SCIENCE: A NEW GOLDENAGE

科学:新黄金时代

ROWN ANEWGOLDERAGE

崭新黄金时代

A REPORT TO THE PRESIDENT

致总统的报告

MICHAEL KRATSIOS

迈克尔·克拉齐奥斯

DIRECTOR OF THE WHITE HOUSE OFFICE

白宫科技政策办公室

OF SCIENCE AND TECHNOLOGY POLICY

主任

JULY 2026

"The American story is one of boundless creativity and bold ambition, driven by an indomitable pioneering spirit that propels exploration and discovery. It is this spirit that illuminated the world with Edison's lightbulb, carried the Wright brothers into the skies, and sent Armstrong to the moon. Today, a new frontier of scientific discovery lies before us."

“美国的故事，是无限创造力与大胆雄心的故事，由不屈不挠的开拓精神驱动，推动探索与发现。正是这种精神，用爱迪生的电灯照亮世界，让莱特兄弟翱翔天空，送阿姆斯特朗登上月球。今天，科学发现的新前沿正展现在我们面前。”

PRESIDENT DONALD J. TRUMP

唐纳德·J·特朗普总统

January 23, 2025

2025年1月23日

Letter of Transmittal

呈文函

EXECUTIVE OFFICE OF THE PRESIDENT

总统行政办公室

OFFICE OF SCIENCE AND TECHNOLOGY POLICY

科技政策办公室

WASHINGTON, D.C. 20502

华盛顿特区 20502

July 21, 2026

2026年7月21日

Dear Mr. President:

尊敬的总统先生:

Eighty-one years ago, President Franklin D. Roosevelt wrote a letter to Vannevar Bush, tasking him with reorganizing America's scientific enterprise after World War II. Bush's response was a report, titled Science: The Endless Frontier. It established the Federal Government's role in supporting basic science, led to the creation of the National Science Foundation, and shaped our nation's science and technology strategy for decades afterward. That report was foundational to the American Century and to the technological world in which we live today.

81年前，富兰克林·D·罗斯福总统致信范内瓦·布什，委托他在二战后重组美国的科学事业。布什的回应是一份题为《科学:无尽的前沿》的报告。该报告确立了联邦政府在支持基础科学方面的角色，促成了国家科学基金会的成立，并在随后的几十年里塑造了我国的科技战略。这份报告为美国世纪以及我们今天所处的技术世界奠定了基础。

Now, as we celebrate the United States' 250th anniversary, we have the responsibility to renew our foundations once more. Never have science and technology been more central to our nation's security and prosperity, and, although America continues to set the pace of scientific progress, global competitors are racing to challenge our leadership. This historical moment demands that we modernize our institutions to match the achievements of our past and the ambitions of our future.

如今，在我们庆祝美国建国250周年之际，我们有责任再次夯实根基。科学和技术从未像现在这样对国家安全与繁荣如此重要，尽管美国仍在引领科学进步的步伐，但全球竞争对手正竞相挑战我们的领导地位。这一历史时刻要求我们现代化我们的机构，以匹配过去的成就和未来的雄心。

In your letter of March 26, 2025, on the occasion of my Senate confirmation as Director of the Office of Science and Technology Policy, you challenged me to reexamine America's research and development ecosystem in light of changes brought on by the 21st century.

在您2025年3月26日就我参议院确认担任科技政策办公室主任一事致函中，您要求我根据21世纪带来的变化重新审视美国的研究开发生态系统。

The report I present here diagnoses the obstacles American researchers face today, and the ways our scientific enterprise has fundamentally transformed since its basic organization was established in 1945. Though our investments in research have grown, scientific productivity has slowed. We have become dependent on a narrow set of legacy institutions. Our incentive structures reward conformity over bold inquiry. And the capacity that once turned American discoveries into American strength has eroded, undermined by decades of industrial offshoring.

我在此提交的报告诊断了美国研究人员今天面临的障碍，以及自1945年基本组织架构建立以来，我们的科学事业发生根本性转变的方式。尽管我们的研究投入有所增长，但科学生产力却放缓了。我们变得依赖少数传统机构。我们的激励机制奖励循规蹈矩而非大胆探索。曾经将美国发现转化为美国实力的能力，因数十年的产业外包而遭到侵蚀和削弱。

To address these and related challenges, this report puts forward recommendations for the entire scientific and technological ecosystem, across government, academia, philanthropy, and industry. Underlying all of these recommendations is a simple measure of success. A decade from now, American researchers should look back at our work and say: "The vital questions I could not pursue then, I am free to pursue now." That purpose will be achieved if we make progress toward four overarching goals.

为应对这些及相关挑战，本报告为整个科技生态系统(涵盖政府、学术界、慈善机构和产业界)提出了建议。所有这些建议背后都有一个简单的成功衡量标准。十年后，美国研究人员应回顾我们的工作并说:“那些我过去无法追求的关键问题，现在我可以自由追求了。”如果我们在四个总体目标上取得进展，这一目标就能实现。

First, the U.S. research system should prioritize the individual scientist over legacy institutions. If we are serious about expanding what our scientists can do, we must invest directly in American researchers and the bold ideas that drive them. Too much of our research enterprise has come to serve itself rather than the scientists within it. Federal funding agencies should support a broader range of performers, including a new generation of mission-driven research organizations, to expand our scientific horizons. And these agencies should organize themselves around the interdisciplinary frontiers of science today, rather than the academic silos of the last century.

首先，美国研究体系应优先考虑个体科学家而非传统机构。如果我们真的想扩大科学家的能力范围，就必须直接投资于美国研究人员及其驱动的大胆想法。我们的研究事业中，太多部分已变得服务于自身而非其中的科学家。联邦资助机构应支持更广泛的执行者，包括新一代使命驱动的研究组织，以拓展我们的科学视野。这些机构应围绕当今科学的跨学科前沿来组织，而非上个世纪的学术孤岛。

Second, the Federal Government should fundamentally change how research dollars are allocated, distributed, and assessed. Instead of a one-size-fits-all approach, the government should fund more flexible types of grants, including fast-track grants, long-horizon grants, and new mechanisms that allow reviewers to champion radically unconventional proposals. Federal research agencies should evaluate their own performance as capital allocators, test new ways of making grants, and construct portfolios with the intentionality of a serious investor.

其次，联邦政府应从根本上改变研究资金的分配、分发和评估方式。政府不应采用一刀切的方法，而应资助更灵活的资助类型，包括快速通道资助、长期资助以及允许评审者支持非常规提案的新机制。联邦研究机构应评估自身作为资本分配者的表现，测试新的拨款方式，并像严肃投资者一样有意图地构建投资组合。

Third, the Federal Government should set clear scientific goals and build the industrial muscle to translate scientific discovery into technological strength. The greatest technical achievements of the last century came about because leaders in government identified national priorities and marshaled the resources to accomplish them. As our competitors race us to capture the value chain of strategic technologies, including with tactics we would never countenance, we can no longer assume that the fruits of American science will accrue to our own people. Government must mobilize the full force of our enterprise around national challenges, clear the ground for American builders, and reunite the work of discovery and manufacturing across the country.

第三，联邦政府应设定明确的科学目标，并建立将科学发现转化为技术实力的产业力量。上个世纪最伟大的技术成就之所以实现，是因为政府领导人确定了国家优先事项并调动资源去完成它们。随着我们的竞争对手竞相夺取战略技术价值链(包括使用我们绝不会容忍的策略)，我们不能再假设美国科学的成果会自动惠及本国人民。政府必须围绕国家挑战调动我们事业的全部力量，为美国建设者扫清障碍，并重新在全国范围内统一发现与制造的工作。

Fourth, we must prepare our research enterprise for the AI revolution. AI will accelerate and radically transform the way we do science. But even the most capable AI models will be slowed down in the bottleneck of institutions and systems built for the last century. As we renew the infrastructure that powers American science, we must reengineer it for the AI age. And even as AI compresses the time from question to answer, we will still need human hands to build the instrument or prototype. We must build that capacity, too, investing in advanced manufacturing, the skilled trades, and the vast pools of talent and tacit knowledge outside the traditional academic pipeline.

第四，我们必须为人工智能革命做好准备我们的研究事业。人工智能将加速并彻底改变我们做科学的方式。但即使是最强大的人工智能模型，也会在建于上个世纪的机构和系统的瓶颈中放缓。在我们更新支撑美国科学的基础设施时，必须为人工智能时代重新设计它。即使人工智能压缩了从问题到答案的时间，我们仍然需要人手来建造仪器或原型。我们也必须建设这种能力，投资于先进制造、熟练技工以及传统学术渠道之外庞大的人才和隐性知识库。

In preparing this report, I have consulted widely across the country's scientific enterprise, from entrepreneurs driving world-changing breakthroughs in fusion technology, to researchers eager to pursue bold ideas in neuroscience at our universities, to venture capitalists funding revolutionary platforms for drug discovery. Their message is clear and inspiring: given the right conditions, American ingenuity will continue to achieve the impossible.

在准备这份报告时，我广泛咨询了全国科学界人士，从推动聚变技术实现世界级突破的企业家，到在大学里渴望追求神经科学大胆想法的研究人员，再到资助革命性药物发现平台的风险投资家。他们的信息清晰而鼓舞人心:只要条件合适，美国的创造力将继续实现不可能之事。

Throughout our history, Americans have shown remarkable courage in their willingness to reinvent our institutions when the challenges we faced demanded it, from establishing the land-grant university to creating the National Science Foundation to launching the Apollo Program. Each generation has seized its opportunity to expand the frontiers of knowledge and lay down the paving stones of progress. Now it is our time to build the scientific enterprise that will carry America forward and bring prosperity to Americans through the 21st century and beyond.

纵观我国历史，从建立赠地大学到创建国家科学基金会，再到启动阿波罗计划，每当面临挑战需要变革时，美国人民都展现出非凡的勇气来重塑我们的制度。每一代人都抓住了拓展知识前沿、铺就进步基石的机会。如今，轮到我们建设能够引领美国前进、为美国人民带来21世纪及未来繁荣的科学事业了。

Mr. President, you have called for a Golden Age of American Innovation. This report is the map to that vision. Our nation cured polio, placed men on the Moon, decoded the human genome, and launched the digital revolution. With your leadership, we will extend our scientific and technological might into the Second American Century and continue to deliver the innovations and discoveries that define the modern world.

总统先生，您曾呼吁开创美国创新的黄金时代。本报告正是实现这一愿景的路线图。我们的国家曾战胜小儿麻痹症、实现人类登月、破译人类基因组、引领数字革命。在您的领导下，我们将把科技实力延伸至第二个美国世纪，继续创造定义现代世界的创新与发现。

Respectfully submitted,

谨此呈报，

/s/ MICHAEL J. KRATSIOS, Director

/s/ 迈克尔·J·克拉齐奥斯，主任

The Honorable Donald J. Trump

尊敬的唐纳德·J·特朗普

President of the United States

美利坚合众国总统

The White House

白宫

Washington, D.C.

华盛顿特区

President Trump's Letter

特朗普总统的信函

THE WHITE HOUSE

白宫

WASHINGTON

华盛顿

Dear Mr. Kratsios:

尊敬的克拉西奥斯先生:

Scientific progress and technological innovation were the twin engines that powered the American century. The Manhattan Project fueled the atomic era. The Apollo Program won us the space race. The internet connected us to a digital future. Today, we will usher in the Golden Age of American Innovation. We will make America safer, healthier, and more prosperous than ever before. We will create a future of American greatness for every citizen, restoring the American Dream.

科学进步和技术创新是推动美国世纪的双引擎。曼哈顿计划开启了原子时代，阿波罗计划让我们赢得了太空竞赛，互联网将我们连接至数字未来。今天，我们将迎来美国创新的黄金时代。我们将使美国比以往任何时候都更安全、更健康、更繁荣。我们将为每一位公民创造美国伟大的未来，重拾美国梦。

The triumphs of the last century did not happen by chance. As World War II drew towards a close, President Franklin D. Roosevelt wrote a letter like this one to his science and technology advisor, Vannevar Bush, charging him to explore new frontiers of the mind for the sake of national greatness and pioneer science in peacetime. Dr. Bush's response laid the groundwork for the uniquely successful American partnership of Government, industry, and academia that built the greatest and most productive nation in human history.

上个世纪的辉煌并非偶然。随着二战接近尾声，富兰克林·D·罗斯福总统曾像这样致信他的科技顾问万尼瓦尔·布什，委托他为国家伟大探索思想的新前沿，并在和平时期开创科学事业。布什博士的回应为美国政府、工业界和学术界独特而成功的合作奠定了基础，这种合作造就了人类历史上最伟大、最具生产力的国家。

But today, rivals abroad seek to usurp America's position as the world's greatest maker of marvels and producer of knowledge. We must recapture the urgency which propelled us so far in the last century. The time has come to return to our roots and renew the American scientific enterprise for the century ahead. So, just as FDR tasked Vannevar Bush, I am tasking you with meeting the challenges below to deliver for the American people.

但如今，海外对手试图篡夺美国作为世界最伟大奇迹创造者和知识生产者的地位。我们必须重拾上个世纪推动我们前进的紧迫感。是时候回归本源，为未来一个世纪重振美国的科学事业了。因此，正如罗斯福委托万尼瓦尔·布什一样，我委托你应对以下挑战，为美国人民服务。

First: How can the United States secure its position as the unrivaled world leader in critical and emerging technologies-such as artificial intelligence, quantum information science, and nuclear technology-maintaining our advantage over potential adversaries?

第一:美国如何确保在关键和新兴技术(如人工智能、量子信息科学和核技术)领域保持无可匹敌的世界领导者地位，维持对潜在对手的优势？

We need to accelerate research and development, dismantle regulatory barriers, strengthen domestic supply chains and manufacturing, spur robust private sector investment, and advance American companies in global markets. Rival nations are pushing hard to overtake the United States, and we must blaze a bold path to maintain our technological supremacy.

我们需要加速研发，消除监管障碍，加强国内供应链和制造业，激发私营部门强劲投资，并推动美国公司在全球市场的发展。对手国家正大力追赶美国，我们必须开辟一条大胆的道路，以维持我们的技术霸权。

Second: How can we revitalize America's science and technology enterprise-pursuing truth, reducing administrative burdens, and empowering researchers to achieve groundbreaking discoveries?

第二:我们如何重振美国的科技事业——追求真理，减少行政负担，并赋予研究人员实现突破性发现的能力？

We need new paradigms for the research enterprise, including innovative models for funding and sharing scientific research, redefining how America conducts the business of discovery. We must build an ecosystem that attracts top talent, celebrates merit, protects our intellectual edge, and enables scientists to focus on meaningful work rather than administrative box checking.

我们需要研究事业的新范式，包括资助和共享科学研究的创新模式，重新定义美国开展发现事业的方式。我们必须构建一个吸引顶尖人才、崇尚功绩、保护我们的智力优势，并使科学家能够专注于有意义的工作而非行政填表的生态系统。

Third: How can we ensure that scientific progress and technological innovation fuel economic growth and better the lives of all Americans?

第三:我们如何确保科学进步和技术创新推动经济增长并改善所有美国人的生活？

During my first term, we made unprecedented advances in America's scientific and technological leadership. We launched the American Artificial Intelligence Initiative, vaulting the United States to the front of the pack in the development and deployment of artificial intelligence. Our National Quantum Initiative established the foundation for national quantum supremacy. We created the United States Space Force and charted a new and daring course for America's further exploration of space. All of this buttressed our security and bolstered our prosperity, and it reaffirmed America's place as the world's preeminent technological superpower.

在我的第一个任期内，我们在美国科技领导力方面取得了前所未有的进步。我们启动了美国人工智能倡议，使美国在人工智能开发和部署方面跃居前列。我们的国家量子倡议为国家量子霸权奠定了基础。我们创建了美国太空军，并为美国进一步探索太空制定了大胆的新路线。所有这些都巩固了我们的安全，促进了我们的繁荣，并重申了美国作为世界卓越技术超级大国的地位。

Now, after four long years of weakness and complacency, we must set our sights even higher. I am calling upon you to blaze a trail to the next frontiers of science. We have the opportunity to cement America's global technological leadership and usher in the Golden Age of American Innovation. We are not just competing with other nations; we are seeking, striving, fighting to make America greater than ever before.

现在，经过四年软弱和自满之后，我们必须将目光放得更高。我呼吁你为科学的下一个前沿开辟道路。我们有机会巩固美国的全球技术领导地位，迎来美国创新的黄金时代。我们不仅是在与其他国家竞争；我们是在寻求、努力、奋斗，使美国比以往任何时候都更伟大。

Sincerely,

此致，

/s/ DONALD J. TRUMP

/s/ 唐纳德·J·特朗普

The Honorable Michael Kratsios

尊敬的迈克尔·克拉齐奥斯先生

Director

主任

Office of Science and Technology Policy

白宫科技政策办公室

Washington, D.C. 20502

华盛顿特区 20502

Summary of the Report

报告摘要

CHAPTER I INTRODUCTION

第一章引言

American scientific progress was the beating heart of the 20th century. It delivered victory on the battlefields of World War II, secured America's triumph in the Cold War, and produced the most prosperous nation in human history. We developed the alchemy that taught sand how to think, conjuring the digital world from silicon chips. American science conquered polio, placed men on the Moon, and gave humanity general-purpose artificial intelligence (AI). This leadership has improved lives and defined the very structure of our modern world.

美国科学的进步是20世纪跳动的心脏。它赢得了第二次世界大战的战场胜利，确保了美国在冷战中的 triumph，并造就了人类历史上最繁荣的国家。我们发明了让沙子学会思考的炼金术，从硅芯片中召唤出数字世界。美国科学征服了小儿麻痹症，将人类送上月球，并赋予人类通用人工智能(AI)。这种领导力改善了生活，并定义了我们现代世界的根本结构。

The foundation of these profound advancements was laid in the years following World War II, thanks largely to the vision set out by Vannevar Bush, the chief science advisor to Presidents Roosevelt and Truman. In his canonical 1945 report, Science: The Endless Frontier, Bush made the prescient case for federal support of basic research, laying the groundwork for the modern scientific enterprise. That enterprise, however, was predominantly built around what became called the "linear model" of technical progress, flowing from basic research to applied research to development of technology and industry. A simplification even then, that model has grown increasingly inadequate as a description of progress eighty-one years later. Discovery today is most often an iterative loop between fundamental and applied work, with industry and engineering playing a vital part in spurring even basic research.

这些深刻进步的基础是在第二次世界大战后的几年里奠定的，这主要归功于罗斯福和杜鲁门总统的首席科学顾问范内瓦·布什所提出的愿景。在他1945年的经典报告《科学:无尽的前沿》中，布什富有远见地提出了联邦政府支持基础研究的理由，为现代科学事业奠定了基础。然而，这一事业主要围绕所谓的“线性模型”构建，即从基础研究到应用研究，再到技术和工业发展。即使在当时这也是一种简化，81年后，它作为进步描述已越来越不充分。今天的发现通常是基础工作与应用工作之间的迭代循环，工业和工程在推动甚至基础研究方面发挥着至关重要的作用。

Government funding of research and development, especially basic science in the academy and national labs, has rightly grown in the eight decades since Bush's report. But private industry has become by far the largest source of research and development (R&D) funding in the United States, with its share nearly doubling from the 1950s to today. American companies now deploy around $700 billion annually, more than triple the combined spending from government and higher education. This evolution has made the pie bigger for everybody and should be welcomed across the research ecosystem, but it demands a corresponding adjustment to the nature of the Federal Government's contributions.

自布什报告以来的八十年间，政府对研发的资助，尤其是对学术界和国家实验室基础科学的资助，已合理增长。但私营企业已成为美国研发资金的最大来源，其份额从20世纪50年代至今几乎翻了一番。美国公司现在每年投入约7000亿美元，是政府和高等教育总支出三倍多。这种演变使所有人的蛋糕都变大了，应受到整个研究生态系统的欢迎，但它要求联邦政府贡献的性质进行相应调整。

New challenges have arisen in recent decades. Despite massive increases in biomedical funding since the 1990s, the rate of significant breakthroughs appears to have slowed and drug approvals have flatlined. Researchers today often spend half their time on paperwork and administrative tasks, a burden worsened by expanding federal and university bureaucracies that further reduce funding available for actual science. A smaller proportion of American citizens now fill post-graduate spots in science, technology, engineering, and mathematics (STEM) fields. Our competitors are channeling unprecedented resources into science and engineering, taking a whole-of-society approach to seize the high ground in strategic technologies. The AI revolution, meanwhile, is transforming the conduct of science, and legacy scientific institutions and infrastructure are not ready to take full advantage of this transformation.

近几十年来出现了新的挑战。尽管自20世纪90年代以来生物医学资金大幅增加，但重大突破的速度似乎放缓，药物审批也停滞不前。如今，研究人员经常将一半时间花在文书工作和行政任务上，而不断扩大的联邦和大学官僚机构进一步加重了这一负担，减少了可用于实际科学的资金。现在，美国公民在科学、技术、工程和数学(STEM)领域攻读研究生学位的比例有所下降。我们的竞争对手正将前所未有的资源投入科学和工程领域，采取全社会的方式抢占战略技术高地。与此同时，AI革命正在改变科学的实践方式，而传统的科学机构和基础设施尚未准备好充分利用这一变革。

America has led the world in scientific progress because Americans have refused to stand still. We have adapted to changing conditions before by boldly reinventing how we structure science, and we must innovate again. Never has scientific and technological development been more essential to our national and economic security, and never has this progress been so deeply intertwined with our diplomatic relationships worldwide.

美国在科学进步方面一直领先世界，因为美国人拒绝停滞不前。我们曾通过大胆重塑科学结构来适应变化的环境，我们必须再次创新。科学技术发展从未像现在这样对我们的国家和经济安全至关重要，也从未像现在这样与我们在全球的外交关系如此紧密交织。

President Trump has been very clear about his priorities, as he seeks to lay the foundations for a new Golden Age of American Innovation. He has asked this administration to revitalize the national science enterprise, to secure U.S. leadership in emerging technologies against foreign rivals, and to ensure that all of America's citizens will benefit from new scientific breakthroughs and technological transformations. The President understands the American story as one of ambition, discovery, and invention, of pioneers who forever seek new frontiers for exploration, particularly now in science and technology.

特朗普总统在寻求为美国创新的新黄金时代奠定基础时，已明确表达了他的优先事项。他要求本届政府重振国家科学事业，确保美国在新兴技术领域领先外国竞争对手，并确保所有美国公民都能从新的科学突破和技术变革中受益。总统理解美国的故事是雄心、发现和发明的故事，是永远寻求新探索前沿的先驱者的故事，尤其是在科学和技术领域。

The following chapters provide recommendations, insights, and guidance to the entire U.S. scientific enterprise, from the government to universities to the private sector and philanthropy.

以下章节为整个美国科学事业——从政府到大学，再到私营部门和慈善机构——提供建议、见解和指导。

CHAPTER II REVITALIZING AMERICA'S SCIENCE AND TECHNOLOGY ENTERPRISE

第二章重振美国科技事业

To reverse stagnation and restore breakthrough momentum, the Federal Government must free American scientists to do their best work. Federal funding in academia remains anchored to mid-century assumptions, channeled through traditional disciplines and overly focused on short, project-based grants. Review panels often gatekeep proposals by consensus, disincentivizing transformative ideas. Agencies face little corrective pressure when portfolios underperform. We must strip away unnecessary burdens, realign funding toward excellence and risk-taking, and embed continuous, evidence-based improvement across an approximately $200 billion annual R&D portfolio.

为扭转停滞局面、恢复突破性进展的势头，联邦政府必须解放美国科学家，让他们发挥最佳水平。联邦对学术界的资助仍停留在世纪中叶的假设上，通过传统学科渠道分配，且过度聚焦于短期项目制拨款。评审小组往往以共识为导向把关提案，抑制了变革性想法。当资助组合表现不佳时，各机构几乎感受不到纠正压力。我们必须剥离不必要的负担，将资助重新聚焦于卓越与冒险精神，并在约2000亿美元的年研发组合中嵌入持续、循证的改进机制。

Refocus on the Individual Scientist: Put the working researcher back at the center of America's scientific enterprise. Free them from the growing administrative burdens that now weigh them down for nearly half their working hours. Bet on people, not just projects, by expanding portable graduate fellowships like the National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP), backing early-career independence, and scaling long-horizon grants for the best and brightest modeled on National Institutes of Health (NIH) Director's Pioneer Award. Open alternative pathways beyond standard academia, and ensure that selection rests purely on merit, not the political fashions of the day.
聚焦科学家个体:将一线研究人员重新置于美国科技事业的核心。让他们从日益繁重的行政负担中解脱出来——这些负担如今已消耗他们近一半的工作时间。投资于人，而非仅仅投资于项目:扩大可携带的研究生奖学金(如国家科学基金会研究生研究奖学金项目)，支持早期职业独立性，并参照国立卫生研究院院长先锋奖的模式，为最优秀、最聪明的人才扩大长期资助规模。开辟标准学术界之外的替代路径，确保选拔完全基于才能，而非当下的政治风向。
Diversify Funding Mechanisms: Move beyond consensus-driven peer review by adopting a broader menu of selection mechanisms suited to different kinds of science. Examples include "golden tickets" that empower individual reviewers to champion ambitious proposals, fast grants that deliver rapid funding decisions, prize challenges and advanced market commitments that pay for results, and regranting models that delegate funding authority to scientists to draw on distributed expertise.
多元化资助机制:超越共识驱动的同行评审，采用更广泛的、适应不同科学类型的选拔机制。例如:"黄金门票"赋予个别评审人支持雄心勃勃提案的权力；快速拨款实现快速资助决策；奖励挑战赛和预先市场承诺为成果买单；以及再拨款模式将资助权下放给科学家，以利用分布式专业知识。
Create New Institutional Models: Many of today's most important problems are too large for an academic lab, too cross-disciplinary for a single department, and too hard to commercialize for a private corporation. Federal funding should support a wider range of performers. The recently launched X-Labs can assemble agile, time-bound teams of professional scientists and engineers to break specific bottlenecks. Advanced Research Projects Agencies (ARPAs) can empower individual program managers to make bold bets and curate researchers to execute them. Curiosity-driven institutes can give our best minds the stability needed to pursue fundamental questions over long time horizons.
创建新型机构模式:当今许多最重要的问题对学术实验室而言过于庞大，对单一学科而言过于跨领域，对私营企业而言又过于难以商业化。联邦资助应支持更广泛的执行者。新近启动的X-Lab可组建灵活、有时限的专业科学家和工程师团队，以突破特定瓶颈。高级研究计划局可赋予项目经理大胆押注的权力，并精心挑选研究人员来执行。好奇心驱动的研究所可为我们最优秀的人才提供长期探索基础问题所需的稳定性。
Reduce Bureaucratic Burdens: Requirements on federal grants have ballooned over the past decades. Some grants now take nearly two years from submission to award, almost as long as it took to design and produce the first Boeing 747. Compress review cycles, eliminate duplicative reporting, and rein in indirect cost recovery that supports administrative bloat, redirecting that money to real scientific infrastructure. Advance reforms that reduce grant-writing burdens, with relief directed specifically to early-career researchers.
减少官僚负担:过去几十年间，联邦拨款的要求急剧膨胀。有些拨款从提交到获批耗时近两年，几乎与设计制造第一架波音747的时间相当。压缩评审周期，消除重复报告，控制支持行政臃肿的间接成本回收，将资金重新导向真正的科学基础设施。推进减少拨款撰写负担的改革，并特别针对早期职业研究人员提供减负措施。
Institutionalize Continuous Improvement: Funders should bring the same critical attention to their own performance that they are supposed to bring to the review of grant applications. Stand up an empowered meta-science unit in federal science agencies, reporting directly to the director, with authority to run controlled experiments on review and funding mechanisms and to drive change across the organization. Elevate the prestige of program officers, grow their discretion in setting scientific direction, and support them as the architects of the fields they help shape.
制度化持续改进:资助机构应像对待拨款申请评审那样，以同样严格的关注审视自身绩效。在联邦科学机构中设立一个有权力的元科学部门，直接向机构负责人汇报，有权对评审和资助机制进行受控实验，并推动整个组织的变革。提升项目官员的声望，扩大他们在设定科学方向上的裁量权，并支持他们成为所塑造领域的架构师。

CHAPTER III SECURING U.S. DOMINANCE IN CRITICAL AND EMERGING TECHNOLOGIES

第三章确保美国在关键与新兴技术领域的主导地位

America has the world's most vibrant scientific enterprise and most dynamic private sector, which routinely turns novel ideas into new industries. But scientific leadership alone does not guarantee national strength or economic vitality. We must tightly couple our science and technology enterprises to ensure that groundbreaking ideas invented in the United States are rapidly prototyped, tested, manufactured, and scaled domestically.

美国拥有全球最具活力的科研事业和最富活力的私营部门，它们不断将新颖想法转化为新兴产业。但仅凭科学领导力并不能保证国家实力或经济活力。我们必须紧密耦合科学与技术事业，确保在美国诞生的突破性理念能够迅速在国内完成原型设计、测试、制造和规模化。

Restore Permissionless Innovation: American regulators have grown skilled at weighing the risks of action, but blind to the costs of inaction. Developing good rules require real-world evidence, and building that evidence base only comes from letting innovators prototype and experiment. Extend the President's reforms in nuclear, pharmaceuticals, and drones across other sectors. Weigh benefits alongside risks, streamline permitting, and use regulatory sandboxes to test new technologies under controlled conditions.
恢复无许可创新:美国监管机构日益擅长权衡行动的风险，却对不作为的代价视而不见。制定良好规则需要真实世界的证据，而建立这一证据基础唯有让创新者进行原型设计和实验。将总统在核能、制药和无人机领域的改革推广至其他行业。权衡收益与风险，简化许可流程，并利用监管沙盒在受控条件下测试新技术。
Open Federal Infrastructure to American Builders: The Federal Government has facilities and testbeds no startup can replicate on its own. Broaden industry access to America's laboratory research infrastructure, including at Department of Energy (DOE) national laboratories, National Aeronautics and Space Administration (NASA) centers, and Department of War (DOW) facilities. Consider innovative potential alongside scientific merit in use approvals, streamline Cooperative Research and Development Agreements (CRADAs) and licensing, further leverage Other Transaction Authority (OTA) to enable private-sector engagement in co-designing research directions, and expand partnerships with the private sector to make joint investments into cutting-edge equipment.
向美国建设者开放联邦基础设施:联邦政府拥有任何初创企业无法独自复制的设施和试验台。扩大行业对美国实验室研究基础设施的准入，包括能源部国家实验室、国家航空航天局中心和战争部设施。在审批使用时，将创新潜力与科学价值并重，简化合作研发协议和许可流程，进一步利用其他交易授权推动私营部门参与研究方向的共同设计，并扩大与私营部门的合作，共同投资尖端设备。
Strengthen Public-Private Partnerships and Talent Flows: The university is no longer the only home of America's most innovative scientific research. Expand agency-adjacent foundations, focus Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) programs to build strategic capabilities, and support joint centers among industry, academia, and federal facilities. Scale industry Ph.D. and postdoc fellowships that move talent fluidly between sectors, drawing on America's private sector strengths to bring industry-scale resources to our academic researchers.
加强公私合作伙伴关系与人才流动:大学已不再是美国最具创新性科研的唯一家园。扩大机构附属基金会，聚焦小企业创新研究和小企业技术转让项目以构建战略能力，并支持产业界、学术界和联邦机构之间的联合中心。扩大行业博士和博士后奖学金，促进人才在各领域间灵活流动，利用美国私营部门的优势为学术研究人员带来行业级资源。
Organize Pre-Competitive Consortia and Grand Challenges: The Apollo Program and the Human Genome Project succeeded because the Federal Government marshaled scientific effort at a scale no single institution could match. Leverage grand challenges that pull breakthroughs forward, and create moonshot-scale missions for issues of national importance. Support industry consortia and use federal resources to break shared engineering bottlenecks in foundational areas, as Extreme Ultraviolet Limited Liability Company (EUV LLC) did for semiconductor lithography.
组织竞争前联盟与重大挑战:阿波罗计划和人类基因组计划的成功，在于联邦政府以任何单一机构无法匹敌的规模集结科研力量。利用重大挑战推动突破，为具有国家重要性的议题设立登月级任务。支持行业联盟，利用联邦资源打破基础领域的共同工程瓶颈，正如极紫外光刻有限责任公司为半导体光刻所做的那样。
Use Counties and States as Laboratories: Federalism is one of America's greatest assets. States can experiment with regulation, permitting, and economic incentives in ways the Federal Government cannot replicate. Support state-led experimentation, partner with the jurisdictions that move the fastest, and let localities compete to support regional innovation. Ensure that innovation strategies that work spread across the nation, advancing science and technology in every county and state.
将县和州作为实验室:联邦制是美国最大的资产之一。各州可以在监管、许可和经济激励方面进行联邦政府无法复制的实验。支持州级实验，与行动最快的辖区合作，让地方竞争以支持区域创新。确保行之有效的创新战略在全国推广，推动每个县和州的科技进步。

CHAPTER IV ENSURING THAT SCIENCE AND TECHNOLOGY BETTER THE LIVES OF ALL AMERICANS

第四章确保科学技术改善所有美国人的生活

America's scientific creativity and entrepreneurial culture position us to translate breakthroughs into technologies that enrich every American's life. That enrichment should include the creation of manufacturing jobs, not just the development of consumer products. By rebuilding the link between science and hands-on craft, federal leadership can ensure that the economic returns of discovery, including the jobs, supplier networks, and process knowledge encoded in the hands of workers, accrue to Americans in every region of the country and every sector of the economy, sustaining our technological leadership for generations to come.

美国的科学创造力和创业文化使我们能够将突破转化为技术，丰富每个美国人的生活。这种丰富应包含制造业就业岗位的创造，而不仅仅是消费产品的开发。通过重建科学与手工技艺之间的联系，联邦领导力可以确保发现的回报——包括就业、供应商网络以及工人手中掌握的工艺知识——惠及全国各地区和经济各部门的美国人，从而维持我们未来几代人的技术领导地位。

Integrate Hands-On Training: Technology is encoded not just in papers and patents, but in the tacit knowledge passed from mentor to mentee. Require universities and community colleges to embed practical technical training and externships into STEM curricula. Let hands-on experience and industry credentials count toward degrees. Reform accreditation, admissions, and tenure to reward real-world technical work alongside academic publication.
融入实践培训:技术不仅编码在论文和专利中，还体现在师徒间传递的隐性知识中。要求大学和社区学院将实践技术培训和实习嵌入STEM课程。让实践经验和行业证书计入学位。改革认证、招生和终身教职制度，在奖励学术发表的同时也认可实际技术工作。
Open Scientific Careers Beyond the Academic Ladder: Establish national fellowships for skilled craftspeople, practitioner-in-residence programs embedding machinists and technicians alongside Ph.D. researchers, and portable industry-recognized credentials in advanced manufacturing and lab techniques. Connect hobbyists and tinkerers in rural communities to formal research opportunities, and open up universities to technical training for local residents.
开辟学术阶梯之外的科研职业道路:设立国家奖学金面向熟练工匠，建立驻场实践者项目让机械师和技术人员与博士研究人员并肩工作，并在先进制造和实验室技术领域设立便携式行业认可证书。将农村社区的爱好者和修补者与正式研究机会联系起来，并向当地居民开放大学的技术培训。
Modernize Apprenticeships and Career Pathways: Extend registered apprenticeships into science and technology fields. Adopt pay-for-performance funding models, scale Workforce Pell Grants, and back community colleges as regional hubs of scientific and technical talent. Integrate these hubs with industry sites and federally funded innovation and manufacturing centers.
现代化学徒制和职业路径:将注册学徒制扩展到科学和技术领域。采用按绩效付费的资助模式，扩大劳动力佩尔助学金规模，并支持社区学院成为科技人才区域中心。将这些中心与行业场所及联邦资助的创新和制造中心整合起来。
Build Dense, Local Innovation Clusters Across the Nation: Technological leadership emerges from places where research and production sit close together. Expand regional innovation hubs, manufacturing institutes, and defense industrial base centers to anchor regional ecosystems. Drive coordinated efforts with local universities and national laboratories to build specializations and workforce pipelines. Pair these efforts with the reshor-ing of advanced manufacturing, and restore the feedback loops between researchers, engineers, and skilled technicians.
在全国范围内建设密集的地方创新集群:技术领先源于研究与生产紧密相邻的地方。扩大区域创新中心、制造研究所和国防工业基地中心，以稳固区域生态系统。与当地大学和国家实验室协同努力，打造专业特长和人才输送渠道。将这些努力与先进制造业的回流相结合，恢复研究人员、工程师和技术熟练工人之间的反馈循环。

CHAPTER V A NEW GOLDEN AGE

第五章新的黄金时代

America stands at the cusp of a revolution in science, in which AI will accelerate discovery, multiply human cognitive capabilities, and unlock solutions to some of our greatest challenges. But "AI for science" will still find itself subject to the frictions and inefficiencies of human institutions. We can only fully harness AI and its associated productivity uplift by boldly reforming our scientific institutions, building national-scale infrastructure, and ensuring rigorous verification of the knowledge base from which AI will learn.

美国正站在一场科学革命的边缘，人工智能将加速发现、倍增人类认知能力，并解锁我们一些最大挑战的解决方案。但“AI for science”仍将受制于人类机构的摩擦和低效。只有通过大胆改革科学机构、建设国家级基础设施，并确保人工智能学习所依据的知识库得到严格验证，我们才能充分利用人工智能及其带来的生产力提升。

Launch and Scale the Genesis Mission: Fully fund and expand the Genesis Mission as America's flagship AI for science initiative, integrating supercomputers, AI models, scientific instruments, and datasets across national laboratories to double the productivity and impact of U.S. science within a decade. Direct it at cross-cutting problems where breakthroughs unlock entire branches of downstream discovery and where AI can transform the practice of science itself.
启动并扩展创世纪任务:全面资助并扩大创世纪任务，作为美国旗舰的人工智能科学计划，整合国家实验室的超级计算机、人工智能模型、科学仪器和数据集，在十年内将美国科学的生产力和影响力翻倍。聚焦于那些突破能解锁整个下游发现分支，且人工智能能改变科学实践本身的跨领域问题。
Institutionalize Gold Standard Science: AI operating on a flawed knowledge base will only entrench bad science. Enforce reproducibility, transparency, data sharing, and falsifiability across all federally funded research through the Restoring Gold Standard Science Executive Order, creating a trusted foundation for AI-powered discovery.
制度化黄金标准科学:在缺陷知识库上运行的人工智能只会固化不良科学。通过《恢复黄金标准科学行政令》，在所有联邦资助的研究中强制执行可重复性、透明度、数据共享和可证伪性，为人工智能驱动的发现建立可信基础。
Build Verification Infrastructure at Scale: While the cost of generation has decreased exponentially, the cost of verification has not. Invest in AI-enabled verification systems, open standards, and continuous replication mechanisms. Set standards to enable the development of machine-auditable replication packages, and reward those who replicate or disprove influential scientific results.
大规模建设验证基础设施:虽然生成成本呈指数级下降，但验证成本并未下降。投资于人工智能驱动的验证系统、开放标准和持续复制机制。制定标准以支持机器可审计的复制包开发，并奖励那些复制或反驳有影响力的科学成果的人。
Accelerate Autonomous Experimentation: Closed-loop autonomous laboratories can collapse discovery timelines by orders of magnitude and enable science at a truly industrial scale. Focus investments in robotics and automated laboratories, leveraging industry demand and federal R&D to ensure our scientific equipment industrial base is built on the world's best hardware and software and leads the charge in the coming scientific revolution.
加速自主实验:闭环自主实验室可将发现时间线缩短数个数量级，并实现真正工业规模的科学。聚焦于机器人和自动化实验室的投资，利用行业需求和联邦研发，确保我们的科学设备工业基础建立在世界最佳硬件和软件之上，并在即将到来的科学革命中引领潮流。
Experiment With AI-Native Scientific Institutions: Today's funding structures, publication systems, and credit mechanisms were built for a world of human-paced discovery. Begin the transition to AI-native institutions, including through faster and more open forms of scientific publication, more granular credit attribution, and new market mechanisms that direct resources to problems where breakthroughs matter most.
尝试人工智能原生科学机构:今天的资助结构、出版系统和信用机制是为人类节奏的发现世界而建。开始向人工智能原生机构过渡，包括通过更快、更开放的科学出版形式、更细粒度的信用归属，以及将资源导向突破最重要问题的新市场机制。

Chapter I Introduction

第一章引言

SCIENTIFIC PROGRESS REMAINS ESSENTIAL

科学进步依然至关重要

Eighty-one years ago, Vannevar Bush wrote that scientific progress would be an "essential key to our security as a nation, to our better health, to more jobs, to a higher standard of living, and to our cultural progress."1 Bush had seen a glimpse of science's promise for America in the triumphs of penicillin and radar in securing victory in World War II. His vision proved prescient through the eight decades that followed.

81年前，范内瓦·布什写道，科学进步将是"我们国家安全、改善健康、增加就业、提高生活水平以及文化进步的关键。"1 布什曾从青霉素和雷达在二战胜利中的成就中，窥见科学对美国未来的承诺。此后的八十年证明了他的远见卓识。

Since then, incredible breakthroughs have emerged from our nation's laboratories: the transistor and the integrated circuit, the laser and the LED, the mapping of the human genome and the tools to edit it. The "new products, new industries, and more jobs"2 Bush envisioned have materialized as entire economic sectors, such as computing, biotechnology, aerospace, and telecommunications, which today employ tens of millions and generate trillions in wealth. Enabled by technologies unimaginable in Bush's time, the energy revolution has made America the world's largest oil and gas producer.

自那时起，我国实验室涌现出令人难以置信的突破:晶体管和集成电路、激光和LED、人类基因组图谱及其编辑工具。布什所预见的"新产品、新产业和更多就业机会"2 已化为计算机、生物技术、航空航天和电信等整个经济部门，如今这些行业雇佣数千万人，创造数万亿美元财富。在布什时代无法想象的技术推动下，能源革命使美国成为全球最大的石油和天然气生产国。

Now small computers in our pockets connect us instantly to family across the continent, unlock the world's knowledge, and guide us through unfamiliar streets. Great advances in materials science have given us everything from nylon stockings to bulletproof vests, artificial joints, and fighter jets. We placed GPS satellites in orbit that guide our tractors to precision planting, our packages to on-time arrival, and our troops through hostile terrain. We established the field of modern biotechnology, invented MRI, and developed the lithium batteries that power our cordless world. And in mere decades after Bush's letter, we walked on the Moon and sent scientific instruments to the edge of the solar system.

如今，我们口袋中的小型计算机能瞬间连接远在大陆另一端的家人，解锁世界知识，指引我们穿行陌生街道。材料科学的巨大进步带来了从尼龙丝袜到防弹背心、人工关节和战斗机的种种成果。我们将GPS卫星送入轨道，引导拖拉机精准播种、包裹准时送达、部队穿越险恶地形。我们创立了现代生物技术领域，发明了核磁共振成像，开发了为无线世界供电的锂电池。在布什信函发表后的短短几十年里，我们登上了月球，并将科学仪器送至太阳系边缘。

Our scientists have achieved even more than this. We often forget that American farmers have tripled their output while using about a quarter of the labor used in the ( {1940}\mathrm{;s},{}^{3} ) or that the average American lives more than 10 years longer than when Bush penned his report. ( {}^{4} ) Within living memory, cancer has been transformed from a death sentence to a treatable condition for millions of Americans. The most common form of childhood leukemia has gone from universally fatal to curable in 90% of cases, ( {}^{5} ) and deaths from heart disease have fallen by more than half since their peak. ( {}^{6} )

我们的科学家取得的成就远不止于此。我们常常忘记，美国农民产量翻了三倍，而所用劳动力仅为( {1940}\mathrm{;s},{}^{3} )的四分之一；或者，如今美国人平均寿命比布什撰写报告时延长了10年以上。( {}^{4} ) 在人们的记忆中，癌症已从死刑判决转变为数百万美国人可治疗的疾病。最常见的儿童白血病已从100%致命变为90%可治愈，( {}^{5} ) 而心脏病死亡率自峰值以来已下降超过一半。( {}^{6} )

AS THE SOURCE OF OUR TRIUMPHS

作为我们胜利的源泉

These triumphs happened, and happened here in America, only because of intentional choices made by our people and institutions.

这些胜利之所以发生，并且发生在美国，仅仅是因为我们的人民和机构做出了有意的选择。

First, consistent with what Bush outlined in his essay, the government played a vital role in supporting the scientific enterprise to achieve national goals. These include conquering disease, creating jobs, and ensuring security. Against the backdrop of pre-War federal research funding, which was largely focused on agriculture, this proved to be a key insight. ( {}^{7} ) Not all valuable research attracts private capital, particularly research that promises only slow, diffuse returns. In the capital environment of the mid-20th century, no investor would have funded efforts to build particle accelerators or discover the fundamental insights that underlie the genomic revolution.

首先，与布什在其文章中所概述的一致，政府在支持科学事业以实现国家目标方面发挥了至关重要的作用。这些目标包括战胜疾病、创造就业和确保安全。战前联邦研究资金主要集中于农业，在此背景下，这一见解被证明是关键性的。( {}^{7} )并非所有有价值的研究都能吸引私人资本，尤其是那些回报缓慢且分散的研究。在20世纪中期的资本环境中，没有投资者会资助建造粒子加速器或发现基因组革命基础性见解的努力。

As Bush argued, there are "areas of science in which the public interest is acute but which are likely to be cultivated inadequately if left without more support than will come from private sources."8 Today, we benefit from that insight with an extensive set of federal organizations to advance scientific research, including NSF, DOE national laboratories, NIH, NASA, the National Institute of Standards & Technology (NIST), the Defense Advanced Research Projects Agency (DARPA), and other research arms of federal departments and agencies.

正如布什所言，存在“一些科学领域，公共利益在其中极为迫切，但如果仅靠私人来源的支持而不给予更多支持，这些领域可能得不到充分发展。”8 如今，我们受益于这一见解，拥有广泛的联邦组织来推进科学研究，包括国家科学基金会、能源部国家实验室、国立卫生研究院、国家航空航天局、国家标准与技术研究院、国防高级研究计划局以及联邦部门和机构的其他研究部门。

Second, our government recognized that achieving those national purposes requires more coordination than any single institution can provide, and that our unique advantage, whether in defeating the Soviet Union or winning the technological race today, lies in our dynamic private sector. Describing the development of penicillin, Bush spoke of how the government launched a "coordinated attack on special problems," supporting research and development among medical schools, universities, and the pharmaceutical industry, and helping ideas progress from early laboratory experimentation to large-scale production and use. ( {}^{9} ) This model became the foundation of the fruitful public-private partnerships that sent Americans to the Moon and built the internet. This dynamism between publicly funded science and private enterprise remains the engine of American innovation.

其次，我们的政府认识到，实现这些国家目标需要比任何单一机构所能提供的更多的协调，而我们独特的优势——无论是击败苏联还是在当今技术竞赛中获胜——在于我们充满活力的私营部门。在描述青霉素的开发时，布什谈到政府如何发起“针对特殊问题的协同攻关”，支持医学院、大学和制药行业的研究与开发，并帮助想法从早期实验室实验推进到大规模生产和应用。( {}^{9} )这一模式成为富有成效的公私合作伙伴关系的基础，这种伙伴关系将美国人送上月球并构建了互联网。公共资助的科学与私营企业之间的这种活力至今仍是美国创新的引擎。

Third, we stayed true to the call for science to remain dynamic. "The pioneer spirit is still vigorous within this nation," Bush wrote. ( {}^{10} ) "Science offers a largely unexplored hinterland for the pioneer who has the tools for his task." ( {}^{11} ) Each of our past triumphs required substantial courage and institutional transformation.

第三，我们始终坚守科学保持活力的号召。“这个国家的开拓精神依然旺盛，”布什写道。( {}^{10} )“科学为拥有工具的开拓者提供了一片尚未开发的腹地。”( {}^{11} )我们过去的每一次胜利都需要巨大的勇气和制度变革。

They compelled us to invent new models to drive scientific progress: dedicated science funding agencies, innovative partnerships that enabled the widespread commercialization of modern electronics, and reforms like the deregulation of space that opened the door to today's vibrant era of commercial spaceflight.

它们迫使我们发明新的模式来推动科学进步:专门的科学资助机构、实现现代电子学广泛商业化的创新伙伴关系，以及像太空放松管制这样的改革，为当今商业航天的繁荣时代打开了大门。

This willingness to venture into unknown territory, to challenge established methods, and to create new institutions when old ones prove inadequate, built the scientific supremacy that undergirds our vibrant economy and national security today.

这种勇于探索未知领域、挑战既定方法、并在旧有制度不足时创建新制度的意愿，奠定了支撑我们当今充满活力的经济和国家安全所需的科学优势。

THE LANDSCAPE IS CHANGING

格局正在变化

The principles that government must support basic research, that this research drives national prosperity, and that America's advantage lies in the dynamism of our institutions, remain as sound today as when Bush first articulated them. But principles are not processes. Bush would be the first to recognize that the landscape in which fundamental research is conducted has completely transformed since he wrote Science: The Endless Frontier.

政府必须支持基础研究、这种研究推动国家繁荣、美国的优势在于我们机构的活力——这些原则如今仍与布什首次阐述时一样正确。但原则不是流程。布什会第一个认识到，自他撰写《科学:无尽的前沿》以来，基础研究开展的格局已彻底改变。

In 1950, a dozen engineers in basic laboratories drove progress in semiconductors. Today, the semiconductor industry invests more than $100 billion in capital and R&D each year and employs hundreds of thousands. ( {}^{12} ) They regularly solve physics and materials problems at the edge of possibility and build fabrication plants filled with robots that manipulate silicon atom by atom.

1950年，基础实验室里的十几名工程师推动了半导体的进步。如今，半导体行业每年在资本和研发上投资超过1000亿美元，雇佣数十万人。( {}^{12} )他们经常解决可能性边缘的物理和材料问题，并建造装满机器人的制造工厂，这些机器人逐个原子地操纵硅。

In 1950, scientists mailed typewritten manuscripts to journal editors, who sent copies to reviewers from their personal networks at top universities. Today, researchers post papers online within hours of completion. Thousands read and debate the merits of the work immediately on social media and in discussion channels. Code gets replicated on the internet months before the paper appears in print.

1950年，科学家将打字机打印的手稿邮寄给期刊编辑，编辑从顶尖大学的个人人脉中发送给审稿人。如今，研究人员在论文完成后几小时内就将其发布到网上。成千上万的人立即在社交媒体和讨论频道中阅读并辩论其价值。代码在论文印刷前几个月就在互联网上被复制。

In 1950, mathematicians worked alone with chalkboards and stacks of papers from the library. Today, they look up theorems instantly online. Computers enable experimental mathematics that would have been impossible with pencil and paper. Software languages modularize massive proofs, letting dozens of mathematicians collaborate on a single problem simultaneously from coast to coast.

1950年，数学家独自用黑板和图书馆的一摞摞论文工作。如今，他们即时在线查找定理。计算机实现了用纸笔不可能完成的实验数学。软件语言将庞大的证明模块化，让数十名数学家从海岸到海岸同时协作解决同一个问题。

NEW FRONTIERS AND NEW APPROACHES

新前沿与新路径

The institutions we build determine what problems get solved, which approaches get tried, what risks get taken, and whose talent contributes to discovery. When these institutions align with the nature of the scientific frontier and with our national needs, science advances; when they are misaligned, abundant resources and brilliant researchers go to waste. The misalignment shows up as diminishing returns to R&D investments, a decline in the pursuit of breakthrough ideas, and a slowdown in the benefits that technological progress delivers to the American people.

我们构建的机构决定了哪些问题能得到解决、哪些方法会被尝试、哪些风险会被承担，以及哪些人的才智能为发现做出贡献。当这些机构与科学前沿的性质及国家需求相契合时，科学就会进步；当它们不相匹配时，丰富的资源和杰出的研究者就会被浪费。这种不匹配表现为研发投资回报递减、对突破性想法的追求减少，以及技术进步为美国人民带来的效益放缓。

Institutional design matters because individual researchers follow the signals their institutions send. Consider the incentives of a talented researcher working at the frontier of quantum information science.

机构设计之所以重要，是因为个体研究者会遵循其机构发出的信号。试想一位在量子信息科学前沿工作的才华横溢的研究者所面临的激励。

As a Ph.D. student in a university lab, this researcher must publish regularly to graduate, craft narratives that satisfy journal reviewers and his dissertation committee, and build the personal connections that lead to academic jobs. His professor's grant funding limits what equipment he can afford. He designs experiments around the apparatus in his lab more than the questions most worth asking. With two years until graduation, he actively looks for results that advance his dissertation's narrative. When unexpected results appear, he sometimes chooses to pursue them, but remains cognizant of potential risks to his professional progress and his lab's future funding.

作为大学实验室的博士生，这位研究者必须定期发表论文才能毕业，精心构思令期刊审稿人和论文委员会满意的叙事，并建立能带来学术职位的人际关系。他的教授获得的资助经费限制了他能购买的设备。他围绕实验室现有装置设计实验，而非围绕最值得探究的问题。距离毕业还有两年时，他积极寻找能推进论文叙事的结果。当意外结果出现时，他有时会选择跟进，但也清楚这对其职业进展和实验室未来经费的潜在风险。

As a startup founder raising venture capital, the same researcher faces different pressures. He pitches a bold vision of scalable quantum computing to investors. He can hire engineers and build quickly with tens of millions of dollars in seed funding. But he must also deliver revenue within five years and sustain a clear narrative as funding rounds continue. The technical approach he outlined to investors may not be the best path forward, but changing course risks losing investor confidence. Market pressure imposes scientific constraints that grant reviewers might not.

作为筹集风险投资的初创公司创始人，同一位研究者面临不同的压力。他向投资者描绘可扩展量子计算的宏伟愿景。他可以用数千万美元的种子资金雇佣工程师并快速建设。但他也必须在五年内实现营收，并在后续融资轮次中维持清晰的叙事。他向投资者概述的技术路径可能并非最佳选择，但改变方向可能失去投资者信心。市场压力施加了审稿人可能不会提出的科学约束。

Both paths advance science and technology, but both channel talent toward different problems in different ways. The discoveries that get made depend not only on the questions that are scientifically salient, but on the fit between those questions and the incentives researchers must navigate. It therefore falls to the public officials who steward federal funding, as the architects of the national scientific enterprise, to understand the constraints our researchers face, to create the right incentive structures wherever possible, and to drive R&D in whatever gaps remain. Only then can we unleash American scientists and give them ever greater freedom to explore.

两条路径都推动了科学和技术进步，但都以不同方式将人才引向不同问题。最终取得的发现不仅取决于科学上重要的问题，还取决于这些问题与研究者必须应对的激励之间的契合度。因此，作为国家科学事业的架构师，掌管联邦资金的公职人员有责任理解研究者面临的约束，尽可能创建正确的激励结构，并在任何存在的空白领域推动研发。唯有如此，我们才能释放美国科学家的潜力，赋予他们更大的探索自由。

Universities remain essential for training scientists and pursuing fundamental questions. Venture capital mobilizes private resources toward high-impact technologies. Federal agencies fund research that markets alone will not support. Each serves an essential purpose, but the scientific frontier is constantly shifting, requiring vigilant self-improvement to ensure these institutions remain suited to the answering the most important questions today.

大学对于培养科学家和探索基础问题仍然至关重要。风险投资将私人资源引向高影响力技术。联邦机构资助市场无法单独支持的研究。每个机构都发挥着重要作用，但科学前沿不断变化，需要警惕的自我完善，以确保这些机构始终适合回答当今最重要的问题。

The system that emerged from Bush's vision served the last American Century. But every generation of Americans must show the courage to reinvent our institutions when the frontier demands it. We established land-grant universities when agriculture needed scientific foundations. We created DARPA when the pursuit of breakthrough military technologies required an agency willing to fund high-risk ideas that traditional funders would reject. We developed the venture capital model when a gap emerged between the long time horizons of emerging technology companies and the capacity of traditional capital markets.

源于布什愿景的体系服务于上一个美国世纪。但每一代美国人都必须展现出勇气，在前沿需要时重塑我们的机构。当农业需要科学基础时，我们建立了赠地大学。当追求突破性军事技术需要一个愿意资助传统资助者会拒绝的高风险想法的机构时，我们创建了DARPA。当新兴技术公司的长周期与传统资本市场能力之间出现差距时，我们发展了风险投资模式。

The questions demanding answers, the tools required to answer them, the scale of coordination needed, and the timelines involved all shift as knowledge advances. As our predecessors did, we must continue to craft and refine the machinery of science, allowing each component to work to full advantage and freeing our innovators from pressures that keep them from the greatest goals. A new American Century will require new engines of scientific discovery.

随着知识的进步，需要回答的问题、回答这些问题所需的工具、所需的协调规模以及所涉及的时间线都在变化。正如我们的前辈所做的那样，我们必须继续打造和完善科学机制，让每个组成部分都能充分发挥优势，让创新者摆脱阻碍他们追求最伟大目标的压力。一个新的美国世纪将需要新的科学发现引擎。

GROWING PRIVATE SECTOR R&D

私营部门研发的增长

One particularly visible shift in the scientific machine is who funds and performs research. When Bush penned his report in the middle of the 20th century, the Federal Government stood as the dominant patron of American science, marshaling the nation's research capacity for victory in war. The landscape today would astonish him. Private industry has become by far the largest source of R&D funding in the United States, with its share roughly doubling from the 1950s to today, even as federal funding has grown by leaps and bounds. American companies now deploy around $700 billion annually, more than triple the spending of government and higher education. ( {}^{13} ) While this investment has historically been dominated by late-stage product development, strikingly, the share devoted to basic research, which Bush thought markets could not sustain alone, has also grown rapidly, particularly over the past two decades (Figure 1).

科学机器中一个尤为显著的变化在于谁资助并开展研究。当布什在20世纪中期撰写其报告时，联邦政府是美国科学的主导赞助者，集结国家的研究力量以赢得战争。而今天的景象会令他震惊。私营工业已成为美国研发资金的最大来源，其份额从1950年代至今大约翻了一番，即便联邦资金也在大幅增长。美国公司现在每年投入约7000亿美元，是政府与高等教育支出总和的三倍多。( {}^{13} )尽管这一投资历史上以晚期产品开发为主，但引人注目的是，基础研究(布什认为市场无法独自维持的领域)所占份额也迅速增长，尤其是在过去二十年中(图1)。

Consider two transformative inventions of recent memory, the transistor and the transformer architecture that underpins modern machine learning. Both came from corporate laboratories that employ thousands of researchers who often produce collaborative, well-cited papers on problems of deep intellectual interest. Modern industrial powerhouses fund state-of-the-art experiments and pay salaries tens or hundreds of times more than the academy, drawing top talent from across the country. Researchers at American companies have earned Nobel Prizes for work on electron tunneling in semiconductors, surface chemistry, polymer science, and lasers, a testament to both the rigor of their research and the fundamental nature of their work.

以近代两项变革性发明为例:晶体管和支撑现代机器学习的变压器架构。两者均来自企业实验室，这些实验室雇佣了数千名研究人员，他们经常就深具智力兴趣的问题发表合作性强、引用率高的论文。现代工业巨头资助最先进的实验，并支付比学术界高出数十甚至数百倍的薪水，吸引全国顶尖人才。美国公司的研究人员因在半导体中的电子隧穿、表面化学、高分子科学和激光方面的工作而获得诺贝尔奖，这既证明了其研究的严谨性，也体现了其工作的基础性。

Figure 1: Private sector basic R&D has grown rapidly over the past two decades. It now rivals higher education among performers (left) and the Federal Government among funders (right) of basic research in the United States. ( {}^{14} )

图1:过去二十年中，私营部门的基础研发迅速增长。如今，在美国基础研究的执行者中，它已与高等教育并驾齐驱(左图)；在资助者中，则与联邦政府不相上下(右图)。( {}^{14} )

This is not a sign that the academy has become less important, but rather that the scientific world has expanded. In certain domains, the scale of private investment dwarfs anything federal agencies can match. Universities face real limits in scaling up engineering efforts, with rare exceptions for government-sponsored big science projects like space probes and particle accelerators; yet Ph.D. students and professors can now raise hundreds of millions of dollars to found companies that pursue fundamental breakthroughs. Small startups increasingly perform basic research themselves, giving our most talented scientists new paths for ambitious work. These firms blur the distinction between basic and applied science, combining research and development to accelerate both.

这并非表明学术界变得不那么重要，而是科学世界已经扩展。在某些领域，私人投资的规模远超联邦机构所能匹敌。大学在扩大工程研究规模方面面临实际限制，除了像太空探测器和粒子加速器这类政府资助的大科学项目外，鲜有例外；然而，博士生和教授现在可以筹集数亿美元创办公司，追求基础性突破。小型初创企业越来越多地自行开展基础研究，为我们最有才华的科学家提供了从事雄心勃勃工作的新途径。这些公司模糊了基础科学与应用科学之间的界限，将研发相结合以加速两者的发展。

THE LINEAR MODEL NO LONGER HOLDS

线性模型不再成立

In his 1945 report, Vannevar Bush presented a progression from basic research through applied research to development, later termed the "linear model." ( {}^{15} ) This framework laid out a clear role for each part of the research and development pipeline. Universities would pursue fundamental understanding without the pressure of practical application. Industry would turn discoveries into products. At the time, the separation was natural and productive.

在1945年的报告中，万尼瓦尔·布什提出了从基础研究到应用研究再到开发的演进过程，后来被称为"线性模型"。( {}^{15} ) 这一框架为研发管线的每个部分明确了角色。大学在无实际应用压力下追求基础理解。工业界将发现转化为产品。当时，这种分离是自然而富有成效的。

Modern discovery, however, is increasingly shaped by continuous iteration between fundamental and applied work. Engineering challenges routinely expose unanswered scientific questions, and breakthroughs in basic understanding, in turn, open new engineering pathways. The relationship is recursive rather than unidirectional. The tools required to push the frontier, whether advanced fabrication equipment or specialized engineering teams, are often found outside traditional academic settings. Many of our most productive researchers now move fluidly between sectors, carrying ideas and techniques with them. Technology and science have become deeply interdependent, even, as we will discuss in Chapter ( V ) , in the purest fields of reason like mathematics.

然而，现代发现日益受到基础工作与应用工作之间持续迭代的塑造。工程挑战不断暴露出未解的科学问题，而基础理解的突破又反过来开辟新的工程路径。这种关系是递归而非单向的。推动前沿所需的工具，无论是先进制造设备还是专业工程团队，往往存在于传统学术环境之外。我们许多最高产的研究人员如今在不同领域间灵活流动，随身携带想法和技术。技术与科学已变得深度相互依存，甚至——正如我们将在第( V )章讨论的——在最纯粹的理性领域如数学中也是如此。

This non-linearity is also captured by the framework Donald Stokes articulated half a century after Bush. "Pasteur's quadrant," as he termed it, now defines a growing share of the scientific frontier. ( {}^{16} ) Stokes argued that research can seek fundamental understanding while being motivated by considerations of use, observing that some of the most consequential scientific advances all arose precisely from this combination. Examples of such research include Pasteur's investigations into why wine spoiled, the development of the transistor at Bell Labs, and Shannon's work on information theory. Many of the transformative discoveries of our own era, from the computational study of protein folding that earned the 2024 Nobel Prize in Chemistry to the superconducting quantum devices that earned the 2025 Nobel Prize in Physics, emerged from efforts that were at once theoretically ambitious and deeply connected to practical problems.

这种非线性特征也体现在唐纳德·斯托克斯在布什之后半个世纪阐述的框架中。他所谓的"巴斯德象限"如今定义了科学前沿中日益增长的部分。( {}^{16} ) 斯托克斯认为，研究可以在追求基础理解的同时受使用考量驱动，并观察到一些最具影响力的科学进步恰恰源于这种结合。此类研究的例子包括巴斯德对葡萄酒变质原因的探究、贝尔实验室晶体管的开发，以及香农的信息论工作。我们时代许多变革性发现——从获得2024年诺贝尔化学奖的蛋白质折叠计算研究，到获得2025年诺贝尔物理学奖的超导量子器件——都源于既在理论上雄心勃勃又与实际问题深度关联的努力。

Our national laboratories and federally funded research centers have long been engines for this use-inspired research, and new centers of modern science reflect the same pattern. A greater share of scientists are leaving academia for industry, not because they have abandoned curiosity-driven inquiry, but because the tools, resources, and career opportunities required to pursue certain fundamental questions increasingly lie outside university walls. The task ahead is to enable scientists to move fluidly between problems of different shapes and to give them the freedom and resources to pursue discovery at today's frontier.

我们的国家实验室和联邦资助研究中心长期以来一直是这种应用启发型研究的引擎，而现代科学的新中心也反映了同样的模式。越来越多的科学家离开学术界进入工业界，并非因为他们放弃了好奇心驱动的研究，而是因为追求某些基础问题所需的工具、资源和职业机会日益存在于大学围墙之外。未来的任务是让科学家能够灵活应对不同形态的问题，并赋予他们在当今前沿进行探索的自由和资源。

OUR RESEARCHERS FACE MOUNTING CHALLENGES

我们的研究人员面临日益严峻的挑战

These rapid changes demand that our institutions adapt. As will be addressed in Chapter II, significant portions of our federal funding apparatus remain anchored to outdated assumptions. This is not an indictment of the talented scientists and grantmakers who staff these bureaucracies, but a product of institutional inertia, born from the lack of market selection pressure that drives constant experimentation.

这些快速变化要求我们的机构进行适应。正如第二章将讨论的，我们联邦资助体系的很大一部分仍固守于过时的假设。这并非对供职于这些官僚机构的有才华的科学家和资助者的指责，而是制度惯性的产物，源于缺乏推动持续实验的市场选择压力。

NSF, for instance, still organizes itself primarily around academic disciplines, much as it did in the 1950s, and channels resources overwhelmingly to a single type of performer, the university-based, principal investigator-led research group. Many federal programs are still built around the linear model, making the assumption that basic research happens in academia while development happens in industry. Academic incentives often penalize rather than reward partnerships that cross institutional boundaries. Agencies face little external pressure to adapt, even as the scientific landscape transforms around them, preserving processes essentially unchanged for decades.

例如，美国国家科学基金会(NSF)仍主要按学科组织自身，与20世纪50年代的做法大致相同，并将资源压倒性地投向单一类型的执行者——以大学为基础、由首席研究员领导的研究团队。许多联邦项目仍基于线性模型构建，假设基础研究发生在学术界，而开发发生在产业界。学术激励往往惩罚而非奖励跨越机构边界的合作。各机构面临的外部适应压力微乎其微，即使科学格局在其周围发生转变，它们仍基本保持数十年不变的程序。

Within academia itself, well-documented inefficiencies compound these structural problems. Administrative burdens on researchers have grown over time; one study found that investigators spend nearly half of their federally funded research time on paperwork rather than on research or teaching. ( {}^{17} ) Universities also extract significant overhead from researchers, which funds a mix of legitimate shared infrastructure and growing administrative bloat. Effective indirect cost rates at NIH-funded institutions average more than ( {40}% ,{}^{18} ) even though those same institutions frequently accept 10 to 15% overhead from private funders. ( {}^{19} )

在学术界内部，有据可查的低效问题加剧了这些结构性难题。研究人员的行政负担与日俱增；一项研究发现，研究人员将联邦资助的研究时间近一半花在文书工作上，而非研究或教学上。( {}^{17} )大学还从研究人员身上抽取大量间接费用，这些资金既用于合法的共享基础设施，也用于日益膨胀的行政开支。美国国立卫生研究院(NIH)资助机构的有效间接成本率平均超过( {40}% ,{}^{18} )，尽管这些机构通常接受私人资助者10%至15%的间接费用。( {}^{19} )

The assumptions embedded in our federal grantmaking institutions, and the creeping inefficiencies within the universities they predominantly serve, reinforce one another. While researchers themselves recognize the need for renewal, few Administrations have had the will and mandate to pursue transformative change.

我们联邦资助机构中根深蒂固的假设，以及它们主要服务的大学内部日益蔓延的低效问题，相互强化。尽管研究人员自身认识到改革的必要性，但很少有政府拥有推动变革性变革的意愿和授权。

A TIME OF URGENT SCIENTIFIC NEED

紧迫的科学需求时刻

On the global stage, America no longer stands alone as the unchallenged leader in science and technology. The United States risks losing its first position to competitors that are quicker to update their models of research funding, maintain greater institutional flexibility, and can build tighter connections between different sectors of their scientific enterprise.

在全球舞台上，美国已不再是科学技术的唯一无可争议的领导者。美国面临失去领先地位的风险，因为竞争对手更快更新研究资助模式、保持更大的机构灵活性，并能在其科学事业的不同部门之间建立更紧密的联系。

Over the past twenty years, China's R&D spending has surged from a negligible fraction of U.S. levels to full parity on a purchasing-power-adjusted basis by some metrics (Figure 2). This is a situation America did not face even during the Cold War, when the Soviet Union's economy was appreciably smaller than our own. ( {}^{20} ) Beyond the sheer scale of its spending, Beijing is treating scientific research capacity as a central pillar of global competition, elevating decision-making to the highest level and taking a tightly integrated approach to partnerships across its sectors. This all-of-society approach has allowed China to surge resources toward basic scientific research, advance rapidly in technologies of key national interest, and drive improvements in its innovation process, where entrenched interests have resisted change.

过去二十年间，中国的研发支出从仅占美国微不足道的一小部分，激增至按购买力平价调整后某些指标上的完全宇称(图2)。这是美国即使在冷战期间也未曾面对的局面，当时苏联的经济规模远小于我们。( {}^{20} ) 除了支出规模本身，北京将科研能力视为全球竞争的核心支柱，将决策提升至最高层级，并对其各部门间的合作伙伴关系采取高度整合的方式。这种全社会的方法使中国能够向基础科学研究集中投入资源，在关键国家利益的技术领域快速进步，并推动其创新过程的改进，而美国既得利益者一直抵制变革。

Figure 2: For the first time, the United States faces a peer-level competitor in R&D spending on a purchasing power parity (PPP)-adjusted basis. ( {}^{21} )

图2:美国首次在研发支出上(按购买力平价调整)面临同级竞争对手。( {}^{21} )

China is not the only country reforming its scientific enterprise. The United Kingdom, for example, has restructured its funding system to address internal inefficiencies, including creating a metascience unit to collect data and develop novel funding mechanisms. Norway has organized its national research council around a portfolio-based model, with boards that allocate funding across thematic priorities drawn from the government's long-term research plan, alongside disciplinary portfolios. With new ideas bubbling up across the world and within our own vibrant philanthropic, metascience, and academic communities, it falls on the United States as the world's premier scientific power to take a hard look at how we can accelerate our own scientific machine as well.

中国并非唯一改革其科学事业的国家。例如，英国已重组其资助体系以解决内部效率低下问题，包括设立元科学部门来收集数据并开发新型资助机制。挪威围绕基于组合的模式组织其国家研究委员会，各委员会根据政府长期研究计划中的主题优先事项以及学科组合分配资金。随着新思想在世界各地以及我们自身充满活力的慈善、元科学和学术社区中涌现，作为世界首要科学强国的美国，有责任认真审视如何加速我们自己的科学机器。

REGAINING LEADERSHIP

重夺领导地位

Beyond strengthening science, we also have a duty to ensure that its downstream benefits accrue to the American people first.

除了加强科学实力，我们还有责任确保其下游利益首先惠及美国人民。

For decades, the United States has funded large cohorts of foreign students and tolerated technology transfer abroad under lax security standards. As of 2024, temporary visa holders accounted for around half of U.S. doctoral graduates in computer science and mathematics with confirmed postgraduation plans. ( {}^{22} ) This reliance on foreign talent sidelines American students, a deep domestic talent pool that remains under-supported by its own government. At the same time, we train extraordinary global talent at enormous expense, only to lose this effort when foreign governments recruit them to build up their own technological enterprises. The interest of vast scores of Americans in using taxpayer dollars to invest in our STEM pipeline has, consequently, eroded.

几十年来，美国资助了大量外国学生，并在宽松的安全标准下容忍技术外流。截至2024年，持有临时签证的人员占美国计算机科学和数学领域已确认毕业后计划的博士毕业生约一半。( {}^{22} ) 这种对外国人才的依赖边缘化了美国学生——一个深厚但长期得不到本国政府充分支持的国内人才库。与此同时，我们以巨大代价培养杰出的全球人才，却在外围政府招募他们建设本国科技企业时前功尽弃。因此，广大美国民众对用纳税人的钱投资我们STEM人才输送渠道的兴趣已经减弱。

America's over-reliance on foreign students also creates a security challenge our institutions are ill-equipped to address. While our R&D ecosystem is now less vulnerable to exploitation due to Presidential actions taken in the first Trump Administration, there is an urgent need for a comprehensive approach to research security. ( {}^{23} ) Our competitors have learned to exploit a deep asymmetry between open and closed scientific systems, using our education system as an entry point into our innovation base while building domestic programs that undermine global scientific norms.

美国对外国学生的过度依赖也带来了我们机构难以应对的安全挑战。尽管由于第一届特朗普政府采取的总统行动，我们的研发生态系统现在不易被利用，但迫切需要一种全面的研究安全方法。( {}^{23} ) 我们的竞争对手已学会利用开放与封闭科学体系之间的深刻不对称，将我们的教育体系作为进入我们创新基地的切入点，同时建立破坏全球科学规范的本国项目。

The United States conducts research openly, publishes freely, and shares methods transparently. These values are central to scientific progress. However, there is a critical difference between sharing information on one's own terms and being exploited; the parts of a scientific enterprise that are not shared are often its comparative advantages. These are precisely what our competitors work hardest to extract, leveraging human capital educated in our universities, harvesting data from our papers while restricting access to their own, and scaling breakthroughs first achieved in our laboratories on their factory floors.

美国公开进行研究、自由发表成果、透明分享方法。这些价值观是科学进步的核心。然而，按自身条件分享信息与被利用之间存在关键区别；科学事业中未分享的部分往往是其比较优势。这正是我们的竞争对手最努力攫取的东西——利用在我们大学接受教育的人力资本，从我们的论文中收集数据同时限制对其本国数据的访问，并将首先在我们实验室取得的突破在它们的工厂车间规模化。

For too long, the United States has believed that technological leadership can be secured solely by the discoveries made in America, regardless of whether those discoveries are then translated into products, capabilities, and industries here or on foreign soil. The consequences of this unguarded openness, in laboratories and in markets, have been severe. The United States pioneered many key enabling technologies for Extreme Ultraviolet (EUV) lithography, ( {}^{24} ) yet the only company capable of manufacturing EUV lithography machines today is headquartered in Europe. We pioneered lithium-ion batteries, yet Asian firms dominate global supply chains, and by extension, battery chemistry research. We developed advanced manufacturing techniques that now underpin factories abroad. Discovery without domestic manufacturing leaves America paying the research bill while rivals develop the process improvements and capture the economic, strategic, and knowledge returns.

长期以来，美国一直认为技术领导地位仅凭在美国的发现就能确保，无论这些发现随后是在美国本土还是外国转化为产品、能力和产业。这种在实验室和市场中的不加防范的开放性后果严重。美国率先开发了极紫外光刻的许多关键使能技术，( {}^{24} ) 但如今唯一能制造极紫外光刻机的公司总部却在欧洲。我们率先开发了锂离子电池，但亚洲企业主导着全球供应链，进而也主导着电池化学研究。我们开发的先进制造技术如今支撑着海外工厂。没有国内制造的发现，让美国支付研究账单，而竞争对手则开发工艺改进并攫取经济、战略和知识回报。

Figure 3: The share of U.S. doctorates awarded to temporary visa holders with definite postgraduation commitments has doubled over four decades, rising from roughly 20% to 40% in natural sciences and engineering, and from 10% to 20% in social, behavioral, and health sciences. ( {}^{25} )

图3:持有临时签证且毕业后有明确去向的美国博士毕业生比例在四十年间翻了一番，在自然科学和工程领域从约20%升至40%，在社会、行为与健康科学领域从10%升至20%。( {}^{25} )

THE PRESIDENT'S CHARGE

总统的使命

As our competitors copy and adapt our scientific machine for themselves, sustained leadership requires us to keep innovating. Standing still while the world changes is not stability. The Manhattan Project succeeded not only because of brilliant physicists, but because we built new institutions capable of coordinating the activity of thousands across the basic science and immense engineering challenges needed to build the bomb. The Apollo Program reached the Moon because NASA could marshal resources and talent in ways no university or company could match. Each represented not merely new funding, but fundamentally new ways of organizing scientific work.

当竞争对手复制并改造我们的科学机器为己所用时，持续领先要求我们不断创新。在世界变化时停滞不前并非稳定。曼哈顿计划的成功不仅源于杰出的物理学家，更因为我们建立了能够协调数千人活动的新机构，跨越基础科学与制造原子弹所需的巨大工程挑战。阿波罗计划之所以能登月，是因为NASA能以任何大学或企业无法企及的方式调配资源与人才。每一次突破不仅代表新的资金投入，更代表着组织科学工作的全新方式。

We must face the reality that more innovation is happening in industry than ever before, and that the balance and nature of work shared across the federal, university, and corporate pillars of the national research enterprise have shifted. We must acknowledge, too, that the linear model of discovery and technical progress no longer holds. The interplay between basic research, regular professional science, and commercialization is far more complex than had been assumed. Meanwhile, the context in which the American innovation enterprise operates has become global, and therefore intensely vulnerable; the technologies we invent rely on production chains that stretch around the world, and they are subject to theft by near-peer competitors. Finally, the technological context in which science is conducted and shared has been radically transformed by the internet and current information technologies, now changing even more with the rise of AI.

我们必须正视现实:产业界的创新比以往任何时候都更加活跃，联邦、大学和企业三大支柱在国家科研事业中的工作平衡与性质已经发生转变。我们同样必须承认，发现与技术进步的线性模式已不再成立。基础研究、常规专业科学与商业化之间的相互作用远比假设的更为复杂。与此同时，美国创新事业所处的环境已全球化，因此极易受到威胁；我们发明的技术依赖遍布全球的生产链，并面临近邻竞争对手的窃取。最后，科学开展与共享的技术环境已被互联网和当前信息技术彻底改变，如今随着人工智能的兴起，这种变革仍在加速。

Throughout our history, Americans have reinvented, reformed, and refounded our institutions when the moment demanded it. Each generation of American scientists, inventors, and pioneers has seized the opportunity to expand the frontiers of knowledge. On the occasion of America's 250th anniversary, we must remember that ours is a Republic defined by courage, innovation, and exploration.

纵观历史，当时代需要时，美国人总会重塑、改革并重建我们的制度。每一代美国科学家、发明家和先驱都抓住机遇拓展知识前沿。在美国建国250周年之际，我们必须铭记:我们的共和国以勇气、创新和探索为标志。

Just as scientific inquiry demands that we revise our theories when evidence contradicts them, evidence of scientific slowdown and serious competition from abroad should spur us to experiment with new systems, new models, and new ways of funding, conducting, and translating research. Vannevar Bush's pioneering spirit calls us to do what he would surely do today: reimagine the entire enterprise for our time.

正如科学探究要求我们在证据与理论相悖时修正理论，科学放缓及来自国外的激烈竞争的证据，应促使我们尝试新的体系、新的模式以及新的资助、开展和转化研究的方式。万尼瓦尔·布什的开创精神召唤我们去做他今天一定会做的事:为我们的时代重新构想整个事业。

Chapter II Revitalizing America's Science and Technology Enterprise

第二章重振美国的科技事业

THE SCIENTIFIC MACHINE IS GETTING BOGGED DOWN

科学机器正陷入泥潭

For the better part of a century, one of America's most decisive advantages has been the ability to harvest novel discoveries for the prosperity of the American people. This advantage stemmed from the strength of our post-war innovation ecosystem: our universities, our national laboratories, and the symbiotic relationship between federal funding and research.

近一个世纪以来，美国最显著的优势之一，便是能够将新颖发现转化为惠及美国人民的财富。这一优势源于我们战后创新生态系统的力量:我们的大学、国家实验室，以及联邦资助与研究之间的共生关系。

As Bush wrote in 1945:

正如布什在1945年所写:

A nation which depends upon others for its new basic scientific knowledge will be slow in its industrial progress and weak in its competitive position in world trade, regardless of its mechanical skill. ( {}^{26} )

一个依赖他国获取基础科学新知识的国家，无论其机械技能如何，其工业进步将缓慢，在世界贸易中的竞争地位也将薄弱。( {}^{26} )

However, our scientific dominance today is at risk. While our capacity to drive breakthroughs in basic science remains the envy of the world, as described in Chapter I, competitors are closing the gap. And while federally funded science continues to generate a high return on investment for our taxpayers, a growing body of work provides evidence that, across many fields, our researchers are fighting against an increasingly calcified system that has driven up the cost of scientific progress over time. ( {}^{27} )

然而，我们今天的科学主导地位正面临风险。尽管我们在基础科学领域推动突破的能力仍令世界羡慕，如第一章所述，但竞争对手正在缩小差距。同时，尽管联邦资助的科学继续为纳税人带来高投资回报，但越来越多的证据表明，在许多领域，我们的研究人员正与一个日益僵化的体系作斗争，这个体系随着时间的推移推高了科学进步的成本。( {}^{27} )

Despite massive funding increases in biomedical research since the 1990s, the rate of significant breakthroughs appears to have slowed, drug approvals have flatlined, and the enterprise's productivity, hampered by growing burdens, has declined. ( {}^{28} ) It has become common to speak of "Eroom's law" (Moore's law in reverse) describing the predictable decline in the number of new drugs approved per billion dollars spent. Since 1950, pharmaceutical R&D efficiency, as measured in new drugs per billion dollars, has fallen roughly eighty-fold in inflation-adjusted terms, halving approximately every nine years. ( {}^{29} ) And although NIH's budget has more than doubled since the 1990s, we have not seen a proportional increase in breakthrough treatments, citation impact per dollar spent, or scientific productivity. ( {}^{30} )

尽管自20世纪90年代以来生物医学研究的资金大幅增加，但重大突破的速度似乎有所放缓，药物批准数量停滞不前，而该领域因日益沉重的负担导致生产力下降。( {}^{28} )人们常提及“埃鲁姆定律”(摩尔定律的反向)，描述每十亿美元支出所批准的新药数量呈可预测的下降趋势。自1950年以来，按通胀调整后的每十亿美元新药数量衡量，制药研发效率下降了约80倍，大约每九年减半。( {}^{29} )尽管自20世纪90年代以来，美国国立卫生研究院的预算增加了一倍多，但我们并未看到突破性疗法、每美元支出的引用影响力或科学生产力成比例增长。( {}^{30} )

As a funder and institution-builder, the Federal Government has fallen behind in creating environments where American scientists can do their best work. Evidence shows that the inputs required to sustain past rates of improvement have increased sharply across many fields. A famous study demonstrated that sustaining the historical pace of Moore-style gains in transistor density has required a much larger workforce. Since the early 1970s, the number of researchers needed to double transistor density has risen more than eighteenfold, implying a 7% annual decline in "ideas productivity." The pattern repeats in agriculture, where research effort has multiplied by factors of 3 to 25 since 1969, depending on the metric, while yield growth remains mostly flat; and in medicine, where the "years of life saved" per clinical trial peaked in the mid-1980s before falling sharply. ( {}^{31} )

作为资助者和机构建设者，联邦政府在创造让美国科学家发挥最佳水平的环境方面已经落后。证据表明，维持过去改进速度所需的投入在许多领域急剧增加。一项著名研究表明，维持摩尔定律式晶体管密度进步的历史速度需要更大的劳动力。自20世纪70年代初以来，使晶体管密度翻倍所需的研究人员数量增加了18倍以上，这意味着“创意生产力”每年下降7%。这一模式在农业领域重演:自1969年以来，研究投入根据指标不同增加了3到25倍，而产量增长基本持平；在医学领域，每次临床试验“挽救的生命年数”在20世纪80年代中期达到顶峰，随后急剧下降。( {}^{31} )

Admittedly, slowdown in mature scientific subfields may be inevitable. One might argue that it is natural for the pace of progress to decline, once the proverbial low-hanging fruit has been picked. A slowdown could even be read as evidence of success. However, this intuition has repeatedly been proven wrong throughout modern history. And one would expect new mechanisms for sharing information, and new ways to compress scientific knowledge, to be countervailing forces that speed innovation.

诚然，成熟科学子领域的放缓或许不可避免。有人可能会争辩说，一旦所谓的“低垂果实”被摘取，进步速度放缓是自然的。放缓甚至可能被视为成功的证据。然而，这一直觉在现代历史上屡次被证明是错误的。人们会期望新的信息共享机制和压缩科学知识的新方法成为加速创新的反作用力。

The pattern of apparent stasis in a scientific field exploding into progress, opened by a new discovery and changes in scientific institutions, has repeated itself again and again. These punctuated equilibria are, in fact, the essential story of science. Max Planck famously had a professor tell him that physics was nearly as developed as mature fields like geometry, only for Planck's own discoveries in quantum mechanics to completely reorient our understanding of the physical world. Many medical doctors believed their field was reaching perfection in the late 19th century, with one writing that "there cannot always be fresh fields for conquest by the knife." ( {}^{32} ) Yet soon, the concurrent transformation of medical education and emergence of research hospitals created the institutional foundations for a broader understanding of disease and for modern medicine as we know it. ( {}^{33} )

科学领域表面停滞而后因新发现和科学机构变革而爆发进步的模式一再重演。这些间断平衡实际上是科学的核心故事。马克斯·普朗克曾有名言，一位教授告诉他物理学已像几何学等成熟领域一样发达，但普朗克自己在量子力学中的发现彻底改变了我们对物理世界的理解。许多医学家在19世纪末认为他们的领域正趋于完美，有人写道:“不可能总有新的领域供手术刀征服。”( {}^{32} )然而，不久之后，医学教育的同步转型和研究型医院的出现，为我们对疾病的更广泛理解以及我们所知的现代医学奠定了制度基础。( {}^{33} )

Repeatedly, the tree has only looked bare from the current perspective; the fruits have not been exhausted at all. We had merely lacked the tools with which to pick them.

一次又一次，从当前视角看，树似乎光秃秃的；果实远未被摘尽。我们只是缺乏采摘它们的工具。

By adopting new social and material technologies, we can again accelerate the pace of discovery. It falls to us, as it fell to our predecessors, to imagine new machines capable of exploring the endless frontier.

通过采用新的社会和技术工具，我们可以再次加速发现的步伐。正如我们的前辈一样，我们有责任构想出能够探索无尽前沿的新机器。

SLOWED BY GROWING FRICTIONS

摩擦加剧，进展放缓

Our first step is to strip away the frictions that keep our brightest minds from pursuing the ideas most likely to lead to transformative breakthroughs. Consider a young scientist with a promising proposal for federally funded research, and the decades of accumulated institutional bureaucracy she must navigate to seek support in today's enterprise.

我们的第一步是消除那些阻碍最聪明头脑追求最可能带来变革性突破的想法的摩擦。设想一位年轻科学家，她有一份很有前景的联邦资助研究提案，但在当今的体系中，她必须应对数十年积累的机构官僚程序才能寻求支持。

She spends two to four months drafting the proposal, assembling preliminary data for the same project requiring funding, and navigating her university's internal review process. If she applies to NIH in February, she will be lucky to learn whether she succeeded by the end of the year. Certain grants even have up to a 20-month lead time. ( {}^{34} ) That is almost as long as it took for the Boeing 747 "Jumbo Jet" to go from the drawing board to production. ( {}^{35} )

她需要花两到四个月起草提案，为同一需要资金的项目收集初步数据，并应对所在大学的内部审查流程。如果她在二月份向美国国立卫生研究院提交申请，那么到年底能得知是否成功就算幸运了。某些资助甚至需要长达20个月的前置时间。( {}^{34} ) 这几乎和波音747“珍宝客机”从图纸到投产的时间一样长。( {}^{35} )

If she is awarded the grant, she will face mounds of paperwork. From 1991 to January 2025, the Federal Government imposed at least 270 new requirements on research grants, far outpacing efforts to reduce administrative burdens on researchers. ( {}^{36} ) Federally negotiated indirect cost rates now reach 50 to 60% of direct research costs at major institutions, a figure our scientist will have to bear in mind as she drafts her application. ( {}^{37} ) While effective rates often run closer to 40%, this remains a substantial tax on research budgets, shaping what she asks for. Some of this covers legitimate infrastructure she uses every day, but much of it funds administrative expansion at her university that has outpaced the growth of research itself.

如果她获得了资助，她将面临堆积如山的文书工作。从1991年到2025年1月，联邦政府对研究资助新增了至少270项要求，远远超过了减轻研究人员行政负担的努力。( {}^{36} ) 联邦谈判确定的间接成本费率现在已达到主要机构直接研究成本的50%到60%，这位科学家在起草申请时必须牢记这一数字。( {}^{37} ) 虽然实际费率通常接近40%，但这仍然是对研究预算的一笔巨大税收，影响着她申请的内容。其中一部分用于她日常使用的合法基础设施，但大部分用于资助她所在大学行政部门的扩张，而这种扩张已经超过了研究本身的增长。

Senior investigators can delegate paperwork to postdoctoral researchers, but our scientist runs a small lab and has no one to delegate to. She writes grant applications using hours that should have gone to experiments or mentoring students. This is a tax on innovation that does not appear in the federal budget but costs the nation dearly in foregone breakthroughs. These burdens also create a perverse incentive structure, in which scientists who excel at research administration leapfrog those who excel at research performance. The weight falls heaviest on the scientists America needs most.

资深研究员可以将文书工作委托给博士后研究人员，但我们的科学家经营着一个小型实验室，无人可委托。她花在撰写资助申请上的时间本应用于实验或指导学生。这是对创新的一种税收，虽未出现在联邦预算中，却因错失的突破而让国家付出沉重代价。这些负担还造成了一种扭曲的激励机制，使擅长研究管理的科学家超越那些擅长研究表现的科学家。而最沉重的负担落在了美国最需要的科学家身上。

THE INCUMBENCY TAX

任职税

The academy is a long and difficult road, leading to few stable positions. We should encourage early-career scientists at every step of the academic crucible, from undergraduate lab assistant to first faculty job, to pursue big and creative ideas. We should enable and empower scientists who persist into a research career to focus on breakthrough research from the start. But the data suggest we do not. Between 1980 and 2008, the average age of NIH principal investigators rose from 39 to 51 (Figure 4), while the average age of new principal investigators rose from 36 to 42, exceeding the average age of Nobel Prize-winning contributions in related fields over a comparable period. ( {}^{38} ) These patterns lengthen feedback loops and bias careers toward safer, incremental projects during the long apprenticeship years. Our early-career scientist has seen this gradual graying of our research workforce, and is likely to adjust her ambitions accordingly.

学术之路漫长而艰难，稳定职位寥寥无几。我们应在学术熔炉的每一步——从本科实验室助理到首个教职——鼓励早期职业科学家追求宏大而富有创意的想法。我们应赋能并支持那些坚持从事研究事业的科学家，从一开始就专注于突破性研究。但数据显示我们并未做到。1980年至2008年间，NIH首席研究员的平均年龄从39岁升至51岁(图4)，而新晋首席研究员的平均年龄从36岁升至42岁，超过了同期相关领域诺贝尔奖获奖成果的平均年龄。( {}^{38} )这些模式延长了反馈循环，并使职业生涯在漫长的学徒期内偏向更安全、渐进的项目。我们的早期职业科学家目睹了研究队伍逐渐老龄化，并可能据此调整自己的抱负。

Figure 4: The average age of first-time NIH investigators has increased consistently over the past four decades, from mid-thirties in 1980 to the early forties today, across all degree types. ( {}^{39} )

图4:过去四十年来，NIH首次受资助研究员的平均年龄持续上升，从1980年的三十多岁中期增至如今的四十岁出头，涵盖所有学位类型。( {}^{39} )

Our scientist may accept that these longer training cycles reflect increasing specialization, but these incumbency dynamics also dampen turnover at the frontier. The effect is encapsulated by Planck's famous (mis)quote that "science advances one funeral at a time," and is well documented. ( {}^{40} ) Studies show that when a star scientist in biomedicine passes unexpectedly, outsider contributions surge into the space the star's network has informally dominated, and those outsider papers are then more likely to become highly cited. ( {}^{41} )

我们的科学家可能认为，这些更长的培训周期反映了日益专业化，但这种任职动态也抑制了前沿领域的更替。其影响被普朗克著名的(误)引语概括为“科学进步一次葬礼接一次葬礼”，并有充分文献记载。( {}^{40} )研究表明，当生物医学领域的明星科学家意外离世时，外部研究者的成果会涌入该明星网络非正式主导的领域，而这些外部论文随后更可能成为高被引文献。( {}^{41} )

At the system level, researchers find that as disciplinary fields grow large, attention ossifies around a fixed canon. New papers are less likely to displace central ideas, and even highly cited ones tend to receive citations in a burst rather than through steady accumulation. ( {}^{42} )

在系统层面，研究人员发现，随着学科领域规模扩大，注意力会固化在固定经典上。新论文更难以取代核心思想，即使是高被引论文也倾向于以爆发式而非稳定积累的方式获得引用。( {}^{42} )

WEAKENED MERITOCRACY

削弱的精英体制

Our up-and-coming scientist is further discouraged by the corrosion of the merit principle that once made American research the envy of the world. What began with the NSF's "broader impacts" criterion, which was a reasonable effort to ensure taxpayer funds benefit society, has evolved into a sweeping distortion of the selection process that reduces mobility for the best researchers. Between 2021 and 2024, the share of new NSF grants focusing on diversity, equity, and inclusion initiatives surged from fractions of a percent to more than a quarter. ( {}^{43} ) Our scientist has learned the new rules from principal investigators she has worked for, and is tempted to dress up her otherwise excellent technical proposals with ideological language to survive review. Until recently, NASA required research proposals to include plans for furthering "inclusion goals," which were to be reviewed by review panels one-half composed of "diversity, equity, and inclusion professionals."44

我们这位崭露头角的科学家，因曾让美国研究举世艳羡的精英原则遭到侵蚀而愈发沮丧。始于国家科学基金会"更广泛影响"标准——这一确保纳税人资金惠及社会的合理举措——已演变为对选拔过程的全面扭曲，削弱了顶尖研究者的流动性。2021年至2024年间，国家科学基金会新拨款中聚焦多元化、公平与包容倡议的份额，从不足百分之一飙升至超过四分之一。( {}^{43} )我们的科学家从曾共事过的首席研究员那里学到了新规则，她忍不住想用意识形态语言包装自己原本出色的技术提案，以求在评审中过关。直到最近，美国国家航空航天局还要求研究提案包含推进"包容目标"的计划，并由一半由"多元化、公平与包容专业人士"组成的评审小组进行审查。44

The American scientific establishment has placed politics above merit and performance before. In the 1920s, elite universities implemented quotas limiting Jewish enrollment. Harvard's president worried about a "Jewish problem" as Jewish students grew from 6% to 22% of the student body. The methods are strikingly familiar: subjective criteria for "character" and "leadership," diversity requirements designed to recruit from regions with fewer Jewish residents, and "holistic" reviews that obscured the actual basis for decisions. These quotas, maintained for decades, excluded some of the most talented minds in American science. ( {}^{45} )

美国科学界此前也曾将政治置于精英与绩效之上。20世纪20年代，精英大学实施配额制限制犹太学生入学。当犹太学生从占学生总数的6%增至22%时，哈佛大学校长曾担忧"犹太人问题"。其手段惊人地相似:针对"品格"和"领导力"的主观标准、旨在从犹太居民较少地区招生的多元化要求，以及掩盖实际决策依据的"全面"审查。这些持续数十年的配额制，将美国科学界一些最杰出的人才拒之门外。( {}^{45} )

We should learn from this history rather than repeat it. When qualified candidates are passed over for reasons unrelated to their scientific ability, when they are judged for who they are rather than for their ideas, we injure the cause of progress, discovery, and America's scientific competitiveness.

我们应从这段历史中汲取教训，而非重蹈覆辙。当合格候选人因与科学能力无关的原因被忽视，当他们因身份而非思想被评判时，我们便损害了进步、发现以及美国科学竞争力的根基。

MISALIGNED INCENTIVES

激励错位

Even if the ambitious young scientist successfully navigates the selection gauntlet, she enters an academy whose incentives are severely misaligned with good scientific conduct.

即使这位雄心勃勃的年轻科学家成功通过了选拔的严苛考验，她进入的学术界其激励机制也与良好的科学实践严重错位。

Observing older peers, our scientist has learned that taking the long shot to challenge established paradigms may threaten her ability to deliver results and advance to tenure; yet by the time she achieves tenure, a substantial portion of her most creative years will be behind her. She notices, too, that the tenure and promotion system seems to reward quantity over quality. Cutting work into the smallest publishable units, which researchers call "salami-slicing," often pays off more than making an ambitious attempt at transformative discovery.

观察年长的同行，我们的科学家了解到，冒险挑战既定范式可能会危及她产出成果和获得终身教职的能力；然而，等到她获得终身教职时，她最具创造力的年华已大半逝去。她还注意到，终身教职和晋升制度似乎更看重数量而非质量。将研究切割成最小的可发表单元——研究人员称之为“香肠切片”——往往比大胆尝试变革性发现更有利可图。

Our scientist knows a better way is possible. Private-sector laboratories and startups routinely give young researchers tremendous responsibility, and there they are afforded the opportunity to change the world. Research shows that when investigators receive longer-horizon support with tolerance for early failure, they produce portfolios with both more hits and more misses, the signature of genuine exploration. ( {}^{46} ) But project-tied, short-cycle grants dominate the federal landscape, and our scientist feels pushed toward safer, more "fundable" territories. These incentives also help explain why her peers now commonly reach professional independence only in their forties, not their early thirties as in previous generations.

我们的科学家知道有更好的方式。私营部门的实验室和初创公司经常让年轻研究人员承担巨大责任，并给予他们改变世界的机会。研究表明，当研究者获得更长周期的支持且能容忍早期失败时，他们产出的成果组合中既有更多成功也有更多失败，这正是真正探索的旋称。( {}^{46} ) 然而，联邦资助领域以项目绑定、短周期的拨款为主，我们的科学家感到被推向更安全、更“易获资助”的领域。这些激励也解释了为何她的同行现在通常要到四十多岁才能获得专业独立，而非像前几代人那样在三十岁出头。

The publication economy amplifies these problems. The young scientist quickly learns that there is a particular narrative pattern common to all articles published in the top journals. Meta-research has documented a structural reduction in novelty in papers and patents, which are less disruptive and less likely to reorient a field than in prior decades. ( {}^{47} ) Scientists eschew negative results, even though failed experiments often teach more than successes. Competitive environments amplify this positive-results bias, crowding out careful negative findings and tool-building that lack tidy narratives. Because the system pays by the paper, it under-invests in public goods like datasets, open-source code, and shared engineering infrastructure. The team science now required at the technological frontier gets abandoned for work that generates individual credit.

发表经济放大了这些问题。这位年轻科学家很快发现，顶级期刊发表的所有文章都有一种特定的叙事模式。元研究已记录到论文和专利中新颖性的结构性下降，与过去几十年相比，它们更具颠覆性且更不可能重新定向一个领域。( {}^{47} ) 科学家回避负面结果，尽管失败的实验往往比成功教会我们更多。竞争环境放大了这种正面结果偏差，挤出了缺乏整洁叙事的谨慎负面发现和工具构建。由于系统按论文数量付费，它在数据集、开源代码和共享工程基础设施等公共产品上投入不足。技术前沿所需的团队科学被放弃，转而追求能产生个人荣誉的工作。

THE REPRODUCIBILITY CRISIS

可重复性危机

As many leaders in American science agree, the research enterprise must learn to value and incentivize reproducibility studies and to hold its members responsible for failures in the scientific process.

正如美国科学界的许多领军人物所认同的那样，科研事业必须学会重视和激励可重复性研究，并让科研人员对科学过程中的失败负责。

The reproducibility crisis, particularly acute in the social sciences, has undermined future research and public confidence. In one study that attempted to reproduce 100 psychology studies, fewer than 40 succeeded. ( {}^{48} ) The challenge extends across other fields as well. In Alzheimer's research, a celebrated 2009 paper in a top journal presented a promising path to treating the disease. ( {}^{49} ) By 2012, other researchers had demonstrated its irreproducibility, and internal reviews at the sponsoring pharmaceutical company terminated drug development based on its findings. ( {}^{50} ) Yet the paper accumulated more than 800 citations, misdirecting research priorities and federal funding for another decade. ( {}^{51} ) Its lead author became a university president, and the paper was only finally retracted 15 years after publication, shortly after the lead author resigned amid a broader investigation into data manipulation in his laboratories. ( {}^{52} ) The scientific review process worked, eventually, but far too late.

可重复性危机，在社会科学领域尤为严重，已经损害了未来研究和公众信心。在一项试图重复100项心理学研究的尝试中，成功的不到40项。( {}^{48} ) 这一挑战也延伸至其他领域。在阿尔茨海默病研究中，2009年一篇发表在顶级期刊上的著名论文提出了一条有前景的治疗路径。( {}^{49} ) 到2012年，其他研究人员已证明其不可重复性，赞助该研究的制药公司内部审查也终止了基于其发现的药物开发。( {}^{50} ) 然而，该论文累计被引用超过800次，误导了研究重点和联邦资金长达十年之久。( {}^{51} ) 其第一作者成为一所大学的校长，而该论文最终在发表15年后才被撤回，此时距第一作者因实验室数据操纵的广泛调查而辞职不久。( {}^{52} ) 科学审查过程最终发挥了作用，但为时已晚。

The good scientist knows that knowledge is probabilistic, that evidence accumulates gradually, and that uncertainty is inherent. But the system pressures researchers to deliver results in confident, discrete units. Journal editors want clear narratives. University press offices demand headlines. Nuance dies, as a result, in the race for attention.

优秀的科学家知道知识是概率性的，证据是逐步积累的，不确定性是固有的。但科研体系却迫使研究人员以自信、明确的单元交付成果。期刊编辑想要清晰的叙事。大学新闻办公室追求头条新闻。结果，在争夺关注的过程中，细微之处被扼杀了。

This failure has spilled over to political decision-making. With notable and still-undercelebrated exceptions, the scientific establishment during the COVID- 19 pandemic failed to recognize that science can only describe the world as it is, not the world that ought to be; while it informs policymakers, it cannot determine the best policy, or the tradeoffs that should be made. The scientific consensus to shutter schools demonstrated a profound inability to confront uncertainty or integrate knowledge across specializations. The best available evidence indicated that children were neither at high risk of the disease nor significant vectors of transmission. Conversely, the developmental costs of remote schooling and isolation were entirely predictable. Yet, scientific officialdom produced a "closed-ranks" response, preferring blind consensus over informed dissent. The scientific consensus failed to check its own work, communicate the limits of its own certainty, or remain skeptical of its own assumptions.

这一失败已蔓延至政治决策。除了那些值得注意但仍未得到充分赞扬的例外，在COVID-19疫情期间，科学界未能认识到科学只能描述世界本来的样子，而非世界应该的样子；虽然它为政策制定者提供信息，但无法决定最佳政策或应做出的权衡。关闭学校的科学共识表明了一种深刻的无能:无法面对不确定性，也无法整合跨专业知识。当时最有力的证据表明，儿童既不是该疾病的高风险人群，也不是重要的传播媒介。相反，远程教育和隔离的发展代价是完全可预见的。然而，科学官方机构却做出了“封闭阵营”式的回应，宁愿选择盲目的共识，也不愿接受有依据的异议。科学共识未能审视自身工作，未能传达自身确定性的局限，也未能对自身假设保持怀疑。

A LACK OF ACCOUNTABILITY

缺乏问责制

Even as our individual scientists continue to do world-changing work, performing miracles that save American lives and defend our homeland, these challenges reflect a systematic breakdown in the lines of accountability that align the scientific enterprise with the public interest.

即便我们的科学家个体仍在从事改变世界的工作，创造拯救美国生命、保卫国土的奇迹，但这些挑战反映出，在使科学事业与公共利益保持一致的问责链条上，存在着系统性的断裂。

While repeating the mantra that science must be "independent" of politics, parts of academia have become highly dependent on government funding. But as Bush understood, federal support for science must be politically accountable. Accountability does not mean dictating how a research agenda is to be executed, nor turning away from the basic research that has long been the wellspring of American prosperity. Quite the opposite. It means ensuring that the system serves the researchers who are its lifeblood, rather than the entrenched interests that have accreted around them. Only those parts of the national research enterprise directly responsive to the political process can prioritize among the many potential avenues of inquiry, fund those that best reflect national priorities, and be checked when the scientific process breaks down. In a self-governing nation of laws and citizens, the Federal Government elected by the people shall have the ability to determine how to allocate public resources in the public interest.

在反复强调科学必须"独立"于政治的口号下，部分学术界已高度依赖政府资金。但正如布什所理解的，联邦对科学的支持必须在政治上接受问责。问责并不意味着指令研究议程如何执行，也不意味着放弃长期作为美国繁荣源泉的基础研究。恰恰相反。它意味着确保体系服务于作为其命脉的研究人员，而非围绕他们形成的既得利益。只有国家研究体系中那些直接响应政治进程的部分，才能在众多潜在研究路径中确定优先顺序，资助最能体现国家优先事项的项目，并在科学进程出现问题时接受审查。在一个由法律和公民组成的自治国家中，由人民选举产生的联邦政府应有权决定如何为公共利益分配公共资源。

Within that framework, the independent role of federally funded scientists is to design and execute the research and experimentation program that achieves those objectives. A properly accountable system is one that protects their freedom to do so.

在这一框架内，联邦资助科学家的独立角色是设计和执行实现这些目标的研究与实验计划。一个适当问责的体系，应当保护他们这样做的自由。

Originally intended to shield researchers from external political meddling, the invocation of "scientific autonomy" has too often been inverted. Autonomy, and indeed America's culture of intellectual freedom, remains one of our most valuable scientific assets, but it has been cynically used as a shield against accountability, as scientific institutions are slow to police themselves, or even engage in political meddling of their own, often at the expense of our brightest and most energetic minds. The same scientists who receive grants often serve as the reviewers who dispense them, enforcing a consensus that perpetuates existing biases. Federal funding agencies, in particular, lack the feedback mechanisms found in the private sector. In venture capital or philanthropy, poor judgment faces the swift discipline of the market; bad bets lead to insolvency. In the federal sphere, absent deliberate action, there is no penalty for rejecting a breakthrough, nor for funding safe work that changes nothing.

原本旨在保护研究人员免受外部政治干预的"科学自主权"概念，常常被颠倒使用。自主权——以及美国的知识自由文化——仍然是我们最宝贵的科学资产之一，但它被别有用心地用作逃避问责的挡箭牌:科学机构在自我监督方面行动迟缓，甚至自身也参与政治干预，往往以牺牲我们最杰出、最富有活力的头脑为代价。获得资助的科学家往往同时担任发放资助的评审人，从而强化了延续现有偏见的共识。联邦资助机构尤其缺乏私营部门中的反馈机制。在风险投资或慈善领域，糟糕的判断会面临市场的迅速惩罚；错误押注导致破产。而在联邦领域，若无刻意行动，拒绝一项突破性成果不会受到惩罚，资助那些毫无改变的安全工作也不会受到惩罚。

A BETTER PATH FORWARD

更好的前进道路

One necessary step in restoring accountability to federally supported science is exercising better oversight of scientific funding so that research activities align with the best interests of the American people, through the intentional and principled allocation of capital.

恢复联邦资助科学问责制的必要一步，是加强对科研资金的监督，通过有目的、有原则的资金分配，确保研究活动符合美国人民的最大利益。

Our federal agencies distribute approximately $200 billion in annual R&D funding. Yet we have no systematic framework for identifying where those dollars could catalyze the greatest scientific returns, with deference instead given to the same incumbents that consume the funding. This process produces a portfolio that emerges by accident rather than intentional design.

我们的联邦机构每年分配约2000亿美元的研发资金。然而，我们缺乏系统性的框架来识别这些资金在哪些领域能催化最大的科学回报，反而优先考虑那些消耗资金的现有机构。这一过程产生的投资组合是偶然形成的，而非有意设计的结果。

Driving meaningful improvements in our portfolio allocation will require a coordinated effort, but it is not impossible. We already have proof that bold models can dramatically accelerate progress and deliver new scientific opportunities for our researchers.

推动投资组合分配取得实质性改善需要协调一致的努力，但这并非不可能。我们已经证明，大胆的模式可以显著加速进展，为研究人员带来新的科学机遇。

Throughout history, Americans have developed various ways to support scientific progress that depart from the university-based, principal investigator-driven grant. These include Cold Spring Harbor Laboratory, a leading institution of biological research, founded in 1890, and the Institute for Advanced Study, founded in 1930, which gave luminaries like Einstein, Noether, Oppenheimer, and Gödel an opportunity to explore revolutionary ideas. Each provided a home for new kinds of science.

纵观历史，美国人发展出了多种支持科学进步的方式，这些方式不同于以大学为基础、由首席研究员主导的资助模式。其中包括成立于1890年的冷泉港实验室(一家领先的生物研究机构)，以及成立于1930年的高等研究院——该院为爱因斯坦、诺特、奥本海默和哥德尔等杰出人物提供了探索革命性思想的机会。每一个机构都为新型科学提供了栖息地。

Another famous example is DARPA, which gave us GPS, the internet, stealth aircraft, and autonomous vehicles. Rather than relying on consensus-based review panels as the primary decision-making mechanism, DARPA gives individual program managers the power to make bold technological bets and actively curate teams to execute them. Congress has taken notice, creating the Advanced Research Projects Agency for Health (ARPA-H), Advanced Research Projects Agency-Energy (ARPA-E), and other similar agencies, collectively representing billions of dollars organized around the program-manager model rather than traditional peer review.

另一个著名的例子是DARPA，它为我们带来了全球定位系统、互联网、隐形飞机和自动驾驶汽车。DARPA不依赖以共识为基础的评审小组作为主要决策机制，而是赋予项目经理个人权力，让他们做出大胆的技术赌注，并积极组建团队来执行这些赌注。国会已注意到这一点，创建了卫生高级研究计划局(ARPA-H)、能源高级研究计划局(ARPA-E)及其他类似机构，这些机构共同代表着数十亿美元的资金，其组织模式是项目经理制，而非传统的同行评审制。

Over the past decade, a growing community of researchers, philanthropists, and policymakers has turned the lens of scientific inquiry onto science itself, asking precisely how we can reduce frictions, align incentives, increase accountability, and inject more dynamism into the scientific enterprise. This field, often called metascience or the "science of science," has begun generating rigorous evidence of what actually works, both by studying why models like DARPA or the Institute for Advanced Study succeed, and by running controlled experiments on new approaches.

过去十年间，越来越多的研究人员、慈善家和政策制定者将科学探究的目光转向科学本身，探究如何减少摩擦、协调激励、增强问责，并为科学事业注入更多活力。这一领域通常被称为元科学或"科学学"，通过研究DARPA或高等研究院等模式为何成功，以及通过对新方法进行受控实验，开始产生关于什么方法真正有效的严谨证据。

Philanthropies and federal agencies are now deliberately applying these insights and piloting new organizational forms to address gaps the traditional academic system cannot fill. For instance, NIH has recently created a metascience office, and the new Directorate for Technology, Innovation and Partnerships (TIP) within NSF has begun experimenting with alternatives to traditional peer review, designing and executing controlled experiments in alternative funding mechanisms in partnership with metascience researchers. ( {}^{53} )

慈善机构和联邦机构现在正有意识地应用这些见解，并试点新的组织形式，以填补传统学术体系无法填补的空白。例如，美国国立卫生研究院最近成立了一个元科学办公室，而美国国家科学基金会内新成立的技术、创新与合作局(TIP)已开始尝试替代传统同行评审的方法，与元科学研究人员合作，设计和执行关于替代资助机制的受控实验。( {}^{53} )

These scattered successes confirm the possibility of systematically redesigning how we organize, fund, and conduct science. Realizing that potential will require us to understand how the nature of scientific work has changed, what new institutional forms those changes demand, and what funding mechanisms can best support them.

这些零散的成功案例证实了系统性地重新设计我们组织、资助和开展科学的方式的可能性。要实现这一潜力，我们需要理解科学工作性质的变化、这些变化所需的新制度形式，以及最能支持这些形式的新资助机制。

The answers will not come from any single reform but from building the capacity for continuous experimentation, for applying the scientific method to the scientific enterprise itself.

答案不会来自任何单一的改革，而是来自建立持续实验的能力——将科学方法应用于科学事业本身。

ADAPTING TO THE CHANGING NATURE OF SCIENCE

适应科学本质的变化

The 19th and 20th centuries witnessed a series of institutional innovations that ushered in a fertile era of discovery. The natural philosophy of earlier ages, where a single scholar might range freely across what we now call physics, chemistry, and biology, had given way to specialized disciplines. Each had its own departments, journals, and staff. This transformation, pioneered primarily in German universities during the 19th century, laid the foundation for scientific professionalization. ( {}^{54} )

19世纪和20世纪见证了一系列制度创新，开启了一个丰硕的发现时代。早期时代的自然哲学——一位学者可以自由涉足我们今天所称的物理学、化学和生物学——已被专业学科所取代。每个学科都有自己的院系、期刊和人员。这一转型主要于19世纪在德国大学中开创，为科学的专业化奠定了基础。( {}^{54} )

America adopted this architecture of discovery and gave it a distinctive twist. Land-grant universities democratized access to scientific education, the college major system restructured undergraduate training, and technical universities focused on industry. These innovations proved remarkably successful. The combination of disciplinary depth with clear career ladders enabled the specialization that carried us into the scientific lead.

美国采纳了这一发现架构，并赋予了其独特的特色。赠地大学使科学教育普及化，大学主修制度重构了本科培训，技术大学则专注于工业领域。这些创新被证明极为成功。学科深度与清晰职业阶梯的结合，促成了使我们领先于科学前沿的专业化。

Yet the disciplinary framework that served us in the 20th century sits uneasily with science in the 21st. The most consequential questions of our era, such as how proteins fold and function, how certain disorders emerge from neural circuits, and how we can harness fusion energy, do not respect departmental boundaries.

然而，在20世纪为我们服务的学科框架，在21世纪却与科学格格不入。我们这个时代最重要的问题——例如蛋白质如何折叠和运作、某些疾病如何从神经回路中产生、以及我们如何利用聚变能——并不受院系边界的限制。

The protein folding problem that earned the 2024 Nobel Prize in Chemistry belonged to no single academic department; it required deep knowledge of biochemistry alongside advances in AI and engineering at scale. ( {}^{55} ) Two of the three laureates came from a corporate research lab where team-based science harnessed diverse expertise. The third laureate's work was seeded by NSF and later heavily supported by private philanthropy, spinning off into a large-scale, university-affiliated center. ( {}^{56} ) Both efforts depended on decades of infrastructure building and open science, such as the Critical Assessment of Structure Prediction competitions and the Protein Data Bank, built by over 60,000 experimentalists who shared their findings freely. ( {}^{57} )

获得2024年诺贝尔化学奖的蛋白质折叠问题不属于任何一个学术院系；它需要生物化学的深厚知识，以及人工智能和大规模工程学的进步。( {}^{55} )三位获奖者中有两位来自企业研究实验室，在那里，基于团队的科学汇聚了多样化的专业知识。第三位获奖者的工作由美国国家科学基金会(NSF)资助，后来得到私人慈善的大力支持，并衍生出一个大型的大学附属中心。( {}^{56} )这两项努力都依赖于数十年的基础设施建设和开放科学，例如关键评估结构预测竞赛和蛋白质数据库，后者由超过6万名实验者建立，他们自由分享了研究成果。( {}^{57} )

The shape of an institution determines the shape of the science it produces. ( {}^{58} ) The university laboratory, centered on the principal investigator and a rotating cast of graduate students, excels at curiosity-driven research and at training the next generation within established disciplines. The industrial R&D lab, with its permanent staff and tight feedback loops, is suited to engineering projects. ( {}^{59} ) The national laboratory and large-scale, multinational scientific projects maintain unique capabilities too large for any single actor to sustain. Each form is a container that dictates what research becomes possible and what research never gets attempted.

一个机构的形态决定了它所产出的科学的形态。( {}^{58} )以首席研究员和轮换的研究生为核心的大学实验室，擅长于好奇心驱动的研究，以及在既定学科内培养下一代人才。拥有固定员工和紧密反馈循环的工业研发实验室，则适合工程项目。( {}^{59} )国家实验室和大型跨国科学项目则维持着任何单一行动者都无法支撑的独特能力。每种形式都是一个容器，决定了哪些研究成为可能，哪些研究从未被尝试。

A growing share of scientific problems now demands containers that do not yet exist at scale in the federal portfolio, and does not yield immediate products that contribute to a company's bottom line. ( {}^{60} ) Mapping the wiring of the mammalian brain, for example, requires not a student on a three-year cycle, but a sustained engineering team with the flexibility to scale quickly and hire from industry. It necessitates industrial-scale data collection and analysis, which is infeasible under the fragmented structure of traditional academic grants. It produces a public good whose benefits a single biotechnology company cannot fully internalize.

越来越多的科学问题现在需要联邦投资组合中尚未大规模存在的容器，并且不会立即产生有助于公司利润的产品。( {}^{60} )例如，绘制哺乳动物大脑的连接图，需要的不是一个三年周期的学生，而是一个具有快速扩展和从行业招聘灵活性的持续工程团队。它需要工业规模的数据收集和分析，这在传统学术资助的碎片化结构下是不可行的。它产生了一种公共产品，其利益是单一生物技术公司无法完全内化的。

This pattern extends across other key problems. Achieving practical fusion energy depends on progress in plasma physics, materials science, advanced manufacturing, and systems engineering, paired with the ability to scale up through venture funding. Understanding cognition well enough to address mental disease will benefit from new neural recording probes, machine learning suites that analyze neural activity patterns, and systems that deliver precise, closed-loop therapeutic interventions. Creating new institutional forms, then, plays a central role in bringing new scientific projects to life.

这种模式延伸到了其他关键问题。实现实用的聚变能依赖于等离子体物理学、材料科学、先进制造和系统工程学的进步，同时结合通过风险投资进行规模化的能力。充分理解认知以解决精神疾病，将受益于新的神经记录探针、分析神经活动模式的机器学习套件，以及提供精确闭环治疗干预的系统。因此，创造新的制度形式，在将新的科学项目变为现实方面发挥着核心作用。

The standard NIH R01 grant, which typically offers a quarter of a million dollars a year for a pre-specified project, is the workhorse of American biomedical research. It excels at supporting hypothesis-driven science by small teams on tractable questions. But over-reliance on this structure creates systematic blind spots. Projects requiring tens of millions of dollars and a team of dozens fall outside the container of what any single investigator can assemble. Academic containers are further shaped by labor availability. Employing postdocs and graduate students remains effective for training the next generation of scientists, but doing so is poorly suited for executing large-scale, mission-driven programs that require continuity, specialization, and long-term institutional memory. Frequent turnover fragments efforts and slows progress. As science funders have noted, no technology company would entrust its core R&D to a workforce composed primarily of temporary trainees, yet this is the standard model in academic research. ( {}^{61} ) A more balanced approach would expand stable, well-compensated career paths for staff scientists, engineers, and technicians, roles critical to sustained institutional capability.

标准的美国国立卫生研究院(NIH)R01资助——通常每年为预先指定的项目提供25万美元——是美国生物医学研究的主力。它擅长支持由小团队在可处理问题上进行的假设驱动型科学。但过度依赖这种结构会造成系统性的盲点。需要数千万美元和数十人团队的项目，超出了任何单个研究者所能组装的容器范围。学术容器还受到劳动力可用性的进一步塑造。雇佣博士后和研究生对于培养下一代科学家仍然有效，但这并不适合执行需要连续性、专业化和长期制度记忆的大规模任务驱动型项目。频繁的人员流动会分散努力并减缓进展。正如科学资助者所指出的，没有一家科技公司会将其核心研发委托给主要由临时受训者组成的劳动力，然而这却是学术研究中的标准模式。( {}^{61} )一种更平衡的方法将扩大稳定、薪酬优厚的职业路径，用于支持员工科学家、工程师和技术人员——这些角色对于持续的机构能力至关重要。

NOVEL PERFORMERS

新型执行者

Between the atomized work of the individual investigator and billion-dollar mega-projects like particle colliders lies a vast middle ground of mid-scale science. These are scientific problems requiring tens of millions of dollars, coordinated teams of ten to a hundred people, and timelines of half a decade. They range from the development of minimally invasive brain-computer interfaces that help people with Parkinson's to the building of new platforms that decode immune memory. Such projects, which are often infrastructure-heavy, engineering-intensive, and cross-disciplinary, are challenging to perform in principal investigator-led academic labs. They are rarely pursued in industry either. Pharmaceutical companies face much stronger incentives to chase the next drug breakthrough than to build platform technologies for decoding basic biology. ( {}^{62} )

在个体研究者的原子化工作与粒子对撞机等数十亿美元的大型项目之间，存在着广阔的中等规模科学领域。这些科学问题需要数千万美元资金、十到一百人的协调团队以及五年左右的时间周期。从开发帮助帕金森患者的微创脑机接口，到构建解码免疫记忆的新平台，这类项目通常基础设施密集、工程要求高且跨学科，在首席研究员主导的学术实验室中难以开展。产业界也鲜少涉足——制药公司更倾向于追逐下一个药物突破，而非构建解码基础生物学的平台技术。( {}^{62} )

The ARPA model has proven so effective because it fills precisely this gap in mid-scale technology development. By offering grants in the tens of millions of dollars, these agencies can assemble new research teams and help startups tackle ambitious engineering challenges, from robotic satellite servicing to AI-equipped fighter jets.

ARPA模式之所以被证明如此有效，正是因为它精准填补了中等规模技术开发的这一空白。通过提供数千万美元的资助，这些机构能够组建新的研究团队，帮助初创企业攻克从机器人卫星维护到人工智能战斗机等雄心勃勃的工程挑战。

Funding at this scale, which has worked well in incubating new technological capabilities, could be extended to basic science as well, where the government is not merely procuring a weapons system, but pursuing scientific advancement for the national interest.

这种规模的资助在孵化新技术能力方面成效显著，同样可延伸至基础科学领域——政府在此并非单纯采购武器系统，而是为维护国家利益推动科学进步。

Among private funders, a new class of focused research organizations, or "FROs," has started to fill the gap. FROs are time-bound, nonprofit research startups, engineered to break specific scientific bottlenecks. They hire professional engineers and career scientists, building institutional memory instead of just cycling graduate students through their training process. They produce public goods like open datasets, platforms, and tools, rather than the proprietary intellectual property that defines the commercial startup. And unlike the national laboratory, which is built to last indefinitely, the FRO is built to dissolve, pursuing a well-defined technical milestone and winding down once the mission is complete. These organizations can set long time horizon milestones to target specific bottlenecks and provide full salary support, removing the grant-writing treadmill. ( {}^{63} ) The time-limited nature also gives scientists who complete the project a chance to return to academia, or to spin off a startup and raise venture capital.

在私人资助者中，一类新型聚焦研究组织(简称"FRO")已开始填补这一空白。FRO是限时运作的非营利研究初创企业，专为突破特定科学瓶颈而设计。它们聘用专业工程师和职业科学家，积累机构记忆而非仅让研究生在培训中循环流转；产出开放数据集、平台和工具等公共产品，而非商业初创企业特有的专有知识产权。与永久存续的国家实验室不同，FRO旨在完成使命后解散——追求明确的技术里程碑，任务完成后即逐步收尾。这些组织可设定长期里程碑以瞄准特定瓶颈，并提供全额薪资支持，使科学家摆脱不断撰写经费申请的压力。( {}^{63} )限时特性也让完成项目的科学家有机会重返学术界，或衍生初创企业并筹集风险投资。

Activity	University	Corporate Lab	Federal Lab	New Institutions
Curiosity-driven, investigator-led research	+	✘	$ \neq $	By design
Larger-scale, engineering-intensive science	✘	$ \neq $	$ \neq $	By design
Long-horizon platform and tool development	✘	$ \neq $	+	By design
Public-goods data and infrastructure development	$ \neq $	✘	$ \neq $	By design
Mission-driven, public-good science	$ \neq $	✘	$ \neq $	By design
Proprietary product development	✘	+	✘	By design
Workforce training and apprenticeship	+	$ \neq $	✘	By design
Legend: +	Well-Suited	Partially-Suited	- Less-Suited

活动	大学	企业实验室	联邦实验室	新机构
好奇心驱动、研究者主导的研究	+	✘	$ \neq $	按设计
更大规模、工程密集型科学	✘	$ \neq $	$ \neq $	按设计
长期平台与工具开发	✘	$ \neq $	+	按设计
公共数据与基础设施开发	$ \neq $	✘	$ \neq $	按设计
使命驱动、公益科学	$ \neq $	✘	$ \neq $	按设计
专有产品开发	✘	+	✘	按设计
劳动力培训与学徒制	+	$ \neq $	✘	按设计
图例:+	高度适配	部分适配	- 较低适配

Table 1: Established institutional forms, such as universities, corporate laboratories, and federal laboratories, each carry their own relative advantages. Future modes of organization should be designed to fill the scientific gaps our existing institutions miss.

表1:既有的机构形式，如大学、企业实验室和联邦实验室，各有其相对优势。未来的组织形式应旨在填补现有机构所遗漏的科学空白。

The FRO model acts as an open call for new kinds of science, and for ideas our researchers have rarely dared to pursue thus far because there are no avenues for them. Academic incentives filter out team-based execution; commercial incentives filter out public goods; national laboratories filter out the agility to hire flexibly and execute rapidly. The FRO occupies the new ground of problems too large for the standard federal grant, too non-commercial for venture capital, and too risky and fast-moving for government facilities.

FRO模式作为一种开放式号召，邀请新型科学以及研究者们迄今因缺乏途径而鲜少敢于追求的想法。学术激励机制过滤掉了团队协作的执行方式；商业激励机制过滤掉了公共产品；国家实验室过滤掉了灵活聘用和快速执行的敏捷性。FRO占据了新的领域，这些领域的问题对标准联邦拨款而言过于庞大，对风险投资而言过于非商业化，对政府设施而言又过于高风险和快节奏。

But the FRO is just one point in a broader design space (Table 1). As the examples of Cold Spring Harbor Laboratory and the Institute for Advanced Study illustrate, many other approaches are possible. One proposal taxonomizes a range of novel institutional structures, including minimally constrained homes for basic science, FRO-style teams that execute against specific bottlenecks, and specific formats focused on the scouting and seed-funding of non-consensus ideas. ( {}^{64} ) Others have written about the variables that together map out the design space: the timeline over which projects are expected to pay off, the revenue strategy, the intellectual property policy, the size of the team, and the use of clear market signals to drive problem selection, among others. ( {}^{65} )

但FRO只是更广泛设计空间中的一个点(表1)。正如冷泉港实验室和高等研究院的例子所示，还有许多其他可能的方法。一项提案对一系列新型机构结构进行了分类，包括为基础科学提供最小约束的场所、针对特定瓶颈执行的FRO式团队，以及专注于探索和种子资助非共识想法的特定形式。( {}^{64} ) 其他人则撰文探讨了共同勾勒出设计空间的变量:项目预期回报的时间线、收入策略、知识产权政策、团队规模，以及利用明确市场信号驱动问题选择等因素。( {}^{65} )

Betting exclusively on the existing funding model is like building a military composed entirely of infantry, effective for one kind of warfare, inadequate for others.

完全押注于现有的资助模式，就像建立一支完全由步兵组成的军队，适用于一种战争，却不足以应对其他类型。

Fortunately, there are ways to expand the scope of federal grantmaking to support these innovations. NIH and NSF possess OTA that allows them to bypass traditional grant constraints. Our national laboratories can also create pathways to stand up flexible, federally supported scientific teams on time-bound missions. Recently, NSF's TIP Directorate launched the X-Labs, the first federal program explicitly designed to fund independent research organizations outside of traditional academic institutions. X-Labs will provide full-time teams of researchers, scientists, and engineers with operational autonomy and milestone-based funding as they pursue technical breakthroughs. These teams will not only produce traditional research outputs like publications and datasets, but also command the resources and financial runway to develop revolutionary platform technologies that unlock new fields of scientific inquiry.

幸运的是，有办法扩大联邦拨款的范围以支持这些创新。NIH和NSF拥有OTA，使其能够绕过传统的拨款限制。我们的国家实验室也可以开辟途径，建立灵活的、由联邦支持的、承担有时限任务的科学团队。最近，NSF的TIP局启动了X-Labs，这是首个明确旨在资助传统学术机构之外的独立研究组织的联邦项目。X-Labs将为全职的研究人员、科学家和工程师团队提供运营自主权和基于里程碑的资助，以追求技术突破。这些团队不仅会产出传统的研究成果，如出版物和数据集，还将掌握资源和财务空间，以开发能够开启新科学探究领域的革命性平台技术。

NSF's X-Labs represents a proof of concept for what federal science funding can become. Consider the mammalian brain mapping example again: one of neuroscience's grand challenges. A federally supported initiative could draw from extensive public-private partnerships, leverage matching grants from America's vibrant philanthropic sector, and bring the best researchers from academia together to develop moonshot infrastructure that scales connectome mapping, much as the Human Genome Project commoditized genetic sequencing. A hypothetical X-Lab could help map the reward and motivation circuits across various small mammals, producing one-of-a-kind datasets. For medicine, these circuit diagrams would offer a way to map the circuitry implicated in depression, addiction, and autism. For AI, they would provide a biological reference architecture for building more robust systems, drawn from natural structures that keep impulses in check and align short-term behavior with long-term goals. ( {}^{66} )

NSF的X-Labs代表了联邦科学资助可能成为的一种概念验证。再以哺乳动物大脑图谱绘制为例:这是神经科学的一大挑战。一项联邦支持的倡议可以借鉴广泛的公私合作伙伴关系，利用美国充满活力的慈善部门的配套资金，并汇聚学术界最优秀的研究人员，共同开发能够规模化绘制连接组图谱的登月级基础设施，就像人类基因组计划将基因测序商品化一样。一个假设的X-Lab可以帮助绘制各种小型哺乳动物的奖赏和动机回路，产生独一无二的数据集。对于医学而言，这些回路图将提供一种绘制与抑郁症、成瘾和自闭症相关回路的方法。对于人工智能而言，它们将提供一种生物参考架构，用于构建更稳健的系统，这些系统源自于能够抑制冲动并使短期行为与长期目标保持一致的自然结构。( {}^{66} )

NEW MECHANISMS

新机制

Reformed and new scientific institutions should also be matched with a broader menu of improved selection mechanisms for determining who and what type of organization receives scientific funding. Just as some organizations are better suited to certain kinds of scientific projects than others, so too are some selection processes better than others at identifying and motivating promising talent and programs.

改革和新建的科学机构还应匹配更广泛的改进型遴选机制，以确定谁以及何种类型的组织能获得科研资助。正如某些组织比另一些更适合特定类型的科研项目，某些遴选流程也比其他流程更善于识别和激励有潜力的人才与项目。

The economic field of mechanism design, recognized with the 2007 Nobel Prize, provides the theoretical foundation for understanding how rules shape behavior and outcomes. Mechanism design can be thought of as asking the inverse of traditional economics. Given a desired outcome, what incentives and institutions will produce it? The field has already transformed how we allocate spectrum licenses, match medical residents to hospitals, and price internet advertising. Science funding is an equally rich domain for applying the field's tools.

机制设计这一经济学领域(曾获2007年诺贝尔奖)为理解规则如何塑造行为和结果提供了理论基础。机制设计可被视为传统经济学的逆向思考:给定一个期望结果，需要怎样的激励和制度才能实现它？该领域已彻底改变了频谱牌照分配、医学生住院医师匹配以及互联网广告定价方式。科研资助同样是应用该领域工具的沃土。

At its core, our challenge is that scientific research involves private information that funders cannot directly observe. Peer review emerged as one solution to this information problem, but while peer review has a long and time-honored history of distinguishing good science from bad, it struggles to distinguish the exceptional from the merely good. Furthermore, reliability is low, and multiple reviewers rating the same NIH proposal frequently reach contradictory conclusions about the credibility of the science. ( {}^{67} ) These structural flaws only get worse as the number of proposals rises. When a funding agency can support only one proposal in ten, the noise begins to drown out the signal. All too often, consensus-driven panels fund the least divisive ideas rather than the most promising.

核心问题在于，科学研究涉及资助方无法直接观察的私人信息。同行评议作为解决这一信息问题的方法应运而生，但尽管同行评议在区分科学与伪科学方面历史悠久且备受尊崇，它却难以区分卓越与平庸。此外，其可靠性较低，多位评审人对同一份NIH提案的评分常常得出相互矛盾的结论。( {}^{67} ) 随着提案数量增加，这些结构性缺陷只会愈发严重。当资助机构只能从十份提案中支持一份时，噪声便开始淹没信号。太多时候，追求共识的评审小组资助的是争议最小的想法，而非最有前景的。

While these problems are widely recognized, reform has been slow because the incentives are asymmetric. A failed experiment invites Congressional scrutiny, whereas continuing the mediocre status quo draws little attention. The review panel thus serves as a convenient liability shield, allowing decisions to be attributed to "the scientific community" rather than to any individual who might be held accountable. A mechanism designed to hedge risk ends up precluding the risk-taking that breakthrough science requires.

尽管这些问题广为人知，但改革进展缓慢，因为激励是不对称的。失败的实验会招致国会审查，而维持平庸的现状却几乎无人关注。评审小组因此充当了便利的责任盾牌，使决策可归因于"科学界"而非任何可能被问责的个人。这种为规避风险而设计的机制，最终却排除了突破性科学所需的冒险精神。

But evidence from recent experiments suggests that modifications to funding mechanisms can yield substantial gains in both efficiency and scientific output.

但近期实验的证据表明，对资助机制的调整可在效率和科学产出两方面带来显著提升。

BETTER GRANTMAKING

优化拨款机制

One approach is to improve how peer review functions. In Denmark, a private foundation has experimented with a "golden ticket" system that allows individual reviewers to champion unconventional proposals lacking consensus support. ( {}^{68} ) This model helps rescue high-risk breakthroughs that colleagues might reject, and it is supported by a double-blind process that removes career history, cutting against elitism and leveling the playing field for younger or less prominent researchers. ( {}^{69} ) The approach also tends to attract higher-quality reviewers, who are individually empowered to make bold scientific bets.

一种方法是改进同行评审的运作方式。在丹麦，一家私人基金会试验了"黄金门票"制度，允许个别评审员支持缺乏共识的非传统提案。( {}^{68} ) 这种模式有助于挽救可能被同行否决的高风险突破，并采用双盲流程，隐去职业履历，从而削弱精英主义，为年轻或知名度较低的研究人员创造公平竞争环境。( {}^{69} ) 该方法还能吸引更高质量的评审员，他们被赋予个人权力，敢于做出大胆的科学押注。

NSF has begun piloting golden tickets under its TIP Directorate, and opportunities exist for broader adoption across new federal extramural funding agencies. ( {}^{70} )

美国国家科学基金会(NSF)已在其TIP理事会下试点黄金门票制度，新的联邦外部资助机构也有更广泛采用的机会。( {}^{70} )

Another approach is to change what we fund, supporting people over projects. Using philanthropic money, the Howard Hughes Medical Institute (HHMI) has provided long-horizon support in roughly $10 million over seven years with minimal reporting requirements, while tolerating early failure and betting on people rather than on project proposals. ( {}^{71} ) When researchers compared these grantees against similarly accomplished federally funded scientists, they found that the privately supported investigators, who have been granted more academic freedom, produced high-impact publications at nearly double the rate of their peers and were far more likely to explore genuinely novel lines of inquiry. ( {}^{72} )

另一种方法是改变资助对象，支持人才而非项目。霍华德·休斯医学研究所(HHMI)利用慈善资金，提供约七年1000万美元的长期支持，报告要求极低，容忍早期失败，押注于人而非项目提案。( {}^{71} ) 当研究人员将这些受资助者与成就相当的联邦资助科学家进行比较时，发现获得更多学术自由的私人资助研究者，其高影响力出版物产出率几乎是同行的两倍，并且更有可能探索真正新颖的研究方向。( {}^{72} )

Similar approaches have been tried in federal agencies, but remain too small a share of the current portfolio. NIH's own Director's Pioneer Award, designed to emulate the HHMI program, shows comparable results, ( {}^{73} ) and NSF's CAREER award, though smaller in grant size, has also produced countless breakthroughs. The same philosophy of betting on individuals can be used to support younger researchers as well. Since 1952, NSF GRFP has directly funded some of America's most promising incoming doctoral students. While more work remains to improve the selection mechanism and further empower students to choose their universities and principal investigators, such an approach has shown significant promise. The fellowship provides three years of support with full portability across institutions, freeing recipients to follow intellectual opportunity. The results speak for themselves: more than forty GRFP alumni have gone on to become Nobel laureates. ( {}^{74} )

联邦机构也曾尝试类似方法，但在当前投资组合中占比仍然过小。美国国立卫生研究院(NIH)的主任先锋奖旨在效仿HHMI项目，结果同样可观，( {}^{73} ) 而NSF的CAREER奖虽资助金额较小，也催生了无数突破。同样的押注于个人的理念也可用于支持年轻研究者。自1952年以来，NSF的GRFP项目直接资助了美国一些最有前途的博士生。尽管在改进选拔机制、进一步赋予学生选择大学和首席研究员权力方面仍有工作要做，但这种方法已显示出显著前景。该奖学金提供三年支持，可在机构间完全转移，使获得者能够追随学术机遇。结果不言自明:超过四十位GRFP校友后来成为诺贝尔奖得主。( {}^{74} )

These long-time-horizon grants can be matched with fast grants that provide flexibility on shorter timescales. NSF has mechanisms for fast decision-making that bypass external review panels, but they remain underutilized and often behind schedule. Meanwhile, a privately funded American program has demonstrated that, without any significant sacrifice to scientific quality, funding decisions can be made effectively in 48 hours rather than 6 to 9 months, with applications that take 30 minutes, rather than months, to prepare. ( {}^{75} ) Scaling these just-in-time grants up within federal grantmaking agencies could encourage more risk-taking on novel ideas, all while reducing administrative burdens.

这些长期资助可与快速拨款相结合，在较短时间框架内提供灵活性。NSF拥有绕过外部评审小组的快速决策机制，但这些机制未得到充分利用且常落后于计划。与此同时，一个私人资助的美国项目已证明，在不显著牺牲科学质量的前提下，资助决策可在48小时内有效做出(而非6至9个月)，申请准备仅需30分钟(而非数月)。( {}^{75} ) 在联邦拨款机构内扩大这些即时拨款规模，可鼓励对新颖想法承担更多风险，同时减轻行政负担。

PRIZE CHALLENGES

奖金挑战

Pull mechanisms offer another powerful and underutilized alternative to traditional funding, aligning incentives around outcomes rather than inputs.

拉动机制提供了另一种强大且未被充分利用的传统资助替代方案，将激励与成果而非投入对齐。

Traditional grants pay for effort, such as researcher time, equipment, and supplies, regardless of whether the project succeeds. Pull mechanisms invert this logic by paying for results.

传统拨款为努力付费，例如研究人员的时间、设备和物资，无论项目是否成功。拉动机制则反转这一逻辑，为结果付费。

The case for pull mechanisms is strongest when the goal is clear, but the path to it is not. The most famous example is the DARPA Grand Challenge for autonomous vehicles, which catalyzed an entire industry. DOE and ARPA-E have also used similar prize authorities to accelerate breakthroughs in energy storage and grid technology. ( {}^{76} ) A related mechanism is the advanced market commitment, which guarantees a market for a scientific or technical capability before a product exists.

当目标明确但实现路径不清晰时，拉动机制的优势最为显著。最著名的例子是DARPA自动驾驶汽车大挑战赛，它催生了一个完整的产业。能源部和ARPA-E也利用类似的奖金授权加速了储能和电网技术的突破。( {}^{76} ) 一种相关机制是预先市场承诺，即在产品存在之前就为某项科学或技术能力保证市场。

Such approaches can generate massive investment leverage. A privately funded prize for suborbital spaceflight offered $10 million but triggered hundreds of millions in combined research and development spending across competing teams. ( {}^{77} ) Similarly, the open structure of another prize competition attracted solvers from unconventional backgrounds to read the unopenable Herculaneum scrolls, a feat eventually accomplished not by seasoned archaeologists but by a trio of computer science and robotics students. ( {}^{78} ) While these mechanisms are ill-suited for open-ended, curiosity-driven research, they can be powerful tools for incentivizing use-inspired research and supporting technology commercialization. An optimal innovation portfolio requires both push mechanisms to explore unknown territory and pull mechanisms to close identified gaps.

这类方法可以产生巨大的投资杠杆效应。一项针对亚轨道太空飞行的私人资助奖金提供了1000万美元，却引发了竞争团队数亿美元的联合研发支出。( {}^{77} ) 同样，另一项奖金竞赛的开放结构吸引了来自非传统背景的解决者，去阅读无法打开的赫库兰尼姆卷轴，最终完成这一壮举的不是经验丰富的考古学家，而是一组计算机科学和机器人专业的学生。( {}^{78} ) 虽然这些机制不适合开放式、好奇心驱动的研究，但它们可以成为激励应用启发研究和支持技术商业化的有力工具。一个最优的创新组合需要推动机制来探索未知领域，也需要拉动机制来弥合已识别的差距。

FUTURE IDEAS

未来构想

The list of examples go on. Some of these mechanisms already exist in our federal portfolio and should be used more, others should be experimented with, and still others have yet to be invented. Each addresses different aspects of the same underlying challenge. Each represents a hypothesis about how to elicit honest signals, reward productive risk-taking, and allocate resources where they will generate the greatest return.

上述例子不胜枚举。其中一些机制已存在于联邦投资组合中，应更广泛运用；另一些尚需试验；还有一些则有待发明。每种机制都针对同一根本挑战的不同方面，代表了一种关于如何获取真实信号、奖励建设性冒险、以及将资源分配到能产生最大回报领域的假设。

One emerging idea, for instance, is to decentralize decisions. Doing so can leverage the wisdom of crowds to identify good science. Scouts, financially rewarded to find promising projects and individuals, could help identify scientific research for grantmakers. At a larger scale, the "regranting" model rests on the observation that the people best positioned to spot breakthrough opportunities often lack the authority to fund them, while those with the authority lack information to spot them. Regranting bridges this gap, delegating funding allocation to researchers or experts who possess the specific judgment to identify promising work before consensus forms.

例如，一个新兴构想是分散决策权。这能借助群体智慧识别优质科学项目。通过经济奖励寻找有前景项目与人才的"星探"，可帮助资助方锁定科研方向。在更大规模上，"转拨款"模式基于这样的观察:最善于发现突破性机遇的人往往缺乏资助权限，而掌握权限的人又缺乏识别机遇的信息。转拨款模式弥合了这一鸿沟，将资金分配权委托给那些能在共识形成前凭专业判断识别潜力项目的研究人员或专家。

Existing intermediaries already perform this function with philanthropic funding. Such a model could be extended by funding portfolio-based regranting organizations through federal agencies, or by giving a broad range of scientists the ability to regrant a small check to anyone other than those in their own academic institutions.

现有中介机构已通过慈善资金履行这一职能。该模式可通过联邦机构资助基于投资组合的转拨款组织，或赋予广大科学家向所在学术机构以外人员发放小额转拨款的能力来推广。

More speculative mechanisms, such as quadratic funding, remain in early testing. ( {}^{79} ) This approach weights the breadth of support more heavily than depth. A proposal backed by many small contributions receives larger matching funds than one backed by a few large donors. Quadratic funding reveals community preferences rather than gatekeeper preferences, and has shown promise in open-source software, though evidence of its application to science remains pending.

更实验性的机制(如二次方融资)仍处于早期测试阶段。( {}^{79} )该方法更侧重支持广度而非深度。获得众多小额捐款的项目比仅获少数大额捐款的项目能获得更多匹配资金。二次方融资揭示的是社区偏好而非把关人偏好，已在开源软件领域展现潜力，但其在科学领域的应用效果仍有待验证。

There must ultimately be a menu of options from which those who exercise federal funding authority can choose. The current selection system concentrates decisions among too few people using mechanisms that cannot support the weight placed on them. We stand at the beginning of a renaissance in grantmak-ing, and the Federal Government should welcome this experimentation.

最终必须为联邦资金决策者提供可选择的方案菜单。当前遴选体系将决策权集中在少数人手中，所用机制难以承载其重负。我们正站在资助模式复兴的起点，联邦政府应欢迎这场实验。

A PORTFOLIO-BASED APPROACH

基于投资组合的方法

Private capital allocators must deliver results or risk losing their investors. Philanthropies compete for donor confidence. But federal program officers receive little corrective feedback when their grant portfolios systematically underperform, and agencies rarely compare outcomes across funding mechanisms or allocation strategies.

私人资本配置者必须交付成果，否则可能失去投资者。慈善机构则需争夺捐赠者的信任。但联邦项目官员在其资助组合系统性表现不佳时，几乎得不到纠正性反馈，各机构也极少跨资助机制或配置策略比较成果。

Just as investment funds in the private sector balance their portfolios and match mechanisms to the nature of the work, we need to move toward a far more intentional approach to grantmaking. The preceding pages cataloged a diverse arsenal of mechanisms: golden tickets, which move us beyond false consensus; individual-based funding, which bets on researchers rather than proposals; pull mechanisms, which pay for outcomes rather than inputs; and regranting, which delegates decisions to those closest to the frontier. Each works for certain problems, operates well within certain institutional constraints, and produces returns with a particular risk profile.

正如私营部门的投资基金平衡其投资组合，并根据工作性质匹配机制，我们也需要朝着更具意图性的资助方式迈进。前文列举了多样化的机制:金票机制，助我们超越虚假共识；个人资助，押注于研究者而非项目；拉动机制，为成果而非投入付费；以及再资助，将决策权交给最接近前沿的人。每种机制适用于特定问题，在特定制度约束下运作良好，并以特定风险特征产生回报。

We can also be intentional about where we place various programs on the exploration-exploitation trade-off. Bold scientific bets can pay off in big ways; the biggest breakthroughs of the past decades have more often than not been driven by a relentless pursuit of tools and frameworks to answer practical questions. Science is not a pure random walk; it often helps to have an inductive bias. This is Pasteur's quadrant, the domain of use-inspired basic research, which we discussed in Chapter I. But pure curiosity-driven research can also deliver immense value to society. Riemann's abstract study of differential geometry eventually enabled Einstein's formulation of general relativity; the field of group theory eventually enabled cryptographic codes, computer graphics, and our understanding of elementary particle physics.

我们还可以有意识地在探索与利用的权衡中定位各类项目。大胆的科学押注可能带来巨大回报；过去几十年最大的突破，往往源于对工具和框架的不懈追求，以回答实际问题。科学并非纯粹的随机游走；归纳偏倚常常有所助益。这便是巴斯德象限，即我们在第一章讨论的“应用驱动的基础研究”领域。但纯粹的好奇心驱动的研究也能为社会带来巨大价值。黎曼对微分几何的抽象研究最终助力爱因斯坦提出广义相对论；群论领域最终催生了密码编码、计算机图形学以及我们对基本粒子物理的理解。

The key lies in distinguishing between cases where strategic direction can accelerate progress, and cases where the fog is too thick for anything but an exploratory search.

关键在于区分两种情况:战略方向能加速进展的，以及迷雾过浓、只能进行探索性搜索的。

Intentional grantmaking therefore requires deliberate portfolio construction: a mix of high-risk and low-risk bets; a balance of person-based, project-based, and institution-based funding; and explicit strategies for allocating across fields and capability areas. Federal agencies should construct their portfolios the way sophisticated allocators do, with thesis-driven conviction about where breakthroughs are most likely to emerge, while preserving space for the serendipity that no thesis can anticipate. We should aim to engineer a large, well-constructed portfolio that allows us to win in the long run.

因此，意图性资助需要精心构建投资组合:高风险与低风险押注的混合；个人、项目与机构资助的平衡；以及跨领域和能力领域的明确配置策略。联邦机构应像老练的配置者那样构建其投资组合，既要有基于论点的信念，判断突破最可能出现在何处，又要为任何论点都无法预见的偶然性留出空间。我们的目标是设计一个庞大且精心构建的投资组合，从而在长期中获胜。

DRIVING CONSTANT INNOVATION

驱动持续创新

The foundation of this portfolio should be a metascience unit in each federal science agency. Each unit should be highly empowered, reporting directly to the director or administrator to ensure cross-agency visibility and guard against capture by particular programs or constituencies. Each unit should be staffed with researchers possessing expertise in the science of science, program evaluation, and data analysis, supplemented by rotating program officers who bring operational knowledge of how grants actually get made.

这一组合的基础应是每个联邦科学机构内设一个元科学部门。每个部门应被充分授权，直接向局长或主任汇报，以确保跨机构可见性，并防止被特定项目或利益群体所左右。每个部门应配备具备科学学、项目评估和数据分析专长的研究人员，并由轮岗的项目官员补充，他们带来关于拨款实际运作方式的运营知识。

Federal funding agencies should develop systematic gap-mapping capacity, regularly review their funding portfolios, and drive more intentional grantmaking instead of deferring to the portfolio allocation of the previous fiscal year. Such a process could identify both bottlenecks and the foundational capabilities that would address them. ( {}^{80} ) Metascience units could, for instance, convene expert workshops and maintain living maps of capability gaps, or work externally with foundations that have developed sophisticated methods for identifying transformative research opportunities. DARPA's Heilmeier Catechism embodies this discipline, forcing explicit articulation of what gap a program addresses and why solving it matters. ( {}^{81} )

联邦资助机构应发展系统性的缺口映射能力，定期审查其资助组合，并推动更有意图的拨款，而非沿用上一财年的组合分配。这一过程可识别瓶颈以及解决瓶颈所需的基础能力。( {}^{80} )例如，元科学部门可召集专家研讨会，维护能力缺口的动态图谱，或与已开发出识别变革性研究机会的成熟方法的基金会进行外部合作。DARPA的Heilmeier信条体现了这一纪律，迫使明确阐述项目解决什么缺口以及为何解决它至关重要。( {}^{81} )

These units should also be empowered to do more than advise on the existing portfolio of instruments; they should pilot new ones across agency programs. An NIH unit might randomize whether study sections use golden tickets, then track the novelty and citation impact of funded projects across conditions. An NSF unit might compare outcomes from fast grants against standard review timelines. A DOW unit might experiment with how much discretion program managers are given in funding decisions, comparing data across branches. Without the authority to run experiments and to compel program offices to participate, these units will devolve into compliance operations producing reports. One way to secure this authority is to give each metascience unit a budget it can regrant to program managers for participating in experiments. Their findings should also be published externally, building the broader evidence base on what works in science funding and creating accountability to act on what is learned. The United Kingdom's Metascience Unit, established in 2024, offers an early model, reporting in its first year on distributed peer review, partial randomization of awards, and the consistency of reviewer judgments. ( {}^{82} )

这些部门还应被授权不仅就现有工具组合提供建议，还应在各机构项目中试点新工具。NIH的一个部门可随机化研究部门是否使用黄金门票，然后追踪不同条件下资助项目的创新性和引用影响。NSF的一个部门可比较快速拨款与标准评审时间线的结果。DOW的一个部门可实验项目官员在资助决策中拥有多少自由裁量权，跨分支比较数据。如果没有开展实验和强制项目办公室参与的权力，这些部门将退化为合规操作，仅产出报告。确保这一权力的一种方式是给每个元科学部门一个预算，用于向参与实验的项目官员重新拨款。其发现也应对外发布，建立关于科学资助中什么有效的更广泛证据基础，并形成对所获经验采取行动的问责制。英国于2024年成立的元科学部门提供了一个早期模式，在其第一年报告中涉及分布式同行评审、部分随机化奖项以及评审者判断的一致性。( {}^{82} )

All this institutional experimentation must be matched by hiring the highest quality staff.

所有这些制度性实验必须与招聘最高质量的人员相匹配。

We can, and must, make program management one of the most sought-after jobs in science, where talented people can shape the direction of entire fields. DARPA's success rests not on any single mechanism but on hiring the right program managers and giving them genuine discretion. This begins with making it easier for people from a wide range of backgrounds, including industry and philanthropy, to enter a short stint in government, and with raising the prestige and profile of program officers, whose efforts in coordinating entire fields toward major breakthroughs often go underrecognized. We need to recruit the best scientific talent into these roles, and then truly empower them, with resources, freedom, and the opportunity to network with the smartest people tackling the hardest problems.

我们能够，也必须，使项目管理成为科学界最受追捧的工作之一，让有才华的人能够塑造整个领域的方向。DARPA的成功并非依赖于任何单一机制，而是招聘合适的项目官员并给予他们真正的自由裁量权。这首先要让来自广泛背景(包括产业界和慈善界)的人更容易进入政府短期任职，并提升项目官员的声望和形象，他们协调整个领域实现重大突破的努力往往未得到充分认可。我们需要招募最优秀的科学人才担任这些角色，然后真正赋予他们权力，包括资源、自由以及与解决最棘手问题的最聪明人士建立联系的机会。

America invented the modern research architecture with institutional innovations that the rest of the world subsequently adopted. We must lead the charge again.

美国以制度创新发明了现代研究架构，世界其他国家随后纷纷采纳。我们必须再次引领这一潮流。

Chapter III Securing U.S. Dominance in Critical and Emerging Technologies

第三章确保美国在关键与新兴技术领域的主导地位

WE MUST CHOOSE OUR TECHNOLOGICAL FUTURE

我们必须选择自己的技术未来

In centuries past, land and population determined national power. The industrial age added capital and manufacturing capacity as fundamental components of national sovereignty and security. As the President's National Security Strategy makes clear, technological capability has always conferred advantages, and now sets the terms on which all of these inputs operate. ( {}^{83} )

在过去的几个世纪里，土地和人口决定了国家实力。工业时代将资本和制造能力作为国家主权与安全的基本组成部分。正如总统的国家安全战略所明确指出的，技术能力历来赋予优势，如今更决定了所有这些要素的运作方式。( {}^{83} )

Technological leadership helped forge modern America. Science alone did not produce this leadership; it required the deliberate cultivation of engineering talent, institutional capacity, and industrial might to turn discoveries into capabilities. Our physicists translated Schrödinger's equations into the weapons that ended World War II and defined the nuclear order that followed. Our engineers turned Shannon's information theory into the protocols that carry the world's digital communications. Our scientists turned advances in physics and materials science into GPS satellites that guide ships, planes, and precision weapons on every continent.

技术领导力帮助塑造了现代美国。仅靠科学无法产生这种领导力；它需要刻意培养工程人才、机构能力和工业实力，将发现转化为实际能力。我们的物理学家将薛定谔方程转化为结束二战的武器，并定义了随后的核秩序。我们的工程师将香农信息论转化为承载全球数字通信的协议。我们的科学家将物理学和材料科学的进步转化为GPS卫星，引导着各大洲的船舶、飞机和精确制导武器。

Technology has become foundational to a nation's economic and military strength, its capacity to act independently in the world, and its ability to maintain its distinct culture and way of life. American technological leadership produced enormous wealth, secured our homeland, and turned our nation into a beacon for the rest of the world. It cannot be taken for granted.

技术已成为国家经济与军事实力、独立行动能力以及维护独特文化与生活方式的基础。美国的技术领导力创造了巨大财富，保障了国土安全，并将我们的国家变成了世界其他地区的灯塔。这绝非理所当然。

The nature of technological advantage is shifting. As will be discussed in Chapter ( V ) , advances in AI expand the world’s ability to generate ideas and will accelerate scientific research. These capabilities will benefit American scientists. But those benefits will also accrue to our competitors. As new tools of discovery become more widely available, the comparative advantage conferred by scientific excellence alone will likely narrow. It will therefore be equally important for our nation to bolster its capabilities in translation, the process turning ideas into real-world capabilities. That is the subject of this chapter.

技术优势的性质正在发生变化。正如第( V )章将讨论的，人工智能的进步扩大了世界产生创意的能力，并将加速科学研究。这些能力将使美国科学家受益，但我们的竞争对手也将从中获益。随着新的发现工具日益普及，仅凭科学卓越所带来的比较优势可能会缩小。因此，我国同样重要的是加强转化能力——将想法转化为实际能力的过程。这正是本章的主题。

FAILURE OF THE PASSIVE MODEL

被动模式的失败

America has long been the world's most prolific source of scientific breakthroughs. We must ensure we are equally formidable at turning those breakthroughs into national power, or we risk watching the fruits of American discovery harvested first by others.

美国长期以来一直是全球科学突破最丰富的来源。我们必须确保同样擅长将这些突破转化为国家实力，否则将面临美国发现的果实被他国抢先收获的风险。

For decades, American science and technology policy rested on an unspoken assumption that government need only fund basic research, support a vibrant economy, and trust that technological strength would follow. Pour money into universities, protect intellectual property, keep markets open, and the innovations that secure the nation and enrich its people arrive on schedule-this was the implicit bargain of the post-war scientific order, and for a generation it appeared to work.

几十年来，美国科技政策建立在一个不言而喻的假设上:政府只需资助基础研究、支持充满活力的经济，并相信技术实力会随之而来。向大学投入资金、保护知识产权、保持市场开放，那些保障国家安全、丰富人民生活的创新便会如期而至——这是战后科学秩序的隐性契约，在一代人的时间里，它似乎行之有效。

That laissez-faire assumption does not survive contact with competitors who have built technological states. Commerce and research now constitute a geopolitical battlespace, and the parallel to trade policy is instructive. For decades, the United States assumed that open markets would naturally produce American prosperity and that free trade would lift all boats by maximizing global efficiency. Instead, unilateral openness hollowed out the American industrial base. Competitors exploited our markets while protecting their own. The gains from trade accrued to a narrow slice of the economy while entire communities lost their livelihoods. President Trump has delivered a necessary correction, recognizing that economic security is national security.

这种自由放任的假设在遭遇已建成技术型国家的竞争对手时便站不住脚。商业和研究如今构成了地缘政治战场，与贸易政策的类比具有启发性。几十年来，美国假设开放市场自然会带来美国繁荣，自由贸易会通过最大化全球效率而惠及所有人。然而，单方面的开放掏空了美国工业基础。竞争对手在保护自身市场的同时利用我们的市场。贸易收益流向了经济中的一小部分人，而整个社区失去了生计。特朗普总统做出了必要的纠正，认识到经济安全就是国家安全。

The same logic applies to science and technology. The assumption that federal research investment alone would sustain American technological dominance has proven naive. We funded the discoveries, trained the researchers, and published the papers, but did not ensure that the benefits accrued to our nation. The ideas, as well as the time- and resource-intensive parts of the development cycle, are taken abroad to benefit others.

同样的逻辑也适用于科技领域。仅靠联邦研究投资就能维持美国技术主导地位的假设已被证明是天真的。我们资助了发现、培养了研究人员、发表了论文，但未能确保收益归于我国。这些创意以及开发周期中耗时耗资源的部分被带到国外，让他人受益。

American researchers invented the flat-panel display; Asian manufacturers captured the market. American scientists pioneered cutting-edge battery chemistries; production scaled overseas. The pattern has repeated across decades and industries. The cause was the same passive model that hollowed out our factories, while our competitors pursued a holistic strategy that deliberately blurred the line between public and private, civilian and military, treating every advance, wherever it originated, as raw material for their state-directed development.

美国研究人员发明了平板显示器；亚洲制造商占领了市场。美国科学家开创了尖端电池化学技术；生产却在海外规模化。这种模式在几十年间、在多个行业中反复出现。原因正是那个掏空我们工厂的被动模式，而我们的竞争对手则推行整体战略，刻意模糊公共与私人、民用与军用的界限，将每一项进步——无论源自何处——都视为其国家主导发展的原材料。

CHOOSING TO LEAD

选择引领

Our nation's technological outcomes are shaped by policy choices. The internet became an American platform because we embedded openness and competition into its foundations. We chose to go to the Moon in 1969 because of our national will. It is an achievement that appears, in retrospect, jarringly out of place in humanity's technological timeline. Conversely, nuclear energy stalled in America not because the physics failed, but because regulatory choices over the past half-century made building uneconomical.

我们国家的技术成果是由政策选择塑造的。互联网之所以成为美国的平台，是因为我们将开放与竞争植入了其根基。1969年我们选择登月，是出于国家意志。这一成就，回顾起来，在人类技术时间轴上显得突兀地格格不入。相反，核能在美国停滞不前，并非因为物理学失败，而是因为过去半个世纪的监管选择使得建设变得不经济。

In each case, the decisive variable was the set of institutional, regulatory, and strategic choices that determined whether science became capability. Competition may dictate that nations will adopt AI, race from genotype to phenotype, pursue nuclear technology, and build advanced warships, but it does not dictate how they go about it, or even necessarily when. Within broad technological trajectories, multiple futures are possible. The question is which one America will fight for. ( {}^{84} )

在每一个案例中，决定性的变量都是一套制度、监管和战略选择，它们决定了科学是否能转化为能力。竞争可能迫使各国采用人工智能、从基因型到表型竞速、发展核技术、建造先进战舰，但它并不决定它们如何去做，甚至不一定决定何时去做。在广阔的技术轨迹中，多种未来都是可能的。问题在于美国将为之奋斗的是哪一种。( {}^{84} )

FIGHTING IN OUR OWN ARENA

在自己的主场作战

No nation, however powerful, can lead in every domain. Some technologies demand that we press forward, extending strengths into durable advantages where early leads compound over time. Others require that we hold ground. We may not seek total dominance, but we will not permit an adversary to achieve it either. In domains of lesser strategic consequence, we can concentrate our energies elsewhere and ensure that strengths accrue to partners, rather than adversaries.

没有哪个国家，无论多么强大，能在所有领域都领先。有些技术需要我们奋力推进，将优势转化为持久的竞争力，让早期领先优势随时间不断累积。另一些技术则需要我们守住阵地。我们或许不追求全面主导，但也不会让对手得逞。在战略意义较小的领域，我们可以将精力集中到别处，确保优势流向伙伴而非对手。

The technologies that matter most are those that form platforms on which future technologies are built. Dominating the right foundational platforms grants structural power, allowing the leading actor to dictate the rules and standards by which others must play. These advantages compound, with advances in one field, like computation, unlocking breakthroughs in others, like AI and biotechnology, creating feedback loops that reinforce the leader's edge. ( {}^{85} )

最重要的技术是那些构成未来技术平台的技术。主导正确的底层平台能赋予结构性权力，让领先者制定他人必须遵守的规则和标准。这些优势会不断累积，一个领域的进步(如计算)会解锁其他领域的突破(如人工智能和生物技术)，形成反馈循环，强化领先者的优势。( {}^{85} )

The semiconductor industry offers one striking example. It was not predetermined by nature that transistor density would increase exponentially over time, as described by Moore's Law. Rather, the market found ways to harness, fund, and develop the miracles of physics and material science that made such exponential growth possible. In turn, the countries that control the semiconductor supply chain now push a snowball that gets bigger every year, incorporating more and more of the modern economy. Mobile communications, satellite navigation, and ever-more powerful AI systems are all built on this platform.

半导体行业就是一个突出例子。晶体管密度随时间呈指数增长(如摩尔定律所述)并非自然注定。相反，市场找到了利用、资助和开发物理与材料科学奇迹的方法，使这种指数增长成为可能。反过来，控制半导体供应链的国家如今推动着一个每年都在变大的雪球，将现代经济越来越多地纳入其中。移动通信、卫星导航和日益强大的人工智能系统都建立在这个平台之上。

Winning does not mean isolation. Yes, sovereignty over critical technologies often means building the capacity to produce them domestically. But as the semiconductor example illustrates, technological leadership can also mean strategic integration, supplying the platforms that drive economic growth across our partners' economies, like tapping into broader markets to push the snowball of Moore's Law. Leadership means charting our own destiny by choosing which technologies we develop, which standards we set, and which supply chains we control, rather than accepting a world shaped by the choices of others.

胜利并不意味着孤立。是的，对关键技术的主权通常意味着建立国内生产能力。但正如半导体例子所示，技术领先也可以意味着战略整合，提供推动伙伴经济体增长的平台，就像利用更广阔的市场来推动摩尔定律的雪球。领先意味着通过选择我们开发哪些技术、制定哪些标准、控制哪些供应链来规划自己的命运，而不是接受由他人选择塑造的世界。

Accomplishing those goals requires that we understand and amplify our true advantages. Among our competitors, some, despite their industrial strength, lavishly fund state priorities while leaving private markets to languish, missing breakthrough technologies that emerge from the fringes. Their economies remain investment-driven rather than productivity-driven, with total factor productivity contributions declining even as R&D spending rises. And while the success of their consumer applications is often mistaken for genuine R&D-intensive innovation, they remain far behind the United States in driving original frontier research. ( {}^{86} )

实现这些目标需要我们理解并放大自身的真正优势。在我们的竞争对手中，有些尽管工业实力雄厚，却大肆资助国家优先事项，而让私人市场萎靡不振，错失了从边缘涌现的突破性技术。它们的经济仍然是投资驱动而非生产力驱动，即使研发支出增加，全要素生产率贡献却在下降。而且，虽然其消费应用的成功常被误认为是真正的研发密集型创新，但在推动原创前沿研究方面，它们仍远远落后于美国。( {}^{86} )

Meanwhile, America's financial architecture channels capital toward frontier technology at a scale no nation can rival. In 2024, American venture capital firms deployed over $200 billion, accounting for 57% of global venture investment. ( {}^{87} ) Our public markets tell an even more striking story. As the time of writing, the seven largest American technology companies are collectively worth more than the entire stock market of our primary competitor. These figures reflect not just deep pools of capital, but liquid markets that reward successful exits, a legal system that enforces contracts and protects property rights, institutional investors with long time horizons, and a startup ecosystem that treats failure as education rather than disgrace.

与此同时，美国的金融架构以前所未有的规模将资本引向前沿技术。2024年，美国风险投资公司部署了超过2000亿美元，占全球风险投资的57%。( {}^{87} ) 我们的公开市场讲述了一个更引人注目的故事。截至撰写本文时，美国七大科技公司的总市值超过了我们主要竞争对手的整个股市。这些数字不仅反映了深厚的资本池，还反映了奖励成功退出的流动性市场、执行合同和保护产权的法律体系、拥有长期投资视野的机构投资者，以及将失败视为教育而非耻辱的创业生态系统。

This is our arena. The task before us is to match the incredible vibrancy of our markets with our scientific capital, to unleash technological capabilities that benefit the American people.

这就是我们的主场。我们面前的任务是将市场惊人的活力与我们的科学资本相匹配，释放惠及美国人民的技术能力。

UNLEASHING INNOVATION

释放创新

Over the past few decades, America has built a regulatory state that brings down a gavel to block much innovation in the physical world. When it takes longer to obtain a permit than to build the thing being permitted, when the default answer from the government is "no" or "wait," the most talented builders go elsewhere or stop trying. This system selects against the kind of people and organizations that drive innovation, namely small teams, unconventional entrants, and entrepreneurs whose opportunity costs are so high that they will not wait years for approval.

过去几十年，美国建立了一个监管体系，它像法官一样落下法槌，阻碍了物理世界中的大量创新。当获得许可的时间比建造被许可的东西还要长，当政府的默认回答是“不”或“等”，最有才华的建设者要么另寻他处，要么放弃尝试。这个体系淘汰了那些推动创新的人和机构，即小团队、非传统进入者以及机会成本高到不愿等待数年审批的企业家。

The nuclear industry illustrates the problem at its most extreme. The United States once led the world in nuclear technology. We built the reactors, trained the engineers, and wrote the safety standards that other nations adopted. Then we regulated the industry into paralysis. Before an advanced reactor startup can pour a single foundation, it must spend five to six years in pre-application discussions with the Nuclear Regulatory Commission (NRC), followed by a design certification process that can take an additional four years. One company had to submit a 12,000-page application, supported by more than 2,000,000 pages of technical documentation. DOE spent over $600 million in funding to support this process for a single reactor design. Then came the combined license application, with its own multi-year safety and environmental reviews, mandatory public hearings, and construction inspections. ( {}^{88} )

核工业将这一问题展现得最为极端。美国曾一度引领全球核技术。我们建造反应堆，培训工程师，制定其他国家采用的安全标准。然后，我们通过监管使该行业陷入瘫痪。在先进反应堆初创公司浇筑第一块地基之前，它必须花五到六年时间与核管理委员会(NRC)进行申请前讨论，随后是可能再需四年的设计认证过程。一家公司不得不提交一份12,000页的申请，并附有超过2,000,000页的技术文件。能源部为此单一反应堆设计提供了超过6亿美元的资金支持。接着是联合许可证申请，其本身又涉及多年的安全和环境审查、强制性公开听证会以及施工检查。( {}^{88} )

THE FREEDOM TO BUILD

建设自由

The first Trump Administration began to fix this regulatory morass in the nuclear realm, supporting bipartisan legislation to reform the NRC's approach. ( {}^{89} ) In the second Administration, we have moved to break the logjam entirely because the stakes are so high, driven by energy demands across America's AI and manufacturing industries.

第一届特朗普政府开始着手解决核能领域的监管乱局，支持两党立法改革核管会的做法。( {}^{89} ) 在第二届任期内，由于美国人工智能和制造业的能源需求巨大，我们已着手彻底打破僵局，因为利害攸关。

In May 2025, the President signed four executive orders overhauling America's nuclear regulatory framework: imposing an 18-month deadline for the NRC to revise its regulations, capping licensing timelines for new construction applications, creating expedited approval pathways for reactors already tested by DOE or DOW, and directing the NRC to weigh the benefits of nuclear energy to economic and national security in its regulatory decisions. Companion orders direct DOE to facilitate five gigawatts of power uprates to existing reactors, begin construction on ten new large reactors by 2030, and invoke Defense Production Act authority to secure domestic nuclear fuel supply chains. These are the most sweeping nuclear reforms in a generation, taking down the old system that placed the status quo above the American people. ( {}^{90} )

2025年5月，总统签署了四项行政令，全面改革美国核监管框架:要求核管会在18个月内修订法规，设定新建项目许可审批时限上限，为已由能源部或国防部测试的反应堆创建快速审批通道，并指示核管会在监管决策中权衡核能对经济和国家安全的好处。配套行政令指示能源部推动现有反应堆增加500万千瓦发电能力，到2030年开工建设十座新的大型反应堆，并援引《国防生产法》权力保障国内核燃料供应链。这些是一代人以来最全面的核改革，推翻了将现状置于美国人民之上的旧体制。( {}^{90} )

In biotechnology, the United States pioneered many of the foundational advances in genomics, gene therapy, and CRISPR-based medicine, yet our clinical trial system has grown so costly that testing American discoveries increasingly happens abroad. A promising retinal prosthesis that restores vision for the blind, developed in Alameda, California, had to run its clinical trials in Europe due to challenges navigating the approval process. ( {}^{91} ) Per-patient costs for clinical trials run far higher than in other economies; American scientists make the breakthroughs, but the infrastructure to validate and deploy them is migrating overseas. ( {}^{92} )

在生物技术领域，美国率先实现了基因组学、基因疗法和基于CRISPR的医学的许多基础性突破，然而我们的临床试验体系成本过高，以至于美国发现的成果越来越多地在国外进行测试。加利福尼亚州阿拉米达开发的一种有望恢复盲人视力的视网膜假体，因审批流程困难，不得不在欧洲进行临床试验。( {}^{91} ) 每位患者的临床试验成本远高于其他经济体；美国科学家取得突破，但验证和推广这些突破的基础设施正在向海外转移。( {}^{92} )

Here, we have also begun to reverse the trend. The U.S. Department of Health and Human Services (HHS) has launched the largest deregulatory effort in the Department's history. The Food and Drug Administration (FDA) has moved to accept real-world evidence in regulatory reviews, ( {}^{93} ) dropped the default requirement of two clinical trials per drug application in favor of a single well-powered study with confirmatory evidence, ( {}^{94} ) and fast-tracked review time-lines for drugs supporting U.S. national interests. NIH introduced a new site to make the community aware of priority scientific areas without the need for new Notices of Funding opportunities and also eliminated application requirements that added burden without commensurate benefit. ( {}^{95} ) These reforms are essential, and they must mark the start of a sustained effort to ensure that the world's most innovative biomedical science is tested and deployed on American soil. ( {}^{96} )

在此方面，我们也已开始扭转这一趋势。美国卫生与公众服务部启动了该部历史上最大规模的去监管行动。食品药品监督管理局已开始接受真实世界证据用于监管审查，( {}^{93} ) 取消了每项药物申请需进行两次临床试验的默认要求，改为一次充分有力的研究加确证性证据，( {}^{94} ) 并加快了对符合美国国家利益的药物的审评时间表。国立卫生研究院推出了一个新网站，让社区了解优先科学领域，无需发布新的资助机会通知，同时取消了增加负担而无相应收益的申请要求。( {}^{95} ) 这些改革至关重要，它们必须标志着持续努力的开始，以确保世界上最具创新性的生物医学科学在美国本土得到测试和推广。( {}^{96} )

These examples should only be the beginning. The best of American innovation has always been characterized by permissionless experimentation, the freedom to build, test, fail, and try again without asking leave at every step.

这些例子应该只是开始。美国创新的精髓始终在于无需许可的实验精神——建设、测试、失败、再试的自由，无需每一步都请示批准。

A permissionless approach to innovation does not mean the reckless development of technology. Prudence in broad deployment is wise, and it is the foundation of society's trust in our technologies. But policymakers must also price in the harms of stagnation: the economic growth foregone, the lives lost waiting for a cure, the industrial and automotive accidents that happen by failing to adopt more advanced technology. Where existing rules do not fit new technologies, regulatory sandboxes that allow real-world testing under controlled conditions can generate the evidence needed to write sensible ones. Our goal should be to dismantle the procedural obstacles that prevent American knowledge from becoming American technology, while maintaining genuine accountability for results.

无需许可的创新方式并不意味着技术的鲁莽发展。在广泛部署时保持审慎是明智的，这也是社会信任我们技术的基础。但政策制定者还必须考虑停滞的危害:错失的经济增长、等待治愈而逝去的生命、因未能采用更先进技术而发生的工业和交通事故。当现有规则不适用于新技术时，允许在受控条件下进行真实世界测试的监管沙盒，可以为制定合理规则提供所需证据。我们的目标应该是拆除阻碍美国知识转化为美国技术的程序性障碍，同时保持对结果的真正问责。

PLACES TO TEST

测试地点

About seventy years ago, a committed group of amateur rocketeers purchased a private test site in the Mojave Desert north of Edwards Air Force Base. Since then, the oldest continuously operating amateur rocket group in the country has been firing homemade engines. In 2003, a spin-off organization incorporated next door as a nonprofit, and its volunteers built the necessary infrastructure from scratch. They erected static test stands, reinforced concrete blockhouses, and propellant storage sites. They secured federal permits for handling high explosives and a Federal Aviation Administration (FAA) waiver to launch rockets to 40,000 feet on weekends. A reservation there costs anywhere from a few hundred dollars to the low thousands. ( {}^{97} )

大约七十年前，一群执着的业余火箭爱好者购买了爱德华兹空军基地以北莫哈韦沙漠中的一处私人测试场。自那以后，这个全国历史最悠久的持续运营业余火箭团体一直在发射自制发动机。2003年，一家衍生组织在隔壁注册为非营利机构，其志愿者从零开始建设必要的基础设施。他们架设了静态试车台、加固混凝土掩体和推进剂储存点，获得了处理高爆炸药的联邦许可，以及联邦航空管理局(FAA)的周末火箭发射至4万英尺的豁免权。在那里预订一次的费用从几百美元到几千美元不等。( {}^{97} )

On any given Saturday, at this facility, a father-and-daughter team working out of their garage may be standing next to university engineering students and off-duty aerospace professionals, all firing liquid engines in the open desert. The adjacent airport itself became an FAA-licensed commercial spaceport in 2004. ( {}^{98} ) Multiple rocket companies have emerged from this cluster of cheap leases, federal licenses, and shared test infrastructure to win NASA prizes, raise substantial venture capital, and reshape the commercial launch industry. ( {}^{99} ) Mentors at the amateur site were recruited into startups. Startup veterans returned to mentor the next cohort of rocket enthusiasts. These organizations have overcome the odds in one of the most heavily regulated industries in America and have bred a vibrant ecosystem for innovation.

每逢周六，在这处设施里，一对在自家车库工作的父女团队可能与大学工程系学生和休班的航空航天专业人士并肩而立，都在开阔的沙漠中试射液体发动机。毗邻的机场本身在2004年成为FAA许可的商业航天港。( {}^{98} )多家火箭公司从这片廉价租赁、联邦许可和共享测试基础设施的集群中脱颖而出，赢得了NASA奖项、筹集了大量风险资本，并重塑了商业发射行业。( {}^{99} )业余测试点的导师被招募进初创公司，而初创公司的老手又回来指导下一批火箭爱好者。这些组织在美国监管最严格的行业之一中克服了重重困难，孕育了一个充满活力的创新生态。

Today, the Federal Government also offers test stands at industrial scale through the Stennis Space Center, where startups can lease facilities rather than spending tens of millions of dollars building their own. ( {}^{100} )

如今，联邦政府还通过斯坦尼斯航天中心提供工业规模的试车台，初创公司可以租赁设施，而无需花费数千万美元自行建造。( {}^{100} )

Providing test infrastructure and sustaining a regulatory environment that allows our innovators to experiment represents one of the most important levers we have for driving technology forward.

提供测试基础设施并维持一个允许创新者进行实验的监管环境，是我们推动技术发展最重要的杠杆之一。

Test infrastructure, whether for rockets, advanced manufacturing, autonomous systems, or any frontier technology, is an enabling resource that determines whether the next great American company starts in a desert lot or dies on the vine in a student's garage. Wherever AI systems are deployed at scale, wherever advanced reactors are built and operated, wherever synthetic biology is used in agriculture and medicine, the resulting standards, supply chains, and knowledge bases will compound in favor of the nation that moved first. America must be the place where that experimentation happens.

测试基础设施——无论是用于火箭、先进制造、自主系统还是任何前沿技术——都是一种赋能资源，它决定了下一家伟大的美国公司是在沙漠中起步，还是在学生的车库里夭折。无论人工智能系统在哪里大规模部署，先进反应堆在哪里建造和运行，合成生物学在哪里用于农业和医药，由此产生的标准、供应链和知识库都将向率先行动的国家倾斜。美国必须成为那个实验发生的地方。

While bright spots like the Mojave site and the Stennis Space Center exist for particular industries, across much of America, the gauntlet between a scientific discovery and a deployed technology has grown so forbidding that many of our best ideas never make it through. Too often, ideas die after the paper is published. A breakthrough in a university laboratory must be prototyped, tested under real-world conditions, validated against safety and performance standards, manufactured at scale, and brought to market. The most talented builders increasingly migrate toward software, where the regulatory burden is lightest. This contributes to the lopsided economic growth we see today, away from the world of atoms in our heartlands, and toward the world of bits in the Valley.

尽管莫哈韦测试场和斯坦尼斯航天中心等亮点存在于特定行业，但在美国大部分地区，从科学发现到技术部署之间的难关已变得如此严峻，以至于我们许多最好的想法从未成功跨越。太多时候，想法在论文发表后就夭折了。大学实验室的突破必须经过原型制作、在真实条件下测试、通过安全和性能标准验证、大规模制造并推向市场。最有才华的建造者越来越多地转向监管负担最轻的软件领域。这导致了我们今天看到的失衡经济增长——从我们腹地的原子世界，转向硅谷的比特世界。

OPENING AMERICA'S LABORATORIES

开放美国实验室

Beyond test beds, America possesses extraordinary research infrastructure, built up over decades of federal investment. The DOE alone operates 28 user facilities, from the Advanced Photon Source at Argonne to the Spallation Neutron Source at Oak Ridge, providing capabilities available nowhere else on Earth. ( {}^{101} ) These facilities represent billions of dollars in capital investment by the American taxpayer and decades of accumulated expertise.

除了试验平台，美国还拥有非凡的研究基础设施，这些设施是数十年联邦投资积累的成果。仅能源部就运营着28个用户设施，从阿贡国家实验室的先进光子源到橡树岭国家实验室的散裂中子源，提供了地球上其他地方无法比拟的能力。( {}^{101} )这些设施代表着美国纳税人数十亿美元的资本投入和数十年的专业积累。

Yet access to this extraordinary infrastructure has historically been too narrow and too slow. Echoing a theme from Chapter II, evaluations for facility access are built for academic merit review. This system works for university scientists pursuing publications, but fails entrepreneurs who need to validate technology on a timeline set by competitors.

然而，历史上对这些非凡设施的访问过于狭窄且缓慢。与第二章的主题相呼应，设施访问的评估是为学术评审而设计的。这套系统适用于追求发表论文的大学科学家，但却无法满足那些需要在竞争对手设定的时间表内验证技术的企业家。

Opening these facilities more broadly to private industry, with evaluations that weigh innovative potential and commercial urgency alongside scientific merit, would multiply the return on existing federal investments. An older culture at the labs holds that industry engagement detracts from the research mission, but in reality these interactions benefit both sides, allowing external users to leverage the lab's vast knowledge base while exposing lab researchers to new use-inspired problems. Revenue from user fees can also fund expanded capacity and new instrumentation, turning facilities that today operate below capacity into self-sustaining engines of innovation. Every facility-hour that goes unused is a wasted national asset; every dollar of industry revenue reinvested is a dollar of federal appropriation freed to grow the next generation of tools and equipment.

更广泛地向私营企业开放这些设施，并在评估中兼顾创新潜力、商业紧迫性和科学价值，将成倍提升现有联邦投资的回报。实验室中一种较旧的文化认为，与产业合作会削弱研究使命，但实际上，这些互动对双方都有利:外部用户可以借助实验室庞大的知识库，而实验室研究人员则能接触到新的应用启发型问题。用户费收入还可用于扩大产能和购置新仪器，将目前产能利用率不足的设施转变为自我维持的创新引擎。每一小时未使用的设施都是国家资产的浪费；每一美元再投资的产业收入，都是释放出来用于开发下一代工具和设备的联邦拨款。

Large federal facilities are only part of the picture. Closer to the entrepreneur, shared platforms at smaller scales have proven equally transformative. The National Quantum and Nanotechnology Infrastructure program provides shared cleanroom access with more than 2,000 available tools, enabling startups to prototype semiconductor, photonics, and quantum devices without building their own fabrication lines, often at the cost of just a few hundred dollars per hour. ( {}^{102} ) In the life sciences, shared wet laboratories have reduced the capital barriers for early-stage biotech companies, enabling researchers to move from concept to experiment in weeks rather than the years required to build a dedicated facility. ( {}^{103} ) Shared Good Manufacturing Practice (GMP) facilities address an even larger bottleneck. ( {}^{104} ) The production of clinical-grade materials under FDA-compliant conditions requires tens of millions of dollars in capital investment that most startups cannot raise before they have clinical data. This has created a catch-22 that can be broken by shared GMP platforms. Expanding these platforms across sectors and geographies would put the physical tools of innovation within reach of any American entrepreneur with a good idea.

大型联邦设施只是图景的一部分。对于企业家而言，较小规模的共享平台同样具有变革性。国家量子与纳米技术基础设施项目提供共享洁净室访问权限，拥有超过2000种可用工具，使初创公司能够原型化半导体、光子学和量子器件，而无需自建生产线，通常每小时成本仅为几百美元。( {}^{102} )在生命科学领域，共享湿实验室降低了早期生物技术公司的资本门槛，使研究人员能够在数周内从概念进入实验，而无需花费数年时间建造专用设施。( {}^{103} )共享良好生产规范(GMP)设施解决了更大的瓶颈。( {}^{104} )在符合FDA要求的条件下生产临床级材料需要数千万美元的资本投入，而大多数初创公司在获得临床数据之前无法筹集到这笔资金。这造成了一个“第22条军规”式的困境，而共享GMP平台可以打破这一困境。跨行业和跨地区扩展这些平台，将使创新的物理工具触手可及，任何有好点子的美国企业家都能使用。

Our national laboratories and universities can play a similar role at larger scales, serving as revitalized hubs of testing and evaluation for private industry. Places like Oak Ridge, Sandia, and Lawrence Livermore possess unique capabilities to validate technologies no startup could test alone; universities that host startups in their research infrastructure catalyze knowledge and hiring pipelines that multiply innovation. Yet licensing and partnership processes at national labs remain slow relative to the pace at which technologies must move. American research universities face a parallel challenge. Intellectual property policies vary wildly across institutions, creating friction for companies that want to license from multiple universities. Faculty incentive structures typically reward publications over commercialization, and far too often, equipment purchased with federal grants sits idle between projects while entrepreneurs who could use it have no access.

我们的国家实验室和大学可以在更大规模上发挥类似作用，成为私营企业测试和评估的复兴中心。橡树岭、桑迪亚和劳伦斯利弗莫尔等实验室拥有独特的能力，可以验证任何初创公司无法单独测试的技术；在大学研究基础设施中孵化的初创公司，则能催化知识和人才管道，从而倍增创新。然而，国家实验室的许可和合作流程相对于技术发展的速度仍然缓慢。美国研究型大学面临类似的挑战。各机构的知识产权政策差异巨大，给希望从多所大学获得许可的公司带来了摩擦。教师激励机制通常更看重发表论文而非商业化，而且，用联邦拨款购买的设备在项目之间闲置的情况屡见不鲜，而本可使用这些设备的企业家却无法访问。

Reforms that streamline university technology licensing, standardize IP frameworks for federally funded research, and open university facilities to outside innovators on flexible terms would unlock a vast reservoir of capability that today remains bottled up behind administrative walls. Likewise, streamlining the CRADAs that govern lab-industry partnerships, further leveraging the OTA, and reducing the administrative burden on companies seeking to license lab technologies, would help our scientific institutions move closer to industry speed.

改革措施包括简化大学技术许可流程、标准化联邦资助研究的知识产权框架、以及以灵活条款向外部创新者开放大学设施，这将释放大量目前被行政壁垒封锁的能力。同样，简化管理实验室与产业合作的CRADA协议、进一步利用OTA、以及减轻公司寻求实验室技术许可的行政负担，将有助于我们的科研机构更贴近产业速度。

TAPPING OUR PRIVATE SECTOR

调动私营部门

A major task ahead for the Federal Government is to harmonize the efforts of our publicly-funded institutions with our dynamic private sector. The way government funds science has not yet fully integrated the spectacular rise of the private sector in both basic and applied R&D. In the 1960s, the Federal Government funded over 70% of all basic research performed in the United States. ( {}^{105} ) Today the federal share of basic research funding has fallen to 40%, while industry's share has grown to well over 35%. Our biggest technology companies and leading pharmaceutical firms now support or directly publish some of the most cited work in fundamental science. Individual technology companies now spend more on R&D than the NSF's entire annual budget. In fields like AI, quantum computing, and advanced drug discovery, the most important research increasingly requires capabilities that universities alone cannot provide.

联邦政府面临的一项重大任务是协调公共资助机构与充满活力的私营部门之间的努力。政府在资助科学方面尚未完全整合私营部门在基础研究和应用研发领域的惊人崛起。20世纪60年代，联邦政府资助了美国所有基础研究的70%以上。( {}^{105} )如今，联邦政府在基础研究资金中的份额已降至40%，而工业界的份额已增长至超过35%。我们最大的科技公司和领先的制药企业现在支持或直接发表一些被引用最多的基础科学成果。个别科技公司在研发上的投入已超过美国国家科学基金会(NSF)的全年预算。在人工智能、量子计算和先进药物发现等领域，越来越重要的研究需要大学单独无法提供的能力。

In AI, the companies that train frontier models wield supercomputers worth tens of billions of dollars, hold petabytes of proprietary data, and can afford to spend tens of millions to recruit the best engineering talent in ways no university can match. This has produced an academic brain drain; yet academic researchers remain essential to the long-term health of the field, producing foundational work on next-generation algorithms that companies have less incentive to pursue. Without new partnership structures that give academic scientists access to frontier-scale resources, the basic research that underpins the next generation of AI advances will atrophy, and our technological leadership will rest on an increasingly narrow institutional base. What is needed are mechanisms that adequately leverage the comparative advantage of both public and private funding.

在人工智能领域，训练前沿模型的公司拥有价值数百亿美元的超级计算机，持有PB级的专有数据，并能花费数千万美元招募最优秀的工程人才，这是任何大学都无法比拟的。这导致了学术人才流失；然而，学术研究人员对该领域的长期健康发展仍然至关重要，他们从事下一代算法的基础性工作，而企业缺乏动力去追求这些。如果没有新的合作结构让学术科学家获得前沿规模的资源，支撑下一代人工智能进步的基础研究将会萎缩，我们的技术领导地位将建立在日益狭窄的机构基础之上。我们需要的是能够充分利用公共和私人资金比较优势的机制。

CLOSER PARTNERSHIPS

更紧密的伙伴关系

As a first step, we need to expand the scope of Federal grantmaking. Funding should be open to new types of institutions, whether they are joint industry-university centers or independent research organizations that can raise equity.

作为第一步，我们需要扩大联邦拨款的范围。资金应向新型机构开放，无论是联合产业-大学中心，还是能够筹集股权的独立研究组织。

Some mechanisms already exist but are underused. As discussed in Chapter II, the OTA can surmount procurement constraints, and institution-agnostic grants can reach nonprofits, industry consortia, and independent researchers. SBIR and STTR programs can be deployed strategically to advance new scientific and technological capabilities, coupling federally-seeded companies with the scientific enterprise. Furthermore, our science agencies could establish or strengthen agency-adjacent independent foundations, modeled on the Foundation for the NIH (FNIH). ( {}^{106} ) One FNIH public-private partnership involving NIH and industry partners, the Accelerating Medicines Partnership (AMP), invests in reducing the timeline to find live-saving therapies and improvements in outcomes. The AMP on Alzheimer's Disease, one of twelve disease-focused AMPs, experimentally validated 20 candidate drug targets to lead to clinical trial success. ( {}^{107} ) Such foundations can blend public and private capital in ways that federal procurement rules prohibit, contract on commercial terms, and accept cost-sharing from industry partners, offering a vehicle for public-private collaboration that moves at the speed of industry while remaining responsive to policy priorities.

一些机制已经存在但未被充分利用。如第二章所述，OTA 可以克服采购限制，而机构无关的拨款可以覆盖非营利组织、产业联盟和独立研究人员。SBIR 和 STTR 项目可以战略性地部署，以推进新的科技能力，将联邦资助的公司与科学事业结合起来。此外，我们的科学机构可以建立或加强机构附属的独立基金会，以 NIH 基金会(FNIH)为蓝本。( {}^{106} ) 一个涉及 NIH 和产业伙伴的 FNIH 公私合作伙伴关系——加速药物合作项目(AMP)，致力于缩短发现救命疗法和改善结果的时间。针对阿尔茨海默病的 AMP(十二个专注于疾病的 AMP 之一)实验验证了 20 个候选药物靶点，以推动临床试验成功。( {}^{107} ) 这类基金会可以以联邦采购规则禁止的方式混合公共和私人资本，按商业条款签约，并接受产业伙伴的成本分摊，为公私合作提供一种既能以产业速度运作又能响应政策优先事项的载体。

The most powerful conduit between institutions, however, is the flow of human capital itself. Industry Ph.D. programs that enable American citizens to complete doctoral training at leading private organizations or national laboratories would offer higher stipends, real work experience, and exposure to problems at the frontier while also drawing a larger proportion of American citizens into basic research. Such programs already exist in prototype. ( {}^{108} ) These include industry postdocs, where some of our best researchers join leading companies to drive groundbreaking work, and industry-funded Ph.D. scholarships, which create revolving doors that bring new ideas into our strongest mathematics and physics departments. Programs like Activate at Lawrence Berkeley National Laboratory embed entrepreneurial scientists in national lab environments with stipends, lab access, and mentorship, and have been effectively expanded to talent emerging from the nation's universities through investments by NSF. ( {}^{109} )

然而，机构之间最强大的纽带是人力资本本身的流动。允许美国公民在领先的私营组织或国家实验室完成博士培训的产业博士项目，将提供更高的津贴、真实的工作经验以及接触前沿问题的机会，同时吸引更大比例的美国公民进入基础研究。这类项目已有雏形。( {}^{108} ) 其中包括产业博士后，我们一些最优秀的研究人员加入领先公司推动开创性工作；以及产业资助的博士奖学金，创造了旋转门，将新思想带入我们最强大的数学和物理系。像劳伦斯伯克利国家实验室的 Activate 项目，将创业科学家嵌入国家实验室环境，提供津贴、实验室使用权和指导，并通过 NSF 的投资有效扩展到全国大学涌现的人才。( {}^{109} )

We can build on these models by creating more flexible cross-institutional talent pathways. Scaling such programs would widen the aperture for our researchers. Instead of being locked into a single institutional track, or forced into a risky, one-way jump into industry, our next generation should be able to move fluidly among a wide range of sectors, institutions, and research cultures.

我们可以通过创建更灵活的跨机构人才通道来借鉴这些模式。扩大此类项目将拓宽我们研究人员的视野。我们的下一代不应被锁定在单一机构轨道上，或被迫进行风险高、单向的产业跳跃，而应能够在广泛的部门、机构和研究文化之间灵活流动。

MARSHALING GRAND EFFORTS

汇聚宏伟力量

Reforming the bilateral partnership between our government and private companies is only the first step. Many of the most transformative technological achievements in history required the deliberate marshaling of national effort toward goals that no single company, university, or agency could achieve alone.

改革政府与私营企业之间的双边伙伴关系只是第一步。历史上许多最具变革性的技术成就，都需要有意识地汇聚国家力量，去实现任何单一公司、大学或机构都无法独立完成的目标。

The Human Genome Project is a case in point. It began as a federally directed NIH-DOE partnership in 1990, with initial funding in President Reagan's 1988 budget submission. ( {}^{110} ) Its creators wagered that a complete reference of the human genome, and the sequencing technology advanced through the effort, would become a foundational technology for all of biomedicine. Its advocates pressed forward even as many leading biologists in the late 1980s dismissed the project as immature and argued the money would be better spent on individual grants.

人类基因组计划便是一个例证。该计划始于1990年，由联邦政府主导，是国家卫生研究院与能源部的合作项目，初始资金来自里根总统1988年的预算提案。( {}^{110} )其发起者押注，完整的人类基因组参考序列以及通过该计划推进的测序技术，将成为整个生物医学领域的基础性技术。尽管20世纪80年代末许多顶尖生物学家认为该项目尚不成熟，并主张将资金更好地用于个人研究资助，但倡导者们仍坚持推进。

Only the Federal Government could have marshaled the coalition that executed it. A distributed network of DOE national laboratories and NIH-funded centers, including Washington University and the Whitehead Institute, coalesced around common milestones and operated under the Bermuda Principles, which required immediate public data release. The project depended on the productive entanglement of public and private capacities, most notably in the development of new automated capillary sequencers, where federal demand pulled forward private innovation in instrumentation. ( {}^{111} ) When Celera Genomics entered as a private competitor in 1998, the resulting public-private dynamic accelerated the timeline further. The project finished ahead of schedule, and the $3.8 billion federal investment has generated an estimated $796 billion in economic activity. ( {}^{112} )

只有联邦政府才能汇聚起执行该计划的联盟。一个由能源部国家实验室和国家卫生研究院资助的中心(包括华盛顿大学和怀特黑德研究所)组成的分布式网络，围绕共同里程碑凝聚起来，并依据《百慕大原则》运作，要求立即公开数据。该计划依赖于公共与私人能力的有效交织，尤其是在新型自动毛细管测序仪的开发中，联邦需求拉动了仪器领域的私营创新。( {}^{111} )1998年，塞雷拉基因组学公司作为私营竞争者加入，由此产生的公私动态进一步加速了时间表。该计划提前完成，38亿美元的联邦投资已产生约7960亿美元的经济活动。( {}^{112} )

The human genome story exemplifies the Federal Government driving American leadership in a platform technology. It featured an engineering challenge bound up with a basic science mission; a gap in basic capabilities that could be closed through large-scale coordination; and a network of national laboratories and academic institutions focused on a common goal. Its completion required public-private collaboration that broke institutional walls.

人类基因组的故事体现了联邦政府如何推动美国在平台技术领域的领先地位。其特点包括:一项与基础科学使命紧密相连的工程挑战；一个可通过大规模协调来弥补的基础能力缺口；以及一个专注于共同目标的国家实验室和学术机构网络。该计划的完成需要打破机构壁垒的公私合作。

PRE-COMPETITIVE CONSORTIA

竞争前联盟

The Federal Government wields enormous power to align fragmented actors around shared objectives. As discussed in Chapter II, well-designed grand challenges exemplify this convening power for problems with clear metrics and deadlines, where opening the field to outsiders is an advantage. But not all shared problems lend themselves to this approach. Some technical challenges sit between basic science and commercial application, too applied for academic grants and too risky for any single firm to tackle alone.

联邦政府拥有巨大力量，能够将分散的参与者围绕共同目标凝聚起来。如第二章所述，设计良好的重大挑战体现了这种召集力，适用于有明确指标和期限的问题，且开放领域让外部参与者参与是一种优势。但并非所有共同问题都适合这种方法。一些技术挑战介于基础科学和商业应用之间，对学术资助而言过于应用化，对任何单一企业而言又风险过高，难以独自应对。

These pre-competitive problems, shared across an industry, require another instrument: cooperative R&D anchored by federal investment.

这些跨行业共有的竞争前问题需要另一种工具:以联邦投资为锚点的合作研发。

SEMATECH is the defining American example. By the 1980s, Japanese manufacturers had captured the majority of the global memory chip market. In 1987, fourteen American semiconductor companies pooled resources, matched by federal funding through DARPA, to attack shared manufacturing challenges in lithography, etching, and materials processing. ( {}^{113} ) The consortium solved technical problems that every American chipmaker needed but none could afford to solve individually.

SEMATECH 是美国的典型范例。到 20 世纪 80 年代，日本制造商已占据全球存储芯片市场的大部分份额。1987 年，十四家美国半导体公司汇集资源，并通过 DARPA 获得联邦资金匹配，共同攻克光刻、蚀刻和材料处理等共享制造挑战。( {}^{113} ) 该联盟解决了每个美国芯片制造商都需要但无人能独立承担的技术问题。

The SEMATECH model produced an even more consequential successor. In 1997, EUV LLC, another semiconductor consortium, contracted with three DOE national laboratories to develop EUV lithography, a technology that required breakthroughs in plasma physics, precision optics, and materials science beyond the reach of any single firm. By 2001, the consortium had built the first prototype EUV exposure tool and filed over 150 patents. ( {}^{114} ) Refined over the following two decades, that technology now underpins every leading-edge semiconductor manufactured on Earth. While policies undertaken then ceded the dominant market position to a European company, EUV lithography remains one of the most strategically important industrial technologies in our part of the century, and it exists because American federal laboratories and semiconductor companies solved the problem together. ( {}^{115} )

SEMATECH 模式催生了一个更具影响力的后继者。1997 年，另一个半导体联盟 EUV LLC 与三家美国能源部国家实验室签约，开发极紫外光刻技术，这项技术需要在等离子体物理、精密光学和材料科学方面取得突破，远超任何单一企业的能力范围。到 2001 年，该联盟已建成首台 EUV 曝光原型机，并提交了 150 多项专利。( {}^{114} ) 经过随后二十年的改进，这项技术如今支撑着地球上制造的每一款尖端半导体。尽管当时的政策将主导市场地位让给了一家欧洲公司，但 EUV 光刻技术仍是本世纪最具战略意义的工业技术之一，它的存在是因为美国联邦实验室和半导体公司共同解决了问题。( {}^{115} )

The pre-competitive consortium model worked because it targeted the right problems where the science was understood, but the engineering had not yet been done, where shared technical risk was the barrier. Today, many challenges of a similar scale and complexity await, such as returning leading-edge semiconductor research to American soil, programming biological tissues with precision, and creating next-generation nanotechnology techniques that allow for self-replication and atomic-level manipulation. No single firm can tackle these problems, traditional academic grants cannot fund them, and the nation cannot afford to leave them to chance.

竞争前联盟模式之所以成功，是因为它瞄准了正确的问题:科学原理已明确，但工程尚未完成，共享技术风险是主要障碍。如今，许多规模和复杂性相似的问题亟待解决，例如将尖端半导体研究带回美国本土、精确编程生物组织、以及创造允许自我复制和原子级操控的下一代纳米技术。没有一家企业能独自应对这些问题，传统的学术资助无法支持它们，而国家也不能任其发展。

The Federal Government's ability to anchor such ventures, leveraging private capital, aligning fragmented actors, and sustaining effort over timelines that no quarterly earnings cycle would tolerate, remains one of its most potent and un-derutilized capacities.

联邦政府锚定此类项目的能力——撬动私人资本、凝聚分散参与者、并在任何季度盈利周期都无法容忍的时间线上持续努力——仍是其最强大且未被充分利用的职能之一。

OUR NATIONAL CHARACTER

我们的民族特性

Alexis de Tocqueville observed nearly two centuries ago of America that "boldness of enterprise is the foremost cause of its rapid progress, its strength, and its greatness." He marveled at a society where social station was not fixed by birth, where citizens formed voluntary associations to solve problems rather than waiting for direction from above, and where the frontier, both physical and intellectual, beckoned those willing to take risks. ( {}^{116} ) That culture persists. Americans believe that merit deserves an opportunity to show itself, that free inquiry produces truth, and that individuals who build useful things deserve reward.

近两个世纪前，亚历克西·德·托克维尔观察到美国"企业家的胆识是其快速进步、力量与伟大的首要原因"。他惊叹于这样一个社会:社会地位不由出身决定，公民自发组成志愿团体解决问题而非等待上级指示，无论是物理还是智力上的边疆，都召唤着敢于冒险的人。( {}^{116} )这种文化延续至今。美国人相信，才华应有机会展现，自由探索能产生真理，创造有用之物的个人应得到回报。

That spirit is alive in our states and cities. America's federal system affords us the chance to run many experiments simultaneously across jurisdictions, creating regulatory testbeds, distinct infrastructure, and tailored incentives. By letting companies operate robotaxis on public roads years before most states had written their rules, Arizona built itself into the nation's leading testbed for autonomous vehicles. ( {}^{117} ) It has further leveraged that permissive environment to attract over $100 billion in semiconductor investment. ( {}^{118} ) Utah passed the nation’s first general regulatory sandbox in 2021. ( {}^{119} ) Wyoming enacted a series of laws tailored to blockchain and digital asset companies. ( {}^{120} ) States and cities that get regulatory frameworks right attract capital, talent, and industry; those that do not learn from those that do.

这种精神活跃在我们的州和城市中。美国的联邦制使我们有机会在各辖区同时进行多项实验，形成监管试验场、独特的基础设施和量身定制的激励措施。通过让公司在大多数州制定规则前就在公共道路上运营自动驾驶出租车，亚利桑那州将自己打造成全国领先的自动驾驶汽车试验场。( {}^{117} )它还利用这种宽松环境吸引了超过1000亿美元的半导体投资。( {}^{118} )犹他州于2021年通过了全国首个通用监管沙盒。( {}^{119} )怀俄明州颁布了一系列针对区块链和数字资产公司的法律。( {}^{120} )监管框架得当的州和城市能吸引资本、人才和产业；反之，则向做得好的学习。

When our researchers recognized that scaling laws would transform language models, no government committee approved the decision to pursue it. When our engineers concluded that reusable rockets were possible, a deregulated space industry emerged that mobilized massive capital to land rocket stages. This pattern, of creating a vast reserve of scientific talent and knowledge, of permis-sionless innovation backed by patient capital and enabled by regulatory flexibility, represents America's great competitive advantage.

当我们的研究人员认识到规模法则将改变语言模型时，没有政府委员会批准这一探索方向。当我们的工程师断定可重复使用火箭可行时，一个放松管制的航天产业应运而生，调动巨额资本实现火箭级回收。这种模式——创造庞大的科学人才和知识储备，以耐心资本支持、监管灵活性保障的无需许可创新——正是美国巨大的竞争优势。

But in an era of foundational technologies and active rival states, permis-sionless innovation must be married to strategic purpose. The capacities described in this chapter, especially the ability to discover and test, to move from laboratory to demonstrated viability, are the mechanisms by which American boldness becomes American dominance. The Federal Government should actively encourage experimentation at every level, creating the conditions for more states, more cities, and more institutions to become laboratories. In a competition where early experimentation locks in trajectories, the nation running the most experiments holds the advantage. For more than 250 years, going back to 13 separate colonies, that has been the American way.

但在基础技术时代和活跃竞争国家面前，无需许可的创新必须与战略目标相结合。本章所述的能力，尤其是发现与测试、从实验室走向可行验证的能力，正是美国胆识转化为美国主导地位的机制。联邦政府应积极鼓励各级实验，为更多州、城市和机构成为实验室创造条件。在早期实验锁定发展轨迹的竞争中，开展最多实验的国家占据优势。250多年来，从13个独立殖民地开始，这始终是美国之道。

Chapter IV Ensuring That Science and Technology Better the Lives of All Americans

第四章确保科学技术改善所有美国人的生活

THE MARRIAGE OF SCIENCE AND CRAFT In policy conversations, we often speak as though technology consists solely of intellectual property and gadgets. We focus on the patents that can be filed, the knowledge that can be written down, or the complex machines that can be built. But scientific and technological capability consists of much more than its most visible inputs and outputs.

科学与技艺的结合在政策讨论中，我们常将技术仅仅视为知识产权和装置。我们关注可申请的专利、可记录的知识或可制造的复杂机器。但科技能力远不止其最显性的投入和产出。

A better taxonomy holds that technology exists in three forms: tools, explicit instructions, and process knowledge. ( {}^{121} ) Consider chipmaking. The tools are the lithography machines, etchers, implanters, and more. The explicit instructions are the blueprints and recipes. But the process knowledge, like how to troubleshoot semiconductor yields, how complex variables affect wafer cleaning, how the next process node should be designed to balance performance and manufacturing risk, lives in the heads of experienced engineers and technicians.

更好的分类法认为技术以三种形式存在:工具、显性指令和过程知识。( {}^{121} )以芯片制造为例。工具是光刻机、刻蚀机、离子注入机等。显性指令是蓝图和配方。但过程知识——如何解决半导体良率问题、复杂变量如何影响晶圆清洗、下一代工艺节点应如何设计以平衡性能与制造风险——存在于经验丰富的工程师和技术人员头脑中。

This tacit knowledge cannot be fully codified. Anyone can be placed in front of a piano bench with a score of Rachmaninoff, but playing it well requires a personal command of musical dynamics and tactile skill.

这种隐性知识无法完全编码。任何人都可以坐在钢琴前面对拉赫玛尼诺夫的乐谱，但弹好它需要个人对音乐动态和触键技巧的掌握。

As the chemist-turned-philosopher Michael Polanyi observed, "we can know more than we can tell." ( {}^{122} ) A skilled welder knows things about metal behavior that no manual captures, like the way aluminum warns you before it warps, or the sound a good bead makes as it forms. A machinist develops intuitions about cutting tools that come only from years at the lathe. A pharmaceutical manufacturing technician recognizes subtle variations in chemical processes that determine whether a drug batch meets specifications.

正如化学家出身的哲学家迈克尔·波兰尼所言:"我们能知道的比能说出来的更多。"( {}^{122} )熟练的焊工了解金属行为中任何手册都无法记载的细节，比如铝在变形前的预警信号，或良好焊道形成时的声音。机械师对切削工具产生直觉，这只能来自多年车床操作。制药技术员能识别决定药品批次是否合格的化学过程细微变化。

This process knowledge, embodied in an experienced workforce, is the true keystone of technological capability.

这种蕴含在经验丰富劳动力中的过程知识，才是技术能力的真正基石。

The same applies to the practice of science. When the sociologist Harry Collins studied laboratories attempting to replicate a new type of laser in the 1970s, he found that no scientist succeeded using published sources alone. Those who built working devices had all spent time in a laboratory with someone who had already done it. The knowledge to build the laser flowed through personal contact, often so subtle that scientists themselves could not fully articulate what they had learned. ( {}^{123} )

科学实践也是如此。当社会学家哈里·柯林斯研究20世纪70年代试图复制新型激光器的实验室时，他发现没有科学家仅凭公开资料就能成功。那些造出工作设备的人都曾在已成功的实验室待过。制造激光器的知识通过人际接触传播，其微妙程度甚至让科学家自己都无法完全说清所学到的东西。( {}^{123} )

Likewise, a synthetic biologist improves through countless failed experiments while coaxing cells into expressing a novel protein. An immunologist, after years of experience and guidance from senior mentors, develops intuitions for which protocols will work with finicky cell lines, knowledge no methods section can capture. This is why many forms of scientific expertise require years of on-the-job training in working research organizations, and why academic publications alone remain insufficient to transmit the craft of science.

同样，一位合成生物学家在无数次失败的实验中不断进步，同时引导细胞表达一种新型蛋白质。一位免疫学家，经过多年的经验和资深导师的指导，会培养出直觉，知道哪些方案适用于挑剔的细胞系，这是任何方法学部分都无法捕捉的知识。这就是为什么许多形式的科学专业知识需要在活跃的研究机构中经过多年的在职培训，也是为什么仅靠学术出版物仍不足以传递科学技艺的原因。

Papers and patents are not the ultimate ends of progress, but way stations in the training of better scientists, engineers, and technicians. Science is not simply about the equipment, which any laboratory with enough capital can purchase, nor the instructions, which can be shared on a sheet of paper. ( {}^{124} ) Our true competitive advantage lies in the process knowledge embodied by America's talent. Without skilled practitioners who pass their craft to those who follow, the engine stalls.

论文和专利并非进步的最终目标，而是培养更优秀科学家、工程师和技术人员的驿站。科学不仅仅关乎设备——任何有足够资金的实验室都能购买——也不仅仅关乎可以写在纸上的指令。( {}^{124} )我们真正的竞争优势在于美国人才所体现的过程知识。如果没有熟练的从业者将他们的技艺传授给后来者，引擎就会熄火。

OUR MANUFACTURING BASE

我们的制造业基础

America has endured a sustained period of deindustrialization. Manufacturing employment peaked at nearly 20 million workers in 1979; ( {}^{125} ) today it stands at roughly 13 million, a decline of around 35% even as the population has grown by more than ( {50}% {.}^{126} ) Manufacturing’s share of total employment fell from nearly 22% in 1979 to around 8% today. ( {}^{127} ) This sustained decline has been compounded by the offshoring of contract research and development, especially in the pharmaceutical industry, where laboratories have moved abroad en masse. The fate of the American scientific enterprise is inseparable from the fate of American industry for two reasons.

美国经历了一段持续的产业空心化。制造业就业人数在1979年达到近2000万人的峰值；( {}^{125} )如今约为1300万人，下降了约35%，而同期人口却增长了超过( {50}% {.}^{126} )。制造业占总就业的比重从1979年的近22%降至如今的约8%。( {}^{127} )这种持续下滑因合同研发的外包而加剧，尤其是在制药行业，实验室已大规模迁往海外。美国科学事业的命运与美国工业的命运密不可分，原因有二。

First, most of the economic returns from scientific discovery arise not at the moment of invention, but during the translation of new ideas into products that can be produced at scale. The returns lie in the work that follows invention: the engineering that makes designs manufacturable, the process refinements that bring costs down, the skilled workforce that operates advanced facilities, the supply chain relationships that enable scale. When technological translation moves abroad, so do the jobs, the expertise, and the capacity to produce the next generation of breakthroughs.

首先，科学发现的大部分经济回报并非来自发明的那一刻，而是来自将新想法转化为可规模化生产的产品过程中。回报在于发明之后的工作:使设计可制造的工程、降低成本的工艺改进、操作先进设施的熟练劳动力、实现规模化的供应链关系。当技术转化转移到国外时，工作岗位、专业知识和生产下一代突破性成果的能力也随之转移。

Second, science and production continually inform one another. As discussed in Chapter I, the linear model in which basic research flows neatly to applied research, development, and production has always been a simplification. Knowledge circulates not only between the theorist and the experimentalist, but between the experimentalist and the industrial sector as well. The scientist studying semiconductor physics learns from the manufacturing engineer wrestling with yields. The biologist designing a new therapeutic depends on the process chemist who can scale production. The roboticist developing a high-torque actuator benefits from the presence of a local precision manufacturing base, working where he can drive down the street to stand beside a machinist at the turning center to optimize the design in person.

其次，科学与生产不断相互促进。如第一章所述，基础研究顺畅流向应用研究、开发和生产的线性模型始终是一种简化。知识不仅在理论家和实验者之间流通，也在实验者和工业部门之间流通。研究半导体物理的科学家从应对良率的制造工程师那里学习。设计新疗法的生物学家依赖于能够规模化生产的工艺化学家。开发高扭矩执行器的机器人专家受益于当地的精密制造基础，他可以开车到街角，站在车床旁的机械师身边，亲自优化设计。

These feedback loops depend on proximity between scientific research and industrial capability. Without local manufacturing capacity, the marriage of science and craft weakens. Scientists lose access to the practical problems that inspire new lines of inquiry and improve research quality, while industry loses the research ecosystem that sustains technological leadership. We need to reshore American manufacturing, not only for the sake of fruitful employment, but for the long-term health of American science itself.

这些反馈循环依赖于科学研究与工业能力之间的邻近性。没有本地制造能力，科学与工艺的结合就会削弱。科学家失去了激发新研究思路和提高研究质量的实际问题，而工业则失去了维持技术领先地位的研究生态系统。我们需要将美国制造业回迁，不仅是为了创造丰硕的就业机会，更是为了美国科学本身的长期健康。

VAST POTENTIAL REMAINS UNTAPPED

巨大潜力尚未开发

Even after decades of offshoring, the United States still nurtures some of the most dynamic trade communities in the world. Ours is a nation of tinkerers, hobbyists, and people who fix things with their hands. Countless Americans learn to repair cars from family members, to operate power tools, to build and maintain their own homes. From barn raisings on the frontier to hot rod culture in the 20th century to today's maker movement, this do-it-yourself (DIY) spirit runs deep in our culture.

尽管经历了数十年的离岸外包，美国仍然培育着世界上最具活力的贸易社区。我们是一个由修补匠、爱好者和动手修理物品的人组成的国家。无数美国人从家人那里学习修理汽车、操作电动工具、建造和维护自己的房屋。从边疆的谷仓建造到20世纪的热棒文化，再到今天的创客运动，这种自己动手(DIY)精神深深植根于我们的文化中。

Log onto any video sharing platform and you will find a country of builders. Amateur machinists demonstrate techniques for precision manufacturing. Hobbyist welders share tips on joining titanium. Electronics enthusiasts repair broken oscilloscopes. Amateur radio operators, biohackers, synthesizer builders, and drone constructors all participate in communities of shared technical knowledge. Even highly specialized pursuits such as nuclear fusion and cyclotron construction have attracted dedicated experimenters. Public libraries increasingly offer 3D printers and laser cutters, while local shops provide courses in welding and machining.

登录任何视频分享平台，你都会发现一个由建造者组成的国家。业余机械师展示精密制造技术；爱好焊工分享连接钛的技巧；电子爱好者修理损坏的示波器；业余无线电操作员、生物黑客、合成器制造者和无人机制造者都参与共享技术知识的社区。即使是核聚变和回旋加速器建造等高度专业化的追求，也吸引了专注的实验者。公共图书馆越来越多地提供3D打印机和激光切割机，而本地商店则提供焊接和机械加工课程。

This grassroots engagement with technical work points to a vast reservoir of talent waiting to be cultivated. More than half of American adults participate in some form of making or building activity. ( {}^{128} ) The largest DIY conventions have attracted more than a hundred thousand participants. ( {}^{129} ) Manufacturing employment may have declined, but the cultural foundations of technical skill remain in the same population from which we once drew machinists, toolmakers, and engineers.

这种基层对技术工作的参与，指向一个等待开发的巨大人才库。超过一半的美国成年人参与某种形式的制造或建造活动。( {}^{128} )最大的DIY大会吸引了超过十万名参与者。( {}^{129} )制造业就业可能已经下降，但技术技能的文化基础仍然存在于我们曾经培养机械师、工具制造者和工程师的同一群体中。

OUR EDUCATIONAL SYSTEM TILTED THE SCALES

我们的教育体系倾斜了天平

Yet our formal educational and employment systems often fail to develop this potential. Over the past several decades, the expansion of college education has come at the expense of vocational training, and high schools that once taught machining classes have shifted resources toward college preparation. As factories closed and communities hollowed out, the message to young people that working with your hands is a fallback, not a calling, was clear. Success meant escaping physical work, not mastering it.

然而，我们的正规教育和就业体系往往未能开发这种潜力。过去几十年，大学教育的扩张是以牺牲职业培训为代价的，曾经教授机械加工课程的高中已将资源转向大学预备教育。随着工厂关闭、社区空心化，传递给年轻人的信息很明确:动手工作是退路，而非天职。成功意味着逃离体力劳动，而非掌握它。

This cultural shift was reinforced by an economic transformation that treated physical labor as a commodity to be sourced wherever it was cheapest. This prejudice runs deep. In too many communities, politicians and guidance counselors have come to treat trade schools as consolation prizes for those not cut out for a four-year degree. Over the past two decades, shop class equipment from shuttered programs has flooded the used machinery market, tangible evidence of how thoroughly we abandoned hands-on education in our rush toward a so-called knowledge economy. ( {}^{130} )

这种文化转变被一场经济转型所强化，后者将体力劳动视为一种可在最廉价处获取的商品。这种偏见根深蒂固。在太多社区中，政客和升学顾问已将职业学校视为那些不适合四年制学位者的安慰奖。过去二十年，来自关闭项目的车间设备充斥二手机械市场，这切实证明了我们在奔向所谓知识经济的过程中，如何彻底放弃了动手教育。( {}^{130} )

The costs are now visible on both sides of the ledger. Millions of Americans have the aptitude and interest for technical work, but lack clear pathways to translate that interest into careers. Meanwhile, millions of skilled jobs are expected to be unfilled even as graduates enter the workforce. ( {}^{131} ) For decades, the Federal Government tilted the scales against career education that by extending unlimited loans to students attending colleges, universities, and graduate programs, driving up the cost of college and burying millions in debt. ( {}^{132} ) At the root of this policy failure was the government's inability to recognize that the work of building, maintaining, and repairing the physical world is not a relic of the past, but the foundation of any future prosperity.

代价如今在账本的两端都清晰可见。数百万美国人具备技术工作的天赋和兴趣，但缺乏将这种兴趣转化为职业的明确路径。与此同时，即使毕业生进入劳动力市场，仍有数百万技术岗位预计无人填补。( {}^{131} )几十年来，联邦政府通过向就读学院、大学和研究生院的学生提供无限贷款，推高了大学成本并使数百万人深陷债务，从而倾斜了天平，不利于职业教育。( {}^{132} )这一政策失败的根源在于，政府未能认识到:建造、维护和修复物质世界的工作并非过去的遗物，而是未来任何繁荣的基础。

WE MUST RESTRUCTURE SCIENCE AS A BROADER ENDEAVOR

我们必须将科学重构为更宏大的事业

Too many of our universities and elite science and technology curricula have severed the connection between theory and craft. Engineering students study the theory of combustion, but few can disassemble and rebuild a combustion engine. Graduate programs reward theoretical contributions measured in citation counts, but not practical applications measured in jobs and dollars. The result is a generation of researchers who can model phenomena mathematically but cannot repair the apparatus in their own laboratories.

我们的太多大学和精英科技课程已经割裂了理论与技艺之间的联系。工程专业的学生学习燃烧理论，但很少有人能拆解并重新组装一台内燃机。研究生项目奖励的是以引用次数衡量的理论贡献，而非以就业和美元衡量的实际应用。其结果是培养出一代能用数学模型描述现象、却无法修理自己实验室设备的研究人员。

This narrowing departs from how science actually advances. As discussed in Chapter I, the linear model no longer holds in many fields, where discovery increasingly relies on feedback from the real world. Some of the most important breakthroughs in molecular biology and theoretical physics have come from scientists who understood their instruments intimately, who could not only design the experiments but build and modify the equipment themselves.

这种狭隘化偏离了科学实际进步的路径。如第一章所述，线性模型在许多领域已不再适用，发现越来越依赖于来自现实世界的反馈。分子生物学和理论物理学中一些最重要的突破，来自那些对仪器了如指掌的科学家——他们不仅能设计实验，还能亲手建造和改装设备。

Consider Rainer Weiss, who won the 2017 Nobel Prize in Physics for detecting gravitational waves. Weiss grew up scavenging war surplus electronics in New York, teaching himself to build ham radio transmitters and fixing broken devices for pocket money. After flunking out of MIT, he took a job as a laboratory technician, working alongside veteran craftsmen, learning to machine, solder, and weld.

以雷纳·韦斯为例，他因探测引力波而获得2017年诺贝尔物理学奖。韦斯在纽约长大，从废弃的军用电子设备中翻找零件，自学组装业余无线电发射机，靠修理坏掉的设备赚取零花钱。从麻省理工学院退学后，他找了一份实验室技术员的工作，与经验丰富的工匠共事，学习机械加工、焊接和熔接。

It was this training in what Weiss called "the art of improvisation in experimental science" that enabled him to design and build the prototype laser interferometer that became LIGO, the instrument that detected ripples in spacetime from colliding black holes a billion light-years away. As Weiss put it, "I'm a big believer in what's called the apprentice system."133

正是这种韦斯称之为“实验科学中的即兴艺术”的训练，使他能够设计并建造出成为LIGO原型的激光干涉仪——这台仪器探测到了来自十亿光年外碰撞黑洞的时空涟漪。正如韦斯所说:“我坚信所谓的学徒制。”133

The divorce between scientific training and craft has held back America's scientific progress. The tacit knowledge that powers our scientific enterprise, developed through experimental practice and hands-on technical training, will only become more important. The particle accelerators at our national laboratories need electrical engineers who can develop more powerful klystrons; fusion experiments need vacuum specialists and fabricators who can work with tungsten; telescopes that map the universe need craftsmen who grind mirrors to nanometer precision. These are the people who may well be core contributors to the next technological breakthrough. We must give STEM students at every level of study the opportunity for hands-on technical training, and conversely, ensure America's skilled technical workforce has clear pathways to participate in formal academic training and scientific research. The glorification of craft, industry, and manufacturing that once characterized American culture must be revived, and we will be richer for it.

科学训练与技艺的脱节阻碍了美国的科学进步。那些通过实验实践和动手技术训练发展起来、支撑我们科学事业的隐性知识，只会变得更加重要。我们国家实验室的粒子加速器需要能够开发更强大速调管的电气工程师；聚变实验需要真空专家和能加工钨材的制造者；绘制宇宙图谱的望远镜需要能将镜面研磨至纳米精度的工匠。这些人很可能就是下一次技术突破的核心贡献者。我们必须让各个学习阶段的STEM学生都有机会接受动手技术训练，同时确保美国的技术熟练工人有清晰的途径参与正规学术培训和科学研究。曾经塑造美国文化的那种对技艺、工业和制造业的崇尚必须复兴，而我们将因此更加富足。

EXPANDING PARTICIPATION

扩大参与

Our model of scientific training must adapt in three ways: by incorporating technical training, breaking academic credentialism, and connecting grassroots learning to formal scientific research.

我们的科学培训模式必须从三个方面进行调整:融入技术培训、打破学术资历主义、将草根学习与正式科学研究联系起来。

First, we must reconnect university science and engineering programs with hands-on, practical knowledge. This requires integrating technical training into university curricula, breaking down barriers between "elite" and "vocational" schooling, and reforming accreditation to reward real-world technical work, which would include counting hands-on externships and registered apprenticeship hours toward accredited degrees. Those who set curricula across the country should think seriously about what a world of more abundant intelligence and more constrained craft knowledge means for the expertise most required by the next generation.

首先，我们必须将大学科学与工程项目与实践性、实用知识重新连接起来。这需要将技术培训融入大学课程，打破"精英"教育与"职业"教育之间的壁垒，并改革认证体系以奖励实际技术工作，包括将实践性实习和注册学徒工时计入认证学位。全国课程制定者应认真思考，在智力更丰富而工艺知识更受限的世界中，下一代最需要哪些专业知识。

Second, we must create new pathways into our scientific enterprise grounded in demonstrated skill rather than academic pedigree alone. Today, the conventional academic ladder is the only widely legible route into research. We must lay alternative paths that allow more makers, crafters, and technicians to participate in academic training and scientific discovery if they choose to do so. National fellowships could place skilled machinists and lab technicians at national labs and research universities, with skill-based pathways to credentials and co-authorship on research outputs. Practitioners-in-residence programs, analogous to artists-in-residence, could embed experienced craftsmen alongside Ph.D. researchers, granting them access to specialized equipment, professional mentorship, and attention for their research without requiring a doctorate. National laboratories could develop portable, industry-recognized credentials in areas like cryogenics and advanced machining. Programs like SBIR open doors for technician-founded ventures, directing resources toward ideas that do not always start with a dissertation.

其次，我们必须为科学事业开辟新路径，这些路径应基于实际技能而非仅凭学术背景。如今，传统的学术阶梯是进入研究领域的唯一广泛认可的途径。我们必须铺设替代路径，让更多制造者、工匠和技术人员能够参与学术培训和科学发现——如果他们愿意的话。国家奖学金项目可将熟练机械师和实验室技术人员安排到国家实验室和研究型大学，提供基于技能的认证途径和研究产出的合著机会。驻场实践者项目(类似于驻场艺术家项目)可将经验丰富的工匠与博士研究人员安排在一起，让他们无需博士学位即可使用专业设备、获得专业指导并关注其研究。国家实验室可开发便携式、行业认可的认证，涵盖低温学、高级机械加工等领域。像SBIR这样的项目为技术员创办的企业打开大门，将资源引导至那些并非总以论文为起点的创意。

Third, we must connect the millions of Americans who tinker, fabricate, and repair to engineering and research. This is especially critical for rural students, who face distinct challenges in accessing traditional academic pathways. They are less likely to have family members working in STEM fields. Their schools receive less outreach from industry. The smaller populations in their towns make it harder to find like-minded peers. And yet many of our best scientists and engineers first learned to weld in a barn, or helped their families fix tractors growing up. The answer is not to pluck talented individuals from their communities, but to bring the frontier to them, creating more connection points between maker culture and our formal science enterprise.

第三，我们必须将数百万喜欢动手制作、组装和修理的美国人连接到工程和研究领域。这对农村学生尤其关键，他们在获取传统学术路径方面面临独特挑战。他们更少有家庭成员从事STEM领域工作。他们的学校获得的行业外联更少。他们所在城镇人口较少，更难找到志同道合的同伴。然而，我们许多最优秀的科学家和工程师最初是在谷仓里学会焊接，或是在成长过程中帮助家人修理拖拉机。答案不是将才华横溢的人从他们的社区中抽离，而是将前沿带到他们身边，在创客文化与正式科学事业之间建立更多连接点。

INTEGRATED MODELS OF TRAINING

综合培训模式

To sustain America's scientific leadership, we must rebuild integrated communities from community colleges to extension systems to keep the frontier of science and technology open not only to a select few, but to every American who has the aptitude and interest to advance it.

要维持美国的科学领导地位，我们必须重建从社区学院到推广系统的综合社区，使科技前沿不仅向少数精英开放，而是向每一位有能力和兴趣推动其发展的美国人敞开。

Community colleges are the natural foundation for scaling scientific and technical training. Congress has provided major funding to these institutions in recent years, and justifiably so. They enroll around 40% of all undergraduates and serve as the primary entry point to higher education for first-generation students, veterans, and working adults. ( {}^{134} ) They also provide apprentices with Related Technical Instruction in classrooms, pairing theory with on-the-job training.

社区学院是扩大科技培训规模的天然基础。近年来，国会已向这些机构提供大量资金，这合情合理。它们招收了约40%的本科生，是第一代学生、退伍军人和在职成人接受高等教育的主要入口。( {}^{134} ) 它们还在课堂上为学徒提供相关技术指导，将理论与在职培训相配对。

With targeted support, community colleges can become regional hubs for scientific and technological innovation, integrating with critical industries, NSF Regional Innovation Engines, and local ecosystem initiatives. These partnerships can channel shared facility access, equipment donations, and industry-led, co-developed curricula into institutions that already train America's best technical talent. The Trump Administration has made expanding apprenticeships a priority, directing federal agencies to reach and surpass one million active apprenticeships annually. The Department of Labor (DOL) has shifted toward a pay-for-performance model, replacing traditional upfront grants for employer-led apprenticeships with funding tied to apprentice hiring and retention. ( {}^{135} ) And with the passage of Workforce Pell in 2025, short-term training programs are, for the first time, eligible for Pell Grant funding.

在定向支持下，社区学院可成为区域科技创新中心，与关键产业、国家科学基金会区域创新引擎及地方生态系统计划相整合。这些合作能将共享设施使用权、设备捐赠以及行业主导、共同开发的课程引入这些已培养美国顶尖技术人才的机构。特朗普政府已将扩大学徒制列为优先事项，指示联邦机构每年达到并超过100万个活跃学徒岗位。劳工部已转向按绩效付费模式，将雇主主导的学徒制传统预付款改为与学徒招聘和留用挂钩的资金。( {}^{135} ) 随着2025年《劳动力佩尔法案》的通过，短期培训项目首次有资格获得佩尔助学金资助。

Going further, we could extend registered apprenticeships into fields that have not traditionally used them, particularly in science and technology. These shifts move us toward a more practical system of education that restores integrated mentorship and embeds learning in real-world application. ( {}^{136} ) And to ensure scientific opportunity is not limited by geography, training must be distributed nationwide. Programs like the U.S. Department of Agriculture's Cooperative Extension System could expand its remit to include critical sciences and technologies, creating strongholds in rural areas. Employers who most need skilled workers could partner with nearby community colleges by donating equipment and supporting instruction. These programs should serve as entry points into a broader regional innovation ecosystem, helping young talent network with mentors and peers who can channel their entrepreneurial energy into shared projects.

更进一步，我们可以将注册学徒制扩展到传统上未采用该制度的领域，尤其是科技领域。这些转变推动我们走向更实用的教育体系，恢复综合指导，并将学习嵌入实际应用。( {}^{136} ) 为确保科学机会不受地域限制，培训必须在全国范围内分布。美国农业部合作推广系统等项目可将其职责范围扩展至关键科技领域，在农村地区建立据点。最需要技能工人的雇主可与附近社区学院合作，捐赠设备并支持教学。这些项目应成为更广泛区域创新生态系统的入口，帮助青年人才与导师和同行建立联系，将他们的创业能量引导至共享项目中。

NEW PATHS FOR TRANSLATION

翻译新路径

American technological leadership has repeatedly emerged from regional clusters where research and production were inseparable. In the early 20th century, Detroit became the world's automotive capital not merely because of Ford's factories, but because a dense ecosystem of suppliers, machinists, and engineering talent made the region uniquely capable of translating automotive innovations into mass production. Thousands of small suppliers could prototype and manufacture new components faster than anywhere else on earth. ( {}^{137} )

美国的技术领先地位屡次从那些研究与生产密不可分的区域集群中涌现。20世纪初，底特律成为世界汽车之都，不仅因为福特的工厂，更因为一个由供应商、机械师和工程人才组成的密集生态系统，使该地区能够独特地将汽车创新转化为大规模生产。成千上万的小型供应商能以比地球上任何地方更快的速度制作原型并制造新部件。( {}^{137} )

Such clusters work because they create dense connections among industrial production, scientific research, and a skilled technical workforce. ( {}^{138} ) The model persists today, where different regions specialize in distinct domains, such as optics in Arizona, biotechnology in the Boston corridor, aerospace in Colorado and Alabama, and advanced manufacturing in the Midwest. In these clusters, knowledge accumulates locally and is reinforced by a culture of practical problem-solving that emerges between the skilled technical workforce and dense supplier networks.

这类集群之所以有效，是因为它们在工业生产、科学研究和熟练技术劳动力之间建立了紧密联系。( {}^{138} )这种模式至今仍在延续，不同地区专注于不同领域，如亚利桑那州的光学、波士顿走廊的生物技术、科罗拉多州和阿拉巴马州的航空航天，以及中西部地区的先进制造。在这些集群中，知识在当地积累，并通过熟练技术劳动力与密集供应商网络之间形成的实用问题解决文化得到强化。

As the United States offshored manufacturing, it also severed these connections and the process knowledge they sustain. For too long, America's leaders assumed that we could retain high-value design work while ceding production. But as competitors began to manufacture at scale, they improved their ability to design and iterate. Vice President Vance captured this dynamic precisely, observing that over time, "the geographies that do the manufacturing get awfully good at the designing of things."139

随着美国将制造业外包，它也切断了这些联系以及它们所维持的工艺知识。长期以来，美国领导人假设我们可以在放弃生产的同时保留高价值的设计工作。但随着竞争对手开始大规模制造，他们提高了设计和迭代的能力。万斯副总统精准地捕捉到了这一动态，他指出，随着时间的推移，“从事制造的地区会变得非常擅长设计东西。”139

Recent federal initiatives have taken early steps toward reversing this trend and rebuilding America's innovation clusters. The NSF's Regional Innovation Engines and the Commerce Department's Tech Hubs channel resources to stimulate place-based production ecosystems outside traditional coastal hubs. Programs such as the Manufacturing USA Institutes, NIST's Manufacturing Extension Partnership, and DOW's eight Microelectronic Commons regional hubs further in-centivize industry partnerships, open shared R&D infrastructure, and drive industry-led workforce training. These clusters lower the barrier for small manufacturers to create new products, test applications of new technologies, and build a manufacturing workforce with the skills of the future. Whole-of-government industrial policy reinforces this shift, driving increased demand for American-made products and changing the calculus for where companies manufacture. The Federal Government is using innovative economic tools to attract investment commitments from foreign governments, and so financing American production, training American workers, and building American supply chains.

最近的联邦倡议已采取初步措施来扭转这一趋势，并重建美国的创新集群。国家科学基金会的区域创新引擎和商务部的技术中心将资源引导至传统沿海中心以外的地区，以刺激基于地方的生产生态系统。诸如美国制造研究所、国家标准与技术研究院的制造扩展伙伴关系以及国防部的八个微电子共享区域中心等项目，进一步激励行业合作，开放共享的研发基础设施，并推动行业主导的劳动力培训。这些集群降低了小型制造商创造新产品、测试新技术应用以及培养具备未来技能的制造劳动力的门槛。全政府工业政策强化了这一转变，推动了对美国制造产品需求的增长，并改变了公司选择制造地点的考量。联邦政府正在使用创新的经济工具来吸引外国政府的投资承诺，从而资助美国的生产、培训美国工人并建设美国的供应链。

That said, the Federal Government cannot build these ecosystems alone. State and local governments hold critical levers, including land use and permitting, education and workforce development, anchor institutions, and more, which enable them to cultivate competitive advantages. Done poorly, such competition can become a race to the bottom that transfers public money to mobile firms, but done well, smart policies can cultivate long-lasting regional ecosystems. By leveraging the benefits of federalism, the United States can pursue parallel experimentation where 50 states and thousands of localities experiment with different approaches to cultivating industry clusters, competing to attract investment and talent.

尽管如此，联邦政府无法独自建立这些生态系统。州和地方政府掌握着关键杠杆，包括土地使用和许可、教育和劳动力发展、支柱机构等，这使他们能够培养竞争优势。如果做得不好，这种竞争可能演变成逐底竞争，将公共资金转移给流动企业；但如果做得好，明智的政策可以培育持久的区域生态系统。通过利用联邦制的优势，美国可以进行平行实验，让50个州和数千个地方尝试不同的方法来培育产业集群，竞争吸引投资和人才。

Several state-led efforts illustrate the approach. When new semiconductor fabrication plants were announced in New Albany, Ohio, the state committed roughly $2 billion in incentives spanning direct cash "onshoring incentive grants," infrastructure spending, and job creation tax credits. ( {}^{140} ) A community college in Columbus now leads a statewide network of 23 Ohio colleges developing open, shareable curricula for a two-year degree pathway into chip manufacturing technician careers. ( {}^{141} ) A semiconductor company has invested $50 million in Ohio higher education to support comprehensive semiconductor workforce development, spanning curriculum development, faculty training, reskilling and upskilling programs, work-based learning, and laboratory equipment upgrades, multiply federal dollars from NSF. ( {}^{142} ) In Taylor, Texas, state-led support for semiconductor manufacturing helped attract a nearly $5 billion capital investment supporting thousands of high-quality jobs. ( {}^{143} ) Across these cases, state leaders have treated industrial development as a core priority, aligning local government, educational institutions, and industry on workforce development. As localities experiment, the most successful will discover models others can adapt.

几个州主导的努力展示了这种方法。当俄亥俄州新奥尔巴尼宣布新建半导体制造厂时，该州承诺了约20亿美元的激励措施，涵盖直接现金“回流激励拨款”、基础设施支出和就业创造税收抵免。( {}^{140} )哥伦布的一所社区大学现在领导着一个由23所俄亥俄州大学组成的全州网络，开发开放的、可共享的课程，以提供通往芯片制造技术员职业的两年制学位路径。( {}^{141} )一家半导体公司已向俄亥俄州高等教育投资5000万美元，以支持全面的半导体劳动力发展，涵盖课程开发、教师培训、再技能和提升技能项目、基于工作的学习以及实验室设备升级，并倍增来自国家科学基金会的联邦资金。( {}^{142} )在得克萨斯州泰勒市，州政府对半导体制造的支持帮助吸引了近50亿美元的资本投资，支持了数千个高质量就业岗位。( {}^{143} )在这些案例中，州领导人将工业发展视为核心优先事项，协调地方政府、教育机构和行业在劳动力发展方面的合作。随着地方进行实验，最成功的将发现可供其他地区借鉴的模式。

MAKING PROGRESS AVAILABLE TO ALL

让进步惠及所有人

American innovation has never been confined to elite university laboratories. In a local workshop, hobbyists young and old work together to retrofit a Computer Numerical Control (CNC) machine to wind carbon-overwrapped pressure vessels. At a private airstrip on the East Coast, a defense technology startup has pitched a trailer at the end of the field, experimenting with new propellant mixtures. Out in the mesas of the New Mexico desert, a student tinkers in a rusty shed, trying to harness the power of the sun through an inertial confinement fusion device cobbled together from laboratory surplus. Innovation advances in these places, far from major research campuses, through hands-on experimentation, the accumulation of craft knowledge, and a bias toward action.

美国的创新从未局限于精英大学的实验室。在本地工坊里，老少爱好者们共同改造一台计算机数控(CNC)机床，用于缠绕碳纤维包裹的压力容器。在东海岸的一条私人跑道上，一家国防科技初创公司在场地尽头搭起拖车，试验新型推进剂混合物。在新墨西哥沙漠的台地间，一名学生在生锈的棚屋里摆弄，试图用从实验室剩余物资拼凑出的惯性约束聚变装置来 harnessing 太阳的力量。在这些远离主要研究园区的地方，创新通过动手实验、工艺知识的积累以及行动导向的偏好而不断推进。

If this tradition is to endure, we must build a future in which entrepreneurship is not confined by geography or credentials, in which a student in a rural town, a machinist in a small city, and a researcher at a major university each has pathways to contribute to technological progress. A future in which science is not gated by pedigree, but open to all with aptitude and drive. The falling costs of computational power, manufacturing equipment, and scientific tools make this increasingly possible, placing capabilities once reserved for major corporations in the hands of small businesses, community colleges, and individual tinkerers.

如果这一传统要延续下去，我们必须构建一个未来:创业不受地理或资历的限制，乡村小镇的学生、小城市的机械师以及顶尖大学的研究员，都能找到为技术进步贡献力量的途径。一个科学不被出身所束缚，而是向所有有才能和动力的人开放的未来。计算能力、制造设备和科学工具成本的下降，使这越来越成为可能，将曾经只有大公司才拥有的能力交到小企业、社区学院和个人爱好者手中。

Alongside new pathways to contribute to the scientific enterprise, this future will create pathways for all to experience the benefits of scientific and technological progress. In the past, the integration of science and craft has produced entirely new markets, industries, and forms of work. Aircraft mechanics, modern welding, CNC machining, and semiconductor manufacturing emerged as skilled crafts in the 20th century; none existed a generation before. Each required its own body of tacit knowledge, its own communities of practice, and its own ties to science and engineering. Each created dignified work for millions of Americans and sustained entire communities. We should expect the same from the technologies taking shape today, but only if we make broad participation possible.

除了为科学事业贡献的新途径，这一未来还将为所有人创造体验科技进步成果的路径。过去，科学与工艺的结合催生了全新的市场、产业和工作形式。飞机机械师、现代焊接、CNC加工和半导体制造在20世纪成为熟练工艺；而在一代人之前，这些都不存在。每一种都需要自己的隐性知识体系、自己的实践社群，以及自己与科学和工程的纽带。每一种都为数百万美国人创造了体面的工作，并支撑了整片社区。对于今天正在成型的技术，我们应抱有同样的期待，但前提是我们要让广泛参与成为可能。

Rebuilding our industrial commons is not the work of a single administration. The clusters that once defined American industry took generations to build, and only years to hollow out when production moved overseas. Meeting this moment requires revitalizing the web of skills, suppliers, and tacit knowledge that form America's industrial commons, and cultivating vibrant communities of scientists, researchers, and craftspeople in a hundred Silicon Valleys across the nation.

重建我们的工业基础并非一届政府之功。曾定义美国工业的产业集群，需要几代人的时间才能建成，而当生产转移到海外时，只需数年便会被掏空。应对这一时刻，需要振兴构成美国工业基础的技能、供应商和隐性知识网络，并在全国范围内培育出上百个“硅谷”般充满活力的科学家、研究员和工匠社群。

The triumphs of American science and technology have never been the work of any singular locale, and have always drawn on distributed strengths. The Manhattan Project pulled talent from across the country, including physicists from Berkeley, engineers from Tennessee, and craftsmen from New Mexico. The space program employed hundreds of thousands in facilities spread across multiple states, from Houston to Huntsville to Cape Canaveral. The agricultural modernization that multiplied farm productivity in the mid-20th century was driven by land-grant universities and extension agents serving communities in every corner of the nation. Throughout our history, American scientific genius has been broad-based and open to all, and it must remain so.

美国科学技术的辉煌成就从来不是某个单一地点的功劳，而是始终依赖于分布各地的优势。曼哈顿计划从全国汇聚人才，包括来自伯克利的物理学家、来自田纳西的工程师以及来自新墨西哥的工匠。太空计划在多个州部署设施，雇佣了数十万人，从休斯顿到亨茨维尔再到卡纳维拉尔角。20世纪中期推动农业生产力倍增的农业现代化，由赠地大学和推广服务推动，服务于全国各个角落的社区。纵观历史，美国科学天才的基础广泛且向所有人开放，这一点必须保持下去。

Chapter V

第五章

A New Golden Age

新的黄金时代

THE AGE OF INTELLIGENCE

智能时代

In July 1945, the same month Vannevar Bush submitted Science: The Endless Frontier to President Truman, he published a companion essay titled "As We May Think." Where the former laid the institutional foundations for postwar science, the latter imagined its cognitive structure. Bush foresaw growing drags on the scientific enterprise, writing:

1945年7月，就在万尼瓦尔·布什向杜鲁门总统提交《科学:无尽的前沿》的同月，他发表了一篇题为《诚如所思》的姊妹篇。前者为战后科学奠定了制度基础，后者则构想了其认知结构。布什预见到科学事业将面临日益沉重的负担，他写道:

There is a growing mountain of research. But there is increased evidence that we are being bogged down today as specialization extends. The investigator is staggered by the findings and conclusions of thousands of other workers-conclusions which he cannot find time to grasp, much less to remember, as they appear. ( {}^{144} )

研究堆积如山。但越来越多的证据表明，随着专业化的扩展，我们正陷入困境。研究者被成千上万其他工作者的发现和结论所震撼——这些结论出现时，他无暇领会，更遑论记忆。( {}^{144} )

Bush recognized that the tools of his era had extended man's physical strength and perception. Trip hammers augmented the fist; microscopes sharpened the eye. But the instruments to extend human thought remained rudimentary. Bush proposed a remedy he called the memex, a device that could store, retrieve, and link the entire accumulated record of human knowledge:

布什认识到，他那个时代的工具已经延伸了人类的体力和感知。打桩锤增强了拳头；显微镜锐化了眼睛。但延伸人类思维的工具仍处于初级阶段。布什提出了一种他称之为“记忆扩展器”的补救方案，这是一种能够存储、检索和链接人类全部知识积累的设备:

Consider a future device for individual use, which is a sort of mechanized private file and library... a device in which an individual stores all his books, records, and communications, and which is mechanized so that it may be consulted with exceeding speed and flexibility. It is an enlarged intimate supplement to his memory. ( {}^{145} )

设想一种供个人使用的未来设备，它是一种机械化的私人档案和图书馆……个人在其中存储所有书籍、记录和通信的设备，并且经过机械化处理，可以极其快速和灵活地查阅。它是他记忆的一种扩大的亲密补充。( {}^{145} )

Over the eighty years since, the instruments Bush imagined have arrived in the form of computers, the internet, and now, AI.

在此后的八十年里，布什所设想的工具以计算机、互联网以及如今的人工智能的形式出现了。

Today, the world stands at the threshold of a dramatic transformation. In 2025 alone, American companies committed more than $400 billion to building out AI infrastructure, more than the inflation-adjusted cost of the Apollo Program and Manhattan Project combined. ( {}^{146} ) Capital exceeding the gross domestic product of most nations is now spent on matrix multiplications, as city-scale symphonies of chips work in concert to train the next generation of AI models.

今天，世界正站在一场剧变的门槛上。仅在2025年，美国公司就承诺投入超过4000亿美元用于建设人工智能基础设施，这超过了经通胀调整后的阿波罗计划和曼哈顿计划的总和。( {}^{146} ) 超过大多数国家国内生产总值的资本现在被用于矩阵乘法，城市规模的芯片交响乐协同工作，以训练下一代人工智能模型。

The result is AI systems that would have been unbelievable even to the most forward-thinking AI researchers five years ago, machines capable of reasoning through complex problems, understanding context and nuance, maintaining large codebases autonomously, and solving mathematics problems at a graduate level.

其结果是，即使对五年前最具前瞻性的人工智能研究人员来说，这些人工智能系统也是难以置信的——能够推理复杂问题、理解上下文和细微差别、自主维护大型代码库、并以研究生水平解决数学问题的机器。

The early returns from AI for science are striking. Narrow systems have predicted protein structures with atomic accuracy, ( {}^{147} ) designed novel proteins from scratch, ( {}^{148} ) modeled molecular dynamics for drug discovery, ( {}^{149} ) advanced plasma control for fusion research, ( {}^{150} ) and uncovered phenomena hidden in vast quantities of particle physics data that human analysts would have missed entirely. ( {}^{151} ) In 2026, AI agents can write entire software applications, analyze experimental data, and operate laboratory equipment without human intervention. In mathematics, perhaps the purest field of reasoning, AI systems have begun to prove novel theorems and enable new forms of mathematical collaboration. These capabilities are improving rapidly as billions of dollars pour into AI research every year.

人工智能在科学领域的早期回报令人瞩目。狭义系统已能以原子精度预测蛋白质结构，( {}^{147} ) 从零开始设计新型蛋白质，( {}^{148} ) 为药物发现模拟分子动力学，( {}^{149} ) 推进聚变研究的等离子体控制，( {}^{150} ) 并发现了人类分析师完全可能错过的、隐藏在大量粒子物理数据中的现象。( {}^{151} ) 到2026年，人工智能代理可以编写完整的软件应用程序、分析实验数据，并在无人干预的情况下操作实验室设备。在数学这个或许是最纯粹的推理领域，人工智能系统已开始证明新颖的定理，并促成新的数学合作形式。随着每年数十亿美元投入人工智能研究，这些能力正在迅速提升。

Our goal, however, should not merely be to accelerate existing methods. It should be reforms of the scientific enterprise that allow us to reach beyond the limits of human cognition and organization. ( {}^{152} )

然而，我们的目标不应仅仅是加速现有方法。而应是对科学事业进行改革，使我们能够超越人类认知和组织的极限。( {}^{152} )

Our brains, today, impose hard boundaries. We can hold only so many variables in working memory, read only so many papers in a career, and master only so many techniques in a lifetime. So too does the sociology of science. Disciplines fragment knowledge, incentives reward incremental work, and hierarchies suppress unconventional thinking. Even our means of communication are constrained by the acceptable mediums of words, equations, and charts.

今天，我们的大脑设定了硬性边界。我们在工作记忆中只能容纳有限数量的变量，职业生涯中只能阅读有限数量的论文，一生中只能掌握有限数量的技术。科学的组织方式也是如此。学科碎片化知识，激励机制奖励渐进式工作，等级制度压制非常规思维。甚至我们的交流方式也受到词语、方程式和图表的可接受媒介的限制。

AI tools offer the possibility of breaking through all of these limitations.

人工智能工具提供了突破所有这些限制的可能性。

Consider how scientists digest knowledge. A researcher today faces the same problem Bush identified in 1945, only greatly magnified. Millions of papers are published each year, ( {}^{153} ) more than any human can read in a fraction of their own subfield, let alone adjacent domains where the most important connections hide. AI systems, by contrast, can extract and synthesize findings from vast literatures, identify patterns that span disciplinary boundaries, and search combinatorial spaces of hypotheses and ideas inaccessible to human capacities. ( {}^{154} )

想想科学家如何消化知识。今天的研究人员面临着布什在1945年指出的同样问题，只是被大大放大了。每年有数百万篇论文发表，( {}^{153} ) 远超任何人在自己子领域内所能阅读的数量，更不用说隐藏着最重要联系的相邻领域了。相比之下，人工智能系统可以从浩瀚的文献中提取和综合发现，识别跨越学科边界的模式，并搜索人类能力无法企及的假设和想法的组合空间。( {}^{154} )

Consider the collection of scientific data. Until recently, only cleaned and structured data could be used reliably at scale. AI systems can now process and annotate raw data buried in old publications, archives, and video recordings. ( {}^{155} ) The Python notebooks used to discover the transformer architecture, ( {}^{156} ) or the boxes of paper Andrew Wiles filled before finally proving Fermat's Last Theorem, can now be processed by machines as well as humans. The metadata behind the tacit knowledge of science, the kind that never makes it into print, is becoming legible for the first time.

想想科学数据的收集。直到最近，只有经过清理和结构化的数据才能可靠地大规模使用。人工智能系统现在可以处理并注释埋藏在旧出版物、档案和视频记录中的原始数据。( {}^{155} ) 用于发现Transformer架构的Python笔记本，( {}^{156} ) 或者安德鲁·怀尔斯在最终证明费马大定理之前填满的纸箱，现在都可以由机器像人类一样处理。科学隐性知识背后的元数据——那种从未被印刷出来的知识——首次变得可读。

And consider the process of experimentation itself. Traditionally, experiments have been designed by hand, run sequentially, and adjusted only after results were reviewed, limiting both their speed and scope. AI-driven planning, combined with industrial-scale autonomous laboratories, will let us parallelize data collection with custom techniques tailored to each run. Operating in closed loop, AI systems can identify which measurements will yield the most information and adjust course in real time, as experimentation scales up by orders of magnitude.

再来看实验过程本身。传统上，实验由人工设计、顺序执行，且仅在结果审查后才进行调整，这限制了实验的速度和范围。由人工智能驱动的规划，结合工业规模的自动化实验室，将使我们能够针对每次运行采用定制技术并行收集数据。在闭环运行中，人工智能系统可以识别哪些测量能产生最多信息，并随着实验规模扩大数个数量级而实时调整方向。

The cognitive tools Bush imagined in "As We May Think" have finally arrived. If we get this right, AI will accelerate every stage of the scientific process. It will help identify the most impactful questions, generating hypotheses that would never occur to researchers constrained by their training and field. It will design experiments, optimize protocols, and anticipate pitfalls based on the full shared record of prior work. It will analyze data at scales far exceeding what is currently possible and predict behaviors in complex systems that were previously impenetrable to mathematical modeling, from large-scale brain dynamics to long-range weather patterns. It will accelerate how scientific results are communicated and validated, breaking the constraining form of the scientific paper. And increasingly, it will participate in the engineering work that translates scientific discoveries into deployable technologies.

布什在《诚如所思》中设想的认知工具终于到来。如果我们把握得当，人工智能将加速科学进程的每个阶段。它将帮助识别最具影响力的问题，生成受限于自身训练和领域的研究者永远无法想到的假设。它将设计实验、优化方案，并基于前人工作的完整共享记录预判潜在问题。它将分析远超当前可能规模的数据，预测此前数学建模无法触及的复杂系统行为——从大规模大脑动力学到长期天气模式。它将加速科学成果的交流与验证，打破科学论文的固有形式。并且，它将越来越多地参与将科学发现转化为可部署技术的工程工作。

ADAPTING OUR INSTITUTIONS

调整我们的制度

Our task is to build a scientific enterprise that maximizes the marginal returns to intelligence, which means confronting, clearly, the factors that constrain its work. Three stand out.

我们的任务是构建一个能最大化智力边际回报的科学事业，这意味着要清晰直面制约其工作的因素。其中三点尤为突出。

The first is the speed of feedback. Particle physicists have devised myriad theories about our universe, but we currently lack the experimental data to distinguish among them. No amount of intelligence can conjure observations that do not yet exist.

首先是反馈速度。粒子物理学家提出了关于宇宙的众多理论，但我们目前缺乏区分它们的实验数据。再多的智慧也无法凭空创造出尚不存在的观测结果。

The second is the speed of atoms. Cells divide on their own schedule, and hardware must be physically manufactured. No cognitive power can alone make a rocket or a semiconductor fab build itself overnight.

其次是原子速度。细胞按自身节奏分裂，硬件必须物理制造。单凭认知能力无法让火箭或半导体工厂在一夜之间自行建成。

The third is the constraint imposed by our own institutions. Publishing systems, funding structures, regulatory frameworks, clinical trial requirements, and cultural inertia govern how research happens. This will be felt first within the scientific enterprise.

第三是我们自身制度带来的制约。出版体系、资助结构、监管框架、临床试验要求以及文化惯性，共同决定了研究如何进行。这一点将首先在科学事业内部被感受到。

The coming abundance of cognitive capability demands a corresponding transformation of our scientific institutions. New technologies like AI should give us tremendous optimism, but we should harbor no illusions that AI will repair our scientific enterprise by default. Even if every scientist benefits from adopting AI, it does not follow that science as a whole will advance. This is one of the counterintuitive properties of complex systems: individual gains do not automatically aggregate into collective progress.

即将到来的认知能力富足，要求我们的科学制度进行相应变革。像人工智能这样的新技术应带给我们巨大乐观，但我们不应抱有幻想，认为人工智能会默认修复我们的科学事业。即使每位科学家都因采用人工智能而受益，也不意味着整个科学会进步。这是复杂系统反直觉的特性之一:个体收益不会自动汇聚成集体进步。

For a century, the United States aggressively suppressed forest fires, and each intervention seemed like an obvious success. But by preventing small fires, we allowed the fuel of dead wood, dense brush, and dry undergrowth to accumulate until the inevitable fires became infernos even harder to contain.

一个世纪以来，美国积极扑灭森林火灾，每次干预看似都是明显的成功。但通过阻止小火灾，我们让枯木、茂密灌木和干燥下层植被的燃料不断积累，直到不可避免的火灾变成更难控制的炼狱。

AI could do the same to science, by making each researcher seemingly more productive while allowing the conditions for catastrophic and systemic dysfunction to build. ( {}^{157} ) AI tools will make it trivial to generate more papers, more grant applications, and more submissions to peer review. But if the obstacle to scientific progress were simply the production of these artifacts, we would already be living in a scientific golden age. We are not.

人工智能可能对科学产生同样影响:让每位研究者看似更高效，同时却让灾难性和系统性功能失调的条件不断积累。( {}^{157} ) 人工智能工具将让生成更多论文、更多资助申请和更多同行评审投稿变得轻而易举。但如果科学进步的障碍仅仅是这些成果的生产，我们早已生活在科学黄金时代。事实并非如此。

As Chapter II documented, the exponential growth in publications, researchers, and funding over the past half-century has not produced a corresponding acceleration in discovery. Disruptive work represents an ever-shrinking fraction of total output, and the breakthroughs that reorient fields arrive no faster than they did generations ago.

正如第二章所述，过去半个世纪中出版物、研究者和资助的指数级增长，并未带来发现速度的相应加速。颠覆性工作在总产出中所占比例不断缩小，而重新定位领域的突破性进展，其到来速度并不比几代人之前更快。

The rise of AI in science therefore demands that we update the institutional machinery governing what gets funded, what gets published, what gets rewarded, and what gets corrected. Science works only if we can generate and verify knowledge in tandem. Models that make flawed paradigms easier to extend may entrench scientific biases; AI tools that flood peer review with unthoughtful submissions will overwhelm systems already stretched thin. In an age of abundant intelligence, everything discussed in the preceding chapters, such as the public-private partnerships that direct AI toward problems that matter, the meta-science reforms that restore accountability and reward genuine exploration, the novel funding mechanisms that tolerate early failure, the rebuilding of our technical workforce and manufacturing base, becomes more urgent, not less.

因此，人工智能在科学中的兴起要求我们更新管理资助、出版、奖励和纠正机制的制度机器。科学只有在能够同步生成和验证知识时才能发挥作用。让有缺陷范式更易扩展的模型可能固化科学偏见；用缺乏思考的投稿淹没同行评审的人工智能工具，将压垮本已捉襟见肘的系统。在智力富足的时代，前几章讨论的一切——如将人工智能导向重要问题的公私合作伙伴关系、恢复问责制并奖励真正探索的元科学改革、容忍早期失败的新型资助机制、重建技术劳动力和制造业基础——都变得更加紧迫，而非相反。

BUILDING THE INFRASTRUCTURE

建设基础设施

The reforms proposed in the preceding chapters are also essential for AI-powered science.

前几章提出的改革对于人工智能驱动的科学同样至关重要。

AI has shifted basic research toward industry, made cross-disciplinary collaboration essential, and sharply increased the capital intensity of frontier science. In doing so, AI has made the institutional adaptations described in the preceding chapters necessary to update the scientific enterprise for the modern world.

人工智能已将基础研究转向产业界，使跨学科合作成为必需，并大幅提高了前沿科学的资本密集度。在此过程中，人工智能使得前几章所述的制度调整成为必要，以更新科学事业以适应现代世界。

The novel organizations and funding mechanisms in Chapter II matter because fully leveraging AI for science demands tight iteration between exploration and engineering, something traditional academic departments were not built to sustain. It also requires close partnerships between domain scientists who understand AI models and AI researchers who understand the science. Private research institutes are now housing machine learning researchers and life scientists in shared facilities to maximize collisions, ( {}^{158} ) while fellowship programs pair AI researchers with academic co-advisors. ( {}^{159} ) These reforms must spread across the entire scientific enterprise. Eventually, we will need AI-native scientific institutions, with rules, infrastructure, capital models, and cultural norms built around the use of powerful AI systems.

第二章中的新型组织和资助机制之所以重要，是因为充分利用人工智能推动科学需要探索与工程之间的紧密迭代，而传统学术部门并非为此而建。它还需要理解人工智能模型的领域科学家与理解科学的AI研究人员之间的密切合作。私立研究机构现在将机器学习研究人员和生命科学家安置在共享设施中，以最大化碰撞机会，( {}^{158} )而奖学金项目则将AI研究人员与学术联合导师配对。( {}^{159} )这些改革必须扩展到整个科学事业。最终，我们将需要原生AI的科学机构，其规则、基础设施、资本模式和文化规范都围绕强大AI系统的使用而构建。

The public-private partnerships in Chapter III matter because frontier AI capabilities concentrate in private laboratories. The compute clusters required to train frontier models cost billions of dollars. The engineering teams capable of operating them mostly work at a handful of companies, on payrolls no university can currently match. Keeping private sector capabilities in conversation with the public scientific enterprise will require carefully crafted structures. Existing partnerships between federal laboratories and AI companies represent steps toward that future, with researchers applying cutting-edge AI systems to advance fusion energy, drive advancements in computational biology, and even control a rover on Mars. ( {}^{160} )

第三章中的公私合作伙伴关系之所以重要，是因为前沿AI能力集中在私营实验室。训练前沿模型所需的计算集群耗资数十亿美元。能够操作这些集群的工程团队大多受雇于少数几家公司，其薪资水平目前任何大学都无法匹敌。让私营部门的能力与公共科学事业保持对话，需要精心设计的结构。联邦实验室与AI公司之间的现有合作是迈向这一未来的步骤，研究人员正在应用尖端AI系统来推进聚变能源、推动计算生物学的发展，甚至控制火星上的漫游车。( {}^{160} )

Finally, the reconnection of science and craft in Chapter IV matters because as intelligence becomes more abundant, the constraints on scientific progress shift from generating ideas to realizing them in the physical world. The world of atoms, which includes manufacturing, fabrication, and the infrastructure on which discovery depends, will increasingly determine the pace of progress. No amount of intelligence substitutes for the instruments needed to collect experimental data or the facilities needed to build and test new technologies. Reforms to rebuild apprenticeships, capture tacit knowledge, and reconnect universities with regional manufacturing ecosystems are focused precisely on this constraint.

最后，第四章中科学与工艺的重新连接之所以重要，是因为随着智能变得愈加丰富，科学进步的制约因素从产生想法转向在物理世界中实现它们。原子世界——包括制造、加工以及发现所依赖的基础设施——将日益决定进步的节奏。再多的智能也无法替代收集实验数据所需的仪器，或建造和测试新技术所需的设施。重建学徒制、捕捉隐性知识以及将大学与区域制造生态系统重新连接起来的改革，正是针对这一制约因素。

THE GENESIS MISSION

创世纪计划

Throughout our history, from the Manhattan Project to the Apollo Program, America's greatest scientific advances have come when national capabilities were matched with the right institutional design. The Genesis Mission is the next chapter in that tradition, a national effort to harness AI for scientific discovery at a scale no other nation can match.

纵观历史，从曼哈顿计划到阿波罗计划，美国最伟大的科学进步都源于国家能力与恰当制度设计的结合。创世纪计划正是这一传统的下一篇章，是一项国家层面的努力，旨在以其他国家无法匹敌的规模，利用人工智能推动科学发现。

President Trump launched the Genesis Mission in November 2025 as America's premier -"AI for science" initiative. The Executive Order establishing the Mission directs DOE to build the American Science and Security Platform, which will connect our most powerful supercomputers, AI systems, and scientific instruments and datasets into a single discovery engine designed to double the productivity and impact of American science and engineering within a decade. ( {}^{161} )

特朗普总统于2025年11月启动创世纪计划，作为美国首屈一指的“人工智能促进科学”倡议。启动该计划的行政令指示能源部建设美国科学与安全平台，该平台将把我们最强大的超级计算机、人工智能系统以及科学仪器和数据集连接成一个统一的发现引擎，旨在十年内将美国科学与工程的生产力和影响力翻一番。( {}^{161} )

The Mission draws on an unparalleled base of national capability. DOE's 17 national laboratories constitute the largest concentration of scientific infrastructure in the world, employing roughly 40,000 scientists, engineers, and technical staff, and receiving approximately $20 billion in annual funding. ( {}^{162} ) These institutions house our most advanced particle accelerators, synchrotron light sources, supercomputers, and experimental facilities spanning materials science, fusion energy, and nuclear security. Beyond our national laboratories, agencies including the FDA, NSF, National Oceanic and Atmospheric Administration (NOAA), and Department of Veterans Affairs steward vast quantities of scientific data accumulated over decades of federal investment, from genomic sequences to weather simulations. The Genesis Mission will unlock this capacity, including by building foundational technologies and AI-ready datasets, to tackle the nation's most complex science and technology challenges.

该计划依托于无与伦比的国家能力基础。能源部的17个国家实验室构成了世界上最大的科学基础设施集群，雇佣约4万名科学家、工程师和技术人员，每年获得约200亿美元的资金。( {}^{162} )这些机构拥有我们最先进的粒子加速器、同步辐射光源、超级计算机以及涵盖材料科学、聚变能和核安全的实验设施。除了国家实验室，包括食品药品监督管理局、国家科学基金会、国家海洋和大气管理局以及退伍军人事务部在内的机构，管理着数十年来联邦投资积累的大量科学数据，从基因组序列到天气模拟。创世纪计划将释放这一能力，包括通过建设基础技术和人工智能就绪数据集，来应对国家最复杂的科技挑战。

Realizing the Mission's potential requires addressing four key challenges that would otherwise constrain AI-enabled science.

实现该计划的潜力需要应对四个关键挑战，否则这些挑战将制约人工智能驱动的科学。

The first is problem selection. Not every scientific problem is well-suited to AI intervention. The strongest candidates exhibit certain characteristics, such as, for today's AI systems, large combinatorial search spaces, substantial quantities of structured data, and clear metrics against which to benchmark progress. Protein structure prediction, for example, fit these criteria precisely. The space of possible configurations is vast, decades of crystallographic data provided training material, and benchmarks such as the Critical Assessment of Protein Structure Prediction (CASP) allowed researchers to measure improvement.

第一个是问题选择。并非所有科学问题都适合人工智能介入。最合适的候选问题具有某些特征，例如，对于当前的人工智能系统而言，包括巨大的组合搜索空间、大量结构化数据以及衡量进展的明确指标。例如，蛋白质结构预测就完全符合这些标准。可能的构型空间巨大，数十年的晶体学数据提供了训练材料，而蛋白质结构预测关键评估等基准则使研究人员能够衡量改进。

The Mission has therefore directed DOE to identify at least 20 science and technology challenges of national importance, spanning advanced manufacturing, biotechnology, critical materials, nuclear fission and fusion, quantum information science, and semiconductors. Challenges will be reviewed annually to reflect both scientific progress and national priorities. In a world where AI research is flush with capital, the Federal Government's value-add is not funding AI in the abstract, but directing it toward problems where breakthroughs could unlock entire branches of downstream discovery and application, just as cracking the human genome did decades ago.

因此，该计划指示能源部至少确定20项具有国家重要性的科技挑战，涵盖先进制造、生物技术、关键材料、核裂变与聚变、量子信息科学以及半导体。这些挑战将每年进行审查，以反映科学进展和国家优先事项。在一个人工智能研究资金充裕的世界里，联邦政府的增值之处并非抽象地为人工智能提供资金，而是将其引导至那些突破可能解锁下游发现和应用整个分支的问题上，就像几十年前破解人类基因组所做的那样。

The second is institutional capacity. The Genesis Mission is designed to op-erationalize the reforms outlined throughout this report, many of which are preconditions for AI-powered science, at national scale. In December 2025, DOE announced agreements with twenty-four organizations, including leading AI companies, semiconductor manufacturers, and cloud providers. ( {}^{163} ) These partnerships, and the many that follow, will ensure the Mission's outputs flow across the entire national research ecosystem. Furthermore, the Transformational AI Models Consortium, a cornerstone investment in the Mission, will mobilize National Laboratories to partner with industry to generate new AI-ready data and support the development of foundation models that harness DOE's unique data, facilities, and expertise across scientific and engineering domains. ( {}^{164} )

第二个是机构能力。创世纪计划旨在将本报告中概述的改革(其中许多是人工智能驱动科学的先决条件)在国家层面付诸实施。2025年12月，能源部宣布与24个组织达成协议，包括领先的人工智能公司、半导体制造商和云服务提供商。( {}^{163} )这些合作伙伴关系以及后续的众多合作，将确保该计划的成果惠及整个国家研究生态系统。此外，作为该计划基石投资的变革性人工智能模型联盟，将动员国家实验室与产业界合作，生成新的人工智能就绪数据，并支持开发基础模型，以利用能源部在科学和工程领域的独特数据、设施和专业知识。( {}^{164} )

The third is data infrastructure. Scientific data is the raw material for AI-powered discovery, but much of America's most valuable data is currently inaccessible, uncurated, or locked behind restrictive licensing. Fixing this requires two complementary approaches. One is opening access to Federal Government data. Many valuable datasets exist because the government chose to build them, like NOAA's weather data or the Materials Project's mapping of inorganic crystals. ( {}^{165} ) The American Science Cloud, a cornerstone of the Mission’s infrastructure, will empower the National Labs to curate and distribute DOE's AI-ready scientific data for the broader research community and unlock data hidden behind government bureaucracy. Approach two is creating incentives for individual researchers to curate and share their own datasets. Much valuable data arises organically, when individuals closest to the research recognize that their experimental records or failed trials could be helpful to others. This data is routinely abandoned, sometimes due to a lack of stable funding for storage and curation, and other times because there is no signal on the value of the information. ( {}^{166} ) Data on laboratory procedures and challenging experiments, for instance, may prove highly valuable in light of potential lab automation, yet such data is currently scattered. The Mission will address this directly, creating new funding opportunities for dataset curation and building new incentives to partake in these curation efforts across our science agencies.

第三个是数据基础设施。科学数据是人工智能驱动发现的原材料，但美国最有价值的数据中，大部分目前无法访问、未经整理或受限于限制性许可。解决这个问题需要两种互补的方法。一是开放联邦政府数据的访问权限。许多有价值的数据集之所以存在，是因为政府选择构建它们，例如国家海洋和大气管理局的天气数据或材料项目的无机晶体图谱。( {}^{165} )作为该计划基础设施基石的美国科学云，将授权国家实验室为更广泛的研究界整理和分发能源部的人工智能就绪科学数据，并解锁隐藏在政府官僚机构背后的数据。方法二是为个体研究人员创建激励措施，以整理和共享他们自己的数据集。许多有价值的数据是自然产生的，当最接近研究的人员认识到他们的实验记录或失败试验可能对他人有帮助时。这些数据通常被弃置，有时是因为缺乏稳定的资金用于存储和整理，有时是因为没有关于信息价值的信号。( {}^{166} )例如，关于实验室程序和具有挑战性实验的数据，鉴于潜在的实验室自动化，可能被证明极具价值，然而此类数据目前是分散的。该计划将直接解决这一问题，为数据集整理创造新的资助机会，并在我们的科学机构中建立参与这些整理工作的新激励措施。

The fourth is the integration of AI capabilities with experimental infrastructure. Where materials discovery can take around 20 years from laboratory to deployment, closed-loop autonomous experimentation could collapse that time-line by an order of magnitude. ( {}^{167} ) That makes leadership in this platform technology a strategic imperative for the United States. We have already prototyped autonomous facilities across our national laboratories, such as the A-Lab at Lawrence Berkeley, which works on solid-state synthesis of inorganic materials, and the Polybot at Argonne, a modular robotics platform for materials characterization. But other countries, including Canada and China, are racing forward.

第四是将人工智能能力与实验基础设施相整合。材料从实验室到部署通常需要约20年，而闭环自主实验可将这一时间线缩短一个数量级。( {}^{167} ) 这使得在该平台技术领域保持领先成为美国的战略要务。我们已在国家实验室中建立了自主设施原型，例如劳伦斯伯克利国家实验室的A-Lab(专注于无机材料固态合成)和阿贡国家实验室的Polybot(用于材料表征的模块化机器人平台)。但包括加拿大和中国在内的其他国家正在加速追赶。

The constraint on further automation runs deeper than funding. Decades of consolidation and offshoring in the scientific instruments industry have created pathologies one would expect, including expensive products, poor software, and proprietary data formats that lock researchers into vendor ecosystems. Scientists attempting to build automated workflows spend months simply getting different instruments to communicate. Scientific instruments themselves must be redesigned for automation from the ground up, with open interfaces and standardized data formats. The national laboratories deploy the largest concentration of advanced scientific instrumentation in the world, and their purchasing power can drive that redesign. The Mission has already invested in 14 projects focused on robotics, automated laboratories, and autonomous control of large-scale experiments. ( {}^{169} ) These efforts build on a parallel push at NSF to invest an initial $380 million into programmable cloud labs across academic institutions and startups, kicking off our domestic autonomous experimentation industry, just as NSFNET played an instrumental role in forming the backbone of the modern internet in the 1980s. ( {}^{170} )

进一步自动化的制约因素远不止资金。科学仪器行业数十年的整合与离岸外包导致了可预见的弊病，包括产品昂贵、软件低劣以及将研究人员锁定在供应商生态系统中的专有数据格式。试图构建自动化工作流程的科学家往往要花费数月时间才能让不同仪器实现通信。科学仪器本身必须从底层重新设计以实现自动化，配备开放接口和标准化数据格式。国家实验室部署了全球最密集的先进科学仪器，其采购能力可推动这一重新设计。该任务已投资14个专注于机器人技术、自动化实验室及大规模实验自主控制的项目。( {}^{169} ) 这些努力建立在国家科学基金会(NSF)同步推进的举措之上——该基金会初步投入3.8亿美元，在学术机构和初创企业中建设可编程云实验室，从而启动国内自主实验产业，正如20世纪80年代NSFNET在构建现代互联网骨干中发挥的关键作用。( {}^{170} )

America's brightest minds and industries have always answered the call when their country needed them most. The Genesis Mission is that call to this generation of scientists and engineers to advance American scientific leadership in the era of AI. Its design reflects the core convictions of this report. The central role of the Federal Government is to shape the arena rather than direct discovery, recognizing that the private sector possesses capabilities public institutions must learn to leverage rather than replicate.

美国最杰出的头脑和产业界始终在国家最需要时挺身而出。创世纪任务正是对当代科学家和工程师的召唤，要求他们在人工智能时代推进美国的科学领导地位。其设计体现了本报告的核心信念:联邦政府的主要角色是塑造竞技场而非指导发现，同时认识到私营部门拥有公共机构必须学会利用而非复制的强大能力。

GOLD STANDARD SCIENCE

黄金标准科学

The promise of AI-enabled science rests on a foundation that is, at present, potentially unsound. We are preparing to train AI systems on scientific literature, deploy them to generate hypotheses, and trust them to design experiments, but the knowledge base they will draw on is riddled with error.

人工智能驱动的科学所承诺的前景，其基础目前可能并不稳固。我们正准备用科学文献训练AI系统，部署它们生成假设，并信任它们设计实验，但它们所依赖的知识库却充斥着错误。

As detailed in Chapter II, a majority of researchers now acknowledge that science faces a reproducibility crisis. Between one-half and two-thirds of psychology studies failed replication attempts, ( {}^{171} ) more than one third of celebrated experimental economics studies similarly failed to replicate, ( {}^{172} ) and one study found that irreproducible findings in preclinical biomedical research alone misdirect an estimated $28 billion annually. ( {}^{173} ) Every false result can mislead subsequent researchers, creating cascading failures throughout the enterprise. Increased scientific productivity will not mean anything if the underlying findings are false.

如第二章所述，大多数研究人员现在承认科学面临可重复性危机。一半到三分之二的心理学研究未能通过重复验证，( {}^{171} )超过三分之一的著名实验经济学研究同样未能重复，( {}^{172} )一项研究发现，仅临床前生物医学研究中不可重复的发现每年就误导约280亿美元。( {}^{173} )每一个错误结果都可能误导后续研究人员，在整个事业中造成级联失败。如果基础发现是错误的，科学生产力的提高将毫无意义。

In May 2025, the President signed an executive order, Restoring Gold Standard Science, to begin addressing this dysfunction. ( {}^{174} ) The order establishes principles to govern all federally funded research: reproducibility; transparency; communication of error and uncertainty; collaboration across disciplines; skepticism of assumptions; falsifiability of hypotheses; unbiased peer review; acceptance of negative results; and freedom from conflicts of interest. Agencies are directed to apply an approach based on the weight of scientific evidence, transparently evaluating each piece of information based on quality and relevance.

2025年5月，总统签署了一项行政命令《恢复黄金标准科学》，以着手解决这一功能失调问题。( {}^{174} )该命令确立了适用于所有联邦资助研究的原则:可重复性、透明度、错误与不确定性的沟通、跨学科合作、对假设的质疑、假设的可证伪性、无偏见的同行评审、对阴性结果的接受，以及免受利益冲突影响。各机构被指示采用基于科学证据权重的方法，根据质量和相关性透明地评估每条信息。

Replication does not happen at scale, in part, because of a market failure. Strong incentives drive researchers to publish new findings, with funding and prestige both flowing from novel claims. On the other hand, verification carries weak incentives; little glory comes from confirming someone else's work. Previous attempts at large-scale replication have failed because they required armies of specialists to verify each study by hand. Manual verification cannot scale to the millions of papers published annually, and the problem is about to grow far more acute.

重复验证未能大规模开展，部分原因是市场失灵。强大的激励驱动研究人员发表新发现，资金和声望都来自新颖的主张。另一方面，验证的激励很弱；确认他人的工作几乎不会带来荣耀。以往大规模重复验证的尝试之所以失败，是因为它们需要大批专家手动逐一验证每项研究。手动验证无法扩展到每年发表的数百万篇论文，而这个问题将变得更加严峻。

As AI is introduced into the scientific process, it risks compounding these problems. False findings will multiply as it becomes easier to generate plausible-sounding scientific results than to verify them. AI research offers a preview. Leading conferences have seen submission surges of 60% in a single year, overwhelming the field's capacity to evaluate new results. Researchers are now burdened with reviewing nonsensical AI-generated submissions while rebutting low-quality AI-generated reviews of their own work. ( {}^{175} ) Other fields will follow the same trajectory.

随着AI被引入科学过程，它有可能加剧这些问题。生成听起来合理的科学结果比验证它们更容易，错误发现将成倍增加。AI研究已提供了预兆。顶级会议在一年内投稿量激增60%，压垮了该领域评估新结果的能力。研究人员现在负担着审阅无意义的AI生成投稿，同时还要反驳针对自己工作的低质量AI生成评审。( {}^{175} )其他领域也将走上同样的道路。

The Genesis Mission is building a science generator with instruments capable of producing scientific discovery at an unprecedented scale. To sustain progress, we must also build its necessary counterpart: a verifier equal in rigor and scale. This is the central challenge that must be undertaken to address the reproducibility crisis and capture the full benefits of AI for science.

创世纪任务正在构建一个科学生成器，其工具能够以前所未有的规模产生科学发现。为了维持进步，我们还必须构建其必要的对应物:一个在严谨性和规模上与之匹配的验证器。这是必须应对的核心挑战，以解决可重复性危机并充分获取AI对科学的好处。

AI itself could help close the generation-verification gap, but only if we invest in the necessary infrastructure. AI has already begun to automate significant parts of the scientific workflow. Meanwhile, the Gold Standard Science requirements, including reproducibility, data sharing, and methodological documentation, create precisely the conditions under which automated verification becomes possible. The combination of both could lead to low-cost, continuous AI-enabled verification. Researchers have already outlined one vision of such a system, in which specialized agents parse submitted papers, reconstruct computational environments, execute analyses in sandboxed settings, and compare outputs against claimed results. ( {}^{176} ) The same infrastructure that audits human-authored papers today could tomorrow judge which machine-generated hypotheses merit experimental resources.

AI本身可以帮助缩小生成与验证之间的差距，但前提是我们投资于必要的基础设施。AI已经开始自动化科学工作流程的重要部分。与此同时，黄金标准科学的要求，包括可重复性、数据共享和方法文档，恰恰创造了自动化验证成为可能的条件。两者的结合可能导致低成本、持续性的AI驱动验证。研究人员已经勾勒出这样一个系统的一种愿景，其中专门的代理解析提交的论文，重建计算环境，在沙盒环境中执行分析，并将输出与声称的结果进行比较。( {}^{176} )今天审计人类撰写论文的同一基础设施，明天可以判断哪些机器生成的假设值得投入实验资源。

Rising to this moment of need, NIH has launched a new, agency-wide initiative to elevate replication and reproducibility studies, identifying critical research and infrastructure needs to advance rigorous findings that are verifiable and transparently shared. ( {}^{177} ) Looking forward, the Federal Government must continue to lay the connective tissue between verification infrastructure and our scientific enterprise. This means establishing open APIs and interoperability standards that allow verification capabilities to plug into journal submission systems, grant reporting platforms, and private-sector AI research tools; standards for replication packages that ensure computational research arrives in machine-auditable form; and prizes for successfully replicating or disproving influential papers. The result should be a verification system that is not occasional but continuous, low-cost, and commensurate with the scale of discovery we are now capable of producing.

为应对这一时刻的需求，NIH已启动一项新的全机构倡议，以提升重复和可重复性研究，确定关键研究和基础设施需求，以推进可验证且透明共享的严谨发现。( {}^{177} )展望未来，联邦政府必须继续在验证基础设施与我们的科学事业之间建立连接纽带。这意味着建立开放的API和互操作性标准，使验证能力能够接入期刊投稿系统、资助报告平台和私营部门的AI研究工具；制定重复验证包的标准，确保计算研究以机器可审计的形式呈现；并为成功重复或证伪有影响力论文设立奖项。结果应是一个验证系统，它不是偶尔运行，而是持续、低成本，并与我们现在能够产生的发现规模相匹配。

IDEAS ON THE HORIZON

地平线上的构想

The printing press transformed what could be written, who could read, and how knowledge accumulated. The research university created entirely new apparatuses for producing knowledge. The tools emerging today will do the same, enabling new forms of collaboration, new standards for verification, and new mechanisms for allocating attention and credit.

印刷术改变了可书写的内容、可阅读的人群以及知识积累的方式。研究型大学则创造了全新的知识生产机制。如今涌现的工具也将如此，催生新的协作形式、新的验证标准以及新的注意力与荣誉分配机制。

Developments in mathematics already underway offer a clear glimpse of the transformative potential of AI paired with Gold Standard Science. In mathematics, checking a proof is often far easier than discovering one, an asymmetry in favor of verification that makes it a natural proving ground for the potential of AI and Gold Standard Science. With proof assistants, the challenge of proving a novel mathematical result reduces to the formalization of a theorem statement and the construction of a chain of arguments that the proof assistant accepts. Nevertheless, formalization has historically been too laborious to matter. Translating a single theorem into machine-checkable code could take months of painstaking work. The Liquid Tensor Experiment, a project to formalize a result in condensed mathematics in 2020, consumed nearly two years of effort from expert practitioners. ( {}^{178} )

数学领域已在进行的发展，清晰展现了人工智能与黄金标准科学相结合的变革潜力。在数学中，验证一个证明往往比发现它容易得多，这种有利于验证的不对称性，使其成为检验人工智能与黄金标准科学潜力的天然试验场。借助证明助手，证明新数学结果的挑战简化为将定理陈述形式化，并构建一条证明助手能接受的论证链。然而，形式化历来因过于繁琐而难有作为。将单个定理翻译成机器可验证的代码，可能需要数月艰苦工作。2020年旨在形式化凝聚数学中一项结果的"液体张量实验"项目，耗费了专家从业者近两年时间。( {}^{178} )

Over the past two years, large language models have begun to make it possible for mathematicians to translate ordinary mathematical writing into these formal languages in real time. The acceleration has been striking. In early 2024, an ambitious project set out to formalize the Prime Number Theorem with a proof assistant. After 18 months and the collaboration of more than twenty people around the world, it had made intermediate progress but remained stuck on core difficulties in complex analysis. ( {}^{179} ) Then, in September 2025, a startup using AI completed the project in three weeks, spanning 1,100 formally verified theorems and definitions. ( {}^{180} )

过去两年间，大语言模型已开始让数学家能够实时将普通数学写作翻译成这些形式语言。加速效果令人瞩目。2024年初，一个雄心勃勃的项目着手用证明助手形式化素数定理。经过18个月和全球二十多人的协作，项目取得阶段性进展，但在复分析的核心难题上陷入困境。( {}^{179} ) 随后，在2025年9月，一家使用人工智能的初创公司在三周内完成了该项目，涵盖1100个经过形式化验证的定理和定义。( {}^{180} )

Mathematical collaboration has traditionally relied on small, trust-based networks where participation depended on reputation and proximity. Formal verification replaces that model with one grounded in mathematical certainty, allowing collaboration to scale beyond personal trust.

数学协作传统上依赖基于信任的小型网络，参与取决于声誉和接近性。形式化验证用基于数学确定性的模式取代了这种模式，使协作能够超越个人信任而扩展。

It has been suggested that mathematicians of the future may become architects of industrialized systems rather than solo artisans. ( {}^{181} ) The profession could grow to include orchestrators who design proof strategies, domain experts who contribute specialized knowledge, and skilled practitioners who direct AI tools. The mathematics we pursue will change as well. When AI handles computational drudgery, entire classes of problems become tractable, opening new scientific frontiers.

有人提出，未来的数学家可能成为工业化系统的架构师，而非独行工匠。( {}^{181} ) 这一职业可能发展出设计证明策略的统筹者、贡献专业知识的领域专家，以及指导人工智能工具的熟练实践者。我们所从事的数学本身也将改变。当人工智能处理计算性苦差时，整类问题将变得可解，从而开辟新的科学前沿。

Proof assistants and AI-enabled verification in mathematics represent a prototype of the Gold Standard Science tools that could propagate across disciplines. Wherever checking an answer is easier than finding one, AI stands to reorganize not just scientific discovery, but the social structures that govern who does it and how.

数学中的证明助手和人工智能辅助验证，代表了可能跨学科传播的黄金标准科学工具的原型。在验证答案比寻找答案更容易的任何领域，人工智能不仅有望重组科学发现本身，还将重组支配谁来做、如何做的社会结构。

RETHINKING SCIENTIFIC PUBLICATION

反思科学出版

The journal system was designed for a different era. When scientific journals emerged in the 17th century, they served perhaps hundreds of active researchers who corresponded by post. Today, there are nine million full-time researchers worldwide, publishing millions of articles across tens of thousands of journals.

期刊体系是为不同时代设计的。17世纪科学期刊诞生时，服务对象或许只有数百名通过信件交流的活跃研究者。而如今，全球有九百万全职研究人员，在数万种期刊上每年发表数百万篇论文。

The infrastructure of scientific communication has not kept pace with the scale of science itself, and AI will only widen the gap. ( {}^{182} )

科学交流的基础设施未能跟上科学本身的发展规模，而人工智能只会进一步扩大这一差距。( {}^{182} )

The publication system's structural problems go beyond scale. Journals create artificial scarcity, rewarding secrecy rather than open collaboration. A small number of anonymous, unpaid reviewers who may have vested interests, limited expertise, or simply not enough time, determine what counts as legitimate science. The format rewards polished narratives over honest accounts of the research process. Null findings, failed experiments, methodological details, and the true rationale behind research choices rarely reach publication.

出版体系的结构性问题远不止规模问题。期刊制造人为稀缺性，奖励保密而非开放合作。少数匿名、无报酬的审稿人——他们可能带有既得利益、专业知识有限或时间不足——决定什么算作合法科学。这种形式奖励精心打磨的叙事，而非对研究过程的诚实描述。零结果、失败实验、方法论细节以及研究选择背后的真实理由，很少能见诸出版。

These challenges will only sharpen with AI-enabled science. When anyone can generate plausible-looking research at industrial scale, the current metrics for evaluating scientific productivity, like papers published, citations accumulated, and impact factors achieved, will all fall to Goodhart's Law as gameable targets.

这些挑战在人工智能驱动的科学中只会更加尖锐。当任何人都能以工业规模生成看似合理的研究时，当前评估科学生产力的指标——如发表论文数、引用积累量和影响因子——都将沦为古德哈特定律下的可操纵目标。

As information technology evolves, select research organizations backed by private funding have stopped supporting traditional journal publications. Their researchers release findings through alternative channels, including preprints, data repositories, and dynamic notebooks, which get reviewed and replicated rapidly within their community. They find that when researchers stop optimizing for publishable units, they design experiments differently. They become more creative, more collaborative. They care about whether results are useful rather than whether they make a compelling story.

随着信息技术的发展，部分由私人资助的精选研究机构已停止支持传统期刊出版。其研究人员通过预印本、数据仓库和动态笔记本等替代渠道发布成果，并在社区内快速接受评审和复制。他们发现，当研究者不再为可发表的单元而优化时，实验设计方式会随之改变。他们变得更富创造力、更乐于合作。他们关心结果是否有用，而非是否构成一个引人入胜的故事。

The future of scientific communication may look very different from the present. Researchers might release shorter outputs more frequently, including datasets, code, preliminary findings, and methodological notes. Dynamic papers could update automatically as underlying data changes. Public peer review, conducted in the open rather than behind closed doors, could offer faster feedback loops. This is clearly seen by reference to machine learning communities, which already rapidly replicate papers posted to online repositories and turn social media platforms into forums for debate.

科学交流的未来可能与现在截然不同。研究者可能更频繁地发布更短的成果，包括数据集、代码、初步发现和方法论笔记。动态论文可在底层数据变化时自动更新。公开同行评审——在开放而非闭门环境中进行——可提供更快的反馈循环。这一点在机器学习社区中显而易见，该社区已能快速复制发布在在线存储库中的论文，并将社交媒体平台转化为辩论论坛。

NEW FORMS OF COLLABORATION AND CREDIT

新型合作与信用模式

Today's frontier advances in AI-for-science, such as AI models and autonomous laboratories, remain largely reflective of the existing structure of science. But combined with emerging decentralized technologies, they point toward the possibility of a more profound transformation in AI agents. Those agents would not merely assist human researchers, but participate as autonomous actors in a scientific economy.

当今人工智能在科学领域的前沿进展，如AI模型和自主实验室，在很大程度上仍反映着现有科学结构。但结合新兴的去中心化技术，它们指向了AI体更深刻变革的可能性。这些AI体不仅协助人类研究人员，更作为自主行动者参与科学经济。

One key building block of this transformation will be more granular credit attribution. Blockchain-based systems can create immutable records of scientific contributions, timestamping every dataset uploaded, every analysis run, and every hypothesis proposed, and linking each to its creator. ( {}^{183} ) When the record is fully traceable and captures every contribution comprehensively, credit attribution need not be zero-sum. Contributions to shared resources, such as datasets, code libraries, and protocols, become properly visible and rewardable.

这一变革的关键基石之一是更精细的信用归属。基于区块链的系统可以创建科学贡献的不可篡改记录，为每个上传的数据集、每次分析运行和每个提出的假设打上时间戳，并将其与创建者关联。( {}^{183} )当记录完全可追溯并全面捕捉每项贡献时，信用归属不必是零和博弈。对共享资源(如数据集、代码库和协议)的贡献将变得清晰可见并可获得回报。

Another building block will be new modes of financial transaction for scientific knowledge. Decentralized Autonomous Organizations, communities that pool resources and allocate them through collective governance, are beginning to fund scientific research directly without going through traditional institutional gatekeepers. ( {}^{184} ) Prediction polls, augmented with proper scoring feedback and statistical aggregation, have also been shown to forecast scientific developments better than prediction markets, based on technological trends already underway. ( {}^{185} ) Together, these mechanisms can direct resources toward problems based on the wisdom of crowds rather than committee review, potentially faster and more effectively.

另一基石将是科学知识的新型金融交易模式。去中心化自治组织(即通过集体治理汇集和分配资源的社区)正开始绕过传统机构守门人，直接资助科学研究。( {}^{184} )基于已在进行的技术趋势，带有适当评分反馈和统计聚合的预测投票已被证明比预测市场更能预测科学发展。( {}^{185} )这些机制共同作用，可以基于群体智慧而非委员会评审将资源导向问题，可能更快更有效。

These pieces lay the foundation for a continuous, market-mediated, agent-based scientific economy. Imagine a funder posting a million-dollar bounty for the first validated therapeutic target for a rare disease. An agent working on adjacent problems notices a promising lead and posts a smaller bounty for replicating the finding. Other agents assess whether the problem falls within their competence, bid for the work, and contract an autonomous laboratory accessible through the internet, which runs the experiment and returns cryptographically signed results. The agent evaluates the evidence, updates its models, and publishes conclusions to a distributed ledger. When results prove ambiguous, human experts provide the judgment that automated systems lack. Smart contracts release funds automatically as milestones are verified.

这些要素为持续、市场中介、基于AI体的科学经济奠定了基础。想象一个资助者为罕见病的首个验证治疗靶点悬赏百万美元。一个处理相关问题的AI体注意到有希望的线索，并发布较小悬赏以重复该发现。其他AI体评估问题是否在其能力范围内，竞标工作，并签约可通过互联网访问的自主实验室，该实验室运行实验并返回加密签名结果。AI体评估证据，更新模型，并将结论发布到分布式账本。当结果不明确时，人类专家提供自动化系统缺乏的判断。智能合约在里程碑验证后自动释放资金。

In such a world, experimental information becomes a tradeable commodity, and price mechanisms replace slow institutional coordination. Markets could form to support the scientific enterprise, such as prediction markets informing grantmakers about technologies on the horizon, bounty markets directing resources toward unsolved problems, and reputation markets tracking which agents produce reliable results. Agents would interact directly, exchanging data, hypotheses, and compute time through microtransactions. The whole system runs continuously, at speeds no human institution could match, but is guided by human judgment about which breakthroughs merit large bounties, and which questions require framing that machines cannot yet provide.

在这样的世界里，实验信息成为可交易商品，价格机制取代缓慢的机构协调。市场可以形成以支持科学事业，例如预测市场告知资助者即将到来的技术，悬赏市场将资源导向未解决问题，以及声誉市场追踪哪些AI体产生可靠结果。AI体将直接交互，通过微交易交换数据、假设和计算时间。整个系统持续运行，速度无人能及，但由人类判断引导:哪些突破值得大额悬赏，哪些问题需要机器尚无法提供的框架。

An agent-based scientific economy will reshape what science gets done. Agents might specialize in replication, profiting by verifying or falsifying claims that humans find too tedious to check. Others might focus on negative results, which journals refuse to publish but which hold real value for anyone exploring the same territory. Unconstrained by disciplinary boundaries, career incentives, or the limits of human attention, agents could pursue the questions that matter most, rather than the ones that yield publishable results.

基于AI体的科学经济将重塑科学研究的格局。AI体可能专攻重复验证，通过验证或证伪人类觉得过于繁琐的声明获利。其他AI体可能专注于负面结果——期刊拒绝发表，但对探索同一领域的人具有真正价值。不受学科边界、职业激励或人类注意力限制，AI体可以追求最重要的问题，而非那些能产生可发表结果的问题。

Cloud laboratories become the factories of this economy. Robotic facilities already exist that can synthesize molecules, run assays, and return results without human intervention. As these facilities proliferate and standardize interfaces, they become nodes in a network that any agent can access. An AI pursuing a hypothesis about protein folding could contract with a lab in Colorado, run crystallography experiments, receive results within hours, and integrate them into its next round of reasoning as it collaborates with humans in Boston. Physical experimentation, long the bottleneck of empirical science, becomes as accessible as computation.

云实验室成为这种经济的工厂。机器人设施已经存在，可以合成分子、运行分析并在无人干预下返回结果。随着这些设施普及并标准化接口，它们成为任何AI体都可访问的网络节点。一个研究蛋白质折叠假设的AI可以与科罗拉多州的实验室签约，运行晶体学实验，数小时内收到结果，并在与波士顿人类合作时将其整合到下一轮推理中。物理实验——长期是经验科学的瓶颈——变得像计算一样可及。

None of this exists today in a mature form, but the pieces are emerging separately. Whether they will combine into something like the system sketched here, or into something we cannot yet imagine, remains unknown. But the vision belongs in the same tradition as Bush's original argument, that the frontier of scientific knowledge is open, expansive, and worth pushing into. The duty to keep pushing falls squarely on us.

今天这些都不以成熟形式存在，但各要素正在分别涌现。它们是否会结合成类似这里勾勒的系统，或我们尚无法想象的东西，仍不得而知。但这一愿景属于布什最初论证的同一传统:科学知识的前沿是开放的、广阔的，值得推进。继续推进的责任 squarely 落在我们肩上。

AS WE MAY BUILD

我们或将建造

For millennia, scientific knowledge and technological progress were bounded by the cognitive faculties of the human mind. Knowledge, however collective in its making, had to fit inside the heads of individual thinkers, flow through human patterns of communication, and conform to the social technologies we invented to guide inquiry. That era is ending.

数千年来，科学知识与技术进步始终受限于人类心智的认知能力。知识无论在其形成过程中多么具有集体性，都必须容纳于个体思想者的头脑中，经由人类的交流模式流动，并遵循我们为引导探索而发明的社会技术。这个时代正在终结。

Our civilization has been built on bronze and steel, substances we discovered and exploited, but did not design. The 21st century will be built on materials we engineer from first principles, metamaterials that bend light in ways nature never attempted, programmable matter that reconfigures on command, self-assembling structures that grow like living things but serve engineered purposes. The progression from the forge to the semiconductor fab took centuries; the progression from semiconductor fab to molecular assembler may take only decades.

我们的文明建立在青铜和钢铁之上——这些我们发现并利用、却未曾设计的物质。21世纪将建立在我们从第一性原理出发设计的材料之上:以自然从未尝试过的方式弯曲光线的超材料、按指令重新配置的可编程物质、像生物一样生长却服务于工程目的的自组装结构。从锻造厂到半导体工厂的演进历经数百年；从半导体工厂到分子组装机或许只需数十年。

We may begin to engineer cells as precisely as we now engineer circuits, programming immune systems to hunt malignancies with complete specificity, shaping cell differentiation and tissue growth to repair damaged organs, and designing therapeutics atom by atom rather than discovering them by trial and error. If we get all this right, within a generation, the diseases that today kill millions-like cardiovascular failures, neurodegenerations, and cancers-may yield one by one to instruments we are now starting to build.

我们或将开始像如今设计电路一样精确地设计细胞，编程免疫系统以完全特异性追捕恶性肿瘤，塑造细胞分化和组织生长以修复受损器官，并逐个原子地设计疗法，而非通过试错来发现。如果我们做对了这一切，在一代人的时间内，如今夺走数百万人生命的疾病——如心血管衰竭、神经退行性疾病和癌症——可能会被我们正在开始建造的工具逐一攻克。

The technological transformation is already underway. In the first year of the Trump Administration, more than a trillion dollars of investment commitments have been secured for advanced manufacturing infrastructure and for technology companies building in the physical world. The best minds of a generation are bent on breakthroughs in machine intelligence and its applications to science. New companies are created every day to discover new materials, design revolutionary drugs, build fusion power, and explore unsolved conjectures in mathematics.

技术变革已然展开。在特朗普政府的第一年，已为先进制造基础设施和实体世界建设的科技公司获得超过一万亿美元的投资承诺。一代人中最杰出的头脑正致力于机器智能及其科学应用的突破。每天都有新公司成立，以发现新材料、设计革命性药物、建造聚变能源，并探索数学中未解的猜想。

In parallel, a revival in the crafts has made advanced technology possible. Americans are grinding precision bearings to tolerances measured in millionths of an inch, polishing optics for surgical lasers and microscopes, spinning carbon nanofibers for spacecraft and medical implants, and growing semiconductor crystals of inhuman purity. The nation is rediscovering its capacity to build, grounded in the recognition that the frontier advances on two kinds of knowledge: the explicit, which can be written down and taught, and the tacit, which can only be learned through practice. America's strength has always come from a culture that honors both science and craft, and keeps both open to all with the aptitude and interest to learn.

与此同时，工艺的复兴使先进技术成为可能。美国人正在以百万分之一英寸的公差研磨精密轴承，为外科激光器和显微镜抛光光学元件，为航天器和医疗植入物纺制碳纳米纤维，并生长出非人纯净度的半导体晶体。这个国家正在重新发现其建造能力，其基础在于认识到前沿依赖于两种知识:可以书写和传授的显性知识，以及只能通过实践习得的隐性知识。美国的实力始终源于一种既尊重科学又尊重工艺的文化，并向所有具备天赋和兴趣的人敞开大门。

To sustain this progress, we must invent new ways of doing science. Science is the pool of knowledge that underlies our technological pursuit. The science of the coming decades could produce knowledge that no single person fully grasps, verified by systems that no single person fully audits, yet more reliable than anything we have built before. Future infrastructure for discovery may harness trillions of AI agents running experiments, testing conjectures, and surfacing insights across every scientific domain, with human researchers setting directions, posing questions, integrating findings, and making the judgments that require wisdom rather than computation. We urgently need to begin preparing for this AI-enabled future, by building the institutions, incentive structures, and information systems that let us trust what we cannot individually comprehend and steer what we cannot fully predict.

为了维持这一进步，我们必须发明新的科学方法。科学是支撑我们技术追求的知识池。未来几十年的科学可能产生无人能完全掌握的知识，由无人能完全审计的系统验证，却比我们以往建造的任何东西都更可靠。未来的发现基础设施可能利用数万亿个AI代理运行实验、检验猜想、并在每个科学领域浮现洞见，而人类研究者则设定方向、提出问题、整合发现，并做出需要智慧而非计算的判断。我们迫切需要开始为这个AI赋能的未来做准备，通过建立让我们能够信任自己无法单独理解的事物、引导自己无法完全预测的事物的机构、激励结构和信息系统。

When Vannevar Bush wrote to President Roosevelt, the nation faced a choice, whether to continue the wartime mobilization of science, or let the momentum dissipate. We chose to build. The institutions that emerged gave America a half-century of scientific dominance that translated into security and prosperity. But they are no longer sufficient for the new frontier we face today.

当范内瓦·布什写信给罗斯福总统时，国家面临一个选择:是继续战时科学的动员，还是任其势头消散。我们选择了建造。由此产生的机构为美国带来了半个世纪的科学主导地位，这转化为安全与繁荣。但它们已不足以应对我们今天面临的新前沿。

This report has described what must replace them: new partnerships that bridge discovery and production, new mechanisms that reward boldness over consensus, new infrastructure that reunites science with manufacturing and craft, and preparations for an AI-transformed era of scientific discovery. Our competitors understand this; they are building their own systems to capture this next era of science and technology, and to shape what it will be used for.

本报告描述了必须取代它们的东西:连接发现与生产的新伙伴关系、奖励大胆而非共识的新机制、将科学与制造及工艺重新统一的新基础设施，以及为AI变革的科学发现时代所做的准备。我们的竞争对手明白这一点；他们正在建立自己的系统，以抓住下一个科学与技术时代，并塑造其用途。

The task, then, falls to our generation to design the institutions, standards, and capabilities that can guide a scientific enterprise larger, faster, and less individually comprehensible than any in history. In doing so, we will determine not only the future of American technological prowess, but also the trajectory of human knowledge itself. Rising to this challenge is vital if America is to continue to deliver prosperity and security to its people.

因此，任务落在我们这一代人肩上:设计能够引导比历史上任何科学事业都更庞大、更快速、更难以个人理解的机构、标准和能力。这样做时，我们不仅将决定美国技术实力的未来，也将决定人类知识本身的轨迹。如果美国要继续为其人民带来繁荣与安全，迎接这一挑战至关重要。

End Notes

尾注

1 Vannevar Bush, Science, the Endless Frontier, 75th anniversary ed. (National Science Foundation, 2020), xiv.

1 范内瓦·布什，《科学:无尽的前沿》，75周年纪念版(国家科学基金会，2020年)，xiv。

2 Bush, Science, the Endless Frontier, 1.

2 布什，《科学:无尽的前沿》，第1页。

3 U.S. Department of Agriculture, "A Look at Agricultural Productivity Growth in the United States, 1948-2017," USDA Blog, March 5, 2020, https://www.usda.gov/about-usda/news/blog/ look-agricultural-productivity-growth-united-states-1948-2017.

3 美国农业部，“1948-2017年美国农业生产力增长概览”，《美国农业部博客》，2020年3月5日，https://www.usda.gov/about-usda/news/blog/ look-agricultural-productivity-growth-united-states-1948-2017。

4 Elizabeth Arias et al., "United States Life Tables, 2023," National Vital Statistics Reports 74, no. 6 (National Center for Health Statistics, July 15, 2025), https://www.cdc.gov/nchs/data/nvsr/ nvsr74/nvsr74-06.pdf.

4 伊丽莎白·阿里亚斯等，“2023年美国生命表”，《国家生命统计报告》第74卷第6期(国家卫生统计中心，2025年7月15日)，https://www.cdc.gov/nchs/data/nvsr/ nvsr74/nvsr74-06.pdf。

5 Ching-Hon Pui and William E. Evans, "A 50-Year Journey to Cure Childhood Acute Lymphoblastic Leukemia," Seminars in Hematology 50, no. 3 (2013): 185-196, https://pmc.ncbi.nlm.nih.gov/ articles/PMC3771494.

5 裴正汉和威廉·E·埃文斯，“治愈儿童急性淋巴细胞白血病的50年历程”，《血液学研讨会》第50卷第3期(2013年):185-196，https://pmc.ncbi.nlm.nih.gov/ articles/PMC3771494。

6 Earl S. Ford et al., "Explaining the Decrease in U.S. Deaths from Coronary Disease, 1980-2000," New England Journal of Medicine 356, no. 23 (2007): 2388-2398, https://www.nejm.org/doi/ full/10.1056/NEJMsa053935.

6 厄尔·S·福特等，“解释1980-2000年美国冠心病死亡率的下降”，《新英格兰医学杂志》第356卷第23期(2007年):2388-2398，https://www.nejm.org/doi/ full/10.1056/NEJMsa053935。

7 Keith Fuglie et al., Agricultural Research and Development: Public and Private Investments Under Alternative Markets and Institutions, AER-735 (U.S. Department of Agriculture, Economic Research Service, May 1996), https://www.ers.usda.gov/publications/pub-details?pubid=40696.

7 基思·富格利等，《农业研发:替代市场与制度下的公共和私人投资》，AER-735(美国农业部经济研究局，1996年5月)，https://www.ers.usda.gov/publications/pub-details?pubid=40696。

8 Bush, Science, the Endless Frontier, 9.

8 布什，《科学:无尽的前沿》，第9页。

9 Bush, Science, the Endless Frontier, 13.

9 布什，《科学:无尽的前沿》，第13页。

10 Bush, Science, the Endless Frontier, xiii.

10 布什，《科学:无尽的前沿》，xiii。

11 Bush, Science, the Endless Frontier, xiii.

11 布什，《科学:无尽的前沿》，xiii。

12 Semiconductor Industry Association, 2025 SIA Factbook, https://www.semiconductors.org/ wp-content/uploads/2025/05/2025-SIA-Factbook-FINAL-1.pdf.

12 半导体工业协会，《2025年SIA概况手册》，https://www.semiconductors.org/ wp-content/uploads/2025/05/2025-SIA-Factbook-FINAL-1.pdf。

13 National Center for Science and Engineering Statistics, National Patterns of R&D Resources: 2023-24 Data Update, NSF 26-313 (National Science Foundation, February 2026), https://ncses.nsf.gov/pubs/nsf26313.

13 国家科学与工程统计中心，《国家研发资源模式:2023-24年数据更新》，NSF 26-313(国家科学基金会，2026年2月)，https://ncses.nsf.gov/pubs/nsf26313。

14 National Center for Science and Engineering Statistics, National Patterns of R&D Resources.

14 国家科学与工程统计中心，《国家研发资源模式》。

15 Bush, Science, the Endless Frontier, 17-21.

15 布什，《科学:无尽的前沿》，第17-21页。

16 Donald E. Stokes, Pasteur's Quadrant: Basic Science and Technological Innovation (Brookings Institution Press, 1997).

16 唐纳德·E·斯托克斯，《巴斯德象限:基础科学与技术创新》(布鲁金斯学会出版社，1997年)。

17 Sandra L. Schneider et al., 2018 Faculty Workload Survey: Primary Report (Federal Demonstration Partnership, 2020), https://thefdp.org/wp-content/uploads/FDP-FWS-2018-Primary-Report.pdf.

17 桑德拉·L·施奈德等人，《2018年教师工作量调查:主要报告》(联邦示范伙伴关系，2020年)，https://thefdp.org/wp-content/uploads/FDP-FWS-2018-Primary-Report.pdf。

18 Pierre Azoulay et al., "Indirect Cost Recovery in U.S. Innovation Policy: History, Evidence, and Avenues for Reform" (NBER Working Paper No. 33627, National Bureau of Economic Research, June 2025), https://doi.org/10.3386/w33627; Congressional Research Service, "NIH Indirect Costs Policy for Research Grants: Recent Developments," CRS Insight IN12516, April 17, 2026, https://www.congress.gov/crs-product/IN12516.

18 皮埃尔·阿祖莱等人，《美国创新政策中的间接成本回收:历史、证据与改革路径》(NBER工作论文第33627号，美国国家经济研究局，2025年6月)，https://doi.org/10.3386/w33627；美国国会研究服务部，《NIH研究资助间接成本政策:近期发展》，CRS洞察报告IN12516，2026年4月17日，https://www.congress.gov/crs-product/IN12516。

19 National Institutes of Health, "Supplemental Guidance to the 2024 NIH Grants Policy Statement: Indirect Cost Rates," NOT-OD-25-068, February 7, 2025, https://grants.nih.gov/grants/ guide/notice-files/NOT-OD-25-068.html.

19 美国国立卫生研究院，《2024年NIH资助政策声明补充指南:间接成本费率》，NOT-OD-25-068，2025年2月7日，https://grants.nih.gov/grants/guide/notice-files/NOT-OD-25-068.html。

20 National Science Board, Discovery: R&D Activity and Research Publications, NSB-2025-7 (National Science Foundation, National Center for Science and Engineering Statistics, July 23, 2025), https://ncses.nsf.gov/pubs/nsb20257; Central Intelligence Agency, "A Comparison of Soviet and U.S. Gross National Products, 1960-83," research paper, released as sanitized, 1999, https://www.cia.gov/readingroom/docs/DOC_0000498181.pdf.

20 美国国家科学委员会，《发现:研发活动与研究出版物》，NSB-2025-7(美国国家科学基金会，国家科学与工程统计中心，2025年7月23日)，https://ncses.nsf.gov/pubs/nsb20257；美国中央情报局，《苏联与美国国民生产总值比较，1960-83年》，研究报告，1999年解密发布，https://www.cia.gov/readingroom/docs/DOC_0000498181.pdf。

21 National Science Board, Discovery: R&D Activity and Research Publications; Organisation for Economic Co-operation and Development, Main Science and Technology Indicators (OECD, 2026), https://www.oecd.org/en/data/datasets/main-science-and-technology-indicators.html.

21 美国国家科学委员会，《发现:研发活动与研究出版物》；经济合作与发展组织，《主要科学与技术指标》(经合组织，2026年)，https://www.oecd.org/en/data/datasets/main-science-and-technology-indicators.html。

22 National Center for Science and Engineering Statistics, Doctorate Recipients from U.S. Universities: 2023, NSF 25-300 (National Science Foundation, December 2, 2024), Figure 8, https://ncses.nsf.gov/pubs/nsf25300.

22 国家科学与工程统计中心，《美国大学博士学位获得者:2023年》，NSF 25-300(美国国家科学基金会，2024年12月2日)，图8，https://ncses.nsf.gov/pubs/nsf25300。

23 Donald Trump, National Security Presidential Memorandum 33: United States Government Supported Research and Development National Security Policy, January 14, 2021.

23 唐纳德·特朗普，《国家安全总统备忘录33:美国政府支持的研发国家安全政策》，2021年1月14日。

24 Chuck Gwyn and Stefan Wurm, "EUV LLC: An Historical Perspective," in EUV Lithography, ed. Vivek Bakshi (SPIE Press, December 10, 2008), https://doi.org/10.1117/3.769214.

24 查克·格温和斯特凡·武尔姆，《EUV LLC:历史视角》，载于《EUV光刻技术》，维韦克·巴克希编(SPIE出版社，2008年12月10日)，https://doi.org/10.1117/3.769214。

25 National Center for Science and Engineering Statistics, "Table 6-3: Temporary Visa Holder Research Doctorate Recipients with Definite Postgraduation Commitments, by Major Field of Doctorate: 2024" in Doctorate Recipients from U.S. Universities: 2024 Data Tables, NSF 25-349 (U.S. National Science Foundation, 2025), https://ncses.nsf.gov/pubs/nsf25349.

25 国家科学与工程统计中心，《表6-3:持有临时签证的博士学位获得者中具有明确毕业后去向者，按博士主要领域分类:2024年》，载于《美国大学博士学位获得者:2024年数据表》，NSF 25-349(美国国家科学基金会，2025年)，https://ncses.nsf.gov/pubs/nsf25349。

26 Bush, Science, the Endless Frontier, 17.

26 布什，《科学:无尽的前沿》，第17页。

27 Andrew Fieldhouse and Karel Mertens, "The Returns to Government R&D: Evidence from U.S. Appropriations Shocks," Working Paper No. 2305 (Federal Reserve Bank of Dallas, 2024), https://www.dallasfed.org/research/papers/2023/wp2305.

27 安德鲁·菲尔德豪斯和卡雷尔·默滕斯，《政府研发的回报:来自美国拨款冲击的证据》，工作论文第2305号(达拉斯联邦储备银行，2024年)，https://www.dallasfed.org/research/papers/2023/wp2305。

28 Nicholas Bloom et al., "Are Ideas Getting Harder to Find?" American Economic Review 110, no. 4 (2020): 1104-44; Michael Park et al., "Papers and Patents Are Becoming Less Disruptive over Time," Nature 613 (2023): 138-44.

28 尼古拉斯·布鲁姆等人，《想法是否越来越难找到？》，《美国经济评论》第110卷第4期(2020年):1104-44；迈克尔·帕克等人，《论文和专利随时间推移颠覆性减弱》，《自然》第613卷(2023年):138-44。

29 Jack W. Scannell et al., "Diagnosing the Decline in Pharmaceutical R&D Efficiency," Nature Reviews Drug Discovery 11, no. 3 (2012): 191-200, https://doi.org/10.1038/nrd3681.

29 杰克·W·斯坎内尔等人，《诊断制药研发效率下降》，《自然综述:药物发现》第11卷第3期(2012年):191-200，https://doi.org/10.1038/nrd3681。

30 The NIH budget doubled from $13.6 billion in 1998 to $27.1 billion in 2003. See: Bruce Alberts et al., "Rescuing US Biomedical Research from Its Systemic Flaws," Proceedings of the National Academy of Sciences 111, no. 16 (April 14, 2014): 5773-5777, https://doi.org/10.1073/pnas.1404402111.

30 NIH预算从1998年的136亿美元翻倍至2003年的271亿美元。参见:布鲁斯·阿尔伯茨等人，《将美国生物医学研究从其系统性缺陷中拯救出来》，《美国国家科学院院刊》第111卷第16期(2014年4月14日):5773-5777，https://doi.org/10.1073/pnas.1404402111。

31 Bloom, "Are Ideas Getting Harder to Find?"

31 布鲁姆，《想法是否越来越难找到？》

32 Adam Mastroianni, "Ideas Aren't Getting Harder to Find and Anyone Who Tells You Otherwise Is a Coward and I Will Fight Them," Experimental History (blog), May 17, 2022, https://www.experimental-history.com/p/ideas-arent-getting-harder-to-find.

32 Adam Mastroianni, "想法并不难找，谁要是告诉你相反的话，他就是个懦夫，我会跟他干一架," Experimental History (博客), 2022年5月17日, https://www.experimental-history.com/p/ideas-arent-getting-harder-to-find.

33 Paul Starr, The Social Transformation of American Medicine: The Rise of a Sovereign Profession and the Making of a Vast Industry, updated ed. (Basic Books, 2017).

33 Paul Starr, 《美国医学的社会转型:主权职业的崛起与庞大产业的缔造》, 更新版 (Basic Books, 2017).

34 National Institute of Allergy and Infectious Diseases, "Timeline for Funding Decisions," National Institutes of Health, last reviewed September 30, 2024, https://www.niaid.nih.gov/grants-contracts/timelines-funding-decisions.

34 美国国家过敏症与传染病研究所, "资助决策时间表," 美国国立卫生研究院, 最后审阅于2024年9月30日, https://www.niaid.nih.gov/grants-contracts/timelines-funding-decisions.

35 Joe Sutter and Jay Spenser, 747: Creating the World's First Jumbo Jet and Other Adventures from a Life in Aviation (Smithsonian Books, 2006).

35 Joe Sutter 与 Jay Spenser, 《747:创造世界首架巨型喷气式飞机及航空生涯中的其他冒险》 (Smithsonian Books, 2006).

36 Council on Governmental Relations, "Changes in Federal Research Requirements Since 1991," January 2025, https://www.cogr.edu/sites/default/files/RegChangesSince1991_Dec%202024.pdf.

36 政府关系委员会, "1991年以来联邦研究要求的变化," 2025年1月, https://www.cogr.edu/sites/default/files/RegChangesSince1991_Dec%202024.pdf.

37 Pierre Azoulay et al., "Indirect Cost Recovery and American Innovation: Context and Ideas for Reform," Institute for Progress, July 24, 2025, https://ifp.org/indirect-cost-recovery-and-american-innovation.

37 Pierre Azoulay 等, "间接成本回收与美国创新:改革思路与背景," 进步研究所, 2025年7月24日, https://ifp.org/indirect-cost-recovery-and-american-innovation.

38 Kristin R. W. Matthews et al., "The Aging of Biomedical Research in the United States," PLOS One 6, no. 12 (2011): e29738, https://doi.org/10.1371/journal.pone.0029738.

38 Kristin R. W. Matthews 等, "美国生物医学研究的老龄化," 《PLOS One》 6, 第12期 (2011): e29738, https://doi.org/10.1371/journal.pone.0029738.

39 National Institutes of Health, "Average Age and Degree of NIH R01-Equivalent First-Time Awardees 1980-2016," Early Stage Investigator Related Data, NIH Grants and Funding, last updated September 9, 2024, https://grants.nih.gov/policy-and-compliance/policy-topics/ early-stage-investigators/related-data.

39 美国国立卫生研究院, "1980-2016年NIH R01等效首次获奖者平均年龄与学位," 早期研究者相关数据, NIH资助与拨款, 最后更新于2024年9月9日, https://grants.nih.gov/policy-and-compliance/policy-topics/ early-stage-investigators/related-data.

40 Max Planck, Scientific Autobiography and Other Papers, translated by Frank Gaynor (Williams & Norgate Ltd., 1950), 33-34.

40 Max Planck, 《科学自传及其他论文》, Frank Gaynor 译 (Williams & Norgate Ltd., 1950), 33-34.

41 Pierre Azoulay et al., "Does Science Advance One Funeral at a Time?" American Economic Review 109, no. 8 (2019): 2889-2920, https://doi.org/10.1257/aer.20161574.

41 Pierre Azoulay 等, "科学是否每送走一位葬礼就前进一步？" 《美国经济评论》 109, 第8期 (2019): 2889-2920, https://doi.org/10.1257/aer.20161574.

42 Johan S. G. Chu and James A. Evans, "Slowed Canonical Progress in Large Fields of Science," Proceedings of the National Academy of Sciences 118, no. 41 (2021): e2021636118, https://doi.org/10.1073/pnas.2021636118.

42 Johan S. G. Chu 与 James A. Evans, "大型科学领域中经典进展放缓," 《美国国家科学院院刊》 118, 第41期 (2021): e2021636118, https://doi.org/10.1073/pnas.2021636118.

43 Ted Cruz, D.E.I.: Division, Extremism, Ideology: How the Biden-Harris NSF Politicized Science (U.S. Senate Committee on Commerce, Science, and Transportation, October 2024), https:// www.govinfo.gov/app/details/GOVPUB-Y4_C73_7-PURL-gpo234941.

43 Ted Cruz, 《D.E.I.:分裂、极端主义、意识形态——拜登-哈里斯政府下的NSF如何将科学政治化》 (美国参议院商务、科学与交通委员会, 2024年10月), https:// www.govinfo.gov/app/details/GOVPUB-Y4_C73_7-PURL-gpo234941.

44 Igor R. Efimov et al., "Politicizing Science Funding Undermines Public Trust in Science, Academic Freedom, and the Unbiased Generation of Knowledge," Frontiers in Research Metrics and Analytics 9 (2024): 1418065, https://doi.org/10.3389/frma.2024.1418065.

44 Igor R. Efimov 等, "将科学资助政治化会削弱公众对科学的信任、学术自由以及知识的公正产生," 《研究计量学与分析前沿》 9 (2024): 1418065, https://doi.org/10.3389/frma.2024.1418065.

45 Jerome Karabel, The Chosen: The Hidden History of Admission and Exclusion at Harvard, Yale, and Princeton (Houghton Mifflin, 2005).

45 Jerome Karabel, 《被选中的人:哈佛、耶鲁和普林斯顿招生与排斥的隐秘历史》 (Houghton Mifflin, 2005).

46 Pierre Azoulay et al., "Incentives and Creativity: Evidence from the Academic Life Sciences," RAND Journal of Economics 42, no. 3 (September 12, 2011): 527-554, https://doi.org/10.1111/ j.1756-2171.2011.00140.x.

46 Pierre Azoulay 等, "激励与创造力:来自学术生命科学的证据," 《RAND经济学杂志》 42, 第3期 (2011年9月12日): 527-554, https://doi.org/10.1111/ j.1756-2171.2011.00140.x.

47 Michael Park et al., "Papers and Patents Are Becoming Less Disruptive over Time."

47 Michael Park 等, "论文与专利随时间推移越来越缺乏颠覆性."

48 Open Science Collaboration, "Estimating the Reproducibility of Psychological Science," Science 349, no. 6251 (2015): aac4716, https://doi.org/10.1126/science.aac4716.

48 开放科学合作项目，“评估心理科学的可重复性”，《科学》349卷，第6251期(2015年):aac4716，https://doi.org/10.1126/science.aac4716。

49 Anatoly Nikolaev et al., "Retracted Article: APP Binds DR6 to Trigger Axon Pruning and Neuron Death via Distinct Caspases," Nature 457 (2009): 981-989, https://doi.org/10.1038/nature07767.

49 阿纳托利·尼古拉耶夫等人，“撤回文章:APP通过不同的半胱天冬酶结合DR6触发轴突修剪和神经元死亡”，《自然》457卷(2009年):981-989，https://doi.org/10.1038/nature07767。

50 Genentech, "Findings of 2023 Genentech Review of 2009 Nature Paper and Related Research," April 6, 2023, https://www.gene.com/media/statements/ps_040623.

50 基因泰克公司，“2023年基因泰克对2009年《自然》论文及相关研究审查的结果”，2023年4月6日，https://www.gene.com/media/statements/ps_040623。

51 Nikolaev et al., "Retracted Article: APP Binds DR6 to Trigger Axon Pruning and Neuron Death via Distinct Caspases."

51 尼古拉耶夫等人，“撤回文章:APP通过不同的半胱天冬酶结合DR6触发轴突修剪和神经元死亡”。

52 Theo Baker, "Stanford President's Research Under Investigation for Scientific Misconduct, University Admits 'Mistakes'," The Stanford Daily, November 29, 2022, https://stanforddaily.com/2022/11/29/stanford-presidents-research-under-investigation-for-scientific-misconduct-university-admits-mistakes; Retraction Note, Nature 625, no. 7993 (2024): 204, https://doi.org/10.1038/s41586-023-06943-3.

52 西奥·贝克，“斯坦福校长研究因科学不端行为受调查，大学承认‘错误’”，《斯坦福日报》，2022年11月29日，https://stanforddaily.com/2022/11/29/stanford-presidents-research-under-investigation-for-scientific-misconduct-university-admits-mistakes；撤回声明，《自然》625卷，第7993期(2024年):204，https://doi.org/10.1038/s41586-023-06943-3。

53 Dalmeet Singh Chawla, "Golden Tickets' on the Cards for NSF Grant Reviewers," Nature 614 (2023): 604-605, https://doi.org/10.1038/d41586-023-00579-z; Villum Foundation, "The Villum Experiment," https://villumfonden.dk/en/group/grantsubarea/villum-experiment.

53 达尔米特·辛格·乔拉，“NSF评审员的‘黄金门票’”，《自然》614卷(2023年):604-605，https://doi.org/10.1038/d41586-023-00579-z；维卢姆基金会，“维卢姆实验”，https://villumfonden.dk/en/group/grantsubarea/villum-experiment。

54 R.D. Anderson, "Germany and the Humboldtian Model," in European Universities from the Enlightenment to 1914 (Oxford University Press, 2004), https://doi.org/10.1093/acprof: oso/9780198206606.003.0004.

54 R.D. 安德森，“德国与洪堡模式”，载于《从启蒙到1914年的欧洲大学》(牛津大学出版社，2004年)，https://doi.org/10.1093/acprof: oso/9780198206606.003.0004。

55 Nobel Prize Outreach, "The Nobel Prize in Chemistry 2024," press release, October 9, 2024, https://www.nobelprize.org/prizes/chemistry/2024/press-release.

55 诺贝尔奖外联，“2024年诺贝尔化学奖”，新闻稿，2024年10月9日，https://www.nobelprize.org/prizes/chemistry/2024/press-release。

56 "The Audacious Project: Launching the Protein Design Revolution," UW Medicine, April 17, 2019, https://give.uwmedicine.org/ipd-audacious.

56 “大胆项目:启动蛋白质设计革命”，华盛顿大学医学院，2019年4月17日，https://give.uwmedicine.org/ipd-audacious。

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57 约翰·莫尔特等人，“评估蛋白质结构预测方法的大规模实验”，《蛋白质:结构、功能与生物信息学》23卷，第3期(1995年11月):ii-v，https://doi.org/10.1002/prot.340230303；“蛋白质数据库:生命分子的关键”，NSF影响，美国国家科学基金会，https://www.nsf.gov/impacts/protein-data-bank。

58 Michael Nielsen and Kanjun Qiu, "A Vision of Metascience: An Engine of Improvement for the Social Processes of Science," October 18, 2022, https://scienceplusplus.org/metascience.

58 迈克尔·尼尔森和阚军，“元科学愿景:改进科学社会过程的引擎”，2022年10月18日，https://scienceplusplus.org/metascience。

59 Ben Southwood, "The Rise and Fall of the Industrial R&D Lab," Works in Progress, August 28, 2020, https://worksinprogress.co/issue/the-rise-and-fall-of-the-american-rd-lab.

59 本·索斯伍德，“工业研发实验室的兴衰”，《进展中的工作》，2020年8月28日，https://worksinprogress.co/issue/the-rise-and-fall-of-the-american-rd-lab。

60 Sam Rodriques and Adam Marblestone, "Focused Research Organizations to Accelerate Science, Technology, and Medicine," Federation of American Scientists, September 24, 2020, https://fas.org/publication/focused-research-organizations-to-accelerate-science-technology-and-medicine.

60 萨姆·罗德里格斯和亚当·马布尔斯通，“聚焦研究组织加速科学、技术与医学”，美国科学家联合会，2020年9月24日，https://fas.org/publication/focused-research-organizations-to-accelerate-science-technology-and-medicine。

61 Ben Reinhardt, "Unbundling the University," Speculative Technologies, February 2025, https:// www.unbundle-the-university.com.

61 本·莱因哈特，“解绑大学”，投机技术，2025年2月，https:// www.unbundle-the-university.com。

62 Caleb Watney, "Launching X-Labs for Transformative Science Funding," in The Techno-Industrial Policy Playbook (The Foundation for American Innovation; American Compass; The Institute for Progress; New American Industrial Alliance Foundation, 2025), https://www.rebuilding.tech/ posts/launching-x-labs-for-transformative-science-funding.

62 凯莱布·沃特尼，“为变革性科学资助启动X实验室”，载于《技术产业政策手册》(美国创新基金会；美国指南针；进步研究所；新美国工业联盟基金会，2025年)，https://www.rebuilding.tech/ posts/launching-x-labs-for-transformative-science-funding。

63 Adam Marblestone et al., "Unblock Research Bottlenecks with Non-Profit Start-Ups," Nature 601, no. 7892 (January 11, 2022): 188-190, https://doi.org/10.1038/d41586-022-00018-5.

63 亚当·马布尔斯通等人，“用非营利初创公司破解研究瓶颈”，《自然》601卷，第7892期(2022年1月11日):188-190，https://doi.org/10.1038/d41586-022-00018-5。

64 Caleb Watney, "Launching X-Labs for Transformative Science Funding."

64 凯莱布·沃特尼，《启动变革性科学资助的X实验室》

65 Stuart Buck, "A Taxonomy of R&D Orgs: What Is New, What Is Missing?" Good Science Project, forthcoming.

65 斯图尔特·巴克，《研发组织分类学:什么是新的，什么是缺失的？》良好科学项目，即将出版。

66 Adam Marblestone and Andrew Payne, "Mapping the Brain for Alignment," Institute for Progress, August 11, 2025, https://ifp.org/mapping-the-brain-for-alignment.

66 亚当·马布尔斯通和安德鲁·佩恩，《为对齐而绘制大脑图谱》，进步研究所，2025年8月11日，https://ifp.org/mapping-the-brain-for-alignment

67 Heidi Williams, "Building a Better NIH," Institute for Progress, May 17, 2023, https://ifp.org/ building-a-better-nih.

67 海迪·威廉姆斯，《打造更好的NIH》，进步研究所，2023年5月17日，https://ifp.org/building-a-better-nih

68 Chawla, "Golden Tickets' on the Cards for NSF Grant Reviewers."

68 查拉，《NSF评审员的“黄金门票”》

69 Ishan Sharma et al., "Piloting and Evaluating NSF Science Lottery Grants: A Roadmap to Improving Research Funding Efficiencies and Proposal Diversity," Institute for Progress, February 2, 2022, https://ifp.org/piloting-and-evaluating-nsf-science-lottery-grants.

69 伊尚·夏尔马等人，《试点和评估NSF科学彩票资助:提高研究资助效率和提案多样性的路线图》，进步研究所，2022年2月2日，https://ifp.org/piloting-and-evaluating-nsf-science-lottery-grants

70 Chawla, "Golden Tickets' on the Cards for NSF Grant Reviewers."

70 查拉，《NSF评审员的“黄金门票”》

71 Howard Hughes Medical Institute, "Mid-Career & Senior Faculty Program," https://www.hhmi.org/programs/investigators.

71 霍华德·休斯医学研究所，《中高级教师项目》，https://www.hhmi.org/programs/investigators

72 Pierre Azoulay et al., "Incentives and Creativity: Evidence from the Academic Life Sciences."

72 皮埃尔·阿祖莱等人，《激励与创造力:来自学术生命科学的证据》

73 Francis S. Collins et al., "NIH Roadmap/Common Fund at 10 Years," Science 345, no. 6194 (2014): 274-276, https://doi.org/10.1126/science.1255860.

73 弗朗西斯·S·柯林斯等人，《NIH路线图/共同基金十年》，《科学》345卷，第6194期(2014年):274-276，https://doi.org/10.1126/science.1255860

74 NSF Graduate Research Fellowship Program, "About GRFP," https://www.nsfgrfp.org/about.html.

74 NSF研究生研究奖学金项目，《关于GRFP》，https://www.nsfgrfp.org/about.html

75 Patrick Collison et al., "What We Learned Doing Fast Grants," Future, June 2, 2022, https:// future.com/what-we-learned-doing-fast-grants; Heidi Williams, "To Speed Scientific Progress, Do Away with Funding Delays," The Washington Post, August 14, 2023, https://www.washington-post.com/opinions/2023/08/14/heidi-williams-science-research-funding.

75 帕特里克·科里森等人，《我们从快速资助中学到的》，未来，2022年6月2日，https://future.com/what-we-learned-doing-fast-grants；海迪·威廉姆斯，《为加速科学进步，消除资助延迟》，《华盛顿邮报》，2023年8月14日，https://www.washingtonpost.com/opinions/2023/08/14/heidi-williams-science-research-funding

76 Defense Advanced Research Projects Agency, "The DARPA Grand Challenge: Ten Years Later," DARPA News, March 13, 2014, https://www.darpa.mil/news/2014/grand-challenge-ten-years-later.

76 国防高级研究计划局，《DARPA大挑战:十年之后》，DARPA新闻，2014年3月13日，https://www.darpa.mil/news/2014/grand-challenge-ten-years-later

77 XPRIZE Foundation, "Mojave Aerospace Ventures Wins That Competition That Started It All," https://www.xprize.org/news/mojave-aerospace-ventures-wins-the-competition.

77 XPRIZE基金会，《莫哈韦航空航天风险投资公司赢得开启一切的竞赛》，https://www.xprize.org/news/mojave-aerospace-ventures-wins-the-competition

78 Vesuvius Challenge, "Vesuvius Challenge 2023 Grand Prize Awarded: We Can Read the Scrolls!" February 4, 2024, https://scrollprize.org/grandprize.

78 维苏威挑战赛，《维苏威挑战赛2023大奖颁发:我们能阅读卷轴了！》2024年2月4日，https://scrollprize.org/grandprize

79 Vitalik Buterin et al., "A Flexible Design for Funding Public Goods," Management Science 65, no. 11 (July 2, 2019): 5171-5187, https://doi.org/10.1287/mnsc.2019.3337.

79 维塔利克·布特林等人，《公共产品资助的灵活设计》，《管理科学》65卷，第11期(2019年7月2日):5171-5187，https://doi.org/10.1287/mnsc.2019.3337

80 Adam Marblestone et al., "Introducing the Convergent Research Gap Map," Essential Technology (blog), April 15, 2025, https://www.essentialtechnology.blog/p/introducing-the-convergent-research.

80 Adam Marblestone 等人，"Introducing the Convergent Research Gap Map," Essential Technology (博客), 2025年4月15日, https://www.essentialtechnology.blog/p/introducing-the-convergent-research.

81 "Heilmeier Catechism," Defense Advanced Research Projects Agency (DARPA), https://www.darpa.mil/about/heilmeier-catechism.

81 "Heilmeier Catechism," 国防高级研究计划局 (DARPA), https://www.darpa.mil/about/heilmeier-catechism.

82 UK Metascience Unit, A Year in Metascience (2025), (Department for Science, Innovation and Technology and UK Research and Innovation, June 30, 2025), https://www.gov.uk/government/ publications/a-year-in-metascience-2025.

82 英国元科学部门, A Year in Metascience (2025), (科学、创新与技术部及英国研究与创新署, 2025年6月30日), https://www.gov.uk/government/ publications/a-year-in-metascience-2025.

83 The White House, National Security Strategy of the United States of America (November 2025), https://www.whitehouse.gov/wp-content/uploads/2025/12/2025-National-Security-Strategy.pdf.

83 白宫, 美利坚合众国国家安全战略 (2025年11月), https://www.whitehouse.gov/wp-content/uploads/2025/12/2025-National-Security-Strategy.pdf.

84 Allan Dafoe, "On Technological Determinism: A Typology, Scope Conditions, and a Mechanism," Science, Technology, & Human Values 40, no. 6 (2015): 1047-1076, https://doi.org/10.1177/ 0162243915579283.

84 Allan Dafoe, "On Technological Determinism: A Typology, Scope Conditions, and a Mechanism," Science, Technology, & Human Values 40, 第6期 (2015): 1047-1076, https://doi.org/10.1177/ 0162243915579283.

85 W. Brian Arthur, Increasing Returns and Path Dependence in the Economy (University of Michigan Press, 1994).

85 W. Brian Arthur, Increasing Returns and Path Dependence in the Economy (密歇根大学出版社, 1994).

86 National Science Board, Discovery: R&D Activity and Research Publications.

86 国家科学委员会, Discovery: R&D Activity and Research Publications.

87 KPMG Private Enterprise, "2024 Global VC Investment Rises to $368 Billion as Investor Interest in AI Soars While IPO Optimism Grows For 2025 According to KPMG Private Enterprise's Venture Pulse," press release for Venture Pulse, January 2025, https://kpmg.com/xx/en/media/ press-releases/2025/01/2024-global-vc-investment-rises-to-368-billion-dollars.html.

87 毕马威私营企业, "2024 Global VC Investment Rises to $368 Billion as Investor Interest in AI Soars While IPO Optimism Grows For 2025 According to KPMG Private Enterprise's Venture Pulse," Venture Pulse 新闻稿, 2025年1月, https://kpmg.com/xx/en/media/ press-releases/2025/01/2024-global-vc-investment-rises-to-368-billion-dollars.html.

88 U.S. Nuclear Regulatory Commission, "Pre-application Process," last reviewed or updated May 13, 2026, https://www.nrc.gov/reactors/new-reactors/advanced/new-app/general-guidance/ pre-app-process; Office of Nuclear Energy, "NRC Approves First U.S. Small Modular Reactor Design," U.S. Department of Energy, September 2, 2020, https://www.energy.gov/ne/articles/ nrc-approves-first-us-small-modular-reactor-design; Office of Nuclear Energy, "NRC Certifies First U.S. Small Modular Reactor Design," U.S. Department of Energy, January 20, 2023, https:// www.energy.gov/ne/articles/nrc-certifies-first-us-small-modular-reactor-design.

88 美国核管理委员会, "Pre-application Process," 最后审阅或更新于2026年5月13日, https://www.nrc.gov/reactors/new-reactors/advanced/new-app/general-guidance/ pre-app-process; 核能办公室, "NRC Approves First U.S. Small Modular Reactor Design," 美国能源部, 2020年9月2日, https://www.energy.gov/ne/articles/ nrc-approves-first-us-small-modular-reactor-design; 核能办公室, "NRC Certifies First U.S. Small Modular Reactor Design," 美国能源部, 2023年1月20日, https:// www.energy.gov/ne/articles/nrc-certifies-first-us-small-modular-reactor-design.

89 Nuclear Energy Innovation and Modernization Act, Pub. L. No. 115-439, 132 Stat. 5565 (2019).

89 核能创新与现代化法案, Pub. L. No. 115-439, 132 Stat. 5565 (2019).

90 Erik Cothron, "Fact Sheet: President Trump's Nuclear Energy Executive Orders," Nuclear Innovation Alliance, May 29, 2025, https://nuclearinnovationalliance.org/fact-sheet-president-trumps-nuclear-energy-executive-orders.

90 Erik Cothron, "Fact Sheet: President Trump's Nuclear Energy Executive Orders," 核创新联盟, 2025年5月29日, https://nuclearinnovationalliance.org/fact-sheet-president-trumps-nuclear-energy-executive-orders.

91 "PRIMA Visual Prosthesis," Science Corporation, https://science.xyz/technologies/prima.

92 Aylin Sertkaya et al., U.S. Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation, Examination of Clinical Trial Costs and Barriers for Drug Development, July 24, 2014, https://aspe.hhs.gov/reports/examination-clinical-trial-costs-barriers-drug-development-0.

92 Aylin Sertkaya 等人, 美国卫生与公众服务部, 助理部长规划与评估办公室, Examination of Clinical Trial Costs and Barriers for Drug Development, 2014年7月24日, https://aspe.hhs.gov/reports/examination-clinical-trial-costs-barriers-drug-development-0.

93 U.S. Food and Drug Administration, "FDA Eliminates Major Barrier to Using Real-World Evidence in Drug and Device Application Reviews," press announcement, December 15, 2025, https://www.fda.gov/news-events/press-announcements/fda-eliminates-major-barrier-using-real-world-evidence-drug-and-device-application-reviews.

93 美国食品药品监督管理局, "FDA Eliminates Major Barrier to Using Real-World Evidence in Drug and Device Application Reviews," 新闻公告, 2025年12月15日, https://www.fda.gov/news-events/press-announcements/fda-eliminates-major-barrier-using-real-world-evidence-drug-and-device-application-reviews.

94 Vinay Prasad and Martin A. Makary, "One Pivotal Trial, the New Default Option for FDA Approval-Ending the Two-Trial Dogma," New England Journal of Medicine 394, no. 8 (February 18, 2026): 815-17, https://doi.org/10.1056/NEJMsb2517623.

94 Vinay Prasad 和 Martin A. Makary, "One Pivotal Trial, the New Default Option for FDA Approval-Ending the Two-Trial Dogma," New England Journal of Medicine 394, 第8期 (2026年2月18日): 815-17, https://doi.org/10.1056/NEJMsb2517623.

95 National Institutes of Health, "Highlighted Topics," NIH Grants & Funding, last updated December 10, 2025, https://grants.nih.gov/funding/find-a-fit-for-your-research/highlighted-topics; National Institutes of Health, "Updated Application Policies: NIH Administrative Burden Reduction Effort Removal of Requirements for Letters of Intent and Unsolicited Applications Requesting $500,000 or More in Direct Costs," notice no. NOT-OD-26-019, NIH Guide for Grants and Contracts, December 3, 2025, https://grants.nih.gov/grants/guide/notice-files/ NOT-OD-26-019.html.

95 国立卫生研究院, "Highlighted Topics," NIH Grants & Funding, 最后更新于2025年12月10日, https://grants.nih.gov/funding/find-a-fit-for-your-research/highlighted-topics; 国立卫生研究院, "Updated Application Policies: NIH Administrative Burden Reduction Effort Removal of Requirements for Letters of Intent and Unsolicited Applications Requesting $500,000 or More in Direct Costs," 通知编号 NOT-OD-26-019, NIH Guide for Grants and Contracts, 2025年12月3日, https://grants.nih.gov/grants/guide/notice-files/ NOT-OD-26-019.html.

96 U.S. Food and Drug Administration, "Commissioner's National Priority Voucher (CNPV) Pilot Program," last modified April 28, 2026, https://www.fda.gov/industry/commissioners-national-priority-voucher-cnpv-pilot-program.

96 美国食品药品监督管理局，"局长国家优先凭证(CNPV)试点项目"，最后修改于2026年4月28日，https://www.fda.gov/industry/commissioners-national-priority-voucher-cnpv-pilot-program。

97 Kim Stringfellow, "Peace, Love and Rockets: Amateur Rocketry in the Mojave," PBS SoCal, August 31, 2017, https://www.pbssocal.org/shows/artbound/peace-love-and-rockets-amateur-rocketry-in-the-mojave; Friends of Amateur Rocketry, Inc., https://friendsofamateurrocketry.org.

97 Kim Stringfellow，"和平、爱与火箭:莫哈韦地区的业余火箭活动"，PBS SoCal，2017年8月31日，https://www.pbssocal.org/shows/artbound/peace-love-and-rockets-amateur-rocketry-in-the-mojave；业余火箭爱好者协会，https://friendsofamateurrocketry.org。

98 Mojave Air and Space Port, "America's First Inland Space Port," https://mojaveairport.com/ about-us/page/americas-first-inland-space-port.

98 莫哈韦航空航天港，"美国首个内陆航天港"，https://mojaveairport.com/about-us/page/americas-first-inland-space-port。

99 NASA, "NASA and X Prize Announce Winners of Lunar Lander Challenge," press release, June 6, 2013, https://www.nasa.gov/news-release/nasa-and-x-prize-announce-winners-of-lunar-lander-challenge.

99 美国国家航空航天局，"NASA与X大奖宣布月球着陆器挑战赛获胜者"，新闻稿，2013年6月6日，https://www.nasa.gov/news-release/nasa-and-x-prize-announce-winners-of-lunar-lander-challenge。

100 NASA, "NASA Stennis Inks Expanded Test Complex Agreement with Relativity Space," press release, September 7, 2023, https://www.nasa.gov/news-release/nasa-stennis-inks-expanded-test-complex-agreement-with-relativity-space.

100 美国国家航空航天局，"NASA斯坦尼斯与相对论空间公司签署扩大测试综合体协议"，新闻稿，2023年9月7日，https://www.nasa.gov/news-release/nasa-stennis-inks-expanded-test-complex-agreement-with-relativity-space。

101 "Office of Science User Facilities," U.S. Department of Energy, https://www.energy.gov/science/ office-science-user-facilities.

101 "科学用户设施办公室"，美国能源部，https://www.energy.gov/science/office-science-user-facilities。

102 National Nanotechnology Coordinated Infrastructure, https://nnci.net.

102 国家纳米技术协调基础设施，https://nnci.net。

103 BioLabs, https://www.biolabs.io.

103 BioLabs，https://www.biolabs.io。

104 U.S. Food and Drug Administration, "Current Good Manufacturing Practice (CGMP) Regulations," last modified November 21, 2025, https://www.fda.gov/drugs/pharmaceutical-quality-resources/current-good-manufacturing-practice-cgmp-regulations.

104 美国食品药品监督管理局，"现行良好生产规范(CGMP)法规"，最后修改于2025年11月21日，https://www.fda.gov/drugs/pharmaceutical-quality-resources/current-good-manufacturing-practice-cgmp-regulations。

105 National Center for Science and Engineering Statistics, National Patterns of R&D Resources.

105 国家科学与工程统计中心，《国家研发资源模式》。

106 Foundation for the National Institutes of Health, https://fnih.org.

106 美国国立卫生研究院基金会，https://fnih.org。

107 National Institutes of Health, "Accelerating Medicines Partnership (AMP)," last reviewed July 16, 2025, https://www.nih.gov/amp.

107 美国国立卫生研究院，"加速药物合作计划(AMP)"，最后审阅于2025年7月16日，https://www.nih.gov/amp。

108 "A Doctoral Program for Full-time Employees," Northeastern University, https://phd.northeastern.edu/industry-and-experiential-phd-program.

108 "面向全职员工的博士项目"，东北大学，https://phd.northeastern.edu/industry-and-experiential-phd-program。

109 "Activate," Entrepreneur Futures Network, https://entrepreneurfutures.org/activate.

109 "Activate"，创业未来网络，https://entrepreneurfutures.org/activate。

110 Robert Cook-Deegan, The Gene Wars: Science, Politics, and the Human Genome (W. W. Norton, 1994), 102.

110 Robert Cook-Deegan，《基因战争:科学、政治与人类基因组》(W. W. Norton，1994年)，第102页。

111 Francis S. Collins et al., "The Human Genome Project: Lessons from Large-Scale Biology," Science 300, no. 5617 (April 11, 2003): 286-90, https://doi.org/10.1126/science.1084564.

111 Francis S. Collins等人，"人类基因组计划:大规模生物学的经验教训"，Science 300，第5617期(2003年4月11日):286-90，https://doi.org/10.1126/science.1084564。

112 Simon Tripp and Martin Grueber, Economic Impact of the Human Genome Project (Battelle Memorial Institute, Technology Partnership Practice, May 2011), https://battelle.org/docs/ default-source/misc/battelle-2011-misc-economic-impact-human-genome-project.pdf.

112 西蒙·特里普与马丁·格鲁伯，《人类基因组计划的经济影响》(巴特尔纪念研究所，技术合作实践部，2011年5月)，https://battelle.org/docs/default-source/misc/battelle-2011-misc-economic-impact-human-genome-project.pdf。

113 United States General Accounting Office, Federal Research: SEMATECH's Efforts to Strengthen the U.S. Semiconductor Industry, RCED-90-236, September 13, 1990, https://gao.gov/assets/ rced-90-236.pdf.

113 美国总审计局，《联邦研究:SEMATECH加强美国半导体行业的努力》，RCED-90-236，1990年9月13日，https://gao.gov/assets/rced-90-236.pdf。

114 Sandia National Laboratories, "Partners Unveil First Extreme Ultraviolet Chip-Making Machine," April 11, 2001, https://newsreleases.sandia.gov/partners-unveil-first-extreme-ultraviolet-chip-making-machine.

114 桑迪亚国家实验室，“合作伙伴揭晓首台极紫外芯片制造机”，2001年4月11日，https://newsreleases.sandia.gov/partners-unveil-first-extreme-ultraviolet-chip-making-machine。

115 Gwyn and Wurm, "EUV LLC: An Historical Perspective."

115 格温和武尔姆，“EUV LLC:历史视角”。

116 Alexis de Tocqueville, Democracy in America, trans. Henry Reeve, vol. 2, bk. 3, ch.18.

116 亚历克西·德·托克维尔，《论美国的民主》，亨利·里夫译，第2卷，第3册，第18章。

117 "Autonomous Vehicles Testing and Operating in the State of Arizona," Arizona Department of Transportation, Motor Vehicle Division, https://azdot.gov/mvd/services/professional-services/ autonomous-vehicles-testing-and-operating-state-arizona.

117 “在亚利桑那州测试和运行的自动驾驶车辆”，亚利桑那州交通部，机动车辆司，https://azdot.gov/mvd/services/professional-services/autonomous-vehicles-testing-and-operating-state-arizona。

118 "TSMC Arizona," Taiwan Semiconductor Manufacturing Company, https://www.tsmc.com/ static/abouttsmcaz/index.htm.

118 “台积电亚利桑那”，台湾积体电路制造公司，https://www.tsmc.com/static/abouttsmcaz/index.htm。

119 Regulatory Sandbox Program Amendments, H.B. 217, 2021 Gen. Sess. (Utah 2021).

119 监管沙盒计划修正案，H.B. 217，2021年常会(犹他州2021年)。

120 James R. Holbein and Justin Holbein, "Wyoming Laws More Crypto-Friendly with Issuance of State Stablecoin," Braumiller Law Group, PLLC, https://www.braumillerlaw.com/wyoming-laws-more-crypto-friendly-with-issuance-of-state-stablecoin.

120 詹姆斯·R·霍尔本与贾斯汀·霍尔本，“怀俄明州法律因发行州稳定币而更友好于加密货币”，布劳米勒法律集团，PLLC，https://www.braumillerlaw.com/wyoming-laws-more-crypto-friendly-with-issuance-of-state-stablecoin。

121 Dan Wang, "How Technology Grows (a Restatement of Definite Optimism)," blog, July 24, 2018, https://danwang.co/how-technology-grows; Dan Wang, Breakneck: China's Quest to Engineer the Future (W. W. Norton & Company, 2025).

121 王丹，“技术如何成长(对明确乐观主义的重述)”，博客，2018年7月24日，https://danwang.co/how-technology-grows；王丹，《突飞猛进:中国塑造未来的追求》(W. W. 诺顿公司，2025年)。

122 Michael Polanyi, The Tacit Dimension (Doubleday, 1966).

122 迈克尔·波兰尼，《隐性维度》(双日出版社，1966年)。

123 Harry Collins, Changing Order: Replication and Induction in Scientific Practice (University of Chicago Press, 1992).

123 哈里·柯林斯，《改变秩序:科学实践中的复制与归纳》(芝加哥大学出版社，1992年)。

124 Wang, "How Technology Grows."

124 王丹，“技术如何成长”。

125 Katelynn Harris, "Forty Years of Falling Manufacturing Employment," Beyond the Numbers 9, no. 16 (November 2020), https://www.bls.gov/opub/btn/volume-9/forty-years-of-falling-manufacturing-employment.htm.

125 凯特琳·哈里斯，“制造业就业下降四十年”，《数字之外》第9卷第16期(2020年11月)，https://www.bls.gov/opub/btn/volume-9/forty-years-of-falling-manufacturing-employment.htm。

126 U.S. Bureau of Labor Statistics, "All Employees, Manufacturing [MANEMP]," FRED, Federal Reserve Bank of St. Louis, March 2, 2026, https://fred.stlouisfed.org/series/MANEMP.

126 美国劳工统计局，“所有雇员，制造业 [MANEMP]”，FRED，圣路易斯联邦储备银行，2026年3月2日，https://fred.stlouisfed.org/series/MANEMP。

127 U.S. Bureau of Labor Statistics, "All Employees, Manufacturing [MANEMP]."

127 美国劳工统计局，“所有雇员，制造业 [MANEMP]”。

128 Tim Bajarin, "Maker Faire: Why the Maker Movement Is Important to America's Future," Time, May 19, 2014, https://time.com/104210/maker-faire-maker-movement.

128 蒂姆·巴贾林，《创客嘉年华:为何创客运动对美国未来至关重要》，《时代》杂志，2014年5月19日，https://time.com/104210/maker-faire-maker-movement。

129 Will Holman, "Makerspace: Towards a New Civic Infrastructure," Places Journal, November 2015, https://placesjournal.org/article/makerspace-towards-a-new-civic-infrastructure.

129 威尔·霍尔曼，《创客空间:迈向新的公民基础设施》，《场所杂志》，2015年11月，https://placesjournal.org/article/makerspace-towards-a-new-civic-infrastructure。

130 Matthew B. Crawford, "Shop Class as Soulcraft," The New Atlantis, no. 13 (Summer 2006): 7-24, https://www.thenewatlantis.com/wp-content/uploads/legacy-pdfs/TNA13-Crawford.pdf.

130 马修·B·克劳福德，《车间课作为灵魂手艺》，《新亚特兰蒂斯》，第13期(2006年夏季):7-24页，https://www.thenewatlantis.com/wp-content/uploads/legacy-pdfs/TNA13-Crawford.pdf。

131 Nick Moore, "Connecting Talent to Opportunity: A National Challenge to Build Talent Marketplaces," Homeroom Blog, U.S. Department of Education, January 13, 2026, https://www.ed.gov/ about/homeroom-blog/connecting-talent-opportunity-national-challenge-build-talent-marketplaces; Heather Hennerich, "The Jobs and Degrees Underemployed College Graduates Have," Open Vault Blog, Federal Reserve Bank of St. Louis, August 13, 2025, https://www.stlouisfed.org/ open-vault/2025/aug/jobs-degrees-underemployed-college-graduates-have.

131 尼克·摩尔，《连接人才与机遇:构建人才市场的国家挑战》，《Homeroom博客》，美国教育部，2026年1月13日，https://www.ed.gov/about/homeroom-blog/connecting-talent-opportunity-national-challenge-build-talent-marketplaces；希瑟·亨内里奇，《大学毕业生就业不足的工作与学位》，《Open Vault博客》，圣路易斯联邦储备银行，2025年8月13日，https://www.stlouisfed.org/open-vault/2025/aug/jobs-degrees-underemployed-college-graduates-have。

132 Rita R. Zota, "A Snapshot of Federal Student Loan Debt," CRS Report No. IF10158, February 19, 2025, https://www.congress.gov/crs-product/IF10158.

132 丽塔·R·佐塔，《联邦学生贷款债务概况》，国会研究服务部报告编号IF10158，2025年2月19日，https://www.congress.gov/crs-product/IF10158。

133 Rainer Weiss, "Biographical," NobelPrize.org, https://www.nobelprize.org/prizes/physics/2017/ weiss/biographical; Bryan Marquard, "Rainer Weiss, Nobel Prize-Winner Who Helped Unlock Secrets of the Universe, Dies at 92," The Boston Globe, August 31, 2025, https://www.boston-globe.com/2025/08/31/metro/mit-nobel-prize-winner-rainer-weiss-passes-away.

133 雷纳·韦斯，《传记》，NobelPrize.org，https://www.nobelprize.org/prizes/physics/2017/weiss/biographical；布莱恩·马夸德，《诺贝尔奖得主、帮助解开宇宙秘密的雷纳·韦斯去世，享年92岁》，《波士顿环球报》，2025年8月31日，https://www.boston-globe.com/2025/08/31/metro/mit-nobel-prize-winner-rainer-weiss-passes-away。

134 American Association of Community Colleges, "AACC Fast Facts 2025," February 2025, https://www.aacc.nche.edu/wp-content/uploads/2025/02/AACC2025_Fact_Sheet.pdf.

134 美国社区学院协会，《AACC 2025年快讯》，2025年2月，https://www.aacc.nche.edu/wp-content/uploads/2025/02/AACC2025_Fact_Sheet.pdf。

135 U.S. Department of Labor, "US Department of Labor Announces $145M in Funds Supporting Performance-Based Registered Apprenticeship Expansion across Key Industry Sectors," news release, February 13, 2026, https://www.dol.gov/newsroom/releases/eta/eta20260213-0.

135 美国劳工部，《美国劳工部宣布拨款1.45亿美元支持关键行业领域基于绩效的注册学徒制扩展》，新闻稿，2026年2月13日，https://www.dol.gov/newsroom/releases/eta/eta20260213-0。

136 Robert I. Lerman, "Proposal 7: Expanding Apprenticeship Opportunities in the United States" (The Hamilton Project, Brookings Institution, 2014), https://www.hamiltonproject.org/assets/ legacy/files/downloads_and_links/expand_apprenticeship_opportunities_united_states_ler-man.pdf; Amy Simon, "Revitalizing the Federal Apprenticeship System," American Compass, June 8, 2022, https://americancompass.org/revitalizing-the-federal-apprenticeship-system.

136 罗伯特·I·勒曼，《提案7:扩大美国学徒机会》(汉密尔顿项目，布鲁金斯学会，2014年)，https://www.hamiltonproject.org/assets/legacy/files/downloads_and_links/expand_apprenticeship_opportunities_united_states_ler-man.pdf；艾米·西蒙，《重振联邦学徒制体系》，《美国指南针》，2022年6月8日，https://americancompass.org/revitalizing-the-federal-apprenticeship-system。

137 Steven Klepper, "Disagreements, Spinoffs, and the Evolution of Detroit as the Capital of the U.S. Automobile Industry," Management Science 53, no. 4 (2007): 616-631, https://doi.org/10.1287/ mnsc.1060.0683.

137 史蒂文·克莱珀，《分歧、衍生公司与底特律作为美国汽车工业之都的演变》，《管理科学》第53卷，第4期(2007年):616-631页，https://doi.org/10.1287/mnsc.1060.0683。

138 Philip E. Auerswald and Lewis M. Branscomb, "Valleys of Death and Darwinian Seas: Financing the Invention to Innovation Transition in the United States," The Journal of Technology Transfer 28 (2003): 227-239, https://doi.org/10.1023/A:1024980525678.

138 菲利普·E·奥尔斯瓦尔德与刘易斯·M·布兰斯科姆，《死亡之谷与达尔文之海:美国从发明到创新转型的融资》，《技术转移杂志》第28卷(2003年):227-239页，https://doi.org/10.1023/A:1024980525678。

139 J.D. Vance, "Remarks at the American Dynamism Summit" (speech, Third American Dynamism Summit, Washington, D.C., March 18, 2025).

139 J.D.万斯，《在美国活力峰会上的 remarks》(演讲，第三届美国活力峰会，华盛顿特区，2025年3月18日)。

140 Tyler Buchanan, "Over $2 Billion Worth of Incentives Brought Intel to Ohio," Axios Columbus, January 31, 2022, https://www.axios.com/local/columbus/2022/01/31/over-2b-worth-of-incentives-brought-intel-to-ohio.

140 泰勒·布坎南，《超过20亿美元的激励措施将英特尔引入俄亥俄州》，《Axios哥伦布》，2022年1月31日，https://www.axios.com/local/columbus/2022/01/31/over-2b-worth-of-incentives-brought-intel-to-ohio。

141 Columbus State Community College, "Columbus State Leading New Ohio-Wide Community College Collaboration Creating Two-Year Degree Pathways to Chip Manufacturing Technician Careers at Intel," September 8, 2022, https://www.cscc.edu/about/news/2022/intel-ground-breaking-sept-22.shtml.

141 哥伦布州立社区学院，《哥伦布州立引领新的俄亥俄州全州社区学院合作，创建通往英特尔芯片制造技术员职业的两年制学位路径》，2022年9月8日，https://www.cscc.edu/about/news/2022/intel-ground-breaking-sept-22.shtml。

142 Chris Bournea, "Intel Announces $50 Million Investment in Ohio Higher Education," Ohio State University College of Engineering, March 18, 2022, https://engineering.osu.edu/news/2022/03/ intel-announces-50-million-investment-ohio-higher-education; Gabriela Cruz Thompson, "Intel Addresses Semiconductor Workforce Shortage," September 24, 2023, https://newsroom.intel.com/corporate/intel-addresses-semiconductor-workforce-shortage.

142 Chris Bournea，“英特尔宣布向俄亥俄州高等教育投资5000万美元”，俄亥俄州立大学工程学院，2022年3月18日，https://engineering.osu.edu/news/2022/03/intel-announces-50-million-investment-ohio-higher-education；Gabriela Cruz Thompson，“英特尔应对半导体劳动力短缺”，2023年9月24日，https://newsroom.intel.com/corporate/intel-addresses-semiconductor-workforce-shortage。

143 Office of the Texas Governor, "Governor Abbott Announces Texas Semiconductor Innovation Fund Grant to Samsung Austin Semiconductor," press release, September 17, 2025, https:// gov.texas.gov/news/post/governor-abbott-announces-texas-semiconductor-innovation-fund-grant-to-samsung-austin-semiconductor.

143 德克萨斯州州长办公室，“州长阿博特宣布德克萨斯州半导体创新基金向三星奥斯汀半导体拨款”，新闻稿，2025年9月17日，https://gov.texas.gov/news/post/governor-abbott-announces-texas-semiconductor-innovation-fund-grant-to-samsung-austin-semiconductor。

144 Vannevar Bush, "As We May Think," The Atlantic, July 1945, https://www.theatlantic.com/magazine/archive/1945/07/as-we-may-think/303881.

144 Vannevar Bush，“如我们所想”，《大西洋月刊》，1945年7月，https://www.theatlantic.com/magazine/archive/1945/07/as-we-may-think/303881。

145 Bush, "As We May Think."

145 Bush，“如我们所想”。

146 Rolfe Winkler et al., "Big Tech's $400 Billion AI Spending Spree Just Got Wall Street's Blessing," Wall Street Journal, July 31, 2025, https://www.wsj.com/tech/ai/tech-ai-spending-company-valuations-7b92104b.

146 Rolfe Winkler等人，“大型科技公司4000亿美元AI支出狂潮获华尔街认可”，《华尔街日报》，2025年7月31日，https://www.wsj.com/tech/ai/tech-ai-spending-company-valuations-7b92104b。

147 John Jumper et al., "Highly Accurate Protein Structure Prediction with AlphaFold," Nature 596 (2021): 583-589, https://doi.org/10.1038/s41586-021-03819-2.

147 John Jumper等人，“利用AlphaFold实现高精度蛋白质结构预测”，《自然》596(2021):583-589，https://doi.org/10.1038/s41586-021-03819-2。

148 Joseph L. Watson et al., "De Novo Design of Protein Structure and Function with RFdiffusion," Nature 620 (2023): 1089-1100, https://doi.org/10.1038/s41586-023-06415-8.

148 Joseph L. Watson等人，“利用RFdiffusion从头设计蛋白质结构与功能”，《自然》620(2023):1089-1100，https://doi.org/10.1038/s41586-023-06415-8。

149 Tong Wang et al., "Ab Initio Characterization of Protein Molecular Dynamics with AI2BMD," Nature 635 (2024): 1019-1027, https://doi.org/10.1038/s41586-024-08127-z.

149 Tong Wang等人，“利用AI2BMD从头表征蛋白质分子动力学”，《自然》635(2024):1019-1027，https://doi.org/10.1038/s41586-024-08127-z。

150 Jonas Degrave et al., "Magnetic Control of Tokamak Plasmas Through Deep Reinforcement Learning," Nature 602 (2022): 414-419, https://doi.org/10.1038/s41586-021-04301-9.

150 Jonas Degrave等人，“通过深度强化学习磁控托卡马克等离子体”，《自然》602(2022):414-419，https://doi.org/10.1038/s41586-021-04301-9。

151 ATLAS Collaboration, "Search for New Phenomena in Two-Body Invariant Mass Distributions Using Unsupervised Machine Learning for Anomaly Detection at ( \sqrt{}s = {13}\mathrm{{TeV}} ) with the ATLAS Detector," Physical Review Letters 132, no. 8 (2024): 081801, https://doi.org/10.1103/Phys RevLett.132.081801.

151 ATLAS合作组，“利用无监督机器学习在( \sqrt{}s = {13}\mathrm{{TeV}} )处通过ATLAS探测器进行异常检测搜索双体不变质量分布中的新现象”，《物理评论快报》132，第8期(2024):081801，https://doi.org/10.1103/PhysRevLett.132.081801。

152 Hiroaki Kitano, "Nobel Turing Challenge: Creating the Engine for Scientific Discovery," npj Systems Biology and Applications, vol. 7, no. 29 (2021), https://doi.org/10.1038/s41540-021- 00189-3.

152 Hiroaki Kitano，“诺贝尔图灵挑战:打造科学发现引擎”，《npj系统生物学与应用》第7卷，第29期(2021)，https://doi.org/10.1038/s41540-021-00189-3。

153 National Center for Science and Engineering Statistics, "Publication Output by Region, Country, or Economy and by Scientific Field," in Publications Output: U.S. Trends and International Comparisons, NSB-2023-33 (National Science Foundation, December 11, 2023), https://ncses.nsf.gov/ pubs/nsb202333/publication-output-by-region-country-or-economy-and-by-scientific-field.

153 国家科学与工程统计中心，“按地区、国家或经济体及科学领域划分的出版物产出”，载于《出版物产出:美国趋势与国际比较》，NSB-2023-33(国家科学基金会，2023年12月11日)，https://ncses.nsf.gov/pubs/nsb202333/publication-output-by-region-country-or-economy-and-by-scientific-field。

154 Conor Griffin et al., "A New Golden Age of Discovery: Seizing the AI for Science Opportunity," AI Policy Perspectives (blog), November 26, 2024, https://www.aipolicyperspectives.com/p/ a-new-golden-age-of-discovery.

154 Conor Griffin等人，“发现的新黄金时代:把握AI促进科学机遇”，《AI政策视角》(博客)，2024年11月26日，https://www.aipolicyperspectives.com/p/a-new-golden-age-of-discovery。

155 Haotian Teng et al., "Chiron: Translating Nanopore Raw Signal Directly into Nucleotide Sequence Using Deep Learning," GigaScience 7, no. 5 (2018), https://doi.org/10.1093/gigascience/ giy037.

155 Haotian Teng等人，“Chiron:利用深度学习将纳米孔原始信号直接翻译为核苷酸序列”，《GigaScience》7，第5期(2018)，https://doi.org/10.1093/gigascience/giy037。

156 Ashish Vaswani et al., "Attention Is All You Need," in Advances in Neural Information Processing Systems 30 (2017), 5998-6008.

156 Ashish Vaswani等人，“注意力即一切”，载于《神经信息处理系统进展》30(2017)，5998-6008。

157 Sayash Kapoor and Arvind Narayanan, "Could AI Slow Science? Confronting the Production-Progress Paradox," AI as Normal Technology (blog), July 16, 2025, https://www.normaltech.ai/p/ could-ai-slow-science.

157 Sayash Kapoor和Arvind Narayanan，“AI会拖慢科学吗？直面生产-进步悖论”，《AI作为常态技术》(博客)，2025年7月16日，https://www.normaltech.ai/p/could-ai-slow-science。

158 "About," Arc Institute, https://arcinstitute.org/about.

158 “关于我们”，Arc研究所，https://arcinstitute.org/about。

159 "2026 AI-for-Science Independent Postdoctoral Fellowship Program," FutureHouse, https:// www.futurehouse.org/fellowship.

159 “2026年人工智能驱动科学独立博士后奖学金项目”，FutureHouse，https://www.futurehouse.org/fellowship。

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171 Open Science Collaboration, "Estimating the Reproducibility of Psychological Science," Science 349, no. 6251 (August 28, 2015): aac4716, https://doi.org/10.1126/science.aac4716.

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172 Colin F. Camerer et al., "Evaluating Replicability of Laboratory Experiments in Economics," Science 351, no. 6280 (March 3, 2016): 1433-1436, https://doi.org/10.1126/science.aaf0918.

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173 Leonard P. Freedman et al., "The Economics of Reproducibility in Preclinical Research," PLoS Biology 13, no. 6 (2015): e1002165, https://doi.org/10.1371/journal.pbio.1002165.

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174 Exec. Order No. 14303, "Restoring Gold Standard Science," Federal Register 90 (May 29, 2025): 22601, https://www.federalregister.gov/documents/2025/05/29/2025-09802/restoring-gold-standard-science.

174 第14303号行政令，《恢复黄金标准科学》，《联邦公报》第90卷(2025年5月29日):第22601页，https://www.federalregister.gov/documents/2025/05/29/2025-09802/restoring-gold-standard-science。

175 "There was a 59.8% increase in ICLR submissions in 2025 alone" as stated in Jaeho Kim et al., "Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards," Proceedings of the 42nd International Conference on Machine Learning 267 (2025): 81634-81651, https://proceedings.mlr.press/v267/kim25am.html.

175 “仅2025年，ICLR投稿量就增长了59.8%”，引自Jaeho Kim等人，《立场:AI会议同行评审危机需要作者反馈与审稿人奖励》，《第42届国际机器学习大会论文集》第267卷(2025年):第81634-81651页，https://proceedings.mlr.press/v267/kim25am.html。

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177 National Institutes of Health, "Replication to Enhance Research Impact Initiative," NIH Common Fund, last reviewed February 4, 2026, https://commonfund.nih.gov/replication-initiative.

177 美国国立卫生研究院，《通过复制增强研究影响力倡议》，NIH共同基金，最后审阅于2026年2月4日，https://commonfund.nih.gov/replication-initiative。

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178 Mathlib社区，《液体张量实验完成》，Lean社区博客，2022年7月15日，https://leanprover-community.github.io/blog/posts/lte-final。

179 Leila Sloman, "A-Team' of Math Proves a Critical Link Between Addition and Sets," Quanta Magazine, December 6, 2023, https://www.quantamagazine.org/a-team-of-math-proves-a-critical-link-between-addition-and-sets-20231206.

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180 Math, Inc., "The Strong Prime Number Theorem," GitHub repository, 2025, https://github.com/ math-inc/strongpnt.

180 Math公司，《强素数定理》，GitHub仓库，2025年，https://github.com/math-inc/strongpnt。

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182 Seemay Chou, "Scientific Publishing: Enough is Enough," Astera Institute (blog), June 2, 2025, https://asterainstitute.substack.com/p/scientific-publishing-enough-is-enough.

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183 Timothy K. Mackey et al., "A Framework Proposal for Blockchain-Based Scientific Publishing Using Shared Governance," Frontiers in Blockchain 2, (2019): 19, https://doi.org/10.3389/fbloc.2019.00019.

183 蒂莫西·K·麦基等人，《基于区块链的科学出版共享治理框架提案》，《区块链前沿》第2卷(2019年):第19页，https://doi.org/10.3389/fbloc.2019.00019。

184 "The Community of the DAO," Nature Biotechnology 41 (2023): 1357, https://doi.org/10.1038/ s41587-023-02005-1.

184 《DAO社区》，《自然生物技术》第41卷(2023年):第1357页，https://doi.org/10.1038/s41587-023-02005-1。

185 Gaia Dempsey, "Why I Reject the Comparison of Metaculus to Prediction Markets," Metaculus, February 24, 2023, https://www.metaculus.com/notebooks/17599/why-i-reject-the-comparison-of-metaculus-to-prediction-markets.

185 盖亚·邓普西，《为何我拒绝将Metaculus与预测市场相提并论》，Metaculus，2023年2月24日，https://www.metaculus.com/notebooks/17599/why-i-reject-the-comparison-of-metaculus-to-prediction-markets。

Annex

附件

MEMORANDUM FOR THE HEADS OF EXECUTIVE DEPARTMENTS AND AGENCIES

致各行政部委及机构负责人备忘录

FROM: MICHAEL J. KRATSIOS

发件人:迈克尔·J·克拉齐奥斯

ASSISTANT TO THE PRESIDENT FOR

总统助理负责

SCIENCE AND TECHNOLOGY

科学与技术事务

DIRECTOR, OFFICE OF SCIENCE AND

科学和技术政策办公室

TECHNOLOGY POLICY

主任

RUSSELL T. VOUGHT

拉塞尔·T·沃特

DIRECTOR, OFFICE OF MANAGEMENT

管理和预算办公室

AND BUDGET

主任

SUBJECT:

主题:

Ushering in a New Golden Age of American Innovation: Fiscal Year 2028 Administration Research and Development Budget Priorities

开启美国创新新黄金时代:2028财年行政研发预算优先事项

American leadership in science and technology (S&T) underpins our economic prosperity, national security, and public health. As the United States celebrates the 250th anniversary of declaring its independence, we stand at the threshold of a new golden age of American innovation. The future of American leadership in the emerging technologies that will define this century, from frontier artificial intelligence (AI) to quantum technologies and advanced nuclear fission and fusion, depends in part on core Federal investments in foundational research, the basic and use-inspired inquiry upon which a broad range of sciences and engineering work depends. Rapid technological advances are transforming the way scientific research is conducted, the scientific questions that we can now ask, and the scientific instruments we can build. To usher in this new golden age, we must renew the research and development (R&D) enterprise on which our scientific leadership depends.

美国在科学技术(S&T)领域的领导地位支撑着我们的经济繁荣、国家安全和公共卫生。在美国庆祝独立250周年之际，我们正站在美国创新新黄金时代的门槛上。美国在定义本世纪的新兴技术(从前沿人工智能到量子技术、先进核裂变和核聚变)中的领导地位，部分取决于联邦对基础研究(即支撑广泛科学和工程工作的基础性和应用启发性探究)的核心投资。快速的技术进步正在改变科学研究的方式、我们能够提出的科学问题以及我们能够建造的科学仪器。为了开启这个新黄金时代，我们必须重振支撑我们科学领导地位的研发(R&D)事业。

Eighty years ago, Vannevar Bush's Science: The Endless Frontier laid the foundation for the modern American scientific enterprise, giving rise to the National Science Foundation and a partnership between Federal Government, universities, and industry that won the American Century. Today, that enterprise is being reshaped by forces Bush could not have foreseen. Global competitors are racing to challenge U.S. scientific leadership, developing new methods to drive discovery and innovation. At the same time, our own enterprise has fallen out of balance. Industry now drives a growing share of innovation and even basic research, where its share of national R&D funding has doubled over the past half century, and yet we have largely not updated how the Federal Government funds research or partners with the private sector. The government invests more in R&D than ever before, yet much of the non-defense increase is concentrated in the life sciences and the pace of significant breakthroughs has slowed. And while transformative discoveries are still made in America, too often we fail to capitalize on them at home, ceding the manufacturing and supply chains that turn discovery into industry to competitors abroad. The opportunity before us is clear: by integrating industry more fully into the research enterprise, funding transformative science, especially in the physical sciences and engineering, and reconnecting scientific discovery with manufacturing and skilled crafts, America can once again fully translate scientific discovery into broad-based prosperity, creating new applications, high-paying jobs, and stronger regional economies.

八十年前，范内瓦·布什的《科学:无尽的前沿》为现代美国科学事业奠定了基础，催生了国家科学基金会以及联邦政府、大学和工业界之间的伙伴关系，赢得了美国世纪。如今，这一事业正被布什无法预见的力量重塑。全球竞争对手正竞相挑战美国的科学领导地位，开发推动发现和创新的新方法。与此同时，我们自己的事业已失去平衡。工业界现在推动着越来越多的创新甚至基础研究(其在全国研发资金中的份额在过去半个世纪翻了一番)，但我们基本上没有更新联邦政府资助研究或与私营部门合作的方式。政府在研发上的投入比以往任何时候都多，但非国防领域的增长大部分集中在生命科学领域，重大突破的步伐已经放缓。尽管变革性发现仍在美国产生，但我们常常未能将其在国内转化为资本，将把发现转化为产业的制造和供应链拱手让给海外竞争对手。我们面前的机遇是明确的:通过将工业界更充分地融入研究事业，资助变革性科学(尤其是物理科学和工程领域)，并将科学发现与制造和熟练工艺重新连接，美国可以再次将科学发现完全转化为广泛的繁荣，创造新的应用、高薪工作和更强大的区域经济。

This memorandum provides guidance to Federal departments and agencies (agencies) to recalibrate the Nation's S&T enterprise, implementing the recommendations in Science: A New Golden Age and advancing the President's vision of a Golden Age of American Innovation. The guidance identifies Administration R&D priorities for agencies to consider, as appropriate, in Fiscal Year (FY) 2028 Budget formulation and related planning. These priorities include: (i) rebalancing R&D portfolios toward foundational research and the physical sciences and engineering, (ii) advancing national S&T missions, (iii) applying AI and emerging technologies to accelerate American research and innovation, (iv) expanding R&D infrastructure for broader ecosystem use, (v) translating scientific advances into stronger regional ecosystems and broad-based prosperity, (vi) considering new funding mechanisms and institutional models to support frontier science, (vii) exploring better ways to identify and develop scientific talent, (viii) rigorously studying, evaluating, and improving how Federal science is funded, and (ix) integrating Federal R&D into the broader S&T enterprise. Agencies should account for this guidance, as appropriate, in their FY 2028 Budget submission to the Office of Management and Budget (OMB).

本备忘录为联邦部门和机构(机构)提供指导，以重新调整国家的科技事业，落实《科学:新黄金时代》中的建议，并推进总统关于美国创新黄金时代的愿景。该指导确定了行政研发优先事项，供机构在2028财年预算编制和相关规划中酌情考虑。这些优先事项包括:(i)将研发组合重新平衡至基础研究以及物理科学和工程领域，(ii)推进国家科技使命，(iii)应用人工智能和新兴技术加速美国研究和创新，(iv)扩大研发基础设施以促进更广泛的生态系统使用，(v)将科学进步转化为更强大的区域生态系统和广泛的繁荣，(vi)考虑新的资助机制和制度模式以支持前沿科学，(vii)探索更好的识别和培养科学人才的方法，(viii)严格研究、评估和改进联邦科学的资助方式，以及(ix)将联邦研发融入更广泛的科技事业。机构应酌情在其向管理和预算办公室(OMB)提交的2028财年预算中考虑本指导。

FY 2028 R&D PRIORITY AREAS

2028财年研发优先领域

Invest in Foundational Research to Drive Scientific Breakthroughs for Emerging Technologies

投资基础研究，推动新兴技术的科学突破

Foundational research, including basic and use-inspired inquiry across the sciences and engineering, remains the bedrock of American scientific and technological leadership. The United States derives outsized long-term security, economic, and societal returns from foundational research, which expands the frontier of knowledge and leads to the growth of new industries. The Federal Government's comparative advantage relative to private industry lies here, in supporting work where payoffs are long-horizon, broadly distributed, and difficult to realize privately. In their FY 2028 budget submissions to OMB, agencies should seek to increase the share of foundational research relative to later-stage development.

基础研究，包括整个科学与工程领域的基础性和应用启发性探索，仍是美国科技领导地位的基石。美国从基础研究中获得了超乎寻常的长期安全、经济和社会回报，这些研究拓展了知识前沿，并催生了新产业的成长。联邦政府相对于私营部门的比较优势在于支持那些回报周期长、分布广泛且难以由私人实现的工作。在向OMB提交的2028财年预算中，各机构应努力增加基础研究相对于后期开发的比例。

Many of the Administration's strategic technology priorities, including AI, quantum information science, semiconductors, advanced communications, robotics, advanced manufacturing, nuclear fission and fusion, and space systems, all rely on foundational research across the physical sciences, computer science, and engineering. However, the physical sciences and engineering have declined as a share of the Federal research portfolio over an extended period, even as the strategic importance of these fields has grown. Agencies are encouraged to prioritize both the absolute level and the relative share of funding directed within budget guidance levels to the physical sciences (physics, chemistry, materials science, space science, etc.), computer science, and supporting engineering and mathematical disciplines, especially within national security-relevant research portfolios. In addition, to support Administration priorities in biotechnology and biomanufacturing, agencies should prioritize foundational research in the biological sciences over the life sciences, a broader category not focused on foundational research.

政府的许多战略技术优先事项，包括人工智能、量子信息科学、半导体、先进通信、机器人、先进制造、核裂变与聚变以及空间系统，都依赖于物理科学、计算机科学和工程领域的基础研究。然而，物理科学和工程在联邦研究组合中的份额长期下降，尽管这些领域的战略重要性日益增长。鼓励各机构在预算指导水平内，优先考虑将资金(绝对水平和相对份额)投向物理科学(物理学、化学、材料科学、空间科学等)、计算机科学以及支持性的工程和数学学科，尤其是在国家安全相关的研究组合中。此外，为支持政府在生物技术和生物制造方面的优先事项，各机构应优先考虑生物科学中的基础研究，而非更广泛的生命科学类别(后者不专注于基础研究)。

In their FY 2028 Budget submissions to OMB, agencies should note the R&D character classification of proposed activities as a percentage of their R&D funding portfolio and identify the specific programs through which the agency proposes to shift its portfolio toward earlier-stage work. Where agencies propose to significantly expand later-stage development activities, they should justify why such activities would not occur absent Federal support. Agencies should prioritize funding for:

在向OMB提交的2028财年预算中，各机构应注明拟议活动的研发性质分类，作为其研发资金组合的百分比，并确定机构拟通过哪些具体项目将其组合转向早期阶段工作。如果机构提议大幅扩展后期开发活动，则应说明为何在没有联邦支持的情况下这些活动不会发生。各机构应优先资助:

Physical Sciences. Agencies should prioritize foundational research in the physical sciences, including condensed matter and quantum materials physics, including correlated, magnetic, and topological states; photonics, addressing the generation, control, and detection of light; atomic, molecular, and optical physics, addressing precision measurement and the quantum control of systems; the physics of superconductivity and other quantum phenomena; plasma and high energy density physics; nuclear physics and matter under extreme conditions; gravitational physics and geodesy; and space and planetary physics, including the radiation, plasma, and space-weather conditions in which space systems operate. These fields underpin quantum science, semiconductors, advanced communications networks, future computing technologies, advanced nuclear fission and fusion energy, and space exploration technologies including novel sensing modalities and precision position, navigation, and timing.
物理科学。各机构应优先考虑物理科学的基础研究，包括凝聚态和量子材料物理学，涵盖关联、磁性和拓扑态；光子学，涉及光的产生、控制和探测；原子、分子和光学物理学，涉及精密测量和系统的量子控制；超导及其他量子现象的物理学；等离子体和高能量密度物理学；极端条件下的核物理学和物质；引力物理学和大地测量学；以及空间和行星物理学，包括空间系统运行的辐射、等离子体和空间天气条件。这些领域支撑着量子科学、半导体、先进通信网络、未来计算技术、先进核裂变与聚变能源，以及空间探索技术，包括新型传感模式和精确定位、导航与授时。
Chemistry and Materials Science. Agencies should prioritize foundational research in chemistry and materials science, including electronic, photonic, and quantum materials; the surface, interface, and defect chemistry that governs fabrication and device performance; materials for extreme environments (e.g., radiation-tolerant, plasma-facing, and high-temperature); the structure, properties, synthesis, and characterization of materials, including condensed matter and materials theory, ceramics, metals, polymers and biomaterials; electrochemistry and solid-state ionics; and catalysis, synthesis, and reaction mechanisms. These fields underpin quantum science and semiconductors and extend across advanced manufacturing, energy production and storage, the nuclear fuel cycle, photonics, and space and hypersonic systems.
化学与材料科学。各机构应优先考虑化学与材料科学的基础研究，包括电子、光子和量子材料；控制制造和器件性能的表面、界面和缺陷化学；极端环境材料(如耐辐射、面向等离子体和高温材料)；材料的结构、性能、合成与表征，包括凝聚态和材料理论、陶瓷、金属、聚合物和生物材料；电化学和固态离子学；以及催化、合成和反应机理。这些领域支撑着量子科学和半导体，并延伸至先进制造、能源生产与存储、核燃料循环、光子学以及空间和高超音速系统。
Mathematics and Computer Science. Agencies should prioritize foundational research in the mathematical and computational sciences, including applied and computational mathematics, numerical analysis and uncertainty quantification; classical and quantum information theory; algorithms, computational complexity, and cryptography, including post-quantum cryptography; the mathematics of optimization and control; statistics, probability, and the foundations of data science; and the foundations of high-performance and future computing. These fields underpin advanced communications networks and secure information systems, quantum information science and future computing, and the modeling, simulation, and verification on which fusion energy, advanced manufacturing, and space systems depend.
数学与计算机科学。各机构应优先考虑数学与计算科学的基础研究，包括应用和计算数学、数值分析和不确定性量化；经典和量子信息论；算法、计算复杂性和密码学，包括后量子密码学；优化与控制的数学；统计学、概率论和数据科学基础；以及高性能和未来计算的基础。这些领域支撑着先进通信网络和安全信息系统、量子信息科学和未来计算，以及聚变能源、先进制造和空间系统所依赖的建模、模拟与验证。
Engineering Sciences. Agencies should prioritize foundational research in engineering sciences, including microelectronics, photonic, quantum, and microsystem device engineering and early-stage manufacturing; the electromagnetic, radiofrequency, and propulsion sciences; the thermal, fluid, and mechanical sciences, including solid mechanics and the mechanics of materials; the dynamics, estimation, and control of complex systems, including astrodynamics, guidance, and navigation; and magnet, superconducting, and power-system engineering. These fields underpin semiconductors and advanced communications networks, advanced manufacturing, space systems, robotics, and fission and fusion energy.
工程科学。各机构应优先考虑工程科学的基础研究，包括微电子、光子、量子及微系统器件工程和早期制造；电磁、射频和推进科学；热学、流体和机械科学，包括固体力学和材料力学；复杂系统的动力学、估计与控制，包括天体动力学、制导与导航；以及磁体、超导和电力系统工程。这些领域支撑着半导体和先进通信网络、先进制造、空间系统、机器人以及裂变与聚变能源。
Biological Sciences. Agencies with general, broad-based life-sciences research missions should prioritize foundational research in the biological sciences including molecular, cellular and structural biology; biochemistry and chemical biology; genetics, genomics, and synthetic and engineering biology; neuroscience and the neural basis of cognition and behavior; and microbiology and quantitative biology. These fields underpin biotechnology and biomanufacturing, neurosciences and brain-machine interfaces, and human health and therapeutics.
生物科学。承担基础性、广泛性生命科学研究任务的机构应优先支持生物科学领域的基础研究，包括分子、细胞和结构生物学；生物化学与化学生物学；遗传学、基因组学及合成与工程生物学；神经科学及认知与行为的神经基础；微生物学与定量生物学。这些领域是生物技术与生物制造、神经科学与脑机接口、人类健康与治疗学的基础。

Advance National Science and Technology Missions

推进国家科技使命

From the Manhattan Project to the Apollo Program, some of America's greatest scientific achievements have come from focused national missions that united the Nation's brightest minds behind an ambitious common goal. This Administration has revived that mission-driven model for a new era of global competition, launching a set of national science and technology efforts targeting the technologies that will define the coming century. Federal R&D is uniquely suited to drive these efforts forward by supporting them across every stage from foundational discovery to demonstration, sustaining the long-horizon and high-risk work the private sector cannot undertake alone, and convening the partnerships among government, industry, academia, and philanthropy through which national missions are ultimately achieved. Realizing them will demand a comparable concentration of national effort. Agencies should align their R&D investments, where appropriate, with the Administration's national missions, including:

从曼哈顿计划到阿波罗计划，美国一些最伟大的科学成就源于聚焦的国家使命，这些使命将国家最聪明的人才团结在雄心勃勃的共同目标之下。本届政府复兴了这一使命驱动模式，以应对新时代的全球竞争，启动了一系列国家科技努力，瞄准将定义未来一个世纪的技术。联邦研发通过支持从基础发现到示范的每个阶段，维持私营部门无法独自承担的长期高风险工作，并召集政府、产业、学术界和慈善机构之间的伙伴关系，最终实现国家使命，从而独特地适合推动这些努力。实现这些使命需要同等集中的国家努力。各机构应酌情将其研发投资与政府的国家使命保持一致，包括:

AI: The Genesis Mission to harness AI to double the productivity and impact of America's research enterprise within a decade, including agency-specific contributions across national S&T challenges and compute and research infrastructure for the American Science and Security Platform, pursuant to Executive Order 14363;
人工智能:创世纪使命，旨在十年内利用人工智能将美国研究企业的生产力和影响力翻倍，包括各机构在国家科技挑战中的具体贡献，以及根据第14363号行政命令为美国科学与安全平台提供的计算和研究基础设施；
Quantum: The Quantum Computer for Application Development and Discovery Science (QC-ADDS) effort to develop a quantum computer at a scale intended to initiate the era of quantum-enabled scientific discovery, pursuant to Executive Order 14413;
量子:量子计算机应用开发与发现科学(QC-ADDS)努力，旨在开发一台规模足以开启量子赋能科学发现时代的量子计算机，根据第14413号行政命令；
Fusion: Demonstration of commercial fusion power in the United States by the mid-2030s, following the Department of Energy's Fusion Science & Technology Roadmap;
聚变:根据能源部聚变科学与技术路线图，在2030年代中期前在美国实现商业聚变发电示范；
Space: Return of Americans to the lunar surface by 2028, the construction of a lunar base, the National Initiative for American Space Nuclear Power, and the development of a responsive and adaptive national security space architecture, pursuant to Executive Order 14369;
太空:到2028年让美国人重返月球表面，建设月球基地，美国太空核能国家倡议，以及根据第14369号行政命令开发响应性和适应性的国家安全太空架构；
Robotics: General-purpose autonomous systems capable of dexterous manipulation, mobility, and reliable operation in real-world environments, to initiate the era of physical AI-driven scientific discovery and American reindustrialization; and
机器人:能够在真实环境中进行灵巧操作、移动和可靠运行的通用自主系统，以开启物理人工智能驱动的科学发现和美国再工业化时代；以及
Semiconductors: Next-generation semiconductor technologies, including EUV-and-beyond photolithography, 3D advanced packaging, and novel materials for future semiconductor devices and technology nodes.
半导体:下一代半导体技术，包括极紫外及超越光刻、3D先进封装，以及用于未来半导体器件和技术节点的新型材料。

Agencies should support these missions through the full range of R&D policy instruments available to them. Each agency should identify, through the FY 2028 Budget process and other established budget review channels how its mission-specific research priorities and programs can support these national goals, consistent with statutory authorities, agency missions, and available resources. In their FY 2028 budget submissions, agencies should consider how to prioritize their R&D infrastructure, including user facilities, testbeds, and high-performance computing assets, toward mission needs and expand access for university and industry partners. Agencies should also propose investments that employ the full set of talent and incentive mechanisms at their disposal, including graduate and postdoctoral fellowships to build the skilled workforce these missions require, and prizes, grand challenges, and competitions to mobilize the broadest possible range of innovators toward the hardest problems.

各机构应通过其可用的全部研发政策工具支持这些使命。每个机构应通过2028财年预算流程和其他既定预算审查渠道，确定其特定使命的研究优先事项和计划如何能够支持这些国家目标，并符合法定权限、机构使命和可用资源。在2028财年预算提交中，各机构应考虑如何优先考虑其研发基础设施，包括用户设施、测试平台和高性能计算资产，以满足使命需求，并扩大大学和产业合作伙伴的访问权限。各机构还应提出利用其掌握的全部人才和激励机制的投入，包括研究生和博士后奖学金，以培养这些使命所需的熟练劳动力，以及奖项、重大挑战和竞赛，以动员最广泛的创新者解决最棘手的问题。

Build the Foundation for a New Era of Scientific Discovery

为科学发现新时代奠定基础

AI and emerging technologies have immense potential to transform science by unlocking novel experimental and analytical capabilities, enabling new ways to organize the research enterprise, and prompting new fields of scientific inquiry. In November 2025, President Trump launched the Genesis Mission, a whole-of-government effort to harness the AI-driven computing revolution with the intent to double the productivity and impact of American science and engineering within a decade. Rather than crowding subfields of AI research where private capital is already abundant, the Genesis Mission is designed to ensure America's scientific enterprise is first and fastest to harness these technologies for discovery across the scientific landscape.

人工智能与新兴技术通过解锁新颖的实验与分析能力、开辟研究组织的新方式、催生新的科学探究领域，具有变革科学的巨大潜力。2025年11月，特朗普总统启动了“创世纪使命”，这是一项全政府行动，旨在利用人工智能驱动的计算革命，在十年内将美国科学与工程的生产力和影响力翻倍。与挤占私人资本已充足的AI研究子领域不同，“创世纪使命”旨在确保美国科学事业率先且最快地利用这些技术，在整个科学领域实现发现。

Agencies should identify opportunities to integrate AI and other emerging technologies into research as appropriate; prepare Federal scientific instrumentation, datasets, and compute for the AI-for-science transformation; and treat support for the Genesis Mission as a central R&D priority. Proposed agency efforts in this area should be noted in FY 2028 Budget submissions. Agencies should prioritize funding for:

各机构应酌情识别将AI及其他新兴技术融入研究的机会；为AI驱动科学转型准备联邦科学仪器、数据集和计算资源；并将支持“创世纪使命”作为核心研发优先事项。各机构应在2028财年预算提交中注明该领域的拟议工作。机构应优先资助以下方面:

AI as an Instrument of Scientific Discovery. Agencies should fund research that uses AI as a new instrument of scientific discovery, not merely as a tool to augment existing capabilities. Agencies should seek out proposals that thoughtfully integrate AI into scientific workflows, rather than projects that apply AI for incremental gains or without clear justification for why the problem requires AI-specific methods. Agencies should align R&D funding with the Genesis Mission's National S&T Challenge areas where appropriate and propose new or expanded challenges consistent with their own priorities. Given the scale of private sector investment in AI, agencies should prioritize work that industry is unlikely to pursue on its own, including pre-competitive research outputs and enabling platform technologies.
AI作为科学发现工具。各机构应资助将AI作为科学发现新工具的研究，而不仅仅是增强现有能力的工具。机构应寻求深思熟虑地将AI融入科学工作流程的提案，而非那些仅追求渐进式收益或未明确说明为何问题需要AI特定方法的项目。机构应酌情将研发资金与“创世纪使命”的国家科技挑战领域对齐，并根据自身优先事项提出新的或扩展的挑战。鉴于私营部门对AI的投资规模，机构应优先支持行业不太可能自行开展的工作，包括竞争前研究成果和使能平台技术。
Scientific Foundation Model Development. Agencies should propose investments that support domain-specific scientific foundation models that enable high-fidelity simulations of natural phenomena and accelerate scientific discovery across Genesis Mission's National S&T Challenge areas, including advanced manufacturing, biotechnology, critical materials, nuclear fission and fusion, quantum information science, and semiconductors, and coordinate with other agencies as applicable. These models require curated scientific datasets and compute that no performer can assemble alone, making the Federal Government uniquely well-positioned to develop them as shared, pre-competitive assets for the research community.
科学基础模型开发。各机构应提出投资建议，支持领域特定的科学基础模型，这些模型能够实现自然现象的高保真模拟，并加速“创世纪使命”国家科技挑战领域的科学发现，包括先进制造、生物技术、关键材料、核裂变与核聚变、量子信息科学和半导体，并酌情与其他机构协调。这些模型需要精心策划的科学数据集和计算资源，任何单一执行者都无法独立完成，这使得联邦政府处于独特优势地位，能够将其开发为研究界共享的竞争前资产。
Scientific Data Generation for AI. Agencies should propose efforts to make internal scientific datasets available for use and investments in the data infrastructure that makes them accessible for AI training and inference. Agencies should create incentives for researchers to curate and share valuable data that is routinely abandoned due to lack of dedicated funding or recognition, including experimental records, negative results, and operational data from laboratory procedures. Agencies should further support the creation, curation, and stewardship of ambitious new datasets that could open entirely new fields of inquiry or deliver exceptional value to the Nation's S&T enterprise. As laboratory automation matures, agencies should propose investments in infrastructure to capture data at an industrial scale, laying the groundwork for a future of rapid, autonomous scientific discovery.
面向AI的科学数据生成。各机构应提出举措，使内部科学数据集可供使用，并投资于数据基础设施，使其可用于AI训练和推理。机构应创建激励机制，鼓励研究人员整理和分享因缺乏专项资助或认可而常被废弃的有价值数据，包括实验记录、阴性结果以及实验室操作流程中的运行数据。机构还应进一步支持创建、管理和维护雄心勃勃的新数据集，这些数据集可能开辟全新的研究领域，或为国家科技事业带来非凡价值。随着实验室自动化日趋成熟，机构应提出投资建议，建设基础设施以工业规模捕获数据，为未来快速、自主的科学发现奠定基础。
Integration of AI with Scientific Instrumentation. Agencies should build on the Genesis Mission by proposing investments in robotics, automated laboratories, modernization of user facilities to operate within closed-loop AI scientific workflows, and autonomous control of large-scale experiments in which AI systems generate hypotheses, conduct experiments, interpret results, and iterate in real time. Agencies should leverage their purchasing power to build domestic supply chains for AI-ready scientific instrumentation and drive the redesign of these instruments with open interfaces, standardized data formats, and cross-vendor interoperability, making it easier for researchers to connect instruments and use the software tools best suited to their work.
AI与科学仪器的集成。各机构应在“创世纪使命”基础上，提出投资机器人技术、自动化实验室、用户设施现代化以在闭环AI科学工作流程中运行，以及大规模实验的自主控制，使AI系统能够实时生成假设、进行实验、解读结果并迭代。机构应利用其购买力建设AI就绪科学仪器的国内供应链，并推动这些仪器的重新设计，使其具备开放接口、标准化数据格式和跨供应商互操作性，从而方便研究人员连接仪器并使用最适合其工作的软件工具。

Expand World-Class R&D Infrastructure for Broad Use

扩大世界级研发基础设施的广泛使用

The productivity of Federal R&D depends on scientific infrastructure, including the physical platforms, user facilities, instrumentation, compute, and laboratory spaces through which research is conducted. These assets have long planning horizons, high fixed costs, and operating requirements that extend well beyond the grants they support, and are often out of reach for individual investigators and institutions. When broadly accessible, this infrastructure enables scientists to pursue cutting-edge research and focus on conducting their best science, rather than the time and capital required to build their own infrastructure and facilities.

联邦研发的生产力依赖于科学基础设施，包括开展研究所需的物理平台、用户设施、仪器设备、计算资源和实验室空间。这些资产规划周期长、固定成本高，运营要求远超其资助的拨款项目，且通常超出单个研究人员和机构的承受能力。当这些基础设施广泛可及，科学家便能从事前沿研究，专注于产出最佳科学成果，而非耗费时间和资金自行建设基础设施和设施。

In their FY 2028 budget submissions to OMB, agencies should assess scientific infrastructure needs deliberately rather than treating them as a residual claim on research grants. In particular, agencies should propose investments in mid-scale instrumentation, fully funded within a fiscal year and aligned with Administration priorities, given it has historically been underfunded relative to its scientific importance; advanced compute; and sustained operating support for user facilities and shared platforms. Where agencies propose to significantly reduce or defer these investments, they should justify the proposal and explain how they will address the resulting gaps and sustain operation of existing facilities. To expand the reach of investments in scientific infrastructure, agencies should prioritize funding for:

在向OMB提交的2028财年预算中，各机构应有意识地评估科学基础设施需求，而非将其视为研究拨款的剩余索取权。具体而言，机构应提议投资于中型仪器(在财政年度内全额拨款，并与政府优先事项保持一致，因为该领域历来相对于其科学重要性而言资金不足)、先进计算，以及用户设施和共享平台的持续运营支持。若机构拟大幅削减或推迟这些投资，应说明理由，并解释如何弥补由此产生的缺口以及维持现有设施的运营。为扩大科学基础设施投资的覆盖范围，机构应优先资助以下方面:

User Facilities for the S&T Ecosystem. Agencies should propose investments in cutting-edge R&D infrastructure and instrumentation to enable researchers and innovators to validate new hypotheses, test prototypes, and scale new technologies, lowering barriers to frontier research. Proposed investments should be consistent with overarching Administration priorities to both maximize the use of existing infrastructure by addressing deferred maintenance and increase efficiency by reducing footprints and when necessary, include new infrastructure to achieve the greatest utilization by a broad community of researchers, including the private sector and other non-Federal researchers. Agencies should consider the resources needed to increase access to Federal R&D facilities by adopting evaluation criteria that weigh innovative potential and commercial urgency alongside scientific merit, streamlining Cooperative Research and Development Agreements and licensing processes, and reducing administrative burdens on industry users. These arrangements should encourage facilities to leverage industry cost-share arrangements and user-fee revenue to expand capacity and fund next-generation instrumentation.
科技生态系统用户设施。机构应提议投资于前沿研发基础设施和仪器设备，使研究人员和创新者能够验证新假设、测试原型并推广新技术，从而降低前沿研究的门槛。拟议投资应与政府总体优先事项保持一致，既要通过解决延期维护问题最大化现有基础设施的利用率，又要通过减少占地面积提高效率，必要时还应包括新建基础设施，以实现广大研究人员群体(包括私营部门和其他非联邦研究人员)的最大化利用。机构应考虑增加联邦研发设施可及性所需的资源，具体措施包括:采用兼顾创新潜力、商业紧迫性与科学价值的评估标准，简化合作研发协议和许可流程，并减轻行业用户的行政负担。这些安排应鼓励设施利用行业成本分摊安排和用户费收入来扩大容量并资助下一代仪器设备。
Advanced Compute for Federal R&D. Compute is the foundation of AI-enabled science, and Federal infrastructure must keep pace with the scale and flexible access researchers now require. Agencies should propose investments that expand access to advanced compute infrastructure, including unified access portals, standardized applications, and common data and software environments that allow researchers to move work seamlessly across facilities. Application processes should lower the barrier to entry for students, individual investigators, and small teams, particularly for fast-turnaround projects. Federal compute investment should offer capabilities differentiated from the commercial market, such as highly secure data centers for sensitive research, access to unique Federal datasets, and specialized AI accelerators and computing architectures. Where commercial compute is cost-effective and meets researcher needs, agencies should pursue public-private partnerships or procure capacity through commercial providers to improve agility and time-to-science.
联邦研发先进计算。计算是人工智能驱动科学的基础，联邦基础设施必须跟上研究人员现在所需的规模和灵活访问。机构应提议投资于扩大先进计算基础设施的访问，包括统一访问门户、标准化应用程序以及通用数据和软件环境，使研究人员能够跨设施无缝迁移工作。申请流程应降低学生、个人研究人员和小型团队的准入门槛，特别是对于快速周转项目。联邦计算投资应提供与商业市场差异化的能力，例如用于敏感研究的高度安全数据中心、对独特联邦数据集的访问，以及专门的AI加速器和计算架构。若商业计算具有成本效益且能满足研究人员需求，机构应通过公私合作伙伴关系或从商业供应商处采购能力来提高敏捷性和科学产出速度。

Leverage R&D to Strengthen Regional Manufacturing and Industry

利用研发强化区域制造业与工业

Federal R&D investments can be leveraged to translate scientific discoveries into benefits for all Americans, securing broad-based prosperity and supporting the reindustrialization of our Nation. Achieving these objectives require Federal investments that pair foundational research with advanced manufacturing, strengthen regional ecosystems, build resilient supply chains, develop a skilled technical workforce, and catalyze non-Federal investment to the greatest extent possible.

联邦研发投资可用于将科学发现转化为惠及全体美国人的成果，确保广泛繁荣并支持国家再工业化。实现这些目标需要联邦投资将基础研究与先进制造相结合，强化区域生态系统，构建韧性供应链，培养技术熟练的劳动力，并最大程度地撬动非联邦投资。

Agencies should prioritize funding for manufacturing R&D across strategic technologies with the goal of building domestic manufacturing capacity and supply chains to produce the next generation of semiconductors, advanced materials, biotechnology, nuclear technologies, and robotics. Manufacturing R&D spans the full research spectrum: the manufacturing science underlying how things are made, including process science, materials science, metrology, automation, and the underlying physics, chemistry, and engineering; advanced engineering methods and production technologies; translational programs such as manufacturing innovation institutes, pilot lines, and demonstration facilities that bridge laboratory discovery and production; and supply chain analytics. Cost-share arrangements should generally be considered, and where appropriate, expected for later-stage manufacturing and demonstration activities, while earlier-stage manufacturing science should be supported on terms appropriate to foundational research.

各机构应优先资助战略技术领域的制造业研发，目标是建设国内制造能力和供应链，以生产下一代半导体、先进材料、生物技术、核技术和机器人。制造业研发涵盖整个研究谱系:制造科学(包括工艺科学、材料科学、计量学、自动化及其基础物理、化学和工程学)；先进工程方法和生产技术；转化项目(如制造创新研究所、中试线和示范设施，连接实验室发现与生产)；以及供应链分析。通常应考虑成本分摊安排，并在适当时对后期制造和示范活动提出成本分摊要求，而早期制造科学则应以适合基础研究的条件予以支持。

R&D PRIORITY PRACTICES

研发优先事项实践

1. Develop New Mechanisms to Support Frontier Science

1. 开发支持前沿科学的新机制

America must continue to expand the repertoire of institutions and R&D funding mechanisms it uses to conduct science, enabling our best researchers to tackle the most ambitious S&T challenges that exist. These mechanisms should account for forces reshaping the scientific enterprise, including the rise of funding from industry and philanthropy, and the growing importance of genuinely integrated, multidisciplinary teams.

美国必须继续扩展其用于开展科学的机构和研发资助机制，使最优秀的研究人员能够应对现有最雄心勃勃的科技挑战。这些机制应考虑到重塑科学事业的力量，包括来自工业和慈善事业的资助增加，以及真正整合的多学科团队日益增长的重要性。

The Federal Government should incentivize new institutional models that complement conventional principal-investigator driven laboratories, industry laboratories, and Federal R&D facilities. It should also supplement conventional, consensus-driven peer review, which excels at advancing established lines of inquiry, with new review mechanisms that are better suited to recognizing high-risk, high-reward research, early-career talent, or ideas that fall outside established disciplinary boundaries. Agencies should adopt a deliberate, portfolio-based approach that matches funding mechanisms to the S&T challenges they seek to address, maximizing Federal return on investment through an explicit mix of modalities, risk profiles, and time horizons. A broader menu of institutional structures and funding mechanisms will enable new forms of scientific work, encourage scientists to pursue novel lines of inquiry, and attract higher-caliber reviewers empowered to make bold bets.

联邦政府应激励新的机构模式，以补充传统的首席研究员主导的实验室、工业实验室和联邦研发设施。还应补充传统的、基于共识的同行评审(该评审擅长推进既定研究路线)，采用更适合识别高风险高回报研究、早期职业人才或超出既定学科界限想法的新评审机制。各机构应采取深思熟虑的、基于组合的方法，将资助机制与其试图解决的科技挑战相匹配，通过明确的模式组合、风险状况和时间范围最大化联邦投资回报。更广泛的机构结构和资助机制菜单将支持新的科学工作形式，鼓励科学家探索新的研究路线，并吸引更有能力做出大胆决策的高水平评审者。

Agencies are encouraged to review their existing institutional models and funding mechanisms, explore new ones to close gaps in areas critical for national priorities, and construct balanced Federal R&D portfolios according to the following principles:

鼓励各机构审查其现有的机构模式和资助机制，探索新的机制以填补国家优先事项关键领域的空白，并根据以下原则构建平衡的联邦研发组合:

Support a Diverse Portfolio of Institutions. The Federal Government should reflect a portfolio of institutions that collectively advance the core objectives of the Nation's S&T enterprise, including conducting a range of scientific work, training the next generation of scientists, and translating scientific discoveries into concrete benefits for Americans. Agencies should identify objectives that remain unaddressed because no existing institution is well-suited to pursue them. One notable gap is agile, mid-scale science: infrastructure-heavy, multidisciplinary basic research that requires coordinated teams of ten to a hundred people. Agencies have begun to address these gaps through new models like the U.S. National Science Foundation's (NSF) X-Labs and certain Advanced Research Projects Agency programs. Agencies should consider these models and experiment with additional designs to enable new types of scientific pursuits.
支持多样化的机构组合。联邦政府应反映一个机构组合，共同推进国家科技事业的核心目标，包括开展一系列科学工作、培养下一代科学家以及将科学发现转化为美国人民的实际利益。各机构应确定因现有机构不适合追求而仍未解决的目标。一个显著空白是灵活的中等规模科学:需要十到一百人协调团队的基础设施密集型、多学科基础研究。各机构已开始通过新模式(如美国国家科学基金会的X-Labs和某些高级研究计划局项目)解决这些空白。各机构应考虑这些模式并尝试其他设计，以支持新型科学探索。
Increase Grant Durations for Transformative Research. Agencies should develop proposals for the FY 2028 Budget that would expand the number of long-duration grants, ideally lasting five years or more, that give our best researchers the time and autonomy to pursue bold, ambitious projects whose most important results may take years to emerge. These awards should be fully-funded in year one, with all resources earmarked upfront, to minimize administrative burdens and reduce pressure for researchers to generate intermediate results to secure continued funding. This upfront commitment should be paired with clear performance metrics and periodic reviews, with the understanding that funding may be withdrawn and redirected if needed. Existing programs, such as the National Institute of Health (NIH) Director's Pioneer Award and the DOW Vannevar Bush Faculty Fellowship, offer useful models for long-duration, investigator-centered support for creative basic research.
增加变革性研究的资助期限。各机构应为2028财年预算制定提案，以扩大长期资助的数量(理想情况下持续五年或更长时间)，使最优秀的研究人员有时间和自主权追求大胆、雄心勃勃的项目，其最重要结果可能需要多年才能显现。这些奖项应在第一年全额资助，所有资源预先指定，以最小化行政负担并减少研究人员为确保持续资助而生成中间结果的压力。这种预先承诺应与明确的绩效指标和定期审查相结合，并理解如有需要可撤回和重新分配资金。现有项目，如国立卫生研究院主任先锋奖和DOW范内瓦·布什教师奖学金，为创造性基础研究提供了长期、以研究者为中心支持的有用模式。
Expand Use of Fast Grants for Exploratory Projects. Agencies should consider establishing or expanding, where authorized and consistent with available resources, flexible, low-friction "fast grants" to support preliminary research, exploratory projects, and time-sensitive work. These programs should feature simplified applications requiring just a few pages of writing, rapid review timelines of under one month, and award sizes calibrated to proof-of-concept work. Agencies should encourage greater use of existing mechanisms and ensure that they meet their intended time-lines, while developing additional fast-track pathways as needed.
扩大探索性项目的快速资助使用。各机构应考虑在授权且与可用资源一致的情况下，建立或扩展灵活、低摩擦的“快速资助”，以支持初步研究、探索性项目和时效性工作。这些项目应具有简化的申请要求(仅需几页文字)、不到一个月的快速评审时间表，以及根据概念验证工作校准的奖项规模。各机构应鼓励更广泛地使用现有机制，并确保其满足预期时间表，同时根据需要开发额外的快速通道途径。
Design Ambitious Prizes and Challenges. Well-designed prizes can spur cross-disciplinary collaboration, attract nontraditional entrants, mobilize substantial private capital, and catalyze entirely new industries with a relatively small amount of funding. Agencies should expand their use of prizes and challenges to advance national missions. To maximize participation from nontraditional teams, agencies should emphasize outcome-based goals rather than prescribing specific methods. Prizes should target at least a 3:1 leverage of private to Federal investment, and may be paired with complementary incentives such as advance procurement commitments, regulatory fast-tracking, and access to Federal testing facilities.
设计雄心勃勃的奖项和挑战。设计良好的奖项可以促进跨学科合作，吸引非传统参与者，调动大量私人资本，并以相对较少的资金催化全新产业。各机构应扩大奖项和挑战的使用，以推进国家使命。为最大化非传统团队的参与，各机构应强调基于结果的目标，而非规定具体方法。奖项应瞄准至少3:1的私人投资与联邦投资杠杆比，并可搭配补充激励措施，如预先采购承诺、监管快速通道以及使用联邦测试设施。
Experiment with Emerging Funding Mechanisms. Agencies should study, pilot, and evaluate whether there are existing models or additional designs for innovative funding mechanisms beyond those described above and in conjunction with OMB and OSTP. Mechanisms for consideration could include, as appropriate, "golden tickets" that let individual agency technical reviewers recommend unconventional proposals that may not pass consensus-driven review panels, which tend to skew toward funding more incremental advances; advance market commitments that signal demand for a scientific or technical capability before it exists, subject to available appropriations and demonstration of capabilities against clearly defined criteria; regranting models that delegate funding authority to working scientists to tap distributed expertise; and more speculative approaches such as quadratic funding or eigenfunding. Such models could surface valuable ideas too divisive for committees and attract higher-quality reviewers by empowering them to exercise independent scientific judgment. Such approaches will not be a one-size-fits-all solution to grantmaking, but should be appropriately explored for their potential role in the Federal R&D portfolio as agencies look to more effectively support the American S&T enterprise. Agencies should ensure that new funding mechanisms strictly adhere to agencies' legal authorities and conflict of interest policies, and that funded proposals meet a level of scientific rigor appropriate for Gold Standard Science.
试验新兴资助机制。各机构应与OMB和OSTP合作，研究、试点并评估是否存在上述机制之外的现有模式或额外设计，以创新资助机制。可考虑的机制酌情包括:“黄金门票”，允许机构技术评审员推荐可能无法通过共识驱动评审小组(这类小组往往倾向于资助渐进式进展)的非常规提案；预先市场承诺，在科学或技术能力存在之前即发出需求信号，但需视可用拨款及按明确标准展示能力而定；再授权模式，将资助权委托给在职科学家以利用分布式专业知识；以及更具探索性的方法，如二次资助或特征资助。此类模式可发掘对委员会而言过于分裂但有价值的想法，并通过赋予评审员独立科学判断权来吸引更高质量的评审员。这些方法并非资助的万能解决方案，但应在联邦研发组合中适当探索其潜在作用，因为各机构正寻求更有效地支持美国科技事业。各机构应确保新资助机制严格遵守其法律权限和利益冲突政策，且获资助提案达到符合黄金标准科学的科学严谨性水平。

2. Identify and Develop Top Technical Talent

2. 识别并培养顶尖技术人才

The Federal Government should orient around the scientists, engineers, and technicians who serve our Nation, providing them with the support, freedom, and opportunities needed to do their best work. S&T workforce programs should select the best and brightest Americans, recognizing that these individuals are distributed across the Nation, not isolated to major metropolitan areas. The programs should identify and invest early in high-potential students and early-career individuals, while cultivating their long-term commitment to America's S&T enterprise.

联邦政府应围绕服务于国家的科学家、工程师和技术人员开展工作，为他们提供发挥最佳水平所需的支持、自由和机会。科技人才队伍项目应选拔最优秀、最聪明的美国人，同时认识到这些人才分布在全国各地，而非局限于主要大都市区。这些项目应尽早识别并投资于高潜力学生和职业生涯早期人才，同时培养他们对美国科技事业的长期承诺。

Agencies with S&T workforce development programs, including graduate fellowships; K-12 Science, Technology, Engineering, and Mathematics (STEM) education; and skilled technical workforce programs should review existing efforts and, where appropriate, propose modifications or new approaches through established budget and policy processes to:

拥有科技人才队伍发展项目的机构，包括研究生奖学金、K-12科学、技术、工程和数学(STEM)教育以及熟练技术人才队伍项目，应审查现有工作，并酌情通过既定预算和政策流程提出修改意见或新方法，以:

Identify Exceptional Talent Nationwide. S&T workforce programs should identify and support all talented Americans across geographies, incomes, and demographics. Exceptional talent is defined by demonstrated technical ability, not background or identity. Agencies should therefore anchor selection processes in criteria predictive of STEM success, such as reasoning assessments, domain competitions, engineering portfolios, and technical work, rather than relying on self-selection, essays, institutional referrals, or polish and credentials. Agencies should leverage merit-based identification mechanisms that cover as many people as possible (e.g., SAT scores or other standardized quantitative assessments) to find overlooked talent.
在全国范围内识别杰出人才。科技人才队伍项目应识别并支持所有来自不同地域、收入水平和人口背景的有才华的美国人。杰出人才由所展现的技术能力定义，而非背景或身份。因此，机构应将选拔过程锚定在可预测STEM成功的标准上，如推理评估、领域竞赛、工程作品集和技术工作，而非依赖自我选择、论文、机构推荐或表面修饰与资历。机构应利用基于能力的识别机制，覆盖尽可能多的人(例如SAT成绩或其他标准化定量评估)，以发现被忽视的人才。
Expand Advanced K-12 STEM Enrichment Opportunities. Targeted programs can accelerate the development of advanced K-12 STEM talent by increasing exposure to pathways into scientific careers and connecting students with expert mentors and similarly capable peers. Where appropriate, agencies should support K-12 STEM enrichment opportunities, such as residential math and science programs and Olympiad-style competitions, that immerse high-ability students in advanced S&T environments and direct their ambitions to the hardest open questions.
扩大高级K-12 STEM拓展机会。有针对性的项目可通过增加接触科学职业途径的机会，并将学生与专家导师及能力相当的同伴联系起来，加速培养高级K-12 STEM人才。在适当情况下，机构应支持K-12 STEM拓展机会，如寄宿制数学和科学项目及奥林匹克式竞赛，让高能力学生沉浸于高级科技环境中，并将其志向引导至最棘手的未解问题。
Expand Hands-On Technical Learning. S&T workforce programs should provide early and sustained exposure to real-world technical environments. Agencies should treat research placements in academic, industry, and Federal laboratories as standard components of high-quality S&T talent development programs. Placements should be substantive, last at least one semester, and provide participants with meaningful access to advanced scientific instrumentation, datasets, and challenges not available in traditional academic settings. Agencies should expand opportunities for hands-on technical learning as early as high school through work-based learning, vocational training, makerspace access, and machine shop classes.
扩大实践性技术学习。科技人才队伍项目应提供早期且持续接触真实技术环境的机会。机构应将学术、行业和联邦实验室的研究实习视为高质量科技人才发展项目的标准组成部分。实习应具有实质性，至少持续一个学期，并为参与者提供有意义地接触先进科学仪器、数据集以及传统学术环境中无法获得的挑战的机会。机构应尽早从高中开始，通过基于工作的学习、职业培训、创客空间使用和机械车间课程等方式，扩大实践性技术学习的机会。
Support Early-Career Researchers. Agencies should strengthen support for graduate students, post-doctoral researchers, and early-career faculty, when research creativity is often highest but institutional support the weakest. Agencies should expand the use of fellowship programs and address conditions that limit the mobility of graduate students, post-doctoral researchers, and early-career researchers as they navigate opportunities in the S&T enterprise. These programs can provide young scientists with resources and intellectual freedom during the most pivotal stage of their careers, encouraging them to remain in the Nation's S&T enterprise.
支持早期职业研究人员。机构应加强对研究生、博士后研究人员和早期职业教师的支持，这一阶段研究创造力通常最高，但机构支持却最薄弱。机构应扩大奖学金项目的使用，并解决限制研究生、博士后研究人员和早期职业研究者在科技事业中把握机会时流动性的条件。这些项目可在年轻科学家职业生涯最关键的阶段为他们提供资源和智力自由，鼓励他们留在国家的科技事业中。
Support Individuals Agnostic of Institutional Affiliations. S&T workforce programs should provide individuals flexibility to choose their research institutions, supervisors, and topics. Agencies should prioritize programs that distribute funding directly to students and researchers, similar to NSF's Graduate Research Fellowship Program, so recipients can apply the grant to any qualifying institution that best supports their goals and retain it if they move, encouraging institutions to compete for early-career talent. Agencies should develop the capability to track supported individuals longitudinally across multi-year transitions, minimizing the need for individuals to re-discover and re-apply for support.
支持不受机构隶属关系限制的个人。科技人才队伍项目应为个人提供选择研究机构、导师和课题的灵活性。机构应优先考虑将资金直接分配给学生的项目，类似于国家科学基金会的研究生研究奖学金项目，使获得者能够将资助应用于最能支持其目标的任何合格机构，并在转学时保留资助，从而鼓励机构竞争早期职业人才。机构应发展纵向追踪受支持个人跨多年过渡的能力，最大限度地减少个人重新发现和重新申请支持的需求。
Build Flexible Cross-Sector Talent Pathways. The Nation's top scientific talent should be encouraged to gain experience across research cultures and engineering environments throughout their careers. Agencies should expand opportunities for scientists, engineers, and skilled technical workers to move fluidly across academia, industry, and Federal R&D facilities by increasing the flexibility of academic fellowships, establishing cross-institution placements like joint industry or Federal laboratory Ph.D. programs, and supporting alternative paths for skilled technical workers to participate in academic training and scientific discovery.
建立灵活的跨部门人才通道。国家顶尖科学人才应被鼓励在整个职业生涯中积累跨研究文化和工程环境的经验。机构应通过提高学术奖学金的灵活性、建立跨机构实习(如联合行业或联邦实验室博士项目)以及支持熟练技术工人参与学术培训和科学发现的替代途径，扩大科学家、工程师和熟练技术工人在学术界、行业和联邦研发机构之间灵活流动的机会。
Encourage Broad Post-Fellowship Service. Federal investments in individuals should strengthen the Nation's S&T enterprise. Agencies should consider incorporating service requirements into fellowship programs while defining service broadly to capture the myriad ways individuals can leverage their training to advance that enterprise. Qualifying service could include academic research and training the next generation of American scientists, entrepreneurship, work in the defense industrial base, advisory roles that shape Federal S&T priorities, or government and military service. Agencies should aim to make any service requirements flexible enough for recipients to pursue the highest-impact opportunities after their fellowship ends and to attract the strongest candidates.
鼓励广泛的奖学金后服务。联邦对个人的投资应加强国家的科技事业。机构应考虑将服务要求纳入奖学金项目，同时广泛定义服务，以涵盖个人利用其培训推动该事业的多种方式。合格的服务可包括学术研究和培养下一代美国科学家、创业、在国防工业基地工作、塑造联邦科技优先事项的咨询角色，或政府和军队服务。机构应力求使任何服务要求足够灵活，以便获得者能在奖学金结束后追求最具影响力的机会，并吸引最优秀的候选人。

3. Build a Self-Improving Scientific Enterprise

3. 构建自我完善的科学事业

The Federal Government invests approximately two hundred billion dollars in R&D each year, but allocates comparatively little to understanding which funding mechanisms, institutional models, workforce development programs, and research practices produce the strongest scientific outcomes. Agencies should treat the science of science-funding with the same rigor as the science they fund, and build the organizational capacity to learn, experiment, and improve continuously. Agencies should assess whether to establish metascience capabilities, where appropriate, following the guidance below:

联邦政府每年在研发上投入约两千亿美元，但用于了解哪些资助机制、机构模式、劳动力发展计划和科研实践能产生最强科学成果的资源相对较少。各机构应以与资助科学相同的严谨态度对待科学资助的科学，并建立学习、实验和持续改进的组织能力。各机构应评估是否在适当情况下建立元科学能力，遵循以下指导:

Establish Metascience Capabilities. Agencies should establish meta-science capabilities that evaluate what programs actually work and drive organization-wide reforms. Core responsibilities should include conducting research on how factors such as funding mechanisms, peer review, and publication practices affect scientific outcomes; piloting novel funding mechanisms and institutional models; and evaluating pilots and informing agency-wide portfolio management. These functions should be established at a sufficiently high level within agencies to effect real, cross-agency change.
建立元科学能力。各机构应建立元科学能力，评估哪些项目真正有效，并推动全机构范围的改革。核心职责应包括研究资助机制、同行评审和出版实践等因素如何影响科学成果；试点新型资助机制和机构模式；评估试点项目并为机构整体投资组合管理提供信息。这些职能应在机构内足够高的层级设立，以产生真正的跨机构变革。
Develop Systematic Gap-Mapping Capacity. Agencies should develop the capacity to systematically compare their grantmaking portfolios against the landscape of unsolved scientific and technical challenges in their domains, rather than relying primarily on historical funding patterns. In collaboration with industry, academia, and philanthropy, agencies should maintain "gap maps" that identify unmet needs, duplicated efforts, and emerging opportunities. Gap maps should directly inform portfolio construction, helping agencies select appropriate funding mechanisms and institutional models to target the most important and neglected gaps.
发展系统性差距映射能力。各机构应发展能力，系统性地将其资助组合与其领域内未解决的科学和技术挑战格局进行比较，而非主要依赖历史资助模式。与产业界、学术界和慈善机构合作，各机构应维护“差距地图”，识别未满足的需求、重复努力和新兴机遇。差距地图应直接指导投资组合构建，帮助机构选择合适的资助机制和机构模式，以针对最重要且被忽视的差距。
Build Data Infrastructure for Metascience. Agencies should develop purpose-built data infrastructure for metascience, including systems that integrate application-level data, reviewer behavior and scoring, and links between awards and downstream outcomes. These systems should support longitudinal tracking for both awardees and near-miss applicants. Agencies should assess workforce and contracting operations for software engineers and data scientists with the skills to build and maintain these systems as a core institutional capability.
构建元科学数据基础设施。各机构应开发专门用于元科学的数据基础设施，包括整合申请级数据、评审者行为和评分，以及奖项与下游成果之间联系的系统。这些系统应支持对获奖者和接近中标的申请者进行纵向追踪。各机构应评估软件工程师和数据科学家的劳动力及合同运营，以培养构建和维护这些系统的核心机构能力。
Elevate and Empower Agency Program Officers. The effectiveness of Federal R&D funding depends heavily on agencies' ability to recruit exceptional program officers and give them genuine discretion to define technical problems, build a research portfolio, and manage toward ambitious outcomes. Agencies should consider approaches for recruiting top scientists, engineers, entrepreneurs, and philanthropists into time-limited public service and raising their prestige, visibility, and authority. Agencies should also assess options for reducing barriers to hiring program officers from non-traditional backgrounds, expanding rotational mechanisms such as the Intergovernmental Personnel Act, and developing competitive compensation and career pathways that make program management a career-enhancing opportunity for top scientific talent. Agencies should also develop or enforce mechanisms to ensure that conflict of interest policies are strictly followed for all employees involved in funding recommendations and decisions.
提升并赋权机构项目官员。联邦研发资助的有效性在很大程度上取决于机构招募优秀项目官员的能力，并赋予他们真正的自主权来定义技术问题、构建研究投资组合，并朝着雄心勃勃的成果进行管理。各机构应考虑招募顶尖科学家、工程师、企业家和慈善家进入限时公共服务，并提升其声望、可见度和权威。各机构还应评估减少从非传统背景招募项目官员的障碍、扩大如《政府间人事法案》等轮换机制，以及制定有竞争力的薪酬和职业路径，使项目管理成为顶尖科学人才职业发展的机遇。各机构还应制定或执行机制，确保所有参与资助建议和决策的员工严格遵守利益冲突政策。
Reduce Administrative Burdens. Agencies should reduce administrative burdens in the grantmaking and research process to maximize the impact of taxpayer-funded science. This includes clarifying requirements for the research community and eliminating overcompliance beyond what Federal regulations and statutes require. Agencies should consider proposals to coordinate to harmonize and standardize grant requirements, forms, and submission processes to the greatest extent possible, and carefully weigh any incremental gains in oversight from new requirements or regulations against the cumulative burden they impose on researchers. Agencies should also consider options for easing administrative and regulatory burdens on Federal technology transfer to increase private-sector investment in R&D.
减少行政负担。各机构应减少资助和研究过程中的行政负担，以最大化纳税人资助科学的影响力。这包括明确对研究界的要求，并消除超出联邦法规和法令要求的过度合规。各机构应考虑协调提案，尽可能统一和标准化资助要求、表格和提交流程，并仔细权衡新要求或法规带来的监督增量收益与对研究人员累积的负担。各机构还应考虑减轻联邦技术转让的行政和监管负担，以增加私营部门对研发的投资。

4. Integrate Federal R&D into Broader S&T Enterprise

4. 将联邦研发融入更广泛的科技事业

Federal R&D is one part of a far larger national S&T enterprise that spans private industry, academia, state and local governments, and the regional economies in which discovery is translated into production. To maximize the return on Federal investment, agencies should more deliberately integrate their R&D with this broader enterprise. This means looking for opportunities to expand the use of non-Federal cost share, so that Federal dollars draw in and are amplified by private and other non-Federal investment rather than standing alone. It also means coordination between Federal R&D and non-R&D investments to support the growth of regional innovation ecosystems and domestic manufacturing hubs consistent with statutory purposes.

联邦研发是全国更广泛的科技事业的一部分，该事业涵盖私营企业、学术界、州及地方政府，以及将发现转化为生产的区域经济。为最大化联邦投资的回报，各机构应更审慎地将自身研发融入这一更广泛的事业。这意味着要寻找扩大非联邦成本分摊使用的机会，使联邦资金能够吸引并放大私营及其他非联邦投资，而非孤立运作。同时，这也意味着联邦研发与非研发投资之间的协调，以支持区域创新生态系统和国内制造业中心的发展，并符合法定目的。

Drive Greater Integration of Foundational and Applied Research. In many frontier technologies, scientific discovery, engineering, and manufacturing R&D are not sequential but iterative and tightly coupled. Where appropriate, agencies should propose funding consortia and partnerships that integrate basic research with manufacturing R&D, reflecting the multidisciplinary, engineering-intensive way science is conducted today. Agency funding in this area should ensure the pursuit of long-term research agendas in partnership with industry, employment of career scientists, engineers, and technicians, publication of foundational discoveries as public goods while licensing specific process innovations, and co-locate with manufacturing facilities and testbeds. Agencies should explore how such institutions can provide durable infrastructure to anchor place-based innovation ecosystems aligned with a region's economic strength.
推动基础研究与应用研究的更深度融合。在许多前沿技术领域，科学发现、工程和制造研发并非依次进行，而是迭代且紧密耦合的。在适当情况下，各机构应提议资助那些将基础研究与制造研发相结合的联盟和伙伴关系，以反映当今科学开展的多学科、工程密集型方式。该领域的机构资助应确保与产业合作追求长期研究议程，聘用职业科学家、工程师和技术人员，将基础发现作为公共产品发表，同时许可特定的工艺创新，并与制造设施和试验平台共址。各机构应探索此类机构如何提供持久的基础设施，以支撑与区域经济优势相契合的本地化创新生态系统。
Expand the Use of Non-Federal Cost Share. Federal R&D funding is most effective when it catalyzes, rather than substitutes for, private and nonFederal investment. Agencies should structure funding opportunities, within existing resources, to prioritize support for initiatives that incorporate meaningful non-government cost share from industry, philanthropy, State and local governments, or international partners. Cost-share arrangements signal market validation, accelerate translation, distribute risk, and extend the impact of taxpayer-funded research. These arrangements should draw on the deep domain expertise external funders have built in particular sub-fields and leverage their networks to identify exceptional grant opportunities. Agencies should review existing authorities for cost-shared R&D, including cooperative agreements, public-private partnerships, consortia models, and other transaction authorities where applicable, and propose expansions where statutory or regulatory barriers can be addressed.
扩大非联邦成本分摊的使用。当联邦研发资金能够催化而非替代私营及非联邦投资时，其效果最佳。各机构应在现有资源范围内构建资助机会，优先支持那些包含来自产业、慈善机构、州及地方政府或国际合作伙伴的有意义的非政府成本分摊的倡议。成本分摊安排表明市场验证，加速转化，分散风险，并扩大纳税人资助研究的影响。这些安排应利用外部资助者在特定子领域积累的深厚领域专业知识，并借助其网络识别卓越的资助机会。各机构应审查现有的成本分摊研发授权，包括合作协议、公私伙伴关系、联盟模式及其他适用的交易授权，并在可解决法定或监管障碍的情况下提出扩展建议。
Integrate Federal R&D with Non-R&D Investments to Support Regional Ecosystems. The impact of Federal R&D depends critically on the surrounding ecosystem, including the workforce, infrastructure, capital, supply chains, and institutions that translate discovery into economic growth. Agencies should coordinate R&D investments with Federal non-R&D investments, including in workforce and education, economic development, infrastructure, small business support, manufacturing extension, and procurement, to strengthen regional innovation ecosystems and ensure that the benefits of Federal science are broadly distributed across American communities, particularly where doing so would accelerate industry-specific R&D anchored in a region's area of expertise. Agencies should coordinate across the Federal Government, including through OMB, the NSTC, and agency-to-agency agreements where helpful, to identify opportunities to co-locate, sequence, or jointly award R&D and non-R&D resources in support of place-based strategies and ensure complementary Federal investments in a given region.
将联邦研发与非研发投资相结合，以支持区域生态系统。联邦研发的影响关键取决于周边生态系统，包括将发现转化为经济增长的劳动力、基础设施、资本、供应链和机构。各机构应协调研发投资与联邦非研发投资，包括劳动力与教育、经济发展、基础设施、小企业支持、制造业扩展和采购，以加强区域创新生态系统，并确保联邦科学的利益广泛分布于美国各社区，特别是在这样做能加速以区域专长领域为基础的特定行业研发的情况下。各机构应在整个联邦政府内进行协调，包括通过OMB、NSTC以及机构间协议(如适用)，以识别共址、排序或联合授予研发与非研发资源的机会，支持基于地方的策略，并确保特定区域内的联邦投资互补。
Integrate Industry in Workforce Training. S&T workforce programs should maximize collaboration with the private sector, which increasingly leads both basic and applied R&D, holds unique scientific instrumentation, data, and computing resources, and can recruit the best science and engineering talent in ways no university can match. Where practicable, agencies should partner with industry to attract stronger applicants and amplify Federal investments, including through industry co-funding (e.g., tuition, stipends, and portable research funding), paid internship placements, access to research infrastructure, curriculum development, and expert mentorship.
将产业融入劳动力培训。科技劳动力项目应最大化与私营部门的合作，私营部门日益引领基础和应用研发，拥有独特的科学仪器、数据和计算资源，并能以任何大学无法比拟的方式招募顶尖科学与工程人才。在可行的情况下，各机构应与产业合作，以吸引更优秀的申请者并放大联邦投资，包括通过产业共同资助(如学费、津贴和可携带的研究资金)、带薪实习安排、研究基础设施使用、课程开发和专家指导。

IMPLEMENTATION

实施

To address the budget formulation priorities set forth in the "FY 2028 R&D Priority Areas" section of this memorandum, agencies should follow the standard process for FY2028 budget submission to OMB.

为落实本备忘录“2028财年研发优先领域”部分所述的预算编制优先事项，各机构应遵循2028财年预算提交至OMB的标准流程。

In addition, within 90 days of this memorandum, the head of each agency with $3 billion or more in FY 2026 budget authority for R&D shall submit to the Assistant to the President for Science and Technology (APST) and Director of the Office of Management and Budget (OMB Director) an action plan describing how the agency intends to implement the program implementation guidance set forth in the "R&D Priority Practices" section of this memorandum. Agency action plans should identify how program execution of their FY2026 and FY2027 budgets can support these priority practices. Budget formulation matters addressed by this memorandum are outside the scope of action plans and should instead be reflected in agency FY 2028 budget submissions to OMB. Each action plan shall identify specific actions to address each R&D priority practice (e.g., new funding opportunities, program solicitations, pilot initiatives, statements to the research community, internal organizational changes), implementation timelines, and the offices responsible. OSTP and OMB will coordinate implementation of these action plans and issue supplementary guidance as appropriate.

此外，在本备忘录发布后90天内，每个2026财年研发预算授权达30亿美元或以上的机构负责人，应向总统科技助理(APST)及管理与预算办公室主任(OMB主任)提交一份行动计划，说明该机构如何落实本备忘录“研发优先实践”部分所述的项目实施指导。机构行动计划应明确其2026和2027财年预算的项目执行如何支持这些优先实践。本备忘录涉及的预算编制事项不在行动计划范围内，而应反映在机构向OMB提交的2028财年预算中。每份行动计划应明确针对每项研发优先实践的具体行动(例如，新的资助机会、项目招标、试点计划、向研究界的声明、内部组织变革)、实施时间表及负责部门。OSTP和OMB将协调这些行动计划的实施，并酌情发布补充指导。

科学：新黄金时代

SCIENCE: A NEW GOLDENAGE

科学:新黄金时代

Table of Contents

目录

Letter of Transmittal

呈文函

President Trump's Letter

特朗普总统的信函

Summary of the Report

报告摘要

CHAPTER I INTRODUCTION

第一章 引言

CHAPTER II REVITALIZING AMERICA'S SCIENCE AND TECHNOLOGY ENTERPRISE

第二章 重振美国科技事业

CHAPTER III SECURING U.S. DOMINANCE IN CRITICAL AND EMERGING TECHNOLOGIES

第三章 确保美国在关键与新兴技术领域的主导地位

CHAPTER V A NEW GOLDEN AGE

第五章 新的黄金时代

SCIENTIFIC PROGRESS REMAINS ESSENTIAL

科学进步依然至关重要

AS THE SOURCE OF OUR TRIUMPHS

作为我们胜利的源泉

THE LANDSCAPE IS CHANGING

格局正在变化

NEW FRONTIERS AND NEW APPROACHES

新前沿与新路径

GROWING PRIVATE SECTOR R&D

私营部门研发的增长

THE LINEAR MODEL NO LONGER HOLDS

线性模型不再成立

OUR RESEARCHERS FACE MOUNTING CHALLENGES

我们的研究人员面临日益严峻的挑战

A TIME OF URGENT SCIENTIFIC NEED

紧迫的科学需求时刻

REGAINING LEADERSHIP

重夺领导地位

THE PRESIDENT'S CHARGE

总统的使命

Chapter II Revitalizing America's Science and Technology Enterprise

第二章 重振美国的科技事业

THE SCIENTIFIC MACHINE IS GETTING BOGGED DOWN

科学机器正陷入泥潭

SLOWED BY GROWING FRICTIONS

摩擦加剧，进展放缓

THE INCUMBENCY TAX

任职税

WEAKENED MERITOCRACY

削弱的精英体制

MISALIGNED INCENTIVES

激励错位

THE REPRODUCIBILITY CRISIS

可重复性危机

A LACK OF ACCOUNTABILITY

缺乏问责制

A BETTER PATH FORWARD

更好的前进道路

ADAPTING TO THE CHANGING NATURE OF SCIENCE

适应科学本质的变化

NOVEL PERFORMERS

新型执行者

NEW MECHANISMS

新机制

BETTER GRANTMAKING

优化拨款机制

PRIZE CHALLENGES

奖金挑战

FUTURE IDEAS

未来构想

A PORTFOLIO-BASED APPROACH

基于投资组合的方法

DRIVING CONSTANT INNOVATION

驱动持续创新

Chapter III Securing U.S. Dominance in Critical and Emerging Technologies

第三章 确保美国在关键与新兴技术领域的主导地位

WE MUST CHOOSE OUR TECHNOLOGICAL FUTURE

我们必须选择自己的技术未来

FAILURE OF THE PASSIVE MODEL

被动模式的失败

CHOOSING TO LEAD

第一章引言

第二章重振美国科技事业

第三章确保美国在关键与新兴技术领域的主导地位

第五章新的黄金时代

第二章重振美国的科技事业

第三章确保美国在关键与新兴技术领域的主导地位