Michael Kratsios
Frontier Insights
Frontier Thesis
AI dominance hinges not merely on algorithmic models, but on industrializing the entire technological stack: pairing 2026 agentic coding breakthroughs and autonomous scientific discovery with physical power, domestic manufacturing, and rapid-market policy missions.
Strategic Decisions
Leverage regulatory policy and targeted national mandates to artificially spur tech-stack demand. Secure global ecosystem market share by exporting full-stack American infrastructure—semiconductors, data centers, and scientific platforms—while modernizing frontline industrial capacity.
Risks & Warnings
Execution risks severe fragmentation under conflicting state-level regulations. Success remains bottlenecked by power grid economics, debt constraints, bureaucratic delays, and geopolitical pushback.
Key Views & Dialogues
Inside Trump’s Science Agenda: Anti-Science Claims, Fauci’s Damage, DEI & China w/ Michael Kratsios
OSTP director Michael Kratsios rejects the “anti-science administration” charge, reframing the $200B federal S&T budget around breakthrough productivity rather than growth. His reset uses metascience experiments, portable fellowships, bold-grant “golden tickets,” and government-funded precompetitive bets including Genesis Mission. Top-five agency implementation plans are due within 90 days, while talent shortages, China’s output, disputed grant disruptions, and rebuilding COVID-era trust remain risks.
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- Michael Kratsios, the OSTP director, flatly rejects the “anti-science administration” charge — despite a Nature poll showing 86% of scientist readers backed Harris vs. 6% for Trump — arguing the scientific community “lost its way” over 15-20 years of politicization that “crescendoed and peaked in COVID.” His counter-thesis is the new report “Science: A New Golden Age”: the question isn’t whether the ~$200B annual federal S&T budget goes up, but whether it “is actually driving the best breakthroughs.”
- Asked what was behind the $3B in unspent funds on active grants that the host says were disrupted, frozen, or terminated in 2025, Kratsios does not itemize those grants. Instead, he frames the issue as resource allocation, citing Ted Cruz’s Senate committee analysis that roughly 25% of Biden-era NSF grants — ~$2B a year, $8B over four years — went to “DEI-related quote-unquote science,” and arguing that “spending more money on the wrong types of things is not the right policy action.”
- On climate, the administration’s line via Secretary Wright: yes, the climate is changing and human-burned fossil fuels contributed CO2, but “what the data does not support is that it is a climate emergency.” Kratsios points to RCP 8.5, the extreme scenario he says was pulled a few months ago because it couldn’t be sustained “with any scientific integrity” — yet it drove media focus and some funding.
- Kratsios says scientific stagnation is real: the host cites NIH’s budget rising from $14B (1998) to $47B (2024) while outcomes per dollar have fallen ~80-fold since 1950, halving every nine years — a trend people call Eroom’s law. The proposed fix is “metascience” — new units at NSF and NIH will run experiments on funding itself: Denmark-tested “golden tickets” letting reviewers unilaterally back bold grants, variable durations instead of the 18-month default, and 2,600 portable GRFP fellowships that fund the scientist rather than lock them to a project.
- The government’s proposed lane is big bets and precompetitive discovery science the private sector won’t fund: American boots on the moon in ’28, a nuclear reactor in space by ’28, moon-base elements by 2030, a “scientifically relevant quantum computer by 2028,” fusion by 2035, and the flagship Genesis Mission to “essentially double the scientific output of the United States” by applying AI to science. With hundreds of billions of private capital in AI, “the government’s not trying to compete in that space.”
- The host cites China’s spend rising from $33B (2000) to $670B (2021) — 19x growth vs. America’s 3x — and estimates that China now publishes ~50% more papers in nearly every domain except some life sciences, while noting the statistic is hard to verify. He also cites estimates of 2x-10x greater output per dollar. But Kratsios argues centralization fails: China’s single-agency model has chased EUV lithography since the 2019 export controls with “no breakthrough,” while America’s fragmented agencies are “a feature, not a bug.”
- The talent pipeline is the loudest alarm: U.S.-born computer-science PhDs inverted from ~70% American 30 years ago to ~70% foreign today, the median NIH intramural researcher is 71 years old, and NSF programs for gifted STEM students were stopped “for some reason.” The host speculates that this was part of a broader DEI effort; Kratsios calls the decision discouraging and says “that’s what’s most tragic.”
- On COVID, Kratsios is categorical: Fauci “did more disservice to science than anyone in modern history,” and rebuilding public trust “is going to take quite a while to dig ourselves out.” Implementation is already moving — an R&D priorities memo co-signed with OMB’s Russ Vought requires the top five research agencies (>$3B R&D each) to report implementation plans within 90 days.
How the White House Plans to 10x Scientific Productivity | Michael Kratsios | EP #276
Michael Kratsios frames regulation as a market catalyst for technologies “born in captivity,” while national missions aim to create demand across AI, chips, robotics, quantum computing, and space. The Golden Age blueprint targets 10x scientific productivity through five-year grants, autonomous laboratories, public datasets, and AI-native research markets, with export financing and semiconductor controls shaping competition; execution and public acceptance remain key risks.
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Kratsios divides technology into products “born free” or “born in captivity.” Internet-like technologies need government restraint; commercial drones and AI medical diagnostics need affirmative rule changes before customers can realize their value. For investors, regulation is therefore both a bottleneck and a market-making catalyst.
Washington’s ambition is moving from AI adoption toward national-scale demand creation: humans back on the Moon in 2028, initial lunar-base elements by 2030, a space nuclear reactor by 2028, a scientifically relevant quantum computer by the end of Trump’s term, and AI-driven science through the Genesis Mission. Kratsios said Genesis should probably aim for 10x scientific productivity, not the publicly stated 2x: “We have to aim big. We have to do 10x.”
The administration wants leadership in both closed and open models, but Kratsios concedes that Chinese open-source models are well-performing and currently cheapest. The American AI Exports Program aims to counter that advantage with turnkey packages spanning chips, models, applications, and government-backed financing—a deliberate effort to ensure that “the world should build on America’s AI and tech stack.”
Kratsios strongly defends semiconductor controls, calling the 2019 restrictions on EUV lithography “probably one of the most impactful export controls” in US history. Diamandis argued that restrictions can incubate foreign competitors; Kratsios’s answer was that China already treated semiconductor independence as a strategic priority, while controls throttled its ability to match US frontier systems.
The Golden Age blueprint treats scientific stagnation as an incentive-design failure, not principally a shortage of money: NIH approaches $45 billion, yet researchers reportedly spend about 45% of their time on grant-related administrative work. Proposed repairs include five-year awards, applications reviewed within a month, unilateral “golden tickets” for unconventional proposals, dedicated meta-science units, prizes, and four-year industry-linked PhDs.
The most radical proposal is an AI-native scientific marketplace where funders post bounties, agents identify leads and hire autonomous labs, cryptographically signed results trigger smart-contract payments, and prediction markets guide resources. Humans would still choose the questions and judge the most consequential results, but coordination and experimentation could run continuously “at machine speed.”
Robotics, autonomous laboratories, data centers, and public scientific datasets form the investable physical layer. Kratsios described new limits on non-US humanoid imports, federal support for closed-loop robotic labs, and a requirement that data-center builders “build, bring, or buy” their own power; meanwhile, 70 years of national-lab data should become AI-ready and remain a public good, analogous to NOAA weather data.