I watched the full Michael Kratsios interview on Moonshots this week, and one number stuck with me long after the rest of the optimism faded: a 54% proposed cut to the National Science Foundation.
Kratsios is the 13th Director of the White House Office of Science and Technology Policy. He’s the architect behind three initiatives that will shape how AI touches science for the next decade: America’s AI Action Plan, the Genesis Mission, and a blueprint called Science in a New Golden Age. The pitch is genuinely thrilling. The arithmetic underneath it is where things get complicated.
This isn’t a piece about politics. It’s about whether the engineering premise holds.
What’s actually being proposed
The Genesis Mission, launched by executive order in November 2025, is the centrepiece. It aims to build what the order calls an American Science and Security Platform: federal supercomputers, secure cloud networks, public and proprietary datasets, and scientific foundation models, all linked into a single discovery engine.
The ambition is to shift science from human-paced to machine-speed. Self-driving labs running experiments around the clock without human intervention. Long-duration grants and what the blueprint calls “golden tickets” for high-risk, unconventional research. Access democratised so far that a high school student could test a hypothesis on federal equipment.
The stated targets are not modest. Astronauts back on the moon by 2028. First elements of a lunar base by 2030. A nuclear reactor in space by 2028.
The case for the prosecution
The strongest argument for this agenda is that scientific discovery per dollar has been falling for decades. If AI is a genuine productivity multiplier, it attacks that decline directly rather than just throwing more money at a broken process.
Kratsios also makes a sharp point about regulation. He rejects the idea of a single AI regulator, arguing AI is horizontal: it touches drones, medical diagnostics, financial services, everything. His position traces back to the 2019 executive order on AI, signed years before ChatGPT made this a mainstream conversation. Sector specialists, the argument goes, understand their own risks better than one central body ever could.
On chips, he’s on solid ground. The EUV lithography export controls were consequential. His framing is revealing: the AI export programme exists because of frustration with Huawei, where a “good enough” heavily subsidised stack propagated globally before anyone could counter it.
The case for the defence
Here’s the contradiction nobody in the interview resolves. You cannot credibly promise a golden age of discovery while cutting the institutions that produce it.
The Brennan Center documented over $3 billion in previously approved NIH and NSF research grants cut or frozen, with roughly $1.4 billion still frozen. The FY26 proposal put NIH base funding at $27 billion, close to a 40% reduction. The following budget proposed a 54% cut to the NSF, 28% to NIST, and 10% to NIH.
Genesis Mission is meant to run on operational efficiencies rather than substantial new funding. Legal analysts have flagged the obvious risk: that it quietly subsidises large AI firms while foundational research gets starved. Self-driving labs still need people who can design the experiment and interpret the result.
Three of Kratsios’s specific claims don’t survive contact with independent evidence.
On export controls. He says the American lead over the best Chinese chip widens year-over-year. Chatham House concluded in April 2026 that hardware controls alone won’t stop China developing advanced AI. The Economist covered a March 2026 smuggling case showing contraband gear still reaching China. CSIS goes further, warning controls have pushed China to double down on subsidised development that could leapfrog the current state of the art.
On open source. Kratsios concedes the US “could be doing better”. That’s a considerable understatement. Stanford HAI has mapped China’s open-weight ecosystem, DeepSeek, Qwen, Kimi and others, and its rapid global diffusion. CNBC reported in July 2026 that American companies are increasingly building on Chinese models as OpenAI and Anthropic costs climb. This is precisely the Huawei dynamic he says he wants to avoid, playing out one layer up the stack. Open weights are how a standard propagates.
On jobs. He’s optimistic long-term, and he may well be right about the aggregate. The near-term picture is narrower and sharper. The Dallas Fed found workers aged 22 to 25 in the most AI-exposed occupations down 13% in employment since 2022. Stanford put young software developer employment down nearly 20% from its 2024 peak. That’s not mass unemployment, it’s something more specific: the bottom rung of the ladder being sawn off.
The missing middle
The weakest moment in the interview is the claim that public fear of AI was largely manufactured by government messaging. People aren’t anxious because politicians told them to be. They’re anxious because they’ve seen deepfakes, AI-branded redundancies, and automated systems failing in public.
Dismissing that as a communications problem is how you lose the argument you most need to win.
What this means for you
If you work in security or technology, three things are worth tracking.
- Sector-by-sector AI regulation is now the default direction. Combined with preemption of state law, accountability for cross-cutting harms falls between agencies. Your compliance map is about to get fragmented, not simpler.
- Open weights are a strategic dependency, not a licensing footnote. If your stack quietly standardises on models from a geopolitical rival, that’s a supply chain decision made by default rather than design.
- The entry-level compression is your succession problem. Cut graduate hiring for two years and you’ll be paying triple for mid-level talent by 2029.
I want the Genesis Mission to work. AI-accelerated discovery is one of the few things that could genuinely bend the curve on problems that have resisted everything else. My scepticism isn’t about the ambition, it’s about the arithmetic.
You can automate the experiment. You cannot automate the scientist who knows which experiment is worth running.
