Dean Ball

Eleven months in, Ball judges America’s AI Action Plan roughly “30 to 40% done,” with real gains in energy, military adoption, manufacturing, and deployment—but a widening gap between competent implementation below and reactive politics above. His largest drafting regret is that it read like “three dozen separate thematic objectives” instead of one strategy for generalist agents, American primacy, and positive-sum global diffusion. The administration then validated allies’ deepest fear by imposing frontier-model export controls on non-US persons with 90 minutes’ notice, even as Ball hopes policymaking remains in a “high neuroplasticity phase.”

The Anthropic supply-chain designation and Fable ban illustrate how legitimate security concerns can become entangled with weak frontier-AI context, personal friction, and post-hoc political justification. The designation remains in litigation and could plausibly reach a Supreme Court disposition by summer 2027; meanwhile, the Department of War appears to be winding down Anthropic while other agencies—and reportedly even the NSA under Anthropic’s surveillance and lethal-weapons red lines—continue using it. Ball sees the Fable restriction as an improvised attempt to remove a model from market, not a coherent universal rule: “If you were a user of Fable, your world became dumber in the last week.”

Moving frontier-model oversight into classified, intelligence-led processes risks giving government a capability monopoly while discarding society’s “parallel compute.” Ball accepts classified work where necessary, but objects to a world where unknown models are tested against undisclosed standards and access decisions are made by roughly 20 officials, perhaps 15 without deep AI context. State-level frontier laws offer a more promising counterexample: California SB 53, New York’s RAISE Act, and Illinois SB 315 substantially converge, while Illinois, Connecticut, Virginia, and potentially Ohio are building auditing or independent-verification machinery.

China’s reluctance to buy US chips is partly strategic signaling, while the larger technical surprise is that world-simulation systems may have pulled dexterous robotics sharply forward. Ball expects Beijing to proclaim semiconductor self-reliance while DeepSeek, Alibaba, Zhipu, and others privately lobby for American chips; he concedes his prediction that DeepSeek’s top model would be closed by the end of Q1 2026 was wrong. Persistent 3D world simulation changed his robotics forecast immediately: synthetic-data pipelines built from human demonstrations could solve manipulation far sooner than he had expected—his reaction was, “Okay, dexterous manipulation in robots is going to be solved in eight months.”

Ball is joining OpenAI because frontier labs have become a new species of political and economic power center whose decisive information and governance choices cannot be understood from outside. He compares them with the emergence of modern finance: government lacks the expertise to write every rule, private standards must fill the gap, and AI itself will become an instrument of statecraft. His new team will look 6–12 months ahead, work closely with researchers, and focus especially on internal deployments that existing regulation—usually triggered by public release—does not reach.

Ball’s base case is that recursive self-improvement produces another steepening of the curve, not an instantaneous singularity, but even a 10%–20% chance of discontinuity warrants concrete contingency planning now. He wants predefined indicators, inter-lab coordination options, and clarity on when government must enter. His sharper concern is execution: labs may have plans yet remain unsure “that we’re going to follow it,” making internal conviction as important as written commitments.

AI has become sufficiently intertwined with semiconductors, energy, startups, and nationally important IP that a 2027 growth disappointment could trigger an implicit government backstop. Ball sketches a slowdown in data collection that cuts capex expectations, knocks equities down 20%–30%, and cascades through interconnected balance sheets; intervention could then become a public-interest necessity even without an explicit bailout promise. Government still holds the Defense Production Act and the monopoly on legitimate force, so labs’ durable defense is broad diffusion: every bank, university, and major industry should have a stake in preventing confiscation or nationalization.

Ball believes the transition may create a brief “main character energy” era in which individual judgment reaches maximum leverage just before machines become primary actors. His safeguards are personal: preserve independent public writing, define resignation lines in advance, resist both government capture and commercial expediency, and leave if his team becomes window dressing. He will use AI deeply for research and thought partnership, but still sees human-authored essays as valuable because “every single path through life is highly improbable”—and a model cannot make observations from experiences it never lived.

Google is treating orbital AI infrastructure as a long-horizon response to Earth’s land, permitting, grid, and community constraints. Project Suncatcher would put TPU clusters and solar arrays in a dawn-dusk low-Earth orbit, where nearly constant sunlight could make panels up to eight times as productive; returning data may add only “a couple more milliseconds.” It is a real option on explosive compute demand, but not a present business: launches still cost many times an equivalent terrestrial center, and repairs may require robots.

The first concrete milestone is a 2027 two-satellite prototype, not an orbital hyperscale build-out. Google says newer TPUs survived proton-beam radiation beyond what a five-year mission should bring, and it is partnering with Planet for the test; Starcloud, Axiom Space, and a vaguely described Chinese effort are also exploring the field, while Eric Schmidt and Jeff Bezos have signaled interest. Google frames Suncatcher beside Waymo and quantum computing—an “eight, 10, 12, 15 years” kind of commitment—making launch economics and operational reliability the gates.

Republican AI policy remains a spectrum of intuitions rather than a settled MAGA doctrine. Dean Ball described the administration as sharing broad intuitions that AI may be the most important opportunity in decades or ever, carries familiar and “more alien” risks, and will shape U.S. leadership; camps range from national-security officials focused on China competition and child-safety conservatives to the contrasting positions the hosts associated with David Sacks and Steve Bannon. Ball expects backlash around a “weird vichyssoise” of slop, electricity, water, jobs, child safety, extinction, and claims that AI is fake.

The labs’ strongest competitive moat may be infrastructure, while their regulatory positions track their incentives. Hyperscalers do not necessarily oppose export controls because Chinese buyers—and even indirect demand for TSMC fabrication capacity—compete with them; frontier labs need chips to sell tokens, but Ball argued that “the parameters of the model are not your moat.” Anthropic’s announced $50 billion data-center commitment joins Google, OpenAI’s Stargate, Meta, and xAI in a capex race whose equilibrium government must arbitrate.

Ball draws a sharp regulatory line: prevent catastrophic tail risk federally, regulate ordinary harms reactively, and let procurement rules govern only government-bought models. He defended the “Woke AI” order as anti-ideological-bias terms and system-prompt disclosure for federal versions, while acknowledging the right’s jawboning tension and calling compelled changes to public model training “unambiguously unconstitutional.” He wants national standards for billion-dollar, globally served models, yet says Congress must act on child safety and that California’s SB 53 transparency rules are “rather reasonable overall.”

An unidentified Gemini test model appears to have pushed handwritten transcription from impressive to professionally usable. On five benchmark documents totaling about 1,000 words, Mark Humphries measured roughly a 1% word-error rate—about a 50% decline against Gemini 2.5 Pro on those tests and comparable to what human transcription experts offer. The result came through Google AI Studio A/B tests, sometimes after 20 or 30 attempts, so Kevin Roose’s “probably Gemini 3” remains a bet, not an identification.

The deeper signal was not transcription but a ledger inference that looked like symbolic reasoning. From an Albany entry, the model inferred that “14 5” meant 14 pounds 5 ounces of sugar sold at 1 shilling 4 pence per pound, reconciling it to 19 shillings 1 penny; Humphries said “models shouldn’t be able to do that.” If replicated, the capability could let models aggregate a ledger and take on broader archive-scale tasks—but the sample was tiny, the model unknown, and release-time replication still required.