The East Room Compact: industry stops going it alone on super intelligence. The rename is cosmetic. The compact is the news. Two papers today. The executive order — Inaugurating the Era of Super Intelligence — tells federal agencies to say Super Intelligence instead of AI in new executive-branch documents. Same statutory definition. Sixty days to propose a law. No auditor. No town. https://www.whitehouse.gov/presidential-actions/2026/09/inaugurating-the-era-of-super-intelligence/ The governance paper is the White House Accord on Super Intelligence, Joint Commitment on Frontier Responsibilities. Trump called it “almost like a constitution” and “morally binding.” Signers in the coverage: Trump, Zuckerberg, Musk, Amodei, Pichai, Huang, Brockman. https://nypost.com/2026/09/29/us-news/trumps-morally-binding-artificial-intelligence-pledge-signed-by-tech-leaders-read-in-full/ Four layers, in the Accord’s words: internal controls on training and deployment (cyber, bio, chemical; no unintended hacking); an internal team to remediate; an independent external auditor or evaluator; an independent committee of the board of directors that receives both reports and sees issues fixed. Companies will meet regularly on standards. “Over time, it may make sense to codify these steps into laws or regulations.” Until then they are “committed to doing this.” Self-regulation plus the Department of Justice and the FBI. Speaker Johnson in the chair with the President. Amodei there because Sunday’s dinner mended a public fight. Brockman in the seat while Altman ran DevDay. Musk at the mic: jobs are going to change because of AI. Data-center towns got a verbal promise — happy, financially beneficial, popular — that is not in the one-pager. A ten-person committee and an SI czar in “three or four days” are still oral. First paragraph of governance. Not the last. The test is whether morally binding grows a consequence.

**Distillation, Efficiency, and State Power: How China Is Closing the AI Gap — and Why the US Is Moving to Restrict Chinese Models** Caught between two superpowers, many scientists and business leaders today are carrying a burden that rarely gets discussed publicly. As the United States and China compete for leadership in artificial intelligence, the people actually building these systems are finding themselves in an increasingly difficult position — one where technical excellence, commercial pressures, ethical responsibility, and national interests are pulling in different directions at once. In my latest essay, I explore how Chinese AI labs have managed to close the performance gap with the West despite facing significant restrictions on advanced chips. Through highly efficient architectures like Mixture-of-Experts, aggressive quantization, and deep hardware-software optimization for domestic accelerators, they are delivering frontier-level capabilities at a fraction of the cost. At the same time, close coordination between the Chinese government and its leading AI companies has created structural advantages in data access, strategic focus, and speed of execution. This progress has not gone unnoticed in Washington. Concerns about large-scale model distillation, the stripping of safety guardrails, and the potential national security implications of widely adopted Chinese models have led to growing calls for restrictions on how U.S. organizations use these systems. The debate is no longer just about chips and compute — it now extends to which models American companies and institutions should be allowed to rely on. What often gets lost in these strategic discussions is the human dimension. Engineers, researchers, and executives are being asked to navigate an environment where every technical decision can carry geopolitical weight. The pressure to move fast, stay competitive, and deliver value to customers exists alongside growing expectations around security, ethics, and alignment with national interests. For many, this creates a quiet but real strain. The image attached tries to capture something of this tension. I wrote this piece because I believe we need a more honest conversation about the real trade-offs involved — not just in terms of technology and policy, but in terms of the people doing the work. If we want responsible AI development, we need to create conditions where those building it can operate with clarity rather than constant conflict. You can read the full essay here: https://kentkaufman.substack.com/p/distillation-efficiency-and-state I'd be interested in your thoughts — especially from those working directly in AI development or policy.

OpenAI Just Showed Us What the First Real U.S. AI Regulatory Process Looks Like This week, OpenAI publicly launched the full GPT-5.6 series (Sol, Terra, and Luna) and GPT-Live voice models — but only after successfully navigating several weeks of government scrutiny. This wasn't a formal approval process, but it revealed the outlines of how the U.S. may begin managing frontier AI going forward. OpenAI initially released GPT-5.6 only to a small group of vetted partners, then worked directly with the Commerce Department and National Cyber Director on targeted testing focused on cyber and biological risks. Once benchmarks were met, OpenAI was allowed to scale. The review was non-binding, meaning the final decision on timing remained with the company. This "voluntary submission + targeted testing + company-led scaling" model may become a template for future oversight. At the same time, market reality is hitting hard. Companies are discovering that rapid, heavy adoption of frontier models can destroy budgets faster than expected. Uber exhausted its entire 2026 AI coding budget by April after widespread use of agentic tools — a clear warning sign. Organizations are now being forced to deliberately manage the speed of AI adoption while they figure out where it actually delivers value. This pressure is exactly why OpenAI split GPT-5.6 into three tiers: Sol for maximum capability Terra for strong performance at roughly half the cost of GPT-5.5 Luna for speed and high-volume use Meanwhile, Chinese labs continue advancing. Multiple Chinese companies (not just those releasing open-weight models) have been accused of large-scale distillation campaigns against U.S. frontier models using tens of thousands of fraudulent accounts. These efforts extract capabilities developed through billions of dollars in U.S. R&D. Longer term, energy and execution speed will matter enormously. As Elon Musk has noted, space-based data centers offer major advantages in energy density, unlimited siting, and avoiding the permitting, grid, and supply chain bottlenecks that are slowing terrestrial infrastructure. The current moment shows both the risks of reactive policy and the first signs of a more pragmatic path. What's increasingly clear is that we need more structured, predictable institutions for AI oversight — rather than case-by-case interventions that create uncertainty for both innovation and security. Full analysis here: https://kentkaufman.substack.com/p/the-new-ai-cold-war-how-us-restrictions How should the U.S. and its allies balance the need for security guardrails with the imperative to maintain long-term technological and economic leadership in AI? .org

Told 5 times, Jun 22, 2026 – Jul 9, 2026

AI Policy