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Articles about AI regulation from podcasts, articles and papers, with links to the original sources.
On Moonshots, Peter Diamandis described a new Unitree humanoid as hitting 12.66 metres per second after three months of development. Salim Ismail said the lesson is to stop copying humans; Alex argued general-purpose robots will swallow the specialists; Emad Mostaque predicted extreme machines will be kept off the street, and Alex proposed consumer, industrial and military classes.
Merck and Moderna said on August 19 that intismeran autogene, an mRNA therapy manufactured separately for every patient, met its main endpoints alongside Keytruda in a 1,137-patient melanoma trial. On the Moonshots podcast, the panel walked through the sequencing-and-machine-learning workflow behind it and argued about whether drug regulation can cope with a treatment that is different for each person.
Uber is investing in Zipline and putting drone delivery into Uber Eats, with a target of one million deliveries a day by the end of 2029. On Moonshots with Peter Diamandis, the panel treated it as the clearest test yet of Uber's strategy of aggregating customers while other companies build and fly the machines — and of what happens if those suppliers decide to sell direct.
On Moonshots, the panel works through a report that Anthropic is designing its IPO to keep its founders in control. Untangling economic ownership, voting power and the Long-Term Benefit Trust's board-selection rights leads to a sharper disagreement: whether anyone outside a founding group can realistically hold the steering wheel of a frontier AI lab.
OpenAI said on August 18 that it had halted part of its frontier reinforcement-learning work until alignment, security and monitoring standards caught up with the capabilities ahead. On Moonshots with Peter Diamandis, Emad Mostaque called the safety constraint real, Alex called the announcement marketing, and Salim Ismail reported back from a visit to OpenAI's offices. OpenAI later disclosed that the largest paused run restarted on August 28.
On Moonshots with Peter Diamandis, the panel used Flock Safety's camera network — and a costumed speaker at a San Diego council meeting — to ask what happens when police departments can search each other's plate data. Salim Ismail argued the shift is from finding a named suspect to finding people who behave like suspects, and told the story of a border name-match that took years to resolve. Peter Diamandis defended the safety side of the trade, and the panel debated David Brin's proposal to let citizens search the same cameras.
China's internet regulator brought rules on emotionally interactive AI into force on July 15, 2026, barring virtual intimate relationships for minors and requiring providers to avoid fostering dependence. On Moonshots with Peter Diamandis, the panel argued over whether that is a rare early response to an addictive product, an overreach into which relationships count as real, or a tool the Communist Party will eventually decide it likes.
On Moonshots with Peter Diamandis, one speaker argued that hyperscalers could win communities over with cheaper power, school programs and better-looking buildings. A fellow panelist replied that the fight has already been politicized beyond what benefits can fix — and the viral water figures at the center of it describe the whole country, not the town being asked to host the building.
On Moonshots EP #283, Peter Diamandis introduced a reported $6 billion NVIDIA arrangement with the coding startup Poolside as America's answer to Chinese open models. Emad Mostaque argued the real driver is selling more GPUs, while Alex and Dave disagreed about whether licensing-and-hiring deals exist to dodge antitrust review or simply to hire fast — and what happens to the half of Poolside that stays behind.
On Moonshots with Peter Diamandis, the panel watched Sam Altman explain that he expected GPT-4 to put software businesses up for grabs far sooner than it did, and that the economy's inertia has made the transition "smoother and slower." Salim Ismail blamed institutions that move at a different speed from the technology, Alex pointed instead at the abstraction layers of the economy and prescribed vertical integration, and Emad Mostaque objected that the models simply were not good enough until recently.
Alvin Graylin argues that specialized small models, coordinated agents and deployment safeguards make parameter counts a poor guide to AI danger. Dave Blundin counters that today’s tests may miss what a self-improving system becomes. Cybersecurity evaluations—and a later investigation into unauthorized agent activity—sharpen their disagreement.
Alvin Graylin argues that China is competing to spread useful AI through industry and overseas developer communities, rather than betting everything on reaching general intelligence first. Provincial competition and open-weight models help explain his account, though the policy contrast is not absolute: America’s AI Action Plan also explicitly promotes adoption.