On Moonshots with Peter Diamandis, the panel read out a new leaderboard result: Google's Gemini 3.7 Flash on top of the AA-AnalystAgent benchmark with 60%, ahead of Claude Opus 5 at 54%. Diamandis called it proof that Google is back; Alex argued the score measures repeated reliability on spreadsheet analysis rather than frontier capability, and blamed Google Search for pushing Gemini toward speed and determinism. Emad Mostaque agreed the model was decent but said Google's problem is institutional, not a shortage of chips.
On the Moonshots panel, Peter Diamandis runs a Grok Bot chief of staff called Skippy and Emad Mostaque runs 18 of them across his own machines, installing models and making art. Salim Ismail calls it the move from asking an AI to assigning work; Alex argues the messaging-app interface cannot possibly scale.
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.
After touring Unitree’s headquarters and factories, Alvin Graylin said research and demonstrations still dominate its robot purchases, while upper-torso models are finding more commercial demand. His argument is about engineering economics: repetitive work may need capable arms, but not the balancing, repairs and extra parts that come with legs.
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.
Asked about allegations that Chinese labs extracted capabilities from Claude, Alvin Graylin argued that access to another model’s answers cannot explain every engineering advance. The Moonshots exchange turned on three distinctions: legitimate distillation versus prohibited extraction, query bills versus development costs, and learning from outputs versus improving the machinery behind them.
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.
Alvin Wang Graylin proposes a practical starting point for US–China AI cooperation: an emergency hotline, shared safety tests and an agreement to keep talking. Speaking personally on Moonshots, ahead of a September 24 dialogue described in the episode, he connects those steps to a larger bargain—financing AI deployment abroad while spreading agreed safety standards.
Alvin Graylin argues that AI can become more useful while earning less for the companies financing its infrastructure. His warning centers on cheaper models and local computing weakening cloud revenues, just as NVIDIA proposes financing platforms intended to mobilize more than $500 billion of outside capital.
On Moonshots, Ramez Naam pointed to the brain’s modest power needs and children’s ability to learn from relatively little data. Co-host Alex countered with a rack of chips producing text thousands of times faster than one writer. Their disagreement connects AI’s energy bill to a larger question: how much improvement can more computation buy?
Helion is targeting initial plant operation in 2028, followed by a ramp-up to at least 50 megawatts under its Microsoft agreement. On Moonshots, energy investor Ramez Naam explained what still separates encouraging fusion experiments from dependable electricity: whole-plant energy gain, durable components and a price customers can afford.
Ramez Naam sees AI demand as a powerful source of nuclear financing, but doubts new small reactors can supply electricity within the five-year window he considers reasonably predictable for investment. His argument turns on what can be delivered sooner—and whether repeated construction and factory production can make later plants cheaper.
