Alibaba's Wan 3.0 and a relayed claim that 70% of Chinese AI token use goes to video sent the Moonshots panel into an argument about money: one guest said American labs chase revenue per token while Chinese labs give their weights away, another said video is the only market that will trust a Chinese model. They ended up disagreeing about whether world models or text models reach self-improving AI first.
On Moonshots with Peter Diamandis, Salim Ismail argued that a Mac Studio with 512GB of unified memory changes AI spending from a perpetual per-token bill into a capital asset, with law firms and mid-sized healthcare organizations as the likely buyers. Two other panelists agreed the machine was worth having and still called Apple's AI record a long-running software failure.
On Moonshots EP #284, the panel read out the performance figures OpenAI published for Jalapeño, the inference chip it built with Broadcom, and argued that inference is moving off NVIDIA. One panelist went further, imagining an "OpenAI Compute" cloud that rents capacity to competitors — possibly even hosting an Anthropic model. Dave Blundin explained why CUDA no longer locks buyers in at inference time, and why NVIDIA's next defense is the networking between chips.
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 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 Diary of a CEO, economist Steve Keen argued that the only thing likely to slow the race for frontier AI is its sheer cost and the physical resources it needs — and that the company left standing will be Chinese. Other speakers pushed back with military necessity and the long unprofitable years of earlier internet giants, and Keen pointed to the recent Kimi release as his example.
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 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.
Meta’s Muse Glimmer is a 30-billion-parameter model designed to run agents on personal computers. Alongside Mark Zuckerberg’s vision of personal superintelligence, it prompted a Moonshots debate about whether open models put users in charge—or strengthen the company that already owns their favorite apps.
The Moonshots panel described The Cully Hill Boys as a 110-minute AI feature made by 28 people in four weeks for roughly $2 million, half of it spent on computation. Emad Mostaque expects cheaper video generation to cut that bill sharply. But the film’s published production materials are study-only, and cheaper footage does not resolve performers’ rights or the need for creative direction.