On Training Data, Box CEO Aaron Levie describes how his customers actually pick models: a default for asking questions of their files, and hard-nosed accuracy evaluations for the high-volume extraction work where most tokens are spent. He endorses Decagon founder Jesse Zhang's argument that mature workflows migrate to open-weight models, and explains why the big labs' revenue and open-weight token volume can climb at the same time.
Philip Johnston spent six months failing to find a cheap trajectory to the nearest star system. A research campaign at the AI physics startup PSI, run on roughly 10 billion tokens and five or six hours of human time, returned an unintuitive answer: slow the spacecraft down first and let it fall toward the sun. The resulting Fermi Explorer mission proposes a 100-kilogram probe, a sub-$15 million budget, a launch by the end of 2029 and a journey of roughly 77,500 years.
On The Diary of a CEO, David Friedberg argued that open-weight AI models will stop the industry's value from pooling in two or three labs, and wagered that someone with no money today will build a billion-dollar company on a model they downloaded. His case runs through the Netscape era, the fight in Washington over Chinese models, and a proposal that data centers generate their own power and sit in ordinary retirement accounts.
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 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 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.
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.