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Articles about US–China AI competition from podcasts, articles and papers, with links to the original sources.
On Moonshots with Peter Diamandis, the panel walked through Waymo's sixth-generation driver: a purpose-built 5-nanometer chip, fewer but sharper cameras, and an autonomous-hardware estimate falling from $115,000 to $20,000. Peter had ridden in the new vehicle; Alex objected that the West is now white-labeling Chinese hardware, and argued Waymo is heading for full vertical integration.
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, Konstantin Kisin tells Steven Bartlett that nobody — not him, not the host, not Trump — can stop AI, and that what remains is living well with family and community. Bartlett refuses to give up on public pressure and expects AI on the ballot in 2028; Kisin expects voters to blame whoever is in office instead.
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.
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.
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.