OpenAI has proposed ending the agreement that supplies its models to Cursor, now owned by SpaceX, on 12 November. On the Moonshots panel, one guest read the move as OpenAI betting on its own enterprise stack; another argued the real prize is reasoning traces — the working a model shows while solving a problem. Both explanations lead to the same awkward conclusion: Elon Musk and Anthropic now need each other.
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.
Rich Sutton and Khurram Javed want deployed AI to change its underlying weights from individual experience, rather than rely on extra context or shared model updates. Their Oak Lab agenda combines learning rates tailored to each weight with a way to refresh a network’s capacity to learn—supported by earlier experiments, but not yet a demonstrated general-purpose system.
xAI’s August 12 release puts Grok 4.6 alongside GPT-5.6 Sol Max in its launch benchmark table, with pricing aimed at sustained agent work. The Moonshots panel’s debate was about the next step: whether training on other models’ reasoning can only help a challenger catch up, and what computing infrastructure it takes to move beyond that.