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Reporting and discussion about Models & Research, with links to the original sources.
John Schulman expects frontier AI models to keep growing as compute and GPUs expand. Charlie O’Neill expects little parameter growth for a few years, because long reinforcement-learning runs reward cheap inference.
Charlie O'Neill told the Dwarkesh Podcast that AI already learns from deployment, but in slow batches, and that many small updates erase earlier learning; John Schulman expects customers to resist feeding a shared model.
AI researcher John Schulman says cheap distillation keeps model competition open. He argues that a copy gets broad behavior only from realistic, multi-turn prompts, and that benchmark-style tasks alone may match scores without it.
On a Dwarkesh Podcast panel, John Schulman forecast that AI will make AI researchers ten times more productive in about two years. Charlie O’Neill put it five to ten years away, arguing that choosing experiments and objectives is hard to automate.
Dwarkesh Patel and Jerry Han found better training data gave a 12-fold compute-efficiency gain from 2019 to 2025 versus 3.7-fold for model recipes, in small models on easy benchmarks; a panel split on how long proprietary data stays valuable.
Nvidia's Jensen Huang declared AGI achieved on September 6 as he announced more GPU capacity, and the Moonshots panel split on what the label means. OpenAI's figure of 3.1 agent research days per human day measures runtime, not finished work.
OpenAI says an internal model running about 10,000 agents for 88 hours produced a forced Navier–Stokes blowup construction with a Lean-checked proof. Moonshots panelists stressed it concerns idealized fluids and noted disputes over credit.
World Labs' Atlas generates video along a user-set camera path and rebuilds scenes from photos, inventing what was not photographed. On Moonshots, Alex inferred from its documentation that it trains on Gaussian splats.
Anthropic's agents formalized Wiles's existing proof of Fermat's Last Theorem in about 13 million Lean lines over eleven days, per its report, with occasional human direction and a dependency-tracking platform keeping runs from losing their work.
OpenAI's GPT-6 Astra nearly saturates ARC-AGI-3 and tops Epoch AI's index but ranks third by vendor family on Artificial Analysis's. Moonshots panelist Alex, lacking access, suspects it was built for fast, low-token computer use.
Sam Altman told TIME he expects an internal OpenAI system he would call AGI by year-end. Moonshots panelists speculated the leap may be agents that keep their memory over very long tasks instead of losing context.
Tim Sweeney tweeted that Elon Musk's predicted 100x gain in intelligence per gigabyte has come true; Musk says specialist AIs add another 100x. Dave asks how 10,000 agents would work together; Alex says specialists are really sparsification.