An AI publication
For readers who want to understand important AI developments and keep following the questions that matter.
Our articles start from AI podcast conversations and the research papers and articles they cite, with links to the original material.
A briefing reports one development at a point in time. We may correct or clarify it later; a new development gets a new briefing. How our formats work
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.
Panelists on Moonshots argue that as agent compute gets cheaper, the scarce skill is choosing targets: OpenAI's claimed Navier–Stokes proof was easy to specify, but models asked for ideas to cure cancer return poor lists.
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.
H100 rental prices rose 22% in a month to $3.28 per GPU-hour on Ornn's transaction index. Dave Blundin, whose fund backs Ornn, called it a reversal of chip depreciation; Emad Mostaque said better models make old Hoppers more useful.
Google DeepMind's AlphaGenome Atlas precomputes predicted molecular effects of every possible single-letter change in the human genome. A Moonshots panelist cautioned that what combinations of variants mean still needs outcome data.
In an exploratory analysis of 42 lung-fibrosis patients, AI-designed drug rentosertib made protein aging clocks read about three years younger by week four. Mortality clocks didn't significantly shift at 60 mg, and the effect plateaued by week 12.
Anthropic's most extreme AI scenario pairs 15% annual GDP growth by 2030 with 17.9% cognitive-worker unemployment and labor's share falling to 45%. On Moonshots, Blundin called it a lower bound; Emad Mostaque said it ignores collapsing demand.
DeepSeek says V4.1-Flash's KV cache needs a quarter of the HBM and an eighth of the SSD cache of its prior generation. Moonshots panelists argued that changes what AI data centers should buy; one estimated HBM is 40% of US buildout spending.
Sam Altman called OpenAI's claimed Navier–Stokes result the strongest evidence yet for pacing AI; on Moonshots, Peter Diamandis urged labs to publish measurable alignment plans, while other panelists disputed that remedy.
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