Reading up on Epoch AI
7 deep · digging since nov 24, 25
- How far behind are open models? — LessWrong
Open models lag behind closed frontier models by 8–10 months on private benchmarks and 4–6 months on public, with the gap growing since DeepSeek R1.
- Frontier labs don’t use most AI compute (yet) - by Josh You
Epoch AI estimates frontier labs use less than half of global AI compute, but OpenAI and Anthropic may soon dominate, requiring economic transformation to sustain scaling.
- What do frontier AI companies' job postings reveal about their plans?
Epoch AI analyzes job postings from OpenAI, Anthropic, xAI, and DeepMind to reveal shifting strategies toward go-to-market roles, hardware bets like robotics and devices, and differing approaches to compute and data sourcing.
- What the hell happened with AGI timelines in 2025?
Expert AGI timelines contracted then expanded in 2025 as reasoning model gains proved costly to scale, though steady progress and growing revenue undercut radical pessimism.
- On Recursive Self-Improvement (Part I) - by Dean W. Ball
Frontier AI labs are automating research to create vast AI workforces that self-improve, radically accelerating capability growth by 2026.
- Global AI computing capacity is doubling every 7 months
Global AI computing capacity from chips has grown 3.3x per year since 2022, doubling every seven months, with NVIDIA supplying over 60%.
- Benchmark Scores = General Capability + Claudiness
Benchmark scores are dominated by a single 'general capability' dimension, with the second component mainly identifying a 'Claudiness' factor.