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Nathan Lambert

What Nathan Lambert thinks about scaling laws

@nathan-lambert · 55 positions · On ppll ↗

Machine-learning researcher on open language models; writes the Interconnects newsletter and the RLHF Book, after leading post-training at Ai2.

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Only Nathan Lambert on scaling laws. See everyone on scaling laws

2 dated positions, 2025 to 2026, in their own words. Our reading of what Nathan Lambert has said — not written or endorsed by them.

2 positions so far — this page is not yet offered to search engines.

  1. Our readingAI scaling laws require exponentially more compute and resources to produce only linear gains in model intelligence.

    Their wordsall of our scaling laws show that you need exponential compute and resources to make linear improvements in intelligence.

    ↗Why I still haven’t bought into true RSIinterconnects.ai 1st of 3 in this piece

  2. 19 months earlier

  3. Our readingPost-training is the best place to work in AI right now, because cheap runs mean a far higher share of your experiments can be all-or-nothing bets.

    Their wordsThis is why you want to work in post-training because the GPU cost for training is lower. So you can make a higher percentage of your training runs YOLO runs.

    ↗DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459youtube.com 3rd of 44 in this recording