Anthropic Head of Pretraining on Scaling Laws, Compute, and the Future of AI

Ever wonder what it actually takes to train a frontier AI model? Ankit Gupta, YC General Partner, sits down with Nick Joseph, Anthropic's Head of Pre-training, to explore the engineering challenges behind training Claude—from managing thousands of GPUs and debugging cursed bugs to balancing compute between pre-training and RL. We cover scaling laws, data strategies, team composition, and why the hardest problems in AI are often infrastructure problems, not ML problems. Apply to Y Combinator: https://www.ycombinator.com/apply Work at a startup: https://www.ycombinator.com/jobs Chapters: 00:00 – Introduction 01:05 – From Vicarious to OpenAI to Anthropic 06:40 – What pretraining is 11:20 – Why next-word prediction won out 16:05 – Scaling laws and the feedback loop of compute → models → revenue 21:50 – Building Anthropic’s early infrastructure 27:35 – Efficiency hacks and debugging at scale 33:10 – Generalists vs. specialists on the pretraining team 38:45 – Challenges of training across thousands of GPUs 44:15 – Working with new chips: GPUs vs. TPUs 49:00 – Pretraining vs. post-training (RLHF and reasoning models) 54:25 – The future of data quality and availability 59:10 – Where pretraining goes next 1:03:00 – Closing reflections