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Karpathy later co-founded OpenAI. As a free agent, his subsequent work has been notable. Baker views the Anthropic move as significant, describing Karpathy as a highly curious individual capable of independent invention. Recursive self-learning places models on both overdrive and autopilot, potentially enabling order-of-magnitude yearly improvement analogous to a new Moore's Law, producing parabolic gains in model quality through compute scaling and rapid learning.

Anthropic's Financial Position and Industry ARR04:31

Gavin Baker states Anthropic achieved EBIT positivity in the most recent quarter per Wall Street Journal reporting. This development supports the broader AI narrative. OpenAI and Anthropic are projected to reach approximately $100 billion combined ARR with roughly 80% gross margins on inference. Adding Gemini, Cursor, xAI, and open-source models could yield $200-400 billion ARR by year-end at high margins across language models.

Baker excludes non-LLM GPU returns such as improved recommender systems, ad targeting, and ad measurement at Facebook, Google, and Amazon. He focuses strictly on LLM economics. The analysis suggests strong ROI this year even when excluding major economically significant GPU use cases.

Recursive Self-Improvement and Continual Learning06:00

Gavin Baker identifies recursive self-improvement and continual learning as the final two frontiers for AI. Recursive self-improvement involves a model contributing input to its own training during forward passes, or another model contributing to training. Chimera's statistic of 10x yearly improvement may prove conservative. Continual learning remains the "holy grail," allowing models to learn from experience analogously to humans.