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Dhaval Adjodah

Co-founder, MakerMaker.AI

Search every verified Dhaval Adjodah interview, podcast appearance, and on-the-record quote — each transcript cross-checked by AI and human review to confirm speaker identity. Dhaval Adjodah, co-founder of MakerMaker.AI, appeared on an a16z podcast on October 12, 2026, to discuss recursive self-improvement (RSI) in AI alongside co-founder Owen He and a16z General Partner Martin Casado. During the conversation, Adjodah distinguished true RSI from merely using AI to automate parts of AI development, arguing that RSI involves a closed loop where the system being improved is also the improver, across components such as models, harnesses, and potentially the broader AI stack. He stated that many uses of the term RSI are overloaded, and that approaches where humans use AI to build better AI are, in his view, a form of autocatalysis rather than genuine self-improvement. Adjodah also described MakerMaker.AI’s goal as optimizing multiple levels of the AI stack, including prompts, harnesses, weights, and eventually chips, using a multi-agent system that has been recursively improved. He emphasized a first-principles approach to research, suggesting that many existing papers and ideas are subclasses of a unified framework, and noted that the company has been thorough and has not released work until they are confident it is correct. When asked about the energy cost relative to improvement, he said the company could not share too much but indicated they had made important efforts in that area, and said more information would be forthcoming soon.

Recent appearances

  • Recursive Self-Improvement and the Future of AI

    a16z General Partner Martin Casado sits down with MakerMaker.AI co-founders Dhaval Adjodah and Owen He to unpack recursive self-improvement, or RSI, and separate the increasingly popular term from what it would actually mean for an AI system to improve itself. Dhaval and Owen distinguish true RSI from simply using AI to automate pieces of AI development. Their goal is a closed loop where the system being improved is also doing the improving, across components including models, harnesses, and eventually more of the AI stack. They explain why closing that loop still doesn’t guarantee runaway “t…

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