Yaron Singer

As Jensen mentioned with Brad Gerstner & Bill Gurley, something that few people know is that NVIDIA is becoming the American open-source leader in AI, with over 400 contributions of models, datasets and apps on Hugging Face in the 12 months. And I have a feeling they're just getting started!

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Axiom, a pre-product AI startup founded by a Stanford math PhD Carina Hong, is raising at a $300m valuation. Goal is to build AI trained on math proofs that can solve complex math problems with certainty, for Wall St. quants. Carina graduated from MIT with a double major in mathematics and physics. She then earned a neuroscience master’s degree with dissertation distinctions from Oxford, now pursuing a law degree and PhD in mathematics at Stanford.

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I was honored to host a fireside chat with Sherwin Wu, head of engineering for OpenAI API at last week's Fellows Forum 2025 organized by Fellows Fund. Sherwin shared his incredible journey at OpenAI in scaling OpenAI's developer platform and building its vibrant ecosystem, and his deep insights into various topics we covered.   A few quick notes:   Model Evolution & Platform Development: Outlined 3 major model capability inflection points 📍GPT-3: Limited to copywriting applications 📍GPT-3.5/4: Enabled the post-ChatGPT explosion (Perplexity, Snapchat integrations) 📍Reasoning paradigm (O1): Unlocked long-term planning for coding and autonomous agents   Lessons from GPT-5 launch 📍Latency concerns: Model's extensive thinking time frustrated users 📍Product showcase mismatch: ChatGPT interface didn't effectively demonstrate the model's capabilities 📍Learning: Future major launches will pair API releases with first-party products that better showcase model strengths   Unexpected use cases 📍Consumer apps like enabling toys for kids to chat with   Common patterns in startups that successfully leverage OpenAI models and API 📍Most successful startups "swim with the current" - building products that push models to their limits and position for future capabilities rather than current limitations   Common patterns in enterprises that successfully leverage OpenAI models and API 📍Common use cases include internal productivity enhancement (e.g., using Codex) and customer interaction transformation (support, sales automation)   Barriers in enterprise Adoption 📍Lack of both top-down and bottom-up organizational buy-in (top-down alone is not sufficient) 📍Insufficient AI-ready infrastructure and data platform investment   Responsible use: OpenAI is investing in 📍Pre-launch Red-teaming in Model Development 📍Post-launch monitoring 📍Tools to help developers manage safety risks, build guiderails, and setup evals   Exciting future breakthroughs 📍Excitement centers on advancing the reasoning paradigm (longer thinking periods) and multimodal breakthroughs, particularly in voice interactions that feel more naturally conversational Full fireside chat video: https://lnkd.in/gJzjzMBm

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