Tyler Edwards

@JensenHuang put our whole roadmap on one slide at #nvidiagtc berlin: "take control of your intelligence, build your own ai" (the @pollenrobotics microducks waddling around his feet still stole the show!!) nvidia calls them ai factories, with gpus at one end and enterprise value at the other. almost every conversation i had was about some stretch of that chain the best debates were about rl. everyone is racing to own the rl stack, yet every compute provider told me the same thing: enterprises still pick 30-200b general models because big feels safe. a focused 1-3b specialist would often do the job better. that is an education gap, not a tech one. huge thanks to the @nvidia startup team. jessica held the stage with so much warmth, naomi ran everything so smoothly you forgot how hard it was, and howard went from the hardwood to nvidia without losing his court vision. bonus: howard and naomi share my altitude, so for once i got to talk to people eye to eye the future we are building at @OvermindLab: every agent in production supervised by one open-source platform that trains on your own data, runs on infra you own, and ships a better version of itself every single day 🚀

We were able to fine tune an open weight model (Qwen3.5-9B) on proprietary internal data at @coinbase, and it out performed frontier models, at much lower cost and speed, for a specialized task (fraud prevention in this case). I think we'll see more of this pattern inside companies. The frontier models are amazing generalists. But increasingly you'll see specialist models out perform on more narrow tasks, either working on teams, or by themselves. We're making it easy to fine tune models internally for our employees. We even have a skill for it in our internal harness that makes it easy to spin these up and start training them: "/fine-tune-and-serve-llm I want to fine-tune Qwen for payments risk review. help me get started" I imagine we'll have some backend services that could be replaced entirely by this new architecture.