Lin Qiao, CEO of Fireworks AI, ends up arguing that the next jump in AI quality will come from the more than 90% of data that sits inside enterprises and applications, not from bigger general models. Her picture is open models continuously and automatically customised to each product, which she calls "autonomous intelligence". The host says Fireworks has raised over $300 million, including a $250 million Series C.
Autonomous intelligence versus AGI. Qiao calls AGI "a great direction", but says only a small fraction of data reaches foundation models. Customisation must be continuous as products change, and automated, since today only a few talent-dense companies can do it. She says coding agents now behave like junior engineers, enough that Fireworks is rethinking coding interviews.
Reinforcement fine-tuning. She likens RL to breeding sweeter fruit over generations. A rewarded model specialises at the cost of breadth, and a constantly learning private model gives a product a moat API users lack. She compares this to mobile forcing A/B testing: product signals should now improve the model.
Churn. Model selection is "exhausting". Leaderboard leaders change every couple of weeks, labs are specialising, and benchmarks are saturated. NVIDIA launched three SKUs last year against one every 3 years before. Fireworks bet early on open models out of its team's PyTorch background, and she expects convergence with closed models to become prominent in 2026.
Who adopts open models. AI-native startups adopt readily because products can "scale into bankruptcy" on inference costs, and Fireworks has no pre-training cost to amortise. Enterprises worry about licences and data leaving the country but are opening up. Fireworks also pools demand across customers, sparing them capacity guesses, much as cloud replaced caged mainframes.
Latency classes. Real-time uses cannot afford reasoning. Her recipe there is "small, big, small": test data quality on a small model, tune the largest MoE, then distil. Offline research agents need slow thinking, long context and tuning on thinking tokens.
Evals and optimisation. Fireworks partners for evals. Because agent builders start with "vibe testing", it open-sourced Eval Protocol to link any eval system to any tuner. Customers want quality, speed and cost together, which turns configuration into a search over more than 100,000 options, like a database query optimiser.