Fireworks AI CEO: 0.1% per-turn numerical drift compounds to ~10% over 100 agent turns
AccBalanced · x · 2026-10-15
Fireworks AI CEO Lin Qiao explained to firesidealpha why the company guarantees bitwise-equivalent inference on customized models:
- Long-horizon agentic tasks are extremely sensitive to numerics: 0.1% divergence per turn compounds to roughly 10% over a 100-turn task, which is "completely unworkable"
- Fireworks customizes not just models but the inference stack, with training and inference co-designed and co-optimized
- That lets it guarantee bitwise-equivalent inference with no precision loss after customization
The same interview surfaced a Meta anecdote: chief AI officer Alexandr Wang recalled Meta CFO Susan Li pulling him aside "many times" at a Hawaii exec offsite to warn that if the model didn't work, it would be "very, very bad in financial terms." Zuckerberg added that the ante for booting up a frontier lab now is extremely high.
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