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Yoko Li (a16z/@stuffyokodraws) argues on 2026-08-06 that AI models don’t…

2026-08-06 · 16:20 UTC ·@a16z ·1 min read

Brief

Yoko Li (a16z) argues that AI systems lack intrinsic termination awareness and currently stop only when compute budgets expire or when engineered checks signal completion. In her loop-engineering piece (Aug 6, 2026), she insists builders must predefine the cost and precise criteria of “done” so systems converge predictably instead of running indefinitely.

Why it matters

Yoko Li (a16z/@stuffyokodraws) argues on 2026-08-06 that AI models don’t inherently know when their work is done; “It stops when the budget runs out or when a check we designed says enough.”

Key details

  • The post frames this as a loop-engineering problem: builders must design stopping checks and decide, in advance, both what “done” means and what done costs.
  • a16z published Li’s piece titled “Knowing When to Stop: The Art of Making a Loop Converge” (a16z.news/p/knowing-when-to-…) advocating predefining convergence criteria rather than relying on indefinite model continuation.
Cleaned source text

"So how does an AI model know its work is done? For now, it doesn’t.

It stops when the budget runs out or when a check we designed says enough, and both of those need to be built.

The systems that matter will not be the ones that can keep going. They all can. They will be the ones whose builders decided, precisely and in advance, what done costs and what done means."

a16z's Yoko Li on loop engineering: a16z.news/p/knowing-when-to-…

Yoko (@stuffyokodraws)

Article

Knowing When to Stop: The Art of Making a Loop Converge

How can an AI model know when its work is done?

Well, how does a human know when our work is done.

A programmer waits for the tests to turn green or waits for PR review from their team. A designer

— https://nitter.net/stuffyokodraws/status/2085392969558089980#m