Adaptive Compute Governor for ChatGPT
I’d like to suggest an automatic compute-routing system for ChatGPT.
The idea is simple: a lightweight, inexpensive model acts as a front-door router and decides the minimum model, reasoning effort, and tools needed for each request.
Conceptually:
Lightweight router → economical model → general reasoning → frontier model → maximum reasoning
Using current models only as an example, this could look like:
Luna → Terra → Sol → Sol High > Astra
A simple request such as “rewrite this paragraph” could remain on the cheapest tier, while difficult coding, mathematics, large-file analysis, or repeated failed attempts would automatically escalate.
Importantly, escalation could also happen during the task. If a lower tier encounters uncertainty, failed tests, conflicting evidence, or unexpected complexity, it could hand the accumulated context to a stronger model without making the user restart.
Examples:
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Rewrite an email → lightweight model
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Summarize a document → economical/general model + tools
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Debug a large Blender add-on → stronger reasoning model
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Several fixes have already failed → automatically escalate
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Complex proof requiring verification → maximum reasoning
Users could still override this with commands such as “use maximum intelligence”, or manually choose models if desired.
The main benefits could be significant at ChatGPT scale:
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lower inference cost
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lower user credit/quota consumption
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less unnecessary frontier-model usage
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reduced GPU and infrastructure demand
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lower energy consumption when extra compute provides no quality benefit
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simpler UX, since users no longer need to understand which model or reasoning level is appropriate
The core principle is:
Users shouldn’t need to decide how much AI a problem requires. A cheap intelligence layer should determine how much intelligence the problem deserves.
This could make ChatGPT simultaneously simpler, cheaper, more scalable, and more computationally efficient.