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Cheap AI models now match the expensive ones. Why did this engineer spend $40?

As AI execution gets cheaper, durable advantage shifts to technical imagination: knowing which new questions are worth asking frontier models.

Nate B Jones frames Mitchell Hashimoto’s experiment as a strategic warning. If cheap models can now match expensive ones on ordinary coding tasks, the important question is no longer only which model executes fastest or cheapest. It is who can imagine the work that is not yet on the list.

Execution is becoming a commodity layer

For well-defined tasks, lower-cost models can already produce acceptable output. That is useful and should be exploited aggressively. But it also means that shared prompts, public playbooks and familiar tasks will push everyone toward similar results.

Where frontier models still matter

The expensive model mattered when Hashimoto applied it to a hard systems optimization rooted in his own expertise. The task was not assigned by a backlog or a product manager; it existed because an expert suspected a new capability had become possible. That is the frontier-model use case: targeted exploration that changes what the execution layer builds next.

Technical imagination is learned through contact

Jones separates imagination from vague creativity. In this context it means hands-on awareness of what models can do, combined with deep domain context. You cannot reliably imagine uses for capabilities you have never touched.

The organizational lesson

Companies should route routine work to cheaper models, but they also need scouting time, permission and infrastructure for people close to the work to try expensive questions. Without that, AI becomes a faster version of the old operating model.

Bottom line

Cheap models are the engine. Frontier models help steer. The advantage goes to teams that redesign the task list itself, not just to teams that execute yesterday’s list at lower cost.

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