I wanted to share an idea and get thoughtful feedback from the community, especially those thinking about the long-term economics of frontier AI.
One challenge I keep coming back to is this: as models become more capable, the cost of intelligence is largely borne by AI providers, while much of the recurring economic value is captured downstream by companies using that intelligence to replace or augment labor.
The idea I’m exploring reframes AI from “intelligence on demand” into productive digital labor.
At a high level:
-
AI agents perform specific, measurable tasks for businesses (operations, support, internal workflows, etc.).
-
Businesses pay a recurring fee for output, not raw intelligence.
-
The agents are economically owned by individuals, while the platform handles deployment, performance tracking, and payments.
From an AI-provider perspective (e.g., OpenAI), this model could:
-
Create long-running, predictable inference demand instead of sporadic chat usage.
-
Enable value-based pricing, where AI is tied to economic output rather than tokens.
-
Externalize operational and liability risk.
-
Capture downstream value by positioning models as the engine behind AI labor, not just APIs.
I’m currently exploring this as a solo founder and am primarily looking for critical feedback:
-
Does this model meaningfully address the value-leakage problem for frontier AI providers?
-
Are there obvious flaws or second-order risks I might be missing?
-
Would this kind of “AI labor infrastructure” align with how you see AI being deployed at scale?
Appreciate any perspectivesespecially from those thinking deeply about AI economics and deployment at scale.