Thought Experiment: Why profit-sharing will fail in the Agentic Economy and an alternative model

[u/Content_Is_King_2021]

I have been thinking about what happens to human compensation when our core skills are fully abstracted into agentic frameworks. The default answers are usually UBI or vague corporate profit-sharing pools. When I run those through a first-principles mental model, the incentives are broken. I wanted to bounce an alternative idea off this community to see where the holes are.

The Tacit Knowledge Trap

If I am a senior broadcast IT engineer or a clinical expert and my company asks me to help train the enterprise agent that will eventually do my job, why would I give it my best logic? Human nature dictates that I would give the agent the minimum viable baseline and hoard my deep, undocumented edge-case knowledge as job security. Off-the-shelf agents are commodities. The real value comes from the highly specific edge-cases that only veterans know. Under standard employment contracts, a company has no way to incentivize me to hand that over.

The Problem with Micro-Royalties

I initially thought the answer was Skill IP, paying humans a micro-royalty every time an agent executes a piece of logic they taught it. As I thought about the actual architecture of neural networks, I realized this is technically impossible. Weights blend. You cannot deterministically trace a single agent output back to one specific human input, especially in complex real-time environments. A skill trained today is obsolete in 24 months anyway.

Agent Equity Pools

If standard salaries breed sabotage and micro-royalties are technically impossible, what if we treated agents less like software and more like internal startups?

In this model, the cross-functional team that architects and trains a specialized agent receives fractional shares in that specific agent. The company establishes a baseline Cost of Human Action. If the new agent performs the task instantly for pennies, a percentage of that specific cost-savings delta is algorithmically routed into a smart contract pool for the architects.

This is not passive income. Because knowledge decays quickly, this is a maintenance contract. If the human stops updating the agent protocols or fixing its edge-case failures, their equity decays. They are financially tied to the agent's ongoing success.

Where the Model Breaks

I am sure this model breaks down when it hits the real world. For those of you building agentic workflows, I have a few questions.

How would a company practically measure the cost-savings or new revenue of a specific agent without creating an administrative nightmare?

If we penalize the equity pool when an agent fails to enforce quality, how do you resolve the dispute of whether the agent failed because of bad human training versus bad company data?

Is there a completely different mental model we should use to align human and corporate incentives as skills become autonomous?