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Seventy percent.

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That’s the number a billion-dollar CEO dropped in Fortune this week. Not in a footnote. Not in a caveat. As his central thesis.

Tanmai Gopal runs PromptQL, a Bay Area AI unicorn that helps Fortune 500 companies deploy artificial intelligence. He has seen, from the inside, what happens when the most powerful technology in a generation meets actual business reality. And his conclusion is that seventy percent of the effort required to make AI useful relies entirely on unwritten knowledge that exists only in human heads.

Not the code. Not the model. Not the data pipeline. The human.

The knowledge that lives in conversations. In judgment calls. In the instinct a twenty-year veteran has when something feels wrong before the data says so. In relationships that took years to build. In context that nobody wrote down because nobody asked and nobody thought it mattered.

Gopal said you fundamentally cannot train a system on this. It changes too fast. It’s too fluid. Too human.

This should be a liberation. Someone with a billion dollars of market credibility just told the world that without human intelligence, the most sophisticated AI on the planet is seventy percent useless.

Then he named the future of the human worker.

“Our job as humans and people is that we are now context gatherers instead of just workers.”

And there it is.

Context gatherers.

Your seventy percent—your judgment, your relationships, your hard-won knowledge—isn’t yours to deploy. It’s yours to donate. To collect the raw material of your own intelligence and feed it into a system that accrues returns to capital.

This is not a liberation philosophy. This is the most sophisticated extraction framework I have heard in thirty-six years of manufacturing. And it is dressed up as empowerment.

I need to be precise about what I mean by extraction, because the word gets thrown around loosely.

Extraction is when you take value from the person who creates it and transfer it to someone who didn’t. It’s when a steelworker’s thirty years of metallurgical knowledge gets captured in a process manual, the worker gets laid off, and the company sells the manual’s output as intellectual property. The worker created the value. Capital captured it. The worker got a severance check. Capital got a revenue stream.

“Context gathering” is that play’s white-collar update.

Here’s what Gopal is actually describing, stripped of the aspirational language: The AI doesn’t work without the human’s knowledge. The human’s job is now to transfer that knowledge to the AI. Once the AI has it, the human’s contribution is to go get more.

At no point in this framework does anyone ask: If the human’s context is worth seventy percent of the AI’s value, shouldn’t the human receive seventy percent of the AI’s return?

Last week I wrote about the SaaSpocalypse—the trillion-dollar software selloff triggered when Wall Street realized AI could do what SaaS tools help humans do. The market’s question was blunt: if the AI can do the task, why do we need the tool?

This week’s Fortune article asks the next question: if the AI can do the task but needs human context to function, what is the human’s role?

Gopal’s answer: context gatherer.

My answer: that’s the wrong question.

The right question is not “what role does the human play in the AI system?” The right question is “what role does the AI play in the human’s work?”

That is not a semantic distinction. It is the difference between an economy that uses humans to serve machines and an economy that uses machines to serve humans. Every dollar of return, every career trajectory, every commu