Who Owns the Intelligence Economy? A VC Says Nobody Has Decided

By Daniel Okafor

Eighty-nine percent of organizations now use AI in at least one business function, yet only 44 percent have started to scale it, according to a McKinsey study. Gordon Ritter of Emergence Capital reads that gap as the last open window to decide who owns the intelligence AI systems absorb.

In a November 2 Fortune commentary, Ritter argues the rules of the AI economy are being written by default. Companies building AI on employees' work are quietly staking claims about who owns what a system learns. "If we don't shape the rules now, the companies controlling the most powerful models will set the terms, and everyone else will pay rent," he writes.

What is the resource in dispute?

Ritter's unit of value is the trace. In a June Fortune essay he argued AI doomers count jobs when they should count traces: corrections, judgment calls, reasoning that closed a deal, workarounds absent from any manual. These are among a company's most valuable assets.

His historical parallel is radio. In the 1920s, American broadcasters staked out frequencies faster than the law could follow. The Radio Act of 1927 arrived only after control had solidified. The Communications Act of 1934 merely ratified it. Ritter warns the same pattern is repeating, with human reasoning standing in for the airwaves. Treat intelligence as something to extract rather than cultivate, he says, and the source of future breakthroughs drains.

How would the three-layer contract work?

Ritter proposes a social contract with three layers: platform, organization, and individual.

• Platform: choice. No single model should become the unavoidable infrastructure of intelligence. As foundation model capabilities converge, the durable advantage sits "above the model," in the learning loop an organization builds on top of it. Providers often bind organizations to the first model they adopted, and access to AI should not require surrendering intelligence as a condition of service.
• Organization: sovereignty. Learning loops, workflows, and domain expertise built on top of AI belong to organizations. RedCompass Labs already trains its Payment Expert Agent with Cognitive Task Analysis, converting thousands of hours of expert work into datasets that teach AI how specialists reason.
• Individual: appreciation. Ritter uses the term the way an investor does: a person's judgment should be worth more when they leave a job than when they arrived.

The sovereignty layer needs discrimination, he argues. Routine operations can stay transparent by default. The outliers deserve protection: the rep who ignores the script and closes the deal, the engineer's fix absent from any runbook. "Sovereignty claims belong with the judgment that changed the outcome."

Employees using public AI tools at work are already teaching an external agent how the organization operates, Ritter notes. Enterprise contracts often prohibit training on internal data, but protections around behavioral traces and process knowledge remain far less settled.

What happens to people?

Ritter's evidence that AI design shapes human capability comes from two studies. Undergraduates who used ChatGPT as a study aid scored significantly lower on a surprise retention test 45 days later than students using traditional methods. Separately, Harvard researchers tested an AI tutor that withheld answers until students reasoned their way to them; those students achieved more learning gains immediately after lessons and spent less time than peers in an active-learning classroom.

A crutch is a design choice, he concludes. This is the next generation of his firm's 2017 Coaching Networks thesis: the first generation collected top performers' moves and spread them; the next must help people invent new moves, and that requires friction.

His sharpest example is an enterprise sales rep leaving her role after four years. The company's AI watched thousands of her calls, learned her negotiation timing, her habit of opening with a question about the buyer's last quarter, and coached the team with her moves. When she exits, the calls, records, pricing, and the coaching model stay behind.

His remedy is a "cognitive passport": a portable, anonymized record of how a person approaches problems, excluding deals, documents, and confidential processes. Her opening question is hers; the company's 18 percent discount floor is not. No common format yet makes a passport from one company mean something at the next.

Ritter discloses his position plainly: Emergence Capital profits when portfolio companies gain value, but twenty years in enterprise software taught him technology creates the most value when the people whose thinking feeds it trust the system. He invites stress-testing from founders, researchers, legal scholars and labor voices, and expects to be wrong in parts.

His closing claim frames the stakes: humans remain the only mutation engine in the age of AI, because AI masters fixed-rule games while markets keep changing the rules. The people whose judgment trains today's systems, he writes, should be treated as co-authors, not raw material.