Gordon Ritter: I predicted AI’s learning loop a decade ago. The doomers are still measuring the wrong thing
- Jun 13
- 1 min read
FORTUNE — Every time a person works with an internal system, that interaction leaves a trace: a record of what the system did and how a human responded — every correction, every preference, every edge case. Traces are not data you can buy or scrape. They are earned one interaction at a time. They feed the domain-specific training loops that turn raw use into steadily better performance.
Better performance drives more use. More use produces more traces. The gap widens with every cycle.
Foundation model providers capture general intelligence from every customer they serve. But the traces that encode how your analysts build a model, how your operators make a call, how your team actually decides — those belong to whoever builds the system to capture them.
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