Gordon Ritter: I predicted AI’s learning loop a decade ago. The doomers are still measuring the wrong thing

In June 2025, Jim Farley said AI would replace half of all white-collar workers in America. Dario Amodei put unemployment as high as 20%. For more than a year, executives have reframed the future of knowledge work as a countdown clock.

I want to make the opposite case — and I want to be honest that I have skin in it.

In 2017, my firm wrote about what we called Coaching Networks: software that uses machine learning to guide workers in real time, gathering data from a distributed network of people and learning the techniques that actually work. The idea that mattered most was this: the human being is the mutation engine in the system. Software learns what’s already proven. But genuinely new moves — the ones no model could have predicted — come from creative people finding a better way. The system spreads those mutations to everyone else. The cycle repeats.

We were early. The technology wasn’t ready. It is now. And the idea has aged a great deal better than the doom has.

The Countdown Gets the Wrong Number

AI is extraordinary at optimization. Give it a goal and it will find a faster, cheaper path than any team you could assemble. What it cannot do is decide which goal is worth pursuing, or make the judgment call when the model has no answer. Those are the moments that move markets and start companies. They are the hardest moments to automate, because there is nothing yet to imitate.

The work that survives is not the work that sits below the model. It is the work that sits above it.

This is not a thought experiment. The companies furthest ahead are already........

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