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 organizing around it. McKinsey built Lilli, an internal assistant that indexes the firm’s proprietary knowledge for tens of thousands of consultants. Bain built thousands of custom GPTs on top of its OpenAI partnership. EY has tens of thousands of AI agents in production, not as a science project but as a remaking of how institutional knowledge gets created. Ramp gives every employee agentic capability; when someone finds a workflow that works, it gets packaged into a reusable skill and shared across the company. Veeva is embedding the same principle into an entire industry, with deep agentic integration across the applications that run life sciences companies.
What these companies share is a direction. They are pointing AI inward, compounding human intelligence and keeping it inside the building. Companies losing ground are pointing it outward, letting their best thinking drain into tools they do not control.
Why the Advantage Compounds
There is a technical reason this works, and it is the most underappreciated idea in enterprise AI right now.
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. A company that starts today holds an advantage a competitor starting next year cannot simply purchase. The traces are structurally exclusive. That is real intellectual property, and it compounds on its own.
The Risk Nobody Is Managing
When your people use public AI tools to describe how your team works, they are teaching those tools how you operate. The exposure most leaders worry about is data — the sensitive file leaving the building. The subtler exposure is process. A multi-step session with an agent encodes your sequencing, your priorities, and your decision logic. That reasoning path is among the most valuable things a model can learn from.
Enterprise contracts usually prohibit training on your data. The protection around behavioral traces is far less settled. Anthropic changed its consumer terms in August 2025 so that chats from free, Pro, and Max accounts train future models by default unless the user opts out. AI labs are actively hiring lawyers, doctors, and financial analysts to teach foundation models how to reason like industry insiders — recruiting from your talent pool to learn what makes your industry tick. One study found that 77% of employees paste data into generative AI tools, with 82% of that activity running through personal accounts nobody is managing.
When Anthropic released legal plugins for Claude in early 2026, Thomson Reuters fell roughly 18% in a single session. RELX and Wolters Kluwer dropped double digits. Analysts called the reaction overdone, and whether those losses hold is still an open question. But the signal underneath the volatility is the one that matters: software by itself is not a durable layer. After a career building and backing technology companies, that is not a small thing to say. The durable layer is how your people think, decide, and work together — captured and sustained within the company.
Where This Lands
Automate everything you can. Optimization is free money and you should take it all. But optimization is not the edge, because soon everyone will have it. The edge is the judgment that changes the goal, and the creativity that produces a move the model has never seen.
Your people are the only truly defensible algorithm you have. Not because AI cannot do more — it will — but because the specific way your organization thinks and adapts cannot be reconstructed from the outside. It is built from billions of lived experiences, recombined fresh whenever someone meets a problem no one has solved before.
The doomers are counting jobs. The winners are counting traces.
Build the layer that captures yours. Keep the mutations yours.
The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.
