The AI risk I think about most is fairly boring.
Expertise becomes easier to capture.
A good analyst working with an AI system is not only producing an answer. She is also showing the system around her how she works.
What does she check first? Which result makes her suspicious? Which objections matter? When is something good enough to send?
Do this every day and parts of that judgment gradually become prompts, evaluation rules, software, and workflow.
The company becomes better at reproducing the work.
I think this is more important than the usual image of AI suddenly replacing a job.
The transition can be much quieter.
A team starts using better tools. One person can cover more work. A junior role is not replaced. Another process gets automated. Two years later, the organization needs fewer people to produce the same output.
Nobody had to do anything obviously wrong.
In fact, the employee doing a great job can help accelerate the process.
That is the uncomfortable part.
I sometimes think of this as organizational distillation. The analogy is imperfect, but the direction is useful: knowledge that used to live mainly inside a person becomes encoded in something cheaper to reproduce.
Attribution then becomes messy.
If an internal AI system improves after two years of interaction with a research team, it is hard to separate what came from the base model, the engineers, the firm’s data, and the experts who repeatedly corrected it.
For the worker, that distinction matters a lot.
For the firm, maybe less so.
This is why I care more about distribution than the headline number of jobs created or destroyed.
AI can make an economy much more productive while making some forms of knowledge work less valuable in the labor market.
Those two things are completely compatible.
And if expertise increasingly becomes an asset owned by institutions rather than something workers rent to institutions through their labor, the economic consequences could be large.
I do not have a confident forecast for how large.
But I think the mechanism is already worth paying attention to.
The change may look less like mass replacement and more like slow compression: smaller teams, fewer entry level roles, and more of the firm’s knowledge living in software.
No dramatic moment.
Just a different labor market a few years later.