But in the two hypotheticals you cite (Wall Street securities analyst and a defense attorney), the army of junior people they used to use to do that work is no longer needed. The juniors are replaced by AI while the seniors are expected to increase work output volume and quality.
Do you see that playing out any other way?
I’ve got two small personal data points. I hike a lot and use a commercial app (MapMyRun) to log my hikes. But its ability to calculate my calorie burn is abysmal. With AI I began researching different physiological models that take account of more information about the hikes and much more information about me. I began just asking AI “Here’s info about my hike and me. Give me calories using these three physiological models.” I made that a quick template and it worked really good.
Then, one day, I asked Grok “Can we create an app so I can run these analyses locally?” It replied “Sure. Do it in Python.” I said “Find. Write me an app with these constraints, these goals, this desired output.” It did a pretty good job.
Since then, I’ve greatly improved it, switched to Claude Code, and added a lot of features. The features I added are of interest to me and probably not to a wider audience, so they would never gotten added to MapMyRun, Strava, AllTrails, etc.
I’m not a professional programmer (did do some limited coding in my career on scientific apps and managed some niche tech dev programs), but I am blown away by what amounts to having a team of junior devs operating under my control. I operated as the project manager, team manager, and product manager.
And I figured all this out on my own without any training. Truly astonishing.
Do you see that playing out any other way?
Yes.
That's the static view–AI does the work previously done by staff, fewer people required. The boss picks up any slack remaining.
But that's not how it plays out in genuinely competitive fields.
AI doesn't just help a securities analyst do his existing job faster. It opens up information horizons that didn't exist before. He can now track operational metrics, supply chain signals, and industry dynamics at a level of granularity that was simply out of reach. That's not the same work done more efficiently–it's a fundamentally larger canvas. The firm that cuts its junior staff to pocket the savings will be outcompeted by the firm that redeploys that capacity into the new territory AI has made accessible.
Same in law. AI doesn't just help a defense attorney process the same evidence faster. It surfaces connections across case law, forensic data, and precedent that no junior associate could have found in time. The attorney who uses AI to shrink his team will eventually face one who used it to build a deeper, more comprehensive case.
The zero-sum assumption treats the work as fixed. In competitive professional fields, AI makes the work larger. The firms and attorneys and analysts who grasp that will eat the lunch of the ones who just used it to cut headcount.
As an aside, I am a mountain biker and recently bought a weighted ruckpack (GoRuck) so I can hike my biking trails. Have you looked into it?
I've only been at it for a couple of months, but am increasingly confident it is going to be great addition to my fitness routine.