For what it’s worth, I think OP hit the nail on the head for you software devs out there in terms of post-AI changes: sure, specialists who can maintain total comprehensive and detailed knowledge of a single system’s codebase will always be in some demand, but the true generalists (who never do dives) and T-shaped (who never specialize enough) are arguably better replaced with AI in many corporate cases. Those in a position to succeed under AI are the owners who do deep dives on-demand to learn or fix things (hi!) or the T-shaped workers willing to start going on more deep dives.
It’s very similar to the reward structure of the IT industry since outsourcing and visa exploitations became the norm: nobody really has immense in-depth specialty experience in a single system, because that track isn’t often needed in work environments. Rather, specialists often end up leading departments while owners lead companies for the same sorts of reasons the author laid out: fundamentals, breadth, and on-demand depth. It’s more valuable in the marketplace knowing how to migrate off VMware to another hypervisor while also being able to fumble-write the new API calls for your sludge pipe to deploy into the new environment and also supporting the identity/collaboration/SSO stack than it is to just be an Okta or VMware guy specifically.
It also means leveraging AI effectively on-device makes you (or me, in this case) that much more valuable, because I don’t need a Claude or OpenAI or Gemini sub to “work”, I just need compute (which is cheap and plentiful). Because I’ve gone on these deep dives before, I know how to steer my agents to solutions that are adapted to product changes (like a recent Mac keychain issue); the same would apply to software devs who have plumbed problems directly when they could rather than sloughed off blindly to the agents.
Thanks for the write-up. It’s nice seeing the topic written about from the lived-in middle as opposed to the extremes.