[jillesvangurp]

I would say agents will come for specialists equally hard. It takes a long time for a specialist to build up expertise in their domains. But given enough background material, an AI agent can absorb a lot of the essentials and do productive things in the same domain. And of course a lot of general purpose frontier models have been trained on an insane amount of research and documentation. The only difference between training data for generalists and specialists is that there's a lot more material for generalists out there.

If you are a generalist with a lot of experience, it's not that hard to "specialize" to different domains that you have no direct experience with the help of AI. Effectively that's what generalists do anyway even without AI support. If this includes proprietary stuff, the job is basically making sense of a lot of specs, documentation, etc. LLMs are pretty good at picking apart stuff like that.

A lot of projects I've been doing in the last 25 years have in common that they always require me to wrap my head around a new business domain, new technology, or framework. That makes me a generalist. I got good at figuring out new things and filtering out things not relevant to the job at hand. That's what makes me a senior (that and my age).

I've on purpose stepped out of my comfort zone and targeted a few tech stacks lately that I've not used before with AI. That works amazingly well. My experience as a generalist is helpful, a lot of concepts are not really that tech stack specific and port well across stacks. I don't need to be a Go specialist to be productive with Go anymore. Same with typescript, rust, and python. I've barely touched Kotlin (my go to tech stack until last year) in the last months.