[Bayko]

> AI reduces the penalty for weak domain context

This is why (personal experience) I am seeing a lot of FullStack jobs compared to specialized Backend, FE, Ops roles. AI does 90% of the job of a senior engineer (What the CEOs believe) and the companies now want someone that can do the full "100" and not just supply the missing "10". So that remaining 90 is now coming from an amalgamation of other responsibilities.

[KittenInABox]

In my mind we will have a bimodal set of skills in software development, likely something like a product engineer (an engineer who is also a product manager-- this person conceptualizes features and systemically considers the software as a whole in terms of ergonomics, business sense, and the delight in building something used by others) and something like a deep-in-the-weeds engineer (an engineer who innovates on the margins of high performance, tuning, deep improvements to libraries and other things of that nature). The former is needing to skill in rapid context switching, keeping the full model of customer journey in their minds, while also executing on technical rigor enough to prevent inefficiencies. The latter will need to skill in being able to dive extremely deeply into nuanced subjects like fine-tuning the garbage collector, compiler, network performance, or internal parts of the DOM or OS or similar.

I would expect a lot of product engineering to specialize further into domains like healthtech, fintech, adtech, etc. While the in-the-weeds engineering will be platform, infra, and embedded systems type folks.

[toldnotmywrath]

Can I take a guess that you believe you will speciate into the former?

[KittenInABox]

Actually, ideally I'd love to dig deep into and specialize in database management systems internals. I think data engineering in general is the underspoken but fundamental necessity to any sort of application, AI or otherwise, but especially any concept of a data warehouse.