Overall it is a slight shift in skills and about knowing how to use the new tools effectively. At the minute for example, reviewing code is as much about making sure it is nice and readable, long term that we can come back and understand it. That the names are good, not too large functions, tests exist. However AI code looks good right out of the bat. But that does not mean good. The heuristic of nice readable code is good code doesn’t work anymore. It might be nice looking but bad code. It might not do the right job or the tests don’t test for the actual thing we want. Does it follow requirements.
We move on from being ones that write code to orchestrating agents. Its not the same work but its not boring work either. There’s still a lot going on. It probably will also involve another layer of abstraction where engineers are up a level, closer to architects, strategists and product managers operate.
What can we do about this then? One option is to focus on improving skills. Before you could specialise narrowly in a specific niche. That is no longer enough on its own. Now you need to be able to tie that in with other things so you can at the very least operate the agents working on adjacent things, rather than just shipping that off to another team
Takeaway
Nothing here is all that groundbreaking. Go through all the big shifts the last decade and you’d find the same stuff. Focus on the fundamental skills that remain relevant throughout the ages. Treat AI as another tech you need to get familiar with. But also remain knowledgeable about the fundamentals and ensure you’re good at working in a team. Software is still a team sport and AI shows no sign of changing that. We might be more productive individually but you’re not going to be the only one on the team. Being able to communicate your work, your achievements, your ideas all remains as relevant as ever. Things might be moving fast, but that’s not different.