SWE was most of the time never thinking and solving problems. Most of the time it was writing boiler plate code or transforming data from one format into another which is now done by AI (most of the time ok-ish). Where AI fails (and I think will keep failing) is keeping coherence, understanding the problem and therefore generating correct solutions (unsupervised).

I could show a colleague that her AI generated code produced exactly the numbers from her Excel which she put in as a template, but that the numbers were wrong as soon as you changed the input (basically the AI hardcoded the current state of the Excel, but forgot that depending on the value of certain fields there were changes to the calculation.

Last time I checked AI was also not capable of choosing the right statistical tools for processing the data.

So understanding the problem, knowing the business needs and being able to check the results of the generated code remains. As I said that was only a small part of SWE, the boiler plate writers are not longer needed. And since even local models can do that to a certain extend I think that will stay even if the AI bubble bursts.

I like coding with AI because I can do POCs real quick, or try a new idea, and I hate coding with AI when it ignores my prompt over and over again and produces the same shit slightly different. But overall it still makes me quicker and more productive. The bottleneck is now reviewing all the generated code, because most of the time it lacks quality.

So my suggestion would be: if you love coding or building models or whatever your niche is, go for it. If you just think that’s the best way to earn a lot of money: don’t. Being passionate about something will make you great at it and there will always be positions for those people. But if it’s just a half assed job you’re doing the enthusiasts will probably get the job before you get it.

The idea the AI bros are selling is: oh, you just define the problem and then the AI creates the whole application. If you need changes the AI just regenerates the software, discarding the old version. That will never work. The reason is that in nearly 40 years working as a developer I never ever saw a correct problem specification. The real world is messy (timezones!), the requirements change constantly etc.

And last but not least: the frontier models are crazy expensive. If they would charge the users the real costs most companys would not use them. There was this idiotic trend of token maxxing (great idea to define being wasteful as KPI.) but now it already shiftet to: um, could you use the smaller model, costs get out of hand. Already saw that some companies found out that they now spend more on tokens than on human developers, but without really gaining anything.

So for me developing is still the greatest thing in the world, I get paid for my hobby. The idea is to solve problems, software is a tool. If AI gets crazy good, there will still be the problems which need a solution and someone to check if the solution is correct.

I have to relearn my job constantly, which I enjoy. Coding with line numbers was suddenly not fashionable, someone came up with dependency injection, unit test where suddenly a must etc. AI is just another tool like an IDE or a compiler. Learn how and when to use it.

So the question for yourself: which problems do you enjoy to solve? Where do these problems show up? How are the problems solved and by whom? Get in contact with people who do what you think you would like and ask them about there field and experiences.

Asking here will just get you unspecific answers because software development and data science is such a vast field there won’t be a correct answer.

My best guess what the next big thing is would be robotics. Have a look at the robot olympics 2026 in China. Try to find videos which do not show only the failures (most english videos just mock them) but a more neutral view on what happend. Also compare the progress to 2025. Learning chinese and electrical engineering may also be worth a shot.