If you're worried about this, I would recommend learning about the "complements" to AI -- data preparation and distributed computing. Those both involve a lot

If you're worried about this, I would recommend learning about the "complements" to AI -- data preparation and distributed computing. Those both involve a lot of conventional software skills. What you don't get from the flashy news articles is that a lot of ML work is programming and sys admin work: writing code to prepare the data, and a cycle of tweaking and re-running large jobs (including debugging them when they run out of resources, etc.). You can also read papers like this which talk about the software engineering challenges of machine learning systems. "Machine Learning: The High Interest Credit Card of Technical Debt" https://research.google.com/pubs/pub43146.html https://research.google.com/pubs/pub43146.html There's a big difference between "we got this thing to work once" (which is most of the new articles you read), and a system that does useful work over a long period of time. That latter is what you should know about as a software developer.