I just finished a PhD in stats, and before that, studied computer science. I had planned to go back to industry to do software engineering or be a data scientist afterwards.
However, it seems that in the meantime, vibe coding has taken over the software industry and swallowed most data scientist roles. Few open positions, and the ones that do exist are just pushing a button instead of thinking and solving problems. Is this a correct assessment? Should I try to get back in or just find a new career? I am not too keen on staying in academia either.
The Jevons paradox point is interesting. Maybe we’ll finally get better software products, so far it seems like everything is as buggy and slow as as ever.
Even if unemployment isn’t an issue, engineers would need to have the flexibility/freedom to do things by hand sometimes though I think. Go into details, understand the lower level work. Just like pilots occasionally hand-fly not to lose their skills. Otherwise, it’s just too mind numbing to do long term, even if humans were involved in the big picture decisions.
It’s not an engineering problem I’d say. Enshittificatiom is an effect of capitalism.
If a Performanz and mostly working software is the goal, that has been possible for a long time.
The bar to be a coder is being lowered, but actual software engineering with metrics, requirements, and deadlines will probably increase. I can easily see a world where everyone rushes into the AI to solve problems that used to not be cost effective and at the end of the first push (hopefully that’s around now) there’s a lot of new, untested code, and no metrics whether programmers were more efficient, and more importantly no metrics (I saw stats PhD) about whether the bottom line increased: https://www.cnbc.com/2026/10/02/ai-redefining-wall-street-jobs.html
How would one measure the efficacy of two different AI generated trading regimes (or however it is they work up there) when the people who understood what they were asking can’t understand any of the code?
Yeah this aligns with my intuition. In the things that are easy to verify (is the div centered, does the server crash) humans are quickly becoming obsolete, in the things that are difficult to verify, you still need a human to make sure the code is doing what you think it is.
I’ve heard from an anquintance that their electricity pricing company just uses black box AI things for pricing. Cool, until it sells 10M worth of electricity for 500k and the grid runs out of electricity. In use cases like that, complex evaluation is required and probably can’t be fully automated anytime soon.
It shouldn’t be, but it seems like there’s an apetite for letting the AI cook and blaming it if something goes wrong (no actual consequences faced). I can’t wait until an AI escapes test lab containment and sends a side channel message to the pricing AI to drop the power price to nothing so it’s cheaper to run more AI. /s
I just don’t see people ever falling all the way out of the loop. Eventually economics will prevent the AI from becoming some tautological circle jerk if it doesn’t translate to profit, no matter how cool it is. I worry more about how techbros are setting the bridge on fire behind us before we find out where it goes.
I liked AlecSadler’s answer from this thread. If you weren’t already comfortable with doing stats in something like python+scipy.stats then now might be a good time. With the pandas library it’s supposed to be a little like R. (ask me for more details if you want)
Sorry for the half-ass answer.