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.
There will always be a need for people who know how to think for themselves.
Stay strong, and to what you like to do. After the AI bubble has burst the software industry is gonna need people like you again.
As a career software engineer, I think you’re right to ask the question.
I’m going to give an answer that will get me downvoted to hell, but you deserve to hear it.
Your PhD is extremely useful in specific cases regardless of AI slop-coding existing. With the caveat that you may need to be open to leveraging AI tooling to go faster.
I work with a few PhDs, smart people, sharp people, amazing ideas, but the things they think of and approaches they try are not something your AI/LLM is going to just magically always suggest. Even if it does, it likely won’t be contextually correct or proper for the situation.
A hybrid approach of someone with the intelligence to steer LLMs towards a goal and who understands the right ways to prompt to achieve or test that goal quickly is valuable.
I don’t know you, haven’t met you, don’t know your actual technical knowledge - but assuming that stuff all checked out it is exactly the type of thing some companies I’ve worked for or work for would hire around.
Unfortunately, the roles that appreciate someone like you are fewer and further between, but they exist and they’re in-demand based on what I’ve seen.
I agree but have downvoted this in order to support your prophecy. /s
I don’t feel comfortable telling anyone to go into software engineering right now. I think the future is really uncertain. That said, I’m also not convinced this is a bad time.
The need for software engineering isn’t going away. LLMs are actually not very good at thinking and solving problems at a high level, so we still need people for that. LLMs are more of a performance multiplier than a replacement for human software engineers.
That does mean that fewer engineers will be needed to do the same work. But also, I think there’s a chance it’s a Jevons Paradox situation, and maybe if the cost of software drops we’ll see even more demand for it - like how we found tons of new uses for electricity when prices dropped. Maybe we’re entering a golden age of bespoke software. Unclear.
This. You still need to know how to write and organize code to guide LLMs in the right direction, you just don’t have to write the code yourself anymore, that’s no longer economical at scale.
The skills you need to guide LLMs though comes from practical application/experience writing code in the industry though, so my concern is how people are going to get those skills now. You don’t get them from college.
The skills you need to guide LLMs though comes from practical application/experience writing code in the industry though, so my concern is how people are going to get those skills now. You don’t get them from college.
Yeah… it’s going to be really rough for new engineers for a while until we figure out a new way to train people. I think there will be people who are self taught and AI native who will do really well, but they will be the minority.
I agree with this take. Lots of uncertainty because LLMs are rubbish at probably solving (which is a human’s strength). LLMs don’t have a semantic understanding of the problem they’ve been tasked with solving.
tl;dr LLMs are useful tool but not software engineers.
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.
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.
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?
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.
As an addendum, this was posted yesterday: AI Needed to Produce More Gas to Power More AI, Tech Firm Cohere Says
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.
At the very least, it’s a very different job than what you remember. It’s more of a manager role now, without the paycheck.
Note this is far from a universal experience in this industry. Even if vibe coding worked as well in my problem domain as others, my manager would still be unable to deliver a vaguely decent solution.
Recently I was asked to review a vibe coded application from a random guy who convinced management he was onto something. But neither he nor anyone he knew could even identify the correct person to review the solution he vibed up. So they went to me and I recognized that I had nothing to do with the problem and forwarded them to the folks that would.
Ultimately, it was a rather visually impressive demo that didn’t do anything vaguely right under the presentation. This is the sort of disconnect between management and what they manage, they have a whole culture of not even understanding the depth of their problem domains.
I mean, yeah, I agree with that. Requirements engineering continues to be a big part of the job and an actual team lead does not have the mental capacity for that.
I meant “manager” more as in:
- You’re supposed to take responsibility for something you only delegated.
- As a result, you spend most of your time checking the output of others/LLMs and trying to put in guardrails to push them towards the right goal and making them more productive.
I guess, everyone’s a lead dev, without the paycheck, is probably more precise.
With a PhD in stats you should be able to make more than most software developers. Companies need scientists to sign off on certain claims for liability reasons and it’s easier for them to just have one in house. That’s a licensed position that AI will not be able to take away.
I left corporate software engineering after 10 years. I wouldn’t recommend it unless you can stomach the worst side of humanity.
I still love development, but I would only return if I can get a guarantee that I don’t have to develop or work with AI, and only if I’m offered more than I was making before
What did you switch to?
I already had a second career in circus so I’m just shifting priorities
circus
wat
Data analysis is only going to be more and more in demand.
I’m a senior developer. I haven’t written any code by hand so far this year, and the highly technical stuff I have on the resume is already done both faster and better by AI than I would be able to do it. However, I find that there’s still a lot of work for me to do because the AI has trouble maintaining coherence and working towards long-term goals in the absence of straightforward metrics. I end up herding a bunch of highly competent but easily distracted AI agents. It’s like being a manager, I think, although I’ve never managed humans myself. You still have to keep track of the overall goal and how the different agents are working (or failing to work) towards it in a way that requires human judgement and experience. I wouldn’t suggest becoming a junior dev in 2026 (because I would rather work with an AI than a junior dev) but senior devs aren’t obsolete yet although the nature of the role is changing rapidly and I don’t know what the future will bring.
YES
