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.
If you want to get into software engineering as a career, please be patient. The bubble is close to bursting, and there will inevitably be a push to hire humans. This time around however, your job will be un-fucking your employers vibe-coded slopware.
You’re like 90% of a financial analyst. Work for a bank, grab that cash. Get an MS in financial engineering, might be able to bump that to a hedge fund job.
Highest salary (straight number, not counting stock options, etc.) I’ve ever seen was an acquaintance who is a financial engineer at a major hedge fund, and rode that to a VP position.
Wait for the bubble to burst.
Anecdotal: at
${DAYJOB}a junior was tasked with “enhancing” a VB6 project, using AI of course, by adding more features1. In a dev meeting today someone asked the junior some basic questions:- If you had to, could you fix a bug alone without codex? No
- What if we can’t use codex anymore (price, end or contract, etc)? shrugs
1by the way the C/C++project i’m solely maintaining already has said features…
This kind of dependence on these systems is breeding a kind of gambling addict like behavior, that one dears what will happen if it goes away.
I also have a theory that because LLM’s use a bit randomization to it that it scratches that same itch that gambling has and this is why so many people are addicted/defend their slop so hard. “Yes, this code is crap but if I rub this chair and switch this light on and off 54 times, maybe the LLM will reward me with exactly what I want.”
I considering (at least dream about it) on switching into indiehacking.
Cuz number of jobs in IT are less and less
The tech industry seeks to introduce complexity to everyday things and disrupt systems to increase prosperity for people at the top. Technology solving problems for wellbeing is far from the ideal use case for companies, non-profits, and universities - and efforts to earnestly do this don’t typically see the light of day.
Join a union or start one if you cannot find one. Figure out if you want to work in a field harming people globally or be a force for change.
If you’re talking about solving problems to enable inequitable wealth for the rich then you probably can move forward, with perseverence. They pay top dollar for skills they tried to eliminate from education.
Looking at job postings and people around me, I am not too much less cynical than you. A lot of gambling, data centers, military tech, police, fossil fuel adjacent etc. companies hiring. Though it used to be realistic to be able to do something morally neutral, and have the job be at least somewhat interesting in a small scale. Like I didn’t think making mobile internet 300mbps instead of 100mbps was solving any real problems 5+ years ago when I did my internship, but I don’t think it was causing too many either.
I wouldn’t recommend anybody pursue software right now, switch if at all possible while you still can.
I couldn’t be happier with my nursing career. I have several buddies that went the tech route and are currently in a revolving cycle of new job for 6 months and either laid off or looking for a new job. They make good money, but it seems like their is zero job security. Most of them have had to relocate more than once.
Right now really is the time to find careers that AI can’t take.
Most people I know that were heavy into corporate software coding, have ‘pivoted’ to their next big hustle “that could be ANYTHING!”. This is the bargaining phase right before depression and acceptance. Their entire career has been gutted from below them and now desperation is setting in.
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.
Ai still needs heavy direction, it doesn’t matter how clear you are, sometimes it just needs to fail repeatedly before it gets something right, sometimes I get hella progress done, sometimes I spend an hour on one thing stubbornly when It would have been 5 minutes max to do myself. Still get caught in a loop ocassionally where you just assume the next prompt/response will fix it. Not as noticable when you work on multiple things at once. Feels like less of a stall then at least.
It also never suggests helpful things? Maybe just the AI I use, ill get days into a project before I encounter something while googling realizing it fits my needs perfectly and the ai never once thought to implement it instead of whatever solution it came up with. It constantly reinvents the wheel.
Try creating a plan document with the AI first, consisting of small steps. Start by telling it to explain different solutions to the problem, with pros and cons so you can decide what you need. Refine the steps. Then let it implement and review the solution step by step. Let it add general things you don’t like to an archtiecture document and refer to that in the prompt. Clear the context inbetween steps.
I do that, the issue usually comes up when new features get added, the plan is helpful, and I do usually start with one that tackles the backend and makes a basic frontend, it’s all the iteration that comes after the mvp where any new features also need to be planned to come out right which annoys me. I always start with locally hosted web frontends to test quickly on my phone, so I can go back and forth while testing to ensure everything works as expected. When doing that, one manually asked for change at a time to test is ideal. Ai tests do not seem to account for if things feel good to use, you just get a basic functional ui in my experience otherwise.
I think at the point SWE and DS are solved problems, most other white collar jobs are too, then you’ll have huge numbers of applicants for the remaining white collar jobs and blue collar jobs which will drive down the marginal value of those skills. Realistically if you like SWE and are seemingly pretty good at it, keep applying and get a job until the music stops
SWE consists of 2 core aspects: Complexity management and requirements engineering. Both work in layers. At each layer, choices need to be made.
Example: what instruction do you compile an “if” statement to? That is making a choice on how to implement a requirement on a level that we have already automated away. (Unless you are a compiler engineer.)
An AI can handle several of the lower layers with ease. But that just means we can start stacking more layers before human engineers get overwhelmed. AI won’t end software, it will just increase the expectations on how wel integrated it is, how much features is has, how fast bugs get resolved, how well tested code is, etc
It can change the job in ways people don’t like, though. In the same way that a compiler engineer isn’t necessarily going to be happy doing web frontend work in Javascript, somebody who enjoys coding in high-level languages by hand isn’t necessarily going to be happy doing AI-driven development.
Agreed. You have a bit different perspective on it than I do but I was coming to say the same thing. It’s funny when the first comment you read was what you were going to say.
I think at the point SWE and DS are solved problems
Do you mean this is an unknown point in the future?
I mean I don’t think they are solved now. In my job AI still needs me for certain aspects, you could probably replace me with someone less skilled potentially but company’s usually pay a premium for reliability etc. Maybe in five years time this isn’t the case I don’t know, but company’s (at least in Europe) will be slow to adapt and I think its quite likely employee protections will be pretty heavily enforced to try and stop all the white collar job money being sent to American tech companies but that’s just my two cents
Think the point is you can put aside the debate about when and if and just take it that all the likely alternative careers are in the same boat, whatever shape the boat is in. Especially if you’ve already done your education.
You might have more need to think on it if you are, say in high school or very early college. Then you might think on dramatically different paths based on risk or generally perceived risk.
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.
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.
seems unwise if you arnt in Programming, CS is right now. CS grads at the bachelors level already have trouble finding jobs after graduation. are willing to go back to school and get another masters, when you spent so much years on a PHD in stats?
have you considered other non-tech fields?
Getting another masters seems quite unwise too. Especially the things I could study (e.g. other types of engineering?) are not AI proof either.
but software/ cs is even less stable now than before the layoffs compared to other fields. since you said you had some CS experience, did you try applying to jobs?
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
dunno why you would get downvoted, i think alot of people agree with your assesment.
Sane people do. But the site is filled with AI haters who press downvote as soon as someone recommends using AI for Amy thing whatsoever.
Who’s Amy?
Auto correct for any
No! I wanna know who Amy is! She deserves a background story!
Amy is ‘any’ in witness protection. The downvoters put her there."
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.
YES
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.