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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 features^1^. In a dev meeting today someone asked the junior some basic questions:
^1^by the way the C/C++project i'm solely maintaining already has said features...
I considering (at least dream about it) on switching into indiehacking.
Cuz number of jobs in IT are less and less
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.
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.
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
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.
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.
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.
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.
not very good at thinking
They neither think no reason.
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.
Why train people? AI is your junior now!™
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.
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?
AI poses one major risk to SE in general: the dropped need for low/entry level employees.
It used to be, a senior would have a team of mid- and junior engineers, senior would be in charge of the big picture while the team learned, and delivered the actual low level tasks.
But with AI, companies started hiring only seniors. Which is an issue - now the market is full of "entry level" graduates who simply can't find a job because nobody's hiring. And seniors are left wrangling AI agents that don't learn, don't work autonomously, don't ask questions, don't bring ideas.
don't ask questions
Skill issue, in the most literal sense possible.
oh yeah? when was the last time your LLM asked you a question, unprompted? when was the last time your LLM had a line of thought and came up with something groundbreaking, something unique, on its own?
when was the last time your LLM asked you a question
The coding agents will do that. Ask which of a couple of approaches you'd like to go with.
I think they meant questions unrelated to the current tasks.
AI agents and Junior engineers both have their pros and cons. But this column definitely goes to the human engineers.
I had no idea at the time that I'd miss those annoying questions once all the juniors are gone.
Yeah, that's a good point if that's what they meant.
Woosh, apparently. Let me explain.
LLMs are virtually nowhere used without some sort of harness these days, most prominently agent harnesses.
Skills are a harness artifact for reusable prompts where we encapsulate small terminology or large processes. People who use them quickly come up with or reuse a skill that will lead the agent to lead interviews to get a common understanding of key issues before going to action.
As for no 2, idk why that's so relevant for automation. Often after an extensive interview the agent proposes solutions like I'd envision them, but much more thoroughly written out, in a quality and consistency that maybe a much reviewed and discussed RFC reaches, but I typically not. Sometimes it suggests solutions that I didn't think of, but when poked it's usually an elegant one. I don't suppose the agent came up with these things itself, but it certainly has read many more books than I did, so that's not objectable.
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
