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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...
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."
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.
It is one of those things. LLM's are brilliant at recombining old code/art/ideas into mixtures of these old things, but they cannot create anything genuinely new. Eventually we will need new idea and means of doing it.
Most of this slop-ware will probably be ditched in years to come as its tidal wave of technical debt becomes more of a burden than the gain.
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.
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.
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.
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.
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.
This is exactly the drum I've been beating for about the last year, since my company has really embraced using AI for development. I'm worried that the industry as a whole is going to become very top heavy. Nobody's going to be hiring new junior devs, and the new devs that do get hired straight out of college won't have the practical experience needed to really be able to fully understand the output of their AI models - what's good, what's bad, and how do you tell the difference?
AI can be incredibly useful for an experienced dev, but if you don't have that baseline experience and understanding and just blindly trust what it gives you, then you're just going to cause more problems than you're solving.
Maybe I'm too cynical for my own good - I guess time will tell.
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!™
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.
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
The whole idea really is to drive the value of everyone, well except those that actually run the generative AI systems.
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.
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.
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
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.
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 considering (at least dream about it) on switching into indiehacking.
Cuz number of jobs in IT are less and less
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
