AI Engineers aren't safe from being replaced by AI
dmanco.dev
dmanco.dev
Same with prompts, most attempts seem to be fidgeting with the models till they get your intend right, which is also a matter of hill-climbing, subtle mutation, and so on.
If I were to clarify anything from the article, I'd probably say that I'd rather do the factorisation of programming roles by how long they already existed. If someone is an AI engineer and his work only became relevant a month ago, very probably it will be obsolete in another month. If they do the same thing for the past 10 years, changes are that their skills would be useful for another 10 years to come.
[1] Some examples: https://danluu.com/nothing-works/
Simple example: Who will renew the SSL cert? Day 1: meh, no impact. Day 2: meh, no impact. Day 700: who the hell manages this and why are we making no revenue?
You might think that is laughable; what a pack of newbs!
But this stuff has already happened without even LLMs in the mix.
https://www.digicert.com/blog/lessons-from-the-equifax-data-... comes to mind.
The number of flea circus level orgs where someone has flubbed it and been on leave, causing a few hours outage? More than one in my experience.
Where it's more hostile? https://www.reddit.com/r/sysadmin/comments/1itiu8n/it_team_f... is a common narrative.
The hard question, one which everyone and everything who isn't a domain expert (so AI, juniors, and quite a lot of managers and politicians) suck at, is "which things are safe to break, and which things really do need quality?"
On the other hand, it will finally not matter any more if AI-generated software is crap, because its bot users have infinite patience and infinite tolerance. Only the people who pay the electricity bill for all that waste will care but that's the price for survival in a world where only bots count.
Of course not, all these sloppers are doing is training the models so at the eyes of management they are good enough for a replacement. The ones who stay will have 10x more work.
And economic progress for the companies. What these engineers who stay get is just a burnout and a pat on the back. I've seen it, and felt it, many times way before all this slop-coding started
Maybe at some point the AI tooling will be good enough for me to say "do my work for me today" and sit back. At that point, yes, I am irrelevant and could be replaced by anyone else. But is it anywhere close to that right now? My experience says no.
Perhaps the current models are capable of that with the right tooling - some system to define clear goals and stick to them. I haven't seen evidence of that yet though. Have other people?
Over the last couple of years, I’ve seen plenty of developers who remain barely competent despite having access to powerful AI tools. Generating code is easy. Evaluating whether it’s actually correct and maintainable is the hard part.
I place very little value in the idea of "getting better at using AI". It's like getting better at using a library, or getting better at using Google. Now that LLMs are widely available, their entire intent is to make it significantly easier to access information held in a truly vast body of written work.
I have also seen no evidence that understanding the resulting generated code is necessary.
If your job has a large component of regurgitating existing information, you are now competing with a machine that can regurgitate hugely more information and with lower-skilled operators.
You'll be replaced by someone cheaper using AI.
But AI can also do that. So, what’s the point? And if you think it can’t, wait one more year
At that point the debate isn’t really about software engineering anymore
What time to be alive, eh?
> But AI can also do that.
Citation needed.
> So, what’s the point?
The point is that there haven't been broad demonstrations of your claim.
> And if you think it can’t, wait one more year
You surely must understand that this isn't an argument? How many hundreds of billions have been burned through now? Yet we still have to suffer "soon" as an argument? I can't take any of this seriously anymore.
PS: Just to be absolutely sure you don't misunderstand me: I am NOT claiming that AI will never be able to do this stuff. Nor am I even claiming that it's too far off or too expensive. Just, for the love of god, you cannot build an industry on promises of how amazing it'll be in the future. Technology is evaluated based on how it performs. Not how you think it might perform in the future.
PPS: The last paragraph does also not mean that I think it's bad to invest in things that haven't yet paid off. On the contrary! What I am saying is you cannot claim success until there's success!
To have a job you have to show up, get in there and figure it out.
Although this may be more relevant to replacing AI researchers, not AI engineers...
(Submitted as https://news.ycombinator.com/item?id=48380643 )
https://www.minimax.io/news/minimax-m27-en
With human productivity already fully unleashed, the natural next step was to initiate self-evolution of both the model and the organization. M2.7 is our first model deeply participating in its own evolution.
Drag it out for a couple of years and you'll be set.
In fact, I believe that the most cost effective way is a collab of human+agent. Ie giving the agent direction as it goes along with the plan I can cut the thinking while keeping the speed. Basically helping the agent going from a breadth first search into a guided depth first one which is much more token efficient.
Additionally, humans have long term memory and knowledge of the context around your codebase. Agents do not, and while you can fit a lot in 1M context window, once you fill that the quality goes down considerably.
To be clear, I'm also not saying LLMs will definitely displace a lot of us very soon. I'm just saying I wouldn't be surprised by either outcome and I don't know how anyone claims to know one way or another given the past year or so of progress.
In a hypothetical world where LLMs have enough context window and "understanding" to have no need for an experienced user to give inputs I would assume its also going to have enough information to make most business decisions and provide well formatted info to the C-Suite.
While tokens costs are going down, the number of token burned is going up and up. Case in point Sam Altman is complaining about their top token users burning through 100B tokens per month [1]. So you have token prices going down but token usage going up 10x per year (if you extrapolate linearly from what Sam was ranting about). This is happening because people trust more and more LLMs and give them more autonomy and more complex tasks (IMHO).
So if you really need a true unsupervised agent that replaces SWEs you need how probably much more than that. Say 20x that number (2T tokens/month) for each SWE. I'm gonna focus on the energy part as this is more tangible. Trying with some realistic numbers:
- To replace 1M SWEs for a year you need 2T tokens/month * 12 months * 1M SWEs ( = 2.410^19 tokens)
- Assuming 0.5J per token you get 1.210^19J [2] (I took the number for an llama3 8B model, probably is much more for SOTA models IMHO).
- A year has 31M seconds
- Over a year that is 380 GW of constant power that is needed only for replacing 1M SWEs and that is around 80% of all the current US energy consumption (450GW). And apparently there are 47ish Million SWEs globally as of 2025 [3]
I don't think there is enough power capacity to deliver all of this without pivoting all of society into building data centers and power plants.
So unless there is some breakthrough in efficiency/intelligence (ie you need way fewer tokens for what you have to do) your job is gonna be safish at least.
Of course I pulled that 20x out of my ass, but I believe it is somewhat realistic for a truly autonomous agent(s) that replace SWEs.
[1] https://finance.yahoo.com/sectors/technology/articles/sam-al... [2] https://arxiv.org/html/2512.03024v1 [3] https://www.slashdata.co/post/global-developer-population-tr...
I think the economics here work out as "OK, so we've bought 80% the electricity in the US and used this to sell software to the 96% of humans not living in the US; this is profitable for the businesses, so nobody with money cares about the Americans who now literally can't afford to keep refrigerators running because we outbid them".
The reasons are many.
These are things I've come to expect from bots, clueless journalists, clueless juniors, clueless expert beginners and clueless members of the professional managerial class but almost never from experienced software engineers.
To be fair, seasoned software engineers always seem to get shouted down online by the former group which is louder and more numerous so you could argue that we "lost" the argument.
Meanwhile big tech's vibe coded monstrosities are increasingly exploding all around us in ever more humiliating ways while the humans who had this tech rammed down their throats get thrown under the bus.
This undeserved halo effect over AI is maintained in order to keep the needle from pricking the ginormous stock market bubble that hinges upon the religious belief in the lie AI Will Replace Us All Soon.
Currently leading an Integration that for the most part needs no new code written and the CEO is breathing down my neck telling me to cut my 4 week estimate down to 1 because "can't i just use AI like the other firms do?".
There's a morbid part of me that wants to give him what he wants and let claude make critical process decisions on internal processes that are very domain specific and have no online documentation, but alas I would rather not have the project go down in flames so I smile and nod.
Physicist sounds like Lab Technician, which sounds like managing samples.
Electrical Engineer sounds like Electrician, which sounds like installing a bunch of wire.
Stunt Driver sounds like Uber Driver which sounds like pushing pedals and turning a wheel.
It’s fun to pretend the world is much simpler than it is.
That, or extreme extrapolation from events that form a vanishingly tiny part of the job of a software engineer. "Last week my AI solved this amazing software problem that I had struggled with" very quickly becomes "the AI is better at software than I am". Any pushback suggesting that the fact that something (or someone) did one tiny part of your job better than you one time does not mean you should be replaced, is quickly met with "yeah, but that's today, imagine how amazing the models will be in n years".
You can't win a debate with this much moving of goalposts.
The more interesting question is: Has the ease of replacibility increased because of AI?
Most training is now actually inference, not directly gradient descent. Reinforcement learning requires the generation of lots of 'rollouts' that are then compared with each other via an algorithm like GRPO. Or they might be compared using a critic model - AI judging AI and causing it to self improve. Generating a rollout means inference. And there's lots of data cleaning by older models. This has been called in the past 'textbook' or 'curriculum' learning, not sure what it's called now. But AI is also used for things like data/document labelling, transcription of videos, detection of images/videos with watermarks or subtitles, elimination of content that shouldn't be in the dataset, creation of new content that should and so on.
AI has proven capable of some routine work, like brute-force optimizing GPU kernels or doing hyperparameter sweeps.
Obviously, researchers are all using coding agents too.
So that's a few ways AI is self-improving. But there are lots of other ways in which even frontier models are still beaten by human researchers. Experiments in closing the loop have failed. For instance, people have tried giving the latest models access to some GPUs and an old version of an AI codebase that was recently optimized by human researchers (a NanoChat speed run goal, I believe). Could the models match the performance of the AI researchers? Nope. They only got 10% as far as the humans did, mostly because their approach was uninspired. They wasted a lot of time and budget doing low-IQ stuff like hyperparameter tuning. The humans had many other tactics like studying the research literature and inventing new algorithms that the models didn't even attempt.
The bottleneck is therefore currently the level of insight and inspiration the models are capable of. I've also seen this in my own work. I come up with an idea I think is novel and see if I can get a frontier model to reach the same idea. It never works without questions so leading it's more or less pointless.
It's very unclear why AI struggles so much with innovation yet can invent new songs, poems etc without apparent difficulty. Obvious answers like "it's not in the training set" don't feel right to me, the issue is deeper.