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If you use AI to do your work, you can be replaced by someone else using AI to do your work.
To have a job you have to show up, get in there and figure it out.
Earlier quoted context omitted.
The lag time between firing your core team and finding out that was a bad idea can be measured in years of slow attrition.
Actually workflow impact in the world of software can be observed in weeks/months at max. And token spending too, is a voice that they see at the high floors. Also, there was never a strong willing in IT companies to reduce cost of work force: it is done sometimes, but it is more common to see them over-hiring.
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.
If you use AI to do your work, you can be replaced by someone else using AI to do your work.
I’d reframe it: you won’t be replaced by someone using AI, you’ll be replaced by someone who is better at using AI and understands the code it generates 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.
If you use AI to do your work, you can be replaced by someone else using AI to do your work.
not convinced, because if you use a shovel to do your work, you can be replaced by someone else using a shovel to do your work. To have a job you have to show up, get in there and figure it out.
Earlier quoted context omitted.
I am pretty familiar with a 500k LOC codebase. If for every feature request/bug the agent has to go through a lot of it, spend a gazillion thinking tokens for understanding what it needs to do, plan, and then execute (assuming it gets it right) given the current cost of tokens I argue I am often more cost effective. In fact, I believe that the most cost effective way is a collab of human+agent. Ie giving the agent di…
This is why you can't be replaced today. I'm not sure you can rely on that remaining true for very long. And this goes for the vast majority of us. 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.
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 have yet to hear a single convincing argument by a person that works in software why they can't be replaced.
The reasons are many.
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.
Indeed this will likely happen in the future, but not today. I was experimeting with SSD streaming in DwarfStar for DeepSeek v4 PRO inference in 128GB systems (and Flash inference iwth 32/64). GPT 5.5 ran the whole night, I checked what it had accomplished regardless of all the hints I provided in the specification document. After reasoning on the problem I gave him the design fixes and the tokens/sec were 4x after 1…
Your job safety doesn’t depend on the capabilities of AI but on what management thinks are the capabilities of AI
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?"
Earlier quoted context omitted.
This is why you can't be replaced today. I'm not sure you can rely on that remaining true for very long. And this goes for the vast majority of us. 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.
I think you are assuming cost per task will become cheaper and that there is unlimited energy supply. 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 rant…
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".