I’m a backend engineer. For the last couple of years a big part of my job has been wiring LLM “agents” into production systems: the kind that answer customer calls and chats, pull up your account, and decide whether a human ever sees your case. Most AI discourse comes either from boosters or from people who have never seen how this stuff actually gets deployed, so here are some notes from the shop floor.
1. The product is not intelligence, it’s deskilling. Braverman laid it out in Labor and Monopoly Capital: management studies the labour process, extracts the knowledge from the worker, and hands it back to them as a fragment. Separate conception from execution, concentrate conception in management. That is literally the project. Transcripts from experienced workers become training and evaluation data. The judgement of someone with ten years on the phones gets turned into prompts, flows and guardrails. Marx called this the real subsumption of labour under capital: capital no longer just buys your time, it rebuilds the work itself in its own image. What’s left for the human is the edge cases, at a faster pace and with less autonomy.
2. “Replace workers” is the pitch, “discipline workers” is the reality. The humans mostly don’t disappear. There are fewer of them, they’re cheaper, and they’re monitored by the same system: handle time, sentiment scores, compliance flags, all generated automatically. The agent is also the foreman. Marx describes exactly this in Capital vol. 1, ch. 15: in the factory it’s not the worker who uses the instrument of labour, it’s the instrument that uses the worker. Dead labour, in this case the frozen knowledge of thousands of past calls, dominating living labour. And the ones who are let go don’t vanish either. They join the reserve army, which is the stick held over everyone still employed.
3. It’s relative surplus value, not magic. Nothing here escapes the old logic. Marx quotes Mill doubting that all the machinery ever invented had lightened a single worker’s day, and his answer is that it was never meant to. Under capital, machinery is a means for producing surplus value: it cheapens labour power and lengthens the part of the day you work for free. The goal is to cut the labour time needed per interaction, and the gain goes to the client’s margins, not to shorter hours for the people doing the work. The same machine under different relations of production would mean the opposite.
4. The general intellect is being enclosed. In the Fragment on Machines, Marx talks about the “general intellect”, society’s accumulated knowledge, becoming a direct force of production. LLMs are that almost literally: trained on the collective writing of humanity, then rented back to us by a handful of firms. Socialised production, private appropriation, the basic contradiction in its purest form. Open-weight models, a lot of them coming out of China, cut against that enclosure, which is part of why Washington treats them as a threat.
5. The tech itself is useful. Don’t burn the data centers. Marx already settled this one. The Luddites smashed the looms, and it took time and experience, he writes, before workers learned to tell machinery apart from its capitalist use, and to aim their attacks at the social form, not the material instruments. Lenin made the same point about Taylorism: the subtle brutality of bourgeois exploitation fused with real scientific achievements, so take the science and abolish the brutality. Automating tedious, repetitive work is good. Under planning it could mean shorter hours, better public services, translation and accessibility for everyone. The problem was never the machine. It’s who owns it and what it’s pointed at.
Question for comrades: if you work in tech, or in a job that’s being “AI-transformed”, what does it look like where you are? And is anyone seeing real organizing among the people who build these systems?
I don’t think that’s all that matters. I know that an LLM was used to some extent to write this. The only alternative would be that OP has used LLMs for so long that they’ve adopted the same writing style, which would be concerning. In any event, because an LLM was almost certainly used here, I can’t tell whether the LLM was just used to polish things up or to generate the whole analysis, and I’m not about to start putting effort into discussing this topic with a mindless LLM. You’ll notice that after OP responded to me using their own words, I wrote a detailed response to them.
No you don’t unless the OP says they have used an LLM. Arranging something in a bullet-point style manner is not exclusive to AI, it’s just something that Instruct models were trained to do because it helps with brevity and is easier for the user to process mentally. Furthermore, the analysis made is not that much different from other ones that people having been trying to say on this instance for a long time. If you’re afraid of wasting time conversing with a bot, you’d have to avoid the internet to be certain of it because there’s long been the possibility of that. It still matters to discuss for the sake of real people who are watching, unless we’re going to say Dead Internet Theory is real.
It has nothing to do with bullet points. It has to do with how the text sounds and is structured. It screams LLM to me. I use bullet points myself, but no person I’ve ever seen writes sentences and formats their text the way this post is written and formatted. I’m sure such people exist, but they’re rare enough that I’ve never met one in my life.
But you don’t actually know. That’s the bottom line. I remember a time on here not too long ago where somebody who was being very anti-AI accused me of writing a response to them with AI (it was 100% from my own head). When I said that, they claimed it must be that my writing was being influenced from using AI.
People would like to think they can tell, but they can’t reliably do so. We need to be able to accept the implications of not having certainty about it, what that means for text and images on the internet. Trying to figure out how to tell without explicit information on what is and isn’t AI generated is a losing battle because if somebody really wants to pass something AI off as human written they can just put more thought into hiding it in plain sight by using more colloquial styles and casual structures, especially with a model that does not have Instruct style drummed into it.
We can best guess in some situations, but that’s all it is, a guess. And if there are concerns about verifiability of information written by an LLM vs. written by a human and not knowing whether to trust it because we don’t know if it’s LLM written or not, well we should be cautious about that regardless. The internet has long been full of real people who may or may not be bullshitting to various degrees. I remember before LLMs, sentiment on reddit about how you’d have people who would share supposed insight that was actually bullshit and it sounded sensible to people who didn’t know the field, but sounded like bullshit to people who did. Point being, that was happening long before LLMs and it wouldn’t suddenly stop being an issue if LLMs disappeared tomorrow.
There are posts where it kind of sounds like maybe there was an LLM used where I don’t say anything because I know it could have been written by a person, and then there’s this, which just screams LLM. I suppose I can never be 100% sure, but I’d say after reading this post, I’m about 95% sure it at least went through an LLM at some point, and at most was generated entirely by one.