I build LLM agents for a living. Notes from the shop floor on what they're actually for

pacemaker7382@lemmygrad.ml · 2 days

I get what you’re saying, but I think there’s a bit of a cultural difference here too. I’m Greek and that’s literally how a lot of political texts are written here, especially in communist and anarchist circles. Pamphlets, announcements, you name it. They’re full of these “it’s not X, it’s Y” constructions, sharp contrasts, numbered arguments etc. I’ve been reading and writing this stuff for years and I guess it just carries over into how I write in English.

I mean I get why you’d associate that style with LLMs nowadays, but these models learned those patterns from human writing in the first place lol. We cant suddenly treat every rhetorical device they picked up as evidence that a human didn’t write something.

pacemaker7382@lemmygrad.ml · 2 days

The criticism about the lack of concrete examples is fair. I probably spent too much time connecting things to Braverman and Marx and not enough describing what actually happens in these projects.

But I really don’t understand when numbered arguments and carefully structured writing became uniquely associated with ChatGPT. Have you read political pamphlets? Party publications? Lenin’s polemics? People have been organising political arguments into numbered theses, headings and neat rhetorical contrasts for generations. That’s practically a genre of political writing.

Anyway, the practical point is that decisions previously made by experienced support workers are increasingly being encoded into prompts, workflows and evaluation criteria. The worker doesn’t necessarily disappear. They lose discretion over the process and increasingly handle whatever the automated system can’t resolve. That’s what I meant by deskilling, and that’s the discussion I was hoping to have.

pacemaker7382@lemmygrad.ml · 2 days

Fair enough, my last reply was a cheap joke.

But I think we’re mixing up two different things here. I’m not arguing that LLMs are intelligent, or that every developer should be outsourcing their thinking to one. I work with these systems, and a considerable part of that work is dealing with their failuresand the ridiculous expectations management has of them.

Where I disagree is the claim that they’re not automation. I’ve seen actual human tasks broken down into workflows, with parts handed over to models and the remaining work pushed onto fewer people. Whether the model understands anything is almost beside the point. The labour process has changed, and that’s what I wanted to discuss.

As for the writing itself, I find the argument about structure a bit odd. Political pamphlets and theoretical texts have always been edited, condensed, divided into numbered points and written around fairly repetitive rhetorical constructions. Half the political literature we’ve inherited is structured that way. It didn’t suddenly become an AI characteristic in 2023.

I do take the criticism that I should’ve included more concrete examples from work rather than leaning so heavily on theory. That’s fair. But I’d rather discuss what’s actually happening to workers than spend the whole thread trying to establish whether a colon or a numbered paragraph was invented by ChatGPT.

pacemaker7382@lemmygrad.ml · 2 days

Would you like me to make this reply more concise, or adjust the tone to sound more casual? Let me know!

pacemaker7382@lemmygrad.ml · 2 days

ngl “sounds like AI” is becoming the new “ok boomer”, a way to skip the argument. braverman is from 1974, take it up with him lol. any thoughts on the actual content tho?

pacemaker7382@lemmygrad.ml · 2 days

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?