But the factory owners and their investors had captured Parliament, which ignored its own laws and did nothing as the "dark, Satanic mills" proliferated. Luddites only turned to property destruction *after* the system failed them.

Now, it's true that *eventually*, the machines improved and the fabric they turned out matched and exceeded the quality of the fabric that preceded the Industrial Revolution.

11/

But there's nothing about the *way* the Industrial Revolution unfolded - increasing the power of capital to pay workers less and treat them worse while flooding the market with inferior products - that was necessary or beneficial to that progress. Every other innovation in textile production up until that time had been undertaken with the cooperation of the guilds, who'd ensured that "progress" meant better lives for workers, better products for consumers, *and* lower prices.

12/

If the Luddites' demands for co-determination in the Industrial Revolution had been met, we might have gotten to the same world of superior products at lower costs, but *without* the immiseration of generations of workers, mass killings to suppress worker uprisings, and decades of defective products being foisted on the public.

13/

So there are two stories about automation and labor: in the dominant narrative, workers are afraid of the automation that delivers benefits to all of us, stand in the way of progress, and get steamrollered for their own good, as well as ours.

14/

In the other narrative, workers are glad to have boring and dangerous parts of their work automated away and happy to produce more high-quality goods and services, and stand ready to assess and plan the rollout of new tools, and when workers object to automation, it's because they see automation being used to crush them and worsen the outputs they care *about*, at the expense of the customers they care *for*.

15/

In modern automation/labor theory, this debate is framed in terms of "centaurs" (humans who are assisted by technology) and "reverse-centaurs" (humans who are conscripted to assist technology):

pluralistic.net/2023/04/12/alg…

There are plenty of workers who are excited at the thought of using AI tools to relieve them of some drudgework. To the extent that these workers have power over their bosses and their working conditions, that excitement might well be justified.

16/

I hear a lot from programmers who work on their own projects about how nice it is to have a kind of hypertrophied macro system that can generate and tweak little automated tools on the fly so the humans can focus on the real, chewy challenges. Those workers are the centaurs, and it's no wonder that they're excited about improved tooling.

17/

But the reverse-centaur version is a lot darker. The reverse-centaur coder is an assistant *to* the AI, charged with being a "human in the loop" who reviews the material that the AI produces. This is a pretty terrible job to have.

For starters, the kinds of mistakes that AI coders make are the *hardest* mistakes for human reviewers to catch.

18/

That's because LLMs are statistical prediction machines, spicy autocomplete that works by ingesting and analyzing a vast corpus of written materials and then producing outputs that represent a series of plausible guesses about which words should follow one another. To the extent that the reality the AI is participating in is statistically smooth and predictable, AI can often make eerily good guesses at words that turn into sentences or code that slot well into that reality.

19/

But where reality is lumpy and irregular, AI stumbles. AI is intrinsically conservative. As a statistically informed guessing program, it wants the future to be like the past:

reallifemag.com/the-apophenic-…

This means that AI coders stumble wherever the world contains rough patches and snags. Take "slopsquatting." For the most part, software libraries follow regular naming conventions.

20/

The Apophenic Machine — Real Life

The conspiratorial mode and the internet’s data hoard were made for each other

^{Real Life}

For example, there might be a series of text-handling libraries with names like "text.parsing.docx," "text.parsing.xml," and "text.parsing.markdown." But for some reason - maybe two different projects were merged, or maybe someone was just inattentive - there's also a library called "text.txt.parsing" (instead of "text.parsing.txt").

21/

AI coders are doing inference based on statistical analysis, and anyone inferring what the .txt parsing library is called would guess, based on the other libraries, that it was "text.parsing.txt." And that's what the AI guesses, and so it tries to import that library to its software projects.

22/

This creates a new security vulnerability, "slopsquatting," in which a malicious actor creates a library with the expected name, which replicates the functionality of the real library, but also contains malicious code:

theregister.com/2025/04/12/ai_…

23/

AI can't stop making up software dependencies and sabotaging everything

: Hallucinated package names fuel 'slopsquatting'

^{Thomas Claburn (The Register)}

Note that slopsquatting errors are extremely hard to spot. As is typical with AI coding errors, these are errors that are based on continuing a historical pattern, which is the sort of thing our own brains do all the time (think of trying to go up a step that isn't there after climbing to the top of a staircase). Notably, these are very *different* from the errors that a beginning programmer whose work is being reviewed by a more senior coder might make.

24/

These are the very hardest errors for humans to spot, and these are the errors that AIs make the most, and they do so at machine speed:

pluralistic.net/2024/04/23/max…

To be a human in the loop for an AI coder, a programmer must engage in sustained, careful, line-by-line and command-by-command scrutiny of the code. This is the *hardest* kind of code to review, and maintaining robotic vigilance over long periods at high speeds is something humans are very bad at.

25/

Indeed, it's the kind of task we try very hard to automate, since machines are much better at being machineline than humans are. This is the essence of reverse-centaurism: when a human is expected to act like a machine in order to help the machine do something it can't do.

26/

Humans routinely fail at spotting these errors, unsurprisingly. If the purpose of automation is to make superior goods at lower prices, then this would be a real concern, since a reverse-centaur coding arrangement is bound to produce code with lurking, pernicious, especially hard-to-spot bugs that present serious risks to users.

27/

But if the purpose of automation is to discipline labor - to force coders to accept worse conditions and pay - irrespective of the impact on quality, then AI is the perfect tool for the job. The point of the human isn't to catch the AI's errors so much as it is to catch *the blame* for the AI's errors - to be what Madeleine Clare Elish calls a "moral crumple zone":

estsjournal.org/index.php/ests…

28/

As has been the case since the Industrial Revolution, the project of automation isn't just about increasing productivity, it's about weakening labor power as a prelude to lowering quality. Take what's happened to the news industry, where mass layoffs are being offset by AI tools. At Hearst's King Features Syndicates, a *single writer* was charged with producing over *30* summer guides, the entire package:

404media.co/viral-ai-generated…

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Viral AI-Generated Summer Guide Printed by Chicago Sun-Times Was Made by Magazine Giant Hearst

The Chicago Sun-Times said "we understand this is unacceptable for us to distribute."

^{Jason Koebler (404 Media)}