Simply put: AI thrives when our need for originality is low and our demand for mediocrity is high.
AI will fill the world with grindingly average texts, passable but derivative illustration and video, and unoriginal but functional new product designs.
What is being mechanized by AI is our tastes—our ability to discern quality (or originality) at all.
People talk about the effectiveness (or lack thereof) of large language models as though all tasks are comparable. But it strikes me that there are three broad categories of work that large language models are applied to:
Compression is when you feed a large language model something big that you want to make small. Summarise this book. Give me the gist of this meeting. Large language models are generally pretty good at this, which makes sense given that they themselves are kind of like compressed artifacts.
Transformation is when large language models convert from one format into another. Turn this audio into text. Turn this jumble of data into structured JSON. A large language model can handle these tasks pretty well. There’ll probably be a few errors so make sure that’s not a deal-breaker.
Expansion is when you give a large language model a prompt to generate something from scratch. An image. A presentation. An email. A poem. This is where slop lives. The output inevitably betrays its origins, glistening with a sheen of mediocrity.
Laurie spotted this three-way split a while back:
Is what you’re doing taking a large amount of text and asking the LLM to convert it into a smaller amount of text? Then it’s probably going to be great at it. If you’re asking it to convert into a roughly equal amount of text it will be so-so. If you’re asking it to create more text than you gave it, forget about it.
I hope that when the bubble finally bursts, we’ll see the surviving large language models put to work on the first two categories. The boring stuff. The work that’s tedious for humans.
But tedious is as tedious does. Something I consider drudgery might be the very thing that gives you life. Like Giles says:
I have a feeling that everyone likes using AI tools to try doing someone else’s profession. They’re much less keen when someone else uses it for their profession.
The big exception seems to be programming. Apparently there are plenty of coders who never before expressed an interest in being managers who are now happily hanging up their coding spurs in favour being the overseer of non-human workers.
It’s a reasonable outlook. It could even be considered a user-centred approach. Users don’t care about the elegance of your code; they care about accomplishing their tasks.
Programming is something of an exception to the efficacy of large language models in general. Instead of relying on the subjectivity of painting, poetry, or prose, programming can be objectively tested. Throw enough money at the worst people in the world and they’ll give you tokens you can use to get the machines to test their own output. So you can get a large language model to create something reasonably good from scratch as long as that something is code.
If you had asked me about the threat model of large language models two years ago, I probably would’ve been worried for artists, writers, and musicians. I thought that software had enough inherent complexity to be relatively safe.
Now my opinion has completely reversed. Software is almost certainly the killer app for large language models.
I think the artists, writers, and musicians will be okay, or at least as okay as they ever were. It turns out that humans like things made by other humans.
And y’know what? If I had to choose which endeavour I’d rather see automated away—programming or art—it’s no competition.
Don’t get me wrong—it would be nice if everyone got paid for doing what they enjoy. It’s just that I’m okay with software engineers not being at the front of that line.
I remember when I first started getting paid money to make websites. “Really?” I thought, “Someone is willing to pay me to do something I’d do anyway?” I kept waiting for the jig to be up. Instead I saw my profession grow and expand.
Perhaps there’s a long-overdue compression happening.
Or maybe it’s more like a transformation.
In 1958, Mao ordered every village in China to produce steel. Farmers melted down their cooking pots in backyard furnaces and reported spectacular numbers. The steel was useless. The crops rotted. Thirty million people starved.
In 2026, every other company is having top down mandate on AI transformation.
Same energy.
slop·py·pas·ta n. Verbatim LLM output copy-pasted at someone, unread, unrefined, and unrequested. From slop (low-quality AI-generated content) + copypasta (text copied and pasted, often as a meme, without critical thought). It is considered rude because it asks the recipient to do work the sender did not bother to do themselves.
Mutually assured Mechanical Turk.
This is genuinely much more interesting and wholesome than a chat interface powered by a large language model.
To be clear, I think AI will be ultimately extremely helpful. I still am using it on my projects. I am going to use it at my next job. I, personally, don’t hate AI.
But I can’t deny that the vibes right now are awful.
Not just bad, awful. It’s not just the “chat we’re cooked you’re the permanent underclass” stuff influencers say. It’s not just the “everybody is fucked” hyperbole CEOs sprout. It’s the actual, day-to-day experience with the technology. I’m a programmer—AI actually helps me a lot. But for normal people, their interactions are profoundly more negative, and none of the people behind this technology seem to care.
I write here for you, not for the benefit of building the machines producing a firehose of spam, scams, and slop. The artificial intelligence companies have already violated the expectations of even a public web. Regardless of the benefits they have created — and I do believe there are benefits to these technologies — they have behaved unethically. Defensive action is the only control a publisher can assume right now.
I wanted to quote an excerpt of this post, but honestly I couldn’t choose just one part—the whole thing is perfect. You should read it for the beauty of the language alone.
(This is Anthony Moser’s first blog post. I fear he has created his Citizen Kane.)
This website is for humans, and LLMs are not welcome here.
Cosigned.
Following on from my earlier link about AI etiquette, what Trys experienced here is utterly deflating:
I spent a couple of hours working through my notes and writing up a review before sending it to my manager, awaiting their equivalent review for me.
However, the review I received back was, quite simply, quintessential AI slop.
When slopagandists talk about “AI” boosting productivity, this is the kind of shite they’re talking about.
For the longest time, writing was more expensive than reading. If you encountered a body of written text, you could be sure that at the very least, a human spent some time writing it down. The text used to have an innate proof-of-thought, a basic token of humanity.
Now, AI has made text very, very, very cheap. … Any text can be AI slop. If you read it, you’re injured in this war. You engaged and replied – you’re as good as dead. The dead internet is not just dead it’s poisoned.
I think that realistically, our main weapon in this war is AI etiquette.
Frankly, I’d rather quit my career than live in the future they’re selling. It’s the sheer dystopian drabness of it. Mediocrity as a service.
I tried the tab-completion slot machines; not my cup of tea. I tried image generation and was overcome with literal depression. I don’t want a future as a “prompt artist”.
I’m mostly linking this for what it says, but oh boy, do I love the way it says it with this wonderful HTML web compenent.
If I’m understanding Greg correctly here, he’s saying it’s okay for people to use large language models …because they’re being forced to?
One persistent piece of slopaganda you’ll hear is this:
“It’s just a tool. What matters is how you use it.”
This isn’t a new tack. The same justification has been applied to many technologies.
Leaving aside Kranzberg’s first law, large language models are the very antithesis of a neutral technology. They’re imbued with bias and political decisions at every level.
There’s the obvious problem of where the training data comes from. It’s stolen. Everyone knows this, but some people would rather pretend they don’t know how the sausage is made.
But if you set aside how the tool is made, it’s still just a tool, right? A building is still a building even if it’s built on stolen land.
Except with large language models, the training data is just the first step. After that you need to traumatise an underpaid workforce to remove the most horrifying content. Then you build an opaque black box that end-users have no control over.
Take temperature, for example. That’s the degree of probability a large language model uses for choosing the next token. Dial the temperature too low and the tool will parrot its training data too closely, making it a plagiarism machine. Dial the temperature too high and the tool generates what we kindly call “hallucinations”.
Either way, you have no control over that dial. Someone else is making that decision for you.
A large language model is as neutral as an AK-47.
I understand why people want to feel in control of the tools they’re using. I know why people will use large language models for some tasks—brainstorming, rubber ducking—but strictly avoid them for any outputs intended for human consumption.
You could even convince yourself that a large language model is like a bicycle for the mind. In truth, a large language model is more like one of those hover chairs on the spaceship in WALL·E.
Large language models don’t amplify your creativity and agency. Large language models stunt your creativity and rob you of agency.
When someone applies a large language model it is an example of tool use. But the large language model isn’t the tool.
I have little understanding for people using large language models to generate slop; words and images that nobody asked for.
I have more understanding for people using large language models to generate code. Code isn’t the thing in the same way that words or images are; code is the thing that gets you to the thing.
And if a large language model hallucinates some code, you’ll find out soon enough:
With code you get a powerful form of fact checking for free. Run the code, see if it works.
But I want to push back on one justification I see repeatedly about using large language models to write code. Here’s Craig:
There are many moral and ethical issues with using LLMs, but building software feels like one of the few truly ethically “clean”(er) uses (trained on open source code, etc.)
That’s not how this works. Yes, the large language models are trained on lots of code (most of it open source), but they’re not only trained on that. That’s on top of everything else; all the stolen books, all the unpaid creative work of others.
Even Robin Sloan, who first says:
I think the case of code is especially clear, and, for me, basically settled.
…goes on to acknowledge:
But, again, it’s important to say: the code only works because of Everything. Take that data away, train a model using GitHub alone, and you’ll get a far less useful tool.
When large language models are trained on domain-specific data, it’s always in addition to the mahoosive amount of content they’ve already stolen. It’s that mohoosive amount of content—not the domain-specific data—that enables them to parse your instructions.
(Note that I’m being very delibarate in saying “parse”, not “understand.” Though make no mistake, I’m astonished at how good these tools are at parsing instructions. I say that as someone who tried to write natural language parsers for text-only adventure games back in the 1980s.)
So, sure, go ahead and use large language models to write code. But don’t fool yourself into thinking that it’s somehow ethical.
What I said here applies to code too:
If you’re going to use generative tools powered by large language models, don’t pretend you don’t know how your sausage is made.
It’s an annoying cognitive task: detecting weird photo artifacts, bizarre movement in videos, impossible animals and body horror, and reading through reams of anodyne text to determine if the person who prompted the synthetic media machine cared enough to dedicate time and energy to the task of communicating to their audience.
I hate that this is the bleak future which venture capitalists and AI boosters have gleefully laid out for us, that they consider this to be a “democratizing” technology in any real sense of the word. Far from strengthening democracy, these are technologies more apt at propping up scam capitalism and multi-level marketing schemes. I would like my time and mental space back.
We wonder often if what is created by AI has any value, and at what cost to artists and creators. These are important considerations. But we need to also wonder what AI is taking from what has already been created.
Using ChatGPT to complete assignments is like bringing a forklift into the weight room; you will never improve your cognitive fitness that way.
Another great piece by Ted Chiang!
The companies promoting generative-A.I. programs claim that they will unleash creativity. In essence, they are saying that art can be all inspiration and no perspiration—but these things cannot be easily separated. I’m not saying that art has to involve tedium. What I’m saying is that art requires making choices at every scale; the countless small-scale choices made during implementation are just as important to the final product as the few large-scale choices made during the conception.
This bit reminded me of Simon’s rule:
Let me offer another generalization: any writing that deserves your attention as a reader is the result of effort expended by the person who wrote it. Effort during the writing process doesn’t guarantee the end product is worth reading, but worthwhile work cannot be made without it. The type of attention you pay when reading a personal e-mail is different from the type you pay when reading a business report, but in both cases it is only warranted when the writer put some thought into it.
Simon also makes an appearance here:
The programmer Simon Willison has described the training for large language models as “money laundering for copyrighted data,” which I find a useful way to think about the appeal of generative-A.I. programs: they let you engage in something like plagiarism, but there’s no guilt associated with it because it’s not clear even to you that you’re copying.
I could quote the whole thing, but I’ll stop with this one:
The task that generative A.I. has been most successful at is lowering our expectations, both of the things we read and of ourselves when we write anything for others to read. It is a fundamentally dehumanizing technology because it treats us as less than what we are: creators and apprehenders of meaning. It reduces the amount of intention in the world.