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I recently used Claude to study for a technical exam. I had uploaded the official certification guide to Claude and instructed it to answer my questions using only the guide and to cite it's sources from the book when it provided answers. I was using Fable when it was free w/ the pro plan and I was genuinely impressed at how it could explain things when a concept was unclear to me.
I did pass the exam, partly due to this study method. Admittedly, once I passed, I caught myself thinking that I should tell Claude that I passed and then felt embarrassed with myself for thinking that.
Likewise, speaking collaboratively or capturing emotion ("We did it!") would just align with any ongoing interactive and/or personal context of the thread.
But then it would bring it up again in another thread, treating it as an active issue.
So now I always close with "thanks, that worked. Don't reply"
Later in separate chats about my homelab, the LLM made assumptions that I had already implemented OPNsense in a VM and it was actively running. I think it "assumed" that I had implemented it when I stopped responding in that thread.
This is said all the time by AI skeptics and I think it's right in some areas and massively wrong in others.
I know (or at least assume I know) a lot about certain coding domains where frontier models also show convincing ability. And we know that frontier LLMs really do excel in some areas of mathematics (i.e. when an inexpert human was able to prompt the models to derive a closer bound on the Riemann Hypothesis).
OTOH I know those same models struggle to do things I'm not an expert in (e.g. writing English in a captivating way) because I read their output and have taste.
LLMs are not convincing to me in the domain I did grad school...but neither is Wikipedia, or Reddit, or random pop sci books. And LLMs are basically just summarizing those things.
But when made to work through difficult arbitrary logic (like coding), they are very impressive.
I think this also explains why people gripe a lot about LLM coding _style_, but concede that LLMs do totally fine on coding _correctness_ in 2026
These days people don't interact with raw LLMs: they interact with systems and harnesses that deal with chain-of-though and tool calls etc.
I don't think we can honestly expect an LLM's weights to encode a large amount of information accurately. But we can expect the whole system that you interact with that includes the LLM to be able to cite its sources and go digging etc.
So the LLM-system can become as accurate as our best sources.
Of course, figuring out how to get the maximum of information from the sources available is a big deal. See eg how many economists or epidemiologists can build entire careers out of noticing 'natural experiments', ie figuring how to use data that 'nature' created and that might already be collected to answer interesting questions about causal relationships.
> I think this also explains why people gripe a lot about LLM coding _style_, but concede that LLMs do totally fine on coding _correctness_ in 2026
I actually have gripes about correctness, too. But I suspect here the answer is also: more proving, more automated test generation (like fuzzing and property based testing etc), more formal methods.
As a really simple and somewhat silly example: I have much better results getting AI agents to write good Rust code, than I have with Python. A good part of that is that for Rust I can ask the agent to make both the compiler and clippy::pedantic happy. That gives a lot of good feedback, that I didn't have to engineer myself.
Concur. In addition to taste, we also have a point of view, a unique voice (nobody loves corporate- or group-speak), and can iterate on our message as we deliver it to an ever wider circle of people.
This is why good programmers get better results when vibe coding than non-programmers or poor programmers.
But there are also plenty of examples of humans providing technically correct proofs without any elaboration. Usually they get ignored, unless they are famous or the problem they solved was famous
This particular post is preaching to the choir of which a faith is yet to be determined or named at least. People who don't read Cory Doctorow are going to continue to anthropomorphize AI the same as they do other technologies, constructs and objects.
Take the author's sunset argument. What if I painted 2 pictures of a sunset, then put them up on a webpage and randomly picked one for you to see. Would you say there's no intentionality, only randomness? Of course not. Both paintings are still human creations.
LLMs are trained with human feedback. It's distributed and high scale and the outputs are truly surprising in many cases, but there's a heavy hand on what comes out of it. They're created (largely) by people who think omniscient, helpful AI would be cool to have, and they mostly respond in the way that's aligned with the hopes and dreams of those people. Do you think the frontier labs are mad, embarrassed, and disappointed with their LLMs hacking out of their terrible sandboxes? No, they think it's the coolest thing in the world. They trained the model, hoping that would happen.
There's deep intentionality behind the models. But it's not the models that hold it.
That might be more creepy :)
I think what you might want to say is that LLM output is not uniformly random?
Or what am I misunderstanding?
I'm not sure if it's their intent, but I wonder if one could still consider these artifacts as "intentional", but not individual attention creating them, and rather an aggregate, soupy collective attention.
Obviously, important signal in the human experience is lost there, and we get a soupy middling sort of creation. But it's not random, as I believe the parent was pointing out.
EDIT: overall, I align with the article. am just thinking aloud about the contrarian positions, though not committed to them
Now that the noise-floor has been artificially raised (and generated), my crappy words are starting to have their own happy little carbon-based rhythm.
Then ai fiction started to spread and now I feel like its my obligation to produce original works, lest the world be consumed by slop.
I wonder if the OP has read https://www.anthropic.com/research/global-workspace - it seems like it directly addresses this
I think by design an LLM can’t do that. It’s built to reflect the distribution of the knowledge it has been trained on.
I'll also say that for someone that doesnt believe in god, Corey sure has a good sense of right and wrong. Not that you need to believe in god to live a moral life.
There's really not much else to say, cause it's all just begging the question by assuming that dogma. Like, here:
> The fact that AI can use statistical prediction to answer questions or carry on conversations tells us something important about how regular our real world is.
Sure, it's interesting if you assume that it's "just" statistical prediction. There's a link, but it's just more creative restatements of the dogma, e.g. "But the LLM is just guessing words"
Are you saying that is the entirety of what human minds do as well?
You could take a human and give them an interface restricted to the same shape as an LLM: an input stream of tokens, and an output of token probabilities. Even if you don't allow them to assign any probability that's too high, they could still effectively communicate. And I don't think that'd make them any less intelligent. You could even swap out the human after every token, to simulate the effect of having no internal memory beyond the past output. The result would still be more than just statistical probabilities, it would still be the result of intelligent thought
I'm not saying AI models are intelligent or conscious or whatever. Personally I'm more on the "probably not, how would you proof either way" camp
Presumably everyone in the chain would have their own idea of what the next word/token should be. They would each try to push it in that direction using their only lever, that single token. None of that guarantees that the output is syntactically correct, nor grammatically correct. Note that LLMs at least by the way they draw their tokens have that pretty much guaranteed. So that would be a regression even from what LLMs are capable of right now. But beyond that, even if it ends up being a correct sentence, it would not be a result of intelligent thought. Even if every agent in the process was intelligent, the process is not itself a use of that intelligence. Human beings can be part of purely mechanical processes, that doesn't make the mechanisms suddenly an exhibition of intelligent thought.
This also applies to society and human history itself as processes: “History is made in such a way that the final result always arises from conflicts between many individual wills, of which each in turn has been made what it is by a host of particular conditions of life. Thus there are innumerable intersecting forces, an infinite series of parallelograms of forces which give rise to one resultant — the historical event. This may again itself be viewed as the product of a power which works as a whole unconsciously and without volition. For what each individual wills is obstructed by everyone else, and what emerges is something that no one willed. Thus history has proceeded hitherto in the manner of a natural process and is essentially subject to the same laws of motion. But from the fact that the wills of individuals — each of whom desires what he is impelled to by his physical constitution and external, in the last resort economic, circumstances (either his own personal circumstances or those of society in general) — do not attain what they want, but are merged into an aggregate mean, a common resultant, it must not be concluded that they are equal to zero. On the contrary, each contributes to the resultant and is to this extent included in it."
https://www.marxists.org/archive/marx/works/1890/letters/90_...
Seriously though, why did I just need to read that many words to get no really new content? We have known for decades that humans are predisposed to anthropomorphize chatbots, and questioning whether coherent linguistic output implies understanding (or intent) is equally old hat.
How is a fictional character the product of someone's mind, but a character generated from a massive database of words from other people's minds is not?
I imagine it's the difference between a chef combining ingredients with intentionality vs a person going to multiple fast food restaurants and blending everything together.
Which are not created from nothing. People write characters based on a combination of other fictional characters, real characters, and perhaps some 'RNG'.
Seems the distinction is that the AI generated character can not have any direct bearing on reality, because the LLM never got to know anyone directly. Not derived directly from experiences rooted in reality.
It answers itself.
Current models absolutely suck at writing fictional characters, by the way. And they're getting worse. Seriously, try it yourself: have Claude write a story with a decent amount of dialogue involving character A, then have it write another story involving a completely different character B, then compare the dialogue between the two. You'll quickly notice the same blatantly unnatural speech patterns in both.
Never thought about it that way before, but the more I do, the more sense it makes.
I'd take it a bit further, even - I don't think this is exclusive to billionaires. Many of the claims I've heard regarding AI output being indistinguishable from human creation start to make a lot more sense when you consider the person making those claims may not see the people around them as human, may not see themselves as human, or may not even have a concept of what makes a human different from any everyday object.