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
When I ask about acoustics, they still often make incorrect assumptions, e.g. overlooking that they are speaking of logarithmic display of digital levels when the discussion has shifted to SPL (sound pressure level in air).
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.
Coding of course is something I know well and it obviously works very well for me there, but computer vision was not, and it has helped me solve lots of useful, bespoke problems because I can very literally "see" if it works.
However, it has even helped me solve problems in arbitrary matters far outside my expertise. Choice example: a complicated multi-airline, multi-jurisdiction flight delay compensation case which companies like AirHelp turned away, and ChatGPT got me literally hundreds of dollars that neither airline was willing to hand out. It told me what to say to whom and why, and when I said it, the responsible airline capitulated.
What could be more convincing than cold, hard $$$?
The trick of course, is to figure out how to validate the output of the AI, which can take some effort on our part. But many would rather downplay the technology than take an honest crack at making it work for them, and I suspect it's because they're already prejudiced against it or their incentives are otherwise not aligned.
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
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.
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.
Likewise, speaking collaboratively or capturing emotion ("We did it!") would just align with any ongoing interactive and/or personal context of the thread.
So now I do it regularly.
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 mean that’s objectively wrong for any model using RLHF.
I think what you might want to say is that LLM output is not uniformly random?
Or what am I misunderstanding?
On your die, if you colour one face in black and the other 5 faces in black, rolling it will still produce a random outcome. It's just that you get a 1:5 skew.
Or look at the probability that a given C-14 carbon isotope will decay tomorrow. As far as we can tell, that's as random as it physically gets. With the die you could theoretically try to run a physics simulation to predict how it rolls, but as far as we can tell, atomic decay is intrinsically random.
However a C-14 atom has about 1 in 3 million chance to decay on any given day.
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
When you randomly choose a picture to show me, I cannot glean any intent from being shown that specific picture, but I can glean some intent from the set of pictures you could have shown me, and in the relationships among the elements of the given picture you did show. Any randomness cuts out some intention.
When a picture is derived from huge model, any intention is mulched up to a degree that analyzing the picture for meaning is pointless.
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.
Don't just create original works for the sake of creating more original works -- there are plenty of them already. You should write if you have something to say.
In other words, the more you talk with claude, the more you tend to interpret all messages as coming from an LLM, and less worthy of respect
https://myscp.onlinelibrary.wiley.com/doi/full/10.1002/jcpy....
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.
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.
As opposed to an "AI atheist" that Doctorow refers to in the article.
The good news for Cory, I guess, is that he probably doesn't want to turn this into a philosophical debate and neither do his more serious opponents. It's mostly politics for both sides.
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.