Despite mentioning a philosopher (good pick!), this is just creative writing rehashing one side of the hard problem -- or, more specifically, restating the dogma that Turing wrote his most famous paper to debunk. It's really good creative writing, at least!

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"

What else could it be doing? That is literally the mechanism of how a model works.

The output method is a probability distribution of the next token. But that tells you nothing about what's going on on the inside

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

How would that process be the result of intelligent thought?

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_...

But that tells you nothing about what's going on on the inside

It does not have to tell us anything. Because we built it..We know what is going on the inside. There is nothing more to it..

Why do security bugs in Linux exist? We built Linux, we know everything that is going on inside.

Yes, we can explain the bugs by looking at the code that we put in and the data it bugs out on. In a similar way, we can explain the behavior of LLMs by look at their code and the data the code operates upon.

There is no mystery anywhere...

Indeed, the chatbot is less real than the character, because the character is the product of another mind, while the chatbot's words are the product of complex mathematical operations conducted over a massive database of all the words humans have uttered, arranged by their frequency in relation to one another.

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?

Silicon math can never be chemical goo math!

Why can't people just admit they believe in souls?

I frequently say what you say, but there is a more charitable reading: today's AIs are not "minds", as they are not stateful. Maybe continual learning ones (like the mini AGI exhibited today on the HN front page) will be perceived as "soulful".

"You're trying to parse HTML with a linear regex. That algorithmic approach is too limited to do what we want with the problem-space."

"Why can't you just admit that you think only tree-parsers have souls!?"

Believing a specific architecture is not capable of consciousness is a different matter. I am seeing statements that simply state things like "it is software or a machine and therefore cannot be conscious" without any further clarification. That clearly betrays a belief in souls.

Sure, if your architecture include hardware that is known to be capable of generation consciousness, the no one will say that.

Problem is that we don't know any such hardware..

Why? The human brain already exists. Unless you have some well defined criterion for what hardware can support consciousness how can you say that?

Oh, I thought you were talking about "architectures" not containing a human brain..

And I am sure that lolakutty will momentarily bestow the world with a consciousness test, solving a problem that has eluded millions of top thinking man hours over millennia of trying...

However, I feel rather certain, that deep down the proposed test will just be another "is it chemical goo?"

The test can only be done on oneself, and not on others. So I know I am conscious, and I am made up of a brain, among other things. So I know for a fact that this set of things that I am comprised of, can generate consciousness.

Nah, it "betrays" an awareness of something implicitly obvious: All the software/machines we might discuss are ones from ~2026, rather than ones from ~2226.

Similarly, "pets can't do my taxes" is a reasonable statement, because it is implicit that it doesn't include hundreds of years of genetic engineering and cyborg implants.

Pets can't speak human languages or do thinking on logic or natural logic. Current AI's can. Its a very bad example. People saying "current archs are limited" are much less than people outright claiming "software therefore not conscious". And I haven't yet seen any proof why the current AI's already don't have some consciousness or not.

Wrong assumption that the chemical goo is only doing math....

What's the non-math part you think? All of currently known physics is turing computable as far as I am aware.

currently known physics is not good enough..

It is quite an arbitrary classification. What is so special about "current" knowledge...

If you think current physics is insufficient but future physics might be sufficient, I can accept that but its still an unfounded belief. If you truly believe something "more" is needed, then you are placing a bet that always lost for centuries. Bets in anything supernatural or aphysical have always and consistently failed till now.

Any sufficiently advanced technology is indistinguishable from magic.

Not my words.

That magic means a different thing from supernatural magic.

Of course, the more you know about a subject, the less convincing the AI's responses are.

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.

I think part of this comes from the fact that LLMs are surprisingly good at logic but roughly about as good as expected on information accuracy.

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

This sounds plausible. And it's also very fixable!

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.

Aren't the recent results in mathematics actually stronger evidence for his point? Although the models may be capable of generating proofs they aren't coming out with the same level of quality of a human discovered and communicated proof. Providing a gobbledy-gook yet technically correct proof (generated at least in part by brute force) lacks the qualities of an expert produced proof because they fail to communicate insight or understanding about why the theorem is true.

I think to me LLMs had the effect of noticing much more the author, the intention behind human-made works of art (books, movies etc.). Before LLMs, I used to frequently consume media in a way as it were generated by a mindless process. Now it's like everything which is not AI-generated has more meaning than ever before, a bit like hypomania.

I'm actually using that as a catalyst for my own writing; beauty/human-ness in its imperfection. Prior to LLMs and their cultural craze, I harbored a fear that my writing would allow for someone to draw a box around me and mark me as a bore, dullard or of lacking originality.

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.

If you go far enough in a field, you start to recognize areas where your personal opinion differs from the “best practices” usually recommended.

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.

Professional tech Cassandra discovers ELIZA and Searle; coins a term for it. More at 10...

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.

systems that cannot form intent, that have nothing to form intent with

I wonder if the OP has read https://www.anthropic.com/research/global-workspace - it seems like it directly addresses this

But there's a second hurdle that makes it hard for a small but important subset of humanity to understand that chatbots aren't people: the billionaires to whom nearly everyone isn't a real person. These solipsists see chatbots as being equivalent (or even superior) to humans, because they don't think most humans are fully people, either

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.

Maybe I am missing the point of the article, but it seems to me that there's always an intender. Maybe the intender created something that doesnt have intent, but there is always someone behind the scenes that is creates the intent behind the creation that lacks it.

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.

Wait, hold up. LLMs may be non-deterministic, but they're not _random_.

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.

LLM output is literally randomly sampled.

I think what you might want to say is that LLM output is not uniformly random?

Or what am I misunderstanding?

when we interact with an AI, we hallucinate the person on the other side of the interaction. Those hallucinations are far more common and far more consequential than any AI-generated "hallucinations" (these are more properly called "errors" or "defects").

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.

I don’t think it’s that silly to tell Claude you passed. That feedback is useful context for that chat session; and could theoretically be used to improve future models.

You're missing the fact the they didn't have this in mind, and thought of it as sharing a positive result with a study partner.

Feedback is one thing, but another is complete memory. I used to stop talking in a thread once gpt solved my issue.

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"

The technology is there to assist you. It can provide valuable feedback to you about what aspects of your studying were particularly productive or less so based on your test results. There is real meaning to developing this kind of interaction with an object, no different than how children use dolls to develop prosocial behaviors.

The doll never learns, though.

It's ironic that real atheists like Richard Dawkins (whose work I believe inspired the title of this post) are public about their own degree of skepticism in favor for the "personhood" of AI. And I'm confident that there are plenty others who are convinced to the tune of total disinterest in the issue of "personhood" altogether—AIs and humans alike.

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.

I stopped reading when the author described an 'atheist' sunset as being empty.