I don’t see how being critical of this is a knee jerk response.

Thinking, like intelligence and many other words designating complex things, isn’t a simple topic. The word and concept developed in a world where it referred to human beings, and in a lesser sense, to animals.

To simply disregard that entire conceptual history and say, “well it’s doing a thing that looks like thinking, ergo it’s thinking” is the lazy move. What’s really needed is an analysis of what thinking actually means, as a word. Unfortunately everyone is loathe to argue about definitions, even when that is fundamentally what this is all about.

Until that conceptual clarification happens, you can expect endless messy debates with no real resolution.

“For every complex problem there is an answer that is clear, simple, and wrong.” - H. L. Mencken

Sometimes after a night’s sleep, we wake up with an insight on a topic or a solution to a problem we encountered the day before. Did we “think” in our sleep to come up with the insight or solution? For all we know, it’s an unconscious process. Would we call it “thinking”?

The term “thinking” is rather ill-defined, too bound to how we perceive our own wakeful thinking.

When conversing with LLMs, I never get the feeling that they have a solid grasp on the conversation. When you dig into topics, there is always a little too much vagueness, a slight but clear lack of coherence, continuity and awareness, a prevalence of cookie-cutter verbiage. It feels like a mind that isn’t fully “there” — and maybe not at all.

I would agree that LLMs reason (well, the reasoning models). But “thinking”? I don’t know. There is something missing.

I don't get why you would say that. it's just auto-completing. It cannot reason. It won't solve an original problem for which it has no prior context to "complete" an approximated solution with. you can give it more context and more data,but you're just helping it complete better. it does not derive an original state machine or algorithm to solve problems for which there are no obvious solutions. it instead approximates a guess (hallucination).

Consciousness and self-awareness are a distraction.

Consider that for the exact same prompt and instructions, small variations in wording or spelling change its output significantly. If it thought and reasoned, it would know to ignore those and focus on the variables and input at hand to produce deterministic and consistent output. However, it only computes in terms of tokens, so when a token changes, the probability of what a correct response would look like changes, so it adapts.

It does not actually add 1+2 when you ask it to do so. it does not distinguish 1 from 2 as discrete units in an addition operation. but it uses descriptions of the operation to approximate a result. and even for something so simple, some phrasings and wordings might not result in 3 as a result.

Having seen photocopiers so many times produce coherent, sensible, and valid chains of words on a page, I am at this point in absolutely no doubt that they are thinking.

Having seen LLMs so many times produce incoherent, nonsensical and invalid chains of reasoning...

LLMs are little more than RNGs. They are the tea leaves and you read whatever you want into them.

The first principle is that you must not fool yourself, and you are the easiest person to fool. - Richard P. Feynman

They're not thinking, we're just really good at seeing patterns and reading into things. Remember, we never evolved with non-living things that could "talk", we're not psychologically prepared for this level of mimicry yet. We're still at the stage of Photography when people didn't know about double exposures or forced perspective, etc.

Yes, I've seen the same things.

But; they don't learn. You can add stuff to their context, but they never get better at doing things, don't really understand feedback. An LLM given a task a thousand times will produce similar results a thousand times; it won't get better at it, or even quicker at it.

And you can't ask them to explain their thinking. If they are thinking, and I agree they might, they don't have any awareness of that process (like we do).

I think if we crack both of those then we'd be a lot closer to something I can recognise as actually thinking.

> Having seen LLMs so many times produce coherent, sensible and valid chains of reasoning to diagnose issues and bugs in software I work on, I am at this point in absolutely no doubt that they are thinking.

While I'm not willing to rule *out* the idea that they're "thinking" (nor "conscious" etc.), the obvious counter-argument here is all the records we have of humans doing thinking, where the records themselves are not doing the thinking that went into creating those records.

And I'm saying this as someone whose cached response to "it's just matrix multiplication it can't think/be conscious/be intelligent" is that, so far as we can measure all of reality, everything in the universe including ourselves can be expressed as matrix multiplication.

Falsification, not verification. What would be measurably different if the null hypothesis was wrong?

I think you are the one dismissing evidence. The valid chains of reasoning you speak of (assuming you are talking about text you see in a “thinking model” as it is preparing its answer) are narratives, not the actual reasoning that leads to the answer you get.

I don’t know what LLMs are doing, but only a little experimentation with getting it to describe its own process shows that it CAN’T describe its own process.

You can call what a TI calculator does “thinking” if you want. But what people are interested in is human-like thinking. We have no reason to believe that the “thinking” of LLMs is human-like.

Counterpoint: The seahorse emoji. The output repeats the same simple pattern of giving a bad result and correcting it with another bad result until it runs out of attempts. There is no reasoning, no diagnosis, just the same error over and over again within a single session.

Isn’t anthropomorphizing LLMs rather than understanding their unique presence in the world a “ lack of imagination and flexibility of thought”? It’s not that I can’t imagine applying the concept “thinking” to the output on the screen, I just don’t think it’s an accurate description.

I guess it depends if you definite thinking thinking as chaining coherent reasoning sentences together 90-some% of the time.

But if you define thinking as the mechanism and process we mentally undergo and follow mentally... I don't think we have any clue if that's the same. Do we also just vector-map attention tokens and predict the next with a softmax? I doubt, and I don't think we have any proof that we do.

It might appear so, but then you could validate it with a simple test. If the LLM would play a 4x4 Tic Tac Toe game, would the agent select the winning move 100% of all time or block a losing move 100% of the time? If these systems were capable of proper reasoning, then they would find the right choice in these obvious but constantly changing scenarios without being specifically trained for it.

[1] https://jdsemrau.substack.com/p/nemotron-vs-qwen-game-theory...

If you understand how they operate and you are reasonable and unbiased there is no way you could consider it thinking

Different PoV: You have a local bug and ask the digital hive mind for a solution, but someone already solved the issue and their solution was incorporated... LLMs are just very effficient at compressing billions of solutions into a few GB.

Try to ask something no one ever came up with a solution so far.

Yeah but if I assign it a long job to process I would also say that an x86 CPU is "thinking" about a problem for me.

What we really mean in both cases is "computing," no?

But all those times the same system produces irrational gibberish don't count? GPT-5 will commonly make mistakes no thinking human could ever make.

Human: I'm trying to get my wolf, sheep and cabbage across the river in this boat, but the wolf keeps eating the sheep or the sheep eats the cabbage

Bot: You should put the sheep in the boat and take it across — if we delve into the biology of Canis lupus we discover that wolves don't eat cabbage!

H: Ok, so that worked great so far, the sheep is on one side and the wolf/cabbage is on the other.

B: Now, Option 1 is to bring the wolf across, or Option 2 you can bring the cabbage. I recommend (2) taking the cabbage as cabbages are smaller and easier to transport in a boat.

H: But then the sheep eats the cabbage, right? Remember that?

B: Exactly, that's sharp thinking. If you put the sheep and the cabbage together on the same side of the river, the sheep is sure to devour the cabbage. We need to not just separate sheep from cabbages — we need to separate cabbages from sheep! :rocketship:

what sound does a falling tree make if no one is listening?

I’ve asked LLMs to write code for me in fields I have little background knowledge, and then had to debug the whole thing after essentially having to learn the language and field.

On the other hand, for things I am well versed in, I can debug the output and avoid entire swathes of failed states, by having a clear prompt.

Its why I now insist that any discussion on GenAI projects also have the speaker mention the level of seniority they have ( proxy for S/W eng experience), Their familiarity with the language, the project itself (level of complexity) - more so than the output.

I also guarantee - that most people have VERY weak express knowledge of how their brains actually work, but deep inherent reflexes and intuitions.

I'd represent the same idea but in a different way:

I don't know what the exact definition of "thinking" is. But if a definition of thinking rejects the possibility of that current LLMs think, I'd consider that definition useless.

>Having seen LLMs so many times produce coherent, sensible and valid chains of reasoning to diagnose issues and bugs in software I work on, I am at this point in absolutely no doubt that they are thinking.

If one could write a quadrillion-line python script of nothing but if/elif/else statements nested 1 million blocks deep that seemingly parsed your questions and produced seemingly coherent, sensible, valid "chains of reasoning"... would that software be thinking?

And if you don't like the answer, how is the LLM fundamentally different from the software I describe?

>Knee jerk dismissing the evidence in front of your eyes because

There is no evidence here. On the very remote possibility that LLMs are at some level doing what humans are doing, I would then feel really pathetic that humans are as non-sapient as the LLMs. The same way that there is a hole in your vision because of a defective retina, there is a hole in your cognition that blinds you to how cognition works. Because of this, you and all the other humans are stumbling around in the dark, trying to invent intelligence by accident, rather than just introspecting and writing it out from scratch. While our species might someday eventually brute force AGI, it would be many thousands of years before we get there.

Having seen LLMs so many time produce incoherent, nonsense, invalid answers to even simplest of questions I cannot agree with categorization of "thinking" or "intelligence" that applies to these models. LLMs do not understand what they "know" or what they output. All they "know" is that based on training data this is most likely what they should output + some intentional randomization to make it seem more "human like". This also makes it seem like they create new and previously unseen outputs but that could be achieved with simple dictionary and random number generator and no-one would call that thinking or intelligent as it is obvious that it isn't. LLMs are better at obfuscating this fact by producing more sensible output than just random words. LLMs can still be useful but they are a dead-end as far as "true" AI goes. They can and will get better but they will never be intelligent or think in the sense that most humans would agree those terms apply. Some other form of hardware/software combination might get closer to AI or even achieve full AI and even sentience but that will not happen with LLMs and current hardware and software.

I think we can call it "thinking" but it's dangerous to anthropomorphize LLMs. The media and AI companies have an agenda when doing so.

They may not be "thinking" in the way you and I think, and instead just finding the correct output from a really incredibly large search space.

> Knee jerk dismissing the evidence in front of your eyes

Anthropomorphizing isn't any better.

That also dismisses the negative evidence, where they output completely _stupid_ things and make mind boggling mistakes that no human with a functioning brain would do. It's clear that there's some "thinking" analog, but there are pieces missing.

I like to say that LLMs are like if we took the part of our brain responsible for language and told it to solve complex problems, without all the other brain parts, no neocortex, etc. Maybe it can do that, but it's just as likely that it is going to produce a bunch of nonsense. And it won't be able to tell those apart without the other brain areas to cross check.

It's reinforcement learning applied to text, at a huge scale. So I'd still say that they are not thinking, but they are still useful. The question of the century IMO is if RL can magically solve all our issues when scaled enough.

>Knee jerk dismissing the evidence in front of your eyes because you find it unbelievable that we can achieve true reasoning via scaled matrix multiplication is understandable, but also betrays a lack of imagination and flexibility of thought.

You go ahead with your imagination. To us unimaginative folks, it betrays a lack of understanding of how LLMs actually work and shows that a lot of people still cannot grasp that it’s actually an extremely elaborate illusion of thinking.

"Convince" the stock Claude Sonnet 4.5 that it's a sentient human being hooked up to Neuralink and then tell me again it's thinking. It's just not.

"Consciousness" as in subjective experience, whatever it is we mean by "the hard problem," is very much in doubt.

But "self-awareness," as in the ability to explicitly describe implicit, inner cognitive processes? That has some very strong evidence for it: https://www.anthropic.com/research/introspection

Code gen is the absolute best case scenario for LLMs though: highly structured language, loads of training data, the ability to automatically error check the responses, etc. If they could mimic reasoning anywhere it would be on this problem.

I'm still not convinced they're thinking though because they faceplant on all sorts of other things that should be easy for something that is able to think.

Then the only thing I have to ask you is: what do you think this means in terms of how we treat LLMs? If they think, that is, they have cognition (which of course means they're self aware and sentient, how can you think and refer to yourself and not be these things), that puts them in a very exclusive club. What rights do you think we should be affording LLMs?

Apparent reasoning can emerge from probabilistic systems that simply reproduce statistical order not genuine understanding.

Weather models sometimes “predict” a real pattern by chance, yet we don’t call the atmosphere intelligent.

If LLMs were truly thinking, we could enroll one at MIT and expect it to graduate, not just autocomplete its way through the syllabus or we could teach one how to drive.

> Having seen LLMs so many times produce coherent, sensible and valid chains of reasoning to diagnose issues and bugs in software I work on, I am at this point in absolutely no doubt that they are thinking.

People said the same thing about ELIZA

> Consciousness or self awareness is of course a different question,

Then how do you define thinking if not a process that requires consciousness?

Thinking as in capable of using basic reasoning and forming chains of logic and action sequences for sure. Ofc we both understand that neither of us are trying to say we think it can think in the human sense at this point in time.

But oh boy have I also seen models come up with stupendously dumb and funny shit as well.

They remind me of the apparitions in Solaris. They have this like mechanical, almost player-piano like quality to them. They both connect with and echo us at the same time. It seems crazy to me and very intellectually uncreative to not think of this as intelligence.

> The world is full of bizarre wonders and this is just one more to add to the list.

https://youtu.be/_-agl0pOQfs?si=Xiyf0InqtjND9BnF

If AI is thinking if slavery is bad then how can somebody own AI. How can investors can shares from AI profits? We are ok with slavery now. Ok i will have two black slaves now. Who can ask me? Why shld that be illegal?

Too many people place their identity in their own thoughts/intellect. Acknowledging what the LLMs are doing as thought would basically be calling them human to people of that perspective.

Is this model thinking too?

https://huggingface.co/PantheonUnbound/Satyr-V0.1-4B

I agree with you.

If you took a Claude session into a time machine to 2019 and called it "rent a programmer buddy," how many people would assume it was a human? The only hint that it wasn't a human programmer would be things where it was clearly better: it types things very fast, and seems to know every language.

You can set expectations in the way you would with a real programmer: "I have this script, it runs like this, please fix it so it does so and so". You can do this without being very precise in your explanation (though it helps) and you can make typos, yet it will still work. You can see it literally doing what you would do yourself: running the program, reading the errors, editing the program, and repeating.

People need to keep in mind two things when they compare LLMs to humans: you don't know the internal process of a human either, he is also just telling you that he ran the program, read the errors, and edited. The other thing is the bar for thinking: a four-year old kid who is incapable of any of these things you would not deny as a thinking person.

Sometimes I start thinking our brains work the same way as an LLM does when it comes to language processing. Are we just using probability based on what we already know and the context of the statement we're making to select the next few words? Maybe we apply a few more rules than an LLM on what comes next as we go.

We train ourselves on content. We give more weight to some content than others. While listening to someone speak, we can often predict their next words.

What is thinking without language? Without language are we just bags of meat reacting to instincts and emotions? Are instincts and emotions what's missing for AGI?

Having seen parrots so many times produce coherent, sensible, and valid chains of sounds and words, I am at this point in absolutely no doubt that they are thinking.

Would they have diagnosed an issue if you hadn't presented it to them?

Life solves problems itself poses or collides with. Tools solve problems only when applied.

Instead of thinking, "Wow. AIs are smart like humans", maybe we should say, "Humans are dumb like matrix multiplication?"

You’re assuming the issues and bugs you’ve been addressing don’t already exist, already encoding human chain of reasoning, in the training data.

If you're sensitive to patterns and have been chronically online for the last few decades it's obvious they are not thinking.

Its overt or unaware religion. The point when you come down to the base of it is that these people believe in "souls".

I'm not so sure. I, for one, do not think purely by talking to myself. I do that sometimes, but a lot of the time when I am working through something, I have many more dimensions to my thought than inner speech.

So an x86 CPU is thinking?

So many times I've seen it produce sensible, valid chains of results.

Yes, I see evidence in that outcome that a person somewhere thought and understood. I even sometimes say that a computer is "thinking hard" about something when it freezes up.

...but ascribing new philosophical meaning to this simple usage of the word "thinking" is a step too far. It's not even a new way of using the word!

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