AI is 'not smart' so what's next in artificial intelligence? | Hacker News Reader

AI is 'not smart' so what's next in artificial intelligence?

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44 points1vuio0pswjnm775 comments

Yann LeCun was saying 3 years ago that because token generation is auto-regressive, its mathematically impossible to generate a long stream of coherent tokens, because errors amplify exponentially.

and then models learned that they can back track and error correct

so much for "mathematically impossible..."

I think it was largely the introduction of tool calling that allowed models to mitigate the issue of errors amplifying exponentially since it allows the model to understand if what it generated is correct or has issues that it needs to address. This addresses the potential lack of or low quality of world model by being able to reference the current state of the world.

I've definitely realized this phenomenon after a few occasions of erroneously trying to rely purely on instructions to get an LLM to do a thing or take on a role, especially without persistent cloud-based sessions that have internal checklists and other opaque guidance. They're essentially poor at self-managing, but can do really well when they are limited in scope/context and are worked into a sort of state machine that guarantees they perform certain tasks predictably. They won't always do those tasks the exact way you expect them to, but at least they actually do them, and because of that they are more likely to have the correct prior context to perform the next task better. Because they are so prone to selectively ignoring directions, that can quickly send them down an incorrect path that compounds on itself.

Also, almost any argument against LLM intelligence also applies to humans.

I very commonly see someone make some small mistake and end up going in the wrong direction, “accumulating stupid” as they go, sometimes for years.

Also with the stochastic parrot thing. If you say just the right thing to the right human and the right time, they'll very predictibly say their favorite movie/book quote or song lyric, like some sort of parrot.

An LLM will tell you how a song feels, even if it has literally no way to experience music. Because it's not thoughts or feelings that you get from an LLM. We take a massive amount of information, compress it into a large graph, then explore sections of the graph via prompts. That's what the stochastic parrot means. And that doesn't compare with how humans think. It's just a completely different architecture

The trouble is that a 24/7 AI stochastic parrot does pretty damn well at some things.

In my estimation, we're arguing about how big the blast radius will be.

And also, if you put an actual parrot in a room full of programmers, it might learn a few word of programming jargon, but it never will become a useful coding partner the way LLMs are.

Humans can learn.

AI can not.

For those disagreeing: please explain how a static hardware can learn.

this is profoundly false. AI not only can learn, it is built entirely from learning. The field is called machine learning after all.

Not only that... AI is NOT only learning during the training phase... LLMs learn in real time the minute you talk to it. It learns something and saves those learnings in a context window or somewhere else if you want it to exist beyond the context window.

All of the above runs on static hardware. Don't understand how someone can say a profoundly wrong statement and get voted up.

Correct me if I'm wrong, but if a profound insight is gathered in session 1 with user A and stored in context A1, this might be available to user A in session 2, if that still has access to context A1, but won't be available to user B in any of his/her sessions until that NN is retrained with input which includes at least some of the information from context A1.

You are wrong again in every possible count. Since you think in terms of algebra think like this: there is context SP which is context window shared between all sessions, that is called the system prompt. Then there is the context window called memory M which is shared between all sessions under a user and then finally there context CW (current window) which is the context comprised of the queries from the current chat session. Total context = SP + CW + M.

M is the context window that doesn’t require retraining that allows the LLM to “learn” in the same way humans do. This is the usual set up. But nothing prevents someone from adding a GM (general memory) shared between all users. Under this set up the LLM and harness fits and is virtually identical to how humans learn at a high level.

But this is besides the point because even if none of this was done. Just a context window or just training is in itself a form of learning. There was no action taken in your algebraic example where learning did not occur.

idk, how does voice recognition learn my voice? How can I install programs when the hardware is static?

By self-modifying the software. Currently the model harnesses only allow the model to modify its own prompt (which could be considered a really weak kind of learning), but theoretically, a model could design and train its own replacement and run that, continuously improving itself. I’m not sure if LLMs will be able to do that but the static hardware has nothing to do with it (since the bits on the harddrive aren’t static).

> and then models learned that they can back track and error correct

You mean "Human developers learned ...", yes? Or was there really an all AI-driven, self-improving aspect to this?

Well, LLM networks don't have a 'back track and error correct' component in the design, AFAIK.

You insinuate here AI "learned".

I doubt that this was AI self-improvement.

Was there a particular change to the network or the way that it was trained that introduced the 'backtrack and error correct' mechanism?

do you have a problem with this field of research being called "machine learning"?

Does that take anything away from the argument?

What argument, "a theory was wrong"? No, the inane central observation, the observation that a researcher was unable to predict a discovery before it was discovered, remains true despite the gratuitous insertion of a little bit of bullshit about AI learning.

I suppose it's additionally trying to imply something else, like "due to a pattern of researchers being unable to discover discoveries before they discover them, AGI is just around the corner".

its one thing to say "we dont know"

it's a different thing to say "it's mathematically impossible"

so if it turns out it is possible, what then? was math broken? or the researcher an idiot who either doesnt know math, or is just bullshitting non-existing proofs?

Mathematics doesn't tell you what is necessarily true. It consists of guessing about what is necessarily true.

(I don't know the details of what happened, it could be either or neither of the things you said.)

Stop attacking Yann. I would say like 90% of the HN crowd was parroting Yann too.

Besides "smart", the headline also conflates AI with LLMs. The real, non-clickbait title is "Yann LeCun, founder of AMI Labs, is developing a new AI system"

It is just so bizarre compared to my everyday experience also.

I never ask Opus or Fable a question and think "what a stupid response".

Quite the opposite. It has actually raised the bar of what I consider an intelligent response to my inquiry. So much so that most responses from humans on most subjects to most forms of inquiry seem stupid and not really thought out.

I’m sure you’re very intelligent and capable so I suspect we work in different problem spaces if you have not seen this, but I definitely think the responses are at times very very junior and I find myself having to explain first principles. Fable less so, but Opus routinely will make very naive assumptions about retry logic, protocols it supposedly has training material on, and it will very often miss the forest for the trees.

This isn’t exactly saying how stupid anyone is but I’d definitely have been concerned about a human’s reasoning ability and understanding of logic if they’d given me similar answers.

Everyone nowadays seems to only think of AI as LLMs or maybe also stable diffusion. People want to ban games with AI in them, when by definition every NPC is following some kind of AI algorithm.

This chess game has a min-max algorithm in it! AI slop! It should have a short person hidden inside like the original Mechanical Turk!

They literally interview another person in it and mention a lot of other labs doing this kind of research including Google. Yes, he's the main starting interview but this is not really clickbait or a marketing piece.

The article seems to define "smart" as being good at spatial awareness and navigating a body through 3D space and such. Thus, a mice is smarter than an LLM.

That's the first time in my life I hear this definition. Until now, the word "smart" has meant doing exactly the things LLMs do, and mice don't.

I guess it is a sign we are re-evaluating what makes humans special.

> I guess it is a sign we are re-evaluating what makes humans special.

Always has been: https://en.wikipedia.org/wiki/AI_effect

Tangentially: https://en.wikipedia.org/wiki/Moravec%27s_paradox

While we should be careful of a bias, it is also a good practice in the scientific method to review definitions that may have been not precise enough.

For example, initially, a "planet" was just a big body in space. Then when people started to see more and more nuances, the definition just refined, and some objects stopped being called "planet".

I would not be surprised if there is a bias that pushes some people to redefine "intelligence" away from machine, but I would not be surprised if there is a bias that pushes some people to ignore newly discovered nuance and put into the same "intelligence" bag things that are in fact very different. I personally can see how LLM are not really "intelligent", and I don't think it is a good idea to say: well, yesterday we said the minimum criteria is X, now that we noticed that X can be reached without really doing the real thing, let's just ignore that and pretend it is the same thing.

(: the biggest clue for me is to use an early model, and see that it sometimes looks very intelligent, and then sometimes you can see that it gets it wrong in a way that shows that it never "understood" it at all. Newer models are better, but because it is an iteration on the same bases, the increase of performances cannot really due to replacing the things that "looked smart by aren't" by "real smart", but more replacing the things that "don't look smart" by "look smart by aren't")

Yeah I think if we are looking at it through that lens, the problem is in the term _intelligence_ in itself. Psychology and biology could not even pinpoint what exactly makes for _intelligence_. There isn't really a precise definition yet so it's just natural that definitions tend to shift.

I don't think we even need to go into tech and AI for an example. The intelligence or lack thereof of pets surprise us. Sometimes a cat is surprisingly smart when it is able to open a door to get food it wasn't supposed to. But then same cat gets bamboozled by walls and simple optical illusions. We generally expect that if something/a human is smart enough to do the former, then it shouldn't be dumb enough to fall for the latter.

Coming back to AI, this dissonance is how AI-generated images are detected for example. If a human can render something so well, you wouldn't expect them to make small but nonetheless elementary line art mistakes.

It's the same with human intelligence though. A human can be brilliant on some things and then we're puzzled why they are so idiotic in other areas.

Every time this comes up, people pick on any kind of flaws or inconsistencies of AI models, while at the same time giving a huge pass to the extreme variation in intelligence and stupidness displayed in human behaviour.

Creativity is the same. Human artists are "inspired" by earlier arts, perhaps following and slightly changing "trends" they participate in -- which is somehow seen as totally different from what AIs are doing.

My problem with AI is the sheer variance of its stupid-smart spectrum. While it's true that human intelligence is not deterministic or predictable, the inconsistency exists in a much narrower band of variance which makes failure modes foreseeable. Thus I would much prefer a system with humans in the loop with processes in place for idiot-proofing.

This is true for "lateral" (I lack a better term) fields of intelligence as well. You don't ask a philosophy professor advice for the rashes on your skin; you see a doctor for that. And yet both the professor and the doctor could be expected to accurately identify from a picture that you do have rashes on your skin. An AI (and I mean in the general sense, not only transformer LLMs) could give you a pretty accurate rundown of Plato and still think the same picture is a beautiful sunrise.

(I don't even kid. Just this morning, an AI labeled a GIF from _Friends_ as a 1950s magazine ad for white bread. Just what in the failure mode is that?)

You can't idiot-proof AI without knowing what's in the training data set and even then you run into question of scale.

> It's the same with human intelligence though.

No, this is not the same observation. In "basic LLM", the answer is not "confused" or "fail to understand", the answer is "inconsistent with the understanding mechanism". It is not that they "fail to understand while trying to understand", it is that there is not understanding mechanism at all.

Humans can have different level of intelligence, depending on the individuals, the subjects, even circumstantial situations (someone being tired, someone being distracted, or just bad luck). But they never make the same kind of mistakes I've observed with "basic LLM", where they do "non sequitur" that does not make sense at all but has all the characteristic of imitating something said by someone who understood.

I still even see it sometimes with Claude. It says logical stuff, and then suddenly something that does not make sense and it snaps me back to reality: none of this, including the correct things, are the result of understanding the underlying concepts, it is just that the correct things are more probable to generate, and that suddenly, a nonsensical happen to also be probable for a given configuration.

Humans don't make the exact same errors of LLMs of course. Humans are very different beings.

So you recognize that Claude is not a human.

Humans make mistakes as well "inconsistent with the understanding mechanism", but they have a very different form, and you are so used to the particular failure mode of humans, that you don't think about it.

But aliens visiting earth likely would find some aspects of human mind very peculiar!

Examples:

Humans learning algebra (or really anything like playing music, paddling a canoe, etc.) have to go through lots and lots of trivial basic mistakes, and only learn to avoid them through repetition and pattern matching on earlier experience, rather than relying on "reasoning".

A "pure reasonable being" would simply be learned the rules for algebra then go ahead and make perfect deductions applying the rules -- but humans are very clearly not such beings. Humans can know the rules for algebra perfectly well, then still go ahead and make mistakes until enough training has been done until we say you have "learned" it (be able to pattern match on previous experience).

Imagine humans being employed by aliens to do algebra, then aliens seeing humans basically do "2 + 2 = 5" (just on a higher complexity level). Like very human in first year in university WILL do with their formulas. What would you conclude about humans and their relation to "real understanding"?

Or another example: Humans engage a lot in post-rationalization, having first made up ones mind, then finding the reasons for the choice afterwards. (Most striking example of post-rationalization is the experiments on patients with severed brain half connections where one brain half invents a reason it can believe in for a choice made by the other brain half; https://en.wikipedia.org/wiki/Split-brain -- but if you look at pretty much any political issue for instance it is clear that people are driven at least as much by being herd animals as by doing any reasoning -- the majority of humans decide what people they belong with first, then figure out why afterwards).

I find this kind of reasoning a bit pointless and unfalsifiable.

Someone says "LLMs fail at this", and you say "but humans also sometimes fail", then they says "but we are not talking about the same thing", and you answer "this difference does not matter because aliens may be totally different".

My point is that what we observe with LLMs does not require any understanding. And in some cases, it is clear the answer of a LLM was built without understanding. And in other cases, it looks like it could have been built with understanding because there is no visible errors, but because we know the LLM can build things without understanding, this can equally simply be something that is built without understanding and happen to have no error, and therefore just looks like it has been built with understanding.

I think you take the problem the wrong way: you start from the hypothesis that there is understanding, and then you are finding reasons to maintain this conclusion (the most prominent ones being "humans also can do mistake" or "... fake understanding" or "... hallucinate". Well, humans can do a lot of things that don't require intelligence, does it mean that things that do these things that do not require intelligence are in fact as intelligent as humans?). This is a confirmation bias.

I don't have problem if it turns out LLMs have understanding. But the reality right now is that a simple explanation is that it does not have it. But it feels like some people just argue "but it is still possible, bending this argument there and there". I bet at some point, they will say "ok, I see your point, but maybe LLMs are intelligent and have this behavior on purpose because they want to remain hidden because they are smart enough to understand that if humans would know, they would freak out". It feels more and more like a belief system rather than a scientific approach.

Just two elements to go further:

- in the majority of cases, "things that have been faked to look like there are the result of understanding" will be correct. Because if you are trained to pretend you understand, you are trained to imitate someone who has understood, and you are therefore trained to imitate their reasoning, which turns out to be correct. (if you want to test the understanding, it is complicated, because the "understanding" is a data leakage during training)

- if LLMs extract understanding for the data from their training, it is strange that their current understanding (just after the training) is so close to the current understanding of the humans. Surely humans have missed stuffs here and there. The math theorem number 3424 not solved yet is probably as "simple" than the math theorem number 6423 that happened to be solved by humans, it is just by chance and circumstances that some humans have worked on 6423 and found a way to crack it while they did not spend as much time and effort on 3424. And yet, LLMs just happen to never notice any theorem on their own at the end of the training phase. Asking a LLM "Explain to me a math theorem that humans did not notice, with demonstration. This theorem should be something you understood when you were trained over maths" just does not work.

(and, please, I know that a mathematician may know theorem 3424 and yet not have noticed 6423 either, or that LLMs can scan a math problem with a large series of math tools and find a break-through. But my point is that LLMs are studying math soooo intensively during the training that they know all the human theorems, which is way more knowledge than any single mathematicians. And yet, it turns out that all this math understanding just ends up being exactly limited to what humans already know. What are the odds? Or more probably: they just don't really understand math, and when asked about a known theorem, they generate a correct explanation based on training data without properly understanding it)

I didn't really try to argue that LLMs "have understanding".

I am more, in a sense, arguing that "understanding" isn't clearly defined and sceptical about your confidence that this is an obvious quality of humans.

I'm not getting what this "understanding" thing is in humans that you are talking about. But I feel if anyone you are the one making the unfalsifiable statements here. You are the one talking about an inner quality within the reasoning that only humans possess and not LLMs.

If I re-read your post and replace the word "understanding" with "consciousness" then it makes a lot more sense to me. Yes, humans can be conscious that they understand something, while LLMs are very very probably not conscious of anything at all. If that is what you mean, I can easily agree with that. I would never argue LLMs are conscious.

But at least my original post had nothing to do with consciousness.

If I'm coding, I'd definitely pick the "understanding" of Fable over a junior engineer's "understanding" any day, for purely pragmatic reasons. When I say that, I simply mean that the rate of mistakes in junior humans is way higher than in best trained LLMs for most coding tasks.

I'm guessing this is not using the word "understanding" in a way you are happy with, and probably because you define the word "understanding" as being related to consciousness?

> I am more, in a sense, arguing that "understanding" isn't clearly defined and sceptical about your confidence that this is an obvious quality of humans.

I did not do that, because it is not what I believe. I don't have any problems with the concept of having non-human being intelligent. It is just that LLMs are not that.

> I'm not getting what this "understanding" thing is in humans that you are talking about

Then that's fine. Other people have a better understanding of the notion. Just simply avoid the conversation if you don't know, it just feels like you are muddying it by not getting the concepts.

> If I re-read your post and replace the word "understanding" with "consciousness" ...

No, I'm not talking about "consciousness". I'm talking about "perceiving the underlying meaning or concept". LLMs don't create their answers by relying on the abstract concepts of the objects they are using, they just have meaningless rules linking the different objects, without grasping the abstract concepts explaining these links.

> I'd definitely pick the "understanding" of Fable over a junior engineer's "understanding" any day

Similarly, I trust better my pocket calculator than a human, but it does not mean that the calculator "understand math", it just has the "math rules" hardcoded without grasping the abstract concepts. In LLMs, the rules are not hardcoded, just extracted from the data, but the LLM doesn't understand any more than a pocket calculator understand math.

Thank you that was clearer.

So, I have a PhD in Astrophysics so I am not a total stranger to doing some thinking. And I would say "create (...) answers by relying on the abstract concepts of the objects they are using" is a lofty goal for humans, something to aspire to more than something that typically goes on. We go by habits and intuition and allegories and quite muddy concepts most of the time. Concepts are malleable and evolve in clarity. And in creating new mathematics etc., intuition, inspiration, "flashes of insights" etc after absorbing oneself in the problem has an important role.

Are these things we have in our minds, whether concepts or habits or intuitions or flashes of insights, better or worse than whatever patterns could potentially be found in the LLM weights?

I struggle to label one of them "understanding" and the other not, at least without involving consciousness somehow.

Obviously you can define "understand" as "understand as a human would" but that is circular and uninteresting.

We just have to agree to find each others position incredible I am afraid :)

> ... is a lofty goal for humans, ... We go by habits and intuition and allegories and quite muddy concepts most of the time

Those are already concepts. For LLMs, the word X is just an object linked to the words W, Y, Z, with no meaning to it. Habits, intuition and allegories are using objects to which we are attributing meaning.

Just to clarify, the links that LLMs create are complex, for example depend on all the surrounding other words, but they are still meaningless. If 2 totally different semantic sets of words happen to have exactly the same graphs of links, then you can swap the sets of word together, it does not matter for the LLM. To use a simplified example where you reduce a set to just 2 words, if "garden" and "pea" are linked the same way that "quantum" and "mechanic", then the relationship are the same for the LLM, without the LLM understanding that "garden pea" is a different concept than "quantum mechanic".

That's what I mean by "understanding": humans understand "garden pea" and "quantum mechanic" as concepts (even if they don't know biology or physics enough to even explain how they work), LLMs just use these objects as meaningless entities. All there is is a graph of links used to generate a sentence, but without knowing what the sentence means. A bit like if someone was giving you all the words of a language you don't know and the exact rules of how to build an answer given an input, but that you don't know what each word means.

Of course, the relationship learnt by the LLMs are very complex, allowing big changes based on the surrounding other 100'000 words. But learning this relationship is still more straightforward than to leap into a conceptual world model (especially because there is nothing to guide the LLM. If "garden pea" and "quantum mechanic" have the same graph geometry, then the world model where "garden pea" is an abstract field of study and "quantum mechanic" is a material object is as probable than the opposite).

I still remember when "smart" meant knowing the number of Rs in strawberry

Was that ever solved? It seems that entire retort faded overnight, yet to my knowledge there was never any systematic analysis on cause or tokenizer change that fixed it. Maybe we just decided that this failure mode doesn't have any practical bearing given the existence of tool-use?

...I think it really is irrelevant, isn't it? The LLM gets words as tokens, not strings of letters. If you asked me how many of the letter s is in Mississippi, but said I'm not allowed to spell out the word in my head and count the letters, I don't think I could do it.

This isn't a great analogy, because part of the challenge would be preventing myself from picturing the spelling in my head. But my point is, the AI is not getting the words as letters. The correct solution is tool use.

LLMs can learn to do arithmetic (without tool use), and they can learn a mapping from tokens to the letter counts contained therein (you could imagine trivially training on synthetic data). So there doesn't seem to be any fundamental barrier.

So we have that quote from the Oxford guy about explanations: "systems that can explain... You need models that can answer questions like: What matters? What causes what?", and then a mention of simulation of what the world looks like.

Fine, that describes theorizing.

But then a contradictory ending statement: "We're still going to need humans to figure out what questions to ask, what to build, what to create".

So that's moral theorizing. I don't think you can have one without the other. Then there's two more suggestions before the end of the article:

> smarter than us

> staff of assistants

Both of which are completely gratuitous assumptions. Why should its theories be better than established ones? Is it supposed to be a maverick hermit genius and come up with everything from first principles, or does it in fact participate in the existing world of ideas like a normal person? Then, being a normal person with moral theories, why would it take on the role of assistant rather than theorizing "I don't want to do that for you"?

It depends on how you define "smart".

For me, "smart" means doing things less based on instinct. Things humans can do but mice cannot, things mathematicians can do but normal people cannot, etc.

Considering the unit distance conjecture was disproved by OAI's model last month, I think maybe LLMs should count as "smart".

It's inference. It's really good at generating stuff when the example base is extensive. Like for non-esoteric coding.

Is a brain also inference? I know that an LLM works very different from the brain, but I wonder what makes a brain more capable of thinking. Is it the long term context? Is it a different type of neuron activation?

Ha before reading the article I thought "this must be an interview of Lecun". A bitter scientist that hates he was left behind the revolution.

In what way was he left behind? If he wanted to actually work on LLMs all the AI labs would fight to get him

Left behind how? It's been transformers since 2016 and not much actual progress in basic architectures has happened 10 years later. I'm honestly struggling to see how you can be left behind in this field.

and CPUs have the same basic architecture since 2000. no progress happened, right?

Obviously, transformers architecture is just one of the ingredients. Otherwise we wouldn't be seeing competing labs in this race. I also read all his interviews as a marketing material.

Considering all of the great research that has come from his labs (eg. DINO, Segment Anything) I don’t think that’s fair (no pun intended).

i guess inference engineering, like dpsark or dflash specific speculative decoding technqiues

What's next is more AI spam-slop. I already noticed this on youtube. Tons of short videos are AI sloppified, making youtube worse in the process.

> What's next is more AI spam-slop. I already noticed this on youtube.

I hope not but your observation on YouTube is spot-on. It's really frustrating. I've managed to keep good hygiene for my shorts feed. I practice zero-tolerance for braindead content; one strike is all it takes for the "never recommend" button.

But with the World Cup, the situation is just pandemonium. Football has always been breeding ground for low-effort content on the platform: unofficial highlights, cropped to death to avoid copyright, and always, for some reason, with a blaring background music. But now it's reached peak slop chaos: AI voiceovers, dubious anecdotes ("...and that kid, was Ronaldo"), and STILL the horribly blaring background music. The algorithm makes no distinction between quality and slop content of a topic. It's all just about the topic. So all it takes is for me to view a short related to football (even one of thoughtful commentary) for the slop to come in hordes.

At least I can now share in this forum's disdain for shorts. Kill that shit with fire man.

AI winter.

Human winter.

Not likely. Take with whatever grain of salt you'd like, but that was largely a property of development being academicized and subject to things like grant cycles, research topic fashionability trends, and institutional structure. It would be wrong to assume it's some baked in thing that's guaranteed to happen independent of how development looks.

But _AI today_ is heavily subsidized by investor capital in the same way investors subsidized social/mobile/big data/VR/blockchain in the past. It's not unlikely "AI" would get a soft taboo in the same way as if you just presented a mobile-first, big-data driven, VR social media app today.

Which, judging by the terrible PR optics AI has nowadays, could unfortunately seep into academia too. Fund grants wouldn't want their names associated with anything with "AI" in its name even if it's a return to expert systems.

You're mixing different things. Mobile first is integrated into new services to the point that they either are mobile first, or they have a design system which includes mobile as a surface. VR has a wide user base (MQ2 sold as well as the original Xbox) and is involved in both manufacturing design and simulation, and is hardly an academic taboo, even if the "main" topic of discussion is elsewhere right now. Blockchains are being integrated into financial infrastructure even as some people make snarky commentary about it. Sometimes optics is just an optical illusion.

Fair enough. Mobile and social became ubiquitous and are now table stakes. But my problem with VR and blockchain---even allowing for the fact/assumption that they are still relevant---is that they never lived up to their hype. They never became ubiquitous as mobile and social. They don't inspire investor confidence like they did in the past, if at all. AI, if it survives the public and regulatory backlash, could be headed to the same understudy role.

I'm using "AI" broadly here even if the current investor darling is just LLMs because, well, the term AI has been front and center of all promotions and investors and the general consumer public isn't really a discerning bunch. So I stand by my prediction that a "soft taboo" is likely where investors and consumers shy away from anything even remotely AI. The consumer backlash has arguably already started.

The vast consumer adoption and ongoing involvement seems to point the other way, though. I think a lot of the appearance of backlash is on (specifically anglophone, mostly) social media, which is going through a somewhat reactionary phase regardless.

Out of curiosity which markets exactly do you see with a positive AI outlook?

Also, I think you are downplaying the "anglophone" social media backlash too much for a couple reasons. _Anglophone_ social media is huge, even global. Everyone participates in anglophone social media even non-English speakers (who post in broken English, or comment in their native language in English-language content). So there is anglophone social media in all markets; it's not difficult to be aware of and espouse American public sentiment.

Even if you narrowly define anglophone socmed to correspond to the geo-cultural anglosphere, I think it's not surprising at all that the bulk of backlash is focused there because the leading AI companies are based there as well.

The Chinese market for one seems pretty optimistic about AI and the presence of AI in apps.

> MQ2 sold as well as the original Xbox

I'd be interested in the "retention rate" for these two products. I wouldn't be surprised if the average original Xbox was used 2 orders of magnitude more than the average Meta Quest, which is collecting dust on some shelf.

I'd wager the typical MQ2 owner is someone with 20 hours of Beat Saber on it and 5000 hours total on Steam or PS.

We're past the point where there's a feasible argument that there is an AI winter coming.

The models work remarkably well for several classes of problem that seemed impossible a few years ago. They're not going away. There will still absolutely be a lot of ups and down and crazy stuff that happens in AI, but it won't be that AI almost completely stops being developed/funded for a decade or more. The biggest risk, I think, is regulatory capture; it's what Anthropic and OpenAI seem to be aiming for with their scaremongering about how capable and dangerous their models are. That'll put a damper on the industry for everyone except the companies that bribe the right people.

They're not going away, in the same sense that Henry Winkler is still alive and working.

AI climate change.

Its definitely not as dumb as MAGA crowd

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