At least within tech, there seem to have been explosive changes and development of new products. While many of these fail, things like agents and other approaches for handling foundation models are only expanding in use cases. Agents themselves are hardly a year old as part of common discourse on AI, though technologists have been building POCs for longer. I've been very impressed with the wave of tools along the lines of Claude Code and friends.
Maybe this will end up relegated to a single field, but from where I'm standing (from within ML / AI), the way in which greenfield projects develop now is fundamentally different as a result of these foundation models. Even if development on these models froze today, MLEs would still likely be prompted to start with feeding something to a LLM, just because it's lightning fast to stand up.
boringg · 2h ago
I think the payment model is still not there which is making everything blurry. Until we figure out how much people have to pay to use it and all the services built on its back it will remain challenging to figure out full value prop. That and a lot of company are going to go belly up when they have to start paying the real cost instead of growth acquisition phase.
andy99 · 3h ago
Its probably cliche but I think it's both overhyped and under hyped, and for the same reason. They hype comes from "leadership" types that don't understand what LLMs actually do and so imagine all sorts of nonsense (replacing vast swaths of jobs or autonomously writing code) but don't understand how valuable a productivity enhancer and automation tool to can be. Eventually hype and reality will converge, but unlike e.g. blockchain or even some of the less bullshit "big data" and similar trends, there's no doubt that access to an LLM is a clear productivity enhancer for many jobs.
No comments yet
wvbdmp · 12h ago
Okay, so AI isn’t exceptional, but I’m also not exceptional. I run on the same tech base as any old chimpanzee, but at one point our differences in degree turned into one of us remaining “normal” and the other burning the entire planet.
Whether the particular current AI tech is it or not, I have yet to be convinced that the singularity is practically impossible, and as long as things develop in the opposite direction, I get increasingly unnerved.
ehnto · 53m ago
I don't think LLMs are building towards an AI singularity at least.
I also wonder if we can even power an AI singularity. I guess it depends on what the technology is. But it is taking us more energy than really reasonable (in my opinion) just to produce and run frontier LLMs. LLMs are this really weird blend of stunningly powerful, yet with a very clear inadequacy in terms of sentient behaviour.
I think the easiest way to demonstrate that, is that it did not take us consuming the entirety of human textual knowledge, to form a much stronger world model.
michaelhoney · 15m ago
True, but our "training" has been a billion years of evolution and multimodal input every waking moment of our lives. We come heavily optimised for reality.
tempodox · 7h ago
We’ll manage to make our own survival on this planet less probable, even without the help of “AI”.
measurablefunc · 7h ago
If you use non-constructive reasoning¹ then you can argue for basically any outcome & even convince yourself that it is inevitable. The basic example is as follows, there is no scientific or physical principle that can prevent the birth of someone much worse than Hitler & therefore if people keep having children one of those children will inevitably be someone who will cause unimaginable death & destruction. My recommendation is to avoid non-constructive inevitability arguments using our current ignorant state of understanding of physical laws as the main premise b/c it's possible to reach any conclusion from that premise & convince yourself that the conclusion is inevitable.
I agree that the mere theoretical possibility isn’t sufficient for the argument, but you’re missing the much less refutable component: that the inevitability is actively driven by universal incentives of competition.
But as I alluded to earlier, we’re working towards plenty of other collapse scenarios, so who knows which we’ll realize first…
marcus_holmes · 1h ago
None of them.
Humans have always believed that we are headed for imminent total disaster. In my youth it was WW3 and the impending nuclear armageddon that was inevitable. Or not, as it turned out. I hear the same language being used now about a whole bunch of other things. Including, of course, the evangelist Rapture that is going to happen any day now, but never does.
You can see the same thing at work in discussions about AI - there's passion in the voices of people predicting that AI will destroy humanity. Something in our makeup revels in the thought that we'll be the last generation of humans, that the future is gone and everything will come to a crashing stop.
This is human psychology at work.
potsandpans · 24m ago
"nothing ever happens."
The observation is, humans tend to think that annihilation is inevitable, it hasn't happened yet so therefore it will never be inevitable.
In fact, _anything_ could happen. Past performance does not guarantee future results.
If you need cognitive behavioral therapy, fine.
But to casually cite nuclear holocaust as something people irrationally believed in as a possibility is dishonest. That was (and still is) a real possible outcome.
Whats somewhat funny here is is if youre wrong, it doesnt matter. But that isnt the same as being right.
> Something in our makeup revels in the thought that we'll be the last generation of humans, that the future is gone and everything will come to a crashing stop
And yet there _will_ (eventually) be one generation that is right.
measurablefunc · 5h ago
My current guess is ecological collapse & increasing frequency of system shocks & disasters. Basically Blade Runner 2049 + Children of Men type of outcome.
jackphilson · 6h ago
Irrelevant but I like this pattern of using Gemini (or AI outputs in general) as sources. Please continue to do so and I encourage any readers to also adopt this pattern. I will also try to implement this pattern.
measurablefunc · 6h ago
The sources are in the report. Gemini provides actual references for all the claims made. You'd know that if you actually looked but lack of intellectual rigor is expected when people are afraid of actually scrutinizing their beliefs of non-constructive inevitability.
jrave · 5h ago
maybe you misread the post you‘re answering to here or are you suspecting sarcasm? the poster commended your usage of the footnote with the gemini convo as far as i can tell?
measurablefunc · 5h ago
Laid it on a little too thick to be sincere & more generally I don't comment on internet forums to be complimented on my response style. Address the substance of my arguments or just save yourself the keystrokes.
jjk166 · 4h ago
I'm pretty sure a lot of work has gone into making institutions resistant to a potential future super-Hitler. Whether those efforts will be effective or not, it is a very real concern, and it would be absurd to ignore it on the grounds of "there is probably some limit to tyranny we're not yet aware of which is not too far beyond what we've previously experienced." I would argue a lot more effort should have gone into preventing the original Hitler, whose rise to power was repeatedly met with the chorus refrain "How much worse can it get?"
1vuio0pswjnm7 · 43m ago
"So a paper published earlier this year by Arvind Narayanan and Sayash Kapoor, two computer scientists at Princeton University, is notable for the unfashionably sober manner in which it treats AI: as "normal technology"."
"Differences about the future of AI are often partly rooted in differing interpretations of evidence about the present. For example, we strongly disagree with the characterization of generative AI adoption as rapid (which reinforces our assumption about the similarity of AI diffusion to past technologies)."
jcranmer · 10h ago
Well, for starters, it would make The Economist's recent article on "What if AI made the world's economic growth explode?" [1] look like the product of overly credulous suckers for AI hype.
Well, it's better the same publication publish views contradicting their past than never changing their views with new info.
jaredklewis · 8h ago
This comment reminds me of the forever present HN comments that take a form like "HN is so hypocritical. In this thread commenters are saying they love X, when just last week in a thready about Y, commenters were saying that they hated X."
kamikazeturtles · 7h ago
All articles published by the Economist are reviewed by its editorial team.
Also, the Economist publishes all articles anonymously so the individual author isn't known. As far as I know, they do this so we take all articles and opinions as the perspective of the Economist publication itself.
janalsncm · 2h ago
Even if articles are reviewed by their editors (which I assume is true of all serious publications) they are probably reviewing for some level of quality and relevance rather than cross-article consistency. If there are interesting arguments for and against a thing it’s worth hearing both imo.
m_fayer · 5h ago
I’m pretty sure the “what if” in that article was meant in earnest. That article was playing out a scenario, in a nod to the ai maximalists. I don’t think it was making any sort of prediction or actually agreeing with those maximalists.
jcranmer · 5h ago
It was the central article of the issue, the one that dictated the headline and image on the cover for the week, and came with a small coterie of other articles discussing the repercussions of such an AI.
If it was disagreeing with AI maximalists, it was primarily in terms of the timeline, not in terms of the outcomes or inevitability of the scenario.
AnIrishDuck · 3h ago
This doesn't seem right to me. From the article I believe you are referencing ("What if AI made the world’s economic growth explode?"):
> If investors thought all this was likely, asset prices would already be shifting accordingly. Yet, despite the sky-high valuations of tech firms, markets are very far from pricing in explosive growth. “Markets are not forecasting it with high probability,” says Basil Halperin of Stanford, one of Mr Chow’s co-authors. A draft paper released on July 15th by Isaiah Andrews and Maryam Farboodi of mit finds that bond yields have on average declined around the release of new ai models by the likes of Openai and DeepSeek, rather than rising.
It absolutely (beyond being clearly titled "what if") presented real counterarguments to its core premise.
There are plenty of other scenarios that they have explored since then, including the totally contrary "What if the AI stock market blows up?" article.
This is pretty typical for them IME. They definitely have a bias, but they do try to explore multiple sides of the same idea in earnest.
naasking · 2h ago
I think any improvements to productivity AI brings will also create uncertainty and disruption to employment, and maybe the latter is greater than the former, and investors see that.
tootie · 2h ago
And a tacit admission that absolutely nobody knows for sure what will happen so maybe let's just game out a few scenarios and be prepared.
shoo · 4h ago
re: Why are The Economist’s writers anonymous?, Frqy3 had a good take on this back in 2017:
> From an economic viewpoint, this also means that the brand value of the articles remains with the masthead rather than the individual authors. This commodifies the authors and makes then more fungible.
> Being The Economist, I am sure they are aware of this.
If you back every horse in a race, you win every time.
svara · 10h ago
I'm perfectly happy reading different, well-argued cases in a magazine even if they contradict each other.
gyomu · 8h ago
Why would you expect opinion pieces from different people to agree with one another?
I’m curious about exploring the topics “What if the war in Ukraine ends in the next 12 months” just as much as “What if the war in Ukraine keeps going for the next 10 years”, doesn’t mean I expect both to happen.
buu700 · 7h ago
To add to your point, both article titles are questions that start with "What if". The same person could have written both and there would be no contradiction.
ranger207 · 11h ago
AI being normal technology would be the expected outcome, and it would be nice if it just hurried up and happened so I could stop seeing so much spam around AI actually being something much greater than normal technology
redwood · 11h ago
I think the "calculator for words" analogy is a good one. It's imperfect since words are inherently ambiguous but then again so is certain forms of digital numbers (floating point anyone?).
Through this lens it's way more normal
sfpotter · 11h ago
Floating point numbers aren't ambiguous in the least. They behave by perfectly deterministic and reliable rules and follow a careful specification.
solid_fuel · 2h ago
I understand what you're saying, but at the same time floating point numbers can only represent a fixed amount of precision. You can't, for example, represent Pi with a floating point. Or 1/3. And certain operations with floating point numbers with lots of decimals will always result in some precision being lost.
They are deterministic, and they follow clear rules, but they can't represent every number with full precision. I think that's a pretty good analogy for LLMs - they can't always represent or manipulate ideas with the same precision that a human can.
sfpotter · 1h ago
It's no more or less a good analogy than any other numerical or computational algorithm.
They're a fixed precision format. That doesn't mean they're ambiguous. They can be used ambiguously, but it isn't inevitable. Tools like interval arithmetic can mitigate this to a considerable extent.
Representing a number like pi to arbitrary precision isn't the purpose of a fixed precision format like IEEE754. It can be used to represent, say, 16 digits of pi, which is used to great effect in something like a discrete Fourier transform or many other scientific computations.
tintor · 5h ago
In theory, yes.
In practice, outcome of floating point computation depends on compiler optimizations, order of operations, and rounding used.
sfpotter · 4h ago
None of this is contradictory.
1. Compiler optimizations can be disabled. If a compiler optimization violates IEEE754 and there is no way to disable it, this is a compiler bug and is understood as such.
2. This is as advertised and follows from IEEE754. Floating point operations aren't associative. You must be aware of the way they work in order to use them productively: this means understanding their limitations.
3. Again, as advertised. The rounding mode is part of the spec and can be controlled. Understand it, use it.
GMoromisato · 10h ago
So are LLMs. Under the covers they are just deterministic matmul.
sfpotter · 5h ago
The purpose of floating point numbers it to provide a reliable, accurate, and precise implementation of fixed-precision arithmetic that is useful for scientific calculations and which has a large dynamic range, which is also capable of handling exceptional states (1/0, 0/0, overflow/underflow, etc) in a logical and predictable manner. In this sense, IEEE754 provides a careful and precise specification which has been implemented consistently on virtually every personal computer in use today.
LLMs are machine learning models used to encode and decode text or other-like data such that it is possible to efficiently do statistical estimation of long sequences of tokens in response to queries or other input. It is obvious that the behavior of LLMs is neither consistent nor standardized (and it's unclear whether this is even desirable---in the case of floating-point arithmetic, it certainly is). Because of the statistical nature of machine learning in general, it's also unclear to what extent any sort of guarantee could be made on the likelihoods of certain responses. So I am not sure it is possible to standardize and specify them along the lines of IEEE754.
The fact that a forward pass on a neural network is "just deterministic matmul" is not really relevant.
Chinjut · 7h ago
Ordinary floating point calculations allow for tractable reasoning about their behavior, reliable hard predictions of their behavior. At the scale used in LLMs, this is not possible; a Pachinko machine may be deterministic in theory, but not in practice. Clearly in practice, it is very difficult to reliably predict or give hard guarantees about the behavioral properties of LLMs.
Workaccount2 · 6h ago
Everything is either deterministic, random, or some combination.
We only have two states of causality, so calling something "just" deterministic doesn't mean much, especially when "just random" would be even worse.
For the record, LLMs in the normal state use both.
No comments yet
mhh__ · 10h ago
And at scale you even have a "sampling" of sorts (even if the distribution is very narrow unless you've done something truly unfortunate in your FP code) via scheduling and parallelism.
Digital spreadsheets (excel, etc) have done much more to change the world than so-called "artificial intelligence," and on the current trajectory it's difficult to see that changing.
thepryz · 10h ago
I don’t know if I would agree.
Spreadsheets don’t really have the ability to promote propaganda and manipulate people the way LLM-powered bots already have. Generative AI is also starting to change the way people think, or perhaps not think, as people begin to offload critical thinking and writing tasks to agentic ai.
Swizec · 10h ago
> Spreadsheets don’t really have the ability to promote propaganda and manipulate people
May I introduce you to the magic of "KPI" and "Bonus tied to performance"?
You'd be surprised how much good and bad in the world has come out of some spreadsheet showing a number to a group of promotion chasing type-a otherwise completely normal people.
Tarsul · 9h ago
social media ruined our brains long before LLMs. Not sure if the LLM-upgrade is is all that newsworthy... Well, for AI fake videos maybe - but it could also be that soon no one believes any video they see online which would have the adverse effect and could arguably even be considered good in our current times (difficult question!).
CuriouslyC · 7h ago
Agents are going to change everything. Once we've got a solid programmatic system driving interface and people get better about exposing non-ui handles for agents to work with programs, agents will make apps obsolete. You're going to have a device that sits by your desk and listens to you, watches your movements and tracks your eyes, and dispatches agents to do everything you ask it to do, using all the information it's taking in along with a learned model of you and your communication patterns, so it can accurately predict what you intend for it to do.
If you need an interface for something (e.g. viewing data, some manual process that needs your input), the agent will essentially "vibe code" whatever interface you need for what you want to do in the moment.
jrm4 · 6h ago
This isn't likely to happen for roughly the same reason Hypercard didn't become the universal way for novices to create apps.
CuriouslyC · 6h ago
I probably spend 80% of my time in front of a computer driving agents, challenge accepted :)
lordhumphrey · 5h ago
Marshall McLuhan called, he said to ask yourself, who's driving who?
CuriouslyC · 4h ago
"We shape our tools, and therefore, our tools shape us."
Ironically the outro of a YouTube video I just watched. I'm just a few hundred ms of latency away from being a cyborg.
hn_acc1 · 3h ago
So basically, the "ideal" state of a human is to be 100% active driving agents to vibe code whatever you need, based on every movement, every thought? Can our brains even handle having every thought being intentional and interpreted as such without collapsing (nervous breakdown)?
I guess I've always been more of a "work to live" type.
coke12 · 2h ago
Consider that a subset of us programmer types pride themselves on never moving their hands off the keyboard. They are already "wired in" so to speak.
alexpotato · 6h ago
The technology for this has been around for the past 10 years but it's still not a reality, what makes AI the kicker here?
e.g. Alexa for voice, REST for talking to APIs, Zapier for inter-app connectdness.
(not trying to be cynical, just pointing out that the technology to make it happen doesn't seem to be the blocker)
CuriouslyC · 6h ago
Alexa is trash. If you have to basically hold an agent's hand through something or it either fails or does something catastrophic nobody's going to use or trust it.
REST is actually a huge enabler for agents for sure, I think agents are going to drive everyone to have at least an API, if not a MCP, because if I can't use your app via my agent and I have to manually screw around in your UI, and your competitor lets my agent do work so I can just delegate via voice commands, who do you think is getting my business?
naasking · 26m ago
Artificial intelligence has solved protein folding. The downstream effects of that alone will be huge, and it's far from the only change coming.
micromacrofoot · 6h ago
hah, just wait until everything you ever do online is moderated through an LLM and tell me that's not world changing
bilsbie · 11h ago
I’m guessing it will be exactly like the internet. Changes everything and changes nothing.
daxfohl · 3h ago
Yeah I can see it being like late 90's and early 2000's for a while. Mostly consulting companies raking in the cash setting up systems for older companies, a ton of flame-out startups, and a few new powerhouses.
Will it change everything? IDK, moving everything self-hosted to the cloud was supposed to make operations a thing of the past, but in a way it just made ops an even bigger industry than it was.
only-one1701 · 6h ago
lol absolutely not
only-one1701 · 6h ago
I think it’ll be like social media
Havoc · 8h ago
A better starting point imo is that it is a general-purpose technology. It can have a profound effect on society yet not be magic/AGI.
j45 · 7h ago
Absolutely. The first version to the world was the 3rd or 4th version of ChatGPT itself.
Some can remember the difference between iPhone 1 and 4 and where it took off with the latter.
marginalia_nu · 11h ago
AI is technology that does not exist yet that can be speculated about. When AI materializes into existence it becomes normal technology.
Let's not forget there has been times when if-else statements were considered AI. NLP used to be AI too.
1c2adbc4 · 11h ago
Do you have a suggestion for a better name? I care more about the utility of a thing, rather than playing endless word games with AI, AGI, ASI, whatever. Call it what you will, it is what it is.
J_McQuade · 10h ago
Broadly Uneconomical Large Language Systems Holding Investors in Thrall.
lordhumphrey · 5h ago
Excellent name! BULLSHIT really captures the spirit of the whole thing.
OJFord · 11h ago
It will depend on the final form the normal useful tools take, but for now it's 'LLMs', 'coding agents', etc.
el_nahual · 11h ago
We have a name: Large Language Models, or "Generative" AI.
It doesn't think, it doesn't reason, and it doesn't listen to instructions, but it does generate pretty good text!
chpatrick · 11h ago
[citation needed]
People constantly assert that LLMs don't think in some magic way that humans do think, when we don't even have any idea how that works.
mindcrime · 9h ago
> People constantly assert that LLMs don't think in some magic way that humans do think,
It doesn't matter anyway. The marquee sign reads "Artificial Intelligence" not "Artificial Human Being". As long as AI displays intelligent behavior, it's "intelligent" in the relevant context. There's no basis for demanding that the mechanism be the same as what humans do.
And of course it should go without saying that Artificial Intelligence exists on a continuum (just like human intelligence as far as that goes) and that we're not "there yet" as far as reaching the extreme high end of the continuum.
hermitcrab · 7h ago
Aircraft don't fly like birds, submarines don't swim like fish and AIs aren't going to think like a human.
chpatrick · 6h ago
Do you need to "think like a human" to think? Is it only thinking if you do it with a meat brain?
hermitcrab · 5h ago
Is the substrate important? If you made an accurate model of a human brain in software, in silicon or using water pipes and valves, would it be able to tnink? Would it be conscious? I have no idea.
chpatrick · 4h ago
Me neither but that's why I don't like arguments that say LLM's can't do X because of their substrate, as if that was self-evident. It's like the aliens saying surely humans can't think because they're made of meat.
utyop22 · 6h ago
Do these comparisons actually make sense though?
Aircraft and submarines belong to a different category and of the same category, than AI.
hermitcrab · 5h ago
I am just trying to make the point that the machines that we make tend to end up rather different to their natural analogues. The effective ones anyway. Ornithopters were not successful. And I suspect that articifial intelligences will end up very different to human intelligence.
utyop22 · 4h ago
Okay... but an airplane in essence is modelling the shape of a bird. Where do you think the inspiration for the shape of a plane came from? lmao. come on.
Humans are not all that original, we take what exists in nature and mangle it in some way to produce a thing.
The same thing will eventually happen with AI - not in our lifetime though.
jbritton · 9h ago
I recently saw an article about LLMs and Towers of Hanoi. An LLM can write code to solve it. It can also output steps to solve it when the disk count is low like 3. It can’t give the steps when the disk count is higher. This indicates LLMs inability to reason and understand. Also see Gotham Chess and the Chatbot Championship. The Chatbots start off making good moves, but then quickly transition to making illegal moves and generally playing unbelievably poorly. They don’t understand the rules or strategy or anything.
naasking · 21m ago
> This indicates LLMs inability to reason and understand.
No it doesn't, this is an overgeneralization.
leptons · 9h ago
Could the LLM "write code to solve it" if no human ever wrote code to solve it? Could it output "steps to solve it" if no human ever wrote about it before to have in its training data? The answer is no.
chpatrick · 8h ago
Could a human code the solution if they didn't learn to code from someone else? No. Could they do it if someone didn't tell them the rules of towers of hanoi? No.
That doesn't mean much.
Gee101 · 8h ago
It does since humans where able to invent a programming language.
chpatrick · 7h ago
Have you tried asking a modern LLM to invent a programming language?
CamperBob2 · 7h ago
Have you? If so, how'd it go? Sounds like an interesting exercise.
A human can learn and understand the rules, an LLM never could. LLMs have famously been incapable of beating humans in chess, a seemingly simple thing to learn, because LLMs can't learn - they just predict the next word and that isn't helpful in solving actual problems, or playing simple games.
It's not some "magical way"--the ways in which a human thinks that an LLM doesn't are pretty obvious, and I dare say self-evidently part of what we think constitutes human intelligence:
- We have a sense of time (ie, ask an LLM to follow up in 2 minutes)
- We can follow negative instructions ("don't hallucinate, if you don't know the answer, say so")
int_19h · 2h ago
We only have a sense of time in the presence of inputs. Stick a human into a sensory deprivation tank for a few hours and then ask them how much time has passed afterwards. They wouldn't know unless they managed to maintain a running count throughout, but that's a trick an LLM can also do (so long as it knows generation speed).
The general notion of passage of time (i.e. time arrow) is the only thing that appears to be intrinsic, but it is also intrinsic for LLMs in a sense that there are "earlier" and "later" tokens in its input.
chpatrick · 4h ago
I think plenty of people have problems with the second one but you wouldn't say that means they can't think.
bluefirebrand · 3h ago
We don't need to prove all humans are capable of this. We can demonstrate that some humans are, therefore humans must be capable, broadly speaking
Until we see an LLM that is capable of this, then they aren't capable of it, period
chpatrick · 1h ago
Sometimes LLMs hallucinate or bullshit, sometimes they don't, sometimes humans hallucinate or bullshit, sometimes they don't. It's not like you can tell a human to stop being delusional on command either. I'm not really seeing the argument.
d3ckard · 10h ago
What can be asserted without proof, can be dismissed without proof.
The proof burden is on AI proponents.
chpatrick · 10h ago
It's more that "thinking" is a vague term that we don't even understand in humans, so for me it's pretty meaningless to claim LLMs think or don't think.
There's this very cliched comment to any AI HN headline which is this:
"LLM's don't REALLY have <vague human behavior we don't really understand>. I know this for sure because I know both how humans work and how gigabytes of LLM weights work."
or its cousin:
"LLMs CAN'T possibly do <vague human behavior we don't really understand> BECAUSE they generate text one character at a time UNLIKE humans who generate text one character a time by typing with their fleshy fingers"
barnacs · 9h ago
To me, it's about motivation.
Intelligent living beings have natural, evolutionary inputs as motivation underlying every rational thought. A biological reward system in the brain, a desire to avoid pain, hunger, boredom and sadness, seek to satisfy physiological needs, socialize, self-actualize, etc. These are the fundamental forces that drive us, even if the rational processes are capable of suppressing or delaying them to some degree.
In contrast, machine learning models have a loss function or reward system purely constructed by humans to achieve a specific goal. They have no intrinsic motivations, feelings or goals. They are statistical models that approximate some mathematical function provided by humans.
chpatrick · 8h ago
Are any of those required for thinking?
barnacs · 8h ago
In my view, absolutely yes. Thinking is a means to an end. It's about acting upon these motivations by abstracting, recollecting past experiences, planning, exploring, innovating. Without any motivation, there is nothing novel about the process. It really is just statistical approximation, "learning" at best, but definitely not "thinking".
chpatrick · 8h ago
Again the problem is that what "thinking" is totally vague. To me if I can ask a computer a difficult question it hasn't seen before and it can give a correct answer, it's thinking. I don't need it to have a full and colorful human life to do that.
barnacs · 7h ago
But it's only able to answer the question because it has been trained on all text in existence written by humans, precisely with the purpose to mimic human language use. It is the humans that produced the training data and then provided feedback in the form of reinforcement that did all the "thinking".
Even if it can extrapolate to some degree (altough that's where "hallucinations" tend to become obvious), it could never, for example, invent a game like chess or a social construct like a legal system. Those require motivations like "boredom", "being social", having a "need for safety".
chpatrick · 6h ago
Humans are also trained on data made by humans.
> it could never, for example, invent a game like chess or a social construct like a legal system. Those require motivations like "boredom", "being social", having a "need for safety".
That's creativity which is a different question from thinking.
barnacs · 6h ago
I guess our definition of "thinking" is just very different.
Yes, humans are also capable of learning in a similar fashion and imitating, even extrapolating from a learned function. But I wouldn't call that intelligent, thinking behavior, even if performed by a human.
But no human would ever perform like that, without trying to intuitively understand the motivations of the humans they learned from, and naturally intermingling the performance with their own motivations.
bluefirebrand · 3h ago
> Humans are also trained on data made by humans
Humans invent new data, humans observe things and create new data. That's where all the stuff the LLMs are trained on came from.
> That's creativity which is a different question from thinking
It's not really though. The process is the same or similar enough don't you think?
chpatrick · 2h ago
I disagree. Creativity is coming up with something out of the blue. Thinking is using what you know to come to a logical conclusion. LLMs so far are not very good at the former but getting pretty damn good at the latter.
shakna · 6h ago
Thinking is better understood than you seem to believe.
We don't just study it in humans. We look at it in trees [0], for example. And whilst trees have distributed systems that ingest data from their surroundings, and use that to make choices, it isn't usually considered to be intelligence.
Organizational complexity is one of the requirements for intelligence, and an LLM does not reach that threshold. They have vast amounts of data, but organizationally, they are still simple - thus "ai slop".
Who says what degree of complexity is enough? Seems like deferring the problem to some other mystical arbiter.
In my opinion AI slop is slop not because AIs are basic but because the prompt is minimal. A human went and put minimal effort into making something with an AI and put it online, producing slop, because the actual informational content is very low.
omnicognate · 4h ago
This seems backwards to me. There's a fully understood thing (LLMs)[1] and a not-understood thing (brains)[2]. You seem to require a person to be able to fully define (presumably in some mathematical or mechanistic way) any behaviour they might observe in the not-understood thing before you will permit them to point out that the fully understood thing does not appear to exhibit that behaviour. In short you are requiring that people explain brains before you will permit them to observe that LLMs don't appear to be the same sort of thing as them. That seems rather unreasonable to me.
That doesn't mean such claims don't need to made as specific as possible. Just saying something like "humans love but machines don't" isn't terribly compelling. I think mathematics is an area where it seems possible to draw a reasonably intuitively clear line. Personally, I've always considered the ability to independently contribute genuinely novel pure mathematical ideas (i.e. to perform significant independent research in pure maths) to be a likely hallmark of true human-like thinking. This is a high bar and one AI has not yet reached, despite the recent successes on the International Mathematical Olympiad [3] and various other recent claims. It isn't a moved goalpost, either - I've been saying the same thing for more than 20 years. I don't have to, and can't, define what "genuinely novel pure mathematical ideas" means, but we have a human system that recognises, verifies and rewards them so I expect us to know them when they are produced.
By the way, your use of "magical" in your earlier comment, is typical of the way that argument is often presented, and I think it's telling. It's very easy to fall into the fallacy of deducing things from one's own lack of imagination. I've certainly fallen into that trap many times before. It's worth honestly considering whether your reasoning is of the form "I can't imagine there being something other than X, therefore there is nothing other than X".
Personally, I think it's likely that to truly "do maths" requires something qualitatively different to a computer. Those who struggle
to imagine anything other than a computer being possible often claim that that view is self-evidently wrong and mock such an imagined device as "magical", but that is not a convincing line of argument. The truth is that the physical Church-Turing thesis is a thesis, not a theorem, and a much shakier one than the original Church-Turing thesis. We have no particularly convincing reason to think such a device is impossible, and certainly no hard proof of it.
[1] Individual behaviours of LLMs are "not understood" in the sense that there is typically not some neat story we can tell about how a particular behaviour arises that contains only the truly relevant information. However, on a more fundamental level LLMs are completely understood and always have been, as they are human inventions that we are able to build from scratch.
[2] Anybody who thinks we understand how brains work isn't worth having this debate with until they read a bit about neuroscience and correct their misunderstanding.
[3] The IMO involves problems in extremely well-trodden areas of mathematics. While the problems are carefully chosen to be novel they are problems to be solved in exam conditions, not mathematical research programs. The performance of the Google and OpenAI models on them, while impressive, is not evidence that they are capable of genuinely novel mathematical thought. What I'm looking for is the crank-the-handle-and-important-new-theorems-come-out machine that people have been trying to build since computers were invented. That isn't here yet, and if and when it arrives it really will turn maths on its head.
chpatrick · 4h ago
LLMs are absolutely not "fully understood". We understand how the math of the architectures work because we designed that. How the hundreds of gigabytes of automatically trained weights work, we have no idea. By that logic we understand how human brains work because we've studied individual neurons.
And here's some more goalpost-shifting. Most humans aren't capable of novel mathematical thought either, but that doesn't mean they can't think.
omnicognate · 3h ago
We don't understand individual neurons either. There is no level on which we understand the brain in the way we very much do understand LLMs. And as much as people like to handwave about how mysterious the weights are we actually perfectly understand both how the weights arise and how they result in the model's outputs. As I mentioned in [1] what we can't do is "explain" individual behaviours with simple stories that omit unnecessary details, but that's just about desiring better (or more convenient/useful) explanations than the utterly complete one we already have.
As for most humans not being mathematicians, it's entirely irrelevant. I gave an example of something that so far LLMs have not shown an ability to do. It's chosen to be something that can be clearly pointed to and for which any change in the status quo should be obvious if/when it happens. Naturally I think that the mechanism humans use to do this is fundamental to other aspects of their behaviour. The fact that only a tiny subset of humans are able to apply it in this particular specialised way changes nothing. I have no idea what you mean by "goalpost-shifting" in this context.
int_19h · 2h ago
> We actually perfectly understand both how the weights arise and how they result in the model's outputs
If we knew that, we wouldn't need LLMs; we could just hardcode the same logic that is encoded in those neural nets directly and far more efficiently.
But we don't actually know what the weights do beyond very broad strokes.
CamperBob2 · 7h ago
The proof burden is on AI proponents.
Why? Team "Stochastic Parrot" will just move the goalposts again, as they've done many times before.
lordhumphrey · 4h ago
Yes, and the name for this behaviour is called "being scientific".
Imagine a process called A, and, as you say, we've no idea how it works.
Imagine, then, a new process, B, comes along. Some people know a lot about how B works, most people don't. But the people selling B, they continuously tell me it works like process A, and even resort to using various cutesy linguistic tricks to make that feel like it's the case.
The people selling B even go so far as to suggest that if we don't accept a future where B takes over, we won't have a job, no matter what our poor A does.
What's the rational thing to do, for a sceptical, scientific mind? Agree with the company, that process B is of course like process A, when we - as you say yourself - don't understand process A in any comprehensive way at all? Or would that be utterly nonsensical?
chpatrick · 1h ago
Again, I'm not claiming that LLMs can think like people (I don't know that). I just don't like that people confidently claim that they can't, just because they work differently from biological brains. That doesn't matter when it comes to the Turing test (which they passed a while ago btw), just what it says.
mvdtnz · 2h ago
The classic God of the Gaps - we don't know how human brains think, so what LLMs do must be it!
chpatrick · 1h ago
I'm not saying that LLMs do anything, just that it's rich to confidently say they don't do something when we don't even understand how humans do it.
It's like we're pretending cognition is a solved problem so we can make grand claims about what LLM's aren't really doing.
exe34 · 10h ago
my favourite game is to try to get them to be more specific - every single time they manage to exclude a whole bunch of people from being "intelligent".
leptons · 9h ago
When I write a sentence, I do it with intent, with specific purpose in mind. When an "AI" does it, it's predicting the next word that might satisfy the input requirement. It doesn't care if the sentence it writes makes any sense, is factual, etc, so long as it is human readable and follows gramatic rules. It does not do this with any specific intent, which is why you get slop and just plain wrong output a fair amount of time. Just because it produces something that sounds correct sometimes does not mean it's doing any thinking at all. Yes, humans do actually think before they speak, LLMs do not, cannot, and will not because that is not what they are designed to do.
chpatrick · 8h ago
Actually LLMs crunch through half a terabyte of weights before they "speak". How are you so confident that nothing happens in that immense amount of processing that has anything to do with thinking? Modern LLMs are also trained to have an inner dialogue before they output an answer to the user.
When you type the next word you also put a word that fits some requirement. That doesn't mean you're not thinking.
leptons · 8h ago
"crunch through half a terabyte of weights" isn't thinking. Following grammatical rules to produce a readable sentence isn't thought, it's statistics, and whether that sentence is factual or foolish isn't something the LLM cares about. If LLMs didn't so constantly produce garbage, I might agree with you more.
chpatrick · 7h ago
They don't follow "grammatical rules", they process inputs with an incredibly large neural net. It's like saying humans aren't really thinking because their brains are made of meat.
stillsut · 7h ago
"Unstructured data learners and generators" is probably the most salient distinction for how current system compare to previous "AI systems" examples (NLP, if-statements) that OP mentioned.
marginalia_nu · 11h ago
I don't particularly mind the term, it's a useful shibboleth separating the marketing and sci-fi from the takes grounded in reality.
bradgessler · 10h ago
Artificial Interpolator
Augmented Intelligence
ronsor · 10h ago
Aye-aye, that's a good name
exe34 · 10h ago
I think it's fine to keep the name, we just have to realise it's like magic. real magic can't be done. magic that can be done is just tricks. AI that works is just tricks.
1c2adbc4 · 10h ago
I didn't realize that magic was the goal. I'm just trying to process unstructured data. Who's here looking for magic?
stillsut · 7h ago
I think the "magic" that we've found a common toolset of methods - embeddings and layers of neural networks - that seem to reveal useful patterns and relationships from a vast array of corpus of unstructured analog sensors (pictures, video, point clouds) and symbolic (text, music) and that we can combine these across modalities like CLIP.
It turns out we didn't need a specialist technique for each domain, there was a reliable method to architect a model that can learn itself, and we could already use the datasets we had, they didn't need to be generated in surveys or experiments. This might seem like magic to an AI researcher working in the 1990's.
int_19h · 2h ago
Many humans like to think that their own intelligence is "magic" that cannot be reduced to physics.
exe34 · 7h ago
did you miss the word "like"? have you come across the concept of an analogy yet?
lo_zamoyski · 10h ago
Statistics.
A lot of this is marketing bullshit. AFAIK, even "machine learning" was a term made up by AI researchers when the AI winter hit who wanted to keep getting a piece of that sweet grant money.
And "neural network" is just a straight up rubbish name. All it does is obscure what's actually happening and leads the proles to think it has something to do with neurons.
janalsncm · 2h ago
To be honest, no one can agree on what “intelligence” is. The “artificial” part is pretty easy to understand though.
jrm4 · 6h ago
One, I doubt your premise ever happens in a meaningfully true and visible way -- but perhaps more important, I'd say you're factually wrong in terms of "what is called AI?"
Among most people, you're thinking of things that were debatably AI, today we have things that are AI (again, not due to any concrete definition, simply due to accepted usage of the term.)
michaeldoron · 11h ago
NLP is still AI - LLMs are using Natural Language Processing, and are considered artificial intelligence.
hermitcrab · 7h ago
>Let's not forget there has been times when if-else statements were considered AI.
They still are, as far as the marketing department is concerned.
Artificial Intelligence is a whole subfield of Computer Science.
Code built of nothing but if/else statements controlling the behavior of game NPCs is AI.
A* search is AI.
NLP is AI.
ML is AI.
Computer vision models are AI.
LLMs are AI.
None of these are AGI, which is what does not yet exist.
One of the big problems underlying the current hype cycle is the overloading of this term, and the hype-men's refusal to clarify that what we have now is not the same type of thing as what Neo fights in the Matrix. (In some cases, because they have genuinely bought into the idea that it is the same thing, and in all cases because they believe they will benefit from other people believing it.)
ACCount37 · 11h ago
"AI" is a wide fucking field. And it occasionally includes systems built entirely on if-else statements.
lo_zamoyski · 10h ago
There is no difference between AI and non-AI save for the model the observer is using to view a particular bit of computation.
OkayPhysicist · 9h ago
Eh, I'd be fairly comfortable delineating between AI and other CS subfields based on the idea of higher-order algorithms. For most things, you have a problem with fixed set of fixed parameters, and you need a solution in the form of fixed solution. (e.g., 1+1=2) In software, we mostly deal with one step up from that: we solve general case problems, for a fixed set of variable parameters, and we produce algorithms that take the parameters as input and produce the desired solution (e.g., f(x,y) = x + y). The field of AI largely concerns itself with algorithms that produce models to solve entire classes of problem, that take the specific problem description itself as input (e.g., SAT solvers, artificial neural networks, etc where g("x+y") => f(x,y) = x + y ). This isn't a perfect definition of the field (it ends up catching some things like parser generators and compilers that aren't typically considered "AI"), but it does pretty fairly, IMO, represent a distinct field in CS.
alanbernstein · 7h ago
I think I misinterpreted your comment as not understanding the AI effect, but actually you're just summarizing it kind of concisely and sarcastically?
LLMs are one of the first technologies that makes me think the term "AI effect" needs to be updated to "AGI effect". The effect is still there, but it's undeniable that LLMs are capable of things that seem impossible with classical CS methods, so they get to retain the designation of AI.
There were _so many_ articles in the late 80s and early 90s about how computers were a big waste of money. And again in the late 90s, about how the internet was a waste of money.
We aren't going to know the true consequences of AI until kids that are in high school now enter the work force. The vast majority of people are not capable of completely reordering how they work. Computers did not help Sally Secretary type faster in the 1980s. That doesn't mean they were a waste of money.
boredtofears · 7h ago
You mean the same kids that are currently cheating their way through their education at record rates due to the same technology? Can't say I'm optimistic.
bnchrch · 1h ago
> The children now love luxury; they have bad manners, contempt for authority; they show disrespect for elders and love chatter in place of exercise
> - Socrates (399 BC)
> The world is passing through troublous times. The young people of today think of nothing but themselves. They have no reverence for parents or old age. They are impatient of all restraint. They talk as if they knew everything, and what passes for wisdom with us is foolishness with them. As for the girls, they are forward, immodest and unladylike in speech, behavior and dress
> - Peter the Hermit (1274)
naasking · 15m ago
> > - Socrates (399 BC)
Context: Ancient Greece went into decline just 70 years after that date. Make of that what you will.
ctoth · 11h ago
What if this paper actually took things seriously?
A serious paper would start by acknowledging that every previous general-purpose technology required human oversight precisely because it couldn't perceive context, make decisions, or correct errors - capabilities that are AI's core value proposition. It would wrestle with the fundamental tension: if AI remains error-prone enough to need human supervisors, it's not transformative; if it becomes reliable enough to be transformative, those supervisory roles evaporate.
These two Princeton computer scientists, however, just spent 50 pages arguing that AI is like electricity while somehow missing that electricity never learned to fix itself, manage itself, or improve itself - which is literally the entire damn point. They're treating "humans will supervise the machines" as an iron law of economics rather than a temporary bug in the automation process that every profit-maximizing firm is racing to patch.
Sometimes I feel like I'm losing my mind when it's obvious that GPT-5 could do better than Narayanan and Kapoor did in their paper at understanding historical analogies.
nottorp · 10h ago
> because it couldn't perceive context, make decisions, or correct errors - capabilities that are AI's core value proposition
I could ask the same thing then. When will you take "AI" seriously and stop attributing the above capabilities to it?
simonh · 11h ago
LLMs do have to be supervised by humans and do not perceive context or correct errors, and it’s not at all clear this is going to change any time soon. In fact it’s plausible that this is due to basic problems with the current technology. So if you’re right, sure, but I’m certainly not taking that as a given.
cubefox · 7h ago
Exactly. People seem to want to underhype AI. It's like a chimpanzee saying: humans are just normal apes.
Delusional.
aredox · 10h ago
The potentially "explosive" part of AI was that it could be self-improving. Using AI to improve AI, or AI improving itself in an exponential growth until it becomes super-human. This is what the "Singularity" and AI "revolution" is based on.
But in the end, despite saying AI has PhD-level intelligence, the truth is that even AI companies can't get AI to help them improve faster. Anything slower than exponential is proof that their claims aren't true.
naasking · 11m ago
LLMs are already superhuman at many tasks. You're also wrong about AI not accelerating AI development. There was at least one paper published this year showing just such a result. It's just beginning.
lioeters · 6h ago
> improving itself in an exponential growth
That seems like a possibly mythical critical point, at which a phase transition will occur that makes the AI system qualitatively different from its predecessors. Exponential to the limit of infinity.
All the mad rush of companies and astronomical investments are being made to get there first, counting on this AGI to be a winner-takes-all scenario, especially if it can be harnessed to grow the company itself. The hype is even infecting governments, for economic and national interest. And maybe somewhere a mad king dreams of world domination.
utyop22 · 6h ago
What world domination though? If such a thing ever existed for example in the US, the government would move to own and control it. No firm or individual would be allowed to acquire and exercise that level of power.
utyop22 · 6h ago
Said another way, will a firm suddenly improve radically because they hired a thousand PhDs folks? Not quite.
Many things sound good on paper. But paper vs reality are very different. Things are more complex in reality.
jrm4 · 6h ago
This is brilliant and I can't believe I haven't heard this idea before.
giardini · 9h ago
How about a link that works?
Neither the OP's URL nor djoldman's archive link allow access to the article!8-((
giardini · 6h ago
OK, now djoldman's archive link above works!
pessimizer · 10h ago
I've come to the conclusion that it is a normal, extremely useful, dramatic improvement over web 1.0. It's going to
1) obsolete search engines powered by marketing and SEO, and give us paid search engines whose selling points are how comprehensive they are, how predictable their queries work (I miss the "grep for the web" they were back when they were useful), and how comprehensive their information sources are.
2) Eliminate the need to call somebody in the Philippines awake in the middle of the night, just for them to read you a script telling you how they can't help you fix the thing they sold you.
3) Allow people to carry local compressed copies of all written knowledge, with 90% fidelity, but with references and access to those paid search engines.
And my favorite part, which is just a footnote I guess, is that everybody can move to a Linux desktop now. The chatbots will tell you how to fix your shit when it breaks, and in a pedagogical way that will gradually give you more control and knowledge of your system than you ever thought you were capable of having. Or you can tell it that you don't care how it works, just fix it. Now's the time to switch.
That's your free business idea for today: LLM Linux support. Train it on everything you can find, tune it to be super-clippy. Charge people $5 a month. The AI that will free you from their AI.
Now we just need to annihilate web 2.0, replace it with peer-to-peer encrypted communications, and we can leave the web to the spammers and the spies.
fsloth · 6h ago
"everybody can move to a Linux desktop now"
People use whatever UI comes with their computer. I don't think that's going to change.
g42gregory · 4h ago
While I feel silly to take seriously something printed in The Economist, I would like to mention that people tend to overestimate the short-term impact of any technology and underestimate its long-term impacts. Maybe AI will follow the same route?
65 · 3h ago
Ah yes, disgraced tabloid The Economist, no one should ever take their writing seriously!
g42gregory · 3h ago
I used to read it and subscribe to it, a while back. I would not technically categorize them as a tabloid. They serve a different purpose.
giardini · 2h ago
...right into the dustbin of computing history. But possibly faster than most other technologies.
westurner · 6h ago
AI is probably more of an amplifier for technological change than fire or digital computers; but IDK why we would use a different model for this technology (and teams and coping with change).
> [ "From Comfort Zone to Performance Management" (2009) ] also suggests management styles for each stage (Commanding, Cooperative, Motivational, Directive, Collaborative); and suggests that team performance is described by chained power curves of re-progression through these stages
Transforming, Performing, Reforming, [Adjourning]
Carnal Coping Cycle: Denial, Defense, Discarding, Adaptation, and Internalization
akomtu · 10h ago
Normal? AI is an alien technology to us, and we are being "normalized" to become compatible with it.
aeternum · 9h ago
AI actually seems far less alien than steam engines, trains, submarines, flight, and space travel.
People weren't sure if human bodies could handle moving at >50mph.
akomtu · 1h ago
All those steam engines, trains and submarines were steps toward what we are seeing now. AI is the logical culmination and the purpose of technology.
josefritzishere · 11h ago
If you read the paper, they make a good case that AI is just a normal technology. They're a bit dissmissive, but they're not alone in that. The AI sector has been all too much hype and far too little substance.
ktallett · 12h ago
What do they mean what if? It is similarly based to something that has existed for around 4 decades. It of course is at a higher standard of efficiency and able to search through and combine more data but it isn't new. It is just a normal technology and this was why myself and many others were shocked at the initial hype.
Eisenstein · 12h ago
> It is similarly based to something that has existed for around 4 decades.
Four decades ago was 1985. The thing is, there was a huge jump in progress from then until now. If we took something which had a nice ramped progress, like computer graphics, and instead of ramping up we went from '1985' to '2025' in progress over the course of a few months, do you think there wouldn't be a lot of hype?
johnbellone · 7h ago
> Four decades ago was 1985
Don't remind me.
ktallett · 11h ago
But we have ramped up slowly, it's just not been given in quite this form before. We have previously only used it in settings where accuracy is a focus.
csours · 5h ago
If you make something cheap, then it will be cheap.
LLMs may set a record for time between specialized/luxury goods and commodity.
There may be a price floor, but it's not very high.
In My Opinion.
---
Ever think about why restaurants pay someone to wash the dishes?
In my house, I have a machine that does that.
In a restaurant, the machine is too slow, and not compatible with the rest of the system of the restaurant.
Until we hit singularity, AI has to be compatible with the rest of the system.
fragmede · 5h ago
Restaurants have machines for washing dishes. They do pay people to do the dish washing, but commercial dishwashing machines exist, and they work differently than home machines. They're large stainless steel monsters, some with a conveyor belt, others operate vertically. They usually use high temp water rather than soap to do the cleaning.
Maybe this will end up relegated to a single field, but from where I'm standing (from within ML / AI), the way in which greenfield projects develop now is fundamentally different as a result of these foundation models. Even if development on these models froze today, MLEs would still likely be prompted to start with feeding something to a LLM, just because it's lightning fast to stand up.
No comments yet
Whether the particular current AI tech is it or not, I have yet to be convinced that the singularity is practically impossible, and as long as things develop in the opposite direction, I get increasingly unnerved.
I also wonder if we can even power an AI singularity. I guess it depends on what the technology is. But it is taking us more energy than really reasonable (in my opinion) just to produce and run frontier LLMs. LLMs are this really weird blend of stunningly powerful, yet with a very clear inadequacy in terms of sentient behaviour.
I think the easiest way to demonstrate that, is that it did not take us consuming the entirety of human textual knowledge, to form a much stronger world model.
¹https://gemini.google.com/share/d9b505fef250
But as I alluded to earlier, we’re working towards plenty of other collapse scenarios, so who knows which we’ll realize first…
Humans have always believed that we are headed for imminent total disaster. In my youth it was WW3 and the impending nuclear armageddon that was inevitable. Or not, as it turned out. I hear the same language being used now about a whole bunch of other things. Including, of course, the evangelist Rapture that is going to happen any day now, but never does.
You can see the same thing at work in discussions about AI - there's passion in the voices of people predicting that AI will destroy humanity. Something in our makeup revels in the thought that we'll be the last generation of humans, that the future is gone and everything will come to a crashing stop.
This is human psychology at work.
The observation is, humans tend to think that annihilation is inevitable, it hasn't happened yet so therefore it will never be inevitable.
In fact, _anything_ could happen. Past performance does not guarantee future results.
If you need cognitive behavioral therapy, fine.
But to casually cite nuclear holocaust as something people irrationally believed in as a possibility is dishonest. That was (and still is) a real possible outcome.
Whats somewhat funny here is is if youre wrong, it doesnt matter. But that isnt the same as being right.
> Something in our makeup revels in the thought that we'll be the last generation of humans, that the future is gone and everything will come to a crashing stop
And yet there _will_ (eventually) be one generation that is right.
The paper:
https://thedocs.worldbank.org/en/doc/d6e33a074ac9269e4511e5d...
"Differences about the future of AI are often partly rooted in differing interpretations of evidence about the present. For example, we strongly disagree with the characterization of generative AI adoption as rapid (which reinforces our assumption about the similarity of AI diffusion to past technologies)."
[1] https://www.economist.com/briefing/2025/07/24/what-if-ai-mad...
Also, the Economist publishes all articles anonymously so the individual author isn't known. As far as I know, they do this so we take all articles and opinions as the perspective of the Economist publication itself.
If it was disagreeing with AI maximalists, it was primarily in terms of the timeline, not in terms of the outcomes or inevitability of the scenario.
> If investors thought all this was likely, asset prices would already be shifting accordingly. Yet, despite the sky-high valuations of tech firms, markets are very far from pricing in explosive growth. “Markets are not forecasting it with high probability,” says Basil Halperin of Stanford, one of Mr Chow’s co-authors. A draft paper released on July 15th by Isaiah Andrews and Maryam Farboodi of mit finds that bond yields have on average declined around the release of new ai models by the likes of Openai and DeepSeek, rather than rising.
It absolutely (beyond being clearly titled "what if") presented real counterarguments to its core premise.
There are plenty of other scenarios that they have explored since then, including the totally contrary "What if the AI stock market blows up?" article.
This is pretty typical for them IME. They definitely have a bias, but they do try to explore multiple sides of the same idea in earnest.
> From an economic viewpoint, this also means that the brand value of the articles remains with the masthead rather than the individual authors. This commodifies the authors and makes then more fungible.
> Being The Economist, I am sure they are aware of this.
https://news.ycombinator.com/item?id=14016517
I’m curious about exploring the topics “What if the war in Ukraine ends in the next 12 months” just as much as “What if the war in Ukraine keeps going for the next 10 years”, doesn’t mean I expect both to happen.
Through this lens it's way more normal
They are deterministic, and they follow clear rules, but they can't represent every number with full precision. I think that's a pretty good analogy for LLMs - they can't always represent or manipulate ideas with the same precision that a human can.
They're a fixed precision format. That doesn't mean they're ambiguous. They can be used ambiguously, but it isn't inevitable. Tools like interval arithmetic can mitigate this to a considerable extent.
Representing a number like pi to arbitrary precision isn't the purpose of a fixed precision format like IEEE754. It can be used to represent, say, 16 digits of pi, which is used to great effect in something like a discrete Fourier transform or many other scientific computations.
In practice, outcome of floating point computation depends on compiler optimizations, order of operations, and rounding used.
1. Compiler optimizations can be disabled. If a compiler optimization violates IEEE754 and there is no way to disable it, this is a compiler bug and is understood as such.
2. This is as advertised and follows from IEEE754. Floating point operations aren't associative. You must be aware of the way they work in order to use them productively: this means understanding their limitations.
3. Again, as advertised. The rounding mode is part of the spec and can be controlled. Understand it, use it.
LLMs are machine learning models used to encode and decode text or other-like data such that it is possible to efficiently do statistical estimation of long sequences of tokens in response to queries or other input. It is obvious that the behavior of LLMs is neither consistent nor standardized (and it's unclear whether this is even desirable---in the case of floating-point arithmetic, it certainly is). Because of the statistical nature of machine learning in general, it's also unclear to what extent any sort of guarantee could be made on the likelihoods of certain responses. So I am not sure it is possible to standardize and specify them along the lines of IEEE754.
The fact that a forward pass on a neural network is "just deterministic matmul" is not really relevant.
We only have two states of causality, so calling something "just" deterministic doesn't mean much, especially when "just random" would be even worse.
For the record, LLMs in the normal state use both.
No comments yet
Seems to be the referenced paper?
If so previously discussed here: https://news.ycombinator.com/item?id=43697717
Spreadsheets don’t really have the ability to promote propaganda and manipulate people the way LLM-powered bots already have. Generative AI is also starting to change the way people think, or perhaps not think, as people begin to offload critical thinking and writing tasks to agentic ai.
May I introduce you to the magic of "KPI" and "Bonus tied to performance"?
You'd be surprised how much good and bad in the world has come out of some spreadsheet showing a number to a group of promotion chasing type-a otherwise completely normal people.
If you need an interface for something (e.g. viewing data, some manual process that needs your input), the agent will essentially "vibe code" whatever interface you need for what you want to do in the moment.
Ironically the outro of a YouTube video I just watched. I'm just a few hundred ms of latency away from being a cyborg.
I guess I've always been more of a "work to live" type.
e.g. Alexa for voice, REST for talking to APIs, Zapier for inter-app connectdness.
(not trying to be cynical, just pointing out that the technology to make it happen doesn't seem to be the blocker)
REST is actually a huge enabler for agents for sure, I think agents are going to drive everyone to have at least an API, if not a MCP, because if I can't use your app via my agent and I have to manually screw around in your UI, and your competitor lets my agent do work so I can just delegate via voice commands, who do you think is getting my business?
Will it change everything? IDK, moving everything self-hosted to the cloud was supposed to make operations a thing of the past, but in a way it just made ops an even bigger industry than it was.
Some can remember the difference between iPhone 1 and 4 and where it took off with the latter.
Let's not forget there has been times when if-else statements were considered AI. NLP used to be AI too.
It doesn't think, it doesn't reason, and it doesn't listen to instructions, but it does generate pretty good text!
People constantly assert that LLMs don't think in some magic way that humans do think, when we don't even have any idea how that works.
It doesn't matter anyway. The marquee sign reads "Artificial Intelligence" not "Artificial Human Being". As long as AI displays intelligent behavior, it's "intelligent" in the relevant context. There's no basis for demanding that the mechanism be the same as what humans do.
And of course it should go without saying that Artificial Intelligence exists on a continuum (just like human intelligence as far as that goes) and that we're not "there yet" as far as reaching the extreme high end of the continuum.
Aircraft and submarines belong to a different category and of the same category, than AI.
Humans are not all that original, we take what exists in nature and mangle it in some way to produce a thing.
The same thing will eventually happen with AI - not in our lifetime though.
No it doesn't, this is an overgeneralization.
That doesn't mean much.
- We have a sense of time (ie, ask an LLM to follow up in 2 minutes)
- We can follow negative instructions ("don't hallucinate, if you don't know the answer, say so")
The general notion of passage of time (i.e. time arrow) is the only thing that appears to be intrinsic, but it is also intrinsic for LLMs in a sense that there are "earlier" and "later" tokens in its input.
Until we see an LLM that is capable of this, then they aren't capable of it, period
The proof burden is on AI proponents.
There's this very cliched comment to any AI HN headline which is this:
"LLM's don't REALLY have <vague human behavior we don't really understand>. I know this for sure because I know both how humans work and how gigabytes of LLM weights work."
or its cousin:
"LLMs CAN'T possibly do <vague human behavior we don't really understand> BECAUSE they generate text one character at a time UNLIKE humans who generate text one character a time by typing with their fleshy fingers"
Intelligent living beings have natural, evolutionary inputs as motivation underlying every rational thought. A biological reward system in the brain, a desire to avoid pain, hunger, boredom and sadness, seek to satisfy physiological needs, socialize, self-actualize, etc. These are the fundamental forces that drive us, even if the rational processes are capable of suppressing or delaying them to some degree.
In contrast, machine learning models have a loss function or reward system purely constructed by humans to achieve a specific goal. They have no intrinsic motivations, feelings or goals. They are statistical models that approximate some mathematical function provided by humans.
Even if it can extrapolate to some degree (altough that's where "hallucinations" tend to become obvious), it could never, for example, invent a game like chess or a social construct like a legal system. Those require motivations like "boredom", "being social", having a "need for safety".
> it could never, for example, invent a game like chess or a social construct like a legal system. Those require motivations like "boredom", "being social", having a "need for safety".
That's creativity which is a different question from thinking.
Yes, humans are also capable of learning in a similar fashion and imitating, even extrapolating from a learned function. But I wouldn't call that intelligent, thinking behavior, even if performed by a human.
But no human would ever perform like that, without trying to intuitively understand the motivations of the humans they learned from, and naturally intermingling the performance with their own motivations.
Humans invent new data, humans observe things and create new data. That's where all the stuff the LLMs are trained on came from.
> That's creativity which is a different question from thinking
It's not really though. The process is the same or similar enough don't you think?
We don't just study it in humans. We look at it in trees [0], for example. And whilst trees have distributed systems that ingest data from their surroundings, and use that to make choices, it isn't usually considered to be intelligence.
Organizational complexity is one of the requirements for intelligence, and an LLM does not reach that threshold. They have vast amounts of data, but organizationally, they are still simple - thus "ai slop".
[0] https://www.cell.com/trends/plant-science/abstract/S1360-138...
In my opinion AI slop is slop not because AIs are basic but because the prompt is minimal. A human went and put minimal effort into making something with an AI and put it online, producing slop, because the actual informational content is very low.
That doesn't mean such claims don't need to made as specific as possible. Just saying something like "humans love but machines don't" isn't terribly compelling. I think mathematics is an area where it seems possible to draw a reasonably intuitively clear line. Personally, I've always considered the ability to independently contribute genuinely novel pure mathematical ideas (i.e. to perform significant independent research in pure maths) to be a likely hallmark of true human-like thinking. This is a high bar and one AI has not yet reached, despite the recent successes on the International Mathematical Olympiad [3] and various other recent claims. It isn't a moved goalpost, either - I've been saying the same thing for more than 20 years. I don't have to, and can't, define what "genuinely novel pure mathematical ideas" means, but we have a human system that recognises, verifies and rewards them so I expect us to know them when they are produced.
By the way, your use of "magical" in your earlier comment, is typical of the way that argument is often presented, and I think it's telling. It's very easy to fall into the fallacy of deducing things from one's own lack of imagination. I've certainly fallen into that trap many times before. It's worth honestly considering whether your reasoning is of the form "I can't imagine there being something other than X, therefore there is nothing other than X".
Personally, I think it's likely that to truly "do maths" requires something qualitatively different to a computer. Those who struggle to imagine anything other than a computer being possible often claim that that view is self-evidently wrong and mock such an imagined device as "magical", but that is not a convincing line of argument. The truth is that the physical Church-Turing thesis is a thesis, not a theorem, and a much shakier one than the original Church-Turing thesis. We have no particularly convincing reason to think such a device is impossible, and certainly no hard proof of it.
[1] Individual behaviours of LLMs are "not understood" in the sense that there is typically not some neat story we can tell about how a particular behaviour arises that contains only the truly relevant information. However, on a more fundamental level LLMs are completely understood and always have been, as they are human inventions that we are able to build from scratch.
[2] Anybody who thinks we understand how brains work isn't worth having this debate with until they read a bit about neuroscience and correct their misunderstanding.
[3] The IMO involves problems in extremely well-trodden areas of mathematics. While the problems are carefully chosen to be novel they are problems to be solved in exam conditions, not mathematical research programs. The performance of the Google and OpenAI models on them, while impressive, is not evidence that they are capable of genuinely novel mathematical thought. What I'm looking for is the crank-the-handle-and-important-new-theorems-come-out machine that people have been trying to build since computers were invented. That isn't here yet, and if and when it arrives it really will turn maths on its head.
And here's some more goalpost-shifting. Most humans aren't capable of novel mathematical thought either, but that doesn't mean they can't think.
As for most humans not being mathematicians, it's entirely irrelevant. I gave an example of something that so far LLMs have not shown an ability to do. It's chosen to be something that can be clearly pointed to and for which any change in the status quo should be obvious if/when it happens. Naturally I think that the mechanism humans use to do this is fundamental to other aspects of their behaviour. The fact that only a tiny subset of humans are able to apply it in this particular specialised way changes nothing. I have no idea what you mean by "goalpost-shifting" in this context.
If we knew that, we wouldn't need LLMs; we could just hardcode the same logic that is encoded in those neural nets directly and far more efficiently.
But we don't actually know what the weights do beyond very broad strokes.
Why? Team "Stochastic Parrot" will just move the goalposts again, as they've done many times before.
Imagine a process called A, and, as you say, we've no idea how it works.
Imagine, then, a new process, B, comes along. Some people know a lot about how B works, most people don't. But the people selling B, they continuously tell me it works like process A, and even resort to using various cutesy linguistic tricks to make that feel like it's the case.
The people selling B even go so far as to suggest that if we don't accept a future where B takes over, we won't have a job, no matter what our poor A does.
What's the rational thing to do, for a sceptical, scientific mind? Agree with the company, that process B is of course like process A, when we - as you say yourself - don't understand process A in any comprehensive way at all? Or would that be utterly nonsensical?
It's like we're pretending cognition is a solved problem so we can make grand claims about what LLM's aren't really doing.
When you type the next word you also put a word that fits some requirement. That doesn't mean you're not thinking.
It turns out we didn't need a specialist technique for each domain, there was a reliable method to architect a model that can learn itself, and we could already use the datasets we had, they didn't need to be generated in surveys or experiments. This might seem like magic to an AI researcher working in the 1990's.
A lot of this is marketing bullshit. AFAIK, even "machine learning" was a term made up by AI researchers when the AI winter hit who wanted to keep getting a piece of that sweet grant money.
And "neural network" is just a straight up rubbish name. All it does is obscure what's actually happening and leads the proles to think it has something to do with neurons.
Among most people, you're thinking of things that were debatably AI, today we have things that are AI (again, not due to any concrete definition, simply due to accepted usage of the term.)
They still are, as far as the marketing department is concerned.
Artificial Intelligence is a whole subfield of Computer Science.
Code built of nothing but if/else statements controlling the behavior of game NPCs is AI.
A* search is AI.
NLP is AI.
ML is AI.
Computer vision models are AI.
LLMs are AI.
None of these are AGI, which is what does not yet exist.
One of the big problems underlying the current hype cycle is the overloading of this term, and the hype-men's refusal to clarify that what we have now is not the same type of thing as what Neo fights in the Matrix. (In some cases, because they have genuinely bought into the idea that it is the same thing, and in all cases because they believe they will benefit from other people believing it.)
LLMs are one of the first technologies that makes me think the term "AI effect" needs to be updated to "AGI effect". The effect is still there, but it's undeniable that LLMs are capable of things that seem impossible with classical CS methods, so they get to retain the designation of AI.
Computer's Aren't Pulling Their Weight (1991)
There were _so many_ articles in the late 80s and early 90s about how computers were a big waste of money. And again in the late 90s, about how the internet was a waste of money.
We aren't going to know the true consequences of AI until kids that are in high school now enter the work force. The vast majority of people are not capable of completely reordering how they work. Computers did not help Sally Secretary type faster in the 1980s. That doesn't mean they were a waste of money.
> - Socrates (399 BC)
> The world is passing through troublous times. The young people of today think of nothing but themselves. They have no reverence for parents or old age. They are impatient of all restraint. They talk as if they knew everything, and what passes for wisdom with us is foolishness with them. As for the girls, they are forward, immodest and unladylike in speech, behavior and dress
> - Peter the Hermit (1274)
Context: Ancient Greece went into decline just 70 years after that date. Make of that what you will.
A serious paper would start by acknowledging that every previous general-purpose technology required human oversight precisely because it couldn't perceive context, make decisions, or correct errors - capabilities that are AI's core value proposition. It would wrestle with the fundamental tension: if AI remains error-prone enough to need human supervisors, it's not transformative; if it becomes reliable enough to be transformative, those supervisory roles evaporate.
These two Princeton computer scientists, however, just spent 50 pages arguing that AI is like electricity while somehow missing that electricity never learned to fix itself, manage itself, or improve itself - which is literally the entire damn point. They're treating "humans will supervise the machines" as an iron law of economics rather than a temporary bug in the automation process that every profit-maximizing firm is racing to patch. Sometimes I feel like I'm losing my mind when it's obvious that GPT-5 could do better than Narayanan and Kapoor did in their paper at understanding historical analogies.
I could ask the same thing then. When will you take "AI" seriously and stop attributing the above capabilities to it?
Delusional.
But in the end, despite saying AI has PhD-level intelligence, the truth is that even AI companies can't get AI to help them improve faster. Anything slower than exponential is proof that their claims aren't true.
That seems like a possibly mythical critical point, at which a phase transition will occur that makes the AI system qualitatively different from its predecessors. Exponential to the limit of infinity.
All the mad rush of companies and astronomical investments are being made to get there first, counting on this AGI to be a winner-takes-all scenario, especially if it can be harnessed to grow the company itself. The hype is even infecting governments, for economic and national interest. And maybe somewhere a mad king dreams of world domination.
Many things sound good on paper. But paper vs reality are very different. Things are more complex in reality.
Neither the OP's URL nor djoldman's archive link allow access to the article!8-((
1) obsolete search engines powered by marketing and SEO, and give us paid search engines whose selling points are how comprehensive they are, how predictable their queries work (I miss the "grep for the web" they were back when they were useful), and how comprehensive their information sources are.
2) Eliminate the need to call somebody in the Philippines awake in the middle of the night, just for them to read you a script telling you how they can't help you fix the thing they sold you.
3) Allow people to carry local compressed copies of all written knowledge, with 90% fidelity, but with references and access to those paid search engines.
And my favorite part, which is just a footnote I guess, is that everybody can move to a Linux desktop now. The chatbots will tell you how to fix your shit when it breaks, and in a pedagogical way that will gradually give you more control and knowledge of your system than you ever thought you were capable of having. Or you can tell it that you don't care how it works, just fix it. Now's the time to switch.
That's your free business idea for today: LLM Linux support. Train it on everything you can find, tune it to be super-clippy. Charge people $5 a month. The AI that will free you from their AI.
Now we just need to annihilate web 2.0, replace it with peer-to-peer encrypted communications, and we can leave the web to the spammers and the spies.
People use whatever UI comes with their computer. I don't think that's going to change.
Diffusion of innovations: https://en.wikipedia.org/wiki/Diffusion_of_innovations :
> The diffusion of an innovation typically follows an S-shaped curve which often resembles a logistic function.
From https://news.ycombinator.com/item?id=42658336 :
> [ "From Comfort Zone to Performance Management" (2009) ] also suggests management styles for each stage (Commanding, Cooperative, Motivational, Directive, Collaborative); and suggests that team performance is described by chained power curves of re-progression through these stages
Transforming, Performing, Reforming, [Adjourning]
Carnal Coping Cycle: Denial, Defense, Discarding, Adaptation, and Internalization
People weren't sure if human bodies could handle moving at >50mph.
Four decades ago was 1985. The thing is, there was a huge jump in progress from then until now. If we took something which had a nice ramped progress, like computer graphics, and instead of ramping up we went from '1985' to '2025' in progress over the course of a few months, do you think there wouldn't be a lot of hype?
Don't remind me.
LLMs may set a record for time between specialized/luxury goods and commodity.
There may be a price floor, but it's not very high.
In My Opinion.
---
Ever think about why restaurants pay someone to wash the dishes?
In my house, I have a machine that does that.
In a restaurant, the machine is too slow, and not compatible with the rest of the system of the restaurant.
Until we hit singularity, AI has to be compatible with the rest of the system.
eg https://youtu.be/Nk_0j936_DY