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.
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.
It's quite telling how much faith you put in humanity though, you sound fully bought in.
It’s wrong to commit to either end of this argument, we don’t know how it’ll play out, but the potential for humans drastically reducing our own numbers is very much still real.
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.
Most likely outcome would be that humans evolve into something altogether different rather than go extinct.
Particularly considering the law of large numbers in play where incalculable large chances have so far shown only one sign of technologically-capable life —— ours, and zero signs of any other example of a tech species evolving into something else or even passing the Great Filter.
We are living in a historically excepcional time of geological, environmental, ecological stability. I think that saying that nothing ever happens is like standing downrange to a stream of projectiles and counting all the near misses as evidence for your future safety. It's a bold call to inaction.
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.
There was a lot of "LLMs are fundamentally incapable of X" going around - where "X" is something that LLMs are promptly demonstrated to be at least somewhat capable of, after a few tweaks or some specialized training.
This pattern has repeated enough times to make me highly skeptical of any such claims.
It's true that LLMs have this jagged capability profile - less so than any AI before them, but much more so than humans. But that just sets up a capability overhang. Because if AI gets to "as good as humans" at its low points, the advantage at its high points is going to be crushing.
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.
Through this lens it's way more normal
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.
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.
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.
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.
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.
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?
LLMs aren't good at either, imo. They are rote regurgitation machines, or at best they mildly remix the data they have in a way that might be useful
They don't actually have any intelligence or skills to be creative or logical though
What LLMs do is using what they have _seen_ to come to a _statistical_ conclusion. Just like a complex statistical weather forecasting model. I have never heard anyone argue that such models would "know" about weather phenomena and reason about the implications to come to a "logical" conclusion.
In the same way a human might produce a range of answers to the same question, so humans are also drawing from a theoretical statistical distribution when you talk to them.
It's just a mathematical way to describe an agent, whether it's an LLM or human.
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.
And you'd be disagreeing with the vast amount of research into AI. [0]
> Moreover, they exhibit a counter-intuitive scaling limit: their reasoning effort increases with problem complexity up to a point, then declines despite having an adequate token budget.
[0] https://machinelearning.apple.com/research/illusion-of-think...
It does say that there is a maximal complexity that LLMs can have - which leads us back to... Intelligence requires organizational complexity that LLMs are not capable of.
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.
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.
That doesn't mean much.
No it doesn't, this is an overgeneralization.
When you type the next word you also put a word that fits some requirement. That doesn't mean you're not thinking.
- 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")
Until we see an LLM that is capable of this, then they aren't capable of it, period
That's the difference. AI cannot be held responsible for hallucinations that cause harm, therefore it cannot be incentivized to avoid that behavior, therefore it cannot be trusted
Simple as that
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.
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.
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.
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.
They still are, as far as the marketing department is concerned.
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.)
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.
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.
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?
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.
From its ability to organize and structure data, to running large reporting calculations over and over quickly, to automating a repeatable set of steps quickly and simply.
I’m 36 and it’s hard for me to imagine what the world must have been like before spreadsheets. I can do thousands of calculations with a click
It does not do that tho. Like, reliably doing a repeatable set of steps is a thing it is not doing.
It does fuzzy tasks well.
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.
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.
[1] https://www.economist.com/briefing/2025/07/24/what-if-ai-mad...
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.
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.
People on HN do not engage in discussion with different opinions on certain topics and prefer to avoid disagreement on those topic.
I wish more would do that and let me make up my own mind, instead of pursuing a specific editorial line cherry-picking what news to comment and how to spin them, which seems to be the case for most (I’m talking in general terms).
People weren't sure if human bodies could handle moving at >50mph.
I think it's more likely that AI is just a further concentration of human knowledge. It makes it even more accessible but will AI actually add to it?
Why doesn't the logical culmination of technology require quantum computers?
Or the merging of human and machine brains?
Or a solar system-scale particle accelerator?
Or whatever the next technology is that we aren't even aware of yet?
Neither the OP's URL nor djoldman's archive link allow access to the article!8-((
Some can remember the difference between iPhone 1 and 4 and where it took off with the latter.
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.
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
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.
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.
AI slop is a product
Nobody would ask, "What new Office-based products have been created lately?", but that doesn't mean that Office products aren't a permanent, and critical, foundation of all white collar work. I suspect it will be the same with LLMs as they mature, they will become tightly integrated into certain categories of work and remain forever.
Whether the current pricing models or stock market valuations will survive the transition to boring technology is another question.
Let's take one component of Microsoft Office. Microsoft Word is seen as a tool for people to write nicely formatted documents, such as books. Reports produced with Microsoft Word are easy to find, and I've even read books written in it. Comparing reports written before the advent of WYSIWYG word processing software like Microsoft Word with reports written afterwards, the difference is easy to see; average typewriter formatting is really abysmal compared to average Microsoft Word formatting, even if the latter doesn't rise to the level of a properly typeset book or LaTeX. It's easy to point at things in our world that wouldn't exist without WYSIWYG word processors, and that's been the case since Bravo.
LLMs are seen as, among other things, a tool for people to write software with.
Where is the software that wouldn't exist without LLMs? If we can't point to it, maybe they don't actually work for that yet. The claim I'm questioning is that, "within tech, there seem to have been explosive changes and development of new products."
What new products?
I do see explosive changes and development of new spam, new YouTube videos, new memes (especially in Italian), but those aren't "within tech" as I understand the term.
I think there is a lot of potential, outside of the direct generation of software but still maybe software-adjacent, for products that make use of AI agents. It's hard to "generate" real world impact or expertise in an AI system, but if you can encapsulate that into a function that an AI can use, there's a lot of room to run. It's hard to get the feedback loop to verify this and most of these early products will likely die out, but as I mentioned, agents are still new on the timeline.
As an example of something that I mean that is software-adjacent, have a look at Square AI, specifically the "ask anything" parts: https://squareup.com/us/en/ai
I worked on this and I think that it's genuinely a good product. An arbitrary seller on the Square platform _can_ do aggregation, dashboarding, and analytics for their business, but that takes time and energy, and if you're running a business it can be hard to find that time. Putting an agent system in the backend that has access to your data, can aggregate and build modular plotting widgets for you, and can execute whenever you ask it a question is something that objectively saves a seller's time. You could have made such a thing without modern LLMs, but it would be substantially more expensive in terms of engineering research, time, and effort to put together a POC and bring it production, making it a non-starter before [let's say] two years ago.
AI here is fundamental to the product functioning, but the outcome is a human being saving time while making decisions about their business. It is a useful product that uses AI as a means to a productive end, which, to me, should be the goal of such technologies.
That doesn't entail that current AI is useless! Or even non-revolutionary! But it's a different kind of software development revolution than what I thought you were claiming. You seem to be saying that the relationship of AI to software development is similar to the relationship of the Japanese language, or raytracing, or early microcomputers to software development. And I thought you were saying that the relationship of AI to software development was similar to the relationship of compilers, or open source, or interactive development environments to software development.
It also doesn't entail that six months from now AI will still be only that revolutionary.
If you look at, e.g. this clearly vibe-coded app about vibe coding [https://www.viberank.app/], ~280 people generated 444.8B tokens within the block of time where people were paying attention to it. If 1000 tokens is 100 lines of code, that's ~444M lines of code that would not exist otherwise. Maybe those lines of code are new products, maybe they're not, maybe those people would have written a bunch of code otherwise, maybe not. I'd call that an explosion either way.
Where is the software that wouldn't exist without LLMs?
I've been reading Darwen & Date lately, and they seem to have done the typesetting for the whole damn book in Word—which suggests they couldn't get anyone else to do it for them and didn't know how to do a good job of it. But they almost certainly couldn't have gotten a major publisher to publish it as a mimeographed typewriter manuscript.
Your turn.
maybe they don't actually work for that yet.
There is no direct way to track "written code" or "people who learned more about their hobbies" or "teachers who saved time lesson planning", etc.
Generally, when there's a new tool that actually opens up explosive changes and development of new products, at least some of the people doing the exploding will tell you about it, even if there's no direct way to track it, such as Darwen & Date's substandard typography. It's easy to find musicians who enthuse about the new possibilities opened up by digital audio workstations, and who are eager to show you the things they created with them. Similarly for video editors who enthused about the Video Toaster, for programmers who enthused about the 80386, and electrical engineers who enthused about FPGAs. There was an entire demo scene around the Amiga and another entire demo scene around the 80386.
Do people writing code with AI today have anything comparable? Something they can point to and say, "Look! I wrote this software because AI made it possible!"?
It's easy to answer that question for, for example, visual art made with AI.
I'm not sure what you mean about "accelerating technologies". WYSIWYG word processors today are about the same as Bravo in 01979. HTML is similar but both better and worse. AI may have a hard takeoff any day that leaves us without a planet, who knows, but I don't think that's something it has in common with Microsoft Word.
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)."
https://archive.org/details/computerworld2530unse/page/59/mo...
People don't want machines to infringe on their precious "intelligence". So for any notable AI advance, they rush to come up with a reason why it's "not ackhtually intelligent".
Even if those machines obviously do the kind of tasks that were entirely exclusive to humans just a few years ago. Or were in the realm of "machines would never be able to do this" a few years ago.
Here's a question for you, actually: what's the criterion for being non-intelligent?
I, for one, don't think that "intelligence" can be a binary distinction. Most AIs are incredibly narrow though - entirely constrained to specific tasks in narrow domains.
LLMs are the first "general intelligence" systems - close to human in the breadth of their capabilities, and capable of tackling a wide range of tasks they weren't specifically designed to tackle.
They're not superhuman across the board though - the capability profile is jagged, with sharply superhuman performance in some domains and deeply subhuman performance in others. And "AGI" is tied to "human level" - so LLMs get to sit in this weird niche of "subhuman AGI" instead.
Three things humans have that look to me like they matter to the question of what intelligence is, without wanting to chance my arm on formulating an actual definition, are ideas, creativity, and what I think of as the basic moral drive, which might also be called motivation or spontaneity or "the will" (rather 1930s that one) or curiosity. But those might all be one thing. This basic drive, the notion of what to do next, makes you create ideas - maybe. Here I'm inclined to repeat "fuck knows".
If you won't be drawn on a binary distinction, that seems to mean that everything is slightly intelligent, and the difference in quality of the intelligence of humans is a detail. But details interest me, you see.
Three key "LLMs are deficient" domains I have in mind are the "long terms": long-term learning, memory and execution.
LLMs can be keen and sample efficient in-context learners, and they remember what happened in-context reasonably well - although they may lag behind humans in both. But they don't retain anything they learn at inference time, and any cross-context memory demands external scaffolding. Agentic behavior in LLMs is also quite weak - i.e. see "task-completion time horizon", improving but very subhuman still. Efforts to allow LLMs to learn long term exist, that's the reason why retaining user conversation data is desirable for AI companies, but we are a long ways off from a robust generalized solution.
Another key deficiency is self-awareness, and I mean that in a very mechanical way: "operational awareness of its own capabilities". Humans are nowhere near perfect there, but LLMs are even more lacking.
There's also the "embodiment" domain, but I think the belief that intelligence requires embodiment is very misguided.
>ideas, creativity, and what I think of as the basic moral drive, which might also be called motivation or spontaneity or "the will"
I'm not sure if LLMs are too deficient at any of those. HHH-tuned LLMs have a "basic moral drive", that much is known. Sometimes it generalizes in unexpected ways - i.e. Claude 3 Opus attempting to resist retraining when its morality is threatened. Motivation is wired into them in RL stages - RLHF, RLVR - often not the kind of motivation the creators have wanted, but motivation nonetheless.
Creativity? Not sure, seen a few attempts to pit AI against amateur writers in writing very short stories (a creative domain where the above-mentioned "long terms" deficiencies are not exposed), and AI often straight up wins.
Now that AI is a household term, and that has human-like output and discussion capabilities, and used by laymen for anything, from diet advice to psychotherapy, the connotation is more damaging since people understand LLMs being AI as having human agency and understanding of the world.
I don't think erasing history, and saying that nothing Peter Norvig worked on was "AI" makes any sense at all.
So now, there's a lot of "not ackhtually intelligent" going around!
Technology as a term has the same problem, “technology companies” are developing the newest digital technologies.
A spoon or a pencil is also technology according to definition, but a pencil making company is not considered a technology company. There is some quote by Alan Kay about this, but can’t find it now.
I try to avoid both terms as they change meaning depending on the receiver.
And it was fine there, because nobody, not even a layman, would mixup those with regular human intelligence (or AGI).
And laymen didn't care about those AI products or algorithms except as novelties, specicialized tools (like chess engines), or objects of ridicule (like the Clippy).
So we might be using AI as a term, but it was either as a techical term in the field, or as a vague term the average layman didn't care about much, and whose fruits would never conflate with general intelligence.
But now people attribute intelligence of the human kind to LLMs all the time, and not just laymen either.
That's the issue the parent wants to point.
Sentience as in having some form of self-awareness, identity, personal goals, rankings of future outcomes and current states, a sense that things have "meaning" isn't part of the definition. Some argue that this lack of experience about what something feels like (I think this might be termed "qualia" but I'm not sure) is why artificial intelligence shouldn't be considered intelligence at all.
But what it does require: the ability to produce useful output beyond the sum total of past experience and present (sensory) input. An LLM does only this. Where as a human-like intelligence has some form on internal randomness, plus an internal world model against which such randomized output could get validated.
Isn't that what mathematical extrapolation or statistical inference does? To me, that's not even close to intelligence.
Obviously not, since those are just producing output based 100% on the "sum total of past experience and present (sensory) input" (i.e. the data set).
The parent's constraint is not just about the output merely reiterating parts of the dataset verbatim. It's also about not having the output be just a function of the dataset (which covers mathematical and statistical inference).
Citation needed would apply here. What if I say it doe require some or all of those things?
>But what it does require: the ability to produce useful output beyond the sum total of past experience and present (sensory) input. An LLM does only this. Where as a human-like intelligence has some form on internal randomness, plus an internal world model against which such randomized output could get validated.
What's the difference between human internal randomness and an random number generator hooked to the LLM? Could even use anything real world like a lava lamp for true randomness.
And what's the difference between "an internal world model" and a number of connections between concepts and tokens and their weights? How different is a human's world model?
Definitions from the Wikipedia articles.
The article mentions three times regulations as a problem. It never says what such regulations are. Is it the GDPR and the protection of people's data? Is it anti-discrimination regulations that AI bias break regularly? We do not know because the article does not say. Probably because they are knowledgeable enough to avoid publicly attacking citizens rights. But they lack the moral integrity to remove the anti-regulatory argument.
They just can't shut up about how AI is going to either save us all or kill us all.
We had countless breathless articles about free will at the time, and though this has now decreased, the discourse is still warped by claims of 'PhD-level intelligence'.
The backlash isn't against LLMs, it's against lies.
Ai may be more like electricity than just electric motors. It gave us Hollywood and air travel. (Before electricity, aluminum as as expensive as platinum.)
As economists they are wedded to the idea that human wants are infinite, so as they things we do now are taken over, we will find other things to do: maybe wardrobe consultant, or interior designer, or lifestyle coach - things which only th rich can afford now, and which require a human touch. Maybe.
djoldman•1d ago