Slightly different cohorts.
I'm not really trying to be snarky; I'm trying to point out to you that you're being really vague. And that when you actually get really, really concrete about what we have it ... starts to seem a little less magical than saying "computers that talk and think". Computers that are really quite good at sampling from a distribution of high-likelihood next language tokens based upon complex and long context window is still a pretty incredible thing, but it seems a little less likely to put us all out of a job in the next 10 years.
And it became and industry that as completely and totally changed the world. The world was just so analog back then.
>starts to seem a little less magical than saying "computers that talk and think"
Computer thinking will never become magical. As soon as we figure something out it becomes "oh that is just X". It is human thinking that will become less magical over time.
LLMs may be a stepping stone to AGI. It's impressive tech. But nobody's proven anything like that yet, and you're running on pure faith not facts here.
I'm enjoying the new LLM based tooling a lot, but nothing about it suggests that we're in any way near to AGI because it's very much a one trick pony so far.
When we see generative AI that updates its weights in real time (currently an intractible problem) as part of the feedback loop then things might get very interesting. Until then it's just another tool in the box. CS interns learn.
I would be interested to hear the way that you see. I don't have any problem seeing a huge number of roadblocks to post-scarcity that AI won't solve, but I am open to a different perspective.
Not that I think you're wrong, but come on - make the case!
I have the very unoriginal view that - yes, it's a (huge) bubble but also, just like the dot com bubble, the tevhnology is a big deal - but it's not obvious to see what will stand and fall in the aftermath.
Remember that Sun Microsystems, a very established pre-dot com business, rose to huge heights on the bubble and was then smashed by the fall when it popped. Who's the AI bubble's Sun and who's its Amazon? Place your bets...
Nah, we aren't. There's a reason the output of generative AI is called slop.
Extraordinary claims demand extraordinary evidence. We have machines that talk, which is corollary to nothing.
That's an extremely speculative view that has been fashionable at several points in the last 50 years.
Prediction is obviously involved in certain forms of cognition, but it obviously isn't all there is to the kinds of beings we are.
Napkin scribbles
It's always different this time.
More seriously: there are decent arguments that say that LLMs have an upper bound of usefulness and that we're not necessarily closer to transcending that with a different AI technology than we were 10 or 30 years ago.
The LLMs we have, even if they are approaching an upper bound, are a big deal. They're very interesting and have lots of applications. These applications might be net-negative or net-positive, it will probably vary by circumstance. But they might not become what you're extrapolating them into.
Even in 2002, my CS profs were talking about how GAI was a long time off bc we had been trying for decades to innovate on neural nets and LLMs and nothing better had been created despite some of the smartest people on the planet trying.
The compute and data are both limitations of NNs.
We've already gotten really close to the data limit (we aren't generating enough useful content as a species and the existing stuff has all been slurped up).
Standard laws of physics restrict the compute side, just like how we know we will hit it with CPUs. Eventually, you just cannot put things closer together that generate more heat because they interfere with each other because we hit the physical laws re miniaturization.
No, GAI will require new architectures no one has thought of in nearly a century.
2. The category of computerized machines (of which self checkouts are one example) has absolutely revolutionized the world. Computerization is the defining technology of the last twenty years.
They revolutionized supermarkets.
I would really like to hear you explain how they revolutionized supermarkets.
I use them every day, and my shopping experience is served far better by going to a place that is smaller than one that has automated checkout machines. (Smaller means so much faster.)
Hell, if you go to Costco, the automated checkout line moves slower than the ones manned by experienced workers.
And for small baskets, sure, but it was scan as you shop that really changed supermarkets and those things thankfully do not talk.
Outside of the software world it's mostly a (much!) better Google.
Between now and a Star Trek world, there's so much to build that we can use any help we can get.
Indeed. I was using speech to text three decades ago. Dragon Naturally Speaking was released in the 90s.
It's blatantly obvious to see if you work with something you personally have a lot of expertise in. They're effectively advanced search engines. Useful sure.. but they're not anywhere close to "making decisions"
From where I look at it, LLMs are flawed in many ways, and people who see progress as inevitable do not have a mental model of the foundation of those systems to be able to extrapolate. Also, people do not know any other forms of AI or have though hard about this stuff on their own.
The most problematic things are:
1) LLMs are probabilistic and a continuous function, forced by gradient descent. (Just having a "temperature" seems so crazy to me.) We need to merge symbolic and discrete forms of AI. Hallucinations are the elephant in the room. They should not be put under the rug. They should just not be there in the first place! If we try to cover them with a layer of varnish, the cost will be very large in the long run (it already is: step-by-step reasoning, mixture of experts, RAG, etc. are all varnish, in my opinion)
2) Even if generalization seems ok, I think it is still really far from where it should be, since humans need exponentially less data and generalize to concepts way more abstract than AI systems. This is related to HASA and ISA relations. Current AI systems do not have any of that. Hierarchy is supposed to be the depth of the network, but it is a guess at best.
3) We are just putting layer upon layer of complexity instead of simplifying. It is the victory of the complexifiers and it is motivated by the rush to win the race. However, I am not so sure that, even if the goal seems so close now, we are going to reach it. What are we gonna do? Keep adding another order of magnitude of compute on top of the last one to move forward? That's the bubble that I see. I think that that is not solving AI at all. And I'm almost sure that a much better way of doing AI is possible, but we have fallen into a bad attractor just because Ilya was very determined.
We need new models, way simpler, symbolic and continuous at the same time (i.e. symbolic that simulate continuous), non-gradient descent learning (just store stuff like a database), HAS-A hierarchies to attend to different levels of structure, IS-A taxonomies as a way to generalize deeply, etc, etc, etc.
Even if we make progress by brute forcing it with resources, there is so much work to simplify and find new ideas that I still don't understand why people are so optimistic.
Hallucinations are incredibly fucking overrated as a problem. They are a consequence of the LLM in question not having a good enough internal model of its own knowledge, which is downstream from how they're trained. Plenty of things could be done to improve on that - and there is no fundamental limitation that would prevent LLMs from matching human hallucination rates - which are significantly above zero.
There is a lot of "transformer LLMs are flawed" going around, and a lot of alternative architectures being proposed, or even trained and demonstrated. But so far? There's nothing that would actually outperform transformer LLMs at their strengths. Most alternatives are sidegrades at best.
For how "naive" transformer LLMs seem, they sure set a high bar.
Saying "I know better" is quite easy. Backing that up is really hard.
In the end leaving the world changed, but not as meaningfully or positively as promised.
Maybe say something concrete? What's a positive real world impact of LLMs where they aren't hideously expensive and error prone to the point of near uselessness? Something that isn't just the equivalent of a crypto-bro saying that their system for semi-regulated speculation (totally not a rugpull!) will end the tyranny of the banks.
---
Less flippantly, they are excellent for self-studying university-level topics. It's like being able to ask questions to a personal tutor/professor.
- documentation
- design reviews
- type systems
- code review
- unit tests
- continuous integration
- integration testing
- Q&A process
- etc.
Is it not the same with AI? With the right processes you can get consistent results from inconsistent tools.
This is a pretty massive difference between the two, and your narrative is part of why AI is proving to be so harmful for education in general. Delusional dreamers and greedy CEOs talking about AI being able to do "PhD level work" have potentially ruined a significant chunk of the next generation into thinking they are genuinely learning from asking AI "a few questions" and taking the answers at face value instead of struggling through the material to build true understanding.
I’ll take a potential solution I can validate over no idea whatsoever of my own any day.
So why's it different this time?
Or did you pop your laundry into a machine and your dishes into another one and press a button?
Is the wear so small that it’s simply negligible ?
Is it going to be that significant though? No idea.
Just ask Intel what happened to 14th gen.
It's not normally an issue, but the edge cases can be very sharp. Otherwise, the bigger concern is the hardware becoming obsolete because of new generations being significantly more power efficient. Over a few years, the power+cooling+location bill of a high end CPU running at 90% utilization can cost more than the CPU itself.
But with that said machines that run at a pretty constant thermal load within range of their capacitors can run a very long time.
Maybe we can finally have a Rosie from the Jetsons.
just what I want, a mobile Alexa that spews ads and spies on me 24/7
The “at a loss” scenario comes from (1) training costs and (2) companies selling tokens below market to get market share. Neither of those imply that people won’t run models in future. Training new frontier-class models could potentially become an issue, but even that seems unlikely given what these models are capable of.
But then, without this huge financial and tech bubble that's driven by these huge companies:
1/ will those models evolve, or new models appear, for a fraction of the cost of building them today?
2/ will GPU (or their replacement) also cost a fraction of what they cost today, so that they are still integrated in end-user processors, so that those model can run efficiently?
These people won't sit still and models will keep getting better as well as cheaper to run.
I have access to quite a few models, and I use them here and there. They are sort of useful, sometimes. But I don't pay directly for any of them. Honestly, I wouldn't.
Creating new LLMs might be out of reach for all but very well-capitalized organizations with clear intentions, and governments.
There might be a viable market for SLMs though. Why does my model need to know about the Boer wars to generate usable code?
Consider how much software is out there that can now be translated into every (human) language continuously, opening up new customers and markets that were previously being ignored due to the logistical complexity and cost of hiring human translation teams. Inferencing that stuff is a no brainer but there's a lot of workflow and integration needed first which takes time.
How about chips during the dotcom period? What was their lifespan?
Maybe in next decade we will have cheap gaming cloud offerings built on repurposed GPUs.
Which is exactly what we expect if technological efficiency is increasing over time. Saying we've invested 1000x in aluminum plants and research over the first steel plants means we've had massive technological growth since then. It's probably better that it's actually moving around in an economy than just being used to consolidate more industries.
>compute/storage servers became obsolete faster than networking
In the 90s extremely rapidly. In the 00s much less rapidly. And by the 10's servers and storage especially the solid components like boards lasted a decade or more. The reason the servers became obsolete in the 90s is much faster units came out fast and were much faster, not that the hardware died. In the 2010-2020 era I repurposed tons of data center hardware to onsite computers for small businesses. I'm guessing a whole lot less of that hardware 'went away' then you'd expect.
Not saying that’s even remotely realistic over the next century, but it does seem to be how some of these people think. Excessive wealth destroys intelligence, it doesn’t enhance it, as countless examples show.
As the noise fades, and with luck, the obsession with slapping "AI" on everything will fade with it. Too many hype-driven CEOs are chasing anything but substance.
Some AI tools may survive because they're genuinely useful, but I worry that most won't be cost-effective without heavy subsidies.
Once the easy money dries up, the real engineers and builders will still be here, quietly making things that work.
Altman's plea -- "Come on guys, we just need a few trillion more!" -- and that error-riddled AI slide deck will be the meme that marks the top of the market.
In 10 years GPUs will have a lifespan for 5-7 years. The rate of improvement on this front has been slowing down faster then CPU.
What is interesting is that it seems like the ever larger sums of money sloshing around are resulting in bigger, faster hype cycles. We are already seeing some companies face issues after blowback from adopting AI too fast.
2. While comprehensive studies were never done, some tech channels did some testing and found used GPUs to be generally reliable or easily repairable, when scamming was excluded. https://youtu.be/UFytB3bb1P8
You can keep a server running for 10-15 years, but usually you do that only when the server is in a good environment and has had a light load.
I said solid state components last decades. 10nm transistors have a thing for over 10 years now and other than manufacturer defect don't show any signs of wearing out from age.
> MTBFs for GPUs are about 5-10 years, and that's not about fans.
That sounds about the right time for a repaste.
> AWS and the other clouds have a 5-8 year depreciation calendar for computers.
Because the manufacturer warranties run out after that + it becomes cost efficient to upgrade to lower power technology. Not because the chips are physically broken.
> Most of the money is being spent on incredibly expensive GPUs that have a 1-3 year lifespan due to becoming obsolete quickly and wearing out under constant, high-intensity use.
So it isn’t entirely tied to the rate of obsolescence, these things apparently get worn down from the workloads.
In terms of performance improvement, it is slightly complicated, right? It turns out that it was possible to do ML training on existing GPGPU. Then there was spurt of improvement as they go after the low-hanging fruit for that application…
If we’re talking about what we might be left with after the bubble pops, the rate of obsolescence doesn’t seem that relevant anyway. The chips as they are after the pop will be usable for the next thing or not, it is hard to guess.
The three year number was a surprisingly low figure sourced to some anonymous Google engineer. Most people were assuming at least 5 years and maybe more. BUT, Google then went on record to deny that the three year figure was accurate. They could have just ignored it, so it seems likely that three years is too low.
Now I read 1-3 years? Where did one year come from?
GPU lifespan is I suspect also affected by whether it's used for training or inference. Inference loads can be made very smooth and don't experience the kind of massive power drops and spikes that training can generate.
Perhaps the author confused "new GPU comes out" with "old GPU is obsolete and needs replacement"
- this is war path funding
- this is geopolitics; and it’s arguably a rational and responsible play
- we should expect to see more nationalization
- whatever is at the other end of this seems like it will be extreme
And, the only way out is through
Seeing the last tariffs and what China done about the rare earth minerals (and also the deal the US made with Ukraine for said minerals), the article might have a point that the super power will cripple each other to be the first with the super intelligence. And you also need money for it so tariffs.
I don't see how some kind of big breakthrough is going to happen with the current model designs. The superintelligence, if it will ever be created, will require a new breakthrough in model architecture. We've pretty much hit the limit of what is possible with current LLMs. The improvements are marginal at this point.
Secondly, hypothetically, the US achieves superintelligence, what is stopping China from doing the same in a month or two, for example?
Even if China achieves a big breakthrough first, it may benefit the rest of the world.
If you read the article carefully, I work hard to keep my priors and the priors of the people in question separate, as their actions may be rational under their priors, but irrational under other priors, and I feel it's worth understanding that nuance.
I'm curious where you got the writer "clinging to power and money desperately."
Also, to be fair, I envy Europe right now, but we can't take that path.
This is suggesting an "end of history" situation. After Fukuyama, we know there is no such thing.
I'm not sure if there is a single strong thesis (as this one tries to be) on how this will end economically and geopolitically. This is hard to predict, much less to bet on.
I mean if you are talking about USA itself falling into dystopic metastability in such a situation, maybe, but even so I think it misses some nuance. I don't see every other country following USA into oblivion, and also I don't see the USA bending the knee to techno-kings and in the process giving up real influence for some bet on total influence.
The only mechanism I see for reaching complete stability (or at least metastability) in that situation is idiocracy / idiotic authoritarianism, i.e. Trump/his minions actually grabbing power for decades and/or complete corruption of USA institutions.
I read an article today in which western business leaders went to China and were wowed by "dark factories" where everything is 100% automated. Lots of photos of factories full of humanoid robots too. Mentioned only further down the article: that happens because the Chinese government has started massively distorting the economy in favor of automation projects. It's widely known that one of the hardest parts of planning a factory is figuring out what to automate and what to use human labour for. Over-automating can be expensive as you lose agility and especially if you have access to cheap labour the costs and opportunity costs of automation can end up not worth it. It's a tricky balance that requires a lot of expertise and experience. But obviously if the government just flat out reimburses you 1/5th of your spending on industrial robots, suddenly it can make sense to automate stuff that maybe in reality should not have been automated.
BTW I'm not sure the Kuppy figures are correct. There's a lot of hidden assumptions about lifespan of the equipment and how valuable inferencing on smaller/older models will be over time that are difficult to know today.
https://www.reddit.com/r/Amtrak/comments/1hnvl3d/chinese_hsr...
https://merics.org/en/report/beyond-overcapacity-chinese-sty...
It's easy to think of Uber/AirBnB style apps as trivialities, but this is the mistake communist countries always make. They struggle to properly invest in consumer goods because only heavy industry is legible to the planners. China has had too low domestic spending for a long time. USSR had the same issue, way too many steel mills and nowhere near enough quality of life stuff for ordinary people. It killed them in the end; Yeltsin's loyalty to communist ideology famously collapsed when he mounted a surprise visit to an American supermarket on a diplomatic mission to NASA. The wealth and variety of goods on sale crushed him and he was in tears on the flight home. A few years later he would end up president of Russia leading it out of communist times.
The US indeed seems destined to fall behind due to decades of economic mismanagement under neoliberalism while China’s public investment has proved to be the wise choice. Yet this fact wounds the pride of many in the US, particularly its leaders, so it now lashes out in a way that hastens its decline.
The AI supremacy bet proposed is nuts. Prior to every societal transition the seeds of that transition were already present. We can see that already with AI: social media echo chambers, polarization, invading one’s own cities, oligarchy, mass surveillance.
So I think the author’s other proposed scenario is right - mass serfdom. The solution to that isn’t magical thinking but building mass solidarity. If you look at history and our present circumstances, our best bet to restore sanity to our society is mass strikes.
I think we are going to get there one way or another. Unfortunately things are probably going to have to get a lot more painful before enough people wake up to what we need to do.
Do you really prefer brutal serfdom to the AI supremacy scenario? From where I sit, people have mixed (trending positive) feelings about AI, and hard negative feelings about being in debt and living paycheck to paycheck. I'd like to understand your position here more.
You can't seriously believe that spending all your income each month while living in the country with the highest standard of living in history is "serfdom."
Hyperbolic nonsense like this makes the rest of the article hard to take seriously, not that I agree with most of it anyway.
This AI bubble already has lots of people with their forks and knifes waiting to capitalize on a myriad of possible surpluses after the burst. There's speculation on top of _the next bubble_ and how it will form, even before this one pops.
That is absolutely disgusting, by the way.
This is how humans have worked with pretty much every area of expansion in at least the last 500 years and probably longer. It's especially noticeable now because the amount of excess capital in the world from technological expansion makes it very noticeable and a lot of the limitations we know of in physics have been ran into, so further work gets very expensive.
If you want to stop the bubbles you have to pretty much end capitalism, if which capitalists will fight you about. If AI replaces human thinking and robots human labor that 'solves' the human capital problem but opens up a whole new field of dangerous new ones.
I think that people doing work in many professions with these offline tools alone could more than double their productivity compared to their productivity two years ago. Furthermore if the usage was shared in order to lower idle time, such as 20 machines for 100 workers, the initial capital outlay is even lower.
Perhaps investors will not see the returns they expect, but it is difficult to image how even the current state of AI doesn't vastly change the economy. There could be significant business failures among cloud providers and attempts to rapidly increase the cost of admission to closed models, but there's essentially no possibility of productivity regressing to a pre-AI levels.
They already work on the most expensive Apple hardware. I expect that price to come down in the next few years.
It’s really just the UX that’s bad but that’s solvable.
Apple isn’t having to pay for each users power and use either. They sell hardware once and folks pay with their own electricity to run it.
I know folks who still use some old Apple laptops, maybe 5+ years old, since they don't see the point in changing (and indeed if you don't work in IT and don't play video games or other power-demanding jobs, I'm not sure it's worth it). Having new models with some performant local LLM built-in might change this for the average user.
the dotcom bubble was a result of investors jumping on the hype train all at once and then getting off of it all at once.
Yes, investors will eventually find another hype train to jump on, but unlike 2000, we have tons of more retail investors and AI is also not a brand new tech sector, it's built upon the existing well established and "too big to fail" internet/ecommerce infrastructure. Random companies slapping AI on things will fail but all the real AI use cases will only expand and require more and more resources.
OpenAI alone just hit 800M MAU. That will easily double in a few years. There will be adjustments,corrections and adaptations of course but the value and wealth it generates is very real.
I'm no seer, I can't predict the future but I don't see a massive popping of some unified AI bubble anytime soon.
OpenAI has ~4B of revenue already, and they aren't even monetizing aggressively. Facebook has an infinite money glitch, and can afford to put billions in the ground in pursuit of moonshots and Zuck's own vanity projects. Google is Google, and xAI is Elon Musk. The most vulnerable frontier lab is probably Anthropic, and Anthropic is still backed by Amazon and, counterintuitively, also Google.
At the same time: there is a glut of questionable AI startups, extreme failure rate is likely - but they aren't the bulk of the market, not by a long shot. The bulk of the "AI money" is concentrated at either the frontier labs themselves, or companies providing equipment and services to them.
The only way I see for the "bubble to pop" is for multiple frontier labs to get fucked at the same time, and I just don't see that happening as it is.
Figuring out which was which was absolutely not possible at the time. Not many people foresaw Sun Microsystems as being a victim and nor was it obvious that Amazon would be a victor.
I wouldn't bet my life savings on OpenAI.
Same for GPUs/LLMs? At some point things will mature and we’ll be left with plentiful, cheap, high end LLM access, on the back of the investment that has been made. Whether or not it’s running on legacy GPUs, like some 90s fiber still carries traffic, is meaningless. It’s what the investment unlocks.
...whether it is profitable is another matter
It will become cool for you to become inaccessible, unreachable, no one knowing your location or what you’re doing. People might carry around little beeper type devices that bounce small pre-defined messages around on encrypted radio mesh networks to say stuff like “I’m okay” or “I love you”, and that’s it. Maybe they are used for contactless payments as well.
People won’t really bother searching the web anymore they’ll just ask AI to pull up whatever information they need.
The question is, with social media on the decline, with the internet no longer used for recreational purposes, what else are people going to do? Feels like the consumer tech sector will shrink dramatically, meaning that most tech written will be made to create “hard value” instead of soft. Think anything having to do with movement of data and matter, or money.
Much of the tech world and government plans are built on the assumption that people will just continue using tech to its maximum utility, even when it is clearly bad for them, but what if that simply weren’t the case? Then a lot of things fall apart.
arisAlexis•2h ago
mjhay•2h ago
gizmo686•2h ago
We are well into this process already. Core chat capabilities have pretty much stalled out. But most of the attempts at application are still very thin layers over chat bots.
abathologist•1h ago
dotnet00•1h ago