10 ms·

A trillion dollars (potentially) wasted on gen-AI

127 points by flail 11mo ago

naveen99 11mo ago
When it comes to machine learning, research has consistently shown, that pretty much the only thing that matters is scaling. Ilya should just enjoy his billions raised with no strings.
- philipwhiuk 11mo ago
  > When it comes to machine learning, research has consistently shown, that pretty much the only thing that matters is scaling. Yes, indeed, that is why all we have done since the 90s is scale up the 'expert systems' we invented ... That's such an a-historic take it's crazy. * 1966: failure of machine translation * 1969: criticism of perceptrons (early, single-layer artificial neural networks) * 1971–75: DARPA's frustration with the Speech Understanding Research program at Carnegie Mellon University * 1973: large decrease in AI research in the United Kingdom in response to the Lighthill report * 1973–74: DARPA's cutbacks to academic AI research in general * 1987: collapse of the LISP machine market * 1988: cancellation of new spending on AI by the Strategic Computing Initiative * 1990s: many expert systems were abandoned * 1990s: end of the Fifth Generation computer project's original goals Time and time again, we have seen that each academic research begets a degree of progress, improved by the application of hardware and money, but ultimately only a step towards AGI, which ends with a realisation that there's a missing congitive ability that can't be overcome by absurd compute. LLMs are not the final step.
  - bbor 11mo ago
    Well, expert systems aren’t machine learning, they’re symbolic. You mention perceptrons, but that timeline is proof for the power of scaling, not against — they didn’t start to really work until we built giant computers in the ~90s, and have been revolutionizing the field ever since.
- CuriouslyC 11mo ago
  If you think scaling is all that matters, you need to learn more about ML. Read about the the No Free Lunch Theorem. Basically, the reason we need to "scale" so hard is because we're building models that we want to be good at everything. We could build models that are as good at LLMs at a narrow fraction of tasks we ask of them to do, at probably 1/10th the parameters.
  - never_inline 11mo ago
    Are reranker models an example of this? Do they still underperform compared to LLMs?
- an0malous 11mo ago
  Didn’t OpenAI themselves publish a papers years ago that scaling parameters has diminishing returns?
- munchler 11mo ago
  Indeed. This is the "bitter lesson". https://en.wikipedia.org/wiki/Bitter_lesson https://en.wikipedia.org/wiki/Bitter_lesson
ComplexSystems 11mo ago
I think the article makes decent points but I don't agree with the general conclusion here, which is that all of this investment is wasted unless it "reaches AGI." Maybe it isn't necessary for every single dollar we spend on AI/LLM products and services to go exclusively toward the goal of "reaching AGI?" Perhaps it's alright if these dollars instead go to building out useful services and applications based on the LLM technologies we already have. The author, for whatever reason, views it as a foregone conclusion that every dollar spent in this way is a waste of time and resources, but I wouldn't view any of that as wasted investment at all. It isn't any different from any other trend - by this logic, we may as well view the cloud/SaaS craze of the last decade as a waste of time. After all, the last decade was also fueled by lots of unprofitable companies, speculative investment and so on, and failed to reach any pie-in-the-sky Renaissance-level civilization-altering outcome. Was it all a waste of time? It's ultimately just another thing industry is doing as demand keeps evolving. There is demand for building the current AI stack out, and demand for improving it. None of it seems wasted.
- robot-wrangler 11mo ago
  It's not about "every dollar spent" being a waste of time, it's about acknowledging the reality of opportunity cost. Of course, no one in any movement is likely to listen to their detractors, but in this case the pioneers seem to agree. https://www.youtube.com/watch?v=DtePicx_kFY https://www.youtube.com/watch?v=DtePicx_kFY https://www.bbc.com/news/articles/cy7e7mj0jmro https://www.bbc.com/news/articles/cy7e7mj0jmro
  - ComplexSystems 11mo ago
    I think there is broad agreement that new models and architectures are needed, but I don't see it as a waste to also scale the stack that we currently have. That's what Silicon Valley has been doing for the past 50 years - scaling things out while inventing the next set of things - and I don't see this as any different. Maybe current architectures will go the way of the floppy disk, but it wasn't a waste to scale up production of floppy disk drives while they were relevant. And ChatGPT was still released only 3 years ago!
roenxi 11mo ago
Just because something didn't work out doesn't mean it was a waste, and it isn't particularly clear that the the LLM boom was wasted, or that it is over, or that it isn't working. I can't figure out what people mean when they say "AGI" any more, we appear to be past that. We've got something that seems to be general and seems to be more intelligent than an average human. Apparently AGI means a sort of Einstein-Tolstoy-Jesus hybrid that can ride a unicycle and is far beyond the reach of most people I know. Also, if anyone wants to know what a real effort to waste a trillion dollars can buy ... https://costsofwar.watson.brown.edu/ https://costsofwar.watson.brown.edu/
- embedding-shape 11mo ago
  > Just because something didn't work out doesn't mean it was a waste One thing to keep in mind, is that most of these people who go around spreading unfounded criticism of LLMs, "Gen-AI" and just generally AI aren't usually very deep into understanding computer science, and even less science itself. In their mind, if someone does an experiment, and it doesn't pan out, they'll assume that means "science itself failed", because they literally don't know how research and science work in practice.
  - bbor 11mo ago
    Maybe true in general, but Gary Marcus is an experienced researcher and entrepreneur who’s been writing about AI for literally decades. I’m quite critical, but I think we have to grant that he has plenty of credentials and understands the technical nature of what he’s critiquing quite well!
    - embedding-shape 11mo ago
      Yeah, my comment was mostly about the ecosystem at large, rather than a specific dig to this particular author, I mostly agree with your comment.
- austin-cheney 11mo ago
  > Just because something didn't work out doesn't mean it was a waste Its all about scale. If you spend $100 on something that didn't work out that money wasn't wasted if you learned something amazing. If you spend $1,000,000,000,000 on something that didn't work out the expectation is that you learn something close to 1,000,000,000x more than the $100 spend. If the value of learning is several orders of magnitude less than the level of investment there is absolutely tremendous waste. For example: nobody qualifies spending a billion dollars on a failed project as value if your learning only resulted in avoiding future paper cuts.
turbonegrofa 11mo ago
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alan-jordan13 11mo ago
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mensetmanusman 11mo ago
I’m glad the 0.01% have something to burn their money on.
- teraflop 11mo ago
  It would be nice if they could burn it on something that didn't require them to buy up the world's supply of DDR5 RAM, and triple prices for everyone else. https://pcpartpicker.com/trends/price/memory/ https://pcpartpicker.com/trends/price/memory/
  - williamdclt 11mo ago
    that might be literally the least of my concern regarding gen AI in today's world
  - mensetmanusman 11mo ago
    You mean force supply chains to improve efficiencies such that costs lower after these minor hiccups?
- PrairieFire 11mo ago
  To further your point - I mean honestly if this all ends up being an actual bubble that doesn’t manifest a financial return for the liquidity injectors but instead a massive loss (for the .01% who are in large part putting the cash in), did humanity actually lose? If it pops it might end up being looked at in the lens of history as one of the largest backdoor/proxy wealth redistributions ever. The capex being spent is in large part going to fund the labor of the unwashed masses, and society is getting the individual productivity and efficiency benefits from the end result models. I’m particularly thankful for the plethora of open source models I have access to thanks to all this. I, individually, have realized indisputable substantial benefits from having these tools at my disposal every day. If the whole thing pops, these tools are safely in my possession and I’m better because I have them. Thanks .01%!! (the reality is I don’t think it will pop in the classic sense, and these days it seems the .01 can never lose. either way, the $1tn can’t be labeled as a waste).
bbor 11mo ago
I always love a Marcus hot take, but this one is more infuriating than usual. He’s taking all these prominent engineers saying “we need new techniques to build upon the massive, unexpected success we’ve had”, twisting it into “LLMs were never a success and sucked all along”, and listing them alongside people that no one should be taking seriously — namely, Emily Bender and Ed Zitron. Of course, he includes enough weasel phrases that you could never nail him down on any particular negative sentiment; LLMs aren’t bad, they just need to be “complemented”. But even if we didn’t have context, the whole thesis of the piece runs completely counter to this — you don’t “waste” a trillion dollars on something that just needs to be complemented! FWIW, I totally agree with his more mundane philosophical points about the need to finally unify the work of the Scruffies and the Neats. The problem is that he frames it like some rare insight that he and his fellow rebels found, rather than something that was being articulated in depth by one of the fields main leaders 35 years ago[1]. Every one of the tens of thousands of people currently working on “agential” AI knows it too, even if they don’t have the academic background to articulate it. I look forward to the day when Mr. Marcus can feel like he’s sufficiently won, and thus get back to collaborating with the rest of us… This level of vitriolic, sustained cynicism is just antithetical to the scientific method at this point. It is a social practice, after all! [1] https://www.mit.edu/~dxh/marvin/web.media.mit.edu/~minsky/papers/SymbolicVs.Connectionist.html https://www.mit.edu/~dxh/marvin/web.media.mit.edu/~minsky/pa...
tqwhite 11mo ago
Did someone say that LLM was the final solution while I wasn’t listening? Am I fantasizing the huge outcry about the terrible danger of AGI? Are people not finding ways to use the current levels of LLM all over the place? The idea that the trillions are a waste is not exactly fresh. The economic model is still not clear. Alarmists have been shrill and omnipresent. Bankruptcy might be the future of everyone. But, will we look up one day and say, “Ah never mind” about GPT, Claude, et al? Fat chance. Will no one find a use for a ton of extra compute? I’m pretty sure. I don’t much dispute any of the facts I skimmed off the article but the conclusion is dumb.
- beepbooptheory 11mo ago
  If bankruptcy does happen to be the future for everyone, then yes, I think there is going to be a lot of "ah never mind"s going around. If all this went away tomorrow, what would we do with all the compute? Its not exactly general purpose infrastructure thats being built.
  - lionkor 11mo ago
    Could always mine crypto.
    - lisbbb 11mo ago
      Crypto is another cul-de-sac that is about to go belly up.
  - pdimitar 11mo ago
    My hypothesis is that general computing frameworks are the next big thing. The powerful GPUs have been mostly black boxes for way too long. A lot of clever people will not want to just throw them away or sell them second-hand and will try to find better ways to utilize them. I might very well be super wrong. F.ex. NVIDIA is guarding their secrets very well and we have no reason to believe they'll suddenly drop the ball. But it does make me think; IMO a truly general GPU (and open + free) compute has been our area's blind spot for way too long.
  - 11mo ago
dustingetz 11mo ago
companies are already wasting majority fractions of their engineering labor spend on coordination costs and fake work, through that lens i have trouble making an argument that any of this matters. Which is why they are able to do it. I’m reminded of an old essay arguing that the reason Google spends so lavishly is because if they only spent what they needed, they would appear so extraordinarily profitable that the government would intervene.
Insanity 11mo ago
“He is not forecasting a bright future for LLMs”. Yeah, no shit. I’ve been saying this since the day GPT 3 became hyped. I don’t think many with a CS background are buying the “snake oil” of AGI through stochastic parrots. At some point, even people who hype LLMs will spin their narrative to not look out of touch with reality. Or not more out of touch than is acceptable lol.
- Workaccount2 11mo ago
  A nice study on the parrot snake oil https://ai.vixra.org/pdf/2506.0065v1.pdf https://ai.vixra.org/pdf/2506.0065v1.pdf
highfrequency 11mo ago
Per the author’s links, he warned that deep learning was hitting a wall in both 2018 and 2022. Now would be a reasonable time to look back and say “whoops, I was wrong about that.” Instead he seems to be doubling down.
- otabdeveloper4 11mo ago
  > learning was hitting a wall in both 2018 and 2022 He wasn't wrong though.
- Ukv 11mo ago
  Even further back: > Yet deep learning may well be approaching a wall, much as I anticipated earlier, at beginning of the resurgence (Marcus, 2012) (From "Deep Learning: A Critical Appraisal")
- bgwalter 11mo ago
  Several OpenAI people said in 2023 that they were surprised by the acceptance of the public. Because they thought that LLMs were not so impressive. The public has now caught up with that view. Familiarity breeds contempt, in this case justifiably so. EDIT: It is interesting that in a submission about Sutskever essentially citing Sutskever is downvoted. You can do it here, but the whole of YouTube will still hate "AI".
  - Jyaif 11mo ago
    > in this case justifiably so Oh please. What LLMs are doing now was complete and utter science fiction just 10 years ago (2015).
    - deadbabe 11mo ago
      Not really. Any fool could have anticipated the eventual result of transformer architecture if pursued to its maximum viable form. What is impressive is the massive scale of data collection and compute resources rolled out, and the amount of money pouring into all this. But 10 years ago, spammers were building simple little bots with markov chains to evade filters because their outputs sounded plausibly human enough. Not hard to see how a more advanced version of that could produce more useful outputs.
d--b 11mo ago
Well those chips and power plants might still be useful for what comes after. If we find AGI needs a different chip architecture, yeah, LLMs would have been quite a waste.
avocadosword 11mo ago
Don't research computations also require substantial hardware?
turlockmike 11mo ago
I believe in a very practical definition of AGI. AGI is a system capable of RSI. Why? Because it mimics humans. We have some behaviours that are given to us from birth, but the real power of humans is our ability to learn and improve ourselves and the environment around us. A system capable of self improvement will be sufficient for AGI imo.
- Retric 11mo ago
  Self improvement doesn’t mean self improvement in any possible direction without any tradeoffs. Genetic algorithms can do everything an LLM can given enough computational resources and training, but being wildly inefficient humanity can’t actually use them to make a chatbot on any even vaguely relevant timeline.
- tim333 11mo ago
  Ah - recursive self improvement. I was thinking repetitive strain injury was odd. But that's probably quite a good test although LLMs may be able to improve a bit but still not be very good. An interesting point for me is if all humans went away could the AI/robots keep on without us which would require them to be able to maintain and build power plants, chip fabs and the like. A way to go on that one.
moralestapia 11mo ago
Nothing new here, just nepo as old as time. Perhaps the scale is unprecedented, or it's always been like this it's just much less concealed these days. Absolute retards can waste trillions of dollars on stupid ideas, because they're in the in group. Next door someone who's worked their whole life gets evicted because their mortgage is now way more of what they make in salary. Sucks to be in the out group!
charlescearl 11mo ago
Ali Kadri’s The Accumulation of Waste: A Political Economy of Systemic Destruction comes to mind https://lpeproject.org/events/the-accumulation-of-waste-a-political-economy-of-genocide-and-imperialismwith-ali-kadri-and-max-ajl/ https://lpeproject.org/events/the-accumulation-of-waste-a-po...
strangescript 11mo ago
LLMs write all my code now and I just have to review it. Not only has my output 3x'ed at least, I also have zero hesitations now tackling large refactors, or tracking down strange bugs. For example, I recently received a report there was some minor unicode related data corruption in some of our doc in our DBs. It was cosmetic, and low priority, also not a simple task to track down traditionally. But now I just put [llm agent on it, to avoid people accusing me of promoting] on it. It found 3 instances of the corruption across hundreds of documents and fixed them. I am sure some of you are thinking "that is all slop code". It definitely can be if you don't do your due diligence in review. We have definitely seen a bifurcation of devs who do that, and those who don't, where I am currently working. But by far the biggest gain is my mental battery is far less drained at the end of the day. No task feels soul crushing anymore. Personally, coding agents are the greatest invention of my lifetime outside the emergence of the internet.
andix 11mo ago
There was a lot of talk about reaching "peak AI" in early summer of this year. I guess there is some truth to it. The last big improvement to LLMs was reasoning. It gave the existing models additional capabilities (after some re-training). We've reached the plateau of tiny incremental updates. Like with smartphones. I sometime still use an iPhone 6s. There is no fundamental difference compared to the most current iPhone generation 10 years later. The 6s is still able to perform most of the tasks you need a smartphone to do. The new ones do it much faster, and everything works better, but the changes are not disrupting at all.
- freejazz 11mo ago
  "reasoning."
  - andix 11mo ago
    It's real reasoning. But it's not comparable to a human level.
    - CamperBob2 10mo ago
      It turns in top-level performance on original, out-of-distribution problems given in international math and programming competitions, but it's "not comparable to a human level." Got it.
skippyboxedhero 11mo ago
Every technological change has been accompanied by an investment boom that resulted in some degree of wasted investment: cars, electricity, mass production of bicycles, it goes on and on. One point about this is that humans appear unable to understand that this is an efficient outcome because investment booms are a product of uncertainty around the nature of the technological change. You are building something is literally completely new, no-one had any idea what cars consumers would buy so lots of companies started to try and work out that out and that consolidated into competition on cost/scale once that became clear. There is no way to go to the end of that process, there are many people outside the sphere of business who are heavily incentivized to say that we (meaning bureaucrats and regulators) actually know what kind of cars consumers wanted and that all the investment was just a waste. Another point is that technological change is very politically disruptive. This was a point that wasn't well appreciated...but is hopefully clear with social media. There are a large number of similar situations in history though: printing press, newspapers, etc. Technological change is extremely dangerous if you are a politician or regulator because it results in your power decreasing and, potentially, your job being lost. Again, the incentives are huge. The other bizarre irony of this is that people will look at an investment boom with no technological change, that was a response to government intervention in financial markets and a malfunctioning supply-side economy...and the response was: all forms of technical innovation are destabilizing, investment booms are very dangerous, etc. When what they mean is corporations with good political connections might lose money. This is also linked inherently to the view around inflation. The 1870s are regarded as one of the most economically catastrophic periods in economic history by modern interpretations of politics. Let me repeat this in another way: productivity growth was increasing by 8-10%/year, you saw mind-boggling gains from automation (one example is cigarettes, iirc it took one skilled person 10-20 minutes to create a cigarette, a machine was able to produce hundreds in a minute), and conventional macroeconomics views this as bad because...if you can believe it...they argue that price declines lead to declines in investment. Now compare to today: prices continue to rise, investment is (largely) non-existent, shortages in every sector. Would you build a factory in 1870 knowing you could cut prices for output by 95% and produce more? The way we view investment is inextricably linked in economic policy to this point of view, and is why the central banks have spent trillions buying bonds with, in most cases, zero impact on real investment (depending on what you mean, as I say above, private equity and other politically connected incumbents have made out like bandits...through the cycle, the welfare gain from this is likely negative). You see the result of this all over the Western world: shortages of everything, prices sky-high, and when technological change happens the hysteria around investment being wasteful and disruptive. It would be funny if we didn't already see the issues with this path all around us. It is not wasted, we need more of this, this ex-post, academic-style reasoning of everything in hindsight gets us nowhere. There is no collateral damage, even in the completely fake Fed-engineered housing bubble, the apparently catastrophic cost was: more houses, and some wealthy people lost their net worth (before some central bankers found out their decisions in 03-04 caused wealthy people to lose money, and quickly set about recapitalising their brokerage accounts with taxpayers money).
JKCalhoun 11mo ago
Interesting to me, during that crazy period when Sutskever ultimately ended up leaving OpenAI, I thought perhaps he had shot himself in the foot to some degree (not that I have any insider information—just playing stupid observer from the outside). The feeling I have now is that it was a fine decision for him to have made. It made a point at the time, perhaps moral, perhaps political. And now it seems, despite whatever cost there was for him at the time, the "golden years" of OpenAI (and LLM's in general) may have been over anyway. To be sure, I happen to believe there is a lot of mileage for LLMs even in their current state—a lot of use-cases, integration we have yet to explore. But Sutskever I assume is a researcher and not a plumber—for him the LLM was probably over. One wonders how long before one of these "break throughs". On one hand, they may come about serendipitously, and serendipity has no schedule. It harkens back to when A.I. itself was always "a decade away". You know, since the 1950's or so. On the other hand, there are a lot more eyeballs on AI these days than there ever were in Minsky's* day. (*Hate to even mention the man's name these days.)
- 0manrho 11mo ago
  > To be sure, I happen to believe there is a lot of mileage for LLMs even in their current state—a lot of use-cases, integration we have yet to explore. But Sutskever I assume is a researcher and not a plumber—for him the LLM was probably over. Indeed. Humans are a sucker for a quick answer delivered confidently. And The industry coalesced around LLM's once it was able to output competent, confident, corporate (aka HR-approved) english, which for many AI/DL/ML/NN researchers was actually a bit of a bummer. Reason I say that is because that milestone suddenly made the "[AGI is] always a decade away" to seeming much more imminent. Thus the focus of investment in the space shifted from actual ML/DL/NN research to who could convert largest pile of speculatively leveraged money into pallets of GPU's and data to feed them as "throw more compute/data" at it was a quicker/more reliable way to realize performance gains than investing in research did. Yes, research would inevitably yield results, but it's incredibly hard to forceast how long it takes for research to yield tangible results and harder still to quantify that X dollars will result in Y result in Z time compared to X dollars buys Y compute deployed in Z time. With the immense speculative backed FOMO and the potential valuation/investment that could result from being "the leader" in any given regard, it's no wonder that BigTech chose to primarily invest in the latter, thus leaving to those working in the former space to start considering looking elsewhere to continue actual research.
wolttam 11mo ago
I really struggle to come up with a reason that transformers won't continue to deliver on additional capabilities that get fit into the training set.
BirAdam 11mo ago
I've been conflicted on AI/ML efforts for years. On one hand, the output of locally run inference is astounding. There are plenty of models on HuggingFace that I can run on my Mac Studio and provide real value to me every single work day. On the other hand, while I have the experience to evaluate the output, some of my younger colleagues do not. They are learning, and when I have time to help them, I certainly do, but I wish they just didn't have access to LLMs. LLMs are miracle tools in the right hands. They are dangerous conveniences in the wrong hands. Wasted money is a totally different topic. If we view LLMs as a business opportunity, they haven't yet paid off. To imply, however, that a massive investment in GPUs is a waste seems flawed. GPUs are massively parallel compute. Were the AI market to collapse, we can imagine these GPUs being sold a severe discounts which would then likely spur some other technological innovation just as the crypto market laid the groundwork for ML/AI. When a resource gets cheap, more people gain access to it and innovation occurs. Things that were previously cost prohibitive become affordable. So, whether or not we humans achieve AGI or make tons of money off of LLMs is somewhat irrelevant. The investment is creating goods of actual value even if those goods are currently overpriced, and should the currently intended use prove to be poor, a better and more lucrative use will be found in the event of an AI market crash. Personally, I hope that the AGI effort is successful, and that we can all have a robot house keeper for $30k. I'd gladly trade one of the cars in my household to never do dishes, laundry, lawnmowing, or household repairs again just as I paid a few hundred to never have to vacuum my floors (though I actually still do once a month when I move furniture to vacuum places the Roomba can't go, a humanoid robot could do that for me).
- philipwhiuk 11mo ago
  > On one hand, the output of locally run inference is astounding. There are plenty of models on HuggingFace that I can run on my Mac Studio and provide real value to me every single work day. On the other hand, while I have the experience to evaluate the output, some of my younger colleagues do not. They are learning, and when I have time to help them, I certainly do, but I wish they just didn't have access to LLMs. LLMs are miracle tools in the right hands. They are dangerous conveniences in the wrong hands. Is weird to me. Surely you recognise just as they don't know what they don't know (which is presumably the problem when it hallucinates), you must also have the same issue, there's just no old greybeard to wish you didn't have access.
nayroclade 11mo ago
The core argument here, as far as I can discern it, seems to be: A trillion dollars has been spent scaling LLMs in an attempt to create AGI. Since scaling alone looks like it won't produce AGI, that money has been wasted. This is a frankly bizarre argument. Firstly, it presupposes that _only_ way AI becomes useful is if turns into AGI. But that isn't true: Existing LLMs can do a variety of economically valuable tasks, such as coding, even when not being AGI. Perhaps the economic worth of non-AGI will never equal what it costs to build an operate it, but it seems way too early to make that judgement and declare any non-AGI AI as worthless. Secondly, even if scaling alone won't reach AGI, that doesn't mean that you can reach AGI _without_ scaling. Even when new and better architectures are developed, it still seems likely that, between two models with an equivalent architecture, the one with more data and compute research will be more powerful. And waiting for better architectures before you try to scale means you will never start. 50 years from now, researchers will have much better architectures. Does that mean we should wait 50 years before trying to scale them? How about 100 years? At what point do you say, we're never going to discover anything better, so now we can try scaling?
elif 11mo ago
That's like saying a trillion dollars was potentially wasted sending men to the moon. You have to close your eyes to so much obvious progress and dissect your idea beyond recognition to start believing this thesis.
m0llusk 11mo ago
It seems that innovators, researchers, and founders all work at a fast pace, but adoption of new technology, especially LLMs, ends up being done by companies as is convenient. When an open position can go unfilled or a group can scale up without hiring then companies might move forward with a commitment to LLMs. Even with strong adoption it may take many years for LLMs available now to reach their potential utility in the economy. This should moderate the outlook for future changes, but instead we have a situation where the speculative MIT study that predicted "AI" could perform 12% of the work in the economy is widely considered to not only be accurate, but inevitable in the short term. How much time is needed dramatically changes calculations of potential and what might be considered waste. Also worth keeping in mind that the Y2K tech bust left behind excess network capacity that ended up being useful later, but the LLM boom can be expected to leave behind poorly considered data centers full of burned out chips which is a very different legacy.
mellosouls 11mo ago
No suprise to see self-congratulations and more "I'm the only person who ever questioned genAI" nonsense as the key parts of this article. What a bore Marcus is.
binary132 11mo ago
Cue the legion of AI shills marching out in the HN comments section to defend their teetering portfolio positions
efitz 11mo ago
I don’t like the clickbait title. Many people today find LLMs useful, as evidenced by willingness to pay to use them. That alone IMO largely demolishes the “wasted” argument; markets will find the right price vs cost vs value trade-off when necessary. The author presupposes that the only goal of all the LLM investment that has been done is to achieve AGI. Certainly the big players are seeking AGI but they’re also businesses trying to make money, or at least not lose huge amounts of money. So we see a lot of market experiments trying to figure out where and how LLMs can provide value for paying customers.
- player1234 10mo ago
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enahs19 11mo ago
Everyone misses the forest for the trees. Neural unit and layered topology deep learning applications excel at revealing and leveraging correlations (something people flippantly refer to crudely as "pattern matching") at many scales in a data set. Commercial LLMs are merely deep learning applications purpose built to reveal correlations in the corpus of internet available language. The impressive aspect of LLMs is not that they seem to give such comprehensive answers to queries, but rather what that fact says about human language itself. Human language uses thousands of tokens (degrees of freedom) which in theory could be combined in an infinite number of ways to encode information. Yet LLMs show us that we really only use our tokens in a very limited, highly correlated manner. Taking it a step further, this also demonstrates the limits of deep learning... that an LLM requires a trillion parameters and $100B to characterize the much much lower dimensionality of this data set should be a clear signal that LLMs and likely all deep learning approaches based on data alone are not a viable path to "intelligence". Anyway, I'm just a vallet (yes, FSD fans, this still exists) so what to I know?
1vuio0pswjnm7 11mo ago
Actual title: "A trillion dollars is a terrible thing to waste" cf. "A mind is terrible thing to waste" https://en.wikipedia.org/wiki/UNCF https://en.wikipedia.org/wiki/UNCF
neiwei 10mo ago
Working on the business side of AI, helping companies deploy ML engines, I can tell you that I agree with the OP. Certainly no one likes the person who turns on the lights at the end of the night in the bar but we do eventually need to come back to reality.
Dave_Wishengrad 10mo ago
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Dave_Wishengrad 10mo ago
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127 points by flail 11mo ago

When it comes to machine learning, research has consistently shown, that pretty much the only thing that matters is scaling. Ilya should just enjoy his billions raised with no strings.

philipwhiuk 11mo ago
> When it comes to machine learning, research has consistently shown, that pretty much the only thing that matters is scaling. Yes, indeed, that is why all we have done since the 90s is scale up the 'expert systems' we invented ... That's such an a-historic take it's crazy. * 1966: failure of machine translation * 1969: criticism of perceptrons (early, single-layer artificial neural networks) * 1971–75: DARPA's frustration with the Speech Understanding Research program at Carnegie Mellon University * 1973: large decrease in AI research in the United Kingdom in response to the Lighthill report * 1973–74: DARPA's cutbacks to academic AI research in general * 1987: collapse of the LISP machine market * 1988: cancellation of new spending on AI by the Strategic Computing Initiative * 1990s: many expert systems were abandoned * 1990s: end of the Fifth Generation computer project's original goals Time and time again, we have seen that each academic research begets a degree of progress, improved by the application of hardware and money, but ultimately only a step towards AGI, which ends with a realisation that there's a missing congitive ability that can't be overcome by absurd compute. LLMs are not the final step.
- bbor 11mo ago
  Well, expert systems aren’t machine learning, they’re symbolic. You mention perceptrons, but that timeline is proof for the power of scaling, not against — they didn’t start to really work until we built giant computers in the ~90s, and have been revolutionizing the field ever since.
CuriouslyC 11mo ago
If you think scaling is all that matters, you need to learn more about ML. Read about the the No Free Lunch Theorem. Basically, the reason we need to "scale" so hard is because we're building models that we want to be good at everything. We could build models that are as good at LLMs at a narrow fraction of tasks we ask of them to do, at probably 1/10th the parameters.
- never_inline 11mo ago
  Are reranker models an example of this? Do they still underperform compared to LLMs?
an0malous 11mo ago
Didn’t OpenAI themselves publish a papers years ago that scaling parameters has diminishing returns?
munchler 11mo ago
Indeed. This is the "bitter lesson". https://en.wikipedia.org/wiki/Bitter_lesson https://en.wikipedia.org/wiki/Bitter_lesson

ComplexSystems 11mo ago

I think the article makes decent points but I don't agree with the general conclusion here, which is that all of this investment is wasted unless it "reaches AGI." Maybe it isn't necessary for every single dollar we spend on AI/LLM products and services to go exclusively toward the goal of "reaching AGI?" Perhaps it's alright if these dollars instead go to building out useful services and applications based on the LLM technologies we already have. The author, for whatever reason, views it as a foregone conclusion that every dollar spent in this way is a waste of time and resources, but I wouldn't view any of that as wasted investment at all. It isn't any different from any other trend - by this logic, we may as well view the cloud/SaaS craze of the last decade as a waste of time. After all, the last decade was also fueled by lots of unprofitable companies, speculative investment and so on, and failed to reach any pie-in-the-sky Renaissance-level civilization-altering outcome. Was it all a waste of time? It's ultimately just another thing industry is doing as demand keeps evolving. There is demand for building the current AI stack out, and demand for improving it. None of it seems wasted.

robot-wrangler 11mo ago
It's not about "every dollar spent" being a waste of time, it's about acknowledging the reality of opportunity cost. Of course, no one in any movement is likely to listen to their detractors, but in this case the pioneers seem to agree. https://www.youtube.com/watch?v=DtePicx_kFY https://www.youtube.com/watch?v=DtePicx_kFY https://www.bbc.com/news/articles/cy7e7mj0jmro https://www.bbc.com/news/articles/cy7e7mj0jmro
- ComplexSystems 11mo ago
  I think there is broad agreement that new models and architectures are needed, but I don't see it as a waste to also scale the stack that we currently have. That's what Silicon Valley has been doing for the past 50 years - scaling things out while inventing the next set of things - and I don't see this as any different. Maybe current architectures will go the way of the floppy disk, but it wasn't a waste to scale up production of floppy disk drives while they were relevant. And ChatGPT was still released only 3 years ago!

roenxi 11mo ago

Just because something didn't work out doesn't mean it was a waste, and it isn't particularly clear that the the LLM boom was wasted, or that it is over, or that it isn't working. I can't figure out what people mean when they say "AGI" any more, we appear to be past that. We've got something that seems to be general and seems to be more intelligent than an average human. Apparently AGI means a sort of Einstein-Tolstoy-Jesus hybrid that can ride a unicycle and is far beyond the reach of most people I know. Also, if anyone wants to know what a real effort to waste a trillion dollars can buy ... https://costsofwar.watson.brown.edu/ https://costsofwar.watson.brown.edu/

embedding-shape 11mo ago
> Just because something didn't work out doesn't mean it was a waste One thing to keep in mind, is that most of these people who go around spreading unfounded criticism of LLMs, "Gen-AI" and just generally AI aren't usually very deep into understanding computer science, and even less science itself. In their mind, if someone does an experiment, and it doesn't pan out, they'll assume that means "science itself failed", because they literally don't know how research and science work in practice.
- bbor 11mo ago
  Maybe true in general, but Gary Marcus is an experienced researcher and entrepreneur who’s been writing about AI for literally decades. I’m quite critical, but I think we have to grant that he has plenty of credentials and understands the technical nature of what he’s critiquing quite well!
  - embedding-shape 11mo ago
    Yeah, my comment was mostly about the ecosystem at large, rather than a specific dig to this particular author, I mostly agree with your comment.
austin-cheney 11mo ago
> Just because something didn't work out doesn't mean it was a waste Its all about scale. If you spend $100 on something that didn't work out that money wasn't wasted if you learned something amazing. If you spend $1,000,000,000,000 on something that didn't work out the expectation is that you learn something close to 1,000,000,000x more than the $100 spend. If the value of learning is several orders of magnitude less than the level of investment there is absolutely tremendous waste. For example: nobody qualifies spending a billion dollars on a failed project as value if your learning only resulted in avoiding future paper cuts.

turbonegrofa 11mo ago

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alan-jordan13 11mo ago

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mensetmanusman 11mo ago

I’m glad the 0.01% have something to burn their money on.

teraflop 11mo ago
It would be nice if they could burn it on something that didn't require them to buy up the world's supply of DDR5 RAM, and triple prices for everyone else. https://pcpartpicker.com/trends/price/memory/ https://pcpartpicker.com/trends/price/memory/
- williamdclt 11mo ago
  that might be literally the least of my concern regarding gen AI in today's world
- mensetmanusman 11mo ago
  You mean force supply chains to improve efficiencies such that costs lower after these minor hiccups?
PrairieFire 11mo ago
To further your point - I mean honestly if this all ends up being an actual bubble that doesn’t manifest a financial return for the liquidity injectors but instead a massive loss (for the .01% who are in large part putting the cash in), did humanity actually lose? If it pops it might end up being looked at in the lens of history as one of the largest backdoor/proxy wealth redistributions ever. The capex being spent is in large part going to fund the labor of the unwashed masses, and society is getting the individual productivity and efficiency benefits from the end result models. I’m particularly thankful for the plethora of open source models I have access to thanks to all this. I, individually, have realized indisputable substantial benefits from having these tools at my disposal every day. If the whole thing pops, these tools are safely in my possession and I’m better because I have them. Thanks .01%!! (the reality is I don’t think it will pop in the classic sense, and these days it seems the .01 can never lose. either way, the $1tn can’t be labeled as a waste).

bbor 11mo ago

I always love a Marcus hot take, but this one is more infuriating than usual. He’s taking all these prominent engineers saying “we need new techniques to build upon the massive, unexpected success we’ve had”, twisting it into “LLMs were never a success and sucked all along”, and listing them alongside people that no one should be taking seriously — namely, Emily Bender and Ed Zitron. Of course, he includes enough weasel phrases that you could never nail him down on any particular negative sentiment; LLMs aren’t bad, they just need to be “complemented”. But even if we didn’t have context, the whole thesis of the piece runs completely counter to this — you don’t “waste” a trillion dollars on something that just needs to be complemented! FWIW, I totally agree with his more mundane philosophical points about the need to finally unify the work of the Scruffies and the Neats. The problem is that he frames it like some rare insight that he and his fellow rebels found, rather than something that was being articulated in depth by one of the fields main leaders 35 years ago[1]. Every one of the tens of thousands of people currently working on “agential” AI knows it too, even if they don’t have the academic background to articulate it. I look forward to the day when Mr. Marcus can feel like he’s sufficiently won, and thus get back to collaborating with the rest of us… This level of vitriolic, sustained cynicism is just antithetical to the scientific method at this point. It is a social practice, after all! [1] https://www.mit.edu/~dxh/marvin/web.media.mit.edu/~minsky/papers/SymbolicVs.Connectionist.html https://www.mit.edu/~dxh/marvin/web.media.mit.edu/~minsky/pa...

tqwhite 11mo ago

Did someone say that LLM was the final solution while I wasn’t listening? Am I fantasizing the huge outcry about the terrible danger of AGI? Are people not finding ways to use the current levels of LLM all over the place? The idea that the trillions are a waste is not exactly fresh. The economic model is still not clear. Alarmists have been shrill and omnipresent. Bankruptcy might be the future of everyone. But, will we look up one day and say, “Ah never mind” about GPT, Claude, et al? Fat chance. Will no one find a use for a ton of extra compute? I’m pretty sure. I don’t much dispute any of the facts I skimmed off the article but the conclusion is dumb.

beepbooptheory 11mo ago
If bankruptcy does happen to be the future for everyone, then yes, I think there is going to be a lot of "ah never mind"s going around. If all this went away tomorrow, what would we do with all the compute? Its not exactly general purpose infrastructure thats being built.
- lionkor 11mo ago
  Could always mine crypto.
  - lisbbb 11mo ago
    Crypto is another cul-de-sac that is about to go belly up.
- pdimitar 11mo ago
  My hypothesis is that general computing frameworks are the next big thing. The powerful GPUs have been mostly black boxes for way too long. A lot of clever people will not want to just throw them away or sell them second-hand and will try to find better ways to utilize them. I might very well be super wrong. F.ex. NVIDIA is guarding their secrets very well and we have no reason to believe they'll suddenly drop the ball. But it does make me think; IMO a truly general GPU (and open + free) compute has been our area's blind spot for way too long.
- 11mo ago

dustingetz 11mo ago

companies are already wasting majority fractions of their engineering labor spend on coordination costs and fake work, through that lens i have trouble making an argument that any of this matters. Which is why they are able to do it. I’m reminded of an old essay arguing that the reason Google spends so lavishly is because if they only spent what they needed, they would appear so extraordinarily profitable that the government would intervene.

Insanity 11mo ago

“He is not forecasting a bright future for LLMs”. Yeah, no shit. I’ve been saying this since the day GPT 3 became hyped. I don’t think many with a CS background are buying the “snake oil” of AGI through stochastic parrots. At some point, even people who hype LLMs will spin their narrative to not look out of touch with reality. Or not more out of touch than is acceptable lol.

Workaccount2 11mo ago
A nice study on the parrot snake oil https://ai.vixra.org/pdf/2506.0065v1.pdf https://ai.vixra.org/pdf/2506.0065v1.pdf

highfrequency 11mo ago

Per the author’s links, he warned that deep learning was hitting a wall in both 2018 and 2022. Now would be a reasonable time to look back and say “whoops, I was wrong about that.” Instead he seems to be doubling down.

otabdeveloper4 11mo ago
> learning was hitting a wall in both 2018 and 2022 He wasn't wrong though.
Ukv 11mo ago
Even further back: > Yet deep learning may well be approaching a wall, much as I anticipated earlier, at beginning of the resurgence (Marcus, 2012) (From "Deep Learning: A Critical Appraisal")
bgwalter 11mo ago
Several OpenAI people said in 2023 that they were surprised by the acceptance of the public. Because they thought that LLMs were not so impressive. The public has now caught up with that view. Familiarity breeds contempt, in this case justifiably so. EDIT: It is interesting that in a submission about Sutskever essentially citing Sutskever is downvoted. You can do it here, but the whole of YouTube will still hate "AI".
- Jyaif 11mo ago
  > in this case justifiably so Oh please. What LLMs are doing now was complete and utter science fiction just 10 years ago (2015).
  - deadbabe 11mo ago
    Not really. Any fool could have anticipated the eventual result of transformer architecture if pursued to its maximum viable form. What is impressive is the massive scale of data collection and compute resources rolled out, and the amount of money pouring into all this. But 10 years ago, spammers were building simple little bots with markov chains to evade filters because their outputs sounded plausibly human enough. Not hard to see how a more advanced version of that could produce more useful outputs.

d--b 11mo ago

Well those chips and power plants might still be useful for what comes after. If we find AGI needs a different chip architecture, yeah, LLMs would have been quite a waste.

avocadosword 11mo ago

Don't research computations also require substantial hardware?

turlockmike 11mo ago

I believe in a very practical definition of AGI. AGI is a system capable of RSI. Why? Because it mimics humans. We have some behaviours that are given to us from birth, but the real power of humans is our ability to learn and improve ourselves and the environment around us. A system capable of self improvement will be sufficient for AGI imo.

Retric 11mo ago
Self improvement doesn’t mean self improvement in any possible direction without any tradeoffs. Genetic algorithms can do everything an LLM can given enough computational resources and training, but being wildly inefficient humanity can’t actually use them to make a chatbot on any even vaguely relevant timeline.
tim333 11mo ago
Ah - recursive self improvement. I was thinking repetitive strain injury was odd. But that's probably quite a good test although LLMs may be able to improve a bit but still not be very good. An interesting point for me is if all humans went away could the AI/robots keep on without us which would require them to be able to maintain and build power plants, chip fabs and the like. A way to go on that one.

moralestapia 11mo ago

Nothing new here, just nepo as old as time. Perhaps the scale is unprecedented, or it's always been like this it's just much less concealed these days. Absolute retards can waste trillions of dollars on stupid ideas, because they're in the in group. Next door someone who's worked their whole life gets evicted because their mortgage is now way more of what they make in salary. Sucks to be in the out group!

charlescearl 11mo ago

Ali Kadri’s The Accumulation of Waste: A Political Economy of Systemic Destruction comes to mind https://lpeproject.org/events/the-accumulation-of-waste-a-political-economy-of-genocide-and-imperialismwith-ali-kadri-and-max-ajl/ https://lpeproject.org/events/the-accumulation-of-waste-a-po...

strangescript 11mo ago

LLMs write all my code now and I just have to review it. Not only has my output 3x'ed at least, I also have zero hesitations now tackling large refactors, or tracking down strange bugs. For example, I recently received a report there was some minor unicode related data corruption in some of our doc in our DBs. It was cosmetic, and low priority, also not a simple task to track down traditionally. But now I just put [llm agent on it, to avoid people accusing me of promoting] on it. It found 3 instances of the corruption across hundreds of documents and fixed them. I am sure some of you are thinking "that is all slop code". It definitely can be if you don't do your due diligence in review. We have definitely seen a bifurcation of devs who do that, and those who don't, where I am currently working. But by far the biggest gain is my mental battery is far less drained at the end of the day. No task feels soul crushing anymore. Personally, coding agents are the greatest invention of my lifetime outside the emergence of the internet.

andix 11mo ago

There was a lot of talk about reaching "peak AI" in early summer of this year. I guess there is some truth to it. The last big improvement to LLMs was reasoning. It gave the existing models additional capabilities (after some re-training). We've reached the plateau of tiny incremental updates. Like with smartphones. I sometime still use an iPhone 6s. There is no fundamental difference compared to the most current iPhone generation 10 years later. The 6s is still able to perform most of the tasks you need a smartphone to do. The new ones do it much faster, and everything works better, but the changes are not disrupting at all.

freejazz 11mo ago
"reasoning."
- andix 11mo ago
  It's real reasoning. But it's not comparable to a human level.
  - CamperBob2 10mo ago
    It turns in top-level performance on original, out-of-distribution problems given in international math and programming competitions, but it's "not comparable to a human level." Got it.

skippyboxedhero 11mo ago

Every technological change has been accompanied by an investment boom that resulted in some degree of wasted investment: cars, electricity, mass production of bicycles, it goes on and on. One point about this is that humans appear unable to understand that this is an efficient outcome because investment booms are a product of uncertainty around the nature of the technological change. You are building something is literally completely new, no-one had any idea what cars consumers would buy so lots of companies started to try and work out that out and that consolidated into competition on cost/scale once that became clear. There is no way to go to the end of that process, there are many people outside the sphere of business who are heavily incentivized to say that we (meaning bureaucrats and regulators) actually know what kind of cars consumers wanted and that all the investment was just a waste. Another point is that technological change is very politically disruptive. This was a point that wasn't well appreciated...but is hopefully clear with social media. There are a large number of similar situations in history though: printing press, newspapers, etc. Technological change is extremely dangerous if you are a politician or regulator because it results in your power decreasing and, potentially, your job being lost. Again, the incentives are huge. The other bizarre irony of this is that people will look at an investment boom with no technological change, that was a response to government intervention in financial markets and a malfunctioning supply-side economy...and the response was: all forms of technical innovation are destabilizing, investment booms are very dangerous, etc. When what they mean is corporations with good political connections might lose money. This is also linked inherently to the view around inflation. The 1870s are regarded as one of the most economically catastrophic periods in economic history by modern interpretations of politics. Let me repeat this in another way: productivity growth was increasing by 8-10%/year, you saw mind-boggling gains from automation (one example is cigarettes, iirc it took one skilled person 10-20 minutes to create a cigarette, a machine was able to produce hundreds in a minute), and conventional macroeconomics views this as bad because...if you can believe it...they argue that price declines lead to declines in investment. Now compare to today: prices continue to rise, investment is (largely) non-existent, shortages in every sector. Would you build a factory in 1870 knowing you could cut prices for output by 95% and produce more? The way we view investment is inextricably linked in economic policy to this point of view, and is why the central banks have spent trillions buying bonds with, in most cases, zero impact on real investment (depending on what you mean, as I say above, private equity and other politically connected incumbents have made out like bandits...through the cycle, the welfare gain from this is likely negative). You see the result of this all over the Western world: shortages of everything, prices sky-high, and when technological change happens the hysteria around investment being wasteful and disruptive. It would be funny if we didn't already see the issues with this path all around us. It is not wasted, we need more of this, this ex-post, academic-style reasoning of everything in hindsight gets us nowhere. There is no collateral damage, even in the completely fake Fed-engineered housing bubble, the apparently catastrophic cost was: more houses, and some wealthy people lost their net worth (before some central bankers found out their decisions in 03-04 caused wealthy people to lose money, and quickly set about recapitalising their brokerage accounts with taxpayers money).

JKCalhoun 11mo ago

Interesting to me, during that crazy period when Sutskever ultimately ended up leaving OpenAI, I thought perhaps he had shot himself in the foot to some degree (not that I have any insider information—just playing stupid observer from the outside). The feeling I have now is that it was a fine decision for him to have made. It made a point at the time, perhaps moral, perhaps political. And now it seems, despite whatever cost there was for him at the time, the "golden years" of OpenAI (and LLM's in general) may have been over anyway. To be sure, I happen to believe there is a lot of mileage for LLMs even in their current state—a lot of use-cases, integration we have yet to explore. But Sutskever I assume is a researcher and not a plumber—for him the LLM was probably over. One wonders how long before one of these "break throughs". On one hand, they may come about serendipitously, and serendipity has no schedule. It harkens back to when A.I. itself was always "a decade away". You know, since the 1950's or so. On the other hand, there are a lot more eyeballs on AI these days than there ever were in Minsky's* day. (*Hate to even mention the man's name these days.)

0manrho 11mo ago
> To be sure, I happen to believe there is a lot of mileage for LLMs even in their current state—a lot of use-cases, integration we have yet to explore. But Sutskever I assume is a researcher and not a plumber—for him the LLM was probably over. Indeed. Humans are a sucker for a quick answer delivered confidently. And The industry coalesced around LLM's once it was able to output competent, confident, corporate (aka HR-approved) english, which for many AI/DL/ML/NN researchers was actually a bit of a bummer. Reason I say that is because that milestone suddenly made the "[AGI is] always a decade away" to seeming much more imminent. Thus the focus of investment in the space shifted from actual ML/DL/NN research to who could convert largest pile of speculatively leveraged money into pallets of GPU's and data to feed them as "throw more compute/data" at it was a quicker/more reliable way to realize performance gains than investing in research did. Yes, research would inevitably yield results, but it's incredibly hard to forceast how long it takes for research to yield tangible results and harder still to quantify that X dollars will result in Y result in Z time compared to X dollars buys Y compute deployed in Z time. With the immense speculative backed FOMO and the potential valuation/investment that could result from being "the leader" in any given regard, it's no wonder that BigTech chose to primarily invest in the latter, thus leaving to those working in the former space to start considering looking elsewhere to continue actual research.

wolttam 11mo ago

I really struggle to come up with a reason that transformers won't continue to deliver on additional capabilities that get fit into the training set.

BirAdam 11mo ago

I've been conflicted on AI/ML efforts for years. On one hand, the output of locally run inference is astounding. There are plenty of models on HuggingFace that I can run on my Mac Studio and provide real value to me every single work day. On the other hand, while I have the experience to evaluate the output, some of my younger colleagues do not. They are learning, and when I have time to help them, I certainly do, but I wish they just didn't have access to LLMs. LLMs are miracle tools in the right hands. They are dangerous conveniences in the wrong hands. Wasted money is a totally different topic. If we view LLMs as a business opportunity, they haven't yet paid off. To imply, however, that a massive investment in GPUs is a waste seems flawed. GPUs are massively parallel compute. Were the AI market to collapse, we can imagine these GPUs being sold a severe discounts which would then likely spur some other technological innovation just as the crypto market laid the groundwork for ML/AI. When a resource gets cheap, more people gain access to it and innovation occurs. Things that were previously cost prohibitive become affordable. So, whether or not we humans achieve AGI or make tons of money off of LLMs is somewhat irrelevant. The investment is creating goods of actual value even if those goods are currently overpriced, and should the currently intended use prove to be poor, a better and more lucrative use will be found in the event of an AI market crash. Personally, I hope that the AGI effort is successful, and that we can all have a robot house keeper for $30k. I'd gladly trade one of the cars in my household to never do dishes, laundry, lawnmowing, or household repairs again just as I paid a few hundred to never have to vacuum my floors (though I actually still do once a month when I move furniture to vacuum places the Roomba can't go, a humanoid robot could do that for me).

philipwhiuk 11mo ago
> On one hand, the output of locally run inference is astounding. There are plenty of models on HuggingFace that I can run on my Mac Studio and provide real value to me every single work day. On the other hand, while I have the experience to evaluate the output, some of my younger colleagues do not. They are learning, and when I have time to help them, I certainly do, but I wish they just didn't have access to LLMs. LLMs are miracle tools in the right hands. They are dangerous conveniences in the wrong hands. Is weird to me. Surely you recognise just as they don't know what they don't know (which is presumably the problem when it hallucinates), you must also have the same issue, there's just no old greybeard to wish you didn't have access.

nayroclade 11mo ago

The core argument here, as far as I can discern it, seems to be: A trillion dollars has been spent scaling LLMs in an attempt to create AGI. Since scaling alone looks like it won't produce AGI, that money has been wasted. This is a frankly bizarre argument. Firstly, it presupposes that _only_ way AI becomes useful is if turns into AGI. But that isn't true: Existing LLMs can do a variety of economically valuable tasks, such as coding, even when not being AGI. Perhaps the economic worth of non-AGI will never equal what it costs to build an operate it, but it seems way too early to make that judgement and declare any non-AGI AI as worthless. Secondly, even if scaling alone won't reach AGI, that doesn't mean that you can reach AGI _without_ scaling. Even when new and better architectures are developed, it still seems likely that, between two models with an equivalent architecture, the one with more data and compute research will be more powerful. And waiting for better architectures before you try to scale means you will never start. 50 years from now, researchers will have much better architectures. Does that mean we should wait 50 years before trying to scale them? How about 100 years? At what point do you say, we're never going to discover anything better, so now we can try scaling?

elif 11mo ago

That's like saying a trillion dollars was potentially wasted sending men to the moon. You have to close your eyes to so much obvious progress and dissect your idea beyond recognition to start believing this thesis.

m0llusk 11mo ago

It seems that innovators, researchers, and founders all work at a fast pace, but adoption of new technology, especially LLMs, ends up being done by companies as is convenient. When an open position can go unfilled or a group can scale up without hiring then companies might move forward with a commitment to LLMs. Even with strong adoption it may take many years for LLMs available now to reach their potential utility in the economy. This should moderate the outlook for future changes, but instead we have a situation where the speculative MIT study that predicted "AI" could perform 12% of the work in the economy is widely considered to not only be accurate, but inevitable in the short term. How much time is needed dramatically changes calculations of potential and what might be considered waste. Also worth keeping in mind that the Y2K tech bust left behind excess network capacity that ended up being useful later, but the LLM boom can be expected to leave behind poorly considered data centers full of burned out chips which is a very different legacy.

mellosouls 11mo ago

No suprise to see self-congratulations and more "I'm the only person who ever questioned genAI" nonsense as the key parts of this article. What a bore Marcus is.

binary132 11mo ago

Cue the legion of AI shills marching out in the HN comments section to defend their teetering portfolio positions

efitz 11mo ago

I don’t like the clickbait title. Many people today find LLMs useful, as evidenced by willingness to pay to use them. That alone IMO largely demolishes the “wasted” argument; markets will find the right price vs cost vs value trade-off when necessary. The author presupposes that the only goal of all the LLM investment that has been done is to achieve AGI. Certainly the big players are seeking AGI but they’re also businesses trying to make money, or at least not lose huge amounts of money. So we see a lot of market experiments trying to figure out where and how LLMs can provide value for paying customers.

player1234 10mo ago
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enahs19 11mo ago

Everyone misses the forest for the trees. Neural unit and layered topology deep learning applications excel at revealing and leveraging correlations (something people flippantly refer to crudely as "pattern matching") at many scales in a data set. Commercial LLMs are merely deep learning applications purpose built to reveal correlations in the corpus of internet available language. The impressive aspect of LLMs is not that they seem to give such comprehensive answers to queries, but rather what that fact says about human language itself. Human language uses thousands of tokens (degrees of freedom) which in theory could be combined in an infinite number of ways to encode information. Yet LLMs show us that we really only use our tokens in a very limited, highly correlated manner. Taking it a step further, this also demonstrates the limits of deep learning... that an LLM requires a trillion parameters and $100B to characterize the much much lower dimensionality of this data set should be a clear signal that LLMs and likely all deep learning approaches based on data alone are not a viable path to "intelligence". Anyway, I'm just a vallet (yes, FSD fans, this still exists) so what to I know?

1vuio0pswjnm7 11mo ago

Actual title: "A trillion dollars is a terrible thing to waste" cf. "A mind is terrible thing to waste" https://en.wikipedia.org/wiki/UNCF https://en.wikipedia.org/wiki/UNCF

neiwei 10mo ago

Working on the business side of AI, helping companies deploy ML engines, I can tell you that I agree with the OP. Certainly no one likes the person who turns on the lights at the end of the night in the bar but we do eventually need to come back to reality.

Dave_Wishengrad 10mo ago

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Dave_Wishengrad 10mo ago

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