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.
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.
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!
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.)
> 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.
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).
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?
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.
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.
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[ 2.6 ms ] story [ 68.6 ms ] threadIlya should just enjoy his billions raised with no strings.
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.
Also, if anyone wants to know what a real effort to waste a trillion dollars can buy ... https://costsofwar.watson.brown.edu/
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/pa...
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.
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.
If we find AGI needs a different chip architecture, yeah, LLMs would have been quite a waste.
A system capable of self improvement will be sufficient for AGI imo.
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!
https://lpeproject.org/events/the-accumulation-of-waste-a-po...
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.
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.
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).
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.)
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.
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).
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?
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.