Seems like this is basically the same theory as what happened in the late 90s internet boom. Lots of speculative investment, over build out of core technology like fiber networks, then a crash, and an eventual recovery where that additional capacity was eventually put to use. Similar thing happened with railroads and the airline industry to some extent (pretty sure airlines as a whole had net negative investment returns until recently).
"Workers supply labor, hold no assets, and consume their wage." Ouch. There was a time in the US when most capital was the assets backing workers' pensions.
We've seen speculative over-growth with a good legacy at least three times in the
last three decades. First was the dot-com boom. Overpromotion made it necessary for every business to have a web site. That wasn't pre-ordained. The Web could have maxed out as a distribution system for catalogs, data sheets, academic papers, and similar business to business info. Overpromotion created the business to consumer web, which turned out to be useful.
The second overbuild was long-haul fiber optics. Look up Global Crossing. So much fiber was put into the ground and water that intercontinental spam is not a problem. That didn't have to happen. If traffic was billed, it wouldn't have happened. It turned out to be useful, but was not pre-ordained from the economics.
A third overbuild was the solar panel industry, especially in China. So much money was thrown at solar panel manufacturing that the price became very, very low. Solar deployment accelerated and started to take over, after decades of panels costing too much.
Now China has a solar panel glut. They're dealing with it intelligently - minimum efficiency standards are coming into effect, and pollution controls on panel manufacturing are being tightened.
So, some of the capital invested due to the AI boom may be for stuff like buildings, energy infrastructure, and other long-lasting stuff. I am reminded of all of the spending done in the runup to the Y2K, when I saw factories able to get all kinds of upgrades to equipment done, outside of the normal budgetary restraints, simply because the vendors were smart enough to say "not fixable, you'll have to buy the new model". It could happen.
However, GPUs and memory chips are some of the fastest-depreciating capital investments one can make. Overinvesting in this generation's GPU model will either be wasted (because the chips are worthless in a few years) or result in a lot of underinvestment in future years (because instead of replacing those GPUs with newer models you keep using them, unwilling to admit you bought several times as much as you should have).
If we have reached the point in the cycle where the boom's proponents are trying to argue that even if it was all a mistake, maybe it's ok, then one suspects were might be late in the boom part of the cycle.
I looked at the actual article and your instinct is exactly right: it's precisely the kind of hand-waving that makes clicking or not clicking a clear decision point.
No word about taxes and the paper describes workers as being “protected on the downside.” while the model has removed the downside risk that workers actually face. I could write a long essay with all the issues this "paper' has.
Truly dismal science of an Economics professor at MIT.
I won't pretend to fully understand the paper, but I did try to read it.
A few notes:
1. This assumes that there is notable ROI on 'AI labor'. That is still up for debate.
2. This assumes that the interests are currently falling, unless I misread the paper.
3. This affirms that we are in an over valuated, speculative bubble which will inevitably correct; but it needs to "correct" at the exact right time defined by multiple factors.
First, "correction" can be an euphemism for a disastrous financial crisis. It could take years and years for most people to see the end of the tunnel.
I don't know if the end justify the means.
Do we really need to engineer a financial crisis to build more energy facilities? And will they be built the 'right way', using renewable energy for example? What if we invested half of those trillions directly in socially impactful measures, instead of having the money flow through a speculative bubble first?
Finally, I am not an economist, but I wonder how accurate a mathematical model is to the real world - i.e. what happens to the model when Donald keep changing the opening hours of the Hormuz?
It does feel a bit like trying to read tea leaves to me.
This reminds me of Hari Seldon's psychohistory:
> In Foundation (1951), famed mathematician and psychologist Hari Seldon has developed the science of psychohistory, which uses sophisticated mathematics and statistical analysis to predict future trends on a galactic scale. He has predicted the unavoidable and relatively imminent fall of the Galactic Empire, and intends to establish the Foundation, "a repository of crucial, civilization-preserving knowledge" that will enable society to revive itself more quickly and efficiently [...] [1]
> What if we invested half of those trillions directly in socially impactful measures
There’s no real “we” in this case. The money is coming from private coffers, people looking for ROI on their hard-earned money. The money isn’t coming from a central planning process.
This reads like a propaganda piece aimed at mathematically inclined knowledge workers to try to stave off risk perception as their economic and political power is undermined by the US shift towards kakistocracy.
"workers
operate with a larger conventional capital stock and wages rise even as the worker share falls."
Liberal Democratic capitalism splits power into two primary buckets: political and economic.
Marx provided the critique of consolidated economic power. The Soviet union proved the dangers of consolidated political power and Hayek made the mechanism explicit.
The "election tampering" BS is their attempt to try to undermine plutocratic political power. This looks like an attempt to justify the insane concentration of economic power that clearly goes against Hayek's description of free markets as a mechanism to discover preferences. Whose preferences?
If worker share is falling, workers are losing their share of the economic voting mechanism. Whether or not the emerging capital ownership class chooses to keep rents and subscriptions affordable to the new working subclass if and when they accomplish this power grab is immaterial, no matter how much math they try to wrap the propaganda in.
A temporary overvaluation can build enough real capital that the economy lands in a permanently higher-capital equilibrium, even after the inflated valuations correct. The future for AI companies may look rather iffy, but the whole economy may not be as screwed as some fear.
The author starts out with a quote from Keynes about the speculation/growth from 1925-1929, is the permanently high-capital equilibrium supposed to happen 10 years after the crash or after we win the world war that follows..?
Surely that outcome requires that the capital retains its value/usefulness. One advantage of crypto and AI is that they can utilise massively parallel computational resources (and don't have tight latency requirements, like gaming) in an age where we've hit the physical limits of sequential computation.
If the "higher capital" that results from an AI boom consists
of massively parallel computational resources that currently can only be fully utilised by AI and crypto, and if those things turn out to be a bust, the "higher capital" only has value if we find something else to do with it.
Do you believe that machine learning or even specifically LLMs will "bust" out of existence?
The model in my head is more like DotCom telecom. The massive overbuild in fiber was eventually used and even used for the purpose that it was imagined for during the boom. It's just that the companies that built it mostly went under and new owners acquired it at a profit-supporting price.
Most of the cost in a fiber rollout is actual fiber in the ground which could be upgraded by simply swapping a few relatively cheap bits of equipment.
Data centers and electrical infrastructure has a similar long term value, but most of the AI investment is in compute/manufacturing capacity for current nodes which doesn’t age nearly as well.
> compute/manufacturing capacity for current nodes which doesn’t age nearly as well
I mean, compute depreciates, but I think there is zero chance that the value of inference or training is going to fall to zero. Market discovery will find the right price provided the market has the right degree of freedom. Given the type of market it is, I don't see how that won't be the case.
Algorthmic improvements in inference could make all that kit redundant very quickly - there are already moderately capable models that can be run on phones or laptops with specifications that are currently high-end but will be mainstream in another year or so.
This will lead to a superabundance of power-hungry compute power in the hyperscalers, and it's not entirely clear what can be done to consume it all and still run at a profit unless they manage to make ever greater gains for ever more compute-hungry models that cannot be run on consumer devices, unless they refresh their hardware at ever faster and more expensive rates.
The joke about data centers used to be that their core business was selling power at a loss; this may end up being true of the hyperscalers next.
I'm a big fan stylistically of what https://taalas.com/ is doing, as far as models baked into silicon. If you haven't tried their chat it's absurdly fast (and also very very dumb)
That implies to me that in the future we'll have models as good or perhaps better than the state of the art at the moment, but on hardware chips that can be put in places where you can't currently locate a datacenter, and operating at hundreds of times better power efficiency, which sounds pretty great.
Some people thought that it was misguided when they extended the depreciation cycle of the current AI build out year(s).
In terms of raw performance, there is still some headroom (maybe) but those gains are going to be marginal when you look at the amount of compute per watt (if its more than 5 percent I will be shocked). And that push is going to create a whole other set of problems (cooling is going to be an issue, it already is).
It is fairly likely that this hardware buildout has more legs than one might suspect based on history.
Doesn't that only apply if the capital is reusable? If we end up with a bunch of data centre GPUs after an AI bubble collapse, there's no guarantee those GPUs will find productive use for other things.
It's like the tulip bubble of the 17th century [1]. Having a bunch of money tied up in useless tulip bulbs didn't do anything productive after the collapse.
Energy infrastructure for powering a data centre isn't the same as energy infrastructure for powering a city. One is a simple point-to-point link (power plant to data centre), the other is a grid.
It's like comparing a railway line from a mine to a smelter with a city's road network.
Seems like this is basically the same theory as what happened in the late 90s internet boom. Lots of speculative investment, over build out of core technology like fiber networks, then a crash, and an eventual recovery where that additional capacity was eventually put to use. Similar thing happened with railroads and the airline industry to some extent (pretty sure airlines as a whole had net negative investment returns until recently).
"Workers supply labor, hold no assets, and consume their wage." Ouch. There was a time in the US when most capital was the assets backing workers' pensions.
We've seen speculative over-growth with a good legacy at least three times in the last three decades. First was the dot-com boom. Overpromotion made it necessary for every business to have a web site. That wasn't pre-ordained. The Web could have maxed out as a distribution system for catalogs, data sheets, academic papers, and similar business to business info. Overpromotion created the business to consumer web, which turned out to be useful.
The second overbuild was long-haul fiber optics. Look up Global Crossing. So much fiber was put into the ground and water that intercontinental spam is not a problem. That didn't have to happen. If traffic was billed, it wouldn't have happened. It turned out to be useful, but was not pre-ordained from the economics.
A third overbuild was the solar panel industry, especially in China. So much money was thrown at solar panel manufacturing that the price became very, very low. Solar deployment accelerated and started to take over, after decades of panels costing too much. Now China has a solar panel glut. They're dealing with it intelligently - minimum efficiency standards are coming into effect, and pollution controls on panel manufacturing are being tightened.
So, some of the capital invested due to the AI boom may be for stuff like buildings, energy infrastructure, and other long-lasting stuff. I am reminded of all of the spending done in the runup to the Y2K, when I saw factories able to get all kinds of upgrades to equipment done, outside of the normal budgetary restraints, simply because the vendors were smart enough to say "not fixable, you'll have to buy the new model". It could happen.
However, GPUs and memory chips are some of the fastest-depreciating capital investments one can make. Overinvesting in this generation's GPU model will either be wasted (because the chips are worthless in a few years) or result in a lot of underinvestment in future years (because instead of replacing those GPUs with newer models you keep using them, unwilling to admit you bought several times as much as you should have).
If we have reached the point in the cycle where the boom's proponents are trying to argue that even if it was all a mistake, maybe it's ok, then one suspects were might be late in the boom part of the cycle.
I looked at the actual article and your instinct is exactly right: it's precisely the kind of hand-waving that makes clicking or not clicking a clear decision point.
No word about taxes and the paper describes workers as being “protected on the downside.” while the model has removed the downside risk that workers actually face. I could write a long essay with all the issues this "paper' has.
Truly dismal science of an Economics professor at MIT.
I won't pretend to fully understand the paper, but I did try to read it.
A few notes:
1. This assumes that there is notable ROI on 'AI labor'. That is still up for debate.
2. This assumes that the interests are currently falling, unless I misread the paper.
3. This affirms that we are in an over valuated, speculative bubble which will inevitably correct; but it needs to "correct" at the exact right time defined by multiple factors.
First, "correction" can be an euphemism for a disastrous financial crisis. It could take years and years for most people to see the end of the tunnel. I don't know if the end justify the means.
Do we really need to engineer a financial crisis to build more energy facilities? And will they be built the 'right way', using renewable energy for example? What if we invested half of those trillions directly in socially impactful measures, instead of having the money flow through a speculative bubble first?
Finally, I am not an economist, but I wonder how accurate a mathematical model is to the real world - i.e. what happens to the model when Donald keep changing the opening hours of the Hormuz?
It does feel a bit like trying to read tea leaves to me. This reminds me of Hari Seldon's psychohistory:
> In Foundation (1951), famed mathematician and psychologist Hari Seldon has developed the science of psychohistory, which uses sophisticated mathematics and statistical analysis to predict future trends on a galactic scale. He has predicted the unavoidable and relatively imminent fall of the Galactic Empire, and intends to establish the Foundation, "a repository of crucial, civilization-preserving knowledge" that will enable society to revive itself more quickly and efficiently [...] [1]
---
[1](https://en.wikipedia.org/wiki/Foundation_universe#Psychohist...)
> What if we invested half of those trillions directly in socially impactful measures
There’s no real “we” in this case. The money is coming from private coffers, people looking for ROI on their hard-earned money. The money isn’t coming from a central planning process.
Fair point.
Yet, "we" will suffer the potential consequences.
This has strong signs of LLM writing, potentially Claude.
The book Boom argues something similar
https://press.stripe.com/boom
This reads like a propaganda piece aimed at mathematically inclined knowledge workers to try to stave off risk perception as their economic and political power is undermined by the US shift towards kakistocracy.
"workers operate with a larger conventional capital stock and wages rise even as the worker share falls."
Liberal Democratic capitalism splits power into two primary buckets: political and economic.
Marx provided the critique of consolidated economic power. The Soviet union proved the dangers of consolidated political power and Hayek made the mechanism explicit.
The "election tampering" BS is their attempt to try to undermine plutocratic political power. This looks like an attempt to justify the insane concentration of economic power that clearly goes against Hayek's description of free markets as a mechanism to discover preferences. Whose preferences?
If worker share is falling, workers are losing their share of the economic voting mechanism. Whether or not the emerging capital ownership class chooses to keep rents and subscriptions affordable to the new working subclass if and when they accomplish this power grab is immaterial, no matter how much math they try to wrap the propaganda in.
high quality post
Tl;dr is:
A temporary overvaluation can build enough real capital that the economy lands in a permanently higher-capital equilibrium, even after the inflated valuations correct. The future for AI companies may look rather iffy, but the whole economy may not be as screwed as some fear.
The author starts out with a quote from Keynes about the speculation/growth from 1925-1929, is the permanently high-capital equilibrium supposed to happen 10 years after the crash or after we win the world war that follows..?
Surely that outcome requires that the capital retains its value/usefulness. One advantage of crypto and AI is that they can utilise massively parallel computational resources (and don't have tight latency requirements, like gaming) in an age where we've hit the physical limits of sequential computation.
If the "higher capital" that results from an AI boom consists of massively parallel computational resources that currently can only be fully utilised by AI and crypto, and if those things turn out to be a bust, the "higher capital" only has value if we find something else to do with it.
Maybe we will...
Do you believe that machine learning or even specifically LLMs will "bust" out of existence?
The model in my head is more like DotCom telecom. The massive overbuild in fiber was eventually used and even used for the purpose that it was imagined for during the boom. It's just that the companies that built it mostly went under and new owners acquired it at a profit-supporting price.
Most of the cost in a fiber rollout is actual fiber in the ground which could be upgraded by simply swapping a few relatively cheap bits of equipment.
Data centers and electrical infrastructure has a similar long term value, but most of the AI investment is in compute/manufacturing capacity for current nodes which doesn’t age nearly as well.
> compute/manufacturing capacity for current nodes which doesn’t age nearly as well
I mean, compute depreciates, but I think there is zero chance that the value of inference or training is going to fall to zero. Market discovery will find the right price provided the market has the right degree of freedom. Given the type of market it is, I don't see how that won't be the case.
Algorthmic improvements in inference could make all that kit redundant very quickly - there are already moderately capable models that can be run on phones or laptops with specifications that are currently high-end but will be mainstream in another year or so.
This will lead to a superabundance of power-hungry compute power in the hyperscalers, and it's not entirely clear what can be done to consume it all and still run at a profit unless they manage to make ever greater gains for ever more compute-hungry models that cannot be run on consumer devices, unless they refresh their hardware at ever faster and more expensive rates.
The joke about data centers used to be that their core business was selling power at a loss; this may end up being true of the hyperscalers next.
I'm a big fan stylistically of what https://taalas.com/ is doing, as far as models baked into silicon. If you haven't tried their chat it's absurdly fast (and also very very dumb)
That implies to me that in the future we'll have models as good or perhaps better than the state of the art at the moment, but on hardware chips that can be put in places where you can't currently locate a datacenter, and operating at hundreds of times better power efficiency, which sounds pretty great.
> but most of the AI investment is in compute
Some people thought that it was misguided when they extended the depreciation cycle of the current AI build out year(s).
In terms of raw performance, there is still some headroom (maybe) but those gains are going to be marginal when you look at the amount of compute per watt (if its more than 5 percent I will be shocked). And that push is going to create a whole other set of problems (cooling is going to be an issue, it already is).
It is fairly likely that this hardware buildout has more legs than one might suspect based on history.
Doesn't that only apply if the capital is reusable? If we end up with a bunch of data centre GPUs after an AI bubble collapse, there's no guarantee those GPUs will find productive use for other things.
It's like the tulip bubble of the 17th century [1]. Having a bunch of money tied up in useless tulip bulbs didn't do anything productive after the collapse.
[1] https://en.wikipedia.org/wiki/Tulip_mania
yeah how will we ever find a usecase for the machines that can run thinking software
But it’s not just GPUs that result from it but also a lot of other infrastructure including, prominently, energy.
And beyond physical infrastructure there are the intangible assets: the learning and the process innovation across multiple fields.
The upfront price for all that may end up steep, or fair, or even cheap… the truth is no one knows yet
Energy infrastructure for powering a data centre isn't the same as energy infrastructure for powering a city. One is a simple point-to-point link (power plant to data centre), the other is a grid.
It's like comparing a railway line from a mine to a smelter with a city's road network.
The reality will be something more like this-
https://en.wikipedia.org/wiki/Minsky_moment
the guy wrote a paper on what a VC is, amazing