Has a competent economist modeled the circularity of these deals ? [ numerically or analytically ]
It seems a healthy economy has a lot of wide circularity .. money circulating is a good thing, a result of a functioning market, tracking the flow of real goods / services. But large corps circulating paper 'self-deals' or debt-swaps seems like a bad thing - a creative accounting practice designed to pump up their stock price/valuation.
How can we _quantify_ the difference ? I guess it would need to match the cash / debt flows against the movement of actual goods and services ??
Not an economist, feel free to weigh in, suggest links.
you'd need to look at prior work associated with the Dot com boom and Enron. Economists rarely look into today's actual activity because America makes it nigh impossible to actually understand who owns what around the fringes; contracts arn't required to be publically disclosed and a bunch of other dark interests make it impossible to really know.
MLMs exist in the same murky waters and tread the same ephermal economics by pushing useless product but hiring people to hire people to hire people, etc.
If healthy and risky circular financing deals look similar at first, how can investors or regulators tell when a circular deal has crossed the line into something dangerous...especially when it's off the balance sheets?
I think the real question in all sorts of financial setups is really “is the dollar actually worth a dollar”. So when company A invests in company B which buys from company A the actual question is whether the valuation of the investment and the valuation of the purchase is correct or are they getting high on their own supply
First cash/dollars are considered bad so they collapse in price. When the psychology shifts, suddenly the dollar climbs in value and itself bubbles over, causing a recession.
Not sure if you're referring to dollars as an alternative to other forms of cash (e.g., treasuries, money market funds, etc.) or as an alternative to other currencies (e.g., Euro, Yen, etc.). If it's the latter, then I think we've seen recessions and booms where the opposite happens, depending on the balance of payments situation.
Cash/dollars with respect to 10Y Bonds, Stocks, or other instruments.
When Cash becomes king, for whatever reason, it behooves the economy to sell of all other assets and buy Cash.
The closer to cash you get (1Y high grade commercial paper, 1Y Treasury, 3M Treasury, 1 month commercial paper, money market, savings account, dollars under a mattress...), the better you deal with such circumstances.
Observing how the US stock market is behaving this week (up-down-up-down...), I feel like many investors don't appreciate the distinction between good and bad circularity.
Part of the market issues this week are Korea imploding and Japan about to implode. Japan's bond rate moving up could mean the end of the carry trade. Korea apparently has many stock tickers but only two distinct companies behind them. This caused their market and their overly leveraged retail investors a lot of pain.
Eventually. I'm a bit concerned about it right now. I think SpaceX showed that the AI story is weaker than promoted. This will make people wonder about all the money going into it. If the bubble pops, the blow back on that particular index will be huge. Maybe dotcom bursting huge. But after 15 years QQQ move positive.
SpaceX is a really interesting one, tbh, especially in the context of circular deals... they're making $2b/month leasing their data centers to Anthropic and Google, which is a huge markup.
... but then again... if they are leasing the compute then it implies their own AI work isn't paying off?
Is HN really this fast!? Everybody already skipped the hard questions whether LLMs are intelligent? 'It' can already be commodified?
So how much time and effort it took from these companies? A couple of years and millions spent on PR? And LLMs are "intelligent" all of a sudden, is what everybody says nowadays?
Especially applicable for programmers: naming things is not only hard, it's also important, and can be very insidious. Remember, marketeers also name things. For example, naming the internet 'the cloud', some 15 years ago.
I can't stop thinking about a quote of one of my favorite authors (Theo Kars): "Progress can only be technical in nature, because knowledge is transferable and wisdom is not." [translated from Dutch]
Exactly this. LLMs are already some form of useful, and have some kind/level of intelligence. We don't need to get to AGI before AI has uses. It isn't a step function and it isn't all or nothing. Even if LLMs get no better than they are today, we will still have valid practical applications that use them.
Think of it like this: if you can offload a task to an AI model that requires reasoning or representing a nonlinear system, you are offloading intelligence.
The problem is that this doesn't distinguish LLMs from other forms of software! The whole point of computers is to offload tasks requiring reasoning, and they've always helped us understand complex nonlinear systems is that computers work through the hideous details.
More significantly: many major advances in scientific or mathematical programming were heralded as a thinking computer! 50's numerical scientific programming, 60's Lisp+ symbolic programming, 70's logic and symbolic algebraic programming, 80's unifying all this with encyclopedias and NLP like Mathematica - all of this is clearly useful and cool, all designed to offload intelligence... and, with modern eyes, we see plainly that not a single shred of intelligence is required to execute any of it. The AI researchers of yesteryear consistently made cool computer programs and consistently overestimated their progress at cracking human intelligence. It is plain as day to me that ANN researchers are making precisely the same scientific and philosophical mistakes as yesteryear's symbolics researchers, and even Alan Turing. This does not diminish from the coolness of the computer programs. But pretending these systems are intelligent, even "verbally" intelligent, is bad for society.
It's bad for computer science, too. None of the progress in LLMs or ANNs gets us any closer to making a robot as smart as a cockroach. I doubt any of us will live to see that milestone.
Whether inference hardware will be a commodity remains to be seen. We had twenty years of most servers being x86-64. That's commodity compute. Inference hardware isn't a mature architecture yet. There will be progress and obsolescence.
Excellent piece, but it does hinge on the idea that what we’re building is actual AI (still hotly debated), if it can be successfully commoditized like utilities (very unlikely given compute continuously evolves and the hardware cycles of datacenters relative to power plants or water filtration systems), and if such a healthy cycle - should it develop at all - survives the multiple decades needed to effectively provide ROI.
Which is an awful lot of “ifs” to be hedging a bet on, especially at nation-state scales.
Has a competent economist modeled the circularity of these deals ? [ numerically or analytically ]
It seems a healthy economy has a lot of wide circularity .. money circulating is a good thing, a result of a functioning market, tracking the flow of real goods / services. But large corps circulating paper 'self-deals' or debt-swaps seems like a bad thing - a creative accounting practice designed to pump up their stock price/valuation.
How can we _quantify_ the difference ? I guess it would need to match the cash / debt flows against the movement of actual goods and services ??
Not an economist, feel free to weigh in, suggest links.
you'd need to look at prior work associated with the Dot com boom and Enron. Economists rarely look into today's actual activity because America makes it nigh impossible to actually understand who owns what around the fringes; contracts arn't required to be publically disclosed and a bunch of other dark interests make it impossible to really know.
MLMs exist in the same murky waters and tread the same ephermal economics by pushing useless product but hiring people to hire people to hire people, etc.
Recently: "Nvidia, CoreWeave, and Nebius: Inside the Circular Financing of the GPU Boom" (io-fund.com)
https://news.ycombinator.com/item?id=48873836
370 points| 18 days ago | 182 comments
If healthy and risky circular financing deals look similar at first, how can investors or regulators tell when a circular deal has crossed the line into something dangerous...especially when it's off the balance sheets?
This has been bugging me quite a bit. I think the more "off-balance-sheet" the deal is, the shadier it gets... But it's really hard to tell early on.
I actually think Nvidia's deals have been above-board, generally speaking, because they are transaprent.
Less true for Meta and Terawulf.
I think the real question in all sorts of financial setups is really “is the dollar actually worth a dollar”. So when company A invests in company B which buys from company A the actual question is whether the valuation of the investment and the valuation of the purchase is correct or are they getting high on their own supply
A recession is a cash/dollar bubble.
First cash/dollars are considered bad so they collapse in price. When the psychology shifts, suddenly the dollar climbs in value and itself bubbles over, causing a recession.
Not sure if you're referring to dollars as an alternative to other forms of cash (e.g., treasuries, money market funds, etc.) or as an alternative to other currencies (e.g., Euro, Yen, etc.). If it's the latter, then I think we've seen recessions and booms where the opposite happens, depending on the balance of payments situation.
Cash/dollars with respect to 10Y Bonds, Stocks, or other instruments.
When Cash becomes king, for whatever reason, it behooves the economy to sell of all other assets and buy Cash.
The closer to cash you get (1Y high grade commercial paper, 1Y Treasury, 3M Treasury, 1 month commercial paper, money market, savings account, dollars under a mattress...), the better you deal with such circumstances.
Observing how the US stock market is behaving this week (up-down-up-down...), I feel like many investors don't appreciate the distinction between good and bad circularity.
Part of the market issues this week are Korea imploding and Japan about to implode. Japan's bond rate moving up could mean the end of the carry trade. Korea apparently has many stock tickers but only two distinct companies behind them. This caused their market and their overly leveraged retail investors a lot of pain.
Good point. Do you think the NASDAQ selloff will recover once Korea stabilizes?
Eventually. I'm a bit concerned about it right now. I think SpaceX showed that the AI story is weaker than promoted. This will make people wonder about all the money going into it. If the bubble pops, the blow back on that particular index will be huge. Maybe dotcom bursting huge. But after 15 years QQQ move positive.
SpaceX is a really interesting one, tbh, especially in the context of circular deals... they're making $2b/month leasing their data centers to Anthropic and Google, which is a huge markup.
... but then again... if they are leasing the compute then it implies their own AI work isn't paying off?
In short: I agree with you.
> commodification of intelligence
Is HN really this fast!? Everybody already skipped the hard questions whether LLMs are intelligent? 'It' can already be commodified?
So how much time and effort it took from these companies? A couple of years and millions spent on PR? And LLMs are "intelligent" all of a sudden, is what everybody says nowadays?
Especially applicable for programmers: naming things is not only hard, it's also important, and can be very insidious. Remember, marketeers also name things. For example, naming the internet 'the cloud', some 15 years ago.
I can't stop thinking about a quote of one of my favorite authors (Theo Kars): "Progress can only be technical in nature, because knowledge is transferable and wisdom is not." [translated from Dutch]
I'm pretty skeptical of the LLM hype, but I'd say it's fair to describe LLMs as displaying some form verbal intelligence.
Exactly this. LLMs are already some form of useful, and have some kind/level of intelligence. We don't need to get to AGI before AI has uses. It isn't a step function and it isn't all or nothing. Even if LLMs get no better than they are today, we will still have valid practical applications that use them.
Intelligence doesn't require sentience.
Think of it like this: if you can offload a task to an AI model that requires reasoning or representing a nonlinear system, you are offloading intelligence.
The problem is that this doesn't distinguish LLMs from other forms of software! The whole point of computers is to offload tasks requiring reasoning, and they've always helped us understand complex nonlinear systems is that computers work through the hideous details.
More significantly: many major advances in scientific or mathematical programming were heralded as a thinking computer! 50's numerical scientific programming, 60's Lisp+ symbolic programming, 70's logic and symbolic algebraic programming, 80's unifying all this with encyclopedias and NLP like Mathematica - all of this is clearly useful and cool, all designed to offload intelligence... and, with modern eyes, we see plainly that not a single shred of intelligence is required to execute any of it. The AI researchers of yesteryear consistently made cool computer programs and consistently overestimated their progress at cracking human intelligence. It is plain as day to me that ANN researchers are making precisely the same scientific and philosophical mistakes as yesteryear's symbolics researchers, and even Alan Turing. This does not diminish from the coolness of the computer programs. But pretending these systems are intelligent, even "verbally" intelligent, is bad for society.
It's bad for computer science, too. None of the progress in LLMs or ANNs gets us any closer to making a robot as smart as a cockroach. I doubt any of us will live to see that milestone.
Whether inference hardware will be a commodity remains to be seen. We had twenty years of most servers being x86-64. That's commodity compute. Inference hardware isn't a mature architecture yet. There will be progress and obsolescence.
Excellent piece, but it does hinge on the idea that what we’re building is actual AI (still hotly debated), if it can be successfully commoditized like utilities (very unlikely given compute continuously evolves and the hardware cycles of datacenters relative to power plants or water filtration systems), and if such a healthy cycle - should it develop at all - survives the multiple decades needed to effectively provide ROI.
Which is an awful lot of “ifs” to be hedging a bet on, especially at nation-state scales.