Streetwise Professor

It’s a story as old as time. An allegedly genius investor has a winning streak, which burnishes his genius image even more. He starts to believe it. Levers up. Then the wave he was riding crests and falls, the iron logic of leverage exerts itself. He becomes illiquid-if not insolvent-and either goes bust or gets bought out. And he’s no longer a genius, but an excuse maker.

One particularly famous example of this is Long Term Capital Management. LTCM had bona fide geniuses, Nobel Prize winners Myron Scholes and Robert Merton. Their genius apparently paid off for quite a while, with LTCM showing stellar risk-adjusted returns. Then, you know, stuff happens. Stuff like Russia effectively defaulting on its debt. Then your supposedly uncorrelated trades become suddenly correlated, and all start going in the wrong direction.

All that leverage that amped your returns now turns on you with a vengeance, and amps your losses. What’s more, your success has allowed you to grow to the point where you effectively are the market, which (a) moves the markets strongly against you when you try to liquidate some positions to meet margin calls and/or reduce leverage, and (b) pushes up the correlations even more.

Then, you are transformed from Golden Child to Systemic Problem Child. In LTCM’s case, it was so large that the Fed had to intervene to coordinate a liquidation of its positions into the tender loving care of the likes of Goldman.

Fast forward 28 years. The Golden Child and putative genius is Leopold Aschenbrenner, head of the oh-so-ironically named Situational Awareness hedge fund. The soi disant “Nostradamus of AI” implemented the picks-and-provisions strategy in the AI space, investing heavily in stocks of companies that provide the inputs to data centers and other AI-related activities. (SA also has private investments in AI firms like Anthropic).

He did time his entry right, and rode prices upwards. He (sound familiar?) amped his returns through extensive leverage (reputedly 4x). But-and there’s usually a but-the market peaked, and began to drop. And as has happened time and time again (it wasn’t just LTCM) the iron logic of leverage took over. Wrong way market moves beget margin calls on leveraged positions. Margin calls mean cash. Mark-to-market paper gains aren’t cash. So the imperiled fund was forced to sell positions to raise money-which only exacerbated the price declines.

Worse, the financial distress of a big position holder can’t be kept a secret. Evidently many others were apparently more situationally aware than Situational Awareness, and started to unload positions in stocks in which it was heavily invested. This front running exacerbated the price declines, and hence severely leveraged SA’s distress.

It happened so fast!, Aschenbrenner is no doubt thinking.

Sure did, (ex) Golden Child. Leverage comes at you fast.

And so do sharks. Jane Street, Millennium, and Citadel circled. In a single night, the last of these negotiated a deal to acquire all of SA’s publicly traded holdings at a discount estimated to be 10 percent.

The markets breathed a collective sigh of relief when news of Citadel’s purchase eliminated fears of a dumping of SA’s shares on the market: tech stocks rallied on the news.

Though no doubt his motives were purely self-interested, and are not as public spirited as J. P. Morgan’s intervention in the Panic of 1907, Citadel’s Ken Griffin did the markets a big favor. It is not from the benevolence of the butcher, the brewer, or the baker–or the hedge fund mogul–etc. (NB: I am not a reflexive Ken Griffin fan, as this old post demonstrates).

But as the saying goes, no good deed goes unpunished. Many-mostly Leopold fanboyz apparently-have argued that Griffin plotted SA’s demise (a reprise of CZ and SBF, maybe) by releasing a note predicted a Fed rate increase some time before the last Fed meeting. When the Fed did meet, and held rates steady-not raise them, mind you-the markets dropped, delivering the coup de grace to SA’s strategy that was already bleeding profusely. So per the fanboyz: See! Citadel caused this and then swooped in to take advantage!

Er, no. SA was already teetering. Citadel’s prediction-wrong, in the event-did not cause the final, fatal sell off. The Fed did.

For his own part, in a letter to investors Aschenbrenner rounded up the usual suspect-short sellers! It’s kind of pathetic. I have more respect for the natural gas fund operator who gave a blubbering, cringing apology when his widow maker strategy imploded.

If you are looking for other culprits here-beyond Aschenbrenner-look at SA’s prime brokers. Why did they supply so much leverage to back a poorly diversified strategy focused on a sector where sentiment had started to turn prior to the initial declines?

And if you want to do an It’s a Wonderful Life homage and wonder what would have happened if Ken Griffin/Citadel hadn’t been born, look at the disastrous unwind of another highly leveraged hedge fund-Archegos. Another example of an undiversified, highly leveraged fund caught in a doom loop when the markets turned against it.

In that instance, its prime brokers got together and agreed to assume the fund’s positions, and undertake a controlled liquidation. So far, so good. But one of them-Morgan Stanley-played “defect” in the prisoners’ dilemma facing the PBs, and sold $5 billion of Archegos’ holdings. Then the others jumped in and a full-scale fire sale occurred, leaving all the PBs badly singed. With Morgan Stanley (a la Margin Call) coming out best by getting out first (and not telling its counterparties what it knew about Archegos’ impending demise).

One firm-Citadel in this instance-didn’t face a prisoner’s dilemma. It-with the support of SA’s prime brokers-took the whole portfolio. Fire sale averted-to the markets’ relief (as noted above).

In sum, the Situational Awareness story is news, but not news. It is a story that has been repeated many times in many places. Leverage makes you look like a genius when the markets move your way. But with probability bounded away from zero, it will kill you when the market turns.

Much of the hysteria and euphoria about AI is implicitly based on the idea that AI will be free, or alternatively, that the marginal cost of AI will be close to zero. This is the basis for the belief that AI will replace everybody, rendering humans worthless, with all the benefits accruing to the creators of AI. (This is based on incomplete, not to say idiotic, economic reasoning, but I leave that for a later post). It is also the basis for the Always Look on the Bright Side of Life way of looking at the same prediction:

There won’t be dollars or scarcity of goods & services for individual consumption in the future

— Elon Musk (@elonmusk) July 17, 2026

But these predictions for ill or bliss are obviously, metaphysically wrong. AI is incredibly resource intensive. I just posted on power and water. But there is also the huge physical capital requirement–GPUs, with all the attendant capital costs they entail, buildings, wire, electronics, elaborate cooling systems, and on and on.

And as I’ve also posted on recently, businesses have been rudely awakened from their AI revery to realize that it is anything but free. It is damned expensive, actually, and will therefore require them to make discriminating choices on using it. “Fire everybody” is not a discriminating choice.

It seems there has been a ubiquitous category error in analyses of AI. It has been analyzed as if it is software. Software, once created, has effectively zero marginal cost. Hell, at one time the marginal cost was a floppy disk or CD: now it can just be downloaded for virtually nothing. Using software is also effectively costless. The cost of software is in its creation, and is largely fixed.

In contrast, although the creation of an advanced AI model is highly costly–that is, involves high fixed costs–using an AI model is also very costly. That is, the marginal cost of use is positive, because it involves consumption of scarce resources like power, water and GPUs.

Moreover, it is highly likely that AI is an increasing cost industry because the supply curves for AI inputs are upward sloping.

Software: high fixed cost, effectively zero marginal cost. AI: high fixed cost, positive and arguably high marginal cost.

All meaning that whereas people like Elon and other AI “visionaries” apparently believe that AI will invalidate the laws of economics–which are predicated on scarcity–this is a category error. Scarce resources are necessary to produce the AI that firms and individuals actually consume. The costs of these resources will drive the costs of such consumption, which will in turn lead to economizing decisions regarding where, how, and how much AI is used.

Almost all of those forecasting the future of AI (e.g., Elon) have ignored the constraining effects of this scarcity. Therefore, their prognistications–both dire and utopian–are worthless. Ignore anyone who does not place such scarcity front and center when attempting to discern how AI will reshape the world.

Like the fracking wars, but only much bigger: the current war on data centers. This confict has achieved some notable results, including an outright ban on new data centers in New York, and restrictions even in Texas.

Much of the opposition is organic. Some is astroturfed, and traceable back to China: it provides fertilizer for even of some of the organic growth. As with all these things, a lot of the rhetoric and analysis is fundamentally confused, but there are important economic issues here that deserve serious consideratoin.

First among these is the impact of data centers on power prices. The industry and its backers have pushed back with studies claiming that the impact of data centers is benign, or actually beneficial. For example, an Institute for Energy Research policy paper does a cross sectional analysis of state electricity prices and concludes that states with the highest concentration of data centers do not have higher electricity prices, and that data center penetration is only weakly positively correlated with power price increases.

This is a superficial, not to say stupid, analysis. With respect to the cross section, data center siting is endogenous: developers tend to site in lower cost areas. With respect to time series, a state level analysis is obviously too crude. Furthermore, the analysis does not take into account capacity utilization.

Furthermore, it is based on EIA data on “Average Price of Electricity to Ultimate Customers by End-User Sector.” These are effectively regulated retail rates that roll in historical capital costs, that are effectively fixed costs, meaning that retail costs are based on average costs. As the authors state: “Electricity systems have large fixed costs. If demand increases, those fixed costs are spread over more kilowatt-hours, reduction average costs.”

The problem is that these are backwards looking measures, and that when demand starts pushing towards capacity, those declines in average cost will stop abruptly and be replaced by sharp increases. As Herb Stein famously said, “If something cannot go on forever, it will stop.” And we are at–or even past–the stopping point. Certainly, given projected data center growth, if the stopping point hasn’t been reached, it will be soon. Meaning that hyperscaling will drive up power prices, unless power producing and transmission capacity is increased commensurately.

It has been recognized for some time that the US electricity generation and transmission system is becoming dangerously overstretched. New capacity–reliable capacity (more on this below)–is essential. Data center growth will only stretch the system more.

In the short term, electricity supply curves–the marginal cost curve, which is what is really relevant for pricing–become vertical, or close to vertical. Additions to capacity push out the supply curves. Those additions are vitally needed.

Want evidence? PJM–the Independent System Operator that manages the power grid in the region where much of the largest data center growth has occurred–uses a capacity market to incentivize investment sufficient to meet power demand. Roughly speaking, PJM determines how much capacity is needed to meet anticipated demand, and holds an auction for that quantity. High capacity prices are an indication that capacity is scarce.

Here are the last 5 PJM auction results:

Delivery Year	BRA Held	RTO Clearing Price ($/MW-day)	Change

2022/2023

2021

$50.00

–64%

2023/2024

2022

$34.13

–32%

2024/2025

2023

$28.92

–15%

2025/2026

July 2024

$269.92

+833%

2026/2027

July 2025

$329.17*

+22%

Oh, 2026/2027 doesn’t look so bad, right? Er, the price hit the FERC-imposed price cap. God only knows what would have cleared the market.

What should you be thinking reading this? Here’s an idea:

Looping back to the study mentioned earlier, the days of declining average cost are over–unless capacity increases to accommodate the higher demand.

The long run effect of data center-induced demand growth on prices depends on the long run supply curve, which depends on a variety of factors, including technology, capital costs, the long run energy supply curve (especially the long run supply curve for natural gas)–and policy.

All but the last of these are largely out of the hands of policymakers. So what policies can make the long run supply curve as flat as possible given these other factors?

Perhaps the most important policy lever is to incentivize efficiently investment in “behind-the-meter” capacity, i.e., vertical integration of new capacity and data centers that will consume the power it produces. This kills a couple of birds. First, it adds to generating capacity. Second, it doesn’t add to the demand for transmission. Given that transmission is the main vulnerability of the grid at present, this is an important consideration.

Some months ago, I analogized an xAI (now SpaceXAI) investment in gas generation co-located with its Memphis data center to mine mouth coal generation plants, and argued that transactions costs considerations provided an incentive for such vertical integration and coordinated co-investment. Those economic considerations apply with data centers, but the electricity market is highly regulated at the state and federal level, and those regulations can affect the incentives for BTM investment.

The crucial issue to provide the appropriate incentives to data center developers. One of the enduring economic lessons that I have learned (from my high school economics teacher, no less) is that a maxim of business success is to “externalize costs, and internalize benefits.” If the costs of connecting a data center to the grid, or the costs of transmission, or other factors that determine the opportunity cost of a BTM investment do not reflect actual costs, developers will have too weak an incentive to vertically integrate because they can externalize their costs. The opportunity cost to colocating generation with a data center is the cost of electricity drawn from the grid. If data centers do not pay the full cost of doing so, they will invest too little in building their own generation.

Data centers don’t want to pay high costs for power, obviously. But if their consumption from the grid is subsidized, they will consume too much from it, and build too little behind the meter capacity.

So, as always: Get the prices right. I say again: Get the prices right. Unfortunately, the politicization of electricity rate setting means that seldom happens.

Some of the biggest subsidies for data centers come from tax benefits granted by states. Just stop. Please. And if you can’t break the habit, limit the subsidies to generating capacity.

The other big policy change that can mitigate the impact of data centers on power prices is to terminate, with extreme prejudice, the monstrosity of “climate crisis”-driven laws and regulations that severely distort incentives to invest in generating capacity. These penalize investment in fossil fuel generation and and encourage investment in renewables. Absent subsidies, renewables are not cheaper than conventional generation. If you want cheaper electricity-where you strip out subsidies in determining cost-reverse these destructive policies.

This is particularly important when one considers that price is not the only relevant consideration here: reliability is too. Greater renewables penetration reduces reliability, all else equal. When you consider the true cost of power, which includes the cost of service interruptions (which can be extreme) and the cost of lower quality power, reliability is an important consideration that has been largely ignored in the force feeding of renewables onto the grid.

Ironically, the unreliability of a grid with a decidedly suboptimal generation mix may encourage behind-the-meter investment. Data centers need reliable power. If they can’t get it from the grid, they’ll decouple from the grid. But there can be too much BTM investment just as there can be too little. Both result from getting the prices (and incentives) wrong.

Some states have responded to impending overwhelming of the grid by data centers in a predicable way: by restricting or stopping altogether data center development. In the case of states like New York this response was foreordained by their previous perverted policy choices which were largely driven by climate hysteria. If they want a long run fix, they need to jettison their hysteria-induced policies.

Will they? As if.

This post has focused on power. Another sensitive issue is water. I don’t know much about the technical issues involved here, so I’ll just make one observation. Markets for water don’t exist, for all practical purposes. It’s almost certain that the prices are wrong. Meaning that it is almost certain that data center water consumption is inefficient. Knowing that doesn’t require knowing anything about the technology.

Unfortunately, it is necessary to remember Chesterson’s Fence in this context. That is, to ask “why aren’t there markets for water?” The answer is ultimately a political one, and as a result hopes for a market-based water pricing mechanism are in vain. Meaning that concerns about data center water consumption are likely to be addressed with a meat cleaver–no data centers!–or not at all–go ahead, build what you want! The discriminating, balancing decisions about water consumption that would occur when water is at least remotely priced correctly will therefore not occur, and the water issue will become a bone of political contention.

In sum, the correct approach to the data center war is Econ 101: GET THE PRICES RIGHT. The prices of connecting to the grid. The price of transmission. The price of energy. The price of reliability. The price of water.

And that’s why I am pessimistic about the ultimate outcome of this war. The products involved-power and water-are highly regulated and highly politicized. Regulation and politicization are inimical to getting prices right. Regulation and politicization were not imposed by aliens. They are the equilibrium outcome of political processes that are unlikely to change. Furthermore, the political salience of energy prices in an era in which “affordability” has heightened political valence means that pricing perversities are likely to get worse, not better.

Sorry to break it to you. But you probably didn’t come here to get sunshine blown up your backside.

Palantir made a long post on X regarding “sovereignty” from AI:

Our thoughts on the importance of AI sovereignty.

1. Your AI sovereignty dictates your institution’s future. Sovereignty is the precondition for choice. Relinquishing sovereignty transfers the future choices of your institution to others, who are likely to exploit it for their…

— Palantir (@PalantirTech) July 1, 2026

The post is a little esoteric, and requires an almost Straussian reading, but the basic theme is clear: protect your IP from prying AI. Fools and their IP are soon parted. This is especially true if you are lured by the supposed cheapness of Chinese models like DeepSeek.

Palantir’s Alex Karp was more straightforward in an interview (see especially starting around 9:20):

BREAKING $PLTR Dr. Karp Full interview CNBC@PalantirTech Dr. Alex Karp new $NVDA Deal

Palantir is profiting from Tokenmaxxing, because AI Labs can't deliver the value they charged customers.

Palantir product is 5 years+ ahead.

We deliver the best products to warfighters and… https://t.co/qtlbrSrcp5 pic.twitter.com/ooglNRobQL

— Mike (@MikeLongTerm) July 1, 2026

Karp is essentially warning against model lock-in, more than having your data scraped and used for training: there are contractual safeguards for that. This brings to mind the interoperability debates involving operating systems and applications (e.g., Office) in the late-90s and early-00s. But the issue with AI is much deeper, given the complexity of the models. Palantir is offering interoperability. But won’t that get you locked in to Palantir?

One area where I part ways with Karp is over pricing. Karp criticizes pricing AI on a usage basis, i.e., through charging for tokens. Instead, he envisions deals in which the frontiers get a share of he value created by their AIs.

Great thought. But such contracts require measurability and verifiability to a third party (a court). Maybe this would be possible for, say, a drug created by AI. You can measure the sales of the drug. But for something like improving the efficiency of an internal business process, measuring the savings and perhaps more importantly proving the value to a court (in the event of a dispute between the parties) is likely impossible. So for many of the AI use cases, such contingent contracting is economically infeasible.

And even the drug case is hard. How do you verify the portion of a company’s AI that is attributable to that project? Companies would have incentives to try to attribute AI usage for other things to the project.

Charging for tokens reminds me of the old case of IBM requiring users of its machines to buy punch cards from IBM. It was a way for IBM to price discriminate–extracting more value from intensive users. IBM couldn’t measure the value an individual computer user captured–which basically was determined by where the user’s demand curve was located–but figured that more intensive users had higher demands and more surplus to extract. By tying cards and computers, and pricing cards at above marginal cost, IBM was able to extract more surplus from the bigger demanders. That is, it could price discriminate.

Charging for tokens works the same way. Indeed, the price discrimination is more subtle, because of the different pricing levels and token restrictions based on access to different models, usage intensity, etc.

The industry is new, and changing rapidly. No doubt pricing models will change as well. There will be attempts to implement Karp’s vision, and some of those will work, but it is not likely to be the default pricing mechanism. Instead, some sort of token-based pricing, likely tweaked from its current implementation, is likely to remain in place. This is especially true for non-siloed, general business purpose uses of AI.

The biggest innovations will occur on the buy side. I’ve referred recently to some of the sticker shock that AI users had experienced, and how they are cutting back on AI usage. Meta (surprise, surprise) was arguably the biggest idiot.

Facebook (Meta) burned through an astronomical 60 to 73 trillion AI tokens in a single month—costing an estimated $221 million—because employees intentionally wasted them to climb a gamified internal leaderboard. Rather than a software glitch or an accidental AI loop, this massive token burn was the result of a corporate culture trend known as “tokenmaxxing.” [1, 3]

To encourage its 85,000 employees to adopt AI tools, Meta launched an internal, voluntary dashboard called “Claudeonomics” (or Clawonomics). [1, 4, 5]

• The Metric: The system ranked employees solely on the raw volume of AI tokens they consumed.
• The Incentive: Meta mistakenly treated high token usage as a proxy for employee productivity. Top users were publicly showcased on the intranet and rewarded with gamified corporate titles like “Token Legend” and “Cache Wizard.”

Because token consumption was tracked as an “input metric”—measuring how much AI you used rather than what you actually accomplished—employees figured out how to fake productivity:

• Ghost AI Agents: Engineers left autonomous AI agents running continuously in the background for hours to execute completely pointless, infinite research tasks.
• Massive Waste: The top individual “Token Legend” at Meta managed to burn 281 billion tokens in 30 days entirely on throwaway work—enough data to reproduce the entirety of Wikipedia more than 33 times over.
• System Strain: The unchecked AI overuse became so extreme that it even caused internal system outages and tech disruptions (SEVs) inside Meta.

Once details of the massive, multi-million dollar waste leaked to the public, Meta quickly shut down the leaderboard. The company has since clamped down on “tokenmaxxing” by introducing a centralized dashboard called AI Gateway, which enforces strict spending controls, real-time tracking, and automated alerts for unexpected token spikes.

Incentives matter! Who knew?

Apparently Meta didn’t, despite the fact that its whole business model revolves around manipulating its users’ incentives.

Other companies (e.g., Uber) have experienced similar, though less extreme outcomes due to incentivizing token usage. They are now adapting.

Which means that the demand curves facing AI firms will change dramatically, as companies experiment with different internal incentive structures–the design of which will be anything but trivial, for similar reasons to those cited above. How can a company determine the value a given employ can generate with AI, and design a system that incentivizes realization of that value rather than mindless waste.

As companies experiment, AI firms will adjust their pricing. Which will induce companies to modify their incentive structures. This will take a long time before things settle down. There will be learning on both sides, and that learning takes time.

No doubt companies will use AI to try to design incentive structures. And the AI firms will use AI to adjust their pricing. The use of AI might accelerate the learning process.

But at the end of the day I predict some form of usage-based pricing, with structures that incorporate a substantial degree of price discrimination. A long way from IBM and punch cards, but it rhymes.