Streetwise Professor

Perpetual futures are all the rage these days. Few things in the futures space have attracted as much attention as “perps,” in particular in the aftermath of a surge in crude oil perpetual futures trading on Hyperliquid over the weekend when the US and Israel unleashed their bombing campaign against Iran–a time when the major exchanges were closed. Below is a policy piece I prepared for ICE that examines the economics of perps. The basic conclusion: they are suitable primarily for a retail trading niche which represents a small fraction of incumbent exchanges’ business.

The Economics of Perpetual Commodity Futures

Introduction

Perpetual futures (“perpetuals” or “perps”) were developed initially to solve specific security problems in trading cryptocurrency (“crypto”). From their birthplace in the crypto markets, perpetual futures have moved into the trading of virtually every major asset class, including inter alia equities, equity indices, and currencies. And now they are making inroads into commodities like oil and corn. The Iran War triggered broader awareness of this latter trend: the volume of a crude oil perpetual futures contract traded on Hyperliquid soared to $1.7 billion with the onset of the war.[1]

This, in turn, has sparked considerable debate over how perpetual futures will affect the traditional conventional commodity futures markets, including ICE and the CME. Herein I present an economic analysis of perpetual futures. [2] The primary conclusions are:

• Due to their design, perpetual futures are ill-suited for market participants who account for a preponderant share of trading on incumbent exchanges. These are large commercial and financial entities who use traditional futures contracts to transfer risk. Crucially, the spot price mimicking structure of perpetual futures does not align with the risks that commercial hedgers manage.
• Instead, perpetual futures are tailored to small “retail” traders. As a result, perpetual futures are likely to be a niche product that is attractive to this clientele.
• The competitive impact of perpetual futures on commodity derivatives markets will depend on whether these contracts attract retail volume from incumbent exchanges, or instead disproportionately attract traders who would not trade on these exchanges.

Commodity Perpetual Futures

Perpetual futures emerged in the cryptocurrency market to solve a particular problem: recurrent thefts of cryptocurrency held by exchanges trading actual crypto (“spot exchanges”).[3] Introduced by BitMEX in 2013, perpetual futures avoided the security issues that had plagued spot exchanges because they were cash-settled instruments that did not allow, require, or countenance the delivery of crypto assets, or the custody thereof.

Incumbent crypto exchanges traded crypto assets on a spot basis, in much the same way that the NYSE or Nasdaq trade equities on a spot basis. Perpetual futures were designed to mimic spot trading but to avoid the security issues just described. Specifically, with perpetual futures, on a periodic basis throughout the day a perpetual futures exchange determines a price from an “oracle” that takes the prices from spot exchanges. The perpetual futures exchange then compares the price of its perpetual contract (for Bitcoin, say) to the price derived from the oracle. If the prevailing price on the exchange’s perpetual diverges from the oracle price (a measure of the spot price of the instrument), the exchange determines a “funding rate.” If the perpetual price is above the oracle price, the contract long pays the funding rate, and the short receives a funding rate payment.

This is intended to, and has the effect of, causing the perpetual price to track the spot price prevailing on spot exchanges. A perpetual price above the spot price incentivizes sale of the perpetual to capture the funding payment, thereby driving the price: conversely, a perpetual price below the spot price incentivizes a purchase of the perpetual, again to capture the funding payment, thereby driving up the price. These sales and purchases tend to drive the perpetual price towards the spot price, thereby causing the perpetual price to track spot prices closely. In this way, a perpetual futures trader can obtain exposure to the underlying spot price as could be obtained directly on a spot exchange, but without the security risks posed by trading on a spot exchange.

Market participants soon realized that perpetual futures offered other advantages.

First, unlike conventional futures contracts, perpetual futures have no fixed expiration date. Thus, whereas trading traditional futures requires those who wish to hold a position over a contract expiration date must “roll” these positions on or before the expiration date, this is unnecessary for a perpetual future. For example, with conventional futures, a long must sell the expiring contract and buy a later-expiring contract to maintain a long position, whereas a trader long the perpetual need not do anything. This economizes on transactions costs. 

Second, like futures but unlike spot transactions, perpetual futures incorporate leverage. For example, perpetual exchanges offer 10x, 20x, and sometimes as much as 50x leverage.

Third, like conventional futures, perpetual futures are easier to short than actual cryptocurrencies.

Fourth, perpetual futures offer 24×7 trading. In contrast, at present, conventional exchanges trade for less than 24 hours a day, and on weekdays.

As a result of these various features, perpetual futures have come to dominate crypto trading. A recent JP Morgan study estimates that perpetuals account for 90 percent of crypto derivatives trading (with conventional futures traded on exchanges like the CME representing the rest), and that perpetual futures volumes exceed spot exchange volumes.

Perpetual futures differ from conventional futures in other ways. Unlike conventional futures traded on ICE or CME, many perpetual futures are not centrally cleared.

Moreover, and relatedly, perpetual futures often have an “auto-liquidation” feature. As an example, on Hyperliquid, as on a traditional cleared market, the exchange sets an initial margin level and a maintenance margin level: these levels effectively determine leverage, with lower margins being associated with higher leverage. If the market moves against a trader and the loss drives the trader’s account equity below the maintenance margin level, Hyperliquid reduces or closes the position by engaging submitting an offsetting market order into the exchange limit order book, for example, selling when a long’s account equity is insufficient.[4] If the trader’s equity falls below two-thirds of the maintenance margin level, the position is transferred to a “Liquidation Vault,” which essentially internalizes the offsetting transaction.[5] In extreme circumstances, when the market liquidation fails and the Liquidation Vault cannot safely absorb the position loss, the exchange may trigger “Auto-Deleveraging,” whereby profitable positions on the opposite side of the market can have their positions reduced. This is effectively a “tear up” procedure.

Auto-liquidation mitigates credit risk in leveraged instruments, but can have destabilizing effects. For example, a large price increase likely results in large auto-liquidations of short positions, which tends to cause prices to increase further. In the case of perpetual futures, the price change will manifest itself primarily in the form of an increased spread between the perpetual price and the spot price it is intended to track, leading to large funding payments that require access to funding liquidity. Liquidity strains can exacerbate this mechanism.[6]

Commodity Perpetual Futures

Although perpetual futures developed in crypto markets to address crypto-specific problems, their market success has spurred the extension of the concept to a wide variety of other underlying instruments, most notably commodities such as crude oil. With respect to commodities specifically, perpetual futures differ in salient dimensions from crypto perpetual futures and even perpetual futures on other non-crypto-underlyings, such as equity indices.

There is one overriding reason for this difference: there are no transparent and liquid spot markets as there are for cryptocurrencies and many other financial assets. Thus, it is impossible to create commodity perpetual futures that track (as a result of funding payments) a spot commodity price, because for all practical purposes such a thing does not exist.

Instead, existing commodity perpetual futures are designed to track the prices of liquid futures contracts, such as CME NYMEX WTI Crude Oil Futures and ICE Brent Crude Oil Futures. With that difference, commodity perpetual futures operate similarly to the crypto perpetuals described above.

Specifically, a perpetual futures exchange obtains the price of the futures it is tracking from an oracle, and sets funding payments based on the difference between the perpetual futures price and the oracle price. For example, on 3 August 2026, Hyperliquid receives the price of September 2026 WTI Crude Oil Futures on an hourly basis from its oracle.[7]  As described above, it determines a funding payment based on the difference between the WTI perpetual futures price at that time and the Crude Oil Futures price determined from the oracle. Further, as already described, funding payments incentivize market participants to make transactions that tend to diminish the difference between the perpetual futures price and the price of the underlying futures contract.

As a result of this mechanism, the perpetual futures price tracks a futures price. But what futures price? The underlying futures contracts expire, meaning that the perpetual futures exchange must periodically shift the identity of the underlying futures contract whose price the perpetual future tracks.

There are many ways to do this. One way would be to shift at the end of trading of a particular futures contract, which would be 2:30 p.m. Eastern Time on the last trading day of an expiring contract, at which time the tracking would shift from the just-expired contract to the now front-month contract.

Due to the nature of commodity forward curves, where prices of adjacent futures contracts can differ substantially (a subject I return to below), such a mechanism would lead to abrupt changes in the perpetual futures price. Exchanges have therefore adopted other mechanisms that result in a smoother transition.

Hyperliquid, for example, has a five-day roll period running from the fifth to the tenth business day of the last month of trading of a contract.[8] On the fifth day, the reference price equals the price of the next expiring (front month) contract. On the fourth day, the reference price equals .8 times the front month price and .2 times the second month price. On each successive day, the weight on the front month contract falls by 20 percent, and the weight on the second month contract rises by that amount. This continues until on the tenth business day of the month, at which time the reference price equals 100 percent of the second month contract price. This mechanism effectively smooths out the effect of differences between front month and second-month underlying prices on the price of the perpetual.

The Economic Function of Commodity Futures

Futures markets serve two primary purposes: risk transfer and price discovery. Risk transfer relates to hedging: those who wish to reduce their exposure to the price of a particular commodity can take an offsetting exposure through the futures market, thereby transferring their risk either to a hedger with a mirror image risk exposure, or to a speculator who is willing to take on the risk in the expectation of earning a profit. Price discovery is a valuable byproduct of futures trading undertaken for hedging or speculative purposes. Those with private information (about fundamentals, for instance) trade on that information, for instance buying when their information suggests that the current price is too low. These privately informed trades tend to move prices in the direction of the private information that motivates them, thereby causing prices to reflect this information. The aggregation of private information in futures prices through this mechanism is valuable, because more informative prices guide better resource allocation decisions.

Different types of market participants have different reasons for trading. In commodities, large commercial entities’ trading is largely (though not exclusively) for hedging purposes. For example, commodity trading firms hedging the price risk of oil inventories or oil producers hedging the prices of their anticipated production are important market participants. Large banks (swap dealers) also use futures markets to hedge the exposure they incur when providing over-the-counter hedging instruments (e.g., swaps) to their clients (which could include firms like the oil producer just mentioned). Speculators now typically consist of large institutions, including hedge funds, family offices, commodity trading advisors, and financial institutions. In addition to large commercial and institutional entities, smaller entities such as individual retail traders also participate in the futures market.

Although imperfect for a variety of reasons, the Commitment of Trader (“COT”) Reports produced weekly by the US Commodity Futures Trading Commission (“CFTC”) provide a useful breakdown of futures trading activity by type of trader. These COT reports disclose the quantity of contracts held by several categories of traders, including Producer, Merchant, Processor, and User (“PMPU”); Swap Dealer; Managed Money; Reporting Traders[9] who do not fall in one of the previous categories; and non-Reporting Traders. Moreover, the COT breaks down positions by long and short, and in the case of Swap Dealers, Managed Money, and Reporting Traders, by spread positions as well (something I discuss further below).

Table 1 reports the shares for long positions for six major commodity futures contracts, and Table 2 does the same for short positions.[10] The main takeaway is that large entities dominate, with small traders holding 5 percent or less of the positions in all of the commodities except corn. Thus, the main function of the traditional futures markets is risk transfer among large commercial and financial entities. This is an important fact when considering the potential role and impact of commodity perpetual futures.

Table 1

Contract	PMPU	SwapDealers	ManagedMoney	OtherRpt	NonRpt
COFFEE C	21.84	12.78	29.90	7.03	5.35
CORN	25.11	16.48	14.83	8.53	8.19
COTTON NO. 2	23.28	21.09	13.43	13.40	4.99
NAT GAS ICE	49.44	8.49	13.77	3.48	0.60
NAT GAS NYMEX	13.40	10.33	12.06	3.39	3.57
WT PHYSICAL NYMEX	32.43	3.25	9.12	7.18	3.64

Table 2

Contract	PMPU	SwapDealers	ManagedMoney	OtherRpt	NonRpt
COFFEE C	41.49	20.52	4.70	6.23	3.97
CORN	41.79	1.09	15.14	4.60	10.50
COTTON NO. 2	24.84	4.45	35.12	6.66	5.14
NAT GAS ICE	24.59	48.51	0.77	1.62	0.29
NAT GAS NYMEX	15.31	2.37	13.21	9.25	2.62
WT PHYSICAL NYMEX	18.70	25.56	4.93	3.63	2.80

The Economic Role of Commodity Perpetual Futures

The structure of perpetual futures means that they are not well-suited to achieve the purposes of futures markets described above, at least for a very large proportion of market participants. Specifically, they are not useful to large commercial hedgers or large speculators (i.e., those supplying risk capital to the market). Instead, they are most suitable for small speculators. In servicing this niche, perpetual futures might undermine the price discovery function of established futures markets.

The most telling indication of perpetual futures’ trader clientele is contract size. The Hyperliquid WTI Crude Oil contract (linked to the CME Crude Oil Futures price) is for one barrel of crude oil, in contrast to the standard CME contract which is for 1,000 barrels. The CME offers smaller contracts, and these are explicitly focused on the retail segment of the market. [11]

Such small contract sizes are impractical for large hedgers, who often hold positions in the millions of barrels. For example, in 2025, the average PMPU long in Crude Oil Futures held a position of 10.3 million barrels, and the average PMPU short’s position was 5.5 million barrels.

These average positions dwarf the entire open interest and volume of the Hyperliquid contract. On the day of the writing of this, Hyperliquid’s Crude Oil 24 hour volume in its 20x leverage contract was a mere 1.03 million barrels, and open interest only 2.13 million barrels. Volumes and open interest for other Hyperliquid commodity contracts is similarly de minimis compared to commercial position sizes.

The very nature of perpetual futures also makes them ill-suited for commercial hedging. They are designed to track a proxy for commodity spot prices—the nearby futures price—but hedgers typically are not hedging spot exposures. Instead, they hedge specific production, inventory, transportation, or consumption commitments occurring at identifiable future dates. A producer expecting to market crude oil six months hence typically hedges using contracts maturing near the anticipated production date. A grain elevator holding inventories evaluates storage opportunities by comparing prices across delivery months.

This relates directly to an important feature of commodity futures markets that distinguishes them from financial futures: the fact that there is trading for numerous contract expiration dates, sometimes stretching years into the future. Although trading volume and open interest is largest for nearby contracts, trading volume and open interest for deferred months is material.

This is reflected in the fact that “calendar spread” trading is extremely active in commodity futures markets.[12] This is illustrated in Table 3, which shows the fraction of open interest accounted for by calendar spread trades in the five contracts analyzed above. These shares are very large. By design, perpetual futures cannot accommodate spread trades. Thus, they are of limited utility to the large numbers of swap dealers and managed money firms who trade large volumes of spreads.[13]

Table 3

Contract	SwapDealer	ManagedMoney
COFFEE C	517	834
CORN	1450	2026
COTTON NO. 2	1110	1516
NAT GAS ICE	27172	17902
NAT GAS NYMEX	4682	5929
WT PHYSICAL NYMEX	10284	5480

The auto-rolling feature of perpetual futures is also attractive to small retail traders. This feature reduces transactions costs: a trader can hold a position in the nearby without incurring the commissions and liquidity costs associated with making offsetting trades in an expiring and first deferred contract.

It bears noting that the auto-rolling feature of perpetual futures does impact the timing of cash flows as compared to a strategy of successively rolling conventional futures contracts. It is well known that the cumulative payoff to a collateralized position in the nearby contract that is rolled periodically equals the change in the spot price (measured by the nearby price) of a commodity from the date the strategy is initiated to the date it is terminated. A perpetual future generates the same payoff over an identical holding period. However, with a conventional rolling strategy, the cash flows at the time of the roll do not depend on the difference (spread) between the nearby and first-deferred contracts at the time of the roll, whereas the cash flow on a perpetual future does. As noted above, the perpetual price at the time of the roll is a function of both the nearby and first-deferred prices, and thus depends on the spread between them. If calendar spreads are large, that is, the market is in a large backwardation or contango, the perpetual price can change dramatically over the roll interval even if the underlying futures prices do not change.

This has important implications for margining and auto-liquidation. Auto-roll during a period of large backwardation or contango generates large perpetual price changes, which in turn generates large mark-to-market gains and losses, and large adjustments to margin balances. Since such changes can trigger equity shortfalls (especially in high-leverage perpetual futures), they can also trigger automatic liquidations even if the underlying futures prices change little. This is not the case for conventional futures.

In sum, perpetual futures are most attractive to retail traders, rather than the large commercial and financial firms that dominate conventional futures markets. This means that perpetual futures are competing for only a relatively small share of traditional exchanges’ customer base.

However, this could have implications for price discovery. The foregoing suggests that perpetual futures will fragment/segment order flow in much the same way as dark pools, payment for order flow, and other off-exchange trading mechanisms do in equity markets.

Since retail order flow is unlikely to be privately informed, such segmentation changes the composition of order flow in ways that can impede price discovery. Although at first blush it may seem that stripping out uninformed order flow should make futures prices more informative, the opposite is true. The subfield of finance known as “market microstructure” shows that informed traders trade less intensively when uninformed order flow declines. Therefore, such declines tend to reduce the informativeness of prices, i.e., to degrade price discovery.

The importance of this cannot be predicted with any certainty, especially given the recentness of the development of perpetual futures, but some factors can be identified. One factor is whether perpetual futures will primarily divert retail traders from traditional exchanges, or will instead attract new traders who would not otherwise trade on such exchanges. The larger the importance of diversion vs. attraction the bigger the adverse impact on price discovery. But if the main effect of perpetual futures is to attract new participants to the markets (due for instance to small contract sizes, or lower commission and account setup and maintenance costs), the effect on incumbent exchanges and price discovery on them will be modest. To the extent that diversion does occur, the relatively modest shares of trading accounted for by retail traders will limit the impact on price discovery.

Summary

Perpetual futures are the new hot thing in commodity markets. They have achieved prominence lately primarily due to a surge in oil perpetual futures trading during the Iran War. This attention exaggerates their importance, and their likely future place in the commodity derivatives landscape.

Why? Because their very design makes them unsuited for hedging of commodity price risk, which is the primary function of commodity derivatives markets. Specifically, by design perpetual futures track a spot price (or in commodities, a proxy for the spot price), but commercial hedgers are primarily exposed to price risks further out on the forward curve. Moreover, calendar spread trading is important in commodity derivatives markets, and spot-focused perpetual futures cannot support such trading. Moreover, existing perpetual futures contracts are far smaller than the contracts that are traded in large volumes on traditional exchanges.

Thus, perpetual futures are tailored to a specific clientele—small “retail” traders who account for a relatively small share of traditional exchange volumes. Although perpetual futures may divert some of this volume from traditional exchanges, they may also facilitate the entry of new traders who would not trade on traditional exchanges. The ultimate competitive effect, and the impact of perpetual futures on the performance of commodity derivatives markets, will depend on the relative importance of these two effects.

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[1] Will Canny and Al Boost, Iran war volatility is driving oil trading boom on Hyperliquid, says JP Morgan. CoinDesk (20 March 2026).

[2] I offer no analysis or opinions regarding the legal status of perpetual futures under the Commodity Exchange Act.

[3] “Perpetual Futures and the Illusion They Were Built to Escape.” John Lotian News, 22 June 2026.

[4] See https://hyperliquid.gitbook.io/hyperliquid-docs/trading/liquidations for a detailed description of the process. The exchange can profit or lose by trading out of the positions taken into the liquidation vault.

[5] Some perpetual futures exchanges do not first attempt to liquidate the position in the open market, but internalize all liquidations.

[6] Below I discuss how the nature of commodity perpetual futures can inherently lead to large price changes during “roll” periods even when underlying market prices are not volatile.

[7] I focus on Hyperliquid because its public disclosures of its methodologies and trading protocols are the most complete and detailed.

[8] This procedure mimics that of some commodity futures exchange traded funds.

[9] Each futures contract has a reporting level. Those holding positions in excess of the reporting level must report them.  Those whose positions fall below the level are not required to report.

[10] The numbers do not sum to 100 percent due to the exclusion of calendar spread positions. Due to their importance, I discuss calendar spread positions separately below.

[11] The CME also offers a “mini” contract for 500 barrels, and a “micro” contract for 100 barrels. The CME has recently announced it will launch a 10 barrel contract. See Financial Times, Oil market opens up as retail traders pour in (4 July 2026).

[12] A calendar spread trade involves simultaneous purchase and sale of futures contracts on the same underlying commodity but with different delivery dates. For example, in natural gas, a February-March calendar spread purchase involves buying a February contract and simultaneously selling a March contract.

[13] Commercial traders in the PMPU category also trade spreads in large volumes, and hold large spread positions, even though the COT reports do not report such positions.

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.

I have a confession. There is a skeleton in my family closet: my great grandfather was a socialist.

And not a barstool, lip service socialist either. A full-on, politically active one. He was an elector for Eugene V. Debs in the 1912 presidential election. The Socialist Party had sounded him out as a potential gubernatorial candidate for Ohio in that election.

He idolized Debs. My grandmother recalled how he would employ his skills as a former concert coronetist to attract crowds in Marietta, Ohio. He would set up on a downtown street corner and play his coronet with my 8 year old grandmother singing accompaniment. When a crowd gathered, he would launch into a speech lauding Debs and socialism.

I don’t know whether he converted anyone. I do know that he didn’t persuade my grandmother. She said “I adored my father, but even when I was 8 I knew socialism was for the birds.”

My great grandfather’s era was America’s first serious brush with socialism. We are now experiencing another. I sometimes wonder now what Frank Martin would have thought about Zohran Mamdani and his ilk. I am guessing one could say Mamdani is not my great grandfather’s socialist.

The most important distinction is that the core of the Debs era socialist support was manual laborers, whereas today’s DSA crowd consists mainly of lumpen bourgeoisie who have never performed physical labor in their lives, and shudder at the thought. Many are largely over credentialed, under educated intellectualoids infuriated at their marginal economic circumstances and the burden of student debt incurred to obtain unmarketable credentials. They are intellectual dilettantes, and living embodiments of Dunning-Kruger Syndrome, midwits convinced of their intellectual and moral superiority outraged that their social status and income falls far short of their lofty (but totally unrealistic) expectations. Rather than blame their bad choices, they attack the system that made those choices possible.

Schumpeter would understand.

In brief. 1912: swingers of hammers and machine operators. 2026: Starbucks baristas and NGO hangers on.

Another core 2026 socialist constituency is America-hating immigrants, of whom Mamdani is an exemplar. There is no better illustration of this than his 3 July speech, infuriatingly delivered while sitting at George Washington’s desk. It was a jeremiad (or more accurately a jihad-miad) against America and capitalism.

Mamdani blasts capitalism, foreign wars in America 250 address – while sitting at George Washington's desk https://t.co/yYGmUy9fmc pic.twitter.com/8HgzR3Vo9O

— New York Post (@nypost) July 3, 2026

Here is a man who achieved prominence in America, pissing on the hand that raised him up. Slandering America sitting at the desk of a far, far greater man on the eve of the country’s most important civic holiday. Indeed, on the eve of the 250th anniversary of the Declaration of Independence.

He should have been at Khamenei’s funeral instead. They have “death to America” in common.

And he’s not the worst. Take his wife. Please. Or Democratic Congressional primary winner Darializa Avila Chevalier, who drips hatred towards this country. I could go on.

The new socialists are also obsessed with “Palestine” and hate Israel. The new socialists are hardcore anti-semites (using “anti-Zionism” as a Trojan Horse). indeed, it is the main thing that differentiates them from the progressives whom they have vanquished in primaries. Old school socialists not so much.

Remember what Lenin said: “Anti-semitism is the socialism of fools.” And a la Lenin, the new socialists are truly fools.

As I noted not long ago, these socialists represent the energy in the Democratic Party, with Chevalier being just one candidate who vanquished extremely progressive incumbents. And other Democrats are bending the knee and kissing the ring, e.g., Kamala Harris reaching out to Mamdani. Or “Bill Clinton” posting a letter that mimics Mamdani’s screed:

My statement on America at 250 and the state of the country: pic.twitter.com/WyIzBFxfBq

— Bill Clinton (@BillClinton) July 4, 2026

I put “Bill Clinton” in quotes because he has been in public recently about as often as Mojtaba Khamenei, and when he has appeared he is clearly seriously mentally and physically diminished. So I strongly suspect that Hillary put this out in his name.

I could possibly take encouragement at this development. These people and their doctrine are poisonous. Their cause has a litany of abject failures stretching back more than a century. One may give my great grandfather a pass because the fatal defects of his doctrine had not been demonstrated by hard experience when he was expounding them. Today’s socialists have no such excuse. The history of socialism since 1917 is a trail of tears of poverty and death.

Certainly Americans–at least a majority thereof–will recognize this, and reject the new socialists outside of the lumpen bourgeois precincts of blue cities right?

But I am not so sure, especially since recent polls demonstrate widespread sympathy for socialism, especially among Democrats and the young, among whom a majority are supporters. The hysteresis of party loyalty, antipathy towards Trump, and the typical dynamics of midterm elections make it not improbable, and arguably probable, that in November Democrats will secure control of the House, and perhaps the Senate. In the event, the socialists will claim credit, and demand their due.

And that, my friends, is a dire prospect.

FT Alphaville ran a long piece on oil prices, and the forward curve specifically. I guess it’s good that a mainstream outlet delves into such an arcane topic, but it would have been better if they’d done better than a Fractured Fairy Tale treatment.

The piece does a service by pointing out that yes Virginia, there is a forward curve, and as a result there are different prices of oil for different delivery dates (not to mention for different qualities). Furthermore, unlike in say the equity or FX markets, there is no real “spot market” (i.e., market for delivery on the spot/immediately) for oil–or for most commodities for that matter.

The article also rightly recognizes that “the relationship between spot and futures prices in oil, as for most commodities, is actually quite complex.” Where it goes wrong is explaining that relationship.

It starts out by explaining the cash-and-carry (no) arbitrage relationship, and claims that it doesn’t hold in oil markets. Which is correct. But it gets the reason wrong.

It says: “In practice, this is difficult because the institutional structure of oil markets impedes the process where the future price is forced to this equilibrium level through arbitrage.” Further, “[o]ne problem is that physical oil is difficult to short.” And participants in the futures market are diverse: “Another is that the participants in the physical and futures market differ. While they use futures to hedge prices, producers, refiners and consumers need the actual oil. Financial investors use the futures market to simply trade price movements.”

In fact, none of these things explain the departure of the oil forward curve from the cash-and-carry (no) arbitrage model, and in particular the existence of backwardation, which is a facial violation of the model.

My teaching mantra is that futures markets price bottlenecks/constraints in transformation processes. And since the forward curve (and thus calendar spreads) provides intertemporal relative prices, the bottleneck in the forward curve relates to transformations in time. And it is a fundamental one: time travel is impossible.

Backwardation signals that the commodity is expected to be more abundant in the future than it is today. If it were possible, we would like to use a time machine to move the commodity from the future to the present. It’s not possible, however. The best we can do is to NOT move the commodity from the present to the future, which would be like carrying coals to Newcastle. That is, backwardation signals that we should consume the commodity today, not add to storage thereby moving the commodity to the future, and in fact draw down on inventories.

Indeed, it is never optimal for inventories of the deliverable underlying a futures contract to always be positive. Instead, sometimes a “stockout” should occur. That’s essentially what we are observing now in WTI, where Cushing stocks are at “tank bottoms.” (If a stockout never occurred, then there would be stuff that is produced yet never consumed. That is suboptimal).

Formal models describe the mechanism. My book is all about such models.

The book (and books and articles by others, including Wright and Williams, and Scheinkman and Schectman) derives forward curves from dynamic programming models that optimize storage and consumption decisions. And in my models. in particular, forward prices are derived from no-arbitrage principles. Namely, to prevent arbitrage, forward prices are expected future spot prices under an equivalent probability measure. So it’s incorrect to say “actual futures prices for commodities don’t follow arbitrage models.”

They don’t follow cash-and-carry arbitrage models, but such models are applicable only to pure assets–things that are always in positive supply like stocks or bonds–and with zero transactions costs. (Positive transactions costs in stocks and bonds can result in deviations from full carry too). And that’s the key point. Commodities are not pure assets. They are consumables, and as noted above, sometimes it is optimal for the commodity underlying a futures contract (e.g., WTI stored in Cushing, Oklahoma) to be in zero supply. The possibility for a stockout breaks the transactions underlying the cash-and-carry model, which implicitly assumes that inventories are always positive.

So all of the stuff Alphaville says differentiate physical from paper (futures) markets are utterly irrelevant in explaining the forward curve, and deviations from full carry. Instead, it’s all about the fact that commodities are storable consumables, not assets analogous to a stock or a bond.

Alphaville really gets into fairy tale territory when it commits the common error of confusing backwardation as the term is used by traders and market participants, and Keynesian backwardation. Backwardation as the term is used in markets means that a nearby price at time T is above a futures price at time T for delivery after T. For example, when the May 2026 price was above the December 2026 price during April.

Keynesian backwardation is when a futures price is above the expected future spot price at the expiration of the futures contract. This is totally different. An expectation is not a traded price. A futures price is a traded price. So Keynesian backwardation compares a tradable price to a non-tradable one. A backwardation backwardation compares a tradeable price to another tradable price. Moreover, whereas backwardation as used in the markets compares, say, a spot price (or nearby futures price) to a futures price with later expiration, Keynesian backwardation compares prices related to the same point in time–the futures expiration date.

Long story short, a market can be in an actual backwardation and not in a Keynesian backwardation, or in a Keynesian backwardation and not an actual backwardation, or different amounts of backwardations. The concepts are not at all related.

Perhaps the best illustration is that stock index futures can be at full carry (adjusted for dividends), and simultaneously in a Keynesian backwardation. In fact, that’s what you’d expect. Futures prices drift up in a Keynesian backwardation, rewarding the long for taking on risk. If the expected rate of return on the index exceeds the risk free rate (as is definitely true), the index will be in a Keynesian backwardation.

Alphaville does get the gist of Keynes’ argument regarding “normal backwardation” correct:

Keynes’ premise was that the participants in the supply chain have asymmetric risk aversion. Refiners, distributors and some consumers might not hedge, as they can pass on price fluctuations. In contrast, producers incur high upfront capital expenditures and bear greater financial risk from lower prices, encouraging hedging. This gives futures prices a downward bias, with producers willing to sell at a discount to the theoretical futures prices.

That is, there is a short hedging imbalance that puts downward pressure on futures prices (relative to expected future spot) to encourage speculators to go long and absorb the imbalance.

The problem with the “downward bias” conclusion that says that hedging imbalance determines the bias in the futures price is that it is based on an archaic view of asset pricing, and arguably an archaic futures market structure. Keynes implicitly assumed futures markets were not integrated with broader financial markets, and also wrote before modern asset pricing theory showed that the risk premium on a given claim depends on its contribution to the risk of some portfolio (e.g., the market portfolio in CAPM), or its correlation with aggregate consumption, or something of the like. In Keynes’ world, speculators in market X were undiversified, and hence their risk exposure and the compensation they demanded for bearing risk was related to the volatility of the futures price. With much demonized “financialization,” however, the marginal market participant is almost certainly well diversified (e.g., a hedge fund) and hence the risk premium he requires to take a position in market X depends not on price volatility but its covariance with his portfolio, or aggregate consumption, or whatever your asset pricing model of choice tells you.

But again, Keynesian backwardation is about risk premiums, and that’s a different issue from what causes an observable forward curve to deviate from the predications of the cash-and-carry arbitrage model.

Alphaville compounds the confusion by mixing up the concept of convenience yields with Keynesian backwardation: “reflecting the fact that producers are prepared to pay a positive insurance premium (the convenience yield) to protect against unforeseen adverse price movements.” Relatedly, it says: “The level of backwardation can be exacerbated by shortages of stock available for immediate delivery from an adverse supply shock — as in the oil market as during the Iran war — or a positive demand shock. This reflects the ‘possession’ value, as refiners and consumers hoard available stocks as a security measure.” This is basically a convenience yield story: the security provides a flow of implicit benefits.

“Convenience yield” is another of my pet peeves. It was a kludge devised by Kaldor to explain deviations from full carry. He reasoned the future price of stocks is less than the future value of the spot price by the amount of dividends. That is, a cash flow on the underlying asset depresses the futures price. Commodities don’t pay a cash flow, but Kaldor argued perhaps they pay some implicit benefit analogous to a dividend that similarly reduces the futures price relative to the spot price and the future value of the spot price (i.e., the spot price plus financing costs).

Very hand-wavy. And there has never been a coherent model that predicts the existence of such an implicit benefit from the optimization decisions of economic agents in equilibrium. In my book, I show that convenience yield only arises when it is essentially assumed (as a kludge) or in models with magical properties (e.g., inventory lowers production costs even if it is never consumed).

So convenience yield is bad enough, but to mix it together with something unrelated to actual backwardation is really ridiculous.

The article does identify some factors that are associated with backwardation. These are factors that feed into structural, dynamic programming models like those in my book. So Alphaville has identified the correlations, but is clueless on the causal mechanism.

What it comes down to is that the extreme backwardation observed during the hot phase of the Iran War is simply explained using the dynamic programming logic. There was a huge adverse short term supply shock. The supply shock was not expected to last forever. That made oil scarce in the short term relative to what was expected in the longer term, when the supply shock dissipated with the end of the war. Under these circumstances, it was optimal to draw down on stocks. The forward curve moved into an extreme backwardation to incentivize this.

This also relates to another issue that has been discussed lately: why is the 2026 oil shock different than the 1979 oil shock in terms of its economic impact? Because the shocks are different. 1979 represented a structural change in the oil market that was expected to last indefinitely. The 2026 shock was expected to be transitory, and indeed that is apparently the case. Old hotness: shortage of oil. New hotness: impending glut. Persistent shocks have very different effects than temporary ones, again as demonstrated in the book.

One last thing. Alphaville starts the piece with a story about Keynes allegedly having to take delivery on wheat futures, and measuring the King’s College Chapel only to learn that it was not large enough to hold the wheat. Keynes told this story, and it is clearly a joke. Delivery on the futures contracts was in store. That is, Keynes received warehouse receipts giving him ownership of wheat in a warehouse. He could have kept it there, as long as he paid storage. No sacrilege required.

But what is revealing is that Saint John Maynard was a welsher: “Keynes cleverly avoided the problem by objecting to taking delivery on quality grounds.” The problem wasn’t finding a place to store the wheat delivered to him. Keynes just didn’t want to take on the obligations (namely paying storage) that delivery entailed. Not clever: commercially disreputable.

And that wheat was ever delivered to Keynes was either the result of his choice or negligence. He could have avoided this problem by selling his futures prior to the delivery period. Did he choose not to do so, then regret the consequences? Or did he have a brain cramp and forget? Either way, he should have manned up, rather than weaseled.

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.