ARTIFICIAL INTELLIGENCE is changing many things about the modern world. Language is no exception. The word “compute”, for instance, used to be regarded as a verb. Today even grammar pedants grudgingly accept it as a noun, referring to the processing power generated by advanced chips. This new, non-verbal compute is the fuel of a technological revolution.
Demand for processing oomph has surged, and it is in short supply. About $360bn was spent on AI compute last year around the world, according to Boston Consulting Group, about 50% more than on copper. By 2030 BCG expects $2.3trn in transactions, roughly the size of the global trade in crude oil today.
For all its impressive size, however, the compute market lacks sophistication. The “hyperscalers” (a still somewhat arcane piece of IT jargon for a handful of giant cloud-computing providers including Amazon, Google and Microsoft) and frontier AI labs (such as Anthropic and OpenAI) make deals for tens or even hundreds of billions of dollars of compute. But such arrangements are unavailable to thousands of smaller firms. Buyers and sellers also have few of the derivatives that in established markets allow them to hedge their exposures and speculate.
The compute supply chain, then, looks ripe for marketisation. In the most simple form, chipmakers (like Nvidia and AMD) sell advanced AI semiconductors to equipment manufacturers (such as Dell and Hewlett Packard Enterprises), which package the silicon into racks and servers to sell to data-centre operators. Besides the hyperscalers, these include a few large “neoclouds” (one more linguistic innovation, denoting independent providers like CoreWeave and Nebius) and hundreds of smaller, less well-known firms.
The operators, in turn, rent out access to compute to customers for a fixed period, often several years. Customers range from the hyperscalers (which supplement their in-house compute with rentals) to startups running open-source AI models.
So far, so simple—in theory. In practice, things quickly get complicated. That is because compute is heterogeneous: different chips are sold in different servers, at different locations, for different time periods. Matching buyers and sellers is finicky. Every business that needs processing power has a “compute guy” (or several) buying second-hand capacity through an opaque network of middlemen. Many of these informal brokers, some shadier than others, tout their offerings through messaging apps like WhatsApp and Telegram. The whole thing looks less like trading a high-tech commodity and more like peddling illegal drugs.
In a market that is so fractured and immature, providers and purchasers alike prefer the safety of long-term contracts. On paper, these guarantee capacity to users and revenues for themselves. In reality, given how fast the technology and the market of AI are evolving, it is hard to tell how much such deals are worth: today’s buyers and sellers may not be around in a few years to honour their commitments. The surging yields on CoreWeave’s bonds highlight the uncertainty. Who will make a killing? And who may not make it at all? With lenders struggling to price risk, many smaller operators buying and renting compute face double-digit interest rates.
Fortunately for everyone involved, proper markets are being built around the compute supply chain. Some budding bourses, such as Compute Exchange, list GPUs (the increasingly familiar shorthand for graphics-processing units, AI’s silicon workhorse) by location, rental duration, price and availability. Titans of global derivatives trading, including CME Group and the Intercontinental Exchange, hope to launch their own compute futures. They have teamed up with startups pioneering compute indices, pegged to the rental price of popular AI chips. They await regulatory approval.
Rather than wait for exchange-traded derivatives to be approved, some compute-market pioneers are creating over-the-counter instruments of the sort common in credit and foreign-exchange markets. In March a startup called Anera Labs arranged a six-figure pilot trade for June with FalconX, a cryptocurrency broker, based on the future rental price of Nvidia’s popular H100 GPUs.
Luxor, another startup, offers operators swap-like contracts that guarantee fixed cashflow per server, per hour. If the spot price for compute is below the price agreed in the swap, the operator is paid the difference. If the spot price is higher, it is the operator that pays. This payment, explains Ethan Vera, one of the firm’s co-founders, may for example be made through a revenue-sharing arrangement. Luxor’s bespoke derivatives are modest for now, in the tens of millions of dollars, but the potential is vast.
As American exchanges await the thumbs-up from regulators, upstarts are getting off the ground elsewhere. From its exchange in Bermuda, Architect Financial Technologies, founded in 2023, offers futures in compute linked to the value of chips in America. The firm has also recently launched a platform where model-makers bid to provide “inference”—the useful output produced by an AI model in response to a prompt—rather than the underlying compute capacity. Brett Harrison, the company’s founder and boss, believes that inference may prove easier to standardise than compute.
Given the uncertainty surrounding the all-new market, it is attracting risk-takers. Many, including Mr Harrison, spent time in crypto, once the most compute-intensive enterprise out there, which attracted financial engineers trying to turn processing power into a tradable asset. Mr Harrison was president of FTX US, the American arm of the crypto exchange founded by Sam Bankman Fried, resigning two months before the parent firm’s bankruptcy amid fraud in November 2022. Luxor once primarily served bitcoin miners. One of Anera’s co-founders, Vishwa Naik, was a crypto entrepreneur. (CoreWeave and some other neoclouds also began life as crypto-mining businesses.)
AI’s finance bros
As opportunities grow, established financial companies are taking more of an interest. DRW and Jump Trading, two large market-makers, have both invested in Silicon Data, the compute-focused research firm with which CME hopes to launch its futures. Each market-maker has also assembled its own team to trade the new commodity. This month Jump Trading acquired Ito Markets, a compute-trading startup, whose founders will lead its efforts to break into the new industry.
David Olsen, Jump’s investment chief, points out that the market will grow more rapidly with the right institutions, such as clearing houses that limit the risk of blow-ups by ensuring that contracts are honoured by both sides of a trade. “We’re a long way from that,” he acknowledges, echoing many in this industry who think it may take years or even decades for the market to mature. “But”, he adds, “we want to be part of that discussion.”
His desire is unsurprising. After all, with surging demand for what remains a scarce commodity, and with so many businesses around the world exposed to it, the prize is enormous.
Mr Naik of Anera, who in his pre-crypto days was an oil trader at Reliance Industries, an Indian conglomerate, likens today’s moment in the compute market to the last days of the “seven sisters”, a cartel of Western giants that ruled the global petroleum trade between the late 1920s and the 1970s. Their chokehold was broken by nationalisation (in many exporting countries) and the rise of the open oil market (just about everywhere). As governments covetously eye the AI labs and ventures like Anera attempt to create a market from scratch, you do not need to squint too hard to see a similar future for compute.
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