One of the best books about trading is The Coffee Trader, David Liss’ account of the development of the coffee market in the seventeenth century Netherlands. Protagonist Miguel Lienzo recognizes coffee’s potential and hatches a scheme to crash the East India Company supply and then buy it up cheap before the market wakes up to exploding European demand. He moves his plan forward with a series of secret auction schemes and shaky partnerships across the taverns of Amsterdam. The legal ethics and bet sizing are a bit hairy. But a modern trader can see in Miguel the seeds of a new world, the trader’s world.
One nice moment in the book is an account of the benefits of caffeine. “Coffee,” a promotional pamphlet proclaims, “has all but destroyed the plague in England. It preserves health in general and makes those who drink it hearty and fat; it helps the digestion and cures consumption and other maladies of the lung. It is wonderful for fluxes, even the bloody flux, and has been known to cure jaundice and every kind of inflammation.”
Respect the copy, but the world didn’t get addicted to coffee because of its health benefits. The pamphlet saves its honest pitch for last: “Besides all that, it imparts astonishing powers of reason and concentration. In the years to come the man who does not drink coffee may never hope to compete with the man who avails himself of its secrets.”
Today, more than was the case with coffee, the man who does not use computation cannot compete with the man who avails himself of its secrets. No hedge fund can price second order effects faster than a 200-megawatt Jane Street data center. No lone genius can produce a better language model than Anthropic.
It’s not just that companies need compute to serve software applications. Many of our biggest companies, with highly scalable business models in things like search and learning, actually have “hyperscale” demand for compute that appears limitless.
Whereas the seventeenth-century market for coffee beans was measured in perhaps the equivalent of millions of modern-day dollars, the computing and cloud market today measures in the hundreds of billions a year, with trillions more committed to building it out. And whereas the coffee trade involved a vast colorful tapestry of characters from Turkey to Ethiopia to Indonesia to Amsterdam, the compute market concentrates into a shockingly small number of multi-trillion-dollar actors.
Magic Beans
Coffee is a simple commodity: coffee beans are grown in farms in places like Colombia or Tanzania, graded, bagged, and exported to global markets. The grading allows a buyer in, say, Hamburg, to price a lot of coffee he’s never seen before. Compute, meanwhile, is harder to even define. The thing that’s sold is not a commodity but a sellable capacity for work done on computers, purchased by the hour. When a startup “buys compute,” it is renting time on racks of specialized computers in massive data centers. You cannot put compute in a bag, store it for later, or sell it in a farmer’s market. No grade exists that tells a buyer what he’s getting.
Why is grading impossible? Compute is extremely nonfungible. Even identical hardware running identical software can produce wildly different performance results depending on a whole variety of subtle differences in how chips might be networked, powered, cooled, and used. Depending on a thousand hidden choices made by your counterparty, the same standard Nvidia H100 — the $30,000 gold standard chip that powers almost all of the AI consumed in America today— from Company A might be 38% more powerful for your workload than the same exact computer from Company B. Think less coffee beans and more Bertie Bott’s Every Flavour Beans. That is nonfungibility.
But nonfungibility is not in itself a fundamental barrier to commodity trading. As Dave Friedman argues in a Substack piece, “Can Compute Commoditize if it’s not Fungible?,” lots of commodities that people trade every day are nonfungible. Electricity is nonfungible at the local level. A watt-hour in a data-center town is different from a watt-hour in New York City. And both are different from a watt-hour next to a solar farm at noon on a sunny day.
Electricity markets solve this with a local reference price. Power is usually indexed at a “node” — roughly, a town or an area served by a plant. Big trading hubs branch into these nodes. Then there is other information that determines what power is worth in one place at one point in time: time, load, generation, transmission constraints, losses. To the extent that this other information is known or predictable, traders are willing to add “basis” to the reference price and trade. Factor in congestion and loss on a $10 reference and you get to $9.83, or $10.17, or whatever the case may be. And just as a crowd at a county fair, averaged together, can guess the weight of a cow almost perfectly, a richly traded market becomes very good at predicting and collapsing all those mysterious long-tail unknowns lurking in the GPUs. For example, an operator with higher incidence of thermal failures would have that factored into its output price like beans from a leaky warehouse. Over time, spreads narrow, and basis risk dissolves into prices.
The reference for GPUs could be something like the price of an H100-hour in a certain region, predicted via an AI trained on the entire history of transaction data available to the reference provider. Nobody needs all GPU-hours to be created equal; the market will learn what the difference is worth, then quote it.
Why would this be exciting? Commodity trading markets would allow people to do all kinds of interesting things that are prohibitively hard or impossible now. A startup could lock in next year’s compute at today’s price, the same way Southwest locked in cheap jet fuel before oil prices spiked. A neocloud that borrowed half a billion dollars to buy GPUs is making two bets: that it runs a good cloud, and that GPU prices will stay high. A market would let it pay someone to take the second bet off its hands, so its survival depends only on what it actually controls — the quality of its cloud. And a trader who thinks compute is mispriced could bet on that opinion without buying racks of computers. Today, none of this is possible.
Markets also make the system smarter. A mature market like oil can make sure no gas pump goes dry without any international coordination around a big event like the closing of the Strait of Hormuz. A compute trading market could do the same: route every bit of the scarce silicon our Taiwanese benefactors allot us towards its most economically useful work, priced and competitively weighted to perfection.
And the information generated by all these open prices and signals — in an otherwise-opaque tech stack — could produce a Hayekian explosion of open data, the mirror image of the advertising-driven Big Data explosion in the 2010s. That decade brought the signals previously hidden in the world into the clouds: every click and purchase and location ping was harvested and centralized, thus financing most current data center sites and catalyzing the success of the transformer and large language models. This decade could bring the knowledge currently hidden in the clouds back out to the open markets, where anyone can read the prices.
A Dutch time traveler who looked at our data-center-covered nation and heard about our mighty capitalist ways might have guessed that trading would be the way things developed from the start. He might have guessed we’d live in a society of compute bartering and bit auctions; after all, Amsterdam pioneered the stock exchange and systems where the trading of companies remained tightly bound to the trading of commodities. He might fail to understand how knowledge could get so locked up in clouds in the first place. But that trading world is not the world we live in.
