sebastien laye

US industrial capacity has been slipping for decades, and so have the prospects of our middle class and, to a meaningful degree, our national security. Data-center construction and continued US leadership in AI finally give us a chance to reverse that decline. The current push to block these projects anywhere and everywhere is effectively handing an advantage to China.

There is a great winner from the 11/4 election that few mentioned: FLORIDA. Now the largest pro business state, with a young and energetic governor. @ByronDonalds

France has found a new thing to tax: the gigabyte. On Friday, with 35 of 577 deputies in the room, the National Assembly voted 11 to 10 to charge 5.5 euro cents for every gigabyte sent to French users. Its Socialist author expects €10 billion a year, aimed at Google, Meta, Amazon and Netflix. The government opposed it. Finance committee chair Éric Coquerel, one of the 11, says he pressed the wrong button. The same debate floated taxes on companies' AI spending, tokens included. Those failed. The instinct did not. Germany is catching up. Digital minister Karsten Wildberger said Berlin must "tax the extremely high revenue from this technology," before backpedaling. Karin Prien, deputy leader of the governing CDU, agreed last week: "There is no other way." Chancellor Friedrich Merz, a skeptic, told Monday's digital summit: "We must, we want to become leaders." Maybe this is why Europe trails in AI. A new industry shows up, and the oldest reflex wakes first: send it a bill. Neither country has built an OpenAI. Both already know how to invoice one.

Join us in that revolution..Whether you want to hedge or invest, let us talk

France's economy unexpectedly shrank in the third quarter while inflation rose more than anticipated, piling pressure on the government ahead of further nationwide protests this weekend that are ​making it ‌more difficult to cut spending.

The Compute-Token Jevons paradox. @ClemDelangue @PeterDiamandis @JP_O @brivael @alexandr_wang @alexwg Intelligence is getting cheaper; people consequently consume vastly more of it. More models, more routing, more inference and more agentic workflows translate into higher aggregate GPU-hours unless efficiency improves even faster. This is why falling token prices can coexist with rising GPU rental prices. The fact that even the older H100 is holding/rising despite expected technological depreciation is an especially compelling scarcity signal.

CME and ICE moving toward GPU rental-price futures is a meaningful step. For the first time, there’s a path to price and transfer pure compute risk — not Nvidia equity, not power or real estate, but the hourly cost of the actual asset that generates AI revenue. From where I sit, this matters less as a pure trading opportunity and more as the missing financial layer for physical compute. We operate and invest in physical GPU capacity. That gives us direct exposure to the asset class: utilization, power, interconnect, and the real rental rate our capacity can command. At the same time, many of our clients — AI companies, inference providers, and enterprises — have revenues that are highly sensitive to the same rental price. When the hourly rate rises, their compute costs rise and margins compress. When it falls, data-center owners (including us) see revenue pressure. GPU futures, once liquid, let us couple the two sides of the same balance sheet. On the physical side, we can hedge the revenue stream of capacity we already own or have under long-term commitment. On the client side, we can offer structured hedges so their compute spend doesn’t blow up their unit economics. In between, the same instruments let us express a directional view on the asset class itself — long compute when we believe scarcity persists, or selective hedges when we think new capacity or efficiency gains will pressure rates — without having to buy or sell the underlying GPUs for every position. The practical reality is still messy. Benchmarks differ (sometimes materially), liquidity is nascent, and cash-settled contracts do not solve physical delivery or capacity-availability risk. That’s why the combination of owning the real asset and using the financial overlay is more useful than either in isolation. Physical compute gives you the actual GPU-hours and the operational edge; the futures (and OTC compute derivatives that will sit alongside them) give you the ability to manage the price risk and transfer it efficiently. This is the infrastructure trade that actually matches the cash-flow profile of the AI build-out. I’m interested in how others who own capacity or underwrite large compute commitments are thinking about integrating these new instruments into their risk and investment process.