AI is turning compute into a strategic resource, and the scramble to secure GPU capacity is starting to look a lot more like a commodity market than a traditional cloud-services business. As data-centre developers, lenders and energy companies try to price the next wave of AI demand, a new question is coming into focus: Can the industry build the kind of benchmark and hedging tools that already exist for oil, gas and power?

Host Ed Crooks is joined by Peter Keavey, Global Head of Energy and Environmental Products at CME Group, and Carmen Li, Founder and CEO of Silicon Data. Together, they explore the case for a futures market in GPU compute: a financial product designed to bring more transparency, liquidity and risk management to one of the fastest-growing corners of the AI economy.

Carmen explains how the market works today. Most users are not buying chips outright; they are renting access to GPU capacity by the hour, often through longer-term agreements with hyperscalers, neo-cloud providers and data-centre operators. That market is already large, global and increasingly active, but it remains fragmented and opaque, with prices varying by provider, chip type and contract structure, and much of the trading still happening through bilateral deals and requests for quotes.

Peter sets out the logic for moving from that over-the-counter world to an exchange-traded one. In his view, a GPU futures contract could do three things at once: reduce counterparty risk through central clearing, concentrate liquidity in a transparent order book, and create forward benchmark prices the wider market can use. The proposed product is financially settled against an index of spot prices, translating an hourly rental market into a standardised monthly contract that could eventually extend several years forward.

The bigger issue, though, is energy. Power is not the whole cost of GPU compute, but it is the most volatile variable input, which means a GPU hedge could eventually sit alongside gas and power hedges for data-centre operators, lenders and infrastructure investors. The discussion keeps returning to what that means for markets such as Texas and Virginia, where the AI build-out is already shaping decisions on generation, grid access and where capital should go next.

Both guests stress that this is still a young market, but already a volatile one. Rental rates have swung sharply as chip scarcity eases and then tightens again, while banks, traders and developers are trying to make long-dated decisions without a reliable forward curve. If this market develops the way Keavey and Li expect, GPU futures would not just serve traders: they could become an important signal for anyone trying to judge how durable the AI boom really is, and how much energy the system will need to support it.

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As the renewable energy and storage industries grow, and enter more hostile geographies, asset resilience is becoming an increasingly pressing concern. Developers, owners and lenders want to know how solar, storage and other facilities can increase their resilience to the growing risks of physical threats such as hailstorms and wildfires. The rush to add capacity to meet surging demand from new data centres is adding to the pressure, making calculations about the value of asset reliability increasingly complex.

In this special episode, host Ed Crooks speaks with Mike Perron, Renewable Energy Market Lead at FM, and Cassian Walker, Operations Vice President and Renewables Engineering Manager at FM, one of the world’s largest commercial property insurers. They explain how insurers are starting to build resilience into renewable project design far earlier in the development process, and why that has become a financing issue as much as a technical one.

For solar power, that means understanding the inherent physical risks such as hail, then deciding what combination of tracker systems, stowing technology and panels is right for the location. The same equipment that performs adequately in California or New Jersey can become a major liability in Texas or Arizona.

Cassian explains how modern trackers can tilt panels away from an incoming storm to turn a direct hit into a glancing blow. Mike contrasts a devastating nine-figure loss at one Texas site with a far smaller loss at another facility that successfully stowed. Those resilience strategies can work only if the system is designed for the local wind and hail conditions, and the performance of the equipment has been tested and verified.

From there, the conversation broadens to the economics. Insurance can account for a large share of a project’s operating costs, and lenders are asking harder questions about resilience before they finance new builds. FM’s case is that better engineering, better hazard modelling and earlier involvement from insurers and independent engineers can lower lifetime risk and improve financial performance, even if they raise up-front costs.

Today, renewable developers still often treat insurance as a late-stage procurement exercise, after key technology decisions have already been locked in. Mike and Cassian argue that that is changing, but the industry is still early in that learning curve. As renewables become more important to the power system, those questions will only get harder to avoid.

This episode of Energy Gang is sponsored by FM. As one of the world's leading commercial property insurers, FM combines engineering expertise, scientific research, and data-driven insights to help organizations understand, mitigate, and prevent loss before it occurs. From utility-scale solar and battery storage projects to thermal power generation, manufacturing operations, and other critical infrastructure, FM helps organizations build stronger, more resilient businesses.

To learn more about FM's engineering-based approach to resilience, visit FM.com. For additional insights on risk, resilience, and business continuity, subscribe to FM's Sound Policy podcast.

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