Karsten Wenzlaff, Advisor
August 26th, 2025
Karsten Wenzlaff is a Crowdfunding and Fintech Expert from Germany. He is Secretary-General of Digit[...]
BGC Launches Compute Infrastructure Markets
June 18, 2026 | NCFA Insight | Artificial Intelligence And Data, Capital Markets And Market Infrastructure
Pricing Access To Scarce AI Capacity
On June 18, 2026, BGC Group launched BGC Compute Infrastructure Markets, a new division focused on the secondary market for compute and memory capacity.
BGC is a financial brokerage and market data firm active in markets such as fixed income, foreign exchange, commodities, energy, shipping, equities, and futures. Its new compute business will operate inside the firm's Energy, Commodities and Shipping group and focus first on over the counter trading.
AI companies need huge amounts of computing power, but that capacity is getting harder to secure. It depends on chips, power, data centres, location, contracts, water, cooling, and timing. When something becomes scarce and expensive, buyers and sellers start asking market questions: who has capacity, who needs it, what is it worth, and how can risk be managed?
Compute Is A Resource Constraint
The United Nations University Institute for Water, Environment and Health report estimates that global data centres consumed 448 TWh of electricity in 2025. If data centres were treated as a country, that would rank 11th globally by electricity consumption. The same report projects data centre electricity use could reach 945 TWh by 2030, with AI workloads rising from roughly 20% of data centre electricity use in 2025 to 40% by 2030.
The report goes well beyond just the issue of power. Data centres' 2025 electricity consumption carried an estimated carbon footprint of 189 million tonnes of CO2e, a water footprint of 4.5 trillion litres, and a land footprint of 6,900 square kilometres. By 2030, projected data centre electricity use could be associated with 9.3 trillion litres of water and more than 14,500 square kilometres of land footprint.
Compute isn't just a cloud bill. It's tied to cost structures of electricity supply, grid connection, cooling, site location, water availability, hardware access, and local permitting. A buyer may need capacity in a specific place, for a specific time, with reliable delivery and known costs. A seller may have unused or contracted capacity that another participant needs. That is where a secondary market starts to make sense.
From Procurement To Risk Management
BGC says the new division is designed to support price discovery, risk management, liquidity access, and execution for participants exposed to AI infrastructure price risk. That statement alone treats compute like market exposure.
The buyers could include AI labs, enterprise AI teams, fintechs, model developers, governments, researchers, and companies that need access to GPUs or memory capacity. The sellers could include cloud providers, data centre operators, colocation firms, infrastructure investors, hardware owners, or firms with contracted capacity they no longer need. Between them is a market matching opportunity. Capacity is unevenly distributed, demand changes quickly, and long term infrastructure commitments are expensive.
See: Goldman Sachs Buys Québec AI Compute Platform QScale
Risk can show up in several ways. As AI demand grows, it's not just the technical issues. They are pricing, financing, and execution issues, too.
- A firm may need compute before a product launch and face higher spot costs
- A data centre operator may hold capacity without matching demand
- An investor may finance infrastructure before knowing whether demand will persist
- A buyer may lock in capacity but later need a different location, duration, or hardware profile.
Environmental Risk Becomes Market Data
UNU-INWEH argues that AI impacts should be measured across carbon, water, and land footprints rather than carbon alone. Investors and financiers should treat electricity, carbon, water, and land footprints as material risks for AI infrastructure portfolios and use comparable footprint metrics in due diligence.
That is where BGC's initiative becomes more interesting. A compute market may eventually need more than bids and offers. It may need location data, power source data, delivery terms, grid risk, sustainability metrics, water exposure, counterparty quality, contract duration, and settlement rules. The more compute resembles infrastructure, the more the market will need infrastructure grade information.
Canada's Compute Question
Canada has its own stake in this. The federal AI Sovereign Compute Infrastructure Program is designed to improve access to advanced computing for Canadian researchers and firms while supporting protected Canadian controlled capacity. This connects directly with NCFA’s earlier coverage of AI data centres testing B.C.’s clean power limits. The strategy is about access, data protection, intellectual property, domestic capacity, and private investment.
Markets like the one BGC is trying to build could affect how Canadian companies think about compute access. Public programs can help anchor capacity, but private AI adoption will still depend on price, availability, power, location, financing, and contract flexibility. If compute capacity becomes easier to price and trade globally, Canadian AI firms and investors will need to understand how that market affects domestic competitiveness.
It's still early days, but financial market infrastructure is beginning to form around AI's hardest operating constraint, and that's worth watching closely.
Talking Point
If compute capacity becomes a priced and tradable infrastructure market, will AI advantage depend less on model design alone and more on who can secure, finance, measure, and manage access to scarce compute?
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