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The Invisible Commodity

Training the latest generative-AI models costs billions, not millions. By mid-2025, leading labs reported that compute budgets had surged, driven by rising demand for high-performance GPUs and data-center scale-up. For example, CoreWeave signed a five-year contract worth US $11.9 billion with OpenAI in March 2025.

In September, CoreWeave strengthened its partnership with OpenAI for up to $6.5 billion. This brings their total deal to about $22.4 billion.

In essence: compute has become a bottleneck, and bottlenecks trade like commodities.

From Cloud to Market

What started off as dealing with cloud compute as a cost center is now turning into a way of looking at it as a tradable asset. Nvidia announced the new software platform Lepton, which will create a marketplace for cloud-based AI chip capacity, thus marking the formalization of compute-markets.

Meanwhile, the global “GPU as a Service” market is projected to grow from US $6.54 billion in 2024 to US $8.21 billion in 2025, and further to US $26.62 billion by 2030 (CAGR ~26.5%).

Running a training job is no longer just a tech operation—it’s a market signal.

“We used to buy capacity; now we’re trading it.”

Liquidity of Intelligence

Investors aren’t just backing software anymore; they’re backing compute infrastructure. Firms treat GPU clusters, training pipelines, and model-hosting farms as utility assets.

The global data-centre GPU market is predicted to grow from about US$119.97 billion in 2025 to US$228.04 billion by 2030.

In other words, compute throughput is becoming the next lever of market advantage.

The Speculation Layer

Compute markets are still thinly regulated but rapidly gaining complexity. For example, Nvidia’s deal to invest up to US $100 billion in OpenAI (and supply chips) shows how compute commitments now resemble infrastructure finance.

The question becomes, if you’re buying access to AI compute, what asset class do you own?

It sits at the intersection of technology, finance, and commodity markets.

The Human Parallel

Commodity trading desks once monitored oil tankers and futures curves. Today some firms are monitoring compute pipelines and GPU lease contracts.

The analogies are real: just as power plants once provided scale for industrial growth, model-labs now seek scale via compute liquidity.

Value is shifting upstream: from software features to underlying infrastructure.

What It Signals

If compute becomes a core asset class:

• Infrastructure providers (not just hardware vendors) gain strategic importance: marketplaces, leasing platforms, and secondary GPU markets become key nodes.

• Software companies face pressure: increasingly the differentiator is access to compute + data, not just features.

• Investors need new metrics: compute utilisation, petaflops per dollar, latency arbitrage become valuable indicators.

• Markets may shift: competition becomes about compute liquidity as much as talent or data.

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