Compute Labs

Solar energy became a bankable asset class only when the risk models matured. Before we had standardized data, solar was treated as venture risk. Once the yield became predictable, it became infrastructure. GPUs are crossing that exact same bridge. We now have the utilization data to underwrite these deployments as revenue-generating assets. That stability is what allows neoclouds to mature into the next group of Nvidia Cloud Partners.

Hyperscalers became enterprise partners by doing a few simple things very well: staying online, delivering consistent performance, and building trust over time. Neoclouds are now starting that same journey. The difference will be in how they fund growth. With revenue-share financing, GPU capacity is paid for upfront and repaid from future workload revenue instead of from scarce cash, equity or debt. That lets neoclouds spend their time on what actually wins enterprise demand: running reliable infrastructure at scale.

There’s roughly $7 trillion in AI infrastructure spending projected this decade. Most of it is still funded like CapEx: bought outright, financed privately, and rarely securitized. This presents an opportunity. Once compute can be tracked, valued, and underwritten like other asset classes, it becomes accessible to broader capital. Our focus is on building the bridge between institutions that understand infrastructure and neoclouds who create it.

From day one, our conviction at Compute Labs was that compute would evolve from an illiquid capital expense into a financial asset class. GPUs have always generated measurable cash flows but they were inaccessible to most investors and expensive for operators to scale. The only way to “invest” in compute was through public equities like Nvidia or cloud stocks, which don’t actually reflect GPU utilization or yield. That gap is what we set out to close. @Compute_Labs exists to make compute liquid — to help operators scale GPU operations without upfront cost, and to give investors direct exposure to the real asset driving the AI economy. Our bet has always been that once capital markets treat compute like energy or towers, the scale game accelerates. Compute as an asset class is arriving.

When governments start buying GPUs like energy reserves, it changes the game. As a result of the compute arms race, governments, operators and capital markets are fighting to secure the same scarce input. If compute is going to be treated like energy, the same financial tools must be in place: instruments that spread cost, transfer risk, and make scaling possible. That's why we built @Compute_Labs. To democratize participation in the infrastructure race of our century.

The next wave of real impact in crypto won’t come from echo chambers. Crypto natives needs to prove value outside of our bubble. We’re building for a broader capital base and it’s already opening doors.

A wave of open-source models is emerging in China, and they’re optimized for lower-cost hardware like NVIDIA’s H20. Z.ai’s new GLM-4.5 runs on just eight H20s, and its pricing undercuts DeepSeek and Moonshot by a wide margin. At @Compute_Labs, we’re designing capital pathways that adapt to this modularity so investors earn yield regardless of ecosystem allegiance.

GPU demand is showing no signs of stopping: • $5.2T for AI-ready data centers • $1.5T for traditional DCs All by 2030. We built @Compute_Labs to capture that value for investors. Turning GPU infra into yield-generating digital assets.