Right now, AI companies are paying a hefty premium for speed of GPU deployments. Neoclouds have customers waiting at the door, but traditional banks are too slow to finance the hardware. That delay creates an attractive opportunity for private capital. By stepping in to fund

We wrote the manual on investing in GPUs. I’m excited to share a whitepaper I co-authored with @WarWren_ and @TFOA_SFO Over the last year, I’ve spoken with hundreds of investors who understand that AI is huge, but don’t know how to touch it without taking massive venture risk.

The best yields often live in the gaps where banks can't move fast enough. Right now, a neocloud with signed enterprise contracts can still struggle to deploy, even with a loan from a traditional lender. That disconnect allows private capital to step in and capture a premium

AI stocks move on sentiment. Infrastructure yield moves on utilization. When you invest in GPUs with @Compute_Labs, you aren't betting on NVIDIA’s quarterly earnings call or interest rate cuts. You are betting on the secular demand for compute capacity. Whether the S&P is up or

Everyone is chasing AI exposure through public equities. But the real arbitrage is in the yield. The quieter, higher-yielding opportunity is securing the economic rights to the GPUs themselves. But yield only exists if the infra is running. We underwrite neoclouds to ensure

The 36% APY we’re seeing on our H200 is a premium paid for liquidity in a constrained system. Right now, neoclouds have end demand contracts but lack the credit history for traditional bank debt. That friction creates a window for private capital to step in and capture the

To a traditional lender, GPUs are often only viewed as hardware with a depreciation schedule. In the Compute Labs model, they function as income-generating infrastructure assets. Hardware book value provides crucial downside protection for the principal. However, the primary

We talk to neoclouds every day who have strong demand but can’t scale due to limited financing tools. On the other hand, investors tell us they’re interested but don’t yet have a framework to underwrite neoclouds and GPU returns. There is a fundamental mismatch. Providers can

More and more capital allocators are reaching out to ask about compute and the same pattern keeps popping up: they want exposure to AI, but are still learning how GPU revenue actually works. That’s where most of the opportunity is right now. Neoclouds have workloads ready to run

There’s a reason a live H200 deal on our platform is earning 36–40% APY: the industry has a GPU demand and financing supply mismatch. Neocloud operators can monetize GPUs quickly after they arrive, but most capital sources aren’t set up to fund these deployments. That gap

Investors can now get AI exposure through the economics of compute itself, not just through public equities. When GPUs run paid workloads, they generate hourly revenue. With clear utilization data and consistent demand, we can underwrite those deployments and structure the cash

We’re in an interesting moment: family offices and HNWIs are beginning to allocate capital to compute because the risk-return profiles are finally clear enough to understand. GPUs as an investment are early and early markets create strong yield opportunities. We’re currently

Family offices like assets with three traits: • Real utility • Cash flow tied to demand • Clear path to underwriting GPUs check all three. The challenge is translating GPU performance into a structure investors can evaluate. As utilization data becomes standardized, more

Neoclouds track a few core metrics to gauge how well their infrastructure is performing: • GPU Utilization: how often GPUs are running paid workloads • PUE (Power Usage Effectiveness): how efficiently the facility turns power into compute • Uptime: how consistently the site

A lot of alternative asset investors are getting AI exposure by buying company equities or funds tied to the big names. Very few are looking at the infrastructure underneath it, even though that’s where a large share of the economic value is being created. GPU deployments

Most neoclouds already track their utilization data. but lack the confidence to share it. They’re unsure how it’ll be used or how it might enable them to access more capital. But utilization data is the foundation of any financing conversation. Without it, investors can’t

Investing in AI doesn’t have to mean buying equity in AI companies. The infrastructure itself (GPUs running with verified utilization) produces significant revenues. When those contracts and cash flows are structured, investors can access exposure to the yield side of AI.

Every data center operator wants to scale GPU ops without massive CapEx. If you can show your GPUs are productive through utilization reports, uptime logs, and signed demand, @Compute_Labs can underwrite you for financing. And for investors, those same deals create a compliant

When investors ask “why GPUs?” I tell them: They’re the cash-flowing real estate of AI. Predictable tenants (workloads), lease durations (contracts), and location premiums (power). We make those economics investable.

Most of the $400B+ in AI infrastructure CapEx this year came from five companies. Outside those few names, small to midsize data centers are sitting on GPU orders and demand they can’t yet finance. That’s where financing structures like revenue-share models open the market to