Saw that "BaZi" (Chinese astrology) ranked #2 on Qwen's annual prompt leaderboard. That surprised me — didn't realize there was such huge demand for this stuff. I don't personally believe in any of it, but figured I'd vibe code a couple apps with Claude Code just to see what

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

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

Since January, AI infrastructure announcements have added up to hundreds of billions in new CapEx. All concentrated among a handful of hyperscalers. But outside those few names, there’s a long list of regional and independent data center operators sitting on GPU purchase orders

Meta just raised $27B from Blue Owl Capital for its biggest data center project yet. That’s the new blueprint for DC operators: share the upfront cost. The same logic works for smaller operators. If you have steady clients and reliable power, you can grow through financing, not

Compute Labs was featured in Diginomica for transforming GPUs into yield-bearing digital assets, creating new financing options for data centers and new structured products for investors. Our approach pairs onchain transparency with off chain underwriting: historical

As the WSJ reports, Texas utility Oncor is fielding an interconnection queue, the waiting list for new power hookups, that runs into the hundreds of gigawatts. Many of those projects won’t get built. At Compute Labs, we connect capital with operators who have power, workloads,

xAI laying out plans for 50M+ H100-class GPUs underscores just how far AI demand is running ahead of supply. This gap drives higher lease rates and fuller utilization for deployed units. It’s why we see compute evolving into a financial asset, not just a tool.

Compute Labs was previously featured in Forbes for our approach to financializing AI infrastructure. Smaller and mid-sized data centers often lack the capital to meet outsized GPU demand. Our model provides financing so operators can acquire hardware with little to no upfront

The LA Times just featured Compute Labs. Why? Because GPUs are one of the most profitable asset classes for investors of the AI era. Read the full story ↓

Canada committed $300M to help AI companies pay for compute. We expect this to drive new leasing opportunities for operators and more asset-backed yield opportunities for investors. Compute Labs bridges the gap by providing the capital needed to procure and deploy this

First of many. With the help of @USDai_Official financing, we will turn capital into GPUs, lease them to operators, and return asset-backed yield to investors All while helping operators scale without dilution.

Been having investor meetings for @Compute_Labs and feel more energized than ever. Even tho some crypto-only VCs have passed, it actually further encourages us that we’re on the right path as our goals were not aligned. We are no longer building for just the "Web3 bubble"

Most common reactions I get from family offices and funds about @Compute_Labs: • "We didn’t know this was investable" • "How do I know it’s real yield?" • "What’s the risk profile?" Everyone believes in AI. What they don’t know is how to invest in it, who to trust, or what

For a long time, investing in AI meant buying equity in companies you didn’t control or hardware you didn’t understand. Compute Labs is changing that. We’re making AI infrastructure investable and the industry is starting to take note. Forbes spotlighted us as a way to access

GLM-4.5 runs on just eight H20s and is priced to compete with some of the cheapest open-source models out there. What matters is how models are being optimized around available infrastructure. Teams are thinking in terms of cost, energy, and uptime as opposed to raw benchmark

Compute Labs primarily serves two groups: 1. Investors seeking yield from AI infrastructure 2. AI companies looking for GPU compute without upfront capex

Most investors can’t access the GPUs powering AI. Compute Labs is changing that. We're featured in Investing News Network's latest on how AI + blockchain are reshaping infrastructure finance. Read the full piece: https://t.co/btLDTgpL1E

Google raised its 2025 capital expenditure forecast by $10B. Most of it will fund data center expansion and custom silicon. That means more demand for GPUs, energy, and floor space. The hyperscalers are racing to secure infrastructure ahead of the next wave of model training.