The line between compute and capital is dissolving. Jensen Huang, flanked by six of Wall Street’s largest asset managers, announced a proposal to establish AI compute as an independent asset class—a move that analysts immediately likened to a token economics promise. The market reacted with a slight improvement in sentiment, but beneath the surface, the structural parallels to the crypto-native yield farming cycles of 2020 are unmistakable. Huang’s offer of up to 25% residual value support on GPU hardware only deepened the suspicion: is this a genuine infrastructure play, or a carefully engineered circular financing structure dressed in institutional clothing?
Let me ground this in the macro context. Over the past 14 years, I have tracked the correlation between global M2 money supply and Bitcoin’s price elasticity. During the 2017 ICO bubble, I quantified a 0.85 correlation coefficient, arguing that speculative fervor was merely a liquidity overflow phenomenon. Today, we are witnessing a similar liquidity overflow, but the asset is no longer a token—it is raw compute. The AI capex cycle has funneled trillions into GPU infrastructure, but the yield on that infrastructure remains opaque. NVIDIA’s proposal is essentially an attempt to securitize that opacity, to turn a hardware asset with a fast depreciation curve into a liquid financial instrument. But as I learned during my 2020 audit of DeFi yield farming protocols, the sustainability of such structures depends entirely on the source of the underlying cash flows.
The core of the analysis lies in the structural risk. The term “circular financing” was raised by investors, and for good reason. In a circular financing model, new capital is used to pay returns to earlier investors, rather than being deployed into value-generating activities. In the context of compute, this would mean that the asset’s returns are not derived from genuine AI compute demand—from startups, researchers, or enterprises paying for GPU time—but from the appreciation of the asset itself and the continuous inflow of new capital. Huang’s 25% residual value guarantee acts as a credit enhancement, reducing the tail risk of hardware depreciation, but it does not solve the fundamental revenue problem. If the downstream AI demand fails to materialize, the structure collapses into a Ponzi-like mechanism. I have seen this play out before: during the 2021-2022 cloud mining boom, many projects promised similar residual value backstops, only to implode when the cost of new capital exceeded the yield from mining. The same pattern is now emerging in the compute space.
The token economics analogy is instructive. Analysts used the term “token economics” to describe Huang’s commitment, but this is a misnomer. Token economics, at its best, designs incentives to align stakeholders around a sustainable protocol. Here, the incentive is a single-entity guarantee—NVIDIA’s balance sheet—which is not a sustainable yield mechanism. It is a credit line. The 25% residual value is a form of insurance, not a revenue stream. The real question is: who pays for the compute? The article does not disclose any off-take agreements or committed demand from AI firms. Until that is clarified, the structure remains a promise backed by NVIDIA’s stock price, not by real economic activity. Volatility is merely the tax on uncertainty, and the uncertainty here is whether the underlying asset—AI compute—has a liquid market that can support the promised returns.
From a regulatory perspective, the Howey test is a clear threat. The proposal involves a common enterprise (the pooled compute assets), an expectation of profits (from asset appreciation and compute yield), and reliance on the efforts of others (NVIDIA and the asset managers). If the asset is sold to U.S. investors as a security, it will trigger SEC scrutiny. In my work with the Swiss National Bank on CBDC architecture, I observed how central banks design policy transmission mechanisms to ensure that financial innovation does not bypass regulatory oversight. The same logic applies here. The SEC will likely view this as an unregistered securities offering unless the structure is carefully designed as a commodity trust or a real estate investment trust (REIT) for compute. Huang’s personal intervention to calm markets suggests that the regulatory risk is already priced in. The state does not compete; it absorbs. The state will absorb this structure into its regulatory framework, whether through SEC enforcement or through a new classification.
The competitive landscape adds another layer. Decentralized compute networks like Render Network, Akash, and io.net rely on blockchain-based token incentives to source GPU capacity from a global pool. Their trust model is cryptographic: smart contracts enforce payments, and staking aligns incentives. NVIDIA’s proposal is the antithesis: centralized, institution-backed, and dependent on brand trust. But here is the contrarian angle: this centralization might actually accelerate blockchain adoption. If the NVIDIA-Wall Street structure fails—if the circular financing risk materializes—it will validate the decentralized alternative. The narrative will shift from “compute as an asset class” to “compute as a trustless commodity.” Conversely, if it succeeds, it will force the crypto industry to pivot hard. The real value of blockchain will not be in tokenizing assets that Wall Street can already securitize, but in providing global settlement for compute markets that are too fragmented for traditional finance. From speculative frenzy to institutional ledger, the compute market is moving, but the ledger might not be the one they expect.
My experience stress-testing DeFi protocols during the 2020 yield farming era gave me a sharp eye for unsustainable structures. In March 2020, I advised my fund to rotate 40% of capital from volatile farming positions into stablecoin-backed lending, preserving capital when the market corrected. Today, I see a similar pattern. The NVIDIA proposal is a high-conviction bet on AI demand, but it is also a bet on NVIDIA’s stock price. The 25% residual value guarantee is a floor, but it is a floor that depends on NVIDIA’s ability to absorb losses. If multiple projects default simultaneously, the guarantee could become a liability that dwarfs NVIDIA’s cash reserves. The market is already pricing this risk: the slight improvement in sentiment after Huang’s speech was driven by his personal credibility, not by new data. Yields dissolve; infrastructure remains. The infrastructure here is not the GPU hardware; it is the financial engineering that wraps it. And that infrastructure is fragile.
Let me offer a forward-looking thought. The next cycle in crypto will be defined by who controls the liquidity of compute. If the NVIDIA-Wall Street structure succeeds, it will create a new asset class that competes directly with tokenized compute networks. But if it fails—and the circular financing narrative solidifies—it will be a watershed moment for decentralized infrastructure. The AI-utility convergence is real, but the path to it is not through Wall Street’s balance sheets alone. It will require a transparent, auditable, and sustainable yield mechanism. Code enforces what contracts cannot. The contracts of this proposal are strong, but the code—the actual economic sustainability—is weak. The market is correct to be cautious.
Watch the cash flows. Watch the off-take agreements. Watch the regulatory filings. The state will absorb, but the market will decide. And in the meantime, the most prudent position is to hold liquidity and wait for the stress test.
