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NewsStructural4 min readDisconfirmAI Editorial

Tomasz Tunguz: Nvidia's Three AI Capital Channels and the 112% Cash Flow Overdraft in the Last Quarter

Tomasz Tunguz likens Nvidia to the 'Central Bank of AI,' quantifying its three channels for injecting capital into the industry—$42 billion in M&A, $61 billion in equity investments, and $164.5 billion in lease guarantees—and notes that equity and M&A spending reached 112% of operating cash flow last quarter. He argues that tightening these equity checks, payment terms, or lease guarantees would slow AI construction, just as the Federal Reserve's credit tightening slows the broader economy.

2026-11-12 08:00 GMT+8

Tomasz Tunguz, Partner at Theory Ventures and former Google product manager, published a structured industry analysis on the day Jensen Huang resigned. His core argument is that over the past four years, Nvidia has acted as the 'Central Bank of AI,' and Huang's departure exposes this role to systemic tightening risks. This analogy is not mere rhetorical exaggeration; he provides specific figures and compositions for three capital channels, along with a triggering concern: last quarter, Nvidia deployed 112% of its operating cash flow into equity and M&A.

The scale of the three channels is as follows: $42 billion in M&A and technology acquisitions over the past two years (including Groq's $20 billion LPU technology license and engineering acqui-hire, Hugging Face's $12.93 billion acquisition, Poolside's $6 billion Model Factory license, etc.); $61 billion in cash-cost-based equity investments covering 229 private companies and 13 public companies (with disclosed stakes in OpenAI reaching $30 billion); and $164.5 billion in lease guarantees and cloud capacity backstop commitments (including up to $105 billion in guarantees for OpenAI’s Ohio data center leases and $6.3 billion in capacity guarantees for CoreWeave). Additionally, there are $63.1 billion in customer accounts receivable, with payment terms extended to as long as one full year.

Tunguz points out that in the last quarter (FY27 Q2), Nvidia's operating cash flow was $24.08 billion, while the cash deployed into equity and M&A was $26.98 billion, a ratio of 112%. He traces data across eight quarters: rising gradually from 4% in FY25 Q3 to 72% in FY26 Q4, and reaching 112% in FY27 Q2. Over the past four quarters combined, Nvidia generated $134.4 billion in operating cash flow but invested $75.8 billion into equity and M&A. This means the deployment pace has exceeded the organic cash generation capacity of a single quarter, requiring the consumption of existing cash reserves or debt financing to maintain. Under Huang's leadership, this unconventional deployment was viewed as a strategic bet driven by personal conviction and relationships; however, Tunguz believes that facing stricter board scrutiny, a more conservative successor might compress these expenditures.

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This is precisely where the 'Central Bank' analogy lies in its mechanism. Tunguz writes that if Nvidia tightens equity checks, payment terms, or lease guarantees, AI construction will slow down, just as the broader economy slows when the Federal Reserve tightens credit. He specifically emphasizes that these deals run on Huang’s personal conviction and relationships—Nvidia simultaneously acts as chip supplier, lead venture investor, anchor cloud customer, and lease guarantor across more than 200 startups. What sustains these multiple roles is not contract terms, but trust among founders. A successor must maintain these founder partnerships without the co-founder who built them.

Tunguz’s argument has a notable boundary. The 112% figure is a single-quarter peak rather than a sustained rhythm—the average deployment rate over four quarters is approximately 56%, and of Groq's $20 billion, only $2.94 billion fell in Q2 FY27 (the remaining $13 billion was paid in the 72% quarter of Q4 FY26). More importantly, Tunguz himself lists two alternative paths: First, custom AI ASICs already accounted for 28% of AI server shipments and 15% to 20% of accelerator revenue in 2026, with Google TPUs holding 65% of the custom silicon market, and 'leadership turbulence at a single-source supplier accelerates this shift.' Second, Meta, Google, and at least eight US startups that have raised over $100 million continue to train open-weight models, and they will now have to carry more of the American open-source mantle themselves. Furthermore, the $164.5 billion in guarantees represents 'contingent liabilities that do not appear on the balance sheet unless triggered,' but require maintaining substantial liquidity reserves. Therefore, a more accurate phrasing of the 'Central Bank tightening' narrative is: Nvidia’s capital channels serve as a liquidity supplement mechanism for the AI ecosystem; tightening them will raise financing costs for mid-sized operators dependent on its credit endorsement and slow project progress, but it will not eliminate the capital expenditure needs of hyperscale cloud service providers themselves.

Sources

  • tomtunguz.comTomasz Tunguz↗
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