From TechCrunch, August 13:
Nvidia announced this week that Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR were willing to commit up to $500 billion to build AI data centers. That eye-popping figure got a lot of the attention, but the bigger story is Nvidia’s effort to create a secondary market for aging GPUs.
To convince those big-name financial companies, Nvidia has agreed to guarantee, with its own money, that its chips used as collateral in these deals will retain their value.
Many have now commented on how unusual, smart, and dangerous this plan is. It is all of those things. The bond markets got so spooked that Nvidia CEO Jensen Huang took to X and business TV to better explain how Nvidia’s risk would be limited.
But underneath the financial maneuvering to fund AI data centers (and keep revenue for Nvidia flowing), is something, perhaps, far more interesting for startups and enterprises: Huang wants to ensure an ecosystem of used AI hardware flourishes, helping sustain demand for Nvidia hardware as it ages.
Specifically, Nvidia is promising that if GPUs used as collateral don’t retain their value as expected, the company will cover up to 25% of the difference. So, if a data center owner defaults on a loan and the lender must liquidate, but the chips can’t command the price the books say they should, Nvidia will chip in.
The dangerous part for Nvidia is that this creates something financiers call “wrong way” risk. That is, Nvidia’s obligations will grow as demand weakens. Should that happen, its revenues will likely be squeezed as well.
Still, the scheme is deliberately unlike the comparison to Lucent Technologies that some have been making. Lucent was the telecommunications equipment provider that rose and crashed with the dotcom bubble after lending its customers money to buy its wares.
The Lucent comparison is a shadow over Nvidia, Huang knows. And not an unfair one. Nvidia definitely has committed billions toward those who buy its chips, including frontier AI labs OpenAI and Anthropic, neoclouds like CoreWeave (the originator of using Nvidia chips as collateral), as well as Nebius, Firmus, and Lambda. And it has been working on another $750 billion worth of circular deals this summer, Bloomberg has calculated....
....MUCH MORE
Recently:
If interested see also:
September 2025 - "OpenAI in talks to lease Nvidia chips instead of buying them, Information says"
September 2025 - "The $4trn accounting puzzle at the heart of the AI cloud"
Nvidia's development cycle is currently around eighteen months, shorter than the magic number that Moore's Law observed for the number of transistors in an integrated circuit, two years.
I don't know how long Nvidia can maintain that pace but Mr. Huang is pushing to shorten the development cycle further, thus making earlier generations of the company's GPUs obsolete even faster, a point we were pitching as a positive earlier this year regarding leakage of state-of-the art chips from the Middle Eastern buyers to China:
As noted exiting from May 18's "U.S. Still Mulling Whether To Allow Nvidia To Sell One Million+ Most Powerful Chips To UAE":
On the one hand with that many chips floating around that part of the world there is no way to keep a bunch of them from ending up in China. On the other hand, Nvidia's development cycle is focused on releasing new, more powerful chips every 12 -14 months meaning the current smoking hot H100 chips will have been superseded by two cycles at the end of the contract period.The first point, that chips will get to China is borne out by the recent news that $1 billion worth of chips had been smuggled into China in three months after the export ban on the more powerful Nvidia chips.
note: the smuggled chips were not the ones destined for the UAE.
The second point is that the real technology transfer deterrent is in the pace of NVDA's development cycle.Although there are hiccups—most recently server racks overheating from the amazing amount of electricity flowing through the systems—the overarching goal is an almost metronomic rhythm to the development of new chips such that the H20s will be out-dated in under 2 1/2 years.If interested in a deeper dive into the pace of development see also:
May 6 - Goldman Sachs: "Tracking Trillions: The Assumptions Shaping the Scale of the AI Build-Out"
