Jensen Huang’s departure leaves more than Nvidia’s leadership in limbo: it also puts at risk the capital that has helped fund much of the artificial intelligence ecosystem. The company participated in 105 investment rounds this year, according to PitchBook, and several investors fear that its interim successor, Colette Kress, will reduce the pace and size of those commitments.
The change comes after Huang, co-founder and CEO for 33 years, resigned amid a crisis combining scrutiny of the company’s business practices with allegations involving a Chinese businesswoman. The reasons for his departure have not been established: The Decoder reports that it has not been disclosed whether Huang left voluntarily or whether the board took advantage of the situation to remove him. It is worth separating that uncertainty from the economic consequences, which are already worrying founders and investors.
An investor who also drove the market
Nvidia’s scale as a funder is not limited to its portfolio of investments. According to TechCrunch, the company backed specialized cloud companies, inference providers, and open-weight models: businesses that require large investments in data centers and computing capacity before generating revenue. In the words of Shamillah Bankiya of Dawn Capital:
“Nvidia is carrying almost every AI-native startup from application to neocloud”
— Shamillah Bankiya, Dawn Capital
Its bets include CoreWeave, before it was publicly traded, and two rounds of Crusoe, including a $1.4 billion Series E. Nvidia also invested in Mistral AI, Lambda, and Black Forest Labs. TechCrunch also reported that the company bought Hugging Face for $13 billion, a deal expected to close in 2027, and that last year it put $800 million into a $2 billion round for Reflection AI.
For young companies, that funding could overcome a difficult initial barrier. An investor quoted by TechCrunch said that infrastructure companies needing between $100 million and $500 million in GPUs could raise about half that amount from Nvidia, at valuations of $1 billion or more. The chipmaker’s participation also helped convince other funds that the bet was financeable.
That backing, however, came with an industrial trade-off. Nvidia was not just providing money: its investments helped companies buy and use its GPUs. TechCrunch describes how the generosity of some rounds could leave startups tied to those chips and strengthen the company’s position. Financial support and market expansion went hand in hand.
Fewer checks, more selectivity
Kress, until now the CFO and interim CEO, could adopt a more cautious policy. An investor told TechCrunch that she is “much more conservative” and that they do not believe she will commit the same amount to support the ecosystem at early stages. The change has not been confirmed as a new policy, but it is an expectation affecting funding decisions.
If Nvidia reduces its contributions, the first consequences could show up in valuations and deal timelines. Without the company on the cap table, some traditional funds could reconsider their bets or postpone deals, according to investors quoted by TechCrunch. Umesh Padval of Seligman Ventures summed up that vote of confidence this way: “When Nvidia was on the cap table, everyone felt safe”.
A retreat could also create room for competitors. Dharmesh Thakker of Battery Ventures expects other big tech companies to challenge Nvidia’s role if they can combine product, ecosystem, and financial capacity to compete with CUDA, the software platform that lets AI models run on Nvidia GPUs. But Thakker himself doubts that Google, Amazon, Microsoft, or AMD will invest hundreds of millions in infrastructure startups with no revenue, as Nvidia did.
That distinction matters: backing mature companies to drive adoption of chips or cloud services is not the same as funding projects that still need to build data centers. If other players cover only the safer part of the portfolio, young startups could find less capital just when their computing needs are greatest. TechCrunch reports, however, that few investors believe a slowdown by Nvidia alone will bring the broader AI boom to a halt.
The cost of funding capacity and guaranteeing revenue
The discussion of Nvidia’s capital is intertwined with a higher-risk mechanism: financial guarantees that would allow cloud providers and data center developers to buy its chips. The Decoder, summarizing a report by The Wall Street Journal, says Nvidia offered guarantees worth billions and expected to receive a share of those providers’ revenue in return. Nico Caprez, Huang’s son-in-law, reportedly championed the program and promised quick closings.
The largest deal was reportedly a $250 billion guarantee for OpenAI, intended for a data center in Ohio. According to The Decoder, Kress questioned how the guarantees were valued and what returns they could generate; she later cut Nvidia’s commitment in half. The reduced deal was signed in August. The difference between the announced amount and the final commitment shows why manufacturer-funded expansion could also worry investors.
The same article attributes an account of an internal clash to The Wall Street Journal: Huang reportedly called Kress “a deer in the headlights” at an August meeting and described her as a “reporting CFO”. These are quotes attributed to that coverage, not a definitive explanation of the departure. The Decoder also reports that Nvidia’s average monthly stock multiple fell from 23 to 16 between May and October and that the company responded with its largest share buyback. Financial pressure thus coexisted with bets on an enormous scale.
The exposure extends beyond Silicon Valley
The continuity risk also reaches projects outside the United States. After reviewing more than 150 hiring announcements, investment plans, contracts, and corporate communications, Reuters identified computing projects and deals in China worth more than 23 billion yuan, equivalent to $3.2 billion. That figure includes planned investments, signed contracts, buildings, and electrical systems; it does not represent GPU purchases alone.
The plans involve Nvidia H100, H200, and H800 processors subject to export restrictions. Reuters says some centers are operational or under construction, while others could face delays, supply disruptions, or costly redesigns using domestic chips. At a facility in Gansu, thousands of chips were expected to be installed before the end of the year, but it was unclear whether they had arrived.
The tension between Nvidia’s statements and ongoing demand in China also requires distinguishing between different measures. The company said that U.S. restrictions had excluded it from China’s data center market; by contrast, an IDC estimate cited by Reuters put its share at 34% of Chinese AI accelerator shipments in the first half of 2026. Reuters cautions that the data do not clarify how the restricted hardware it found reached users.
Huang’s resignation could weaken Nvidia’s ability to defend its interests in Washington as calls for stricter controls grow, according to Reuters. Even so, the projects identified by the agency have not been linked to Megaspeed or to the allegations against Huang. Nvidia said it had no reason to believe its trade secrets or U.S. national security had been compromised; there is no public finding that Huang violated export rules.
Asia awaits signals on supply and projects
Responses from Asian governments show that the uncertainty already has an industrial dimension. South Korea said Nvidia reaffirmed its plan to supply 260,000 advanced GPUs and continue joint AI projects; its government AI strategy council held an emergency meeting to assess the impact. Samsung Electronics and SK Hynix supply memory used in Nvidia AI processors.
In Taiwan, Economy Minister Kung Ming-shin considered it unlikely that the departure would significantly alter global AI growth, while the Taipei government said Nvidia’s planned international headquarters was still moving forward. Japan announced that it would closely monitor the company and stressed its interest in securing a stable semiconductor supply. These are signs of continuity, although they do not resolve what investment policy or influence Nvidia will have under new leadership.
For developers and companies building on AI infrastructure, the immediate question is whether the capital that made new providers possible will remain available, and on what terms. A more selective Nvidia could reduce funding for projects dependent on large upfront purchases; it could also leave more room for rivals and other chip suppliers. The balance between these two outcomes will depend on whether other investors fund early-stage infrastructure and on how the new leadership manages Nvidia’s guarantees and investments.
Sources: TechCrunch, TechCrunch, The Decoder, Reuters (republished by Euronext), Ministry of Foreign Affairs of the People’s Republic of China
