Nvidia CEO in Taipei: Firm will make a “huge” OpenAI investment, denies $100 billion deal has stalled
Nvidia CEO Jensen Huang, speaking in Taipei on Jan. 31, 2026, confirmed a “huge” investment in OpenAI’s current funding round and rejected reports the potential $100 billion deal had stalled, calling them “nonsense.” He reaffirmed the commitment.
Key takeaways
- Huang confirmed“huge” investment in OpenAI and denied that a $100 billion OpenAI investment had stalled (Sources: TechCrunch, BloomingBit).
- Nvidia and OpenAI signed a letter of intent in September 2025 to deploy at least 10 gigawatts of Nvidia systems, tying investment to compute capacity (Nvidia press release).
- Reports of internal doubts and a non-binding $100 billion figure prompted Huang to push back publicly in Taipei (TechCrunch).
- OpenAI and Nvidia say they are “actively working through details” while final dollar figures remain unpublished (TechCrunch).
What Huang said in Taipei
Speaking to reporters in Taipei on Jan. 31, Jensen Huang rejected stories that he was unhappy with OpenAI and strongly dismissed suggestions the talks had stalled. In his words he called those stories “nonsense.”
“It’s such a good investment,” Huang said, adding OpenAI’s work is “incredible.” He declined to name an exact amount and noted the widely reported $100 billion figure is not final.
Coverage of Huang’s comments and the earlier reporting is available from TechCrunch and a summary report from BloomingBit.
Background: the September 2025 agreement
On Sept. 22, 2025, Nvidia issued a press release announcing a strategic plan with OpenAI to deploy at least 10 gigawatts of Nvidia systems. The letter of intent ties Nvidia investment to each gigawatt deployed and names Nvidia as a preferred compute partner, describing the Vera Rubin platform as a starting point in late 2026.
That release made clear the partnership would happen in stages and depend on conditions such as the economy and competition. It framed Nvidia as central to OpenAI’s ability to train significantly larger models.
Why the $100 billion number matters
Reports since September suggested Nvidia could invest up to $100 billion over time to support OpenAI’s scaling. Such a figure would be among the largest private investments into an AI company and would pair capital with priority access to hardware, enabling OpenAI to buy advanced chips required to train and run massive models.
Recent reports of doubts and negotiations
On Jan. 30, multiple outlets reported internal friction at Nvidia over the size and structure of the deal: some executives reportedly preferred a smaller arrangement, and the $100 billion total was described as non-binding. Reports also indicated private criticism from Huang about OpenAI’s business discipline and competitive pressure from Google and Anthropic (TechCrunch).
One line of reporting suggested negotiations shifted toward a smaller equity stake — in the tens of billions rather than $100 billion — and that some internal discussions questioned the plan’s structure. Another outlet reported parts of the plan were paused amid internal concern (MLQ).
Huang’s response and the companies’ statements
Huang addressed the reports directly in Taipei, saying it was false to claim he was unhappy with OpenAI and insisting the investment would move forward. He declined to state a specific dollar amount and said OpenAI CEO Sam Altman would announce the final round total. OpenAI stated both companies are “actively working through details.” (See BloomingBit and TechCrunch).
What the deal would do technically and commercially
Under the September letter, the investment is meant to fund and match compute deployments. Deploying 10 gigawatts of systems represents large-scale electrical and hardware capacity and would allow OpenAI to train and run much larger models. Nvidia’s role as a preferred compute partner includes priority access to hardware and software stacks such as the Vera Rubin platform (Nvidia press release).
For Nvidia, the deal could lock in long-term demand for chips; for OpenAI, it could ease hardware bottlenecks. Both companies already work with partners like Microsoft and Oracle named in the original announcement.
Points of caution and regulatory interest
The September announcement highlighted risks: economic shifts or competition could affect outcomes. A very large Nvidia investment could draw scrutiny from regulators and lawmakers concerned about market concentration, compute access, and power dynamics among major tech firms. A lower final figure could reflect internal risk assessments and governance questions at AI startups (Nvidia press release; TechCrunch).
Why this matters now
AI investment shapes leadership in the next wave of technology. A large Nvidia–OpenAI pact would be more than capital: it would be a long-term pact to share hardware, engineering, and priority access, influencing which companies set standards and which models and products reach markets first.
Implications for San Diego, California
Economic impact
- Tech manufacturing and services: Increased demand for data-center services and high-performance computing could create contracts for local suppliers and integrators (Nvidia press release).
- Jobs and hiring: Startups and established firms may hire more engineers, data-center technicians, and AI specialists, benefiting workers experienced in GPU and cloud operations.
Political consequences
- Local tech policy debates: City and county leaders may pursue incentives, training programs, or zoning changes to capture AI-related growth.
- Regional competitiveness: Pressure to build capacity quickly could intensify if AI partnerships concentrate around a few hubs.
Social effects
- Services and startups: More compute access could help San Diego startups in health care, defense, and biotech build advanced AI products.
- Cost and equity: Increased data-center demand may raise utility and real-estate costs, requiring planners to balance growth with community impacts.
Cultural relevance & practical applications
Research ties: Universities like UC San Diego could gain stronger links to AI infrastructure, grants, and student opportunities. New products: Residents could see more AI-powered tools in health care and government. Training: More bootcamps, short courses, and university programs may emerge for GPU programming and cloud operations.
Reporting and sources
This article draws on the Sept. 22, 2025 Nvidia press release, TechCrunch coverage of Huang’s Jan. 31 comments, a summary report from BloomingBit, and coverage of internal pause reports from MLQ. Ongoing details remain in flux; Nvidia has said it will participate in OpenAI’s round but has not published a final dollar figure.
