Nvidia to Invest in Thinking Machines Lab and Supply AI Chips
Key Facts
- What: Nvidia Corp. is investing in Thinking Machines Lab, the AI startup founded by former OpenAI executive Mira Murati, and will supply its chips to help train and run the company’s AI models.
- Status: The announcement is based on a single Bloomberg report; multiple cross-referenced sources show no corroborating details about the specific investment in Thinking Machines Lab.
- Context: Thinking Machines Lab previously raised a $2 billion seed round in July that valued the startup at $12 billion, according to TechCrunch reporting.
- Broader Ecosystem: Thinking Machines Lab is listed among AI labs expected to adopt Nvidia’s upcoming Rubin platform, alongside OpenAI, Anthropic, Meta, xAI and others.
- Verification Note: Claims of a “new investment” remain unverifiable based on available cross-references, which discuss Nvidia’s other investments and partnerships.
Nvidia Corp. is making a new investment in Thinking Machines Lab, an artificial intelligence company founded by former OpenAI executive Mira Murati, and will supply chips to help train and run the startup’s AI models, according to Bloomberg.
The move underscores Nvidia’s continued strategy of backing promising AI startups while ensuring its hardware remains the preferred platform for training next-generation models. Thinking Machines Lab, which emerged from stealth with significant funding earlier in 2026, joins a growing list of high-profile AI companies that rely on Nvidia’s graphics processing units as the backbone of their compute infrastructure.
Investment Details and Funding Background
According to the Bloomberg report, Nvidia’s investment in Thinking Machines Lab is part of a broader pattern of strategic bets on AI talent and infrastructure. The startup, led by Murati who previously served as OpenAI’s chief technology officer, formally announced a $2 billion seed round in July. That round valued the company at $12 billion post-money, according to TechCrunch’s overview of Nvidia’s top startup investments.
Specific terms of Nvidia’s investment — including the size of the stake or valuation at which it is investing — were not disclosed in the Bloomberg article. The report simply states that Nvidia “is making a new investment” and “will supply chips” to the lab. Cross-referenced sources, including Nvidia’s own press releases about its Rubin platform and other investments, do not mention this particular deal, leaving some details unverified at the time of publication.
Nvidia’s Expanding AI Ecosystem
Thinking Machines Lab is already cited in Nvidia’s official announcements as one of the AI labs expected to adopt the company’s next-generation Rubin platform. In materials detailing the Rubin launch, Nvidia lists Thinking Machines Lab alongside Amazon Web Services, Anthropic, Black Forest Labs, Cohere, CoreWeave, Cursor, Dell Technologies, Google, Harvey, HPE, Lambda, Lenovo, Meta, Microsoft, Mistral AI, Nebius, Nscale, OpenAI, OpenEvidence, Oracle Cloud Infrastructure, Perplexity, Runway, Supermicro, and xAI as organizations looking to the Rubin architecture.
The Rubin platform includes six new chips and is designed to power an “incredible AI supercomputer” capable of training larger, more capable models and serving long-context, multimodal systems at lower latency. While exact technical specifications, pricing, and benchmark numbers for Rubin were not included in the provided Bloomberg coverage, Nvidia has positioned the platform as the successor to its current Blackwell and Hopper architectures.
Nvidia has demonstrated a clear pattern of investing in the AI ecosystem it powers. Separate reports show the company channeling $2 billion each into Lumentum and Coherent — developers of optical technologies that use light rather than electrical signals to connect AI chips — as part of efforts to scale AI infrastructure. Nvidia has also announced intentions to invest up to $100 billion in OpenAI as part of a strategic partnership to deploy 10 gigawatts of Nvidia systems, highlighting the scale of capital the chipmaker is willing to deploy to secure its position.
Mira Murati and Thinking Machines Lab
Mira Murati’s departure from OpenAI and subsequent founding of Thinking Machines Lab drew significant attention in AI circles. As a highly regarded technical leader during the development of GPT-4 and earlier models, Murati brings substantial credibility to the new venture. The company’s $12 billion valuation at the seed stage reflects strong investor confidence in its potential to compete with established players like OpenAI, Anthropic, and xAI.
The name “Thinking Machines Lab” evokes the history of artificial intelligence research while signaling ambitious goals. Industry observers expect the startup to focus on developing advanced AI models that require massive compute resources — precisely the kind of workload where Nvidia’s GPUs have historically dominated.
Competitive Landscape and Industry Implications
Nvidia’s investment comes at a time when competition for both AI talent and compute resources remains intense. Major cloud providers, hyperscalers, and specialized AI infrastructure companies are all racing to secure GPU supply. By investing directly in startups like Thinking Machines Lab, Nvidia not only gains potential financial upside but also helps ensure that emerging AI labs build their infrastructure around its technology stack.
The company’s broader ecosystem strategy appears designed to maintain its market leadership as AI training demands continue to grow exponentially. Partnerships and investments with both established players like OpenAI and newer entrants like Thinking Machines Lab create a flywheel effect: more leading AI organizations train on Nvidia hardware, which in turn strengthens the company’s software ecosystem (CUDA) and makes it harder for competitors to displace.
Impact on Developers, Startups, and the AI Industry
For AI developers and startups, Nvidia’s deepening involvement in the funding landscape sends a clear signal about the importance of hardware choices. Startups that align with Nvidia’s roadmap may gain preferred access to chips during periods of tight supply, as well as potential investment relationships.
The news also highlights the increasing convergence between AI model development and semiconductor infrastructure. Founders like Murati must now navigate not only technical and talent challenges but also complex strategic relationships with the companies that supply the silicon their models run on.
What’s Next
Specific timelines for Nvidia’s investment closing or when Thinking Machines Lab will begin receiving new chip allocations were not disclosed. The company is expected to continue rolling out details about its Rubin platform in the coming months, which could provide additional clarity on how startups like Thinking Machines Lab plan to leverage the new architecture.
As more details emerge about the investment terms and technical collaboration, the AI industry will gain a clearer picture of how Nvidia’s capital deployment strategy evolves. For now, the announcement reinforces Nvidia’s central role in the AI stack — from silicon to strategic investment.
Sources
- Bloomberg: Nvidia to Invest in Mira Murati’s Thinking Machines Lab and Supply Chips
- NVIDIA Newsroom: NVIDIA Kicks Off the Next Generation of AI With Rubin
- TechCrunch: Nvidia’s AI empire: A look at its top startup investments
- NVIDIA Investor: NVIDIA Kicks Off the Next Generation of AI With Rubin
Note: The core claim of a new Nvidia investment in Thinking Machines Lab is currently unverifiable against multiple cross-referenced sources, which discuss other Nvidia investments and partnerships instead.