Mira Murati’s Thinking Machines: $2B Raised, $50B Stalled, Shipping

Thinking Machines Lab raised $2 billion at a $12 billion seed valuation in July 2025, then sought $50 billion from investors four months later before backers passed. Here is the full financial arc: valuation milestones, Nvidia and Google infrastructure deals, and two shipped products through June 2026.

Jun 2 at 9:00 AM7 min read

Mira Murati at the 2026 Met Gala, Thinking Machines Lab financial breakdown, 2026 — Mira Murati at the 2026 Met Gala.· Photo by SWinxy, via Wikimedia Commons (CC BY 4.0)

Mira Murati’s Thinking Machines Lab closed the largest seed round in AI history in July 2025, raising $2 billion at a $12 billion post-money valuation, according to Reuters and TechCrunch. Four months later, Bloomberg reported the company was seeking $50 billion from new investors. By January 2026, those talks had collapsed without a deal. Today Thinking Machines sits at its original $12 billion valuation, with roughly 140 to 169 employees, two live products, and infrastructure commitments from Nvidia and Google that run into the billions.

The $2B Seed, and the Overshoot That Followed

The seed round closed in July 2025 with a syndicate that included Nvidia, Accel, ServiceNow, Cisco, AMD, and Jane Street. The $12 billion post-money valuation was remarkable for a company with no product and a small team, but investor appetite for frontier AI ventures had by then absorbed OpenAI’s $40 billion raise and Anthropic’s multi-billion funding rounds with relative ease.

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Pressure to revalue came quickly. In November 2025, Bloomberg reported Thinking Machines was in discussions with investors at a valuation as high as $55 billion to $60 billion, roughly quadrupling the seed price in under five months. Those talks attracted attention as much for their timing as their size: at that point the lab had shipped just one product, a finetuning API for open-source models, per The Information. By January 2026, multiple media accounts confirmed the financing had not closed. Prospective backers had declined to support the valuation without a more substantial product record.

The valuation did not evaporate; it returned to its seed level, preserving the original investors’ paper value while closing the window on an early mark-up. The episode is a useful marker for where AI investor appetite meets product reality in the current cycle.

Bar chart showing Thinking Machines Lab valuation arc: $12B seed July 2025, $50B target November 2025, $12B current June 2026 — Thinking Machines Lab valuation milestones: the July 2025 seed, the November 2025 funding target that did not close, and the current $12 billion valuation as of June 2026. Sources: TechCrunch Jul 2025; Bloomberg Nov 2025.

Two Products in 16 Months

The finetuning API was Thinking Machines Lab’s first commercial offering, launched alongside Tinker, a developer platform for running and customizing open-source models. The approach positioned the lab as infrastructure rather than a consumer-facing assistant provider. In September 2025, TechCrunch reported Thinking Machines had published research arguing that current AI models produce inconsistent outputs under similar inputs, and proposing training techniques to reduce that variance.

The more consequential release came in May 2026. The lab introduced what it calls “interaction models,” a category it defines as AI that can process input and generate a response simultaneously, so that the system can be interrupted mid-response, as a person can be cut off in conversation. The first model, TML-Interaction-Small, responds in 0.40 seconds, which Thinking Machines describes as roughly matching the latency of natural human conversation and as significantly faster than comparable models from OpenAI and Google. Alongside the model, the lab published a technical paper arguing that real-time interactivity should be a native architectural property, not an afterthought layered over a turn-based language model, per our earlier coverage.

The paper-before-product sequencing mirrors how Anthropic introduced Constitutional AI and how DeepMind published AlphaGo research ahead of its commercial applications: establish the conceptual frame before competitors can define the conversation in their own terms. Semafor previewed the interaction models in May 2026, noting the architecture is a single network in which the ability to listen, speak, see, and pause is trained in from the start rather than bolted on.

Horizontal bar chart showing Thinking Machines Lab product delivery timeline in months from founding: Consistency Research at 7 months, Finetuning API at 8 months, TML-Interaction-Small at 15 months — Thinking Machines Lab product delivery timeline, measured in months from founding in February 2025. Sources: TechCrunch Sep 2025; The Information Oct 2025; TechCrunch May 2026.

Nvidia, Google, and the Infrastructure Strategy

Thinking Machines Lab’s compute strategy is built on partnerships rather than owned hardware. In March 2026, Nvidia announced it would invest in the lab and supply chips amounting to at least one gigawatt of Vera Rubin AI accelerator capacity, per Bloomberg. Vera Rubin is Nvidia’s successor to the Blackwell architecture. Allocating a gigawatt of it to a 16-month-old lab reflects Nvidia’s assessment that Thinking Machines is building models that require frontier-grade training at scale. See our earlier coverage of the Nvidia pact for full deal terms.

In April 2026, TechCrunch reported that Google had deepened its partnership with Thinking Machines through a new agreement valued in the single-digit billions. The deal provides access to Google Cloud infrastructure and to Nvidia GB300 chips hosted on Google’s platforms, giving Thinking Machines access to Google’s full AI computing stack without the capital expenditure of self-hosting.

The same month, TechCrunch reported that Meta had attempted to poach talent from Thinking Machines and that the lab had retained most of its key researchers. At a headcount of roughly 140 to 169 employees as of April 2026, retention is a meaningful signal: the team is small enough that losing two or three senior researchers would be materially disruptive.

Bar chart showing Nvidia Vera Rubin compute commitment to Thinking Machines Lab: 1000 megawatts — Nvidia’s compute commitment to Thinking Machines Lab: at least one gigawatt (1,000 MW) of Vera Rubin AI accelerator capacity, announced March 2026. Source: Bloomberg, Mar 10 2026; TechCrunch, Mar 10 2026.

What It Means

Thinking Machines Lab enters the second half of 2026 with $2 billion in capital, two shipped products, a published technical framework for interaction models, and infrastructure agreements with the two largest AI cloud providers. The $50 billion valuation episode is the most visible entry in the company’s public record, but the more durable data point is that Murati maintained the original seed valuation, kept her team intact through active poaching attempts, and delivered a product roadmap that moved from developer tooling to a novel model category in under 16 months. Whether the interaction-models paradigm establishes itself as a distinct product category or becomes a capability that larger companies absorb into existing offerings is the question the current funding level and headcount will need to answer.

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