Accel Said to Be in Talks to Lead $1B Thinking Machines Round at $40B Valuation

Accel is reportedly in talks to lead a $1 billion funding round for Thinking Machines, the artificial intelligence startup founded by former OpenAI chief technology officer Mira Murati. If completed, the deal would value the company at about $40 billion and underscore how aggressively investors continue to price elite AI labs with limited but fast-growing revenue.

The reported round matters because Thinking Machines is barely a year old, yet it is already being discussed in the same valuation range as some of the most valuable private AI companies in the world. The financing would also mark a sharp test of investor appetite after the startup previously raised a record-setting seed round and later faced notable staff departures.

According to reporting from The Information, the new financing is still under discussion and has not been finalized. Thinking Machines and Accel did not immediately comment.

What Thinking Machines is building

Thinking Machines was launched early last year by Murati after her high-profile exit from OpenAI. The company has moved quickly to establish itself in the competitive market for frontier AI tools and model development, leaning on the reputation of its founder and the technical pedigree of the researchers who initially joined her.

In July, the startup unveiled Inkling, an open-weight model designed to work with proprietary data through its Tinker platform. Rather than relying only on traditional model licensing, Thinking Machines appears to be building a revenue engine around usage-based compute fees, charging customers for the work required to adapt models to their own data.

That model reflects a broader trend in AI: the fastest-growing companies are not just selling access to chat interfaces or standalone models, but offering infrastructure that helps enterprises customize systems for their own workflows. In that sense, Thinking Machines is positioning itself as both a model company and a platform company.

How does the Tinker platform generate revenue?

It generates revenue by charging customers for compute usage when they adapt models on top of proprietary data. That approach can be attractive to enterprises that want more control over their AI systems, while giving the startup a direct link between adoption and revenue.

It also creates a business model that can scale quickly if demand rises. But it comes with a cost: compute-intensive services can be expensive to deliver, and profitability depends heavily on pricing power, infrastructure efficiency and customer retention.

Why a $40 billion valuation stands out

A $40 billion valuation is striking not just because of the company’s youth, but because it would imply a very steep multiple relative to revenue. A source familiar with the company’s financials said Thinking Machines has reached an annual revenue run rate of more than $100 million. At that level, a $40 billion price tag implies a revenue multiple that is unusually high even by the standards of the current AI boom.

For comparison, the startup reportedly sought a $50 billion valuation late last year. If this financing closes near $40 billion, it would still represent a major leap from earlier pricing and indicate that top-tier investors remain willing to pay a premium for companies perceived as category-defining in AI.

Below is a quick snapshot of the company’s reported financing history and current position.

How did Thinking Machines get here so quickly?

It got here by combining founder prestige, a concentrated investor base and the current market’s hunger for frontier AI exposure. Murati’s move from OpenAI instantly made Thinking Machines one of the most watched startups in the sector, and her early recruiting wins helped cement the company’s image as a serious contender.

The startup’s first major funding round reinforced that perception. In that transaction, Andreessen Horowitz led a $2 billion investment at a $12 billion valuation, with participation from Nvidia, GV, Lightspeed and Conviction Partners. At the time, investors were said to be betting heavily on Murati’s leadership and on the former OpenAI researchers who joined her.

That original round now looks even more extraordinary. It was large enough to make Thinking Machines one of the best-capitalized early-stage AI startups in history, and it set the stage for the company to pursue rapid product development rather than the slower fundraising cycles typical of young startups.

Key investors in the earlier round

• Andreessen Horowitz, which led the financing
• Nvidia, which joined as a backer
• GV, Alphabet’s venture arm
• Lightspeed
• Conviction Partners

Who is Mira Murati, and why do investors care?

Mira Murati is the former OpenAI CTO whose departure from one of the world’s most important AI labs made her a marquee founder almost overnight. Investors care because she combines technical credibility, product experience and deep visibility across the AI ecosystem, which can dramatically lower the perceived risk of backing a new startup.

Her reputation is especially influential in a market where top researchers and operators are scarce. In frontier AI, founder quality is often treated as a proxy for execution quality, and that can translate into very large checks at very early stages.

Still, the market has also learned that prestige alone does not guarantee stability. Thinking Machines has already seen several high-profile departures, including some co-founders who returned to OpenAI, among them Lilian Weng and Luke Metz.

Investors initially backed Thinking Machines largely because of Murati’s background and the team she assembled from OpenAI, according to the original financing narrative. The company’s later personnel changes, however, have shown how difficult it can be to keep a star-studded AI bench intact.

What the departures signal for the company

The exits do not necessarily weaken Thinking Machines’ market position, but they do add nuance to the story. In AI, a startup’s early reputation can be built around a founder, yet long-term execution depends on retaining talent, shipping products and proving that the business can operate beyond the founding halo.

Several of the company’s early supporters were betting that Murati could recreate the kind of talent magnetism that helped OpenAI scale rapidly. The return of some key personnel to OpenAI suggests the usual pressures of the AI talent market are still at work: competition is fierce, compensation is intense and the best researchers often have multiple attractive options.

For investors, the departures may matter less than the underlying fundamentals. If Thinking Machines can continue growing revenue and convert its platform into a widely used enterprise product, the startup could still justify a premium valuation. But a rich price means expectations rise faster than the company can afford to miss them.

How does this compare with other AI startup fundraises?

This potential deal would place Thinking Machines among the most aggressively valued private AI companies. While the market has already seen enormous capital commitments to frontier AI groups, the combination of a relatively young company, a large proposed round and a valuation in the tens of billions remains unusual.

There are two reasons these transactions keep happening. First, a small number of investors believe the next foundational AI platforms will capture enormous share. Second, the supply of proven teams capable of building frontier systems is limited, which pushes capital toward the few names that appear to have a credible shot.

Thinking Machines fits that template. It has a well-known founder, a technical team with elite credentials, a product that is beginning to show commercial traction and a lead investor that already knows the company. Those ingredients can be enough to keep large rounds moving, even when the math looks stretched.

What a deal at this valuation would mean

A completed round at or near $40 billion would send a clear message that investors still view top AI labs as premium assets, even when the valuation is disconnected from current revenue by traditional standards. It would also deepen the divide between a handful of elite AI startups and the broader market of companies trying to build in the sector.

For Thinking Machines, the financing would provide more than cash. It would function as a public signal that the market still believes in Murati’s company despite the departures and despite a valuation already far ahead of many established technology firms.

For Accel, leading the round would reinforce its standing as one of the most aggressive venture firms in AI. Existing investors often have the easiest path to doubling down when a company is still ascending quickly, and in this case the firm would be backing a startup that has already shown both fundraising power and commercial momentum.

Why investors keep paying up

They keep paying up because a small number of frontier AI companies are viewed as possible platform winners. If one of them becomes the default infrastructure layer for enterprise model adaptation, the upside could be enormous enough to justify extraordinary early valuations.

That logic is speculative, but it is not irrational. The AI market has repeatedly rewarded companies that control core tooling, training workflows or enterprise deployment paths. The challenge is that very few startups can turn that strategic promise into durable economics.

What happens next?

The next step is simple: the talks either turn into a financing announcement or they do not. Until that happens, the reported valuation and funding amount should be treated as part of an active negotiation rather than a completed transaction.

Still, the reporting itself is informative. It shows that despite the company’s age, its prior mega-round, its talent churn and the extraordinary bar set by its last valuation, Thinking Machines remains one of the most closely watched private AI startups in the industry.

If a $1 billion round does close, it will likely become another reference point in the fast-moving story of AI capital formation: a market where huge checks, compressed timelines and founder mythology continue to reshape what investors consider normal.

Timeline of Thinking Machines’ rise

For now, the headline is straightforward: Thinking Machines is in talks for another massive financing at a stunning valuation, and the deal — if it happens — would underline just how far the AI funding market remains willing to stretch for a startup with the right founder, the right story and the promise of frontier technology.