Thinking Machines Lab funding round at $40B valuation

When talks for a new financing round at Thinking Machines Lab surfaced this week, the headline number - a $40 billion pre-money valuation - looks like a retreat. ^[1]It sits below the $50-60 billion the company reportedly sought late last year, before those talks fell apart without a deal. ^[1]But the comparison that matters isn't to the failed ask - it's to the company's actual last completed round: a $10 billion pre-money valuation in July 2025. ^[2]Measured against that baseline, $40 billion is still roughly a fourfold jump in about fourteen months. ^[2]What's changed in between is that Thinking Machines now has revenue to point to - an annualized run rate that has crossed $100 million, generated through usage-based fees on its Tinker fine-tuning platform and the ecosystem around its open-weight model Inkling - rather than the product-less balance sheet that made the earlier $50 billion pitch hard to defend. ^[2]The new number isn't really a discount so much as a recalibration: investors who balked at paying for reputation alone in 2025 are now being asked to pay a smaller premium for reputation plus early, real revenue.

Buried inside the $1 billion-plus round is a detail that says more about AI's capital structure than about Thinking Machines itself: Nvidia is in talks to supply roughly $2.5 billion of the new money. ^[3]That's not Nvidia's first move here - in March 2026 the chipmaker struck a multiyear deal committing Thinking Machines to at least one gigawatt of its next-generation Vera Rubin platform, making Nvidia both the startup's primary compute vendor and now one of its largest prospective equity backers. ^[4]Commentary on the deal has framed this as Nvidia extending its role from chip supplier into direct capital allocator across the AI model layer - the same company that sells the GPUs also writing a check that helps the buyer afford them. ^[9]That structure can inflate apparent demand for Nvidia's hardware without a proportional increase in fully independent outside capital entering the system. ^[9]For Thinking Machines, the upside is obvious: locked-in compute plus a marquee investor. For everyone else pricing this round, it means a meaningful share of the 'new' money isn't fully external - it's a chip supplier recycling revenue back into a customer relationship it already depends on.

The valuation conversation is happening against a backdrop of unusual churn at the top of the company. Co-founders Barret Zoph and Luke Metz, along with researcher Sam Schoenholz, returned to OpenAI around the same time the earlier $50-60 billion round collapsed in early 2026. ^[5][6]Co-founder Lilian Weng left in July 2026, citing health reasons, before also joining OpenAI. ^[7]That's three co-founders and a researcher gone from the small founding and early-research bench that was Thinking Machines' entire pitch to investors in the first place - Murati's reputation and the team she assembled, more than any shipped product, justified the 2025 seed valuation. A round priced at roughly four times that seed valuation is, implicitly, a bet that the team's departures matter less than its remaining talent and its now-real revenue. Whether investors are underwriting the team or the product is no longer a rhetorical question - it's the actual risk they're pricing.

Even as this round looks more grounded than the one that fell apart, the loudest skepticism about Thinking Machines predates the revenue numbers and hasn't gone away. GIC's Group Chief Investment Officer Bryan Yeo described the broader early-stage AI venture market as showing 'a little bit of a hype bubble,' using Thinking Machines' earlier $50 billion ask as a reference point. ^[8]TPG President Todd Sisitsky went further, calling per-employee valuation multiples at startups like this one - as high as '$400 million and $1.2 billion per employee' - simply 'breathtaking.' ^[8]Those comments were aimed at the failed higher ask, not the current $40 billion figure, but the underlying critique - that founder reputation is being priced ahead of durable product-market fit - applies just as easily to a company that, even with $100 million in revenue, is still being valued at roughly 400 times that figure.