Thinking Machines Lab, the AI startup founded by former OpenAI CTO Mira Murati, unveiled its first proprietary AI model, Inkling, on Wednesday morning. Unlike flagship models from OpenAI, Anthropic, or Google, Inkling is open-weight, meaning external developers and companies can download and modify it directly. This model uses a mixture-of-experts architecture with 975 billion total parameters, though it only activates roughly 41 billion for any given task—a common design choice that keeps large models faster and cheaper to run. According to the company's own release materials, Inkling was trained on 45 trillion tokens of text, image, audio, and video, and it reasons natively across all three modalities.

This release marks the company's first public proof of concept after a year and a half of building AI infrastructure largely out of the public eye. Some of that work had already surfaced in a May research preview of "interaction models"—AI designed to listen, speak, and even interrupt, rather than stopping and waiting like typical chatbots. Inkling also tests the central bet behind Thinking Machines: that AI which organizations can adapt for themselves will outperform the one-size-fits-all models currently sold by the biggest labs. The model is designed to give calibrated answers, including flagging uncertainty instead of guessing, and it allows users to adjust "thinking effort" up or down to trade for speed. On one benchmark, the company says Inkling uses a third as many tokens as Nvidia's Nemotron 3 Ultra to achieve the same coding performance.

It's worth noting that Thinking Machines does not claim Inkling is best-in-class. Its briefing materials explicitly state that Inkling is "not the strongest model available today, closed or open." Instead, the company is aiming for well-rounded performance. This raises a big question: who is this product targeting? Beyond the obvious—this is definitely an enterprise product—Thinking Machines is marketing it less as a finished work and more as a starting point. Organizations can fine-tune it themselves through Tinker, the company's model-customization platform. (OpenAI, Anthropic, and Google have taken a very different approach with ChatGPT, Claude, and Gemini, respectively, which were built to compete as general-purpose chatbots first, with agentic, autonomous features layered on top.)

A post published by Thinking Machines last week was clearly meant as the backdrop for this release. The company argued that AI trained centrally by one company and then set in stone underperforms AI that organizations shape themselves, because so much expertise is specific to the people who hold it. The broader idea is that centralized labs sell everyone the same product, repeatedly refined by the lab that built it, while enterprises willing to own and customize their own models can wring far more value from them. This argument is gaining steam. In a blog post published Sunday, Microsoft CEO Satya Nadella—whose company has invested billions in both OpenAI and Anthropic—warned that enterprises using proprietary AI models effectively pay twice: once in subscription costs, and again by handing over business knowledge embedded in their thousands of prompts and corrections, which can be absorbed into future model versions. Frontier models, he said, will increasingly be reserved for experimentation and high-value tasks, while most production AI work shifts to private or open-source alternatives—the exact split Thinking Machines is building around.

The clearest evidence for that argument came recently from a project with Bridgewater Associates, the world's largest hedge fund (which is not, for what it's worth, a Thinking Machines investor). Researchers from both companies took an existing open-source model and trained it further on Bridgewater's own financial expertise. The result scored 84.7% on financial reasoning tests, beating top proprietary AI models, while costing roughly a fourteenth as much to run. Those results, published jointly in late June, come from the two companies' own evaluation, not an independent one. Thinking Machines has also emphasized how quickly it got here: OpenAI took roughly five years, and Anthropic roughly three, to bring tech to market and show revenue; Thinking Machines says it did the same in about nine months.

Some will wonder whether Inkling was trained on outputs from competitors' models, a practice known as distillation that has drawn scrutiny industry-wide. The short answer, per the company's own materials, is partly. Thinking Machines pretrained Inkling from scratch, but it says it used other open-weight models—including Moonshot AI's Kimi K2.5—to help generate some of its early post-training data before large-scale reinforcement learning took over. The next model, the company insists, will use fully self-contained post-training instead. On the cost side, Thinking Machines has been more guarded. It struck a strategic partnership with Nvidia in March to deploy a gigawatt of Vera Rubin computing capacity, and says Inkling itself was trained entirely on Nvidia's GB300 NVL72 systems. But the company hasn't said how it plans to balance that against revenue that, by most accounts, hasn't been a primary focus so far. (A reported $50 billion fundraising round was said to be coming together last November, which multiple outlets reported had stalled by January; the company has declined to talk about its funding picture since, though Nvidia said it made a "significant investment" in Thinking Machines when the companies announced that March partnership.)

A related question is whether Thinking Machines' spending will ever reach the scale of OpenAI's or Anthropic's, or whether its efficiency-driven approach means the economics look different. Put another way, the company's bet may be less that it will eventually spend like its larger rivals than that it won't need to at all—because once weights are public, nothing obligates anyone who downloads them to pay Thinking Machines to run them, unlike the metered access OpenAI and Anthropic sell. It's Tinker, not the model itself, where the company's revenue has to come from, via training, fine-tuning, and, now, a cut of the hosting ecosystem built around it. Headcount, at least, looks more settled. Thinking Machines now employs roughly 200 people, up from levels reported after a wave of departures earlier this year, including two co-founders who left for OpenAI in January. Thinking Machines, for its part, doesn't seem interested in playing up individual moves the way much of the industry does. According to a source inside the company, its culture, by design, favors continuity over reliance on any one personality. It makes sense: it's less of a setback when people change teams if they were never put on a pedestal to begin with. It's also a remarkable thing for a company to insist on, given how much of its own story is still associated with the name of its now-famous co-founder, whether she planned it or not.