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Thinking Machines Lab, the AI startup founded by former OpenAI CTO Mira Murati, released its first in-house AI model Wednesday morning, called Inkling<\/a>. And unlike the flagship models from OpenAI, Anthropic, or Google, it\u2019s open-weight, meaning outside developers and companies can download it and modify it directly.<\/p>\n Inkling is a mixture-of-experts system with 975 billion total parameters, though it only draws on a fraction of that \u2014 about 41 billion \u2014 for any given task, a common design that keeps very large models faster and cheaper to run. It was trained on 45 trillion tokens of text, image, audio, and video, and reasons natively across all four, according to the company\u2019s own release materials. For now, though, its outputs are limited to text, including code, styled artifacts, and structured data.<\/p>\n The model is Thinking Machines Labs\u2019 first public proof point after a year and a half spent building AI infrastructure largely out of public view. Some of that work had already surfaced in a May research preview<\/a> of \u201cinteraction models\u201d \u2014 AI designed to listen and speak (and even interrupt) instead of stop and wait as with typical chatbots. It\u2019s also a test of the central bet behind the startup, which is that AI that organizations can adapt for themselves will outperform the one-size-fits-all models the biggest labs currently sell.<\/p>\n Inkling is designed to give calibrated answers, including flagging uncertainty rather than guessing, and lets users dial \u201cthinking effort\u201d up or down when they want to trade for speed. On one benchmark, the company says, Inkling uses a third as many tokens as Nvidia\u2019s Nemotron 3 Ultra \u2014 its latest generation open-weight model \u2014 to hit the same coding performance.<\/p>\n Thinking Machines doesn\u2019t claim Inkling is best-in-class. Its newest blog post states explicitly that Inkling is \u201cnot the strongest overall model available today, open or closed.\u201d What it\u2019s evidently going for instead is well-rounded performance.<\/p>\n That raises the question of who, within the enterprise market it\u2019s targeting, this product is really for. Thinking Machines is, for now, marketing Inkling less as a finished product than as a starting point, something for organizations to fine-tune themselves through Tinker, the company\u2019s model-customization platform. This also means customers, not Thinking Machines, are responsible for making sure their customizations are safe, for example. (Fine-tuning requires serious machine-earning talent.)<\/p>\n OpenAI, Anthropic, and Google have all taken a very different approach with ChatGPT, Claude, and Gemini, respectively, which were all built to compete as general-purpose chatbots first, with agentic, autonomous features layered on top.<\/p>\n A post published by Thinking Machines last week<\/a> was clearly meant as the backdrop for this release. AI that\u2019s trained centrally by one company and then set in stone, the company argued in that post, underperforms AI that organizations shape themselves because so much expertise is specific to the people who hold it. <\/p>\n Other arguments against closed models are gaining steam. In a blog post published Sunday, Microsoft CEO Satya Nadella \u2014 whose company has invested billions in both OpenAI and Anthropic \u2014 warned that enterprises using proprietary AI models effectively pay twice<\/a>: once in subscription costs, and again by handing over business knowledge embedded in their prompts and corrections, which can be absorbed into future model versions.<\/p>\n Hugging Face CEO Clem Delangue made a similar prediction<\/a> in conversation with TechCrunch last week. 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 \u2014 the exact split Thinking Machines is building around.<\/p>\n The clearest argument for Thinking Machines\u2019 approach came from a recent project<\/a> with Bridgewater Associates, the world\u2019s largest hedge fund (which is not, for what it\u2019s worth, a Thinking Machines investor). Researchers from both companies took an existing open-source model and trained it further on Bridgewater\u2019s own financial expertise. The result was said to score 84.7% on financial reasoning tests, beating top proprietary AI models, while costing roughly a fourteenth as much to run \u2014 though those results come from the two companies\u2019 own evaluation, not an independent one.<\/p>\n Either way, Thinking Machines is emphasizing how quickly it got here. OpenAI took roughly five years to bring its tech to market and show revenue, and Anthropic roughly three. Thinking Machines says it did the same in about nine months.<\/p>\n Some will wonder whether Inkling was trained on outputs from competitors\u2019 models, a practice known as \u201cdistillation<\/a>\u201d that has drawn scrutiny<\/a> across the industry. The short answer, per the company\u2019s own materials, is partly. Thinking Machines pre-trained Inkling from scratch, but it says it used other open-weight models \u2014 including Moonshot AI\u2019s Kimi K2.5 \u2014 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.<\/p>\n On the cost side, Thinking Machines has been more guarded. It struck a partnership with Nvidia in March to deploy a gigawatt of Vera Rubin computing capacity and trained Inkling entirely on Nvidia\u2019s GB300 NVL72 systems \u2014 but hasn\u2019t said how it plans to cover those costs, and revenue, by most accounts, hasn\u2019t been a priority. (A reported $50 billion fundraising round was said to be coming together last November but had stalled by January; the company has declined to talk about its funding picture since.)<\/p>\n A related question is whether Thinking Machines\u2019 spending will ever reach the scale of OpenAI\u2019s or Anthropic\u2019s, or whether its efficiency-driven approach means the economics look different. Put another way, the company\u2019s bet may be less that it will eventually spend like its larger rivals than that it won\u2019t need to at all \u2014 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\u2019s Tinker, not the model itself, where the company\u2019s revenue has to come from, via training, fine-tuning, and, now, a cut of the hosting ecosystem built around it. <\/p>\n 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<\/a> in January.<\/p>\n Thinking Machines, for its part, doesn\u2019t 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\u2019s less of a setback when people change teams if they were never put on a pedestal to begin with. It\u2019s 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.<\/p>\n<\/div>\n When you purchase through links in our articles, we may earn a small commission<\/a>. This doesn\u2019t affect our editorial independence.<\/em><\/p>\n