Thinking Machines Lab launches Inkling, an open-weight AI model built for enterprise customization

BitcoinWorld Thinking Machines Lab launches Inkling, an open-weight AI model built for enterprise customization Thinking Machines Lab, the artificial intelligence startup founded by former OpenAI chief technology officer Mira Murati, released its first proprietary AI model Wednesday morning, called Inkling — an open-weight system that marks a significant departure from the one-size-fits-all approach of larger competitors. The model, which uses a mixture-of-experts architecture with 975 billion total parameters but activates only about 41 billion per task, is designed to be downloaded and modified directly by outside developers and enterprises, positioning it as a flexible alternative to the closed models sold by OpenAI, Anthropic, and Google. What makes Inkling different from other AI models Inkling is trained on 45 trillion tokens spanning text, image, audio, and video, and reasons natively across all three modalities, according to the company’s release materials. Unlike flagship models from larger labs that are marketed primarily as general-purpose chatbots, Inkling is designed for organizations that want to adapt AI to their own specific needs. The model includes features such as calibrated responses — flagging uncertainty rather than guessing — and a user-adjustable ‘thinking effort’ dial that trades depth for speed. On one internal benchmark, the company claims Inkling uses a third as many tokens as Nvidia’s Nemotron 3 Ultra to achieve the same coding performance, though the company explicitly states that Inkling is ‘not the strongest model available today, closed or open.’ The strategic bet behind open-weight AI Thinking Machines Lab is positioning Inkling not as a finished product but as a starting point for enterprise customization. The company’s Tinker platform allows organizations to fine-tune the model for their own data and workflows. This approach is underpinned by a broader argument that centralized AI labs selling the same product to everyone underperform models that organizations can shape themselves. A blog post published by Thinking Machines last week argued that expertise specific to individual organizations is lost when AI is trained centrally and set in stone. The argument is gaining traction: Microsoft CEO Satya Nadella warned in a Sunday blog post that enterprises using proprietary AI models effectively pay twice — 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. Hugging Face CEO Clem Delangue made a similar prediction last week, saying frontier models will increasingly be reserved for experimentation while most production AI work shifts to private or open-source alternatives. Evidence from the Bridgewater Associates project Perhaps the clearest evidence for this argument comes from a recent project involving Bridgewater Associates, the world’s largest hedge fund. Researchers from both companies took an existing open-source model and trained it further on Bridgewater’s own financial expertise.