Mira Murati’s Thinking Machines Lab Makes The Technical Case For Human-Centered AI Built On Customizable Model Weights
Thinking Machines Lab published a report to build AI that extends human will and judgment. Most AI in use today is trained in a handful of places, then frozen. The report argues that this design excludes the people a model serves. Instead, the Thinking Machines lab researchers want AI that is distributed, customizable, and shaped by its users. Thinking Machines Lab’s Proposal The lab names four technical directions. First, it trains strong models with multimodal interaction and cust
Thinking Machines Lab published a report arguing that AI systems should be distributed, customizable, and shaped by their users rather than trained in a handful of places and then frozen. The lab proposes four technical directions: training models with multimodal interaction and customizability, building tools for fine-tuning model weights, developing interfaces for better human-machine communication, and publishing research so more engineers understand how models work. The report frames this approach around the idea that much real-world knowledge is tacit and local, constantly updated through feedback, and therefore cannot be extracted into databases or centralized systems. The lab introduced Tinker, a tool that lets organizations fine-tune open-weights models and encode their own values into portable adapter weights, keeping both data and control in-house rather than renting fixed models from central providers.
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