Llama Risks Thinking Machines' Valuation

The real risk is that open models can turn a frontier lab into a services vendor before it ever becomes a software platform. Thinking Machines is already exposing fine tuning primitives and supporting outside open models like Llama, Qwen, and DeepSeek through Tinker, which makes it easier to win developer adoption but also trains customers to expect portability, downloadable checkpoints, and lower prices rather than lock in around a proprietary model.

• Meta changed buyer behavior by making strong base models widely available, and that shifted enterprise usage toward open and on premises options. In practice, many teams now start with a model like Llama, fine tune it for one narrow workflow, and cut inference cost dramatically versus staying on a premium API for every call.
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• That dynamic pushes value down the stack. If the base model is interchangeable, the winner is often the layer that makes deployment, monitoring, and workflow integration easiest. Cloud platforms like Bedrock, Azure AI, and Vertex already sell that multi model control plane, which competes directly with Thinking Machines' customization layer.
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• Mistral shows the more durable open model playbook. It uses open weights as the top of funnel, then sells API usage, private deployments, and enterprise support around sovereignty and data residency. That is a much clearer monetization path than relying on frontier scarcity alone, and it is still exposed to pricing pressure from Meta and other open model labs.
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The next phase of the market will reward labs that own a concrete workflow, distribution channel, or regulated deployment advantage, not just strong base models. For Thinking Machines, that means the moat has to harden quickly around enterprise operations, proprietary data loops, or domain specific systems, because open model supply is growing faster than customer willingness to pay premium model prices.