Thinking Machines Risks Ceding Platform Power

This kind of open release only works if the company keeps the highest value layer for itself. Meta used Llama to seed an ecosystem of hosted APIs, fine tuning tools, and enterprise deployments across AWS, Azure, Databricks, Snowflake, and others, which shows how quickly an open model can become raw material for everyone else’s product. Thinking Machines has already taken this path with the open source Tinker Cookbook and later with Inkling open weights, so the risk is not just copycats, it is a fast shift of bargaining power from the model creator to the platforms wrapping it.1 2 3 4

• Open models make it easier for buyers to swap vendors. In practice, many companies deploy pre trained models like Llama and fine tune them or host them through third parties, which means distribution and workflow ownership often sit with the infrastructure layer, not the original lab.2 5 6
• The ecosystem around open weights fills in quickly. vLLM became a widely used runtime for serving open models, and Meta explicitly positioned Llama as a base for developer tools, inference optimizations, and downstream applications, which is the same pattern that can surround Thinking Machines releases.2 6 7
• There is still a viable upside if open components are a wedge into a proprietary system. Thinking Machines has paired open releases with Tinker, a managed API for custom post training, which suggests the durable business would come from the workflow where customers bring data, tune behavior, and stay for deployment and iteration.1 4

The likely end state is a split market. Open components will keep spreading model capability outward and pushing prices down, while lasting value concentrates in managed tuning, deployment, security, and customer workflow. For Thinking Machines, the winning path is to make open releases the top of funnel for a closed operating layer that customers depend on every day.1 2 5 6