According to Together AI's CEO, China's leadership in open-source AI is a function of market structure, not a philosophical preference. The market is organized around open models, with companies competing by building APIs and applications on top, creating a different game-theoretic equilibrium than the closed-model US market.

Intense competition in China's AI market has led to a prevalence of open-source models. This creates a dynamic where competitors share best practices, allowing all models to learn from one another. This ecosystem structure is capable of innovating far faster than a closed, proprietary system.

Despite being open-source, leading Chinese AI firms are profitable. They generate hundreds of millions in revenue by selling managed services and API access, saving customers the complexity of self-hosting, GPU management, security, and deployment.

Despite powerful open-source AI models, companies like Anthropic post record revenue. This indicates the total addressable market (TAM) is dramatically larger than anticipated, supporting both paid and open-source ecosystems simultaneously rather than one cannibalizing the other.

AI21 exemplifies a winning AI business model: give away the foundational model (Jamba) to drive adoption, then monetize a proprietary orchestration layer (Maestro) that helps enterprises manage multiple models for cost and performance, capturing value higher up the stack.

OpenAI has seen no cannibalization from its open source model releases. The use cases, customer profiles, and immense difficulty of operating inference at scale create a natural separation. Open source serves different needs and helps grow the entire AI ecosystem, which benefits the platform leader.

VLLM thrives by creating a multi-sided ecosystem where stakeholders contribute for their own self-interest. Model providers contribute to ensure their models run well. Silicon providers (NVIDIA, AMD) contribute to support their hardware. This flywheel effect establishes the platform as a de facto standard, benefiting the entire ecosystem.

Open source AI models don't need to become the dominant platform to fundamentally alter the market. Their existence alone acts as a powerful price compressor. Proprietary model providers are forced to lower their prices to match the inference cost of open-source alternatives, squeezing profit margins and shifting value to other parts of the stack.

Analyst Gavin Baker argues a few dominant AI labs create a monopsony (a dominant buyer) for compute, suppressing margins for everyone else. The rise of competitive open-source models decentralizes this power, shifting value back to other layers of the AI stack, from chips to software and cloud providers.