The market frets that cheaper open-source models cannibalize expensive frontier models. This is a misconception. Open source drives token elasticity, increasing total compute demand. It merely shifts high margins away from model providers to the underlying AI infrastructure players who provide the compute.

Vercel data shows open source models have flipped to over 60% of AI token volume, indicating mass adoption for high-volume tasks. However, analysts predict closed, frontier models will still capture the vast majority of economic value, as premium intelligence for critical tasks commands a significant price premium.

For consumer AI products with low, flat subscription fees, the cost of using frontier proprietary models at scale becomes prohibitive. This economic pressure is forcing startups to aggressively adopt high-performing open-source models to control costs and maintain a positive unit economic model without capping usage.

As enterprises become more cost-conscious about token spend, they are actively seeking cheaper alternatives to OpenAI and Anthropic. Data from Ramp shows China's DeepSeek is the top trending software vendor, indicating a new willingness to use foreign or open-source models despite potential data privacy concerns.

Recent data from Ramp shows frontier models' usage share fell from 53% to 45% in a single month, while standard models gained share. This indicates a market shift towards cost-effectiveness and "good enough" performance over cutting-edge capabilities for many use cases, challenging the moat and pricing power of companies like OpenAI and Anthropic.

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.

The current software pricing war is a direct result of dependence on expensive, proprietary AI models from OpenAI and Anthropic. Executives believe that as open-source models become more capable and widely adopted, the underlying cost of AI will fall, commoditizing LLMs and stabilizing prices across the industry.

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.