Despite fears that cheaper, open-source models would commoditize the market, the opposite is happening. While token usage for cheaper models is rising, the actual share of economic value (wallet share) is increasingly flowing to expensive frontier labs like Anthropic and OpenAI.

The AI market will bifurcate. Open models will dominate most commodity tasks. However, the most economically significant problems—like advanced scientific research—will rely on closed, frontier models, allowing them to capture a disproportionate share (30-40%) of the total economic value.

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.

Decagon's CEO explains a paradox: while open-source AI usage grows, its market share shrinks. This is because open-source is ideal for scaled, defined tasks, but most enterprise AI is still in the experimental phase, where powerful, flexible frontier models are preferred.

Frontier models from giants like OpenAI force enterprises to share sensitive data, creating platform risk. The future of corporate AI lies in private, fine-tuned, open-source models that keep a company's "intelligence" in-house, preventing it from training potential competitors.

Large enterprises like AT&T manage soaring AI costs with a tiered strategy. They aim to use cheaper open-source models for 60-70% of internal tasks, keeping spending on expensive frontier models flat while overall AI usage grows. This treats premium models as specialized tools, not defaults.

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.

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.

Though leading closed-source models are marginally superior, open-source alternatives provide a much better price-to-performance ratio. Users pay a steep premium for the last few percentage points of intelligence offered by proprietary models, making open source a highly cost-effective choice for many applications.