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.

Glean's co-founder argues that most enterprise tasks don't require expensive frontier models. Open-source alternatives are now capable enough for the vast majority of use cases. The primary adoption driver has shifted from data privacy to pure cost savings, as enterprises seek to control skyrocketing AI bills.

Today, 80% of Box's AI spend is on frontier models. CEO Aaron Levy predicts that in 3-5 years, this will evolve into a stratified portfolio: roughly one-third on cutting-edge frontier models, one-third on near-frontier, and one-third on cheaper models for high-volume tasks. This reflects a maturation of AI usage towards cost-optimization.

The AI model market has two clear segments: expensive, high-IQ frontier models for critical tasks like cybersecurity, and small, cheap, fast models for high-volume, simple tasks. Mid-tier models are struggling to find a clear product-market fit, as users gravitate to either extreme.

Just as developers use various databases for different needs, AI applications will rely on a "constellation" of specialized models. Some tasks will require expensive, high-reasoning models, while others will prioritize low-latency or low-cost models. The market will become heterogeneous, not monolithic.

The market for AI models is bifurcating. Users either pay a premium for top-tier frontier models for high-stakes tasks like cybersecurity or use extremely cheap, small models for high-volume, simple tasks. Mid-tier models struggle to find a viable use case, getting squeezed from both ends.

The greatest value in AI won't be captured by frontier labs alone. Instead, companies in the "applied layer" are incentivized to build routing systems that use expensive frontier models for high-level orchestration while deploying cheaper open-source models for bulk tasks, creating a more efficient, barbell-shaped cost structure.

While the most powerful AI will reside in large "god models" (like supercomputers), the majority of the market volume will come from smaller, specialized models. These will cascade down in size and cost, eventually being embedded in every device, much like microchips proliferated from mainframes.

The AI market is bifurcating. Large, general-purpose frontier models will dominate the massive consumer sector. However, the enterprise world, where "good enough is not good enough," will increasingly adopt more accurate, cost-effective, and accountable domain-specific sovereign models to achieve real productivity benefits.