In a future where open-source models commoditize the model layer itself, closed-source labs will likely adapt their business models. Monetization will move up the stack to the application layer (where the "last mile" value is) and down to the infrastructure layer (optimizing costs with custom chips).

Frontier AI labs like Anthropic are creating their own chip design teams not just to cut costs but to "co-design hardware and models." This allows for optimized performance and efficiency at massive scale, a benefit not achievable with general-purpose chips. The trend suggests future AI dominance will require a deeply integrated, full-stack approach from silicon to software.

To avoid having their core inference services commoditized, frontier labs like OpenAI and Anthropic will inevitably move up the stack. They will build applications that compete directly with their largest customers, such as those in legal tech or design, posing an existential risk for any startup building on their platform.

Nebius's competitive edge is full vertical integration. By controlling the stack "down" to building its own data centers, it gains cost and speed advantages. By building "up" with software platforms, it accesses enterprise markets that competitors focused on raw compute cannot.

Unprofitable frontier AI companies are expanding into application-layer verticals like drug development as a defensive strategy. They aim to build defensible, high-margin SaaS revenue streams to prove their business model to investors before their core inference and training services are fully commoditized by cheaper open-source alternatives.

Relying solely on expensive frontier models is unsustainable. Vertical AI companies must build a portfolio of smaller, specialized models that match frontier performance on specific tasks but cost 100x less, effectively allocating intelligence where it's needed most.

Value in the AI stack will concentrate at the infrastructure layer (e.g., chips) and the horizontal application layer. The "middle layer" of vertical SaaS companies, whose value is primarily encoded business logic, is at risk of being commoditized by powerful, general AI agents.

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.

Leading AI companies like Anthropic are positioning themselves as the infrastructure layer for intelligence, akin to how AWS provides infrastructure for computing. Their strategy is to partner with and enable existing SaaS companies, not to destroy them by competing directly at the application level.