While AI compute demand seems limitless, its price is not infinitely elastic. As inference becomes a core cost of goods sold (COGS) for AI products, excessively high compute prices will break the business models of infrastructure customers, ultimately limiting demand.
Roman Chernin argues that if the AI market consolidates into a few dominant players, infrastructure companies like Nebius lose their value-add software stack and become simple commodity providers. A diverse ecosystem of builders is essential for their long-term viability.
Nebius's co-founder believes the key to its crucial relationship with NVIDIA isn't business development, but engineering excellence. Because NVIDIA is an engineering-driven company, the foundation of a strong partnership is gaining the respect of their technical teams by proving your own team's capabilities.
The fear that open source will erode the business of OpenAI and Anthropic is misplaced. As open source models make existing solutions cheaper, they compel frontier model providers to tackle the vast number of more complex, unsolved problems, effectively expanding the entire market.
Companies like Revolut initially struggle with AI adoption, not due to technology, but because they must first build a "cold start" foundation of evaluation frameworks, metrics, and CI/CD for models. Once this is in place, their AI consumption grows exponentially, matching AI-native firms.
When an efficient model like DeepSeek was released, Nebius's stock fell on fears of reduced compute demand. Internally, they had their best sales week ever. Cheaper intelligence makes new products economically viable, increasing overall compute consumption, not decreasing it.
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.
Nebius conceptualizes its growth in four layers: 1) Bare metal (megawatts) for hyperscalers, 2) Managed cloud (GPU hours) for researchers, 3) Managed inference (tokens) for AI companies, and 4) Agentic platforms (tasks) for developers. This strategy moves them up the value stack, away from pure commodity infrastructure.