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Foundational AI models will commoditize into a utility layer where companies buy "intelligence on the fly." The real, sustainable profit will be captured by application companies that leverage various models to solve specific business problems, as most enterprises lack the expertise to use raw models effectively.
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The specific AI model used is becoming as irrelevant as the specific variety of corn in a gourmet dish. The true value and differentiation lie not in the commodity model itself, but in the entire system—the agentic harnesses, workflows, and user experience—that prepares and presents the final product.
The AI value stack has evolved from chips (NVIDIA) to models (OpenAI). The next critical phase is the application layer. It's unclear if value will be captured by new application companies or if the underlying model providers will absorb all the profits, a key question for investors and founders.
Similar to how blockchain protocols like Bitcoin and Ethereum accrued more value than the apps built on them, AI foundation models are getting 'fatter.' They are absorbing more capabilities, allowing users to perform complex tasks in a single step within the base model, reducing the need for specialized application-layer companies.
Gurley notes that major AI model providers like OpenAI and Anthropic are shifting from solely selling API access to building their own applications. This move up the stack signals a fear that being a pure model provider is not a defensible moat and could lead to commoditization.
Mobile networks built expensive global infrastructure with massive usage but captured little value as profits moved "up the stack" to apps. Foundation models, despite huge CapEx, face a similar risk of becoming a commoditized infrastructure layer with low pricing power.
Comparing AI to 1995-era internet bandwidth, the hosts argue that selling raw 'intelligence' is a low-margin, commodity business. The significant financial upside will be captured not by the infrastructure providers, but by the creators who build novel applications and experiences using that intelligence as a building block.
If AI makes intelligence cheap and universally available, its economic value may collapse. This theory suggests that selling raw AI models could become a low-margin, utility-like business. Profitability will depend on building moats through specialized applications or regulatory capture, not on selling base intelligence.
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.
Sam Altman's analogy of selling AI like electricity is flawed because utility providers are low-margin businesses. Without strong differentiation, model labs will face price competition, becoming a commodity. The real value will be captured by applications built on top, just as apps, not telcos, captured mobile's value.
As foundational AI models become commoditized 'intelligence utilities,' the economic value moves up the stack. Orchestrators like OpenClaw, which can intelligently route tasks to the most efficient model based on cost or use case, are positioned to capture the margin that the underlying model providers cannot.