LLMs are becoming commoditized. Like gas from different stations, models can be swapped based on price or marginal performance. This means competitive advantage doesn't come from the model itself, but how you use it with proprietary data.

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.

As customers increasingly adopt model orchestration—routing tasks to the most efficient model for the job—value shifts away from individual frontier models. This trend commoditizes the raw intelligence layer, posing a significant threat to companies focused solely on building the largest models.

In previous tech waves, proprietary technology was a key differentiator. Now, with powerful AI models widely available, the advantage shifts to deeply understanding customer problems. The question "Should we even build this?" is more critical to creating a moat than the technology itself.

Since LLMs are commodities, sustainable competitive advantage in AI comes from leveraging proprietary data and unique business processes that competitors cannot replicate. Companies must focus on building AI that understands their specific "secret sauce."

The common practice of model distillation suggests that AI capabilities will eventually be commoditized. As smaller models can cheaply mimic larger ones, differentiation will shift away from raw performance to product integration and price, likely triggering a massive price war among providers.

Leading AI models are becoming increasingly similar in capability. This rapid convergence suggests the underlying technology is becoming a commodity, and competitive advantage will likely shift to user interface, distribution, and specific applications rather than the core model itself.

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.

Contrary to the 'winner-takes-all' narrative, the rapid pace of innovation in AI is leading to a different outcome. As rival labs quickly match or exceed each other's model capabilities, the underlying Large Language Models (LLMs) risk becoming commodities, making it difficult for any single player to justify stratospheric valuations long-term.