Creating frontier AI models is incredibly expensive, yet their value depreciates rapidly as they are quickly copied or replicated by lower-cost open-source alternatives. This forces model providers to evolve into more defensible application companies to survive.

The fact that the "best" AI model shifts every few months between players like OpenAI and Anthropic signals that no company has a sustainable, compounding moat. This lack of durable advantage makes the entire sector precarious and vulnerable to commoditization.

To survive against foundation models, startups need moats that are structurally different from what large AI labs will build. This includes integrating with physical sensors, creating marketplaces with network effects, or building full-stack businesses that become the service provider (e.g., an AI-powered wealth management firm), not just a software vendor.

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.

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.

Much like 'big data' evolved from a competitive advantage into a widely available commodity, AI models will likely follow the same path. So many sources will offer powerful models that they will cease to be a unique differentiator or a durable moat for businesses.

Despite billions in funding, large AI models face a difficult path to profitability. The immense training cost is undercut by competitors creating similar models for a fraction of the price and, more critically, the ability for others to reverse-engineer and extract the weights from existing models, eroding any competitive moat.

The assumption that building the most advanced AI model creates a defensible, high-margin business is collapsing. With competitors offering comparable performance at lower prices, the sustainable advantage shifts from owning the best intelligence to how that intelligence is productized and integrated.

Despite high valuations, foundation models lack sustainable differentiation. Users will switch providers based on cost-per-token and performance, making it a highly competitive, low-margin commodity business, akin to a utility, that is currently mispriced by the market.