Current AI pricing models, which pass on expensive LLM costs to users, are temporary. As LLM costs inevitably collapse and become commoditized, the winning companies will be those who have already evolved their monetization to be based on the value their product delivers.

The current subsidized AI subscription model is unsustainable. The inevitable shift to pay-per-token pricing will expose the true cost of inference. For tasks like coding, where AI can "hallucinate" and burn tokens in loops, this creates unpredictable and potentially exorbitant costs, akin to gambling.

A liquid futures market for GPU compute would create price transparency, threatening the business models of hyperscale cloud providers. These giants benefit from opaque, bundled pricing and controlling supply. They will naturally resist the standardization and transparency that an open futures market would bring.

Contrary to the idea of AI for all, the most powerful models will likely be restricted to a few high-paying clients to prevent distillation and maximize revenue. This creates a future where competitive advantage is defined by exclusive AI access, potentially allowing large incumbents to crush smaller competitors.

Current unprofitability in some AI applications, like subsidizing tokens for coding, is a deliberate strategy. Similar to Uber's early city-by-city expansion, AI labs are subsidizing usage to rapidly gain market share, gather data, and build a powerful flywheel effect that will serve as a long-term competitive moat.

As the current low-cost producer of AI tokens via its custom TPUs, Google's rational strategy is to operate at low or even negative margins. This "sucks the economic oxygen out of the AI ecosystem," making it difficult for capital-dependent competitors to justify their high costs and raise new funding rounds.

Unlike traditional SaaS where high switching costs prevent price wars, the AI market faces a unique threat. The portability of prompts and reliance on interchangeable models could enable rapid commoditization. A price war could be "terrifying" and "brutal" for the entire ecosystem, posing a significant downside risk.

Major AI labs operate as an oligopoly, competing on the quantity of supply (compute, GPUs) rather than price. This dynamic, known as a Cournot equilibrium, keeps costs for frontier model access high as labs strategically predict and counter each other's investments.

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.