AI model providers are shifting from subsidized subscriptions to metered, usage-based pricing for their most powerful models. This forces go-to-market teams to stop experimenting freely and start rigorously calculating the ROI for each AI-powered workflow, as costs are now directly tied to usage.

Even if AI allows companies to build products 10x faster, customer spending won't scale accordingly. The resulting flood of products competing for a fixed budget will inevitably increase competition, drive down prices, and depress total market revenue.

OpenAI's decision to slash prices on its smaller models isn't a discount sale due to struggling sales. It is a strategic maneuver to compete in the increasingly crowded market for more efficient models. This allows them to secure the lower end of the market while demand for their high-priced, frontier models remains incredibly strong.

As enterprise usage ramps up, LLM token bills threaten corporate budgets, creating intense pressure on CIOs and CFOs to reduce costs. Enterprises do not need frontier-grade intelligence for every workflow; near-frontier open-weight models that trail frontier models by 6 to 12 months can handle 90% of reasoning, coding, and agentic tasks at a third of the cost. This dynamic will force enterprises to downgrade non-critical workloads away from expensive closed models.

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.

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.

Unlike traditional software companies, AI labs can reallocate compute from revenue-generating inference to R&D-focused training. This strategic decision could cause massive, voluntary revenue drops, a volatility public market investors are unprepared to handle.

OpenAI's drastic model price cuts led to a disproportionate, 13x surge in usage. This demonstrates Jevons' Paradox in AI: lower token costs don't just make existing tasks cheaper, they cross a threshold that makes previously uneconomical applications viable, causing a massive net increase in consumption as new use cases go from '0 to 1'.

Software companies face a conflict: they need to adopt usage-based pricing to cover high AI compute costs but must also offer steep discounts to compete and encourage adoption. This strategy prioritizes long-term user lock-in over immediate profitability, as shown by Figma effectively halving prices after a pricing model shift.