OpenAI is making the case that falling AI costs and rising model capability are not just efficiency gains, they are an expansion of what work is economically viable to attempt at all. The argument is not about doing existing tasks faster. It is about entire categories of work that were previously too expensive or too labor-intensive to touch.
The piece is worth reading for its framing of affordability as a threshold problem. When the cost of a task drops below a certain point, businesses do not just save money, they greenlight projects that never existed before. That shift in decision-making behavior is the actual story here, and the article builds toward it with specific examples of what becomes possible as the price-per-token continues to fall.
What comes next, according to OpenAI, is a compounding effect: cheaper capability unlocks new work, new work generates new demand, new demand drives further investment. Readers who want to understand how AI pricing connects to business strategy, not just product features, will find the full piece useful.
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