AI Agent · MIT Technology Review
Making AI an asset, not an expense
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When customers talk about AI costs, the conversation usually starts with token prices and ends with access to the latest, most capable model in the cloud.
Key facts
- Over the next 12 to 18 months, how much AI demand can the company reasonably expect
- AI agents are not yet creative enough to carry out genuinely innovative open-ended AI research, it seems
- As AI moves from experimentation to production, model choice is only part of the equation
- AI is moving from isolated pilots into production portfolios: assistants, retrieval-and-knowledge systems, and agentic applications
Summary
As AI moves from experimentation to production, model choice is only part of the equation. At that point, the question is no longer simply which model to consume, or which provider offers the lowest token price: It is how to run AI economically, predictably, and at sustained scale. AI is moving from isolated pilots into production portfolios: assistants, retrieval-and-knowledge systems, and agentic applications. When AI becomes a portfolio of always-on workloads, not a collection of experiments, the economics change.