The future of AI inference is not premium but ubiquitous and commoditized, according to the article. Conventional wisdom suggests commoditization destroys value, but history shows technologies that reshape industries rarely remain scarce: electricity, broadband, cloud computing and storage became more affordable, reliable and easier to deploy, and demand exploded rather than shrank. AI inference is approaching the same inflection point, and the next phase of AI will be defined by driving the cost of inference low enough that organizations stop rationing its use and start embedding AI into everything they do. Currently AI is priced, marketed and deployed as a luxury product, with the industry focused on scarce accelerators, premium systems, expensive deployments and extracting maximum performance from every available resource. This limits adoption, encouraging enterprises to treat AI as a precious resource; engineering teams ration token usage, throttle application programming interface calls and cap deployments to keep cloud computing bills from spiraling out of control, and even Microsoft Corp. reportedly limits AI usage. Lower inference costs create new customers, workloads and business models, as illustrated by a gourmet chocolatier selling handcrafted truffles for $27 each versus a 99-cent chocolate bar: the market expands because millions can afford it. As inference becomes more affordable, organizations that previously couldn’t justify significant AI deployments suddenly can, existing AI services become more profitable, and companies can redesign services around continuous AI usage, including ambient intelligence, autonomous systems and always-on assistants.