IBM CEO Arvind Krishna says rationing AI is a losing strategy

As inference spending overtakes training for the first time, Krishna argues token caps miss the point and cheaper open-weight models are the smarter move For years, the big AI expense was building the models. In 2026, the bill for actually using them is expected to be bigger. IBM CEO Arvind Krishna, writing in Fortune, argues that companies are responding to that shift in the wrong way. Their instinct is to ration AI. He calls that a losing strategy. The inference bill comes due Global spending on inference, the cost of running AI systems once they are deployed, is expected to reach $23.3 billion in 2026. Training spend is projected at $19 billion. That would mark the first time running AI costs more than building it. Krishna frames the distinction in accounting terms. Training was a capital bet made by a small group of companies. Inference is an operating expense, and it shows up on the books of every business that uses the technology, every single day the system stays switched on. Why rationing feels right but isn’t So companies have started capping tokens, the units of text that AI models process and bill by. Some have put spending meters on employee screens, and others reserve ...