IBM CEO: rationing AI is a slow way to lose

Arvind Krishna is the chairman, president and CEO of IBM. IBM sells the AI operating layer this piece describes, including watsonx and Red Hat, which IBM owns, and helped build the open-source llm-d project cited here.

This year, for the first time, the world will spend more money running AI systems than training them. Training them was a capital bet placed by a handful of companies. Running them is an operating expense, and it lands on everyone’s P&L, every day, for as long as the system is in use. The question for business leaders is no longer whether AI works. It is whether they can afford to run it at scale.

The response so far has been rationing. Pilots cost so little that nobody tracked them. Production costs real money. So, companies are capping tokens, putting spending meters on screens and reserving the most capable models for an approved few. I understand the instinct. I have long argued that the discipline is in picking three or four focused AI projects and funding them properly so they can scale, rather than running a hundred disparate experiments. But that is a decision about where to spend. Rationing is a decision to underfund what you have already chosen. Rationing the most productive technology you have ever put in your people’s hands is a slow way to lose.

Instead, winning the economics of enterprise AI is determined by three questions: What drives the cost? Who sets the price? And what are you actually spending?

First, what drives the cost? The answer is simple: engineering does. AI agents, like the ones writing software, read roughly 600 words for every one they write. And since nearly all of that reading is repetitive, companies are really paying to reread their own documents........

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