Vendors Build the Meter: MSP Risk Rises as AI Usage Moves to Consumption Pricing

Dave Sobel (0:02)

A technician rewrites her prompt for the fourth time and finally gets what she needed. Under this year's license, the first three tries cost nothing. Under next year's, all four are on the bill. This is The Business of Tech, I'm Dave Sobel.

The way you are charged for artificial intelligence is changing, and the change arrives as a line on an invoice rather than an announcement. Start with Gartner, which projects that by 2028, consumption-based pricing will account for more than 35 percent of net new corporate legal technology spending. You pay for what you use. Gartner's Shannon Nakamoto puts the cause plainly. As legal AI adoption grows, the tools consume ever more computing power, and that puts vendors under pressure to align pricing more closely with usage. Now the accounting and CIO dive carry two findings worth putting side by side. The first is Accenture's. From a report it calls the CIO's Guide to AI Tokenomics, built on a survey of 750 senior executives. And that is a consultancy selling advisory work on this exact problem, so weigh it accordingly. Accenture's finding is that only $1 in 5 of enterprise token spend shows up as a quantified financial outcome. The second is the scale sitting on top of that. Global AI spending reaches $2.7 trillion in 2026, up almost 50 percent year over year, most of it infrastructure. And Gartner's John David Lovelock names how a lot of it happens. CIOs are frequently funding AI without intending to, because it arrives embedded inside products and services they already bought. Third, where the rest of the usage comes from. Deloitte surveyed workers in the United Kingdom and found 31 percent using generative AI at work without their employer's knowledge, and 17 percent paying for at least one tool out of their own pockets, £958 million between them. Three counts. The pricing model is moving to metered, the spend is not tied to an outcome, and a large share of it was never a decision anybody made. And the usage nobody in the company bought is measured in the hundreds of millions. So that is what the counting shows. The harder question is why nobody has a number of their own to check any of it against.

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A seat license does not care what you do with it. You pay the same whether the person logs in every day or never, which means nobody in the chain ever needed to count what happened after the log in. Not the vendor who had already been paid, not the buyer who had nothing to gain from the number. The absence of a meter is not an oversight. It's what the pricing model made unnecessary. So look at what shows up when you do start counting. Bamboo HR put a breakdown on it. That is an HR software company researching the workplace it sells into, so weigh it accordingly. Across 1600 American desk workers, about 35% of AI usage time goes to productive work. 42% goes to troubleshooting errors and iterating on prompts. That second number works out to roughly 20 full working days a year, per person, spent getting the tool to do the thing.

Hold on to the 42 Under a seat license, all of that is free. You rephrase the question six times, you run it again after you get something wrong, and the bill does not move. Under consumption pricing, every one of those is billable. The unit of payment stops being the person and becomes the attempt. Now, you might reasonably say that iteration is not waste. That refining a prompt is how the tool is meant to be used. And somebody on their fourth try is working, not failing. That is fair. And it does not help. The meter cannot tell skillful iteration from flailing. Both render as tokens. And the attempts land less often than the marketing suggests. The Real SWE Benchmark tested the top coding agents against private production-grade code and found the best of them failing more than 60% of the time, with the failures traced to integration errors and missing requirements rather than anything exotic. So the bill arrives per attempt against work where most attempts do not land. To argue with that bill, you would need a count of your own. And the UK's National Cyber Security Center has named the reason. When an organization tries to prevent employees from using AI, employees turn to the tools the organization never approved, introducing risks that, in its words, can be hard to identify. And notice who's on the other side of that. Under a seat license, a vendor whose tool needed six tries instead of two had a retention problem. Under a meter, that vendor has a better month. In plain terms, the only complete record of what was consumed sits with the party sending the invoice.