AI for MSPs: The Price You're Quoting Is Borrowed

Dave Sobel (0:02)

A client signs a flat-B agreement with AI folded in. Then the model vendor cuts what the plan delivers in half, and the MSP finds out from its own margin. That cut has already happened to OpenAI's $200 plan. The only question is whose agreement saw it coming. This is the Business of Tech. I'm Dave Sobel. AI services are growing fast, and almost nobody agrees on what they should cost. Start with Omnia. In its September sentiment poll of channel partners, 44% expected third-quarter revenue to fall from a year earlier, and 41% expected it to grow. On this show, Omnia principal analyst Jessica Davis said the MSPs in that poll looked about the same as everyone else. Managed services overall are still growing. This isn't a market shrinking, it's a market splitting. The fast growth is narrow. Omnia's Robin Odey told partners in Barcelona that AI-related managed services are growing around 60%, against about 10% of traditional managed services. Davis said most of that comes from a small minority of organizations. She described what most MSPs are doing with AI as internal. More tickets resolved per technician, level one work automated. What they sell to clients, she said, is still experimental. Her read on where the industry sits is, quote, the 2008 part of it, not the 2012 part, end quote. In plain terms, before the playbook. Omnia's Lisa Lawson put a size on that advanced group. She told Channel Dive that North American partners could be looking at nearly $150 billion in AI services revenue by 2031 Only 20% of partners have the maturity to capture it today. Another 44% sit at a low level of maturity. On the buyer side, most enterprises report that fewer than one in five proof of concept projects has produced any real value.

Now look at what one MSP did with its price list. Manhattan managed IT services in its own announcement, put AI strategy and implementation inside its fully managed plan. That plan starts at $150 per user per month, alongside quarterly VCIO sessions. The AI work is in a separate line. It rides inside the flat fee.

So there are a few partners growing fast and most still experimenting. At least one has already folded the work into a fixed monthly number. Nobody has settled what AI services should cost. The question is why it can't be settled yet. To see why, look at what sits underneath the price.

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The price of AI isn't a cost. It's a decision the vendor makes, and the vendor can make again. Start with what a subscription actually covers. SemiAnalysis, which tests the usage limits on every major AI plan, calls subscription plans highly subsidized. In its rough model of Anthropic, subscriptions bring in about 10 percent of revenue, but can take up more than 40 percent of the compute used to run the models. That gap is deliberate. The plans work as customer acquisition, and the limits move. By SemiAnalysis' tracking, OpenAI cut the value of its $200 plan in half and added a $500 tier. While testing, SemiAnalysis also found one of three identical subscriptions with limits about 20 percent lower than the other two. The provider confirmed it was a small A-B test. SemiAnalysis' conclusion is that providers can change subscription limits silently at any time. Usage-based pricing doesn't escape this. On this show, Techaisle's Anorak Agarwal explained what he calls token shock. The price per token is falling. But an agent didn't use one token. It uses thousands per task because it loops, reasons, and checks its own worth. Consumption per task is rising faster than the price per token is falling so the bill goes up. In Techaisle's data, Bill Shock ranks as buyer's top frustration with their vendors at 42%.

So who pays the gap between price and cost? Investors. Gartner estimates AI providers will spend nearly a trillion dollars on infrastructure in 2026 and 6 trillion more from 2027 through 2030 To cover that, plus financing and a 12% return on invested capital, data center owners would need another 8.5 trillion dollars in revenue. Gartner forecasts no more than 3.6 trillion. For every dollar the buildout needs to earn back, Gartner sees about 42 cents. That's why it puts more than 3.5 trillion dollars of that spending at risk of being written down. Gartner also expects more than 80% of the smaller AI cloud providers to stop operating independently by 2030