Operator Implications of Cloud Scarcity: Why AI Spending Now Demands Active Monitoring

[00:00:02] Pull up a client's Zoom invoice. $20 a user and it looks like every other per seat line you have ever billed against. Nothing on that invoice will tell you where the seat ends and the meter starts. This is the Business of Tech, I'm Dave Sobel. The way your clients pay for AI changed shape, and most of them haven't noticed, because it still looks like the thing it replaced.

[00:00:29] We'll start today with Forrester, which surveyed more than 2,600 business and technology decision makers about their software and AI budgets. 80% said those budgets are going up. But the finding underneath that number matters more. Forrester points at three vendors, Anthropic, OpenAI and GitHub, that have already shifted services off flat rate subscriptions and onto usage-based billing.

[00:00:54] Then it adds Microsoft to the list for a different reason. A new premium license tier that bolts co-pilot and security tools on top of what customers already buy. Three moving to a meter, one moving the price. Same direction. And Forrester's advice to customers is to build a financial operations practice around token spend, fund controls like model routing, and set guardrails to stop runaway consumption.

[00:01:21] Think about that plainly. The subscription, a fixed number a business could write into a budget and forget about, is being replaced by a meter. Except at the scale your clients operate, it doesn't arrive looking like a meter. Look at how Zoom sells its new AI assistant. $20 per user per month, and that price is described as including AI credits.

[00:01:45] Microsoft prices co-pilot on the same pattern. The seat survived. The per-user line on the invoice survived. There is simply a consumption meter running inside it now. And nothing on that invoice tells a small business where the seat ends and the meter begins. Now look at how many people that meter is running for. Gallup found 52% of American workers using AI tools on the job.

[00:02:11] 30% frequently. 15% every single day. And 47% saying their organization has integrated AI into how it operates. Up from 41%. This is not a pilot in a lab somewhere. It's half the workforce. Then the part that doesn't fit.

[00:02:29] The UK's Office for National Statistics, a government statistics agency, not a vendor with something to sell, measured AI adoption across British business, and found it had roughly tripled since 2023. Enormous growth. Then it measured the depth of that adoption, and found it had barely moved. Most firms are still running surface-level pilots rather than pushing AI into their processes their business actually runs on.

[00:02:57] Think about the shape of all that. Adoption tripled. Depth didn't. So the price of AI quietly turned into a meter. Half the workforce is running it. And the usage is a mile wide and an inch deep. Those three arrived together. And the reason has almost nothing to do with software. To see why, you have to stop looking at the software and start looking at the building it runs in. If you're listening to this and you haven't hit follow yet, on Apple Podcasts, search Business of Tech.

[00:03:26] It takes five seconds, and you'll get the next episode automatically. I track conversations from the MSP community every week. And the frustration I keep seeing isn't that MSPs don't know what AI can do. It's that no one clearly explains how to start building and monetizing AI for their business and clients. Pax8 does it differently. A curated cloud marketplace where AI works for you.

[00:03:53] Education built for MSPs and the infrastructure to deliver managed services and intelligence at scale. 47,000 partners have already made it the center of their operations. If you're ready to cut through the AI noise and grow with agentic solutions, start at Pax8.com. That's P-A-X, the number eight, dot com. Cloud computing was sold on a promise, and the promise was abundance.

[00:04:21] Capacity is effectively infinite, you pay for what you use, and there is always more where that came from. That promise is breaking, and everything else follows from it. Computer Weekly examined whether Microsoft has overstretched its own cloud elasticity, and found the answers showing up in operations rather than in press releases. Real capacity limits, service rollbacks in some regions, customers discovering that the resource they've been told was bottomless has a floor.

[00:04:49] The response from those customers is to stop depending on one provider for it. That's not a software problem, that's a shortage. And the shortage has a second source that has nothing to do with engineering. New York's governor signed an executive order pausing state permits for new, large-scale data centers for up to a year. The first statewide moratorium of its kind in the country. The politics underneath it matter more than the ban itself.

[00:05:16] Candidates have been winning local races by running against data centers. And Gallup has confidence in big technology companies down to one in five Americans. The lowest since it started measuring. And the only institution this year with a share with almost no confidence actually surged. Sit with what that does to supply. Compute now requires land, power, and permission. The first two are getting more expensive. The third can be voted away. Here's the fair objection.

[00:05:46] Scarce things get more expensive, and vendors raise prices all the time. Why would that change the shape of the bill instead of just the size of it? Because of what the build-out is doing to the companies funding it. The Next Web reports that big tech's AI capital spending has accelerated to the point where it's straining cash flow and compressing margins. And that the pressure could push those vendors to cut other investments, raise prices on AI services, or go looking for outside financing.

[00:06:16] That's the pivot. That's the pivot. A company selling an abundant good can price it flat, because one more customer costs it almost nothing. A company selling a scarce good, it is borrowing money to produce, cannot do that. It has to charge by the unit, because the unit is what costs it money. So the meter isn't a pricing strategy. It's what a scarce good does on its way to market. And a bill attached to something physical only stays under control if someone is watching it.

[00:06:46] Which raises the question of who, exactly, is doing that watching inside a 12-person business. So bring this down to the client you actually have, because the hidden meter is only half of their problem. Research from IDC, conducted on behalf of the software firm SAS, so weigh the framing accordingly, put a number on where small and mid-sized businesses genuinely stand with AI. 1,600 SMB leaders across 28 countries.

[00:07:14] Only 9% worldwide have fully embedded it into daily operations. Roughly 70% are still in experimental or opportunistic use. Trying things, not running on them. And nearly half report their data ownership is fragmented enough that scaling AI stalls on it. Think about that 9%. It isn't that small businesses haven't started. Most of them have. It's that almost none of them finished.

[00:07:42] And the distance between starting and finishing is exactly where the money for the next phase is supposed to come from. Because nobody funds a second project when they can't say when the first one produced. So now watch a vendor act on precisely that. Microsoft is launching a program called Copilot in 30. A 30-day trial, 25 users, aimed at businesses under 300 employees, and delivered through partners rather than sold direct. Look at what Microsoft packed into it.

[00:08:12] Alongside the setup guides and the adoption content, Microsoft is shipping a success planner. A tool to help the customer work out who should be using it, what for, and what success would even look like. Microsoft built the measuring scaffolding into the trial itself. Read why. A 30-day trial converts to paid only if somebody can say at the end of it whether the thing worked.

[00:08:40] And Microsoft's embedding that on their own the customer can't. That's the whole opportunity. Handed to you by a vendor inside a motion you already run. So here's the choice. You can become the provider who can state plainly what a client's AI consumed and what it returned. And then sell the second deployment that proof unlocks, because the budget for phase 2 is sitting behind a question nobody's answered.

[00:09:07] Or you can keep competing to sell first deployments into a market where 9 clients in 10 are already stuck inside one. And never find out whether any of it worked. That choice sounds like a strategy question. It arrives as a pricing. Managing security across a dozen client tools is eating your margins. And your team's time.

[00:09:33] OpenText's secure cloud bundles let you consolidate protection across endpoint, cloud, and identity into a single conversation with your clients. Simpler renewals. Better coverage. Healthier margins. Learn more at cybersecurity.opentext.com Why do we care? Because the first billable thing here isn't the measurement. It's the finding. And you can quote that next week.

[00:10:02] Your client cannot tell you what they spend on AI because it isn't a line item. It's scattered across seat prices with credits buried inside of them. Sell the discovery as a scoped, fixed price engagement first. Then price the ongoing reporting against the number that discovery uncovers. A fee anchored to the spend it governs is the one that survives the spend going up. So what to consider?

[00:10:29] Inventory the hidden meters before you quote anything. Go through the client's stack and flag every per-seat subscription whose description mentions credits, including AI usage or AI capacity. The Zooms, the co-pilots, the tools that quietly added an assistant last year. For each one, write down what's included, what happens when they hit the ceiling, and what the overage costs. You can't put a price on governing a spend that nobody's totaled.

[00:10:58] And in most shops, that total has never been assembled once. Price discovery and ongoing measurement as two separate things. The one-time work of finding and totaling AI spend across the stack is fixed scope and finite, which makes it easy to quote and easy for a client to say yes to. The ongoing work of reporting consumption against what it returned is recurring, and it should be priced that way.

[00:11:24] Blend them into one number, and you'll make the recurring fee look enormous while giving away the discovery work that proves you're worth it. Anchor the recurring fee to spend under management and baseline it at the start. A percentage of the AI spend you govern scales as the meter runs, which means you aren't renegotiating every time consumption grows. But set the baseline when the engagement begins.

[00:11:51] Otherwise, the first time you eliminate waste, you cut your own fee, and you've built a service that pays you less the better you do at it. If this trend continues, within a year, what does your AI actually cost you, becomes a question no small business can answer without outside help. And the provider who put a price on answering it first is the one every renewal conversation after that runs through.

[00:12:19] This is the business of tech. The hardest part of running an MSP? Doing it alone. The Small Biz Thoughts community has been the room where independent operators compare nodes for nearly 20 years. Real peers, real numbers, real answers from people running businesses just like yours. Pull up a chair at smallbizthoughts.org. Interested in advertising?

[00:12:46] Head to mspradio.com slash engage. The Business of Tech is written and produced by me, Dave Sobel, under ethics guidelines posted at businessof.tech. Thanks for listening. I'll see you on the next episode. Proud member of the MSP Radio Network.