Automation's Cost Curve: Why AI Usage Is Squeezing Profits Across IT Services

[00:00:02] Somebody just raised $2 billion on the belief that most of what your team does all day can be done without them. You don't have to sell your company for that to matter. You only have to compete with someone who read the same memo. This is the Business of Tech. I'm Dave Sobel. Three numbers describe the same pressure from three different directions.

[00:00:26] We'll start with Trend Micro, which reported its second quarter results. The headline looked healthy. Total net sales up 13% year-over-year and its AI security product, Vision One, growing annual recurring revenue 49%. That's the number a vendor puts on a slide. Then read the next slide. Cloud costs nearly doubled in a year, from 6.8 billion yen to 12.7 billion.

[00:00:54] And the company named AI Token Costs directly. Operating income for the quarter fell 54%. Then it cut its full-year profit forecast by 12 billion yen, taking its expected operating margin from 19% down to 15%. Net sales guidance did not change at all. Only the profit did. So the AI product is selling exactly as planned. And the cost of running it is eating the margin underneath.

[00:01:23] That's not a company in trouble. That is a company disclosing in public that the thing customers want most now carries a cost that scales with how much they use it. Now the second one, and it comes from the other end of the market. A firm called Thrive Holdings raised $2 billion at a $12 billion valuation from SoftBank and two other investors. Its purpose is buying service businesses and rebuilding how the work gets done with AI.

[00:01:51] It already owns more than 70 of them. One arm, called Current, holds over 50 accounting firms and more than 2,000 professionals. The other arm is called Shield, and it holds about 20 information technology services companies. OpenAI owns a stake in the parent and sends its own people in to work inside them. Set aside a moment whether anyone is knocking on your door. $2 billion is what it costs to state a conviction out loud.

[00:02:19] And the conviction is that a meaningful share of the labor inside professional service delivery can be removed without the customer noticing. It's not a prediction from a conference stage. That is professional investors putting a number on it. And third, which says the separation has already started. TechIsle's Global Channel Partner Survey found that partners under $10 million in revenue project growth of 8.4%.

[00:02:47] Partners above $500 million project 16.8%. Same market, same vendors, same technology available to both. One group is growing at roughly half the rate of the other. And the gap is in the forecast, not the history. So a vendor's cost going up, $2 billion betting service costs come down, and a growth gap that's already opened between the top of this channel and the bottom of it. Those three things are not a coincidence.

[00:03:15] They're the same shift arriving from opposite ends of your own books. If you're listening to this and haven't hit follow yet, on Apple Podcasts search The Business of Tech. It takes five seconds and you'll get the next episode automatically. This episode is brought to you by Control Map. Growing MSPs are using Control Map to build recurring revenue by expanding their GRC services.

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[00:04:11] Software has always had one economic property that made it different from every other business. Once it's written, the next copy costs nothing. That single fact is why software companies carry the margins they do, and why reselling software has been a reasonable living for 30 years. Inference breaks that. When a product answers a question using a model, that answer costs money. Every time, for every customer, forever.

[00:04:39] Gartner captures where this is heading in its infrastructure forecast. AI-optimized cloud spending is climbing toward $42 billion, and the spend is shifting from training models to running them. Training is something you pay for once. Running is something you pay for on every use. And here is how large that appetite has become. The memory those systems need is in such short supply that it's repriced a market with nothing to do with AI.

[00:05:07] Asus and Gigabyte have now raised graphics card prices twice on gaming cards because AI data centers are consuming the memory supply those cards depend on. The most popular models are up roughly 40%. Nobody buying a gaming card is buying AI. They're paying for it anyway. Now, the obvious objection, because it's a good one. Model prices are collapsing. OpenAI cut its GPT 5.6 pricing hard. One model by 80%, another by 20%.

[00:05:37] If the per unit cost is falling that fast, where is the problem? The problem is that per unit price and total cost are different numbers. When something gets cheaper, products will use more of it. The vendor's bill is volume times price, and volume is winning. That is precisely what a software company watching its cloud costs nearly double is telling you in a year where the unit prices it pays went down. Now, the other side of the ledger.

[00:06:06] Services never had software's economics. The marginal cost of service delivery is a person, and that is what justified the price. Automation removes it. Halo announced an AI studio letting providers build unlimited service desk agents alongside price reductions. Sophos announced it is putting OpenAI's model directly into its security platform, sold to providers. That's the company's own announcement, so weigh it accordingly. Read who receives those releases.

[00:06:35] Everyone, simultaneously. And when every competitor's delivery cost falls in the same quarter, that savings does not become margin, it becomes somebody's lower quote. Which is, when it stops being an industry trend, it becomes a number on your own invoice. So the question is which of your numbers still belongs to you? So, look at what your client is actually watching.

[00:07:00] Research from Maverick, a cost-governance vendor, so weigh it accordingly, found that 62% of organizations have altered a business decision because of unexpected AI expense. And here's the number underneath all of those. In the last few months, 98% of them can see their AI bill. 11% can predict it. Down from 15% the year before. Visibility is solved.

[00:07:30] Forecasting is getting worse. Set that alongside Gartner's independent read on the same pressure. Global IT spending revised up to $6.37 trillion, growth of 14.2% driven by AI infrastructure. Both point at the same thing. AI costs to stop being a technology topic and become a line item somebody's board is asking about by name. That is an opening, and it needs nothing you don't already have.

[00:08:00] When a cost has a number, a governance process, and a nervous executive attached to it, a provider can take that number down and say so in the client's own units. This much was spent, this much is being spent now, here is the difference. That is not a promise about service quality. It is arithmetic on a statement the client already keeps, that their accountant already reads, and that nobody has to take your word for.

[00:08:28] The second one is harder and worth far more. Semaphore reported that the gap between corporate AI leaders and everyone else is widening sharply, with the heaviest users consuming vastly more than the median firm. The separation is not between companies that bought AI and companies that didn't. It's between companies that pointed at where they actually make money, and companies that pointed at whatever was easiest. Knowing where a business makes money is not a technology skill.

[00:08:58] It is knowing which customers are profitable, which quotes get lost and why, where the delay between doing the work and getting paid actually sits. A provider who can name a revenue line and explain how they moved it for their customer has proven they understand the business, and that provider does not get replaced because the knowledge lives in them and not in the tooling. So here's your choice.

[00:09:21] Put your services lines on the client's income statement, the cost you took out, and eventually the revenue you enabled in their numbers on their statement. Or stay denominated in licenses and hours, and watch the price of both get set by a vendor's inference bill and by every competitor who just bought the same automation you did. Which sounds like an easy call, right up until you count what you'd be giving up. Here's a reality every MSP knows.

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[00:10:20] Built on Hornet security, now part of Proofpoint. See it at proofpoint-total-protection.com Why do we care? The fair objection is that the commodity tier is not deadweight. Repeatable work at scale is where the leverage lives, and advisory work is headcount bound and first to get cut in a downturn. But moving up does not mean giving that up.

[00:10:47] The automated tier is how you produce the cost reduction you now get paid for. You keep the leverage and stop selling it as the product. The provider who loses is not the one who kept the commodity work. It's the one who kept charging for it as though the client couldn't see what it costs. So what to consider? Find out whether you can even name the number before you build a pitch around it. Pick your three largest clients and try to write one sentence for each.

[00:11:17] The cost that went down in their dollars because of something you did. Most providers discover they can describe the work in detail and cannot state the figure at all. That gap is the actual project. Not a new service line, not a new stack, just the arithmetic you never had to produce before because the employees never asked for it. Separate what automation saves you from what it saves the client and decide deliberately who gets it.

[00:11:45] When your service desk agents take hours out of your delivery, that savings is real and it is yours until a competitor's identical savings turns into a lower quote. Keeping it quiet buys you a quarter or two. Handing it to the client as a named, quantified reduction buys you the position of the provider who told them first. Both are defensible choices, but the default, which is letting it leak out slowly as discounting, is the only one that is not.

[00:12:16] Assign the client's business model to a person. The same way you assign a stack to a person. Somebody in your shop should be able to answer, without looking anything up, how each major client makes money. Which customers are profitable, where quotes get lost, where the lag between doing the work and getting paid is. That is the knowledge revenue mapping runs on. It takes months to build, and it's the one capability in your business that's not arriving in anybody's product release.

[00:12:45] If this trend continues within 12 to 18 months, the first question in a competitive services bid stops being what you run, and becomes which line on a client's income statement you moved last year. And the provider who has never opened one will be answering from a stack sheet, while somebody else answers from the client's own books. This is the business of tech. What would you fix in your business with the right playbook?

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[00:13:38] 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.