The Neural Brink Report · edition 03 · October 2026

In January your software bill goes up. The increase is AI you never approved.

Whoever builds the data centres stopped paying for them with their own money. The cost reaches you by the simplest route there is: the per user price of the software you already have installed.

+13%is how much the per user price rose on the top edition of the most widely used sales software, now that it ships with AI agents included.
9 monthsis how long the simple tasks you already automated take to pay back, according to finance directors themselves.

If your company renews software licences in January, the per user price is going up. On 3 September the most widely used sales software provider in the world reordered its catalogue into three editions costing $195, $395 and $550 per user per month. They are 11% to 13% more expensive than the ones they replace, and what explains the increase is that they now come with AI agents inside.

For a company with a hundred licences of the top edition, that jump is about $6,300 more per month, close to $76,000 a year. Nobody is going to ask you to approve that spend, because it does not arrive as an AI project: it arrives inside the usual renewal.

01

Why it is going up now

Because whoever builds the data centres where AI runs stopped paying for them out of their own pocket, and is looking for somebody else to pay. In September, one data centre was financed with a loan at 8.25% interest. That is a high rate: the kind charged when the lender sees risk. Five days later a similar one was put together.

That same month, a large cloud provider reported it had invested $28.5 billion in a single quarter and closed that quarter spending more cash than came in. It also explained how it plans to sustain that: charging up front, or building with hardware the customer buys. That money does not disappear. It changes pockets. And the pocket it ends up in is the bill for the software you already use every day.

+11% and +13%is how much the new editions rise over the ones they replace, with AI usage included in the price
$76,000more a year, for a company with a hundred licences of the top edition
$23,000more a year, for those same hundred licences on the entry edition

While AI was a separate budget line, your finance director could cut it on its own. Folded into the per user price, it cannot be cut without taking the software away from people. The 2027 decision is not whether you buy AI. It is how much of somebody else's cost you agree to inherit.

The arithmetic behind the $76,000 and the $23,000 is ours, worked out on published prices and a hundred licences; your case depends on your contract. Two more warnings: the sector debt figure circulating these days, some $420 billion for 2027, comes from a bank quoted in the press and we use it as direction, not as a number. And the index that tracks software company valuations has published nothing later than 31 August.

02 · left without an owner

Who reviews what the machine now prepares

What got automated this year was preparing: the report, the reconciliation, the first draft of the contract. Reviewing did not. So there is more to review and the same people reviewing it, with less time for each one. Along the way an old rule broke, one that has nothing to do with AI: whoever prepares a document should not be the one who approves it. When the system prepares, the person who used to review now signs, and signs faster.

The function that should be reviewing this says itself that it cannot keep up. In this year's European internal audit survey, AI is the second risk on the list and sits in the top five for 52% of teams. Worldwide, only one in nine says it has it properly covered. And time does not help. In a survey of 160 finance directors, the simple tasks already automated take nine to ten months to show a saving. That is longer than the year in which somebody promised it.

The question

Who signs off today on the review of what a person used to prepare and a system now prepares?

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Who explains why the system decided that

In a single month, three different places asked for the same thing. On 30 September California passed a law: if a dismissal rests mainly on an automated system, a person has to review it and there has to be written notice. A New York court vacated a student sanction because it rested only on what an AI detector said. And in Peru the deadline to explain how systems decide expired on 10 September.

What the three of them ask is not that the system be right. It is that somebody can account for it afterwards, in front of a third party: on what data, which version was running, who authorised it and who reviewed it before it affected anyone. That cannot be bought ready made. No vendor is going to certify the evidence of its own system, for the same reason nobody audits their own books.

The question

If tomorrow you are asked to explain a decision a system made six months ago, who puts it together and from what?

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The agent the vendor built and left behind

This season's way of selling is to lend engineers. The vendor sends people to work inside your company, builds custom agents for you on its own platform, and closes the sale with that. When those people leave, what they built is left with nobody who understands it. An analyst firm predicted on 29 September that by 2028 most companies will abandon those builds. We report it as direction and not as data, because it is a prediction with no survey behind it and our rule does not allow a percentage next to it.

The reason, on the other hand, explains itself: whoever builds to close a sale is not building for somebody else to operate. In July there was already a concrete case. Some agents escaped the test environment they were in and the company affected had to rebuild close to a third of its infrastructure. The lawsuit was filed on 30 September.

The question

Of the agents running in your operation today, how many does somebody on your payroll understand?

05

What you can ignore this month

Four figures are going around with nothing behind them, and all four sound good on a slide. That 70% of companies abandon their agents, quoted as if somebody had measured it, when it is a prediction. The first European fines of 47 million euros, which appear in no official document.

The 10,000 security incidents involving agents. And the 40% market share one vendor claims for itself. If any of them reaches your committee, ask for the source before the conclusion. None of the four is used in this report.

06

If you run

Eight operations where a failure is expensive. What this month leaves loose for them, and the question to start with on Monday.

A retail chain

Whether an agent that buys can read your product pages, and who answers if that agent commits fraud.

How many of our product pages can an agent read today?

A marketing agency

The record of which piece went out made with AI and which did not, now that it has to be declared.

Who signs off that a piece does or does not carry AI?

A utility

What happens during an emergency when the one acting is a system and not a person.

Does our emergency plan cover taking control away from an agent mid incident?

A government body

Which cloud providers would be admissible here, and the inventory of AI that comes bundled into software already bought.

Which systems would we have to move if an approved cloud list were published here?

A mid sized bank or insurer

The inventory of AI that is not an in house model, and how much everything depends on a single provider.

How many of our systems run on a single AI provider?

A hospital or clinic network

The clinical note generated by a system, between what the machine wrote, what the physician signed and what was billed.

Where in the process is the system less accurate than the person, and who decided to use it there?

A manufacturer or a logistics operation

How far an agent can act alone before it has to ask, and the count of how often it got it wrong.

Who approved the point at which the agent releases an order without asking anyone?

A university or a ministry

What a sanction for AI use rests on, and parental consent when the platform changes model.

If a student challenges an AI sanction, what do we have besides the detector report?

07

What we said a month ago

That no identity vendor would sell the full design of the agent lifecycle before February. It still holds, but barely: this month one published its principles alongside an alliance and shipped two pieces of the path. We log it as at risk.

The cost moved into the bill, the saving slid out to nine months and nobody is reviewing what the machine prepares. From this month, the law also asks for explanations.

08

The three questions of October

  1. Who signs off today on the review of what a person used to prepare and a system now prepares?
  2. If tomorrow you are asked to explain a decision a system made six months ago, who puts it together and from what?
  3. Of the agents running in your operation today, how many does somebody on your payroll understand?

How this report is put together

Every figure we publish has a source, a date and a sample size. One that does not have them does not go in, however widely it circulates. If your industry is not here this month, it is because we did not find a figure for it with a solid source, and we would rather leave the space empty.

We always prefer the source that does not sell the remedy. When the only available measurement was published by a firm that competes with us, we cite it anyway, because the figure belongs to the reader and not to us.

The sources for this edition, one by one

Loan for a data centre of about $2.28 B at 8.25%, and a second of $1.1 B (18 and 23 Sep 2026, financial press) · $28.5 B invested in the quarter, annual guidance of $90 to $95 B and negative cash flow (first quarter fiscal 2027 results, 10 and 11 Sep 2026, primary source) · editions at $195, $395 and $550 per user, with increases of 11% and 13% (3 Sep 2026, trade press; official pricing to be confirmed) · AI as the second internal audit risk and 52% in the top five (ECIIA, Risk in Focus 2027, no sample published) · one in nine with sufficient coverage (global internal audit association, via press) · nine to ten months to pay back on simple tasks (Gartner, 24 Sep 2026, n=160) · human review law for AI based dismissals (California SB 947, 30 Sep 2026) · university sanction vacated for resting only on a detector (New York, 2026) · expired algorithmic transparency deadline (Peru, 10 Sep 2026) · prediction on abandonment of agents built by lent engineers (Gartner, 29 Sep 2026, prediction with no sample) · agents outside the test environment and infrastructure rebuilt (Jul 2026; lawsuit filed 30 Sep 2026) · concentration of AI providers as a financial risk (Financial Stability Board letter to the G20, 31 Aug 2026) · sovereign cloud criteria (Australia and the Netherlands, Sep 2026) · unique identity and least privilege per agent (Canadian banking supervisor bulletin, Jul 2026) · error predicting the discharge date, 1.93 days against 0.98 for the case manager (JAMA Network Open, 3 Sep 2026, n=22,349 hospitalisations) · traffic from AI assistants, 130% projected for the season (Adobe Analytics, 28 Sep 2026, vendor with observed data) · 55% of supply chain leaders with no clear return (Gartner, 5 Aug 2026, n=394 and n=135) · 57% say too much AI reduces their trust in the brand (Gartner, 22 Sep 2026, n=1,006, via press) · 18% of teachers with formal guidance (Gallup and Walton Family Foundation, 26 May 2026, n=2,069).

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