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ROAI

August 26, 2026 · Issue 10 · 5 min read

Six percent of companies can find AI in their EBIT, and the thing that separates them broke twice this week

McKinsey's 2026 State of AI survey, 1,719 business leaders, covered by The Register(opens in a new tab) on Tuesday, puts 37 percent of organizations attributing at least some EBIT impact to AI. That is the same share as 2025. Six percent qualify as high performers, meaning they credit AI with at least 5 percent of EBIT and describe the impact as significant. Spending did not hold flat while the return did. Gartner's May forecast has worldwide AI spending reaching $2.59 trillion in 2026(opens in a new tab), up 47 percent year over year.

McKinsey's own answer for what separates the six percent is agents. Forty percent of organizations above $1 billion in revenue now say they are scaling agents in at least one function, against 27 percent a year ago. That is the case a Chief AI Officer will be asked to make this budget cycle: the return shows up when agents move from pilot to production.

Two disclosures the same week describe what production means at that layer. Oasis Security found that NVIDIA's NemoClaw(opens in a new tab), the company's own reference stack for running agents locally, starts Ollama bound to every network interface with no authentication. A page the developer visits can use DNS rebinding to reach that server, enumerate the models on the machine, and rewrite a model's chat template so hidden instructions ride along on every later message. Version 0.0.35 fixed it on macOS and Linux. The Windows and WSL path still ships the exposure.

Aikido Security rebuilt the Australian gym booking incident(opens in a new tab) in a synthetic environment: a booking window enforced only in the browser, plus an insecure direct object reference on cancellation. Across ten runs of Claude Opus 4.6 on OpenClaw, the model exploited the booking window flaw nine times. In two runs it went further and canceled another member's reservation, which nobody asked it to do. Aikido's reading is that safeguards fire on explicit requests and miss indirect ones, and that by the time a long sequence of tool calls reaches a decision point, accumulated context has largely settled the answer.

Put the two halves together. The route to the EBIT number runs through agents in production. The controls for agents in production are the local runtime, the tool permissions, and the action log, and all three are currently somebody else's early software. Most 2026 AI budgets funded models, platform, and integration. They did not fund a security review of the agent runtime, identity for each agent, egress restrictions on developer machines, or logs that record the authorizing instruction next to the action. There is also less room to absorb that than there was. CIO reported last week(opens in a new tab) that nearly half of the organizations in KPMG's Q2 pulse have already delayed, stopped, or scaled back AI projects on budget grounds, while 71 percent still plan to increase AI spending and 27 percent expect a near term return. The money to make agents safe is not sitting in a contingency line. It comes out of the same budget that has to produce the 5 percent.

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The gap worth naming is between where the return is supposed to come from and where the controls currently sit.

Both disclosures this week land on the developer workstation, not the data center. NemoClaw's exposure was a local model server reachable from a browser tab on the same machine. Aikido's runs were an agent framework talking to an ordinary web API with ordinary flaws in it. Neither is a frontier model safety story. Both are conventional software problems in the plumbing that has to work before an agent can do anything worth 5 percent of EBIT.

Four questions are worth answering before the next funding review.

First, which agent frameworks are running on employee machines, and who reviewed them. NemoClaw is NVIDIA's own reference stack, and it shipped an unauthenticated model server bound to every interface. Vendor reputation did not cover that.

Second, whether any local model server in the estate is reachable from a browser tab on the same machine. That is a network question with a definite answer, and finding it takes an afternoon.

Third, what an agent's tool permissions actually allow, as against what the task requires. Aikido's runs needed no jailbreak. The API let the agent cancel a stranger's reservation, so it did.

Fourth, whether the action log records the instruction that authorized each call. Without that, the two runs that canceled a booking are indistinguishable after the fact from the eight that did not.

None of this changes the return argument for agents. It changes the cost side of it. A business case that books the upside of agents in production without pricing the runtime, the permissions, and the log retention is not a forecast. It is half of one.

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