The Multiplier Myth.

The thing you are sitting in front of right now is a forty-year-old logistics network pretending to be a workspace.

A mouse to point. A keyboard to spell out words. A screen to render them back. Underneath that: a stack of standards — TCP/IP, browser engines, window managers, file formats, drag-and-drop protocols — that the world has spent trillions of dollars building, layer over layer, since roughly 1985.

The single job of all that infrastructure is unglamorous when you say it out loud. Take a messy idea inside one person's head. Funnel it through a sequence of brittle physical and software protocols. Push it across an undersea cable. Unpack it on the other side into an identical setup so another person's head can read it back.

We have treated that round-trip as the peak of modern efficiency. It is not. It is an extraordinary high-friction detour, and we accepted it for forty years because we couldn't see anything else. The mouse and the menu and the export-as-PDF were the cheapest way we had to move an idea from one head to another. We had no choice but to slow our thinking down to the speed of the interface.

That is what is about to change. And almost nobody in the boardroom is reading the change correctly.

This is where the boardroom miscalculation happens, and it happens almost universally.

I sit in a lot of meetings where senior leaders are looking at this shift through the narrowest possible lens — the spreadsheet. They are treating a cognitive multiplier as a margin-chopper. The first question they ask is "how many headcount hours can this save me in Q3?" rather than "what new market can my people now reach that they couldn't last year?"

That single question reorientation — multiplier vs. margin — is, in my read, the single most expensive strategic mistake of the next ten years. And the uncomfortable thing is that the evidence is sitting in plain sight, in three different waves of technology, all telling the same story.

The ATM was supposed to eliminate bank tellers. Between the late 1980s and 2010, roughly 400,000 ATMs were installed across the United States. The average number of tellers per branch did fall — from about 20 down to 13. But the banks that read the shift correctly did not pocket the savings. They responded to the lower per-branch overhead by expanding their physical branch footprint by 43%, aggressively going after regional market share. Total teller employment wentup, from roughly 500,000 to nearly 600,000. The ATM did not kill the teller. It collapsed the unit cost of branch operations, and the winners used that compression to expand the surface area of their business.

The spreadsheet was supposed to eliminate bookkeepers. It did — about 900,000 fewer routine clerical roles between 1990 and 2015. But during the same window, financial managers, analysts, and accountants who used the same tool to do something larger grew at roughly 3% annually. The spreadsheet did not shrink the financial industry. It expanded what counted as financial work, and the market for strategic financial analysis followed.

The current AI cycle is the same dynamic on faster compounding. The most useful data point I keep coming back to is from the PwC 2026 AI Performance Study: 74% of the total economic value being generated by AI right now is being captured by 20% of organisations. The leaders in that cohort are 2.6 times more likely than their peers to use AI to reinvent their core business model, twice as likely to redesign operational workflows, and 2.8 times more likely to increase the volume of decisions executed autonomously inside a real governance frame. They are not optimising the horse. They are building a different vehicle.

The Gartner data is the warning shot for everyone running the defensive playbook. When an AI deployment is targeted at individual task efficiency — the "everyday AI" frame — the measured productivity leakage runs between 10% and 30% in tightly coordinated cases, and up to 69% in uncoordinated ones. Translation: most of the time savings get eaten by administrative friction before they hit the P&L. CFOs in those deployments report headcount reductions in the 0–3% range. The savings story largely doesn't show up.

The risk story, on the other hand, does show up. Air Canada was ordered to pay damages after its customer-service chatbot invented a bereavement refund policy that didn't exist. DPD pulled its chatbot offline when screenshots of it swearing at a customer reached 1.3 million viewers. The Commonwealth Bank of Australia reversed 45 customer-service layoffs because the automation was producing systemic operational failures. The pattern is unambiguous: pure cost-cutting AI deployments produce small savings, big tail risks, and zero structural advantage.

The pattern that works is structurally different. McKinsey tracked 80 global commercial banks from 2018 to 2022. Digital leaders — the ones treating digital infrastructure as a growth platform rather than a cost-saving project — delivered 8.1% annual total shareholder return against 4.9% for digital laggards. They grew retail revenue at +0.8% annually while laggards lost −1.4%. They expanded their share of digital sales from 40% to 70%; laggards moved from 8% to 17%. Same technology. Same five years. Completely different outcomes — entirely a function of whether leadership read the shift as a margin-chop or a market expansion.

This is what I mean by the Multiplier Mandate. The job of senior leadership in 2026 is not to count the hours an agent saves. It is to count the markets the team can now enter because the cognitive bandwidth constraint has finally moved. The brief becomes: what would we go after if the limiting factor was not how many things our best people could hold in their heads at the same time? Because that limit, for the first time in human history, is no longer the limit.

Treating that shift as an OpEx exercise is like treating the automobile as a way to save 10% on horse feed. It is technically not wrong. It is missing the entire point.