The question

Professional services means the firms that sell thinking: accountants, auditors, lawyers and management consultants. A big one is shaped like a pyramid. A few partners, the senior people who own the firm, sit at the top, and many junior analysts sit at the bottom. The profit comes from billing junior hours at several times what the juniors cost, and the trade calls that bill the billable hour. AI is now good at much of the work the juniors do.

So every managing partner, the partner who runs the firm, is asking the same question in private. When AI makes analysis cheap to produce, do we protect the pyramid that pays us, or undercut our own prices before somebody else does?

Our answer is that this is the wrong question. What decides the outcome is where the price cut lands and who is holding the blame when it does. Blame here is literal. Serious professional work must be signed by a licensed person. A licence is a professional body’s permission to practise, and the person can lose it if the work is wrong.

Two parties the partners never negotiate with decide where the cut lands and who holds the blame. One is the insurer that pays the client when the firm’s advice is wrong. Its price makes one thing scarce: a licensed person who can be punished when AI work is wrong. That person is the throat to choke of the title. The other party is the client’s own internal team.

How the simulation works

The box above says who played: an AI, one role at a time, and one referee, Dr Dan Epstein. The AI argued five roles: the big firm, the challenger, the buyer, the workforce and the regulator. Those are not the five players who carry the story. A role is a side the AI argued. A player is a character the referee wrote to carry what that argument found.

So the Analysts stand for the workforce. The Underwriter carries the finding that the first rule on AI work came from an insurer, not from the regulator. The story runs as seventeen dated steps from 2026 to 2030, told from 2030 looking back. No dice and no repeated games, so there are no counts of how often anything happened.

The real-world snapshot was taken on 1 July 2026, and every date after that is the story. Everything on the public record is checked against the evidence pack the box names. The deep mode marks every sentence that rests on one of its facts. A number with no source is an assumption we wrote to be consistent with the others, which the deep mode calls an archetype. Four such scales run through the story, listed here and drawn below so you know they are ours.

The Firm keeps the saving by hiring fewer graduates and holding its price

The Firm opens with a loud move and a quiet one. The loud move is an alliance with a leading AI lab and a cloud provider, under an “AI investment” headline in the billions. Most of that number is existing technology spending relabelled. The real firms did this: EY’s own 2023 release describes its US$1.4 billion as money already spent. The quiet move is the cut: fifteen to forty per cent fewer graduates, a band we chose, and no replacements for the juniors who leave.

Fewer juniors at the same price sends the saving to the partner draw, the money the owners take home. The Firm’s new AI platform cuts what the work costs, not what the client pays, so no invoice explains the gap. Then it moves its proposals to fixed fees and subscriptions, so the invoice stops showing how many hours the work took. In the story that hides a sixty per cent fall in delivery hours, a figure we chose. The real McKinsey already earns about a quarter of its fees from prices tied to results rather than hours.

The Firm also builds a cheap unit of its own, with a separate brand and a lower price. It walls the unit off so a failure cannot hurt the core. Starved of talent, pricing freedom and partner status, the unit stays a toy. It is not ready when the insurer changes its prices in 2027.

The Firm’s own staff hide the speed-up, so the partners see no gain

The Analysts become the most AI-fluent people in the building, and they hide it. In the story a slide deck that took twenty hours takes four, and they pace the work to look like twenty. Owning up means a smaller bonus pool or being the obvious candidate for the next cut. The sharpest build reputations of their own on the side.

So the workforce absorbs the saving before it reaches the Firm’s accounts. The partners look at their own numbers and conclude, reasonably, that AI is not changing their economics yet. That holds until a challenger with no hidden slack to protect underprices them in the open. The hiding is not our invention. Two large 2024 surveys measured it.

The insurer writes the first rule on AI work, by changing a price

Every professional firm carries professional-indemnity insurance, the cover that pays the client when the firm’s advice turns out to be wrong. The insurer reprices it once a year, at the renewal, and can refuse to insure a whole class of work. No statute and no consultation is needed. Unsigned AI work means AI-heavy work that no named professional has checked and put their name to.

Through late 2026 and 2027, in the story, unsigned AI work draws new questions at renewal. A quiet argument starts over “silent AI”, a policy that never says whether AI-caused losses are covered. That argument is real and already named in insurance-law commentary. The Underwriter holds a firm’s price flat when the firm can name the licensed person who owns each output. It reprices firms selling unsigned AI work by fifteen to forty per cent, a band we chose, or refuses them a quote.

So selling unsigned AI work becomes the expensive and personally risky path. Paying a human to check and sign the work becomes the cheap one. The brake on the Firm’s AI strategy is its chief financial officer reading the renewal, not a regulator reading a statute.

Then in 2027 a wrong AI-produced piece of work lands on one licensed person rather than on the Firm. That person is struck off, meaning the licence is taken away and the career ends. “The AI did it” is no defence. Every consequential piece of work now needs one named person who can be punished when it is wrong. That person is the throat to choke.

The Buyer learns what the work costs and takes the fee back

The Buyer commissions a normal piece of work from her usual strategy firm. Behind a closed door, two of her own people rebuild sixty to seventy per cent of it with off-the-shelf AI tools, a share we chose. She compares the two versions and tells almost nobody. What she gains is a reference price: what the routine part of the work costs her company to make itself. That price never appears on an invoice, and it is the most important number in the story.

She moves while her bargaining power is highest. She re-cuts her contracts away from hours and towards results, and cancels first the consultants hired by the day to sit inside her own team. She builds an internal team of four to eight people.

In 2027 she splits her supplier list into two tiers. Brand-name firms sit on top, kept so the board sees a famous name on the work, and challengers underneath do the routine work. She gives both tiers the same brief every quarter, which puts a dollar figure on what the famous name is worth.

The Challenger sells commercial due diligence, the research a buyer commissions before it buys a company. The story prices that at a fixed thirty-five thousand dollars and names no currency, with the money back if the Challenger misses its deadline. On our numbers that is about a third of what the big firms charge for the same job. The Challenger wins the routine work she does not do herself.

By 2029 the written rules arrive, and they mostly ratify what the insurer priced in 2027. They name an accountable human on the record, make firms tell clients when AI was involved, and recognise a kind of insurance for AI-heavy work. The Firm buys the challengers that won the fixed-price work, at a premium. The market settles into three tiers. A few expensive accountable judges plus AI sit at the top, fixed products in the middle, and the buyers’ own teams underneath.

A famous name protects whether clients hire you. It does not protect what the work costs.

What it means

One idea transfers to any business that sells expertise, and three questions follow. A reputation for judgement and accountability protects whether clients hire you, not what the work costs to make. Buyers may confine you to the part of the job that needs a signature, thirty per cent in our story. They do the rest themselves. The question is whether that part is a viable business at your cost base.

What this does not prove

This is one refereed story, not a set of repeated games, so nothing was rolled and nothing was counted. We cannot tell you how often any of it happens, only that each move survived one sceptical referee. The deep mode ends on three possible endings, ranked by Dr Epstein’s own judgement. In the first, which he thinks most likely, the big firms survive smaller and more senior, behind compliance costs the challengers cannot afford. In the second the buyers do the work themselves, and in the third nothing much changes through 2030.

The most flattering claim is also the least trustworthy. All five roles conclude that human judgement, accountability and the apprenticeship, meaning training juniors under senior supervision, are the one advantage AI cannot copy. An AI wrote the first pass of all five. An AI trained to sound authoritative will tend to say those abilities belong to humans. So will Dr Epstein, because judgement is what he sells, and if AI keeps getting better at judgement the whole story falls over.

Every number without a source is one we chose. That includes the fifteen to forty per cent intake cut, the sixty per cent fall in delivery hours, the seventy-thirty split between routine and signed work, the insurance repricing, and the four scales above. The checked evidence pushes back in eight places. Two are already on this page as figures, the insurance discounts and the buyers paying for more AI help. The largest of the rest is the time saving.

Five more places push back, starting with the real challengers, which price lower and promise less than ours. One sells due diligence at about US$50,000, against US$500,000 to a million from the big firms. That is about a tenth of the big firms’ price, where our Challenger charges about a third, and nobody publishes our money-back guarantee. In consulting the blame still lands on firms. Deloitte’s Australian firm partly refunded a US$290,000 government report with AI-invented references, and nobody lost a licence.

The hiring collapse is uneven, since EY and PwC cut by single digits and BCG grew its workforce to 33,500 in 2025. An insurer already covers AI failures. Since April 2025 a Lloyd’s product from Armilla and Chaucer has covered hallucinations, meaning AI inventing false information that reads as fact. And the small firms in our story die of not being found. Trade coverage says independents now lose on proving their method, not on the quality of the work.

What to watch

Six real-world signals, and where each one shows up.

The renewal form changes before any law does, and it is the thing to read first.