What is the judgment class?

In the New York Observer on 17 July 2026 I argued that the next AI power class will not be the people who build the models. It will be the people whose judgement others rely on: who decide whether what the machine produced is good enough, who can stop it, and who answer for the result.

Why power moves away from production#

When anyone can produce competent-looking output, competent-looking output stops being evidence of anything. Drafting, summarising and first analysis get cheap. What stays scarce is the part that cannot be generated: deciding what the output is worth, noticing what it missed, and being the person accountable when it goes wrong.

That is the verifier's role, and organisations tend to underpay it, because verification looks like checking rather than like producing. The argument is developed on staying valuable in the age of AI.

The trap at the start of a career#

Membership is earned through practice. AI lets someone produce work that looks like it carries fifteen years of judgement when it carries eighteen months, and the people who skip the hard early work never build what the judgment class is paid for. This research calls that synthetic seniority, and the disappearing junior work behind it the missing rungs.

What it asks of an organisation#

Two things. First, decide which decisions stay with people and who those people are, which is judgement allocation, written down as custody. Second, keep building the judgement those people need, on purpose, because the routine work that used to build it is the work being automated first.

2 to 8 October 2026: the checking turns up as a cost#

Two reports in one week put figures on the part of the work this page says stays scarce. Scarlett Evans at CIO Dive reported on 2 October, from Bain’s 2026 Global Technology Report, that AI coding tools are helping developers complete “roughly 21% more tasks, but time spent reviewing outputs rose 91%”. A Bain partner, Purna Doddapaneni, told her: “The bottleneck has moved from writing code to trusting it”, and “the system settles at the speed of its slowest human checkpoint.” Alexei Alexis at CFO Dive reported on 6 October a Datarails survey of 270 chief financial officers and finance leaders at large US organisations. They put the share of their teams’ work time spent “verifying or correcting AI-generated finance outputs” at an average of 26 per cent. One figure is from a consultancy’s report and the other from a company’s own survey, and the second is the leaders’ estimate, not a timesheet. They point the same way.

Two more describe what is then sold. Legal Cheek reported on 7 October that Falcon, a firm co-founded by the former Allen & Overy senior partner Wim Dejonghe, will review up to six commercial contracts a month for €1,000, returned the same day, and that “Every contract review is signed off by a qualified lawyer”, because clients still want what Dejonghe called “the judgment of a lawyer”. The Thomson Reuters Institute, writing on 2 October about the tax, audit and accounting firms its parent company serves, said hourly billing is still “used by 78% of firms” and forecast that AI will force them “to stop billing by the hour and start pricing for their judgment instead”. Christian Catalini’s essay in Harvard Business Review the same day gives the pattern a name. In the words of HBR’s summary, firms turn into “verification factories: institutions capable of properly steering AI-generated output and standing behind the results.” Only the opening of that essay could be read without a subscription.

The same summary carries the warning in the second half of this page. Verification factories, it says, rely on “unique ground truth and talent”, and “many firms are adopting AI in ways that erode both of these critical assets.” A trial published the same week shows where the erosion would start. It is set out on whether AI makes junior staff better or only makes their work look better. One caution about who is in the class today. Gallup reported on 6 October, from a survey of US employees fielded from January to March, that 37 per cent of managers use AI at least weekly against 25 per cent of individual contributors. Among employees who use it, 63 per cent say it makes them faster, 46 per cent say it raises the quality of their work and 43 per cent say it does not. Speed is widely reported. Whether the work is better is contested, and deciding that is the job this page describes.

9 to 15 October 2026: the reviewer is the limit, and accountability is what stays#

Three accounts from the people building the tools put the reviewer where this page puts the judgement. George Weale, Bo Yang and Andrew Biernat at Google Cloud reported on 13 October that an agent ran unattended for eleven hours on a 205,000-line codebase and proposed six optimisations; the maintainers shipped two and rejected four in review. Their summary: “the agent explores, the maintainer subtracts.” Anthropic’s source control team wrote the same day that the company averaged around 2,500 code changes merged a day in May 2026 and over 30,000 a day by September, and that for the system they built to carry it, “Our team members still review every change request for the system, but they now examine the intent of the change as opposed to reading every line of code.” Kelly Hong and Greg Burnham at Epoch AI, testing whether models could do their own organisation’s work, concluded on 8 October that the best models are reliable on well-defined tasks but “consistently fall short on the judgment that drives real work”.

The people doing the reviewing say what it costs them to trust. Stack Overflow’s 2026 developer survey, from over 30,000 respondents in 169 countries, found that 48 per cent trust AI when they can easily validate the answers and 6.6 per cent would trust it for important decisions; 78.7 per cent say source attribution matters. The survey’s own reading: “human-in-the-loop is the dominant mode of working with these tools.” Rabihah Butler at the Thomson Reuters Institute reported on 12 October, from her company’s 2026 Future of Professionals Report, that 47 per cent of professionals say they hold responsibility for errors in AI-assisted work, 48 per cent fear a negative effect on the development of their independent judgement, and legal professionals “expect the timeline to trusted judgment to extend by nearly two years under current conditions”. Her diagnosis: “The disease is the absence of clear professional standards for what AI-assisted work should look like in practice.”

What stays with the person was named most plainly in medicine. Andrew Parsons of the University of Virginia and Adam Rodman of Harvard, in a commentary in the Annals of Internal Medicine published on 12 October, accept that AI can match or exceed doctors at some diagnostic tasks and contend that what stays with the clinician is accountability, “owning the risk of acting under uncertainty and answering for the decision that follows”. They add: “A clinician who knows they will answer for the result weighs options differently than a system with nothing to lose.” And: “Deviating from the technically correct answer is often good medicine, and someone must answer for that deviation.” That is the class this page describes, in a profession’s own words. The week’s clearest case of the reviewer being removed rather than named is on whether a decision can rest on an AI summary.

The limits of the argument#

It is an argument about where value moves, and no count of the class is claimed here. The evidence underneath it, on what AI does to expertise and how judgement is built, is graded on AI and human judgement. The longer treatment is in SuperSkills (Kogan Page, 2026).