The Missing Rungs Problem

AI compresses entry-level work. That creates a pipeline risk most organisations do not notice until it is too late.

What changes#

• Fewer apprenticeship tasks exist.
• Junior roles lose meaningful learning loops.
• Leadership readiness becomes harder to build.
• Organisations become dependent on external hiring.

Why this is hard to see#

The problem does not show up in quarterly metrics. Junior employees still complete tasks. AI makes them look productive. But the learning that used to happen invisibly, through struggle, feedback, and correction, is compressing or vanishing.

By the time organisations notice, they have a leadership bench that was never built. Succession plans depend on external hiring. Institutional knowledge thins. The cost compounds.

Rebuilding the rungs#

1. Redesign early-career roles around judgement, communication, and problem framing.
2. Create structured rotations that expose people to decision contexts.
3. Measure capability growth, not output.
4. Protect human learning friction in the right places.
5. Build new rungs intentionally.

The instinct to automate the junior work is understandable: it is the most visibly repetitive, the easiest to hand to a machine, the fastest efficiency win. But that work was never only production. It was the training ground where judgement was formed. Remove it without replacing it, and you save money this year at the cost of a capability you will need in five. The organisations that manage this well do not refuse to automate. They rebuild the ladder deliberately, so that the people who will lead them later are still learning to think now.

The same problem, described in 1983#

The strongest corroboration of this argument is forty-three years old and was written about process control rather than knowledge work. In Ironies of Automation, published in Automatica in 1983, Lisanne Bainbridge observed that automating a system removes the routine operation through which operators stayed practised, while leaving them responsible for the difficult cases. Her wording is precise: "the more advanced a control system is, so the more crucial may be the contribution of the human operator", and "by taking away the easy parts of his task, automation can make the difficult parts of the human operator's task more difficult."

That is the missing rungs, stated before the personal computer was widespread. It matters for two reasons. It shows the mechanism is a property of automation rather than a novelty of AI, which makes it considerably harder to dismiss as technophobia. And it means the aviation and process industries have four decades of hard-won practice in designing against it, which knowledge work has not yet bothered to read.

Independent field evidence#

Matt Beane reached the same problem from a different direction. In The Skill Code (Harper Business, 2024), built on his own ethnographic research including years observing robotic surgery, he documented surgical residents losing the hands-on time through which surgeons have always been made. The senior surgeon at the console works alone; the resident watches. The operation goes well. The training does not happen.

Beane calls what remains shadow learning: the informal, often rule-bending ways juniors scavenge the practice the official system has removed. It is a useful and uncomfortable finding, because it suggests the ladder does not simply vanish. It goes underground, becomes unevenly distributed, and rewards the confident and well-connected over the diligent.

And now, measured#

The 2026 Global AI Jobs Barometer from PwC put numbers on it. Analysing 2.4 million US entry-level jobs, it found that entry-level roles most exposed to AI are seven times more likely to require traditionally senior, human-intensive capabilities such as leadership, creativity and face-to-face interaction. Those roles grew 35 percent since 2019, while other entry-level roles shrank 10 percent.

Read that carefully, because it is not the story the headlines told. Entry-level work is being seniorised rather than simply disappearing. The junior tasks through which people used to climb are being automated away, and the expectation of senior judgement is arriving on the first day instead, before there has been any opportunity to build it. The ladder has not been shortened. Its bottom rungs have been replaced with a demand to already be at the top. That finding is US-only and drawn from job advertisements, which describe what employers ask for rather than what the work requires, but it is the closest thing to direct measurement this argument has.

The Big Four have started acting on this#

On 27 August 2026 the Financial Times reported that consulting firms are weighing requiring junior staff into the office more often, on the grounds that AI has made interpersonal skills more valuable. EY's UK head of consulting is quoted saying firms will "have to reduce flexibility, but in order to help the human skills", and that training in empathy, storytelling and leadership was dropped during the remote-working period while AI and technical skills were prioritised. KPMG is reinventing its in-person training. Deloitte and PwC began extra coaching for their youngest UK recruits in 2023 after finding weaker teamwork and communication than earlier cohorts.

This is the argument on this page arriving in the trade press with named executives attached. It is the strongest external corroboration this research has.

The diagnosis is right. The remedy does not follow from it. If juniors are weaker because the tasks that built judgement were absorbed, attendance does not restore them. A junior in an office while a model still writes the first draft has gained proximity, not repetitions. Presence and practice were bundled together for a century, so they are easy to confuse now that AI has separated them.

And the reporting contains no measurement. The 2023 cohort effects are attributed to pandemic lockdowns rather than to AI, EY as a firm restated its existing flexibility policy alongside its executive's comments, and everyone quoted has an interest in the answer. It evidences what large firms now believe and are doing. It does not evidence the mechanism.

The progression this breaks#

Why removing early-stage work is not simply an acceleration is best explained by Hubert and Stuart Dreyfus in Mind Over Machine (1986). They describe expertise as a progression through stages, from rule-following novice to situational, intuitive expert, in which each stage depends on the accumulated experience of the one below. Intuition, in their account, is compressed experience rather than a shortcut around it.

If that is right, and forty years of naturalistic decision research broadly supports it, then removing the early stages does not speed up the climb. It removes it. You cannot arrive at stage five having skipped one through three, because stages one to three are what stage five is made of.

Measured in German manufacturing#

The most rigorous test of this argument comes from a different technology and a different continent. Dauth, Findeisen, Suedekum and Woessner, publishing in the Journal of the European Economic Association in 2021, used German administrative worker and plant records from 1994 to 2014 to trace what industrial robots actually did to careers.

Incumbent workers were largely fine. They kept their jobs and moved into new, higher-quality tasks inside their original plants. The cost fell somewhere else entirely: on young labour-market entrants, who shifted away from vocational manufacturing training towards university because the entry route had closed behind them.

That is twenty years of hard administrative data finding exactly this pattern. The damage from automation fell on skill formation, not skill possession, and it was invisible if you only looked at the people already doing the job. Generative AI is not an industrial robot and the analogy should not be pushed too far. But the mechanism has been observed at national scale before, which makes it considerably harder to dismiss as speculation about knowledge work.

Further reading#

The full graded evidence is in the evidence base, and the wider map of the field, including Bainbridge, Beane and Dreyfus in full, is in the essential works.

Related SuperSkills research#

The individual version of this is synthetic seniority; the practice version is the missed reps; the organisational accumulation is capability debt. See also will AI replace entry-level jobs and how humans learn with AI.

Development of the idea#

The term was introduced in the Box of Amazing essay The Missing Rungs: What Nobody Will Tell You About AI and Your Job on 21 September 2025. The argument was anticipated a year earlier in Critical thinking (10 November 2024) and developed alongside The Great Unbundling of Work (25 May 2025) and The Half-Life of Skills (8 June 2025). For the full dated record, see the timeline.

The older name for what is being lost is tacit knowledge: the knowledge that resists articulation, is acquired by doing, and passes through shared work rather than documentation. Its transmission route is the work being automated.

About this research#

Written by Rahim Hirji, author of SuperSkills (Kogan Page, 2026), keynote speaker on AI and human capability, and founder of The SuperSkills Intelligence Company.

How this research works · Reviewed quarterly · Found an error? Tell me and it is corrected on the page.