Ageless Management: A Sustainable Organizational Growth Model

A theory of cognitive complementarity, the audit value of experience, and the lifelong extension of brain capital in the age of AI

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In an era of 100-year lives, companies still assign roles by asking how old you are. This paper explains why that is about to stop working, and what to measure instead.

The conclusion first. Age has become too coarse a proxy for ability. The answer is not to make age a category deserving special care, but to remove age as a variable from management decisions and put in its place an ability measured by detection performance.

VURA Working Paper No.9. This page explains the paper's core argument in plain English. The full paper (Japanese original, 132 pages; English edition, 188 pages) is linked below.

This paper in three minutes

• It discards three popular claims first. "Seniors will become AI's auditors." "Keep working and you stay healthy." "Multigenerational teams perform better." The current evidence supports none of them as stated.

• There is no single human prime. Different abilities peak at different ages. One yardstick called "age" cannot stand in for a bundle of abilities.

• AI erased only the experience gap on the producing side. Generative AI lifted novices the most. Research on the checking side, catching errors, points the other way. These are two different abilities.

• So it narrows what to measure to one thing: experiential audit capacity, defined not by title or age but by how many errors a person detects.

• Get the design wrong, though, and the mechanism destroys itself. If every auditor sees the world through the same AI summary, their blind spots overlap however many auditors you line up.

• And the paper writes down its own weaknesses. Its central Propositions 3, 4 and 5 are untested. Measurement runs into legal limits. The author stands to benefit from the framework. All of this is stated in the paper.

The question this paper tries to answer

As AI makes the execution of knowledge work cheap, where do people add value? This series has argued repeatedly that scarcity shifts to the side that defines, evaluates and assigns meaning.

Then who has that ability? Most discussions answer "experienced people" and quietly rewrite that as "older people." Does the rewrite hold? This paper takes that question head-on.

How does AI-enabled cognitive complementation turn the asymmetries of ability tied to experience and age into two-way complementarity of value? And under what conditions does that conversion succeed?

The paper sits in the series' third layer (micro / corporate management). It extends Brain Capital Management (No.5) along the age axis and brings an "experience" axis into the supervisory structure of Human-on-the-Loop (No.8).

The starting point: set aside the "senior talent utilization" script

The original draft behind this paper carried three intuitive, crowd-pleasing claims. Seniors will become AI's auditors. Keep working and you stay healthy. Multigenerational teams perform better.

The paper adopts none of them as written. The existing evidence does not support them.

There is no single human prime

Hartshorne & Germine (2015, Psychological Science) showed that cognitive abilities peak at widely different ages. Processing speed peaks around 18. Working memory, around 30. Reading other people's emotions, in the 40s and 50s. Vocabulary and crystallized knowledge, in the late 60s to early 70s. A single summit called "cognitive prime" does not exist in the data.

An analogy: a field with staggered harvests. One field grows strawberries, corn, apples and persimmons, each ripening in a different season. So "when is this field in season?" has no answer. Likewise, "what age is the peak?" cannot be answered until you name the ability.

The paper does not, however, use this finding to claim that "older people do not decline." The study is a cross-sectional design: it measures people of many ages at one point in time. That design cannot separate differences caused by age itself from cohort differences, the differing education and living conditions of each generation. If one age group is faster, the design cannot say whether aging or schooling is the reason.

The paper takes exactly one point from this study. A one-dimensional scale called "age" cannot represent a bundle of abilities.

"Working keeps the brain healthy" has not been proven

On continued employment and cognitive function, supportive findings have accumulated: Rohwedder & Willis (2010), "Mental Retirement"; the large study by Dufouil and colleagues finding lower dementia risk with later retirement (hazard ratio 0.968, n = 429,803; a hazard ratio below 1 indicates lower risk); the randomized trials of Experience Corps; Japan's JAGES.

But evidence in the opposite direction exists with equal standing. The systematic review by Meng and colleagues (2017), a method that collects and re-evaluates every eligible study, concluded that the results are mixed. Studies finding that retirement actually improves health (Insler, Eibich, Coe and others) sit alongside. Salthouse (2006) is an influential critique of the "use it or lose it" hypothesis itself.

Reverse causation also remains. Are people healthy because they worked longer, or did they work longer because they were healthy? The direction has not yet been adequately separated.

The paper therefore takes this up not as a conclusion but as Hypothesis H1, to be tested. It writes down an identification strategy: exogenous changes in pension rules and mandatory retirement systems as instrumental variables, a method that separates cause from effect using changes imposed from outside, unrelated to the person's own choices. And it states explicitly that H1 is untested. No assertion about health or medicine is part of this paper's claims.

Diversity does not produce results by itself

On age diversity and team performance there are three meta-analyses, a method that statistically pools many studies' results. Joshi & Roh report r = −.06; Schneid and colleagues and Wallrich and colleagues report r = .014. Here r is a correlation coefficient: 0 means "no relationship," 1 means "perfect match." In other words, the average effect is near zero. Average across the studies and the positives and negatives cancel; practically nothing remains.

The paper fixes this as its baseline.

Positive effects appear only under conditions. Backes-Gellner & Veen found one only in creative tasks. Wegge and colleagues showed dependence on moderators, conditions that change an effect's direction or size, such as task complexity and an age-discriminatory climate.

The unconditional claim that "multigenerational means more value" is extremely easy to refute. The paper does not make it.

What is happening now: AI erased the gap only on the producing side

On top of these three rejections, the paper places a different observation. Studies measuring the effect of generative AI consistently show that it helps the less experienced most. Brynjolfsson, Li & Raymond (2025, QJE) report a 15% average productivity gain in a call center, n = 5,172, and roughly 30% for novices. Noy & Zhang point the same way. AI transfers tacit knowledge, and it really has lowered the "experience barrier."

But the same literature shows the opposite on the other side.

Dell'Acqua and colleagues (758 participants) showed performance falling 19 points on tasks outside the boundary of AI's competence. The follow-up "Falling Asleep at the Wheel" shows that reliance on AI produces slackened monitoring and missed errors. Budzyń and colleagues (2025, Lancet Gastroenterology) studied endoscopy under AI assistance: the detection rate without assistance fell from 28.4% to 22.4% (an observation suggesting skill depreciation, not a causal claim).

From here the paper draws its central boundary line.

Proposition 3 | Gaps in production versus gaps in verification What AI compresses is the experience gap in production, not the experience gap in verification. The two are distinct abilities with distinct distributions.

In plain terms — AI took over only the "making" side of the experience gap. The ability to spot "this must not go out" remains a separate ability.

An analogy: the hands that cook and the palate that tastes. With better tools, a novice quickly gets good at following a recipe. But tasting the finished dish and judging "this cannot be served" does not improve with the same tools. AI lifted the first; this paper measures the second.

The paper does not claim the boundary is permanent. Proposition 3's refutation condition explicitly includes a pathway in which verification ability itself is compressed over time. The learning retention shown by Brynjolfsson and colleagues and the skill depreciation shown by Budzyń and colleagues are treated as a pair.

Experiential audit capacity: only those who detect are counted

What, then, is the ability on the verification side? The paper defines it as experiential audit capacity (Definition 4).

Definition 4 | Experiential audit capacity An ability operationally defined by performance in detecting errors and contextual risks contained in AI output. It manifests as the interaction of long-term domain experience, crystallized intelligence (Gc) and metacognition.

In plain terms — Not "people who seem experienced," but only "people who actually found the errors" are counted as having this ability.

Operationally defined means the measurement procedure decides what qualifies: what is measured, how, and what result earns the label. Crystallized intelligence (Gc) is accumulated knowledge, such as vocabulary and specialist expertise. Metacognition is knowing how far your own understanding reaches.

This definition went through one major correction during production. In the first draft, a "formation mechanism" and the phrase "not a function of age" had leaked into the definition itself, so the corresponding Proposition 4 had become analytically true: true by definition, and so impossible to refute. Adversarial review caught this, and the definition was purified into an operational definition based on detection performance. The formation mechanism now belongs to Proposition 4 alone.

The purification has a practical consequence. Experiential audit capacity is judged by detection performance on a task, not by title or age. Who has it is unknown until measured.

Re-attributing from age to experience

The "experts" measured by Brynjolfsson and colleagues are people with long domain experience in the task in question, not older people. Confusing the two leaves the theory unable to answer an obvious objection: can a senior with no domain experience audit AI?

The paper's answer is direct. Correct. The paper does not claim the value of age; it claims the value of accumulated experience. Age is merely a function of the time that makes accumulation possible. That is why the theory is "ageless," not a celebration of seniors.

An analogy: estimating income from a postal code. Region and income are correlated, so on average the estimate lands. Applied to the one person in front of you, it misses. Age is the same: a proxy variable loosely correlated with accumulated experience. If you can measure the person, there is no reason to use the proxy.

The same frame covers the reverse problem: as AI takes over entry-level work, younger people lose the opportunities in which experience is built.

Removing the cognitive bottleneck

Why has using senior talent traditionally been so hard? The paper's reading: Gf-type tasks sat at the entrance to jobs, meaning operating speed, memory load, tool proficiency. Gf is fluid intelligence, the capacity to process on the spot. So people exited their jobs before their accumulated Gc could be exercised. AI removes that bottleneck and makes Gc "extractable" for the first time.

Here again the paper splits its claim, because adversarial review found a problem. The first half, that "a substantial part of exit is caused by the Gf bottleneck," carried no refutation condition and collided with the known main pathways of exit: mandatory retirement, health and discrimination. The current version splits it into (a) whether the bottleneck exists as an independent exit pathway, and (b) the effect of removing it, each with a refutation condition. Quantifying "a substantial part" is an open empirical question.

Different generations, different blind spots: with one fatal condition

Human-on-the-Loop (No.8) formalized the value of supervision as independence × probability of detection, on the condition that auditors' errors be decorrelated: the places where they miss things must not overlap. This paper supplies the supply-side answer.

Proposition 5 | Decorrelation of errors through generational heterogeneity Generations that differ in training period, tools used and language habits hold different blind spots. This heterogeneity is a source of decorrelation among auditors' errors.

In plain terms — People who grew up in different environments miss different things. So generational difference can be raw material for reducing overlapping misses in a checking system.

Here came the heaviest correction in the paper. The authors' own design was breaking their own proposition. Suppose you use AI summaries or pre-screening to reduce the auditing load. Then the context that AI dropped is seen by no auditor at all. However different the generations, if the input channel is identical, decorrelation is lost at the channel level.

An analogy: handing three proofreaders the same excerpt. However different their eyes, no one is looking at the passages the excerpt left out. Adding proofreaders does not reduce that miss.

In response, Proposition 5 now has a non-mediated access condition built in. The design side makes the trade-off between load reduction and independence explicit, and requires that an audit slot always remain in which raw output is seen without summary or pre-screening (the independence counterpart of "protected unassisted practice" in Brain Capital Management). Operationally this becomes dual-track auditing, with an audit-channel factor added to the metric for overlap in detection.

Social problems become resources, conditionally

Population aging, constrained participation of the young, the exclusion of people with limited access to work: the paper treats these as undeveloped sources of supply for brain capital. But it does not claim unconditional conversion.

There are two conditions. AI-mediated complementarity (Definition 6): not one side merely supplementing the other, but complementarity in both directions. And Brain Safety (Definition 8): meeting standards that amount to occupational health and safety for the brain.

There is a reason for the conditions. Omri and colleagues (2025) showed that without conditions, AI can actually increase unemployment among people with disabilities (the effect turns negative only through an indirect pathway via education). The need for design conditions comes from here.

And the "conditional" form itself became unrefutable once during production, because the success criterion had been embedded in the conditions. A failed case could be reclassified afterward as "that one did not meet the conditions." In logic this is the no-true-Scotsman move: excluding inconvenient cases afterward as "not the real thing."

The current version rewrites the conditions as an ex-ante checklist a third party can judge before outcomes are observed, and the refutation condition now states that "retrospective denial of the conditions after the fact is not admitted as a defense."

Brain Safety runs in both directions. Symmetrical to protecting the young (primarily educational purpose, safeguards against exploitation), the framework also covers preventing exploitation of the old: being turned into unpaid volunteers or unpaid advisers.

The institutional wall: Japan and the world

Try to remove age as a variable, and institutions stand in the way first. The paper treats the employment and non-employment forms separately.

AreaContent

Older workers, employment form (Japan)The Act on Stabilization of Employment of Elderly Persons (高年齢者雇用安定法) makes securing work opportunities up to age 70 a duty of effort. The April 2026 amendment to the in-service old-age pension (在職老齢年金) earnings test raises the suspension threshold from ¥510,000 to a statutory ¥620,000 (¥650,000 after wage indexation)

Treatment at 70 and aboveThe suspension mechanism continues to apply to employed persons aged 70 and above. Only premium contributions and insured status end at 70; turning 70 does not put a person "outside the system"

Older workers, non-employment formThe measures to support business start-ups, etc. under Article 10-2 of the same Act are the statutory non-employment option. From the 70s onward, many shift to advisory or contract work, creating a protection gap in workers' accident compensation and employment insurance (Proposition 9). The Freelance Act (フリーランス法; Act No. 25 of 2023, in force 1 November 2024) and expanded special enrollment in workers' accident compensation insurance partly fill this gap

United States, GermanyThe US abolished the earnings test after full retirement age (FRA) through P.L. 106-182 in 2000. Germany abolished the earnings limit on early pensions (Hinzuverdienstgrenze) in January 2023

Younger peopleILO Convention No. 138 (the three-tier 15 / 13 / 18 structure) and Convention No. 182. Participation needs project-based-learning and contest designs that do not rely on employment contracts

Proposition 9 (the protection gap) in one line — When work moves from employment to contract, a stretch opens in which the same work falls outside the umbrella of workers' accident compensation and employment insurance.

The institutional descriptions are fixed at their reference dates, and the paper says so. It is not legal advice.

H2 | Twelve propositions, three hypotheses, and refutation conditions

The paper consists of 8 definitions, 12 propositions and 3 hypotheses, and every proposition carries a refutation condition. Writing down the conditions under which the theory would be shown wrong, at the same time as the theory, is the discipline of this series. The three hypotheses:

H1 (brain health) Work that exercises Gc and is complemented by AI slows cognitive decline through sustained cognitive engagement, not through the number of hours worked.

In plain terms — If anything is working, it should be "continuing to use the mind," not "working long." This is a hypothesis, not a proven conclusion.

H2 (multigenerational teams) Under AI use, mixed-generation teams score higher than homogeneous teams on both the novelty and the feasibility of their ideas.

In plain terms — The question is not whether being multigenerational matters in itself, but what happens under AI use. A qualified prediction that takes the near-zero average effect as its baseline.

H3 (non-compressibility of experience) In verification tasks on AI output seeded with errors, people with long-term domain experience outperform the less experienced in detecting contextual risks, and the gap does not narrow under AI assistance.

In plain terms — Hand out AI and the gap in "noticing" should still not close. If it closes, the center of this paper collapses.

H3 comes with a 2 × 2 experimental protocol as Appendix A (experience level × with or without AI assistance). The dependent variables are the detection rate for surface errors, the detection rate for contextual risks, the quality of proposed corrections, and the over-rejection rate: the share of cases in which a person judged AI output wrong when it was in fact correct. Detection rates alone make anyone who doubts everything look excellent; the over-rejection rate cancels that. The protocol can be run with a few dozen participants and is positioned as the first in-house empirical study in the series.

What the paper admits it cannot yet show

• The central propositions are untested. Propositions 3, 4 and 5 are theoretical claims built from existing evidence. A search for studies testing them directly along the axis of age or years of experience found none. That absence is the basis of the paper's research-gap claim, and also its weakness.

• The eligibility criterion for H3 (left the workforce within the past 5 years) excludes people long out of the labor market. This limit on generalization is stated in the text, and estimating the depreciation function of ability is left as a research task.

• Measuring cognition and health is subject to legal constraints. The paper devotes a section to the Act on Employment Promotion etc. of Persons with Disabilities (障害者雇用促進法), the ADA, and the handling of special care-required personal information (要配慮個人情報), as the responsibility of anyone who proposes measurement.

• Conflict of interest. The entity proposing this framework stands to benefit from assessment and advisory work based on it. The paper discloses its self-citations in three categories and states the structural conflict of interest.

• The operational numbers in the appendices are initial design values, not thresholds with empirical grounding.

Implications

For executives. Where in your company's decisions is age an input? Mandatory retirement, retirement from managerial posts at a set age, the age distribution of hiring, the cut-off for training. For each one, can you say what age is standing in for? If you cannot, you are not measuring it.

For HR. Experiential audit capacity can be measured by detection performance. The practical tools are public, from assessment criteria and levels to task construction and the sealed-registration procedure. Measure first. Assign roles afterward. Note that the paper takes dynamic role rotation as its principle. Fixing "seniors as auditors" is just a new age stereotype in place of the old one.

For investors. Has the organization that adopted AI become a structure in which everyone sees the world through the same AI summary? If so, supervision exists on paper but has lost real independence. This is hard to see in disclosures; it is first observed as an accident.