Ageless Management

DVs: (i) surface-error detection (ii) contextual/practical-risk detection (iii) quality of corrections (blind-rated)

(iv) over-rejection rate — errors span confirmatory/anomalous and high/low fluency — prediction: gap shrinks on (i), persists on (ii)(iii); (iv) Working Paper | Ageless Management in the AI Era 147

Section 10.6), making after-the-fact substitution of hypotheses structurally impossible is

demanded even more strongly than in an ordinary experiment.

9.3 Organization-Level Indicators — Extending K × u to the Ecosystem

In parallel with hypothesis testing, an organization implementing Ageless Management needs

indicators for measuring its own state. This paper extends the framework of brain capital =

stock (K) × utilization rate (u), formalized by BCM (Kadowaki 2026e), from the employees of a

single firm to the participants in a multigenerational ecosystem not limited to the

employment boundary (Definition 7). What Proposition 8 asserts is that an ecosystem

satisfying its conditions increases K and u bidirectionally — restrained depreciation of K

among super-seniors, early formation of K among youth and socially marginalized groups,

and a rise in u for the organization. Making this claim measurable requires participant-level

K indicators, organization-level u indicators, and, as the third dimension this paper adds,

indicators of decorrelation. Furthermore, since these indicators become inputs to role

allocation, the ramparts that keep measurement itself robust (the operational requirements

of Proposition 12) must be built into the operation of the indicators.

First, participants' K-maintenance indicators. As a premise, the structure of K should be

restated. As made explicit in the commentary on Proposition 8 (Section 5), K is not a single

number, nor is it an additive composite score of three dimensions — (1) clinical cognitivefunction

scores (standardized tests such as MoCA), (2) structural and functional brain

measures, and (3) standardized domain-knowledge measures. K is conceptualized as a

hierarchical function K = 1[Kbase ≥ θ] × f(Kbase, Kdomain), gated by the foundational

cognitive function Kbase measured by (1) and (2) exceeding a threshold θ, on top of which the

domain knowledge and operational schemata Kdomain of (3) are multiplied (an inheritance

and refinement of BCM's measurement framework). This structure has two implications for

measurement procedure. The dimensions must be measured and reported independently,

and no additive composite may be constructed in which high domain-knowledge scores offset

declines in clinical scores — that would resurrect, on the measurement side, the substitution

the hierarchical function forbids. Also, the gate judgment on Kbase logically precedes the

measurement of the other dimensions — this threshold is the same gate as the cognitivescreening

lower bound of Proposition 2(b) (the theory's internal consistency), and its

judgment is also the judgment of the boundary for allocation to experiential-audit roles. The

"systematic deterioration of brain-capital indicators" in Proposition 8's refutation condition is

adjudicated by dimension-wise measurement in accordance with this hierarchical structure.

Of these, direct measurement of (1) and (2) should be conducted only under medical

collaboration (Section 9.4); what an organization can handle day to day is limited to proxy

indicators for them. There are three candidates. (1) Measurement of cognitive engagement —

the share of Gc-type components in the role occupied, and the subjective absorption in and

challenge level of role occupation. This is the mediating variable of Proposition 7 and also

connects to the testing of H1. (2) Periodic performance on detection tasks under unassisted

Working Paper | Ageless Management in the AI Era 148

conditions — small H3-type tasks administered periodically without assistance, tracking the

level and trajectory of experiential audit capacity. This can function as an early warning

against the skill-depreciation risk discussed in Section 4.3, but it must be stated repeatedly

that whether unassisted practice is itself effective for maintaining capacity is untested. (3)

Continuity of participation and changes of role — tracking whether exits due to the cognitive

bottleneck (Definition 3) are actually decreasing. All of these are apprehensions of state, not

measurements of the effect of interventions, and the distinction between state and

intervention that BCM made a discipline is maintained here as well.

Second, the organization's u indicators. The utilization rate (u) is operationalized as the

share of the cognitive assets the ecosystem could connect that are actually connected to roles.

Concretely: (1) the fit rate between participants' held assets (measured cognitive

characteristics and domain experience) and their current role requirements; (2) the fill rate

of Gc-type and audit-type roles — how far the supply of experiential audit capacity is

connected to oversight demand (the verification function shown in Section 4 to be growing

scarce); and (3) the distribution of participation and remuneration by contractual form —

data to be read alongside the Brain Safety indicators (Section 8, Table 9) to check that nonemployment

participation is not falling into the protection vacuum (Proposition 9). A rise in

u, if it becomes an end in itself, slides into exploitation (Definition 8(ii)). u indicators must

always be reported paired with indicators of load and remuneration.

Third, the measurement of decorrelation. Proposition 5 (generational decorrelation) can be

measured directly within an organization. The core of the proposed measurement is the

detection-overlap rate. The same set of AI outputs is audited independently by multiple

supervisors, and the sets of missed errors are compared across supervisors. The overlap rate

of misses for generationally homogeneous supervisor pairs is contrasted with that for

heterogeneous pairs, and if the latter is systematically lower, Proposition 5's claim — that the

correlation of blind spots is lower between generations than within them — is supported.

Conversely, if there is no difference in overlap rates, Proposition 5 is rejected exactly as its

refutation condition states. This measurement adds the audit channel as a factor — a

condition in which auditors access the raw output unmediated, and a condition in which they

access it through summarization and pre-screening by the same AI. Proposition 5 is

formulated conditional on unmediated access (Section 5), and the claim of its latter half —

that sharing an AI-mediated channel destroys, at the channel level, the decorrelation supplied

by generational heterogeneity — can be directly tested through this design as the difference

in overlap rates between the unmediated and mediated conditions. For the same reason, a

foundation-model condition is also added as a factor — a condition in which the audited AI

outputs derive from a single foundation model and a condition in which they derive from

multiple models of different architectures and developers. As condition (iii) of Proposition 5

asserts, in a situation where a single model's systematic blind spots dominate all outputs,

generational heterogeneity on the human side should not be able to override them, and this

claim becomes testable as an interaction in which the reduction of overlap rates from

Working Paper | Ageless Management in the AI Era 149

generational heterogeneity shrinks or disappears under the single-model condition. This

measurement is an operationalization of the independence side of WP8's (Kadowaki 2026h)

oversight value = independence × detection probability; the detection-probability side is

measured separately with H3-type tasks — to conflate the two is to commit the error of rating

highly a supervisor pool whose blind spots merely fail to overlap while detecting nothing. It

should be noted that experimental evidence that information exchange broadens and factual

errors decrease in diversely composed groups exists in the context of racial diversity

(Sommers 2006), but replication on the age-and-generation axis is unconfirmed, and the

measurement of detection-overlap rates is precisely an attempt to fill that gap.

Fourth, the robustness of measurement itself — the ramparts against Goodhart's law. Since

this section's indicators, above all detection-task performance and role-fit rates, become

inputs to dynamic role allocation (Definition 1), measurement connects directly to allocation

and is therefore structurally exposed to gaming (Proposition 12). The operational

requirements of Proposition 12 are here given concrete form as measurement procedures.

First, the non-periodic replacement of detection and measurement tasks. Repetition of the

same tasks permits overfitting to the tasks — improvement in test-taking rather than in

experiential audit capacity — so the task pool is refreshed without notice, and discontinuities

in performance across refreshes are monitored as an indicator of overfitting. Second,

backtesting against real-work logs. Whether performance on the verification tasks used as

grounds for role allocation diverges from audit performance in real work — the record of

detections and misses measured by ex-post verification through unmediated audit of realwork

logs — is checked periodically, and any indicator for which divergence is systematically

observed is removed from the grounds of allocation. Only operation accompanied by ex-post

verification, not one-off test scores, can sustain measurement as a ground of allocation. Third,

unannounced, real-time blind in-situ verification (unannounced in-situ verification). What

backtesting verifies is the logs that remain, and as long as how logs are kept is itself under the

control of those subject to allocation, a deeper level of gaming remains — adaptation toward

"ways of keeping logs that backtesting does not detect" (Proposition 12). Accordingly, a

procedure of blindly observing, in real time and without advance notice, randomly selected

audit scenes in real work, and collating the judgments made on the spot with the entries in

the logs, is operated as a pair with backtesting — a double rampart against the two levels of

gaming: adaptation to the test and adaptation of log formation. In addition, monitoring of

each auditor's false-alarm rate is built into the operation of the indicators. Monitoring only

the detection rate (hit rate) cannot distinguish a shift of the response criterion toward

suspecting everything — a criterion shift that raises apparent detection while increasing false

alarms and verification costs — from a true improvement in detection capacity. Sensitivity d′

and response criterion c are separated within the framework of signal detection theory

(Appendix A), and each auditor's false-alarm rate is continuously monitored as an estimate of

the response criterion c. This measurement is the monitoring of the same quantity as the

rampart against gridlock through excessive flagging (Section 8), and it is also a condition for

Working Paper | Ageless Management in the AI Era 150

preserving the interpretability of detection performance as an indicator. These are, however,

ramparts, not guarantees. No means exists to completely prevent divergence between

measurement and true capacity, and the implications of that residual risk are discussed in

Section 10.3.

Finally, the risk of measurement misuse is stated explicitly. This section's indicators are

designed for improving role allocation and monitoring Brain Safety; the moment they are

diverted into instruments for rating individuals, adjudicating exit, or cutting remuneration,

Ageless Management degenerates into a device that merely replaces discrimination by

chronological age with discrimination by measurement. A decline in K-maintenance

indicators is an occasion for support and role redesign, not a ground for exclusion. This

danger is inherent in this paper's theory, and it is confronted head-on in Section 10.3.

9.4 Implementation Roadmap

Verification and implementation cannot be separated. The propositions of Ageless

Management include some that can be tested only inside an implemented multigenerational

ecosystem (Propositions 8 and 11), and conversely, implementation without verification does

not rise above the level of an ideal. This section presents a four-stage roadmap that raises cost

and the strength of causal inference step by step.

The first stage is a pilot. In the small-scale ecosystems of the author's own organization and

its partners, the H3-type verification tasks and the measurement of detection-overlap rates

are trialed, and the collection procedures for the indicators of Section 9.3 — proxy indicators

of K maintenance, u indicators, Brain Safety indicators — are established. The purpose of this

stage is not the estimation of effects but the verification of the feasibility of measurement. For

super-seniors, for whom participation in the tasks is itself a load, applying Brain Safety

(Section 8) — load ceilings and the adequacy of compensation — to the design of the

measurement itself is also among the procedures to be established at this stage.

The second stage is case studies. WP3 of this series (Kadowaki 2026c) collated theory with

observation for the theoretical framework of enterprise redefinition through structured

observation of company cases based on public information. The same observational method

is re-applied to this paper's framework: cases of organizations in the process of implementing

multigenerational role composition, non-employment participation, and AI-mediated

complementarity are described in structured form along the design variables of Section 6 (the

default design of Table 5 and deviations from it, the operation of dynamic role allocation, the

organizational design of decorrelation). Case studies do not settle causation, but they reveal

whether the propositions take observable form in real organizations and whether there are

unanticipated failure modes. The selection bias by which observed cases skew toward

successes is unavoidable here, and its implications must be interpreted together with the

survivorship-bias discussion of Section 10.4.

Working Paper | Ageless Management in the AI Era 151

The third stage is a longitudinal study. Ecosystem participants across multiple organizations

are formed into a panel, and the K and u indicators together with data on roles, contracts, and

load are tracked continuously. Because a single organization lacks both sample size and

exogenous variation, the formation of a multi-organization consortium is a precondition. At

this stage, H1's identification strategy comes into view. Exogenous institutional variation —

reform of Japan's in-work old-age pension offset (Section 7) or changes to mandatoryretirement

rules — is a candidate instrumental variable linking changes in work and role

occupation to cognitive outcomes, and if the panel is maintained across an institutional

change, it can be used as a quasi-experiment. As seen in Section 3, however, this field has a

history of conclusions reversing with the choice of instrument. From the outset, the design

incorporates the discipline of not betting on a single identification strategy and of reporting

including the discrepancies among estimates. The verification tasks at this stage include

estimating the depreciation function of detection capacity with years since leaving work as a

continuous variable. For the stratum long past exit, to which H3's eligibility criterion (within

five years of leaving practice) does not permit extrapolation, the speed at which experiential

audit capacity depreciates with idle time is an unresolved empirical question that directly

demarcates the scope of the resource-conversion claim (Propositions 2 and 11).

The fourth stage is medical collaboration. Measuring the rate of cognitive decline (MoCA,

etc.), H1's dependent variable, requires the ethics review, clinical expertise, and long-term

follow-up infrastructure of medical research, and cannot be carried out by management

studies alone. There is precedent. The effects of structured productive social engagement by

older adults on cognitive function and brain structure have been tested jointly by medicine

and social science in the randomized controlled trials of Experience Corps (Carlson et al.

2008; Carlson et al. 2015). These, however, are effects of fifteen hours per week of structured

volunteering in a limited population (chiefly low-income urban populations in the United

States), and extrapolation to work requires bridging through the superordinate concept of

productive social engagement (Section 3). What corresponds to this in the context of Ageless

Management is the randomized or quasi-experimental evaluation of the intervention of

occupation in Gc-exercising, AI-complemented roles. Including the design of ethically

permissible forms of assignment — such as randomizing the order in which roles are offered

to those wishing to participate — this stage presupposes joint design with medical

researchers.

This roadmap is at the same time a declaration of the provisional character of this paper's

claims. The first and second stages can be undertaken by practitioners including the author's

own organization, but the execution of the third and fourth stages — and above all the

interpretation of results — must be open to execution and replication by independent

researchers. A configuration in which the proposer of a theory monopolizes its verification is

to be avoided, particularly under the conflict of interest disclosed in Section 10.6. The results

of each stage — above all a rejection of H3 — connect directly to the revision or abandonment

of the propositions. Until verified, this paper's propositions remain proposals.

Working Paper | Ageless Management in the AI Era 152

10. Limitations and Self-Critique

This section is not a ritual enumeration of the paper's limitations. The theory of Ageless

Management carries an internal structure by which it can itself turn into a new apparatus of

discrimination, selection, and exclusion, and the proposer of the theory knows the pathways

of that transformation most concretely. This section discusses six limitations — the limits of

the evidence, new stereotyping, the risks of measurement, survivorship bias, economic

presuppositions, and conflict of interest — naming names wherever possible. The paper's

core thesis bears restating. Whereas conventional senior-employment and D&I arguments

have rested on a paradigm of "accommodation and compensation (cost/CSR)," this paper

presents a management model that, through bidirectional complementarity between

heterogeneous cognitive abilities mediated by AI, converts multigenerational and diverse

talent into co-creating agents of the managerial resource of brain capital. The sections that

follow specify the conditions under which this thesis fails, and the conditions under which,

even succeeding, it does harm.

10.1 The Limits of the Evidence — Most of the Propositions Are Untested

Begin with the most basic limitation. Of this paper's twelve propositions, not one has been

directly tested. The empirical work this paper relies on is in every case not a test of the

propositions themselves but peripheral evidence consistent with them. And that peripheral

evidence itself has the following three weaknesses.

The first is misalignment of axes. What the empirical work on compression, oversight, and

depreciation organized in Section 4 measured was tenure, skill, and expertise, not

chronological age, and no study that operationalized age or years of tenure and measured

performance in AI oversight and verification exists within the range of this paper's search

(Section 4.4, search record). Neither the reality of experiential audit capacity (Definition 4)

nor the claim that it is not compressed by AI assistance (Proposition 3) has direct evidence

until Hypothesis H3 is carried out. That the keystone of this paper's theory is placed at the

point where the evidence is currently thinnest should be stated explicitly.

The second is the mixed character of the evidence. The relation between work and brain

health (the background of Propositions 7 and 8) is a field in which the sign of the estimates

has reversed with the choice of instrumental variable (Rohwedder & Willis 2010 vs. Coe et al.

2012), and the summation of the systematic reviews goes no further than "the evidence is

mixed, with large research gaps" (Meng et al. 2017; van Ours 2022). On age diversity and team

outcomes (the background of Propositions 5 and 6), the meta-analytic average effect is near

zero (Joshi & Roh 2009; Schneid et al. 2016; Wallrich et al. 2024), and this paper's Proposition 6

is a hypothesis that stacks an untested moderator — AI mediation — on top of this headwind

baseline. Citing only the favorable side of the estimates would make this paper's claims look

strong, but that is not the state of the evidence.

Working Paper | Ageless Management in the AI Era 153

The third is the mixture of evidence grades. This paper has referred, alongside peerreviewed

research, to preprints, technical reports, surveys by NGOs and membership

organizations, and practitioners' essays, with the grades made explicit. In particular, the

headwind data of Section 4.5 and parts of the description of institutional operation in Section

7 rest on grey literature, and these are not measurements of ability or outcomes. Making

grades explicit is a requirement of honesty, but it does not cure the weakness of the evidence

itself. In addition, there are limits of external validity. Much of the empirical work this paper

cites is data from the United States and Europe, often from a single firm, a single occupation,

or a limited population (noted individually at various points in Sections 3, 4, and 6), and its

transferability to the context of Japanese employment practice, pension institutions, and

elderly employment is itself an assumption requiring verification. The institutional

comparison of Section 7 has done no more than roughly demarcate the conditions of that

transfer. Taken together, the accurate positioning of this paper is not a report of established

facts but a proposal of theory equipped with refutation conditions and a map of verification

(Section 9). This qualification, stated in the abstract, becomes all the more important when

read together with the COI disclosure at the end of this section (Section 10.6).

Alongside the limits of the evidence, the limits of the concepts should also be stated. This

paper's theory is built on the two-axis classification of intelligence into Gf and Gc, but as

Definition 2 made explicit, this classification is a relative weighting on a continuum, not a

binary, and many real tasks are inseparable bundles of both components. If, as the terms Gftype

and Gc-type recur through this paper, they harden in the reader's mind into binary

categories, that is a misreading of Definition 2 — and at the same time a misreading invited

by this paper's own mode of exposition. Moreover, the construct validity of experiential audit

capacity (Definition 4) is not established. Years of experience, used to operationalize longterm

domain experience, is a coarse proxy variable, and there is no guarantee that the same

years of experience form the same detection capacity — the quality of experience (exposure

to failure cases, the density of feedback) is very likely the true explanatory variable, and this

refinement can only be carried out once H3's results are in.

10.2 The Risk of New Stereotyping — A Self-Critique of Table 5

This paper has repeatedly used the role image of "seniors as auditors." Although this image

was introduced in order to dismantle the presumption of ability from age, it carries the risk

of itself hardening into a new presumption. The statement that older people are suited to

auditing carries on its reverse side the implication that older people are not suited to

execution, and it revives the very operation Definition 1 was meant to remove: inferring role

aptitude from chronological age. Even when the direction is celebratory, the structure of

inferring an individual's characteristics from age is isomorphic with age discrimination, and

the fixation of seniors-as-auditors can become age discrimination's inverted twin.

This danger lies not outside this paper but inside it. To name names, it is Table 5 of Section

6. Table 5 presented as a "default design" a role composition assigning experiential audit and

Working Paper | Ageless Management in the AI Era 154

contextual evaluation to super-seniors, rapid prototyping to youth, orchestration to the midcareer

generation, and lived-experience problem perception to socially marginalized groups.

This paper positioned it as a convenience of departure and made deviation through dynamic

role allocation (Proposition 12) the rule, but the danger remains that practice will operate the

table as a norm. Tables circulate faster than theories. If Table 5 alone is excerpted and

transcribed into training materials and HR systems, then older people who want and can

perform Gf-type roles, early specializers with high experiential audit capacity at a young age

(the thought experiment of Section 4.4), and socially marginalized people who want to

participate in execution rather than audit are rendered invisible once again inside the new

template. That low age stereotyping is a condition for the success of age diversity is also

empirically suggested (Wegge et al. 2012), and if this paper's schema supplies new

stereotypes, that is a self-destructive consequence that undermines the paper's own

theoretical premises.

The danger of fixation is not limited to super-seniors. Table 5 assigns youth to rapid

prototyping, but if this role image hardens, youth will keep specializing in AI-assisted

execution, and by the crutch-effect logic seen in Section 4.3 they risk being deprived of the

opportunity to form contextual verification capacity — that is, the acquisition pathway of

future experiential audit capacity. This stands in direct tension with Proposition 8, which

asserts the early formation of K among youth. Unless the role design of the multigenerational

ecosystem includes intergenerational role transition (the staged entry of youth into audit

experience), this paper's schema fixes the present division of labor and dries up the future

supply. Likewise, the description that positions socially marginalized groups as suppliers of

lived-experience problem perception carries a pathway of sliding into the exploitation of

lived experience — tokenism in which opinions alone are solicited while decisions and

rewards are withheld. For the provision of a lived-experience perspective to be respected as a

role means that it carries compensation and decision rights (Section 8), and it must be

distinguished from the staging of symbolic participation.

Three counter-devices are made explicit. First, Definition 1 removes chronological age from

the criteria of role allocation, leaving only measured characteristics, experience, health

status, and the person's own intent. Second, Proposition 12 asserts the inferiority of age-fixed

allocation, on the grounds of intra-individual variation in cognitive characteristics and the

overlap of distributions across age groups, and attaches a refutation condition. Third, the

operating model of Section 6, as the name dynamic role allocation indicates, requires periodic

re-measurement and re-allocation of roles. But the existence of counter-devices does not

mean the disappearance of the danger. What this paper can do extends no further than to

state here in plain words: any reading that cites Table 5 as a norm is a misreading of this

paper.

Deeper still beneath the danger of stereotyping lies the critique of commodification. This

paper's vocabulary — brain capital, stock and utilization rate, the decomposition of job

bundles into Gf-type and Gc-type components, the connection of cognitive assets — can be

Working Paper | Ageless Management in the AI Era 155

read as a schema that disassembles human beings into parts of brain function, selects the

parts with market value, and reuses them as managerial resources, and this reading cannot

be dismissed as a mere misreading. This paper has in fact consistently discussed the

operations of decomposing jobs into components, measuring individuals' cognitive

characteristics, and optimizing their connection, and what the critique points at is not this

paper's periphery but its method itself. This paper's answer to it is placed, as stated in Section

1.4, in the normative anchor that connects its concept of capital to Sen's (1999) capability

approach. The measurement and connection of brain capital can be legitimate only insofar as

it serves not the maximization of organizational output for its own sake, but the return to

individuals of the freedom of participation that the coarse proxy variable of chronological

age has taken from them — the substantive freedom to live a life one has reason to value.

That Definition 1 includes the person's own intent among the allocation criteria, that

Definition 8 makes protection a precondition of participation, and that Propositions 8 and 11

demand the bidirectionality of benefits across all participating strata are the expressions of

this limitation inside the theory. But the existence of the anchor does not extinguish the

danger of the vocabulary. The words capital, asset, and utilization rate can circulate stripped

of the limiting clauses of intent and dignity, and at that point this paper's framework

functions as a lexicon for the instrumentalization of human beings. This danger too, like

Table 5, is one this paper has itself supplied.

10.3 The Risks of Measurement — Transformation into an Apparatus of

Selection

Definition 1 placed measured cognitive characteristics in the vacancy left by removing

chronological age. This substitution is the core of this paper's theory, and at the same time its

most dangerous point. If measurement is diverted into selection for hiring, treatment, and

exit, Ageless Management will not have abolished age discrimination but merely replaced it

with trait discrimination. Discrimination by chronological age is at least visible, and is

established as an object of legal regulation (Section 7). Selection based on the measurement of

cognitive characteristics, clothed in the appearance of objectivity, is that much harder to

make visible and to regulate. This paper cannot deny the possibility that Definition 1 will be

read as the blueprint of a new apparatus of selection.

There are four concrete dangers. The first is mismeasurement. The measurement of

cognitive characteristics is accompanied by measurement error, situational dependence, and

practice effects, and a low score at a single point in time is misread as a permanent deficit of

ability. The second is gaming. If measurement connects directly to treatment, the

optimization of measured performance becomes an end in itself, and the validity of the

measurement itself collapses. The third is cultural and attribute bias. When the language,

format, and context of a test work against particular groups, measurement automates bias

under the appearance of neutrality. The observation that algorithmic hiring tools can

perpetuate discrimination against people with disabilities (El Morr et al. 2024) is a real

Working Paper | Ageless Management in the AI Era 156

instance, in an adjacent field, of the pathway by which a measurement apparatus that

proclaims inclusion reproduces exclusion. The fourth is use beyond purpose. The pathway by

which cognitive-characteristic data collected for role allocation is diverted to the person's

detriment — exit inducement, insurance, credit — is, under the protection vacuum of nonemployment

participation (Proposition 9), especially unclosed.

The safety devices internal to this paper are the refutation condition of Proposition 12 — if

the costs of trait measurement, mismeasurement, and gaming exceed the gains of dynamic

allocation, the superiority of dynamic allocation is rejected — together with the explicit

prohibition of misuse in Section 9.3 and the Brain Safety of Section 8 (a design that restricts

measurement to an instrument of protection). As to gaming, Proposition 12 wrote into the

proposition itself, as operational requirements, the ramparts against Goodhart's law — the

non-periodic replacement of measurement tasks and ex-post verification through

unmediated audit of real-work logs (backtesting) — and Section 9.3 gave these concrete form

as procedures of indicator operation. But ramparts do not extinguish residual risk. As long as

measurement is connected to allocation and treatment, no means exists to completely

prevent divergence between measured performance and true capacity — overfitting to the

tasks, manipulation of the very real-work logs that backtesting targets, the skewing of effort

toward measurable components. The formula that when a measure becomes a target, it

ceases to be a good measure (Strathern 1997) can be mitigated by replacement and

backtesting but not repealed. The superiority of dynamic role allocation (Proposition 12) must

therefore be read as a net-benefit claim that prices in this residual cost of gaming, and that is

why its refutation condition explicitly names the costs of measurement, mismeasurement,

and gaming. On the institutional side, the minimum line is purpose limitation of

measurement (for role allocation, not for exit adjudication), the person's right of access to

results and right to re-measurement, and the guarantee of alternative pathways upon refusal

of measurement. But explicit statement is not prevention. The observation of an operational

reality in which the misuse of measurement exceeds its gains is a legitimate ground for the

normative criticism that the organization in question lacks the standing to claim the benefits

of Ageless Management. This normative statement, however, must not be diverted into a

defense of the propositions. In testing Propositions 8 and 11, the adjudication of whether the

conditions were satisfied is by ex-ante determination prior to the observation of outcomes

(Section 5). Reclassifying failure cases after the fact as "not having been implemented" in

order to protect the propositions is, as the refutation conditions of both propositions state

explicitly, not admitted as a defense, and the moment this epistemic discipline is broken, the

refutability that is this paper's signboard loses its substance.

Before the risk of misuse lies a design-level problem: the legality of the measurement

apparatus itself. Section 7 compared institutions organized around age, but the measurement

apparatus Definition 1 puts in age's place itself intersects with a different lineage of legal

regulation. First, it is a general demand of anti-discrimination law that cognitive testing in the

employment context be job-related, and comprehensive cognitive measurement unconnected

Working Paper | Ageless Management in the AI Era 157

to role requirements may not be justifiable as a selection procedure. Second, using health

status as an allocation criterion can stand in tension with the legal regimes prohibiting

discrimination on the basis of disability — the ADA (Americans with Disabilities Act) in the

United States; in Japan, the Act on Employment Promotion of Persons with Disabilities and

the Act for Eliminating Discrimination against Persons with Disabilities. The pathway by

which restriction of roles based on health status collides with duties to provide reasonable

accommodation and with limits on medical examinations cannot be excluded. Third, data on

cognitive characteristics and health status are of a class that may constitute special carerequired

personal information under Japanese law, subject to heightened discipline in

acquisition and use. That is, the apparatus introduced to remove discrimination by

chronological age can be assessed as unlawful or improper as trait discrimination or healthinformation

discrimination — a problem of the institutional viability of the design itself, prior

to the misuse discussed at the opening of Section 10.3. Furthermore, because years of

experience correlate strongly with chronological age, experience-based role allocation, even

where at the individual level it is reattribution away from age (Section 4.4), can appear at the

group level as a difference in treatment correlated with age — indirect discrimination. The

implementation of Ageless Management presupposes passing these legality reviews at the

design stage. The description in this section is an identification of issues, not legal advice, and

individual assessments depend on the statutes of each jurisdiction and the facts of each case

(the same reservation as at the opening of Section 7).

The handling of data carries its own dangers. In the non-employment participation on

which Ageless Management depends (outsourced engagement, advisory roles, PBL, NPO

partnership), the disciplines protecting workers' personal information and governing health

information that presuppose an employment relationship may not apply, or their application

may be unclear. The data whose collection this paper's framework demands — cognitive

characteristics, health status, load — belong by their nature to the class whose misuse carries

the heaviest consequences. Minimization of collection, limitation of retention periods, giving

substance to the person's consent (under asymmetric bargaining power, consent easily

becomes a formality), and prohibition of data linkage across organizations are the minimum

line (Section 8), but these remain voluntary standards without legal backing. The protection

vacuum Proposition 9 identified extends not only to workers' accident compensation and

remuneration but to data.

10.4 Survivorship Bias — The Re-Invisibilization of Those Who Cannot Work

This paper's theory is, by its very materials, skewed toward survivors. The evidence on

capacity maintenance in Section 2 is data from those who could keep participating in testing;

the association between work and health in Section 3, from those who could keep working;

the evidence on older workers' productivity in Section 6, from those who remained in the

factory (Börsch-Supan & Weiss 2016 corrects statistically for survivorship bias, but complete

removal is impossible). A theory standing on this skew carries the danger that, by speaking of

Working Paper | Ageless Management in the AI Era 158

the possibilities of older people who can work, it renders invisible once again those who

cannot.

One should have a sense of scale. Healthy working life expectancy at age 50 in England —

the years a person can expect to spend both healthy and in work — is estimated at about 9

years, below the years remaining to state pension age (Parker et al. 2020). And this figure has

steep gradients: 10.9 years for men against 8.3 for women, 6.8 years in the most deprived

areas, with regional differences reaching about 4.5 years. In the sample of a large Japanese

longitudinal survey as well, 66.0% of those aged 65 and over are retirees, and 10.2% have no

work experience at all (Takeuchi et al. 2024). The image this paper draws — "super-seniors

exercising experiential audit capacity into their 90s" — is legitimate as a description of

possibility, but if it circulates as a standard image, it turns into a pressure to reinterpret the

condition of the many who cannot participate because of health, caregiving, poverty, or

disability as if it were a matter of will or effort. The pathway by which the vocabulary of

Ageless Management is appropriated to justify policies such as raising the pension eligibility

age or tightening work requirements lies outside this paper's control, but being foreseeable, it

is warned against explicitly here.

Furthermore, this paper's theory has, even regarding change in cognitive function itself,

shone its light on the side that can be maintained — Gc-type abilities and metacognition. But

the changes of cognition that accompany aging do actually include decline to a level at which,

if it progresses, experiential audit capacity itself ceases to hold. For people in states of

dementia or long-term care need, this paper's prescription of participation in audit-type roles

is no answer. What Ageless Management can speak to extends only to role design within the

range where cognitive participation-capability is preserved, and to the provision of the

environment that preserves that capability (Brain Safety, cognitive engagement); dignity and

care after the capability is lost are a separate task outside this paper's theory — and one that

does not rank below it. Talk of "lifelong active engagement" that blurs this boundary runs

counter to this paper's intent.

The second boundary condition of Proposition 2 (Section 5) writes this fact into the theory

in plain terms. AI's complementation of Gf-type components holds only within the range in

which standard cognitive screening does not fall below the threshold of mild cognitive

impairment, and this model cannot complement a state in which attentional resources

themselves are exhausted — this lower bound is the acknowledgment that the

complementation model has a neurological limit point. This paper makes this boundary

explicit not to justify exclusion but to draw the theory's honest boundary. A theory that does

not write down its lower bound would, by the overclaim that AI complementation is possible

for people in every cognitive state, instead blur the distinctiveness of the real support — care,

medicine, income security — needed by those outside the boundary. At the same time, as

Proposition 2 itself states explicitly, this lower bound is a boundary on allocation to

experiential-audit roles, not a constraint on participation in the multigenerational ecosystem

in general. The possibility that a person below the threshold participates in forms other than

Working Paper | Ageless Management in the AI Era 159

audit — the provision of lived-experience problem perception, the handing down of

narrative, loose social connection — remains open outside the boundary, and its concrete

design remains a task this paper has not discharged. The rampart against survivorship bias is

not to hide the existence of the limit point. It is to make the location of the boundary explicit

in measurable form, and to condition the dignity of people on neither side of it on

participation — the distinction drawn in the preceding passage is the minimum line for that

purpose.

The breakwaters within this paper's framework are three. First, Definition 1 includes

health status and the person's own intent among the allocation criteria, and participation is a

right, not a duty. Second, Definition 8 (Brain Safety) requires a design that caps the intensity

and load of participation, and Proposition 10 requires that this protection be designed

symmetrically as a response to asymmetric bargaining power. Third, the socioeconomic

gradient of healthy life expectancy requires that the ecosystem's design object include not

only "the utilization of those who can participate" but "the distribution of participationcapability

itself" — this is also the reason the institutional analysis of Section 7 is a

component of the management theory, not an external condition to it. Even so, this paper

presents no theory of its own about the welfare of people in states where they cannot work.

Ageless Management is a theory of inclusion through work and participation, and it is no

substitute for inclusion that does not pass through participation — income security, care,

dignity without participation. Making this boundary explicit is the limit of the honesty this

paper can offer.

10.5 The Limits of the Economic Presuppositions

This paper's theory places several economic presuppositions implicitly. In domains where

they break down, implementation fails even if the propositions are theoretically correct. The

first is the cost of AI use. This paper took the fall in the marginal cost of Gf-type components

(Proposition 1) as its point of departure, but what an organization bears is not only the unit

price of inference. Systems that present output and grounds in auditable form, work

environments compliant with Brain Safety, and the cost of keeping up with model updates

remain as fixed costs. The second is the cost of task decomposition. The work of decomposing

job bundles into Gf-type and Gc-type components and re-allocating them across AI and

multiple participants itself generates costs of analysis, coordination, and contracting.

Moreover, the boundary of AI's zone of competence is hard to see in advance (the jagged

frontier of Section 4), and errors of decomposition surface only after the fact, as oversight

failures. The third is the transaction cost of running the ecosystem. Managing the multiple

contractual forms of employment, outsourced engagement, advisory roles, PBL, and NPO

partnership; measuring and re-allocating participants; and maintaining a composition that

preserves decorrelation all require administrative cost.

A large part of these costs diminishes with organizational scale, and this paper's design

theory may be tilted in favor of large organizations or consortia. This point must be accepted

Working Paper | Ageless Management in the AI Era 160

explicitly as a critique of external validity. The cost of building and maintaining, firm by firm,

the measurement and governance apparatus this paper demands — measurement and remeasurement

of cognitive characteristics, dual-track auditing, risk-weighted sampling,

unannounced in-situ verification, a layer-separated feedback infrastructure — is enormous,

and this paper cannot exclude the possibility that net benefit turns positive only in large

organizations. The response to this critique rests on two pathways. The first is the platform

pathway presented as a possibility in Section 6.5. If the administrative and measurement

infrastructure is standardized as common infrastructure, the marginal cost per firm can fall,

and if this holds, the scale threshold of net benefit comes down — though this is the

presentation of a possibility, not an assertion that it holds. The second is the net-benefit

requirement of Hypothesis H2. Because H2 records time to decision and verification effort as

cost variables and makes a positive net benefit after their deduction a requirement for

support, if administrative overhead eats up the benefits, Proposition 6 is by design not

supported — that is, the testing of this external-validity critique is built in not as an objection

from outside the theory but as an adjudication criterion inside this paper's verification plan.

Implementability in small, medium-sized, and regional organizations could not be examined

in this paper, and it is an important observation item for the pilot and the case studies in the

roadmap of Section 9. Also, regarding the time lag until the marginal value shift of

Proposition 1 is actually reflected in the labor market's reward structure, and the possibility

that the reward premium of Gc-type tasks is concentrated in particular occupations and

strata, this paper has nothing beyond theoretical prediction. In domains where the economic

presuppositions are not met, the multigenerational ecosystem will remain a cost center, and

pressure to revert to the "accommodation and compensation" paradigm will operate. That is

not a refutation of this paper's thesis but a demarcation of its scope — yet narrowness of

scope directly diminishes the value of a theory.

Finally, the temporal reach (Temporal Robustness) of this paper's theory should be made

clear. As declared in Section 1.4, what this paper's propositions are anchored to is not the

performance profile of the current generation of generative AI — the transient performance

gap that "today's LLMs are poor at contextual verification." The anchor point is the structure

of verification independence formalized by WP8 (Kadowaki 2026h, Propositions 10 and 11).

Namely: for any intelligent agent whatsoever, a verifier whose errors correlate with its own

training distribution cannot supply itself with statistical independence in detecting its own

systematic blind spots. The independence of verification is not supplied by improvements in

capability — however capable the verifier, so long as it shares an error distribution with the

generator, its misses remain correlated with the generator's misses. Therefore, even in a

world where future AI has made self-correction and self-verification highly sophisticated, (a)

the detection of systematic blind spots deriving from the training distribution will still

require decorrelated verifiers, and (b) the sources of their supply will be both humans with

heterogeneous experience and models of different lineages, so that this paper's framework

Working Paper | Ageless Management in the AI Era 161

persists as the allocation problem between the two. The audit-certification pathway shown in

the commentary on Proposition 1 (Section 5) is likewise anchored to this structure.

This structural anchoring, however, does not guarantee the size of the human contribution

in that allocation. Stated honestly, this paper cannot exclude the possibility that the advance

of machine-side decorrelation — cross-verification by models of different architectures and

developers (Proposition 5's model condition) — and the acceleration of verification-skill

acquisition in AI-use environments will progressively shrink the residual niche of humansupplied

decorrelation. Because this shrinkage would appear not as a one-stroke refutation of

the theory but as a gradual narrowing of its scope, it is all the harder to detect. What this

paper designates as its detector is the second sentence of Proposition 3's refutation condition

— longitudinal evidence that the acquisition of verification capacity accelerates in AI-use

environments and substantially substitutes for the effect of accumulated experience

(longitudinal compression). This paper's propositions should therefore be read as dynamic

claims to be re-tested at each point of the technology's development; the results of H3 must

always be annotated with the model generation at the time of data collection; and the

longitudinal measurement in the roadmap of Section 9 must include, among its objects of

observation, the very speed at which this residual niche shrinks.

10.6 Disclosure of Conflict of Interest and Classification of Self-Citations

Finally, this paper discloses the conditions of its own production. The author of this paper is

affiliated with VURA Capital Innovation Holdings, Inc. The company is a business operating in

the domains of longevity and brain capital, and it stands to gain business benefit from the

spread of the ideas of Ageless Management and brain capital management. That is, this paper

is written not by an observer with no stake in the theory's consequences, but by an interested

party who may benefit from the theory's diffusion. Readers should read this paper with this

structure discounted, and this paper's disciplines — the attachment of refutation conditions

to every proposition, the explicit grading of evidence, the explicit presentation of headwind

data (Section 4.5), and this section — are designed on the premise of that discount. It is for the

same reason that the roadmap of Section 9 makes it a requirement that verification from the

third stage onward, and the interpretation of results, be open to execution and replication by

independent researchers. A configuration in which the proposer of a theory monopolizes its

verification is not permissible under this conflict of interest.

The classification of self-citations is also made explicit. This paper has cited eight papers of

the VURA Working Paper Series, and their positioning divides into three types. The first is

inheritance. The inversion of valuation in Future Value Theory (Kadowaki 2026a); the K × u

framework and the state-versus-intervention distinction of Brain Capital Management

(Kadowaki 2026e); and the oversight value = independence × detection probability, error

decorrelation, the solvency condition, and the non-delegable residual of the Human on the

Loop argument (Kadowaki 2026h) were used by this paper as presupposed frameworks. The

second is reference. Enterprise redefinition and its observation (Kadowaki 2026b; 2026c),

Working Paper | Ageless Management in the AI Era 162

purpose-description-based role design (Kadowaki 2026d), the self-defined society (Kadowaki

2026f), and redefinition capitalism (Kadowaki 2026g) were cited solely to indicate position

within the series. The third is what newly becomes an object of verification in this paper. The

relation between BCM's "protected unassisted practice" and Hypothesis H3, and the extension

of HOTL's decorrelation condition to the generational axis (Proposition 5), are new claims of

this paper, and they are untested.

The point of this classification lies in the following discipline. The inherited frameworks

themselves are all unrefereed working papers, and mutual citation within the series is no

substitute for external evidence. This paper does not make claims for which no independent

supporting evidence outside the series exists appear established through a chain of selfcitations.

The strength of this paper's claims is supported not by the internal coherence of the

series but solely by the independent empirical literature cited in Sections 2 through 4 and by

the executability of the verification plan of Section 9. And since the core of that verification

plan (H3) has not yet been executed, what this paper has reached is a proposal put into

refutable form — this sentence is the conclusion of this section, and the premise of the

conclusion of the next.

11. Conclusion

This paper has presented the theory of a management regime — Ageless Management — that

removes the variable of chronological age from decisions on role allocation, evaluation,

participation, and exit, and that dynamically allocates roles, within an ecosystem not limited

to the boundary of employment, on the basis of measured cognitive characteristics,

accumulated domain experience, health status, and the person's own intent. The skeleton of

the theory consists of three operations. First, the operation of reattributing the oversight

value of older workers from age to "the interaction of long-term domain experience × Gc ×

metacognition" — that is, to experiential audit capacity. Second, the operation of formalizing

generational heterogeneity as an organizational source of error decorrelation in AI oversight.

Third, the operation of converting the three social problems of population aging, constrained

youth participation, and the exclusion of socially marginalized groups into untapped sources

of brain capital (K × u), conditional on AI-mediated complementarity and on Brain Safety

operated in an ex-ante verifiable form. These were cast into eight definitions, twelve

propositions each carrying a refutation condition, and three testable hypotheses (Section 9).

This paper's position within the series should also be confirmed. This paper extends Brain

Capital Management (Kadowaki 2026e), which treated brain capital as an asset of a single

firm's employees, vertically into a multigenerational ecosystem not limited to the boundary of

employment, and at the same time bridges the theory of future value (Kadowaki 2026a) and

the theory of oversight structure (Kadowaki 2026h) along the axis of age. WP8, which

demonstrated the failure of oversight, and this paper, which asks about the supply side of

oversight, divide a single question between demand side and supply side — who bears the

Working Paper | Ageless Management in the AI Era 163

residual that cannot be delegated to AI, under what protections, and at what cost. This paper's

answer — experiential audit capacity and generational decorrelation — is not the only

answer to this question, but the first candidate arranged in a testable form.

The meta-structure in the lineage of theory declared in the introduction (Section 1.4) should

also be reconfirmed here. The main axis of this paper is a theory of firms' competitive

advantage. Experiential audit capacity, as a path-dependent product that can be accumulated

only over time through long-term domain experience, is a candidate for a scarce and hard-toimitate

oversight resource (the resource-based view — Barney 1991), and the configurations

that organize it — the oversight portfolio that designs error decorrelation, and dynamic role

allocation — sit at the level of dynamic capabilities that integrate and reconfigure resources

under change (Teece, Pisano & Shuen 1997). By contrast, the institutional argument (Section

7) and the health argument (Section 3) are not parallel claims of this paper but the

institutional context and complementary assets that make the acquisition of competitive

advantage possible. The protective institutions for non-employment participation are the

institutional context that makes connection to oversight resources possible, and Brain Safety

and brain health are the complementary assets that prevent the depreciation of the resource

and sustain its utilization. That this paper — a theory of management — discussed

institutions and health at length is due to this complementary structure; conversely, when the

institutional context and complementary assets are absent, experiential audit capacity

remains unexpressed as a source of competitive advantage.