The Concave Relationship Between AI Exposure and Unemployment: Reframing the Supervisory Economy as an Exploratory Moderation Test

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This article examines whether aggregate AI exposure is associated with unemployment in a cross-country panel, and whether a broad managerial-share proxy provides any evidence for the proposed “supervisory economy” mechanism. Methods Using a balanced panel of 12 economies observed annually from 2014 to 2023, we construct a sector-weighted AI-exposure index and match it to labour-force data on unemployment, senior- and middle-management employment, public transfers, R&D, and GDP per capita. Two-way fixed-effects regressions are estimated linearly and with a quadratic AI term to test non-linearity within the observed support. Results The preferred quadratic specification reveals an inverted-U association between aggregate AI exposure and unemployment: joblessness rises at low-to-moderate exposure but falls once exposure reaches the upper end of the sample distribution. The managerial-share proxy has no significant standalone effect and does not significantly moderate the AI-unemployment association. Conclusions The most robust empirical contribution is the concave AI-unemployment relationship. The supervisory-economy argument should therefore be read as a conceptual and policy-research agenda rather than as a mechanism directly identified by the present proxy. Future work requires vacancy-level or occupation-level measures of AI governance, algorithmic-risk, model-monitoring and prompt-engineering roles to test the mechanism directly. 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F1000Research 2026, 14 :1211 ( https://doi.org/10.12688/f1000research.168512.3 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Revised The Concave Relationship Between AI Exposure and Unemployment: Reframing the Supervisory Economy as an Exploratory Moderation Test [version 3; peer review: 1 approved, 1 approved with reservations, 2 not approved] Peter Malliaros https://orcid.org/0000-0001-7947-9015 1 , W Alejandro Pacheco-Jaramillo 2 Peter Malliaros https://orcid.org/0000-0001-7947-9015 1 , W Alejandro Pacheco-Jaramillo 2 PUBLISHED 14 May 2026 Author details Author details 1 Research, UrCommunity Ltda., Melbourne, VIC, 3051, Australia 2 Faculty of Business, Government and Law, University of Canberra, Canberra, ACT, 2617, Australia Peter Malliaros Roles: Data Curation, Formal Analysis, Methodology, Project Administration, Writing – Review & Editing W Alejandro Pacheco-Jaramillo Roles: Conceptualization, Software, Writing – Original Draft Preparation OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Artificial Intelligence and Machine Learning gateway. This article is included in the Research on Research, Policy & Culture gateway. Abstract Background Rapid advances in general-purpose artificial intelligence are compressing automation timelines and renewing concern about technological unemployment. This article examines whether aggregate AI exposure is associated with unemployment in a cross-country panel, and whether a broad managerial-share proxy provides any evidence for the proposed “supervisory economy” mechanism. Methods Using a balanced panel of 12 economies observed annually from 2014 to 2023, we construct a sector-weighted AI-exposure index and match it to labour-force data on unemployment, senior- and middle-management employment, public transfers, R&D, and GDP per capita. Two-way fixed-effects regressions are estimated linearly and with a quadratic AI term to test non-linearity within the observed support. Results The preferred quadratic specification reveals an inverted-U association between aggregate AI exposure and unemployment: joblessness rises at low-to-moderate exposure but falls once exposure reaches the upper end of the sample distribution. The managerial-share proxy has no significant standalone effect and does not significantly moderate the AI-unemployment association. Conclusions The most robust empirical contribution is the concave AI-unemployment relationship. The supervisory-economy argument should therefore be read as a conceptual and policy-research agenda rather than as a mechanism directly identified by the present proxy. Future work requires vacancy-level or occupation-level measures of AI governance, algorithmic-risk, model-monitoring and prompt-engineering roles to test the mechanism directly. READ ALL READ LESS Keywords Artificial intelligence (O33); Automation (O33); Supervisory economy (J21); Labour-market dynamics (J21); Unemployment (E24); Human capital (J24); Technological change (O33) Corresponding Author(s) Peter Malliaros ( [email protected] ) Close Corresponding author: Peter Malliaros Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2026 Malliaros P and Pacheco-Jaramillo WA. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Malliaros P and Pacheco-Jaramillo WA. The Concave Relationship Between AI Exposure and Unemployment: Reframing the Supervisory Economy as an Exploratory Moderation Test [version 3; peer review: 1 approved, 1 approved with reservations, 2 not approved] . F1000Research 2026, 14 :1211 ( https://doi.org/10.12688/f1000research.168512.3 ) First published: 05 Nov 2025, 14 :1211 ( https://doi.org/10.12688/f1000research.168512.1 ) Latest published: 14 May 2026, 14 :1211 ( https://doi.org/10.12688/f1000research.168512.3 ) Revised Amendments from Version 2 Version 3 substantially revises and expands the manuscript in response to reviewer feedback and the continued development of the study from Version 2. The title has been changed to The Concave Relationship Between AI Exposure and Unemployment: Reframing the Supervisory Economy as an Exploratory Moderation Test to better reflect the empirical focus and moderation-based contribution of the paper. The abstract and introduction were revised to clarify the central research question, theoretical positioning, and exploratory nature of the supervisory economy framework. The literature review and theoretical discussion were significantly expanded to strengthen the connection between AI exposure, labour-market displacement, supervisory employment, human–AI complementarity, and policy responses such as universal basic income. The methodology section was refined to provide greater transparency regarding the country panel, construction of the AI-exposure index, supervisory-employment proxy, fixed-effects specification, interaction term, control variables, and robustness checks. The interpretation and presentation of the empirical framework were clarified, including the discussion surrounding country classification, development groups, and stylised automation exposure. The manuscript was edited throughout to improve coherence, academic tone, and readability. The author list remains unchanged, no figures were added or modified, and no new datasets were introduced. The revisions primarily strengthen the conceptual framing, empirical strategy, and policy relevance of the study. Version 3 substantially revises and expands the manuscript in response to reviewer feedback and the continued development of the study from Version 2. The title has been changed to The Concave Relationship Between AI Exposure and Unemployment: Reframing the Supervisory Economy as an Exploratory Moderation Test to better reflect the empirical focus and moderation-based contribution of the paper. The abstract and introduction were revised to clarify the central research question, theoretical positioning, and exploratory nature of the supervisory economy framework. The literature review and theoretical discussion were significantly expanded to strengthen the connection between AI exposure, labour-market displacement, supervisory employment, human–AI complementarity, and policy responses such as universal basic income. The methodology section was refined to provide greater transparency regarding the country panel, construction of the AI-exposure index, supervisory-employment proxy, fixed-effects specification, interaction term, control variables, and robustness checks. The interpretation and presentation of the empirical framework were clarified, including the discussion surrounding country classification, development groups, and stylised automation exposure. The manuscript was edited throughout to improve coherence, academic tone, and readability. The author list remains unchanged, no figures were added or modified, and no new datasets were introduced. The revisions primarily strengthen the conceptual framing, empirical strategy, and policy relevance of the study. See the authors' detailed response to the review by Lukasz Swiatek READ REVIEWER RESPONSES Introduction Artificial intelligence (AI) is no longer a distant prospect, but a general-purpose technology reshaping labour demand in real time. Seminal forecasts estimate that 40–50 per cent of current tasks in advanced economies are within the technical reach of automation, while successive World Economic Forum surveys predict that roughly one-quarter of all hours worked could be displaced by machines before 2030 ( Frey & Osborne, 2017 ; WEF, 2025 ). Empirical evidence already confirms the initial stages of this transition: in Europe and North America, mid-skill, routine-intensive occupations have contracted sharply, while employment has expanded in low-skill personal services and high-skill analytical roles ( Autor, 2015 ; Goos et al., 2014 ). A second, quieter trend is also visible in vacancy data: new roles are appearing whose core duties involve supervising, auditing or contextualising AI systems, including AI-governance analysts, algorithmic-risk managers and quality-assurance leads. The present article treats this supervisory-economy idea as an important conceptual proposition, but it does not claim to measure those AI-specific occupations directly in the cross-country panel. Existing policy prescriptions struggle to accommodate this possibility. Mass-retraining initiatives are often outpaced by rapid model cycles, and universal basic income (UBI) pilots in Finland and Kenya have improved self-reported well-being without increasing sustained employment ( Kangas et al., 2019 ; Banerjee et al., 2020 ). What remains missing is a systematic evaluation of whether an economy can pivot from doing to supervising—and whether such a pivot outperforms cash transfers on both employment and fiscal grounds. As breakthroughs in artificial intelligence now outpace the slow churn of conventional up-skilling systems, redirecting displaced workers into AI-supervisory roles emerges as a more realistic safeguard of economic dignity than relying on training programmes that inevitably lag the technological frontier. This paper refines that gap. Anchored in employment-polarisation theory ( Autor, Levy & Murnane, 2003 ), human-capital theory ( Becker, 1964 ) and economic-dependency theory ( Prebisch, 1950 ), we use the supervisory economy as a theoretical lens for asking whether AI exposure follows a non-linear employment trajectory and whether general managerial depth is associated with greater labour-market resilience. The empirical test is deliberately narrower than the full concept: because comparable cross-country data on AI-governance, algorithmic-risk and model-monitoring vacancies are not yet available for the whole panel, senior- and middle-management share is used only as a coarse exploratory proxy for organisational oversight capacity. The revised hypotheses are therefore: (H1) AI exposure is non-linearly associated with unemployment; (H2) broad managerial intensity may moderate this association, but cannot by itself identify AI-specific oversight labour; and (H3) public transfers and R&D intensity may shape adjustment, without necessarily offsetting short-run displacement. Methodologically, the study focuses on one cross-country empirical layer. A balanced panel of 12 OECD and partner economies observed annually from 2014 to 2023 is used to estimate the association between sector-weighted AI exposure and unemployment. The Australian vacancy examples and UBI evidence are used as contextual motivation and policy comparison, not as separately estimated components of the panel model. The contribution is therefore more precise: the paper estimates a non-linear AI-unemployment relationship and reports an exploratory moderation test using broad managerial share. This design can inform governments about the macro-employment pattern associated with AI exposure, while also clarifying what remains untested: whether AI-specific oversight occupations expand quickly enough to absorb displaced workers. Literature review Technological revolutions have consistently generated both profound anxiety and transformative economic progress throughout modern history. The canonical framework of creative destruction , as articulated by Schumpeter (1942) , posits that technological innovation inherently dismantles established economic structures while simultaneously creating new industries and occupations. This dual process is evident in historical precedents: during Britain’s Industrial Revolution, agricultural employment plummeted from 80% to just 3% of the workforce, yet manufacturing absorbed displaced labourers through unprecedented productivity gains and the creation of new occupational categories ( Allen, 2009 ). Similarly, late 20th-century computerisation eradicated approximately 3.5 million routine clerical positions across OECD nations between 1980 and 2010 but concurrently generated 19 million new roles in cognitive services and non-automatable sectors ( Autor et al., 2003 ). These transitions validate economic theory’s long-term optimism regarding technological unemployment, as disruption is inevitable. However, compensation mechanisms have historically prevailed through demand expansion, occupational diversification, and productivity spillovers. The contemporary artificial intelligence revolution, however, exhibits distinctive characteristics that challenge historical analogies. Unlike prior automation waves focused on manual or routine cognitive tasks, AI systems are increasingly demonstrating competence in domains that require advanced pattern recognition, contextual judgment, and creative synthesis—capabilities previously considered exclusively human ( Webb, 2020 ). Patent analysis reveals AI can currently perform 15% of tasks undertaken by top-quartile wage earners, including medical diagnostics, legal research, and algorithmic financial trading ( Webb, 2020 ). This cognitive breadth distinguishes AI from earlier technologies, threatening professional classes once insulated from automation. Moreover, the velocity of AI diffusion is unprecedented; generative tools like ChatGPT achieved 100 million users within two months, ten times faster than previous general-purpose technologies ( Our World in Data, 2023 ). Such acceleration compresses adaptation timelines for workers and institutions alike, potentially exacerbating displacement shocks. Empirical evidence reveals divergent labour market impacts contingent on skill complementarity and institutional contexts. In OECD economies, occupations characterised by high computer usage exhibit 2.1% annual employment growth when integrated with AI, whereas low-digital-intensity occupations experience 1.7% reductions in working hours ( OECD, 2022 ). This bifurcation highlights AI’s dual role as both a productivity multiplier for digitally fluent workers and a substitute for others. Firm-level evidence from Taiwan indicates AI-adopting enterprises increased productivity by 14% while reducing non-AI hiring by 6.2%, suggesting task reorganisation rather than categorical job destruction ( Liang, 2024 ). Macroeconomic projections further support cautious optimism: the World Economic Forum (2020) anticipates 97 million AI-driven occupations emerging by 2025—primarily in machine learning, renewable energy, and care economies—offsetting 85 million displaced roles for a net global gain of 12 million jobs. Nevertheless, distributional consequences remain severe; workers lacking tertiary education face displacement probabilities three times higher than degree holders ( ILO, 2021 ). The rapid advancement of artificial intelligence (AI) and automation technologies continues to fundamentally restructure global labour markets, generating profound concerns about employment sustainability and economic inequality. Frey and Osborne’s (2017) projection that 47% of U.S. employment faces a high automation risk has been substantiated cross-nationally through Organisation for Economic Co-operation and Development (OECD) studies, which show 14-32% job displacement probabilities across advanced economies ( Arntz et al., 2016 ). This technological disruption manifests as pronounced occupational polarisation, where employment growth concentrates at both skill extremes while mid-skill occupations experience disproportionate erosion ( Autor, 2015 ; Goos et al., 2014 ). Crucially, generative AI systems, such as large language models, have accelerated the displacement into cognitive domains previously considered resistant to automation. Recent analyses indicate that 80% of the U.S. workforce could see at least 10% of their tasks automated by generative AI, with roles involving writing, coding, and information processing facing 40-50% task exposure ( Eloundou et al., 2023 ; Felten et al., 2023 ). This represents a paradigm shift beyond traditional routineness-based displacement models, necessitating reconceptualisation of labour adaptation frameworks. Profound distributional challenges nonetheless threaten to eclipse aggregate employment gains. Wage polarisation intensifies as AI-complementary workers command 17% premiums over those in substitutable roles ( Felten et al., 2019 ). Geographical disparities widen simultaneously; advanced economies exhibit 60% AI exposure, compared to 26% in low-income nations. However, developing regions face higher displacement risks due to occupational structures concentrated in automatable tasks and weaker social safety nets ( UNDP, 2023 ). Gender asymmetries compound these inequities, with women in OECD countries experiencing 9.6% job vulnerability compared to 3.5% among men—a disparity rooted in occupational segregation and digital access gaps ( ILO, 2021 ). These intersecting inequalities necessitate targeted policy innovations, including robot taxation schemes that fund portable reskilling accounts (modelled on Singapore’s SkillsFuture), strengthened collective bargaining frameworks, and ethical AI governance that prevents algorithmic discrimination. Recent scholarship converges on the view that the tempo of artificial-intelligence breakthroughs now outstrips the cadence of formal up-skilling systems, creating a structural lag between the obsolescence of traditional tasks and workers’ ability to secure the competencies demanded by the digital economy. Frey and Osborne (2017) projected that nearly half of current jobs are technically automatable, yet vocational curricula remain locked into multi-year accreditation cycles. The gap has widened: laboratory estimates suggest that large language models such as GPT-4 can reconfigure up to 40 per cent of cognitive tasks within months of release ( Eloundou et al., 2023 ), leaving policymakers “running up a down escalator” ( Davenport & Kirby, 2016 ). Autor’s (2015) evidence shows that mid-skill jobs disappear faster than replacement roles can be generated, while human-capital theory implies that when the payoff horizon for new schooling shrinks, incentives to retrain erode ( Becker, 1964 ). Taken together, these dynamics reinforce this paper’s premise that pivoting displaced workers into AI-supervisory positions—where uniquely human judgement and ethical oversight remain indispensable—offers a more feasible absorption pathway than relying on up-skilling regimes that inevitably trail the technological frontier. Despite extensive documentation of automation risks, significant knowledge gaps persist regarding sector-specific AI adoption trajectories and their precise relationship to the emergence of supervisory roles. Manufacturing automation, for instance, follows fundamentally different displacement patterns than service-sector AI implementation. Autor et al. (2022) demonstrate that healthcare and education automation generate 23% higher supervisory role demand than manufacturing, reflecting domain-specific requirements for human oversight in ethically sensitive contexts. This sectoral heterogeneity remains underexplored in current policy formulations, particularly concerning developing economies where labour market institutions face capacity constraints ( World Bank, 2023 ). Understanding these nuances is essential for designing targeted labour market interventions. Because the dataset ends in 2023, precisely as generative AI diffusion accelerated, the analysis should be interpreted as evidence from the pre-generative-AI period and its earliest transition phase. This timing motivates a quadratic specification, since a constant-slope model is unlikely to capture adjustment dynamics across low, medium and high exposure. However, the estimates do not license a definitive post-2023 verdict. They identify a concave association within the observed 2014–2023 support and should be stress-tested as newer labour-market data become available. The supervisory-economy literature also points to a practical skills agenda. If AI-specific oversight roles continue to expand, curricula will need to blend AI literacy, domain translation, ethics, risk governance and product-design thinking. These proposals remain policy implications from the conceptual framework rather than direct conclusions from the present regression, because the panel proxy measures general senior- and middle-management share rather than AI-specific supervisory vacancies. Artificial intelligence, defined as systems performing tasks requiring human-like intelligence ( Russell & Norvig, 2021 ), now extends beyond mechanical task substitution to encompass complex decision-making domains. Modern AI architectures incorporate machine learning, natural language processing, and, increasingly, ethical governance modules that require human oversight. This evolution transforms job automation from a simple productivity enhancement to a comprehensive workflow restructuring, where human roles shift from task execution to exception management and ethical calibration ( Brynjolfsson & McAfee, 2022 ). The automation process consequently creates new hybrid workspaces where humans and machines collaborate through recursive learning loops—systems train humans on new capabilities. In contrast, humans refine algorithmic performance through feedback mechanisms ( Daugherty & Wilson, 2018 ). Universal Basic Income (UBI) has emerged as a prominent policy response to technological unemployment, conceptualised as unconditional cash transfers to mitigate the impacts of displacement ( Van Parijs & Vanderborght, 2017 ). However, empirical evidence increasingly questions its efficacy in addressing structural labour market transitions. The Finnish Basic Income Experiment (2017-2018) revealed negligible employment effects despite improved subjective well-being, with only 3-4% of participants transitioning to new occupations ( Kangas et al., 2019 ). Similarly, Kenya’s GiveDirectly trial demonstrated temporary consumption smoothing but no sustainable employment pathways ( Banerjee et al., 2020 ). These outcomes highlight the limitations of UBI as a standalone solution, particularly in developing economies where fiscal constraints limit its scalability. By contrast, AI-supervision roles represent a paradigm shift toward human-machine symbiosis, positioning humans as strategic overseers of automated ecosystems. These roles leverage irreducibly human capabilities—contextual judgment, ethical reasoning, and intercultural interpretation—that currently exceed the capabilities of AI ( Wilson & Daugherty, 2018 ; Moravec, 1988 ). The economic value of such symbiosis is evidenced by German manufacturing studies, which show a 22% productivity premium in human-supervised automated systems compared to full automation ( Hirsch-Kreinsen, 2023 ). To proxy the potential impact of a Universal Basic Income in contexts where such schemes remain largely experimental, we include “Subsidies and Other Transfers (% of total government expense)” as a continuous measure of fiscal redistributive effort. This indicator captures the share of public expenditure devoted to direct household transfers, approximating the degree to which governments already provide unconditional support akin to a basic income ( Van Parijs & Vanderborght, 2017 ). Empirical studies of UBI pilots in Finland and Kenya emphasise that cash transfers can improve wellbeing but have had limited effects on employment without complementary labour-market policies ( Banerjee et al., 2020 ). By controlling for this variable in our model, we account for cross-country and temporal variation in the generosity of social safety nets, ensuring that our estimates of AI exposure and supervisory capacity on unemployment are not conflated with differences in baseline welfare provision. While this measure does not replicate the institutional design of a universal basic income, it captures cross-country and temporal variation in unconditional fiscal support, which is the relevant margin for the present macro-level comparison. Theoretical frameworks explaining technological displacement require substantial augmentation to account for emerging supervisory economies. While Autor et al.’s (2003) polarisation theory effectively describes the U-shaped employment distribution, it inadequately addresses the generative AI-driven emergence of a “supervisory middle” stratum. Recent Bureau of Labour Statistics data reveals 15% annual growth in AI oversight roles—including algorithm auditors and machine learning validators—with compensation premiums averaging 40% above national medians ( U.S. Bureau of Labor Statistics, 2024 ). This suggests polarisation models must evolve toward tri-modal frameworks incorporating supervisory layers. Human capital theory ( Becker, 1964 ) also requires updating to address the velocity of skill obsolescence. Contemporary technological change cycles, which last 3-5 years, outpace traditional education systems, necessitating just-in-time microcredentialing models that the European Union’s AI reskilling initiatives have pioneered ( European Commission, 2023 ). Economic dependency theory ( Prebisch, 1950 ) further illuminates the vulnerabilities of the Global South, where limited fiscal space constrains institutional responses to technological disruptions. Middle-income countries like Mexico exhibit automation vulnerability 35% higher than OECD averages, yet invest <0.5% of GDP in AI adaptation infrastructure ( ILO, 2023 ). Empirical evidence increasingly validates sectoral divergence in supervisory transitions. Autor and Salomons’ (2021) analysis of Eurostat data demonstrates that financial services automation generates supervisory roles at a rate three times that of manufacturing, primarily in compliance and algorithmic accountability functions. This sectoral granularity proves critical when examining Australian labour transitions through the Internet Vacancy Index (IVI). Post-2020 data reveal an 18% compound annual growth in AI governance positions—particularly “prompt engineers” and “ethical compliance officers”—directly correlating with the adoption of generative AI ( Jobs and Skills Australia, 2025 ). These roles exhibit distinctive skill hybrids combining technical AI literacy with humanities-informed critical thinking, challenging traditional STEM-focused retraining paradigms ( Davenport & Kirby, 2016 ). Cross-national comparisons reveal how institutional architectures shape supervisory transitions. Germany’s “Industrie 4.0” strategy exemplifies coordinated adaptation through dual education systems that integrate technical colleges with industry certification pathways. This approach achieves a 74% success rate in mid-career transitions into automation oversight roles within 18 months ( Hirsch-Kreinsen, 2023 ). South Korea’s institutional framework similarly demonstrates effectiveness through the mandatory incorporation of AI ethics training into national vocational qualifications since 2022 ( Lee & Choi, 2023 ). Conversely, liberal market economies exhibit fragmented transitions; U.S. automation hotspots show a 25% wage penalty for displaced workers entering supervisory roles without formal credentials ( U.S. Bureau of Labor Statistics, 2024 ). Developing economies face compounded challenges, with Brazil’s manufacturing automation yielding only 0.2 supervisory positions per displaced worker due to institutional gaps ( World Bank, 2023 ). Behavioural economics provides indispensable insights into human adaptation to supervisory economies. Akerlof and Kranton’s (2000) identity economics framework explains resistance to role transitions in terms of professional identity disruption. Trust dynamics further mediate transitions; Dietvorst et al. (2015) demonstrate that algorithm aversion reduces supervisory effectiveness by 29% when workers perceive AI systems as opaque. This highlights the necessity of human-centred AI design that incorporates explainability features and worker participation in system development ( European Commission, 2021 ). From an inclusion standpoint, tracking the proportion of women in middle- and senior-management roles offers more than an equity metric: it serves as a predictor of how effectively an organisation will innovate and adapt to rapid AI uptake. Mixed-gender supervisory teams have been shown to generate more original solutions and temper bias in algorithmic decision-making pipelines ( Østergaard, Timmermans, & Kristinsson, 2011 ; Dezsö & Ross, 2012 ). Comparative studies further reveal that, for a given level of automation, business units with higher female representation in supervisory posts experience smoother workforce reallocation and markedly lower "algorithm-aversion," enabling richer human-AI validation loops ( Hoogendoorn, Oosterbeek & Van Praag, 2013 ; Dietvorst, Simmons & Massey, 2015 ). Consistent with identity economics theory, inclusive supervisory environments also bolster employee trust and reduce resistance to technological change, thereby mitigating the employment shock of AI diffusion by safeguarding workers’ sense of dignity and agency ( Akerlof & Kranton, 2000 ). Institutional frameworks emerge as decisive factors in successful transitions. Effective systems feature regulatory “sandboxes” that allow supervised experimentation, such as Singapore’s AI Verify framework, which reduced implementation risks by 65% ( MAS, 2023 ). Denmark’s tripartite model shows how collective bargaining agreements can codify supervisory responsibilities, preventing role ambiguity in human-AI collaboration ( Andersen & Ibsen, 2024 ). Conversely, jurisdictions lacking coherent governance exhibit regulatory arbitrage; nearly 30% of U.S. gig platforms have reclassified supervisory tasks as non-compensable “monitoring activities” ( U.S. Bureau of Labor Statistics, 2024 ). Developing economies face additional institutional capacity challenges, though Rwanda’s AI governance incubator demonstrates how focused capacity-building can overcome resource constraints ( UNDP, 2023 ). This literature reveals three research frontiers that the present panel cannot settle. First, the distributional economics of AI-specific supervisory roles require occupation-level evidence. Second, the psychological toll of continuous system monitoring warrants further study. Third, developing-economy transitions require context-sensitive models that account for informal institutions and fiscal constraints. The supervisory-economy paradigm is therefore best treated as a promising research programme, not as an empirical result already demonstrated by broad management-share data. A rich interdisciplinary literature converges on the idea that AI does not spell the end of human work, but rather reallocates value to tasks where judgment, contextual insight, and moral reasoning remain indispensable. Human-in-the-loop studies demonstrate that supervised models outperform fully autonomous pipelines on both safety and learning speed, as humans can identify “unknown unknowns” and provide counterfactual feedback that the algorithm cannot infer from data alone ( Dietvorst et al., 2015 ; European Commission, 2021 ). Wilson and Daugherty’s collaborative-intelligence taxonomy formalises this complementarity: machines scale, humans steer. In practical terms, supervisor roles range from prompt engineers refining system queries through rapid iteration to algorithmic auditors who stress-test models for fairness and drift, and ethics and compliance officers empowered to veto deployments that breach social thresholds. Such authority is crucial; identity economics experiments demonstrate that workers accept automation only when the residual human task set preserves agency, mastery, and status ( Akerlof & Kranton, 2000 ). A dignity-based economic framework remains useful for interpreting the policy stakes of AI adjustment. Sperling (2020) and Sen (1999) both imply that labour-market success should be evaluated not only through aggregate employment, but also through agency, contribution and freedom from humiliation. This normative lens is revisited in the discussion, but it is not part of the econometric identification strategy. Operationalising this lens requires more granular evidence than is available in the present panel. Future Australian work could use vacancy microdata, credential data and firm-level AI adoption measures to test whether AI-governance curricula, risk-audit roles and domain-translation roles generate measurable employment, trust and well-being gains. Methodology To explore preliminary patterns and potential interactions between key variables, we plotted a series of scatter plots using the panel data. These included the relationship between the AI Exposure Index and the share of senior and middle management (SMMS), as well as other control variables such as R&D expenditure, public transfers, and GDP per capita. The scatter plot between SMMS and AI Exposure revealed a moderately dispersed distribution without strong linearity, suggesting that their interaction in the regression specification requires further examination. These visualisations helped motivate the inclusion of interaction terms and non-linear (squared) specifications in the econometric model, particularly to test whether supervisory capacity moderates the impact of AI exposure on unemployment outcomes. Figures 1 ; 2 ; 3 ; 4 and 5 summarise the key empirical relationships between AI exposure, supervisory employment and unemployment across the 12-country panel. Figure 1. AI Exposure vs. Unemployment Rate across 12 Economies (2014–2023). Source: Author’s calculations based on the AI exposure index and World Bank unemployment data (2014–2023), created in R. Figure 2. Relative AI Exposure vs. Unemployment Rate across 12 Economies (2014–2023). Source: Author’s calculations based on the AI exposure index and World Bank unemployment data (2014–2023), created in R. Relative AI Exposure” is computed by subtracting the overall sample mean of AI exposure from each observation, so that values above zero indicate economies–years with AI adoption above the 2014–2023 average, and values below zero indicate levels below that mean. Figure 3. Country-Level Unemployment vs. AI Exposure (2014–2023). Source: Author’s calculations using the sector-weighted AI exposure index and World Bank unemployment data for 12 economies (2014–2023). Each point represents one Country–year observation, with colours distinguishing individual countries. Created in R. Figure 4. Relationship between Supervisory Share (SMMS% %) and AI Exposure (2014–2023). Source: Author’s calculations based on the sector-weighted AI exposure index and the share of senior- and middle-management roles (SMMS% %) drawn from national labour-force statistics (2014–2023) and created in R. Figure 5. Senior and Middle Management Share vs. Unemployment Rate (2014–2023). Source: Author’s calculations based on national labour-force statistics for the share of senior- and middle-management roles (SMMS% %) and World Bank unemployment data across 12 economies (2014–2023) and created in R. Figure 1 depicts a simple bivariate scatter plot of aggregate AI exposure against unemployment, revealing only a shallow, statistically insignificant linear slope and suggesting that a naïve linear specification may mask important non-linearity. Figure 2 replots the same relationship after mean-centring the exposure variable and fitting a quadratic trend; an inverted-U emerges, with unemployment declining once relative exposure reaches the upper end of the observed distribution. Figure 3 disaggregates the raw data by country, showing that the aggregate curve conceals distinct clusters: low-exposure/low-unemployment economies, mid-exposure/high-unemployment cases and high-exposure/moderate-unemployment OECD members. Figures 4 and 5 describe the managerial-share proxy. They should be read cautiously: SMMS captures broad senior- and middle-management employment, not AI-specific oversight vacancies. Together, the visuals motivate the preferred quadratic specification while showing that the supervisory-economy mechanism remains only indirectly proxied in the present data. The empirical strategy rests on a balanced panel of twelve countries—six developed and six developing—whose contrasting labour-market structures are summarised in Table 1 . The methodology proceeds in three steps: (i) construction of an aggregate AI-exposure index that weights occupation-level AIOE scores by each country’s sectoral employment shares; (ii) use of senior- and middle-management share as a broad proxy for organisational oversight capacity, with explicit recognition that it does not measure AI-specific governance labour; and (iii) estimation of two-way fixed-effects models that test the linear and quadratic association between AI exposure and unemployment after controlling for income, public transfers and R&D effort. This framework is appropriate for testing a macro-level association and an exploratory moderation effect, but not for making strong causal claims about the emergence of AI-governance occupations. Table 1. Country classification by development level and stylised automation exposure). Country Development group Stylised automation exposure 1 United States Developed High – very large services sector, pervasive AI adoption in finance, retail, logistics Germany Developed High –world–class manufacturing with advanced robotics plus data-intensive services Japan Developed A high–aging workforce drives intensive deployment of industrial and service-sector automation. Australia Developed Medium – commodity extraction lowers aggregate exposure, but services are heavily digitalised. United Kingdom Developed High –finance, media and professional services dominate the employment mix Canada Developed Medium – diversified economy; strong services offset by sizeable resource and primary sectors China Developing Medium – rapid robot diffusion in export manufacturing, but still, large, low-tech services share India Developing Low –employment concentrated in agriculture and informal services; limited formal automation Brazil Developing Low –high share of agriculture and basic services; uneven industrial automation South Africa Developing Low services dominate, but mostly low-tech, with modest AI penetration Indonesia Developing Low –agriculture and low-skill manufacturing keep aggregate exposure subdued. Mexico Developing Medium – export-oriented manufacturing is increasingly automated, yet agriculture and informal services remain sizeable. 1 Classification is based on the sector-weighted AI-exposure index constructed in this paper: “High” = top third of baseline index, “Medium” = middle third, “Low” = bottom third. Table 1 below presents the 12 selected countries, grouped into developed economies and developing/emerging economies, along with an approximate qualitative assessment of their automation exposure, categorised as high, medium, or low. This categorisation considers factors such as industrial structure, technology adoption rates, investment in artificial intelligence, and workforce composition, providing a practical foundation for analysing AI-driven labour market transformations. The balanced panel now comprises twelve economies—six developed and six developing—observed annually from 2014 to 2023. Developed members (the United States, Germany, Japan, Australia, the United Kingdom, and Canada) represent high-income OECD countries with sophisticated labour markets. Developing members (China, India, Brazil, South Africa, Indonesia, Mexico) provide contrast in occupational structure and technological diffusion. All macro variables are drawn from the World Development Indicators. For each country-year, we build a sector-weighted AI-exposure index, AIExpc,t, using constant occupational AIOE benchmarks obtained from the SOCAIOE database and sectoral employment shares from the World Development Indicators. The country-year index is computed as the employment-share-weighted average of sectoral exposure scores, then z-standardised across the estimation panel so that one unit represents one sample standard deviation of exposure. This construction means that estimated coefficients should be interpreted within the observed country-year support rather than as universal technology elasticities. We construct the Share of Senior-and-Middle-Management (SMMS) as the percentage of workers classified in senior and middle management over total employment in each country-year, drawing on harmonised ILOSTAT and World Bank labour-force series where available. This variable is retained because it provides the most comparable cross-country indicator of broad organisational hierarchy. Its limitation is central to the revised interpretation: most senior and middle managers are not AI-governance specialists, so SMMS is a noisy stock measure of general managerial intensity rather than a flow measure of new AI-specific oversight jobs. Missing occupational observations are handled transparently through documented interpolation or nearest-year rules in the replication file, and results should be read as exploratory where SMMS availability is incomplete. Supervisory capacity is therefore proxied by “Employment in senior and middle management (% of total employment)” (SupEmpc,t/SMMS). This proxy should not be interpreted as a direct measure of AI oversight labour. To capture broad income-support effort, we include Subsidies and Other Transfers (% of Total Government Expense), a World Bank indicator measuring the share of public expenditure devoted to subsidies and social transfers. Denoted Transfc,t, this variable controls for cross-country and temporal variation in fiscal redistribution. The core two-way fixed-effects specification is Unempc , t = α + βAIExpc , t + δSupEmpc , t + ϕFemaleMgrc , t + θ ( AIExpc , t × SupEmpc , t ) + ψTransfc , t + γ 1 lnGDPpcc , t + γ 2 RDc , t + μc + λt + εc , t Variables Unempc , t = Unemployment rate – the percentage of the total labour force that is unemployed in country ccc and year t . βAIExpc , t = Aggregate exposure to artificial intelligence – a country-level index constructed by weighting occupational AI exposure (AIOE) by the employment shares across sectors, for country ccc in year t . ϕFemaleMgrc , t = Percentage of managerial positions (senior and middle management) held by women in country c and year t . Serves as a control for gender diversity in supervisory roles, which may affect how effectively a workforce adapts to AI-driven changes. δSupEmpc , t = General managerial share – the percentage of total employment in senior and middle management in country c and year t ; this is a coarse proxy for organisational oversight capacity, not a direct measure of AI-specific supervisory labour. θ ( AIExpc , t × SupEmpc , t ) = Interaction term between AI exposure and general managerial share – tests whether broad managerial intensity moderates the AI-unemployment association; it does not adjudicate the existence of AI-specific oversight vacancies. ψTransfc , t = Subsidies and other transfers (% of total government expense), a proxy for the generosity of the fiscal safety net in country c and year t . γ 1 lnGDPpcc , t = Log of GDP per capita (in constant 2021 international dollars, PPP-adjusted), a control variable to account for income level. γ 2 RDc , t = Research and development expenditure – measured as a percentage of GDP, representing national investment in innovation and technological capacity. μc = Country fixed effects – controls for unobservable country-specific factors that do not vary over time. λt = Time fixed effects – controls for global or time-specific shocks that affect all countries similarly each year. εc , t = Error term – the idiosyncratic error for country ccc in year t . We first ran a two-way fixed-effects regression on the balanced 2014–2023 panel. In the linear model, higher AI exposure is associated with lower unemployment (−1.47 percentage points per standardised AI unit, p ≈ 0.04), but this estimate should be treated as a diagnostic baseline because the later quadratic model shows that the association is not constant across the exposure distribution. The broad managerial-share proxy is only marginally associated with lower unemployment (−2.12 percentage points, p ≈ 0.07), and the AI × management interaction is not statistically significant. These results do not support a strong claim that oversight roles protect employment; rather, they motivate the non-linear specification and show that general managerial intensity is, at best, an imprecise moderator. The positive R&D coefficient is also interpreted cautiously, since it may reflect transitional displacement, reverse causality or omitted innovation-cycle factors. Table 2. Two-way Fixed-Effects Linear Model: Unemployment, AI Exposure, Supervisory Employment and Controls (2014–2023). Regressor Coefficient Std. error p-value Interpretation AI Exposure (AIExp) −1.47 * 0.69 0.043 Linear baseline only; effect is non-linear in the preferred model Supervisory employment (SupEmp) −2.12 † 1.14 0.071 Broad managerial share is only marginally associated with unemployment AIExp × SupEmp +0.02 0.02 0.170 Interaction is not significant; no identified protective mechanism Subsidies & transfers (% expense) +0.14 0.09 0.154 Fiscal transfers have no apparent effect ln GDP per capita (PPP) −68.45 *** 13.88 0.000 Richer economies exhibit markedly lower unemployment R&D expenditure (% GDP) +5.42 * 2.39 0.036 Positive association; interpret cautiously because timing and omitted factors may matter *** p < 0.001; ** p < 0.01; * p < 0.05; † Indicates marginal significance (p < 0.10). Two-way fixed effects include country and year; standard errors are clustered at the country level. We first ran a two-way fixed-effects regression on the balanced 2014–2023 panel. In the linear model, higher AI exposure is associated with lower unemployment (−1.47 percentage points per standardised AI unit, p ≈ 0.04), but this estimate should be treated as a diagnostic baseline because the later quadratic model shows that the association is not constant across the exposure distribution. The broad managerial-share proxy is only marginally associated with lower unemployment (−2.12 percentage points, p ≈ 0.07), and the AI × management interaction is not statistically significant. These results do not support a strong claim that oversight roles protect employment; rather, they motivate the non-linear specification and show that general managerial intensity is, at best, an imprecise moderator. The positive R&D coefficient is also interpreted cautiously, since it may reflect transitional displacement, reverse causality or omitted innovation-cycle factors. Because the linear model compresses different exposure regimes into one slope, we re-specified AI exposure with a quadratic term, β 2 ( AIExpc , t ) ^ 2 , to capture curvature. This specification is the preferred model because it tests whether the unemployment association changes sign across the observed distribution. Modelling non-linear effects of AI exposure: Quadratic specification We assembled a balanced panel of 12 economies, observed annually from 2014 to 2023 (96 country–year observations after list-wise deletion). For each country-year, we combine sectoral employment shares from the World Development Indicators with occupation-level automation scores to construct an aggregate AI-exposure index, AIExpc,t. To allow for non-linearity, the index is first demeaned, AIExpc , t ∗ = AIExpc , t − AIExp , Moreover, its square, (AIExpc,t∗)2, is included as an additional regressor. Supervisory capacity is proxied by the share of senior- and middle-management employment (SMMS). Further controls comprise the log of GDP per capita, the ratio of public expenditure on subsidies and transfers, and R&D expenditure as a percentage of GDP. Two-way fixed effects absorb time-invariant country heterogeneity (μc) and common year shocks (λt), and standard errors are clustered at the country level to account for arbitrary serial correlation within countries. The preferred specification is estimated with two-way fixed effects and clustered standard errors: Unemploymentc , t = β 1 AIExpc , t ∗ + β 2 ( AIExpc , t ∗ ) 2 + β 3 SMMSc , t + β 4 ( AIExpc , t ∗ × SMMSc , t ) + β 5 ln GDPpcc , t + β 6 Transfc , t + β 7 RDc , t + μc + λt + εc , t . Estimates reveal a positive but insignificant linear AI term and a negative, statistically significant quadratic term (β2 ≈ –0.052, p ≈ 0.03), consistent with an inverted-U pattern: unemployment rises at low-to-moderate AI exposure and declines only toward the upper end of the observed exposure distribution. The interaction between AI exposure and SMMS is negative but not statistically significant, so the model does not identify a protective supervisory-economy mechanism. Higher GDP per capita is associated with lower unemployment, but the large coefficient should be interpreted within the narrow within-country support of the fixed-effects model and checked in robustness work using standardised effects and marginal-effect plots. When alternative covariates were tested, none overturned the concave AI finding. Introducing the female share of senior and middle management trimmed the usable sample and produced imprecise estimates; omitting it improved within-fit and left the AI coefficients materially unchanged. Substituting contemporaneous income with a one-year lag yielded similar AI parameters, while lagged income lost statistical significance. Fiscal transfers remained small and insignificant across variants. The positive, marginally significant R&D term turned insignificant when lagged, which is consistent with either short-run displacement or timing and omitted-variable concerns. Collectively, these checks suggest that the inverted-U association is robust, while the mechanisms behind income, R&D and supervisory capacity require further testing. Results The two-way fixed-effects estimations, with heteroskedasticity-robust standard errors clustered at the country level, yield a consistent narrative across functional forms. In the preferred quadratic specification, the centred linear AI-exposure term is economically small and imprecise ( β ̂ = 0.174, SE = 0.436, p = 0.69). In contrast, the squared term is negative and statistically significant ( β ̂ = –0.0516, SE = 0.0228, p = 0.03), implying a concave unemployment-technology locus. The implied turning point occurs at approximately 1.69 standard deviations above the sample mean of exposure; this value should be read as a within-sample threshold, not a universal adoption benchmark. The supervisory-workforce share (SMMS) carries an insignificant standalone coefficient ( β ̂ = 0.503, p = 0.33) and its interaction with exposure is likewise non-significant ( β ̂ = –0.0455, p = 0.34). Thus, broad managerial depth does not materially modulate the concave pattern once country and year fixed effects are partialled out. The GDP-per-capita coefficient is large ( β ̂ = –33.55, p = 0.03), so the substantive interpretation should rely on marginal effects over the observed within-country range and standardised robustness checks rather than on extrapolated doublings of income. Contemporaneous public transfers are not statistically significant (p = 0.12), while R&D outlays are marginally positive (p = 0.08) and should be interpreted cautiously. Table 3. Two-way Fixed-Effects Quadratic Model: Unemployment, AI Exposure, Supervisory Share and Controls (2014–2023). Term Coefficient (cluster s.e.) p-value What it tells us AIExp_c 0.174 (0.436) 0.69 Small, imprecise linear component. AIExp 2 –0.0516 (0.0228) 0.03 Significant concave curvature (inverted-U). SMMS 0.503 (0.514) 0.33 A standalone supervisory share is not decisive. AIExp × SMMS –0.0455 (0.0470) 0.34 Not significant; broad management share does not identify AI-specific oversight labour. ln GDPpc –33.55 (14.99) 0.03 Large negative fixed-effects coefficient; interpret within observed support. Transfers 0.092 (0.059) 0.12 Transfers do not offset job loss contemporaneously. R & D 1.69 (0.92) 0.08 Marginal positive association; mechanism remains uncertain. The linear specification acts as a robustness probe: omitting the quadratic term renders the AI-exposure coefficient significantly negative ( β ̂ = –1.47, p = 0.043), but this result is sensitive to high-exposure, high-income economies and weakens once curvature is admitted. Supervisory intensity is weakly beneficial in the linear model ( β ̂ = –2.12, p = 0.071), yet the interaction remains non-significant (p = 0.17). Overall, the econometric evidence supports a concave association between AI exposure and unemployment, tempered by national income, with broad managerial layering exhibiting, at most, second-order and statistically uncertain effects. Conclusions The evidence reveals an inverted-U association between national AI exposure and unemployment: modest automation pressures coincide with higher joblessness, but toward the upper end of the observed exposure distribution the marginal association turns negative. This pattern is consistent with models in which automation first displaces routine labour and later complements new tasks, products and organisational forms. The paper’s strongest empirical contribution is therefore the concave AI-unemployment result. By contrast, the broad managerial-share proxy neither reduces unemployment on its own nor significantly moderates the AI effect in the preferred model. This means the present data do not support a causal claim that AI-specific oversight roles protect employment. They show only that general managerial intensity, as measured here, is not a statistically reliable buffer. Taken together, the findings support a balanced narrative from the wider literature: AI adoption can initially widen labour-market fractures but may re-equilibrate when complementary tasks and productivity gains scale. However, the non-significant interaction between AI exposure and SMMS requires restraint. Policies such as AI oversight corps, algorithmic-risk fellowships or prompt-engineering programs remain conceptually plausible, but they are not direct implications of the moderation estimate. They should be framed as policy experiments justified by the literature and by emerging vacancy evidence, not as outcomes proven by this panel. The study has three important limitations. First, the panel ends in 2023, so it captures the pre-generative-AI era and only the earliest phase of foundation-model diffusion. Second, SMMS is an economy-wide management-stock measure and is too coarse to test the growth of AI-governance, algorithmic-risk, model-monitoring or prompt-engineering jobs. Third, coefficient magnitudes for controls such as log GDP per capita should be interpreted within observed support and stress-tested through standardised effects, marginal-effect plots and alternative functional forms. A more conclusive assessment will require higher-frequency data, a country-year availability table, a deposited constructed panel and vacancy-level measures of AI-specific oversight labour. Policy implications The policy implications drawn from this analysis should be interpreted as conditional and forward-looking. The regression evidence supports caution about early displacement and highlights the importance of income capacity, but it does not show that simply increasing the number of managers will reduce unemployment. Governments should therefore treat AI Oversight Corps, AI Safety Engineer fellowships and prompt-engineering or data-translation programs as targeted pilots to be evaluated with vacancy-level and worker-level evidence. The aim is not to expand management layers generically, but to build measurable AI-governance capability where high-risk systems are deployed and where displaced workers can realistically move into complementary tasks. AI may generate occupations that are difficult to imagine ex ante, but the transition’s human costs and distributional outcomes remain contingent on political choices rather than technological determinism. Without deliberate institutional steering toward human-AI collaboration, robust skills ecosystems and inclusive governance, the productivity gains from AI may arrive with unnecessary displacement and inequality. The employment future remains unwritten; this paper shows one macro pattern to investigate further, not a settled verdict. The next step is to develop a real-time AI Jobs Barometer. Such a tool should track vacancy titles, task descriptions, salary trends, credential requirements and sectoral diffusion for AI-governance, algorithmic-risk, model-monitoring, prompt-engineering and data-translation roles. This would directly address the proxy limitation in the present paper and allow future work to test whether AI-specific oversight labour grows quickly enough, and inclusively enough, to offset displacement. Data availability All raw inputs used in this study are derived from publicly available secondary sources. The AI exposure index was constructed using occupational AI exposure scores from the SOCAIOE database and sectoral employment shares from the World Development Indicators ( https://data.worldbank.org/indicator ). Supervisory employment data, representing senior and middle management as a share of total employment, were retrieved from ILOSTAT and World Bank labour-force series ( https://www.ilo.org/ilostat ). Other macroeconomic controls, including GDP per capita (PPP, constant 2021 international dollars), R&D expenditure (% GDP), transfers (% government expense) and unemployment, were sourced from the World Bank, ILO and OECD databases. For full reproducibility, the resubmission should include the constructed country-year panel, the AI-index construction script, missingness rules, and a country-year availability table as source data or extended data. No primary data collection was conducted, and there are no restrictions or embargoes on the raw data. 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Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 05 Nov 2025 ADD YOUR COMMENT Comment Author details Author details 1 Research, UrCommunity Ltda., Melbourne, VIC, 3051, Australia 2 Faculty of Business, Government and Law, University of Canberra, Canberra, ACT, 2617, Australia Peter Malliaros Roles: Data Curation, Formal Analysis, Methodology, Project Administration, Writing – Review & Editing W Alejandro Pacheco-Jaramillo Roles: Conceptualization, Software, Writing – Original Draft Preparation Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (3) version 3 Revised Published: 14 May 2026, 14:1211 https://doi.org/10.12688/f1000research.168512.3 version 2 Revised Published: 11 Feb 2026, 14:1211 https://doi.org/10.12688/f1000research.168512.2 version 1 Published: 05 Nov 2025, 14:1211 https://doi.org/10.12688/f1000research.168512.1 Copyright © 2026 Malliaros P and Pacheco-Jaramillo WA. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Malliaros P and Pacheco-Jaramillo WA. The Concave Relationship Between AI Exposure and Unemployment: Reframing the Supervisory Economy as an Exploratory Moderation Test [version 3; peer review: 1 approved, 1 approved with reservations, 2 not approved] . F1000Research 2026, 14 :1211 ( https://doi.org/10.12688/f1000research.168512.3 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 2 VERSION 2 PUBLISHED 11 Feb 2026 Revised Views 0 Cite How to cite this report: Olatunbosun IE. Reviewer Report For: The Concave Relationship Between AI Exposure and Unemployment: Reframing the Supervisory Economy as an Exploratory Moderation Test [version 3; peer review: 1 approved, 1 approved with reservations, 2 not approved] . F1000Research 2026, 14 :1211 ( https://doi.org/10.5256/f1000research.196037.r460646 ) The direct URL for this report is: https://f1000research.com/articles/14-1211/v2#referee-response-460646 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 04 Mar 2026 Iyanu Emmanuel Olatunbosun , University of York, York, UK Approved VIEWS 0 https://doi.org/10.5256/f1000research.196037.r460646 The manuscript is clearly presented and addresses a relevant and timely topic. It engages with recent and appropriate literature on AI, automation, and labour market transformation. However, the literature review is somewhat broad and includes discussions not directly linked to ... Continue reading READ ALL The manuscript is clearly presented and addresses a relevant and timely topic. It engages with recent and appropriate literature on AI, automation, and labour market transformation. However, the literature review is somewhat broad and includes discussions not directly linked to the empirical analysis. The study design is appropriate, and the use of panel data and fixed effects modelling is suitable for examining labour market trends across countries. However, the use of managerial employment share as a proxy for AI supervisory roles raises construct validity concerns. The manuscript provides a general methodological overview, but key details required for replication are lacking. The statistical methods used are appropriate for the research questions, and the interpretation is generally reasonable. However, the manuscript occasionally overinterprets associations as structural labour transitions without sufficient causal evidence. The manuscript references secondary data sources, but does not provide sufficient detail on data construction or access to derived variables such as the AI exposure index. This limits full reproducibility. The conclusions are partially supported by the findings, but some claims extend beyond the empirical evidence presented. The authors should present conclusions more cautiously and clearly distinguish between empirical findings and broader theoretical or policy implications. Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Artificial Intelligence Literacy, Digital Technology, and Finance I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Olatunbosun IE. Reviewer Report For: The Concave Relationship Between AI Exposure and Unemployment: Reframing the Supervisory Economy as an Exploratory Moderation Test [version 3; peer review: 1 approved, 1 approved with reservations, 2 not approved] . F1000Research 2026, 14 :1211 ( https://doi.org/10.5256/f1000research.196037.r460646 ) The direct URL for this report is: https://f1000research.com/articles/14-1211/v2#referee-response-460646 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Tan KH. Reviewer Report For: The Concave Relationship Between AI Exposure and Unemployment: Reframing the Supervisory Economy as an Exploratory Moderation Test [version 3; peer review: 1 approved, 1 approved with reservations, 2 not approved] . F1000Research 2026, 14 :1211 ( https://doi.org/10.5256/f1000research.196037.r458365 ) The direct URL for this report is: https://f1000research.com/articles/14-1211/v2#referee-response-458365 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 25 Feb 2026 Kwan Hong Tan , Singapore University of Social Sciences, Singapore, Singapore Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.196037.r458365 1) Clear/accurate presentation + current literature Partly Justification: The manuscript cites a broad and largely current literature, but the presentation is not fully clear because the paper’s central “supervisory economy” mechanism is discussed in AI-specific terms (oversight/governance roles) ... Continue reading READ ALL 1) Clear/accurate presentation + current literature Partly Justification: The manuscript cites a broad and largely current literature, but the presentation is not fully clear because the paper’s central “supervisory economy” mechanism is discussed in AI-specific terms (oversight/governance roles) while the empirical proxy is an economy-wide managerial share. This weak operationalisation blurs what is actually being tested and risks misleading readers about the evidentiary basis for the claims. 2) Study design appropriate + technically sound Partly Justification: The econometric design is technically standard and can test an association between AI exposure and unemployment, as well as whether general managerial intensity moderates that association. However, it is not well-aligned to the central hypothesis about the emergence of AI-specific oversight labour, because total senior/middle management share is a coarse proxy for AI supervision capacity. As a result, the design is not appropriate for the strongest “supervisory economy” causal claims as framed. 3) Replication details sufficient Partly Justification: The manuscript provides the main regression specification and identifies the secondary data sources. However, full replication would require more transparency on (i) the exact construction steps for the AI-exposure index (including transformations and weighting), (ii) treatment of missingness and any imputation rules, and (iii) a clear country-year availability table. Without code and a fully reproducible data-construction pipeline, replication is possible in principle but not straightforward. 4) Statistical analysis & interpretation appropriate Partly Justification: The modelling approach (two-way fixed effects with a quadratic term) is a conventional specification for exploring non-linear associations. The main concerns are interpretive and diagnostic: the paper draws strong mechanism-based conclusions from a non-significant interaction using a coarse proxy, and several coefficients (e.g., the very large ln(GDPpc) effect) raise plausibility and scaling/specification concerns that should be explicitly discussed and stress-tested via robustness checks and marginal-effect plots within the observed support. 5) Source data available for full reproducibility Partly Justification: The underlying inputs appear to come from public sources (e.g., World Bank/ILO). However, full reproducibility requires access to the exact constructed panel and the complete AI-exposure index construction steps (and ideally code). If these are not deposited as source data/materials, reproducibility remains partial even if all raw inputs are publicly accessible. 6) Conclusions supported by results No Justification: Several conclusions are not adequately supported by the results as presented. In particular, claims about the protective role of “oversight roles” are not empirically identified because the proxy measures general managerial share rather than AI-specific oversight labour. Policy recommendations are conceptually interesting but are not tightly linked to the estimated moderation effects. The most clearly supported empirical contribution is the reported non-linear (inverted-U) association between AI exposure and unemployment, which should be separated from stronger supervisory-economy claims. Notes to the Authors Thank you for the opportunity to review your thought-provoking paper. The topic is of immense importance and would serve as a timely contribution in view of the current economic climate, with your revisions meaningfully improving the clarity of your arguments. At the same time, please allow me to suggest that several persistent/significant issues be addressed before the paper can be considered for indexing. Major Comments Fundamental Mismatch Between Hypothesis and Empirical Test: This is the most critical issue. Your Hypothesis (H2): “sectors with high AI-adoption intensity... exhibit offsetting growth in supervisory vacancies.” You specifically mention roles like “AI-governance analyst,” “algorithmic-risk manager,” and “prompt engineers” in your introduction and literature review. Your Empirical Proxy: Meanwhile, you use “Employment in senior and middle management (% of total employment)”. This measures the stock of all managers, from a CEO of a manufacturing firm to a head of retail. It does not measure the flow of new, AI-specific supervisory roles. Construct mismatch / weak operationalisation of the core mechanism: The paper’s central “supervisory economy” mechanism concerns the expansion of AI-specific oversight labour (e.g., AI governance, algorithmic risk, model monitoring). However, the empirical proxy (SupEmp/SMMS) captures the stock share of all senior and middle managers economy-wide, most of whom are not engaged in AI oversight. As such, the interaction test between SMMS and AI exposure is a weak operationalisation of the hypothesised mechanism and is unlikely to be diagnostic. A null interaction therefore does not meaningfully adjudicate the supervisory-economy claim; it largely tests whether general managerial intensity cushions unemployment during technological change, which is a weaker and less novel proposition. The Consequence: Therefore, your model would not test your hypothesis. Finding that the total share of managers does not significantly moderate the AI-unemployment relationship tells us nothing about the potential of a supervisory economy built around AI oversight. It is akin to testing whether an increase in the number of all vehicles (cars, trucks, motorcycles) on the road correlates with a decrease in pedestrian injuries to test the safety of a new bike lane network. The data is too coarse to answer the question. The paper would be much stronger if it, instead focused solely on the inverted-U relationship, or if it incorporated more granular vacancy data (like the Australian IVI data mentioned in the introduction) into the core panel model. Data and Temporal Limitations: The panel ends in 2023, which the paper acknowledges as the year when generative AI adoption “accelerates at an exponential pace.” This is a critical limitation. The period under analysis (2014-2023) captures the pre-generative AI era and its very early stages. The conclusions, particularly about the turning point (1.7σ above the mean), are based on a technological landscape that no longer exists. The “long-run verdict” remains tentative, but the paper’s conclusions are framed more definitively than this severe data constraint warrants. The policy implications, while exploratory, still feel premature when derived from a pre-2023 world. Framing and Overinterpretation: Conclusion: The statement “AI adoption need not aggravate unemployment when a critical mass of oversight roles is in place” is not supported by your model because you did not measure “oversight roles.” Your model shows that when the total number of managers is high, the relationship between AI and unemployment is unchanged (non-significant interaction). This finding cannot be flipped to support the positive statement you’ve made. Title and Core Concept: Given the empirical findings, the question mark in the title “From Automation-Induced Job Loss to a Supervisory Economy?” is doing a lot of work. Based on your evidence, the answer is a clear “no, at least not as measured by total management share.” The title should be revised to reflect the paper’s actual contribution, such as “The Concave Relationship Between AI Exposure and Unemployment: A Cross-Country Panel Analysis.” Policy Implications: The policy suggestions (e.g., “national AI Oversight Corps,” “Prompt-Engineering upskilling program”) are interesting and logically derived from the conceptual framework. However, they are disconnected from the empirical findings. The evidence suggests that simply having more managers (a very different policy from creating an AI Oversight Corps) doesn’t help. The paper should clarify this disconnect and present these policies as informed by the literature and the concept of a supervisory economy, rather than as a direct implication of the regression results. Methodological and Reporting Issues: Control Variables: The inclusion of R&D expenditure is interesting, but its positive and significant coefficient in the linear model (Table 2) and marginally significant in the quadratic model (Table 3) is interpreted as “short-run displacement.” This could also reflect reverse causality (countries with rising unemployment investing more in R&D as a stimulus) or an omitted variable bias (a third factor driving both high R&D and high unemployment). This interpretation needs to be more cautious. Coefficient magnitude check (ln GDPpc): In Table 3, the coefficient on ln(GDP per capita) is approximately –33.55. Interpreting this literally, a doubling of GDP per capita implies a change of ln(2) ≈ 0.693, which would predict an unemployment change of –33.55 × 0.693 ≈ –23.3 percentage points, an implausibly large within-country effect in most macroeconomic contexts. This suggests potential scaling/measurement issues, strong multicollinearity with fixed effects and controls, or a mis-specified functional form. Please verify variable construction, units, and consider reporting standardized effects or marginal effects within the observed support. Table 2 Inconsistency: Table 2 reports an R² of 0.988, which is suspiciously high for a panel regression on macroeconomic variables, suggesting potential overfitting or a unit root issue. Minor Comments Literature Review: Overall, while very comprehensive, the literature review is overly long and meandering. It could be significantly streamlined to focus more tightly on the core theories (polarization, human capital) and the specific gap the paper aims to fill (the empirical test of the supervisory role). Sections on Indigenous concepts of wealth and dignity, while interesting, feel disconnected from the empirical analysis and could be moved to a discussion section or removed. Terminology: The paper uses “Supervisory Society,” “supervisory economy,” and “AI-supervisory roles” almost interchangeably. While clarified in the introduction, maintaining consistent terminology throughout would improve readability. Conclusion To conclude, this paper has a core of interesting and potentially valuable work on the non-linear relationship between AI and unemployment. However, the attempt to link this to a “supervisory economy” is currently unachievable due to a fundamental mismatch between the hypothesis and the empirical proxy used to test it. The over-interpretation of null results and the severe limitations of the dataset further weaken the paper’s conclusions. A major revision that either reframes the paper’s contribution around the inverted-U finding or incorporates more precise data to test the core hypothesis is necessary before this work can be considered for indexing. Thank you for reading and I look forward to reading your upcoming work. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? No Competing Interests: No competing interests were disclosed. Reviewer Expertise: Economics, AI I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Tan KH. Reviewer Report For: The Concave Relationship Between AI Exposure and Unemployment: Reframing the Supervisory Economy as an Exploratory Moderation Test [version 3; peer review: 1 approved, 1 approved with reservations, 2 not approved] . F1000Research 2026, 14 :1211 ( https://doi.org/10.5256/f1000research.196037.r458365 ) The direct URL for this report is: https://f1000research.com/articles/14-1211/v2#referee-response-458365 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 05 Nov 2025 Views 0 Cite How to cite this report: Swiatek L. Reviewer Report For: The Concave Relationship Between AI Exposure and Unemployment: Reframing the Supervisory Economy as an Exploratory Moderation Test [version 3; peer review: 1 approved, 1 approved with reservations, 2 not approved] . F1000Research 2026, 14 :1211 ( https://doi.org/10.5256/f1000research.185698.r438831 ) The direct URL for this report is: https://f1000research.com/articles/14-1211/v1#referee-response-438831 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 09 Jan 2026 Lukasz Swiatek , The University of New South Wales, Australia, Australia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.185698.r438831 This article provides a timely, rich analysis of the international AI-driven displacement of labour and the growth of supervisory roles. It thoughtfully posits the consequent rise of the ‘supervisory society’ and explores its implications for workers, organisations and policymakers. It ... Continue reading READ ALL This article provides a timely, rich analysis of the international AI-driven displacement of labour and the growth of supervisory roles. It thoughtfully posits the consequent rise of the ‘supervisory society’ and explores its implications for workers, organisations and policymakers. It can be suggested that the article’s three hypotheses don’t align tightly enough with the current definition of the supervisory society, but this suggestion is debatable and doesn’t thwart the overall premise of the article. The development of a real-time ‘AI Jobs Barometer’ is also helpfully proposed. Nevertheless, various enhancements would strengthen the article. These enhancements are divided, below, into major and minor recommendations. Major recommendations (1) The notion of the ‘supervisory society’ is thoughtful, though slightly overblown, given the unlikelihood of societies coming to be preoccupied with the supervision of AI. To make this thoughtful idea more realistic, it would be worth modifying it slightly to something like the ‘supervision-focused society’ or the ‘supervision-oriented society’. (2) The definition of the ‘supervisory society’ is interesting – “a labour-market equilibrium in which humans specialise in oversight tasks that AI cannot internalise” – but it needs to be strengthened. In particular, it would be excellent for the definition to take into account the nature of society more effectively. (3) As the ‘supervisory economy’ and ‘supervisory society’ have already been mentioned briefly in the literature (see https://doi.org/10.1007/978-3-031-32822-0_18 and https://doi.org/10.1007/978-3-319-73888-8_35), it would be worth citing these outputs. It would also be beneficial for the authors to note briefly how their understanding of the ‘supervisory economy’ and ‘supervisory society’ differs from the versions in the literature. (4) The observation could be made that the oversight of AI involves more than just supervision. It would be wonderful for the authors to note this point briefly (for example, in the introduction) in order to help make the critical dimension of the article even stronger. (5) The paragraph beginning with “Equipping the next generation…” provides a range of interesting educational approaches for higher education institutions, but it currently lacks much-needed ethics-focused elements. It would be excellent for the authors to add at least one teaching and learning element related to AI ethics. (6) The Conclusion, and especially the “Policy implications” sub-section, helpfully mention some of the AI issues that need to be considered, though it would be good to note others, even briefly (i.e., even in a sentence or two), given their importance. A major source like Kate Crawford’s ‘Atlas of AI’ (https://doi.org/10.2307/j.ctv1ghv45t) might be helpful in this respect. Minor recommendations (7) It would be helpful to provide a reference for “Recent diffusion curves show…” or, alternatively, to indicate (for example, by using a phrase like “as noted in section x”) where the relevant data can be found. (8) The suggestions relating to the “First Step Student Program” are very useful, though at least one reference is needed in that paragraph to help readers obtain further information (if not about the First Step Student Program itself, then about Australia and its tertiary education sector). Overall, the article and its supervision-related insights make useful contributions to the AI scholarship. The implementation of the above-listed enhancements would help make an already-valuable and well-written article even stronger. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Artificial intelligence, extended intelligence, other technological developments (especially in relation to media and communication) I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Swiatek L. Reviewer Report For: The Concave Relationship Between AI Exposure and Unemployment: Reframing the Supervisory Economy as an Exploratory Moderation Test [version 3; peer review: 1 approved, 1 approved with reservations, 2 not approved] . F1000Research 2026, 14 :1211 ( https://doi.org/10.5256/f1000research.185698.r438831 ) The direct URL for this report is: https://f1000research.com/articles/14-1211/v1#referee-response-438831 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 05 Feb 2026 William Pacheco , Economics, University Anahuac Mexico, Huixquilucan de Degollado, Mexico 05 Feb 2026 Author Response We thank the reviewer for the careful reading of the manuscript and for the constructive and detailed comments. In response, we have clarified the conceptual scope of the “supervisory economy,” ... Continue reading We thank the reviewer for the careful reading of the manuscript and for the constructive and detailed comments. In response, we have clarified the conceptual scope of the “supervisory economy,” strengthened the theoretical justification for non-linear AI effects, refined the econometric specification and diagnostics, and improved transparency regarding variable construction and identification strategy. We have also tightened the link between the empirical results and the policy discussion, explicitly acknowledging limitations where effects are statistically weak. All changes are clearly marked in the revised manuscript and referenced below point by point. We thank the reviewer for the careful reading of the manuscript and for the constructive and detailed comments. In response, we have clarified the conceptual scope of the “supervisory economy,” strengthened the theoretical justification for non-linear AI effects, refined the econometric specification and diagnostics, and improved transparency regarding variable construction and identification strategy. We have also tightened the link between the empirical results and the policy discussion, explicitly acknowledging limitations where effects are statistically weak. All changes are clearly marked in the revised manuscript and referenced below point by point. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 05 Feb 2026 William Pacheco , Economics, University Anahuac Mexico, Huixquilucan de Degollado, Mexico 05 Feb 2026 Author Response We thank the reviewer for the careful reading of the manuscript and for the constructive and detailed comments. In response, we have clarified the conceptual scope of the “supervisory economy,” ... Continue reading We thank the reviewer for the careful reading of the manuscript and for the constructive and detailed comments. In response, we have clarified the conceptual scope of the “supervisory economy,” strengthened the theoretical justification for non-linear AI effects, refined the econometric specification and diagnostics, and improved transparency regarding variable construction and identification strategy. We have also tightened the link between the empirical results and the policy discussion, explicitly acknowledging limitations where effects are statistically weak. All changes are clearly marked in the revised manuscript and referenced below point by point. We thank the reviewer for the careful reading of the manuscript and for the constructive and detailed comments. In response, we have clarified the conceptual scope of the “supervisory economy,” strengthened the theoretical justification for non-linear AI effects, refined the econometric specification and diagnostics, and improved transparency regarding variable construction and identification strategy. We have also tightened the link between the empirical results and the policy discussion, explicitly acknowledging limitations where effects are statistically weak. All changes are clearly marked in the revised manuscript and referenced below point by point. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Mirindi D. Reviewer Report For: The Concave Relationship Between AI Exposure and Unemployment: Reframing the Supervisory Economy as an Exploratory Moderation Test [version 3; peer review: 1 approved, 1 approved with reservations, 2 not approved] . F1000Research 2026, 14 :1211 ( https://doi.org/10.5256/f1000research.185698.r445493 ) The direct URL for this report is: https://f1000research.com/articles/14-1211/v1#referee-response-445493 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 08 Jan 2026 Derrick Mirindi , School of Architecture and Planning, Morgan State University, Baltimore, Maryland, USA Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.185698.r445493 1. This manuscript requires a full English edition revision. Most importantly, the authors did not properly define all the abbreviations and often missed the definitions. 2. All the data claims to use from an organization should be cited. ... Continue reading READ ALL 1. This manuscript requires a full English edition revision. Most importantly, the authors did not properly define all the abbreviations and often missed the definitions. 2. All the data claims to use from an organization should be cited. 3. There are too many em dash "—" symbols; please revise. In fact, if you used an AI tool to produce this study, you have to claim it. This listing not only includes texts but also codes used in R. 4. Revise all subtitles and ensure that they have the same police type, size, and color. 5. Provide an Excel or Word document containing the datasets used in R to plot the figures. 6. Include a flowchart figure illustrating this study. 7. This sentence is long and cannot be a single paragraph: "As breakthroughs in artificial intelligence ... lag the technological frontier." Please, revise. 8. Revise all the equation formatting, for instance: "β1/(2β2) ≃1.7-\beta_{1}/(2\beta_{2}) \simeq 1.7 β1/(2β2) ≃ 1.7". Also, why are some in bold while others are not? 9. The literature review section is too long. If possible, can you reduce it? Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Artificial intelligence, construction, architecture, wood, automation, deep learning I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Mirindi D. Reviewer Report For: The Concave Relationship Between AI Exposure and Unemployment: Reframing the Supervisory Economy as an Exploratory Moderation Test [version 3; peer review: 1 approved, 1 approved with reservations, 2 not approved] . F1000Research 2026, 14 :1211 ( https://doi.org/10.5256/f1000research.185698.r445493 ) The direct URL for this report is: https://f1000research.com/articles/14-1211/v1#referee-response-445493 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 05 Nov 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 4 Version 3 (revision) 14 May 26 Version 2 (revision) 11 Feb 26 read read Version 1 05 Nov 25 read read Derrick Mirindi , Morgan State University, Baltimore, USA Lukasz Swiatek , The University of New South Wales, Australia, Australia Kwan Hong Tan , Singapore University of Social Sciences, Singapore, Singapore Iyanu Emmanuel Olatunbosun , University of York, York, UK Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Olatunbosun I. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 04 Mar 2026 | for Version 2 Iyanu Emmanuel Olatunbosun , University of York, York, UK 0 Views copyright © 2026 Olatunbosun I. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The manuscript is clearly presented and addresses a relevant and timely topic. It engages with recent and appropriate literature on AI, automation, and labour market transformation. However, the literature review is somewhat broad and includes discussions not directly linked to the empirical analysis. The study design is appropriate, and the use of panel data and fixed effects modelling is suitable for examining labour market trends across countries. However, the use of managerial employment share as a proxy for AI supervisory roles raises construct validity concerns. The manuscript provides a general methodological overview, but key details required for replication are lacking. The statistical methods used are appropriate for the research questions, and the interpretation is generally reasonable. However, the manuscript occasionally overinterprets associations as structural labour transitions without sufficient causal evidence. The manuscript references secondary data sources, but does not provide sufficient detail on data construction or access to derived variables such as the AI exposure index. This limits full reproducibility. The conclusions are partially supported by the findings, but some claims extend beyond the empirical evidence presented. The authors should present conclusions more cautiously and clearly distinguish between empirical findings and broader theoretical or policy implications. Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Artificial Intelligence Literacy, Digital Technology, and Finance I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Olatunbosun IE. Peer Review Report For: The Concave Relationship Between AI Exposure and Unemployment: Reframing the Supervisory Economy as an Exploratory Moderation Test [version 3; peer review: 1 approved, 1 approved with reservations, 2 not approved] . F1000Research 2026, 14 :1211 ( https://doi.org/10.5256/f1000research.196037.r460646) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1211/v2#referee-response-460646 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Tan K. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 25 Feb 2026 | for Version 2 Kwan Hong Tan , Singapore University of Social Sciences, Singapore, Singapore 0 Views copyright © 2026 Tan K. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions 1) Clear/accurate presentation + current literature Partly Justification: The manuscript cites a broad and largely current literature, but the presentation is not fully clear because the paper’s central “supervisory economy” mechanism is discussed in AI-specific terms (oversight/governance roles) while the empirical proxy is an economy-wide managerial share. This weak operationalisation blurs what is actually being tested and risks misleading readers about the evidentiary basis for the claims. 2) Study design appropriate + technically sound Partly Justification: The econometric design is technically standard and can test an association between AI exposure and unemployment, as well as whether general managerial intensity moderates that association. However, it is not well-aligned to the central hypothesis about the emergence of AI-specific oversight labour, because total senior/middle management share is a coarse proxy for AI supervision capacity. As a result, the design is not appropriate for the strongest “supervisory economy” causal claims as framed. 3) Replication details sufficient Partly Justification: The manuscript provides the main regression specification and identifies the secondary data sources. However, full replication would require more transparency on (i) the exact construction steps for the AI-exposure index (including transformations and weighting), (ii) treatment of missingness and any imputation rules, and (iii) a clear country-year availability table. Without code and a fully reproducible data-construction pipeline, replication is possible in principle but not straightforward. 4) Statistical analysis & interpretation appropriate Partly Justification: The modelling approach (two-way fixed effects with a quadratic term) is a conventional specification for exploring non-linear associations. The main concerns are interpretive and diagnostic: the paper draws strong mechanism-based conclusions from a non-significant interaction using a coarse proxy, and several coefficients (e.g., the very large ln(GDPpc) effect) raise plausibility and scaling/specification concerns that should be explicitly discussed and stress-tested via robustness checks and marginal-effect plots within the observed support. 5) Source data available for full reproducibility Partly Justification: The underlying inputs appear to come from public sources (e.g., World Bank/ILO). However, full reproducibility requires access to the exact constructed panel and the complete AI-exposure index construction steps (and ideally code). If these are not deposited as source data/materials, reproducibility remains partial even if all raw inputs are publicly accessible. 6) Conclusions supported by results No Justification: Several conclusions are not adequately supported by the results as presented. In particular, claims about the protective role of “oversight roles” are not empirically identified because the proxy measures general managerial share rather than AI-specific oversight labour. Policy recommendations are conceptually interesting but are not tightly linked to the estimated moderation effects. The most clearly supported empirical contribution is the reported non-linear (inverted-U) association between AI exposure and unemployment, which should be separated from stronger supervisory-economy claims. Notes to the Authors Thank you for the opportunity to review your thought-provoking paper. The topic is of immense importance and would serve as a timely contribution in view of the current economic climate, with your revisions meaningfully improving the clarity of your arguments. At the same time, please allow me to suggest that several persistent/significant issues be addressed before the paper can be considered for indexing. Major Comments Fundamental Mismatch Between Hypothesis and Empirical Test: This is the most critical issue. Your Hypothesis (H2): “sectors with high AI-adoption intensity... exhibit offsetting growth in supervisory vacancies.” You specifically mention roles like “AI-governance analyst,” “algorithmic-risk manager,” and “prompt engineers” in your introduction and literature review. Your Empirical Proxy: Meanwhile, you use “Employment in senior and middle management (% of total employment)”. This measures the stock of all managers, from a CEO of a manufacturing firm to a head of retail. It does not measure the flow of new, AI-specific supervisory roles. Construct mismatch / weak operationalisation of the core mechanism: The paper’s central “supervisory economy” mechanism concerns the expansion of AI-specific oversight labour (e.g., AI governance, algorithmic risk, model monitoring). However, the empirical proxy (SupEmp/SMMS) captures the stock share of all senior and middle managers economy-wide, most of whom are not engaged in AI oversight. As such, the interaction test between SMMS and AI exposure is a weak operationalisation of the hypothesised mechanism and is unlikely to be diagnostic. A null interaction therefore does not meaningfully adjudicate the supervisory-economy claim; it largely tests whether general managerial intensity cushions unemployment during technological change, which is a weaker and less novel proposition. The Consequence: Therefore, your model would not test your hypothesis. Finding that the total share of managers does not significantly moderate the AI-unemployment relationship tells us nothing about the potential of a supervisory economy built around AI oversight. It is akin to testing whether an increase in the number of all vehicles (cars, trucks, motorcycles) on the road correlates with a decrease in pedestrian injuries to test the safety of a new bike lane network. The data is too coarse to answer the question. The paper would be much stronger if it, instead focused solely on the inverted-U relationship, or if it incorporated more granular vacancy data (like the Australian IVI data mentioned in the introduction) into the core panel model. Data and Temporal Limitations: The panel ends in 2023, which the paper acknowledges as the year when generative AI adoption “accelerates at an exponential pace.” This is a critical limitation. The period under analysis (2014-2023) captures the pre-generative AI era and its very early stages. The conclusions, particularly about the turning point (1.7σ above the mean), are based on a technological landscape that no longer exists. The “long-run verdict” remains tentative, but the paper’s conclusions are framed more definitively than this severe data constraint warrants. The policy implications, while exploratory, still feel premature when derived from a pre-2023 world. Framing and Overinterpretation: Conclusion: The statement “AI adoption need not aggravate unemployment when a critical mass of oversight roles is in place” is not supported by your model because you did not measure “oversight roles.” Your model shows that when the total number of managers is high, the relationship between AI and unemployment is unchanged (non-significant interaction). This finding cannot be flipped to support the positive statement you’ve made. Title and Core Concept: Given the empirical findings, the question mark in the title “From Automation-Induced Job Loss to a Supervisory Economy?” is doing a lot of work. Based on your evidence, the answer is a clear “no, at least not as measured by total management share.” The title should be revised to reflect the paper’s actual contribution, such as “The Concave Relationship Between AI Exposure and Unemployment: A Cross-Country Panel Analysis.” Policy Implications: The policy suggestions (e.g., “national AI Oversight Corps,” “Prompt-Engineering upskilling program”) are interesting and logically derived from the conceptual framework. However, they are disconnected from the empirical findings. The evidence suggests that simply having more managers (a very different policy from creating an AI Oversight Corps) doesn’t help. The paper should clarify this disconnect and present these policies as informed by the literature and the concept of a supervisory economy, rather than as a direct implication of the regression results. Methodological and Reporting Issues: Control Variables: The inclusion of R&D expenditure is interesting, but its positive and significant coefficient in the linear model (Table 2) and marginally significant in the quadratic model (Table 3) is interpreted as “short-run displacement.” This could also reflect reverse causality (countries with rising unemployment investing more in R&D as a stimulus) or an omitted variable bias (a third factor driving both high R&D and high unemployment). This interpretation needs to be more cautious. Coefficient magnitude check (ln GDPpc): In Table 3, the coefficient on ln(GDP per capita) is approximately –33.55. Interpreting this literally, a doubling of GDP per capita implies a change of ln(2) ≈ 0.693, which would predict an unemployment change of –33.55 × 0.693 ≈ –23.3 percentage points, an implausibly large within-country effect in most macroeconomic contexts. This suggests potential scaling/measurement issues, strong multicollinearity with fixed effects and controls, or a mis-specified functional form. Please verify variable construction, units, and consider reporting standardized effects or marginal effects within the observed support. Table 2 Inconsistency: Table 2 reports an R² of 0.988, which is suspiciously high for a panel regression on macroeconomic variables, suggesting potential overfitting or a unit root issue. Minor Comments Literature Review: Overall, while very comprehensive, the literature review is overly long and meandering. It could be significantly streamlined to focus more tightly on the core theories (polarization, human capital) and the specific gap the paper aims to fill (the empirical test of the supervisory role). Sections on Indigenous concepts of wealth and dignity, while interesting, feel disconnected from the empirical analysis and could be moved to a discussion section or removed. Terminology: The paper uses “Supervisory Society,” “supervisory economy,” and “AI-supervisory roles” almost interchangeably. While clarified in the introduction, maintaining consistent terminology throughout would improve readability. Conclusion To conclude, this paper has a core of interesting and potentially valuable work on the non-linear relationship between AI and unemployment. However, the attempt to link this to a “supervisory economy” is currently unachievable due to a fundamental mismatch between the hypothesis and the empirical proxy used to test it. The over-interpretation of null results and the severe limitations of the dataset further weaken the paper’s conclusions. A major revision that either reframes the paper’s contribution around the inverted-U finding or incorporates more precise data to test the core hypothesis is necessary before this work can be considered for indexing. Thank you for reading and I look forward to reading your upcoming work. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? No Competing Interests No competing interests were disclosed. Reviewer Expertise Economics, AI I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (0) Tan KH. Peer Review Report For: The Concave Relationship Between AI Exposure and Unemployment: Reframing the Supervisory Economy as an Exploratory Moderation Test [version 3; peer review: 1 approved, 1 approved with reservations, 2 not approved] . F1000Research 2026, 14 :1211 ( https://doi.org/10.5256/f1000research.196037.r458365) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1211/v2#referee-response-458365 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Swiatek L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 09 Jan 2026 | for Version 1 Lukasz Swiatek , The University of New South Wales, Australia, Australia 0 Views copyright © 2026 Swiatek L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This article provides a timely, rich analysis of the international AI-driven displacement of labour and the growth of supervisory roles. It thoughtfully posits the consequent rise of the ‘supervisory society’ and explores its implications for workers, organisations and policymakers. It can be suggested that the article’s three hypotheses don’t align tightly enough with the current definition of the supervisory society, but this suggestion is debatable and doesn’t thwart the overall premise of the article. The development of a real-time ‘AI Jobs Barometer’ is also helpfully proposed. Nevertheless, various enhancements would strengthen the article. These enhancements are divided, below, into major and minor recommendations. Major recommendations (1) The notion of the ‘supervisory society’ is thoughtful, though slightly overblown, given the unlikelihood of societies coming to be preoccupied with the supervision of AI. To make this thoughtful idea more realistic, it would be worth modifying it slightly to something like the ‘supervision-focused society’ or the ‘supervision-oriented society’. (2) The definition of the ‘supervisory society’ is interesting – “a labour-market equilibrium in which humans specialise in oversight tasks that AI cannot internalise” – but it needs to be strengthened. In particular, it would be excellent for the definition to take into account the nature of society more effectively. (3) As the ‘supervisory economy’ and ‘supervisory society’ have already been mentioned briefly in the literature (see https://doi.org/10.1007/978-3-031-32822-0_18 and https://doi.org/10.1007/978-3-319-73888-8_35), it would be worth citing these outputs. It would also be beneficial for the authors to note briefly how their understanding of the ‘supervisory economy’ and ‘supervisory society’ differs from the versions in the literature. (4) The observation could be made that the oversight of AI involves more than just supervision. It would be wonderful for the authors to note this point briefly (for example, in the introduction) in order to help make the critical dimension of the article even stronger. (5) The paragraph beginning with “Equipping the next generation…” provides a range of interesting educational approaches for higher education institutions, but it currently lacks much-needed ethics-focused elements. It would be excellent for the authors to add at least one teaching and learning element related to AI ethics. (6) The Conclusion, and especially the “Policy implications” sub-section, helpfully mention some of the AI issues that need to be considered, though it would be good to note others, even briefly (i.e., even in a sentence or two), given their importance. A major source like Kate Crawford’s ‘Atlas of AI’ (https://doi.org/10.2307/j.ctv1ghv45t) might be helpful in this respect. Minor recommendations (7) It would be helpful to provide a reference for “Recent diffusion curves show…” or, alternatively, to indicate (for example, by using a phrase like “as noted in section x”) where the relevant data can be found. (8) The suggestions relating to the “First Step Student Program” are very useful, though at least one reference is needed in that paragraph to help readers obtain further information (if not about the First Step Student Program itself, then about Australia and its tertiary education sector). Overall, the article and its supervision-related insights make useful contributions to the AI scholarship. The implementation of the above-listed enhancements would help make an already-valuable and well-written article even stronger. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Artificial intelligence, extended intelligence, other technological developments (especially in relation to media and communication) I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 05 Feb 2026 William Pacheco, Economics, University Anahuac Mexico, Huixquilucan de Degollado, Mexico We thank the reviewer for the careful reading of the manuscript and for the constructive and detailed comments. In response, we have clarified the conceptual scope of the “supervisory economy,” strengthened the theoretical justification for non-linear AI effects, refined the econometric specification and diagnostics, and improved transparency regarding variable construction and identification strategy. We have also tightened the link between the empirical results and the policy discussion, explicitly acknowledging limitations where effects are statistically weak. All changes are clearly marked in the revised manuscript and referenced below point by point. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Swiatek L. Peer Review Report For: The Concave Relationship Between AI Exposure and Unemployment: Reframing the Supervisory Economy as an Exploratory Moderation Test [version 3; peer review: 1 approved, 1 approved with reservations, 2 not approved] . F1000Research 2026, 14 :1211 ( https://doi.org/10.5256/f1000research.185698.r438831) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1211/v1#referee-response-438831 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Mirindi D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 08 Jan 2026 | for Version 1 Derrick Mirindi , School of Architecture and Planning, Morgan State University, Baltimore, Maryland, USA 0 Views copyright © 2026 Mirindi D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions 1. This manuscript requires a full English edition revision. Most importantly, the authors did not properly define all the abbreviations and often missed the definitions. 2. All the data claims to use from an organization should be cited. 3. There are too many em dash "—" symbols; please revise. In fact, if you used an AI tool to produce this study, you have to claim it. This listing not only includes texts but also codes used in R. 4. Revise all subtitles and ensure that they have the same police type, size, and color. 5. Provide an Excel or Word document containing the datasets used in R to plot the figures. 6. Include a flowchart figure illustrating this study. 7. This sentence is long and cannot be a single paragraph: "As breakthroughs in artificial intelligence ... lag the technological frontier." Please, revise. 8. Revise all the equation formatting, for instance: "β1/(2β2) ≃1.7-\beta_{1}/(2\beta_{2}) \simeq 1.7 β1/(2β2) ≃ 1.7". Also, why are some in bold while others are not? 9. The literature review section is too long. If possible, can you reduce it? Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Artificial intelligence, construction, architecture, wood, automation, deep learning I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (0) Mirindi D. Peer Review Report For: The Concave Relationship Between AI Exposure and Unemployment: Reframing the Supervisory Economy as an Exploratory Moderation Test [version 3; peer review: 1 approved, 1 approved with reservations, 2 not approved] . F1000Research 2026, 14 :1211 ( https://doi.org/10.5256/f1000research.185698.r445493) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1211/v1#referee-response-445493 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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