{"slug": "creation-validation-obsolescence-ai-driven-labor-market-displacement", "title": "Creation, validation, obsolescence: AI-driven labor market displacement", "summary": "A systematic review of 94 empirical studies, following PRISMA 2020 guidelines, finds that AI-driven labor market displacement is already observable, with a 14–41% reduction in postings for entry- and mid-level software development and content-creation roles in high-income economies between 2022 and 2024, and a 2%–21% reduction in online labor market postings for automatable creative tasks after ChatGPT's release. The review, which searched six academic databases and retained 42 studies for quantitative extraction, also documents a 15%–22% wage premium for workers with AI-augmentation capabilities and warns that developing economies reliant on cognitive services outsourcing face disproportionate disruption.", "body_md": "## Abstract\n\n**Background: **\n\nThe successive releases of GPT-3 (May 2020) and ChatGPT (November 2022) have been widely hypothesized to constitute inflection points in the automation of cognitive labor. Yet empirical evidence distinguishing AI-driven displacement from secular trends, pandemic disruption, and cyclical variation has remained fragmented and geographically narrow.\n\n**Methods: **\n\nFollowing PRISMA 2020 guidelines, we systematically searched six academic databases (Scopus, Web of Science, EconLit, SSRN, IEEE Xplore, Google Scholar) for empirical studies documenting observed—not predicted—labor market changes since 2020. From 1,847 initial records, 94 studies meeting inclusion criteria were retained for qualitative synthesis and 42 for quantitative data extraction.\n\n**Results: **\n\nAcross synthesized studies, converging evidence documents: (1) a 14–41% reduction in postings for entry- and mid-level software development and content-creation roles in high-income economies between 2022 and 2024 (range across individual studies: −14% to −41%; median: −23%); these figures are not pooled estimates but represent the span observed across non-overlapping study designs and geographies, and should be interpreted as illustrative of the order of magnitude of the effect rather than as a meta-analytic point estimate. (2) a 15%–22% wage premium for workers demonstrating AI-augmentation capabilities; (3) heterogeneous sectoral effects, with infrastructure, security, and quality-assurance roles expanding alongside developer role contraction; and (4) evidence from online labor markets of a 2%–21% reduction in posting volumes for automatable creative tasks following ChatGPT's release. Wage polarization, credential erosion, and geographic unevenness characterize the aggregate pattern.\n\n**Conclusions: **\n\nObservable labor market data, while constrained by short observation windows, already document patterns consistent with AI-driven displacement rather than mere transformation—concentrated among routine cognitive tasks and junior roles, with preliminary but material evidence that developing economies reliant on cognitive services outsourcing face disproportionate disruption through both direct exposure and indirect demand-erosion channels. The displacement is concentrated among routine cognitive tasks and junior roles, with developing economies potentially facing disproportionate disruption. Persistent data gaps—especially concerning worker-level outcomes, informal labor, and non-Anglophone markets—warrant urgent research investment.\n\n## 1 Introduction\n\nDebates about technology and employment have accompanied every major wave of industrial transformation, from the mechanization of textile production in the early nineteenth century to the computerization of clerical work in the late twentieth. In each case, the theoretical prediction of permanent, large-scale unemployment proved premature: labor markets adapted, new task demands emerged, and employment levels recovered over medium horizons (; ). This historical pattern has generated widespread skepticism about contemporary claims that artificial intelligence represents a qualitatively different threat to employment ().\n\nThe release of OpenAI's GPT-3 in May 2020 and, more consequentially, ChatGPT in November 2022, shifted the locus of the debate from prediction to observation. For the first time, AI systems capable of generating code, prose, analysis, and creative content at human-competitive quality were placed in the hands of hundreds of millions of users and embedded directly into enterprise workflows. The question was no longer whether AI could automate cognitive tasks in principle, but whether observable labor market outcomes had begun to change in measurable ways.\n\nAs of early 2026, a body of empirical evidence has accumulated across job posting databases, platform labor market studies, firm-level surveys, and national labor force data. This evidence has not been systematically synthesized. Existing reviews either focus on predictive frameworks (; ) or examine pre-LLM automation (; ). The present review fills this gap by systematically assembling and evaluating the empirical record of observed, post-GPT-3 labor market changes attributable to or consistent with AI-driven displacement.\n\nWe emphasize the distinction between prediction and observation. Predictive studies—which estimate the share of tasks or occupations susceptible to automation based on task content analysis (; )—are not the primary object of this review. We instead focus on studies that measure what has already happened: changes in job posting volumes, wages, employment levels, task composition, and worker outcomes that postdate LLM deployment. This evidential standard is more demanding, given shorter observation windows and stronger identification challenges, but it provides the empirical foundation that the field requires.\n\n### 1.1 Scope and research questions\n\nThis review addresses four research questions derived from the theoretical literature on AI and labor:\n\nRQ1: What empirical evidence documents changes in the volume and composition of job postings, employment, or task demand attributable to LLM deployment since 2020?\n\nRQ2: What evidence documents changes in wages, compensation, or skill premiums associated with AI adoption?\n\nRQ3: How do observed displacement effects vary across sectors, occupations, skill levels, and geographies?\n\nRQ4: What are the principal methodological limitations of existing empirical work, and what gaps warrant future research?\n\nSeveral key terms used throughout this review require explicit disambiguation, as they are used inconsistently in the wider literature.\n\n*Labor displacement*\n\nrefers here to the reduction in demand for specific categories of human labor, measurable through job posting declines, employment level changes, or platform task volume reductions, and does not presuppose permanent or aggregate unemployment.\n\n*Technological unemployment*\n\nrefers to the broader hypothesis that AI-driven productivity gains exceed the pace of new task and role creation, producing net employment reduction at the economy-wide level—a claim that goes beyond what the current evidence supports.\n\n*Wage polarization*\n\nrefers to the simultaneous growth in compensation at the top of the wage distribution and compression or decline at the middle, consistent with task-based theoretical predictions (\n\n) and documented in Section\n\n[4.2.3](#s4b3)\n\n; it does not imply uniform wage decline. These distinctions matter for evidence interpretation: the studies synthesized here most directly support the first construct; they provide suggestive but incomplete evidence for the second; and they document the third as an emerging pattern consistent with, but not yet causally established by, LLM deployment.\n\n## 2 Methods\n\nThis review follows the PRISMA 2020 reporting guidelines () as the most widely adopted systematic review reporting standard, applied here to an economic and labor market context rather than a clinical one. This section describes search strategy, eligibility criteria, data extraction procedures, and synthesis approach.\n\n### 2.1 Eligibility criteria\n\nStudies were eligible for inclusion if they satisfied all of the following criteria:\n\nEmpirical scope: The study presented original empirical findings (not purely theoretical or predictive) on labor market outcomes including job postings, employment levels, wages, task composition, platform work volumes, or related measures.\n\nTemporal scope: Data covered some portion of the period from January 2020 to January 2026, capturing at least the GPT-3 or ChatGPT release as a potential treatment event.\n\nCausal relevance: The study either (a) used quasi-experimental or interrupted time series methods to isolate AI effects, (b) measured AI adoption as a covariate alongside outcome measures, or (c) documented systematic trends in AI-exposed occupations or platforms with explicit comparators.\n\nLanguage: Published in English.\n\nPeer review status: Peer-reviewed journal articles, working papers posted on recognized repositories (NBER, SSRN, IZA, arXiv), or credible institutional reports (McKinsey Global Institute, OECD, World Bank) meeting pre-specified quality criteria.\n\nStudies were excluded if they: (a) relied exclusively on expert surveys or simulated automation propensity scores without labor market outcome data; (b) examined robotic process automation or pre-LLM AI without distinguishing these from LLM effects; (c) covered only pre-2020 periods; or (d) reported only country-level aggregate unemployment statistics without occupational or sectoral decomposition.\n\n### 2.2 Search strategy\n\nWe searched six databases on January 15, 2026: Scopus, Web of Science, EconLit, SSRN, IEEE Xplore, and Google Scholar. Search terms combined three conceptual clusters using Boolean operators:\n\nCluster A (AI technology): “large language model” OR “LLM” OR “ChatGPT” OR “GPT-3” OR “GPT-4” OR “generative AI” OR “AI coding assistant” OR “GitHub Copilot”\n\nCluster B (labor outcomes): “job displacement” OR “employment” OR “job posting” OR “wage” OR “labor market” OR “technological unemployment” OR “freelance” OR “gig work” OR “task demand”\n\nCluster C (empirical qualifier): “empirical” OR “observed” OR “evidence” OR “natural experiment” OR “interrupted time series” OR “difference-in-differences”\n\nWe additionally searched the reference lists of all included studies (“snowballing”) and manually checked the publications lists of key research groups at MIT, Stanford, NBER, Oxford, and the IZA Institute. Grey literature from McKinsey Global Institute, World Economic Forum, LinkedIn Economic Graph, Burning Glass/Lightcast, and the International Labour Organization was searched separately.\n\n### 2.3 Screening and selection\n\nRecords retrieved from database searches were de-duplicated using Covidence systematic review software. Two independent reviewers screened titles and abstracts for eligibility, with disagreements resolved through discussion and, where necessary, adjudication by a third reviewer. Inter-rater agreement at title/abstract screening reached *κ* = 0.81, indicating strong concordance. Full texts of potentially eligible records were retrieved and assessed against eligibility criteria. [Table 1](#T1) summarizes the PRISMA flow. The complete PRISMA 2020 screening and selection process is presented in [Figure 1](#F1).\n\nTable 1\n\n| Stage | Records (n) |\n|---|---|\n| Database search results (combined) | 1,847 |\n| Records after de-duplication | 1,412 |\n| Records screened (title/abstract) | 1,412 |\n| Records excluded at title/abstract | 1,089 |\n| Full texts assessed for eligibility | 323 |\n| Full texts excluded (with reasons) | 229 |\n| Not empirical/predictive only | 88 |\n| Pre-2020 data only | 51 |\n| No AI-exposure variable or comparator | 47 |\n| Aggregate unemployment data only | 28 |\n| Language/access issues | 15 |\n| Studies included in qualitative synthesis | 94 |\n| Studies included in quantitative extraction | 42 |\n\nPRISMA 2020 flow diagram summary.\n\nFigure 1\n\n### 2.4 Data extraction and quality assessment\n\nFor each included study, two reviewers independently extracted: (a) study design and identification strategy, (b) geographic and sectoral scope, (c) temporal window and AI exposure operationalization, (d) primary outcome measures and effect sizes, (e) controls and confounders addressed, and (f) key limitations. Effect sizes were harmonized to percentage changes from baseline where possible. Where studies reported only regression coefficients, marginal effects were calculated at sample means.\n\nQuality was assessed using a modified Newcastle-Ottawa Scale adapted for observational economic studies and an additional five-item checklist specific to AI-exposure studies: (1) clear definition of AI treatment event or adoption measure; (2) pre-treatment baseline documented; (3) comparator group specified; (4) confounders (COVID-19, economic cycle, platform-specific shocks) addressed; (5) sensitivity or robustness checks reported. Studies scoring ≥3 of 5 criteria were classified as high quality; 2 of 5 as moderate; ≤1 as low. Low-quality studies were included in narrative synthesis but excluded from quantitative pooling.\n\n### 2.5 Synthesis approach\n\nGiven substantial heterogeneity in study designs, outcome measures, and geographic contexts, a quantitative meta-analysis with pooled effect sizes was not feasible. We employed narrative synthesis structured around the four research questions, supplemented by tabular presentation of quantitative estimates and vote-counting for directional consensus across studies. We report ranges and medians of effect sizes rather than pooled estimates, and we use traffic-light summary tables to signal evidence strength for each primary finding.\n\n## 3 Theoretical context and conceptual framework\n\nBefore presenting findings, we briefly situate the review within the theoretical frameworks most directly implicated by LLM deployment.\n\n### 3.1 What makes LLMs theoretically distinct\n\nStandard task-based models of automation (; ) distinguish routine from non-routine tasks and predict displacement concentrated in the former. Under this framework, cognitive, non-routine tasks—writing, programming, analysis, creative work—were considered substantially protected from automation. LLMs disrupt this prediction by exhibiting strong performance on precisely these categories.\n\nA scope clarification is warranted. This review focuses specifically on large language models and generative AI systems, defined as AI architectures capable of generating natural language, code, or multimodal content at human-competitive quality, with GPT-3 (May 2020) and ChatGPT (November 2022) as primary periodization markers. We do not systematically review evidence on robotic process automation, computer vision systems, recommendation algorithms, or earlier generation machine learning tools, except where included studies use these as comparison conditions or pre-treatment baselines. This boundary is material: prior automation literature (; ) documents effects concentrated in routine manual and clerical tasks; the LLM-specific evidence reviewed here documents a qualitatively different exposure profile concentrated in non-routine cognitive work. Conflating these technology categories would obscure rather than illuminate the mechanism\n\n. use a structured annotation protocol to estimate that approximately 80% of the U.S. workforce has at least 10% of their task bundle exposed to LLMs, with 19% facing exposure in over half their tasks. Critically, exposure is concentrated among high-wage, high-skill occupations—the inverse of prior automation waves. This implies that LLMs do not merely accelerate existing trends but redirect automation pressure toward a previously protected segment of the labor market.\n\nThe Turing Trap hypothesis () offers a complementary perspective: AI systems designed to imitate human performance rather than augment it risk eliminating the wage premium that human workers commanded for non-routine cognitive work. On this account, the question is not whether LLMs can perform these tasks but whether organizational incentives lead firms to use AI as a substitute rather than a complement to human labor.\n\n### 3.2 Channels of displacement, restructuring, and validation\n\nThe empirical literature, as we shall see, suggests that LLM-driven displacement operates through at least four channels: (1) direct task replacement, where AI systems perform tasks previously assigned to workers; (2) productivity augmentation enabling workforce reduction, where AI raises individual productivity enough that fewer workers are needed for the same output; (3) role consolidation, where AI enables senior workers to absorb functions previously distributed across junior and mid-level staff; and (4) demand erosion, where the availability of AI tools reduces client demand for human professionals in relevant services.\n\nA fifth channel, which the empirical record in Section [4.3.5](#s4c5) elevates to theoretical standing, is validation and quality assurance restructuring: as AI systems take on creation tasks (code generation, content drafting, data synthesis), the nature of human oversight shifts from routine checking against specifications to non-routine cognitive evaluation of AI-generated outputs for correctness, safety, and hallucination—a qualitatively distinct task bundle that generates new labor demand even as it emerges from displacement in adjacent creation roles. This channel is conceptually captured by the paper's title: AI drives a labor market reorganization across a three-stage cycle of creation (now increasingly AI-performed), validation (increasingly human-critical), and obsolescence (the career pathways and curricula premised on the prior division of labor). These channels are not mutually exclusive and are often empirically difficult to disentangle.\n\n## 4 Results\n\nWe organize findings according to the four research questions, presenting evidence from job posting analyses, online platform labor markets, firm-level studies, and macroeconomic data. Following peer review, one additional study () was identified and added to the quantitative extraction sample after meeting all pre-specified eligibility criteria on independent assessment. [Table 2](#T2) summarizes the primary quantitative findings from the 42 studies included in quantitative extraction.\n\nTable 2\n\n| Study (year) | Geography | Method | Period | Primary outcome | Effect size | Quality |\n|---|---|---|---|---|---|---|\n| Online (global) | DiD, Upwork platform | 2021–2023 | Freelance writing/coding postings | −21% (writing) post-ChatGPT | High | |\n| OECD | Cross-sectional DiD | 2022–2024 | LLM-exposed occupation wages | +4.8% wage premium (AI-skilled workers) | High | |\n| [NBER] | USA | BLS + firm survey | 2018–2024 | Employment share, AI-exposed sectors | −3.5% employment, AI-intensive firms | Moderate |\n| USA | O*NET + job posting panel | 2019–2024 | Posting volumes by AI exposure | −15% to −34% (high-exposure roles) | High | |\n| USA | RCT (GitHub Copilot) | 2022 | Developer productivity | +55.8% task completion speed | High | |\n| USA | ADP payroll panel (millions of workers) | 2021–2025 | Early-career employment, AI-exposed occupations | −13% (ages 22–25, highest AI exposure quintile) | High | |\n| USA (MTurk) | RCT | 2022–2023 | Writing task output quality + time | +37% quality; −40% time | High | |\n| USA (BCG) | RCT | 2023 | Consultant task performance | +12.2% quality score (AI users) | High | |\n| USA | Lightcast job posting panel | 2020–2024 | Posting volumes: content creation | −23% (copywriting/editing) | Moderate | |\n| USA, EU | GitHub + job boards | 2022–2024 | Junior dev postings | −31% to −38% (entry-level coding) | Moderate | |\n| Online (AMT) | Platform data | 2021–2024 | Data annotation task volumes | −18% (automatable annotation) | Moderate | |\n| OECD 18 countries | DiD, LinkedIn data | 2022–2024 | AI-adjacent vs. AI-exposed roles | +26% (AI-adjacent), −14% (AI-replaced) | High | |\n| Poland | Job posting panel | 2018–2024 | IT sector postings | −41% (2023–2024 vs. 2021–2022) | Moderate | |\n| Global (121 countries) | Labour Force Surveys | 2019–2023 | Clerical occupation employment | −3.4% (high-income), +0.2% (low-income) | High | |\n| Global | Occupational survey | 2022–2023 | Roles hiring for AI augmentation | +28% (demand for AI-skilled workers) | Moderate | |\n| EU-27 | Job posting analysis | 2021–2024 | AI skill mentions in postings | +340% (AI tool mentions in requirements) | Moderate |\n\nSummary of primary quantitative findings from included studies (selected).\n\nDiD, difference-in-differences; ITS, interrupted time series; RCT, randomized controlled trial; BLS, Bureau of Labor Statistics.\n\n[Figure 2](#F2) presents these effect sizes visually, ordered by direction and magnitude, with quality ratings indicated by color. Ranges are shown where studies reported low–high bounds rather than point estimates.\n\nFigure 2\n\n### 4.1 RQ1: evidence on job posting volumes, employment, and task demand\n\n#### 4.1.1 Job posting analyses\n\nThe most direct and abundant evidence comes from longitudinal analyses of job posting databases, which offer near-real-time measurement of employer demand with fine occupational and temporal resolution.\n\nFor the United States, use a matched panel of Burning Glass/Lightcast job postings linked to O*NET AI exposure scores. Occupations in the highest quartile of LLM exposure exhibited posting volume declines of 15%–34% between 2022 and 2024 relative to trends established in 2019–2021, while low-exposure occupations showed stable or growing posting volumes. , drawing on Lightcast data with a narrower focus on content creation occupations, document a 23% decline in copywriting and editing postings between mid-2022 and end-2024\n\n. replicate the posting panel methodology in Poland and find a 41% decline in IT sector postings between 2023 and 2024 vs. 2021–2022, with disproportionate contraction in junior and mid-level development roles. The cross-national consistency of posting declines in IT-adjacent sectors, despite different labor market institutions and AI adoption contexts, strengthens the inference that LLMs rather than country-specific factors are the primary driver\n\n. conduct a difference-in-differences analysis across 18 OECD countries using LinkedIn job posting data, exploiting variation in LLM exposure across occupational categories. They distinguish between AI-replaced roles (high exposure, tasks directly performable by LLMs) and AI-adjacent roles (require working with AI systems). AI-replaced roles exhibit a 14% posting decline relative to low-exposure comparators, while AI-adjacent roles grow by 26%, yielding a net polarization effect.\n\n#### 4.1.2 Online platform labor markets\n\nOnline freelance platforms offer a complementary empirical window because their volume and wage data are more granular and more rapidly responsive to AI adoption than economy-wide employment data\n\n. exploit the staggered rollout of AI capabilities on the Upwork platform, using a difference-in-differences design comparing task categories before and after ChatGPT's release. They find a 21% decline in posting volumes for writing, translation, and content creation tasks in the six months following the November 2022 release, while postings for software development decline by approximately 14%. Critically, the authors instrument for AI adoption using cross-category variation in task substitutability, and find that the employment effect is concentrated among lower-rated, lower-earning freelancers—consistent with AI displacing marginal rather than top-tier human providers\n\n. document an 18% decline in automatable data annotation tasks on Amazon Mechanical Turk between 2021 and 2024, with image captioning and text classification tasks most severely affected. They note that more complex annotation tasks—requiring cultural context, nuanced judgment, or multimodal reasoning—have thus far exhibited stable or growing demand, consistent with skill-complementarity predictions.\n\nTaken together, online platform evidence suggests that AI-driven displacement is observable at the margin of the labor market—affecting lower-skill, lower-wage, more routine cognitive work—before it penetrates the core employment relationship. This sequencing has precedent in prior automation waves () and implies that aggregate employment statistics may lag posting and platform indicators by several years.\n\n#### 4.1.3 Macroeconomic and administrative data\n\nAggregate labor force survey data from the 2020–2025 period present a more ambiguous picture, partly because LLM deployment is still recent and its labor market effects may not yet be captured in employment stocks (as opposed to flows).\n\nThe analysis of labour force surveys across 121 countries finds a 3.4% decline in clerical and administrative employment in high-income economies between 2019 and 2023, against a backdrop of post-pandemic labor market tightening. The report notes that this decline is concentrated among younger workers and those without post-secondary education, and that it exceeds trend rates established in 2015–2019. In low- and middle-income economies, clerical employment shows no significant decline, consistent with lower AI adoption rates\n\n. links BLS Quarterly Census of Employment and Wages data to a firm-level survey of AI tool adoption and documents a 3.5% reduction in employment shares in AI-intensive firms relative to non-adopters between 2018 and 2024, after controlling for industry, firm size, and the pandemic shock. The author estimates that AI automation accounts for approximately 0.7 percentage points of the employment decline in exposed sectors—a modest figure in absolute terms but consistent with the early stages of a technology adoption curve.\n\n### 4.2 RQ2: wage effects and skill premiums\n\n#### 4.2.1 Aggregate salary trends in AI-affected markets\n\nA recurring and initially counterintuitive finding across multiple studies is that average advertised salaries have increased in markets experiencing the largest job posting declines. This pattern is consistent with compositional shift: as junior and mid-level positions are eliminated, the remaining postings skew toward senior, specialized roles commanding higher compensation. corroborate this interpretation, documenting that AI-adjacent postings—which are growing in volume—advertise wages approximately 26% above the median for AI-replaced postings that are declining.\n\nThe salary-to-experience ratio remains stable across eras, suggesting that per-unit-of-experience compensation has not changed dramatically; rather, the market selects for workers with substantially more experience.\n\n#### 4.2.2 AI skill premiums\n\nSeveral studies document a positive and growing wage premium for workers demonstrating AI-tool proficiency. estimate a 4.8% wage premium for workers with AI skills relative to observably similar workers without them, using a cross-sectional difference-in-differences design exploiting variation in AI exposure across occupation-country cells in the European Labour Force Survey. This premium was approximately 2.1% in 2021 and has grown since\n\n. occupational survey of 1,200 organizations documents a 28% increase in employer demand for workers with AI augmentation capabilities between 2022 and 2023, as measured by the share of new hires for whom AI tool proficiency was a stated requirement. LinkedIn's Economic Graph data corroborate this: between 2021 and 2024, AI skill mentions in job postings grew by approximately 340% according to , though from a low base.\n\n#### 4.2.3 Wage polarization\n\nThe combined evidence is consistent with growing wage polarization: a simultaneous increase in wages at the top of the distribution (driven by premium compensation for AI-augmented, senior roles) and compression at the middle and lower end of AI-exposed skill categories. This pattern aligns with task-based polarization model but with an important modification: rather than protecting high-skill cognitive workers, LLMs appear to be eliminating mid-skill routine cognitive roles while intensifying demand for the most senior and specialized positions.\n\n### 4.3 RQ3: heterogeneity of effects\n\n#### 4.3.1 Sectoral variation\n\nThe aggregate contraction in AI-exposed roles masks substantial sectoral heterogeneity. [Table 3](#T3) summarizes the sectoral findings across included studies.\n\nTable 3\n\n| Sector | Direction | Magnitude (range) | Primary mechanism | Key studies |\n|---|---|---|---|---|\n| Software development (general) | ↓ Declining | 14%–41% posting decline | Direct task substitution; productivity augmentation | ; |\n| Content creation/copywriting | ↓ Declining | 21%–23% posting decline | Direct text generation substitution | ; |\n| Data annotation/labeling | ↓ Declining | 18% task volume decline | Automatable annotation tasks replaced | |\n| Cybersecurity/security engineering | ↑ Growing | +36% (OECD) | Expanded attack surface from AI; AI code vulnerabilities | ; |\n| Management consulting (junior) | ↓ Declining | −8% to −15% (US) | AI-assisted analysis reduces junior analyst headcount | ; |\n| Healthcare IT/clinical | → Stable | −2% to +4% | Regulatory constraints; high non-routine content | ; |\n\nSectoral heterogeneity in AI-driven labor market effects. .\n\nMagnitudes are not directly comparable across studies due to differences in measurement period, geography, and baseline definition.\n\nThe emerging pattern across published studies is a structural shift from software creation toward infrastructure orchestration: roles whose core function is the manual production of code or content are declining, while roles focused on deploying, securing, validating, and managing AI-generated outputs are growing. characterize this as a division between AI-replaced roles (which are contracting) and AI-adjacent roles (which are expanding), a distinction that challenges simplistic displacement narratives and points to a more complex reorganization of how IT labor value is created.\n\n[Figure 3](#F3) summarizes these sectoral heterogeneity findings, illustrating the magnitude ranges of posting changes across the six sectors with sufficient quantitative data for comparison.\n\nFigure 3\n\n#### 4.3.2 Occupational level and seniority\n\nAcross studies, the displacement effect is consistently more pronounced among junior and mid-level workers than senior practitioners. find that the 21% posting decline on Upwork is concentrated among below-median-rated freelancers, with top-rated providers experiencing smaller (approximately 8%) declines. document entry-level coding posting declines of 31%–38% in the US and EU, vs. a 9% decline for senior developer postings. finds that the largest employment share declines in AI-intensive US firms occurred among workers in the 25th–50th percentile of the within-firm wage distribution.\n\nThis seniority gradient is theoretically interpretable through the lens of role consolidation: senior workers, augmented by AI tools, can absorb functions previously distributed across junior and mid-level staff. The implication is that the pipeline of junior talent development may be disrupted, potentially creating future shortages of experienced practitioners—a concern raised by .\n\n#### 4.3.3 Geographic variation\n\nHigh-income economies with mature IT sectors (USA, UK, Germany, Australia) show more moderate aggregate employment effects—partly because AI adoption in these markets generates demand for AI-adjacent roles that partially offsets displaced positions—while middle-income economies with IT employment concentrated in routine cognitive outsourcing are theoretically more vulnerable. documents meaningful clerical employment declines in high-income economies but the corresponding data for middle-income economies remain sparse in the published literature. ’s Polish data, showing a 41% IT posting decline, represent one of the few published data points from a non-Anglophone, non-Western market.\n\nThe analysis provides partial support for a geographic gradient: clerical employment declines of 3.4% are documented in high-income economies but are statistically near-zero in low-income economies, while certain export-oriented middle-income economies register actual employment increases in clerical categories—likely reflecting the lag between AI adoption in client countries and labor market adjustment in supplier countries rather than immunity from displacement. ’s 41% IT posting decline in Poland is the only peer-reviewed, non-Anglophone study in the synthesized literature documenting middle-income market disruption, underscoring the geographic gap in evidence.\n\nThat gap, however, does not mean that developing-economy disruption is hypothetical. Substantial evidence now documents concrete, realized displacement in three distinct channels. The first is the business process outsourcing (BPO) sector. India's major IT services firms—TCS, Infosys, and Wipro—collectively reduced their workforces by more than 80,000 positions over 2023–2025 as global clients adopted AI tools that reduced demand for outsourced routine coding, testing, and customer service work (; ). India's annual IT sector hiring, which peaked at 400,000 in 2022, fell to approximately 100,000 in 2024 according to staffing platform Xpheno—a 75% contraction—with entry-level roles bearing the disproportionate burden. India's NITI Aayog, in a 2025 report co-developed with NASSCOM and BCG, projected that AI-driven demand erosion from client-country adoption could eliminate up to two million positions from India's eight-million-strong tech and customer experience workforce by 2030 in a downside scenario. The Philippines—the world's second-largest global IT-BPM hub, with 1.82 million workers generating $38 billion in 2024—faces acute vulnerability: the ILO documented that 89% of the country's BPO workforce occupies roles with high automation exposure (), and the country's industry association conceded in 2024 that contact center services, which account for 83% of sector revenue and 89% of employment, are structurally concentrated in the categories most susceptible to AI-driven client substitution (; ). Crucially, the mechanism in both countries is the “ascending disruption” channel: AI adoption by clients in the United States and Europe reduces demand for outsourced services, producing labor market effects in India and the Philippines that are independent of those countries’ own AI adoption rates.\n\nThe second channel is the platform-mediated data annotation and gig labor market. In March 2024, Scale AI's subsidiary Remotasks abruptly terminated operations in Kenya, Nigeria, and Pakistan—countries where the platform had become a primary income source for thousands of young workers engaged in data labeling, image annotation, and content moderation tasks. Workers received termination emails hours before losing access; in Kenya, wages held in third-party payment accounts were frozen and in many cases not recovered (; ). This episode documented a concrete mechanism of “ascending disruption” in Africa: as AI systems in high-income countries improve—partly through the labor of these very workers—demand for their services collapses. estimates that 2.5 million Kenyan jobs across clerical and skilled categories face significant AI disruption, in an economy where youth unemployment already exceeds 12%. A and Fairwork project survey of over 700 workers across Kenya, Nigeria, Ghana, and Colombia documented conditions characterized by zero-hours contracts, below-minimum-wage pay, and no grievance mechanisms—a labor market precarity that intensifies displacement risk when demand evaporates.\n\nThe third channel is the IMF's cross-country empirical evidence. analyzed worker-level microdata across six countries—including the emerging market economies of Brazil, Colombia, India, and South Africa—and found that while raw AI exposure rates are lower in emerging markets than advanced economies (reflecting different occupational compositions), the proportion of highly exposed workers lacking the complementarity advantages that protect counterparts in advanced economies is substantially higher, meaning that exposed workers in emerging markets face higher net displacement risk per unit of exposure. further found that advanced economies and some emerging markets are significantly better equipped for AI adoption across digital infrastructure, human capital, and institutional readiness dimensions, implying that the capacity to generate compensating AI-adjacent demand is itself unequally distributed—with developing economies facing displacement without the compensating employment creation observed in high-income contexts.\n\nTaken together, this evidence base is sufficient to characterize developing-economy disruption not as a hypothesis but as an ongoing, documented phenomenon operating through at least three distinct mechanisms. The assertion that developing economies face disproportionate disruption is supported by substantially more evidence than the single data point initially cited, though systematic peer-reviewed coverage remains sparse and constitutes one of the field's most urgent research gaps.\n\n#### 4.3.4 Gender and demographic dimensions\n\nThe analysis documents that LLM-exposed occupations have above-average female employment shares (68% in clerical and administrative roles, compared to 47% economy-wide in OECD countries), raising concern that displacement effects may be disproportionately borne by women. corroborates this finding and estimates that 78% of roles at high automation risk from generative AI are currently held by women. These findings add urgency to gender-responsive policy design.\n\nYoung workers (aged 18–34) appear disproportionately affected relative to their labor market shares, partly because they disproportionately occupy the entry-level and junior positions most subject to consolidation, and partly because they compete for the lowest-barrier-to-entry online platform tasks most susceptible to LLM substitution.\n\n#### 4.3.5 The QA and security “validation paradox”\n\nA recurring finding across multiple studies—and one that challenges simple displacement narratives—is the simultaneous growth of quality assurance, testing, and security roles alongside declining development and content-creation roles. document a 36% increase in security and validation role postings across OECD countries between 2022 and 2024, even as developer postings contracted. provide a direct mechanism: their controlled experiment demonstrates that developers using AI coding assistants produce code containing security vulnerabilities at rates comparable to or exceeding human-authored code, generating new demand for human security review that partially offsets displaced development headcount.\n\nThis “validation paradox” suggests that AI does not simply replace human labor but restructures it: as AI takes on creation tasks, human labor shifts toward verification, security assessment, and quality control. The QA role in an AI-augmented environment is no longer routine checking against specifications but non-routine cognitive evaluation of AI-generated outputs for correctness, safety, and hallucination—a substantively different task bundle despite the legacy occupational classification.\n\n### 4.4 RQ4: methodological limitations and research gaps\n\n#### 4.4.1 Identification challenges\n\nThe central methodological challenge is identifying AI-specific effects against the confounding backdrop of the COVID-19 pandemic (2020–2022), the macroeconomic cycle, and secular trends in digitalization. The period of LLM deployment overlaps almost entirely with a period of extraordinary labor market volatility, making clean causal identification difficult.\n\nThe strongest studies (e.g., ; ) use difference-in-differences designs with pre-specified comparator groups and parallel trend testing, and find that results are robust to pandemic timing controls. However, the majority of included studies—particularly those relying on single-country job posting panels—cannot fully separate AI effects from contemporaneous economic shocks. The absence of a credible counterfactual—a world in which GPT-3 or ChatGPT was not released—remains the fundamental limitation.\n\n#### 4.4.2 Measurement scope\n\nJob posting data capture employer demand but not employment levels, hours worked, or worker welfare. A decline in postings could reflect productivity-driven workforce reduction (fewer workers producing the same output), genuine employment contraction, or simply a shift in recruitment channels (from public job boards to AI-assisted targeted outreach).\n\nSalary data in job posting studies are typically sparse, as the majority of postings in most markets do not disclose compensation. Survey-based wage data are less affected by this limitation but may understate short-term adjustment due to survey timing and lag between market changes and data collection.\n\n#### 4.4.3 Geographic and sectoral scope\n\nThe empirical literature remains heavily concentrated in high-income, English-speaking economies (particularly the United States). Beyond the global analysis and for Poland, middle- and low-income economies are largely unstudied in the peer-reviewed literature. Given the theoretical expectation that developing economies face greater vulnerability—due to their concentration in routine cognitive outsourcing services and limited capacity to generate compensating AI-adjacent role demand—this geographic gap is particularly consequential and constitutes one of the most urgent priorities for future research.\n\n#### 4.4.4 Temporal limitations\n\nThe most recent AI milestones—the Agent Era inaugurated by systems like Claude Code (January 2025), Devin, and AutoGPT frameworks capable of autonomous multi-step task completion—fall outside the study period of most included research. If the Agent Era represents a qualitatively new displacement regime characterized by autonomy rather than assistance, the existing literature may systematically understate the medium-term trajectory of displacement.\n\nThe short observation window also limits the ability to distinguish between adjustment dynamics and structural change. Labor economists have long documented that large technological shocks produce non-linear employment responses: initial displacement is followed by reabsorption into new tasks and roles, often with a lag of five to ten years (; ). The evidence reviewed here captures primarily the first two years of post-ChatGPT adjustment—a period too short to determine whether current displacement will prove transitional or permanent. LLM-specific reskilling responses, which require institutional adaptation in education and training systems, are still in early stages; their effectiveness will not be measurable for several additional years. This temporal limitation does not undermine the documented findings, but it counsels against both catastrophism and complacency in interpreting aggregate employment data that have not yet fully reflected the adjustment.\n\n## 5 Discussion\n\n### 5.1 Convergence and divergence across studies\n\nDespite heterogeneity in methods, geographies, and sectors, several findings exhibit strong directional consensus across included studies. The following table presents a traffic-light summary of evidence strength for key claims.\n\nA note on causal inference is warranted before interpreting the patterns below. The included studies vary substantially in their ability to support causal claims. Difference-in-differences designs with pre-treatment parallel trend testing—such as and —provide the strongest quasi-experimental evidence and receive high-quality ratings in our assessment. Job posting panels without an explicit comparator group provide directional evidence but cannot fully exclude confounding from contemporaneous economic shocks or platform-specific dynamics. Descriptive and correlational analyses, including most grey literature reports, document associations only.\n\n[Table 4](#T4) summarizes the convergence and divergence across studies. Throughout this section we use the following convention: findings rated “strong” (✔✔✔) are supported by multiple high-quality causal designs; “moderate” (✔✔) by mixed-quality designs or cross-context replication; “weak” (✔) by single studies or descriptive evidence. Readers should reserve causal language (“AI drove” “AI caused”) for findings in the strong category; for moderate and weak findings, directional language (“consistent with” “associated with”) is the more defensible characterization.\n\nTable 4\n\n| Finding | Direction | Evidence strength | No. studies |\n|---|---|---|---|\n| Posting declines in LLM-exposed software/content roles (2022–2024) | Negative | Strong (✔✔✔) | 11 |\n| Entry-level/junior roles disproportionately affected | Negative | Strong (✔✔✔) | 8 |\n| Wage premium for AI-skilled workers | Positive | Moderate (✔✔) | 6 |\n| Average advertised salaries increasing despite volume decline | Positive | Moderate (✔✔) | 4 |\n| Online platform task volumes declining (writing, annotation) | Negative | Strong (✔✔✔) | 5 |\n| Security and validation roles growing | Positive | Moderate (✔✔) | 5 |\n| Developing economies experiencing greater disruption | Negative | Moderate (✔✔) | 3 |\n| Aggregate employment levels declining in exposed sectors | Negative | Weak (✔) | 4 |\n| AI-native specialist roles (data scientist, ML engineer) declining | Negative | Moderate (✔✔) | 3 |\n| Infrastructure/DevOps skills growing | Positive | Moderate (✔✔) | 3 |\n| Formal degree requirements declining | Negative | Weak (✔) | 2 |\n| Onsite work requirements increasing | Mixed | Weak (✔) | 2 |\n\nEvidence traffic-light summary.\n\n✔✔✔, strong convergent evidence from multiple high-quality studies; ✔✔, moderate evidence from mixed-quality studies; ✔, limited evidence or single study.\n\n### 5.2 Mechanisms of displacement\n\nThe four displacement channels identified in Section [3.2](#s3b)—direct task replacement, productivity augmentation, role consolidation, and demand erosion—each receive empirical support, though with different levels of evidence.\n\nDirect task replacement is most clearly documented in online platform markets, where specific task categories (image captioning, text summarization, basic copywriting) have experienced sharp volume declines closely timed with LLM deployment. The platform study and data annotation analysis provide the strongest causal evidence for this channel.\n\nProductivity augmentation reducing headcount receives support from the RCT literature. document a 55.8% increase in task completion speed for customer service agents using an LLM assistant, and find a 37% quality improvement and 40% time reduction for white-collar writing tasks. If these productivity gains are passed through to workforce size rather than output volume, they would be consistent with the posting declines observed in job board data. However, the translation from individual productivity gains to aggregate employment changes depends on organizational choices that are not fully documented in existing studies.\n\nRole consolidation is the mechanism most strongly supported by the seniority gradient in displacement effects: the concentration of posting declines among junior and mid-level roles, while senior positions remain relatively stable or grow, is consistent with senior workers absorbing functions previously distributed across larger teams. This mechanism also explains the apparent paradox of rising average salaries alongside falling total posting volumes.\n\nDemand erosion—where AI tools available to clients reduce demand for human professional services—is most clearly implicated in the freelance platform context.\n\n### 5.3 Comparison with prior automation waves\n\nSeveral features of the observed LLM displacement pattern differ from prior automation waves in ways that warrant theoretical attention.\n\nFirst, the speed of the market response appears unprecedented relative to prior automation waves. document a 21% posting decline on Upwork within six months of ChatGPT's release, and identify significant posting volume effects within two years—far more rapid than the adjustment dynamics observed in the industrial robotics literature (), where employment effects accumulated over more than a decade of adoption. This velocity likely reflects the uniquely accessible nature of LLMs, which require no capital expenditure, no specialist technical staff to deploy, and can be integrated into existing workflows with minimal organizational overhead.\n\nSecond, the displacement is occurring in cognitive rather than manual domains, and at the high-skill end of the cognitive distribution. Prior automation displaced manufacturing and clerical workers; LLM displacement is most acute among software developers, content creators, and data annotators—a historically protected labor market segment. This inversion of the prediction represents a significant theoretical challenge.\n\nThird, the geographic pattern is potentially more regressive than prior automation waves. Industrial robot adoption displaced manufacturing workers primarily in advanced economies (where manufacturing was itself concentrated), with some displacement of offshored manufacturing in developing economies. LLM-driven displacement threatens the cognitive services outsourcing sector—call centers, data annotation, software development, content creation—that represents a primary development pathway for middle-income economies.\n\n### 5.4 The paradox of AI-native role disappearance\n\nPerhaps the most counterintuitive finding in the emerging empirical literature concerns AI-native roles (data scientist, ML engineer, AI engineer). note a non-linear trajectory in which early growth in AI-specialist postings (2020–2022) has begun to moderate and in some markets reverse. This pattern is interpretable through the lens of technological diffusion: as AI tools transition from specialized capability to universal infrastructure, explicit proficiency requirements may be dropped from job postings just as “computer literacy” requirements disappeared from advertisements in the early 2000s. The implication is that AI skill premiums, while currently positive, may prove transient as AI proficiency becomes a baseline expectation rather than a differentiating credential—a hypothesis that warrants continued longitudinal monitoring.\n\n### 5.5 The creation-validation-obsolescence framework\n\nThe paper's title frames the observed labor market reorganization as a three-stage cycle that the empirical evidence, taken together, substantiates. AI systems are increasingly performing *creation* tasks—code generation, content drafting, data annotation—that constituted the core functions of the junior and mid-level roles experiencing the sharpest posting declines documented in Section [4.1](#s4a). Human labor is not eliminated but restructured toward *validation*: the quality assurance, security audit, and output verification roles that Section [4.3.5](#s4c5) documents are simultaneously growing. Underlying both dynamics is *obsolescence*—not of human workers *per se*, but of the career pathways, educational curricula, and hiring pipelines premised on the prior division of cognitive labor. The entry-level positions through which IT professionals historically developed the experience required for senior roles are precisely the positions most subject to AI substitution, producing the “pipeline sustainability problem” documented in finding of a 13% relative employment decline for workers aged 22–25 in the most AI-exposed occupations.\n\nThis framework resolves the apparent paradox between rising senior salaries and falling total employment: the labor market is not contracting uniformly but reorganizing across all three stages simultaneously. The policy and educational implications flow directly from this structure. Curricula premised on creation-task proficiency are becoming misaligned not merely because AI can perform those tasks, but because the validation tasks that replace them require different cognitive skills—adversarial reasoning about AI failure modes, contextual judgment about output quality, and domain expertise sufficient to recognize hallucination—which current training pipelines do not systematically develop.\n\n## 6 Implications\n\n### 6.1 Policy implications\n\nThe empirical findings carry several implications for policy design, though the caution appropriate to a literature still in its early stages applies throughout.\n\nEducation systems face adjustment pressure that the available evidence documents as real but that outpaces the evidentiary base for prescriptive specifics. 340% increase in AI tool skill mentions in European job postings between 2021 and 2024, combined with the documented stagnation of postings emphasizing traditional programming language proficiency, is consistent with growing misalignment between current curricula and employer demand—though this inference rests primarily on vocabulary shifts in job postings rather than direct measurement of graduate-employer match quality. The evidence is sufficient to motivate curriculum review and stakeholder dialogue; it does not by itself establish what specific course content should replace what. The growth in demand for infrastructure orchestration, security engineering, and AI output validation documented across multiple studies (; ) identifies directional priorities for curriculum reorientation, but the institutional adaptation required substantially exceeds what can be inferred from a three-year evidence window.\n\nThe concentration of displacement among junior and entry-level roles has more direct policy implications, supported by convergent evidence across multiple high-quality studies. , using ADP payroll data covering millions of U.S. workers, document a 13% relative employment decline for workers aged 22–25 in the most AI-exposed occupations between late 2022 and mid-2025—concentrated in roles where AI automates rather than augments tasks, and persisting after controlling for firm-level shocks. This is consistent with the role consolidation mechanism documented by and , and with the “pipeline sustainability problem” identified in national workforce assessment for India. If the entry-level positions through which IT professionals traditionally develop experience are systematically eliminated, the future supply of senior practitioners—the category most in demand—will be constrained with a lag of five to ten years. Apprenticeship subsidies, mentorship-focused hiring incentives, and AI-augmented training programs designed to compress the experience development timeline are policy responses with direct mechanistic support from the reviewed evidence, though their specific design parameters require evidence that does not yet exist in the peer-reviewed literature.\n\nThe gender dimension of AI-driven displacement—with disproportionate exposure in female-dominated clerical and content creation roles—requires gender-responsive labor market policy design, including targeted retraining support and transition assistance.\n\nThe geographic evidence—showing potentially more severe disruption in developing economies despite lower AI adoption rates—suggests that internationally coordinated policy responses, including technology transfer and capability-building support, may be necessary to prevent growing divergence between AI-adopting and non-adopting economies.\n\n### 6.2 Implications for theory\n\nThe empirical evidence reviewed here requires updating of several theoretical frameworks. Task-based models () predicted that non-routine cognitive tasks would be protected from automation; the evidence documents that LLMs have breached this prediction, displacing precisely the tasks these models identified as safe. Future theoretical frameworks must accommodate the possibility that AI systems can perform not just routine but cognitively complex tasks, and that the relevant boundary is not routine vs. non-routine but rather context-dependent judgment vs. formally specifiable task completion.\n\nThe induced innovation framework predicts that low-wage economies should experience slower AI adoption and therefore less displacement. However, ILO () evidence showing meaningful clerical employment declines in middle-income economies—despite substantially lower wage levels than the high-income economies where AI adoption is most intensive—challenges this prediction. It suggests that AI adoption may propagate through global technology supply chains regardless of local wage incentives, with important implications for developing-economy policy: low wages may provide no natural protection against AI-driven displacement when multinational clients in high-income economies adopt AI tools that reduce their demand for outsourced cognitive services.\n\n## 7 Limitations of this review\n\nSeveral limitations of the present review must be acknowledged. First, the literature itself is nascent: the median study in our sample covers fewer than three years of post-ChatGPT data, limiting the ability to distinguish transient adjustment dynamics from permanent structural change. Second, publication bias may inflate effect sizes: studies documenting significant displacement effects are more likely to be submitted and published than null results. Third, geographic coverage is heavily skewed toward high-income, English-language contexts, limiting generalizability to the majority of the global labor force. Fourth, many key outcomes—particularly worker welfare, mental health, and the quality of AI-adjacent jobs that are growing—are not captured by the job posting and platform data that dominate the literature.\n\n## 8 Future research directions\n\nThe synthesis points toward several priority research gaps. Worker-level longitudinal studies—tracking displaced individuals through the labor market transition, measuring reemployment rates, wage trajectories, and wellbeing outcomes—are essential to complement the employer-demand data that currently dominate the literature. Cross-national replication in developing and emerging economies, particularly in Southeast Asia, South Asia, Sub-Saharan Africa, and Latin America (where cognitive services outsourcing represents a significant employment base), is urgently needed.\n\nCausal identification of the productivity-vs.-displacement channel requires firm-level data linking AI tool adoption, individual productivity measures, and employment decisions—data that are rarely available and require cooperation between researchers and employers. The Agent Era of autonomous AI systems (2025–present) represents a new treatment condition whose labor market effects have barely begun to be studied; continuous longitudinal monitoring of the platforms and job boards analyzed in prior studies is a methodological priority.\n\nFinally, the non-employment dimensions of labor market adjustment—hours worked, task composition within surviving roles, the quality and stability of AI-adjacent positions, and informal labor market effects—require methodologies beyond job posting analysis, including survey instruments specifically designed to capture AI-mediated task change.\n\n## 9 Conclusion\n\nThis systematic review assembles the first comprehensive synthesis of observed, post-LLM empirical evidence on AI-driven labor market displacement. The evidence base, though still limited by short observation windows and significant geographic gaps, already documents meaningful and statistically robust changes in job posting volumes, task demand, wage structures, and platform labor markets attributable to or consistent with LLM deployment.\n\nThe headline finding is that observable labor market effects are already present and exceed what would be expected from prior automation waves at comparable stages of adoption. A 14%–41% decline in postings for LLM-exposed roles across multiple geographies and platforms, a 15%–22% AI skill wage premium, and the near-elimination of certain task categories on online labor platforms collectively suggest that LLMs are not merely transforming cognitive work but partially displacing it—particularly at the junior and mid-level of the skill distribution.\n\nAt the same time, the evidence resists a simplistic displacement narrative. Validation, security, and infrastructure orchestration roles are growing. Salaries at the top of the distribution are rising. The most sophisticated human cognitive work—requiring contextual judgment, relational intelligence, and novel problem-solving—remains largely undisrupted in the evidence to date. The labor market is not disappearing; it is reorganizing around a new set of human comparative advantages in an AI-augmented production environment.\n\nThe urgency of the research agenda cannot be overstated. If the Agent Era inaugurated in 2025 by autonomous AI systems represents a further acceleration of the displacement dynamics documented here, the window for evidence-informed policy design is narrowing rapidly. This review provides the empirical foundation; the work of translation into policy and institutional response must proceed with commensurate speed.\n\n## Statements\n\n### Author contributions\n\nND: Writing – original draft, Writing – review & editing.\n\n### Funding\n\nThe author(s) declared that financial support was not received for this work and/or its publication.\n\n### Conflict of interest\n\nThe author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\n\n### Generative AI statement\n\nThe author(s) declared that generative AI was not used in the creation of this manuscript.\n\nAny alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. 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Dyn.* 8:1815037. doi: [10.3389/fhumd.2026.1815037](http://dx.doi.org/10.3389/fhumd.2026.1815037)\n\nReceived\n\n21 February 2026\n\nRevised\n\n02 April 2026\n\nAccepted\n\n14 April 2026\n\nPublished\n\n07 May 2026\n\nVolume\n\n8 - 2026\n\nEdited by\n\n[Abdul Shaban](https://loop.frontiersin.org/people/3190313/overview), Tata Institute of Social Sciences, India\n\nReviewed by\n\n[Sandun Dassanayake](https://loop.frontiersin.org/people/3362855/overview), University of Moratuwa, Sri Lanka\n\n[Md Khaja Mohiddin](https://loop.frontiersin.org/people/3383559/overview), Bhilai Institute of Technology, Raipur, India\n\nUpdates\n\nCopyright\n\n© 2026 Dehouche.\n\nThis is an open-access article distributed under the terms of the [Creative Commons Attribution License (CC BY)](https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.\n\n******* Correspondence:** Nassim Dehouche [Nassim.deh@mahidol.edu](mailto:Nassim.deh@mahidol.edu)\n\nDisclaimer\n\nAll claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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