Evidence (3224 claims)
Adoption
7395 claims
Productivity
6507 claims
Governance
5877 claims
Human-AI Collaboration
5157 claims
Innovation
3492 claims
Org Design
3470 claims
Labor Markets
3224 claims
Skills & Training
2608 claims
Inequality
1835 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 609 | 159 | 77 | 736 | 1615 |
| Governance & Regulation | 664 | 329 | 160 | 99 | 1273 |
| Organizational Efficiency | 624 | 143 | 105 | 70 | 949 |
| Technology Adoption Rate | 502 | 176 | 98 | 78 | 861 |
| Research Productivity | 348 | 109 | 48 | 322 | 836 |
| Output Quality | 391 | 120 | 44 | 40 | 595 |
| Firm Productivity | 385 | 46 | 85 | 17 | 539 |
| Decision Quality | 275 | 143 | 62 | 34 | 521 |
| AI Safety & Ethics | 183 | 241 | 59 | 30 | 517 |
| Market Structure | 152 | 154 | 109 | 20 | 440 |
| Task Allocation | 158 | 50 | 56 | 26 | 295 |
| Innovation Output | 178 | 23 | 38 | 17 | 257 |
| Skill Acquisition | 137 | 52 | 50 | 13 | 252 |
| Fiscal & Macroeconomic | 120 | 64 | 38 | 23 | 252 |
| Employment Level | 93 | 46 | 96 | 12 | 249 |
| Firm Revenue | 130 | 43 | 26 | 3 | 202 |
| Consumer Welfare | 99 | 51 | 40 | 11 | 201 |
| Inequality Measures | 36 | 105 | 40 | 6 | 187 |
| Task Completion Time | 134 | 18 | 6 | 5 | 163 |
| Worker Satisfaction | 79 | 54 | 16 | 11 | 160 |
| Error Rate | 64 | 78 | 8 | 1 | 151 |
| Regulatory Compliance | 69 | 64 | 14 | 3 | 150 |
| Training Effectiveness | 81 | 15 | 13 | 18 | 129 |
| Wages & Compensation | 70 | 25 | 22 | 6 | 123 |
| Team Performance | 74 | 16 | 21 | 9 | 121 |
| Automation Exposure | 41 | 48 | 19 | 9 | 120 |
| Job Displacement | 11 | 71 | 16 | 1 | 99 |
| Developer Productivity | 71 | 14 | 9 | 3 | 98 |
| Hiring & Recruitment | 49 | 7 | 8 | 3 | 67 |
| Social Protection | 26 | 14 | 8 | 2 | 50 |
| Creative Output | 26 | 14 | 6 | 2 | 49 |
| Skill Obsolescence | 5 | 37 | 5 | 1 | 48 |
| Labor Share of Income | 12 | 13 | 12 | — | 37 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Labor Markets
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No regulatory framework requires disclosure of machine/AI labor output.
Author's assertion in the paper (policy claim; no legislative survey or quantification reported).
No index tracks machine labor output over time.
Author's assertion in the paper (stated lack of existing indices; no systematic review/sample reported).
This labor force is entirely invisible to the economic infrastructure humanity has built to measure work: no standardized unit of measurement exists.
Author's assertion/diagnosis in the paper (argumentative/observational, no empirical survey or sample reported).
Specific occupations such as credit analysts, judges, and sustainability specialists reach ATE scores of 0.43-0.47 by 2030.
Reported model outputs / ATE score estimates for individual occupations within the paper's 2025-2030 regional application.
Applying the ATE framework across five major US technology regions (Seattle-Tacoma, San Francisco Bay Area, Austin, New York, and Boston) over a 2025-2030 horizon, 93.2% of the 236 analyzed occupations across six information-intensive SOC groups cross the moderate-risk threshold (ATE >= 0.35) in Tier 1 regions by 2030.
Modeling/application of the ATE score to O*NET-derived tasks for 236 occupations in six SOC groups across five named US regions with forecasts for 2025-2030; explicit numeric result reported (93.2%).
Agentic AI systems execute end-to-end workflows (multi-step reasoning, tool invocation, autonomous decision-making) and substantially expand occupational displacement risk beyond what existing task-level analyses capture.
Theoretical extension of the Acemoglu-Restrepo task exposure framework described in the paper; conceptual argument contrasting prior automation (subtask substitution) with agentic AI (end-to-end workflow automation). No empirical sample size reported for this conceptual claim.
Informal workers cannot capture augmentation rents: the estimated coefficient for H^A in informal sector is negative (beta_2 = -0.044).
Subsample or interaction estimate from the augmented Mincer regression using the same merged dataset (N = 105,517); reported coefficient beta_2 = -0.044 for informal workers.
New mechanisms of surplus value distribution operate in platform-based business models and through AI-mediated processes.
Analytical/theoretical argumentation and literature synthesis in the conceptual paper (no empirical validation reported).
Extreme automation (high AI intensity) causes employment decline.
Part of the U-shaped relationship reported by the paper's empirical results; described qualitatively in the abstract/summary.
Task complexity shapes substitution: low-complexity tasks see high substitution, while high-complexity tasks favor limited partial automation.
Calibration of the model to O*NET tasks + expert survey + GPT-4o decompositions; implementation results reported for computer vision showing substitution varies with task complexity.
AI systems exhibit predictable but diminishing returns to data, compute, and model size (scaling-law experiments), implying the cost of higher accuracy is convex: good performance may be inexpensive, but near-perfect accuracy is disproportionately costly.
Scaling-law experiments estimating performance as a function of data, compute, and model size; described experimental estimation of production function.
Kerangka hukum ketenagakerjaan Indonesia saat ini bersifat reaktif, dengan fokus pada kompensasi pasca-PHK yang belum mampu menjawab dampak jangka panjang disrupsi AI.
Analisis normatif terhadap peraturan perundang-undangan dan temuan dari literatur yang ditinjau; kesimpulan yang dilaporkan oleh penulis penelitian.
Belum terdapat pengaturan eksplisit mengenai kewajiban pelatihan ulang (retraining) maupun mekanisme distribusi manfaat teknologi secara adil dalam kerangka hukum ketenagakerjaan Indonesia saat ini.
Temuan dari analisis peraturan perundang-undangan nasional (UU Cipta Kerja dan peraturan turunannya) dan literatur yang dikaji dalam penelitian normatif.
Fenomena adopsi AI menimbulkan tantangan hukum terkait perlindungan hak pekerja, keadilan sosial, dan keberlanjutan sistem ketenagakerjaan.
Analisis normatif terhadap konsekuensi sosial-ekonomi AI yang disintesis dari literatur nasional (SINTA) dan internasional; pendekatan konseptual dan komparatif dijelaskan dalam metode.
Perkembangan pesat Artificial Intelligence (AI) telah membawa perubahan mendasar dalam struktur pasar tenaga kerja di Indonesia dengan meningkatnya risiko penggantian pekerjaan manusia oleh teknologi otomatisasi.
Pernyataan latar belakang yang didukung oleh tinjauan literatur pada jurnal nasional terindeks SINTA dan jurnal internasional bereputasi (metode: penelitian hukum normatif dengan pendekatan perundang-undangan, konseptual, dan komparatif).
The intersection of IoT, artificial intelligence, cloud computing, and robotics collectively impacts social security systems.
The paper presents this as the focal analytic topic—an argument based on theoretical discussion and synthesis rather than reported empirical measurement (no sample size given).
New technologies are initially skill intensive (demand more college-educated workers) but become less so as they age (they get standardized and accessible to less-skilled workers).
Empirical descriptive evidence from novel text-based data combining patent text and job postings (building on Kalyani et al., 2025) tracking technologies and their changing demand for skills as they age.
Azar et al. (2023) show that monopsonistic employers have stronger incentives to automate and document that US commuting zones with higher labor market concentration experienced more robot adoption.
Citation reported in the paper summarizing Azar et al. (2023); empirical analysis across US commuting zones (no sample size provided here).
Acemoglu and Restrepo (2022) attribute 50–70% of the increase in US wage inequality between 1980 and 2016 to displacement of workers from tasks by automation.
Citation reported in the paper summarizing Acemoglu and Restrepo (2022)'s attribution of the rise in wage inequality to automation-driven task displacement.
Dechezleprêtre et al. (2025) exploit Germany's Hartz reforms to estimate an elasticity of automation innovation to low-skill wages of 2–5 at the firm level.
Citation reported in the paper summarizing Dechezleprêtre et al. (2025)'s empirical estimate (elasticity 2–5); the paper states this was estimated at the firm level.
Eloundou et al. (2024) predict that half of US jobs are significantly exposed to recent advances in generative AI.
Citation reported in the paper summarizing Eloundou et al. (2024)'s prediction; no sample size provided in the excerpt.
When employers have monopsony power, they choose technologies that expand this power beyond what a social planner would consider optimal.
Model results on monopsonistic employer incentives and their technological choices; discussion supported by citations.
Profit-maximizing firms pursue innovations that erode workers' market power by making them more easily replaceable, even at the expense of production efficiency; a social planner who values worker welfare would employ technologies that preserve workers' market power.
Theoretical analysis of interactions between technological choice and market power; supported by cited empirical evidence (e.g., Azar et al. 2023) in the paper.
A welfare-maximizing planner would choose to automate fewer tasks than production efficiency would dictate when workers' welfare is heavily weighted.
Model analysis of welfare-maximizing automation level compared to production-efficient automation; analytical result in the automation application.
Prominent studies predict substantial job displacement due to automation.
Paper asserts this as background, referencing the existence of prominent studies in the literature (no specific citations or sample sizes provided in the abstract).
The regime divide deepens under AI capital concentration, admits a permanent displacement attractor in shallow markets, and generates equity market participation hysteresis in which the ERP remains elevated after employment has normalised.
Model-based assertions: analysis shows capital concentration magnifies regime separation, yields a permanent displacement attractor in shallow-market parameterizations, and produces hysteresis in participation leading to persistently elevated ERP after employment recovery.
The alignment risk channel is specific to agentic AI: correlated misalignment in AI objectives generates aggregate output shocks with fat left tails; formalised via Hansen-Sargent multiplier preferences, the resulting alignment risk premium (ARP) enters the equilibrium ERP decomposition as a priced factor additively separable from the participation wedge.
Theoretical formalisation in the paper: uses Hansen-Sargent multiplier preferences to capture model uncertainty/robustness and defines an ARP that is additively separable in the ERP decomposition.
The participation compression channel operates through household wealth: displacement pushes marginal households below the equity market entry cost κ, concentrating aggregate consumption risk on a shrinking investor pool and—by the Basak-Cuoco mechanism—raising the required risk premium even as fundamentals improve.
Model mechanism described in the paper: heterogeneous-agent model with an explicit market entry cost κ and reference to the Basak-Cuoco mechanism leading to a higher required risk premium when investor base shrinks.
Data reveals that less than 0.7% of the Indian population uses AI-induced ride services.
Empirical statistic reported in the paper (declared as data) quantifying the share of the population using AI-induced ride services.
The lack of a significant worsening in transportation-sector inequality can be attributed to sluggish demand switching from non-AI to AI-based services in India.
Argument in the paper linking empirical finding (no significant increase in inequality) to low observed adoption rates of AI-based ride services; supported by reported adoption statistic.
The financial planning and investment management profession is undergoing a radical transformation driven by Generative AI (GenAI) and Agentic AI, creating urgent workforce displacement challenges that require coordinated government policy intervention alongside educational reform.
Author assertion in the paper's introduction/abstract; framing argument based on the paper's synthesized analysis (no empirical sample, no reported statistical test).
Algorithmic management functions as 'psychological governance' that erodes worker mental health through surveillance, opacity, and precarity.
Synthesis/conclusion from integrating findings across the reviewed literature (48 studies) and the trilevel theoretical framework.
Fear of deactivation (automated sanctions) creates chronic precarity; 78% report chronic fear.
Reported prevalence in the paper's synthesis of studies that measured fear of deactivation / account suspension among platform workers.
Task defragmentation (fragmenting tasks via platform algorithms) leads to a reduced sense of accomplishment among drivers.
Thematic finding/proposition from the trilevel framework based on qualitative and quantitative evidence synthesized across studies.
Rating pressure is associated with emotional exhaustion, with 41–67% reporting high burnout.
Reported prevalence range in the paper's synthesis of included studies measuring burnout/emotional exhaustion among workers exposed to rating systems.
Income volatility from dynamic pricing is associated with depressive symptoms (reported prevalence range 23–41%).
Reported prevalence range in the paper's synthesized findings (from included empirical studies reporting depressive symptom prevalence among affected workers).
Algorithmic opacity is linked to procedural anxiety.
Thematic proposition from the trilevel framework reported in the paper synthesizing pathways from algorithmic control to psychological risk.
AI can promote enterprises to adopt different income distribution modes by improving the marginal output of capital and substituting low-skilled labor (technology bias).
Theoretical mechanism articulated in the paper based on capital-labor substitution principle and factor reward theory; implied empirical testing using firm-level data.
AI-driven job displacement disproportionately affects low-skilled workers.
Reported empirical result from the paper's PLS-SEM analysis on the 351-respondent dataset.
Improvements in AI ('better' AI) amplify the excess automation as well.
Model comparative statics: increased AI capabilities raise private incentives to automate, leading to more displacement than is socially optimal; theoretical analysis only.
More competition amplifies the excess automation (the automation arms race).
Comparative-statics result in the competitive task-based theoretical model showing increased competition raises firms' incentives to automate; no empirical sample.
The resulting loss from excess automation harms both workers and firm owners.
Welfare comparisons from the model showing negative payoff changes for workers (lower wages/less employment) and reduced owner returns when automation is excessive; theoretical analysis, no empirical data.
In a competitive task-based model, demand externalities trap rational firms in an automation arms race, displacing workers well beyond what is collectively optimal.
Formal equilibrium analysis in the paper's theoretical competitive task-based model; comparative statics and welfare analysis (no empirical sample).
Knowing that AI-driven displacement can erode demand is not enough for firms to stop automating.
Analytical result from the paper's competitive task-based model showing firms' incentives do not internalize demand externalities; no empirical sample.
If AI displaces human workers faster than the economy can reabsorb them, it risks eroding the very consumer demand firms depend on.
Theoretical statement in the paper's motivating premise; no empirical sample reported (conceptual argument about aggregate demand effects when displacement outpaces reabsorption).
The most vulnerable occupational groups to AI-driven transformation are office workers, data entry operators, call center workers, accountants, and administrative staff with routine analytical and administrative tasks.
Results of the envelope-model assessment for the sampled European Union countries that identify occupations with high exposure/vulnerability to AI-driven change; occupations are listed explicitly in the paper.
AI appears to be a diffusing technology, not an emerging occupation.
Synthesis of empirical findings: presence of a shared vocabulary but lack of a coherent practitioner population in resume data, interpreted as diffusion of AI skills/vocabulary across existing roles.
These findings uncover critical threats to judicial integrity and public trust and underscore the urgent need for robust safeguards against non-legal influences in AI legal systems.
Interpretation/conclusion drawn from the empirical results (observed deviations, sentiment amplification, and subgroup vulnerabilities).
These safety risks are compounded for emotionally charged topics.
Subgroup analyses where emotionally charged case topics showed larger deviations and stronger effects from injected sentiment.
These safety risks are compounded (stronger) for low-skilled occupational categories.
Subgroup analyses reported in the paper showing larger model deviations and/or greater sentiment amplification effects for cases involving low-skilled occupations.