Evidence (4004 claims)
Search and filter individual claims pulled from the papers. Looking for a specific finding ("what's the effect on wages?"), you're in the right place. Want to compare whole outcome categories against each other instead? Use the Evidence Explorer.
The board below groups claims two ways: by broad theme (nine paper-level topics) and by outcome category (the 34 claim-level outcomes that the Explorer and Syntheses also use).
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Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
9875 claims
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Productivity
8807 claims
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Governance
7870 claims
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Human-AI Collaboration
7560 claims
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Org Design
4892 claims
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Innovation
4781 claims
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Labor Markets
4004 claims
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Skills & Training
3308 claims
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Inequality
2332 claims
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Claims by outcome category
Counts by direction of finding. These are the same 34 outcome categories the Explorer compares and the Syntheses are written for. A linked row has a published synthesis.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 870 | 233 | 116 | 1066 | 2363 |
| Governance & Regulation | 976 | 451 | 218 | 133 | 1809 |
| Organizational Efficiency | 949 | 224 | 144 | 88 | 1416 |
| Technology Adoption Rate | 764 | 287 | 141 | 122 | 1325 |
| Research Productivity | 501 | 152 | 74 | 362 | 1101 |
| Output Quality | 542 | 216 | 69 | 69 | 896 |
| Decision Quality | 387 | 198 | 94 | 54 | 740 |
| Firm Productivity | 513 | 67 | 101 | 27 | 714 |
| AI Safety & Ethics | 249 | 303 | 73 | 36 | 667 |
| Market Structure | 190 | 192 | 134 | 27 | 548 |
| Task Allocation | 243 | 77 | 91 | 36 | 452 |
| Innovation Output | 291 | 33 | 55 | 20 | 401 |
| Skill Acquisition | 206 | 72 | 65 | 21 | 364 |
| Employment Level | 133 | 63 | 115 | 22 | 335 |
| Fiscal & Macroeconomic | 153 | 79 | 52 | 32 | 323 |
| Task Completion Time | 206 | 37 | 12 | 15 | 272 |
| Firm Revenue | 179 | 52 | 29 | 5 | 266 |
| Consumer Welfare | 130 | 76 | 47 | 13 | 266 |
| Inequality Measures | 48 | 137 | 51 | 6 | 242 |
| Worker Satisfaction | 101 | 81 | 25 | 13 | 220 |
| Error Rate | 84 | 110 | 11 | 5 | 210 |
| Wages & Compensation | 98 | 47 | 30 | 10 | 185 |
| Regulatory Compliance | 88 | 73 | 17 | 7 | 185 |
| Automation Exposure | 66 | 64 | 33 | 16 | 182 |
| Team Performance | 105 | 29 | 30 | 11 | 176 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 114 | 21 | 14 | 8 | 158 |
| Job Displacement | 12 | 90 | 24 | 1 | 127 |
| Hiring & Recruitment | 57 | 9 | 9 | 5 | 80 |
| Skill Obsolescence | 6 | 56 | 9 | 1 | 72 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 21 | 17 | 1 | 57 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Labor Markets
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In our central scenario — drawn from credible international estimates — around 7 per cent of current jobs could be displaced in the short–medium run.
Scenario simulation based on international estimates of AI exposure/adoption; central scenario reported in the report (linked to SWITCH microsimulation for distributional analysis).
AI tends to place higher earning and highly educated workers at greater risk of disruption, because the occupations most exposed to AI are predominantly in these groups.
Synthesis of international research on occupational exposure to AI and the report's analysis linking exposure to worker characteristics (education and earnings); presented as descriptive finding in the report.
These dynamics risk trapping workers in a 'low-skill trap'.
Synthesis of observed labour-market polarisation, persistent low-skill segment, and limited reskilling coverage from secondary sources (2020–2024); presented as a likely risk/consequence.
Limited reskilling coverage constrains workers' ability to adapt to AI-driven changes.
Paper reviews official reports and secondary data (2020–2024) indicating low coverage/uptake of reskilling programs in India and links this to limited adaptation capacity.
AI-driven change is intensifying wage disparities.
Paper links observed occupational shifts in secondary data (2020–2024) with widening wage gaps between high- and lower-skilled groups.
Routine middle-skilled roles are declining.
Secondary data and official reports from 2020–2024 documenting reductions in middle-skill occupations, interpreted through SBTC/Human Capital frameworks.
There is a 'capability-demand inversion' where skills most demanded in AI-exposed jobs are those LLMs perform least well at in our benchmark.
Cross-referencing SAFI performance with Anthropic Economic Index demand data (reported in paper); described as an observed inversion pattern.
The effective altruism community's near-exclusive focus on existential risk from AI has created a dangerous blind spot around the political economy of who controls AI and who benefits from it.
Critical evaluation of the effective altruism movement's priorities as presented in the paper; argued via literature/agenda analysis rather than empirical survey data in the abstract.
AI infrastructure owners may come to command more wealth and capability than most governments, undermining the future viability of the nation-state.
Predictive economic and political analysis / modeling in the paper; claim presented as a projection without empirically quantified comparisons or sample size in the abstract.
Universal Basic Income (UBI), absent a revolutionary threat that historically forced redistribution, will default to a pacification mechanism rather than a genuine solution to mass loss of labor value.
Normative/incentive-structure analysis and historical comparison presented in the paper; no empirical trial data or sample sizes cited in the abstract.
Unlike previous feudal orders, this AI-enabled feudal order may be uniquely resistant to revolution because enforcement mechanisms (autonomous weapons, AI surveillance, algorithmic propaganda) do not require human cooperation and therefore cannot be undermined by human dissent.
Conceptual argument drawing on descriptions of autonomous weapons, surveillance, and propaganda systems; presented as a theoretical vulnerability analysis rather than empirically validated case studies in the abstract.
The convergence of geopolitical fragmentation and AI-driven economic concentration could produce a structural transformation that stabilizes into a neo-feudal equilibrium, in which a vanishingly small class of infrastructure owners wields power comparable to pre-Enlightenment monarchs while the vast majority loses labor value and political leverage.
Theoretical/modeling exercise and historical analogy presented in the paper; argumentative prediction rather than reported empirical measurement (no sample size or quantified projection in the abstract).
Advances in artificial intelligence are producing an accelerating concentration of economic power.
Paper asserts causal link based on theoretical argument and economic/political analysis of AI-driven accumulation; no quantitative sample size or empirical estimate reported in the abstract.
The post-World War II international order is undergoing geopolitical fragmentation driven by twenty consecutive years of democratic decline.
Statement in paper referencing long-term democratic trend data (20-year decline) and historical/political analysis; no specific sample size or statistical details provided in the abstract.
Occupations are not eradicated instantaneously, but gradually encroached upon via atomic actions.
Conceptual argument presented by the authors as part of their theoretical framing (Tech-Risk Dual-Factor Model); no empirical count reported for this specific claim.
Existing task-based evaluations predominantly measure theoretical "exposure" to AI capabilities, ignoring critical frictions of real-world commercial adoption: liability, compliance, and physical safety.
Authoritative statement in paper contrasting prior task-based exposure evaluations with the paper's focus on business/institutional frictions (liability, compliance, physical safety). No numeric sample; literature critique based on conceptual analysis.
Up to 25% of routine administrative tasks face high automation risk.
Quantitative survey of 150 leading Nigerian firms across finance, tech, and manufacturing reporting the share of tasks at high automation risk.
There is a significant deficit in high-demand technical competencies such as data engineering, machine learning maintenance, and AI ethics within the Nigerian workforce.
Findings reported from the quantitative survey of 150 leading Nigerian firms (finance, tech, manufacturing) supplemented by qualitative workforce interviews and policy analysis.
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 to Acemoglu and Restrepo (2022) empirical decomposition reported in the paper.
Dechezleprêtre et al. (2025), exploiting Germany's Hartz reforms, estimate an elasticity of automation innovation to low-skill wages of 2–5 at the firm level.
Citation to Dechezleprêtre et al. (2025) empirical estimate reported in the literature review.
When employers have monopsony power, they choose technologies that expand this power beyond what a social planner would consider optimal.
Model results and discussion in Section 7 on interaction of technological choices and monopsony power.
Profit-maximizing firms pursue innovations that erode workers' market power (make them more replaceable), even at the expense of production efficiency; a social planner would instead prefer technologies that preserve workers' market power.
Theoretical analysis in the paper of firms' profit-maximizing technology choices under market power considerations, plus comparative planner outcome.
A welfare-maximizing planner chooses to automate fewer tasks than a production-efficiency benchmark would dictate when workers' welfare is heavily weighted.
Model analysis of optimal task automation vs. production efficiency under different welfare weights on workers.
Occupations whose AI-exposed steps are more dispersed across the production workflow (higher fragmentation) exhibit a substantially lower share of their steps actually executed by AI, conditional on AI exposure share.
Empirical regression analysis controlling for share of AI-exposed steps; uses dataset linking O*NET tasks, human AI exposure assessments, Anthropic Economic Index execution outcomes, and GPT-generated workflow orderings (details in Sections 5.1 and 7).
Under the rapid scenario, economists forecast the share of wealth held by the wealthiest 10% of households rising to 80.0% by 2050.
Conditional forecasts in Key Findings for the economist respondent group under the rapid AI scenario (2050 horizon).
Conditional on the rapid scenario, economists forecast the labor force participation rate falling from its current level of 62% to 55% by 2050.
Conditional forecasts in Key Findings for the economist respondent group under the rapid AI scenario (2050 horizon).
There are macroeconomic risks associated with AI-led unemployment.
Paper's macroeconomic analysis drawing on labor economics and technology adoption research; no quantitative estimates or sample sizes provided in the summary.
Managerial incentives drive premature workforce contraction during AI adoption.
Analytical claim grounded in labor economics and organizational behavior review; the summary indicates examination of managerial incentives but does not report primary empirical tests or sample sizes.
Premature workforce contraction in response to AI adoption foreshadows deeper structural challenges as AI systems mature.
Forward-looking claim based on synthesis of literature and theoretical projection; no empirical quantification or sample provided in the summary.
This pattern of premature workforce reductions reflects longstanding corporate short-termism rather than genuine technological displacement.
The paper's interpretation drawing on labor economics and organizational behavior literature; no empirical study or sample size reported in the summary.
Organizations face mounting pressure to demonstrate immediate returns on AI investments, often through workforce reductions that outpace actual automation capabilities.
Argument in paper citing accelerating AI adoption across sectors and observed managerial responses; no primary dataset or sample size reported in the text.
Such predatory-hiring cases often fall outside the scope of merger control because they fail to meet the applicable thresholds, warranting consideration under the abuse of dominance prohibition in Article 102 TFEU.
Legal analysis stated in abstract referencing merger control thresholds and Article 102 TFEU (no quantitative sample provided in abstract).
When a dominant undertaking in a concentrated market strategically targets and hires a large portion—or the entirety—of a smaller competitor’s key personnel, this behavior can raise significant competition concerns.
Legal argument presented in abstract; draws on relevant case law and scholarship (no empirical sample or experimental method reported in abstract).
There is a governance window—estimated at 10–15 years—before current deployment trajectories risk path-dependent social, economic, and institutional lock-in.
Forward-looking estimate/projection provided in the paper based on the authors' characterization of deployment trajectories and governance dynamics (no empirical sample size provided in the excerpt).
Societal consequences of labor displacement intensify the governance gap by concentrating consequential AI decision-making among an increasingly narrow class of technical and capital actors.
Analytic/theoretical claim in the paper drawing on the paper's multi-domain argument (no empirical sample size or quantified concentration metrics provided in the excerpt).
This nominal-vs-genuine oversight distinction represents the primary architectural failure mode in deployed AI governance.
Argumentative claim based on the paper's multi-domain synthesis and theoretical analysis; no empirical sample size or quantified causal inference provided in the excerpt.
The distinction between nominal and genuine human oversight is largely absent from current governance frameworks, including the EU AI Act and NIST AI Risk Management Framework 1.0.
Comparative policy/regulatory review claimed in the paper (explicit reference to the EU AI Act and NIST AI RMF 1.0); no sample size—based on textual/regulatory analysis rather than statistical data in the provided excerpt.
There exists a critical and underexamined governance gap between nominal human oversight of AI systems (humans in formal authority positions) and genuine human oversight (humans with cognitive access, technical capability, and institutional authority to understand, evaluate, and override AI outputs).
Conceptual/qualitative analysis and argumentation presented in the paper; implied synthesis of case examples and theoretical considerations rather than a quantified empirical study in the provided excerpt.
The accelerating displacement of human labor by artificial intelligence (AI) and robotic systems represents a structural transformation whose societal consequences extend far beyond conventional labor market analysis.
Stated as a framing claim in the paper; supported by the paper's literature review and multi-domain conceptual argument (no empirical sample size or quantitative data reported in the provided excerpt).
The interaction between strict algorithmic control and worker counter-strategies leads to persistent limit cycles in strategy frequencies rather than convergence to a stable compliant workforce.
Dynamical systems analysis and simulation trajectories from the EGT model showing limit cycles / oscillatory equilibria in strategy proportions; model-based (no empirical sample).
Over time the equalizing channel weakened because market valuation (wage exposure) became increasingly unfavorable to female-concentrated occupations, contributing to a renewed widening of the gender wage gap in 2015–2019.
Decomposition results showing a temporal decline in the wage-exposure contribution to equality and a negative wage-exposure trend for female-concentrated occupations, coinciding with gap widening in 2015–2019.
Women experienced greater exposure to displacement compared with men.
Gender-disaggregated results from stacked first-difference estimations and dynamic shift-share decomposition showing higher displacement exposure for female workers.
Routine displacement unfolds episodically rather than simultaneously, with relative contraction in routine cognitive jobs (2001–2005), routine manual jobs (2005–2010), and renewed routine cognitive pressures (2015–2019).
Empirical results from stacked first-difference estimations and a dynamic shift-share decomposition applied to Indonesian formal wage-worker data over 2001–2019.
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.