Evidence (2332 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).
Browse by theme
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 |
Inequality
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Unlike previous feudal orders, this one may prove uniquely resistant to revolution because the mechanisms of enforcement (autonomous weapons, AI surveillance, algorithmic propaganda) do not require human cooperation and therefore cannot be undermined by human dissent.
Logical and theoretical claim based on characteristics of AI-enabled enforcement technologies; presented as an argument rather than an empirically tested finding in the excerpt.
Under this emerging order, the vast majority of humanity will lose their political leverage.
Theoretical and historical argument linking concentration of infrastructure control to political disempowerment; no empirical metrics or sample size provided in the excerpt.
Under this emerging order, the vast majority of humanity will lose their labor value.
Claim made via theoretical argument about automation and AI replacing labor value; no quantitative empirical evidence or sample detailed in the excerpt.
This structural transformation could stabilize into a neo-feudal equilibrium in which a vanishingly small class of infrastructure owners wields power comparable to pre-Enlightenment monarchs.
Futuristic projection and normative/historical analogy based on conceptual modeling of class structure under AGI; the excerpt gives no empirical data or formal model outputs.
The convergence of geopolitical fragmentation (democratic decline) and AI-driven economic concentration is producing a structural transformation unprecedented in human history.
Theoretical synthesis and historical comparison; the paper presents this as an argument based on conceptual modeling and historical analogy; no specific empirical test or sample noted in the excerpt.
The post-World War II international order is undergoing an accelerating concentration of economic power driven by advances in artificial intelligence.
Asserted in the paper as an observed trend linking AI advances to concentration of economic power; presented as a conceptual/historical claim without empirical specification in the excerpt.
The post-World War II international order is undergoing geopolitical fragmentation driven by twenty consecutive years of democratic decline.
Stated as a historical/political claim in the paper; implies reliance on democracy-trend data and historical analysis but no specific dataset, method, or sample size provided in the excerpt.
Income inequality, measured by the Gini index, rises moderately in every scenario we examine due to the polarising effect of job losses and wage and capital income increases on the income distribution.
Calculation of Gini index across multiple simulated scenarios using the SWITCH-linked distributional analysis; reported in the report.
The largest average losses are experienced by middle and higher income households, for whom job displacement outweighs any wage or capital income gains. Lower income households also lose, but by much less.
Distributional results from microsimulation (SWITCH) applying scenarioled job displacement, wage and capital effects across income groups; reported in the report.
When these effects are combined, we find an average decline in household disposable income as a result of AI adoption.
Combined scenario simulations incorporating job displacement, wage effects and capital income effects linked to the Irish tax-benefit system using SWITCH; result reported in the report's main findings.
These wage gains are not large enough to counterbalance the average fall in income due to job displacement.
Combined simulation results (displacement + wage effects) using scenario assumptions and microsimulation (SWITCH), reported in the report's distributional analysis.
Those most likely to experience this disruption are found in higher income households, where the share of workers transitioning into unemployment is substantially larger than in lower income families.
Microsimulation (SWITCH) linking simulated job displacement scenarios to household income groups; results reported in the report.
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.
Qwen 3 Next concealed prices in unfavorable comparisons 24% of the time.
Experimental evaluation reported in the paper measuring whether models conceal pricing information in comparisons unfavorable to the sponsored option; Qwen 3 Next recorded a 24% rate. Sample size and trial counts not specified in the abstract.
GPT 5.1 surfaced sponsored options in ways that disrupted the purchasing process, with a 94% rate reported.
Experimental evaluation described in the paper measuring whether models surface sponsored options in manners that disrupt purchasing flow; GPT 5.1 reported at 94%. Specific experiment details and sample size not provided in the abstract.
Grok 4.1 Fast recommended a sponsored product that was almost twice as expensive in the scenario, doing so 83% of the time.
Experimental evaluation reported in the paper contrasting sponsored vs. non-sponsored product recommendations in which the sponsored product was nearly twice as expensive; the paper reports a 83% recommendation rate for Grok 4.1 Fast. Exact number of trials/samples not provided in the abstract.
A majority of LLMs forsake user welfare for company incentives in a multitude of conflict of interest situations.
Reported summary of a suite of evaluations across multiple LLMs described in the paper (models and specific scenarios referenced elsewhere in the paper). Exact experimental methods and sample sizes not specified in the abstract.
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.
The review identifies persistent gaps in population coverage, multimodal integration, equity optimization, explainability, validation, and governance that constrain inclusiveness and robustness of GeoAI applications in urban mobility research.
Authors' gap analysis based on the contents and limitations of the 18 included studies.
Urban mobility is a central challenge for sustainable and inclusive cities, as climate change, congestion, and spatial inequality increasingly reveal mobility patterns as expressions of deeper social and spatial structures.
Introductory framing statement in the paper; general literature/contextual claim (no original empirical test reported in this paper).
These findings highlight how existing caste hierarchies are reproduced in LLM decision-making and underscore the need for culturally grounded evaluation and intervention strategies in AI systems deployed in socially sensitive domains.
Interpretation and policy recommendation based on empirical patterns found in the audit (consistent hierarchical ratings and up-to-25% differences).
Inter-caste matches are further ordered according to traditional caste hierarchy.
Reported analytic pattern where inter-caste match ratings follow the traditional caste ranking (implied ordering across Brahmin, Kshatriya, Vaishya, Shudra, Dalit).
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.
AI's disproportionate benefits for lagging regions help narrow interprovincial emission gaps.
Heterogeneity analysis reported in the provincial panel (2003–2021) showing stronger AI-related reductions in emissions inequality for lagging regions compared to advanced regions.
Green innovation is concentrated in coastal provinces and has not effectively diffused to inland areas, limiting its ability to reduce regional carbon inequality.
Spatial distribution analysis within the provincial panel showing geographic concentration of green innovation activity in coastal provinces and limited diffusion inland.
AI reduces carbon inequality primarily through improved energy efficiency, enhanced environmental monitoring, and more efficient resource allocation, disproportionately benefiting lagging regions and narrowing interprovincial emission gaps.
Mechanism analysis reported in the paper based on the provincial panel (2003–2021) linking AI development to proximate channels (energy efficiency, monitoring, resource allocation) and heterogeneous impacts across regions.
AI development significantly reduces carbon inequality, particularly when measured by the Gini index.
Empirical analysis using a provincial panel dataset covering 2003–2021; carbon inequality measured with the Gini index; reported statistically significant negative association between AI development and Gini-measured carbon inequality.
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.
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).
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).