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REVIEW 3 major objections 6 minor 2 cited by

The Widespread Adoption of Large Language Model-Assisted Writing Across Society

T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper estimates that LLMs assisted with roughly 18% of consumer complaint text, 24% of corporate press releases, 10% of small-firm job postings, and 14% of UN press releases by late 2024.

desk verdict Solid cross-domain measurement of the post-ChatGPT surge, but the absolute adoption percentages need debiasing and external validation before I'd quote them. read the letter →

arxiv 2502.09747 v2 pith:TU35BESO submitted 2025-02-13 cs.CL

Weixin Liang , Yaohui Zhang , Mihai Codreanu , Jiayu Wang , Hancheng Cao , James Zou This is my paper · ORCID
classification cs.CL
keywords largelanguagemodelsLLMadoptionAI-assistedwritingtextattributionconsumercomplaintsjobpostingspressreleasesChatGPT
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper sets out to measure how much of everyday writing is now done with help from large language models, using four large text corpora: consumer financial complaints, corporate press releases, online job postings from small firms, and United Nations press releases. Tracking these from before ChatGPT's launch through late 2024, it estimates that roughly 18% of consumer complaint text, up to 24% of corporate press release text, just under 10% of small-firm job posting text, and nearly 14% of UN press release text is LLM-assisted. It also finds that adoption rose sharply about three to five months after ChatGPT's release and then flattened out by 2023–2024. A sympathetic reader would take these as the first population-level, cross-domain estimates of LLM adoption in writing, with implications for how we read corporate, institutional, and consumer-facing text.

What carries the argument

The engine of the analysis is a domain-adapted text-mixture estimator. For each corpus, word-frequency distributions are built from two reference pools: human writing collected before ChatGPT's release and synthetic text produced by prompting current GPT models. Fitting a mixture model to observed monthly text yields an estimate of $\alpha$, the proportion of sentences substantially modified by LLMs. The same estimator is fitted separately for each press-release platform, job category, and complaint/UN corpus, and its bias is checked by mixing pre-ChatGPT human text with GPT output at known concentrations. That calibration, with prediction error below 3.3 percentage points in the validation tables, is what turns raw detection scores into population-level adoption numbers.

What would settle it

Take a corpus of documents whose true AI-assistance status is known independently—for example, job postings or press releases drafted with an LLM and then edited by humans, with editing logs intact—and run this estimator on it. If the estimated $\alpha$ falls well below the true fraction of LLM-influenced text, the reported population figures are best read as lower bounds rather than point estimates.

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Extended reading notes

Core claim

The central claim is that LLM-assisted writing has become a measurable, large-scale feature of public and commercial text, not an edge case. Using a statistical estimator of the fraction $\alpha$ of sentences that were generated or substantially modified by an LLM, the paper reports adoption levels of about 18% for financial consumer complaints, 23–24% for at least one corporate press-release platform, up to 15% for job postings from young small firms, and 14% for UN press releases by the end of the study period. The paper further claims that these adoption curves share a common shape: a lag of several months after ChatGPT's debut, a steep rise through 2023, and a plateau by 2024, which it reads as either saturation or the growing indistinguishability of AI output.

Load-bearing premise

The method assumes that text generated by current GPT models in the validation setup is a faithful stand-in for all real-world LLM-assisted writing, and that the pre-ChatGPT false-positive rate stays constant through 2024.

Editorial extensions

If this is right

  • Adoption of LLM-assisted writing is now a majority-adjacent phenomenon in corporate press releases, with the top platform reaching about 24% of text by late 2024.
  • The consistent plateau across domains suggests the first wave of adoption had largely run its course by 2024, whether through saturation or through models becoming harder to detect.
  • Smaller and younger firms lead in job-posting adoption, with post-2015 firms reaching 10–15% in some roles, pointing to an organizational-age gradient in AI uptake.
  • Geographic and demographic heterogeneity is modest but real: more urbanized areas show higher complaint adoption, while lower-education areas show slightly higher rates.
  • International organizations, exemplified by UN press releases, reach roughly 14% LLM-modified content, indicating institutional adoption in high-stakes communication.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the method misses heavily edited or highly human-like LLM text, the paper's own numbers are lower bounds; the true prevalence in late 2024 could be materially higher than 18–24%.
  • If the plateau reflects detector blindness rather than saturation, apparent stabilization may mask continued growth—a testable prediction when future detectors calibrated on newer models are applied to the same 2024 corpora.
  • A direct extension would compare these population estimates with self-reported usage from surveys or platform telemetry, which could cross-validate the framework without relying on synthetic ground truth.
  • The finding that consumer complaints in lower-education areas show higher LLM adoption suggests these tools may be functioning as an equalizer in consumer advocacy, but the paper does not test whether AI-assisted complaints are more likely to receive redress.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. This paper applies a word-frequency mixture estimator, originally developed by the authors to measure AI-modified text in peer reviews, to four large English-language text corpora: 687,241 consumer complaints to the CFPB, 537,413 corporate press releases from Newswire/PRNewswire/PRWeb, 304.3 million LinkedIn job postings, and 15,919 UN press releases. The central empirical claim is that LLM-assisted writing rose sharply starting 3–5 months after ChatGPT's November 2022 release and then plateaued, reaching roughly 18% of consumer complaint text, up to 24% of corporate press release text, just below 10% of small-firm job postings, and nearly 14% of UN press releases by late 2024. The paper also reports heterogeneity in adoption by geography, urbanization, education, and firm age/size. The detection method's ground truth is built entirely from a synthetic pipeline in which GPT-3.5-turbo compresses pre-ChatGPT human text into bullet skeletons and re-expands it, and the validation sets mix only this synthetic positive class with pre-ChatGPT human text.

Significance. If the headline percentages are unbiased, the paper provides the first population-level, cross-domain measurement of LLM-assisted writing, with immediate relevance to policy discussions about AI adoption, labor markets, and institutional communication. The study's strengths are its scale, the consistency of the temporal pattern across four independent domains, the use of an open and transparent estimator rather than commercial black-box detectors, and the robustness check across GPT model versions in Supplementary Figure 4. The authors also honestly acknowledge that heavily edited or human-like LLM text escapes detection and frame their numbers as lower bounds. However, the central estimates inherit a load-bearing external-validity assumption: that synthetic GPT-generated outlines-and-expansions are representative of real-world human-in-the-loop LLM-assisted writing. That assumption is not tested against any independently labeled real documents, and the validation tables in the supplement show a systematic positive bias at every ground-truth level, including at zero.

major comments (3)
  1. [Supplementary Information, Model Fitting; Supp. Figs. 5–6; Supp. Tables 1–5] The ground truth for the 'LLM-assisted' class is entirely synthetic: pre-ChatGPT human text is compressed into bullet-point skeletons and then re-expanded by GPT-3.5-turbo (Supp. Figs. 5–6), and the validation corpora in Supp. Tables 1–5 mix only this synthetic positive class with pre-ChatGPT human text. No independently labeled set of real documents produced by humans using LLMs in the wild is used for validation. The reported prediction error of less than 3.3 percentage points therefore measures how well the estimator recovers the fraction of text that resembles this specific two-prompt pipeline, not the fraction of text actually written or substantially modified by LLMs. Direct prompting, human editing of model drafts, and other model families can produce different lexical signatures. The paper's 'lower bound' framing covers heavily edited or very human-like LLM output being missed, but it does not cover the opposite risk: human text whose style drifts toward GPT-like patterns, from either temporal style drift or humans imitating LLM output. This assumption is load-bearing for every headline percentage.
  2. [Supp. Tables 1–5] The validation tables show a systematic positive bias of roughly 2–3 percentage points at every ground-truth level, including at α=0. For example, Supp. Table 1 reports an estimate of 1.8% at ground-truth 0.0%; Supp. Table 3 reports 2.9%, 2.1%, and 2.3% for PRNewswire, PRWeb, and Newswire at α=0; and Supp. Table 5 reports 2.0% for Scientist at α=0. This offset is not subtracted or otherwise corrected in the headline estimates. For the smallest headline claim ('just below 10%' in small-firm job postings), this is a substantial relative correction, and even for the largest estimates (18–24%) it is a non-negligible absolute correction. The manuscript should either explicitly debias the estimates using the measured false-positive rates at α=0 and at the relevant mixing levels, or demonstrate that the qualitative conclusions are unchanged after applying the corresponding correction.
  3. [Fig. 1; Results; Discussion] The stabilization/plateau pattern is presented as a main finding, but the interpretation is confounded with possible changes in detector sensitivity over time. Because the synthetic positive class is generated by specific GPT models (GPT-3.5-turbo and GPT-4 variants), improvements in LLM indistinguishability—or in human writers' tendency to imitate LLM style—could cause the estimated fraction to flatten or decline even if true adoption continues to rise. The manuscript acknowledges this in the Discussion and in footnote 2, but the abstract and results still present stabilization as an empirical regularity. The validation in Supp. Tables 1–5 only assesses calibration on pre-ChatGPT human text mixed with synthetic LLM text; it does not test whether the detector's sensitivity is stationary across 2023–2024. A sensitivity analysis that varies the assumed sensitivity trajectory over time (e.g., by re-generating the synthetic positive class with successive model versions and showing the time-series conclusions are robust) is needed to support the plateau claim.
minor comments (6)
  1. [Results, LLM Adoption in LinkedIn Job Postings; Fig. 1] The sentence 'Using the sample of small companies based on the number of vacancies posted, our findings reveal... (Fig. 1d, Fig. 4)' refers to Figure 1d, which displays UN press releases; the correct panel for job postings appears to be Fig. 1c.
  2. [Supplementary Figure 4] The caption states that GPT-3.5-turbo, 'used in main analysis,' was 'released January 25, 2024'; GPT-3.5-turbo was released earlier, so this date likely refers to a specific model snapshot rather than the model family, and the caption should be corrected or clarified.
  3. [Results, LLM Adoption in Corporate Press Releases] The Introduction says adoption surged '3-4 months' after ChatGPT's release, while this section says 'about 2 quarters post rollout'; please reconcile the timing statements.
  4. [Results, Geographic and Demographic Disparities] The p-values ('less than 0.001') and 'highly statistically significant' claims are reported without specifying the statistical test, the unit of analysis, or whether any multiple-comparison correction was applied; please add these details.
  5. [Abstract] The consumer-complaint data end in August 2024, so 'By late 2024' is slightly imprecise; please adjust to 'by August 2024' or 'by late summer 2024.'
  6. [Supplementary Information, LinkedIn Job Posting Data] The definition of small firms as 'companies with either 10 or fewer registered employees in 2021 or companies posting less than or equal to about 2 postings per year' is ambiguous in light of the later statement that the median number of postings is 3; please clarify whether the threshold is 2 or 3.

Circularity Check

0 steps flagged · score 0.0 of 10

No formal circularity: synthetic ground truth is a validity limitation, not a self-referential reduction; the post-ChatGPT surge is external.

full rationale

The paper's derivation chain is not circular in the formal sense. The population estimates are produced by a word-frequency mixture estimator whose positive class is constructed by prompting GPT-3.5-turbo to compress a pre-ChatGPT human text into a bullet skeleton and then expand it (Supp. Figs. 5-6). This means the validation error (<3.3 percentage points) is calibrated on synthetic 'LLM-modified' text rather than on independently labeled real documents, and the headline percentages inherit that construct as a validity limitation; the paper explicitly acknowledges that heavily edited or human-like LLM output is missed and offers the numbers as a lower bound. But a calibration gap is not a circular reduction: the fitted model does not take the headline values as inputs, no equation in the paper is defined in terms of its own outputs, and the post-ChatGPT temporal surge in estimated alpha is an external, non-encoded pattern. The self-citations to the authors' prior method [12] are supporting references, and the paper re-describes the fitting procedure and points to code, so the citations do not substitute for the derivation. Therefore the appropriate circularity score is 0.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

No new particles, forces, mediators, or conserved quantities are introduced. The paper's only new objects are population-level alpha estimates, which depend on the fitted detector, the baseline corpora, and the synthetic ground truth described above.

free parameters (1)
  • Alpha estimator word-frequency model parameters = not disclosed
    The detector that produces every headline alpha is fitted to pre-ChatGPT human text and synthetic LLM text; the estimates in Fig. 1 depend directly on these fitted parameters. A separate model is fit for consumer complaints, for UN releases, for each press-release platform, and for each job category (Supp. Info, Model Fitting).
assumptions (4)
  • domain assumption Pre-ChatGPT corpora (2021, or 2019 for UN) are representative of human writing without LLM assistance.
    Used as the baseline for fitting word frequencies; any LLM-like style present before ChatGPT would be absorbed into the false-positive baseline. Located in Supplementary Information, Data Split, Model Fitting, and Evaluation.
  • ad hoc to paper Synthetic LLM-modified texts generated by GPT models are representative of real-world LLM-assisted writing in 2023-2024.
    The validation sets and detector training rely on GPT-generated summaries and expansions of pre-ChatGPT documents; if real users edit, constrain, or use other models, the detector's alpha is miscalibrated. See Supp. Figs. 4-6.
  • domain assumption The false-positive rate measured on pre-ChatGPT text remains stable over the study window.
    The paper does not re-estimate the baseline during 2023-2024; its Discussion notes that shifts in user demographics or language usage could affect detection accuracy.
  • domain assumption English-language text is sufficient for the claimed adoption patterns.
    All four corpora are English-only; the Discussion states the analysis may overlook non-English adoption trends.

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Cite this review

Pith. "Pith review of The Widespread Adoption of Large Language Model-Assisted Writing Across Society." pith.science (2026). https://pith.science/paper/TU35BESO

@misc{pith2026250209747,
  author       = {Pith},
  title        = {Pith review of: The Widespread Adoption of Large Language Model-Assisted Writing Across Society},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TU35BESO}},
  note         = {Machine review of arXiv:2502.09747}
}
read the original abstract

The recent advances in large language models (LLMs) attracted significant public and policymaker interest in its adoption patterns. In this paper, we systematically analyze LLM-assisted writing across four domains-consumer complaints, corporate communications, job postings, and international organization press releases-from January 2022 to September 2024. Our dataset includes 687,241 consumer complaints, 537,413 corporate press releases, 304.3 million job postings, and 15,919 United Nations (UN) press releases. Using a robust population-level statistical framework, we find that LLM usage surged following the release of ChatGPT in November 2022. By late 2024, roughly 18% of financial consumer complaint text appears to be LLM-assisted, with adoption patterns spread broadly across regions and slightly higher in urban areas. For corporate press releases, up to 24% of the text is attributable to LLMs. In job postings, LLM-assisted writing accounts for just below 10% in small firms, and is even more common among younger firms. UN press releases also reflect this trend, with nearly 14% of content being generated or modified by LLMs. Although adoption climbed rapidly post-ChatGPT, growth appears to have stabilized by 2024, reflecting either saturation in LLM adoption or increasing subtlety of more advanced models. Our study shows the emergence of a new reality in which firms, consumers and even international organizations substantially rely on generative AI for communications.

Figures

Figures reproduced from arXiv: 2502.09747 by the authors.

Figure 1
Figure 1. Temporal dynamics of large language model (LLM) adoption across diverse writing domains. Analysis of LLM-generated or substantially modified content across four domains: (a) Consumer complaints filed with the Consumer Financial Protection Bureau showed algorithm false positive rate of 1.5% pre-ChatGPT release (November 2022), followed by genuine LLM adoption rising to 15.3% by August 2023, before plateauing at 17.7%… view at source ↗
Figure 2
Figure 2. Geographic and demographic patterns of LLM adoption in Consumer Financial Protection Bureau complaints. (a) State-level analysis (January-August 2024) revealed substantial geographic variation, with highest adoption in Arkansas (29.2%), Missouri (26.9%), and North Dakota (24.8%), contrasting with lowest rates in West Virginia (2.6%), Idaho (3.8%), and Vermont (4.8%). Notable population centers showed moderate adopti… view at source ↗
Figure 3
Figure 3. Sectoral patterns of LLM adoption in corporate press releases across major distribution platforms. Analysis of press releases by sector revealed consistent patterns across platforms, with Science & Technology showing marginally higher adoption rates. (a) PRNewswire demonstrated similar sectoral patterns by 2023Q4: Science & Technology (16.8%), People & Culture (14.3%), Business & Money (14.0%), and Other sectors (11… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Organization age and LLM adoption patterns in LinkedIn job postings from small organizations across professional categories. (a) Among small organizations (less than median job vacancies), analysis stratified by number of employees revealed higher LLM adoption rates in…

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Forward citations

Cited by 2 Pith papers

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Reference graph

Works this paper leans on

30 extracted references · 15 canonical work pages · cited by 2 Pith papers

  1. [1]

    Blueprint for an AI bill of rights (2022)

    The White House Office of Science and Technology Policy. Blueprint for an AI bill of rights (2022). Accessed: 2023-09-08

    work page 2022

  2. [2]

    Bommasani, R. et al. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2022)

    arXiv 2022

  3. [3]

    Artificial intelligence index report 2024

    Stanford Institute for Human-Centered Artificial Intelligence. Artificial intelligence index report 2024. Tech. Rep., Stanford University (2024)

    work page 2024

  4. [4]

    so what if chatgpt wrote it?

    Dwivedi, Y . K.et al. Opinion paper:“so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. Int. J. Inf. Manag. 71, 102642, DOI: 10.1016/j.ijinfomgt.2023.102642 (2023)

    arXiv 2023

  5. [5]

    Kasneci, E. et al. Chatgpt for good? on opportunities and challenges of large language models for education. Learn. Individ. Differ. 103, 102274, DOI: 10.1016/j.lindif.2023.102274 (2023)

    arXiv 2023

  6. [6]

    M., Gebru, T., McMillan-Major, A

    Bender, E. M., Gebru, T., McMillan-Major, A. & Smitchell, M. On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’21, 610–623, DOI: 10.1145/3442188.3445922 (ACM, 2021)

    arXiv 2021

  7. [7]

    R., John Mellor & other authors

    Laura Weidinger, M. R., John Mellor & other authors. Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021)

    arXiv 2021

  8. [8]

    & Vestergaard, E

    Humlum, A. & Vestergaard, E. The adoption of chatgpt. IZA Discuss. Pap. No. 16992 DOI: 10.2139/ssrn. 4827166 (2024)

    doi:10.2139/ssrn 2024

Show all 30 references
  1. [9]

    & Deming, D

    Bick, A., Blandin, A. & Deming, D. J. The rapid adoption of generative ai. Working Paper 32966, National Bureau of Economic Research (2024). DOI: 10.3386/w32966. Revised February 2025

    2024 doi

  2. [10]

    & Peskoff, D

    Brooks, C., Eggert, S. & Peskoff, D. The rise of ai-generated content in wikipedia (2024). 2410.08044

    2024 arXiv

  3. [11]

    & Shin, J

    Shin, M., Kim, J. & Shin, J. The adoption and efficacy of large language models: Evidence from consumer complaints in the financial industry. Available at SSRN 5004194 (2024)

    2024

  4. [12]

    Liang, W. et al. Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews. arXiv preprint arXiv:2403.07183 (2024)

    2024 arXiv

  5. [13]

    Liang, W. et al. Mapping the increasing use of LLMs in scientific papers. In First Conference on Language Modeling (2024)

    2024

  6. [14]

    & Zou, J

    Liang, W., Yuksekgonul, M., Mao, Y ., Wu, E. & Zou, J. Y . Gpt detectors are biased against non-native english writers. arXiv (2023). 2304.02819

    2023 arXiv

  7. [15]

    & Raymond, L

    Brynjolfsson, E., Li, D. & Raymond, L. Generative ai at work. Natl. Bureau Econ. Res. DOI: 10.3386/w31161 (2023)

    2023 doi

  8. [16]

    M., Singhal, A

    Rogers, E. M., Singhal, A. & Quinlan, M. M. Diffusion of innovations. In Salwen, M. B. & Stacks, D. W. (eds.) An Integrated Approach to Communication Theory and Research , 432–448 (Routledge, 2008), 2 edn

    2008

  9. [17]

    Kalyani, A. et al. The diffusion of new technologies. Working Paper 28999, National Bureau of Economic Research (2021). DOI: 10.3386/w28999

    2021 doi

  10. [18]

    Kalyani, A. et al. The diffusion of new technologies*. The Q. J. Econ. qjaf002, DOI: 10.1093/qje/qjaf002 (2025). https://academic.oup.com/qje/advance-article-pdf/doi/10.1093/qje/qjaf002/61489798/qjaf002.pdf

    2025 doi

  11. [19]

    Davis, F. D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13, 319–340, DOI: 10.2307/249008 (1989)

    1989 doi

  12. [20]

    G., Davis, G

    Venkatesh, V ., Morris, M. G., Davis, G. B. & Davis, F. D. User acceptance of information technology: Toward a unified view. MIS quarterly 425–478 (2003). 8/23

    2003

  13. [21]

    I., Parasuraman, A

    Rojas-Mendez, J. I., Parasuraman, A. & Papadopoulos, N. Demographics, attitudes, and technology readiness: A cross-cultural analysis and model validation. Mark. Intell. & Plan. 35, 18–39 (2017)

    2017

  14. [22]

    Foster, A. D. & Rosenzweig, M. R. Microeconomics of technology adoption. Annu. Rev. Econ. 2, 395–424 (2010)

    2010

  15. [23]

    Jakesch, M., French, M., Ma, X., Hancock, J. T. & Naaman, M. Ai-mediated communication: How the perception that profile text was written by ai affects trustworthiness. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems , 1–13 (2019)

    2019

  16. [24]

    Bias in perception of art produced by artificial intelligence

    Hong, J.-W. Bias in perception of art produced by artificial intelligence. In Human-Computer Interaction. Interaction in Context: 20th International Conference, HCI International 2018, Las V egas, NV , USA, July 15–20, 2018, Proceedings, Part II 20, 290–303 (Springer, 2018)

    2018

  17. [25]

    & Naaman, M

    Kadoma, K., Metaxa, D. & Naaman, M. Generative ai and perceptual harms: Who’s suspected of using llms? arXiv preprint arXiv:2410.00906 (2024)

    2024 arXiv

  18. [26]

    & Hodson, J

    Babina, T., Fedyk, A., He, A. & Hodson, J. Artificial intelligence, firm growth, and product innovation. J. Financial Econ. 151, 103745, DOI: 10.1016/j.jfineco.2023.103745 (2024)

    2024

  19. [27]

    & Horton, J

    Wiles, E. & Horton, J. J. More, but worse: The impact of ai writing assistance on the supply and quality of job posts. Mass. Inst. Technol. (MIT) Sloan (2024)

    2024

  20. [28]

    Generative AI Top 150: The World’s Most Used AI Tools

    Van Rossum, D. Generative AI Top 150: The World’s Most Used AI Tools. https://www.flexos.work/learn/ generative-ai-top-150 (2024)

    2024

  21. [29]

    The Global AI Talent Tracker (2024)

    MacroPolo. The Global AI Talent Tracker (2024). 9/23 a d Consumer Complaint UN Press Release bPress Release cLinkedIn Job Posting Figure 1. Temporal dynamics of large language model (LLM) adoption across diverse writing domains. Analysis of LLM-generated or substantially modif...

    2024

  22. [2024]

    Our analysis primarily focused on the full body text. Due to the limited number of articles post-ChatGPT introduction available from Newswire, we conducted detailed robustness checks only on PR Newswire and PRWeb data, which provided sufficient volume for heterogeneity analysi...

    1945

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Reviewed August 7, 2026 · model on record in the stance chip above.