How Organizations Use AI: Evidence from ChatGPT

Aaron Chatterji; David Holtz; Gawesha Weeratunga; Neel Rakholia; Prasanna Tambe

REVIEW 3 major objections 5 minor 80 references

How Organizations Use AI: Evidence from ChatGPT

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Enterprise AI use grows fast, skews to large R&D-intensive firms, and spans every job level.

desk verdict A serious descriptive paper whose worker-level and task facts are likely to stick, but whose firm-size gradient depends on an unvalidated account-to-ticker crosswalk that the authors need to address. read the letter →

arxiv 2608.12236 v1 pith:WITLOC4N submitted 2026-08-12 econ.GN cs.AIcs.HCq-fin.EC

Aaron Chatterji , David Holtz , Neel Rakholia , Prasanna Tambe , Gawesha Weeratunga This is my paper · ORCID

classification econ.GNcs.AIcs.HCq-fin.EC

keywords enterpriseAIadoptionChatGPTgenerativeusagetelemetryjobtitleclassificationtaskknowledgeworkintangiblecapital

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 uses de-identified ChatGPT Enterprise telemetry—more than 1,500 organizations and 17 million messages at the six-month adoption horizon—to establish four descriptive facts about how firms actually use frontier generative AI. Enterprise usage grew roughly sevenfold between June 2025 and March 2026, through both new adoption and deepening use inside existing customers. U.S. public-company adopters are larger, more valuable, and more R&D- and SG&A-intensive than non-adopters, and within adopting firms active use spans job functions and seniority levels, with early-career workers the most intensive users. Usage also covers a broad range of knowledge-work tasks rather than a single workflow. The point of these facts is to show that adoption is only the beginning of deployment: firms are still learning where AI belongs in their workflows.

What carries the argument

The central machinery is the linked telemetry panel: an organization-week record of ChatGPT Enterprise adoption and usage joined to worker-level job-title metadata, a message-level task classifier, and, for public firms, annual financial data through an LLM-assisted account-to-ticker crosswalk. The job-title classifier maps raw administrative titles to departments, seniority levels, and manager status; the task classifier assigns each message to one of 60 work-task categories. These links allow the paper to measure the extensive margin (which firms and workers adopt), the intensive margin (how much they use), and task breadth within a single privacy-preserving dataset, which is what carries all four facts.

What would settle it

Audit the crosswalk: take a random sample of matched and unmatched U.S. public firms, verify against public procurement records or a separate enterprise-customer list whether each truly runs a ChatGPT Enterprise workspace, and compare the resulting error rates; if unmatched firms are often actual adopters, the adopter/non-adopter regressions in Tables 1, 3, and 4 would be biased.

Watch

Extended reading notes

Core claim

The paper's central claim is that four stylized facts about enterprise AI adoption and use hold in the ChatGPT Enterprise workspaces it observes. First, output tokens generated by enterprise customers grew roughly sevenfold between June 2025 and March 2026, and about fourfold within a fixed cohort of firms that had already adopted by June 2025, so roughly half of aggregate growth came from deepening use rather than new customers. Second, among U.S. public companies, adopters are larger, more valuable, and more intensive in R&D and SG&A spending per employee, with the largest firms in each industry significantly more likely to adopt. Third, six months after adoption, active users are spread across job functions and seniority levels, but intensity is uneven: early-career workers and trainees send roughly eight to nine more weekly messages than the average active user in the same firm, while executives send fewer. Fourth, message-level classification shows a long tail of knowledge-work tasks—writing, technical work, communication, and synthesis—rather than a single dominant use, with task mix varying across industries and roles. The authors emphasize that these are conditional associations, not causal effects.

Load-bearing premise

The load-bearing premise is that the account-to-ticker crosswalk correctly identifies which public firms have ChatGPT Enterprise, so that labeling a firm a non-adopter because it has no match does not systematically misclassify enterprise users.

Editorial extensions

If this is right

Firm-level 'adoption' measured by a signed contract understates deployment: a large share of enterprise growth comes from existing customers using the product more, so studies that count adopters miss the intensive margin.
Early AI adoption is tied to pre-existing intangible capital, so if the pattern holds, generative AI diffusion may widen productivity and value gaps between large, capability-rich firms and the rest.
Because early-career workers are the heaviest users, the productivity and employment effects of generative AI are most likely to show up first among junior employees.
Task breadth across industries and roles supports treating generative AI as a general purpose technology for knowledge work, implying that complements—training, workflow redesign, and organizational change—will determine realized value.
Comparisons of AI use across firms should separate the task-prevalence margin (how many workers try a task) from the message-share margin (where the volume sits), since a task can be widespread but low-volume or narrow but high-volume.

Reading between the lines

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

A natural next step the authors leave open is following the same worker cohorts beyond six months to see whether the early-career intensity gap is a learning phase that fades or a stable feature of how junior staff use AI.
A testable prediction from the complements result: among adopters, firms with larger SG&A stocks should show steeper growth in per-employee message volume after adoption, because the same capabilities that predict entry should also predict deployment success.
The paper's task taxonomy could be applied to usage from other enterprise AI products to check whether the observed breadth is specific to ChatGPT Enterprise or common to workplace LLM tools generally.
If the negative seniority gradient in message volume is causal, then productivity studies that average over all workers will understate early-career gains; measuring effects separately by seniority would be a sharper test.

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Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

First thing to know: this is the first large-sample administrative look at how organizations actually use ChatGPT Enterprise, and the strongest part of the paper is the within-firm stuff—who uses it, how intensively, and for what tasks. The early-career intensity gradient and the breadth of task use across job functions will be the facts people remember. The second thing: the firm-level adoption results in Tables 1, 3, and 4 rest on an account-to-ticker crosswalk whose precision and recall are not reported, and that is a real problem, not a nitpick.

What the paper does well: the data are genuinely new. 1,500+ organizations, 17 million messages, linked to administrative job titles and task classifications. The four facts are clearly written and mostly honestly caveated. The authors repeatedly note that the worker-level results are conditional on covered samples and lack workforce denominators, and they present the regressions as conditional associations rather than causal claims. That is the right posture.

Where the soft spots are: the Compustat-company analysis is the load-bearing part for Fact 2. The paper defines non-adopters as public firms with no match in the bridge, but never tells us how good the bridge is. If recall is lower for small firms—which is likely, since they have less public coverage and messier name-to-ticker mappings—then the matched adopter sample overrepresents large firms and the size and intangibles gradients are inflated. Footnote 10 discusses attenuation from non-adopters who use other AI products, but it does not address differential measurement error in the bridge itself. With 410 adopters against 11,784 unmatched firms, even a small false-negative rate concentrated among small firms could move the coefficients noticeably. That does not mean the qualitative pattern is false, but the paper currently cannot rule out that the magnitude is driven by the bridge.

Two slightly smaller points: the task classifier is described as validated against an internal benchmark, but no numbers are given, and the job-title validation is only top-five lists per category. These are minor relative to the bridge, but they are easy fixes.

Bottom line: this is a paper for anyone studying AI adoption or the future of work, and the descriptive facts will be cited. It deserves a serious referee. The referee should return with a request for validation of the crosswalk—either a hand-labeled precision/recall sample or bounds under differential recall. I would want that before treating the firm-size facts as established.

Serious thinker: yes. The authors are honest about the scope of the data and the limits of what they can see. The missing validation is a gap in the paper, not a sign of sloppiness.

Referee Report

3 major / 5 minor

Summary. This paper uses administrative telemetry from ChatGPT Enterprise, linked to employee job titles, a message-level task classifier, and Compustat financials, to document four descriptive facts: (i) enterprise usage grew rapidly between June 2025 and March 2026, with substantial growth within fixed adoption cohorts; (ii) U.S. public-company adopters are larger, higher-revenue-per-employee, and more R&D- and SG&A-intensive than non-adopters; (iii) among firms with usable title data, active use spans job functions and seniority levels, with early-career workers showing the highest messages-per-active-user; and (iv) task composition is broad, with writing, communication, and information tasks most common and with industry and role variation. The authors are careful to frame the regressions as conditional associations and to state several data limitations.

Significance. If the measurement concerns can be addressed, this is an unusually rich descriptive contribution: it moves beyond surveys to observe actual enterprise usage at scale, with 1,764 organizations and 17.4 million messages in the worker sample, and it connects usage to firm financials, roles, and tasks. The paper is also commendably explicit about what it does not measure: no workforce denominators, no downstream outcomes, single-vendor scope, and incomplete title coverage. The four facts, especially the within-firm worker and task heterogeneity, would be useful calibration for theories of GPT diffusion and for future work on complements. The main risk is that Fact 2, and to a lesser extent Facts 3 and 4, could be partly generated by measurement error in the account-to-ticker bridge and in title coverage rather than by true adoption patterns.

major comments (3)

[Section 3.3, Tables 1, 3, and 4] The central firm-level results rest on the LLM-assisted account-to-ticker crosswalk, but the paper reports no precision or recall for this bridge. Because non-adopters are defined as public firms with no bridge match, and because only 410 matched tickers have positive usage versus 11,784 unmatched tickers, even a small false-negative rate that is correlated with size, revenue, or intangible intensity can generate the documented gradients in Tables 1, 3, and 4. Footnote 10 discusses attenuation from non-adopters using other AI products, which is a different source of error and does not bound differential measurement error in the bridge itself. Please report validation results for the crosswalk, for example a hand-audited sample with precision and recall by firm size and industry, and provide a sensitivity analysis that bounds the adoption probability under alternative assumptions about false-negative rates.
[Section 3.2, Figures 5 and 6] The worker-level facts in Section 4.3 are stated as general facts about enterprise AI use, but the underlying sample requires high-quality job title information and an active organization-week at week 26. The paper notes in Section 3.2 that title coverage is incomplete, yet it never reports the coverage rate, how it varies by organization size or industry, or whether the 1,764-organization sample is representative of the full Enterprise population. If title coverage is higher among firms with more IT administration, or among particular roles, the composition shares in Figure 5 and the seniority gradient in Panel B of Figure 6 could reflect selection. Please report the fraction of active users with usable titles, decompose missingness by role and firm size, and re-estimate the main figures on a high-coverage subsample.
[Section 3.2 and Section 4.4] The task-classification analysis in Section 4.4 relies on a classifier available only from October 30, 2025, and measures use at week 26 after adoption. This combination implies that the task sample of 973 organizations can only include firms whose week-26 horizon falls after that date, effectively restricting the analysis to organizations that adopted in a particular window (roughly May through October 2025). The paper does not report the adoption-date distribution of the task sample or compare it with the worker sample or the full Enterprise sample. Because Section 4.1 documents a simultaneous acceleration across cohorts in early 2026, the task composition measured in this window may not generalize to earlier or later adopters. Please document the cohort composition of the task subsample and, if possible, show task results for multiple adoption horizons.

minor comments (5)

[Section 3.3] The random subsample of matched accounts is not described in terms of the sampling rate or whether sampling weights are used; please clarify whether all regression analyses use the unweighted random sample and whether any disclosure-related subsampling affects precision.
[Section 4.2.4 and Table 4] The SG&A and R&D stocks rely on assumed depreciation rates of 20 percent and 15 percent and a zero-growth steady-state opening stock; please report sensitivity to alternative depreciation rates and opening-stock assumptions.
[Figures 5 and 6] The category 'Other / unknown' pools missing, malformed, and genuinely unclassifiable job titles; because the reported shares for substantive categories are sensitive to the size of this residual, please report the unclassified share explicitly or show results excluding unclassified users.
[Section 4.1 and Figure 3] The sevenfold and fourfold growth figures are stated in the text but the underlying indexed series are not tabulated; please include a small table of index values in an appendix.
[Section 4.3, figure notes] The figure notes describe firm-bootstrap confidence intervals in Figure 5 and firm-clustered standard errors in Figure 6; please specify the bootstrap procedure, including the number of replications and whether resampling is clustered by firm.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the four facts are empirical measurements from internal telemetry, and the firm-level adoption associations are regressions of a match-based adoption indicator rather than conclusions that reduce to their own inputs by construction.

full rationale

This paper is a descriptive empirical study rather than a derivation, so the circularity patterns based on equations or fitted parameters called predictions largely do not apply. Fact 1 (rapid growth) is read directly from the organization-week panel of output tokens; there is no construction by which growth must be sevenfold or fourfold. Facts 2 and the Tables 1-4 regressions define the adopter indicator from an account-to-ticker crosswalk, not from revenue, employment, R&D, or SG&A, so the correlations with those characteristics are not true by construction. The concern that crosswalk recall may be correlated with firm size is a measurement-validity threat, not circularity: the paper does not claim that the crosswalk was fit to the financial outcomes it later correlates with adoption, and there is no exhibited equation-level reduction. Fact 3 is explicitly conditioned on the covered job-title sample; Section 4.3 states that 'job title coverage is not universal and varies across firms, so these estimates describe observed use within the covered job title sample rather than the full workforce of all adopting firms,' and it also notes the absence of role-specific denominators. This is honest conditioning, not a self-definitional claim. Fact 4 uses a 60-category task classifier, but breadth is an empirical distributional finding; the taxonomy could in principle have shown a single dominant category, and the paper reports a substantial residual 'other' category. The self-citations to Chatterji et al. (2025) and Johnston et al. (2026) involve overlapping authors, and Appendix C discloses that the job-title classifier was also used in Johnston et al., but these citations are used for context and data provenance rather than as load-bearing justification for the paper's conclusions. The classifier is validated in this paper with top-title tables, and no uniqueness theorem or ansatz is imported from the authors' prior work. The conclusion section's limitations are explicit and do not conceal a circular step. Overall, the central claims are measurements from linked administrative data; no prediction reduces by construction to a fitted input, and no load-bearing argument rests on an unverified self-citation.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new theoretical entities or fitted physical parameters. Its dependence is on measurement assumptions: the accuracy of the Compustat linkage, the job-title classifier, the task classifier, and the zero-usage coding convention. The depreciation rates used to construct intangible capital stocks are chosen by the authors and affect the Table 4 results.

free parameters (3)

SG&A depreciation rate = 20% annually
Used to construct the SG&A stock in Table 4; the stock value, and hence its coefficient, depends on this choice.
R&D depreciation rate = 15% annually
Used to construct the R&D stock in Table 4; the stock value depends on this choice.
Opening stock assumption = zero-growth steady-state
Determines the initial capital stock in the perpetual-inventory construction; affects measured SG&A and R&D stocks.

assumptions (5)

domain assumption Compustat financial data are accurate and comparable across firms.
All public-company measures come from Compustat (Section 3.3); any measurement error would propagate to the regressions.
domain assumption The job-title classifier (gpt-5-mini) produces unbiased classifications of department, seniority, and manager status.
Section 3.2 and Appendix C; the paper validates by listing top titles per class but reports no accuracy metric, and the classifier is applied to titles that may be stale.
domain assumption The task classifier assigns each message to exactly one task category with acceptable accuracy.
Appendix D states it is evaluated against an internal benchmark but provides no performance numbers; the message-share statistics in Figures 7-10 rely on it.
domain assumption Organizations without observed weekly activity are recorded as zero usage rather than missing.
Section 3.1 states zero-usage weeks are retained; if the product is used through other channels, this understates usage.
domain assumption The LLM-assisted account-to-ticker crosswalk has low error.
Section 3.3; the financial adoption and intensity analyses depend on this linkage and on the definition of non-adopters as unmatched firms.

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Cite this review

Pith. "Pith review of How Organizations Use AI: Evidence from ChatGPT." pith.science (2026). https://pith.science/paper/WITLOC4N

@misc{pith2026260812236,
  author       = {Pith},
  title        = {Pith review of: How Organizations Use AI: Evidence from ChatGPT},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WITLOC4N}},
  note         = {Machine review of arXiv:2608.12236}
}

read the original abstract

We study how organizations use frontier generative AI by linking ChatGPT Enterprise account records to usage, worker roles, task classifications, and public-company financial data through March 2026. These linked data enable a privacy-preserving analysis of adoption, worker roles, and message-level tasks at scale: for instance, the worker-level sample we analyze at the six-month adoption horizon includes over 1,500 organizations and over 17 million messages. We document four facts about enterprise AI adoption and use. First, ChatGPT Enterprise usage has grown rapidly due to a combination of new firm adoption and growing intensity among existing adopters. Second, U.S.-based public company adoption is concentrated among larger, more valuable, and more R&D- and SG&A-intensive firms. Third, active use within adopting firms spans job functions and seniority levels, with especially high usage intensity among early-career workers. Fourth, ChatGPT Enterprise usage encompasses a broad range of knowledge work tasks, including writing, technical work, communication, and information synthesis. In aggregate, these results suggest that firms differ widely in the speed, breadth and purpose of their enterprise AI adoption, and that they are still actively learning how to integrate AI into organizational workflows.

Figures

Figures reproduced from arXiv: 2608.12236 by the authors.

**Figure 2.** SUMMARY STATISTICS: COMPARISON OF CHATGPT [PITH_FULL_IMAGE:figures/full_fig_p025_2.png] view at source ↗

**Figure 3.** ENTERPRISE OUTPUT TOKEN GROWTH Note: This figure plots monthly ChatGPT and Codex output tokens for firms in the ChatGPT Enterprise analysis sample, stacked by the quarter in which each firm first adopted ChatGPT Enterprise. Tokens are counted only after each firm’s ChatGPT Enterprise adoption date. Values are indexed to total combined ChatGPT and Codex output tokens in June 2025, with the dashed horizontal line mark… view at source ↗

Figures from the paper (7 more)

**Figure 4.** Figure 4: FINANCIAL MEASURES BY USAGE INTENSITY: ECDF [PITH_FULL_IMAGE:figures/full_fig_p027_4.png]

**Figure 5.** Figure 5: COMPOSITION OF ACTIVE USERS BY WORKER TYPE [PITH_FULL_IMAGE:figures/full_fig_p028_5.png]

**Figure 6.** Figure 6: DIFFERENCES IN AI USAGE INTENSITY ACROSS WORKER [PITH_FULL_IMAGE:figures/full_fig_p029_6.png]

**Figure 7.** Figure 7: DISTRIBUTION OF AI USE ACROSS TASKS Panel A. Task Prevalence Among Active Users Panel B. Distribution of Messages Across Tasks Note: The figure describes the distribution of ChatGPT Enterprise use across tasks among ChatGPT Enterprise firms with task-classification dat…

**Figure 8.** Figure 8: DIFFERENCES IN AI TASK USE ACROSS INDUSTRIES [PITH_FULL_IMAGE:figures/full_fig_p031_8.png]

**Figure 9.** Figure 9: DIFFERENCES IN AI TASK USE ACROSS JOB TITLE CLASSES [PITH_FULL_IMAGE:figures/full_fig_p032_9.png]

**Figure 10.** Figure 10: DIFFERENCES IN AI TASK USE ACROSS SENIORITY [PITH_FULL_IMAGE:figures/full_fig_p033_10.png]

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Reference graph

Works this paper leans on

80 extracted references · 73 canonical work pages

[1]

2026 , month = jun, eprint =

The Shift to Agentic AI: Evidence from Codex , author =. 2026 , month = jun, eprint =. doi:10.48550/arXiv.2606.26959 , url =

arXiv doi:10.48550/arxiv.2606.26959 2026
[2]

Marketing Science , volume=

When the data are out: measuring behavioral changes following a data breach , author=. Marketing Science , volume=. 2024 , publisher=

work page 2024
[3]

Journal of the American Statistical Association , volume=

Estimation and inference of heterogeneous treatment effects using random forests , author=. Journal of the American Statistical Association , volume=

work page
[4]

1996 , publisher=

Diffusion of general purpose technologies , author=. 1996 , publisher=

work page 1996
[5]

Research Policy , volume=

Could machine learning be a general purpose technology? A comparison of emerging technologies using data from online job postings , author=. Research Policy , volume=. 2023 , publisher=

work page 2023
[6]

2020 , institution=

Digital capital and superstar firms , author=. 2020 , institution=

work page 2020
[7]

2025 , publisher=

Canaries in the Coal Mine?: Six Facts about the Recent Employment Effects of Artificial Intelligence , author=. 2025 , publisher=

work page 2025
[8]

2020 , publisher=

A behavioral theory of the firm , author=. 2020 , publisher=

work page 2020

Show all 80 references

[9]

The RAND Journal of Economics , pages=

The determinants of technology adoption: The case of the banking firm , author=. The RAND Journal of Economics , pages=. 1984 , publisher=

1984
[10]

An integrated approach to communication theory and research , pages=

Diffusion of innovations , author=. An integrated approach to communication theory and research , pages=. 2014 , publisher=

2014
[11]

American economic review , volume=

Learning about a new technology: Pineapple in Ghana , author=. American economic review , volume=. 2010 , publisher=

2010
[12]

2008 , publisher=

The economics of growth , author=. 2008 , publisher=

2008
[13]

The American economic review , volume=

Investment in humans, technological diffusion, and economic growth , author=. The American economic review , volume=. 1966 , publisher=

1966
[14]

Journal of political Economy , volume=

Learning by doing and learning from others: Human capital and technical change in agriculture , author=. Journal of political Economy , volume=. 1995 , publisher=

1995
[15]

Eloundou, Tyna and Manning, Sam and Mishkin, Pamela and Rock, Daniel , journal=
[16]

Brookings Papers on Economic Activity

Technical progress and co-invention in computing and in the uses of computers , author=. Brookings Papers on Economic Activity. Microeconomics , pages=
[17]

Econometrica , volume=

Hybrid corn: An exploration in the economics of technological change , author=. Econometrica , volume=
[18]

Econometrica , volume=

Technical change and the rate of imitation , author=. Econometrica , volume=
[19]

Adoption of new technology , author=
[20]

American Economic Review , volume=

An exploration of technology diffusion , author=. American Economic Review , volume=
[21]

Quarterly Journal of Economics , volume=

Information technology, workplace organization, and the demand for skilled labor: Firm-level evidence , author=. Quarterly Journal of Economics , volume=
[22]

, institution=

Brynjolfsson, Erik and Li, Danielle and Raymond, Lindsey R. , institution=. Generative
[23]

Journal of Economic Perspectives , volume=

Automation and new tasks: How technology displaces and reinstates labor , author=. Journal of Economic Perspectives , volume=
[24]

2023 , note=

The State of. 2023 , note=

2023
[25]

General Purpose Technologies and Economic Growth , publisher=
[26]

, title=

David, Paul A. , title=. The American Economic Review , volume=
[27]

Information Systems Research , volume=

The Illusory Diffusion of Innovation: An Examination of Assimilation Gaps , author=. Information Systems Research , volume=. 1999 , doi=

1999
[28]

Management Science , volume=

The Assimilation of Software Process Innovations: An Organizational Learning Perspective , author=. Management Science , volume=. 1997 , doi=

1997
[29]

Research Policy , volume=

Intrafirm Diffusion of New Technologies: An Empirical Application , author=. Research Policy , volume=. 2003 , doi=

2003
[30]

Research Policy , volume=

Inter- and Intra-firm Effects in the Diffusion of New Process Technology , author=. Research Policy , volume=. 2003 , doi=

2003
[31]

Organization Science , volume=

Technology Diffusion and Organizational Learning: The Case of Business Computing , author=. Organization Science , volume=. 1992 , doi=

1992
[32]

Economics of Innovation and New Technology , volume=

Inter and Intra Firm Diffusion of ICT in the United Kingdom (UK) and Switzerland (CH): An Internationally Comparative Study Based on Firm-Level Data , author=. Economics of Innovation and New Technology , volume=. 2007 , doi=

2007
[33]

Management Science , volume=

Information Technology Implementation Research: A Technological Diffusion Approach , author=. Management Science , volume=. 1990 , doi=

1990
[34]

MIS Quarterly , volume=

A Comprehensive Conceptualization of Post-Adoptive Behaviors Associated with Information Technology Enabled Work Systems , author=. MIS Quarterly , volume=. 2005 , doi=

2005
[35]

Available at SSRN 4670714 , year=

Generative AI and content creators: Evidence from digital art platforms , author=. Available at SSRN 4670714 , year=
[36]

Available at SSRN 4766534 , year=

Token tradability as a platform governance mechanism: Evidence from a policy change , author=. Available at SSRN 4766534 , year=
[37]

and Trajtenberg, Manuel , title =

Bresnahan, Timothy F. and Trajtenberg, Manuel , title =. Journal of Econometrics , year =
[38]

, title =

Jovanovic, Boyan and Rousseau, Peter L. , title =. Handbook of Economic Growth , editor =. 2005 , volume =

2005
[39]

and Brynjolfsson, Erik and Hitt, Lorin M

Bresnahan, Timothy F. and Brynjolfsson, Erik and Hitt, Lorin M. , title =. Quarterly Journal of Economics , year =
[40]

, title =

Bresnahan, Timothy F. , title =. Journal of Economics & Management Strategy , year =
[41]

American Economic Journal: Macroeconomics , year =

Brynjolfsson, Erik and Rock, Daniel and Syverson, Chad , title =. American Economic Journal: Macroeconomics , year =
[42]

Review of Economics and Statistics , year =

Mansfield, Edwin , title =. Review of Economics and Statistics , year =
[43]

Frank and Brynjolfsson, Erik and Kroff, Zachary and Dinlersoz, Emin and Foster, Lucia and Zolas, Nikolas , title =

McElheran, Kristina and Li, J. Frank and Brynjolfsson, Erik and Kroff, Zachary and Dinlersoz, Emin and Foster, Lucia and Zolas, Nikolas , title =. Journal of Economics & Management Strategy , year =
[44]

and Foster, Kevin M

Yotzov, Ivan and Barrero, Jose Maria and Bloom, Nicholas and Bunn, Philip and Davis, Steven J. and Foster, Kevin M. and Jalca, Aaron and Meyer, Brent H. and Mizen, Paul and Navarrete, Michael A. and Smietanka, Pawel and Thwaites, Gregory and Wang, Ben Zhe , title =. 2026 , mon...

2026
[45]

, title =

Bick, Alexander and Blandin, Adam and Deming, David J. , title =. Management Science , year =
[46]

and Fuchs-Sch

Bick, Alexander and Blandin, Adam and Deming, David J. and Fuchs-Sch. Mind the Gap:. 2026 , month = mar, doi =

2026
[47]

and Dinlersoz, Emin and Foster, Lucia S

Bonney, Kathryn and Breaux, Cory L. and Dinlersoz, Emin and Foster, Lucia S. and Haltiwanger, John C. and Pande, Aditya A. , title =. 2026 , doi =

2026
[48]

and Demirer, Mert and Finucane, Connor and Kreps, Avner A

Brand, James M. and Demirer, Mert and Finucane, Connor and Kreps, Avner A. , title =. 2024 , note =. doi:10.3386/w32938 , url =

2024 doi
[49]

2026 , month = mar, doi =

Kim, Hyunjin and Kim, Dahyeon and Koning, Rembrand , title =. 2026 , month = mar, doi =

2026
[50]

2026 , month = mar, howpublished =

Massenkoff, Maxim and Lyubich, Eva and McCrory, Peter and Appel, Ruth and Heller, Ryan , title =. 2026 , month = mar, howpublished =

2026
[51]

Journal of Financial Economics , year =

Babina, Tania and Fedyk, Anastassia and He, Alex and Hodson, James , title =. Journal of Financial Economics , year =
[52]

and Schubert, Gregor and Taska, Bledi and Zhang, Miao Ben , title =

Eisfeldt, Andrea L. and Schubert, Gregor and Taska, Bledi and Zhang, Miao Ben , title =. Journal of Finance , year =
[53]

2026 , month = jul, eprint =

Yu, Yang and Fleming, Martin and Hampton, Lucy and Combemale, Christophe and Thompson, Neil , title =. 2026 , month = jul, eprint =. doi:10.48550/arXiv.2607.08920 , url =

2026 doi arXiv
[54]

and Amodei, Dario and Kaplan, Jared and Clark, Jack and Ganguli, Deep , title =

Handa, Kunal and Tamkin, Alex and McCain, Miles and Huang, Saffron and Durmus, Esin and Heck, Sarah and Mueller, Jared and Hong, Jerry and Ritchie, Stuart and Belonax, Tim and Troy, Kevin K. and Amodei, Dario and Kaplan, Jared and Clark, Jack and Ganguli, Deep , title =. 2025 ...

2025
[55]

2026 , month = jan, howpublished =

Appel, Ruth and Massenkoff, Maxim and McCrory, Peter and McCain, Miles and Heller, Ryan and Neylon, Tyler and Tamkin, Alex , title =. 2026 , month = jan, howpublished =

2026
[56]

2026 , month = jun, howpublished =

Massenkoff, Maxim and Lyubich, Eva and Sacher, Szymon and Hitzig, Zoe and Zhang, Shaoyi and Heller, Ryan and McCrory, Peter , title =. 2026 , month = jun, howpublished =

2026
[57]

and Hitzig, Zoe and Ong, Christopher and Shan, Carl Yan and Wadman, Kevin , title =

Chatterji, Aaron and Cunningham, Thomas and Deming, David J. and Hitzig, Zoe and Ong, Christopher and Shan, Carl Yan and Wadman, Kevin , title =. 2025 , month = sep, doi =

2025
[58]

2025 , eprint =

Tomlinson, Kiran and Jaffe, Sonia and Wang, Will and Counts, Scott and Suri, Siddharth , title =. 2025 , eprint =

2025
[59]

2025 , month = dec, doi =

Demirer, Mert and Fradkin, Andrey and Tadelis, Nadav and Peng, Sida , title =. 2025 , month = dec, doi =

2025
[60]

2025 , eprint =

Fradkin, Andrey , title =. 2025 , eprint =

2025
[61]

2025 , month = sep, howpublished =

Appel, Ruth and McCrory, Peter and Tamkin, Alex and Stern, Michael and McCain, Miles and Neylon, Tyler , title =. 2025 , month = sep, howpublished =

2025
[62]

Science , year =

Daniotti, Simone and Wachs, Johannes and Feng, Xiangnan and Neffke, Frank , title =. Science , year =
[63]

2026 , note =

Chen, Fiona and Stratton, James , title =. 2026 , note =

2026
[64]

2026 , month = may, doi =

Demirer, Mert and Musolff, Leon and Yang, Liyuan , title =. 2026 , month = may, doi =

2026
[65]

Beyond Exposure: Predicting

Lindenlaub, Ilse and Oh, Ryungha and Rodr. Beyond Exposure: Predicting. 2026 , month = may, doi =

2026
[66]

2025 , eprint =

Patwardhan, Tejal and Dias, Rachel and Proehl, Elizabeth and Kim, Grace and Wang, Michele and Watkins, Olivia and Posada Fishman, Sim. 2025 , eprint =

2025
[67]

Science , year =

Noy, Shakked and Zhang, Whitney , title =. Science , year =
[68]

, title =

Brynjolfsson, Erik and Li, Danielle and Raymond, Lindsey R. , title =. Quarterly Journal of Economics , year =
[69]

and Rajendran, Saran and Krayer, Lisa and Candelon, Fran

Dell'Acqua, Fabrizio and McFowland, III, Edward and Mollick, Ethan and Lifshitz-Assaf, Hila and Kellogg, Katherine C. and Rajendran, Saran and Krayer, Lisa and Candelon, Fran. Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificia...

2026
[70]

Management Science , year =

Cui, Kevin Zheyuan and Demirer, Mert and Jaffe, Sonia and Musolff, Leon and Peng, Sida and Salz, Tobias , title =. Management Science , year =
[71]

and Clarke, Rowan and Delecourt, Sol

Otis, Nicholas G. and Clarke, Rowan and Delecourt, Sol. The Uneven Impact of Generative Artificial Intelligence on Entrepreneurial Performance: Evidence from a Field Experiment in. Management Science , year =
[72]

Journal of Political Economy , year =

Garicano, Luis , title =. Journal of Political Economy , year =
[73]

Quarterly Journal of Economics , year =

Garicano, Luis and Rossi-Hansberg, Esteban , title =. Quarterly Journal of Economics , year =
[74]

Management Science , year =

Bloom, Nicholas and Garicano, Luis and Sadun, Raffaella and Van Reenen, John , title =. Management Science , year =
[75]

The Quarterly Journal of Economics , volume =

The Fall of the Labor Share and the Rise of Superstar Firms , author =. The Quarterly Journal of Economics , volume =. 2020 , doi =

2020
[76]

and Thompson, Neil , title =

Autor, David H. and Thompson, Neil , title =. 2025 , month = jun, doi =

2025
[77]

and Immorlica, Nicole and Lucier, Brendan and Shahidi, Peyman , title =

Demirer, Mert and Horton, John J. and Immorlica, Nicole and Lucier, Brendan and Shahidi, Peyman , title =. 2026 , month = feb, doi =

2026
[78]

2025 , month = dec, howpublished =

Huang, Saffron and Seethor, Bryan and Durmus, Esin and Handa, Kunal and McCain, Miles and Stern, Michael and Ganguli, Deep , title =. 2025 , month = dec, howpublished =

2025
[79]

2026 , month = jun, doi =

Kim, Hyunjin and Koning, Rembrand , title =. 2026 , month = jun, doi =

2026
[80]

arXiv preprint arXiv:2605.23958 , year=

AI in the Enterprise: How People Use M365 Copilot Chat , author=. arXiv preprint arXiv:2605.23958 , year=

arXiv

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

[1] [1]

2026 , month = jun, eprint =

The Shift to Agentic AI: Evidence from Codex , author =. 2026 , month = jun, eprint =. doi:10.48550/arXiv.2606.26959 , url =

arXiv doi:10.48550/arxiv.2606.26959 2026

[2] [2]

Marketing Science , volume=

When the data are out: measuring behavioral changes following a data breach , author=. Marketing Science , volume=. 2024 , publisher=

work page 2024

[3] [3]

Journal of the American Statistical Association , volume=

Estimation and inference of heterogeneous treatment effects using random forests , author=. Journal of the American Statistical Association , volume=

work page

[4] [4]

1996 , publisher=

Diffusion of general purpose technologies , author=. 1996 , publisher=

work page 1996

[5] [5]

Research Policy , volume=

Could machine learning be a general purpose technology? A comparison of emerging technologies using data from online job postings , author=. Research Policy , volume=. 2023 , publisher=

work page 2023

[6] [6]

2020 , institution=

Digital capital and superstar firms , author=. 2020 , institution=

work page 2020

[7] [7]

2025 , publisher=

Canaries in the Coal Mine?: Six Facts about the Recent Employment Effects of Artificial Intelligence , author=. 2025 , publisher=

work page 2025

[8] [8]

2020 , publisher=

A behavioral theory of the firm , author=. 2020 , publisher=

work page 2020