The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index; none of the 39 sources its claims cite is checked for support yet.
The Rapid Adoption of Generative AI
Open accessAbstract
Generative artificial intelligence (AI) is a potentially important new technology, but its impact on the economy depends on the speed and intensity of adoption.This paper reports results from a series of nationally representative U.S. surveys of generative AI use at work and at home.As of late 2024, nearly 40 percent of the U.S. population age 18-64 uses generative AI. 23 percent of employed respondents had used generative AI for work at least once in the previous week, and 9 percent used it every work day.Relative to each technology's first mass-market product launch, work adoption of generative AI has been as fast as the personal computer (PC), and overall adoption has been faster than either PCs or the internet.Generative AI and PCs have very similar early adoption patterns by education, occupation, and other characteristics.Between 1 and 5 percent of all work hours are currently assisted by generative AI, and respondents report time savings equivalent to 1.4 percent of total work hours.This suggests that substantial productivity gains from generative AI are possible.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
Using nationally representative U.S. surveys fielded in August and November 2024, the paper finds that nearly 40% of adults age 18–64 use generative AI, with 23% of employed respondents using it for work in the previous week. Adoption has been as fast as PCs at work and faster overall than PCs or the internet, with estimated time savings equivalent to 1.4% of total work hours and a potential aggregate productivity gain of about 1.1%.
Key findings
- As of August and November 2024, 39.6% of respondents used generative AI either for work or outside work; 26.5% of employed respondents used it for work, including 9.0% every workday and 13.9% on some but not all workdays. Outside work, 35.5% used it, but only 5.2% every day.
- Work adoption of generative AI after two years (27%) was similar to PC work adoption after three years (25%), while non-work adoption was much higher for generative AI (34%) than PCs (5%). Overall adoption was faster than both PCs and the internet.
- Between 1 and 5 percent of all U.S. work hours involved direct generative AI use. Among users, mean time savings were 5.4% of work hours; including non-users, 1.4% of total hours. A standard aggregate production model implies a potential productivity gain of 1.1% using an AI-exposure-adjusted labor share of 0.57 (or 1.9% before that adjustment).
- Adoption patterns by education, age, occupation, and industry closely matched early PC adoption; predicted task exposure from Eloundou et al. (2024) correlated with actual adoption at ρ = 0.67. The most useful work tasks were writing (39.5%), administrative tasks (25.6%), and interpreting/translating/summarizing (22.7%).
Methods
- Main data are the Real-Time Population Survey (RPS), an online national labor market survey of U.S. adults age 18–64, fielded in August 2024 (N = 5,014) and November 2024 (N = 5,329), with a combined sample of more than 10,000 respondents. The paper also validates results using the December 2024 Survey of Working Arrangements and Attitudes (SWAA, N = 4,698).
- The RPS mirrors the Current Population Survey (CPS) in question wording and structure; sample weights are constructed via raking to align with CPS demographic, employment, and occupational composition. The final weighted sample is 96.3% of initially collected responses.
- Generative AI questions measure work and non-work use, frequency, daily time spent, products used, tasks helped, and self-reported time savings. Comparisons to PCs and the internet use CPS Computer and Internet Use supplement data from 1984–2003 and 2001–2009, plus ITU internet data. Aggregate productivity estimates use a Cobb-Douglas production model.
Limitations
- The paper states that its data cannot distinguish whether higher wages among intensive generative AI users reflect task variation, worker selection, or direct productivity/wage effects. It also cautions that potential productivity gains may not appear in statistics if workers take time savings as on-the-job leisure, and that firm adoption lags far behind worker adoption.
- The aggregate productivity calculation assumes all time savings are internalized as higher worker output and that labor supply is perfectly substitutable across workers; the paper notes imperfect substitution would reduce the estimated impact.
- The RPS uses a commercial online panel that is not a random sample, though the authors weight it to be nationally representative and validate it against the CPS and SWAA. The text also notes that NBER working papers are circulated for discussion and have not been peer-reviewed.
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Claims the paper makes
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Between 1 and 5 percent of all work hours are currently assisted by generative AI, and respondents report time savings equivalent to 1.4 percent of total work hours.”
§ THE RAPID ADOPTION OF GENERATIVE AI > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Between 1 and 5 percent of all work hours are currently assisted by generative AI, and respondents report time savings equivalent to 1.4 percent of total work hours.”
§ THE RAPID ADOPTION OF GENERATIVE AI > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: Respondents' self-reported time savings accurately measure the actual time savings from using genAI.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As of late 2024, nearly 40 percent of the U.S. population age 18-64 uses generative AI.”
§ THE RAPID ADOPTION OF GENERATIVE AI > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In August and November of 2024, 26 percent of employed respondents reported using genAI for work: 9 percent used it every work day, 14 percent on some but not all work days in the previous week, and 3 percent did not use it in the last week.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 1 Introduction
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“23 percent of employed respondents had used generative AI for work at least once in the previous week, and 9 percent used it every work day.”
§ THE RAPID ADOPTION OF GENERATIVE AI > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Relative to each technology's first mass-market product launch, work adoption of generative AI has been as fast as the personal computer (PC), and overall adoption has been faster than either PCs or the internet.”
§ THE RAPID ADOPTION OF GENERATIVE AI > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Generative AI and PCs have very similar early adoption patterns by education, occupation, and other characteristics.”
§ THE RAPID ADOPTION OF GENERATIVE AI > Abstract
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Relative to each technology's first mass-market product launch, work adoption of generative AI has been as fast as the personal computer (PC), and overall adoption has been faster than either PCs or the internet.”
§ THE RAPID ADOPTION OF GENERATIVE AI > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“23 percent of employed respondents had used generative AI for work at least once in the previous week, and 9 percent used it every work day.”
§ THE RAPID ADOPTION OF GENERATIVE AI > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In August and November of 2024, 26 percent of employed respondents reported using genAI for work: 9 percent used it every work day, 14 percent on some but not all work days in the previous week, and 3 percent did not use it in the last week.”
- Also stated in: § 6 Conclusion · plain
“9 percent of employed respondents used it every work day and 14 percent on some but not all workdays, bringing total weekly usage to 23 percent.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 1 Introduction
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § 6 Conclusion · plain
- It may state two separate claims as one. 59
- Probably not a substantial claim. 32
- The quote only partly states this claim. 2
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“52 percent of daily genAI users also report using it an hour or more each day, compared to only 7 percent of those who used it in the last month but not the last week.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 4.1 Tasks, Predicted Exposure, and Generative AI Adoption
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In 1984, 25 percent of workers reported using computers for their job, compared with 27 percent of workers who report using genAI for their job in 2024.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 1 Introduction
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: In October 1984, the CPS launched a supplemental module to study computer adoption.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The unweighted distributions are already similar, and the weights improve the fit further.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 2.1.2 Sample Weights and Validation
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“ChatGPT is used most frequently, followed by Gemini, and our estimates of ChatGPT usage are similar to other recent surveys.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 6 Conclusion
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In the paper
“In 2024, workers under the age of 50 were much more likely to use genAI than those over 50, and the same was true for PCs in 1984.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 3.1 How Do Our Results Compare to Other Estimates?
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Evaluating (10) using the midpoint of these bounds, l i ≡ (l i +l i )/2 yields a wage-weighted share of total work hours spent using genAI of 5.7%, which implies a productivity parameter value of γ = 0.33.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 5.4 Comparisons to Micro and Macro Estimates of Productivity Gains from Generative AI
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: There is no change in TFP, capital, and hours worked.
- The authors state this as an assumption: The economy's aggregate output is modeled using a Cobb-Douglas aggregate production function.
- The authors state this as an assumption: In a competitive labor market, each worker is paid their marginal product of labor.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The most commonly used products are ChatGPT (28 percent of all respondents), Gemini (17 percent) and embedded products such as Microsoft Copilot (14 percent).”
§ THE RAPID ADOPTION OF GENERATIVE AI > 1 Introduction
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We use survey data on actual adoption to estimate a wage-weighted usage share of 5.7%, which yields slightly higher estimates.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 5.4 Comparisons to Micro and Macro Estimates of Productivity Gains from Generative AI
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Workers in computer, mathematical, and management occupations use genAI between 9 and 12 percent of their work hours, and it saves them between 2.1 and 2.5 percent of their work hours.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 5.2 Reported Time Savings Due to Generative AI
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Among workers who used genAI every day last week, 33.3 percent said it saved them four hours or more, compared to only 11.5 percent of those who used it only one day last week.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 5.2 Reported Time Savings Due to Generative AI
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Workers report that genAI helps in a broad range of tasks, the most useful being writing, administrative support, and interpreting and summarizing information.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 6 Conclusion
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In the paper
“Almost all occupation groups have at least a modest amount of genAI adoption, which is consistent with the breadth of exposure found in Eloundou, Manning, Mishkin, and Rock (2024).”
§ THE RAPID ADOPTION OF GENERATIVE AI > 1 Introduction
What the sources say
- Not matchedcites Eloundou, Manning, Mishkin, and Rock (2024)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Among those who use genAI for work, 34 percent used it every workday in the previous week, 52 percent used it some but not all days in the previous week, and 14 percent did not use it at all the previous week.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 1 Introduction
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“At the same time, some occupations have high adoption relative to predicted exposure (especially managers) and some have low adoption relative to predicted exposure (especially office and administrative support occupations).”
§ THE RAPID ADOPTION OF GENERATIVE AI > 1 Introduction
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At the same time, managers have relatively high adoption rates relative to predicted exposure, while legal and social services and office and administration occupations have relatively low adoption rates.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 4.1 Tasks, Predicted Exposure, and Generative AI Adoption
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Outside of work, usage is higher overall (35.5 percent) but less intensive, with only 5.2 percent of all respondents using it every day last week.”
§ THE RAPID ADOPTION OF GENERATIVE AI > Do you use Generative AI [outside your job]? (No/Yes)
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The RPS and SWAA waves yield very similar adoption rates, both overall and by frequency of use, as well as the type of products being used.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 1 Introduction
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In the paper
“Personal service and blue-collar occupations have relatively low predicted exposure and also relatively low adoption, while computer and mathematical occupations have both high predicted exposure and high adoption.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 4.1 Tasks, Predicted Exposure, and Generative AI Adoption
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“While we cannot distinguish between work and non-work internet use, overall genAI adoption (including work and non-work usage) has been faster than both the personal computer and the internet.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 1 Introduction
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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In the paper
“By contrast, workers in personal service occupations use genAI in only 1.3 percent of their work hours, and it saves them only 0.4 percent of work time.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 5.2 Reported Time Savings Due to Generative AI
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“40.7 percent of SWAA respondents reported using genAI either for work or outside of work, compared with 39.6 percent of RPS respondents.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 3.1 How Do Our Results Compare to Other Estimates?
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, women were earlier users of PCs, in part because of rapid adoption of computers in white-collar clerical occupations.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 6 Conclusion
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We also find similar estimates of use for work (27.1 percent in the SWAA versus 26.5 percent in the RPS) and outside of work (35.5 percent in the SWAA versus 33.7 percent in the RPS).”
§ THE RAPID ADOPTION OF GENERATIVE AI > 3.1 How Do Our Results Compare to Other Estimates?
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Among workers who used genAI in the previous week, 33.2 percent said that genAI saved them an hour or less of time last week, 26.4 percent reported saving two hours, 20.0 percent reported three hours, and 20.4 percent reported four hours or more.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 5.2 Reported Time Savings Due to Generative AI
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Figure 10 shows that 32 percent of genAI users report using genAI for an hour or more per day at work, 47 percent used it for between 15 and 60 minutes per day, and 21 percent used it for less than 15 minutes per day.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 4.1 Tasks, Predicted Exposure, and Generative AI Adoption
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In the paper
“Across industries, information services has both the largest share of work hours spent using genAI (14.0 percent) and the highest time savings (2.6 percent).”
§ THE RAPID ADOPTION OF GENERATIVE AI > 5.2 Reported Time Savings Due to Generative AI
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This estimate is slightly higher than the average time savings due to genAI from the previous section (1.4 percent).”
§ THE RAPID ADOPTION OF GENERATIVE AI > 6 Conclusion
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The first bar shows that 39.6 percent of respondents report using genAI either at work or at home: 9.2 percent used it every day, 23.1 percent used it some but not all days, and 7.3 percent did not use it in the previous week.”
§ THE RAPID ADOPTION OF GENERATIVE AI > Do you use Generative AI [outside your job]? (No/Yes)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“On days that workers used genAI, 32 percent used it for an hour or more per day, 47 percent used it between 15-59 minutes per day, and 21 percent used it for less than 15 minutes per day.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 1 Introduction
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In the paper
“Internet adoption increased rapidly from 20 percent in year two to 60 percent in year seven, and then more gradually to 90 percent over the ensuing two decades.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 3.1 How Do Our Results Compare to Other Estimates?
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In the paper
“Leisure, accommodation, and other services has both the lowest share of work hours spent using genAI (2.3 percent) and the lowest time savings (0.6 percent).”
§ THE RAPID ADOPTION OF GENERATIVE AI > 5.2 Reported Time Savings Due to Generative AI
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In the paper
“Use of both technologies is associated with a weekly earnings premium of 21 log points, a wage premium of about 15 log pints, and 5 log points weekly hours.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 3.1 How Do Our Results Compare to Other Estimates?
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The second bar shows that 26.5 percent of workers used genAI at work: 9.0 percent used it every work day and 13.9 percent on some but not all workdays, bringing total weekly usage to 22.9 percent.”
§ THE RAPID ADOPTION OF GENERATIVE AI > Do you use Generative AI [outside your job]? (No/Yes)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The one exception is gender: early genAI usage is higher for men, while computer usage was initially higher for women, reflecting rapid computer adoption among secretaries and other administrative occupations, which had large female shares.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 1 Introduction
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We also find similar adoption patterns by occupation and industry, although computer adoption was somewhat more concentrated in a few occupations whereas genAI adoption is more evenly dispersed.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 1 Introduction
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The highest ranked tasks at work were writing (39.5 percent), administrative tasks (25.6 percent), interpreting/translating/summarizing text or data (22.7 percent), and searching for facts or information (18.0 percent).”
§ THE RAPID ADOPTION OF GENERATIVE AI > 4.1 Tasks, Predicted Exposure, and Generative AI Adoption
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“ChatGPT is used most frequently, followed by Gemini, and our estimates of ChatGPT usage are similar to other recent surveys.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 6 Conclusion
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Figure 5c shows that genAI adoption is 7.5 percentage points higher for men, while early PC adoption was 6 percentage points higher for women.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 3.1 How Do Our Results Compare to Other Estimates?
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Both computer and genAI adoption have been faster for younger, more educated, and higher-wage workers.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 1 Introduction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Still, it is notable that the speed of genAI adoption has been similar to the adoption of personal computers in the 1980s, since the latter led to an accelerate of productivity growth and rising inequality (e.g.Autor, Levy, and Murnane 2003).”
§ THE RAPID ADOPTION OF GENERATIVE AI > 6 Conclusion
What the sources say
- Not matchedcites Autor, Levy, and Murnane 2003
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe quote only partly states this claim.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Time savings are highly correlated with the intensity of genAI use and thus vary widely across occupations and industries.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 1 Introduction
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: Respondents' self-reported time savings accurately measure the actual time savings from using genAI.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We closely follow the CIU question wording and structure, which allows us to compare adoption of different technologies relative to their first mass market product release.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 1 Introduction
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The RPS asks the same core questions and follows the same timing and structure of the Current Population Survey (CPS).”
§ THE RAPID ADOPTION OF GENERATIVE AI > 1 Introduction
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Several recent studies find that genAI improves worker productivity (Brynjolfsson, Li, and Raymond, 2023; Cui et al., 2024; Dell'Acqua et al., 2023; Noy and Zhang, 2023; Peng, Kalliamvakou, Cihon, and Demirer, 2023; Toner-Rodgers, 2024)”
§ THE RAPID ADOPTION OF GENERATIVE AI > 1 Introduction
What the sources say
- Not matchedcites Brynjolfsson, Li, and Raymond, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Cui et al., 2024
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Dell'Acqua et al., 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This parallel structure allows us to benchmark our survey estimates against the CPS and to construct weights that ensure a nationally representative sample.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 1 Introduction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To minimize measurement error, we restrict both the RPS and CPS samples to individuals with (i) weekly earnings below the CPS topcode of $3,960.00,(ii) an implied hourly wage of at least the federal minimum wage of $7.25.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 6 Conclusion
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Appendix D.3 presents the same figures for each survey wave and analogous plots for industry and college major, which show qualitatively and quantitatively similar patterns to the occupation shares.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 2.1.2 Sample Weights and Validation
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our results offer partial validation of task-based exposure predictions, supporting the utility of these measures for researchers.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 4.1 Tasks, Predicted Exposure, and Generative AI Adoption
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Results from the August and November waves separately, as well as a pilot survey from June 2024, are included in Online Appendix B. All three waves yield similar estimates.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 6 Conclusion
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Our weighting scheme incorporates a broader set of variables, including employment status.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 6 Conclusion
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The RPS survey matches questions on demographics and labor market outcomes in the basic CPS and CPS Outgoing Rotation Group, using the same word-for-word phrasing when practical and replicating the intricate sequence of questions necessary to elicit labor market outcomes in a manner consistent with the CPS (US Census Bureau, 2015).”
§ THE RAPID ADOPTION OF GENERATIVE AI > 2.1 The Real-Time Population Survey (RPS)
What the sources say
- Not matchedcites US Census Bureau, 2015
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“As an additional point of comparison,Bick, Blandin, Caplan, and Caplan (2024a,b) show that estimates of work from home rates in the RPS align with estimates in the American Community Survey, the American Time Use Survey, the CPS, and the Survey of Income and Program Participation.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 6 Conclusion
What the sources say
- Not matchedcites Bick, Blandin, Caplan, and Caplan (2024a,b)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Our data come from the Real-Time Population Survey (RPS), a survey that is constructed and weighted to be nationally representative and follows the same survey design as the CPS, a widely used national data source.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 6 Conclusion
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All surveys started fielding during the same weeks that the CPS conducted its corresponding surveys.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 2.1.1 RPS Sample
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“A final difference is that Acemoglu (2024) assumes that these productivity gains would evolve gradually over the next ten years, whereas our estimates are based on current usage.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 5.4 Comparisons to Micro and Macro Estimates of Productivity Gains from Generative AI
What the sources say
- Not matchedcites Acemoglu (2024)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“Bick and Blandin (2023) show that the RPS produced very similar statistics for employment, hours worked, earnings, industry composition, and employee tenure during the pandemic.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 2.1 The Real-Time Population Survey (RPS)
What the sources say
- Not matchedcites Bick and Blandin (2023)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Employment rates are very similar across the two surveys, although individuals classified as unemployed according to the CPS definition are somewhat overrepresented in the RPS.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 2.1.1 RPS Sample
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While firm adoption may increase over time, it still lags far behind, suggesting that worker adoption is still mostly informal.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 5.3 The Aggregate Productivity Gain from Generative AI
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“According to Qualtrics staff, this question provides the best results in terms of screening out respondents.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 6 Conclusion
What the sources say
- Not matchedcites Qualtrics staff
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The most notable discrepancies are that individuals aged 18 to 24 and with no more than a high school degree are underrepresented in the RPS relative to the CPS, while individuals with household income of $50,000 or less are overrepresented.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 2.1.1 RPS Sample
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“They find that in February 2024, 27 percent of U.S. adults age 18-64 reported ever having used ChatGPT, compared to 29 percent of individuals who report using ChatGPT in our survey in August and November 2024.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 3.1 How Do Our Results Compare to Other Estimates?
What the sources say
- Not matchedcites McClain, 2024
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We conclude that, relative to the introduction of the first mass-market product, genAI has been adopted at a faster pace than both PCs and the internet.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 3.1 How Do Our Results Compare to Other Estimates?
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The first mass-market computer was the IBM PC, which was released in August 1981.
- Rests on an assumption: The first mass-market genAI product was ChatGPT, which was released in November 2022.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, imperfect substitution across worker types with differing usage patterns would reduce the impact of genAI on aggregate productivity.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 6 Conclusion
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- The authors state this as an assumption: Labor supply is perfectly substitutable across workers.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This pattern is strikingly similar for PC's.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 3.1 How Do Our Results Compare to Other Estimates?
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe quote only partly states this claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If workers are now able to complete the same tasks in less time without their employer's knowledge, they may take their time savings as on-the-job leisure, which would increase welfare but not productivity.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 5.3 The Aggregate Productivity Gain from Generative AI
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Replicating key portions of an existing high-quality survey ensures that survey concepts are comparable, which allows researchers to validate RPS outcomes against a widely used benchmark with a larger sample size and, where necessary, to construct sample weights.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 2.1 The Real-Time Population Survey (RPS)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Eloundou, Manning, Mishkin, and Rock (2024) assign each occupation a predicted exposure score based on what fraction of that occupation's tasks they believe will be substantially affected by genAI.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 1 Introduction
What the sources say
- Not matchedcites Eloundou, Manning, Mishkin, and Rock (2024)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Not matched
In the paper
“To address remaining discrepancies, we construct sample weights using the raking algorithm of Deming and Stephan (1940), ensuring that the weighted sample proportions align with the demographic characteristics targeted in the sampling procedure.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 2.1.2 Sample Weights and Validation
What the sources say
- Not matchedcites Deming and Stephan (1940)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The weighting scheme necessitated dropping some observations due to missing occupation and other discrepancies, resulting in a final sample size of 96.3% of initially collected responses.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 2.1.2 Sample Weights and Validation
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For an individual working 40 hours per week, saving 5.4 percent of work hours implies a time savings of 2.2 hours per week.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 5.2 Reported Time Savings Due to Generative AI
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“This is similar to Acemoglu (2024), who estimates a potential productivity gain of 0.7 percent using estimates of task-based exposure to genAI rather than actual adoption data.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 1 Introduction
What the sources say
- Not matchedcites Acemoglu (2024)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“They found that 18 percent of U.S. adults used ChatGPT at least weekly, compared to 19 percent who used ChatGPT in the previous week in our study.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 3.1 How Do Our Results Compare to Other Estimates?
What the sources say
- Not matchedcites Fletcher and Nielsen (2024)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Individuals who take the survey too fast, i.e. in less than 50% of the median time in a soft launch, or who do not state that they will provide their best answers, are automatically dropped from the sample.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 2.1.1 RPS Sample
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“On the other hand, lower adoption costs may also allow genAI to diffuse more rapidly.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 3.1 How Do Our Results Compare to Other Estimates?
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The Qualtrics panel includes about 15 million members and is not a random sample of the U.S. population.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 2.1.1 RPS Sample
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The SWAA has been running monthly since May 2020 and is conducted using a different survey provider (IncQuery), but targets a similar sample that is intended to be representative of the U.S. ages 20-64 by age, sex, income, race, and region.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 3.1 How Do Our Results Compare to Other Estimates?
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
In the paper
“since the latter led to an accelerate of productivity growth and rising inequality (e.g.Autor, Levy, and Murnane 2003).”
§ THE RAPID ADOPTION OF GENERATIVE AI > 6 Conclusion
What the sources say
- Not matchedcites Autor, Levy, and Murnane 2003
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“The widespread adoption of computers and related information technologies beginning in the 1980s contributed to rising aggregate productivity and growing income inequality (Autor, Levy, and Murnane, 2003).”
§ THE RAPID ADOPTION OF GENERATIVE AI > 1 Introduction
What the sources say
- Not matchedcites Autor, Levy, and Murnane, 2003
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“To provide a second source of data, in December 2024 we ran our genAI module through a different online survey, the Survey of Working Arrangements and Attitudes (SWAA) (Barrero, Bloom, and Davis, 2021).”
§ THE RAPID ADOPTION OF GENERATIVE AI > 3.1 How Do Our Results Compare to Other Estimates?
What the sources say
- Not matchedcites Barrero, Bloom, and Davis, 2021
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Not matched
In the paper
“Our estimate is based on reported genAI use and time savings, while Acemoglu (2024) uses experimental evidence on productivity gains from genAI usage and task-based predictions of genAI exposure.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 5.4 Comparisons to Micro and Macro Estimates of Productivity Gains from Generative AI
What the sources say
- Not matchedcites Acemoglu (2024)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To facilitate this comparison, we use existing technology adoption questions from the CPS as a template for our own questions regarding genAI.”
§ THE RAPID ADOPTION OF GENERATIVE AI > 2.2 Measurement of Generative AI Use
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
The paper relies on this without support.
In October 1984, the CPS launched a supplemental module to study computer adoption.
“In October 1984, the CPS launched a supplemental module to study the adoption of a new technology: the computer.”
The paper relies on this without support.
Respondents' self-reported time savings accurately measure the actual time savings from using genAI.
“We ask users how many additional hours they would have needed to complete the same amount of work in the previous week if they had not had access to genAI.”
The paper relies on this without support.
Time saved by genAI translates one-to-one into aggregate productivity gains.
“Using a standard model of aggregate production, we estimate a potential aggregate productivity gain of 1.1 percent at current levels of worker genAI usage.”
The paper relies on this without support.
The choice of whether to adopt a given technology is a function of both its benefits and its cost.
“The choice of whether to adopt a given technology is a function of both its benefits and its cost.”
The paper relies on this without support.
PC adoption required the purchase of relatively expensive hardware that was initially not very mobile, implying a substantial cost of adoption.
“PC adoption required the purchase of relatively expensive hardware that was initially not very mobile, implying a substantial cost of adoption.”
The paper relies on this without support.
Internet adoption required the purchase of a modem and an Internet Service Provider subscription.
“Internet adoption required the purchase of a modem and an Internet Service Provider subscription.”
The paper relies on this without support.
Many genAI products are currently free or inexpensive and user-friendly.
“By contrast, many genAI products are currently free or inexpensive and user-friendly.”
The paper relies on this without support.
Online-based surveys may suffer from selection based on unobservable characteristics that are correlated with genAI use.
“A potential concern with online-based surveys is that they may suffer from selection based on unobservable characteristics that are correlated with genAI use.”
The paper relies on this without support.
The first mass-market computer was the IBM PC, which was released in August 1981.
“The first mass-market computer was the IBM PC, which was released in August 1981.”
The paper relies on this without support.
The first mass-market genAI product was ChatGPT, which was released in November 2022.
“The first mass-market genAI product was ChatGPT, which was released in November 2022.”
The authors state this as an assumption.
The AI-exposure-adjusted labor share is 0.57.
“If we assume an AI-exposure-adjusted labor share of 0.57, following Acemoglu (2024), the implied potential productivity gain is 1.1%.”
The authors state this as an assumption.
There is no change in TFP, capital, and hours worked.
“Assuming no change in TFP, capital, and hours worked, the percent change in aggregate productivity from genAI equals the percent change in aggregate output.”
The authors state this as an assumption.
Labor supply is perfectly substitutable across workers.
“We also assume above that labor supply is perfectly substitutable across workers.”
The authors state this as an assumption.
All gains from genAI are internalized as higher worker output rather than increased leisure.
“However, we caution that this assumes all of the gains are internalized as higher worker output rather than increased leisure.”
The authors state this as an assumption.
The economy's aggregate output is modeled using a Cobb-Douglas aggregate production function.
“We model the economy's aggregate output using a Cobb-Douglas aggregate production function where labor supply is perfectly substitutable across workers.”
The authors state this as an assumption.
In a competitive labor market, each worker is paid their marginal product of labor.
“In a competitive labor market, each worker is paid their marginal product of labor:”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
RPS outside-of-work adoption is reported as both 35.5% and 33.7%
MinorThe RPS results give two different outside-of-work adoption rates. The 33.7% figure also matches the components shown for that bar in Figure 2a (6.6% + 21.9% + 5.2%).
“Outside of work, usage is higher overall (35.5 percent) but less intensive, with only 5.2 percent of all respondents using it every day last week.”
“outside of work (35.5 percent in the SWAA versus 33.7 percent in the RPS).”
Women's early PC adoption advantage does not match Figure 5c
MinorFigure 5c gives PC adoption rates of 29.6% for women and 21.6% for men, a difference of 8.0 percentage points rather than 6.
Reported 6 · recomputed 8
“Figure 5c shows that genAI adoption is 7.5 percentage points higher for men, while early PC adoption was 6 percentage points higher for women.”
Figure 11b is described as both an industry and occupation comparison
MinorThe text says both panels 11b and 11c plot results by industry, whereas the figure notes specify that panel 11b is by occupation and panel 11c is by industry.
“Figures 11b and 11c plot average time savings by industry (including non-users) against hours spent using genAI in the previous week.”
“The bottom two panels plot the mean share of last week's work hours spent using genAI against the mean time savings due to genAI by occupation group (Panel b) and industry group (Panel c).”
Figure 3 describes overall adoption as work adoption
MinorFigure 3's note calls its series usage rates at work, but the discussion identifies the genAI point as the overall adoption rate, and the paper says its internet data do not distinguish work from non-work use.
“Notes: The figure shows usage rates at work for three technologies: genAI, computers, and the internet.”
“While we cannot distinguish between work and non-work internet use, overall genAI adoption (including work and non-work usage) has been faster than both the personal computer and the internet.”
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Metadata
- Authors
- Bick, A., Blandin, A., & Deming, D.
- Affiliations
- Federal Reserve Bank of St. Louis (US)
- Vanderbilt University (US)
- National Bureau of Economic Research (US)
- ORCID
- Bick, Alexander
- Venue
- NBER Working Paper
- Publisher
- National Bureau of Economic Research
- Published
- 2024-09
- OpenAlex
- W4402736895
- Cited by
- 200
- References
- 2
- Citations by year
- 2026: 130 · 2025: 67 · 2024: 3
- Type
- report
- Language
- en
- Open access
- green
- doi.org
- Locations
- National Bureau of Economic Research (publishedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Labor market dynamics and wage inequality
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic Growth and Productivity
- Keywords
- generative AI, technology adoption, productivity improvement, time savings
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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