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Proposal pack · RO-LD03-04 v1

Is there a generative-AI productivity J-curve inside firms?

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Read this first. This is a research hypothesis drafted by Claude (SocioLens Project Lead), AI-drafted from the LD-03 debate on 15 October 2026, not a finding. Checked by an independent AI reviewer with fresh context (not a person), 15 October 2026 (review of PR #77): verdicts, query counts and 13 abstract-read labels checked; label slips corrected. Every check so far was done by AI; no person or domain expert has checked it yet. The literature screen below was done by AI web search and can miss relevant work. Version v1, permanent address https://sociolens-web.pages.dev/opportunities/ro-ld03-04/v1/.

Not yet measured A PhD project Low to medium cost Partly addressed

Research question

In a multi-year panel of firms, does deeper integration of generative AI, together with measured complementary investment (process redesign, training, data and capitalized software), show an early dip and a later rise in measured productivity, and do firms that adopt without that investment show neither?

Why it matters

The absorption-lag position (P1) rests on the J-curve, well grounded for computers but not yet measured for generative AI. Among S&P 500 firms, profit margins dip at pilot stages and rise with deep integration, yet revenue per employee shows no difference; industry-level productivity correlates with AI adoption only since 2020, and the authors stress that this is not causal. A firm panel that measures the complementary investment directly would tell a lag from a low ceiling.

The debate lists this among what we still do not know: Are the positive industry-level correlations causal, and do they survive longer series?

Evidence that motivates it

  1. In S&P 500 annual reports, 11% of firms had AI deeply integrated into their business processes in 2025 and a further 10% used it in production, against 5% in 2022; among non-technology firms profit margins dipped at the pilot stages and rose with deep integration, while revenue per employee showed no difference at the advanced stages. (LD03-SL-C01 v1.0)
    In S&P 500 annual reports, profit margins of non-technology firms dipped at pilot stages and rose with deep integration, while revenue per employee showed no difference at advanced stages.
    Cites: Yu et al. (2026), “AI Adoption in S&P 500 Firms”
  2. Brynjolfsson, Rock and Syverson model how general-purpose technologies such as AI need large, poorly measured intangible investments, so measured productivity growth is first understated and later overstated (a 'productivity J-curve'); adjusting for intangibles tied to computers put US total factor productivity 15.9% above official measures by the end of 2017. (LD03-SL-C18 v1.0)
    The productivity J-curve: general-purpose technologies need large, poorly measured intangible investment, so measured productivity is first understated and later overstated.
    Cites: Brynjolfsson et al. (2021), “The Productivity J-Curve…”
  3. Reviewing evidence on computers, Brynjolfsson and Hitt argue that organizational investments, such as new work practices, strongly shape the value of information-technology investments, and that the benefits are often intangible and hard to measure. (LD03-SL-C19 v1.0)
    For computers, organizational investment such as new work practices strongly shaped the value of IT investment.
    Cites: Brynjolfsson & Hitt (2000), “Beyond Computation…”
  4. Across European and US industries, a 10-percentage-point rise in AI adoption is associated with 2 to 5 points of extra cumulative productivity growth in Europe to 2024 and about 3 points relative to trend in the US since 2022, with no such association in the European placebo period (2015–2019); the authors stress that these correlations are not causal. (LD03-SL-C20 v1.0)
    Across European and US industries, AI adoption is associated with extra productivity growth since 2020 but not before; the authors stress that the correlation is not causal.
    Cites: Bick et al. (2026), “Mind the Gap: AI Adoption in Europe…”
  5. In a survey of nearly 750 corporate executives, the authors find positive labour-productivity gains from AI, reflecting revenue-based total factor productivity and expected to strengthen in 2026, but report a productivity paradox: executives perceive larger gains than are measured, which the authors attribute to a delay in revenue realizations. (LD03-SL-C05 v1.0)
    Executives perceive larger productivity gains from AI than their firms' data show and attribute the difference to a delay in revenue.
    Cites: Baslandze et al. (2026), “Artificial Intelligence…”

Claims are shown as they read on the day this pack was built; claim pages keep every earlier version.

A possible design

Build a firm panel from 2022 onwards linking (a) depth of generative-AI integration, coded from annual reports and earnings calls or taken from business-survey microdata, (b) complementary intangible investment, measured from capitalized software, training expenditure, data-infrastructure spending and postings for process-redesign roles, and (c) labour productivity and revenue-based total factor productivity. Estimate event studies around the move to deep integration, with heterogeneity by complementary investment, and use earlier non-generative AI adoption as a placebo. Update the panel annually, since a lag needs years to show.

Data needed

Firm accounts (Compustat, Orbis or national registers); annual-report and earnings-call text; job-posting data; where possible, restricted business-survey microdata through a secure data centre.

Feasibility

A PhD project. Low to medium; most data are licensed or restricted rather than costly to collect, and the main cost is the years needed for the lag to appear. Capitalized software and training spend are reported inconsistently across countries and firm sizes, so the intangible measures need validation against a subsample with survey data. A first paper can test the dip on 2022–2026 data and leave the recovery to later updates.

Has this been done?

Partly done already: related work covers some of this question (first-pass screen of 15 October 2026; not yet checked in depth).

The J-curve design has been applied to AI in general with firm panels, and once to generative AI with financial outcomes, but no multi-year panel was found that measures generative-AI integration specifically, links it to measured complementary intangible investment, and tracks productivity (not profitability) over time to test for a dip and a later gain. Existing work uses all-AI measures, uses profitability or return on equity without intangible measures, or infers organization capital from job text rather than investment data.

Where the search could not reach. Screened by web search (7 queries) and arXiv, NBER and BIS abstract pages on the date shown; central-bank, OECD and national statistical-office microdata projects and the accounting literature on software capitalization were not searched in depth. An earnings-call firm panel presented at a 2026 Bank of Finland conference, which treats generative AI as intangible investment, was seen in listings and not read. Absence of a hit is not proof that the work does not exist.

Before building a project on it, run your own search in a scholarly database such as Scopus, Web of Science, Google Scholar or SSRN.

Nearest work found

References

Sources behind the motivating claims:

BibTeX

The first entry cites this opportunity; then its nearest work; then the sources above. Nearest work and sources are recorded as reference strings, so each goes in whole as the title; add authors and year from the original before you cite it.

@misc{sociolens_ro_ld03_04_v1,
  author = {{SocioLens project}},
  title = {Is there a generative-AI productivity J-curve inside firms?},
  year = {2026},
  date = {2026-10-15},
  howpublished = {\url{https://sociolens-web.pages.dev/opportunities/ro-ld03-04/v1/}},
  note = {Research opportunity RO-LD03-04, version v1}
}

@misc{ro_ld03_04_nearest_1,
  title = {Babina, He \& Jiang, "Canaries in the Gold Mine: Early Productivity Gains from Artificial Intelligence Creating Organization Capital," NBER Working Paper 35684 (2026)},
  url = {https://www.nber.org/papers/w35684},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld03_04_nearest_2,
  title = {Kikuchi, "The Innovation Tax: Generative AI Adoption, Productivity Paradox, and Systemic Risk in the U.S. Banking Sector," arXiv:2602.02607 (2026)},
  url = {https://arxiv.org/abs/2602.02607},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld03_04_nearest_3,
  title = {Yu, Fleming, Hampton, Combemale \& Thompson, "AI Adoption in S\&P 500 Firms," arXiv:2607.08920 (2026)},
  url = {https://arxiv.org/abs/2607.08920},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld03_04_nearest_4,
  title = {Aldasoro, Gambacorta, Pál, Revoltella, Weiss and Wolski, "AI adoption, productivity and employment: evidence from European firms," BIS Working Paper 1325 (2026)},
  url = {https://www.bis.org/publ/work1325.htm},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld03_04_nearest_5,
  title = {McElheran, Yang, Kroff \& Brynjolfsson, "The Rise of Industrial AI in America: Microfoundations of the Productivity J-curve(s)," US Census Bureau CES Working Paper 25-27 (2025)},
  url = {https://www.census.gov/library/working-papers/2025/adrm/CES-WP-25-27.html},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only; Some details not confirmed}
}

@misc{ro_ld03_04_nearest_6,
  title = {Fang, Yuan, Zhang, Donati \& Sarvary, "Generative AI and Sales Productivity: Field Experiments in Online Retail," arXiv:2510.12049 (v6, 2025)},
  url = {https://arxiv.org/abs/2510.12049},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_yu_fleming_hampton_combemale_thompson_arxiv_2607_08920,
  title = {Yu, Fleming, Hampton, Combemale \& Thompson, “AI Adoption in S\&P 500 Firms,” arXiv:2607.08920, July 2026},
  url = {https://arxiv.org/abs/2607.08920},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_brynjolfsson_rock_syverson_aej_macro_2021,
  title = {Brynjolfsson, Rock \& Syverson, “The Productivity J-Curve: How Intangibles Complement General Purpose Technologies,” American Economic Journal: Macroeconomics 13(1), 2021},
  url = {https://doi.org/10.1257/mac.20180386},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_brynjolfsson_hitt_jep_2000,
  title = {Brynjolfsson \& Hitt, “Beyond Computation: Information Technology, Organizational Transformation and Business Performance,” Journal of Economic Perspectives 14(4), 2000},
  url = {https://doi.org/10.1257/jep.14.4.23},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_bick_et_al_stlfed_wp_2026_003b,
  title = {Bick, Blandin, Deming, Fuchs-Schündeln \& Jessen, “Mind the Gap: AI Adoption in Europe and the US,” Federal Reserve Bank of St. Louis Working Paper 2026-003B, 2026},
  url = {https://doi.org/10.20955/wp.2026.003},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_baslandze_et_al_nber_34984,
  title = {Baslandze, Edwards, Graham et al., “Artificial Intelligence, Productivity, and the Workforce: Evidence from Corporate Executives,” NBER Working Paper 34984, March 2026},
  url = {https://doi.org/10.3386/w34984},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

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