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Proposal pack · RO-LD02-04 v2

Are juniors who still get hired at AI-adopting firms promoted and trained less?

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Read this first. This is a research hypothesis drafted by Claude (SocioLens Project Lead), AI-drafted from the LD-02 debate on 12 October 2026, not a finding. Checked by Claude (SocioLens Project Lead) with AI research agents, by web search; not a person and not a domain expert; an independent AI reviewer with fresh context (not a person), 12 October 2026: every nearest-work citation checked against its source page; corrections applied. 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 v2, permanent address https://sociolens-web.pages.dev/opportunities/ro-ld02-04/v2/.

Causal mechanism not isolated One PhD chapter or paper Low to medium cost Partly addressed

Research question

Among workers hired into junior positions before and after their employer began using AI, do promotion into higher-skill occupations, wage growth, recorded training and retention over the following two to four years differ between AI-adopting and comparable non-adopting firms?

Why it matters

The worry behind "eroding career ladders" is not only that fewer juniors are hired, but that those who are hired learn less, because AI now does the routine work through which novices used to learn. Current studies mostly count junior hires and junior shares; the one that looks at junior promotions covers only a short window, and none found follows the juniors inside adopting firms over several years. If promotion and wage growth hold up, the ladder is narrower but intact; if they fall, the pipeline to senior roles is at risk.

The debate lists this among what we still do not know: Will entry-level learning ladders erode if firms hire fewer juniors?

Evidence that motivates it

  1. In a 41-country working paper, AI-adopting multinational affiliates shift toward more senior workforces: junior share falls by 1.9 percentage points, driven mainly by 6.7% senior employment growth rather than a statistically clear decline in junior headcount. (LD02-HQ-C07 v1.0)
    In a 41-country working paper, AI-adopting affiliates shift toward more senior workforces mainly through senior growth, not a clear fall in junior headcount; the claim does not license junior job destruction, so the open question is the quality of junior careers, not their number.
    Cites: Chandar & Klein Teeselink (2026), “How Does AI Change…”
  2. Danish firms that began using AI in 2023 had roughly 11% lower relative employment growth by late 2025 than comparable non-adopters, mainly through lower recruitment, while economy-wide employment remained relatively unaffected. (LD02-HQ-C03 v1.1)
    Danish firms that began using AI in 2023 grew employment about 11% less than non-adopters, mainly through lower recruitment; this identifies a register population of adopting firms whose junior hires can be followed, and it does not license reading the gap as layoffs.
    Cites: Danmarks Nationalbank Working Paper No. 223, “AI Adoption…”

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

A possible design

Matched employer–employee panel. Identify firms that began using AI from an official enterprise ICT-usage survey linked to registers, and match them to non-adopters on size, sector, pre-period growth and occupational mix. Take every worker hired into a junior position (by age, experience or occupational level) in 2018–2025. Compare, by hiring year and adopter status, the probability of moving to a higher-skill occupation code within the firm, wage growth, separation, and, where the register records it, participation in employer-related training. Use a difference-in-differences across pre- and post-adoption hiring cohorts. A small set of interviews with managers in adopting firms can show how junior tasks were reallocated.

Data needed

Linked employer–employee registers with occupation codes and earnings (Denmark, Norway, Sweden, Finland or the Netherlands); firm-level AI-use answers from the national ICT-usage-in-enterprises survey linked by firm identifier; training records where they exist (coverage varies by country and must be checked).

Feasibility

One PhD chapter or paper. Low to medium; register access fees and time for approvals; interviews add little cost. The harmonised EU version of the ICT survey covers only enterprises with at least 10 employees and is a sample, and its AI questions differ across years, so adoption timing may be coarse and should be checked wave by wave for the chosen country. Occupation codes change slowly within firms, so promotion must be measured as code changes plus wage jumps, and the measure should be validated before use.

Has this been done?

Partly done already: related work covers some of this question (first-pass screen of 12 October 2026, checked again on 13 October 2026).

US résumé data show that adopting firms hire fewer juniors with no significant short-window change in junior promotions, and the authors list longer-run adjustments in training and career ladders as an open question; a US spending-based study instead finds entry-level headcount rising at high-intensity adopters. The 41-country paper measures affiliate headcounts and junior shares, not individual trajectories, and the Danish central-bank paper reports firm employment growth and recruitment, according to the pages read. Wage studies by seniority use sector exposure, not firm adoption, and interview studies describe lost junior learning without measuring careers. No study was found that follows juniors hired at adopting firms over several years for promotion, wage growth and training in population registers.

Literature check. No study was found that follows individual juniors hired at AI-adopting firms over several years for promotion, wage growth and training. New related work counts entry-level headcount at adopters (Kharazian, Simon & Stevens find entry-level headcount rising 12% at high-intensity adopters, contrary to the seniority-bias pattern), measures wages by seniority in exposed sectors (Azar, Giné & Sanz-Espín), or describes the erosion of junior learning qualitatively (Yu & Moon, 14 interviews in Korea). None does the core design. Checked by Claude (SocioLens Project Lead) with AI research agents, by web search; not a person and not a domain expert.

Where the search could not reach. Screened by web search on the date shown; Google Scholar, SSRN and paywalled journal pages were not systematically searched, and the Danish central-bank paper was read at landing-page level only. Several small interview studies of junior software developers exist and were not treated as nearest work because they do not measure careers. Absence of a hit is not proof that the work does not exist. Literature check of 2026-10-13: The full texts of the SSRN papers were not read (abstract or third-party summary only). Google Scholar and paywalled journals were not searched. Five queries ran in the earlier round and the sixth (preprint forward search on Hosseini Maasoum & Lichtinger) ran in a follow-up round; it found only the original paper, the Yu & Moon interview study and a conceptual skill-formation preprint. 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_ld02_04_v2,
  author = {{SocioLens project}},
  title = {Are juniors who still get hired at AI-adopting firms promoted and trained less?},
  year = {2026},
  date = {2026-10-13},
  howpublished = {\url{https://sociolens-web.pages.dev/opportunities/ro-ld02-04/v2/}},
  note = {Research opportunity RO-LD02-04, version v2}
}

@misc{ro_ld02_04_nearest_1,
  title = {Hosseini Maasoum \& Lichtinger, "Generative AI as Seniority-Biased Technological Change: Evidence from U.S. Résumé and Job Posting Data," SSRN Working Paper No. 5425555 (2025)},
  url = {https://doi.org/10.2139/ssrn.5425555},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld02_04_nearest_2,
  title = {Chandar \& Klein Teeselink, "How Does AI Change Labor Demand? Evidence from 41 Countries," Stanford Digital Economy Lab working paper (September 2026)},
  url = {https://digitaleconomy.stanford.edu/publication/how-does-ai-change-labor-demand},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld02_04_nearest_3,
  title = {Danmarks Nationalbank, "AI Adoption and Firm Size: Employment Effects in Monthly Administrative Data," Working Paper No. 223 (16 September 2026)},
  url = {https://www.nationalbanken.dk/en/news-and-knowledge/publications-and-speeches/working-paper/2026/ai-adoption-and-firm-size-employment-effects-in-monthly-administrative-data},
  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_ld02_04_nearest_4,
  title = {Friebel, Huang, Li, Shukla \& Zhang, "Pyramids, Diamonds, and Oscillations: AI and the Structure of Internal Labor Markets," NBER conference paper (10 April 2026)},
  url = {https://conference.nber.org/conf_papers/f232578.pdf},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld02_04_nearest_5,
  title = {Kharazian, Simon \& Stevens, "A New Look at AI's Impact on Jobs: Firm-Level AI Spending and Workforce Adjustment," SSRN Working Paper No. 7024799 (posted 22 July 2026)},
  url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7024799},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld02_04_nearest_6,
  title = {Azar, Giné \& Sanz-Espín, "The Wage Effects of Generative AI," SSRN Working Paper No. 5842084 (2025), as summarised by IESE Insight (12 December 2025)},
  url = {https://www.iese.edu/insight/articles/artificial-intelligence-junior-employees-wages/},
  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_ld02_04_nearest_7,
  title = {Yu \& Moon, "Who Will Become the Next Senior? How Generative AI Erodes the Development Pathway in Software Engineering," arXiv:2607.17067 (v3, 25 August 2026)},
  url = {https://arxiv.org/abs/2607.17067},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld02_04_nearest_8,
  title = {"Toward Measuring AI's Effects on Skill Formation: The Stock-Formation Gap," arXiv:2605.16283 (2026; authors not confirmed this round)},
  url = {https://arxiv.org/abs/2605.16283},
  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{src_ld02_chandar_kleinteeselink_2026,
  title = {Chandar \& Klein Teeselink, “How Does AI Change Labor Demand? Evidence from 41 Countries,” September 2026},
  url = {https://digitaleconomy.stanford.edu/publication/how-does-ai-change-labor-demand},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_ld02_nationalbank_wp_223_2026,
  title = {Danmarks Nationalbank Working Paper No. 223, “AI Adoption and Firm Size: Employment Effects in Monthly Administrative Data,” 16 September 2026},
  url = {https://www.nationalbanken.dk/en/news-and-knowledge/publications-and-speeches/working-paper/2026/ai-adoption-and-firm-size-employment-effects-in-monthly-administrative-data},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

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