Apr 27, 2025

Impact of generative AI on tobacco, investment and tourism industry

jalokim

Details

Details

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I examine how introducing Gen AI solutions in companies in the tourism, investment and tobacco sectors affects labour productivity using DiD. This falls under the Growth track, which studies how technological innovations drive economic growth. Economic theory (and intuition) suggests that AI-driven automation can boost output per worker. I use publicly available data such as revenue, employee count and news about Gen AI adoption to determine if companies that adopted AI increased their labour productivity since 2022 when ChatGPT was launched. Labour productivity is defined as revenue per employee.

Cite this work:

@misc {

title={

Impact of generative AI on tobacco, investment and tourism industry

},

author={

jalokim

},

date={

4/27/25

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Reviewer's Comments

Reviewer's Comments

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Joel Christoph

The paper asks whether firms that adopted generative AI after 2022 experienced higher labour productivity, defined as revenue per employee, in the investment, tourism, and tobacco industries. Using revenue and headcount scraped from StockAnalysis and a hand coded flag for AI adoption, the author implements a two-period difference in differences design covering 2022 to 2024. The results table suggests investment firms gained about 0.14 million dollars per worker, tourism firms about 0.09 million, and tobacco firms saw no effect. The inclusion of a GitHub link and an appendix listing the sampled companies is a positive step toward transparency.

Inference is weak because the sample is small and convenience based, AI adoption dates are uncertain, and the two-period panel offers no way to test common trend assumptions. Revenue per employee is a noisy proxy that ignores capital intensity, hours worked, and currency movements. The regression excludes control variables and firm fixed effects, and confidence intervals are bootstrapped on the same limited cross section. Sector choice is not justified by theory, and the literature review overlooks recent work on firm level AI productivity and task exposure indices.

AI safety relevance is thin. Productivity effects matter for growth trajectories yet the paper does not connect its findings to distributional stability, governance incentives, or funding for alignment research.

Technical documentation is partial. Input numbers are listed but the scraping scripts, dummy construction, and regression code are missing, and robustness checks are absent.

The study would benefit from a larger balanced panel with multiple pre-treatment years, validated adoption dates, firm fixed effects with appropriate controls, a richer literature discussion, full code release, and an explicit link between sectoral productivity shifts and AI safety policy levers such as redistribution or compute taxation.

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This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.