Great work on analyzing the real-world impact of AI in the economy! The article is well written, has super clear language which I appreciate (particularly today where many use LLMs that produce text with unnecessarily complex language). The method is well described, and highlights the limitations of the approach used in a good way.

The article could have been further strengthened with references backing up claims, e.g. "Some claim that AI adoption is correlated with size". I do appreciate however that you included the prompts you used to get the findings – it is good inspiration on a meta level for researchers.

I also think the article would have benefitted from a longer discussion on the findings, to answer questions like why you think Investment has higher productivity boost from AI than other sectors you looked at. But I also understand that the time and format constraints made this difficult – so overall, well scoped research and clearly written article.

* There was little to no rationale provided for selecting the 3 completely unrelated industries, paper would have been much better if it had focused on a particular single industry .

* It is fundamentally illogical to extrapolate metrics of the 2 or 3 largest companies across long-tail industries that are highly fragmented (like Tourism).

* The paper makes many claims without any evidence or reason (why would generative AI increase productivity of tobacco production) .

* The discussion section was just two short sentences.. it should have been several pages.

* The appendix content was good but not formatted well

* AI was was clearly used in the writing of this paper

* Poor grammar and use of prompting

* Not a single citation or reference?

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.

A few points of improvement:

- The paper mostly doesn't engage with prior literature, which would help for providing a theoretical grounding for which industries to analyse

- The paper uses revenue per employee as a proxy, but this is extremely noisy (for example, firms which are more capital intensive could be more likely to use AI - as one of many forms of capital - without having higher total factor productivity, but would have higher revenue per employee)

- Methodology of sample selection was non-random, would have benefitted from usinfg some more systematic method (such as S&P500 firms within industry, or using more granular industry data to ensure small as well as large firms were captured)

- Mean difference is below reporting accuracy, and could vanish at higher sample sizes (so would be good to collect a larger sample to check this)

- DiD implicitly assumes parallel trends - would have been good to verify this with pre-2020 data