Interesting planned work on the impact of AI on LMICs, with a well-developed methodology. Still, the report would have benefitted a lot if some empirical analysis would have been conducted.

* Formatting was highly unusual and difficult to read. (Also provided in .docx, not pdf as suggested)

* Generally, Little evidence provided to suggest

* No details provided for the simulation environments used to model AI-driven labor markets

* It seems the Paper skips from Methods to Anticipated Results, leaving out the most important part (The process)

* No evidence or citations supporting the claim that "AI-driven automation could reduce employment opportunities in clerical and service sectors

* No definitions of key concepts (e.g., "Transformative AI"

* Very few (only 3) references and citations

* The appendix doesn't actually contain any of the claimed Appendix items (e.g., no survey, no model parameters etc) The appendix is just a vague description of what should be there.

The submission spotlights a neglected region by examining how transformative AI may widen or reduce income inequality in African low and middle-income countries. It combines a task based exposure framework adapted from Acemoglu and Restrepo with macro simulations and proposes to triangulate results through case studies and stakeholder surveys. The emphasis on informal employment and locally owned AI cooperatives shows awareness of distinctive African labor market realities. The outline commits to releasing an open dataset and code which, if delivered, would add value for future researchers. ​

At this stage the study is mainly a project plan rather than completed research. The macroeconomic model is not specified, no parameter values are reported, and the expected results section presents qualitative predictions without data. References are few and omit recent empirical work on AI exposure metrics, digital labor platforms in Africa, and inequality projections under automation. Links to AI safety are indirect because the paper does not explain how inequality trends in African LMICs feed back into global catastrophic risk, alignment funding, or governance of advanced models.

Technical documentation is thin. The survey instrument and model parameters are only described in prose; no questionnaire file, code repository, or calibration table accompanies the manuscript. Without these materials reviewers cannot judge methodological soundness or replicability. The promised predictive model, policy toolkit, and dataset remain future deliverables. Clarifying the modeling equations, publishing a minimal working dataset, and running an illustrative calibration for one pilot country would greatly strengthen the submission.

1. Innovation & Literature Foundation

1. 1

2. Should engage with existing work on AI impacts in Africa (Otis et al. 2024, Dan Björkegren's work). This is relatively new stuff, but gives a better grounding to what

3. Of the three citations, the Acemoglu+Restrepo is very relevant, especially if you plan to identify exposed professions. I couldn't find either of the other two citations, the ILO or World Bank reports. The report cited as "World Bank. (2024). AI and Inequality in Low- and Middle-Income Countries. Washington, DC: World Bank Group." sounds really interesting; please share if this exists or is in drafting stages.

2. Practical Impact on AI Risk Reduction

1. 1.5

2. The question is highly policy relevant, under transformative AI. Generally neglected as well — even when African welfare under TAI is discussed, it's usually in the context of American/European policies. Good to account for African policy maker sovereignty.

3. I am initially skeptical of the second policy lever (farmer-owned agritech co-ops). If you believe this would be a very high-impact policy, please sketch the theory of change.

4. No discussion of regulation (outside of appendix). That's the first lever which will be pulled.

3. Methodological Rigor & Scientific Quality

1. 2

2. There's potential here — I would be really interested in someone doing the analysis gestured at in section 3. The anticipated results are plausible (urban clerical workers and outsourced service roles being the most vulnerable), but I don't believe we have the data to support this yet. A full research project here might require new data collection, or an RCT.

3. I'm a bit more sceptical of the macro simulations. Lay out what assumptions would go into this.