Very interesting idea to combine traditional economic growth and innovation diffusion models to study the impact AI will have on growth and disparities. The report would have been more compelling if the results of the theoretical model would have not been described in text only, but also with some simulations or, even better, contrasted with some data (even if those would have been only proxies).

The rates of AI adoption across firms, industries, countries, etc determine how the impact of AI plays out. Therefore, it is important to identify the determinants of AI adoption to understand the aggregate implications of AI and the heterogeneity across different segments of the economy. This is where this paper comes in. The paper attempts to combine a growth model and theories of technology adoption.

The idea could be advanced further by highlighting the role of the factors influencing adoption sharper in the context of the economic growth theory. Which factors will have the first-order importance in determining the adoption pattern of AI? Which aggregate dynamics will they matter for? The authors could start by thinking about these questions to narrow down their contributions.

The paper sets out to explain how differing rates of artificial intelligence adoption will shape productivity, inequality, and regional gaps. It extends the Solow growth framework with an automation variable and overlays Rogers diffusion theory, then sketches a logistic adoption process and an aggregate regional production function. The conceptual synthesis is logically organised and the notation is clear. Figures outlining Gini trajectories are helpful.

The contribution is limited by the absence of empirical grounding. All parameters in the models are hypothetical and no real adoption or productivity data are used for calibration. As a result the results section repeats the intuition that faster adoption raises output and widens gaps, but offers no quantification beyond stylised claims. The references list omits key recent papers that estimate industry level adoption curves, intangible capital spillovers, or distributional effects of large language models. The mechanisms that link AI adoption to regional inequality are described qualitatively and remain untested.

AI safety relevance is only implicit. The paper notes that widening disparities could undermine social stability but does not connect its framework to concrete safety levers such as compute governance, labour transition funds for alignment workers, or incentives for safer model deployment. Without tracing pathways from distributional outcomes to tail risk mitigation the impact on the safety agenda is weak.

Technical quality is hindered by the lack of data, code, or sensitivity tests. Several symbols in the equations are introduced without units. The logistic adoption curve is presented but no baseline or comparative static is shown. The Google Drive link mentioned in the appendix is not integrated into the submission so reproducibility is not possible.

Future work would benefit from collecting cross‐industry panel data on AI investment, estimating adoption rates, and embedding those estimates into the model. A calibrated simulation with Monte Carlo uncertainty would allow credible policy experiments. Engaging with current empirical studies and mapping specific safety channels such as funding for red teaming would strengthen both foundation and relevance. ​

Appreciate the effort especially under short time constraints. Extending the Solow model to this topic was clever. I would have liked to see more engagement with existing literature (e.g. Acemoglu has lots of relevant work here) and a much more thorough policy and/or recommendations section. I would have liked to see you spell out in greater detail where you expect adoption to be faster and slower and what downstream impacts this has that could be relevant to economists or policymakers. Additionally, as you acknowledge, a lack of empirical data made this challenging to evaluate.