Comparing tax proposals to fund a UBI is a good start, but the details on the proposals were light. Along with providing these details, it would be helpful to compare them on a level playing field, for example in order to raise 1% or 10% of global GDP under each of them, what would the distortionary effects be?

For the AIDI, which seems to be the centerpiece of this project, the distortionary effects on AI investment will be important and don't seem to be considered. It also appears to assume global coordination, which may be a major barrier in practice.

It's unclear how the AI Job Threat Index relates to the rest of the model.

Some points made, like "the Global North consolidates technological and economic power at the expense of the Global South" go against standard economic theory and would benefit from further justification.

The paper offers a timely analysis of income guarantee programs and, importantly, how to finance such policies. This kind of analysis is what we need to make informed decisions about how to prepare for potential disruptions by AI.

To further advance the analysis, the authors could examine potential costs of distortionary taxes. Importantly, distortions may differ across different stages of automation. For example, progressive income tax may incur large distortions at low levels of automation as labor is still a crucial input to production. However, as the economy approaches full automation such distortions may decrease. Then the paper could even discuss the optimal mix of different financing schemes as automatio advances.

Lastly, it may be worth taking a step back and asking: Is it ideal to have everyone rely on the income provided by the government? Such centralization of power and resources could be a source of distinct risks that are not yet discussed much.

The paper sets an ambitious goal of financing a universal high income in a world where artificial intelligence removes the majority of paid jobs. It gathers four revenue pillars, a unity wealth tax, a land and property tax on idle assets, a redesigned progressive income tax schedule, and an artificial intelligence dividend charged on the profits of highly automated firms. The abstract claims that the combined package can raise eight to twelve percent of gross domestic product, a figure the authors argue would cover transfers even if unemployment rises to ninety percent. The narrative is accessible and the use of established macro frameworks such as the IS MP Phillips curve for the wealth tax and the Diamond Saez approach for income taxation shows familiarity with modern public finance. The inclusion of an original concept called AIDI brings a creative twist that aligns revenue with the pace of automation. Figures on pages five and six display the anticipated distributional gains, for example the bar chart on page five estimates a fall in the Gini coefficient of roughly five one hundredths under the wealth tax alone ​

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Despite this breadth the study remains largely illustrative. All parameter values are hypothetical and no calibration to existing national accounts or tax bases is attempted. The dynamic general equilibrium modelling is referenced but no model equations beyond skeletal identities are shown, and the paper supplies no code or sensitivity analysis. Key assumptions such as capital flight of thirty to forty percent under unilateral wealth taxation are asserted without evidence. The land value tax results rely on external citations but the authors do not produce their own simulations. As a result the headline claim that the package funds two thousand dollars per adult per month is not verifiable. The reference list is extensive yet recent quantitative work on automation driven tax bases and optimal redistribution under artificial intelligence is missing, so the literature anchoring is only partial.

The link to AI safety is acknowledged but indirect. The authors argue that maintaining consumer demand and curbing extreme inequality will support social stability during a high automation transition. They do not trace how the proposed taxes would influence alignment research incentives, catastrophic misuse risk, or international compute races. A deeper discussion of how large public transfers could be conditioned on safe development norms or how AIDI could internalise externalities from risky deployment would make the paper more relevant to safety.

Technical documentation is thin. Several variables in the model statements lack units, tables omit standard errors, and the Kaggle job threat dataset mentioned in methods is not integrated into the fiscal projections. The appendix points to a Google Drive folder that is not included, so the study cannot be replicated. The graphical results are clear but no underlying data are provided.

The research offers a solid, theoretically grounded macroeconomic analysis of four distinct tax proposals for funding UHI in a future automated economy. It effectively uses established economic models and provides quantitative estimates for potential revenue, distributional impacts, and behavioural responses for each measure. A key strength is the comprehensive approach covering both governmental and market-side effects, as well as identifying crucial implementation challenges like international coordination and tax avoidance. While the analysis of individual measures is thorough, a more integrated modelling of the combined effects and a deeper dive into practical implementation hurdles could enhance the research further.

Thanks for the paper! It lays out several key considerations for designing tax systems in general, and discusses how some of them could relate to AI. Here's a few potential areas for improvement:

- IS-MP is normally used as a short run framework analysing the world given fixed inflation expectations, and has very little relevance to the long run impacts of tax policies, where other considerations (such as capital stock adjustment and labour supply) are more relevant

- It is unclear what the tax rates of some of the taxes in the paper are, especially for the income tax and land tax changes proposed, making it difficult to evaluate the feasibility of their contribution to the end revenue stream

- Additionally, it is unclear how behavioural response is governed, and what model drives the levels of relocation observed in response to the tax, or how the optimal level is computed

- Lastly, any parameters included don't mention any extrapolation from economic data today, but AI would likely change several of them (for example, a rise in the capital share would raise the revenue from a wealth tax and reduce the value of the income tax changes), so it would be desirable to extend this to exploring those