Economics of AI Data Center Energy Infrastructure: Strategic Blueprint for 2030

* Inconsistent formatting (e.g., section 6)

* Generally Well structured

* Some fundamentally mis-informed conclusions ( For example, "Regions with existing robust transmission infrastructure and clean energy resources will have significant competitive advantages in attracting AI development." When in fact there is no correlation becuase AI development is almost never located at or near the datacenter... and datacenters are most often colocated.

Good job not just citing the use of AI but actually using AI appropriately

* Good Citations and references

* Policy recommendations inconsistent with analysis, there was no mention of generation, only transmission

1. Innovation & Literature Foundation

1. 4

2. Great command of literature; you know this space well. Some staff at Rand might be willing to share more detailed analyses if you contact them.

3. Not a ton of innovation evident; mostly improvements to existing models and estimates.

2. Practical Impact on AI Risk Reduction

1. 4

2. I like all of these policies for the problem you chose.

3. But you should justify that problem more.

4. I would challenge you to name your assumptions/worldview (maybe they're in the longer doc!). E.g., from "assume exponential demand" to "it's urgent to meet that demand" because "beat China" (my words, not yours, but that's kinda what I assume underlies these policy proposals).

3. Methodological Rigor & Scientific Quality

1. 3

2. I kinda have to take you at your word regarding your model. Please share the comprehensive research document which this is based on. I'd be particularly interested in the derivation and the sensitivity analyses.

3. This document is a 3; I could imagine it being higher given more details.

4. I would love more discussion of the assumptions. You say you get 22% higher consumption based on your exponential growth factors; Rand also assumes that, and their curve is pretty well justified. Why is your model better?

The paper tackles an underexplored but critical bottleneck for transformative AI: the electricity infrastructure required to power large scale data centers. By combining recent DOE, RAND, Lazard, and EIA datasets, the author estimates that AI facilities could draw 325 to 580 TWh by 2030, equal to 6.7 to 12 percent of projected US demand, and translates this into region specific capital requirements of 360 to 600 billion dollars for grid upgrades.

The scenario tables and the bar chart on page 5 clearly communicate the trade offs among cost, reliability, and emissions and help policymakers see the scale of investment needed. Framing the problem as three market failures gives a coherent structure that links the quantitative projections to policy levers.

Innovation is solid but not ground breaking. The balanced portfolio heuristic and the carbon abatement ladder build on existing LCOE and cost curve work, yet applying them to AI specific load is novel for this sprint. The literature review cites key government and industry reports, but it overlooks recent academic studies on dynamic load management, long duration storage economics, and demand side bidding by data centers. A brief comparison with international experiences such as Ireland or Singapore would broaden the perspective.

The AI safety relevance is indirect. Reliable low carbon power can reduce the climate externalities of AI expansion and mitigate local grid stability risks, but the study does not connect energy policy to core safety issues like catastrophic misuse, concentration of compute, or governance of frontier models. A discussion of how transmission constraints shape compute access and thus influence safety relevant bargaining power would strengthen this dimension.

Technical quality is mixed. The demand model is presented as a single equation yet the parameter values and elasticities are not shown, and no sensitivity analysis accompanies the headline forecast. The regional cost numbers rely on secondary sources without showing the underlying calculations, and uncertainty ranges are wide. Still, the tables are transparent about input assumptions and the reference list is comprehensive. The absence of a public spreadsheet or code repository limits reproducibility.

Suggestions for improvement

1. Publish the demand model workbook with Monte Carlo sensitivity around growth rates and load factors.

2. Compare alternative supply strategies such as on site nuclear microreactors and demand response contracts.

3. Incorporate international case studies and link the coordination failure discussion to recent FERC Order 1920 on transmission planning.

4. Explain how a reliability pricing mechanism could interact with potential compute usage caps or safety taxes.

This was a well-researched synthesis of existing energy publications with novel analysis and clear implications for AI and public policy -- well done. Your incorporation of existing literature was clean. I particularly appreciated your threat model, which both diagnoses the problem and sets the stage for your policy recommendations. That being said, I would have liked to see more detailed justification for both the threat model and solutions. The scale of the problem (6% of US energy consumption by 2030) justifies a much more detailed set of recommendations. I'm also not sure if it's directly AI safety relevant, though I could see the case for grid constraints being a limiting factor for the US vs Chinese output. In a world where the US bottlenecked and China is not, I think you could credibly argue that this poses a safety risk.

The project tackles an important problem: how will low and middle income countries be affected by AI. The authors could have been more precise which risks beyond inequality they are addressing. I would have liked to see the code and not only the prompt which created it (GPT sometimes hallucinates)

The author shows a solid understanding of research around energy projections. It would have been good to see an integration of the project in the literature around the risks of AI. Moreover, the author does not make clear what measure of welfare is used in the project and why it is impropriate. The project seems to be targeted to provide the maximal amount of energy at the lowest possible price. This is common economic reasoning, but if anything, this is likely to speed up AI development and thus increase risks. The project is technically well done