How should the global south think and act about compute policy?

The research question is interesting and a strong result could have actionable policy implications. Sadly, the methodology chosen was not the best fit to answer their research question.

Monte-carlo simulations are best suited for cases where randomness affects the outcome. In this case, it does not. Their main results are outcomes that happen 0% and 100% of the time, no randomness involved given their input parameters. And instead of explaining what are the key parameters the conclusion depends on and how they can be used to mechanistically deduce the final conclusions, the authors get lost on the details of the model instead of trying to explain in a couple sentences what's going on. I suspect the answer might be something like "if there's a few countries with bigger budgets than you that also want to have the largest supercomputer, it's really hard to beat them". Precise modelling is still really valuable, but only when it clarifies the final explanation of the mechanism, instead of hiding it.

Still, the writing was clear and direct, which made it easy and fun to read.

This is a rigorous and unusually well-presented modeling exercise: the provenance labeling, single-file reproducibility, robustness checks, and the disclosure that a validation fix strengthened rather than weakened the result are all exemplary. The central finding, that absolute, budget-anchored targets are reachable on a fixed budget while ranking targets move away at the frontier's growth rate, is correct and policy-relevant.

Three things would sharpen it. First, the actors that constitute the TOP500 frontier are the same actors Brazil competes with for chips, yet they enter only as an exogenous bar rising at a fixed 2.2×/yr. This makes the showcase verdict partly definitional: the model assumes the frontier runs away and then concludes Brazil cannot catch it.

Modeling at least the hyperscaler/frontier dynamics endogenously would test whether the result is discovered or assumed.

Second, the generalization to "the Global South" overreaches against the paper's own evidence: the Gulf plans clear feasibility thresholds within the plausible growth range, so the operative variable is budget-scale-relative-to-target-framing, not geography. Reframing the lesson around target design (absolute-and-needs-sized vs ranking-pinned) would be both more defensible and more useful, since not every relative target is infeasible for every endowment.

Third, the constructive half of the argument is asserted but not modeled. The doubling target is met 17× over in 100% of scenarios, so it functions as a trivial floor rather than a meaningful test, and the genuinely interesting question the paper raises (what absolute target sized to Brazil's actual evaluation, audit, and public-service needs would cost) is never quantified. The paper diagnoses the bad target with precision and leaves the good target as a principle; modeling even a rough needs-based figure would complete the contribution.

This is a valuable contribution to governance. It enables better-defined objectives in compute governance and allows their feasibility to be reviewed over time. The project delivers on the contributions it sets out to make, and the most important one, in my view, is its ability to identify design flaws in the objectives shared by many Global South AI plans.

It fills a real gap by providing a reusable model that takes a national compute target as written, converts the committed budget into installable compute under explicit physical constraints, and reports the share of scenarios in which the target is met.

As for future work, I would encourage the team to think along policy lines so that the results can inform decision-making. Circulating a policy brief among decision-makers would be one useful way to do this.