The paper on AI agents flooding government services, noted here on 21 August, scanned twelve countries: Australia, Brazil, Denmark, Estonia, France, Germany, Japan, the Netherlands, Singapore, South Korea, the United Kingdom and the United States. One of them, Brazil, is middle income.
It collects 84 cases of agents driving demand into a public service, and calls them potential, having posited flooding rather than measured it. In fourteen, the answer was to make applying harder. Australia has considered reinstating freedom of information fees after a wave of AI-generated submissions. Japan blocked a comment procedure by IP address.
Two answers are available: suppress demand with fees and rate limits, or add capacity by redesigning the service. Most friction measures, the paper notes, “require little infrastructural change, have precedent, and can be deployed quickly”, and finds they deter poorer and less digitally literate users first.
Adding capacity costs money a treasury has to have. A two-column menu describes administrations that can afford the second column, and eleven of these twelve are high-income countries.
Whether that column exists where an administration is thinner is a question this evidence cannot settle, and the paper does not ask it. The alternative to a fee is not a fair queue. A flooded queue goes to whoever automates first, which is also not the poor. What nobody is counting is whether the fee comes off once the surge does.
Assorted links
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Brazil’s R$2.5 billion AI package, announced 20 August, puts R$1.276 billion into a five-year Rio project with Huawei and iFlytek covering technology transfer, training 3,000 professionals and Portuguese-language models.
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British public bodies awarded 453 AI contracts worth £1.41 billion to 3 August 2026 on Tussell’s count, past £1.18 billion across 521 contracts in all of 2025, which makes the average deal more than a third larger.
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Regional benchmarks that test models in Indian, African and Arabic languages already exist, and no rule requires a global leaderboard to carry them, so a procurement officer comparing rankings reads a scoreboard that never ran in the service’s language.