Economics of AI
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The U.S. Army is running low on AI tokens
A little over a month after the Department of Defense (DOD) bragged that nearly half of its 3.5 million employees were using AI at work, members of the Army’s Combat Capabilities Development Command (DEVCOM) received an email informing them that they were burning through tokens, and needed to limit use.
“Although the Army CIO announced in May 2026 that they were offering unlimited tokens, by mid-June the Army CIO pool was exhausted of tokens and had to re-establish limits,” the email reads. The email goes on to say that although the Army has chosen to renew token usage at “its current levels,” it’s unclear “if the Army CIO pool will be renewed after 1 Oct.”
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I have read about tokens before, and one of the problems is that the definition of a token is pretty fluid (as far as I can tell) and not only is it inconsistent across platforms, it’s inconsistent within the same platform. So it’s not like character count or word count, where the user could monitor their token use with 100% accuracy.
From @axtremus ’s article:
Tokens represent a unit of output, either in text or image, from an LLM. For the Ask Sage tool, a single token equates to about 3.7 characters, according to documents viewed by WIRED
From OpenAI (https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them)
Spaces, punctuation, and partial words all contribute to token counts. This is how the API internally segments your text before generating a response.
One thing that makes it particularly hard to monitor token usage is that the same amount of text could be interpreted as different amounts of tokens, depending on how it’s entered. At least, that’s according to what I’ve read, and the OpenAi article recommends to “break large text into smaller chunks” if you exceed your token limit.
So what does a “chunk” mean? Does that mean that the same prompt content would be interpreted as fewer tokens if you entered it little by little, rather than all at once? It would seem so, but that doesn’t make sense to me.
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https://www.nytimes.com/2026/08/05/technology/ai-china-africa.html
In African tech hubs, developers are picking China’s cheap, freely available artificial intelligence models over more powerful U.S. ones.
China’s A.I. is surging, especially in developing countries, as people look for the best possible system at the lowest possible cost. Unlike models made by the leading American A.I. companies OpenAI and Anthropic — which are closed and charge fees — the Chinese systems are publicly available to download and modify without payment or approval.
In more than two dozen interviews across Kenya, Uganda and other countries, developers, policymakers and executives described a rapidly changing A.I. market where cost, computing resources, data control and customization mattered more than where a model was made. Often they were not looking for bleeding edge A.I. They just wanted something that worked well enough on limited resources.
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