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Against the IFP

Centralizing control over a currency’s infrastructure is a seemingly obvious mistake.

One would think any Austro-libertarian worth their salt would be able to see thru such a charade. Yet here we are, again. Face to face with economic illiteracy. Not garden variety lefist economic illiteracy, but one far more stinging and painful - one which comes from within our own community, rather than from without. 

First, Bitcoiners faced the economic illiteracy of maximalism and small blockers. Attempts to masquerade money’s primary function as value storage (Ammous) or rejecting Menger’s Regression Theorem altogether (Szabo) are luckily demonstrably false. Nevertheless, the shock of our fellow Bitcoiners illiteracy was like an unexpected slap in the face. Suddenly, we were forced to confront the fact that the ignorance of our allies in the fight for sound money, had led them astray. Yet, thru BCH we were thankfully able to keep Satoshi’s dream of peer to peer cash intact. 
Well, crypto anarch…

Technological Agorism Part III: AI & the Agora




There are two types of artificial intelligence: the rules-based, & the neural network-based approach. To illustrate the differences, I'll borrow an example from AI blogger Janelle Shane's book, You Look Like A Thing & I Love You, & pretend we're training an AI to recognize dogs.



Using a rules-based approach, we’d create parameters which the AI would then use to determine whether or not the thing it’s looking at, is in fact a dog. Our rules would include things like “must have four legs” & “must have tail,” etc. When all of our conditions have been satisfied, the AI will recognize a dog.

With a neural network-based approach, we show the AI images of dogs & it learns to recognize patterns. The more pictures of dogs we show it, the more accurate the AI becomes. Nowadays, this is usually the preferred approach & will be the subject of this article.

The interesting thing about the neural-network approach to AI - as we’ve already noted, is its reliance on data volume for accuracy. The more data the AI is fed, the smarter it gets. Thus, the different ways eastern & western societies collect data will impact both the development of AI & the global balance of power.


In China, the state extracts an astonishing amount of data from the citizenry. Chinese super apps like WeChat, function as an all-in-one app & make a one-stop-shop for the CCP's data collection efforts. In the US, the government doesn’t collect data like this, at least not transparently.

Rather, the West has traditionally relied on commercial enterprises for data collection. As was discussed in Part I, data collection in the west is currently dominated by big tech, although even this is becoming hyper-individualized with the dawning of tokenization.

At first glance, China’s massive data-collection program compared to the US’s lack thereof, seems to give China the geopolitical edge, but closer inspection reveals that this is not the case. Indeed, this is the mistake Kai Fu Lee makes in his otherwise stellar book, AI Superpowers. Recall from Mises that only the market can properly allocate resources. The state, at it’s best, only imitates the market.



Thus, western reliance on market participants ensures that data will be collected & put to use in the most productive manner possible. Chinese reliance on the state to accurately guess at these variables, will at best delay the development of AI, & at worst, empower the CCP to use it for malicious purposes.

In either case, the more market-friendly entity will, as always, stand poised to take the higher ground.





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