Welcome to the DeepSeek Disruption (DSD)

In summary: bloated headcounts, no new sources of revenue from AI, and limitless content with no scarcity value. Welcome to the DSD: DeepSeek Disruption.

I've been fruitfully engaged in a lively dialog with readers on my Substack regarding my post yesterday Is DeepSeek a Sputnik Moment? (titled Is (Chinese) Software Suddenly Eating The World? on Zero Hedge) and my conversation with Adam Taggart on his Thoughtful Money channel: SPECIAL REPORT: Did China's DeepSeek Just Pop The AI Stock Bubble? (56 minutes).

I can't summarize all the topics discussed on the thread, but these two comments and my responses illustrate the tremendous range of dynamics now in play in the DSD: DeepSeek Disruption. (One favor to ask: since a bunch of folks borrowed my "Sputnik Moment" phrase without crediting me yesterday, if you use DSD - DeepSeek Disruption, please credit me. Thank you.)

Although the reader comments are only visible to paid subscribers, I post my own comments as Notes on Substack, which are visible to everyone.

Comment posted by Brad M.:
"I've always had a hunch that there are simpler methods to achieve high performing artificial intelligence. Of course, we should note that the chips China did this on are vastly superior than anything prior to the year 2000 lets say. And China does have decades of manufacturing experience and a literal army worth of engineers to throw at the problem. So we shouldn't say that what China did was easy. It was just easier than throwing a mountain of money at the problem. You can't fake scarcity that easily and the Mag 7 as you call them will continue to struggle while the smaller teams given freedom to try anything with their limited resources will continue to find success.

Or that mountain of money might end up working. Who knows for sure until it happens?"

My response:

Brad, those are very interesting points for exploration.

I'm reminded of various stories in TechLand about small teams developing the breakthroughs while thousands of employees in the corporation generated little value beyond maintaining the status quo. So each Big Tech has (for example) 175K employees, all smart, all dedicated, but a much smaller team blew the 175K teams of the Mag 7 away. Steve Jobs famously kept the Macintosh team isolated from Corporate Apple and exerted obsessive control over the development team. He knew better than to let Corporate do the Corporate Thing to the team.

Since finance and tech dominate the economy now, the big question is: how do I make a killing by investing in DeepSeek? This is a koan because there is no "owner" of the software techniques DeepSeek has shared with the world.

To Brad's question, so what do the tech monopolies / behemoths do with their 175K "teams" now? For sure they can try to replicate DeepSeek, but does that require 175K employees? And since everyone else has access to the same concepts, techniques and approaches, then where is the scarcity value that generates revenues? There is none.

This all leads to a sobering conclusion: there is little justification for these huge headcounts going forward.

Comment posted by Simple John:
"I'd appreciate correction if I'm wrong. I believe I've read that the DeepSeek models are strongest on math and physics. In fact, aren't LLM and image generating AI playing in a universe that is immensely less specific than math and physics and thus really just playing with words and images without any real insights?"

My response:

This is very insightful, as the examples of DeepSeek (or competing tools) solving math problems are "problems" where the "correct answer" can be determined. The "answer" to the "question" *write an essay on Charles Darwin for my class assignment" has no equivalent "correct answer." The AI Bot can hallucinate a response that might pass muster if the hallucination is not too wild.

I've played around with Big Tech free AI tools for generating podcasts, essay summaries, etc., and have watched developments in the video-creation space. These are examples of brute-force processing working with templates assembled from "machine learning," i.e. sampling human-generated videos, stories, essays, etc. As John noted, this kind of extrapolation of existing content is a different kind of "answer" to a different kind of "problem."

So there are video-generation tools where you enter text instructions such as "a young man is walking through a 4th of July party holding a beer," and the program generates a video clip of this scene based on its vast database of 4th of July clips, people holding a beer, etc. This is fascinating and fun, because there is no "correct answer."

But what's the value proposition here when everyone (or anyone with a keen interest) can access the same tools and generate limitless AI content? Who's going to watch all this in an Attention Economy that's already saturated with content?

Based on my limited understanding of the many software techniques DeepSeek employs, it seems likely that these structures may well be superior ways to solve "problems" that have testable "correct" answers.

Without going too deeply into specifics, a key concept in high-end machine learning (Google's DeepMind, etc.) is estimating the accuracy of the answer, i.e. the probabilities of various potential solutions being correct. The "wait a minute, maybe this isn't the best path, let's start over" function of DeepSeek is based on the same idea, which is extremely valuable when the "correctness" of the answer actually matters.

In summary: bloated headcounts, no new sources of revenue from AI, and limitless content with no scarcity value. Welcome to the DSD: DeepSeek Disruption.

There is much more to explore in the DSD, which I'll lay out tomorrow. In the meantime, let's check in on HAL 9000's conclusions:




New podcasts:

I just recorded a wide-ranging discussion of DeepSeek with Adam Taggart of Thoughtful Money: SPECIAL REPORT: Did China's DeepSeek Just Pop The AI Stock Bubble? (56 minutes)

CHS on Geopolitics and Empire: Anti-Progress, Resource Constraints, & Digital Neofeudalism (1:29 hrs)

KunstlerCast417: Charles Hugh Smith, Progress and Anti-Progress (1 hour)

Charles Hugh Smith on the Extremes in the U.S. Economy and Markets. (26 min)

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