Even if it doesn’t, I’m sure it won’t be long until China overtakes the West in manufacturing the best GPUs and at a much cheaper cost, too.

You can’t just ban OpenSource. Anyone company/person can just copy the code and use it. Profitable A. I in the U.S is finished. All it took is 6 million to train and a Chinese tech wizard.

Even if Chinese models were banned, and Chinese cloud services for running models remotely were banned, the episode shows that the vast VC money spent on AI firms was wasteful. Since most AI firms are dependent on investment to cover overhead, a threat to those VC cashflows would prove ill to the companies’ stock prices regardless of access/pratical impact of Deepseek R1.

i think investors are putting money into AI because they don’t really know where else to put it … economic growth is stagnating practically everywhere except maybe africa, but there it’s difficult as well for instability reasons.

i think technological development is coming to a halt in general. the last steps will be renewable energy and cleaning up the dirty industries, but apart from that, only space exploration (think mars) will provide further economic growth.

Lol I would love an intl housing crash right now. Houses where I’m looking are on the market for 200k which were bought for 100k in 2015 🫠 Landlords are even placing offers on places without even viewing them

Not really. Firstly, I think the two “production differences” are more similar than you think, in that they are closely coupled. Secondly, this is just a petite bourgeois company (relatively petite, at least) doing what a petite bourgeois company does. This is the entire “point” of this niche of the bourgeoisie and is not unique to China right now, although China is executing it better than some other countries obviously. The structures are not meaningfully different between the west and China, really just goes to show how ripe “AI” (LLMs) are for further “development”.

I’d still be a little apprehensive. it’ll take more than an AI sputnik moment to topple the US stock market in the short term.

That being said, I’m just glad the savings I do have in investments are explicitly divested from the US tech bubble.

Down to $125ish now!

Nvidia’s down 11% premarket

Down 17% atm

Stock markets haven’t all opened yet. NASDAQ futures down 3.5%, S&P futures down 2%, NVIDIA down 12% in pre-market. A lot of other microchip/AI companies are also down in the 7-10% range.

We’ll have to see what happens to the market once it opens, so checking back in 4-5 hours or so might give a better picture.

Market open in 7mins (NYSE)

S&P Opened 1% lower than Friday

my analysis is roughly the same as yours. I also think 80% of the buzz around AI is just that… every US tech company has been rebranding existing services as somehow incorporating “AI”. I don’t watch a ton of commercials, but when I do see them, tech firms seem to all be investor-oriented hype messaging about how IBM or Apple or Dell Systems or whoever are using “AI” to do/make computer better-good for happy human.

everyone is just saying “AI” about everything.

to me, the market wealth associated is a speculative bubble around a term that has become meaningless.

of course an open source project from China is blowing proprietary performance benchmarking out of the water. the proprietary black box is always smoke and mirrors to attract capital to the secret, inscrutable sauce that is really just watered down ketchup and mayo.

So LLM’s the “AI” that everyone is typically talking about are really good at one statistical thing:

“CLASSIFYING”

What is “CLASSIFYING” you ask? Well it’s basically attempting to take a data and put it into specific boxes. If you want to classify all the dogs you could classify them based on breed for example. LLMs are really good at classifying better than anything we’ve ever made and they adapt very well to new scenarios and create emergent classifications of data fed to them.

However they are not good at basically anything else. The “generation” that these LLMs do is based on the classifier and the model, which basically generates responses based on statistically what the next word is. So for example it’s entirely possible that if you fed an LLM the entirety of Shakespeare and only Shakespeare and you gave it “Two households both alike” as a prompt, it practically may spit out the rest or Romeo and Juliet.

However this means AI’s are not good at the following:

• discerning truth from fiction
• following technical processes (like counting r’s in strawberry)
• having “human like” understandings of the connections between concepts (think of the “is soup a salad” type memes)

So… is what I said above really just how AI is being used in the US, and is that the reason for the huge bubble in asset values of companies like Nvidia and Microsoft.

Don’t get me wrong, yes this is a solution in search of problem. But the real reason that there is a bubble in the US for these things is because companies are making that bubble on purpose. The reason isn’t even rooted in any economic reality. The reason is rooted in protectionism. If it takes a small lake of water and 10 data centers to run ChatGPT, that means it’s unlikely you will lose a competitive edge because you are misleading your competition. If every year you need more and more compute to run the models it concentrates who can run them and who ultimately has control of them. This is what the market has been doing for about 3 years now. This is what DeepSeek has undone.

The similarities to BitCoin and crypto bubbles are very obvious in the sense that the mining network is controlled by whoever has the most compute. Etherium specifically decided to cut out the “middle man” of who owns compute and basically says whoever pays into the network’s central bank the most controls the network.

This is what ‘tech as assets’ means practically. Inflate your asset as much as possible regardless of it’s technical usefulness.

and is that the reason for the huge bubble in asset values of companies like Nvidia and Microsoft?

No not, actually. The bubble is in the idea that AI requires large amounts of power, cooling, and processing throughput to achieve things like the current OpenAI O1 Reasoning and Logic models. The circle is like this:

The New AI Model Is Bigger --> Needs Bigger Hardware --> Bigger Hardware Needs Better Cooling --> More Cooling and Bigger Hardware Needs More Power --> More Cooling and Bigger Hardware means we can train the next Bigger Model --> Back to Start

So long as the Newest AI model is “bigger” then the last AI model, then everyone in this chain keeps making more money and has higher demand.

However, what Deepseek has done is put out an equivalent to the newest AI model that:

A) Required less up front money to train,
B) Uses considerably less resources than the previous model,
C) Is released on an Open Source MIT License, so anyone can host the model for their use case.

Now the whole snake is unraveling because all this investment that was being dumped into power, cooling, and hardware initiatives are fucked because less power and cooling is required, and older hardware can run the model.

I’d like some LLM features centered around “here is my organized folder of documents for context” but without paying $20/month or buying a $2,000 GPU or giving all of my data to Google/Microsoft. I couldn’t find anything that actually worked even if I did pay money or give my data to Google/Microsoft. Ignoring even the folder thing, I remember asking Claude to summarize a novel I’m writing and it kept mixing character’s with details that unambiguously only applied to other characters.

3. Text analysis.
   It’s great at digging thru piles of documents looking for specific things. CGP grey (lib) has an old video called “humans need not apply” that touches on this.