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Is DeepSeek about to cause a stock market crash?
(uk.finance.yahoo.com)
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this post was submitted on 27 Jan 2025
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I haven’t followed developments in AI all that closely. In a very general sense, it seems to me that the potential benefits of AI (or high-powered automation) are limitless. I think it’s hard to fully comprehend all the practical uses it will have and how it could transform the economy.
HOWEVER, currently the only applications I seem to see are: 1.) ChatGPT-like applications, i.e. ask a question and get a response that at best, writes up some text it would have taken you much longer to write or get a maybe right maybe wrong answer, or 2.) shitty art that can’t even make hands right; and even if it did, it’s just images. Not exactly economically impactful like the spinning jenny.
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?
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.
It doesn't work in the average case. I've seen this tactic from the company that I work for and multiple companies I have contacts at. Bosses think they can simply use "AI" to fix their hollowed out documentation, on-boarding, employee education systems by pushing a bunch of half correct, barely legible "documentation" through an LLM.
It just spits out garbage for 90% of people doing this. It's a garbage in garbage out process. In order for it to even be useful you need a specific type of LLM (a RAG) and for your documentation to be high quality.
Here's an example project: https://github.com/snexus/llm-search
The demo works well because it uses a well documented open source library. It's also not a guarantee that it won't hallucinate or get mixed up. A RAG works simply by priming the generator with "context" related to your query, if your model weights are strong enough your context won't outweigh the allure of statistical hallucination.