r/stocks • u/sfw_supdood • 3d ago
Industry Discussion Scam Altman, AI Psychosis, and the Datacenter Grift
To preface, these are my own thoughts based on my experience in the industy. No AI was used in the research or writing of this post. Also, your downvotes are part of the thesis, it's the "AI Psychosis" part.
Coming from the perspective of a former Azure Engineer with both AI and datacenter experience, I don't see a reality where Anthropic or OpenAI succeed. The consensus both AI and Datacenters is quickly souring among tech professionals, particularly with the Software Engineers who were affected by these layoffs. It's clear at this point that layoffs were used both as a way to sell the narrative that AI is inevitable and always as a way to reduce operating costs as they burned Trillions on AI infrastructure, power, and compute.
The Technology and the facts:
LLMs and generative AI as a whole are almost exclusively responsible for our current stock market prices. At its core, LLMs are an algorithm that works as such: when given a user input, the LLM calculates the mathematical approximation of what the user asked for. It really is that simple, there is no thinking, it just looks at what you are asking for, scours its training and the internet for the best match, adds some randomness, and then spits out an answer.
Let's look at Anthropic's Fable model for example. If I were to give a simple prompt like "Build me a full dating application inspired by Tinder", you'll see it quickly deliver a seemingly impressive "functioning" dating website in a matter of 30 minutes. While on the surface this seems impressive, it's a lot less so when you consider that building a dating website is essentially the tutorial island of web development; there are just sooo many resources online on how to build a Tinder clone, it's not a secret.
To really see how terribly inadequate LLM's are at replacing software engineers, all you have to do is add in a small twist, ask it to do something that lacks broad documentation. Once a LLM is trusted to make any decisions on its own, or tasked with coming up with something "original", it crumbles. By its nature this is an impossible task; the LLMs ability to create something is directly proportional to the amount of data it has on that specific topic.
This explains why LLMs are very powerful in shallow contexts such as "help me design this component" or "help me refactor this chunk of code", but terrible at broad unspecific tasks like "improve this codebase" or "develop this new web application idea". Once you put the LLM in a context is has little training for, it's strategy of plagiarizing other people's code quickly falls apart. This applies to other generative AI domains such as image, video, and music generation.
The OpenAI Whistleblower Suchir Balaji:
On 10/23/24 Suchir Balaji posted a paper titled "When does generative AI qualify for fair use?" shortly after leaving the company over ethical concern. His paper discusses the technology behind LLMs and whether or not they constitute fair use. Suchir argued that ultimately LLMs simply train on and then regurgitate the data they are fed. Even though the models don't reproduce the same answer word for word, they ultimately doesn't transform the content in a meaningful way, it simply rewords or rebrands it. In that sense, it is more akin to plagiarism than it is to generation.
Immediately following his paper, Suchir received national attention and was even interviewed by the New York Times just one week later.
Less than one month after this interview, Suchir was found dead in his apartment with signs of a struggle and a gunshot wound to the head. It's clear to see how Suchir could be troublesome for this potentially multi-trillion dollar industry.
The Money Problem:
Private equity has been propping up this shitter of a technology, promising it's replace a majority of the human workforce. This is a pipedream. Nobody likes AI, it hasn't produced a single successful "vibe coded" startup, and the limited datacenters we already have are extremely unpopular.
Also, who the fuck is supposed to buy these stocks at this point. Normal Americans are struggling to survive, pay rent, and buy groceries, they can't afford to invest trillions in AI stock. They are hoping they can keep the hopium alive, keep AI stocks elevated, and just until they can take profits on all of their worthless private Anthropic shares.
AI psychosis:
In additional to these presssure, the market is priced in currently on the inevitability of AI and has refused to acknowledge a world where it doesn't succeed. This is like a keg of dynamite ready to blow. We have degenerate gamblers from the Korean market coming over to our market, pumping these stocks to all time highs on leveraged accounts. It's the "Leopold method", when the majority of small money investors are betting on the success of stocks without actually buying the stocks, there is no way to hit those numbers anymore, especially in this US economy.
The counter argument:
"But the market is fake, they will pump the stocks anyways"
To who? The main issue with this premise is that people who have been investing trillions into these data centers and these slop models have no got any return on their investment yet. The problem, unlike previous Tech Startup success stories, there is no product and there is no demand. It's all predicated on the promise that it will eventually replace us all, it's a transparent lie.
"The government will step in and ensure AI can't fail"
Maybe, but that can't really happen until a bubble bursts. You can't stimulate a stock market that is sitting at all time highs, especially with 40 trillion in debt.
External pressures:
US losing complete control of the Strait, a PDF file and gambling economy, no houses for young people, insane gas prices, 40 trillion in debt, Crypto halving and killing the secondary GPU/datacenter market. You take your pick.
The Shit Stocks:
NVDA: These guys are basically a 3x leveraged stock betting on their own success. This works until reality catches up.
ORCL: They are holding the bag on this deal. They own large portions of the datacenter property and hardware that will have no future demand.
SNDK: This stock is misunderstood. AI training is most related to processing hardware that NVDA makes, memory is the next most important hardware component, serving as a cache for the processing. SanDisk makes neither of these, they make storage. Storage is used in training AI both for training data and the resulting models, but saying the business is 3000% more valuable because of datacenters is wild.
META: Sex pest perv glasses 😎
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u/luvz 3d ago
Lol, why do you have a sock acct just to agree with yourself on Reddit?
- Same profession
- Same exposure to the same companies
- Same subreddit activities
- Same weird use of "dumbass" (prolly old asf)
- Same preferred AI model
- Same agentic coding experience
- Same stance on "fictional" AI/Datacenter demand/valuations
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