Thesis:
LLM's are unpopular, they successfully replaced the "multi-trillion" dollar market of StackOverflow dot com, and they want you to carry the bag with your 401k.
My background:
I'm a 35 year old software engineer, former Microsoft Azure, and was laid off due to AI. I have 17+ years of dev experience, the most recent of which has been spent working on Datacenter technology (Azure).
The Technology and the facts:
LLMs and generative AI as a whole are almost exclusively responsible for the recent. 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 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 ca