The purpose of AI is to allow the wealthy to access the benefits of the talented, while preventing the talented from accessing the benefits of wealth.

1. The business class has been promised they can cut workers and use AI instead, which has been their dream since forever.

2. AI has been shown to deskill workers. Experts lose their skills, new workers don’t gain new skills. Then the owners of AI can rent skills back to people at a profit.

3. If businesses can deskill and fire enough workers, they can take advantage to reinstate things like scrip and slavery.

You rightfully notice that AI isn’t useful enough for businesses to throw an entire civilization worth of money at because you’re thinking about it as a tool, but it’s not a tool, it’s a weapon. A weapon pointed at you and me.

To culturally associate tech workers as low skilled and low paid so that worker leverage can be reduced.

The strategy has succeeded spectacularly even though AI is mostly a scam and will never replace skilled human tech workers, just help rationalize dehumanizing them.

Are you refering to the companies adopting AI into the workflow or the AI companies themselves investing billions into developing these models?

In the latter case they’re trying to rush towards AGI because who ever gets there first will have their investments paid back millionfold and the one placing second gets nothing.

ai is just the first step. step three is profit.

It has already revolutionized software development. As my friend put it:

On the subject of claude code, I went to a high-level founder talk about code ai and the general consensus was that CEOs were telling their staff 100% of code must be written by an agent within 6 months or they have to leave the company. And all the focus was switching on to how you verify, for example, a 15000 line PR written entirely by AI.

100% of my own code is now written by AI. I’ve been programming for 20 years - I can write code myself, but the AI is so much faster than I could possibly be that managing it is far more productive. It makes mistakes, but so do humans.

The CEOs are getting paid. In America, that’s the only thing that matters.

AI is the shiny new tech and investors are more worried about missing out on “the next big thing” than they areabout pissing away a few billion dollars. For investors at that level, it’s all a game of numbers. If they invest a billion dollars in 20 different things, and 19 of those things fail, but one has a return of 100 billion dollars, that’s a win.

Sure, there is no guarantee that AI will pan out. But, that’s a very hard thing to determine ahead of time. Everyone thought e-commerce would pay off big in the 90’s/00’s. And it did, for a few companies (Google, Amazon). The rest got slaughtered and we got the dot-com bubble. Then folks expected Crypto currencies, EFTs and NFTs to pay off big. Mostly, that didn’t happen, though they do thrive in a few places. Again, investors just wanted to ride that profit wave.

In short, investments are all about making money. And at a sufficient scale, it’s not about picking winners or losers, it’s about picking everything and making sure the wins from the winners cover the losses from the losers.

Tech companies like MS, Meta, Google and Amazon bought a lot if the infrastructure for the internet over the years. In order to monetize this, they need people and companies to generate data traffic. Before they were pushing stuff like block chain and cloud services, AI is the next thing they’re focussing on. At the same time there’s the pumping around of investments like between NVidia, OpenAI and Oracle to artificially inflate stock value. So, in short, it’s all about hoarding money.

“You can fire like most of your expensive staff who have real leverage and only be noticably worse”

LLMs are another (large) step in the broad trend of concentrating more power in fewer hands that has been going on since late mesolithic food storage tech.

Most things that we’ve lionized as technological breakthroughs since the end of the ice age ~20kya have been power-concentration tools.

Money laundering for billionaires.

It has one potential use: to increase profits by replacing the jobs of millions of millions of workers. That is the sole reason that these C-Suite Executives are excited about, not considering the fact that their jobs are among the easiest to replace by AI.

Unfortunately there are a lot of answers that aren’t really properly explaining why and just taking the opportunity to piss on AI…

LLMs (the currently hyped form of AI) is seen by many as the “next big thing”. And honestly, it’s not surprising. LLMs are impressive. They’re the closest we’ve ever gotten to mimicking human behavior. A lot of us have seen a lot of LLM-generated stuff and are sick of it, but think back to the first time you saw or heard an LLM string together sentences that are grammatically not gibberish, and in many cases quite coherent. That’s already enough to spark interest from companies and investors.

And the development isn’t stopping, at least not in the next while. Even in the last few years LLMs have noticeably improved. And the question is: where does it go from here? There’s definitely a scenario where this whole thing is a bubble that pops at some point, 90% of the things the current use cases for LLMs disappear as pointless, and we’re left with the remaining 10% that actually help people. But there’s also a scenario where LLMs improve to the point where they can vastly improve productivity. There’s definitely precedent - think computer and Internet or steam engines. And in those cases, those who got in at the right time made a lot of money. Think of the railroad barons and today’s tech companies. There were people then who said “why do we need all this, it’s terribly unreliable and not worth the expense”, as well. The flip side of this is: there are many people who thought other technologies would be really successful who turned out to be completely wrong, think blimps and Google Glass (stories which, by the way, are often quickly forgotten since they are, in hindsight, irrelevant). That’s one part of the answer.

The other part of the answer is that in the long term, there’s generally an increase of productivity, and companies try to outcompete each other. It’s nothing new - companies who were able to use the Internet to be more efficient, for instance, survived much better than those who didn’t. The companies who used steam engines to decrease the costs survived much better than those who didn’t. So, naturally, if companies see something they might be able to use to become more efficient, they do what they can to adopt it before everyone else. (Who should profit from those efficiencies is an entirely different question.)

And that’s really the problem: it’s hard to know exactly where this whole thing is going. After all the hype dies down I see some use cases, improving search results being one of the main ones, or improving the auto complete functions so we can use them much better than we currently do. I don’t see the current environment with AI chatbots being offered at every corner as sustainable or something that will last. But I could also be wrong. You generally never know it 100% for sure when you’re in a bubble, you only know for sure when the bubble pops.