Investors are dumb. It’s a hot new tech that looks convincing (since LLMs are designed specifically to appear correct, not be correct), so anything with that buzzword gets a ton of money thrown at it. The same phenomenon has occurred with blockchain, big data, even the World Wide Web. After each bubble bursts, some residue remains that actually might have some value.

Money. If you paid to use those services, they got what they wanted.

Generative AI has allowed us to do some things that we could not do before. A lot of people very foolishly took that to mean it would let us do everything we couldn’t do before.

Rich assholes have spent a ton of money on it and they need to manufacture reasons why that wasn’t a waste.

A dumb person thinks AI is really smart, because they just listen to anyone that answers confidentially

And no matter what, AI is going to give its answer like it’s is 100% definitely the truth.

That’s why there’s such a large crossover with AI and crypto, the same people fall for everything.

There’s new supporting evidence for Penrose’s theory that natural intelligence involves just an absolute shit ton of quantum interactions, because we just found out how the body can create an environment where quantom super position can not only be achieved, but incredibly simply.

AI got a boost because we didn’t really (still dont) understand consciousness. Tech bro’s convinced investors that neurons were what mattered, and made predictions for when that amount of neurons can be simulated.

But if it include billions of molecules in quantum superposition, we’re not getting there in our lifetimes. But there’s a lot of money sunk in to it already, so there’s a lot of money to lose if people suddenly get realistic about what it takes to make a real artificial intelligence.

The natural general hype is not new… I even see it in 1970’s scifi. It’s like once something pierced the long-thought-impossible turing test, decades of hype pressure suddenly and freely flowed.

There is also an unnatural hype (that with one breakthrough will come another) and that the next one might yield a technocratic singularity to the first-mover: money, market dominance, and control.

Which brings the tertiary effect (closer to your question)… companies are so quickly and blindly eating so many billions of dollars of first-mover costs that the corporate copium wants to believe there will be a return (or at least cost defrayal)… so you get a bunch of shitty AI products, and pressure towards them.

Disclaimer: I’m going to ignore all moral questions here

Because it represents a potentially large leap in the types of problems we can solve with computers. Previously the only comparable tool we had to solve problems were algorithms, which are fast, well-defined, and repeatable, but cannot deal with arbitrary or fuzzy inputs in a meaningful way. AI excels at dealing with fuzzy inputs (including natural language, which was a huge barrier previously), at the expense of speed and reliability. It’s basically an entire missing half to our toolkit.

Be careful not to conflate AI in general with LLMs. AI is usually implemented as Machine Learning, which is a method of fitting an output to training data. LLMs are a specific instance of this that are trained on language (hence, large language models). I suspect that if AI becomes more widely adopted, most users will be interacting with LLMs like you are now, but most of the business benefit would come from classifiers that have a more restricted input/output space. As an example, you could use ML to train an AI that can be used to detect potentially suspicious bank transactions. The more data you have to sort through, the better AI can learn from it*, so I suspect the companies that have been collecting terabytes of data will start using AI to try to analyze it. I’m curious if that will be effective.

*technically it depends a lot on the training parameters

IIRC When ChatGPT was first announced I believe the hype was because it was the first real usable interface a layman could interact with using normal language and have an intelligible response from the software. Normally to talk with computers we use their language (programming) but this allowed plain language speakers to interact and get it to do things with simple language in a more pervasive way than something like Siri for instance.

This then got over hyped and over promised to people with dollars in their eyes at the thought of large savings from labor reduction and capabilities far greater than it had. They were sold a product that has no real “product” as it’s something most people would prefer to interact with on their own terms when needed, like any tool. That’s really hard to sell and make people believe they need it. So they doubled down with the promise it would be so much better down the road. And, having spent an ungodly amount into it already, they have that sunken cost fallacy and keep doubling down.

This is my personal take and understanding of what’s happening. Though there’s probably more nuances, like staying ahead of the competition that also fell for the same promises.

It amazed people when it first launched and capitalists took that to mean replace all their jobs with AI. Where we wanted AI to make shit jobs easier, they used it to replace whole swaths of talent across the industry’s. Recent movies read like they were written almost entirely by AI. Like when Cartman was a robot and kept giving out terrible movie ideas.

Like was said: money.

In addition, they need training data. Both conversations and raw material. Shoving “AI” into everything whether you want it or not gives them the real world conversational data to train on. If you feed it any documents, etc it’s also sucking that up for the raw data to train on.

Ultimately the best we can do is ignore it and refuse to use it or feed it garbage data so it chokes on its own excrement.

They were pretty cool when they first blew up. Getting them to generate semi useful information wasn’t hard and anything hard factual they would usually avoid answering or defer.

They’ve legitimately gotten worse over time. As user volume has gone up necessitating faster, shallower model responses, and further training on Internet content has resulted in model degradation as it trains on its own output, the models gradually begin to break. They’ve also been pushed harder than they were meant to, to show “improvement” to investors demanding more accurate human like fact responses.

At this point it’s a race to the bottom on a poorly understood technology. Every money sucking corporation latched on to LLM’s like a piglet finding a teat, thinking it was going to be their golden goose to finally eliminate those stupid whiny expensive workers that always ask for annoying unprofitable things like “paid time off” and “healthcare”. In reality they’ve been sold a bill of goods by Sam Altman and the rest of the tech bros currently raking in a few extra hundred billion dollars.

A lot of jobs are bullshit. Generative AI is good at generating bullshit. This led to a perception that AI could be used in place of humans. But unfortunately, curating that bullshit enough to produce any value for a company still requires a person, so the AI doesn’t add much value. The bullshit AI generates needs some kind of oversight.

Novelty, lack of understanding, and avarice.

• automation by companies so they can "streamline"their workforces.

• innovation by “teaching” it enough to solve bigger problems (cancer, climate, etc).

• creating a sentient species that is the next evolution of life and watching it systematically eradicate every last human to save the planet.

There is no artificial intelligence, just very large statistical models.