Then again, I could have accomplished the same thing by knocking off work for the day and going to the driving range.

Hey, look at the bright side, as long as you were chained to your desk instead, that's all that matters.

At one point I tried to use a local model to generate something for me. It was full of errors, but after some searching online to look for a library or existing examples I found a github repo that was almost an exact copy of what it generated. The comments were the same, and the code was mostly the same, except this version wasn't fucked up.

It turns out text prediction isn't that great at understanding the logic of code. It's only good at copying existing code, but it doesn't understand why it works, so the predictive model fucks things up when it takes the less likely result. Maybe if you turn the temperature to only give the highest prediction it wouldn't be horrible, but you might as well just search online and copy the code that it's going to generate anyway.

The bigger problem is that your skills are weakened a bit every time you use an assistant to write code.

The issue isn't the percentage, it's that inverse of growth. Most investors desire growth to see returns on investment for their upfront capital. If growth isn't occurring, that's a good sign to read the room and pull your funding.

Similar issues occurred with streaming services. Netflix is still profitable, but because the userbase isn't growing, investors and the financial world stopped seeing it as a valuable platform to invest in.

The ai companies haven't even found a viable business model yet, are bleeding money while the user base is shrinking

That is more than a 10% loss of that customer base in 2 month.

For any industry that is huge.

But they're already not making money, losing customers during the supposed growth phase is absolutely devastating. It's occuring all while AI is being subsidized by massive investments from the likes of microsoft and google, and many more namelesss VCs through OpenAI, anthropic etc.

Not for all use cases, but for most it is.

Could you point me to a model to do that and instructions on how get it up and running?

This contradicts what I'm reading in that AI model costs grow with each generation, not shrink.

We were initially excited by AI at my company, but after we used it a bit we didnt find any really meaningful use cases for it in our business model. And in most cases we spent a lot of time correcting its many errors which would actually slow down our processes...

It would also slow if companies were told insane lies about the capability of "AI" ("it's living having a team of PHD level experts at your disposal!") and then companies realized that many of these promises were total bullshit.

Marx tapping the big sign marked "Tendency of the rate of profit is to fall", but then looking at the already unprofitable AI spin-offs and just throwing his hands up in disgust.

I think there's an argument to be made that the AI hype got a bunch of early adopters, but failed to entice more traditional mainstream clients. But the idea that we just ran out of new AI users in... barely two years? No. Nobody is really paying for this shit in a meaningful way. Not at the Enterprise Application scale of subscriptions. That's why Microsoft is consistently losing money (on the scale of billions) on its OpenAI investment.

If people were adopting AI like they'd adopted the latest Windows OS, these firms would be seeing a steady growth in the pool of users that would signal profitability soon (if not already). But the estimates they're throwing out - one billion AI adoptions in barely a year - are entirely predicated on how many people just kinda popped in, looked at the web interface, and lost interest.

and they will drop it altogether.

I mean the automatic speech recognition and transcription capabilities are quite useful.

That's what LLM are made for; text stuff, not knowledge stuff.

You might as well put your money on red at the casino.