> it's just a text prediction algorithm that can pull out entire sentences when you use it to infer output on topics it trained on

This downplays the incredible things that can be done with it.

There's a lot of noise, yes. How long has the web existed? And yet we're still figuring out how to optimize (HTTP/3).

Disregard the signal at your own expense.

Maybe, but I think the downplay is worth it to emphasize that the things have to be done. The point is precisely that token generation is a phenomenal power source but it does not give you anything but debt unless you build the tools to leverage it.

None of the big players involved right now seem to be doing anything to disarm the perception that the LLM does all the work.

I feel like we're at a state fair 180 years ago having just seen an impressive demonstration of small animals momentarily coming back to life, someone in the crowd said 'eternal life' and everyone lost their minds trying to give money to the 'inventor' on the stage who applied the electric current.

Ask some lay people or junior devs how they feel about LLM mistakes, listen for answers about "learning" etc. Do they realize the model doesn't change? The same set of weights and tensors are deployed to every GPU hosting it. If you use a power tool that lets you specify the temperature and seed, it will generate the exact same output time after time.

"But I told it to always speak like a pirate, and it does": No, you signed up to pay for the model to be told to speak like a pirate every time you use it.

There - that's the curtain I'm talking about that you're lifting/tearing. I think momentarily downplaying the potential of LLMs themselves is worth it to expose that.

What incredible things can be done with it?