Ok but there are SO MANY resources out there for Japanese learning that do not have any mistakes, or if they do have them, they get fixed by people who actually understand what they’re doing. This does not even seem more convenient than them. Jiten.moe lets you customize premade decks for stuff you wanna read, and when the text parser gets stuff wrong and links an incorrect definition (which are normal definitions from, like, a dictionary, written by people), there are people who can go in and fix it.
It seems to me like the only way to “extract value” from an LLM, as it was put earlier, is to understand exactly what it is. Which is not a thinking machine, but an algorithm that produces a statistically likely series of sentences based on text that is input. That’s not the product that’s being sold. They are selling thinking machines. So they add in a layer of traditional software to affect the image of a thinking machine, but that software can’t make the model capable of more than what that software is already capable of elsewhere. In the end you’re still left with what it is: juiced up predictive text.
Now, you can see why that’s useful for a coder. But coding is genuinely unique in this. There is a tolerance for the errors an LLM might make because first of all the program has to compile, second, you still generally have to document the LLM code as if it’s yours, and finally, the key thing, really, there is an existing infrastructure to test the output. There’s QA, there’s a polish phase, all that.
Nothing else has, or even can or should have, an infrastructure like that. So you’re left with a product that’s really only useful for a very small group of things that’s being pitched as the omni-product. The ultimate answer to the tendency for the rate of profit to fall. Virtually the entire apparatus of finance capital has bet its existence on this. It put the final bullet in the back of higher education. It’s permanently poisoned the information environment and turned every teenager into a plagiarist. What happens when people realize what it’s actually capable of doing? How limited it is?
What about when people realize how much it actually costs to use? People just repeat over and over “well costs will go down because of economies of scale.” They genuinely will not. The scaling curve is inverted. I have been talking about this for years. The first time I saw someone within the industry say it was this calendar year. People have only recently been made wise to what inference actually costs. It was essentially covered up. But really, that shouldn’t have mattered. You would expect people whose job it is to invest money would know how much stuff costs, and say “well that doesn’t make sense.” Too late now.
