d0liver

I already really liked candy corn, but this made me like it even more? This is awesome.

Right. Corporations own people, not cash.

Not really. Like, yes, relative to your competitors. But, if the cost of growing potatoes is ten cents per potato, and then I make something that can produce potatoes for one cent then I'd expect everyone to just have more potatoes. This would drive down the cost of potatoes, because the supply would be saturated eventually, but AI is supposed to just make more of everything. So, if AI works as advertised, then you'd probably expect a brief period of highly increased profit margins (by the consumers of AI) followed by rapid _deflation_ as the value of everyone's dollar increases exponentially. Of course, neither of those things is going to happen, because the AI sales pitch is actually a pyramid scheme, so you get the people at the top raking in cash, while everyone else tries to sucker the next tier of buyers so that they can make their buck.

The fear is the sales pitch. AI has some uses but nowhere near enough to justify the price tag, so we need something more to sell it.

Came here to say this. What AI is doing with math is essentially fuzzing, but looking for valid inputs that solve a problem and the inputs are mathematical abstractions. It's a cool approach, but AI doesn't understand what's going on, and without understanding it won't be able to create the next generation of mathematical abstractions needed for the next generation of proofs.

I don't think that's true. If what you're doing is building a fuzzer for mathematical proofs then just say that? The fact that it's doing some of the initial work on hard problems is cool. So is the fact that the promise of that new approach is having a social effect of crowd sourcing talented people to follow up on that work. No need to try to misrepresent it as more than that.

You seem to be misunderstanding the premise of the statement. Go write, "print 'foo';" and copy and paste that statement one hundred thousand times. Congratulations, you just wrote one hundred thousand lines of functioning code! Was that hard? No? Okay, so how is this different from other code? The difference is that it's easy to _understand_ how that code works, because it's the same behavior repeated over and over. So, writing code is easy, but understanding code is difficult! And, when we code with AI, undertanding is precisely the part that it can't handle, even though that's where almost all of the value is!

If, by your definition of understanding, you can understand something without knowing how it works, then what would be the point in understanding it?

Your argument underscores the fact that understanding exists on a spectrum and that deep understanding can be difficult, or even impossible to achieve. Even formal verification seeks to verify certain properties of a system, and doesn't represent anything close to comprehensive understanding, and the process of formal verification is also guided by a fallible human. In a lot of ways, formal verification is just pushing up to review of a different "programming language."

So, good review is really hard, but ignoring the difficulty doesn't make it go away.

What if I told you that understanding what it is doing and finding bugs is actually the same problem?

I think you just made some strong arguments for classifying them as gatekeepers. The idea, like you said, is to make it so that competitors can enter the market and pick up the slack in areas where AWS and Azure are dropping the ball.

In other words, for the primary uses of search things have gotten much worse, but some workarounds still exist.

> software was already in a horrible state before AI, so your dichotomy doesn't work.

It depends on the software. But, generally speaking, I try to use and write the best software available that solves my problem, even if it's one of a kind; it doesn't really matter if the other 99.9999% of the software in the space is broken.

Given 1000 hours to work on a problem, an LLM will continue to yeet out mediocre variations on a theme. Give me 1000 hours to work, and my product will keep getting 1% or 2% better until it's much better than any shit an LLM would spit out.

Similarly, I would much rather use someone else's emulator that they spent 1000 hours on than have AI yeet out some mediocre shit that kinda works, but is really just a mindless exploitation of something that someone else wrote that was actually good.

Then, you follow that with, "Yeah, but AI just allows you to iterate faster and skip the boring stuff, so that you make your product better even faster."

And then I follow that with, "The part where you take it apart piece by piece and study each piece and get kicked in the head by the realities of your lack of understanding is the part that's actually valuable, and it's precisely what you're skipping with an LLM"

I would much rather have software that works but lacks accessibility features than software that's broken but also has some broken accessibility features sprinkled in. The former is useful to many people, while the latter is useful to no one.

But the key here is: LLMs don't have latent rigor, nor any other kind of rigor.

What makes you say that AI is not going anywhere? I hear this overwhelmingly, "AI is here to stay", as if y'all are so caught up in the movement that you've started taking that conclusion as being the axiom. TBH, it feels like a religion.

Getting people into coding is both cool and also not specific to AI.