#1 and #2 are easy to conceptualize but very difficult to build and maintain. Basically, the problem is that people don't fund open source unless they can monetize it.
I think the problem is that they don't control what happens next. The whole premise of AI data center buildouts is basically, "We think we can replace you with a robot so we'll be able to make even more money." So, even if _this data center in particular_ is okay, it's just like saying, "Well, we haven't driven over the cliff yet, so why are you complaining?"
Worse is that there's every evidence that AI is actually just a waste of money, but because there's so much investment in it, we'll all get to lose our jobs when it flops anyway. In the long-term it's just a lose/lose for everyone, and I think people are starting to figure that out. There's general mistrust, because the investors and the implementers are not trustworthy.
Thank you for being one of the few thoughtful responses. I do feel like people's opinions here are more a position on AI in general than this data center. I come from a stance of, "this is happening so we might as well benefit from it"
Well yes. If a car window gets broken in your town, well that's a simple crime. But if entire parking lots full of cars get their windows broken out that's a crime spree and resources need setup in dealing with it.
The issue with incremental problems and quantity problems is you can't point at a single one and go "here, here is the problem" (at least most of the time), you have to look at the entire problem and go "Shit, the paperclip maximizer building one paperclip isn't an issue, it's turning everything into paperclips that's the issue".
I'm over "this is happening". Like Silicon Valley seems to think it should be implicitly true that their libertarian maximalism should be accepted by the rest of the country. Maybe this is thinking "conservatively" and that's a dirty word, but I don't think it's crazy to ask the capitalists to go back to talking about how their businesses will help normies, and not just steamrolling people with "you can't stop growth". If opposing datacenters makes SV rethink their growth strategies. I'm all for it.
Notes end up being process, too. It's basically a way of being intentional with how you apply your attention throughout changing mental contexts. I might have a really good idea today and then six months from now it's just totally off of my radar. Notes help with that context switching.
> It’s possible that some of this jump is the result of committers making more mistakes this year than they did last year, but I don’t think anyone who works on the project really believes that’s the main cause. Rather, it seems likely that PostgreSQL committers were about as conservative in 2026 as they were in preceding years, but a lot more of the mistakes they made were quickly found by LLMs.
This is pretty lazy. Especially considering the fact that later in the post author talks about how humans have to author the fixes, because the AI ones aren't up to snuff. It's not a stretch to think that more of the AI generated stuff that's not up to snuff just got through. And, focusing on bug fixes is going to yield more but fixes, regardless of the technologies involved.
I'm not saying that LLMs aren't helping, necessarily, but this isn't very convincing.
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.
I think the problem is moreso that making useful things for enterprise with AI right now is actually pretty hard because context and attention management is hard, the token cost for complex tasks scales fast, and model's lack of intuition for fuzzy decision-making.
There are hordes of grifters promising things they can't deliver and reaping in millions in capital from investors who are equally confused about the challenges of designing useful AI.
That said, AI can do useful things when properly designed. The market finding the people who can make this happen won't happen as fast as people expect.
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.
This technology has surpassed the the world's best mathematicians at the (explicitly and openly stated) goal by which they measure and reward mathematical progress. Such an advancement that it seems to have thrown the entire field into existential self-doubt. And then someone sits at their keyboard and says it is just a fuzzer.
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