Can you point to some examples of where that's happening?
These data centers are specifically being scouted for communities whose governance is too weak to negotiate for some "sizeable share of their profits" and too ill-prepared to have suitable environmental regulations on the books already. The Ivy League sharks planning these buildout initiatives are sharp people who are looking out for the interests of their employers and know how to pick locations where they have the best opportunity to exploit locals unprepared for their kind of esoteric deal-making, political lobbying, and lawfare. They'd be failing at their job if they did what you're suggesting, and that's why we don't really see that happen.
For any benefit to national or global society AI data centers might provide to someone, the buildout looks a lot like the dirty and exploitative stories of rail expansion in the 19th century. That rail infrastructure proved a good thing for the US, but that doesn't mean the process of making it happen was honest or good for the people immediately affected.
People have a dozen reasons to refuse data centers being built in their communities and zero reasons to encourage it. They're little more than post-industrial mines that take limited resources (power, land, water, quiet) from a community, sell them for profit as compute, and siphon those profits away onto the books of far-flung megacorps with no community reward.
Meanwhile, golf courses are a traditional green space where people in a community gather for both work and leisure. They're not ideal themselves, but they at least provide some benefit against which their negatives can be weighed.
If all you hear from critics of data center building is water use complaints, that's strictly because you've chosen not to listen to people.
> I write the code myself. I find it hard to believe why so many engineers try to avoid it.
Gradually over its recent booming years, software work went from one of several practical engineering refuges for curious tinkers and puzzle-addicts to a career path for financially ambitious bright people akin to finance, law, or medicine.
Many people carrying a "software engineer" title now never really enjoyed that part of the work at all but were suitably clever and responsible to accomplish what modest ends they were tasked to by their very generous employers. Mostly (but not entirely), those people are the ones most eager to have AI agents shield them from rigorous design and puzzle work and enable them to leverage their innate cleverness more lazily. They never really internalized the coding and engineering principles of the industry and so can't foresee what might be down the road for them with this technique, especially when they're surrounded by peers with the same mindset.
> AI isn’t always faster.
It is when coding was an extremely frustrating and high friction experience for you in the first place, as is the case for many who work among us now.
> shipping 10x as many features and bugfixes sounds better
I understand you're excited about the tool, but for the sake of earnest discussion here, maybe commenters like yourself can tone the hype down to plausibility?
Claims like this are just nonsense. It's not how product development works.
How do you even have so many bugs left to fix if the tool is so fast and productive? Surely, you didn't have a backlog of tens of thousands of bugs that you're still chewing through? And of course, the volume of new bugs much be minimal since the AI-composed additions introduce "no problems so far". If it works like you say, which we'll accept in good faith per HN guidelines, you must have exhausted your backlog long ago.
And if you've indeed exhausted your bug backlog long ago (incredible!), you're left to talking about shipping "10x as many features". Yet no product has a limitless capacity for features. Nobody would want to use software so bloated and churning that was gaining features at such a pace. And who is designing and specifying them so quickly anyway? If it works like you say, which we again accept in good faith, you must have stalled out on your feature list long ago.
If the AI indeed allows you to "[ship] 10x as many features and bugfixes", and we take what you say in good faith, then one of the following seems to be implied:
* you've fixed all your bugs and blew through your mature feature designs already, leaving your AI agents sitting idle for all but a few hours a week, while you're bottlenecked on feature design and your software product is bloated beyond imagination
* your coding productivity before AI was absolutely glacial by industry standards such that "10x" productivity for you is actually much closer to "0.5-2x" for others
We rely on subtle distinction in writing style to distinguish sources from each other and to read important cues into their communication intent, the same as with vocalized speech.
Scrubbing that away from writing with AI has the same effect as would running everyone's speech through an anonymizer.
Some people may be blind to that sort of thing entirely, much like some people can't recognize faces, but for most people it just creates a sense of noise and discomfort when everything everywhere is written in the same style. And that impression is only made worse when that increasingly uniquitous anonymized style is so painfully average and repetitive.
If there was a tool that let you edit select characteristics of your writing while preserving your voice, the way a professional editor might help a professional writer, that might be amazing for helping people communicate better and more easily. But that's not this. This is loud, articulate noise.
Probably not a ton of people in the largely-peaceful, largely-comfortable world that many people here have only ever experienced.
But history (and world-awareness) shows that those periods don't last forever, so having mature decentralized technology ready and warm for periods of crisis or devolotion is hugely valuable in the long term. It can be hard to maintain commitment to maturing and seeding that kind of technology when there's not yet a pressing need, exactly because it's hard to gain enough traction to overcome the relative inconveniences. It's admirable and important work regardless.
> can better infer the user’s underlying goal and intended level of work
This is a trap.
It's the optimistic fallacy that poisons all "consumer scale" machine learning products and what's going to effectively ruin these models as they keep chasing it in the same way that web queries were ruined, social media feeds were ruined, and media recommenders were ruined.
For the vendor, optimizing metrics across their whole user base, they always see positive technological progress as their system gets better at making assumptions and accumulating user engagement scores in aggregate. But for the individual user, most of which has some weird tail intent/interest and some of whom have many weird tail intent/interests, the experience quietly but catastrophically degrades. Output/results become more generic, more divergent with the underspecified "weird tail" intent, and more stubbornly hard to ever wrangle towards that "weird tail" altogether.
We've been watching this cycle happen for 20 years now and it's proving hard for anybody to escape because it works so well for the trillion dollar company driving it forward. But while each step might feel ergonomic and welcome to individual users, there's a frog boiling enshitification at play.
In pursuit of output quality and capability (rather than simply the vendor's user count), what we need rather than "makes better guesses" is "presses for more clarity", even where it feels kind of annoying.
Even among human professionals, one of the first hurdles of breaking out of junior tier work is gaining the confidence to press your colleagues and clients to be more specific in their thoughts and expressions despite their desire to have you do it all for them. But they're often coming to you with incomplete, muddy, and conflicting ideas for which there is no safe and correct assumption that you might just run with, and it's your expertise (i.e. relevant "intelligence") that's critical to bringing attention to that. To achieve professional progression, you need to learn to do that and to not just optimize appeasing the ambiguous client/colleague today in exchange for mutual expense tomorrow. To avoid enshitification, which is probably not possible, we need these models to be learning that too.
The "open" in "open source" is traditionally about respecting a user's right to modify a library/application to suit their needs. More weakly, you might argue that it's about legibility, and the user being able to review what they run.
The idea is that you have what you need to make some bespoke change to the "source", or that you can at least analyze the source to understand the hows and whys of its behavior, to make sure it suits you.
Maybe reality isn't represented by the git logs, but according to them, you appear to have composed, reviewed and merged 131 commits affecting 58 files and 6000 lines -- a task that you say amounts to "replacing the engine while driving" -- in the span of one week.
If that's accurate, of course your reliance on AI is at fault here. It invites you to mistake velocity for quality and conflate test satisfaction for completion.
Without AI, this effort would likely have taken much longer and an experienced team would have approached it with due meticulousness, being especially stringent in review. Absorbed in it, the work would have weighed on their unconscious while they slept, with "Ah shoot, did I consider XYZ?" moments striking them in the shower. Ideally, familiar with the specific contributors (i.e. you) and their style of work, the reviewer who later consider merging the work would have ideas what to watch out for and would spend extra time looking at the details you might not have fully considered. It's a whole decades-matured craft practice that can do a pretty good job of making sure that a refactor of this scale doesn't land in main too broken.
And while that version still probably wouldn't have been entirely flawless either, or might never have even been attempted because of its greater calendar and attention burden, that doesn't mean that using AI wasn't responsible for why and how this version is broken.
This is exactly how AI makes things worse and why many people are wary of relying on third-party projects that embrace it too blithely. If you're going to use it, that's great, and maybe it will help you keep landing big features more quickly than otherwise -- but at least accept responsibility for the tradeoffs that you're inviting when you do so.
It's easy for some to give away money if they have it, but it's hard to accumulate that much money if you're one of those people.
Newmark maybe representing the occasional exception, most people who accumulate great wealth through their effort and ideas are afflicted with an addiction problem and have a hard time saying "this is enough for me". They become attached to how much more wealth they might be able to gather and recognize how the wealth they already hold plays a role in making the most of that.
Even if they somehow imagine themselves as philanthropists or minimalists, they tend to put off giving away too much too soon under the rationale that they'll be able to eventually share more if they hold out and use it more practically.
And if you don't have that kind of mindset, you're probably just not going to climb your way into the level of riches we're discussing here. You might still do quite well as far as most people are concerned, but that billionaire milestone is hard to catch without some propensity for wealth addiction.
That's not a distinction that people really benefit from.
Approximately nobody can read other people's code for intent or quality, let alone to surface malware meant to be hidden in it.
For almost everyone, the only hope is that somebody else validated the code you want to use before you choose to use it and successfully interfered with its distribution upon finding an issue. That's why the culture of automatic-updating package managers and bloated dependency graphs are so dangerous and why inserting delays into package managers can make such a difference in exposure to supply chain attacks for those that are intent to use them.
It's true that open source provides the transparency that makes any kind of third-party validation possible, but closed source benefits from commercial vendors staking their brand on what they release. It's a tradeoff, not a straightforward win for one side.
Either way, many of us see it happen all the time there too. For GitHub especially, I almost never get the canonical repo for a project in my Google results. Phishing or innocuous, it's almost always some fork at the top and then a bunch of non-github.com sites.
Search is more or less "cooked" now, as they say. Google vs Bing vs DDG vs Kagi is mostly in the noise.
Early work in any trade is mostly junk, and academia no exception.
But the process of creating that work, engaged throughought that process with those purported to be more practiced, is usually pretty good at seeding enough expertise and confidence that you might be able to proceed more independently and with real novelty, or might at least be prepared to share the trade with others new to it.
That's the point of those years, and so it's more than a little ironic that AI is being used to undermine a practicing expert while simultaneously eroding the traditional process for becoming one by making it so easy to just generate slop and engage with hallucinations than to actually practice writing deep work or engaging with primary sources.
Regardless of what their hiring process screens for, it's safe to say that people able to pass screening for Meta are able to get work elsewhere. It is never an engineers only option, although it may be the only one in a certain luxurious compensation tier.
And while it's true that many organizations carry dirty laundry, especially as they scale into larger organizations with fingers in more pies, Meta's business model is specifically to maximize engagement by any means available so that it can sell their eyeballs to the most lucrative advertising opportunities.
In Facebook's early days and as Whatsapp continues to do, their products may incidentally provided a useful societal function for earnestly connecting people in the way that those people wanted to be connected. But there's no way to look at it through the lens of a socially responsible business -- a qualitative difference from an organization simply not having "clean hands".
The shifting sands of commercial models or pay-per-use managed models are just really not appealing to a lot of people.
Most come with huge privacy concerns, total costs and availability are impossible to forecast very far out, and the specific behavior of frontier models in particular is not something anybody can rely on as those are subscription products that are subject behavior on their publisher's whims (whether from changing system prompts, new "safeguards", retired models, forced "updates", new regulations, etc).
It's quite hard to put a price on all that, and as more people find local models productive enough or develop curiosity to explore models, training, or harness-crafting in their own ways, the marginal cost of buying some shop hardware just sort of disappears into the budget noise for plenty enough people.
Same with many and their shop tools in other trades.
Most hobbyists and many professionals could end up far ahead financially by leveraging makerspaces, tool rentals, and co-op shops or even by hiring out a professional to prep certain intermediates for them, but they get psychological value -- as well as flexibility, reliability, and resale opportunity -- from having their own well-outfitted shop.
And they can afford that premium, so they do. At the scale of individuals and small shops, not everything that matters gets captured in financial models.