You’re missing their point, they’re not defending EU policy and in fact agree that current capability is poor. They’re saying that it can change and that the US is also self sabotaging in other ways.
dude, I don't want to be a hater, but this is a pretty crappy game that barely works. I honest to god could have made this in a week without AI. Is it cool that AI made it in 2 days? Sure, but it's not groundbreaking.
Also, I don't think we're at an inflection point when companies are starting to wisen up to just how much this shit costs.
I have never seen such a hive of negativity and pessimism in my life.
This thinking sand did your job 5x faster. And you could have used the opportunity cost to do other things.
The engineers and artists in this country that are dunking on AI are insane. Asia is openly embracing this tech. They're going to leapfrog us if we keep shooting ourselves in the foot.
I mean many people have seen the same technology multiplier in many other industries too in their life time. And to them calling this an inflection point in humanity and expecting them to either panic or cheer seems crazy. So we automated software tasks and made it faster, welcome to declining wages and marginal improvements to consumers.
> But! Hold my beer… in February 2026 METR effectively walked it back : their follow-up estimates flipped to a speedup (with error bars wide enough to ride a Moto Guzzi, with panniers, through!), and they abandoned the study design entirely - because developers now refuse to work without AI, and can’t reliably self-report time on agentic work. Their latest position: AI probably speeds developers up in 2026, and we can no longer cleanly measure by how much.
This may be true, but they followed in May with this [0]:
> Importantly, survey results are not necessarily grounded in reality. There are reasons to be skeptical of people’s responses to counterfactual questions such as about AI’s effect on productivity — for instance, our study in early 2025 found that people overestimated AI’s effect on their time spent on tasks by 40 percentage points on average.
Author here. Thanks, this is a useful addition I'd missed. The 40-point overestimate from their early-2025 work is exactly why I read METR's current position as "we can no longer cleanly measure this" rather than "AI definitely speeds you up now"; self-reports are doing a lot of load-bearing work in every direction, including in my own sense of my productivity.
> you could delay adopting “the cloud” for a couple of years and survive. With AI you might get a few months.
I really dislike these claims that act like they know the future of engineering, that they’ve been let in on some enlightenment that we haven’t been. What’s going to happen in a few months? Is Sam Altman going to nuke my house from orbit? Or is it because my CTO is going to fire me for not using AI? If it’s the latter, that’s not a curiosity problem, that’s a “there’s a gun to my head” problem.
It seems to be based on some idea that there's no way you can be productive enough without AI. But I've yet to see any companies really shipping meaningful software at some unprecedented speed that was not possible pre-AI. Instead, I see a lot of half baked features and buggy apps. I am not convinced that those that choose to either NOT use AI or use it more sparingly / judiciously (my preference), are somehow going to be "left in the dust".
Yeah, I’ll second that. I see folks moving _fast_, but boy oh boy are they breaking things (or delivering something that never worked) which if anything makes _me_ slower lol
I don't think it's fair to these very real humans to try and distill their essence from what they presented publicly. Real humans are messy and complicated.
This feels really, really disrespectful. Just because someone died a long time ago doesn't mean it's any less weird to do digital necromancy.
You are right, we can not capture a messy and complicated human being. Therefore we tried our best to frame it right with the “Echo“ and disclaimer. We have the fact check sheets to show what is recreated and what the facts are. We also have the shadow section in the fact check sheets to show that these humans were messy, but tried to portray them inside the platform for what they gave to the world, all under the objective to make hard accessible wisdom / philosophy more accessible, as a doorway, that people outgrow us and move from our introduction to primary texts and human teachers.
We have writing, artifacts and objects from ancient peoples which we then use to try to construct historiographies of those cultures, as well as interpretations of their lived experience and circumstances.
This is just doing it for specific historical figures with a different type of technology. Why is it more disrespectful than what historians do?
Because this is placing words in their mouth and pretending it’s something they’d say, not just analyzing it. It pretends in knows their inner world and mind when we only have public artifacts.
Historians also generally adhere to a standard when making a claim, not throwing it to the math machine for regurgitating.
Because it's generating that output using a simulacra, which may mislead and act as if it is Socrates, not using real human reasoning to extrapolate.
There isn't an objective right or wrong here, but it just strikes me as gross. It's fine if you disagree and I appreciate you interrogating the reasons as it helps us both strengthen our thinking.
Improvements in model performance aren't always strictly compute-constrained in a way that makes them reliant on Moore's Law. Open weight models-- in particular, from Chinese labs-- are optimizing model intelligence with less compute. They're "behind" frontier models by months, but as others have noted, it's possible to get Sonnet 4.5+ level performance at reduced cost, today, from open weight labs.
No, I'm not assuming Moore's law. The efficiency of AI datacenters will continue to improve even without Moore's law, but more importantly the efficiency of packing intelligence into gigabytes and FLOPS will improve by leaps and bounds over the coming years, just as it has for the past few years if not faster.
Then you're assuming an efficiency that is analogous to how Moore's law made it efficient for chips. Same difference. The problem is that AI scaling in the longest term is a completely unknown problem.
Training improvements and Moore's Law are "analogous" but not "same difference." They are far from the same thing, governed by completely different factors, and one can happen and has been happening independently from the other.
Well I never said nor meant that, rather, my third (3) sentence should've hinted that I already believe what you are saying in your second sentence (2). Whereas my second (2) sentence was handwaving at the notion that if the parent commenter's remark (about improvment trends) were to be assumed then the rational argument must be subject to the same standards, ergo same difference (in argument standards). (Also I use a phone, please excuse any confusion due to not spelling out my online opinions in full)
To clarify another way, it seems the parent commenter and obviously many, many lay people seem to think ALL sorts of technology improves eventually and are always very assured of that. That's a common mistaken premise or axiom used in their arguments. (Arguably Moore's law (up until now) has been a factor in confounding this observation because so much other tech has historically benefited from it directly or indirectly)
Sorry, but a plain reading of your comment does not imply at all that you agree with me, rather the opposite. I'm not basing my opinion on any mistaken axiom of inevitable technology improvement, of course. I'm projecting obvious trends of the past few years which are overwhelmingly likely to continue in the medium term.
"Same difference" could only mean that you believe my argument should fail in the same way as an argument based on Moore's law. If that's not what you meant then you should have used different words. If that is what you meant, with the justification that "AI scaling in the longest term is a completely unknown problem", I disagree with that too.
In the "longest term" the ultimate scaling of AI doesn't matter for the original question of whether "AGI will most likely only be for the rich". Nobody looks at the TOP500 list today and says "computing is only for the rich". This is because we have an abundance of iPhones and gaming PCs in the consumer market, providing practically any application of computing that a consumer could want at very attainable prices. Similarly, practically any application of AGI will be accessible to consumers at attainable prices. Continued AI scaling after a certain point will be relevant mostly to industry (whose products will still be priced attainably, analogously to the way weather forecasts produced on TOP500 supercomputers are readily accessible to the public today).
They didn't say that HN is turning into Reddit, they said that the conversation quality has gone to shit.
I don't agree with that statement universally, but I have to say I do when it comes to this article. I came here hoping for substantive discussion from those who'd had a chance to try it out; instead what I got was a seemingly endless stream of venting. There's a place for venting - and plenty to vent about with the state of AI nowadays - but to borrow from the HN guidelines you linked, it does very little to gratify my personal intellectual curiosity.
In my own (admittedly limited) experience, 2 employees in my company (that had no programming knowledge or experience) have vibe coded apps that simplify their daily roles. The apps basically automate a flowchart of steps where multiple people need to submit certain pieces of info and as they do, a "project" moves through stages and the employees get notified on Telegram.
The app really is just several simple forms with some if/else logic, but claude code allowed them to get the app up and running and deployed on vercel's free tier, and it's Good Enough™ to save them an hour or so each day lost in messaging and chasing up things.
I don't think anyone would ever have targeted an app for sale to them, and it would have been hard to twist some sort of flow management app and integrate it with Zapier or something to handle external api calls. With claude code they could just tell it what they wanted and solve their very niche issue. That's why I think that even though LLM coding has improved so much you might not see more software for sale - it's easier for people to just...make their own software.
The best part of this workflow - which I see often - is that by having someone build custom software to automate some process they often step back away from the process being their job. That eventually translates into them understanding that some (or sometimes most or all) of that process is not needed. There are so many corporate processes that were implemented and then become the way... and if there are people who identify that process as being their job those people resist attempts to optimize that process.
I have seem several people use AI to write apps to automate a process and along they way finally ask the question 'do we even need this process?'.
Don’t get me wrong, :) that’s pretty cool! I’ve also made highly personalized mini apps for my own personal life. Currently working on an iOS one to log mood and correlate it with HealthKit data since the native health app does a bad job of it.
That said, I meant more like production grade apps that have to serve N>1, which is IME where the hard part LLMs suck at comes in. I saw a tweet somewhere along the lines of “CEOs/execs are so divorced from the last mile effort that they are uniquely susceptible to believing AI can replace engineers end to end”
Let me stop you right there - any time you generalize to that degree, you’ve already failed to think critically and charitably about the issue.
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