Writing emails and summarizing other emails...
At least the author admit he is biased..
So you disagree with the quote from Feynman? (the quote that forms the basis of the article)
I'm genuinely curious to know what part of the article you feel is "so far off from reality"?
> AI will just be turned off.
it's a combination of wishlful thinking and straightup delusion to think that AI will just poof and disappear (maybe the specific LLM approach eventually, but not the general idea of AI/the industry/product as a whole)
So do you disagree that AI companies are in very large amounts of debt? And do you also disagree that debt will have real consequences (and one of those consequences might be either the lights going out or a buyout or merger)?
People said this a year ago, then all the companies admitted they were incinerating cash and had to increase to token pricing. Consumers basically mutinied and managed to push back a bit, but that is the struggle right now - to keep prices from increasing 25+ times, not to see them decrease.
If you're premising this on some future technology like ASICs, even if that works out then we probably shouldn't be spending a trillion dollars on datacenters that it will make obsolete.
A year ago Opus 4.1 was $75 per million tokens, and now Opus 4.8 is $25 and Fable is $50.
Why do you feel this way? Are you talking about frontier models from US companies or smaller weight open source models?
From the perspective of US companies, it seems pretty clear they're severely under water and will need some way to become profitable or risk collapse, so how else will they do that or they than jacking up prices or some unknown future miracle advancement?
I could see the argument applied to self-hosted/OSS models tho.
Would you commission a study about whether cars are really a faster form of locomotion before deciding to purchase one?
There is empirical data right in front of your face. You're just choosing to exclude it because of where you already stand.
That's exactly what Feynman was warning about.
Thats a pretty strong outcome. It implies that not only are GPUs that power AI too expensive long term, but they cost too much to operate even if they were free.
Seems to me the more likely outcome is a wave of dotCom style bankruptcies wiping out equity holders for companies who contracted to buy chips and datacenters at MSRP, and a second wave for the groups that step in after to operate whats left without the absurd financing charges and lower capex.
They are free. You can download glm-5.2 and run it on your own hardware. Even you got hardware for free, electricity would cost you more than the sub, in most places.
Ironically, the author would have been better asking an LLM to write the article. At least it would have found better sources and developed more convincing arguments.
I'd be much more curious on a study that used regular developers on brand new-to-them codebases for significant features or migrations. I'm personally encountering what I'd describe as a 200%+ productivity boost.
I'll just have to move up a level of abstraction to get that same feeling, I guess.
That kills all arguments from the article instantly.
December 2025 through today GenAI has become massively better. I’ve built things with Claude in contracted spans of time that would have been man-years in the before times.
> Maybe that was true in 2025, but it's clearly not true any more - I feel so productive now!
Even if you are right, this is a completely baseless argument. The people in the study felt the exact same way! And "it's 6 months old, so I won't listen to it" is an insanely high standard. You're eliminating all the evidence!
This is largely an infrastructure problem, and it will be solved just as similar problems were solved with storage, network capacity, and computing power.
There will be bankruptcies, but the technology will prevail, just as the web did after the dot com bubble. Personally, I see the loss of craft and practical skill, which can only be built through doing, as a much greater danger. I still practise LeetCode from time to time so I do not lose the ability to write code myself.
METR reran the study early this year and, while they caveat it, this time they found a speedup, which is consistent with subjective estimates of productivity also having increased -- the simplest explanation is that subjective estimates exaggerate, but there's still a speedup with current models: https://metr.org/blog/2026-02-24-uplift-update/#wider-adopti...
(Nobody seems to cite the followup since it's not such a fun counterintuitive finding.)
> Meanwhile, a study (late 2025) seems to report that although participating developers felt they completed tasks faster using AI, they where around 19% slower.
I think you might be fooling yourself if you build your entire worldview concerning the productivity benefits of AI-assisted programming around that one study from one organization that confirms your priors.
I could use current models forever. LLM does things for me that couldn't be done in 2023. I don't want to go back to a world without it, but I'm also ok with progress stalling right here.
As for the productivity benefits, I find there is a kind of skeptic that just has his eyes closed. Open an AI page, ask it to write you an app that renames all your scientific papers so they have their title as the filename instead of the weird long number.
You could easily waste an hour looking for the right PDF library and working out how to use it. Or fiddling with the file search mechanism so that you only get the articles and not every file in your downloads. Now you will have the files renamed in a few minutes, and a tool that keeps them correct every time you have downloaded some more.
How is this not a productivity win? It clearly is. Do we really need a double blind test to check this kind of thing?
Whether it gets squandered in modern orgs is another matter, but the core win is clear for everyone to see.
The same thing as you, man. Die in the chaos.
Zitron has been predicting an imminent bubble pop since 2023 (although in less and less falsifiable terms), as OpenAI and Anthropic revenues have steadily grown 3-10x per year, but people keep listening. Zitron has done a lot to misinform the public but at some point you have to conclude it's demand-driven, and people won't re-evaluate his credibility because he tells them what they want to hear.
Useful: https://www.theargumentmag.com/p/ais-biggest-critic-has-lost...
But imagine that Ctrl+F was just introduced as a feature and you had a bunch of people saying "All these people saying Ctrl+F is increasing their productivity are fooling themselves", it's just a silly thing to say.
Yeah this article is safe to ignore
> This is why I'm not using AI. I'm not using AI on moral, ethical grounds.
The funny thing is - they are using AI and they will be using even more AI as it soaks into world economy even more; they just won‘t be using it directly. Well, not for a while at least, until practicality overcomes subjective morals.
This opinion piece is more what he wants to happen rather than what's actually happening on the ground
- a lot of folks use ai poorly and innudate you with slop, reducing your own efficiency
- because you can just prompt it doesn't mean you should. many let the machine crank out tokens until it works, rather than thinking about the problem first.
of course, if AI solves my second point automatically, it'll solve the first as well, and we'll call that AGI. But we don't have that yet.
This "Anti AI Social Club" is just the latest example, and reality has nothing to do with it. Denying reality might just be the whole point.
It's a very human thing really.
Which ones? That there is no evidence (not personal anecdotes but the numbers) of productivity increase? Or that our subscriptions are heavy subsidized (then try to compare produced value against real price of tokens)? Or that AI companies are burning tonn of cash without clear plan for monetization?
Article has good points which are worth discussing rather than discarding. We need to bring arguments from both sides of the fense.
I hear this a lot and it seems to be far too optimistic. Call me skeptical but plan pricing continues to increase with tighter usage constraints. We’re still at the point in time that companies like Anthropic, OpenAI, and GitHub charge seemingly reasonable prices in order to get people hooked onto AI before raising prices to get their return on investment.
foxyv•1h ago
One thing I've noticed is, that coding manually is exponentially more stressful than the slow AI approach. I'm not going any faster than I normally would, but the burnout doesn't show up on the fifth day of the week. The hyper-focus is kind of fun but utterly exhausting.
With the fast approach I can create impressive demos and POCs that are nowhere near ready for production. But sometimes that's exactly what I need.
dd8601fn•34m ago
The requirements didn’t really change, opus just couldn’t see all the real world potential issues with its various suggestions.
But it was helpful for me to talk it out. So I guess it fell in the (very) slow category of productive use.
foxyv•5m ago
gwerbin•2m ago
You do have to be very careful of when the AI might be confidently wrong, or leading you down some kind of weird narrow path.
When searching the web to gather info on a topic, all have a tendency to find 2-4 search results and fixate on those as if they represented the entire scope of the topic. No surprise about the fixation because that's kind of how LLMs work (X, Y, and Z are in context -> more tokens about X, Y, and Z). But you have to be aware of it, and I often have to prompt it to do another search and keep turning up more options.
solarkraft•28m ago
vadansky•24m ago
CharlesW•17m ago
I just tell Claude Claude code, "commit all, grouping logically" after a tranche of work.
maccard•10m ago
deadbabe•22m ago
Also, once you build something with slow AI, it becomes more practical to actually rewrite it by hand quickly and refactor it in a way that makes more sense for humans. It’s “humanwashing” the code.
gwerbin•6m ago
I can have an agent put together a shitty prototype of something just to get a general idea of what I'm looking at, and save probably half a day of work and avoid physically typing potentially hundreds of lines of code. And I can rubber duck with it, and very quickly search through software packages and literature, produce summary reports, dig through my own company source code and wiki and Slack messages to figure out how some specific detail works, etc. These things are important but take my time and energy away from focusing on the hard parts of what I'm supposed to be doing.
My new favorite thing with AI is that my pile of random helper functions is now a thoughtfully-designed library with an extensive test suite for every single thing. I'm not auditing every line of code but I'm reading the plans carefully and I do take a glance at what the agent wrote before I start using it. Most of the time it's not perfect or it's ugly in some way I wouldn't have done, but it's good enough to do research with, and it's way better than what I could have put together by hand, and in much less time. I trust my own research results more because I can put together these kinds of helper libraries much faster, and if I have to make changes I have a big test suite to catch me when I make a mistake.
The only hard part is managing the context switching where instead of putting my work on pause to go write a helper routine, I now have to account for periodically interrupting my work in order to keep an eye on what the agent is doing. For now my approach has been to just ignore it for a while and then take a break to cycle through AI work sessions/chats.
notnullorvoid•5m ago
I find the exact opposite. Typically the AI will make too many mistakes (even with latest advanced models), they usually aren't major/program breaking mistakes, but they still require fixing if I want to keep the code quality good enough, and make future changes possible without a falling house of cards. In order to describe the correct approach I have to write more English to describe the problem than the amount of code I could write myself. I could maybe see it working by writing rough pseudo code and having the AI translate, but then I loose out on any code analysis or tab completion as I write. It's also just innately frustrating to have to babysit and correct somethings work all the time. This all may be in part caused by the nature of my current work for the last couple years, which has mostly been on library code rather than application code.
I suspect I'd have a very different experience if I was working on CRUD related tasks like making new API endpoints, interfacing with a DB, or writing FE components. Not to make light of those tasks, I enjoy that work, though it is much more repetitive.
I agree with the parts about using AI to ask questions and gain better understanding though. I do that constantly for APIs and languages that I don't have a clear mental model of. It's like an interactive search.
Also agree on POCs, sometimes there are small tools, demos, or prototype features that I don't need to care deeply about.