Comment by didip
20 hours ago
Indeed. I don't understand why Hacker News is so dismissive about the coming of LLMs, maybe HN readers are going through 5 stages of grief?
But LLM is certainly a game changer, I can see it delivering impact bigger than the internet itself. Both require a lot of investments.
> I don't understand why Hacker News is so dismissive about the coming of LLMs
I find LLMs incredibly useful, but if you were following along the last few years the promise was for “exponential progress” with a teaser world destroying super intelligence.
We objectively are not on that path. There is no “coming of LLMs”. We might get some incremental improvement, but we’re very clearly seeing sigmoid progress.
I can’t speak for everyone, but I’m tired of hyperbolic rants that are unquestionably not justified (the nice thing about exponential progress is you don’t need to argue about it)
I'm not sure I understand: we are _objectively on that path_ -- we are increasing exponentially on a number of metrics that may be imperfect but seem to paint a pretty consistent picture. Scaling laws are exponential. METR's time horizon benchmark is exponential. Lots of performance measures are exponential, so why do you say we're objectively not on that path?
> We might get some incremental improvement, but we’re very clearly seeing sigmoid progress.
again, if it is "very clear" can you point to some concrete examples to illustrate what you mean?
> I can’t speak for everyone, but I’m tired of hyperbolic rants that are unquestionably not justified (the nice thing about exponential progress is you don’t need to argue about it)
OK but what specifically do you have an issue with here?
> exponential progress
First you need to define what it means. What's the metric? Otherwise it's very much something you can argue about.
Time spent being human and enjoying life.
I can’t point at many problems it has meaningfully solved for me. I mean real problems , not tasks that I have to do for my employer. It seems like it just made parts of my existence more miserable, poisoned many of the things I love, and generally made the future feel a lot less certain.
> What's the metric?
Language model capability at generating text output.
The model progress this year has been a lot of:
- “We added multimodal”
- “We added a lot of non AI tooling” (ie agents)
- “We put more compute into inference” (ie thinking mode)
So yes, there is still rapid progress, but these ^ make it clear, at least to me, that next gen models are significantly harder to build.
Simultaneously we see a distinct narrowing between players (openai, deepseek, mistral, google, anthropic) in their offerings.
Thats usually a signal that the rate of progress is slowing.
Remind me what was so great about gpt 5? How about gpt4 from from gpt 3?
Do you even remember the releases? Yeah. I dont. I had to look it up.
Just another model with more or less the same capabilities.
“Mixed reception”
That is not what exponential progress looks like, by any measure.
The progress this year has been in the tooling around the models, smaller faster models with similar capabilities. Multimodal add ons that no one asked for, because its easier to add image and audio processing than improve text handling.
That may still be on a path to AGI, but it not an exponential path to it.
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Define it however you like. There's not a single chart you can draw that even begins to look like a signoid.
I wrote an article complaining about the whole hype over a year ago:
https://chrisfrewin.medium.com/why-llms-will-never-be-agi-70...
Seems to be playing out that way.
> but we’re very clearly seeing sigmoid progress.
Yeah, probably. But no chart actually shows it yet. For now we are firmly in exponential zone of the signoid curve and can't really tell if it's going to end in a year, decade or a century.
Doesn't even matter if the goal is extremely high. Talking about exponential when we clearly see matching energy needs proves there is no way we can maintain that pace without radical (and thus unpredictable) improvements.
My own "feeling" is that it's definitely not exponential but again, doesn't matter if it's unsustainable.
I’ve been reading this comment multiple times a week for the last couple years. Constant assertions that we’re starting to hit limits, plateau, etc. But a cursory glance at where we are today vs a year ago, let alone two years ago, makes it wildly obvious that this is bullshit. The pace of improvement of both models and tooling has been breathtaking. I could give a shit whether you think it’s “exponential”, people like you were dismissing all of this years ago, meanwhile I just keep getting more and more productive.
People keep saying stuff like this. That the improvements are so obvious and breathtaking and astronomical and then I go check out the frontier LLMs again and they're maybe a tiny bit better than they were last year but I can't actually be sure bcuz it's hard to tell.
sometimes it seems like people are just living in another timeline.
We're very clearly seeing exponential progress - even above trend, on METR, whose slope keeps getting revised to a higher and higher estimate each time. Explain your perspective on the objective evidence against exponential progress?
Pretty neat how this exponential progress hasn't resulted in exponential productivity. Perhaps you could explain your perspective on that?
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It feels like there are several conversations happening that sound the same but are actually quite different.
One of them is whether or not large models are useful and/or becoming more useful over time. (To me, clearly the answer is yes)
The other is whether or not they live up to the hype. (To me, clearly the answer is no)
There are other skirmishes around capability for novelty, their role in the economy, their impact on human cognition, if/when AGI might happen and the overall impact to the largely tech-oriented community on HN.
The negatives outweigh the positives, if only because the positives are so small. A bunch of coders making their lives easier doesn't really matter, but pupils and students skipping education does. As a meme said: you had better start eating healthy, because your future doctor vibed his way through med school.
Based on quite a few comments recently, it also looks like many have tried LLMs in the past, but haven't seriously revisited either the modern or more expensive models. And I get it. Not everyone wants to keep up to date every month, or burn cash on experiments. But at the same time, people seem to have opinions formed in 2024. (Especially if they talk about just hallucinations and broken code - tell the agent to search for docs and fix stuff) I'd really like to give them Opus 4.5 as an agent to refresh their views. There's lots to complain about, but the world has moved on significantly.
This has been the argument since day one. You just have to try the latest model, that's where you went wrong. For the record I use Claude Code quite a bit and I can't see much meaningful improvements from the last few models. It is a useful tool but it's shortcomings are very obvious.
Just last week Opus 4.5 decided that the way to fix a test was to change the code so that everything else but the test broke.
When people say ”fix stuff” I always wonder if it actually means fix, or just make it look like it works (which is extremely common in software, LLM or not).
Sure, I get an occasional bad result from Opus - then I revert and try again, or ask it for a fix. Even with a couple of restarts, it's going to be faster than me on average. (And that's ignoring the situations where I have to restart myself)
Basically, you're saying it's not perfect. I don't think anyone is claiming otherwise.
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What did Opus do when you told it that it shouldn't have done that?
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Nice. Did it realize the mistake and corrected it?
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The idea of HN being dismissive of impactful technology is as old as HN. And indeed, the crowd often appears stuck in the past with hindsight. That said, HN discussions aren't homogeneous, and as demonstrated by Karpathy in his recent blogpost "Auto-grading decade-old Hacker News", at least some commenters have impressive foresight: https://karpathy.bearblog.dev/auto-grade-hn/
So exactly 10 years ago a lot of people believed that the game Go would not be “conquered” by AI, but after just a few months it was. People will always be skeptical of new things, even people who are in tech, because many hyped things indeed go nowhere… while it may look obvious in hindsight, it’s really hard to predict what will and what won’t be successful. On the LLM front I personally think it’s extremely foolish to still consider LLMs as going nowhere. There’s a lot more evidence today of the usefulness of LLMs than there was of DeepMind being able to beat top human players in Go 10 years ago.
It is an over correction because of all the empty promises of LLMs. I use Claude and chatgpt daily at work and am amazed at what they can do and how far they can come.
BUT when I hear my executive team talk and see demos of "Agentforce" and every saas company becoming an AI company promising the world, I have to roll my eyes.
The challenge I have with LLMs is they are great at creating first draft shiny objects and the LLMs themselves over promise. I am handed half baked work created by non technical people that now I have to clean up. And they don't realize how much work it is to take something from a 60% solution to a 100% solution because it was so easy for them to get to the 60%.
Amazing, game changing tools in the right hands but also give people false confidence.
Not that they are not also useful for non-technical people but I have had to spend a ton of time explaining to copywriters on the marketing team that they shouldn't paste their credentials into the chat even if it tells them to and their vibe coded app is a security nightmare.
This seems like the right take. The claims of the imminence of AGI are exhausting and to me appear dissonant with reality. I've tried gemini-cli and Claude Code and while they're both genuinely quite impressive, they absolutely suffer from a kind of prototype syndrome. While I could learn to use these tools effectively for large-scale projects, I still at present feel more comfortable writing such things by hand.
The NVIDIA CEO says people should stop learning to code. Now if LLMs will really end up as reliable as compilers, such that they can write code that's better and faster than I can 99% of the time, then he might be right. As things stand now, that reality seems far-fetched. To claim that they're useless because this reality has not yet been achieved would be silly, but not more silly than claiming programming is a dead art.
It’s not the technology I’m dismissive about. It’s the economics.
25 years ago I was optimistic about the internet, web sites, video streaming, online social systems. All of that. Look at what we have now. It was a fun ride until it all ended up “enshitified”. And it will happen to LLMs, too. Fool me once.
Some developer tools might survive in a useful state on subscriptions. But soon enough the whole A.I. economy will centralise into 2 or 3 major players extracting more and more revenue over time until everyone is sick of them. In fact, this process seems to be happening at a pretty high speed.
Once the users are captured, they’ll orient the ad-spend market around themselves. And then they’ll start taking advantage of the advertisers.
I really hope it doesn’t turn out this way. But it’s hard to be optimistic.
Contrary to the case for the internet, there is a way out, however - if local, open-source LLMs get good. I really hope they do, because enshittification does seem unavoidable if we depend on commercial offerings.
Well the "solution" for that will be the GPU vendors focusing solely on B2B sales because it's more profitable, therefore keeping GPUs out of the hands of average consumers. There's leaks suggesting that nVidia will gradually hike the prices of their 5090 cards from $2000 to $5000 due to RAM price increases ( https://wccftech.com/geforce-rtx-5090-prices-to-soar-to-5000... ). At that point, why even bother with the R&D for newer consumer cards when you know that barely anyone will be able to afford them?
Maybe because the hype for an next gen search engine that can also just make things up when you query it is a bit much?
Speaking for myself: because if the hype were to be believed we should have no relational databases when there's MongoDB, no need for dollars when there's cryptocoins, all virtual goods would be exclusively sold as NFTs, and we would be all driving self-driving cars by now.
LLMs are being driven mostly by grifters trying to achieve a monopoly before they run out of cash. Under those conditions I find their promises hard to believe. I'll wait until they either go broke or stop losing money left and right, and whatever is left is probably actually useful.
The way I've been handling the deafening hype is to focus exclusively on what the models that we have right now can do.
You'll note I don't mention AGI or future model releases in my annual roundup at all. The closest I get to that is expressing doubt that the METR chart will continue at the same rate.
If you focus exclusively on what actually works the LLM space is a whole lot more interesting and less frustrating.
> focus exclusively on what the models that we have right now can do
I'm just a casual user, but I've been doing the same and have noticed the sharp improvements of the models we have now vs a year ago. I have OpenAI Business subscription through work, I signed up for Gemini at home after Gemini 3, and I run local models on my GPU.
I just ask them various questions where I know the answer well, or I can easily verify. Rewrite some code, factual stuff etc. I compare and contrast by asking the same question to different models.
AGI? Hell no. Very useful for some things? Hell yes.
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Many people feel threatened by the rapid advancements in LLMs, fearing that their skills may become obsolete, and in turn act irrationally. To navigate this change effectively, we must keep open minds, keep adaptable, and embrace continuous learning.
I'm not threatened by LLMs taking my job as much as they are taking away my sanity. Every time I tell someone no and they come back to me with a "but copilot said.." it's followed by something entirely incorrect it makes me want to autodefenestrate.
I am happy “autodefenestrate” is the first new word I learned in 2026. Thank you.
Autodefenestrate - To eject or hurl oneself from a window, especially lethally
Many comments discussing LLMs involve emotions, sure. :) Including, obviously, comments in favour of LLMs.
But most discussion I see is vague and without specificity and without nuance.
Recognising the shortcomings of LLMs makes comments praising LLMs that much more believable; and recognising the benefits of LLMs makes comments criticising LLMs more believable.
I'd completely believe anyone who says they've found the LLM very helpful at greenfield frontend tasks, and I'd believe someone who found the LLM unable to carry out subtle refactors on an old codebase in a language that's not Python or JavaScript.
> in turn act irrationally
it isn't irrational to act in self-interest. If LLM threatens someone's livelihood, it matters not that it helps humanity overall one bit - they will oppose it. I don't blame them. But i also hope that they cannot succeed in opposing it.
It's irrational to genuinely hold false beliefs about capabilities of LLMs. But at this point I assume around half of the skeptics are emotionally motivated anyway.
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rapid advancements in what? hallucinations..? FOMO marketing? certainly nothing productive.
> I don't understand why Hacker News is so dismissive about the coming of LLMs.
Eh. I wouldn’t be so quick to speak for the entirety of HN. Several articles related to LLMs easily hit the front page every single day, so clearly there are plenty of HN users upvoting them.
I think you're just reading too much into what is more likely classic HN cynicism and/or fatigue.
It's because both "side" tries to re-adjust.
When an "AI skeptic" sees a very positive AI comment, they try to argue that it is indeed interesting but nowhere near close to AI/AGI/ASI or whatever the hype at the moment uses.
When an "AI optimistic" sees a very negative AI comment, they try to list all the amazing things they have done that they were convinced was until then impossible.
Exactly. There was a stretch of 6 months or so right after ChatGPT was released where approximately 50% of front page posts at any given time were related to LLMs. And these days every other Show HN is some kind of agentic dev tool and Anthropic/OpenAI announcements routinely get 500+ comments in a matter of hours.
> HN readers are going through 5 stages of grief
So we are just irrational and sour?
LLMs hold some real utility. But that real utility is buried under a mountain of fake hype and over-promises to keep shareholder value high.
LLMs have real limitations that aren't going away any time soon - not until we move to a new technology fundamentally different and separate from them - sharing almost nothing in common. There's a lot of 'progress-washing' going on where people claim that these shortfalls will magically disappear if we throw enough data and compute at it when they clearly will not.
Pretty much. What actually exists is very impressive. But what was promised and marketed has not been delivered.
I think the missing ingredient is not something the LLMs lack, but something we as developers don't do - we need to constrain, channel, and guide agents by creating reactive test environments around them. Not vibes, but hard tests, they are the missing ingredient to coding agents. You can even use AI to write most of these tests but the end result depends on how well you structured your code to be testable.
If you inherit 9000 tests from an existing project you can vibe code a replacement on your phone in a holiday, like Simon Willison's JustHTML port. We are moving from agents semi-randomly flailing around to constraint satisfaction.
Yes and most of the investment has been kind of post-GPT4 betting that things will get exponentially more impressive
I find opus 4.5 and gpt 5.2 mind blowing more often than I find them dumb as rocks. I don’t listen to or read any marketing material, I just use the tools. I couldn’t care less about what the promises are, what I have now available to me is fundamentally different from what I had in August and it changed completely how I work.
Markets never deliver. That isnt new, i do think llms are not far off from google in terms of impact.
Search, as of today, is inferior to frontier models as a product. However, best case still misses expected returns by miles which is where the growsing comes from.
Generative art/ai is still up in the air for staying power but id predict it isnt going away.
The internet and smartphones were immediately useful in a million different ways for almost every person. AI is not even close to that level. Very to somewhat useful in some fields (like programming) but the average person will easily be able to go through their day without using AI.
The most wide-appeal possibility is people loving 100%-AI-slop entertainment like that AI Instagram Reels product. Maybe I'm just too disconnected with normies but I don't see this taking off. Fun as a novelty like those Ring cam vids but I would never spend all day watching AI generated media.
ChatGPT has roughly 800 million weekly active users. Almost everyone around me uses it daily. I think you are underestimating the adoption.
Usage plunges on the weekends and during the summer, suggesting that a significant portion of users are students using ChatGPT for free or at heavily subsidized rates to do homework (i.e., extremely basic work that is extraordinarily well-represented in the training data). That usage will almost certainly never be monetizable, and it suggests nothing about the trajectory of the technology’s capability or popularity. I suspect ChatGPT, in particular, will see its usage slip considerably as the education system (hopefully) adapts.
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How many pay? And out of that how many are willing to pay the amount to at least cover the inference costs (not loss leading?)
Outside the verifiable domains I think the impact is more assistance/augmentation than outright disruption (i.e. a novelty which is still nice). A little tiny bit of value sprinkled over a very large user base but each person deriving little value overall.
Even as they use it as search it is at best an incrementable improvement on what they used to do - not life changing.
“Almost everyone will use it at free or effectively subsidized prices” and “It delivers utility which justifies its variable costs + fixed costs amortized over useful lifetime” are not the same thing, and its not clear how much of the use is tied to novelty such that if new and progressively more expensive to train releases at a regular cadence dropped off, usage, even at subsidized prices, would, too.
The adoption is just so weird to me. I cannot for the life of me get LLM chatbot to work for me. Every time I try I get into an argument with the stupid thing. They are still wrong constantly, and when I'm wrong they won't correct me.
I have great faith in AI in e.g. medical equipment, or otherwise as something built in, working on a single problem in the background, but the chat interface is terrible.
Even my mom and aunts are using it frequently for all sorts of things, and it took a long time for them to hop onto internet and smartphones at first.
The early internet and smartphones (the Japanese ones, not iPhone) were definitely not "immediately" adopted by the mass, unlike LLM.
If "immediate" usefulness is the metric we measure, then the internet and smartphones are pretty insignificant inventions compared to LLM.
(of course it's not a meaningful metric, as there is no clear line between a dumb phone and a smart phone, or a moderately sized language model and a LLM)
> AI is not even close to that level
Kagi’s Research Assistant is pretty damn useful, particularly when I can have it poll different models. I remember when the first iPhone lacked copy-paste. This feels similar.
(And I don’t think we’re heading towards AGI.)
… the internet was not immediately useful in a million different ways for almost every person.
Even if you skip ARPAnet, you’re forgetting the Gopher days and even if you jump straight to WWW+email==the internet, you’re forgetting the mosaic days.
The applications that became useful to the masses emerged a decade+ after the public internet and even then, it took 2+ decades to reach anything approaching saturation.
Your dismissal is not likely to age well, for similar reasons.
the "usefulness" excuse is irrelevant, and the claim that phones/internet is "immediately useful" is just a post hoc rationalization. It's basically trying to find a reasonable reason why opposition to AI is valid, and is not in self-interest.
The opposition to AI is from people who feel threatened by it, because it either threatens their livelihood (or family/friends'), and that they feel they are unable to benefit from AI in the same way as they had internet/mobile phones.
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> The internet and smartphones were immediately useful in a million different ways for almost every person. AI is not even close to that level.
Those are some very rosy glasses you've got on there. The nascent Internet took forever to catch on. It was for weird nerds at universities and it'll never catch on, but here we are.
A year after the iPhone came out… it didn’t have an App Store, barely was able to play video, barely had enough power to last a day. You just don’t remember or were not around for it.
A year after llms came out… are you kidding me?
Two years?
10 years?
Today, by adding an MCP server to wrap the same API that’s been around forever for some system, makes the users of that system prefer NLI over the gui almost immediately.
> Very to somewhat useful in some fields (like programming) but the average person will easily be able to go through their day without using AI.
I know a lot of "normal" people who have completely replaced their search engine with AI. It's increasingly a staple for people.
Smartphones were absolutely NOT immediately useful in a million different ways for almost every person, that's total revisionist history. I remember when the iPhone came out, it was AT&T only, it did almost nothing useful. Smartphones were a novelty for quite a while.
I agree with most points but as a tech enthusiast, I was using a smart phone years before the iPhone, and I could already use the internet, make video calls, email etc around 2005. It was a small flip phone but it was not uncommon for phones to do that already at that time, at least in Australia and parts of Asia (a Singaporean friend told me about the phone).
because lies. all the people involved in this, the one a C title, tell us about how great is now.
Have you tried using it for anything actually complicated?
Lol. It's worse than nothing at all.
I think the split between vibe coding and AI-assisted coding will only widen over time. If you ask LLMs to do something complex, they will fail and you waste your time. If you work with them as a peer, and you delegate tasks to them, they will succeed and you save your time.
I work with leers by delegating complex task to them while I do other complex tasks.
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