alt Hacker NewsA new AI winter is coming?
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LLMs are an amazing advancement. The tech side of things is very impressive. Credit where credit is due.
Where the current wave all falls apart is on the financials. None of that makes any sense and there’s no obvious path forward.
Folks say handwavy things like “oh they’ll just sell ads” but even a cursory analysis shows that math doesn’t ad up relative to the sums of money being invested at the moment.
Tech wise I’m bullish. Business wise, AI is setting up to be a big disaster. Those that aimlessly chased the hype are heading for a world of financial pain.
This has convinced many non-programmers that they can program, but the results are consistently disastrous, because it still requires genuine expertise to spot the hallucinations.
I've been programming for 30+ years and now a people manager. Claude Code has enabled me to code again and I'm several times more productive than I ever was as an IC in the 2000s and 2010s. I suspect this person hasn't really tried the most recent generation, it is quite impressive and works very well if you do know what you are doing
Winter is coming[1]
I am simply stunned at the negativity.
Yes, there is hype.
But if you actually filter it out, instead of (over) reacting to it in either direction, progress has been phenomenal and the fact there is visible progress in many areas, including LLMs, in the order of months demonstrates no walls.
Visible progress doesn’t mean astounding progress. But any tech that is improving year to year is moving at a good speed.
Huge apparent leaps in recent years seem to have spoiled some people. Or perhaps desensitized them. Or perhaps, created frustration that big leaps don’t happen every week.
I can’t fathom anyone not using models for 1000 things. But we all operate differently, and have different kinds of lives, work and problems. So I take claims that individuals are not getting much from models at face value.
But that some people are not finding the value isn’t an argument that those of us getting value, increasing value isn’t real.
Interesting take. His argument is basically that LLMs have hit their architectural ceiling and the industry is running on hype and unsustainable economics. I’m not fully convinced, but the points about rising costs and diminishing returns are worth paying attention to. The gap between what these models can actually do and what they’re marketed as might become a real problem if progress slows.
I’m fascinated by people who say that LLMs have failed in practice.
Last week, when I was on PTO, I used AI to to a full redesign of a music community website I run. I touched about 40k lines of code in a week. The redesign is shipped and everyone is using it. AI let me go about 5-10x faster than if I would have done this by hand. (In fact, I have tried doing this in the past, so I really do have an apples to apples comparison for velocity. AI enabled it happening at all: I’ve tried a few other times in the past but never been able to squeeze it into a week.)
The cited 40% inaccuracy rate doesn’t track for me at all. Claude basically one-shot anything I asked for, to the point that the bottleneck was mostly thinking of what I should ask it to do next.
At this point, saying AI has failed feels like denying reality.
Well, the original "AI winter" was caused by defense contracts running out without anything to show for it -- turns out, the generals of the time could only be fooled by Eliza clones for so long...
The current AI hype is fueled by public markets, and as they found out during the pandemic, the first one to blink and acknowledge the elephant in the room loses, bigly.
So, even in the face of a devastating demonstration of "AI" ineffectiveness (which I personally haven't seen, despite things being, well, entirely underwhelming), we may very well stuck in this cycle for a while yet...
When the hype is infinite (technological singularity and utopia), any reality will be a let down.
But there is so much real economic value being created - not speculation, but actual business processes - billions of dollars - it’s hard to seriously defend the claim that LLMs are “failures” in any practical sense.
Doesn’t mean we aren’t headed for a winter of sobering reality… but it doesn’t invalidate the disruption either.
Blog posts like this make me think model adoption and appropriate use case for the model is...lumpy at best. Every time I read something like it I wonder what tools they are using and how? Modern systems are not raw transformers. A raw transformer will “always output something,” they're right, but nobody deploys naked transformers. This is like claiming CPUs can’t do long division because the ALU doesn’t natively understand decimals. Also, a model is stat aprox trained on the empirical distribution of human knowledge work. It is not trying to compute the exact solution to NP complete problems? Nature does not require worst case complexity, real world cognitive tasks are not worst case NP hardness instances...
I've run into what I think might be an even bigger problem with the "just keep reading/outputting the next token" design: there's no going backwards.
This means that LLMs don't do well with input where text that comes later in the input alters how text that came earlier should be interpreted. Which is really quite common in practice.
Humans like to do things where you start with a dump of information, and then ask questions about it afterward. If a human reader didn't remember something pertinent to the question, they can go back and re-read now that they know what they're looking for. An LLM can't do that by itself. You can paper over that limitation with hacks, but they're hacky and unreliable.
Humans also correct misunderstandings and resolve ambiguities after the fact. LLMs have a really hard time with this. Once one way of interpreting some text has already made it into the LLM's context vectors, it can be surprisingly difficult for subsequent text to significantly alter that interpretation.
It's these sorts of aggravating failure modes stemming from a fundamental feature of the algorithm that seem to always produce the next AI winter. Because they tend to mean the same thing: research has gone down (and then, quote often, massively over-invested in) yet another blind alley that doesn't put us on a short easy path to AGI any more than the previous 75 years' worth of breakthroughs have. And getting out of it is going to involve not only fixing that one problem, but also a huge reset on some substantial portion of whatever tools and techniques were built up to paper over the previous algorithm's limitations in an effort to generate positive ROI on all that massive over-investment.
> AI has failed. >The rumor mill has it that about 95% of generative AI projects in the corporate world are failures.
AI tooling has only just barely reached the point where enterprise CRUD developers can start thinking about. Langchain only reached v1.0.0 in the last 60 days (Q4 2025); OpenAI effectively announced support for MCP in Q2 2025. The spec didn't even approach maturity until Q4 of 2024. Heck most LLMs didn't have support for tools in 2024.
In 2-3 years a lot of these libraries will be part way through their roadmap towards v2.0.0 to fix many of the pain points and fleshing out QOL improvements, and standard patterns evolved for integrating different workflows. Consumer streaming of audio and video on the web was a disaster of a mess until around ~2009 despite browsers having plugins for it going back over a decade. LLMs continue to improve at a rapid rate, but tooling matures more slowly.
Of course previous experiments failed or were abandoned; the technology has been moving faster than the average CRUD developer can implement features. A lot of "cutting edge" technology we put into our product in 2023 are now standard features for the free tier of market leaders like ChatGPT etc. Why bother maintaining a custom fork of 2023-era (effectively stone age) technology when free tier APIs do it better in 2025? MCP might not be the be-all, end-all, but at least it is a standard interface that's at least maintainable in a way that developers of mature software can begin conceiving of integrating it into their product as a permanent feature, rather than a curiosity MVP at the behest of a non technical exec.
A lot of AI-adjacent libraries we've been using finally hit v1.0.0 this year, or creeping close to it; providing stable interfaces for maintainable software. It's time to hit the reset button on "X% of internal AI initiatives failed"
I am of a belief that upcoming winter will look more like normalization than collapse.
The reason is hype deflation and technical stagnation don't have to arrive together. Once people stop promising AGI by Christmas and clamp down on infinite growth + infinite GPU spend, things will start to look more normal.
At this point, it feels more like the financing story was the shaky part not the tech or the workflows. LLMs’ve changed workflows in a way that’s very hard to unwind now.
I agree, I have over 20 years of software engineering experience and after vibe coding/engineering/architecting (or whatever you want to call it) for a couple months, I also don't see the technology progressing further. LLMs are more or less the same as 6 months ago, incremental improvements, but no meaningful progress. And what we have is just bad. I can use it because I know the exact code I want generated and I will just re-prompt if I don't get what I want, but I'm unconvinced that this is faster than a good search engine and writing code myself.
I think I will keep using it while it's cheap, but once I have to pay the real costs of training/running a flagship modell I think I will quit. It's too expensive as it is for what it does.
Is "A New AI Winter Is Coming" the new "The Year of Linux Desktop"?
The author is conflating a financial correction with a technological failure.
I agree that the economics of GenAI are currently upside down. The CapEx spend is eye-watering, and the path to profitability for the foundational model providers is still hazy. We are almost certainly in an age of inflated-expectations hype-cycle peak that will self-correct, and yes, "winter is harsh on tulips".
However, the claim that the technology itself is a failure is objectively disconnected from reality. Unlike crypto or VR (in their hype cycles), LLMs found immediate, massive product-market fit. I use K-means clustering and logistic regression every day; they aren't AGI either, but they aren't failures.
If 95% of corporate AI projects fail, it's not because the tech is broken; it's because middle management is aspiring to replace humans with a terminal-bound chatbot instead of giving workers an AI companion. The tech isn't going away, even if AI valuations might be questioned in the short term.
Token economics are very sound - it's the training which is expensive.
Tokens/week have gone up 23x year-over-year according to https://openrouter.ai/rankings. This is probably around $500M-1B in sales per year.
The real question is where the trajectory of this rocket ship is going. Will per-token pricing be a race to the bottom against budget chinese model providers? Will we see another 20x year year over the next 3 years, or will it level out sooner?
I think its worth discounting against the failure rates of humans also.
> Depending on the context, and how picky you need to be about recognizing good or bad output, this might be anywhere from a 60% to a 95% success rate, with the remaining 5%-40% being bad results. This just isn't good enough for most practical purposes
This seems to suggest that humans are 100%. I'd be surprised if i was anywhere close to that after 10 years of programming professionally
> it's a horrible liability to have to maintain a codebase that nobody on the team actually authored.
exactly - just like 99.9873% of all codebases currently running in production worldwide :)
I think the author is onto something. but (s)he didn’t highlight that there are some scenarios where factual accuracy is unimportant, or maybe even a detractor.
for example, fictional stories. If you want to be entertained and it doesn’t matter if it’s true or not, there’s no downsides to “hallucinations”. you could argue that stories ARE hallucinations.
another example is advertisements. what matters is how people perceive them, not what’s actually true.
or, content for a political campaign.
the more i think about it, genAI really is a perfect match for social media companies
Can't wait
While both are unlikely, if I have to choose one I would bet on AGI than AI winter in the next five years.
AI just got better and better. People thought it couldn't solve math problems without some human formalizes them first. Then it did. People thought it couldn't generate legible text. Then it did.
All while people swore it had reached a "plateau," "architecture ceiling," "inherent limit," or whatever synonym of the goalpost.
As illustrated by the top comment, it seems that "AI" relies on hype, i.e., careful manipulation of how it is perceived. Its continued existence depends on relentless fund raising because generally no one is going to pay fees to use it long-term
As with so much of Silicon Valley's output the past few decades, "AI" is not something people will pay for. It could be a new form of Trojan Horse for data collection, surveillance and ads, though, adopting a hyperfocus on "growth" at any cost (human, environmental, etc.) but this might not even be necessary^1
Time will tell
In order to raise funds in the absence of any clear business plan, "AI" cannot just be ordinary and useful. It cannot be "autocomplete on steroids". It must be "world-changing"
No one shall underestimate or discount the value of "AI". Overestimation is permitted, however. Speculation is encouraged, but only if it's wild and extreme
1. "AI" is not depending on being brought inside the gates of Troy by the city's inhabitants. It's being placed inside the gates by others. Companies with large numbers of users are installing "AI" on users' computers without the users' consent
These sort of articles are new "economists have predicted 17 of the past 3 recessions".
AlexNet was only released in 2011. The progress made in just 14 years has been insane. So while I do agree that we are approaching a "back to the drawing board" era, calling the past 14 years a "failure" is just not right.
> This means that they should never be used in medicine, for evaluation in school or college, for law enforcement, for tax assessment, or a myriad of other similar cases.
If AI models can deliver measurably better accuracy than doctors, clearer evaluations than professors and fairer prosecutions than courts, then it should be adopted. Waymo has already shown a measurable decrease in loss of life by eliminating humans from driving.
I believe, technically, moderns LLMs are sufficiently advanced to meaningfully disrupt the aforementioned professions as Waymo has done for taxis. Waymo's success relies on 2 non-llm factors that we've yet to see for other professions. First is exhaustive collection and labelling of in-domain high quality data. Second is the destruction of the pro-human regulatory lobby (thanks to work done by Uber in the Zirp era that came before).
To me, an AI winter isn't a concern, because AI is not the bottleneck. It is regulatory opposition and sourcing human experts who will train their own replacements. Both are significantly harder to get around for high-status white collar work. The great-AI-replacement may still fail, but it won't be because of the limitations of LLMs.
> My advice: unwind as much exposure as possible you might have to a forthcoming AI bubble crash.
Hedging when you have much at stake is always a good idea. Bubble or no bubble.
The winters are the best part, economic harm aside.
Winters are when technology falls out of the vice grip of Capital and into the hands of the everyman.
Winters are when you’ll see folks abandon this AIaaS model for every conceivable use case, and start shifting processing power back to the end user.
Winters ensure only the strongest survive into the next Spring. They’re consequences for hubris (“LLMs will replace all the jobs”) that give space for new things to emerge.
So, yeah, I’m looking forward to another AI winter, because that’s when we finally see what does and does not work. My personal guess is that agents and programming-assistants will be more tightly integrated into some local IDEs instead of pricey software subscriptions, foundational models won’t be trained nearly as often, and some accessibility interfaces will see improvement from the language processing capabilities of LLMs (real-time translation, as an example, or speech-to-action).
That, I’m looking forward to. AI in the hands of the common man, not locked behind subscription paywalls, advertising slop, or VC Capital.
I'm so annoyed by this negativity. Is AI perfect? No, far from it. Does it have a positive impact on productivity? 100%. Do I care about financials? Absolutely not. There's the regular hype cycle. But these idiotic takes like "dotcom 2.0" and "AI winter" are just and show that the author has no clue what they are talking about.
Most “AI is rubbish” takes treat it as an open-loop system: prompt → code → judgment. That’s not how development works. Even humans can’t read a spec, dump code, and ship it. Real work is closed-loop: test, compare to spec, refine, repeat. AI shines in that iterative feedback cycle, which is where these critiques miss the point.
I sure hope another winter is on the way, the LLM hype wagon is beyond exhausting.
Eric S. Raymond (yes, that ESR; insert long-winded DonHopkins rant, extensively cross-referenced to his own USENET and UNIX-HATERS mailing list postings, about what a horrible person "Eric the Flute" is) reports a shortening of time to implement his latest project from weeks to hours. He says the advantages of LLMs in the programming space are yet to truly unfold, because they not only let you do things faster, they let you try things you wouldn't have started otherwise because it'd be too much of a time/effort commitment.
Assuming these claims are even partially true, we'd be stupid—at the personal and societal level—not to avail ourselves of these tools and reap the productivity gains. So I don't see AI going away any time soon. Nor will it be a passing fad like Krugman assumed the internet would be. We'd have to course-correct on its usage, but it truly is a game changer.
what a curious coincidence that a soft-hard AI landing would happen to begin at the exact same time as the US government launches a Totally Not a Bailout Strategic Investment Plan Bro I Promise. who could have predicted this?
Every critique of AI assumes to some degree that contemporary implementations will not, or cannot, be improved upon.
Lemma: any statement about AI which uses the word "never" to preclude some feature from future realization is false.
Lemma: contemporary implementations have almost always already been improved upon, but are unevenly distributed.
(Ximm's Law)
LLMs have failed to live up to the hype, but they haven't failed outright.
> The technology is essentially a failure
Really? I derive a ton of value from it. For me it’s a phenomenal advancement and not a failure at all.
Zero citations, random speculations. Random blogpost versus the most rapidly adopted technology in history...
As someone who is an expert in the area, everything in this article is misleading nonsense, failing at even the most basic CS101 principles. The level of confusion here is astounding.
> People were saying that this meant that the AI winter was over
The last AI winter was over 20 years ago. Transformers came during an AI boom.
> First time around, AI was largely symbolic
Neural networks were already hot and the state of the art across many disciplines when Transformers came out.
> The other huge problem with traditional AI was that many of its algorithms were NP-complete
Algorithms are not NP-complete. That's a type error. Problems can be NP-complete, not algorithms.
> with the algorithm taking an arbitrarily long time to terminate
This has no relationship to something being NP-complete at all.
> but I strongly suspect that 'true AI', for useful definitions of that term, is at best NP-complete, possibly much worse
I think the author means that "true AI" returns answers quickly and with high accuracy? A statement that has no relationship to NP-completeness at all.
> For the uninitiated, a transformer is basically a big pile of linear algebra that takes a sequence of tokens and computes the likeliest next token
This is wrong on many levels. A Transformer is not a linear network, linear networks are well-characterized and they aren't powerful enough to do much. It's the non-linearities in the Transformer that allows it to work. And only Decoders compute the distribution over the next token.
> More specifically, they are fed one token at a time, which builds an internal state that ultimately guides the generation of the next token
Totally wrong. This is why Transformers killed RNNs. Transformers are provided all tokens simultaneously and then produce a next token one at a time. RNNs don't have that ability to simultaneously process tokens. This is just totally the wrong mental model of what a Transformer is.
> This sounds bizarre and probably impossible, but the huge research breakthrough was figuring out that, by starting with essentially random coefficients (weights and biases) in the linear algebra, and during training back-propagating errors, these weights and biases could eventually converge on something that worked.
Again, totally wrong. Gradient descent dates back to the late 1800s early 1900s. Backprop dates back to the 60s and 70s. So this clearly wasn't the key breakthrough of Transformers.
> This inner loop isn't Turing-complete – a simple program with a while loop in it is computationally more powerful. If you allow a transformer to keep generating tokens indefinitely this is probably Turing-complete, though nobody actually does that because of the cost.
This isn't what Turing-completeness is. And by definition all practical computing is not a Turing Machine, simply because TMs require an infinite tape. Our actual machines are all roughly Linear Bounded Automata. What's interesting is that this doesn't really provide us with anything useful.
> Transformers also solved scaling, because their training can be unsupervised
Unsupervised methods predate Transformers by decades and were already the state of the art in computer vision by the time Transformers came out.
> In practice, the transformer actually generates a number for every possible output token, with the highest number being chosen in order to determine the token.
Greedy decoding isn't the default in most applications.
> The problem with this approach is that the model will always generate a token, regardless of whether the context has anything to do with its training data.
Absolutely not. We have things like end tokens exactly for this, to allow the model to stop generating.
I got tired of reading at this point. This is drivel by someone who has no clue what's going on.
I've been reading about this supposed AI winter for at least 5 years by now, and in the meantime any AI-related stock has gone 10x and more.
I trully hate how absurdly over-hyped this LLM concept of AI is. However, it can do some cool things. Normally that's the path to PMF. The real problem is the revenue. There is no extant revenue model whatsoever. But there are industries that are described as "a business of pennies" Ex. telephony. Someone may yet eek out a win. But the hype-to-reality conversion will come first.
The poster is right. LLMs are Gish Gallop machines that produce convincing sounding output.
People have figured it out by now. Generative "AI" will fail, other forms may continue, though it it would be interesting to hear from experts in other fields how much fraud there is. There are tons of material science "AI" startups, it is hard to believe they all deliver.
>Expect OpenAI to crash, hard, with investors losing their shirts.
Lol someone doesn't understand how the power structure system works "the golden rule". There is a saying if you owe the bank 100k you have a problem. If you owe the bank ten million the bank has a problem. OpenAI and the other players have made this bubble so big that there is no way the power system will allow themselves to take the hit. Expect some sort of tax subsided bailout in the near future.
Modern LLMs could be like the equivalent of those steam powered toys the Romans had in their time. Steam tech went through a long winter before finally being fully utilized for the Industrial Revolution. We’ll probably never see the true AI revolution in our lifetime, only glimpses of what could be, through toys like LLMs.
What we should underscore though, is that even if there is a new AI winter, the world isn’t going back to what it was before AI. This is it, forever.
Generations ahead will gaslight themselves into thinking this AI world is better, because who wants to grow up knowing they live in a shitty era full of slop? Don’t believe it.
This blog post is full of bizarre statements and the author seems almost entirely ignorant of the history or present of AI. I think it's fair to argue there may be an AI bubble that will burst, but this blog post is plainly wrong in many ways.
Here's a few clarifications (sorry this is so long...):
"I should explain for anyone who hasn't heard that term [AI winter]... there was much hope, as there is now, but ultimately the technology stagnated. "
The term AI winter typically refers to a period of reduced funding for AI research/development, not the technology stagnating (the technology failing to deliver on expectations was the cause of the AI winter, not the definition of AI winter).
"[When GPT3 came out, pre-ChatGPT] People were saying that this meant that the AI winter was over, and a new era was beginning."
People tend to agree there were two AI winters already, one having to do with symbolic AI disappointments/general lack of progress (70s), and the latter related to expert systems (late 80s). That AI winter has long been over. The Deep Learning revolution started in ~2012, and by 2020 (GPT 3) huge amount of talent and money were already going into AI for years. This trend just accelerated with ChatGPT.
"[After symbolic AI] So then came transformers. Seemingly capable of true AI, or, at least, scaling to being good enough to be called true AI, with astonishing capabilities ... the huge research breakthrough was figuring out that, by starting with essentially random coefficients (weights and biases) in the linear algebra, and during training back-propagating errors, these weights and biases could eventually converge on something that worked."
Transformers came about in 2017. The first wave of excitement about neural nets and backpropagation goes all the way back to the late 80s/early 90s, and AI (computer vision, NLP, to a lesser extent robotics) were already heavily ML-based by the 2000s, just not neural-net based (this changed in roughly 2012).
"All transformers have a fundamental limitation, which can not be eliminated by scaling to larger models, more training data or better fine-tuning ... This is the root of the hallucination problem in transformers, and is unsolveable because hallucinating is all that transformers can do."
The 'highest number' token is not necessarily chosen, this depends on the decoding algorithm. That aside, 'the next token will be generated to match that bad choice' makes it sound like once you generate one 'wrong' token the rest of the output is also wrong. A token is a few characters, and need not 'poison' the rest of the output.
That aside, there are plenty of ways to 'recover' from starting to go down the wrong route. A key aspect of why reasoning in LLMs works well is that it typically incorporates backtracking - going earlier in the reasoning to verify details or whatnot. You can do uncertainty estimation in the decoding algorithm, use a secondary model, plenty of things (here is a detailed survey https://arxiv.org/pdf/2311.05232 , one of several that is easy to find).
"The technology won't disappear – existing models, particularly in the open source domain, will still be available, and will still be used, but expect a few 'killer app' use cases to remain, with the rest falling away."
A quick google search shows ChatGPT currently has 800 million weekly active users who are using it for all sorts of things. AI-assisted programming is certainly here to stay, and there are plenty of other industries in which AI will be part of the workflow (helping do research, take notes, summarize, build presentations, etc.)
I think discussion is good, but it's disappointing to see stuff with this level of accuracy being on front page of HN.
"The technology is essentially a failure" is in the headline of this article. I have to disagree with that. For the first time in the history of the UNIVERSE, an entity exists that can converse in human language at the same level that humans can.
But that's me being a sucker. Because in reality this is just a clickbait headline for an article basically saying that the tech won't fully get us to AGI and that the bubble will likely pop and only a few players will remain. Which I completely agree with. It's really not that profound.
This article uses the computational complexity hammer way too hard, discounts huge progress in every field of AI outside of the hot trend of transformers and LLMs. Nobody is saying the future of AI is autoregressive and this article pretty much ignores any of the research that has been posted here around diffusion based text generation or how it can be combined with autoregressive methods… discounts multi-modal models entirely. He also pretty much discounts everything that’s happened with AlphaFold, Alpha Go etc. reinforcement learning etc.
The argument that computational complexity has something to do with this could have merit but the article certainly doesn’t give indication as to why. Is the brain NP complete? Maybe maybe not. I could see many arguments about why modern research will fail to create AGI but just hand waving “reality is NP-hard” is not enough.
The fact is: something fundamental has changed that enables a computer to pretty effectively understand natural language. That’s a discovery on the scale of the internet or google search and shouldn’t be discounted… and usage proves it. In 2 years there is a platform with billions of users. On top of that huge fields of new research are making leaps and bounds with novel methods utilizing AI for chemistry, computational geometry, biology etc.
It’s a paradigm shift.