I'd say most of the recent AI model progress has been on price.
A 4-bit quant of QwQ-32B is surprisingly close to Claude 3.5 in coding performance. But it's small enough to run on a consumer GPU, which means deployment price is now down to $0.10 per hour. (from $12+ for models requiring 8x H100)
One thing I’ve seen is large enterprises extracting money from consumers by putting administrative burden on them.
For example, you can see this in health insurance reimbursements and wireless carriers plan changes. (ie, Verizon’s shift from Do More, etc to what they have now)
Companies basically set up circumstances where consumers lose small amounts of money on a recurring basis or sporadically enough that the people will just pay the money rather than a maze of calls, website navigation and time suck to recover funds due to them or that shouldn’t have been taken in the first place.
I’m hopeful well commoditized AI will give consumers a fighting chance at this and other types of disenfranchisement that seems to be increasingly normalized by companies that have consultants that do nothing but optimize for their own financial position.
I think the real meaningful progress is getting ChatGPT 3.5 level quality running anywhere you want rather than AIs getting smarter at high level tasks. This capability being ubiquitous and not tied to one vendor is really what’s revolutionary.
This was published the day before Gemini 2.5 was released. I'd be interested if they see any difference with that model. Anecdotally, that is the first model that really made me go wow and made a big difference for my productivity.
FWIW 2.5-exp was the only one that managed to get a problem I asked it right, compared to Claude 3.7 and o1 (or any of the other free models in Cursor).
It was reverse engineering ~550MB of Hermes bytecode from a react native app, with each function split into a separate file for grep-ability and LLM compatibility.
The others would all start off right then quickly default to just greping randomly what they expected it to be, which failed quickly. 2.5 traced the function all the way back to the networking call and provided the expected response payload.
All the others hallucinated the networking response I was trying to figure out. 2.5 Provided it exactly enough for me to intercept the request and using the response it provided to get what I wanted to show up.
I manually pre-parsed the bytecode file with awk into a bazillion individual files that were each just one function, and gave it the hint to grep to sort through them. This was all done in Cursor.
awk '/^=> \[Function #/ {
if (out) close(out);
fn = $0; sub(/^.*#/, "", fn); sub(/ .*/, "", fn);
out = "function_" fn ".txt"
}
{ if (out) print > out }' bundle.hasm
Quick example of the output it gave and it's process.
As someone who was wildly disappointed with the hype around Claude 3.7, Gemini 2.5 is easily the best programmer-assistant LLM available, IMO.
But it still feels more like a small incremental improvement rather than a radical change, and I still feel its limitations constantly.
Like... it gives me the sort of decent but uninspired solution I would expect it to generate without predictably walking me through a bunch of obvious wrong turns as I repeatedly correct it as I would have to have done with earlier models.
And that's certainly not nothing and makes the experience of using it much nicer, but I'm still going to roll my eyes anytime someone suggests that LLMs are the clear path to imminently available AGI.
This is exactly my sentiment. Sonnet-3.5-latest was the perfect code companion: wrote just the right amount of okay quality code but its strength was it really tried to adhere to your instructions. sonnet-3.7 was the exact opposite, wrote waaay too much code and overengineered things like crazy while having very poor instruction adherence. Gemini 2.5 Pro is basically what I hoped sonnet-3.7 would be: follows instructions well but still softly opinionated, massive (usable) context window, fast response, more biased towards latest best practices and a up to date knowledge cutoff.
I'm wondering how much gemini 2.5 being "amazing" comes from sonnet-3.7 being such a disappointment.
Ya, I find this hard to imagine aging well. Gemini 2.5 solved (at least much better than) multiple real world systems questions I've had in the past that other models could not. Its visual reasoning also jumped significantly on charts (e.g. planning around train schedules)
Even Sonnet 3.7 was able to do refactoring work on my codebase sonnet 3.6 could not.
There's somehow this belief that "newer models will disprove <insert LLM criticism here>" despite the "newer" models being... just a scaled-up version of a previous model, or some anciliary features tacked on. An LLM is an LLM is an LLM: I'll believe it when I see otherwise.
> So maybe there's no mystery: The AI lab companies are lying, and when they improve benchmark results it's because they have seen the answers before and are writing them down. [...then says maybe not...]
Well.. they've been caught again and again red handed doing exactly this. Fool me once shame on you, fool me 100 times shame on me.
Hate to say this but the incentive is growth, not progress. Progress is what enabled the growth, but is also extremely hard to plan and deliver. On the other hand, hype is probably somewhat easier and well-tested approach so no surprise lot of the effort goes into marketing. Markets had repeatedly confirmed that there aren't any significant immediate repercussions for cranking up BS levels in marketing materials, while there are some rewards when it works.
> Since 3.5-sonnet, we have been monitoring AI model announcements, and trying pretty much every major new release that claims some sort of improvement. Unexpectedly by me, aside from a minor bump with 3.6 and an even smaller bump with 3.7, literally none of the new models we've tried have made a significant difference on either our internal benchmarks or in our developers' ability to find new bugs. This includes the new test-time OpenAI models.
This is likely a manifestation of the bitter lesson[1], specifically this part:
> The ultimate reason for this is Moore's law, or rather its generalization of continued exponentially falling cost per unit of computation. Most AI research has been conducted as if the computation available to the agent were constant (in which case leveraging human knowledge would be one of the only ways to improve performance) but, over a slightly longer time than a typical research project [like an incremental model update], massively more computation inevitably becomes available.
(Emphasis mine.)
Since the ultimate success strategy of the scruffies[2] or proponents of search and learning strategies in AI is Moore's Law, short term gains using these strategies will be miniscule. It is over at least a five year period that their gains will be felt the most. The neats win the day in the short term, but the hare in this race will ultimately give away to the steady plod of the tortoise.
For who? Nvidia sell GPUs, OpenAI and co sell proprietary models and API access, and the startups resell GPT and Claude with custom prompts. Each one is hoping that the layer above has a breakthrough that makes their current spend viable.
If they do, then you don’t want to be left behind, because _everything_ changes. It probably won’t, but it might.
This bubble will be burst by the Trump tariffs and the end of the zirp era. When inflation and a recession hit together hope and dream business models and valuations no longer work.
Which one? Nvidia are doing pretty ok selling GPU's, and OpenAI and Anthropic are doing ok selling their models. They're not _viable_ business models, but they could be.
NVDA will crash when the AI bubble implodes, and none of those Generative AI companies are actually making money, nor will they. They have already hit limiting returns in LLM improvements after staggering investments and it is clear are nowhere near general intelligence.
All of this can be true, and has nothing to do with them having a business model.
> NVDA will crash when the AI bubble implodes,
> making money, nor will they
> They have already hit limiting returns in LLM improvements after staggering investments
> and it is clear are nowhere near general intelligence.
These are all assumptions and opinions, and have nothing to do with whether or not they have a business model. You mightn't like their business model, but they do have one.
I consider it a business model if they have plans to make money at some point (no sign of that at openai which are not based on hopium) and are not engaged in fraud like bundling and selling to their own subsidiaries (nvda).
These are of course just opinions, I’m not sure we can know facts about such companies except in retrospect.
Than any silly idea can be a business model. Suppose I collect dust from my attic and hope to sell it as an add-on on my neighbor's lemonade stand, with a hefty profit for the neighbor, who is getting paid by me $10 to add a handful of dust in each glass and sell it to the customers for $1. The neighbor accepts. It's a business model, at least until I don't run of existing funds or the last customer leaves in disguist. At which point exactly that silly idea stops being an unsustainable business model and becomes a silly idea? I guess at least as early as I see that the funds are running up, and I need to borrow larger an larger lumps of money each time to keep spinning the wheel...
Indeed it can. The difference between a business model and a viable business model is one word - viable.
If you asked me 18 years ago was "giving away a video game and selling cosmetics" a viable business model I would have laughed at you.If you asked me in 2019 I would probably give you money. If you asked me in 2025, I'd probably laugh at you again.
> and I need to borrow larger an larger lumps of money each time to keep spinning the wheel...
Or you figure out a way to to sell it to your neighbour for $0.50 and he can sell it on for $1.
The play is clear at every level - Nvidia Sell GPUs, OpenAI sell models, and SAAS sell prompts + UI's. Whether or not any of them are viable remains to be seen. Personally, I wouldn't take the bet.
You’re on a startup forum complaining that vc backed startups don’t have a business model when the business model is the same as it has been for almost 15 years - be a unicorn in your space.
Im able to get substantially more coding done than three months ago. This could be largely in the tooling (coding agents, deep research). But the models are better too, for both coding and brainstorming. And tooling counts, to me, as progress.
Learning to harness current tools helps to harness future tools. Work on projects that will benefit from advancements, but can succeed without them.
I'm not sure if I'm able to do more of the hard stuff, but a lot of the easy but time consuming stuff is now easily done by LLMs.
Example: I frequently get requests for data from Customer Support that used to require 15 minutes of my time noodling around writing SQL queries. I can cut that down to less than a minute now.
Will LLMs end up like compilers? Compilers are also fundamentally important to modern industrial civilization - but they're not profit centers, they're mostly free and open-source outside a few niche areas. Knowing how to use a compiler effectively to write secure and performative software is still a valuable skill - and LLMs are a valuable tool that can help with that process, especially if the programmer is on the steep end of the learning curve - but it doesn't look like anything short of real AGI can do novel software creation without a human constantly in the loop. The same argument applies to new fundamental research, even to reviewing and analyzing new discoveries that aren't in the training corpus.
Wasn't it back in the 1980s that you had to pay $1000s for a good compiler? The entire LLM industry might just be following in the compiler's footsteps.
Yep. I'm looking forward to LLMs/deepnets being considered a standard GOFAI technique with uses and limitations and not "we asked the God we're building to draw us a picture of a gun and then it did and we got scared"
Objectively speaking a chess engine is artificially intelligent. Just because it's not human level doesn't mean it's not intelligent. Repeat for any N of 100s of different technologies we've built. We've been calling this stuff "thinking machines" since Turing and it's honestly just not useful at this point.
The fact is, the phrase "artificial intelligence" is a memetic hazard: it immediately positions the subject of conversation as "default capable", and then forces the conversation into trying to describe what it can't do, which is rarely a useful way to approach it.
Whereas with LLMs (and chess engines and every other tech advancement) it would be more useful to start with what the tech _can_ do and go from there.
My personal experience is right in line with the author's.
Also:
> I think what's going on is that large language models are trained to "sound smart" in a live conversation with users, and so they prefer to highlight possible problems instead of confirming that the code looks fine, just like human beings do when they want to sound smart.
I immediately thought: That's because in most situations this is the purpose of language, at least partially, and LLMs are trained on language.
> [T]here are ~basically~ no public benchmarks for security research... nothing that gets at the hard parts of application pentesting for LLMs, which are 1. Navigating a real repository of code too large to put in context, 2. Inferring a target application's security model, and 3. Understanding its implementation deeply enough to learn where that security model is broken.
A few months ago I looked at essentially this problem from a different angle (generating system diagrams from a codebase). My conclusion[0] was the same as here: LLMs really struggle to understand codebases in a holistic way, especially when it comes to the codebase's strategy and purpose. They therefore struggle to produce something meaningful from it like a security assessment or a system diagram.
> ...whatever gains these companies are reporting to the public, they are not reflective of economic usefulness or generality.
I'm not surprised, because I don't expect pattern matching systems to grow into something more general and useful. I think LLM's are essentially running into the same limitations that the "expert systems" of the 1980's ran into.
My experience as someone who uses LLMs and a coding assist plugin (sometimes), but is somewhat bearish on AI is that GPT/Claude and friends have gotten worse in the last 12 months or so, and local LLMs have gone from useless to borderline functional but still not really usable for day to day.
Personally, I think the models are “good enough” that we need to start seeing the improvements in tooling and applications that come with them now. I think MCP is a good step in the right direction, but I’m sceptical on the whole thing (and have been since the beginning, despite being a user of the tech).
The whole MCP hype really shows how much of AI is bullshit. These LLMs have consumed more API documentation than possible for a single human and still need software engineers to write glue layers so they can use the APIs.
Because it’s lossy compression. I also consumed a lot of books and even more movies and I don’t have good memory of it all. But some core facts and intuition from it.
AI is far better at regurgitating facts than me even if it's lossy compression but if someone gives me an api doc I can figure out how to use it without them writing a wrapper library around the parts that I need to use to solve whatever problem I'm working on.
> but if someone gives me an api doc I can figure out how to use it without them writing a wrapper library around the parts that I need to use to solve whatever problem I'm working on.
I think this is where AI is faling short hugely. AI _should_ be able to integrate with IDEs and tooling (e.g. LSP, Treesitter, Editorconfig) to make sure that it's contextually doing the right thin.
The problem is that up until _very_ recently, it's been possible to get LLMs to generate interesting and exciting results (as a result of all the API documentation and codebases they've inhaled), but it's been very hard to make that usable. I think we need to be able to control the output format of the LLMs in a better way before we can work on what's in the output. I don't konw if MCP is the actual solution to that, but it's certainly an attempt at it...
That's reasonable along with your comment below too, but when you have the ceo of anthropic saying "AI will write all code for software engineers within a year" last month I would say that is pretty hard to believe given how it performs without user intervention (MCP etc...). It feels like bullshit just like the self driving car stuff did ~10 years ago.
I completely agree with you there. I think we're a generation away from these tools being usable with light supervision in the way I _want_ to use them, and I think the gap between now and that is about 10x smaller than the gap between that and autonomous agents.
I think overall quality with Gemini 2.5 is not much better than Gemini 2 in my experience. Gemini 2 was already really good, but just like Claude 3.7, Gemini 2.5 goes some steps forward and some steps backwards. It sometimes generates some really verbose code even when you tell it to be succinct. I am pretty confident that if you evaluate 2.5 for a bit longer you'll come to the same conclusion eventually.
People are really fundamentally asking two different questions when they talk about AI "importance": AI's utility and AI's "intelligence". There's a careful line between both.
1) AI undoubtedly has utility. In many agentic uses, it has very significant utility. There's absolute utility and perceived utility, which is more of user experience. In absolute utility, it is likely git is the single most game changing piece of software there is. It is likely git has saved some ten, maybe eleven digit number in engineer hours times salary in how it enables massive teams to work together in very seamless ways. In user experience, AI is amazing because it can generate so much so quickly. But it is very far from an engineer. For example, recently I tried to use cursor to bootstrap a website in NextJS for me. It produced errors it could not fix, and each rewrite seemed to dig it deeper into its own hole. The reasons were quite obvious. A lot of it had to do with NextJS 15 and the breaking changes it introduces in cookies and auth. It's quite clear if you have masses of NextJS code, which disproportionately is older versions, but none labeled well with versions, it messes up the LLM. Eventually I scrapped what it wrote and did it myself. I don't mean to use this anecdote to say LLMs are useless, but they have pretty clear limitations. They work well on problems with massive data (like front end) and don't require much principled understanding (like understanding how NextJS 15 would break so and so's auth). Another example of this is when I tried to use it to generate flags for a V8 build, it failed horribly and would simply hallucinate flags all the time. This seemed very likely to be (despite the existence of a list of V8 flags online) that many flags had very close representations in vector embeddings, and that there was almost close to zero data/detailed examples on their use.
2) In the more theoretical side, the performance of LLMs on benchmarks (claiming to be elite IMO solvers, competitive programming solvers) have become incredibly suspicious. When the new USAMO 2025 was released, the highest score was 5%, despite claims a year ago that SOTA when was at least a silver IMO. This is against the backdrop of exponential compute and data being fed in. Combined with apparently diminishing returns, this suggests that the gains from that are running really thin.
I hope it's true. Even if LLMs development stopped now, we would still keep finding new uses for them at least for the next ten years. The technology is evolving way faster than we can meaningfully absorb it and I am genuinely frightened by the consequences. So I hope we're hitting some point of diminishing returns, although I don't believe it a bit.
For three years now, my experience with LLMs has been "mostly useless, prefer ELIZA".
Which is software written 1966, but the web version is a little newer. Does occasional psychotherapy assistance/brainstorming just as well, and I more easily know when I stepped out of its known range into the extrapolated.
That said, it can vibe code in a framework unknown to me in half the time that I would need to school myself and add the feature.
Or vibe coding takes twice as long, if I mostly know how to achieve what I want and read no framework documentation but only our own project's source code to add a new feature. But on a day with a headache, I can still call the LLM a dumb twat and ask it to follow my instructions instead of doing bullshit.
But, vibe coding always makes my pulse go to 105, from 65 and question my life choices. Since few instructions are rarely ever followed and loops never left once entered. Except for on the first try getting 80% of the structure kinda right, but then getting stuck for the whole workday.
My mom told me yesterday that Paul Newman had massive problems with alcohol. I was somewhat skeptical, so this morning I asked ChatGPT a very simple question:
"Is Paul Newman known for having had problems with alcohol?"
All of the models up to o3-mini-high told me he had no known problems. Here's o3-mini-high's response:
"Paul Newman is not widely known for having had problems with alcohol. While he portrayed characters who sometimes dealt with personal struggles on screen, his personal life and public image were more focused on his celebrated acting career, philanthropic work, and passion for auto racing rather than any issues with alcohol. There is no substantial or widely reported evidence in reputable biographies or interviews that indicates he struggled with alcohol abuse."
There is plenty of evidence online that he struggled a lot with alcohol, including testimony from his long-time wife Joanne Woodward.
I sent my mom the ChatGPT reply and in five minutes she found an authoritative source to back her argument [1].
I use ChatGPT for many tasks every day, but I couldn't fathom that it would get so wrong something so simple.
Lesson(s) learned... Including not doubting my mother's movie trivia knowledge.
3-4 hours is enough time for It to have crawled the hacker news comments section. That's about the frequency the AI bots crawl my little out of the way blog.
Thats not really 'simple' for an LLM. This is a niche information about a specifc person, LLM's train on massive amount of data, the more a topic is being present in the data, the better will the answers be.
Also, you can/should use the "research" mode for questions like this.
The question is simple and verifiable - it is impressive to me that it’s not contained in the LLM’s body of knowledge - or rather that it can’t reach the answer.
This is niche in the grand scheme of knowledge but Paul Newman is easily one of the biggest actors in history, and the LLM has been trained on a massive corpus that includes references to this.
Where is the threshold for topics with enough presence in the data?
Ah, but isn’t that the problem here - asking an LLM for facts without requesting a search is like asking a PhD to answer a question “off the top of your head”. For pop culture questions the PhD likely brings little value.
I don't think they mean "knowledge" when they talk about "intelligence." LLMs are definitely not knowledge bases. They can transform information given to them in impressive ways, but asking a raw (non-RAG-enabled) LLM to provide its own information will probably always be a mistake.
They kind of are knowledge bases, just not in the usual way. The knowledge is encoded in the words they were trained on. They weren't trained on words chosen at random; they were trained on words written by humans to encode some information. In fact, that's the only thing that makes LLMs somewhat useful.
Does the as yet unwritten prequel of Idiocracy tell the tale of when we started asking Ai chat bots for facts and this was the point of no return for humanity?
Can you blame the users for asking it, when everyone is selling that as a key defining feature?
I use it for asking - often very niche - questions on advanced probability and simulation modeling, and it often gets those right - why those and not a simple verifiable fact about one of the most popular actors in history?
I don’t know about Idiocracy, but something that I have read specific warnings about is that people will often blame the user for any of the tool’s misgivings.
I like that it's unmonetized, of course, but that's not why I use AI. I use AI because it's better at search. When I can't remember the right keywords to find something, or when the keywords aren't unique, I frequently find that web search doesn't return what I need and AI does.
It's impressive how often AI returns the right answer to vague questions. (not always though)
So, in other words, are you saying that AI model progress is the real deal and is not bullshit?
That is, as you point out, "all of the models up to o3-mini-high" give an incorrect answer, while other comments say that OpenAIs later models give correct answers, with web citations. So it would seem to follow that "recent AI model progress" actually made a verifiable improvement in this case.
I am pretty sure that they must have meant "up through", not "up to", as the answer from o3-mini-high is also wrong in a way which seems to fit the same description, no?
I tried with 4o and it gave me what I thought was a correct answer:
> Paul Newman was not publicly known for having major problems with alcohol in the way some other celebrities have been. However, he was open about enjoying drinking, particularly beer. He even co-founded a line of food products (Newman’s Own) where profits go to charity, and he once joked that he consumed a lot of the product himself — including beer when it was briefly offered.
> In his later years, Newman did reflect on how he had changed from being more of a heavy drinker in his youth, particularly during his time in the Navy and early acting career, to moderating his habits. But there’s no strong public record of alcohol abuse or addiction problems that significantly affected his career or personal life.
> So while he liked to drink and sometimes joked about it, Paul Newman isn't generally considered someone who had problems with alcohol in the serious sense.
As other's have noted, LLMs are much more likely to be cautious in providing information that could be construed as libel. While Paul Newman may have been an alcoholic, I couldn't find any articles about it being "public" in the same way as others, e.g. with admitted rehab stays.
This is less an LLM thing than an information retrieval question. If you choose a model and tell it to “Search,” you find citation based analysis that discusses that he indeed had problems with alcohol. I do find it interesting it quibbles whether he was an alcoholic or not - it seems pretty clear from the rest that he was - but regardless.
This is indicative of something crucial when placing LLMs into a toolkit. They are not omniscient nor are they deductive reasoning tools. Information retrieval systems are excellent at information retrieval and should be used for information retrieval. Solvers are excellent at solving deductive problems. Use them. The better they get at these tasks alone is cool but is IMO a parlor trick since we have nearly optimal or actually optimal techniques that don’t need an LLM. The LLM should use those tools.
So, click search next time you have an information retrieval question.
https://chatgpt.com/share/67f2dac0-3478-8000-9055-2ae5347037...
Any information found in a web search about Newman will be available in the training set (more or less). It's almost certainly a problem of alignment / "safety" causing this issue.
There’s a simpler explanation than that’s that the model weights aren’t an information retrieval system and other sequences of tokens are more likely given the totality of training data. This is why for an information retrieval task you use an information retrieval tool similarly to how for driving nails you use a hammer rather than a screw driver. It may very well be you could drive the nail with the screw driver, but why?
You think that's a simpler explanation? Ok. I think given the amount of effort that goes into "safety" on these systems that my explanation is vastly more likely than somehow this information got lost in the vector soup despite being attached to his name at the top of every search result[0].
Except if safety blocked this, it would have also blocked the linked conversation. Alignment definitely distorts behaviors of models, but treating them as information retrieval systems is using a screw driver to drive nails. Your example didn’t refute this.
"Any information found in a web search about Newman will be available in the training set"
I don't think that is a safe assumption these days. Training modern LLM isn't about dumping in everything on the Internet. To get a really good model you have to be selective about your sources of training data.
They still rip off vast amounts of copyrighted data, but I get the impression they are increasingly picky about what they dump into their training runs.
I realise your answer wasn't assertive, but if I heard this from someone actively defending AI it would be a copout. If the selling point is that you can ask these AIs anything then one can't retroactively go "oh but not that" when a particular query doesn't pan out.
This is a bit of a strawman. There are certainly people who claim that you can ask AIs anything but I don't think the parent commenter ever made that claim.
"AI is making incredible progress but still struggles with certain subsets of tasks" is self-consistent position.
My point is the opposite of this point of view. I believe generative AI is the most significant advance since hypertext and the overlay of inferred semantic relationships via pagerank etc. In fact the creation of hypertext and the toolchains around it led to this point at all - neural networks were understood at that point and transformer attention is just an innovation. It’s the collective human assembly of language and visual interconnected knowledge at a pan cultural and global scale that enabled the current state.
The abilities of LLM alone to do astounding natural language processing beyond the ability of anything prior by unthinkable Turing test passing miles. The fact it can reason abductively, which computing techniques to date have been unable to is amazing. The fact you can mix it with multimodal regimes - images, motion, virtually anything that can be semantically linked via language, is breathtaking. The fact it can be augmented with prior computing techniques - IR, optimization, deductive solvers, and literally everything we’ve achieved to date should give anyone knowledgeable of such things shivers for what the future holds.
But I would never hold that generative AI techniques are replacements for known optimal techniques. But the ensemble is probably the solution to nearly every challenge we face. When we hit the limits of LLMs today, I think, well, at least we already have grand master beating chess solvers and it’s irrelevant the LLM can’t directly. The LLM and other generative AI techniques in my mind are like gasses that fill through learned approximation the things we’ve not been able to solve directly, including the assembly of those solutions ad hoc. This is why since the first time BERT came along I knew agent based techniques were the future.
Right now we live at time like early hypertext with respect to AI. Toolchains suck, LLMs are basically geocities pages with “under construction” signs. We will go through an explosive exploration, some stunning insights that’ll change the basic nature of our shared reality (some wonderful some insidious), then if we aren’t careful - and we rarely are - enshitification at scale unseen before.
LLMs aren't good at being search engines, they're good at understanding things. Put an LLM on top of a search engine, and that's the appropriate tool for this use case.
I guess the problem with LLMs is that they're too usable for their own good, so people don't realizing that they can't perfectly know all the trivia in the world, exactly the same as any human.
For them to work at all they need to have some representation of concepts. Recent research at anthropic has shown a surprising complexity in their reasoning behavior. Perhaps the parrot here is you.
It's the first time I've ever used that phrase on HN. Anyway, what phrase do you think works better than 'stochastic parrot' to describe how LLMs function?
Try to come up with a way to prove humans aren't stochastic parrots then maybe people will atart taking you seriously. Just childish reddit angst rn nothing else.
> Try to come up with a way to prove humans aren't stochastic parrots
Look around you
Look at Skyscrapers. Rocket ships. Agriculture.
If you want to make a claim that humans are nothing more than stochastic parrots then you need to explain where all of this came from. What were we parroting?
Meanwhile all that LLMs do is parrot things that humans created
Skyscrapers: trees, mountains, cliffs, caves in mountainsides, termite mounds, humans knew things could go high, the Colosseum was built two thousand years ago as a huge multi-storey building.
Rocket ships: volcanic eruptions show heat and explosive outbursts can fling things high, gunpowder and cannons, bellows showing air moves things.
Agriculture: forests, plains, jungle, desert oases, humans knew plants grew from seeds, grew with rain, grew near water, and grew where animals trampled them into the ground.
We need a list of all atempted ideas, all inventions and patents that were ever tried or conceived, and then we see how inventions are the same random permutations on ideas with Darwinian style survivorship as everything else; there were steel boats with multiple levels in them before skyscrapers; is the idea of a tall steel building really so magical when there were over a billion people on Earth in 1800 who could have come up with it?
You’re likening actual rocketry to LLMs being mildly successful at describing Paul Newman’s alcohol use on average when they already have the entire internet handed to them.
> when there were over a billion people on Earth in 1800 who could have come up with it
My point is that humans did come up with it. Humans did not parrot it from someone or something else that showed it to us. We didn't "parrot" splitting the atom. We didn't learn how to build skyscrapers from looking at termite hills and we didn't learn to build rockets that can send a person to the moon from seeing a volcano
It's obvious that humans imitate concepts and don't come up with things de-novo from a blank slate of pure intelligence. So your claim hinges on LLMs parrotting the words they are trained on. But they don't do that, their training makes them abstract over concepts and remix them in new ways to output sentences they weren't trained on, e.g.:
Prompt: "Can you give me a URL with some novel components, please?"
An living parrot echoing "pieces of eight" cannot do this, it cannot say "pieces of <currency>" or "pieces of <valuable mineral>" even if asked to do that. The LLM training has abstracted some concept of what it means for a text pattern to be a URL and what it means for things to be "novel" and what it means to switch out the components of a URL but keep them individually valid. It can also give a reasonable answer asking for a new kind of protocol. So your position hinges on the word "stochastic" which is used as a slur to mean "the LLM isn't innovating like we do it's just a dice roll of remixing parts it was taught". But if you are arguing that makes it a "stochastic parrot" then you need to consider splitting the atom in its wider context...
> "We didn't "parrot" splitting the atom"
That's because we didn't "split the atom" in one blank-slate experiment with no surrounding context. Rutherford and team disintegrated the atom in 1914-1919 ish, they were building on the surrounding scientific work happening at that time: 1869 Johann Hittorf recognising that there was something coming in a straight line from or near the cathode of a Crookes vacuum tube, 1876 Eugen Goldstein proving they were coming from the cathode and naming them cathode rays (see: Cathode Ray Tube computer monitors), and 1897 J.J Thompson proving the rays are much lighter than the lightest known element and naming them Electrons, the first proof of sub-atomic particles existing. He proposed the model of the atom as a 'plum pudding' (concept parroting). Hey guess who JJ Thomspon was an academic advisor of? Ernest Rutherford! 1911 Rutherford discovery of the atomic nucleus. 1909 Rutherford demonstrated sub-atomic scattering and Millikan determined the charge on an electron. Eugen Goldstein also discovered the anode rays travelling the other way in the Crookes tube and that was picked up by Wilhelm Wien and it became Mass Spectrometry for identifying elements. In 1887 Heinrich Hertz was investigating the Photoelectric effect building on the work of Alexandre Becquerel, Johann Elster, Hans Geitel. Dalton's atomic theory of 1803.
Not to mention Rutherford's 1899 studies of radioactivity, following Henri Becquerel's work on Uranium, following Marie Curie's work on Radium and her suggestion of radioactivity being atoms breaking up, and Rutherford's student Frederick Soddy and his work on Radon, and Paul Villard's work on Gamma Ray emissions from Radon.
When Philipp Lenard was studying cathode rays in the 1890s he bought up all the supply of one phosphorescent material which meant Röntgen had to buy a different one to reproduce the results and bought one which responded to X-Rays as well, and that's how he discovered them - not by pure blank-sheet intelligence but by probability and randomness applied to an earlier concept.
That is, nobody taught humans to split the atom and then humans literally parotted the mechanism and did it, but you attempting to present splitting the atom as a thing which appeared out of nowhere and not remixing any existing concepts is, in your terms, absolute dr...
> fireworks, cannons, jellyfish squeezing water out to accelerate, no sudies of orbits from moons and planets, no chemistry experiments, no inspiration from thousands of years of flamethrowers
Fireworks, cannons, chemistry experiments and flamethrowers are all human inventions
And yes, exactly! We studied orbits of moons and planets. We studied animals like Jellyfish. We choose to observe the world, we extracted data, we experimented, we saw what worked, refined, improved, and succeeded
LLMs are not capable of observing anything. They can only regurgitate and remix the information they are fed by humans! By us, because we can observe
An LLM trained on 100% wrong information will always return wrong information for anything you ask it.
Say you train an LLM with the knowledge that fire can burn underwater. It "thinks" that the step by step instructions for building a fire is to pile wood and then pour water on the wood. It has no conflicting information in its model. It cannot go try to build a fire this way and observe that it is wrong. It is a parrot. It repeats the information that you give it. At best it can find some relationships between data points that humans haven't realized might
be related
A human could easily go attempt this, realize it doesn't work, and learn from the experience. Humans are not simply parrots. We are capable of exploring our surroundings and internalizing things without needing someone else to tell us how everything works
> That is, nobody taught humans to split the atom and then humans literally parotted the mechanism and did it, but you attempting to present splitting the atom as a thing which appeared out of nowhere and not remixing any existing concepts is, in your terms, absolute drivel
Building on the work of other humans is not parroting
You outlined the absolute genius of humanity building from first principles all the way to splitting the atom and you still think we're just parroting,
I hate to be the burden of proof guy, but in this case I'll say: the burden of proof is on you to prove that humans are stochastic parrots. For millenia, nobody thought to assert that the human brain was computational in nature, until people invented computers, and all of a sudden started asserting that many the human brain was just like a classical computer.
Of course, this turned out to be completely false, with advances in understanding of neural networks. Now, again with no evidence other than "we invented this thing that's, useful to us" people have been asserting that humans are just like this thing we invented. Why? What's the evidence? There never is any. It's high dorm room behavior. "What if we're all just machines, man???" And the argument is always that if I disagree with you when you assert this, then I am acting unscientifically and arguing for some kind of magic.
But there's no magic. The human brain just functions in a way different than the new shiny toys that humans have invented, in terms of ability to model an external world, in terms of the way emotions and sense experience are inseparable from our capacity to process information, in terms of consciousness. The hardware is entirely different, and we're functionally different.
The closest things to human minds are out there, and they've been out there for as long as we have: other animals. The real unscientific perspective is that to get high on your own supply and assert that some kind of fake, creepily ingratiating Spock we made up (who is far less charming than Leonard Nimony) is more like us than a chimp is.
It’s good rhetoric but bad analogy. LLMs can be very creative (to the point of failure, in hallucinations).
I don’t know if there is a pithy shirt phrase to accurately describe how LLMs function. Can you give me a similar one for how humans think? That might spur my own creativity here.
I've always been amazed by this. I have never not been frustrated with the profound stupidity of LLMs. Obviously I must be using it differently because I've never been able to trust it with anything and more than half the time I fact check it even for information retrieval it's objectively incorrect.
If you got as far as checking the output it must have appeared to understand your question.
I wouldn't claim LLMs are good at being factual, or good at arithmetic, or at drawing wine glasses, or that they are "clever". What they are very good at is responding to questions in a way which gives you the very strong impression they've understood you.
I vehemently disagree. If I ask a question with an objective answer, and it simply makes something up and is very confident the answer is correct, what the fuck has it understood other than how to piss me off?
It clearly doesn't understand that the question has a correct answer, or that it does not know the answer. It also clearly does not understand that I hate bullshit, no matter how many dozens of times I prompt it to not make something up and would prefer an admittance of ignorance.
It didn't understand you but the response was plausible enough to require fact checking.
Although that isn't literally indistinguishable from 'understanding' (because your fact checking easily discerned that) it suggests that at a surface level it did appear to understand your question and knew what a plausible answer might look like. This is not necessarily useful but it's quite impressive.
There are times it just generates complete nonsense that has nothing to do with what I said, but it's certainly not most of the time. I do not know how often, but I'd say it's definitely under 10% and almost certainly under 5% that the above happens.
Sure, LLMs are incredibly impressive from a technical standpoint. But they're so fucking stupid I hate using them.
> This is not necessarily useful but it's quite impressive.
An ability to answer questions with a train of thought showing how the answer was derived, or the self-awareness to recognize you do not have the ability to answer the question and declare as much. More than half the time I've used LLMs they will simply make answers up, and when I point out the answer is wrong it simply regurgitates another incorrect answer ad nauseum (regularly cycling through answers I've already pointed out are incorrect).
Rather than give you a technical answer - if I ever feel like an LLM can recognize its limitations rather than make something up, I would say it understands. In my experience LLMs are just algorithmic bullshitters. I would consider a function that just returns "I do not understand" to be an improvement, since most of the time I get confidently incorrect answers instead.
Yes, I read Anthropic's paper from a few days ago. I remain unimpressed until talking to an LLM isn't a profoundly frustrating experience.
A stochastic parrot with a sufficiently tiny residual error rate needs a stochastic model so precisely compressing the world and sophisticated decompression algorithms that it could be called reasoning.
Take two 4K frames of a falling vase, ask a model to predict the next token... I mean the following images. Your model now needs include some approximations of physics - and the ability to apply it correctly - to produce a realistic outcome. I'm not aware of any model capable of doing that, but that's what it would mean to predict the unseen with high enough fidelity.
Ironically though an LLM powered search engine (some word about being perplexed) is becoming way better than the undisputed king of traditional search engines (something oogle)
It expands what they had before with AI Overviews, but I’m not sure how new either of those are. It showed up for me organically as an AI Mode tab on a native Google search in Firefox ironically.
It asks me to change some permissions, but that help page says this is only available in the US, so I suppose I'll get blocked right after I change them.
> I guess the problem with LLMs is that they're too usable for their own good, so people don't realizing that they can't perfectly know all the trivia in the world, exactly the same as any human.
They're quite literally being sold as a replacement for human intellectual labor by people that have received uncountable sums of investment money towards that goal.
The author of the post even says this:
"These machines will soon become the beating hearts of the society in which we live. The social and political structures they create as they compose and interact with each other will define everything we see around us."
Can't blame people "fact checking" something that's supposed to fill these shoes.
People should be (far) more critical of LLMs given all of these style of bold claims, not less.
Also, telling people they're "holding it wrong" when they interact with alleged "Ay Gee Eye" "superintelligence" really is a poor selling point, and no way to increase confidence in these offerings.
These people and these companies don't get to make these claims that threaten the livelihood of millions of people, inflate a massive bubble, impact hiring decisions and everything else we've seen and then get excused cause "whoops you're not supposed to use it like that, dummy."
Your point is still trivially disproven by the fact that not even humans are expected to know all the world's trivia off the top of their heads.
We can discuss whether LLMs live up to the hype, or we can discuss how to use this new tool in the best way. I'm really tired of HN insisting on discussing the former, and I don't want to take part in that. I'm happy to discuss the latter, though.
> Put an LLM on top of a search engine, and that's the appropriate tool for this use case.
Hm nope, now that the web if flooded by LLM generated content it's game over. I can't tell how many times I almost got fooled by recipes &co which seem legit at first but are utter non sense. And now we're feeding that garbage back to where it came from
Though a local model I'm running (gemma-3-27b-it; https://huggingface.co/lmstudio-community/gemma-3-27b-it-GGU...) just told me various correct sounding bits about his history with alcohol (correctly citing his alma mater and first wife), but threw in:
"Sobriety & AA: Newman got sober in 1964 and remained so for the rest of his life."
Which doesn't check out. And it includes plausible but completely hallucinated URLs (as well as a valid biography.com URL that completely omits information about alcohol.)
Gemma 3 4B (QAT quant):
Yes, Paul Newman was indeed known to have struggled with alcohol throughout his life. While he maintained a public image of a charming, clean-cut star, he privately battled alcoholism for many years. He sought treatment in the late 1980s and early 1990s and was reportedly very open about his struggles and the importance of seeking help.
> Yes, Paul Newman was known to have struggled with alcohol at certain points in his life. In his early years, particularly during his time in the Navy and into his acting career, Newman admitted to heavy drinking. He was open about his fondness for beer and once jokingly referred to himself as a “functional alcoholic.” In a 1988 interview with The New York Times, he acknowledged that he had a period where he drank too much, stating, “I was a very good drinker. I could put it away.” ...
This may have hit the nail on the head about the weaknesses of LLM's.
They're going to regurgitate something not so much based on facts, but based on things that are accessible as perceived facts. Those might be right, but they might be wrong also; and no one can tell without doing the hard work of checking original sources. Many of what are considered accepted facts, and also accessible to LLM harvesting, are at best derived facts, often mediated by motivated individuals, and published to accessible sources by "people with an interest".
The weightings used by any AI should be based on the facts, and not the compounded volume of derived, "mediated", or "directed" facts - simply, because they're not really facts; they're reports.
It all seems like dumber, lazier search engine stuff. Honestly, what do I know about Paul Newman? But, Joanne Woodward and others who knew and worked with him should be weighted as being, at least, slightly more credible that others; no matter how many text patterns "catch the match" flow.
These models are not reliable sources of information. They are either out of date, subject to hallucination, or just plain wrong for a variety of reasons. They are untrustworthy to ask facts like this.
I appreciate your consideration of a subjective question and how you explained it and understand these nuances. But please - do not trust chatgpt etc. I continue to be frustrated at the endless people claiming something is true from chatgpt. I support the conclusions of this author.
Yes, Paul Newman struggled with alcohol. His issues with alcohol were explored in the HBO Max documentary, The Last Movie Stars, and Shawn Levy's biography, Paul Newman: A Life. According to a posthumous memoir, Newman was tormented by self-doubt and insecurities and questioned his acting ability. His struggles with alcohol led to a brief separation from Joanne Woodward, though it had nothing to do with cheating.
(4x Source footnotes omitted for readability)
# Ki Multi-step Research Assistant
Paul Newman is known to have struggled with alcohol. According to his posthumous memoir, Newman candidly discussed his issues with drinking and self-doubt, describing himself as an alcoholic who was tormented by insecurities[^1][^2]. He reportedly drank a significant amount of beer daily and later moved on to stronger drinks like Scotch[^3][^4]. His drinking habits were a notable part of his life, and he was often identified by his beer drinking[^5][^6]. Despite these struggles, Newman was also recognized for his generosity and devotion to his family[^7].
> "According to a posthumous memoir, Newman was tormented by self-doubt and insecurities and questioned his acting ability. His struggles with alcohol led to a brief separation from Joanne Woodward, though it had nothing to do with cheating."
'though it had nothing to do with cheating' is a weird inclusion.
I can’t reproduce. Maybe others reported the error and someone adjusted the expected answer, I do not know enough about OpenAI operations to say for sure.
The reason this bothers me is that comments like this reinforce the believes of people that could otherwise find value in these tools.
But I think points like this would be better made in shared chats or screenshots, since we do not have something like a core dump or stacktrace to attach.
And while I am not saying OP did this, I have seen technically skilled engineers asserting/implying that llm/chatbots aren’t good or not useful to them look at their chat log that a multitude of topics that I am sure would impact the result of the query.
Yes. It can be an UX problem. Yes. It can be an algorithmc problem.
But they are just tools that can be used wrong and not a perfect mechanical brain.
Yes, Paul Newman did experience significant struggles with alcohol. In his posthumously published memoir, The Extraordinary Life of an Ordinary Man, Newman candidly discusses his drinking habits and acknowledges his long-term battle with alcoholism. He describes himself as a "functioning alcoholic," a trait he noted was shared with his father. At one point, Newman was reported to consume a case of beer daily, followed by spirits, until he eventually gave up hard liquor.
Excluding the ones that do not support chat completions, all but one (qwen-qwq-32b) answered in the affirmative. The answer from qwen-qwq-32b said:
Paul Newman, the renowned actor and humanitarian, did not have a widely publicized
struggle with alcohol addiction throughout most of his life, but there were
specific instances that indicated challenges.
Using lack of progress in a specialized field as a barometer for overall progress is kind of silly. I just spent the last few days 'vibe coding' an application and I have to say that it's pretty remarkable how capable it is now relative to my experience last year.
It took three minutes for me to do the above from the time I created my API key to when I had an answer.
I find that everyone who replies with examples like this is an expert using expert skills to get the LLM to perform. Which makes me think why is this a skill that is useful to general public as opposed to another useful skill for technical knowledge workers to add to their tool belt?
I agree. But I will say that at least in my social circles I'm finding that a lot of people outside of tech are using these tools, and almost all of them seem to have a healthy skepticism about the information they get back. The ones that don't will learn one way or the other.
Perplexity had no problem with the question, replying in part, "Paul Newman is known to have struggled with alcohol throughout his life, as revealed in various accounts, including his own memoir and documentaries about his life...."
You may know this, but that doesn't mean we all do. LLMs are marketed towards many different groups, including folks who don't have much tech knowledge.
I don't think this reflects a flaw in LLM's at all. It is true that Paul Newman is not "known for" having alcohol problems. Paul Newman is known for acting, and salad dressing. Paul Newman has been "known to" have had alcohol problems, which formed as a question correctly one-shots accurate results for me across a few common LLMs. Someone like Betty Ford could be "known for" having alcohol problems. Paul Newman's alcohol problems are relatively specialized knowledge.
good point, though a great LLM answer would DWIM here and explain both interpretations as you have done here, as opposed to (what presumably happened) focus on one and ignore the other.
Yes, Paul Newman was known for being a heavy drinker, particularly of beer. 1 He acknowledged his high consumption levels himself.
1.
Review: Paul Newman memoir stuns with brutal honesty - AP News
apnews.com
While he maintained an incredibly successful career and public life, accounts and biographies note his significant alcohol intake, often describing it as a functional habit rather than debilitating alcoholism, although the distinction can be debated. He reportedly cut back significantly in his later years.
Unless you're expecting an LLM to have access to literally all information on earth at all times I find it really hard to care about this particular type of complaint.
My calculator can't conjugate German verbs. That's fine IMO. It's just a tool
Yes but a tool for what? When asked a question individuals that don't already have detailed knowledge of a topic are left with no way to tell if the AI generated response is complete bullshit, uselessly superficial, or detailed and on point. The only way to be sure is to then go do the standard search engine grovel looking for authoritative sources.
Counterpoint: Paul Newman was absolutely a famous drunk, as evidenced by this Wikipedia page.* Any query for "paul newman alcohol" online will return dozens of reputable sources on the topic. Your post is easily interpretable as handwaving apologetics, and it gives big "Its the children who are wrong" energy.
How else does an LLM distinguish what is widely known, given there are no statistics collected on the general populations awareness of any given celebrities vices? Robo-apologetics in full force here.
> I use ChatGPT for many tasks every day, but I couldn't fathom that it would get so wrong something so simple.
I think we'll have a term like we have for parents/grandparents that believe everything they see on the internet but specifically for people using LLMs.
The core point in this article is that the LLM wants to report _something_, and so it tends to exaggerate. It’s not very good at saying “no” or not as good as a programmer would hope.
When you ask it a question, it tends to say yes.
So while the LLM arms race is incrementally increasing benchmark scores, those improvements are illusory.
The real challenge is that the LLM’s fundamentally want to seem agreeable, and that’s not improving. So even if the model gets an extra 5/100 math problems right, it feels about the same in a series of prompts which are more complicated than just a ChatGPT scenario.
I would say the industry knows it’s missing a tool but doesn’t know what that tool is yet. Truly agentic performance is getting better (Cursor is amazing!) but it’s still evolving.
I totally agree that the core benchmarks that matter should be ones which evaluate a model in agentic scenario, not just on the basis of individual responses.
Yeah, and they probably have more "agreeable" stuff in their corpus simply because very disagreeable stuff tend to be either much shorter or a prelude to a flamewar.
You're right that LLMs don't actually want anything. That said, in reinforcement learning, it's common to describe models as wanting things because they're trained to maximize rewards. It’s just a standard way of talking, not a claim about real agency.
Reinforcement learning, maximise rewards? They work because rabbits like carrots. What does an LLM want? Haven't we already committed the fundamental error when we're saying we're using reinforcement learning and they want rewards?
That sound reasonable to me, but the those companies forget that there's different types of agreeable. There's the LLM approach, similar to the coworker who will answer all your questions about .NET but not stop you from coding yourself into a corner, and then there's the "Let's sit down and review what it actually is that you're doing, because you're asking a fairly large number of disjoint questions right now".
I've dropped trying to use LLMs for anything, due to political convictions and because I don't feel like they are particularly useful for my line of work. Where I have tried to use various models in the past is for software development, and the common mistake I see the LLMs make is that they can't pick up on mistakes in my line of thinking, or won't point them out. Most of my problems are often down to design errors or thinking about a problem in a wrong way. The LLMs will never once tell me that what I'm trying to do is an indication of a wrong/bad design. There are ways to be agreeable and still point out problems with previously made decisions.
I think it's your responsibility to control the LLM. Sometimes, I worry that I'm beginning to code myself into a corner, and I ask if this is the dumbest idea it's ever heard and it says there might be a better way to do it. Sometimes I'm totally sceptical and ask that question first thing. (Usually it hallucinates when I'm being really obtuse though, and in a bad case that's the first time I notice it.)
> I think it's your responsibility to control the LLM.
Yes. The issue here is control and NLP is a poor interface to exercise control over the computer. Code on the other hand is a great way. That is the whole point of skepticism around LLM in software development.
> The core point in this article is that the LLM wants to report _something_, and so it tends to exaggerate. It’s not very good at saying “no” or not as good as a programmer would hope.
umm, it seems to me that it is this (tfa):
But I would nevertheless like to submit, based off of internal
benchmarks, and my own and colleagues' perceptions using these models,
that whatever gains these companies are reporting to the public, they
are not reflective of economic usefulness or generality.
and then couple of lines down from the above statement, we have this:
So maybe there's no mystery: The AI lab companies are lying, and when
they improve benchmark results it's because they have seen the answers
before and are writing them down.
[this went way outside the edit-window and hence a separate comment]
imho, state of varying experience with llm's can aptly summed in this poem by Mr. Longfellow
There was a little girl,
Who had a little curl,
Right in the middle of her forehead.
When she was good,
She was very good indeed,
But when she was bad she was horrid.
It's, in many ways, the same problem as having too many "yes men" on a team at work or in your middle management layer. You end up getting wishy-washy, half-assed "yes" answers to questions that everyone would have been better off if they'd been answered as "no" or "yes, with caveats" with predictable results.
In fact, this might be why so many business executives are enamored with LLMS/GenAI: It's a yes-man they don't even have to employ, and because they're not domain experts, as per usual, they can't tell that they're being fed a line of bullshit.
This rings true. What I notice is that the longer i let Claude work on some code for instance, the more bullshit it invents. I usually can delete about 50-60% of the code & tests it came up with.
And when you ask it to 'just write a test' 50/50 it will try to run it, fail on some trivial issues, delete 90% of your test code and start to loop deeper and deeper into the rabit hole of it's own halliciations.
Every time someone argues for the utility of LLMs in software development by saying you need to be better at prompting, or add more rules for the LLM on the repository, they are making an argument against using NLP in software development.
The whole point of code is that it is a way to be very specific and exact and to exercise control over the computer behavior. The entire value proposition of using an LLM is that it is easier because you don't need to be so specific and exact. If then you say you need to be more specific and exact with the prompting, you are slowly getting at the fact that using NLP for coding is a bad idea.
This is a bit of a meta-comment, but reading through the responses to a post like this is really interesting because it demonstrates how our collective response to this stuff is (a) wildly divergent and (b) entirely anecdote-driven.
I have my own opinions, but I can't really say that they're not also based on anecdotes and personal decision-making heuristics.
But some of us are going to end up right and some of us are going to end up wrong and I'm really curious what features signal an ability to make "better choices" w/r/t AI, even if we don't know (or can't prove) what "better" is yet.
Agreed! And with all the gaming of the evals going on, I think we're going to be stuck with anecdotal for some time to come.
I do feel (anecdotally) that models are getting better on every major release, but the gains certainly don't seem evenly distributed.
I am hopeful the coming waves of vertical integration/guardrails/grounding applications will move us away from having to hop between models every few weeks.
Frankly the overarching story about evals (which receives very little coverage) is how much gaming is going on. On the recent USAMO 2025, SOTA models scored 5%, despite claiming silver/gold in IMOs. And ARC-AGI: one very easy way to "solve" it is to generate masses of synthetic examples by extrapolating the basic rules of ARC AGI questions and train it on that.
It's not surprising that responses are anecdotal. An easy way to communicate a generic sentiment often requires being brief.
A majority of what makes a "better AI" can be condensed to how effective the slope-gradient algorithms are at getting the local maxima we want it to get to. Until a generative model shows actual progress of "making decisions" it will forever be seen as a glorified linear algebra solver. Generative machine learning is all about giving a pleasing answer to the end user, not about creating something that is on the level of human decision making.
At risk of being annoying, answers that feel like high quality human decision making are extremely pleasing and desirable. In the same way, image generators aren't generating six fingered hands because they think it's more pleasing, they're doing it because they're trying to please and not good enough yet.
I'm just most baffled by the "flashes of brilliance" combined with utter stupidity. I remember having a run with early GPT 4 (gpt-4-0314) where it did refactoring work that amazed me. In the past few days I asked a bunch of AIs about similar characters between a popular gacha mobile game and a popular TV show. OpenAI's models were terrible and hallucinated aggressively (4, 4o, 4.5, o3-mini, o3-mini-high), with the exception of o1. DeepSeek R1 only mildly hallucinated and gave bad answers. Gemini 2.5 was the only flagship model that did not hallucinate and gave some decent answers.
I probably should have used some type of grounding, but I honestly assumed the stuff I was asking about should have been in their training datasets.
Good observation but also somewhat trivial. We are not omniscient gods, ultimately all our opinions and decisions will have to be based on our own limited experiences.
There is nothing wrong with sharing anecdotal experiences. Reading through anecdotal experiences here can help understand how one's own experience are relatable or not. Moreover, if I have X experience it could help to know if it is because of me doing sth wrong that others have figured out.
Furthermore, as we are talking about actual impact of LLMs, as is the point of the article, a bunch of anecdotal experiences may be more valuable than a bunch of benchmarks to figure it out. Also, apart from the right/wrong dichotomy, people use LLMs with different goals and contexts. It may not mean that some people do something wrong if they do not see the same impact as others. Everytime a web developer says that they do not understand how others may be so skeptical of LLMs, conclude with certainty that they must be doing sth wrong and move on to explain how to actually use LLMs properly, I chuckle.
Indeed, there’s nothing at all wrong with sharing anecdotes. The problem is when people make broad assumptions and conclusions based solely on personal experience, which unfortunately happens all too often. Doing so is wired into our brains, though, and we have to work very consciously to intercept our survival instincts.
I think you might be caught up in a bit of the rationalist delusion.
People -only!- draw conclusions based on personal experience. At best you have personal experience with truly objective evidence gathered in a statistically valid manner.
But that only happens in a few vanishingly rare circumstances here on earth. And wherever it happens, people are driven to subvert the evidence gathering process.
Often “working against your instincts” to be more rational only means more time spent choosing which unreliable evidence to concoct a belief from.
People "make conclusions" because they have to take decisions day to day. We cannot wait for the perfect bulletproof evidence before that. Data is useful to take into account, but if I try to use X llm that has some perfect objective benchmark backing it, while I cannot make it be useful to me while Y llm has better results, it would be stupid not to base my decision on my anecdotal experience. Or vice versa, if I have a great workflow with llms, it may be not make sense to drop it because some others may think that llms don't work.
In the absence of actually good evidence, anecdotal data may be the best we can get now. The point imo is try to understand why some anecdotes are contrasting each other, which, imo, is mostly due to contextual factors that may not be very clear, and to be flexible enough to change priors/conclusions when something changes in the current situation.
Agreed 100%. When insufficient data exists, you have to fall back to other sources like analogies, personal observations, secondhand knowledge, etc. However, I’ve seen too many instances of people claiming their own limited experience is the truth when overwhelming and easily attainable evidence and data exists that proves it to be false.
Totally agree... this space is still so new and unpredictable that everyone is operating off vibes, gut instinct, and whatever personal anecdotes they've collected. We're all sort of fumbling around in the dark, trying to reverse-engineer the flashlight
You want to block subjectivity? Write some formulas.
There are three questions to consider:
a) Have we, without any reasonable doubt, hit a wall for AI development? Emphasis on "reasonable doubt". There is no reasonable doubt that the Earth is roughly spherical. That level of certainty.
b) Depending on your answer for (a), the next question to consider is if we the humans have motivations to continue developing AI.
c) And then the last question: will AI continue improving?
If taken as boolean values, (a), (b) and (c) have a truth table with eight values, the most interesting row being false, true, true: "(not a) and b => c". Note the implication sign, "=>". Give some values to (a) and (b), and you get a value for (c).
There are more variables you can add to your formula, but I'll abstain from giving any silly examples. I, however, think that the row (false, true, false) implied by many commentators is just fear and denial. Fear is justified, but denial doesn't help.
If you're gonna formulate this conversation as a satisfiability problem you should be aware that this is an NP-complete problem (and actually working on that problem is the source of the insight that there is such as thing as NP-completeness).
>"This is a bit of a meta-comment, but reading through the responses to a post like this is really interesting because it demonstrates how our collective response to this stuff is (a) wildly divergent and (b) entirely anecdote-driven."
People having vastly different opinions on AI simply comes down to token usage. If you are using millions of tokens on a regular basis, you completely understand the revolutionary point we are at. If you are just chatting back and forth a bit with something here and there, you'll never see it.
It's a tool and like all tools, it's sensitive to how you use it, and it's better for some purposes than others.
Someone who lacks experience, skill, training, or even the ability to evaluate results may try to use a tool and blame the tool when it doesn't give good results.
That said, the hype around LLMs certainly overstates their capabilities.
So this is interesting because it's anecdotal (I presume you're a high-token user who believes it's revolutionary), but it's actually a measurable, falsifiable hypothesis in principle.
I'd love to see a survey from a major LLM API provider that correlated LLM spend (and/or tokens) with optimism for future transformativity. Correlation with a view of "current utility" would be a tautology, obviously.
I actually have the opposite intuition from you: I suspect the people using the most tokens are using it for very well-defined tasks that it's good at _now_ (entity extraction, classification, etc) and have an uncorrelated position on future potential. Full disclosure, I'm in that camp.
Token usage meaning via agentic processes. Essentially every gripe about LLMs over the last few years (hallucinations, lack of real time data, etc.) was a result of single shot prompting directly to models. No one is seriously doing that for anything at this point anymore. Yes, you spend ten times more on a task, and it takes much longer. But your results are meaningful and useful at the end, and you can actually begin to engineer systems on top of that now.
It seems like the models are getting more reliable at the things they always could do, but they’re not showing any ability to move past that goalpost. Whereas in the past, they could occasionally write some very solid code, but often return nonsense, the nonsense is now getting adequately filtered by so-called “reasoning”, but I see no indication that they could do software design.
> how the hell is it going to develop metrics for assessing the impact of AIs when they're doing things like managing companies or developing public policy?
Why on earth do people want AI to do either of these things? As if our society isn’t fucked enough, having an untouchable oligarchy already managing companies and developing public policies, we want to have the oligarchy’s AI do this, so policy can get even more out of touch with the needs of common people? This should never come to pass. It’s like people read a pile of 90s cyberpunk dystopian novels and decided, “Yeah, let’s do that.” I think it’ll fail, but I don’t understand how anyone with less than 10 billion in assets would want this.
> Why on earth do people want AI to do either of these things?
This is the really important question, and the only answer I can drum up is that people have been fed a consistent diet of propaganda for decades centered around a message that ultimately boils down to a justification of oligarchy and the concentration of wealth. That and the consumer-focus facade makes people think the LLMS are technology for them—they aren't. As soon as these things get good enough business owners aren't going to expect workers to use them to be more productive, they are just going to fire workers and/or use the tooling as another mechanism by which to let wages stagnate.
The disconnect between improved benchmark results and lack of improvement on real world tasks doesn't have to imply cheating - it's just a reflection of the nature of LLMs, which at the end of the day are just prediction systems - these are language models, not cognitive architectures built for generality.
Of course, if you train an LLM heavily on narrow benchmark domains then its prediction performance will improve on those domains, but why would you expect that to improve performance in unrelated areas?
If you trained yourself extensively on advanced math, would you expect that to improve your programming ability? If not, they why would you expect it to improve programming ability of a far less sophisticated "intelligence" (prediction engine) such as a language model?! If you trained yourself on LeetCode programming, would you expect that to help hardening corporate production systems?!
That's fair. But look up the recent experiment on SOTA models on the then just released USAMO 2025 questions. Highest score was 5%, supposedly SOTA last year was IMO silver level. There could be some methodological differences - ie USAMO paper required correct proofs and not just numerical answers. But it really strongly suggests even within limited domains, it's cheating. I'd wager a significant amount that if you tested SOTA models on a new ICPC set of questions, actual performance would be far, far worse than their supposed benchmarks.
Your analogy is perfect. Training an LLM on math olympiad problems and then expecting it to secure enterprise software is like teaching someone chess and handing them a wrench
I honestly can’t notice any difference in outdoor quality between GPT 4o and GPT 4.5. I also can’t notice any difference in programming quality in cursor when using Claude 3.7 vs 3.5. I’m told there is a clear difference, but I don’t notice it.
Who would assume that LLM companies were to hyper optimise on public to make their share prices go up and bubble keep afloat ... What a unserious thought to maintain ...
475 comments
[ 3.1 ms ] story [ 408 ms ] threadA 4-bit quant of QwQ-32B is surprisingly close to Claude 3.5 in coding performance. But it's small enough to run on a consumer GPU, which means deployment price is now down to $0.10 per hour. (from $12+ for models requiring 8x H100)
What innovation opens up when AI gets sufficiently commoditized?
For example, you can see this in health insurance reimbursements and wireless carriers plan changes. (ie, Verizon’s shift from Do More, etc to what they have now)
Companies basically set up circumstances where consumers lose small amounts of money on a recurring basis or sporadically enough that the people will just pay the money rather than a maze of calls, website navigation and time suck to recover funds due to them or that shouldn’t have been taken in the first place.
I’m hopeful well commoditized AI will give consumers a fighting chance at this and other types of disenfranchisement that seems to be increasingly normalized by companies that have consultants that do nothing but optimize for their own financial position.
In my evals 8 bit quantized smaller Qwen models were better, but again evaluating is hard.
I think they should’ve named it something else.
It was reverse engineering ~550MB of Hermes bytecode from a react native app, with each function split into a separate file for grep-ability and LLM compatibility.
The others would all start off right then quickly default to just greping randomly what they expected it to be, which failed quickly. 2.5 traced the function all the way back to the networking call and provided the expected response payload.
All the others hallucinated the networking response I was trying to figure out. 2.5 Provided it exactly enough for me to intercept the request and using the response it provided to get what I wanted to show up.
https://i.imgur.com/Cmg4KK1.png
https://i.imgur.com/ApNxUkB.png
But it still feels more like a small incremental improvement rather than a radical change, and I still feel its limitations constantly.
Like... it gives me the sort of decent but uninspired solution I would expect it to generate without predictably walking me through a bunch of obvious wrong turns as I repeatedly correct it as I would have to have done with earlier models.
And that's certainly not nothing and makes the experience of using it much nicer, but I'm still going to roll my eyes anytime someone suggests that LLMs are the clear path to imminently available AGI.
I'm wondering how much gemini 2.5 being "amazing" comes from sonnet-3.7 being such a disappointment.
Even Sonnet 3.7 was able to do refactoring work on my codebase sonnet 3.6 could not.
Really not seeing the "LLMs not improving" story
Well.. they've been caught again and again red handed doing exactly this. Fool me once shame on you, fool me 100 times shame on me.
https://www.youtube.com/shorts/LmFN8iENTPc
This is likely a manifestation of the bitter lesson[1], specifically this part:
> The ultimate reason for this is Moore's law, or rather its generalization of continued exponentially falling cost per unit of computation. Most AI research has been conducted as if the computation available to the agent were constant (in which case leveraging human knowledge would be one of the only ways to improve performance) but, over a slightly longer time than a typical research project [like an incremental model update], massively more computation inevitably becomes available.
(Emphasis mine.)
Since the ultimate success strategy of the scruffies[2] or proponents of search and learning strategies in AI is Moore's Law, short term gains using these strategies will be miniscule. It is over at least a five year period that their gains will be felt the most. The neats win the day in the short term, but the hare in this race will ultimately give away to the steady plod of the tortoise.
1: http://www.incompleteideas.net/IncIdeas/BitterLesson.html
2: https://en.m.wikipedia.org/wiki/Neats_and_scruffies#CITEREFM...
Where’s the business model? Suck investors dry at the start of a financial collapse? Yeah that’s going to end well…
For who? Nvidia sell GPUs, OpenAI and co sell proprietary models and API access, and the startups resell GPT and Claude with custom prompts. Each one is hoping that the layer above has a breakthrough that makes their current spend viable.
If they do, then you don’t want to be left behind, because _everything_ changes. It probably won’t, but it might.
That’s the business model
This bubble will be burst by the Trump tariffs and the end of the zirp era. When inflation and a recession hit together hope and dream business models and valuations no longer work.
> NVDA will crash when the AI bubble implodes, > making money, nor will they > They have already hit limiting returns in LLM improvements after staggering investments > and it is clear are nowhere near general intelligence.
These are all assumptions and opinions, and have nothing to do with whether or not they have a business model. You mightn't like their business model, but they do have one.
These are of course just opinions, I’m not sure we can know facts about such companies except in retrospect.
Now we get to see if Bitcoin’s use value of 0 is really supporting 1.5 trillion market cap and if OpenAI is really worth $300 billion.
I mean softbank just invested in openai, and they’ve never been wrong, right?
The same is true for any non essential good or service.
Indeed it can. The difference between a business model and a viable business model is one word - viable.
If you asked me 18 years ago was "giving away a video game and selling cosmetics" a viable business model I would have laughed at you.If you asked me in 2019 I would probably give you money. If you asked me in 2025, I'd probably laugh at you again.
> and I need to borrow larger an larger lumps of money each time to keep spinning the wheel...
Or you figure out a way to to sell it to your neighbour for $0.50 and he can sell it on for $1.
The play is clear at every level - Nvidia Sell GPUs, OpenAI sell models, and SAAS sell prompts + UI's. Whether or not any of them are viable remains to be seen. Personally, I wouldn't take the bet.
Just because it's not reaching the insane hype being pushed doesn't mean it's totally useless
Learning to harness current tools helps to harness future tools. Work on projects that will benefit from advancements, but can succeed without them.
Example: I frequently get requests for data from Customer Support that used to require 15 minutes of my time noodling around writing SQL queries. I can cut that down to less than a minute now.
Wasn't it back in the 1980s that you had to pay $1000s for a good compiler? The entire LLM industry might just be following in the compiler's footsteps.
The fact is, the phrase "artificial intelligence" is a memetic hazard: it immediately positions the subject of conversation as "default capable", and then forces the conversation into trying to describe what it can't do, which is rarely a useful way to approach it.
Whereas with LLMs (and chess engines and every other tech advancement) it would be more useful to start with what the tech _can_ do and go from there.
Also:
> I think what's going on is that large language models are trained to "sound smart" in a live conversation with users, and so they prefer to highlight possible problems instead of confirming that the code looks fine, just like human beings do when they want to sound smart.
I immediately thought: That's because in most situations this is the purpose of language, at least partially, and LLMs are trained on language.
A few months ago I looked at essentially this problem from a different angle (generating system diagrams from a codebase). My conclusion[0] was the same as here: LLMs really struggle to understand codebases in a holistic way, especially when it comes to the codebase's strategy and purpose. They therefore struggle to produce something meaningful from it like a security assessment or a system diagram.
[0] https://www.ilograph.com/blog/posts/diagrams-ai-can-and-cann...
I'm not surprised, because I don't expect pattern matching systems to grow into something more general and useful. I think LLM's are essentially running into the same limitations that the "expert systems" of the 1980's ran into.
Personally, I think the models are “good enough” that we need to start seeing the improvements in tooling and applications that come with them now. I think MCP is a good step in the right direction, but I’m sceptical on the whole thing (and have been since the beginning, despite being a user of the tech).
I think this is where AI is faling short hugely. AI _should_ be able to integrate with IDEs and tooling (e.g. LSP, Treesitter, Editorconfig) to make sure that it's contextually doing the right thin.
But it's not.
The problem is that up until _very_ recently, it's been possible to get LLMs to generate interesting and exciting results (as a result of all the API documentation and codebases they've inhaled), but it's been very hard to make that usable. I think we need to be able to control the output format of the LLMs in a better way before we can work on what's in the output. I don't konw if MCP is the actual solution to that, but it's certainly an attempt at it...
It probably depends a lot on what you are using them for, and in general, I think it's still too early to say exactly where LLMs will lead us.
People who can’t recognize this intentionally have their heads in the sand
1) AI undoubtedly has utility. In many agentic uses, it has very significant utility. There's absolute utility and perceived utility, which is more of user experience. In absolute utility, it is likely git is the single most game changing piece of software there is. It is likely git has saved some ten, maybe eleven digit number in engineer hours times salary in how it enables massive teams to work together in very seamless ways. In user experience, AI is amazing because it can generate so much so quickly. But it is very far from an engineer. For example, recently I tried to use cursor to bootstrap a website in NextJS for me. It produced errors it could not fix, and each rewrite seemed to dig it deeper into its own hole. The reasons were quite obvious. A lot of it had to do with NextJS 15 and the breaking changes it introduces in cookies and auth. It's quite clear if you have masses of NextJS code, which disproportionately is older versions, but none labeled well with versions, it messes up the LLM. Eventually I scrapped what it wrote and did it myself. I don't mean to use this anecdote to say LLMs are useless, but they have pretty clear limitations. They work well on problems with massive data (like front end) and don't require much principled understanding (like understanding how NextJS 15 would break so and so's auth). Another example of this is when I tried to use it to generate flags for a V8 build, it failed horribly and would simply hallucinate flags all the time. This seemed very likely to be (despite the existence of a list of V8 flags online) that many flags had very close representations in vector embeddings, and that there was almost close to zero data/detailed examples on their use.
2) In the more theoretical side, the performance of LLMs on benchmarks (claiming to be elite IMO solvers, competitive programming solvers) have become incredibly suspicious. When the new USAMO 2025 was released, the highest score was 5%, despite claims a year ago that SOTA when was at least a silver IMO. This is against the backdrop of exponential compute and data being fed in. Combined with apparently diminishing returns, this suggests that the gains from that are running really thin.
"game changing" isn't exactly the sentiment there the last couple months.
Even approximations must be right to be meaningful. If information is wrong, it's rubbish.
Presorting/labelling various data has value. Humans have done the real work there.
What is "leading" us at present are the exaggerated valuations of corporations. You/we are in a bubble, working to justify the bubble.
Until a tool is reliable, it is not installed where people can get hurt. Unless we have revised our concern for people.
Which is software written 1966, but the web version is a little newer. Does occasional psychotherapy assistance/brainstorming just as well, and I more easily know when I stepped out of its known range into the extrapolated.
That said, it can vibe code in a framework unknown to me in half the time that I would need to school myself and add the feature.
Or vibe coding takes twice as long, if I mostly know how to achieve what I want and read no framework documentation but only our own project's source code to add a new feature. But on a day with a headache, I can still call the LLM a dumb twat and ask it to follow my instructions instead of doing bullshit.
But, vibe coding always makes my pulse go to 105, from 65 and question my life choices. Since few instructions are rarely ever followed and loops never left once entered. Except for on the first try getting 80% of the structure kinda right, but then getting stuck for the whole workday.
"Is Paul Newman known for having had problems with alcohol?"
All of the models up to o3-mini-high told me he had no known problems. Here's o3-mini-high's response:
"Paul Newman is not widely known for having had problems with alcohol. While he portrayed characters who sometimes dealt with personal struggles on screen, his personal life and public image were more focused on his celebrated acting career, philanthropic work, and passion for auto racing rather than any issues with alcohol. There is no substantial or widely reported evidence in reputable biographies or interviews that indicates he struggled with alcohol abuse."
There is plenty of evidence online that he struggled a lot with alcohol, including testimony from his long-time wife Joanne Woodward.
I sent my mom the ChatGPT reply and in five minutes she found an authoritative source to back her argument [1].
I use ChatGPT for many tasks every day, but I couldn't fathom that it would get so wrong something so simple.
Lesson(s) learned... Including not doubting my mother's movie trivia knowledge.
[1] https://www.newyorker.com/magazine/2022/10/24/who-paul-newma...
Also, you can/should use the "research" mode for questions like this.
This is niche in the grand scheme of knowledge but Paul Newman is easily one of the biggest actors in history, and the LLM has been trained on a massive corpus that includes references to this.
Where is the threshold for topics with enough presence in the data?
An LLM does not care about your question, it is a bunch of math that will spit out a result based on what you typed in.
Its good for broad general overview such as most popular categories of books in the world.
I use it for asking - often very niche - questions on advanced probability and simulation modeling, and it often gets those right - why those and not a simple verifiable fact about one of the most popular actors in history?
I don’t know about Idiocracy, but something that I have read specific warnings about is that people will often blame the user for any of the tool’s misgivings.
It's impressive how often AI returns the right answer to vague questions. (not always though)
Edit: and, more importantly, plenty of people willing to pay a subscription for good quality.
That is, as you point out, "all of the models up to o3-mini-high" give an incorrect answer, while other comments say that OpenAIs later models give correct answers, with web citations. So it would seem to follow that "recent AI model progress" actually made a verifiable improvement in this case.
> Paul Newman was not publicly known for having major problems with alcohol in the way some other celebrities have been. However, he was open about enjoying drinking, particularly beer. He even co-founded a line of food products (Newman’s Own) where profits go to charity, and he once joked that he consumed a lot of the product himself — including beer when it was briefly offered.
> In his later years, Newman did reflect on how he had changed from being more of a heavy drinker in his youth, particularly during his time in the Navy and early acting career, to moderating his habits. But there’s no strong public record of alcohol abuse or addiction problems that significantly affected his career or personal life.
> So while he liked to drink and sometimes joked about it, Paul Newman isn't generally considered someone who had problems with alcohol in the serious sense.
As other's have noted, LLMs are much more likely to be cautious in providing information that could be construed as libel. While Paul Newman may have been an alcoholic, I couldn't find any articles about it being "public" in the same way as others, e.g. with admitted rehab stays.
0 https://www.google.com/search?q=did+paul+newman+have+a+drink...
I don't think that is a safe assumption these days. Training modern LLM isn't about dumping in everything on the Internet. To get a really good model you have to be selective about your sources of training data.
They still rip off vast amounts of copyrighted data, but I get the impression they are increasingly picky about what they dump into their training runs.
"AI is making incredible progress but still struggles with certain subsets of tasks" is self-consistent position.
The abilities of LLM alone to do astounding natural language processing beyond the ability of anything prior by unthinkable Turing test passing miles. The fact it can reason abductively, which computing techniques to date have been unable to is amazing. The fact you can mix it with multimodal regimes - images, motion, virtually anything that can be semantically linked via language, is breathtaking. The fact it can be augmented with prior computing techniques - IR, optimization, deductive solvers, and literally everything we’ve achieved to date should give anyone knowledgeable of such things shivers for what the future holds.
But I would never hold that generative AI techniques are replacements for known optimal techniques. But the ensemble is probably the solution to nearly every challenge we face. When we hit the limits of LLMs today, I think, well, at least we already have grand master beating chess solvers and it’s irrelevant the LLM can’t directly. The LLM and other generative AI techniques in my mind are like gasses that fill through learned approximation the things we’ve not been able to solve directly, including the assembly of those solutions ad hoc. This is why since the first time BERT came along I knew agent based techniques were the future.
Right now we live at time like early hypertext with respect to AI. Toolchains suck, LLMs are basically geocities pages with “under construction” signs. We will go through an explosive exploration, some stunning insights that’ll change the basic nature of our shared reality (some wonderful some insidious), then if we aren’t careful - and we rarely are - enshitification at scale unseen before.
I guess the problem with LLMs is that they're too usable for their own good, so people don't realizing that they can't perfectly know all the trivia in the world, exactly the same as any human.
LLMs are literally fundamentally incapable of understanding things. They are stochastic parrots and you've been fooled.
Look around you
Look at Skyscrapers. Rocket ships. Agriculture.
If you want to make a claim that humans are nothing more than stochastic parrots then you need to explain where all of this came from. What were we parroting?
Meanwhile all that LLMs do is parrot things that humans created
Rocket ships: volcanic eruptions show heat and explosive outbursts can fling things high, gunpowder and cannons, bellows showing air moves things.
Agriculture: forests, plains, jungle, desert oases, humans knew plants grew from seeds, grew with rain, grew near water, and grew where animals trampled them into the ground.
We need a list of all atempted ideas, all inventions and patents that were ever tried or conceived, and then we see how inventions are the same random permutations on ideas with Darwinian style survivorship as everything else; there were steel boats with multiple levels in them before skyscrapers; is the idea of a tall steel building really so magical when there were over a billion people on Earth in 1800 who could have come up with it?
My point is that humans did come up with it. Humans did not parrot it from someone or something else that showed it to us. We didn't "parrot" splitting the atom. We didn't learn how to build skyscrapers from looking at termite hills and we didn't learn to build rockets that can send a person to the moon from seeing a volcano
You are just speaking absolute drivel
Prompt: "Can you give me a URL with some novel components, please?"
DuckDuckGo LLM returns: "Sure! Here’s a fictional URL with some novel components: https://www.example-novels.com/2023/unique-tales/whimsical-j..."
An living parrot echoing "pieces of eight" cannot do this, it cannot say "pieces of <currency>" or "pieces of <valuable mineral>" even if asked to do that. The LLM training has abstracted some concept of what it means for a text pattern to be a URL and what it means for things to be "novel" and what it means to switch out the components of a URL but keep them individually valid. It can also give a reasonable answer asking for a new kind of protocol. So your position hinges on the word "stochastic" which is used as a slur to mean "the LLM isn't innovating like we do it's just a dice roll of remixing parts it was taught". But if you are arguing that makes it a "stochastic parrot" then you need to consider splitting the atom in its wider context...
> "We didn't "parrot" splitting the atom"
That's because we didn't "split the atom" in one blank-slate experiment with no surrounding context. Rutherford and team disintegrated the atom in 1914-1919 ish, they were building on the surrounding scientific work happening at that time: 1869 Johann Hittorf recognising that there was something coming in a straight line from or near the cathode of a Crookes vacuum tube, 1876 Eugen Goldstein proving they were coming from the cathode and naming them cathode rays (see: Cathode Ray Tube computer monitors), and 1897 J.J Thompson proving the rays are much lighter than the lightest known element and naming them Electrons, the first proof of sub-atomic particles existing. He proposed the model of the atom as a 'plum pudding' (concept parroting). Hey guess who JJ Thomspon was an academic advisor of? Ernest Rutherford! 1911 Rutherford discovery of the atomic nucleus. 1909 Rutherford demonstrated sub-atomic scattering and Millikan determined the charge on an electron. Eugen Goldstein also discovered the anode rays travelling the other way in the Crookes tube and that was picked up by Wilhelm Wien and it became Mass Spectrometry for identifying elements. In 1887 Heinrich Hertz was investigating the Photoelectric effect building on the work of Alexandre Becquerel, Johann Elster, Hans Geitel. Dalton's atomic theory of 1803.
Not to mention Rutherford's 1899 studies of radioactivity, following Henri Becquerel's work on Uranium, following Marie Curie's work on Radium and her suggestion of radioactivity being atoms breaking up, and Rutherford's student Frederick Soddy and his work on Radon, and Paul Villard's work on Gamma Ray emissions from Radon.
When Philipp Lenard was studying cathode rays in the 1890s he bought up all the supply of one phosphorescent material which meant Röntgen had to buy a different one to reproduce the results and bought one which responded to X-Rays as well, and that's how he discovered them - not by pure blank-sheet intelligence but by probability and randomness applied to an earlier concept.
That is, nobody taught humans to split the atom and then humans literally parotted the mechanism and did it, but you attempting to present splitting the atom as a thing which appeared out of nowhere and not remixing any existing concepts is, in your terms, absolute dr...
Fireworks, cannons, chemistry experiments and flamethrowers are all human inventions
And yes, exactly! We studied orbits of moons and planets. We studied animals like Jellyfish. We choose to observe the world, we extracted data, we experimented, we saw what worked, refined, improved, and succeeded
LLMs are not capable of observing anything. They can only regurgitate and remix the information they are fed by humans! By us, because we can observe
An LLM trained on 100% wrong information will always return wrong information for anything you ask it.
Say you train an LLM with the knowledge that fire can burn underwater. It "thinks" that the step by step instructions for building a fire is to pile wood and then pour water on the wood. It has no conflicting information in its model. It cannot go try to build a fire this way and observe that it is wrong. It is a parrot. It repeats the information that you give it. At best it can find some relationships between data points that humans haven't realized might be related
A human could easily go attempt this, realize it doesn't work, and learn from the experience. Humans are not simply parrots. We are capable of exploring our surroundings and internalizing things without needing someone else to tell us how everything works
> That is, nobody taught humans to split the atom and then humans literally parotted the mechanism and did it, but you attempting to present splitting the atom as a thing which appeared out of nowhere and not remixing any existing concepts is, in your terms, absolute drivel
Building on the work of other humans is not parroting
You outlined the absolute genius of humanity building from first principles all the way to splitting the atom and you still think we're just parroting,
I think we disagree what parroting is entirely.
Of course, this turned out to be completely false, with advances in understanding of neural networks. Now, again with no evidence other than "we invented this thing that's, useful to us" people have been asserting that humans are just like this thing we invented. Why? What's the evidence? There never is any. It's high dorm room behavior. "What if we're all just machines, man???" And the argument is always that if I disagree with you when you assert this, then I am acting unscientifically and arguing for some kind of magic.
But there's no magic. The human brain just functions in a way different than the new shiny toys that humans have invented, in terms of ability to model an external world, in terms of the way emotions and sense experience are inseparable from our capacity to process information, in terms of consciousness. The hardware is entirely different, and we're functionally different.
The closest things to human minds are out there, and they've been out there for as long as we have: other animals. The real unscientific perspective is that to get high on your own supply and assert that some kind of fake, creepily ingratiating Spock we made up (who is far less charming than Leonard Nimony) is more like us than a chimp is.
I don’t know if there is a pithy shirt phrase to accurately describe how LLMs function. Can you give me a similar one for how humans think? That might spur my own creativity here.
I'm fairly sure I've never seen a deterministic parrot which makes me think the term is tautological.
I wouldn't claim LLMs are good at being factual, or good at arithmetic, or at drawing wine glasses, or that they are "clever". What they are very good at is responding to questions in a way which gives you the very strong impression they've understood you.
It clearly doesn't understand that the question has a correct answer, or that it does not know the answer. It also clearly does not understand that I hate bullshit, no matter how many dozens of times I prompt it to not make something up and would prefer an admittance of ignorance.
Although that isn't literally indistinguishable from 'understanding' (because your fact checking easily discerned that) it suggests that at a surface level it did appear to understand your question and knew what a plausible answer might look like. This is not necessarily useful but it's quite impressive.
Sure, LLMs are incredibly impressive from a technical standpoint. But they're so fucking stupid I hate using them.
> This is not necessarily useful but it's quite impressive.
I think we mostly agree on this. Cheers.
Rather than give you a technical answer - if I ever feel like an LLM can recognize its limitations rather than make something up, I would say it understands. In my experience LLMs are just algorithmic bullshitters. I would consider a function that just returns "I do not understand" to be an improvement, since most of the time I get confidently incorrect answers instead.
Yes, I read Anthropic's paper from a few days ago. I remain unimpressed until talking to an LLM isn't a profoundly frustrating experience.
Take two 4K frames of a falling vase, ask a model to predict the next token... I mean the following images. Your model now needs include some approximations of physics - and the ability to apply it correctly - to produce a realistic outcome. I'm not aware of any model capable of doing that, but that's what it would mean to predict the unseen with high enough fidelity.
https://labs.google.com/search/experiment/22
It expands what they had before with AI Overviews, but I’m not sure how new either of those are. It showed up for me organically as an AI Mode tab on a native Google search in Firefox ironically.
https://support.google.com/websearch/answer/16011537
What happens if you go directly to https://google.com/aimode ?
They're quite literally being sold as a replacement for human intellectual labor by people that have received uncountable sums of investment money towards that goal.
The author of the post even says this:
"These machines will soon become the beating hearts of the society in which we live. The social and political structures they create as they compose and interact with each other will define everything we see around us."
Can't blame people "fact checking" something that's supposed to fill these shoes.
People should be (far) more critical of LLMs given all of these style of bold claims, not less.
Also, telling people they're "holding it wrong" when they interact with alleged "Ay Gee Eye" "superintelligence" really is a poor selling point, and no way to increase confidence in these offerings.
These people and these companies don't get to make these claims that threaten the livelihood of millions of people, inflate a massive bubble, impact hiring decisions and everything else we've seen and then get excused cause "whoops you're not supposed to use it like that, dummy."
Nah.
We can discuss whether LLMs live up to the hype, or we can discuss how to use this new tool in the best way. I'm really tired of HN insisting on discussing the former, and I don't want to take part in that. I'm happy to discuss the latter, though.
Hm nope, now that the web if flooded by LLM generated content it's game over. I can't tell how many times I almost got fooled by recipes &co which seem legit at first but are utter non sense. And now we're feeding that garbage back to where it came from
"Yes, Paul Newman was widely known for being a heavy drinker, particularly of beer. He himself acknowledged his significant alcohol consumption."
The answer I got (https://gemini.google.com/share/9e327dc4be03) includes references such as https://apnews.com/article/entertainment-reviews-movies-paul... and https://www.psychologytoday.com/us/blog/the-playing-field/20... although they are redacted from the public-sharing link.
"Sobriety & AA: Newman got sober in 1964 and remained so for the rest of his life."
Which doesn't check out. And it includes plausible but completely hallucinated URLs (as well as a valid biography.com URL that completely omits information about alcohol.)
>Paul Newman is indeed known for having struggled with alcohol during his life. Accounts from various sources, including his own memoir and the documentary ... (https://www.perplexity.ai/search/is-paul-newman-known-for-ha...)
I guess there's something about ChatGPT's set up that makes it different? Maybe they wanted it to avoid libeling people?
> Yes, Paul Newman was known to have struggled with alcohol at certain points in his life. In his early years, particularly during his time in the Navy and into his acting career, Newman admitted to heavy drinking. He was open about his fondness for beer and once jokingly referred to himself as a “functional alcoholic.” In a 1988 interview with The New York Times, he acknowledged that he had a period where he drank too much, stating, “I was a very good drinker. I could put it away.” ...
https://grok.com/share/bGVnYWN5_86843e8a-39ee-415d-8785-4f8e...
They're going to regurgitate something not so much based on facts, but based on things that are accessible as perceived facts. Those might be right, but they might be wrong also; and no one can tell without doing the hard work of checking original sources. Many of what are considered accepted facts, and also accessible to LLM harvesting, are at best derived facts, often mediated by motivated individuals, and published to accessible sources by "people with an interest".
The weightings used by any AI should be based on the facts, and not the compounded volume of derived, "mediated", or "directed" facts - simply, because they're not really facts; they're reports.
It all seems like dumber, lazier search engine stuff. Honestly, what do I know about Paul Newman? But, Joanne Woodward and others who knew and worked with him should be weighted as being, at least, slightly more credible that others; no matter how many text patterns "catch the match" flow.
I appreciate your consideration of a subjective question and how you explained it and understand these nuances. But please - do not trust chatgpt etc. I continue to be frustrated at the endless people claiming something is true from chatgpt. I support the conclusions of this author.
https://g.co/gemini/share/ffa5a7cd6f46
# Quick Answer
Yes, Paul Newman struggled with alcohol. His issues with alcohol were explored in the HBO Max documentary, The Last Movie Stars, and Shawn Levy's biography, Paul Newman: A Life. According to a posthumous memoir, Newman was tormented by self-doubt and insecurities and questioned his acting ability. His struggles with alcohol led to a brief separation from Joanne Woodward, though it had nothing to do with cheating.
(4x Source footnotes omitted for readability)
# Ki Multi-step Research Assistant
Paul Newman is known to have struggled with alcohol. According to his posthumous memoir, Newman candidly discussed his issues with drinking and self-doubt, describing himself as an alcoholic who was tormented by insecurities[^1][^2]. He reportedly drank a significant amount of beer daily and later moved on to stronger drinks like Scotch[^3][^4]. His drinking habits were a notable part of his life, and he was often identified by his beer drinking[^5][^6]. Despite these struggles, Newman was also recognized for his generosity and devotion to his family[^7].
[^1]: [Posthumous memoir reveals Paul Newman's struggle with alcohol](https://www.express.co.uk/entertainment/films/1688113/paul-n...) [^2]: [Review: Paul Newman memoir stuns with brutal honesty - AP News](https://apnews.com/article/entertainment-reviews-movies-paul...) [^3]: [Was Paul Newman Really an Alcoholic? - The Cinemaholic](https://thecinemaholic.com/was-paul-newman-really-an-alcohol...) [^4]: [Who Paul Newman Was—and Who He Wanted to Be](https://www.newyorker.com/magazine/2022/10/24/who-paul-newma...) [^5]: [Paul Newman's Beer Drinking - Psychology Today](https://www.psychologytoday.com/us/blog/addiction-in-society...) [^6]: [Paul Newman's Beer Drinking | Psychology Today United Kingdom](https://www.psychologytoday.com/gb/blog/addiction-in-society...) [^7]: [The troubled life of Paul Newman | The Spectator](https://www.spectator.co.uk/article/the-troubled-life-of-pau...)
'though it had nothing to do with cheating' is a weird inclusion.
The reason this bothers me is that comments like this reinforce the believes of people that could otherwise find value in these tools.
But I think points like this would be better made in shared chats or screenshots, since we do not have something like a core dump or stacktrace to attach.
And while I am not saying OP did this, I have seen technically skilled engineers asserting/implying that llm/chatbots aren’t good or not useful to them look at their chat log that a multitude of topics that I am sure would impact the result of the query.
Yes. It can be an UX problem. Yes. It can be an algorithmc problem. But they are just tools that can be used wrong and not a perfect mechanical brain.
Yes, Paul Newman did experience significant struggles with alcohol. In his posthumously published memoir, The Extraordinary Life of an Ordinary Man, Newman candidly discusses his drinking habits and acknowledges his long-term battle with alcoholism. He describes himself as a "functioning alcoholic," a trait he noted was shared with his father. At one point, Newman was reported to consume a case of beer daily, followed by spirits, until he eventually gave up hard liquor.
>Found 24 models: llama3-70b-8192, llama-3.2-3b-preview, meta-llama/llama-4-scout-17b-16e-instruct, allam-2-7b, llama-guard-3-8b, qwen-qwq-32b, llama-3.2-1b-preview, playai-tts-arabic, deepseek-r1-distill-llama-70b, llama-3.1-8b-instant, llama3-8b-8192, qwen-2.5-coder-32b, distil-whisper-large-v3-en, qwen-2.5-32b, llama-3.2-90b-vision-preview, deepseek-r1-distill-qwen-32b, whisper-large-v3, llama-3.3-70b-specdec, llama-3.3-70b-versatile, playai-tts, whisper-large-v3-turbo, llama-3.2-11b-vision-preview, mistral-saba-24b, gemma2-9b-it
Excluding the ones that do not support chat completions, all but one (qwen-qwq-32b) answered in the affirmative. The answer from qwen-qwq-32b said:
Using lack of progress in a specialized field as a barometer for overall progress is kind of silly. I just spent the last few days 'vibe coding' an application and I have to say that it's pretty remarkable how capable it is now relative to my experience last year.It took three minutes for me to do the above from the time I created my API key to when I had an answer.
Apparently it isn’t so specialized that a pretty obvious old fashioned web search on Google wouldn’t immediately return an authoritative source.
Yes, Paul Newman was known for being a heavy drinker, particularly of beer. 1 He acknowledged his high consumption levels himself. 1. Review: Paul Newman memoir stuns with brutal honesty - AP News
apnews.com
While he maintained an incredibly successful career and public life, accounts and biographies note his significant alcohol intake, often describing it as a functional habit rather than debilitating alcoholism, although the distinction can be debated. He reportedly cut back significantly in his later years.
My calculator can't conjugate German verbs. That's fine IMO. It's just a tool
https://chatgpt.com/share/67f332e5-1548-8012-bd76-e18b3f8d52...
Your query indeed answers "...not widely known..."
"Did Paul Newman have problems with alcoholism?"
https://chatgpt.com/share/67f3329a-5118-8012-afd0-97cc4c9b72...
"Yes, Paul Newman was open about having struggled with alcoholism"
What's the issue? Perhaps Paul Newman isn't _famous_ ("known") for struggling with alcoholism. But he did struggle with alcoholism.
Your usage of "known for" isn't incorrect, but it's indeed slightly ambiguous.
*https://en.wikipedia.org/wiki/Newman_Day
I think we'll have a term like we have for parents/grandparents that believe everything they see on the internet but specifically for people using LLMs.
When you ask it a question, it tends to say yes.
So while the LLM arms race is incrementally increasing benchmark scores, those improvements are illusory.
The real challenge is that the LLM’s fundamentally want to seem agreeable, and that’s not improving. So even if the model gets an extra 5/100 math problems right, it feels about the same in a series of prompts which are more complicated than just a ChatGPT scenario.
I would say the industry knows it’s missing a tool but doesn’t know what that tool is yet. Truly agentic performance is getting better (Cursor is amazing!) but it’s still evolving.
I totally agree that the core benchmarks that matter should be ones which evaluate a model in agentic scenario, not just on the basis of individual responses.
LLMs fundamentally do not want to seem anything
But the companies that are training them and making models available for professional use sure want them to seem agreeable
You're right that LLMs don't actually want anything. That said, in reinforcement learning, it's common to describe models as wanting things because they're trained to maximize rewards. It’s just a standard way of talking, not a claim about real agency.
A standard way of talking used by people who do also frequently claim real agency.
I've dropped trying to use LLMs for anything, due to political convictions and because I don't feel like they are particularly useful for my line of work. Where I have tried to use various models in the past is for software development, and the common mistake I see the LLMs make is that they can't pick up on mistakes in my line of thinking, or won't point them out. Most of my problems are often down to design errors or thinking about a problem in a wrong way. The LLMs will never once tell me that what I'm trying to do is an indication of a wrong/bad design. There are ways to be agreeable and still point out problems with previously made decisions.
Yes. The issue here is control and NLP is a poor interface to exercise control over the computer. Code on the other hand is a great way. That is the whole point of skepticism around LLM in software development.
umm, it seems to me that it is this (tfa):
and then couple of lines down from the above statement, we have this:In fact, this might be why so many business executives are enamored with LLMS/GenAI: It's a yes-man they don't even have to employ, and because they're not domain experts, as per usual, they can't tell that they're being fed a line of bullshit.
And when you ask it to 'just write a test' 50/50 it will try to run it, fail on some trivial issues, delete 90% of your test code and start to loop deeper and deeper into the rabit hole of it's own halliciations.
Or maybe I just suck at prompting hehe
Every time someone argues for the utility of LLMs in software development by saying you need to be better at prompting, or add more rules for the LLM on the repository, they are making an argument against using NLP in software development.
The whole point of code is that it is a way to be very specific and exact and to exercise control over the computer behavior. The entire value proposition of using an LLM is that it is easier because you don't need to be so specific and exact. If then you say you need to be more specific and exact with the prompting, you are slowly getting at the fact that using NLP for coding is a bad idea.
I have my own opinions, but I can't really say that they're not also based on anecdotes and personal decision-making heuristics.
But some of us are going to end up right and some of us are going to end up wrong and I'm really curious what features signal an ability to make "better choices" w/r/t AI, even if we don't know (or can't prove) what "better" is yet.
I do feel (anecdotally) that models are getting better on every major release, but the gains certainly don't seem evenly distributed.
I am hopeful the coming waves of vertical integration/guardrails/grounding applications will move us away from having to hop between models every few weeks.
A majority of what makes a "better AI" can be condensed to how effective the slope-gradient algorithms are at getting the local maxima we want it to get to. Until a generative model shows actual progress of "making decisions" it will forever be seen as a glorified linear algebra solver. Generative machine learning is all about giving a pleasing answer to the end user, not about creating something that is on the level of human decision making.
I'm just most baffled by the "flashes of brilliance" combined with utter stupidity. I remember having a run with early GPT 4 (gpt-4-0314) where it did refactoring work that amazed me. In the past few days I asked a bunch of AIs about similar characters between a popular gacha mobile game and a popular TV show. OpenAI's models were terrible and hallucinated aggressively (4, 4o, 4.5, o3-mini, o3-mini-high), with the exception of o1. DeepSeek R1 only mildly hallucinated and gave bad answers. Gemini 2.5 was the only flagship model that did not hallucinate and gave some decent answers.
I probably should have used some type of grounding, but I honestly assumed the stuff I was asking about should have been in their training datasets.
Furthermore, as we are talking about actual impact of LLMs, as is the point of the article, a bunch of anecdotal experiences may be more valuable than a bunch of benchmarks to figure it out. Also, apart from the right/wrong dichotomy, people use LLMs with different goals and contexts. It may not mean that some people do something wrong if they do not see the same impact as others. Everytime a web developer says that they do not understand how others may be so skeptical of LLMs, conclude with certainty that they must be doing sth wrong and move on to explain how to actually use LLMs properly, I chuckle.
People -only!- draw conclusions based on personal experience. At best you have personal experience with truly objective evidence gathered in a statistically valid manner.
But that only happens in a few vanishingly rare circumstances here on earth. And wherever it happens, people are driven to subvert the evidence gathering process.
Often “working against your instincts” to be more rational only means more time spent choosing which unreliable evidence to concoct a belief from.
In the absence of actually good evidence, anecdotal data may be the best we can get now. The point imo is try to understand why some anecdotes are contrasting each other, which, imo, is mostly due to contextual factors that may not be very clear, and to be flexible enough to change priors/conclusions when something changes in the current situation.
So am I. If you promise you'll tell me after you time travel to the future and find out, I'll promise you the same in return.
There are three questions to consider:
a) Have we, without any reasonable doubt, hit a wall for AI development? Emphasis on "reasonable doubt". There is no reasonable doubt that the Earth is roughly spherical. That level of certainty.
b) Depending on your answer for (a), the next question to consider is if we the humans have motivations to continue developing AI.
c) And then the last question: will AI continue improving?
If taken as boolean values, (a), (b) and (c) have a truth table with eight values, the most interesting row being false, true, true: "(not a) and b => c". Note the implication sign, "=>". Give some values to (a) and (b), and you get a value for (c).
There are more variables you can add to your formula, but I'll abstain from giving any silly examples. I, however, think that the row (false, true, false) implied by many commentators is just fear and denial. Fear is justified, but denial doesn't help.
People having vastly different opinions on AI simply comes down to token usage. If you are using millions of tokens on a regular basis, you completely understand the revolutionary point we are at. If you are just chatting back and forth a bit with something here and there, you'll never see it.
Someone who lacks experience, skill, training, or even the ability to evaluate results may try to use a tool and blame the tool when it doesn't give good results.
That said, the hype around LLMs certainly overstates their capabilities.
I'd love to see a survey from a major LLM API provider that correlated LLM spend (and/or tokens) with optimism for future transformativity. Correlation with a view of "current utility" would be a tautology, obviously.
I actually have the opposite intuition from you: I suspect the people using the most tokens are using it for very well-defined tasks that it's good at _now_ (entity extraction, classification, etc) and have an uncorrelated position on future potential. Full disclosure, I'm in that camp.
> how the hell is it going to develop metrics for assessing the impact of AIs when they're doing things like managing companies or developing public policy?
Why on earth do people want AI to do either of these things? As if our society isn’t fucked enough, having an untouchable oligarchy already managing companies and developing public policies, we want to have the oligarchy’s AI do this, so policy can get even more out of touch with the needs of common people? This should never come to pass. It’s like people read a pile of 90s cyberpunk dystopian novels and decided, “Yeah, let’s do that.” I think it’ll fail, but I don’t understand how anyone with less than 10 billion in assets would want this.
This is the really important question, and the only answer I can drum up is that people have been fed a consistent diet of propaganda for decades centered around a message that ultimately boils down to a justification of oligarchy and the concentration of wealth. That and the consumer-focus facade makes people think the LLMS are technology for them—they aren't. As soon as these things get good enough business owners aren't going to expect workers to use them to be more productive, they are just going to fire workers and/or use the tooling as another mechanism by which to let wages stagnate.
Of course, if you train an LLM heavily on narrow benchmark domains then its prediction performance will improve on those domains, but why would you expect that to improve performance in unrelated areas?
If you trained yourself extensively on advanced math, would you expect that to improve your programming ability? If not, they why would you expect it to improve programming ability of a far less sophisticated "intelligence" (prediction engine) such as a language model?! If you trained yourself on LeetCode programming, would you expect that to help hardening corporate production systems?!
No LLM last year got silver. Deepmind had a highly specialized AI system earning that
If a model doesn't do good in the benchmarks it will either be retrained until it does or you won't hear about it.