8/4/2026 at 4:48:29 PM
This seems to be the end of the road for LLM's. There's only so much accuracy on a highly non-linear space you can get from a regression.If the Pareto rule is any indicator, 80% of results come from 20% of causes. It seems that we have alot more to learn about intelligence.
I am reminded of a statement on truth from an ancient philosopher, this sentiment seems to be exactly the opposite of the LLM training paradigm
“The seeker after truth is not one who studies the writings of the ancients and, following his natural disposition, puts his trust in them, but rather the one who suspects his faith in them and questions what he gathers from them, the one who submits to argument and demonstration and not the sayings of human beings whose nature is fraught with all kinds of imperfection and deficiency. Thus the duty of the man who investigates the writings of scientists, if learning the truth is his goal, is to make himself an enemy of all that he reads, and, applying his mind to the core and margins of of its content, attack it from every side. he should also suspect himself as he performs his critical examination of it, so that he may avoid falling into either prejudice or leniency.” - ibn al-Haytham
by otterdude
8/4/2026 at 5:19:31 PM
>This seems to be the end of the road for LLM'sThis is an amazingly ignorant thing to say given the current pace of progress.
by heaney-555
8/4/2026 at 5:46:28 PM
Can you quantify this rate of progress? Because someone always comes around and says there's been exponential progress in the past <short timeline> every time someone complains that models just aren't very good. Both can't be trueby malfist
8/4/2026 at 7:47:17 PM
Both can be true, because the experience depends on the skill of the user. The article the other day here on HN that LLMs reward skill is my exact experience. If you are are good at what you are trying to use it for they can be a skill amplifier, and they are definitely getting much better rapidly for the work I do with them. At the same time people are complaining that they are getting dumber. Saying that both can't be true ignores the skill requirement to use them and the completely different perspectives of people using them.Even if both aren't true, your evidence was people saying two opposing things. The truth (if there is a single objective truth on a giving thing) has little bearing on whether or not different people agree on it.
by NyxWulf
8/4/2026 at 6:02:10 PM
In research we are still seeing massive jumps. Subjects that LLMs were completely useless for half a year ago are now definitely in scope.And there are benchmarks that cleanly separate the SOTA models:
Saturation of benchmarks is a property of benchmarks just as much as of the models.
by Certhas
8/4/2026 at 7:19:12 PM
one of these claimed is backed by data, one is backed by anecdotes, you can decide which you trust moreby jake-coworker
8/4/2026 at 5:24:28 PM
There is high rate of progress in specific domains, not high rate of progress in generalness. The models haven't gotten generally smarter, for things they didn't focus on the models are just as bad as a year ago.by Jensson
8/4/2026 at 5:48:48 PM
unless you assume endless progress, fast pace of progress will get you quicker to the plateau.by pasquinelli
8/4/2026 at 5:34:25 PM
And sales of disco records were up 400% for the year ending 1976. If these trends continue...by runarberg
8/4/2026 at 5:02:16 PM
I really don’t think we know enough about what „intelligence“ is or how LLMs actually work to confidently say that this is the end of the road for LLM.by adrianN
8/4/2026 at 5:22:29 PM
You aren't contradicting the person.by Jensson
8/4/2026 at 5:20:04 PM
I'm sorry, we know exactly how LLMs work, this myth that we "dont know how they work" was perpetuated by executives that dont know how they work.We know exactly how attention layers work and how they produce the next word as well as draw them from larger feature spaces.
by tsunamifury
8/4/2026 at 5:44:17 PM
Right, but we have no clue why, and how the emergent behavior they show works.If we would know that, there would be no need for interpretability research.
by soiax
8/4/2026 at 5:25:34 PM
If you know all this, can you explain how these models produce advanced mathematical proofs? (as recently done by OpenAI, for example)I tried to generate the next word to the best of my ability, starting with a mathematical problem, but I did not create a valid proof. How do these LLMs work when they create math proofs to problems not yet solved?
by warkdarrior
8/4/2026 at 6:19:42 PM
Yes, it turns out that matrix math over a feature space of math works pretty well because unlike poetry or real world work, maths are internally coherent and entirely theoretical.by tsunamifury
8/4/2026 at 8:48:21 PM
This not understanding "understanding".> maths are internally coherent and entirely theoretical
Nope. This kind of wish-washy thinking is not what we mean by understanding.
by thesmtsolver2
8/4/2026 at 5:38:41 PM
How did you generate the next word? Did you first read pretty much every written work ever published, including blog posts, forum posts, books, etc? Learn how to imagine everything as a point in a gigantic abstract space where similar meanings cluster together? How did you manage training with gradient descent? And then did you do a lifetime of matrix multiplication for each token you predicted?by slopinthebag
8/4/2026 at 5:35:45 PM
You can see how an LLM works here https://bbycroft.net/llm they are not magic.by ForHackernews
8/4/2026 at 5:31:02 PM
You don’t have the computational ability to process as many calculations as a datacenter. You can hardly transpose a 5×5 matrix in your mind, so you won’t be able to do what datacenters do.This is like saying we don‘t know how a car works because a car can beat the best human athletes in 100 meter dash.
by runarberg
8/4/2026 at 5:28:06 PM
We know plenty about human cognition, and we know everything about how LLMs work. True we don’t know anything about intelligence but that is because “intelligence” is it self a fraught and vague term, and we haven’t (and perhaps never will) settled on what it means exactly.by runarberg
8/4/2026 at 9:09:56 PM
Assuming you are completely correct about the 80/20 rule, we have evidently not yet reached that 80%. Who can say when it will be achieved? The ceiling is glass, we have to touch it to know where it is.by grim_io
8/4/2026 at 9:23:27 PM
[dead]by huflungdung
8/4/2026 at 4:56:00 PM
I am wondering whether the reason he needed to say it was because he was arguing with those who did out their trust in the writings of the ancients.by graemep
8/4/2026 at 5:42:43 PM
Idk about end of the road, I’m sure they can squeeze out some more performance by curating even more data and doing even more RL.But I would bet that pretty much all of the improvement we’ve seen over the last year with coding has come from RL, not from the models becoming particularly stronger. And this makes sense, if models grow sublinearly with compute. And it seems like they do.
by slopinthebag
8/4/2026 at 8:57:10 PM
It seems pretty obvious from the steep 'intelligence' drop-off on out-of-distribution tasks that the performance improvement is from throwing untold tens of billions at RL. There are legions of highly skilled people employed solely to feed the RL loop. Evidently effective, but there's an unmistakable feeling this won't ultimately be the way forward.by gr_norm
8/4/2026 at 5:37:51 PM
It's also important not to put too much faith into ancient sayings and aphorisms.As a civilization, we are currently brushing up against the physics of efficiency. In many areas we have achieved close to what is theoretically possible, based on physics.
Such was not the case for the majority of human existence.
The body of research a.k.a. "writings of the ancients" is now insurmountably higher than it would have been during the time of ibn al-Haytham, when any kind of writing at all was scarce and literacy was low.
by antisthenes
8/4/2026 at 5:08:22 PM
>This seems to be the end of the road for LLM's. There's only so much accuracy on a highly non-linear space you can get from a regression.It's just inadequate benchmarks. Anyone who has used Fable for anything particularly difficult will have seen that it's miles ahead of Opus 5.0, yet the majority of benchmarks are completely unable to capture this.
by logicchains
8/4/2026 at 7:20:37 PM
> Anyone who has used Fable for anything particularly difficult will have seen that it's miles ahead of Opus 5.0, yet the majority of benchmarks are completely unable to capture this.I have seen people claim the exact opposite. If there was so much progress, then you wouldn't have endless disagreements with people championing their own favourite model as the strongest.
by Planktonne
8/4/2026 at 5:27:13 PM
Yes, and good senior software engineer is ahead of fable, but benchmarks can't capture that either.We already know from testing humans that test scores don't correlate that well with how effective a person is at work. Same applies here, we just aren't that great at making good tests.
by Jensson
8/4/2026 at 9:07:11 PM
The two times I tried to use fable I had it attempt something I had already had Opus 4.6 do with no issues. It blasted through 10% of my weekly allowance on a 100$ a month sub and produced something broken and nonsensical.by ofjcihen
8/4/2026 at 5:33:08 PM
If most models were getting 100% on the test it would be an inadequate benchmarks.What were seeing is all models failing to ace these tests.
"Benchmark Saturation" is term that promotes lowering the bar.
by otterdude
8/4/2026 at 5:06:46 PM
The seeker of truth must also hold his breath.by 0xdeadbeefbabe
8/4/2026 at 4:57:14 PM
Do you draw that conclusion from the fact that AI surprisingly quickly reaches the end of each ruler we try to measure it with?by scotty79
8/4/2026 at 5:05:52 PM
Its not really that surprising when models are trained on the examsby otterdude
8/4/2026 at 5:10:10 PM
I work in AI evaluation, lots of problems and leakage is an issue as is ecological validity, but they definitely do not explain the progress we see.I think Epoch has the best analysis I’ve seen on evaluation trends; they use IRT to basically model a variety of benchmark difficulties, and then model a capability parameter for each model. This is as robust a sort of “meta-study” of evaluations as I’ve seen and the trend in capabilities show no sign of slowing down.
So I think people’s feelings clash with reality, and that’s because releases are more frequent and the jumps between releases are smaller, but the growth in capabilities _over time_ has not changed for the better or worse over a very very long period of time.
by astro1234
8/4/2026 at 5:22:11 PM
Benchmarks saturate around 80-90%?This is not "Acing" a test, this is hitting a wall.
by otterdude
8/4/2026 at 5:29:58 PM
Even on very small tests a fraction of questions might have wrong answers in the key.If models can't get more than 90% of the benchmark right I think it's a strong indication that they were not trained on the answers and that benchmark itself is messy enough that <10% desired answers might be wrong or misleading.
by scotty79
8/4/2026 at 7:07:00 PM
Yea this may explain part of it or all of it, it’s likely a case by case kind of thing.Also to respond to the parent comment: benchmarks have a variety of difficulty levels. Humanity’s Last Exam, though now hitting the beginning of a saturation phase with Fable, was long unsaturated while other benchmarks saturated awhile ago. So that’s what I meant by Epoch capability index: using IRT models this effect so that you gather robust signals from variety of benchmark difficulties and can track progress over time as model capabilities have evolved (and so benchmarks have had to evolve to keep up).
But yes like I was saying: all benchmarks are problematic, some are useful. Benchmark quality problems abound, so 90% being the true ceiling is not surprising. There may be other factors at play here too, I haven’t studied this problem that deeply to have a good thorough answer to this. But keep in mind there are probably 50,000 benchmarks in the literature and that is not a joke number. A crapload of noise in that signal but it’s not all noise.
by astro1234
8/4/2026 at 5:03:36 PM
Can't call it AI like that without discrediting yourself. You mean LLMs?by freejazz
8/4/2026 at 5:10:04 PM
Jumping in here, frankly I hate the trend of calling every type of automation intelligence.Most "AI" is really an optimization algorithm in software tools, same as its always been. This really isnt anything new, aside from adding a chatbot / MCP interface to the same tools.
by otterdude
8/4/2026 at 5:24:40 PM
When a Big Killing Robot comes to murder you be sure to always call it BKR and don't discredit yourself by calling it AI.by scotty79
8/4/2026 at 5:40:15 PM
That's a bit hyperbolic when we're all just posting on HNby freejazz
8/4/2026 at 5:09:35 PM
Talk about moving the goalposts. Pray tell, exactly what must an LLM do before you're willing to consider it AI? Be specific, otherwise you're just woo-mongering.by logicchains
8/4/2026 at 5:39:56 PM
Everyone here is talking about LLMs, why bother calling them something elseby freejazz
8/4/2026 at 5:19:34 PM
Weird moment for this take. We're seeing some of the fastest and most impressive progress ever right now.Frontier labs have categorically different & better set ups for evaluation, they're fine. It's work but it's not a crisis.
by hiddencost
8/4/2026 at 5:48:10 PM
I've heard that every week of every month for the past three years. And yet, ask an LLM about a seahorse emoji and see what happens.by malfist
8/4/2026 at 9:30:00 PM
The seahorse emoji thing appears to be fixed actually. (Sonnet 5)by CollinEMac