What I would have never have guessed. I’m shocked I say. /s
I’m pretty sure this sort of thing was tried with the prior era of neural nets too. When the field hits a ceiling they grasp at the make-AI-teach-itself straw. It’s the Hail Mary pass. What if we keep stacking AIs on top of each other. Maybe they’ll somehow break out of their own limitations.
There’s a cadence. Once in a while a breakthrough happens. The tech is incorporated into the world. There are variations of the tech, but all have the same fundamental ceiling.
The AI Effect takes place. People forget about AI for a while. Time passes. A breakthrough paper is published. AI is upon the world once again.
Only this time with LLMs, it’s seemingly passed the Turing Test so people think it’s close to the fictional AGI. Not just recognizing handwriting, speech, or images. Or putting an annoying animated character on your desktop. This time it’s being freakishly good at predicting what the next words should be based on known sum total of human knowledge. Making it be creative isn’t it this time. That’s the ceiling.
deleted by creator
not a reflection on the quality of OP’s submission, but man… like every day now I wish we had an active “noshitsherlock” sub for headlines like these
It’s not being said for the benefit of those who already know.
Someone should DLSS this meme
Turing Test is fundamentally based on fooling humans. Not sure it’s smart to pin humankind’s future on what a birthday magician can do.
How is the Turing test related to the article ?
Lol what? Of course it won’t, If the AI slop ends with recursive edits it’s just going to cause degradation and collapse. I swear techbros have reality confused with their favorite fantasy fiction books.
EDIT: To demonstrate, 90% accuracy of 90% is 81%. Even the best most specific models on earth are not capable of self improvement because they will never reach much less exceed their training data’s capability even if the largest most perfect dataset existed. They might think that by simply adding more layers of machines running in parallel and killing off models which underperform creating a system similar to evolutionary adaptation that it might eventually reach that 91%, but our current approach and level of technology have never demonstrated that capability not even theoretically.
Removed by mod
They Don’t pass 4/5.
They pass 0/5 because they are 80% (that number is way too optimistic btw) accurate to human output on every one of the five attempts.
They also can’t be forced to learn and retake the trial because they don’t have any contextual awareness, they just guess the next word in a sequence.
Even if a machine made 4 self edits sucessfully, it would be permanently disfigured by the one failure and no longer be capable of making good edits.
That’s cool but you’re describing the models from 2 years ago and also not considering harnesses, which account for most of the progress of the last year or so.
You’re entirely right. This won’t replicate to other fields like writing and creative arts in general, but software engineering is just not that hard and can basically be brute forced with a good harness.
It’s a done deal and there is no world where people will write professional code by hand. I like it cause it really separates coding (the job) from coding (the art form). People will code by hand for aesthetic reasons just like people learn the violin instead of using a synth and we’ll have a generation of lovingly crafted stuff. But boring software will be entirely automated, if not generated on the fly based on immediate needs.
Removed by mod
it’s precisely because software engineering is hard (not difficult, but rigid - objective) that makes it a good fit for LLM agent execution
Yes i think we’re actually in agreement here. I said “hard” (not difficult) as a reference to “hard problems”, a term that comes from complexity theory but is now commonly used to describe problems which can’t be reduced to an algorithm or evaluated objectively, and thus can’t readily be “solved”.
Squishy subjects like music and sociology are full of hard problems, while solid subjects like math and coding are full of easy problems. Now the change introduced by LLMs is that as long as a problem is “easy”, it can no longer be so laborious as to be impossible. Every software problem is solvable, modulo the effort/computing power you can spend on it.
Your job as a software architect / engineer is actually to find the easiest workable solution(s).
You also get bonus point if your solution is average (standard, unsurprising etc…), which makes it particularly soluble in LLMs which, by definition, can only produce output that is within the distribution of their training set.
As for relative difficulty, the “soft” fields have a very sliding scale for that with a lot of allowance given to newbies that isn’t accepted “at the highest levels”
That’s not where i would put the difference. If you take a field like music, the problem is that it can’t “just work”. A nostalgic song may move the masses today but you can’t say “okay we’ve solved nostalgia let’s get to serenity next”. Soon enough you’ll need a new nostalgic song and by definition it will be out of distribution. You can’t find it in a high dimensional representation of past music, and, well, you can’t train on future data, so there is no way an LLM finds it and recognizes it for what it is.
with a high bar of standards required before publication and release, LLMs are a powerful tool that can accelerate many creative processes
I still believe they’ll never amount to much regarding artistic processes, and not just for the reasons i already mentioned. To make something good you need to sit with it and walk with it and spend some time in it doing all the tedious little tasks until it really feels like home and you can express yourself in it. You can’t achieve that if a machine speedruns all the little tasks for you.
Removed by mod
squishy is a better term than “hard”
Eh, agree to disagree. My use of the phrase might be easier to parse if you think about the “hard problem of consciousness” as opposed to the “easy problem of consciousness”. Also, more remotely, the notion of NP-hard vs NP-easy in computational complexity. I guess we are arguing about semantics aren’t we ?
I’m fairly certain that’s not a strict definition - LLMs also seem very heavily influencable via their context window inputs, though those are more limited and transient than the training set.
While LLMs don’t simply “statistically predict the next token” (common over-simplification which bears no relation to what is actually going on), the output they produce does exhibit the same statistical distribution as their training data. That’s how you get intelligible language, code that compiles, chains of thought that makes sense etc… The context input will steer the output towards a certain subset of the corpus (highly optimized C functions have slightly different distribution than throw-away Python scripts), but it can never direct the model fully out of distribution as those out of distribution vectors cannot be expressed within the model’s embedding space.
she was a commodity - as talented as you could ask, more talented than many national acts, but not unique
Yes exactly, if music was solvable then the most skilled people would systematically outclass the least skilled ones which is obviously not the case.
Removed by mod
found that AI agents could solve the engineering problems necessary to do AI research but lacked the judgment and creativity to produce original research at the caliber of papers accepted by a top machine-learning conference.
I mean that describes a majority of engineers. No small feat
If the standard of ML talks at conferences I’ve attended is anything to go by then a top machine learning conference is functionally a daycare for the most annoying people you’ve ever met
Tbh a lot of ai conference papers would absolutely be writable from most engineers. A lot of papers are : we trained a model and got these results. They didn’t publish the weight though, so you would be unable to check the paper.
The fingerprinting they’re doing now is meant to help with this. If they see a fingerprint, it was probably not human generated so don’t ingest it.
They are not only using human written data any more. Reinforcement learning through human feedback (RLHF) is a big part of it now, that’s the AI running through a problem multiple times with a human picking the best attempt and then using that best attempt as training data going forwards. The model collapse stuff from a few years ago was from an AI repeatedly ingesting its own input with no guidance over many training iterations.
I think people made some crazy magical assumptions about AI based on scifi that doesn’t apply to real life. Real things to consider and prepare for, but not likely.
Recursive exponential self improvement? Just because something knows how to code doesnt mean it can make the best ultimate next version of itself. Even physical evolution takes millions of years, and it creates mistakes and has setbacks.
We might be seeing logarithmic AI improvement today, like evolution hitting a hill that it can’t cross. We might need trillions more gigabytes of clean training data that isn’t LLM generated to hit the next level, and it might not even be worth it.
I think what the AI developers are doing by constantly promoting ai fear is linking the idea of exponential ai self improvement to it, because their biggest fear right now might be investors realizing that isn’t real, and every dollar they invest is getting less and less back.
Removed by mod
Indeed, throughout nature it’s almost all sigmoids. The trick is that sigmoids look exponential before the inflection point and it’s hard to predict when that inflection point is going to come.
Removed by mod
Looking at LLM benchmarks over time, there was still doubling of benchmark scores as of 2024 to mid-2025, then it inflected to more modest, incremental improvements, so sigmoid seems about right. Now we are seeing open models rapidly catching up now that the frontier has significantly slowed.
Removed by mod
I saw this article today summarizing a Mozilla report that open weight models are about 4 months behind the frontier: https://arstechnica.com/ai/2026/09/exclusive-open-chinese-models-close-gap-with-silicon-valleys-frontier-ai-models/
Progress has slowed at the frontier and open weight is right on their heels. The general advice is that Fable and the like are best for niche use cases and to make an open weight model your daily driver because they’re almost as good and are a lot cheaper. In my case for personal use, I’m perfectly happy with Qwen 3.8 27B running locally and don’t care to deal with data collection or huge bills to get a bit stronger of a model.
Removed by mod
Won’t come at all. It’s mathematically impossible.
Proof?
The output of a statistical model cannot contain more information than what it already had.
So you think that AI increasing the capability of new AI just involves training a new model on the output of the old model? Or are you pretending to be a science?
deleted by creator
“That” is not what evolution did.
deleted by creator
I want to believe this but every biology major I talked to is like “that’s the middle-school explanation of evolution” and mention the actual process had more things going on.
All that an AI has to do to disprove that claim is pay someone to weigh one orange through fivr. So either your claim is wrong, or LLMs as they exist now are no longer statistical models. Go ahead and move the goal post if you want, my complaint was about your overly broad claim.
I think their point is the LLM didn’t contain the information on the weight of that orange, it had to outsource it.
I picked the way in which an AI model (as the exist now) could most trivially produce a piece of novel (and deliberately trivial) information.
I wasn’t worried about whether the credit could fairly be attributed to it. We’re talking about whether they can produce information with which to grow their knowledge. Moral credit for the growth of their knowledge is outside the scope of the discussion.
It’s entirely possible to improve current AI using only information available to us right now. Once that well runs dry, current AI is in theory capable of running experiments and training on their results if we give it a harness to do that. This gives it access to new information. Could it succeed doing this? Unclear, but it is capable of trying. How do you think we discover AI improvements? Divine inspiration? No, we follow a relatively simple research loop.
you’re drowning in slop my man
You assume too much. I don’t even use AI, I just keep up with the research. I haven’t been wrong so far.
Everything is false until it’s true? There’s governments and corporations around the world right now racing to make that happen. What’s the point of the article? If it was so easy it would have been delivered already
Removed by mod
They would need to be trained for open ended creative tasks, which is just hard in the current reinforcement learning paradigm.
I think they’ll find infinite ways to fuck up. The guardrails will never be high enough, or strong enough.
They still don’t get it right all the time, they just stacked a few together to filter out the obviously wrong stuff.
Mmmh… They used Opus 4.8 (which is already outdated) as the model, and OpenClaw (which is utter garbage) as the harness…
heavy criticism of openclaw seems to be the consensus. recently, model makers have all been overly obssesed with toolcalling models. i expect that aspect to be improving the fastest.
