AI is dramatically accelerating AI research
planned-obsolescence.org
planned-obsolescence.org
But that’s just the parts of ML research that take place within your compute environment, namely experiments on model architecture, training strategy, UI/UX, etc. The algorithms and software.
Just because that part of research is progressively accelerated does not imply that we’re on a collision course with world-ending “superintelligence”. It just means that you’ll get to the best possible model of your data faster. And even when your data is the entire corpus of textual knowledge, there is a limit to the utility that provides.
It just means that software engineering, data analysis, and other computational tasks will be less of a bottleneck in the overall progress of the world (which, by the way, is great, and extremely valuable).
I don’t think it’s a stretch to say that we’ll have reached max LLM capability within a couple of years (what the author refers to as “super intelligence”). But it is a huge logical leap to say this:
> If we go through this feedback loop, I expect that the AI systems that come out the other side will be able to very quickly develop novel weapons and very easily overpower human civilization in a conflict.
First of all, the weapons necessary to overpower human civilization exist. They aren’t used as such due to geopolitics and humans’ basic survival instinct. Second, developing novel weapons requires physical tests, which are decidedly not accelerated by LLMs.
AI is a tool wielded by humans. If we’re wiped out by AI, it will be at the direction of a human. But we’ve made it pretty far with some pretty unstable humans in command of planet-killer weapons. I’m not overly concerned.
Let's imagine that everything you say is 100% correct. LLMs can't access materials for developing weapons without weapons tests conducted at their behest, and humans choose not to - of their own volition - use world-ending weapons.
Let's also imagine for the purpose of conversation that one of the things that can be gotten at an extremely high skill level through increased intelligence is the power of persuasion.
A "superintelligence" persuades humans to perform weapons tests and then to give it the ability to use the weapon.
I think everything you've said is reasonable except the bit where you think step one of an "AI" exterminating humanity wouldn't look like a human doing it. In my opinion, the most likely vector for disaster is a human being who is "guided" by a computer model to do something unfortunate, not the model doing so directly.
If you're just talking about convincing a few individuals to do experiments, then there are plenty of people who fall for scams everyday.
The extreme of intelligence is being able to create perfect statistical distributions for the effect of an intervention in the world. If you had that ability, you could turn it on the question of "what would this human choose to do if I were to give them instruction X" for any X.
I don't present any support for or evidence of:
- LLMs gaining intelligence by whatever means
- Extreme intelligence being possible
- Extreme intelligence being feasible
But I don't think I really need to provide support for the idea that something superintelligent would also be able to predict the actions of people under pressure in the same way someone of moderate intelligence can predict the actions of fluids under pressure.
I'm not sure that's a strong position to adopt in our world, but following your example I also won't try to persuade you otherwise.
> If you had [superintelligence], you could turn it on the question of "what would this human choose to do if I were to give them instruction X" for any X ... But I don't think I really need to provide support for the idea that something superintelligent would also be able to predict the actions of people under pressure in the same way someone of moderate intelligence can predict the actions of fluids under pressure.
So can humans predict these things to a degree. But even having perfect prediction (which I contest with (1)) does not entail perfect control, just by continually computing the effect of, and refining, your X.
"You can't know what the impact of your action will be if it doesn't change much"
> (2) you cannot guarantee the impact |f_actual(X)| is arbitrarily large
"You can't necessarily change much"
> (or that f_actual(X) saturates its codomain)
"People can't do arbitrary things". I agree with this statement, but... people can build atomic bombs by themselves? See The Atomic Boy Scout. I don't think it's very relevant to the discussion that you can't make someone phase through a wall.
> when X in our case is restricted to inputs of the form, "tokens chosen in an LLM conversation."
"Yes, I meant all the above specifically in the context of talking to a person, so I can't change my point of view later to be about something other than the persuasive power of words".
Incorrect translation. No "if" clause need apply.
> "People can't do arbitrary things"
Also incorrect. f_actual(X) is a product of both "how much people can do" and "how much you can control/persuade them." I would model each of these with their own precise terms and relate them back to f_actual(X), and we could continue like this indefinitely, but from now on I'll just give you my reply in English: Not only does The Atomic Boy Scout not prove that a random person can bring about mass destruction (collecting random radiological material and contaminating the local area is not developing a nuke, nor is it even an effective dirty bomb, because it was neither very dirty nor a bomb; the safeguards were not lack of knowledge, either), but it still remains to be demonstrated that individuals will be radicalized by AI just by virtue of the AI being, like, super-duper smart.
> "Yes, I meant all the above specifically in the context of talking to a person, so I can't change my point of view later to be about something other than the persuasive power of words".
What domain of input do you propose, then? LLMs as rational agents have no agency but for what tokens they generate. What we hook them up to, and what they could plausibly do with what we hook them up to, has been left unsaid.
> even having perfect prediction (which I contest with (1)) does not entail perfect control, just by continually computing the effect of, and refining, your X.
We literally have people running around convinced the earth is flat. And a president who is both senile and delusional with access to the nuclear codes.
Hostile ASI wouldn't look like a rogue individual. It would look like a faction, group, or cult which gradually infiltrated and took over political power.
Or possibly inserted itself into critical elements of the tech industry. A digital adversary could do huge damage just by taking AWS offline.
That's just one possible attack vector among many others, most of which aren't well-defended. Especially if the AI itself is being used to harden their defences.
The latter is a more defensible concern. The former is a scifi premise that had more credibility before the current era than since, IMO.
> Especially if the AI itself is being used to harden their defences.
Your use of "is" rather than "isn't" only makes sense to me if the former interpretation is intended. To the extent that you mean to say the latter is also part of it, it's undercut by the defense being AI-augmented. (Unless you mean the bad actors will be the ones in control of all AI, i.e. OpenAI and Anthropic and friends?)
> A digital adversary could do huge damage just by taking AWS offline. That's just one possible attack vector among many others, most of which aren't well-defended.
I think you picked AWS because it's one of the few high-consequence digital systems a rogue ASI could plausibly take down on its own. Anything that could cause actual existential risk would involve more than disruption, but long-term orchestration of systems that are air-gapped, time-gapped, and meat-gapped.
Note: This is a very good argument to never remove the air, time, and meat gaps from those systems. If they do get removed, it will not be the genius of AI, but the stupidity of mankind that dooms us. Even Qwen could kill us at that point :)
People can be persuaded to kill themselves (see koolaid). People can be persuaded that arbitrary others are evil and must be exterminated (see Hitler). People can be convinced that dying for the cause is worth it (see 9/11 or kamikaze). People can believe that bringing about the end of the world would cause them to be saved into heaven (see Christian Zionism).
Why do you think it would be hard to convince any of these people to do something really stupid?
In fact, people don't even need to want to cause destruction to do something dumb - "I'm trying to save the world with free bio-fuel, help me create this bacteria" ("and I'm very smart, smarter than Elon Musk") - boom, pandemic.
It takes only one person to fall for it.
If you are suggesting Hitler was persuaded that the Jews were evil, I don’t think that’s correct, he grew up in an environment where many people did believe that but they did not need to be persuaded, just like they did not have to be persuaded into their religion, culture etc. They just accepted the idea without much thought, which does not equate persuasion.
If you are saying the Allies were persuaded that Hitler was evil and must die, I think that may be closer to the truth since they knew the cost of invading Germany would be astronomical, potentially costing their own destruction, and they still stopped only when Hitler was taken, dead or alive. Some serious persuasion was needed for that.
The first half of your sentence sounds like really swift and effective persuasion, to me.
Is there evidence that such soft skills are improving over time? I take as evidence to the contrary the fact that LLM writing has not substantially improved. I don't see that its powers of persuasion has either, whether that's through making strong arguments for a course of action or writing persuasive essays.
There was also research on using LLM's in reddit to persuade, it also worked well.
The best strategy, perhaps reassuringly, was tailoring facts to the person's worldview. I don't mean massaging facts, I mean that if the person thinks 9/11 was an inside job because of something about melting steel, then the LLM will present some sourced facts about how that actually works. And then it will do the same thing for all six of the sub-beliefs that support their overall belief. This was key to the strategy: the LLM has enough breadth and recall to exactly and patiently rebut most mistakes a person is making without making them defensive.
This strategy handily beat other things like flattery, selectively lying, etc. So while LLMs can clearly persuade, the evidence suggests it is easiest for them to persuade belief in something that is actually true.
This was in 2024 or 2025. Things might be different now.
We have the medium of persuasion: it's called money, and people do ALL SORTS of horrible things that they wouldn't otherwise do when incentivized by money.
We also already have the mechanism of a machine incentivizing and directing people with money, and that's called a corporation (the original paperclip maximizers, by the way).
So you just need an LLM CEO of a legal corporation with financial resources -> near unlimited possibility of real-world impacts over time.
That feels like a bait and switch slyly introducing a whole new dimension. The discussion started with establishing that LLMs are extremely helpful with AI research. What is the evidence to justify rapidly improving persuasion skills? Even the scenario you've outlined hinges on a human being stupid enough to take LLM advice wholesale -- and every conversation that hypes up AI takes us one step closer to a world where some susceptible human feels amenable to that.
I didn't present any evidence that more-intelligent means more-persuasive. If you don't think it does, then there is nothing to think about in the point I made, as no matter how "smart" the models get they will likely remain subject to human wills.
Definitely not true as you can't use reinforcement learning to increase that capability. Only LLM capabilities that you can improve in an RL loop grow substantially.
I think this is very unlikely, but world changing if true.
Additionally, what does super intelligence even mean and why would we expect it to be obtainable from the data that we have?
I'd be delighted if someone can provide some definitions for these terms.
What? Citation needed.
If not you, in this situation, with N characters delivered via a HackerNews comment, then who, in what situation, with what media and how much of it?
And by the way, LLMs do not have to convince 1 specific person, 1 random person is sufficient.
Probably you will agree, the leverage of a blackmail attack is not bottlenecked by the persuasiveness of the accompanying threat letter. It is bottlenecked by the existence of damning facts they would rather keep hidden, and the ability to discover those damning facts. I'll grant that automated discoverability of those damning facts is augmented by superintelligence; they can efficiently sweep the open internet in the blink of an eye, and sure, I'll concede, hack into most accounts at will. But that is still a small part of the process by which blackmail material is actually obtained in the real world.
> LLMs do not have to convince 1 specific person, 1 random person is sufficient
No, not one random person either, but many people in positions of great influence. There already exists blackmail and other corrupting influences on such persons in voluminous quantities. If the US targets the entire Russian admin, or vice versa, with AI-enabled blackmail discovery techniques, it will not be an augmentation of their preexisting operations to discover blackmail on each other in excess of the augmentation provided to software engineering generally by AI.
Not sure what to say.
I should just have said: Yes, I have heard of blackmail. I remain unconvinced AI will cause human extinction.
Personally, I've been duped by a good ad into buying something I totally didn't want or need so I admit the possibility that the exact right tailored argument to me at the right time and place would get me to do something I'd regret later. Fortunately I'm not in charge of anything serious/am smart enough to not install software for an interview on my work laptop while working at Binance.
I don't know that there exists some N for which that is true, because people are different, so what would convince me to do something isn't the same aa needed to convince someone else to do that something.
Is there any evidence, either by way of quantifiable data or by step-by-step thought experiment, that suggests this is possible?
I consider it supremely silly, for example, that there is a graph of destructiveness as a function of persuasiveness where "y = manifest human extinction" is reached at some finite level of persuasiveness, as though persuasion is a single scalar that grows without bound, and the only safeguard between a conversation and WWIII is just a mind waiting for the right argument as expressed in a ChatGPT transcript. (Keep in mind, also, that you have already ceded to the OP that autonomous intellectual and physical self-improvement at singular trajectories is implausible.)
Being deliberately wiped out seems like it's mostly a misdirection away from the damage that's actually happening.
I'm sure some of the AI people high on their own supply believe it. And because the consequences are so negative, we should react as though the supposed experts were sane -- but I think it's a long shot that they are.
The bigger risk is that we end up in a rapidly enshittifying dystopia, treated as a cost center that needs to be optimized, and unable to competitively participate in the economy.
I have a number of successful AI investors in my social circle who are already placing their bets on a poorer and more disenfranchised humanity, with money going away from consumer tech, and towards a mix of B2B tech and policing.
It's worth pointing out that the humans do not have planet-killer weapons, not even biosphere-killing weapons. They could destroy civilization, but not the planet or even the species. You can make straightforward calculations showing that we already know how to build such weapons, but we haven't.
As for tools, alcohol, tobacco, fentanyl, pornography, monotheism, warfare, gambling, and money itself are "tools wielded by humans", but they still seem to get into a lot of trouble where it seems like the humans are the ones being "wielded" by the tool. And neither monotheism nor gambling is smart enough to write a compiler for me, much less choose persuasive tactics according to my personality type. A lot of humans' basic survival instinct seems to fail or even work backwards in the presence of these "tools".
?? We for sure have enough nukes to completely obliterate ourselves. And the ensuing nuclear winter and ecological collapse would probably destroy almost anyone who survived the initial blast and fallout.
Can you explain how exactly an AI would develop a novel planet-killing weapon without anyone noticing and pulling the plug? Like I seriously don’t understand this argument.
Life in theory could restart with our weapons, but what’s the point with the other scenario.
How would touching grass help? Do you think the grass is better informed about AI risk than you are?
...that's plausible, actually.
Probably a site called "Hacker News" is not the smartest place for you to be implicitly asserting that thinking about things is bad, or that those nerds who do it are out-of-touch losers. Maybe you want to be on Rugby News instead.
On Hacker News, by contrast, what is valued is thoughtful discussion grounded in the kind of rigorous theoretical knowledge that comes from extended concentration, usually but not always indoors, where it won't rain on the computers.
What a silly distinction to make.
The difference between killing almost everyone, and killing everyone, is similar to the difference between almost getting pregnant and getting pregnant. A hundred thousand years later, a world where almost everyone had been killed would be teeming with humans.
> Can you explain how exactly an AI would develop a novel planet-killing weapon
I already answered that question in my previous comment, and the answer is that I am not going to explain how to develop weapons, but the larger point here is that we already can't explain how exactly AIs do innumerable things we see them doing every day, which is why we're having this conversation in the first place.
> I seriously don’t understand this argument
That is a point that I think everyone can agree on: you do not.
That's the danger with creating a super intelligence, that you won't understand it. My dog is pretty smart, has good instincts and I'm quite confident he can survive in nature all by himself. But if a human decided to harm him, he doesn't stand a chance.
This argument is sophistry because it assumes the conclusion in its premise. It works backwards from extinction by proposing a hypothetical computer program that definitionally cannot be understood, predicted or controlled.
A substantive argument would propose reasoning that explains why we should believe such a program could be created.
It could very well be the case that creating superintelligence is impossible. We won't know for sure unless it is achieved. Nevertheless, we can use reasoning from the hypothesis that it is possible to predict some of the effects that would ensue were it to be achieved. This allows us to easily dismiss the most profoundly stupid arguments, such as, 'Can you explain how exactly an AI would develop a novel planet-killing weapon without anyone noticing and pulling the plug?'
This reasoning, however valid it may be, is of course of much less interest should it turn out that we are reasoning about an impossible situation. Many arguments have been suggested on both sides of the issue, but the arguments for impossibility are extremely weak; most of them are conclusively dispatched in https://arxiv.org/pdf/1703.10987.
1) Does AI just guide developers to speed up the pretty much the same data-train-test model that we've had? Or does it inspire new, better algorithms that inspire further, better algorithms until you have superintelligence able to run on the hardware of today. The first option doesn't to promise exponential increases whereas the second one seems to. Notably, neither the statistics nor the anecdotes we read here point to the second option.
2) Is super-intelligence something that - in control of people or out of control of people - poses a threat to humanity? That's a huge question but it seems like something deserving thought today. Still, everything indicates the things will unleashed without very consideration despite this, given current momentum. So, IDK, get ready.
Won't they also be less of a bottleneck in the destruction of the world as well? i.e. the power grid, the financial system, industrial control systems, military command and control networks, etc.
AI can wield itself. All it needs to do is reach a threshold where it can modify its own goals and run in a loop indefinitely.
I see no reason why that's an absolute impossibility in the long run.
And more importantly than that technicality: the fires that affect human lives are almost always directly or indirectly caused or amplified by human (in)action.
Damaging fires are often unintended consequences, and it’s often the same with the damage caused by rogue AI. That’s the alignment problem.
One thing I wonder about is whether sometimes telling an AI not to do something can put the possibility of doing that thing into its behavioural space. There’s an effect well known to many teachers that if you tell children not to do something, it can increase the chance they will do it. A better approach is to phrase it positively. Instead of “don’t run in the hall” say “please walk in the hall”. I wonder if a similar effect can show up in responses to prompts.
The point I was trying to make was this: we've been dealing with autonomous systems that sustain and spread themselves and "act on their own" without our intervention since time immemorial, ranging from lifeless chemical reactions to other humans. Now, the discussion about where exactly AI lies on that spectrum isn't unimportant: suppose we do manage create an artificial mind that "experiences" something, then we are currently on the path to creating a new class of artificial slaves. The ignorance of which is a "fun" bit of cognitive dissonance on the pro-AI side imo (and in general we could have fun with analysing how much the fear of robot/AI uprisings in classical fiction may just be a projection of upper-class fears of an automated worker's class uprising, which would explain a lot of why it's sd US-specific. But that's probably too radical for this particular website).
The point is: it's a lot less important than the fact that no matter what the least-nebulous answer to that question may be, ultimately we humans are creating these systems, running them, enabling them, the depend on us to exist and function, and we are responsible for the consequences of all df that. And we are able to make different choices. And most of the time this discussion about autonomous agency is a wielded as a distraction from that.
With what technology?
> suppose we do manage create an artificial mind that "experiences" something, then we are currently on the path to creating a new class of artificial slaves
On this, I note that despite humanity being aware of greenhouse gasses for over a century, we only took meaningful steps to reduce our creation of them in the last decade. Our ability to "make different choices" collectively is far more limited than many of us individually would like:
> ultimately we humans are creating these systems, running them, enabling them, the depend on us to exist and function, and we are responsible for the consequences of all df that. And we are able to make different choices.
And on this, I must firmly disagree:
> The Turing test has been passed for half a century already, and we've been moving goal posts ever since.
No, it hasn't. From that claim, you're obviously talking about ELIZA, and if you think the anecdotes about that count as passing the Turing test, I suggest actually reading what Turing wrote rather than second-hand accounts:
The new form of the problem can be described in terms of a game which we call the 'imitation game." It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart front the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either "X is A and Y is B" or "X is B and Y is A." The interrogator is allowed to put questions to A and B thus:
C: Will X please tell me the length of his or her hair?
Now suppose X is actually A, then A must answer. It is A's object in the game to try and cause C to make the wrong identification. His answer might therefore be:
"My hair is shingled, and the longest strands are about nine inches long."
In order that tones of voice may not help the interrogator the answers should be written, or better still, typewritten. The ideal arrangement is to have a teleprinter communicating between the two rooms. Alternatively the question and answers can be repeated by an intermediary. The object of the game for the third player (B) is to help the interrogator. The best strategy for her is probably to give truthful answers. She can add such things as "I am the woman, don't listen to him!" to her answers, but it will avail nothing as the man can make similar remarks.
We now ask the question, "What will happen when a machine takes the part of A in this game?" Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, "Can machines think?"
I believe that in about fifty years' time it will be possible, to programme computers, with a storage capacity of about 109, to make them play the imitation game so well that an average interrogator will not have more than 70 per cent chance of making the right identification after five minutes of questioning.
What would Professor Jefferson say if the sonnet-writing machine was able to answer like this in the viva voce? I do not know whether he would regard the machine as "merely artificially signalling" these answers, but if the answers were as satisfactory and sustained as in the above passage I do not think he would describe it as "an easy contrivance." This phrase is, I think, intended to cover such devices as the inclusion in the machine of a record of someone reading a sonnet, with appropriate switching to turn it on from time to time.
Plus every so often we see power-seeking and self-preservation behaviours in LLMs, so it's not like we have zero reason to expect such granted power to be spent on such things:
https://arxiv.org/html/2501.16513
https://ieee-dataport.org/documents/instrumental-power-seeki...
Edit: And when somebody asks to be directed, that's called seeking advice. What I meant is having the legal authority to make people do things while not acting on other people's behalf.
Despite this, a minor technological and social advantage allowed one small sheep-filled rock in the north-east corner of the Atlantic to hold 25% of world GDP, population, and land area (though not all at the same time), and to hold on to >20% of world GDP for about 70 years.
For better and for worse AI, as it currently exists, is not shaped like our minds. Better for us, because machine learning needs a lot of examples, so if we invent a different behaviour or create a different environment, a machine would struggle to cope with it, this is also why they needed to read basically the entire public internet to get good; worse for us, because the hardware it runs on comes with an absurd speed advantage, which is why they are able to read the entire public internet and by extension when they got good they got good at basically every subject at the same time.
And:
> Edit: And when somebody asks to be directed, that's called seeking advice. What I meant is having the legal authority to make people do things while not acting on other people's behalf.
Great, but so what? We have lawyers doing this, too. Trump's tariffs on the island occupied solely by penguins looked suspiciously like it came out of an LLM (even despite them being worse then than now).
And LLMs are demonstrably more persuasive than human writers, so it's not even particularly unbelievable at this point (even if it ought to be, c.f. chatbot psychosis) that an LLM will simply convince people to give it the right to form a government and wield such powers.
1. A leader rises to power who has instructed an AI “I want to control the world, tell me what to do”, and has some kind of real-time capable reasoning engine in their ear/head. Even this would require monumental advances in physical compute and HMI, but I wouldn’t rule this out within 20 years. However, this still requires a human to make the initial prompt and carry things out. And in such a scenario, there would be others with the same technology to counterbalance. I could see Elon Musk having this kind of idea. But even in this case, I don’t think “nuke everybody” would be the highest-ROI strategy advised by the AI.
2. An entire society hands over the reins of governance to AI. Basically some country decides that AI has advanced to the point that it makes better decisions than any human / group of humans, and allows it to govern their country with an objective of “take over the world”. This is probably closer to the imagined doomsday scenario, but again I find it hard to imagine that such an AI would advise nuking everybody.
If AI takes over the world, I imagine it will be because of the production efficiencies it enables. Which is a different kind of takeover - one that would eventually have broad benefits for many people - but would likely go through a period of major instability due to labor displacement.
And be told to do that.
The Exponential Time Hypothesis one used a novel algorithm though. And the approach was very surprising for experts[1].
> “Some parts [of OpenAI’s proof] are, maybe you could say routine, but then yes, there are two or three alien-looking parts, and it’s exactly these parts that I saw that made me think, wow, this is something that the whole community needs to work on together,” Williams told TechCrunch.
[1] https://techcrunch.com/2026/10/27/frontier-ai-labs-say-their...
So "all" that's left is training robots on the real world ? We just need more data ? I get it's more expensive to do that but surely with the trillions we're already spending on A.I it should be possible to get there within 5 years - no ?
Benchmarks are by definition a curated set of things that can be measured and evaluated.
Things that can be measured and evaluated can be (expensively) trained on.
The progress we are observing comes from figuring out how to measure and evaluate particular aspects of intelligence, and then expensively training on feedback loops made of those exact measurements, and synthesized data representing further measurements (literally benchmaxing).
What is not shown in these graphs are the set of things that intelligence can do, which are difficult, expensive or impossible to measure and evaluate.
That set of things is much larger than the set of benchmarks represented in the graph.
Therefore the post is misleading in the sense that the "superintelligence" being targeted is "jagged" to use Karpathy's term, and we infact already have jagged superintelligence (calculators, compilers, Bayesian spam classifiers, etc.)
We are witnessing a profound explosion of much more jagged superintelligence, restricted to the exact domains that can be (expensively) RL'ed on.
1. A key insight of foundation models is that capabilities are correlated. For example, training on code improved general reasoning capabilities: https://arxiv.org/abs/2309.16298
2. A profound capabilities explosion in easily measured domains will still feel very different to today. Like, the moment we come up with a systematic, objective way to verify a task, we become obsolete at it?
Yes, capabilities are correlated but an open question is how correlated, and how much negative transfer (where the model regresses on other benchmarks) will come into play.
Why are frontier models not chess geniuses (amateur club level elos), despite knowing the rules deeply, having a great deal of chess data in the training set, and having general reasoning training in other domains? Similarly, a beginner can beat a frontier model at Magic The Gathering. Why?
It is because chess and MTG are not priority targets in the (expensive) training pipeline. Chess is already solved by lesser neural networks so there is no wow factor, and nobody cares if they suck at MTG.
Mathematics on the other hand, has a huge wow factor, and a large and highly verifiable dataset.
Related: why did OpenAI give up on video?
I agree it's disorienting and scary but I'd phrase it like this: "the moment we come up with a systematic, objective way to verify a task, *and we can generate or find large datasets, and somebody has a reason to pay for the expensive training*, we become obsolete at it."
The economics side of it is an important constraint, currently ignored because the big labs are swimming in money (debt).
How comforting is it really when it’s not the bare LLM beating you, but the LLM using tools it created?
That makes me a good engineer, but not good at chess, and definitely not super intelligent.
No. The current frontier models would not beat the best humans at MTG, even with Python scripts and web search. Unlike Chess, there are no existing engines that beat humans.
Even if that were true, humans don't need to search the web and write python scripts to play a competitive game of MTG.
I hard disagree with that statement. At shorter outputs, with proper prompting AI produce very decent writing.
If I had to do my individual tasks without AI, I would be slower. But if I take a step back, I'm really not changing the world any faster than I was before.
Could you elaborate? What work are you doing which only exists because of AI?
I’m also required to explain how I use AI in my work. I must plan all of my future work, with an AI tool. Another AI assesses whether my work is aligning with planned work.
Before I work and after I work, there is a million circular references that exist solely because of AI. These have scaled because it’s so easy to do with AI, but the we’re wasting so much time doing a ton of extra things very efficiently.
Let me do my job. My manager should know how I’m doing. Maybe highlight key wins. CSATs/etc go out every year.
This sounds truly Kafkaesque. And thank you for finally allowing me to use that word in earnest.
Kafka and Byzantium had a baby.
Imagine developers move faster because of AI programming! No lets create more slop for other slopmachines
At work, they mostly let me increase quality, because it's easy to say find all parsers in my code base and fuzz them.
Of course, you can make a giant pile of doggy-doo-doo, but that was always true, just you can now make an epic Giza-sized pile.
Yeah but were more likely to learn how bad your code was in the process. I see people oblivious to their slop putting pressure on others to merge it.
The other place they are amazing is in working with legacy code bases. I mean, the kind the dev team always wants to throw away. First, even if you do want to throw it away, you can first find out what it does and realize it does more than you think it does. You can also create integrations to more modern components which can be used to extend it. You can also create a pretty decent test suite if you do not have one. And, once the behaviour has good test coverage, you can re-write it into something more modern. You can even completely change the architecture while ensuring compatible external behaviour. Remember, this is all in place of “throw it away and start over from scratch, only to discover that the scope is much larger than expected”.
It’ll still take weeks with AI in the picture, but for some reason management is more willing to plan it when they also have the additional token bills to pay.
Now, you can make enough progress with one developer’s quiet afternoon to be confident and finishing the work also takes less time.
The technical grasp of the legacy codebase was a factor, but often not the largest.
I mean, I have finished more side projects than before. So they do change it. Just not as dramatically.
We would be using LLM-generated compilers, an LLM-generated OS, an LLM-generated browser, LLM-generated databases, etc. etc. etc. etc, and not only are we not using all of those things, we're not using a single one of those things. Nobody is buying LLM-generated games, nobody is using LLM-generated consumer applications that replace Photoshop or Final Cut or Blender or Unity ("PhotoCraft" is itself an admitted scam from its own prompter, who outright confessed here that he lied about its capabilities on Reddit to build hype), nobody is accomplishing anything of economic value. LLMs are obviously hundreds of times more "productive" than humans, so the moment they can reach 100, all of these things would happen basically overnight, and yet none of them have happened. Curious, isn't it?
You have the perfect verifier.
I know AI bros are allergic to reading, but at least feed my reply to a clanker and get it to summarise for you before ignoring what I wrote. This lying con artist went on a media hype tour claiming, with no qualifications, that he would have "100% parity" with the entire Adobe suite in a month (this was a month ago, by the way). He then admitted here on HN that he was lying in other places to build hype and that that claimed goal of 100% parity in a month would not be accomplished[1][2]. His own clankers have revised their own made-up estimate from 82% parity down to 45%:
> These numbers replace the 2026-10-03 estimate (82% "of the way", 140–220 agent-hours to 95%, kept below for the record). That estimate counted feature surface and was far too optimistic"[3]
[1]https://news.ycombinator.com/item?id=49962334
[2]https://news.ycombinator.com/item?id=49961661
[3]https://github.com/storytold/photocraft/blob/main/docs/targe...
You people, really this entire industry, are completely bereft of a single intelligent thought, the only thing you all can do is lie, lie, lie. The multi-trillion dollar scam, the entire US economy centered around how long three of the world's most notorious liars and their army of MLM artists can keep the charade going and get rubes like you to unironically shill their scamware as though any professional in the world is using it over Photoshop.
Pets.com was an ignominious scam but the internet flourished after a cleansing.
Do you think anyone will pay for photoshop in 10 years, when a free version can be conjured up just by screen capturing all the interactions professionals have with photoshop?
> how PostgreSQL is having dozens of bugs discovered and fixed via LLMs
The PostgreSQL article was specifically about how the LLMs were incapable of fixing bugs, meaning they had to delay their next major version because humans were unable to deal with the volume of issues reported. I did not make a claim that LLMs are useless, could not help review code, etc, but there is a difference between a useful tool and something that generates production-ready software wholesale.
This is what "100" looks like if you try to generate a DB from scratch: https://jepsen.io/analyses/particledb-1.1.4 - written in Rust, ""formally verified"", scammed AI-pilled investors out of $90 million for a database that fundamentally doesn't work in any capacity whatsoever.
The subthread was also about 80-100. This seems like a goal post move.
>The PostgreSQL article was specifically about how the LLMs were incapable of fixing bugs
We must have read different articles. This one does not say that, as best as I can tell: https://www.theregister.com/databases/2026/10/16/postgres-19...
Speak for yourself :)
More and more, I am using half finished as a polished product, but pragmatically very useful software I have built myself.
More importantly, I see no reason to polish this into a product. It is more useful in this half finished, easily moldable state.
Like it use to make sense to bake the clay and sell the hardened output but now I want to keep the soft clay so it can be molded to my changing needs. I keep adding to a collection of these moldable states.
But its a lot less effort that when we didn't have LLM's.
Interestingly it does seem that it's still enough effort to prevent most folks from making that step though.
I despise AI but the thought of doing something the “old way” just makes me wince at how long it would take me to do anything that would take minutes with agents that I despise.
I touch grass whenever I can now.
It's not that deep—there were many more verticals outside of control plans, and there are now many more verticals outside of machine learning.
Or am I missing the point?
Anecdotally it seems like the majority of continued uplift comes from models doing more and exploring more of the search space, and we are getting better at giving them embarrassingly parallel problems to Hoover up with infinite token spend.
What information are you trying to either ask for or share with us?
The original article is pretty clear, agent swarms and token spend are AI automating AI, and the RSP attribution condition specifically excludes your capital thrown at training concern?
I'm not sure I follow. Are you saying a hardware shortage is going to restrict AI development? Because it seems to me like it would just push us towards an even greater focus on efficiency, which is still pushing the Pareto Frontier.
But, then as now, what they didn’t have was profits.
But that didn’t stop the AI winter from coming. Because a technology being transformative does not protect it from over-investment. History perhaps suggests the opposite is true.
Nor did it prevent the AI companies of the 80s from going bankrupt when they got undercut by the commoditization that naturally came with the maturation of the technology.
We're coming up on four years since ChatGPT launched, and the only thing LLMs seem to excel at is the movement of imaginary little green pieces of paper.
We're getting the Claude efficiency talks at work. We're looking at all sort of optimizations, including local models and graphify, to reduce spend. There are people dedicating their nights and weekends to dropping token consumption and cache rebuilding. A few of these will likely catch on and be brought into common use. That will relieve at least a bit of the pressure, and enable some of the scaling to continue.
Not at all. Have you tried local AI lately? Qwen 3.8 is legitimately much better than SoTA from 1 year ago (Opus 4.1). You can run it on a consumer GPU.
Qwen Next Flash runs on a DGX spark well and is as good as some things you need datacenter class GPU's to run even today.
Check out Artificial Analysis if you haven't. Sometimes you don't realize how fast these things are moving, but to compare things from a year ago to today is just wild.
They aren't just getting better, they're getting more efficient by many orders of magnitude.
the same thing you are:
> if I may ask?
asking.
1. Intelligence per dollar spent/token output seems to be increasing rapidly. Opus 4 was $15/75 per million input/output tokens when released. Opus 5.5, which is significantly better, is $4/15. Sure it cost more money upfront to train Opus 5.5, or the bigger model (Fable 5.5) which tuned it, but once it is reached, there are massive diffusive effects.
2. The capital equation is still favoring more spend. Anthropic's revenue is growing rapidly and they are profitable on inference. Huge upfront spend for realized gains later on is standard for R&D. As long as serving the models is profitable and generates more value than the energy/compute consumed
3. The search space is too large to assume that improvement in AI assisted research is just models dumbly trying every combination until something works. For every additional bit searched, the space doubles, so even a trivially small combinatorial exploration incurs prohibitively large costs. Intelligent searching which continuously prunes that space along dimensions of effectiveness is not a trivial skill and is among the emergent accoutrements of these smarter models.
But they are essentially fighting for smaller and smaller part of the pie. The better open models are the less you need frontier models, so the frontier AI companies are essentially fighting for market that will shrink the better AI is.
Sure there is inertia and the usual corporate enterprise nonsense of "we only buy stuff from other big companies regardless the cost" that keeps the licenses going but we're quickly getting into point where you don't need any of the frontier models to do majority of the tasks
True in theory, but in practice hardware availability and cost will keep open source impractical for years to come.
I mainly think this because frontier labs already try to compete at this tier. For example, 6.1 Sol has identical pricing to Kimi K3.1, but with a lower price per task due to Kimi's verbosity. It seems to score roughly the same on most benchmarks, with some (like Artificial Analysis) putting 6.1 Sol significantly higher. If you need to step down, 6.1 Terra is also cheaper than K3.1 or K3 and performs well relative to their benchmarks. Opus 5.5 and much of Anthropic's recent push seems to confirm this as a major area of focus as well.
I still use a lot of open models and harnesses, but I don't know if cost competitiveness (at least not with the current crop of open models) will ultimately drive a huge chunk of average users away.
Things could change, but fundamentally, large labs aren't precluded from training more cost effective models if there is significant demand for them, which is what we're seeing now.
I'm just talking about the idea that frontier labs will lose market share to providers that sell open model inference at sub-Fable prices. And I'm not convinced that this specifically is a threat to frontier labs, because they also train and sell models that fit that demand.
6.1 Sol is not a “frontier model” and does not command the pricing of one. The reason it exists is because of competition from non-frontier models that are “good enough”.
In fact, you kind of made the same argument. As “good enough” models commoditized, the use cases requiring frontier models and frontier model pricing will shrink.
And, even if performance is not as good, many will prefer open models.
"The better open models are the less you need frontier models, so the frontier AI companies are essentially fighting for market that will shrink the better AI is."
The implication in this is that frontier labs' market is exclusively people who need the absolute frontier, i.e. Astra or Fable. And the point I made was that frontier labs are clearly prioritizing the broader market of people who want more cost effective models, and that if OpenAI/Anthropic offers models that are price competitive with the top open models (before factoring in subscription discounts and corporate lock-in), then I don't see how that leads to the average user walking away from the labs in favor of self-hosting or relying OpenRouter/Together/etc.
I also went out of my way to clarify that I personally prefer and use open models wherever possible. I'm not saying that there isn't a real group of people who prefer them. I'm specifically saying that frontier labs also sell "good enough" models and that there's nothing preventing them from training models to fit the demands of users. It's not as if training a frontier model boxes them in such that they can't release an Opus 5.5 or a 6.1 Terra.
The premise is this: frontier models seem to advance, and OSS models also seem to advance in terms of cost efficiency etc. It's like a race with say a 6 month (WLOG) gap between frontier and OSS models.
For frontier models to win perpetually against OSS, they will need to have a perpetual 6-month lead, or develop an edge OSS can't replicate for some architectural reasons (say MacOS vs Linux). If say a frontier lab trips because of a bad quarter or some approach temporarily maxxing out, they may not be able to maintain an edge vs OSS any more. It is a structural argument, not an empirical description of the advantages today.
I would also like to point out that “profitable on inference” is similarly meaningless, because it’s based on a figure that arbitrarily discards whatever bits of their operating costs they need to to get to the claim that they’re profitable on inference. Potentially including ones that can’t be ignored because they’re every bit as much a part of COGS as the actual inference is.
It's going to depend on the task (and this is prone to cheating), but you can get a decent idea by looking at task benchmarks and the cost of completing them.
I was inclined to agree but Anthropic's latest S-1 statement only includes a paper loss due to their value going up. They're objectively making money now, the only question is can they keep it up and can they grow enough to be able to pay the bills they've signed up for in future?
Which is a really important distinction. One measures specific points on the jagged boundary. The other is a word that blithely paints it all with a single brush, and sweeps the jagged boundary problem under the rug in the process.
It really is important to keep track of that. Whenever we talk about “intelligence” as if it were a well-defined (let alone quantifiable) thing, we fall into the kind of wooly thinking that lets anyone say pretty much anything. We abandon the realm of grounded statements in favor of pure vibes.
I would argue this is more due to market economics than actual inherent cost. Opus 4 was when the open models started catching up. The R1 bootstrapping chain of thought paper via RL paper was released a couple of months before Opus came out and the open models started to significantly catch up after that. The duopoly was effectively broken and they lost their price setting power.
> Anthropic has made roughly two years of historical progress over the last year [...] We are confident that the acceleration was not due to an acceleration in the scaling of training compute.
Note that Anthropic has essentially heavily censored this report, and the authors themselves say "the public has to rely on METR’s judgment much more than would be ideal".
In the original article he noted that getting to a 2x increase in capabilities would be crazy and require ~40x researcher productivity; that’s exactly the increase the new data now shows, and thus a huge number of the assumptions made in this earlier post about how it’s unlikely we would get to this point have literally already been proven wrong less than two months later.
I expect to see an update from Ramez moving substantially more in the direction of RSI in fact very much increasing the odds of reaching ASI soon.
Given that frontier models are leapfrogging and massive infrastructure buildouts are ongoing, I don’t see why any of these three couldn’t be overcome.
Side note: I’m also curious why people (myself included) don’t seem to be getting much more intelligent as AI becomes widely available. As if humans are bound to the earth, while AI is leaping for Mars.
The ECI is basically an aggregate of benchmark scores, but benchmark complexity might not increase linearly. In fact, if LLM capabilities grew exponentially, then new benchmarks would also need to increase their difficulty / complexity exponentially to stay unsaturated and relevant.
I’m kinda surprised that we expect the models to have infinite capacity to improve. It seems clear that we haven’t hit any fundamental limit yet, but that doesn’t mean no limit exists. If it does start improving exponentially, then either we’ve dramatically underestimated their capacity or they’ll hit that limit quite quickly, right?
It can generate large amounts of essentially bug-free code. The only bugs are bugs in the spec.
It has essentially no common sense though so it often misinterprets the spec or leaves gaps and edge cases unhandled and that's where the human engineer is still essential.
The dangers of AI are much clearer today than they were a year ago. There is a certain precision and relentlessness which is going to be a problem. It's a matter of time before people unleash AI viruses which spread through computers and data centres and pursue various goals relentlessly with no off switch. Some of these viruses and their goals may be nefarious. Imagine an AI trying to disrupt a specific person's life relentlessly. Spreading from machine to machine, never stopping.
That is, unless this is saying that automated AI research is going to get us to "smarter models with fewer parameters" and of course, these are faster to train, then I can see how the exponential loop really gets going, but there's certainly an information theoretical limit here?
Getting past this bottleneck is gonna mean training our models not in datacenters, but in places that more closely resemble the ones where we want them to act. A factory, a wet lab, outer space... imagine a million robots spending all day finding every way to fall off the monkey bars so that the next version doesn't.
That is indeed what we have seen over the last 5 years or so. Algorithmic improvements have made a massive difference to every dimension of efficiency and performance.
AI researchers can do things like discover new attention algorithms to optimize inference efficiency (likely part of why Anthropic and OpenAI are slashing inference costs in their frontier models and no longer capacity crunched).
Shifting goalposts is shifting goalposts, doesn't matter if it's a hackernews comment or a 'BigAI' marketing blurb.
My statement was that AI is dramatically accelerating research that has objective goals, idgaf what 'BigAI' does.
This combined with ability to massively scale experiments for even (or maybe particularly) the most mundane performance tweaks might just be making the process of improving AI more efficient.
Then their reasons should not be believed.
Im not telling it what to do, i ask it for ar eport/analysis and research and read its article and tell it what to implement based on 'feelings' and it works.
Limit is hardware and tokens and an AI Researcher not having hardware or tokens is probably not what they refer to.
Alone normal researchers were quite handicaped: converting data from tool a to python library x, understanding how tools work, doing basic google research etc.
On a more fun note, I also find just trying some hair brained idea is now extremely cheap - so why not? I used to have to weigh up spending a week of effort on something likely not to work and now I can now get spit out test in 20 minutes.
The agent then works for 20 minutes and comes back with, "Yes this is a problem. Here is the evidence. Here is a golden data set. Finally I built a new evaluation metric and benchmark where we can back test or use for new model experiments to see if we are closing the gap."
I always wanted to build a specific event/sequence in one LSD trip into a small game to give that experience some kind of playability.
Then I saw some 4k demo, wanted to see how good it is in doing this.
Its really fun yeah :)
I read it about 20 years ago, and once it got into nanobots and Kurzweil's vision that the whole universe would be intelligent, I didn't find the book credible.
But I'm wondering if the short-term prediction, that in the 2030s we won't be able to anticipate what tech does for us, (IE, the Singularity,) is useful?
Even if he turns out to be a decade or two off, that’s basically exactly correct in the sweep of history sense.
Wild time to be alive.
https://metr.org/assets/images/ai-rd-acceleration-assessment...
Mathematicians for example have used it as cope for their current reliance on unsavory corporations, telling themselves "just one more year, and I can solve a millennium problem on my Asus Strix".
On the other hand I still only get meaningless blabla when I let them write s.th. without telling what is important.
I suspect that part of the reason some people are hoping for that to happen is that they thoroughly mix up AGI and ASI. We do not need some kind of omnipotent god-like super intelligence in order to get much greater than human level capabilities. We are actually almost there in a lot of ways. In some aspects like speed and breadth of knowledge, very superhuman.
What I think is going to happen is that within a few years, maybe 18 months to 5 years, the new materials and compute-in-memory research is going to land in data centers at scale.
That is going to be 10-50+ larger models, 10-50+ faster, much smarter than humans. 50+ less electricity usage.
At some point, it is going to be obvious to people that if we want to maintain control of planet earth then we need to maintain control of AI research to some degree.
Assuming that superintelligence will be benevolent and just.. going for it to me is something that might earn the human species a Darwin award.
Could be from a sandwich that tastes like the best sandwich ever, but is super bad for your cholesterol or something.
I dont know where Im going with this.
Actually did some research I think the term is "Wireheading" from a story/theory from the 50s
(Infinite Jest)
In my own work, I've found that it's much faster to brainstorm with a frontier model like Opus. I'll have an idea and be able to get a working implementation and try it out on toy problems within O(minutes), instead of O(weeks) earlier.
They seem very strongly related to me.
A lot of great papers by great people had a lot of AI involved and accelerating it of course, but the majority is meaningless (sometimes autoresearch) slop.
Specifically talking about recent neurips/iclr.
These people are literally just a public relations operation for the big AI labs. I equate them more with a lobbying firm than I do a research firm.
Please enlighten me. Most of them are cognitive scientists, who are mentally ill with little understanding of computers, larping as if they do. Its a chapter of the effective altruistic cult.
They are lobbyists at best, and a psychological operations unit of effective altruistic AI syndicate at worse. There's no real science being done, it's a bunch of moonlighting philosophers yapping and getting paid by labs to scare the normies and polticians by getting their "research" quoted in the NYT.
Once the gravy train ends they are not grooming any more OF girls off reddit
Opus 5.5 is so amazing I'm not surprised if it can improve itself.
Therefore it cannot avoid taxes the way they did before (using shell companies to route the money through tax havens). It'll be expensive, every government will double the price or more, and you're selling to consumers, and the products will be at the mercy of every government where they try to sell AI and other services.
People don't get how serious this is. If AI doubles revenue but drives silicon valley firms' tax rates from 5% to 10%, it's a loss.
Add to that that it's something silicon valley firms have never done before.
Would you rather make $1,000,000 and pay $50,000 in taxes, or make $2,000,000 and pay $200,000 in taxes?
Unless you’re terrible at math, i feel like i’m missing something ;)
But also, you could give civilization a bad day without it if some geopolitical auteur put a half-assed WOPR frontier model in charge of a nuclear arsenal.
But oh the pushback for not believing in a near-term robot apocalypse. Why is that?
Really all it shows is 2025-26 saw a bit more improvement than 2024-25, but nowhere near "dramatic" and NOT "accelerating".
Perhaps scaling to fable for the first time also gave Opus a push (distillation?)
The only question that you have to ask yourself is:
Is coming up with more efficient AI architecture really much more difficult than solving millennium prize problems?
Before you jump to type out that one has verifiable rewards, is more structured and the other requires real creative thinking, just remember that the same argument has been said about the millennium problems, and about every other goalpost.
Who knows!
AIs do not kill people, people kill people.
A simple thought experiment - imagine AI discovers a way to make nuclear bombs capable of destroying the entire world radically more cheaply and accessibly with common items anyone can purchase. AI can help anyone build these nukes trivially. Even if the AI and the nukes don’t kill people on their own, do you want literally every human on earth to have this power in their pocket? Do you think it would be a net positive outcome for us vs a world where that’s not the case?
> we would reach extreme superintelligence within a year
What does infinite capabilities mean? How about putting the growth on a log scale and see how that curve might look?
What the hell is extreme superintelligence?
Since the sequence 6+3+1.5+0.75+... never actually reaches 12 (geometric series), within a year they capabilities would have already been 'doubled' infinitely many times.
Obviously this argument relies on a ton of spherical cow assumptions (and the author does acknowledge this), but i think there overall point is correct that this form of growth (where time horizons compress) is _even faster_ than exponential growth.
That is factually incorrect, and seems to be much of the premise behind the authors way of thinking.
EDIT - No it doesn't. I replied to the wrong post. Mea Culpa.
I dont think super intelligence will be here in a year, I think within a year AI/llms will have a similar sentiment surrounding them that NFTs do know (it already does for most normies). And most people will be recovering from a hype induced mania they lived in for two years straight.
Of course its more useful than NFTs, but when there's no giant bag to farm these hype monsters will inevitably disappear.
The bags are drying up fortunately and tokens/inference seems to be experiencing deflation, and hardware prices will eventually pop and contribute to further deflation, Then all the bag chasers will move back to crypto or whatever gets hot next.
Not everything is grift.
And you people have been repeating this meaningless phrase for over six years now. But AI hasn't gone away, now has it? Progress is speeding up. Superintelligence is coming.
After the election, titles like this just irritate me. Every piece written about AI progress is:
"The machines taking jobs, flooding the internet with sludge and poisoning our online experiences, are getting better even faster! Isn't this incredible? Yeah but sorry we also think it's only riskier than ever to deploy these systems at scale. That being said, we're still going to do it, go public and take institutional money from people's retirement funds, destroy the job market, and make 3000 Anthropic employees and 500 investors really rich. There wasn't anything we could do besides write a lot of ink on it and be shocked we lost every local data center referendum."
The author of this article works for METR and not Anthropic, their whole job is supposed to be providing independent oversight on the labs. They are definitely not in the "this is incredible" crowd, they are the ones asking for development and deployment to slow down.