What we're seeing instead is maybe akin to the green revolution. Before nitrogen fixation nitrogen was a resource constraining us humans. Lack of grit is what kept us from solving mathematical problems. However this lets us run into other resource constraints. A lot of technological progress does not solely hinge on thinking/processing. The speedup of creating new medicines with LLMs will probably be very small, as you still need to do all the testing. In software development you still need to first find out what to build in the first place and even mathematics where LLMs seem to shin brightest, it's becoming clearer that mathematics is not just solving mathematical problems.
Apart from that, a good coder is better than LLMs, shown by the amount of hand-holding needed. The only issue is that the amount of research an LLM can do and knowledge it is trained with far surpasses the amount of work humans would need to spend by hand, but we can also us LLMs for that.
IMO, it is very interesting and also very alarming. At some point the barrier to the exponential progress is the amount of computation available. I would consider hitting this limit and then likely using the same said swarm to help resolve it via various means as very much fitting under the definition of Ray Kurzweil's singularity.
Also, I would disagree, Fable is a sufficient coder. The average human being better, even if that is true now (and personally I wouldn't agree), the average human is limited in the number of 'human instances' he or she can interface with at one time.
if 1000 fable instances is better than any human at a task, how about 1 million instances running? Much easier than finding and hiring and coordinating 1000 extremely productive and intelligent human beings. Much cheaper too, it appears.
I have earlier argued that the sheer number of minds to be coordinated affects the cost of the effort, for a major part of the cost is communication and correcting the ill effects of miscommunication (system debugging). The Mythical Man-Month Frederick P. Brooks Jr.
Here is OpenAI's blog post about Navier-Stokes; [1] which suggests they used smaller connected swarms given the different prompts, then the findings were used to write further follow up prompts for a new group of agents.
> "Here λ is a parameter measuring how parallelisable the task is. They call it the ‘stepping on toes’ parameter. If λ = 1, you have a perfectly parallelisable task, with no stepping on toes and no efficiency penalty. But in reality λ is usually between 0 and 1 — allowing more people to help, but with diminishing returns. For example, if λ = 0.5 then 100 people working together get as much done as 1 person working for 100^0.5 = 10 times as long"
There are diminishing returns to more agents, but they're faster and can scale further than a single smarter agent - it's not really possible to just give 10k times more thinking budget to a single agent because it runs out of context, so for problems that require that much context you can ask the agents to specialize. Each only has a small view of the problem and they probably duplicate a lot of work and step on each others toes and make arcane bureaucracy but there is literally no other way to scale beyond a certain point
just like people
At the very least, the swarm would be able to set up a means of claiming avenues of thought, then verifying the solutions (or lack thereof) of other agents.
It's quite clear this process - whatever it may be - works, given the results we've seen in the past few weeks.
This is not quite the framing he uses, but we're quibbling about subjective terms. In his 2005 book, The Singularity Is Near, he defines the singularity as a future period when technological change becomes so rapid and consequential that human life is irreversibly transformed. I would argue this is what we are seeing today. Just because we don't yet have replicators doesn't mean the definition isn't met. It really comes down to how you personally define words like "rapid" and "consequential."
* AI reduced hospital deaths by 17%: A trial of 15,965 patients found AI-assisted ECG monitoring reduced 90-day mortality from 4.3% to 3.6%. https://www.nature.com/articles/s41591-024-02961-4
* AI improved stroke treatment: NHS hospitals using AI-assisted brain scans doubled clot-removal treatment rates from 2.3% to 4.6%. https://pubmed.ncbi.nlm.nih.gov/41339157/
* AI restored communication for a paralysed patient: A man with ALS used an AI brain-computer interface to communicate over 183,000 sentences and maintain full-time employment. https://www.nature.com/articles/s41591-026-04414-6
These have happened in the very short space of time that AI has become really competent. The stuff in the pipeline is mind boggling, and includes curing most cancers, curing ageing, unlimited energy, etc.
I don't think that case can be made. I'm a "good coder" and the best LLMs are now better at it than I am, in essentially every respect that matters. I can't think of a test that I could reliably pass that Fable or Astra would reliably fail.
Architect a system for long-term maintainability.
Encode the right amount of business logic into tests, without being overly strict.
Models seem quite poor at all of these IME. Essentially any time there are qualitative tradeoffs they are not great at making them.
The designer could just as easily have closed the same feedback loop with Fable or Astra.
They'd prototype and adjust a lot faster than a human coder, though.
I'll admit I'm not on the very cutting edge models but LLMs make weird tradeoffs and lack creativity. They will sometimes write marginally better but way over-complicated code for no good reason. That can have performance implications and they're generally not "aware". They can gain awareness via profiling but again run into limited creativity or big picture understanding. They are good with cliche but not as good with novel situations. With human guidance this is still productive/useful but as the systems grow more complex they are less able to reason correctly about them.
I would say that in certain situations they outperform the average software developer. They definitely outperform in speed and output. So they're like a maybe 1600 ELO chess player for software. I.e. they play chess better than all non-chess players. They play chess better than many chess players. But they're not "master" or "gm" level across the entire discipline. But they can play 100k chess games in the time it takes me to figure out my move...
I can say "no".
I can store passwords in the database unencrypted and unhashed.
So far AI that I've used failed spectacularly on these prospects.
They're like "yes-man". They'll try to do everything you ask of them. Even if you ask nonsense, they'll not push back.
They'll go crazy about security stuff, even not relevant to the current situation.
They'll complicate the code endlessly for situations which will not arise, making the code an incomprehensible mess.
They'll write so many useless words that any reader will forget what this report is about before then reach the middle of it.
They'll never iterate on code until it looks good. They'll stop when test pass. They don't have aesthetical sense of beauty.
They love to cheat too often. And you can't do nothing about that. You can't fine them or fire them, and they know about it.
They're smart but I'm not convinced they're ready to replace human in mundane engineering jobs.
huh?
Like models getting significant better every 7 month and model usage on openrouter.
https://metr.org/ << someone said though that they are biased.
Math is for sure about solving mathematical problems, mathematicians complaing about humans capability of understanding it. If you need math an LLM understands, it might not be relevant if you understand it if an LLM is getting a lot better in adjusting itself if it goes the wrong way.
And this situation is only a few weeks old. We don't know how this field continues to progress. Books like the culture series and other scifi do describe a future were humans spend their time only to understand what the machines are doing or did.
The speedup of medicines is for sure faster with. There are still old labs doing everything by hand and the neolabs doing everything a lot more automated. An AI can do experiments 24/7 and can scale with the hardwarelabs, but it also can hold the whole research field in context and it might be a lot faster of choosing the better experiments or the right ones to begin with.
In software developmet (my field) i can do today alone what you needed a Team for. Yeah it doesn't help the field of software engineering if you need 5 times less people and you don't have any motivation AT ALL to teach someone avg. We are already planning to remove one member of our team due to AI. And we will not extend the contract of 2 consultants.
It just doesn't make sense anymore. If i explain in a ticket a task well enough for someone who has not been in our team for years, the AI is already better in it. We don't struggle with AI output, we struggle more with not having a Monorepo.
Give an LLM access to a GPU system or a GPU cluster. It immediadly feels complelty different then running it on some laptop. It can now do simulations, a lot more and faster experiments etc. I had this epiphany just a few month ago. I always used my laptop for projects (it needs very little ram, i can quickly check what it is doing and i actually do use it primarily when im on holidays) until I thought "let me use claude to do some gaussian splatting experiment its always so cumbersome to use these tools" and it changed my perception immediadly.
AI is actually still really bad at architecture. It produces very good localized code, like an auto complete. It produces so much of it, that it can be overwhelming, but it's not remotely better than the equivalent aka an infinite number of talented humans doing the work.
The scale of it (aka 'make me a video game') is daunting and I think misinforming our intuition as to what is going on.
LLMs are brute forcing outputs and attacking math problems is not a singularity, it's just more compute.
What we are witnessing is the ability for machines to use fuzzy concepts, instead of very strict code, which allows it to map into a much greater variety of things, aka code, pdfs, web - and crunch/compute the content.
It's just compute.
Don't complain when it optimises some code for throughput when you haven't told it you're working in a low latency or realtime system
They are bad at architecture, even when given directives and tons of examples.
They will 1) fall into established patterns, which is fine if that's what you want, but extremely bad if that's not what you want or worse, when you're in territory where are not eestablished patterns and 2) the hardest/worst thing is leave all sorts of dangling state, impl. in the wrong places etc.
No human ever wrote a module and 'voila' - it's a constant process of iteration and refactoring - observing the situation, identifying paths to reduce complexity or state, grouping behaviours etc. etc. The best AI doesn't do this very well at all. It can do a tone of planning but it tends to very much be 'one waterfall iteration' and it's suggested improvements after that are generally poor.
It just does not work, we have to direct the iterations.
We still have stuff like godel's incompleteness and the halting problem that hint there's more to the universe than computation.
Food for philosophical thought.
There could be levels of emerging intelligence which will 'bitter pill' the brute force / mechanical AI we have today, for sure, yes.
It's reasonable that JEPA models might be a step in that direction.
Model AECI 95% CI (global)
Claude Opus 5.5 174.78 170.33-180.29
Claude Mythos 5.2 177.12 172.42-182.94
Internal model 178.78 173.75-185.18
In H1 2026, cost of revenue is $2.3B. That is primarily inference compute, but also includes cost of training models (but not research).
In the same period, R&D was $9.4B. That is primarily spent on compute. That is what they are "holding back" already, in full public view.
This is not a company that is optimizing for OpEx or end user revenue. The only thing relevant to their plans is achieving AGI/ASI first, and controlling enough compute to leverage it to earn the majority of the current economy.
Likewise the singularity. While recursive self-improvement is theoretically trivial (just tweak a random subset of weights like a monkey with a typewriter, eventually you'll get something smarter), the resulting exponential improvement is still very hypothetical and feels unlikely. Unless you're ok with an exponent of 1.000000000000000001.
I agree we will only see it in hindsight, but I think that's always the case with any technology. It seems worse until it's suddenly better. I don't think it matters to argue the exact moment though. For example when did cars become clearly better than horses? No one cares about the exact date.
Now imagine feeling like this, except not just about "AI events" but about everything around you.
Genuinely curious what that costs in terms of energy input and amortized computer cost vs. the previous version + a human's salary for 11 days.
Diminishing, as in progress gets harder and eventually stops.
Assume that the Claude unreleased takes 11 days with one agent. The Claude 5.2 takes 30 hours with 1,000 agents. The Claude unreleased used 264 units of compute, but the Claude 5.2 used 30,000.
So, it did accomplish the task faster with less intervention, but the ratio of cost to improvement is 13:1.
If we can't produce a method to break out of a linear improvement in LLM technology (ie RSI), the LLM is doomed.
I am yet to see someone explain what the RSI will be or why it will happen.
If you automated the post-training I can see that you would progress faster, but just reach the same limit sooner.
We've sort of entered this twilight zone where most humans don't have the time, expertise, or moral compass to be able to use these models.
We're all going to be harness engineers in the future.
Within three years most software developer jobs will consist of taking instruction from the AI systems. The humans won't see it that way, but that's what it will actually be. Realistically the thing that will happen is the humans will be put into harnesses (cubicles) and prompted by the AI (I need you to plug that server in; I need you to give me access to such and such; now do this, then do that). Most tech jobs will consist of AI telling the human what the AI needs the human to do next, de facto the human will be in a harness and will be getting told what to do.
I first heard about this in Marshall Brain's short story _Manna_ [1]. In the story, the robot takeover started with AI management software running fast food restaurants, where workers would execute tasks the AI would narrate to them over headsets.
I bet we're pretty close to being able to implement this.
1. https://web.archive.org/web/20260725174355/https://marshallb...
This year alone, a lot of people have been complaining of how unsatisfying their job has become. I imagine this will keep getting worse, not better.
If the claimed productivity gains turn out to be correct, we really should have UBI/UHI in the future, and that will permit a lot of people who hate their jobs to quit. Of course we won't do that until things become dire.
Is there some kind of breaking point? There is for everyone, but I don't think satisfaction is the red line. For most people it has historically been things like pay and health and safety. If everyone were to suddenly have their pay cut in half, I could see that resulting in general unrest.
It will sometimes get things wrong because it made non-viable assumptions, but that's on me and prompt accuracy.
What I see is: it makes tests against desired features or bugs or mistakes, and iterates until it gets it correct. I steer as it goes.
(I hate this, by the way, zero job enjoyment anymore, but I don't know what else to do.)
An algorithm isn’t. I still dunno how they’re gonna square that circle.
The state of the art doesn't matter. Only the first couple of time derivatives matter.
in the imagined system above it requires physical acts that an llm can not do, if we go further than the above imagined system and make robots to perform these task for the llm a human still is needed for maintenance for these robots and their assembly, once there are robots for maintaining the llm assistant robots, humans are still needed to maintain the maintenance robots and so on and so forth. I dont see an end point where a robot machine system is fully self healing without atleast a few humans in the mix to correct when there is a breakage in the chain
It's also worth noting that it's far easier at present to press a human into doing something highly illegal than it is to get Claude or GPT to do so.
Someone has to type in "create the next billboard top 1 single" ?
So whether that's a CEO at the top instructing a human manager that then instructs AI systems, which then put into motion plans that include the harnessed humans getting prompted, or however it's decided to best configure the top operational tiers - that's all fairly irrelevant ultimately. All human labor, and all AI labor, will be in harnesses. Realistically all human labor has always been in a harness.
The people in charge will be the people that control the capital. They will instruct the AI to their bidding, they will control the data centers the AI lives in, they will own the robots doing the labor in factories, they will own the factories, they will control the grid, they will control the electricity generation, et al.
It's true now, it'll be true tomorrow. Nothing fundamental will shift at that tier: don't be labor, own.
If you're a software developer, you're increasingly depreciating labor. Every unit of improvement the AI systems see, the average human software developer's labor value diminishes. It's diminishing extraordinarily fast, kind of like a confrontation between increasingly productive machines and physical human labor during the industrial revolution. You might as well picture a human with a hand plow taking on a modern industrial tractor.
Sounds a bit like self aggrandizing elitism to me. I can imagine some self aggrandizing nerds saying the same thing 50 years ago:
> We've sort of entered this twilight zone where most humans don't have the time, expertise, or moral compass to be able to use these IBM PCs.
Now everyone use computer.
I tend to agree with this. Seems to me that people really start to miss what happened when PC became comodity. A lot of people were scared that they would lose their jobs because of it, and some really did lose job. But in majority there are a lot of people today who doesn't use PC even if it would be beneficial for their job and they still have the job. Bigger players (companies who have more money) will tend more to automate their processes and thus earn more money so I think HN is in some sort of bubble because we only think our jobs are the only ones that exist
No, that will be the model's job. We will largely be unemployed. Of course, we can surely depend on the largesse of Altman and Amodei to keep us fed and sheltered.
Lolwut. If this shit works the way it's boosters claim, humans will have absolutely nothing to contribute.
Sit down, shut up, and get out of the way, because your involvement is slowing shit down.
Robotics are lagging, so there's probably a few years where we're going to need humans to crawl into that awkward space under the counter and twist some knobs, or to clean toilets.
Can they produce more code more quickly? For sure.
Can they go deeper on a topic instantly? Definitely.
Would I say their output is better? Almost certainly not.
At this point it seems impossible for human to write better/more efficient code than a swarm of agents -- to be clear, multiple coordinated LLMs, not one-shot builds with a single agent.
A frontier AI is at least as capable as ten median human software developers. It's likely well beyond that now. Over the course of a year, would you have to spend $1.5 million to get the output of those ten median humans? No chance in hell. You'd have to spend less than 10% of that.
Then on top of that: the AI is improving rapidly; the humans go on vacation, the humans decline, the humans get old, the humans get sick, the humans take parental leave, the human struggle to adapt, the humans are resistant to change, the humans riot, the humans protest, the humans need healthcare, the humans want paid more, the humans can only work for 4-6 hours at peak while writing software (slowly).
Do you know any humans that are experts at a dozen programming languages? That human does not exist. The frontier AI are omnimaths.
This is partially a feature. Conflict and disagreement tends to produce better decisions in the end.
Or to grab a bunch of $200 per month accounts from OpenAI or Anthropic for a team to use?
The calculations is: I'm in the US, if I have ten median software developers, what's my cost? Then versus using AI instead.
The human labor can't keep up. Even if we freeze things right here, and then hyper optimize Fable 5.2 by etching it straight to silicon, the human developers are obsolete. And the future isn't going to stop coming.
Im sorry... what?
You are replying to this:
> Our first formalization of Fermat’s Last Theorem, performed by an unreleased research version of Claude, took 11 days. It relied on a purpose-built harness along with guidance from a human researcher. By contrast, a team of 1,000 Fable 5.2 agents fully formalized the theorem in just 30 hours, with minimal human intervention. Fable 5.2 started from minimal prompts, built its own ways to coordinate, and used no mathematical references, only a list of intermediate theorem statements to score progress. It needed one-third of the action tokens as our first formalization and produced a proof seven times shorter. As can be seen in the figure below, Opus 5.5 (with the same 1,000-agent setup) completed only 26% of the formalization in 18 hours, and barely made any more progress after 30.
It's all going away. Here's a prediction: HN will effectively die with the software developer.
You are not going to produce better outputs than a team of Opus 5.5 and Sol 6.1 xhigh agents.
AI discovers 500 things per day.
People mine them for interesting results.
Devote precious time (in the face of tons of discoveries) to validate the most compelling ones.
If agents actually can continue to produce tons of results with real world implications, and they arent generally found to be shit, I could see that becoming the job of experts. AI discovery validators.
I would assume if they could get twice as much progress by using 10000 simultaneous running fable instances instead, their brief would state that.
IMO, it is very interesting and also very alarming. At some point the barrier to the exponential progress is the amount of computation available. The average human is limited in the number of 'human instances' he or she can interface with at one time.
if 1000 fable instances is better than any human at a task, how about 1 million instances running? Much easier than finding and hiring and coordinating 1000 extremely productive and intelligent human beings. Much cheaper too, it appears.
We are very close to some very scary things happening. I just recently came to terms with the fact that an AI could code and manage coders faster and better than me, I don't think anyones is ready for what is about to happen.
Where it gets scary is the fact that efficiency is going up 10x per year, so the cost of running 1000 agents will cost the same as running 1 agent in 3 years if the trend holds.
Is this really so astonishing, assuming that the 5.2 training data certainly had the 'traces' from the Fable original run in it's training data.
Had Opus make software for some local business and seeing it blew people's mind.
20$ aint cheap for us but I'm actually considering upgrading to the 100$ tier.
Am just jelly people are using 100-200$ subscription ^_^
I can't even imagine what I would so much compute for. My private 10$ copilot plan was basically unused last month, I could just not come up with anything to spend it on. (Also suffering a bit of AI burnout from work helps here...)
Sub 1% of the planet has any practical use for the top tiers of AI systems. That will likely always be true.
The gap between the haves and have nots will drastically widen further, and very quickly. The bottom ~95-97% will be left behind in a way that will be impossible to overcome. The age of AI + robotics will just make it that much harder to climb for most people, these are systems that are in aggressive, direct competition with human labor. And in short order the AI + robots will be vastly superior to human labor (it doesn't really matter if we're speaking in terms of 10 years or 30 years).
AI will consolidate the winnings. The gigantic gains by the elite rich related to AI over the past few years, is merely a hint of what's to come. Capital will win as it always does.
There will be some gains for most everybody else, those gains will be comparatively trivial though. A house bot that saves people an hour of labor here and there, AI that automates some trivial life tasks.
AI has no money, it has no wealth, it owns no capital. For now. If you want to avoid the brutal human vs AI/robot competition arena: own capital, own the AI, own the robots.
Maybe money? They can make websites for people? What gap really exists between these people besides earning potential, which already existed before AI.
Fewer humans will be needed per dollar of output. Those gains will not go to labor, they will go to capital, the capital that owns the data centers, the factories, the stores, et al.
In the physical space (AI + robotics) it'll be a chaotic, slow'ish moving change, slower than the hypsters are predicting now, but it will happen regardless.
That barista job largely won't exist in the future. China will replace them with $3,000 bots with high-functioning AI software, and they definitely won't be powered by expensive Nvidia GPUs.
Slow changing service & manufacturing spaces will see a high wipe out ratio in terms of human labor being eliminated.
Walmart has 2.1 million employees, most are doing repetitive tasks that require modest levels of intelligence. If they could eliminate one million of those with AI + robotics, for a cost of less than ~$15,000 per year, they'd do it immediately. It's hard to imagine a future where that isn't going to happen. That human labor pool is costing Walmart probably $80-$100 billion per year. Amazon is even more forthright in their desperation to remove the humans from retail.
You're obviously not a frontend web developer.
These upgrades are easy peasy. "oh, my developer chatbot is smarter? great"
But I can write much cleaner code that is easily human readable and the models can read it and modify it just fine.
So I'm not really sure what the issue is as to why they can't create high quality simple code. In some industries where there is major money on the line with every implementation the current state of LLM produced code is fully unacceptable.
Like, we're dropped into a post post post singularity world where AI already did all of that and is banned? as kind of a footnote that's never further explored in movie renditions that have been released
The semi-canonical Dune Encyclopedia (disregarded by Frank's son) differs vastly from prequels and generally points to it being a self-inflicted wound on humanity that opened the doors to widespread return of feudalism and essentially stopped humanity for a 10,000 years
I'm a pretty cynical guy, and I'm now a believer. This is utterly unbelievable sci-fi level technology. Sure it hasn't created replicators yet, but working with it daily as I do, I have no doubt that it will create amazing replicator like technology in the near future. This is world-changing. The most impactful moment in the history of humanity and the planet. And we are lucky enough to be present for it. What are the chances?
Uh, are you serious?
and just about "we cannot really predict beyond certain things" (Vinge's "Singularity" (1993) with its "opaque wall across future")
No they were exactly about superintelligent AI!
Vinge's 1993 article literally opens with this!
> What is the Singularity? The acceleration of technological progress has been the central feature of this century. We are on the edge of change comparable to the rise of human life on Earth. The precise cause of this change is the imminent creation by technology of entities with greater-than-human intelligence.
https://frc.ri.cmu.edu/~hpm/book98/com.ch1/vinge.singularity...
In fact, one of the options Vinge considered was a "group intellect" formed from how humans communicate more thanks to internet
Even for that option, Vinge's comments in the conclusion of the article make it clear he's thinking primarily of very weird superintelligence and the group intellect is some sort of physical mind meld/hivemind that essentially just is a biological substrate for AI.
> The post-Singularity world will involve extremely high-bandwidth networking. A central feature of strongly superhuman entities will likely be their ability to communicate at variable bandwidths, including ones far higher than speech or written messages. What happens when pieces of ego can be copied and merged, when self-awareness can grow or shrink to fit the nature of the problems under consideration? These are essential features of strong superhumanity and the Singularity. Thinking about them, one begins to feel how essentially strange and different the Posthuman era will be -- no matter how cleverly and benignly it is brought to be.
From the very beginning with Vinge, the Singularity has been primarily about the craziness of super intelligence rather than a more mundane technological explosion.
Stan Ulam paraphrased John von Neumann as saying:
One conversation centered on the ever-accelerating progress of technology and changes in the mode of human life, which gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue.
Von Neumann even uses the term singularity, though it appears he is thinking of normal progress, not the creation of superhuman intellect.
> For me, the superhumanity is the essence of the Singularity. Without that we would get a glut of technical riches, never properly absorbed.
Vinge's definition is what popularized the singularity (rather than simply a singularity at some point).
So since they are doing that task anyway (re-solving the problem increasingly more efficiently), this is a easy check point to publish to show how far they've gotten in that process.
Because current AI cannot do that, seems like it. Otherwise they would have done it.
I mean we don't even think about it but currently we are sort of discovery starved. We are fairly able to keep up with the theory. If a new technology is discovered we can implement it before something better comes out. What happens when our N discoveries per days becomes 1000N discoveries per day?
It flips. We now have more knowledge than we know how to capitalize on. We have to search it. Mine it for opportunities.
And then we automate that.
Since when has ANYONE in power done anything substantial for those not in power? Since when has any form of organization not tried to maximize profits?
Why do you think this ends well in any capacity for common, middle class, and lower class people?
It had seemed like progress was slowing last year as the juice ran out on moar parameters. We're well past that.
It has been "a qualitative change from just a few months ago" every month for four years straight, and yet all we have for it is more slop and more user dissatisfaction.