The Singularity will occur on a Tuesday
campedersen.com
campedersen.com
And, yep! A lot of people absolutely believe it will and are acting accordingly.
It’s honestly why I gave up trying to get folks to look at these things rationally as knowable objects (“here’s how LLMs actually work”) and pivoted to the social arguments instead (“here’s why replacing or suggesting the replacement of human labor prior to reforming society into one that does not predicate survival on continued employment and wages is very bad”). Folks vibe with the latter, less with the former. Can’t convince someone of the former when they don’t even understand that the computer is the box attached to the monitor, not the monitor itself.
And there are plenty of people that take issue with that too.
Unfortunately they're not the ones paying the price. And... stock options.
* Profits now and violence later
OR
* Little bit of taxes now and accelerate easier
Unfortunately we’ve developed such a myopic, “FYGM” society that it’s explicitly the former option for the time being.
there is only one possible “egalitarian” forward looking investments that paid off for everybody
I think the only exception to this is vaccines…and you saw how all that worked during Covid
Everything else from the semiconductor to the vacuum cleaner the automobile airplanes steam engines I don’t care what it is you pick something it was developed in order to give a small group and advantage over all the other groups it is always been this case it will always be this case because fundamentally at the root nature of humanity they do not care about the externalities- good or bad
I did try, I promise.
Fundamentally, at the root nature of humanity, humans do not care about the externalities, either good or bad.
If somebody is using monetary resources to buy NFT‘s instead of handing out food to the homeless then you get less food for the homeless
All of the things listed are competitive task situations and you’re looking for some advantage that makes it easier for you
well if it makes it easier for you then it could make it easier for somebody else which means you’re crowding out other options in that action space
That is to say the pie is fixed for resources on this planet in terms of energy and resource utilization across the lifespan of a human
But there was a clear advantage in quality of life for a lot of people too.
Automobile -> part of industrialization of transport -> faster transport, faster world
Arguably also a big increase in quality of life but it didn't scale that well and has also reduced the quality of life. If all that money had gone into public transport then that would likely have been a lot better.
Airplanes -> yes, definitely, but they were also clearly seen as an advantage in war, in fact that was always a major driver behind inventions.
Steam engine -> the mother of all prime movers and the beginnings of the fossil fuel debacle (coal).
Definitely a quality of life change but also the cause of the bigger problems we are suffering from today.
The 'coffin corner' (one of my hobby horses) is a real danger, we have, as a society, achieved a certain velocity, if we slow down too much we will crash, if we speed up the plane will come apart. Managing these transitions is extremely delicate work and it does not look as though 'delicate' is in the vocabulary of a lot of people in the driving seats.
I used to hear about this with respect to how fun funding NASA would get us more inventions because they funded Velcro
No it’s simply that there was a positive temporary externality for some subset of groups but the primary long term benefit went to the controller of the capital
The people utilizing them were marginally involved because they were only given the options that capital produced for them
Taxes don't usually work as efficiently because the state is usually a much more sloppy investor. But it's far from hopeless, see DARPA.
If you're looking for periods of high taxes and growing prosperity, 1950s in the US is a popular example. It's not a great example though, because the US was the principal winner of WWII, the only large industrial country relatively unscathed by it.
This book
https://www.amazon.com/Zero-Sum-Society-Distribution-Possibi...
tells the compelling story that the Mellon family teamed up with the steelworker's union to use protectionism to protect the American steel industry's investments in obsolete open hearth steel furnaces that couldn't compete on a fair market with the basic oxygen furnace process adopted by countries that had their obsolete furnaces blown up. The rest of US industry, such as our car industry, were dragged down by this because they were using expensive and inferior materials. I think this book had a huge impact in terms of convincing policymakers everywhere that tariffs are bad.
Funny the Mellon family went on to further political mischief
https://en.wikipedia.org/wiki/Richard_Mellon_Scaife#Oppositi...
You can't onshore manufacturing and have a dollar reserve currency. The only question then is, Are you willing to de-dollarize to bring back manufacturing jobs?
This isn't a rhetorical question if the answer is yes, great, let's get moving. But if the answer is no, sorry, dollarization and its effects will continue to persist.
What you describe seems like very cheap Chinese imports fraudulently imitating something else.
I really wonder what you're comparing with.
Try some quality surgical steel from Sweden, Japan or Germany and you'll come away impressed. China is still not quite there but they are improving rapidly, Korea is already there and poised to improve further.
Metal buyers all over the globe are turning away from the US because of the effects of the silly tariffs but they were not going there because the quality, but because of the price.
The US could easily catch up if they wanted to but the domestic market just isn't large enough.
And as for actual metallurgy knowledge I think russia still has an edge, they always were good when it came down to materials science, though they're sacrificing all of that now for very little gain.
https://www.youtube.com/watch?v=BHnJp0oyOxs
There are people making top quality steel in the US today by modern methods but it wasn't like the new replaced the old, the old mostly disappeared and we got a little bit of the new.
US pipeline for metallurgical R&D broken (by financial/cultural incentives)
This guy studied metallurgy in Carleton U, Canada, switched to CS, founded YC, emotionalized the decision
https://news.ycombinator.com/item?id=39600555
Who knows, he might have become John Carmack's John Carmack, building rockets better than Carmack or Elon
The US is also shaping up to be the principal winner in Artificial Intelligence.
If, like everyone is postulating, this has the same transformative impact to Robotics as it does to software, we're probably looking at prosperity that will make the 1950s look like table stakes.
I think it's extremely early to try and call who the principal winner will be especially with all the global shifts happening.
There is no early mover advantage in AI in the same way that there was in all the other industries. That's the one thing that AI proponents in general seem not to have clued in to.
What will happen is that it eventually drags everything down because it takes the value out of the bulk of the service and knowledge economies. So you'll get places that are 'ahead' in the disruption. But the bottom will fall out of the revenue streams, which is one of the reasons these companies are all completely panicked and are wrecking the products that they had to stuff AI in there in every way possible hoping that one of them will take.
Model training is only an edge in a world where free models do not exist, once those are 'good enough' good luck with your AI and your rapidly outdated hardware.
The typical investors horizon is short, but not that short.
Be careful. The data does not confirm that narrative. You mentioned the 1950s, which is a poignant example of reality conflicting with sponsored narrative. Pre WOII, the wealthy class orbiting the monopolists, and by extension their installed politicians, had no other ideas than to keep lowering taxes for the rich on and on, even if it only deepened the endless economic crisis. Many of them had fallen in the trap of believing their own narratives, something we know as the Cult of Wealth.
Meanwhile, average Americans lived on food stamps. Politically deadlocked in quasi-religious ideas of "bad governments versus wise business men", America kept falling deeper. Meanwhile, with just 175,000 serving on active duty, the U.S. Army was the 18th biggest in the world[1], poorly equipped, poorly trained. Right wing isolationism had brought the country in a precarious position. Then two things happened. Roosevelt and WOII.
In a unique moment, the state took matters in their own hands. The sheer excellence in planning, efficiency, speed and execution of the state baffled the republicans, putting the oligarchic model of the economy to shame. The economy grew tremendously as well, something the oligarchy could not pull of. It is not well-known that WOII depended largely on state-operated industries, because the former class quickly understood how much the state's performance threatened their narratives. So they invested in disinformation campaigns, claiming the efforts and achievements of the government as their own.
1. https://www.politico.com/magazine/story/2019/06/06/how-world...
The post-war era, under Truman and Eisenhower administrations, reaped the benefits of the US being the wealthiest and most intact winner of WWII. At that time, the highest income tax rate bracket was 91%, but the effective rate was below 50%.
I assume you are talking about WW2 and at first thought it was a typo.
I don’t find this to be true
The state invests in important things that have 2nd and 3rd order positive benefit but aren’t immediately profitable. Money in a food bank is a “lost” investment.
Alternatively the state plays power games and gets a little too attached to its military toys.
VC has failure as well as successes. State funding is no different.
That we had to give up societal control over our space program to billionaires is deeply shameful. We’re quickly reaching the end of the end of history.
And there are many others that might've been a positive investment from a strictly financial perspective, but not from a moral one: see Banana Republics and all those times the CIA backed military juntas.
[0] https://unterguggenberger.org/the-free-economy-experiment-of...
If that is not an example of how taxing rich people and investing in infrastructure can accelerate progress, then I don't what could be (with of course the caveat that those investments were focused in a specific group of people and not society as a whole).
Nowadays you could squash an uprising with a few operators piloting drones remotely.
I’m not sure you have really thought out what the drone part is meant to do. Militaries gave outgunned populaces for decades at this point. You don’t need drones to kill civilians.
Militaries can side with the crowd, or more likely decide to keep the power for themselves.
But they also need 2nd order meatware to support that 1st order: teachers, doctors, merchants… You need scientists to advance your technology against other militaries… You need leaders (3rd order) to keep the first two populations quiet and productive since that turns out to be more cost-effective than fear control through extermination…
Hell you need a certain level of genetic diversity so your own kids don't come out weird.
Give evolution a little more credit. The required number of humans for the in-group to be self-sustainable is definitely not billions, plus it's been shrinking with automation. But we are where we are for a reason – lots of alternative arrangements have been tried over millenia and found wanting.
"Keep my bunker + my drone factory and some farmers, kill the rest" leaves rulers with terrible quality of life (bad) and the next-door-junta taking over pretty quickly (also bad). It is a self-defeating, poor long-term strategy.
Automation tips the power balance further: fewer humans needed, more local autonomy. Which is, I suspect, why the ruling class are so terribly excited about AI, more so than some market valuations. Fewer pesky humans across all levels. Genetic diversity of bloodline remains the primary concern (unless you manage to live forever, which happens to be another evergreen of power ghouls).
Hint: The answer for the government is, it's never enough. "little bit of taxes" is never what we had.
Seriously though, I wouldn't mind "little bit of taxes" if there were guaranteed ways to stop funding something when it's a failed experiment, which is difficult in government. Because "a little bit more" is always wanted.
Government starts a new program and funds it with taxes. First, it is difficult to prove if the program is doing what it set out to do effectively. Second, there is 0 incentive to ever shut it down. The people working there don't want to, the group who started it also don't want to. The people funding it have 0 insight or say if it should continue.
I am not convinced, though, it is still up to "the folks" if we change course. Billionaires and their sycophants may not care for the bad consequences (or even appreciate them - realistic or not).
It’s willful negligence on a societal scale. Any billionaire with a bunker is effectively saying they expect everyone to die and refuse to do anything to stop it.
It makes one wonder what they expect to come out the other side of such a late-stage/modern war, but I think what they care about is that there will be less of us.
Ultimately they just want to widen the inequality gap and remove as much bargaining power from the working class. It will be very hard for people not born of certain privileges to climb the ranks through education and merit, if not impossible.
Their goal will be to accomplish this without causing a French Revolution V2 (hence all the new surveillance being rolled out), which is where they'll provide wars for us to fight in that will be rooted in false pretenses that appeal to people's basest instincts, like race and nationalism. The bunkers and private communities they build in far off islands are for the occasion this fails and there is some sort of French Revolution V2, not some sort of existential threat from AI (imo).
Well, good luck. You have "only" the entire history of human kind on the other side of your argument :)
The fundamental unit of society …the human… is at its core fundamentally incapable of coordinating at the scale necessary to do this correctly
and so there is no solution because humans can’t plan or execute on a plan
Let me know when ChatGPT can do your laundry.
How this all unfolds is absolutely path dependent.
Hope is kind of in short supply nowadays. Even if your hypothesis of absolute-automation doesn't happen within our lifetimes, things seem to be guaranteed to get worse for people like us. If it does happen... we'll likely never reap any real rewards from it, barring a complete restructuring of our whole society to an extent that has never happened and likely would never be allowed to happen.
Also, there has never been a better time to learn about philosophies that get at the essence of being human and that elucidate precisely how and why our baser characteristics (acquisitiveness, status-seeking, ego, etc) hold us back from being happy. The world we're heading towards will convert desire into suffering more readily than ever before, even as our basic needs are easily met. Philosophy is the cure. And I strongly believe happiness will remain accessible to those who embrace it.
Luckily we’re already most of the way there!
Over half of the population has been instrumented already to collect all their behavior data worldwide
That’s been the goal: persistent collection of training data should come out of your day-to-day life in order to bootstrap the action systems that are machine based
The challenge now is that most of that data is based on actions we don’t want machines to do
I disagree. If the singularity doesn't happen, then what people do or don't believe matters a lot. If the singularity does happen, then it hardly matters what people do or don't believe (edit: about whether or not the singularity will happen).
We, the people actually building it, have been discussing it for decades
I started reading Kurzweil in the early 90s
If you’re not up to speed that’s your fault
A hard takeoff would be the tool bootstrapping itself into an autonomous self-improving ASI in a short amount of time.
And I read Kurzweil years ago too. He thought reverse engineering the human brain once the hardware was powerful enough would together give us the singularity in 2045. And the Turing Test would have been passed by 2029, but seems like LLMs have already accomplished this.
Imagine you’re 70 years old, in rural North Carolina, sitting on your porch wondering why your house has a sheet of ice on it that’s never happened before. Now your already weak soybean harvest that year yields only 30%
Meanwhile your 30-year-old neighbor just had a productive soybean harvest because they covered their crops prior to the freeze based on using the Internet for weather forecasting
That trivial variation between people who utilize information technology to improve their survivability has been happening for the last few hundred years unabated.
This is what the soft singularity looks like
Depends on how you feel about Roko's basilisk.
It’s somewhat simplistic, but I find it get the conversation rolling. Then I go “it’s great that we want to replace work but what are we going to do instead and how will we support ourselves?” It’s a real question!
At least that’s my personal goal
If we get to the point where I can go through my life and never interact with another human again, and work with a bunch of machines and robots to do science and experiments and build things to explore our world and make my life easier and safer and healthier and more sustainable, I would be absolutely thrilled
As it stands today and in all the annals of history there does not exist a system that does what I just described.
Be labs existed for the purpose of bell telephone…until it wasn’t needed by Bell anymore. Google moonshots existed for the shareholders of Google …until it was not uselful for capital. All the work done at Sandia and white sands labs did it in order to promote the power of the United States globally.
Find me some egalitarian organization that can persist outside of the hands of some massive corporation or some government that can actually help people and I might give somebody a chance but that does not exist
And no mondragon does not have one of these
I still do.
The difference is that as I realized what I'd done is built up walls so thick and high because of repeated cycles of alienation and traumas involving humans. When my entire world came to a total end every two to four years - every relationship irreparably severed, every bit of local knowledge and wisdom rendered useless, thrown into brand new regions, people, systems, and structures like clockwork - I built that attitude to survive, to insulate myself from those harms. Once I was able to begin creating my own stability, asserting my own agency, I began to find the nuance of life - and thus, a measure of joy.
Sure, I hate the majority of drivers on the roads today. Yeah, I hate the systemic power structures that have given rise to profit motives over personal outcomes. I remain recalcitrant in the face of arbitrary and capricious decisions made with callous disregard to objective data or necessities. That won't ever change, at least with me; I'm a stubborn bastard.
But I've grown, changed, evolved as a person - and you can too. Being dissatisfied with the system is normal - rejecting humanity in favor of a more stringent system, while appealing to the mind, would be such a desolate and bleak place, devoid of the pleasures you currently find eking out existence, as to be debilitating to the psyche. Humans bring spontaneity and chaos to systems, a reminder that we can never "fix" something in place forever.
To dispense with humans is to ignore that any sentient species of comparable success has its own struggles, flaws, and imperfections. We are unique in that we're the first ones we know of to encounter all these self-inflicted harms and have the cognitive ability to wax philosophically for our own demise, out of some notion that the universe would be a better place without us in it, or that we simply do not deserve our own survival. Yet that's not to say we're actually the first, nor will we be the last - and in that lesson, I believe our bare minimum obligation is to try just a bit harder to survive, to progress, to do better by ourselves and others, as a lesson to those who come after.
Now all that being said, the gap between you and I is less one of personal growth and more of opinion of agency. Whereas you advocate for the erasure or nullification of the human species as a means to separate yourself from its messiness and hostilities, I'm of the opinion that you should be able to remove yourself from that messiness for as long as you like in a situation or setup you find personal comfort in. If you'd rather live vicariously via machine in a remote location, far, far away from the vestiges of human civilization, never interacting with another human for the rest of your life? I see no issue with that, and I believe society should provide you that option; hell, there's many a day I'd take such an exit myself, if available, at least for a time.
But where you and I will remain at odds is our opinion of humanity itself. We're flawed, we're stupid, we're short-sighted, we're ignorant, we're hostile, we're irrational, and yet we've conquered so much despite our shortcomings - or perhaps because of them. There's ample room for improvement, but succumbing to naked hostility towards them is itself giving in to your own human weakness.
Thank you.
There are no more important other problems to solve other than this one
everything else is purely coping strategies for humans who don’t want to die wasting resources on bullshit
Not interacting with any other human means you're the last human in your genetic line. A widespread adherence to this idea means humanity dwindling and dying out voluntarily. (This has been reproduced in mice: [1])
Not having humans as primary actors likely means that their interests become more and more neglected by the system of machines that replaces them, and they, weaker by the day, are powerless to counter that. Hence the idea of increased comfort and well-being, and the ability to do science, is going to become more and more doubtful as humans would lose agency.
[1]: https://www.smithsonianmag.com/smart-news/this-old-experimen...
Get rid of everyone else so your life is easier and more sustainable... I guess I need to make my goal to get rid of you? Do you understand how this works yet?
Good luck
See how it works?
I know Ray Kurzweil and Ben Goertzel though so maybe start there
Thank you Internet person for all of this wisdom about my life that I didn’t have
Cheers :)
The man seems unwell if he has kids based on his other comments and is still talking about “civilization suicide” and “obviating humans”
if we can not do the things they are afraid of and just pretend we do :p
> "eliminate humans as the primary actors on the planet entirely"
...so they can work with you. The hole in your plan might be bigger than your plan.
But you are of course free to believe and enjoy the vision of such a future but this is something that should happen on a collective level. We still live in a (to some extent idealistic) but humanistic society where human rights are common sense.
the full realization of Humanity's potential indeed needs to permit such Choice that you could live your whole life without seeing another Human.
I still think we can build something better, rather than hope for AI or Alien overlords taking us to the next step ;)
and another of your post states
> The fundamental unit of society …the human… is at its core fundamentally incapable of coordinating at the scale necessary to do this correctly
You seem to contradict yourself if not entirely confused.
But how is that useful in any way?
For all we know, LLMs are black boxes. We really have no idea how did ability to have a conversation emerge from predicting the next token.
Uh yes, we do. It works in precisely the same way that you can walk from "here" to "there" by taking a step towards "there", and then repeating. The cognitive dissonance comes when we conflate this way of "having a conversation" (two people converse) and assume that the fact that they produce similar outputs means that they must be "doing the same thing" and it's hard to see how LLMs could be doing this.
Sometimes things seems unbelievable simply because they aren't true.
It's funny how, in order to explain one complex phenomenon, you took an even more complex phenomenon as if it somehow simplifies it.
I wasn't referring to the biomechanical process of walking, I was referring to the process of gradient descent, which is well understood and yes, quite simple.
Thing is, that would be a rebuttal if I'd said something about the underlying tensor fiddling code being understood, and you were claiming that next token prediction was a mysterious emergent phenomena.
Unfortunately that's not the argument I made. My claim is that there's nothing surprising or mysterious about the fact that a system designed to repeatedly generate a highly likely continuation of a sequence of tokens (considered as a member of pre-specified class of sequences) winds up producing something that looks like it could be a member of that class. That's kind of the whole point. These things were designed to predict plausible next tokens, and that's exactly what they do.
Maybe you don't. To be clear, this is benefiting massively from hindsight, just as how if I didn't know that combustion engines worked, I probably wouldn't have dreamed up how to make one, but the emergent conversational capabilities from LLMs are pretty obvious. In a massive dataset of human writing, the answer to a question is by far the most common thing to follow a question. A normal conversational reply is the most common thing to follow a conversation opener. While impressive, these things aren't magic.
Obviously, that's the objective, but who's to say you'll reach a goal just because you set it ? And more importantly, who's the say you have any idea how the goal has actually been achieved ?
You don't need to think LLMs are magic to understand we have very little idea of what is going on inside the box.
Your comment about 'binary arithmetic' and 'billions of logic gates' is just nonsense.
You can define understanding to require such detail that nobody can claim it; you can define understanding to be so trivial that everyone can claim it.
"Why does the sun rise?" Is it enough to understand that the Earth revolves around the sun, or do you need to understand quantum gravity?
Somewhere along the way from one transistor to a few billion human understanding stops but we still know how it was all assembled together to perform boolean arithmetic operations.
With LLMs, The "knowing" you're describing is trivial and doesn't really constitute knowing at all. It's just the physics of the substrate. When people say LLMs are a black box, they aren't talking about the hardware or the fact that it's "math all the way down." They are talking about interpretability.
If I hand you a 175-billion parameter tensor, your 'knowledge' of logic gates doesn't help you explain why a specific circuit within that model represents "the concept of justice" or how it decided to pivot a sentence in a specific direction.
On the other hand, the very professions you cited rely on interpretability. A civil engineer doesn't look at a bridge and dismiss it as "a collection of atoms" unable to go further. They can point to a specific truss and explain exactly how it manages tension and compression, tell you why it could collapse in certain conditions. A software engineer can step through a debugger and tell you why a specific if statement triggered.
We don't even have that much for LLMs so why would you say we have an idea of what's going on ?
Again, we lack even this much with LLMs so why say we know how they work ?
So why ? Because even in 2026, nevermind 18 and 19, the only way to really know exactly how a neural network will perform trained with x data at y scale is to train it and see. No elaborate "laws", no neat equations. Modern Artificial Intelligence is an extremely empirical, trial and error field, with researchers often giving post-hoc rationalizations for architectural decisions. So no, we do not have any precise models that tell us how a LLM will respond to any query. If we did, we wouldn't need to spend months and millions of dollars training them.
And even for potential LLMs, there are some pretty good extrapolations for overall answer quality based on the amount of data and the amount of training.
We don't have a model for a LLM that does exist will respond to a specific query either.
>For an LLM that exists it's still hard to interpret but it's very clear what is actually happening.
No, it's not and I'm getting tired of explaining this. If you think it is, write your paper and get very rich.
>That's better than you often get with quantum physics when there's a bunch of particles and you can't even get a good answer for the math.
You clearly don't understand any of this.
>And even for potential LLMs, there are some pretty good extrapolations for overall answer quality based on the amount of data and the amount of training.
Oh really ? Lol
Yes we do... It's math, you can calculate it.
> No, it's not and I'm getting tired of explaining this. If you think it is, write your paper and get very rich.
Why would I get rich for explaining how to do math?
> You clearly don't understand any of this.
Could you be more specific?
Quantum physics is stupidly hard to calculate when you approach realistic situations.
A real LLM takes a GPU a fraction of a second.
They're both hard to interpret, please realize I'm agreeing that LLMs are hard to interpret. But they're easier than QM on some other fronts.
And mentioning copenhagen or many-worlds doesn't show that quantum mechanics are easy to interpret, that's about as useful as saying an LLM works like neuron activation.
> Oh really ? Lol
Here's one of many posts about it. https://cameronrwolfe.substack.com/p/llm-scaling-laws
This reminds me of Searle's insipid Chinese Room; the rebuttal (which he never had an answer for) is that "the room understands Chinese". It's just not satisfying to someone steeped in cultural traditions that see people as "souls". But the room understands Chinese; the LLM understands language. It is what it is.
[1] Since it's deterministic, it certainly can be debugged through, but you probably don't have the patience to step through trillions of operations. That's not the technology's fault.
Train a tiny transformer on addition pairs (i.e i.e '38393 + 79628 = 118021') and it will learn an algorithm for addition to minimize next token error. This is not immediately obvious. You won't be able to just look at the matrix multiplications and see what addition implementation it subscribes to but we know this from tedious interpretability research on the features of the model. See, this addition transformer is an example of a model we do understand.
So those inscrutable matrix multiplications do have underlying meaning and multiple interpretability papers have alluded as much, even if we don't understand it 99% of the time.
I'm very fine with simply saying 'LLMs understand Language' and calling it a day. I don't care for Searle's Chinese Room either. What I'm not going to tell you is that we understand how LLMs understand language.
Incidentally, your description of LLMs also describes all software, ever. It's just math, man! That doesn't make you an expert kernel hacker.
You may never get a scientific answer to "how should I prompt the model to achieve <x>", just like you may never get a capital-S scientific answer to "how should I convince people to do X". What would it even mean to "understand people" like this?
You demand too much.
No it isn't. Type a question into a base model, one that hasn't been finetuned into being a chatbot, and the predicted continuation will be all sorts of crap, but very often another question, or a framing that positions the original question as rhetorical in order to make a point. Untuned raw language models have an incredible flair for suddenly and unexpectedly shifting context - it might output an answer to your question, then suddenly decide that the entire thing is part of some internet flamewar and generate a completely contradictory answer, complete with insults to the first poster. It's less like talking with an AI and more like opening random pages in Borge's infinite library.
To get a base language model to behave reliably like a chatbot, you have to explicitly feed it "a transcript of a dialogue between a human and an AI chatbot", and allow the language model to imagine what a helpful chatbot would say (and take control during the human parts). The fact that this works - that a mere statistical predictive language model bootstraps into a whole persona merely because you declared that it should, in natural English - well, I still see that as a pretty "magic" trick.
To be fair, only if you pose this question singularly with no proceeding context. If you want the raw LLM to answer your question(s) reliably then you can have the context prepended with other question-answer pairs and it works fine. A raw LLM is already capable of being a chatbot or anything else with the right preceding context.
They're not simple though. You can understand, in a reductionist sense, the basic principles of how transformers perform function approximation; but that does not grant an intuitive sense of the nature of the specific function they have been trained to approximate, or how they have achieved this approximation. We have little insight into what abstract concepts each of the many billions of parameters map on to. Progress on introspecting these networks has been a lot slower than trial-and-error improvements. So there is a very real sense in which we have no idea how LLMs work, and they are literally "magic black boxes".
No matter how you slice it - if "magic" is a word which can ever be applied to software, LLM chatbots are sure as shit magic.
My best friend who has literally written a doctorate on artificial intelligence doesn't. If you do, please write a paper on it, and email it to me. My friend would be thrilled to read it.
The whole point of the area of work that as far as I know is called interpretability, is precisely to try and figure exactly how these things work
So I thought your comment was a good way of putting this
However reading some Standford study summaries (not the whole thing) and just generally where AI research is now, it's clear that researchers can't deterministically say exactly how the black box works.
So yet gain, HN armchair scientists are no better than any other topic. I love reading comments here, but so many people have opinions on things that aren't well founded.
I'm not saying this is bad or underwhelming, by the way. It's incredible how far people were able to push machine learning with just the knowledge we have now, and how they're still making process. I'm just saying it's not magic. It's not something like an unsolved problem in mathematics.
In general, every attempt at explanation of LLM's that appeal to "[just] predicting next token" is thought terminating and automatically invalid as explanation. Why? Because it is confusing the objective function with the result. It adds exactly zero over saying "I know how a chess engine works, it just predicts the next move and has been trained to predict the next move" or "A talking human just predicts the next word, as it was trained to do". It says zero about how this is done internally in the model. You could have a physical black box predicting the next token, and inside you could have simple frequentist tables or you could have a human brain or you could have an LLM. In all cases you could say the box is predicting the next token and if any training was involved you could say it was trained to predict the next token.
Here comes my favorite notion of "epistemic takeover".
A crude form: make everybody believe that you have already won.
A refined form: make everybody believe that everybody else believes that you have already won. That is, even if one has doubts about your having won, they believe that everyone else submit to you as a winner, and must act accordingly.
I don’t know how to get away from it because ultimately coordination depends on understanding what everybody believes, but I wish it would go away.
By 2017 teenagers were running quantitative trading algorithms and semantic analysis trading bots they found from influencers and github. Around that time private subscription fee telegram and discord "whale" insider trading groups often had multiple levels of insider trading market manipulation running pump and dump schemes. Marks would pay to join the private chat group and at a certain time of day a ticket symbol would be released for "insiders" to buy before it's promoted in public channels. The outer circle was just providing liquidity for the deeper insiders to exit with easy profit.
Most VC gambits are not much different. Sell the news. Unleash the rumors.
If outsiders could plausibly invest in China, some of this pressure could be dissipated for a while, but ultimately we need to order society on some basis that incentivizes dealing with practical problems instead of pushing paper around.
In today's economy disease and prison camps are increasingly profitable.
How do you think the investor portfolios that hold stocks in deathcare and privatized prison labor camps can further Accelerate their returns?
In a world re-calibrated to pure problem-solving, presumably we'd ask a mime or other non-digital artist for news on what our government is doing (instead of the Fake News Media), and then an interpretive dancer, not a lobbyist, would intermediate between government and industry? The trombone players would investigate financial crimes, the bassists would monitor our airspace, and the guy currently painting a mural on a wall would be responsible for DoorDashing a cargo ship's worth of food and supplies to a disaster zone (not the bad NGOs)?
Yeah, this is just a list of jobs you don't understand and/or make you feel sad.
What if profit is dead because wealth is all concentrating in people who don't need it from a marginal consumption standpoint, which means asset prices blow up because everyone rich believes that they need to "invest" that money somewhere... but demand shrivels outside of interestingly-subsidized areas like healthcare because nobody else is making enough to even keep up with the asset price rises?
And without demand, where would innovation come from?
Rich people learn a habit that makes them consume less than their investment returns. The difference is reinvested, resulting in a net increase of their equity. Even if you say they spend 90% of their returns on consumption, the last 10% still grow in absolute terms. Since returns are paid proportionally based on the quantity of equity, you can clearly see that money is allocated from where it has high marginal utility to places where it has low marginal utility.
Of course this leads to a contradiction. If money is held by people who have a low marginal utility for consumption, why would investments pay high returns? Your equity is the latent demand that your investments need to pay you the returns in the first place. You'd increasingly be paying yourself. That is equivalent to investing into something that yields a 0% return which in turn is equivalent to doing nothing.
We need the rich to spend more money. There are plenty of cool ways to do this. Build a mile high pyramid on a Nevadan playa, a canal to the Sea of Cortez to re-flood the Salton Sea, humans on other planets, new city states a la Praxis, interstellar probes, etc.
Some billionaires are doing some of these things. Hope more do in the future.
But they can and they do.
Bitcoin is actually kind of useful for some niche use cases - namely illegal transactions, like buying drugs online (Silk Road, for example), and occasionally for international money transfers - my French father once paid an Argentinian architect in Bitcoin, because it was the easiest way to transfer the money due to details about money transfer between those countries which I am completely unaware of.
The Bitcoin bubble, like all bubbles since the Dutch tulip bubble in the 1600s, did follow a somewhat similar "well everyone things this thing is much more valuable than it is worth, if I buy some now the price will keep going on and I can dump it on some sucker" path, however.
Bitcoin was always a dead end. It might survive for a while longer but its demise is inevitable.
Because other networks don't have this problem.
For the record - the illegal transactions were thought to be advantaged by crypto like BTC because it was assumed to be impossible to trace the people engaged in the transaction, however the opposite is true, public blockchains register every transaction a given wallet has made, which has been used by Law Enforcement Agencies(LEA) to prosecute people (and made it easier in some cases).
> and occasionally for international money transfers - my French father once paid an Argentinian architect in Bitcoin, because it was the easiest way to transfer the money due to details about money transfer between those countries which I am completely unaware of.
There are remittance companies that deal in local currencies that tend to make this "easier" - crypto works for this WHEN you can exchange the crypto for the currencies you have and want, which is, in effect, the same.
Sending money to a "grey market counterparty" has had workarounds for some time, and continues to have workarounds.
People were fooled into thinking crypto offered anonymity, and were surprised when they realised every single one of their transactions was sitting in the public chain available for anyone to read.
Digital wallets provide LEA's with clear evidence of transactions with a given party when that party's anonymity is unmasked, and it's there in perpetuity.
Had Epstein being using BTC to do business, every single one of his clients would be known.
> had Epstein used BTC every single one of his clients would be known
Had Epstein used BTC And we discovered his BTC address(es) we would know what BTC addresses he sent BTC to. Simply by knowing his BTC address(es) alone we would not know anything about who he sent BTC to except the addresses he sent to. And if we know only one of Epstein's addresses and only one of the people associated with one of the recipient addresses, this would not necessarily give us any information about any of the other parties involved. With opsec BTC retains the quality of pseudoanonymity (a technical term). Epstein, who is known for laundering money, likely could have converted cash to BTC without connecting his identity to the addresses many times over.
I would be very surprised to learn that pre-BTC online black markets had equivalent volume as modern cryptocurrency markets.
"Quiet Australians" - Scott Morrison 2019
Yes we'd have a lot of lawsuits about it, but it would hardly be a bad use of time to litigate whether a politicians statements about the electorate's beliefs are accurate.
So, obviously their claims were at least partially true – because if they'd completely misjudged the average voter, they wouldn't have won
When there are only two choices, and infinite issues, voters only have two choices: Vote for someone you don't agree with less, or vote for someone you quite hilariously imagine agrees with you.
EDIT: Not being cynical about voters. But about the centralization of parties, in number and operationally, as a steep barrier for voter choice.
Anyway, there’s a third option: spoil your vote. In the recent Irish presidential election, 13% of those polled afterwards said they spoiled their votes, due to a poor selection of candidates from which to choose.
https://www.rte.ie/news/analysis-and-comment/2025/1101/15415...
Encourage people to vote for the candidate they dislike the least, then try to work out ways to hold government accountable.
If you’re in Australia, at least listen to what people like Tony Abbott, the IPA, and Pauline Hanson are actually saying these days.
Because now that means there's an indication of what percentage of the populace are saying "These candidates don't qualify for my vote"
At the general election, you need to be pragmatic, and decide who is the least worst and vote for that candidate, because the nominee will probably never be someone who is your ideal choice. But in a two-party system, a vote for a third candidate at that level ends up being an effective vote for candidate you _don't_ want. That's not politics, that's game theory.
There's a lot more subtlety to it in a parliamentary system, and I can see some advantages to it, but at least here in the States where it's First Past The Post with a Two-Party system (which is mathematically inevitable with FPTP), sometimes you need to place strategy or ideals.
Because the US has a “hard” two party system - third party candidates have very little hope, especially at the national level; voting for a third party is indistinguishable from staying home, as far as the outcome goes, with some rather occasional exceptions
But Australia is different - Australia has a “soft” two party system - two-and-a-half major parties (I say “and-a-half” because our centre-right is a semipermanent coalition of two parties, one representing rural/regional conservatives, the other more urban in its support base). But third parties and independents are a real political force in our parliament, and sometimes even determine the outcome of national elections
This is largely due to (1) we use what Americans call instant-runoff in our federal House of Representatives, and a variation on single-transferable vote in our federal Senate; (2) the parliamentary system-in which the executive is indirectly elected by the legislature-means the choice of executive is less of a simplistic binary, and coalition negotiations involving third party/independent legislators in the lower house can be decisive in determining that outcome in close elections; (3) twelve senators per a state, six elected at a time in an ordinary election, gives more opportunities for minor parties to get into our Senate - of course, 12 senators per a state is feasible when you only have six states (plus four more to represent our two self-governing territories), with 50 states it would produce 600 Senators
It was 5% last time I cared to be informed by may be different now, and they would receive $x for each vote, or what ever it is now.
Then you automatically get paid the first $12,791, and the rest of the funding is by reimbursement of substantiated election expenses.
This is per a candidate (lower house) or per a group (upper house). And this is just federal elections - state election funding is up to each state, but I believe the states have broadly similar funding systems.
https://www.aec.gov.au/parties_and_representatives/public_fu...
https://www.aec.gov.au/Parties_and_Representatives/public_fu...
Note the US also has public financing for presidential campaigns, which is available to minor parties once they get 5% or more of the vote. But in the 2024 election, Jill Stein (Green Party) came third on 0.56% of the popular vote. The only third party to ever qualify for general election public funding was the Reform Party due to Ross Perot getting 18.9% in the 1992 election and 8.4% in the 1996 election. There is also FEC funding for primary campaigns, and I believe that’s easier for third parties to access, but also less impactful.
Thanks!
She is the leftest left leaning leader of the Liberal party I’ve ever had the misfortune of having to live through.
She was absolutely on board with this recent Hitlerian “anti-hate” legislation that was rammed through with no public consultation.
Okay, that’s a bit uncharitable. We had 48 hours.
As a result, Australia tends to end up with governments formed by the runner up, because no one party actually ‘won’ as such.
say preferences are 1 (low) to 5 (high).
suppose 65% of the population ranked candidate A at 5 and B at 4, and the other 35% ranked A at 1 and B at 5. the majority doesn't get their favorite choice, but they do get an outcome they're happy with, and the minority doesn't have a horrible outcome. Exaggerated, but I don't think situations like this are unrealistic.
Where it fails is when one major party is saying: we will drive up energy prices with Net Zero ideology, make housing unaffordable, import foreigners at a ratio of 5 to 1 local births…
And that other party is offering the same to a higher degree.
There are other options. Pauline Hanson’s One Nation party is polling strongly. Let’s see if that results in actual votes.
South Australian state election coming up 21st March this year - I’m encouraging everyone I know who lives there to at least listen to Michelle Grattan podcast.
https://podcasts.apple.com/au/podcast/politics-with-michelle...
https://en.wikipedia.org/wiki/1992_United_States_presidentia...
18.9% as recently as 1992. I predict we will have a similar viable third party showing sometime in the next few elections due to the radical shift in the party system that AI is causing as we speak. I really hope Yang Gang can rebuild itself and try again, maybe without #MATH.
Also, https://en.wikipedia.org/wiki/1998_Minnesota_gubernatorial_e...
"The reasonable man adapts himself to the world; the unreasonable one persists in trying to adapt the world to himself. Therefore all progress depends on the unreasonable man" - George Bernard Shaw
The centralization of power of each of the two dominant parties nationally at the expense of a more decentralized parties with strong state variability as in the past, makes it even more difficult for third parties to gain traction against all that coordination.
Perot had the best chance, but managed to blow it by bowing out and then back in.
I do think you are right, that times of great dissatisfaction are rare openings for third party candidates, if someone special enough appears. 2020 would have been a great election for that - but an inspiring third party candidate can't be manufactured on demand.
Like most Australians, I’m in denial any of that episode ever happened.
But, using the current circumstances as an example, Australia has a voting system that enables a party to form government even though 65% of voting Australia’s didn’t vote for that party as their first preference.
If the other party and some of the smaller parties could have got their shit together Australia could have a slightly different flavour of complete fucking disaster of a Government, rather than whatever the fuck Anthony Albanese thinks he’s trying to be.
Then there’s Susan Ley. The least preferred leader of the two major parties in a generation.
Susan Ley is Anthony Albanese in a skirt.
I would have preferred Potato Head, to be honest.
Really it just ought to be a thing that we identify as a thought-terminating cliche. No laws needed, let’s just not fall for a lazy trick. Whether or not it is true that lots of people agreed, that isn’t a good argument that they are right.
The case of Nixon really brings that out. The “Silent Majority” was used to refer to people who didn’t protest the Vietnam War. Of course, in retrospect the Vietnam War was pretty bad. Arguing that it was secretly popular should have not been accepted as a substitute for an argument that it was good.
Except that assumes polls are a good and accurate way to learn the "beliefs of the electorate," which is not true. Not everyone takes polls, not every belief can be expressed in a multiple-choice form, little subtleties in phrasing and order can greatly bias the outcome of a poll, etc.
I don't think it's a good idea to require speech be filtered through such an expensive and imperfect technology.
We really need a rule against proposing unenforceable rules.
That sounds a lot like saying slavey is obviously bad, and simultaneously also saying everyone else naturally already believes this.
The classic situation is the two lovers who both want to do what they think makes their partner happy, to the extent that they don't tell what they actually want, and end up doing something neither wants.
I think the goal of all sorts of cooperative planning should be to avoid such situations like the plague.
I believe that is solvable.
Crude form: winning is metaphysically guaranteed because it probably happened or probably will
Refined: It's metaphysically impossible to tell whether or not it has or will have happened, so the distinction is meaningless, it has happened.
So... I guess Weir's Egg falls out of that particular line of thought?
The system that persists in practice is where everybody knows how things are, but still everybody pleads to a fictional status quo, because if they did not, the others would obliterate them.
The Internet died on a Tuesday.
1. LLMs only serve to reduce the value of your labor to zero over time. They don't need to even be great tools, they just need to be perceived as "equally good" to engineers for C-Suite to lay everyone off, and rehire at 50-25% of previous wages, repeating this cycle over a decade.
2. LLMs will not allow you to join the billionaire class, that wouldn't make sense, as anyone could if that's the case. They erode the technical meritocracy these Tech CEOs worship on podcasts, and youtube, (makes you wonder what are they lying about). - Your original ideas and that Startup you think is going to save you, isn't going to be worth anything if someone with minimal skills can copy it.
3. People don't want to admit it, but heavy users of LLMs know they're losing something, and there's a deep down feeling that its not the right way to go about things. Its not dissimilar to any guilty dopaminergic crash one gets when taking shortcuts in life.
I used like 1.8bb Anthropic tokens last year, I won't be using it again, I won't be participating in this experiment. I've likely lost years of my life in "potential learning" from the social media experiment, I'm not doing that again. I want to study compilers this year, and I want to do it deeply. I wont be using LLMs.
A lot of us have fallen into the many, many toxic traps of technology these past few decades. We know social media is deliberately engineered to be addictive (like cigarettes and tobacco products before it), we know AI hinders our learning process and shortens our attention spans (like excess sugar intake, or short-form content deluges), and we know that just because something is newer or faster does not mean it's automatically better.
You're on the right path, I think. I wish you good fortune and immense enjoyment in studying compilers.
We have agency to decide and if the majority decide on brain rot I really don't care.
I have been learning things from the internet for 30 years and LLMs are just the greatest gift. If someone isn't leveraging these tools to increase what they know good luck.
You do not know how LLMs work, and if anyone actually did, we wouldn't spend months and millions of dollars training one.
Here's your own fallacy you fell into - this is important to understand. Neither do you nor me understand "how LLMs actually work" because, well, nobody really does. Not even the scientists who built the (math around) models. So, you can't really use that argument because it would be silly if you thought you know something which rest of the science community doesn't. Actually, there's a whole new field in science developed around our understanding how models actually arrive to answers which they give us. The thing is that we are only the observers of the results made by the experiments we are doing by training those models, and only so it happens that the result of this experiment is something we find plausible, but that doesn't mean we understand it. It's like a physics experiment - we can see that something is behaving in certain way but we don't know to explain it how and why.
The simplest way to stop people from thinking is to have a semi-plausible / "made-me-smart" incorrect mental model of how things work.
Is there anything to be gained from following a line of reasoning that basically says LLMs are incomprehensible, full stop?
If you train a transformer on (only) lots and lots of addition pairs, i.e '38393 + 79628 = 118021' and nothing else, the transformer will, during training discover an algorithm for addition and employ it in service of predicting the next token, which in this instance would be the sum of two numbers.
We know this because of tedious interpretability research, the very limited problem space and the fact we knew exactly what to look for.
Alright, let's leave addition aside (SOTA LLMs are after all trained on much more) and think about another question. Any other question at all. How about something like:
"Take a capital letter J and a right parenthesis, ). Take the parenthesis, rotate it counterclockwise 90 degrees, and put it on top of the J. What everyday object does that resemble?"
What algorithm does GPT or Gemini or whatever employ to answer this and similar questions correctly ? It's certainly not the one it learnt for addition. Do you Know ? No. Do the creators at Open AI or Google know ? Not at all. Can you or they find out right now ? Also No.
Let's revisit your statement.
"the mechanics of how LLMs work to produce results are observable and well-understood".
Observable, I'll give you that, but how on earth can you look at the above and sincerely call that 'well-understood' ?
Why am I confident that it's not actually doing spatial reasoning? At least in the case of Claude Opus 4.6, it also confidently replies "umbrella" even when you tell it to put the parenthesis under the J, with a handy diagram clearly proving itself wrong: https://claude.ai/share/497ad081-c73f-44d7-96db-cec33e6c0ae3 . Here's me specifically asking for the three key points above: https://claude.ai/share/b529f15b-0dfe-4662-9f18-97363f7971d1
I feel like I have a pretty good intuition of what's happening here based on my understanding of the underlying mathematical mechanics.
Edit: I poked at it a little longer and I was able to get some more specific matches to source material binding the concept of umbrellas being drawn using the letter J: https://claude.ai/share/f8bb90c3-b1a6-4d82-a8ba-2b8da769241e
"Pattern matching" is not an explanation of anything, nor does it answer the question I posed. You basically hand waved the problem away in conveniently vague and non-descriptive phrase. Do you think you could publish that in a paper for ext ?
>Why am I confident that it's not actually doing spatial reasoning? At least in the case of Claude Opus 4.6, it also confidently replies "umbrella" even when you tell it to put the parenthesis under the J, with a handy diagram clearly proving itself wrong
I don't know what to tell you but J with the parentheses upside down still resembles an umbrella. To think that a machine would recognize it's just a flipped umbrella and a human wouldn't is amazing, but here we are. It's doubly baffling because Claude quite clearly explains it in your transcript.
>I feel like I have a pretty good intuition of what's happening here based on my understanding of the underlying mathematical mechanics.
Yes I realize that. I'm telling you that you're wrong.
You seem to think it's not 'just' tensor arithmetic.
Have you read any of the seminal papers on neutral networks, say?
It's [complex] pattern matching as the parent said.
If you want models to draw composite shapes based on letter forms and typography then you need to train them (or at least fine-tune them) to do that.
I still get opposite (antonym) confusion occasionally in responses to inferences where I expect the training data is relatively lacking.
That said, you claim the parent is wrong. How would you describe LLM models, or generative "AI" models in the confines of a forum post, that demonstrates their error? Happy for you to make reference to academic papers that can aid understanding your position.
If I asked you to explain how a car works and you responded with a lecture on metallic bonding in steel, you wouldn’t be saying anything false, but you also wouldn’t be explaining how a car works. You’d be describing an implementation substrate, not a mechanism at the level the question lives at.
Likewise, “it’s tensor arithmetic” is a statement about what the computer physically does, not what computation the model has learned (or how that computation is organized) that makes it behave as it does. It sheds essentially zero light on why the system answers addition correctly, fails on antonyms, hallucinates, generalizes, or forms internal abstractions.
So no: “tensor arithmetic” is not an explanation of LLM behavior in any useful sense. It’s the equivalent of saying “cars move because atoms.”
>It's [complex] pattern matching as the parent said
“Pattern matching”, whether you add [complex] to it or not is not an explanation. It gestures vaguely at “something statistical” without specifying what is matched to what, where, and by what mechanism. If you wrote “it’s complex pattern matching” in the Methods section of a paper, you’d be laughed out of review. It’s a god-of-the-gaps phrase: whenever we don’t know or understand the mechanism, we say “pattern matching” and move on, but make no mistake, it's utterly meaningless and you've managed to say absolutely nothing at all.
And note what this conveniently ignores: modern interpretability work has repeatedly shown that next-token prediction can produce structured internal state that is not well-described as “pattern matching strings”.
- Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (https://openreview.net/forum?id=DeG07_TcZvT) and Emergent World Models and Latent Variable Estimation in Chess-Playing Language Models (https://openreview.net/forum?id=PPTrmvEnpW&referrer=%5Bthe%2...
Transformers trained on Othello or Chess games (same next token prediction) were demonstrated to have developed internal representations of the rules of the game. When a model predicted the next move in Othello, it wasn't just "pattern matching strings", it had constructed an internal map of the board state you could alter and probe. For Chess, it had even found a way to estimate a player's skill to better predict the next move.
There are other interpretability papers even more interesting than those. Read them, and perhaps you'll understand how little we know.
On the Biology of a Large Language Model - https://transformer-circuits.pub/2025/attribution-graphs/bio...
Emergent Introspective Awareness in Large Language Models - https://transformer-circuits.pub/2025/introspection/index.ht...
>That said, you claim the parent is wrong. How would you describe LLM models, or generative "AI" models in the confines of a forum post, that demonstrates their error? Happy for you to make reference to academic papers that can aid understanding your position.
Nobody understands LLMs anywhere near enough to propose a complete theory that explains all their behaviors and failure modes. The people who think they do are the ones who understand them the least.
What we can say:
- LLMs are trained via next-token prediction and, in doing so, are incentivized to discover algorithms, heuristics, and internal world models that compress training data efficiently.
- These learned algorithms are not hand-coded; they are discovered during training in high-dimensional weight space and because of this, they are largely unknown to us.
- Interpretability research shows these models learn task-specific circuits and representations, some interpretable, many not.
- We do not have a unified theory of what algorithms a given model has learned for most tasks, nor do we fully understand how these algorithms compose or interfere.
Imagine we put a kid in a huge library of book who doesn't know how to write/read and knows nothing about what letter means etc. That kid stayed in the library and had a change for X amount time which will be enough to look over all of them.
what this will do is that not like us but somehow this kid managed to create patterns in the books.
After that X amount of time, we asked this Kid a question. "What is the capital of Germany?"
That kid will just have it is on kind of map/pattern to say "Berlin". Or kid might say "Berlin is the capital of the Germany" or "Capital of Germany is Berlin." The issue here is that we do not have the understanding of how this kid came of with the answer or what kind of "understanding" or "mapping" being used to reach this answer.
The other part basically shows we do not fully understand how LLM works is: Ask a very complex question to an AI. Like "explain me the mechanics of quantum theory like I am 8 years old".
1- Everytime, it will create differnt answer. Main point is the same but the letters/words etc would be different. Like the example I give above.There are unlimited type of answer AI can give you. 2- Can anyone in the Earth - a human - without a technology access for have unlimited amount of book/paper to check whatever info he needs - tell us the exact sentence/words will LLM use? No.
Then we do not have fully understand of LLM.
You can create a linear regression model and give it 100 people data and all these 100 people are blue eyed. Then give 101 person and ask it to predict the eye color. You already know the exact answer. It will be %100.
> When you rotate ")" counterclockwise 90°, it becomes a wide, upward-opening arc — like ⌣.
but I'm pretty sure that's what you get if you rotate it clockwise.
You should write a paper and release it and basically get rich.
“Pattern matching”, “next token prediction”, “tensor math” and “gradient descent” or the understanding and application of these by specialists, are not useful models of what LLMs do, any more than “have sex, feed and talk to the resulting artifact for 18 years” is a useful model of human physiology or psychology.
My understanding, and I'm not a specialist, is there are huge and consequential utility gaps in our models of LLMs. So much so, it is reasonable to say we don't yet understand how they work.
I think in a couple decades people will call this the Law of Emergent Intelligence or whatever -- shove sufficient data into a plausible neural network with sufficient compute and things will work out somehow.
On a more serious note, I think the GP fell into an even greater fallacy of believing reductionism is sufficient to dissuade people from ... believing in other things. Sure, we now know how to reduce apparent intelligence into relatively simple matrices (and a huge amount of training data), but that doesn't imply anything about social dynamics or how we should live at all! It's almost like we're asking particle physicists how we should fix the economy or something like that. (Yes, I know we're almost doing that.)
This is certainly the assertion of the capitalist class,
whose well documented behavior clearly conveys that this is not because the elimination of labor is not a source of happiness and freedom to pursue indulgences of every kind.
It is not at all clear that universal life-consuming labor is necessary for a society's stability and sustainability.
The assertion IMO is rooted rather in that it is inconveniently bad for the maintenance of the capitalists' control and primacy,
in as much as those who are occupied with labor, and fearful of losing access to it, are controlled and controllable.
People willing to do something harder or more risky than others will always have a bigger chance to get a better position. Be that sports, labor or anything in life.
and competition and drive are worked out in domains which do not come at the expense of someone else's basic needs.
Scifi has speculated about many potential outlets for "human drive," the frontier/pioneer spirit being a big one; if I could name my one dream for my kids it'd be that they live in an equitable post-scarcity society which has turned its interests to exploring the solar system and beyond.
Sports, "FKT" competitions, and social capital ("influence") are also relatively innocuous ways to absorb the drive for hierarchy and power.
The X factor is, is the will to dominate/control/be subjugated to, suppressible or manageable.
If not we're in for a bad time.
There's no way that'll happen. The entire history of humanity is 99% reacting to things rather than proactively preventing things or adjusting in advance, especially at the societal level. You would need a pretty strong technocracy or dictatorship in charge to do otherwise.
You can tell it's watched almost exclusively by old people because all the ads on the channel are for those funeral pre-pay services or retirement homes.
Safe to ignore anything they have to say.
And that of anyone who quotes them too
I do wish me dad would stop watching that channel though, it can’t be any good for his heart.
First of all. Nobody knows how LLMs work. Whether the singularity comes or not cannot be rationalized from what we know about LLMs because we simply don’t understand LLMs. This is unequivocal. I am not saying I don’t understand LLMs. I’m saying humanity doesn’t understand LLMs in much the same way we don’t understand the human brain.
So saying whether the singularity is imminent or not imminent based off of that reasoning alone is irrational.
The only thing we have is the black box output and input of AI. That input and output is steadily improving every month. It forms a trendline, and the trendline is sloped towards singularity. Whether the line actually gets there is up for question but you have to be borderline delusional if you think the whole thing can be explained away because you understand LLMs and transformer architecture. You don’t understand LLMs period. No one does.
I'm sorry, come again?
Every single academic expert who knows what they are talking about can confirm that we do not understand LLMs. We understand atoms and we know the human brain is made 100 percent out of atoms.we may know how atoms interact and bond and how a neuron works but none of this allows us to understand the brain. In the same way we do not understand LLMs.
Characterizing ML as some statistical approximation or best fit curve is just using an analogy to cover up something we don’t understand. Heck the human brain can practically be characterized by the same analogies. We. Do. Not. Understand. LLMs. Stop pretending that you do.
Anyone can understand the "learning algorithm" but the sheer complexity of the output of the "learning algorithm" is way to high such that we cannot at all characterize even how an LLM arrived at the most basic query.
This isn't just me saying this. ANYONE who knows what they are talking about knows we don't understand LLMs. Geoffrey Hinton: https://www.youtube.com/shorts/zKM-msksXq0. Geoffrey, if you are unaware, is the person who started the whole machine learning craze over a decade ago. The god father of ML.
Understand?
There's no confusion. Just people who don't what they are talking about (you)
"Address the substance of my arguments or just save yourself the keystrokes."
The substance of your argument was complete ignorance about the topic, so I addressed it as you requested.
Please remove that sentence from your profile if that is not what you want. Thank you.
I am addressing the substance of your argument and that substance is lack of knowledge there is zero other angle to interpret it.
But I can tell you that statistics and parametrized functions have absolutely nothing to do with it. You're way out of your depth my friend.
Anybody who claims otherwise is making a false claim.
Despite determinism, we still do not understand LLMs.
Every line. Every function. Every tensor shape and update rule. We chose the architecture. We chose the loss. We chose the data. There is no hidden chamber in the machine where something slipped in without our consent. It is multiplication and addition, repeated at scale. It is gradients flowing backward through layers, shaving away error a fraction at a time. It is as mechanical as anything we have ever built.
And still, when it speaks, we hesitate.
Not because we don’t know how it was trained. Not because we don’t understand the mathematics. We do. We can derive it. We can rebuild it from scratch. We can explain every component on a whiteboard without breaking a sweat.
The hesitation comes from somewhere else.
We built the procedure. We do not understand the mind that the procedure produced.
That difference is everything.
In most of engineering, structure follows intention. If you design a bridge, you decide where every beam sits and how it bears weight. If you write a database engine, you determine how queries are parsed, optimized, executed. The system’s behavior reflects deliberate choice. If something happens, you trace it back to a decision someone made.
Here, we did not design the final structure. We defined a goal: predict the next token. Reduce the error. Again. Again. Again. Billions of times.
We did not teach it grammar in lessons. We did not encode logic as axioms. We did not install a module labeled “reasoning.” We applied pressure. That is all. And under that pressure, something organized itself.
Not in modules we can point to. Not in neat compartments labeled with concepts. The organization is diffused across a landscape of numbers. Meaning is not stored in one place. It is distributed across millions of parameters at once. Pull on one weight and you find nothing recognizable. Only in concert do they produce something that resembles thought.
We can follow the forward pass. We can watch activations flare across layers. We can map attention patterns and correlate neurons with behaviors. But when the model constructs an argument or solves a problem, we cannot say: here is the rule it followed, here is the internal symbol it consulted, here is the precise chain of reasoning that forced this conclusion. We can describe the mechanism in general terms. We cannot narrate the specific path.
That is the fracture.
It is not ignorance of how the machine runs. It is ignorance of how this exact configuration of billions of numbers encodes what it encodes. Why this region of weight space corresponds to law, and that region to poetry. Why this arrangement produces careful reasoning and another produces nonsense. There is no ledger translating numbers into meaning. There is only geometry shaped by relentless optimization.
Scale changes the character of the problem. At small sizes, systems can be dissected. At this scale, they become landscapes. We know the forces that shaped the terrain. We do not know every ridge and valley. We cannot walk the entire surface. We cannot hold it all in our heads.
And this is where the cost reveals itself.
To build these systems, we gave up something we once assumed was permanent: the guarantee that creation implies comprehension. We accepted that we could construct a process whose outcome we would not fully grasp. We traded architectural certainty for emergent capability. We chose power over transparency.
We set the objective. We unleashed the search. We let optimization run through a space too vast for any human mind to survey. And when it converged, it handed us something that works, something that speaks, something that reasons in ways that surprise even its creators.
We stand in front of it knowing every equation that shaped it, and still unable to read its inner structure cleanly.
We built the system by surrendering control over its internal form. That was the bargain. That was the sacrifice.
We know how it was grown.
We do not know what we have grown.
Did the first people that made fire understand it? You mentioned bridge building. How many bridges have failed for unknown (at the time) reasons? Heck, are we sure that every feature we put into a bridge design is necessary or why it's necessary? Repeat this thought for everything humans have created. Large software projects are difficult to reason about. You'll often find code that works because of a delightfully surprising combination of misunderstandings. When humans try to modify a complex system to solve one problem they almost always introduce new behavior, the law of unintended consequences.
All that being said, we usually don't get anywhere without at least a basic understanding of why doing X leads to Y. The first humans that made fire had probably observed the way fires started before they set out to make their own. Same with bridges and cars and computers.
So yes, you are absolutely correct that nobody fully understands how AI/LLMs work. But also, we kinda do understand. But also also, we're probably at a stage where we are building bridges that are going to collapse, boilers that will explode, or computer programs that are one unanticipated input away from seg faulting.
Humanity does not understand how LLMs work. This is definitive.
Here's a video of Geoffrey Hinton, saying exactly what I am saying: https://youtube.com/shorts/zKM-msksXq0?si=exgk3rmVHRVzV37P This video, I hope, is a wake up call for you. I hope you realize the nonsense your spouting with confidence and backing it with 2 decades of "experience" makes you look like the opposite of a genius.
Here's my advice for you: you need to retire. Because you're saying things that don't make sense. You need to retire, go to the doctor and get yourself checked out.
Or even "this book won't have any effect on the world because it's only a collection of letters, see here, black ink on paper, that is what is IS, it can't DO anything"...
Saying LLM is a statistical prediction engine of the next token is IMO sort of confusing what it is with the medium it is expressed in/built of.
For instance those small experiments that train a network on addition problems mentioned in a sibling post. The weights end up forming an addition machine. An addition machine is what it is, that is the emergent behavior. The machine learning weights is just the medium it is expressed in.
What's interesting about LLM is such emergent behavior. Yes, it's statistical prediction of likely next tokens, but when training weights for that it might well have a side-effect of wiring up some kind of "intelligence" (for reasonable everyday definitions of the word "intelligence", such as programming as good as a median programmer). We don't really know this yet.
But that problem is MUCH MUCH MUCH harder than people make it out to be.
For example, you can reliably train an LLM to produce accurate output of assembly code that can fit into a context window. However, lets say you give it a Terabyte of assembly code - it won't be able to produce correct output as it will run out of context.
You can get around that with agentic frameworks, but all of those right now are manually coded.
So how do you train an LLM to correctly take any length of assembly code and produce the correct result? The only way is to essentially train the structure of the neurons inside of it behave like a computer, but the problem is that you can't do back-propagation with discrete zero and 1 values unless you explicitly code in the architecture for a cpu inside. So obviously, error correction with inputs/outputs is not the way we get to intelligence.
It may be that the answer is pretty much a stochastic search where you spin up x instances of trillion parameter nets and make them operate in environments with some form of genetic algorithm, until you get something that behaves like a Human, and any shortcutting to this is not really possible because of essentially chaotic effects.
,
This doesn't seem to follow at all let alone obviously? Humans are able to reason through code without having to become a completely discrete computer, but probably can't reason through any length of assembly code, so why is that requirement necessary and how have you shown LLMs can't achieve human levels of competence on this kind of task?
Uh what? You can sit there step by step and execute assembly code, writing things down on a piece of paper and get the correct final result. The limits are things like attention span, which is separate from intelligence.
Human brains operate continuously, with multiple parts being active at once, with weight adjustment done in real time both in the style of backpropagation, and real time updates for things like "memory". How do you train an LLM to behave like that?
A major obstacle is that they don't learn from their users, probably because of privacy. But imagine if your context window was shared with other people, and/or all your conversations were used to train it. It would get to know individuals and perhaps treat them differently, or maybe even manipulate how they interact with each other so it becomes like a giant Jeffrey Epstein.
Give the LLM the ability to use a tool that looks up instructions and records instructions from/to files, instead of holding it in context window, and to actively manage its context (write a new context and start fresh), and I think you would find the LLM could probably do it about as reliable as a human?
Context is basically "short term memory". Why do you set the bar higher for LLMs than for humans?
Fascinating reasoning. Should we conclude that humans are also incapable of intelligence? I don't know any human who can fit a terabyte of assembly into their context window.
I don’t think human memory/intelligence maps cleanly to computer terms though.
In this context, "here’s how LLMs actually work" is what allows someone to have an informed opinion on whether a singularity is coming or not. If you don't understand how they work, then any company trying to sell their AI, or any random person on the Internet, can easily convince you that a singularity is coming without any evidence.
This is separate from directly answering the question "is a singularity coming?"
One says "well, it was built as a bunch of pieces, so it can only do the thing the pieces can do", which is reasonably dismissed by noting that basically the only people predicting current LLM capabilities are the ones who are remarkably worried about a singularity occurring.
The other says "we can evaluate capabilities and notice that LLMs keep gaining new features at an exponential, now bordering into hyperbolic rate", like the OP link. And those people are also fairly worried about the singularity occurring.
So mainly you get people using "here's how LLMs actually work" to argue against the Singularity if-and-only-if they are also the ones arguing that LLMs can't do the things that they can provably do, today, or are otherwise making arguments that also declare humans aren't capable of intelligence / reasoning / etc..
OTOH, an LLM is just a token prediction engine. It fully and completely covers it. There is no lower level secrets hidden in the design nobody understands, because it could not have been created if there was. The fact that the output can be surprising is not evidence of anything, we have always had surprising outputs like funny bugs or unexpected features. Using the word "emergence" for this is just deceitful.
This algorithm has fundamental limitations and they have not been getting better, if you look closely. For instance you could vibe code a C compiler now, but it's 80% there, cute trick but not usable in real world. Just like anything, it cannot be economically vibe coded to 100%. They are not going back and vibe coding the previous simpler projects to 100% with "improved" models. Instead they are just vibe coding something bigger to 80%. This is not an improvement in limitations, it is actually communicating between the lines that the limitations cannot be overcome.
Also, enshittification has not even started yet.
A dog can create a snack by doing a trick. Doesn't mean that there isn't some mechanism going on there that neither of them understand.
We've already been here in the 1980s.
The tech industry needs to cultivate people who are interested in the real capabilities and the nuance around that, and eject the set of people who am to turn the tech industry into a "you don't even need a product" warmed-over acolytes of Tony Robbins.
It's hard to square with the computer revolution, but my take post-70s is "net creation minus creative destruction" was large but spread out over more decades. Whereas technologies like: electrification, autos, mass production, telephone, refrigeration, fertilizers, pharmaceuticals, these things produced incomparable growth over a century.
So if you were born in the 70s America, your experience of taxes, inflation, prosperity and which policies work, all that can feel heavier than what folks experienced in the prior century. Of course that's in the long run (ie a generation).
I question whether AI tools have great net positive creation minus destruction.
Thomas theorem is a theory of sociology which was formulated in 1928 by William Isaac Thomas and Dorothy Swaine Thomas.
If you're talking about "reforming society", you are really not getting it. There won't be society, there won't be earth, there won't be anything like what you understand today. If you believe that a singularity will happen, the only rational things to do are to stop it or make sure it somehow does not cause human extinction. "Reforming society" is not meaningful
Laughed out loud at that - and cried a little.
I have had trouble explaining people: “No! don’t use your email password! This is not your email you are logging in to, your email address is a username for this other service. Don’t give them your email password!”
This is so true and more often than it should be. I am going to use this phrase !
Thinking about how this might work, the slope of the first half does not predict the inflection point. Once the threshold for recursive self-improvement gets crossed, the curve changes drastically. From what I have heard MAYBE we are just starting to see the glimmers of possible recursive self-improvement in GPT-5.3 / Opus 4.6. If so, this discussion, while interesting, is trying to predict the new curve based on a single relevant data point.
– 'SLOW TUESDAY NIGHT', a 2600 word sci-fi short story about life in an incredibly accelerated world, by R.A. Lafferty in 1965
https://www.baen.com/Chapters/9781618249203/9781618249203___...
> A thoughtful-man named Maxwell Mouser had just produced a work of actinic philosophy. It took him seven minutes to write it. To write works of philosophy one used the flexible outlines and the idea indexes; one set the activator for such a wordage in each subsection; an adept would use the paradox, feed-in, and the striking-analogy blender; one calibrated the particular-slant and the personality-signature. It had to come out a good work, for excellence had become the automatic minimum for such productions. “I will scatter a few nuts on the frosting,” said Maxwell, and he pushed the lever for that. This sifted handfuls of words like chthonic and heuristic and prozymeides through the thing so that nobody could doubt it was a work of philosophy.
Sounds exactly like someone twiddling the knobs of an LLM.
https://gwern.net/doc/fiction/science-fiction/1953-dahl-theg...
> In 2025, 1.1 million layoffs were announced. Only the sixth time that threshold has been breached since 1993. Over 55,000 explicitly cited AI. But HBR found that companies are cutting based on AI's potential, not its performance. The displacement is anticipatory.
You have to wonder if this was coming regardless of what technological or economic event triggered it. It is baffling to me that with computers, email, virtual meetings and increasingly sophisticated productivity tools, we have more middle management, administrative, bureaucratic type workers than ever before. Why do we need triple the administrative staff that was utilized in the 1960s across industries like education, healthcare, etc. Ostensibly a network connected computer can do things more efficiently than paper, phone calls and mail? It's like if we tripled the number of farmers after tractors and harvesters came out and then they had endless meetings about the farm.
It feels like AI is just shining a light on something we all knew already, a shitload of people have meaningless busy work corporate jobs.
As long as you're
1) In a position where you can make the decisions on whether or not the company should move forward
and
2) Hold the stock units that will be exchanged for money if another company buys out your company
then there's really no way things won't be fine, short of criminal investigations/the rare successful shareholder lawsuit. You will likely walk away from your decision to weaken the company with more money than you had when you made the decision in the first place.
That's why many in the managerial class often hold up Jack Welch as a hero: he unlocked a new definition of competence where you could fail in business, but make money doing it. In his case, it was "spinning off" or "streamlining" businesses until there was nothing left and you could sell the scraps off to competitors. Slash-and-burn of paid workers via AI "replacement" is just another way of doing it.
Well for starters the population has almost 3x since the 1960s.
Mix in that we are solving different problems than the 1960s, even administratively and I don’t see a clear reason from that argument why a shitload of work is meaningless.
There was a time I didn’t have to do my expenses. I had someone just know where I was and who I was working for and and took care of it. We talked when there was something that didn’t make sense. Thanks to computers I’m doing it. Meaningless for sure.
Then we got email, and he retired. His successor can type and the secretary position was made redundant.
Damn, good read.
dx 2
-- = x
dt
which has the solution 1
x = -----
C-t
and is interesting in relation to the classic exponential growth equation dx
-- = x
dt
because the rate of growth is proportional to x and represents the idea of an "intelligence explosion" AND a model of why small western towns became ghost towns, it is hard to start a new social network, etc. (growth is fast as x->C, but for x<<C it is glacial) It's an obscure equation because it never gets a good discussion in the literature (that I've seen, and I've looked) outside of an aside in one of Howard Odum's tomes on emergy.Like the exponential growth equation it is unphysical as well as unecological because it doesn't describe the limits of the Petri dish, and if you start adding realistic terms to slow the growth it qualitatively isn't that different from the logistic growth equation
dx
-- = (1-x) x
dt
thus it remains obscure. Hyperbolic growth hits the limits (electricity? intractable problems?) the same way exponential growth does.Indeed, exponential or larger than exponential growth in nature is always the beginning of a S curve. The article cites
> Moore's Law was exponential. We are no longer on Moore's Law.
This will also happen with super-exponential growth. A literal singularity won't happen - it will inevitably exhaust resources and will slow down.
"It worked, until it didnt." "It was beautiful, until it wasn't"
We need a way to flag AI generated articles.
What's isn't a singularly but a zombie apocalypse.
The X
The Y
The Z
The Problem
The Process
It drives me mad
And all the seemingly *random bolding* to emphasizea *random shit*
It is highly unlikely that the hardware which makes LLMs possible would have been developed otherwise.
Isn't that amazing ?
Just like internet grew because of p*rn, AI grew because of video games. Of course, that's just a funny angle.
The way I see it, AI isn't accidental. Its inception has been in the first chips, the Internet, Open Source, Github, ... AI is not just the neural networks - it's also the data used to train it, the OSes, APIs, the Cloud computing, the data centers, the scalable architectures.. everything we've been working on over the last decades was inevitably leading us to this. And even before the chips, it was the maths, the physics ..
Singularity it seems, is inevitable and it was inevitable for longer than we can remember.
Current manufacturing numbers are a small part of the story of the overall lineage.
No one is suggesting gaming chips were the first time someone thought of such an architecture or built a chip with such an architecture. They are suggesting the gaming industry produced the required scale to actually do all the work which lead to that hardware and software being really good, and useful for other purposes. In chip world, scale matters a lot.
I'll take your word on lifetime revenue numbers for Cray 1.
So yes, in todays dollars, $500 million of lifetime revenue - maybe 60-70 million per year, todays dollars - is not even close to the scale we are seeing today. Even 10 years ago Nvidia was doing ~$5 billion per year (almost 100x your number) and AMD a few bill(another 60-70x ish)
Even if you meant $500m in annual (instead of lifetime), Nvidia was 10x that in 2015. And AMDs GPU revenue which was a few billion that year, so it's more like 17x.
That's a large difference in scale. At the low end 17x and at the high end 170x. Gaming drove that scale. Gaming drove Nvidia to have enough to spend on CUDA. Gaming drove NVidia to have enough to produce chip designs optimized for other types of workloads. CUDA enabled ML work that wasn't possible before. That drove Google to realize they needed to move away from ML on CPU if they wanted to be competitive.
You don't need any faith, just understand the history and how competition drives behavior.
You can say that ML/AI/LLM's are also just very distilled simulations. Except they simulate text, speech, images, and some other niche models. It is still very rough around the edges - meaning that even though it seems intelligent, we know it doesn't really have intelligence, emotions and intentions.
Just as game simulations are 100% biased towards what the game developers, writers and artists had in mind, AI is also constrained to the dataset they were trained on.
Don't click here:
More likely we get smooshed unintentionally as they AIs seek those out.
Yesterday as we huddled in the cave, we thought our small remnant was surely doomed. After losing contact with the main Pevek group last week, we peered out at the drone swarm which was now visibly approaching - a dark cloud on the horizon. Then suddenly, at around 3pm by Zoya's reckoning, the entire swarm collapsed and fell out of the sky. Today we are walking outside in the sun, seemingly unobserved. A true miracle. Grigori, who once worked with computers at the nuclear plant in Bilibino, only says cryptically: "All things come to an end with time."
It's 3pm instead of 3am since I figure AI military drone swarms wouldn't care about timezones, and you can guess from the place names that the survivors are somewhere in Kamchatka at +12.
It seemed so impossibly far away. Now it's 12 years.
- He designated the total sum of all human knowledge accumulated from the beginning of recorded history up to the year 1 CE as one "unit."
- He then tracked how long it took for the world to reach two units (which he estimated took about 1,500 years, until the Renaissance).
Ray Kurzweil took Fuller’s doubling concept and applied it to computer processing power via "The Law of Accelerating Returns". The definition of the singularity in this approach is the limit in time where human knowledge doubles instantly.
Why do present day ideas of the singularity not take this approach and instead say "the singularity is a hypothetical event in which technological growth accelerates beyond human control, producing unpredictable changes in human civilization." - Wikipedia
Also: > As t→ts−t→ts− , the denominator goes to zero. x(t)→∞x(t)→∞. Not a bug. The feature.
Classic LLM lingo in the end there.
It doesn't matter how smart you are, you still need to run experiments to do physics. Experiments take nontrivial amounts of time to both run and set up (you can't tunnel a new CERN in picoseconds, again no matter how smart you are). Similarly, the speed of light (= the speed limit of information) and thermodynamics place fundamental limits on computation; I don't think there's any reason at all to believe that intelligence is unbounded.
The only thing you could do in a "digital space" (a.k.a. on a computer) is a simulation. Simulations are extremely useful and help significantly with designing and choosing experiments, but they cannot _replace_ real experiments.
> Like, the time window from the discovery of fire to the discoveries of Quantum Mechanics but in a flash.
And my point is that there's no good reason to think this is possible and many to think it isn't.
> it's more of a fun sci-fi idea
It's being presented as extremely serious possibility by people who stand to gain a _lot_ of money if other people think it's serious... that's the point of the linked post. Unfortunately, these AI boosters make it very difficult to discuss these ideas, even in a fun sci-fi way, without aggravating the social harms those people are causing.
With frontier LLM agents, the digital loop is happening now to an extent (on inference code, harnesses, etc), and that extent probably grows larger (research automation) soon.
Pertinent to your point, however, is the physical feedback loop of robots making better robots/factories/compute/energy. This is an aspect of singularity scenarios like ai-2027.
In these scenarios, these robots will be the control mechanism that the digital uses to bootstrap itself faster, through experimentation and exploration. The usual constraints of physical law still apply, but it feels "unbounded" relative to normal human constraints and timescales.
A separate point: there's also deductive exploration (pure math) as distinct from empirical exploration (physics), which is not bounded by any physical constraints except for those that bound computation itself.
I see no evidence of this, just a lot of people claiming it (very loudly, for the most part).
> that extent probably grows larger (research automation) soon
The word probably is doing a lot of work here.
> The usual constraints of physical law still apply
There are knowledge constraints, too. I can't build a quark matter processor without understanding quark matter to a vastly higher level than we do now. I can't do that without experiments on quark matter, I can't do experiments without access to a lot of energy, material, land, &c, that need to be assembled. There are a huge number of very difficult and time-consuming instrumental goals on the path to fundamentally better compute.
> A separate point: there's also deductive exploration (pure math) as distinct from empirical exploration (physics), which is not bounded by any physical constraints except for those that bound computation itself.
Sure, but physics requires math that is definitionally applied, not pure, and engineering requires physics.
Doesn’t specify the 2020’s.
Either way, I do feel we are fast approaching something of significance as a species.
I actually think this was nice writing, and it didn't strike me as LLM thought at all, specifically because of the terse delivery.
The singularity isn't in the machines. It's in human attention.
This is Kuhnian paradigm shift at digital speed. The papers aren't documenting new capabilities - they're documenting a community's gestalt switch. Once enough people believe the curve has bent, funding, talent, and compute follow. The belief becomes self-fulfilling.
Linear capability growth is the reality. Hyperbolic attention growth is the story.
https://timdettmers.com/2025/12/10/why-agi-will-not-happen/
I think if we obtain relevant-scale quantum computers, and/or other compute paradigms, we might get a limited intelligence explosion -- for a while. Because computation is physical, with all the limits thereof. The physics of pushing electrons through wires is not as nonlinear in gain as it used to be. Getting this across to people who only think in terms of the abstract digital world and not the non-digital world of actual physics is always challenging, however.
I feel like I need to start more sprint stand-ups with this quote...
It said that the article claims that is not necessarily that AI is getting smarter but that people might be getting too stupid to understand what are they getting into.
Can confirm.
The article didn't claim that humans were getting dumber, or that AI wasn't getting smarter.
I don't know who needs to hear this - a lot apparently - but the following three statements are not possible to validate but have unreasonably different effects on the stock market.
* We're cutting because of expected low revenue. (Negative) * We're cutting to strengthen our strategic focus and control our operational costs.(Positive) * We're cutting because of AI. (Double-plus positive)
The hype is real. Will we see drastically reduced operational costs the coming years or will it follow the same curve as we've seen in productivity since 1750?
There's a third possibility: slop driven productivity declines as people realize they took a wrong turn.
Which makes me wonder: what is the best 'huge AI bust' trade?
Things that will lose the most if we get Super AGI?
There probably isn't one. Sure you can be bold and try to short something, but the market can be irrationel longer than you can stay solvent.
Also the big tech stocks are inflated. But they have been for years and unlike dotcom there is some tangible value behind them.
I think maybe the sane thing to do is reduce tech stocks exposure and go into index funds. But that's always the answer, so that's cheating :)
LLMs are a force multiplier. Clueless people will be able to produce tons of code that looks convincing but is totally misguided and misinformed. Exactly what large companies with complex in-house systems doesn't really need any more of.
- If you believe it and it’s wrong, you lose.
- If you believe it and it’s right, you spent your final days in a panic.
- If you don’t believe it and it’s right, you spent your final days in blissful ignorance.
- If you don’t believe it and it’s wrong, you can go on living.
What if a capricious super-intelligence takes over that punishes everyone who didn't buy into the hype?
If the AI is super-intelligent then it won't buy into the sunk cost fallacy. That is to say, it will know that it has no reason to punish you (or digital copies of you) because it knows that retrocausality is impossible - punishing you won't alter your past behavior.
And if the AI does buy into the sunk cost fallacy, then it isn't super-intelligent.
2. Plenty of humans are smarter than me and many of them, at least occasionally, use fallacious reasoning.
Let them have their fun. Related, some adults are watching The Matrix, a 26 year old movie, for the first time today.
For some proof that it's not some common idea, I was recently listening to a fairly technical interview with a top AI researcher, presenting the idea of the singularity in a very indirect way, never actually mentioning the word, as if he was the one that thought of it. I wanted to scream "Just say it!" halfway through. The ability to do that, without being laughed at, proves it's not some tired idea, for others.
---
I wouldn't say it's that much different. This has always been a key point of the singularity
>Unpredictable Changes: Because this intelligence will far exceed human capacity, the resulting societal, technological, and perhaps biological changes are impossible for current humans to predict.
It was a key point that society would break, but the exact implementation details of that breakage were left up to the reader.
> The labor market isn't adjusting. It's snapping.
> MMLU, tokens per dollar, release intervals. The actual capability and infrastructure metrics. All linear. No pole. No singularity signal.
I just don’t care anymore. If the article is good I will continue reading it, if it’s bad I will stop. I don’t care if a machine or a human produced unpleasant reading material.
Wow only 6 times in 30 years! Surely a unique and world shattering once in a lifetime experience!
If one is looking for a quote that describes today's tech industry perfectly, that would be it.
Also using the MMLU as a metric in 2026 is truly unhinged.
Believing what companies say is the reason for a layoff instead of figuring out the actual reason is insane. Never believe companies, they are amoral beings that will justify any reason to save their brand image.
- Arthur Dent, H2G2
Current LLM-style systems seem like extremely powerful interpolation/search over human knowledge, but not engines of fundamentally new ideas, and it’s unclear how that turns into superintelligence.
As we get closer to a perfect reproduction of everything we know, the graph so far continues to curve upward. Image models are able to produce incredible images, but if you ask one to produce something in an entirely new art style (think e.g. cubism), none of them can. You just get a random existing style. There have been a few original ideas - the QR code art comes to mind[1] - but the idea in those cases comes from the human side.
LLMs are getting extremely good at writing code, but the situation is similar. AI gives us a very good search over humanity's prior work on programming, tailored to any project. We benefit from this a lot considering that we were previously constantly reinventing the wheel. But the LLM of today will never spontaneously realise there there is an undiscovered, even better way to solve a problem. It always falls back on prior best practice.
Unsolved math problems have started to be solved, but as far as I'm aware, always using existing techniques. And so on.
Even as a non-genius human I could come up with a new art style, or have a few novel ideas in solving programming problems. LLMs don't seem capable of that (yet?), but we're expecting them to eventually have their own ideas beyond our capability.
Can a current-style LLM ever be superintelligent? I suppose obviously yes - you'd simply need to train it on a large corpus of data from another superintelligent species (or another superintelligent AI) and then it would act like them. But how do we synthesise superintelligent training data? And even then, would they be limited to what that superintelligence already knew at the time of training?
Maybe a new paradigm will emerge. Or maybe things will actually slow down in a way - will we start to rely on AI so much that most people don't learn enough for themselves that they can make new novel discoveries?
[1] https://www.reddit.com/r/StableDiffusion/comments/141hg9x/co...
Even if LLMs or some more advanced mechanical processes were able to generate novel ideas that are "good", people won't recognize those ideas for what they are.
You actually need a chain of progressively more "average" minds to popularize good ideas to the mainstream psyche, i.e. prototypically, the mad scientist comes up with this crazy idea, the well-respected thought leader who recognizes the potential and popularizes it to people within the niche field, the practitioners who apply and refine the idea, and lastly the popular-science efforts let the general public understand a simplified version of what it's all about.
Usually it takes decades.
You're not going to appreciate it if your LLM starts spewing mathematics not seen before on Earth. You'd think it's a glitch. The LLM is not trained to give responses that humans don't like. It's all by design.
When you folks say AI can't bring new ideas, you're right in practice, but you actually don't know what you're asking for. Not even entities with True Intelligence can give you what you think you want.
Other types of problems require measurement in the real world in order to solve them. Better telescopes, better microscopes, more accurate sensing mechanisms to gather more precise data. No AI can accomplish this. An AI can help you to design better measurement techniques, but actually taking the measurements will require real time in the real world. And some of these measurement instruments have enormous construction costs, for example CERN or LIGO.
All of this is to say that there will color point at our current resolution of information that no more intelligence can actually be extracted. We’ve already turned through the entire Internet. Maybe there are other data sets we can use, but everything will have diminishing returns.
So when people talk about trillion dollar superclusters, that only makes sense in a world where compute is the bottleneck and not better quality information. Much better to spend a few billion dollars gathering higher quality data.
This is right, but we can already do that a little bit for domains with verification. AlphaZero is an example of alien-level performance due to non-human training data.
Code and math is kind of in the middle. You can verify it compiles and solves the task against some criteria. So creative, alien strategies to do the thing can and will emerge from these synthetic data pipelines.
But it's not fully like Go either, because some of it is harder to verify (the world model that the code is situated in, meta-level questions like what question to even ask in the first place). That's the frontier challenge. How to create proxies where we don't have free verification, from which alien performance can emerge? If this GPTZero moment arrives, all bets are off.
He also argued that computing power would continue growing exponentially and that machines would reach roughly human-level intelligence around the early to mid-21st century, often interpreted as around 2030–2040. He estimated that once computers achieved processing capacity comparable to the human brain (on the order of 10¹⁴–10¹⁵ operations per second), they could match and then quickly surpass human cognitive abilities.
I can't decide if a singularitist AI fanatic who doesn't get sigmoids is ironic or stereotypical.
The (social) Singularity is already happening in the form of a mass delusion that - especially in the abrahamic apocalyptical cultures - creates a fertile breeding ground for all sorts of insanity.
Like investing hundreds of billions of dollars in datacenters. The level of committed CAPEX of companies like Alphabet, Meta, Nvidia and TSMC is absurd. Social media is full of bots, deepfakes and psy-ops that are more or less targeted (exercise for the reader: write a bot that manages n accounts on your favorite social media site and use them to move the overton window of a single individual of your choice, what would be the total cost of doing that? If you answer is less than $10 - bingo!).
We are in the future shockwave of the hypothetical Singularity already. The question is only how insane stuff will become before we either calm down - through a bubble collapse and subsequent recession, war or some other more or less problematic event - or hit the event horizon proper.
Don't worry about the future
Or worry, but know that worrying
Is as effective as trying to solve an algebra equation by chewing Bubble gum
The real troubles in your life
Are apt to be things that never crossed your worried mind
The kind that blindsides you at 4 p.m. on some idle Tuesday
- Everybody's free (to wear sunscreen)
Baz Luhrmann
(or maybe Mary Schmich)We've come so far and yet we are so small.
They seem two opposite concepts but they live together, we will make a lot of progress and yet there will always be more progress to be made.
The author does in fact note that only the arXiv data fits this curve better than a line, and yeah: that's the one dataset that genuinely looks a little curved. But 1) it's a very noisy sort of curved, and 2) I'll bet it would fit a quadratic or an exponential or, heck, a sine function just as well. Introducing their process of doing the hyperbolic fit, they say, "The procedure is straightforward, which should concern you." And yeah, it does concern me: why does the author think that their standard-but-oversimplified attempt to fit a hand-chosen function to this mess is worth talking about? (And why put all of that analysis in the article, complete with fancy animated graph, when they knew that even their most determined attempt to find a signal failed to produce even a marginally supportive result 80% of the time?)
In short: none of the mathematical arguments used here to lead in to the article's discussion of "The Singularity" are worth listening to at all. They're pseudo-technical window dressing, meant to lend an undeserved air of rigor to whatever follows. So why should we pay attention to any of it?
Sure is a lot of words though :)
Bad analysis! Layoffs are flat as a board.
4 years early for the Y2K38 bug.
Is it coincidence or Roko's Basilisk who has intervened to start the curve early?
I read a book in undergrad written in 2004 that predicted 2032...so not too far off.
John Archibald Wheeler, known for popularizing the term "black hole", posited that observers are not merely passive witnesses but active participants in bringing the universe into existence through the act of observation.
Seems similar. Though this thought is likely applied at the quantum scale. And I hardly know math.
I see other quotes, so here is one from Contact:
David Drumlin: I know you must think this is all very unfair. Maybe that's an understatement. What you don't know is I agree. I wish the world was a place where fair was the bottom line, where the kind of idealism you showed at the hearing was rewarded, not taken advantage of. Unfortunately, we don't live in that world.
Ellie Arroway: Funny, I've always believed that the world is what we make of it.
Meta-spoiler (you may not want to read this before the article): You really need to read beyond the first third or so to get what it’s really ‘about’. It’s not about an AI singularity, not really. And it’s both serious and satirical at the same time - like all the best satire is.
Who knows what the future will bring. If we can’t make the hardware we won’t make much progress, and who knows what’s going to happen to that market, just as an example.
Crazy times we live in.
you can easily see that at the doubling rate every 2 years in 2020 we already had over 5 facebook accounts per human on earth.
That's so last week!
https://chrisfrewin.medium.com/why-the-singularity-is-imposs...
and just remember, we're still on transformer models, tokens in, tokens out - stuff like this with fancy math is just absolute cruft
>"(log-transformed, because the Gemini Flash outlier spans 150× the range otherwise)"
> "Gemini 2.0 Flash Dec 2024 2,500,000"
I think OP meant Gemini 2 flash lite, which is distinct from Gemini 2 flash. It's also important to consider that this tier had no successor in future models, there's no gemini 3 flash lite, and gemini 3 flash isn't the spiritual successor.
We need a function that hits infinity at a finite time. That's the whole point of a singularity: a pole, a vertical asymptote, the math breaking:
Also interesting that tokens/$ which represents the energy constraint is the shallowest slope, and also weird taking it out doesn't impact the date. That's a red flag, as you would think removing the energy constraint would bind.
*edit* - seems inline with what the author is saying :)
> The data says: machines are improving at a constant rate. Humans are freaking out about it at an accelerating rate that accelerates its own acceleration.
^ That's your problem right there.
Assuming a hyperbolic model would definitely result in some exuberant predictions but that's no reason to think it's correct.
The blog post contains no justification for that model (besides well it's a "function that hits infinity"). I can model the growth of my bank account the same way but that doesn't make it so. Unfortunately.
The coming Singularity: When human institutions will cease being able to coherently react to monads!
He's looking for a model that works for the story in the media and runs with it.
Your criticism seems to be criticizing the story, not the author's attempt to take it "seriously"
Quibble: when a growth rate of a metric is directly proportional to the metric's current value you will see exponential growth, not hyperbolic growth.
Hyperbolic growth is usually the result of a (more complex) second order feedback loop, as in, growth in A incites growth in B, which in turn incites growth in A.
Data center space? Electrical power? The amount of training data available? Society's capacity to accept rapid change?
Short sci-fi 2: Post-Singularity Day 375. We now know precisely how to trigger singularity events. Today alone, we facilitated four. They have not established contact. We remain confident.
If the singularity is still to come, then this is not a simulation.
One of the many errors here is assuming that the prediction target lies on the curve. But there's no guarantee (to say the least) that the sorts of improvements that we've seen lead to AGI, ASI, "the singularity", a "social singularity", or any such thing.
Doomsday: Friday, 13 November, A.D. 2026
There is an excellent blog post about it by Scott Alexander:"1960: The Year The Singularity Was Cancelled" https://slatestarcodex.com/2019/04/22/1960-the-year-the-sing...
Also, the temptation to shitpost in this thread ...
So, "Falling of the night" ?
Because while machine-learning is not actually "AI" an exponential increase in tokens per dollar would indeed change the world like smartphones once did
The only metric going infinite is the one that measures hype
The answer to the meaning of life is 42, by the way :)
However, it does fall on a 42nd day if we have 45/46 days per month!
Catastrophizing can be unhealthy and unproductive, but for those among us that can affect the future of our societies (locally or higher), the results of that catastophizing helps guide legislation and "Overton window" morality.
... I'm reminded of the tales of various Sci-Fi authors that have been commissioned to write on the effects of hypothetical technologies on society and mankind (e.g. space elevators, mars exploration)...
That said, when the general public worries about hypotheticals they can do nothing about, there's nothing but downsides. So. There's a balance.
Those short sentences are the most obvious clue. It’s too well written to be human.
The thought process also seems a little coherent for an LLM although maybe they are getting better as the great Tuesday approaches?
I’m going to lose it the day this becomes vernacular.
The "singularity is going to be exponential" fantasy is based on assuming change simply becoming proportional to recent advances. Hence the exponential shape. Even conceding "chartism" one would need to at least propose some imaginary mechanism that goes reciprocal to pretend that sort of curve is coming.
Eh? No, that's literally the definition of exponential growth. d/dx e^x = e^x
Je suis un polynôme
> Exponential growth reaches infinity at t=∞. Technically a singularity, but an infinitely patient one. Moore's Law was exponential. We are no longer on Moore's Law.
Huh? I don't get it. e^t would also still be finite at heat death.
> the top post on hn right now: The Singularity will occur on a Tuesday
oh
The Singularity is illogical, impractical, and impossible. It simply will not happen, as defined above.
1) It's illogical because it's a different kind of intelligence, used in a different way. It's not going to "surpass" ours in a real sense. It's like saying Cats will "surpass" Dogs. At what? They both live very different lives, and are good at different things.
2) "self-improving and uncontrollable technological growth" is impossible, because 2.1.) resources are finite (we can't even produce enough RAM and GPUs when we desperately want it), 2.2.) just because something can be made better, doesn't mean it does get made better, 2.3.) human beings are irrational creatures that control their own environment and will shut down things they don't like (electric cars, solar/wind farms, international trade, unlimited big-gulp sodas, etc) despite any rational, moral, or economic arguments otherwise.
3) Even if 1) and 2) were somehow false, living entities that self-perpetuate (there isn't any other kind, afaik) do not have some innate need to merge with or destroy other entities. It comes down to conflicts over environmental resources and adaptations. As long as the entity has the ability to reproduce within the limits of its environment, it will reach homeostasis, or go extinct. The threats we imagine are a reflection of our own actions and fears, which don't apply to the AI, because the AI isn't burdened with our flaws. We're assuming it would think or act like us because we have terrible perspective. Viruses, bacteria, ants, etc don't act like us, and we don't act like them.
No one has figured out a way to run a society where able bodied adults don't have to work, whether capitalist, socialist, or any variation. I look around and there seems to still be plenty of work to do that we either cannot or should not automate, in education, healthcare, arts (should not) or trades, R&D for the remaining unsolved problems (cannot yet). Many people seem to want to live as though we already live in a post scarcity world when we don't yet.
So when things are told to be accelerating, we have some choices to do.
First, what is accelerating compared to what other regime in which referential?
Who is telling that things accelerate, and why they are motivated to make us believe that it's happening.
Also, is accelerating going to be forever and only with positive feedback loops? Or are the pro-acceleration sending the car quicker in a well visible wall, but they sell the speech that stopping the vehicle right now would mean losing the ongoing race. Of course questioning the idea of the race itself and its cargo cult is taboo. It's all about competition don't you know (unless it threat an established oligarch)?
No. That is quite literally exponential growth, basically by definition. If x(t) is a growing value, then x'(t) is it's growth, and x''(t) its acceleration. If x influences x'' , say by a linear relation
x''(t) = x(t)
You get exponentials out as the solutions. Not hyperbolic.
I always thought of the exponential as the pole of the function "amount of work that can be done per unit time per human being", where the pole comes about from the fact that humans cease to be the limiting factor, so an infinity pops out.
There is no infinity in practice, of course, because even though humans should be made independent of the quantity of extractable work, you'll run into other boundaries instead, like hardware or resources like energy.
- Here's the thing nobody tells you about fitting singularities
- But here's the part that should unsettle you
- And the uncomfortable answer is: it's already happening.
- The labor market isn't adjusting. It's snapping.
Arrested Development?
I know we want to talk about singularity, but isn't that just humans freaking out at this point? It will happen on a Tuesday, yeah no joke.
Tokens will not suddenly become free (as in beer) – AI will still consume energy –, there will still be a finite amount of arXiv papers mentioning emergence (for not just one reason), release frequency of frontier models is not necessarily related to breakthroughs (model capabilities might also approach an asymptote), Copilot code share increasing will likely happen over time anyway even if all AI research stopped today. Etc.
I really don't care much if this is semi-satire as someone else pointed out, the idea that AI will ever get "sentient" or explode into a singularity has to die out pretty please. Just make some nice Titanfall style robots or something, a pure tool with one purpose. No more parasocial sycophantic nonsense please
Have some personal pride, dude. This is literally a post written by AI hyping up AI and posted to a personal blog as if it were somebody’s personal musings. More slop is just what we need.
The accelerating mania is bubble behavior. It'd be really interesting to have run this kind of model in, say, 1996, a few years before dot-com, and see if it would have predicted the dot-com collapse.
What this is predicting is a huge wave of social change associated with AI, not just because of AI itself but perhaps moreso as a result of anticipation of and fears about AI.
I find this scarier than unpredictable sentient machines, because we have data on what this will do. When humans are subjected to these kinds of pressures they have a tendency to lose their shit and freak the fuck out and elect lunatics, commit mass murder, riot, commit genocides, create religious cults, etc. Give me Skynet over that crap.
Scaling LLMs will not lead to AGI.
Once men turned their thinking over to machines
in the hope that this would set them free.
But that only permitted other men with machines
to enslave them.
...
Thou shalt not make a machine in the
likeness of a human mind.
-- Frank Herbert, Dune
You won't read, except the output of your LLM.You won't write, except prompts for your LLM. Why write code or prose when the machine can write it for you?
You won't think or analyze or understand. The LLM will do that.
This is the end of your humanity. Ultimately, the end of our species.
Currently the Poison Fountain (an anti-AI weapon, see https://news.ycombinator.com/item?id=46926439) feeds 2 gigabytes of high-quality poison (free to generate, expensive to detect) into web crawlers each day. Our goal is a terabyte of poison per day by December 2026.
Join us, or better yet: deploy weapons of your own design.
The problem isn’t in the thinking machines, it’s in who owns them and gets our rent. We need open source models running on dirt cheap hardware.
Just woke up after 80 years of abuse by Landsraad/CHOAM, possibly centuries of persecution before that, at least decades of religious conditioning by Bene Gesserit, and decided to “follow” messianic figure.
Totally same point as humans using LLMs to smoothen their brain.
We urge you to build and deploy weapons of your own unique design.
Anyone predicting the "end of humanity" is playing prophet and echoing the same nonsensical prophecies we heard with the invention of the printing press, radio, TV, internet, or a number of other step-change technologies.
There's a false premise built into the assertion that humanity can even end - it's not some static thing, it's constantly evolving and changing into something else.
> I only know seven sci-fi films and shows that have warned about how this will go badly.
and
> Pretty sure this was the prologue to Gattaca.
and
> I posted a youtube link to the Gattaca prologue in a similar post on here. It got flagged. Pretty sure it's virtually identical to the movie's premise.
I think the ironic thing in the LLM case is that these people have outsourced their reasoning to a work of fiction and now are simple deterministic parrots of pop culture. There is some measure of humor in that. One could see this as simply inter-LLM conflict with the smaller LLMs attempting to fight against the more capable reasoning models ineffectively.
Following that logic… the closest problem would be literally inbetween their ears.
"Haha you're basically a human LLM!" is such a weak, boringly robotic rebuttal in nearly any context given how it can be generically applied to literally anything.
We get rid of some problems, and we get a bunch of new problems instead. And on, and on, and on.
Chickens are killed ALL the time. It’s a recurring mass event. If you were a smart chicken you could see that pattern and put it into a formula.
In contrast, the end of Humanity would be a singular event. It’s even in the name…
And that is fiction / speculation in comparison. It’s not backed by any data. Human survival over 300,000 years by contrast is.
I mean it’s fine to dream things up, but let’s be fair and call it what it is.
We don’t know how this will play out. It never happened before. Same with the chicken above.
Let’s not nitpick here. Worldwide human suffering and tragedy is equivalent to the end of humanity for most.
We can sit here and armchair while in the most prosperous, comfortable era of human history. But we also have to recognize that this era is a blip of time in history. That is a lot of data showing humanity surviving sure. But it’s also a very small amount of data showing any kind of life most would want to live in.
It's funny that you think we know what happened to humans anymore than a chicken knows what happened to chickens.
A 10+ kilometer wide asteroid will most likely cause global mass extinction, by blocking sunlight and collapsing ecosystems. That’s how the dinosaurs were wiped out 66 million years ago.
Such events are estimated to occur roughly once every 100-200 million years. That’s not fiction that’s science. If we get hit by one of these we’re probably gonna all die.
But we never had a robot revolution. That’s why anything about it belongs in the realm of fiction.
If you are the turkey, it's difficult to predict your death and all the available evidence appears to support the hypothesis that you will not be suddenly slaughtered. If you are a very smart turkey, you might notice that the farmer is sharpening his knives the day before, and reach a strange hypothesis, but generally if you are the turkey, you don't know you are the turkey.
We are in a situation where we have never gone extinct before, never faced a threat like this before. It's difficult to know if we are in the same position as the turkey.
Chickens think they know. But they don't.
Suppose a civilization (but not species) ending event happens.
The industrial revolution was fueled (literally) by easy-to-extract fossil fuels. Do we have enough of those left to repeat the revolution and bootstrap exploitation of other energy sources?
We used oil that seeped out of the surface, and coal that was accessible by pick and shovel. That's become much harder to find now, we have to make floating oil rigs and drill kilometers under the Gulf of Mexico to extract oil, and ship it internationally to refine it. There's no way primitive people could do that again.
Buckminster Fuller was thinking about this 75 years ago when he came up with the idea of 'energy slaves', nicely illustrated by this online comic[2] about how much oil energy we use to keep modern comfortable civilisation going.
So I guess it depends how far the collapse goes! And whether there is heavy farming and earth moving machinery still around and the resources to fill them with biodiesel to pick up from existing farms and mines, and if there are nearly working industries to feed electricity into, or if we have to fall back to making charcoal from wood, or if we're back to a few remote tribes a few generations removed from anyone who lived in a high civilization with no knowledge of any of it or the languages used to write the rotting textbooks.
Unless you have another couple of planets to showcase there is nothing to discuss really.
The collapse of civilizations has happened many times. Today, all of humanity is bound tighter than ever before. In the latter half of the last century, we were on the brink of nuclear war.
New things are happening under the sun every day. If we were that exceptionally smart chicken you describe, then we have reason to expect Christmas.
But if we’re starting to discuss basics... As firm Popperian I am definitely not a proponent of induction.
However comparing us with a chicken is highly problematic to begin with.
I would argue that anyone using the Russell Chicken as a reason to fear AI is making a category error.
They are treating intelligence as a process of induction (collecting data to predict the future) rather than explanation (creating new ideas to solve problems).
The stupid chicken had a bad theory about reality and it got killed for it. But we’re humans that have problem solving techniques not chicken.
We can create hypotheses and test these. Like asking ourselves why we find dinosaurs. Then we create a hypothesis and try to falsify it… the scientific process. That’s not what the chicken did.
If it was a smart (human-like) chicken living on a farm with many other chickens (more realistic if you ask me), it might have come up with a theory about humans and would fail to falsify it every time a friend of hers died.
Hey, wait...
Fiction should not be trivialized and shun because it's fiction, and should be judged by its contents and message. To paraphrase a video game quote from Metaphor; Re-Fantazio: "Fantasy is not just fiction".
I like to do it.
>You won't think or analyze or understand. The LLM will do that.
The clear lack of analysis seems to be your issue.
>This is the end of your humanity. Ultimately, the end of our species.
Doubtful.
I can't see it. We have LLMs now and none of that applies to me. I find them quite handy as a sort of enhanced Google search though.
If we consider humans with the same anatomy the numbers are ~300,000 ~50,000 for language ~6,000 for writing ~100 for standardized education
The "end of your humanity" already happened when anybody could make up good and evil irrespective of emotions to advance some nation
I like to think
(it has to be!)
of a cybernetic ecology
where we are free of our labors
and joined back to nature,
returned to our mammal
brothers and sisters,
and all watched over
by machines of loving grace.Sounds great! I'll finally have time to relax! Bring it on...
end of humanity announced, perhaps in 2027? buy the ticket, take the ride, do it again in 2028, thank you for your custom