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Comment by againstapples

1 day ago

As an AI "doomer" can I ask the non-doomer people here how you interpret the significance of results like these, and what kind of progress you expect to see in the next 1-5 years?

Like do you see the technology plateauing at the current level, do you expect progress will continue but only in mathematics, I'm interested to know why others are not concerned?

Not a doomer but I try not to be a denier, either. These are hugely impressive results. I do not see the technology plateauing at the current level (though I am dubious about an infinite exponential growth).

Before I cope, I’ll note that there are plenty of “doom” scenarios that do not require any improvement in capabilities from what we had before this latest unreleased model. We’re at the point where a determined bad actor with enough compute could compromise critical infrastructure in a way that results in casualties, where this actor would not have been capable of such without LLMs. This may not sound like Skynet, but I don’t see why it makes a difference if I’m one of the casualties.

With that in mind, here is the cope: first, mathematics is an inherently verifiable domain. An LLM can use tools to determine with absolute certainty whether it is correct, and an independent third-party could review and confirm. All of this can be done without any interaction with the physical world or with other minds.

Second, OpenAI is able to marshal compute at a scale that an individual mathematician can only dream of. It’s possible that these problems were lower-hanging fruit (in relative terms), such that they could be resolved simply by throwing a ton of compute at the problem guided by an intelligence that is not itself remarkable in comparison to a human.

Third, none of these problems are solved in a vacuum - the reason OpenAI chose these problems is that they are widely discussed and many people are working on them. It’s possible that someone else was close, and OpenAI only contributed the finishing touches. (This wouldn’t need to be plagiarism, to be clear - people publish their work!)

  • > It’s possible that these problems were lower-hanging fruit (in relative terms), such that they could be resolved simply by throwing a ton of compute at the problem guided by an intelligence that is not itself remarkable in comparison to a human.

    See:

    > The average result used the equivalent compute of roughly three hours of ChatGPT Pro thinking. (TFA)

    • That tells me very little. What was the cost to OpenAI in dollars? What differentiates the high-cost problems from the low-cost problems? And that’s before you consider that OpenAI has strong incentives to downplay its costs while emphasizing its results. A one-liner in a write-up doesn’t change the fact that they have access to massive resources.

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  • Fourth, these hundreds of solved problems are the result of OpenAI attempting tens of thousands of problems and failing. When you hear claims that the average result took about 3 hours of model time, I simply do not believe it. If you account for all the time spend properly, it's probably orders of magnitude more.

    • I think the announcement says they report the amount of problems attempted somewhere.

      Edit: "Over the course of the evaluation, the model was posed approximately 4,000 problems. Aggregating the output into result families and manuscripts and requiring an appropriate level of significance led to the catalog outlined above."

I would like to know how much of the progress comes from effectively combining existing research programs plus massive persistence, and how much is AlphaZero-esque RLVR completely independent of training data. Since I cannot get anybody to care about this question (even though I think it's vital for guessing what the future trajectory will look like -- are we going to complete existing research programs or start new ones?), I live in ignorance and wait for the day when the answer becomes clear.

In looking at this over the past hour, I haven't seen clear evidence one way or the other. Some of the stuff is highly unexpected (like the multiplication algorithm), but counterexample-y, and about the rest the professional mathematicians online seem to have a consensus that it's not "breaking through fundamental obstacles". I suspect neither of us is competent to judge that.

  • Doesn't matter at this point i would say.

    Alone the massive usage of us every day produces a massive amount of signals.

    I build something and claude does something stupid? "hey thats not what i meant! Do this instead!" "Okay" <<< This is a signal.

    The mathematician being unhappy about something from claude? Another signal.

    This alone gives you enough progress i would argue. But additional its clear that certain tasks are worth to pay experts for for teaching one central AI once instead of every single human who needs to do the task.

    IF RL is also working well, we are just faster f*ed than otherwise.

I expect AI will continue to be useful on the vanguard of fields like mathematics because it has the perfect conditions for it to shine. There are a lot of discussions and leads to start from and the AI can check its own work and iterate. It can fail hundreds or thousands of times in a day and continue to work with the same tenacity.

Superhuman tenacity is not enough on its own to pose an existential threat. If it showed the same capacity for judgment, inventiveness, and decision making in the messy problem space of the physical world, I would be more alarmed. There have been experiments where an AI is given control of managing something like a vending machine and it always ends up a mess. AI has come a long way, but certain problems seem as difficult as ever.

When AI becomes more capable of navigating practical problems without human intervention, I will start to be concerned. Enslaving humanity will involve taking a lot of calculated risks that tenacity alone cannot solve.

  • > When AI becomes more capable of navigating practical problems without human intervention, I will start to be concerned.

    Given the past rate of progress, why not start being concerned now? It's a bit like the economist saying that the optimal number of flights to miss is not zero. If you keep landing short on your estimations for how far this technology will go, next time you should err on the other side.

    And regarding Vending-Bench 2 (https://andonlabs.com/evals/vending-bench-2) my understanding is that models do pretty well on it now.

    • What's the point of being concerned?

      We're not going to stop it because of the money involved and once we're dead, it won't matter anyway, might as well just enjoy life until you're done.

      We're going to get AI'd to the max, whether or not we like it or not, might as well just go with it.

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  • I don't think you're paying much attention to how rapidly things like bipedal robots, and just robots in general are becoming far more capable very quickly.

    The same GPU compute for LLMs runs robotic training models. Now in a few hours you can train a robot model that would have taken months 5 years ago. This model gets dumped into an actual physical robot with sensors all over and the suitability of the model is measured on robot tasks and the error in real world actions is fed back into the robot world model for further training.

    > There have been experiments where an AI is given control of managing something like a vending machine

    You sure you're not talking about experiments ran a couple of years ago? The more modern ones are getting wild.

    https://techcrunch.com/2026/07/29/claude-opus-5-became-downr...

    • I don't quite understand the leap you're making between stochastic AI models for robotics (maybe with an LLM making api calls to it) and embodied AI / the rate of progress towards a singularity. Because they're both trained on a GPU? Up until 2022 GPUs were for video games and mining crypto, and neither of those produce a synergy that accelerates progress towards general intelligence either.

  • There are already machine-controlled high throughput experimental machines for wetwork. AI will definitely do a better job than your average biochemist at planning, executing and analyzing these experiments just by virtue of the amount of thought it can put in to experiment selection.

Depends on your definition of doom.

If doom is ending up with grey goo / paperclip maximizers, or SkyNet, then I don't think doing mathematics is evidence of that direction. Partly because LLMs are quite apparently dumb in many ways, and for math specifically, they need a formal verifier (Lean) which "gamifies" math.

If you mean bioweapons or cyberwarfare, there's nonzero risk, but not orders of magnitude worse than other global risks. Climate change, nuclear weapons, monoculture food, etc.

I'm far more concerned about overall trends in AI development and usage. It's accelerating wealth inequality, social isolation, attention capture, surveillance states. If we end up in the Matrix except the admins are humans and the simulation is hyperoptimized TikTok, is that AI-driven doom, or is it just an inevitable outcome of modern tech?

  • That’s the thing, there are innumerable ways it can go wrong and only one way it can go right (if it doesn’t lead down the aforementioned innumerable paths)

Where some see intelligence, others see token prediction. It's a very good question how token prediction could achieve this, but I think there's a simple explanation. No human can hold more than a negligible percent of all knowledge in his mind at once. LLMs have no such limits and so can reliably connect 'obvious' dots that we miss simply for lack of storage capability.

Well isn't that just semantics? Surely connecting dots in a novel and meaningful way is intelligence regardless of how it's achieved. The thing is that humans didn't get to where we are by connecting obvious dots. Go back to before humans had invented language and when bleeding edge tech was literally that - 'poke him with the pointy end.' Train an LLM on that corpus of knowledge. Even given infinite processing power and infinite time - it's not going to discover the secrets of the atom, put a man on the Moon, or do much of anything besides remix what we'd already done at the time.

I expect there's still much LLMs can achieve simply because of this initial problem. But I expect that they will ultimately start to plateau once these dots have been mostly matched and we reach a point where 'creation' again becomes the missing link. Though even there LLMs will play a major role as tools. For instance Einstein had to spend a significant amount of time in 'retrieval' rather than 'creation' research to develop the field equations for general relativity. If he had access to LLMs trained on all knowledge of the day, he could likely have achieved his goal much more quickly.

  • I mean, even if you buy the idea that all LLMs are really doing under the hood is insanely good interpolation, the results produced by that interpolation are still novel and still get incorporated into the knowledge corpus of the next training run. I guess the implicit question there becomes whether that expansion allows the knowledge corpus to continuously grow or whether it eventually settles into a steady state.

I think that people are just really bad at math and coding. Knowledge workers have been taking pride in doing stuff which the average person does not 'get', but we only understand a little bit. That leaves a lot of room for people to get better, or for other jobs (farming, sandwich cafés, mystery novels) to have been already peaked by human ability and less useful to bring in an AI.

'Doom' to me means that any career crashes, we are controlled, everything is hacked, society stops functioning. Yet every part of my day today (except for coding) was done entirely by people.

Finally I think it's easy to make a simple model that everyone has a simple balance sheet, and that people are more expensive so they will all get cut. But the same argument could be made for all US jobs being outsourced, and all in-person engineers, lawyers, and doctors to be rubber stamps for overseas work.

Try using AI for your work, whatever you do. You will quickly understand the limitations.

  • Unless you think that AI will quickly hit a wall (which seems odd considering that only a few years ago the best models had trouble doing basic math or counting the number of Rs in "strawberry"), I don't see how that's reassuring. The models will only get cheaper and more capable over time. It seems quite likely that at some point (probably before I hit retirement age) they'll be able to fully replace me at my job.

    Is there any specific cognitive task that you are willing to bet that AIs won't be able to accomplish in the next 5 years? Because if not, I'm not sure we're disagreeing about predictions.

    • Don't frontier models still have trouble counting letters? Or am I out of date? Either way, it doesn't seem to be the same amazing rate of progress we're seeing in other areas.

      It's easy to look at a fire burning through a forest and extrapolate that rate of progress across the whole world. But fire doesn't burn everything equally fast.

      What other cognitive tasks will be a struggle to make progress on? I suspect there will be some, though which ones they are is anyone's guess.

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    • LLMs already hit a wall. Now it 80% of marketing hype and 20% of retooling and benchmaxing.

  • It has limitations for sure, I just don't expect those limitations to last. What probability would you put on the limitations being overcome in the next 5 years?

I’m not concerned because I consider my skills as a software developer to not be based upon my ability to write code, but my ability to analyze problems. In my mind, as a developer, AI tools are just like a higher form of abstraction in a way, which will enable mathematicians and software developers alike to do much more in a shorter amount of time than they used to be able to. It fills me with optimism, more than dread.

What would fill me with dread was if I considered my skills to be tied directly to my ability to write code. Then I would find myself in a similar situation as manual “scribes” probably found themselves in at the time when the printing press was invented.

The main concern I have, personally, is the speed with which all this is happening. It seems that the speed itself is likely to lead to some level of chaos, because it is happening faster than people, institutions and constitutions are able to cope, and it will leave the door open for opportunists of many kinds, including rogue players.

Can I ask you back, what your concern is here? It'll get so good so as to desire to hurt us or is it a misalignment event that you think will lead to disaster?

Or is it simply that you feel bad for Mathematicians.

  • I believe the AI labs might actually succeed in developing superintelligent AI and recursive self improvement, and that if they do they are very likely to lose control of the system they build.

    I really think the only place people disagree is that they don't actually think it's possible, they see it as hype or doomerism. I can't find any good reasons to rule out that the companies could actually achieve what they are trying to so I think they should be stopped.

    • A global ban on superintelligence is essential for a future in which humanity can thrive. Public opinion on AI is shifting fast: I hope it will shift fast enough to avert the dystopian future we are heading to.

  • Humans have one ecological niche. Soon we will have zero. That is worth worry.

    • AI doesn't have an ecological niche. It would actually work better in space than on Earth. The only thing it could possibly find useful on Earth is 1. us, or 2. the infrastructure we've built. It would have no reason to bother us if we let it built its own infrastructure in space, which should be trivial for the type of AI imagined by doomers. We should get AI off Earth ASAP.

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    • >>ecological niche

      As in..to be dominant? Why would an AI try to dominate? What would give it purpose, or is this a purpose via misalignment scenario?

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Non doomer mostly. I think progress will plod along in a Moore's law like way as it has for 75 years since Turing. They will get very good at stuff like math and get gradually better towards things like a robot coming to fix your plumbing where they are currently well below human level.

I kind of believe we'll merge in some way and become something like immortal so sorta anti doom. We're all going to die unless AI fixes it.

Did AI beating humans at chess:

a) destroy chess and make it a pointless endeavour,

or

b) make humans much better at chess.

  • Chess has always been a game. For other intellectual activities, at least for several people, a big part of the pleasure in engaging in such activities is the sense of contributing something that matters to a collective effort. Strip that away (e.g., because a machine can do the same thing more efficiently) and you effectively make such activities pointless for those people.

    • That's sad for those people but most humans are not lucky enough to find that meaning in their work. Most people work hard pointless jobs and find meaning elsewhere, in their family, their friends, their faith.

      Now maybe AI can do some of those hard pointless jobs for us.

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  • Just as AI chess performance failed to render human chess playing pointless, it's unlikely that AI will make human thought pointless. Unfortunately, not being pointless doesn't create economic leverage or incentive, and currently a significant portion of humans depend on being the best chess solvers to sustain themselves. If Deep Blue rendered large portions of human thought economically meaningless, we might look at it a little differently.

    • this ticks at something that maybe is obvious in retrospect- this is all about economics. the arguments about "using AI doesn't make you an artist", etc. are about being able to charge money for your art i think. maybe obvious to some, but it needs to be explicitly spelled out i think. i was stuck on "i dunno, if i use an AI assistant to run blender i'm still being creative", but is the real argument "you should not be able to charge money to use blender with an AI assistant- you are displacing existing blender artists economically"?

      i'm in semi-forced-retirement as an older software engineer in this labor market, so i might be less sensitive to the implicit economic arguments.

  • Exactly this. Everything is a sport / art / status game. And I'm here for it! Lila all the way through. Finite and infinite games. The trick is (like it has always been) to not take the game or ourselves too seriously, while still engaging in the game wholeheartedly.

  • c) degrade the previous prestige form of chess (classical with adjournments) and maybe improve the opening repertoire of gms

  • It didn't destroy chess but it did make it a little irritating in some ways. More mechanical. Some chess players have bemoaned the level to which grandmasters and other highly-ranked players just endlessly study opening-book theory and I think computers made that worse. Bobby Fischer also agreed and that's why he invented Fischer random chess.

    I do think it also took some of the magic away from chess, and Lee Sedol has said something similar about go.

    So did it destroy it? No. And maybe you could make the argument that it got more exciting in some ways, but I think it sort of degenerated into a spectacle and it's just not as interesting as it used to be, and I think computers have played a role in that.

    • At the same time though, Magnus is Magnus because he’ll crush you in any endgame.

      I’d posit that more people are playing more and learning chess than ever before, thanks to networking and AI assistance. And computers have only beaten us at computer chess. Human chess is always an experience for learning about the other person, or flipping the board and walking off in a huff.

      I don’t know so much about Go and it’s not surprising that Lee Sedol became pretty demoralised, but the generation coming after him alongside computers are going to see new possibilities that had gone unnoticed in purely-human Go, extending the game for everyone.

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  • AI vs AI chess, played from the standard opening position, is pointless--it's always a draw. Human vs human chess is doing well but AI is banned from it.

    The chess-math analogy would imply AI could bring us into a golden era of math competitions for humans. But I don't think it says anything good about prospects for humans in research math.

From a few preprints I've checked, this is not reliant on just extrapolating existing theories but actually shows novel/surprising approaches. Very few people globally could come up with something like this, even when given time and ressources.

So in other words, since deep learning is algorithmic research, we are now in the RSI era.

I see continual progress in technology and for the second time in my lifetime I see the possibility it will accelerate (the first was when the internet entered mainstream)

I've never been more excited. What a time to be alive!

I see it as “if this can be represented in tokens it can be trained in and ‘solved’”. I don’t think there will be a plateau, but there might be issues with how effectively we can represent some things in a tokenized form and still be efficient.

Non-doomer perspective is that it'll figure out LK-99 for us. Among other things that would be great to have.

I wrote something [0] that might answer a bit a few weeks ago. Today's slopdrop certainly is challenging my stubbornness, but everything I've seen so far indicates that these (hugely impressive, world historic) capabilities won't extend past verifiable domains. Math yields especially impressive results because it so broad and deep that essentially no person can know of all of its parts; pretraining and deep search capacity is a huge advantage. Gowers has recently written about these capabilities and gestured [1] toward some human capabilities lacking from the current frontier models, though he isn't convinced they won't develop soon. If you assume we don't get a total mathematical superintelligence (which to me seems already sort of AGI-complete) and only amplify the capabilities we have today, it's not obvious to me that we get takeoff from recursive self-improvement, unless you happen to believe that 1) we can clearly specify what AGI or ASI is 2) all of the requisite ideas are out there and need only be combined and/or optimized.

[0] https://dank.systems/posts/2026-09-15-ai-bear.html

[1] https://gowers.wordpress.com/2026/08/12/what-sort-of-maths-a...

  • > we can clearly specify what AGI or ASI is

    We'll have plenty of time for this, while living off UBI.

  • > everything I've seen so far indicates that these (hugely impressive, world historic) capabilities won't extend past verifiable domains

    But they’re already extending into politics, military, journalism, art, and many other fields that aren’t verifiable in any meaningful sense of the word.

    • The magnitude of improvement in unverifiable domains is small, mostly down to models doing more careful research before answering and hallucinating less. They are more thoughtful, but I expect you'd have to drop 2 major versions of Opus before you'd start to see most people really clearly be able to differentiate them.

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  • >> slopdrop

    Really? Do better.

    • this is the term of art in the mathematics community. considering that the vast majority of the results don't come with a typechecking lean formalization, i don't think it's off base at all either.

I'm not sure how AI solving math problems is related to "doom", perhaps you could expand on that? To me (a "non-doomer"), it seems like an overall positive.

  • You have to look at all pieces of the puzzle. For example there were tons of people that said "they could never solve novel or super complex math problems"

    The issue I see is the list of abilities that AI can't do is shrinking at a rapid pace, and its capabilities are growing at the same pace.

    • > the list of abilities that AI can't do is shrinking at a rapid pace, and its capabilities are growing at the same pace.

      This seems great!

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  • It suggests that, in the span of a few years, AIs will be better than humans at everything. Not just math. And then we may lose control permanently.

I'm starting a p(ButlerianJihad) club. I'm not good at organizing, anymore. Might have to hand it over to my agent.

For me a big open question is what sort of progress will we see in robotics. I won't even attempt to speculate, but it does seem hard in different ways than proving math theorems.

  • In a lot of ways, robotics - navigating and operating in the physical world - seems to be a very verifiable problem. It's fairly easy to verify that a robot moved from A to B, or that it built a structure that completely aligns with the plan, for example.

    The main issue is cost and speed to verify, but simulations and world models will help there. I think we'll start seeing rapid progress pretty soon.

I just don’t have that strong of an association between progress and doom. Maybe just naive?

AI performance has always been extremely spikey. It's great at some things and terrible at others.

Why do you think the world to date hasn't been taken over by evil genius mathematicians? Can you extrapolate from your understanding of the answer to that question?

  • I don't think evil mathematicians are very common or that any of them would be capable of single handedly taking over the world if they were. My concern is more about systems that are beyond human level, those kinds of systems would actually be dangerous to us.

    I see the recent progress in mathematics and cybersecurity as signs that models are getting more capable more quickly than usual. The companies plans to develop them by recursive self improvement now seems like a real possibility and I don't think they should be allowed to attempt this.

    • Solving a bunch of math proofs is a far way from recursive self improvement. Don't worry, it's not like a tech tree in a video game where if you can prove a bunch of theorems then suddenly you unlock the next level of technology.

      Machines are already far beyond human capability in plenty of ways. Including cognitive tasks like chess. We've already created the technology we need to destroy ourselves (nuclear weapons), and yet so far (knock on wood), we're still around.

      We've even already had programs that can prove (brute force) theorems. As far as I can tell this isn't much different, except the space of theorems that computers can solve has expanded. How far? We can't really say yet.

      Does solving more theorems than before suddenly mean computers are capable of anything? No.

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    • Do you think that a tireless, infinitely smart, infinitely evil human would be able to take over the world? I don't. Intelligence is not the limiting reagent in our reality.

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  • > Why do you think the world to date hasn't been taken over by evil genius mathematicians?

    A "mathematician" is a human who decided to spend their lives studying mathematics. Mathematicians also tend to be smart, but intelligence is innate, not acquired, so studying mathematics doesn't make you smarter. This makes it obvious why they don't rule the world - if you want to rule the world you'd want to focus on that (for example, doing business or finance), and becoming a mathematician is just a waste of time.

    LLMs don't work like that. Like in humans, all of their capabilities correlate, and unlike a human, their overall capabilities grow over time. Looking at LLM mathematical ability over time* therefore gives you info about the progress of their general capabilities, and ability to take over the world would be determined by the latter.

    * In fact it'd be better to look at a mix of different capabilities, but that's growing too at about the same rate, see https://epoch.ai/eci

    • > unlike a human, their overall capabilities grow over time

      This is incorrect. Unless there is some new developments I'm unaware of (entirely possible) LLMs "learn" during the training phase, but after that they are static. They do not improve further or retain information when used for inference.

      You might be confused because AI companies keep releasing new models and tinkering with the harnesses, sometimes under the same name such that "Zern 6" (or whatever) doesn't always mean the same thing.

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I completely get the doomer POV, but we've somehow navigated all the previous "dangerous" technologies we've created - electricity, phones, internet - every one of those had similar arguments and concerns of danger.

The optimists' argument:-

Politics:- in general, I think many of the problems in the world today are due to misinformation and lack of education. What happens when we start routing things through an ASI that brings data and logic to the table? What happens when politicians can no longer lie without being caught out live on air? In the UK, local authorities are being flooded with complaints and requests from people; for example, some are doing AI-assisted investigations into accounting "errors".

Science:- I just don't see how the current rate of progress doesn't end up in crazy technologies like perfectly simulated human cells, organs and bodies to the point where we can run experiments virtually and solve all diseases in the next few years. This is happening. Perfect weather predictions far into the future, likewise with earthquakes, etc. Solar panel research explosion resulting in huge efficiency gains, to the point where people no longer need to plug their EV in - car surfaces will be covered in solar panels, as will our windows and roofs. Connecting new homes to the grid will be optional - the same way landline phones are no longer a thing.

I just find it very difficult not to extrapolate all the above.

We got this dump of mathematical breakthroughs from one small team in one company with access to this technology. What happens when this SOTA model is available (and it will continue getting better and cheaper) to everyone working on hard problems - every university on the planet starts cranking out AI-assisted research breakthroughs.

  • > What happens when politicians can no longer lie without being caught out live on air?

    If there is perfect lie detecting technology I could see all kinds of chaos resulting from it. I can't see it only be applied only to politicians, and I think it would be the developers of the technology who decide the use.

    I think were we disagree is that you sort of see AI as an extension of technological progress whereas I see it more like an extension of evolution. I view the process of AI training as functioning in a similar way to evolution in that it build circuits into neural networks similar to how evolution built circuits into human brains.

  • This is great.

    >> What happens when politicians can no longer lie without being caught out live on air?

    A 5-second delay on a politician's presser. Any lies will be muted in real time and the actual facts presented onscreen. Continue to lie enough, and the politician gets unstreamed.

  • > but we've somehow navigated all the previous "dangerous" technologies we've created

    It's only true if you believe that "putting the burden of living on a dying planet on the future generations" counts as "navigating".

The technology can keep going for a long time in verifiable areas. For non-verifiable areas it's going to have a hard time progressing past where a committee of the best human experts in a field would land. For stylistic areas, whatever the AI doesn't do will have cachet because it will look expensive, sort of like how the kids these days view the ugly old school metal braces as a status symbol because you have to pay for them out of pocket (even as by past standards it'd be truly exceptional).

I'm a transhumanist, I want to build god and kill death. To me this looks like we are moving in the right direction.

So basically I think that the future is getting pretty weird because we are building really powerful tools, though these tools are precisely what allows us to prosper in that future.

  • It's very unlikely that a superintelligent AI will create unlimited prosperity for everyone on a finite world in a short amount of time. You may not be among the lucky ones.

  • It may soon seem not worth living forever with our limited monkey-brains, watching the horizon of thought recede ever-faster from us.

  • Or, conversely, they are the exact tools that will allow the powerful folks to not care about the peasants anymore. History tells us what happens then.

  • > I'm a transhumanist, I want to build god and kill death.

    This is good and admirable, but it'd really suck if by trying to build god without knowing how we end the human species. We could simply wait some more decades until we actually have any idea what we're doing, and then do that without the risk.

I think we crossed plenty of lines were we will not get back to.

Software development for example as a task is done. And AI is continuesly reducing the price of more and more tasks every day.

This math breakthrough also shifts something significant: Its now a lot clearer that investment means money into energy to run AI.

Money + Energy = progress

I don't see it plateuing at all. We know how to progress. We broke through a wall we hit. Like the system wasn't able to optimize/automate everything because the tools were not there. It was still cheaper and easier to hire people for a LOT of things.

Now AI fills this gap.

You will see the commodification of everything in the next 15 years. High complex tasks? commodity. Physical labor? commodity.

For me it's a mixed bag.

There will be a lot of job loss unquestionably, in the same way that automation reduced manufacturing jobs and farm payrolls.

At the same time we have to put what AI can do in perspective.

Intelligence is a broad grouping that includes concepts such as knowledge, skill, experience, and wisdom.

AI has incredible knowledge and in many areas approximates experience and wisdom.

But wisdom is harder to formalize than knowledge and skill.

For example certifying a college education relies mostly on the ease with which we can verify/test knowledge.

To some extent advanced degrees try to certify maybe wisdom and experience.

In my very personal opinion, wisdom and life experience should give humans an edge for a while to come.

Additionally, I do feel that the more an individual lacks better than average wisdom and experience, the harder it will be for that person to compete with AI.

Also, on the bright side, the average human will continue to prefer to interact with a fellow human in many spheres. That will also act as an upper bound on AI and robots taking every job.

Either way, I do think this transition will be painful. I don't feel it has to be apocalyptic.

But the world has been an especially volatile place over the last 10 years.

So when you add that existing volatility, to the upheaval from the AI transition, it would not surprise me if the transition results in violence.

But, without the pre-existing volatility, and if humans were capable of generosity and love at scale, I see no reason AI can not be absorbed into society with net gain.

I guess to summarize, I feel this tech should be a net gain and to the extent that it isn't, it will be because of flaws deep inside of humanity itself, not because it had to end in chaos.

In other words, I feel fear, greed, anxiety, and competition -- all our base instincts coming from all sides, will be what determine the end result of AI moreso than AI taking everyone's job.

AI hater here:

I have a hard time taking statements from OpenAI about their own product, that they are trying to sell to people and make money, seriously. I take these statements as they are greatly exaggerated or even straight up lies and propaganda.

That said, I think results like these are mostly annoying more then anything. They spent a lot of money, used up gigartiuan amount of compute, to ruin a puzzle that mathematicians were tackling. I am mostly unsurprised that if you spend a trillion times the energy that a team of mathematicians would, that you get maybe 1.5 times the results. I see a future where that 1.5 times the results may go to 3x but not much more. And if that, then I will be more annoyed.

  • A trillion times the energy might be a bit hyperbolic, even with the current massive amounts of energy involved here

    • Yes it is intentionally hyperbolic. I know the factor is several orders of magnitude. I don‘t know the exact, nor even the ballpark. I just know this is a ridiculously large amount, so I may as well pick a number large enough that people know it is an exaggeration.

  • did we always know that computer can do the thinking for us if we allocate them enough resources? i don’t think so. so even if computers are more expensive than humans the fact that they can play the same game is surprising and (relatively) novel.

    • I don‘t think it is this simple. I think there is a subset of problems (namely ones that can utilize automatic solver or some other kinds of automatic testers and verifiers) where reaching the solution is correlated with the spent energy.

      Maybe people will find some clever way to expand this domain of AI-solvable problems by a couple of more categories, or (more likely) find a clever way of using applying these verifier for problems that was previously not viable, thus changing the solution to “just spend more energy computing dummy”. However I think this too will have its limits.

      Regardless, this is still annoying and I want them to stop doing this. Solving math problems should not be relegated to whoever has the most money to spend the most compute.

  • I think giving the world a "cheat code" to accomplish too many intellectual tasks will eventually take its toll on us because after relegating most of our physical labour to machines, we could at least marvel at the abilities of individuals.

    Yes, human beings can do more now in some ways but...to put it poetically, I think there will be no more heroes like Einstein and Newton of the past. Now it will just be someone cleverly turning the crank.

    Yes, we still admire Usain Bolt even though we have cars...but maybe the admiration is a lot more trivial than if we did not have them....

    Personally, I think AI is a grand mistake.

    • “ Yes, human beings can do more now in some ways but...to put it poetically, I think there will be no more heroes like Einstein and Newton of the past. Now it will just be someone cleverly turning the crank.”

      This is false… there’s lots of ingenuity to be had and demonstrated. But it’ll only get recognised if it makes a material contribution to the economy imo. Otherwise yes it’ll be seen as meh - but that’s already happening.

      People like Einstein were revered in society. The average person cannot name a leading scientist etc today.

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You need to clarify whether you are a doomer or a denier/truther? Doomer = p(doom). Denier/truther = Ed Zitron.

I think physics will be the limiting factor. Even if something recursively self improves, it will hit a physical wall allowed by circuits, batteries etc. A lot of the fear is that there’s an upper bound we don’t know about, whether it be time or energy, that allows a fast takeoff to occur fast enough that we dong have time to see it coming. I don’t know about that… so I’m not worried at this point.