"We make a tentative calibration of the self-sustaining ac
celeration condition using the existing data that is available, measuring AI capabilities using the Epoch Capabilities Index (Ho et al., 2025).
We find that the condition is met if a one-unit increase in AI model capabilities results in at least 15% higher AI R&D pro ductivity.
A rough back-of-the-envelope calculation based on reported AI engineer uplift
suggests this return has been around 9% since the launch of coding agents.
This number is below the model-implied threshold, suggesting we are not experiencing a self-sustaining acceleration."
And the source of this data seems to be self-reported productivity gains from
surveys: 1.4–2X in METR’s survey of technical workers (Becker, 2026).
A bit flimsy basis but an interesting paper nonetheless.
When I hear recursive self improvement all I remember are the ridiculous articles a few years ago about how 3d printers were going to make themselves and take over the world.
So because some optimistic wacko said something implausible in the past you dismiss the entire topic and have nothing to contribute to the conversation. Gotcha.
RSI isn't anything new though; computers have been used to make computers better for about 80 years now.
Imagine having a secretary who could read 1 million records and give you back your answer in 100 microseconds, for just 10 cents an hour. That's Postgres.
So I'd imagine that is R&D can be automated, that everything becomes better and cheaper but we'd all lose our jobs, as secretaries did to postgres. UBI season
> Imagine having a secretary who could read 1 million records and give you back your answer in 100 microseconds, for just 10 cents an hour. That's Postgres.
Well, that secretary can only answer very specific questions in a rather peculiar format.
> [...] but we'd all lose our jobs, as secretaries did to postgres.
I doubt many secretaries were replaced by postgres.
"I doubt many secretaries were replaced by postgres."
The maximal relative share of typists, secretaries, admin assistants et al. in the US workforce was 4% and this apogee was reached in 1980 after ~ 100 years of sustained growth. In 1980, the curve bent and started declining as systematically as it used to rise. Now it is less than half of that. So are other office roles.
(Look at the "The Rise and Fall of Office Work" graph in the middle of the page.)
Given the neat correlation with the tech revolution, I'd be surprised if there wasn't a causal relation. Maybe not exactly with Postgres, but with automation in general. Data collection and management does not involve nearly as many people as it once did.
That would be a failure of imagination when it comes to everything secretaries actually do. Which is a problem with the idea that AI is coming for all our jobs anytime soon. It totally undersells everything humans do on the job.
We could have coffee made today by automated coffee vending machines, but many people still prefer to go to a coffee shop. Why is that?
Sometimes we don’t choose the cheaper automated product, and instead opt for the “human experience”. I don’t imagine this will change any time soon. Humans like being around humans.
If that is true, then, why is it that the "human experience" has been on decline for decades - since the very moment it became possible to start cutting down on it?
If "humans like being around humans", why does human behavior say otherwise?
Estimates on the count of close friendships, amount of acquaintances, dating, sexual activity, etc - all falling.
People have fewer and fewer relationships, and even fewer close or intimate relationships. "Third places" are in decline, demand-induced - social activity takes place "in person" less and less, and more and more of it moves "online".
If your idea of "human experience" is "close relationship in person" or "group activities offline", and not something like "a friend chat group with memes and games, most of them never met in person", "social media posting", "parasocial relationship with a streamer" or "public Minecraft and Roblox servers", then "human experience" is on decline by just about any metric available.
Those aren't new trends either. They predate things like companion/roleplay AI chatbots - which seem popular with Gen Z and Gen Alpha in particular.
How that new thing shakes out is still unclear, but modern AIs are probably the closest thing to a "human experience" one can get without involving a human at all. So there's even more room for the metrics to fall now - regardless of whether "hanging out in a streamer's chat" counts as "human experience" to you.
> Estimates on the count of close friendships, amount of acquaintances, dating, sexual activity, etc - all falling.
Personally I would blame this on smartphones and/or apps, not the people. There's a lot of people who report finding these things addictive, and Meta's getting sued right now by people who have shown that Meta knew the users were upset about being addicted to the platform:
Some ingredients for a good coffee still need to be fresh, like milk - refilled daily. And the machine cleaned. Might as well employ a human at this point.
Mostly because machine coffee tastes like shit, the incentives are off for it; its usually in a place where you cant get good coffee and they do not give a fuck if you like it or not, enjoy your caffeine that you are addicted to.
Nobody has a job that can be fully automated away. Instead, they might lose their job to automation when some non-automatable parts can be done by fewer people and the remaining non-automatable parts can be made optional and the resulting cost savings are enough to pay for a machine to do the automatable parts.
Lots of tasks that could be automated with current technology aren't, simply because there's a non-automatable task that requires a baseline labor force (e.g. widget maker gets jammed once in a while, need someone nearby to remove the jammed widget and restart the machine) which then becomes essentially free when used for ancillary tasks (e.g. sealing boxes full of widgets, which also draws attention to drops in widget output volume). And sometimes humans are simply the cheaper option.
No doubt many people will lose their jobs to AI, and others will have to accept wage cuts to compete with the falling cost of automation, but that doesn't necessarily mean the AI is doing the humans' job now, or that none of the original workforce stays.
> So I'd imagine that if R&D can be automated, everything becomes better and cheaper but we'd all lose our jobs, as secretaries did to postgres. UBI season
I don't have any formal training in economics, so I can't argue the for and against arguments listed in the Wikipedia page, but it is at least possible that having full automation means that the underclass gets left behind and ignored, cut out of all progress forever and without any help from UBI as we cease to be important to those who control the AI.
(Even owning shares in successful AI companies may not help: Tesla doesn't pay dividends, NVIDIA's dividends are tiny).
> But our models make it clear that such an [intelligence] explosion may not follow if there are diminishing returns (“ideas become harder to find”) or if feedback loops become bottlenecked.
How is this not obvious to everyone? As we advance it becomes more difficult to advance. You obviously make most advancements around the things that are easiest to improve. Then all the easy things are done. So you go onto the next easiest things. They're "the easy things" from that standpoint but that doesn't mean they aren't harder than "the easy things" when you started. Complexity increases as precision increases.
Because everyone's thinking around intelligence is incredibly muddled by a variety of factors, and no one is particularly motivated to actually correct anyone's mistaken notions on the matter.
I'm conflicted. On one hand I think we should more openly call people idiots and push back. On the other hand there's Descartes argument for idiots in good company.
I just wish all the people that claimed to care only about truth would actually care about truth. Feels like society is more that trope where someone says a joke to a crowd and no one hears it except one charismatic person who repeats it and gets all the laughs. In reality it feels like the repeated version of the joke doesn't even make sense, it is just vibes.
That RSI can be bottlenecked? I guess this is obvious to many people. Whether RSI will be bottlenecked (at some not very interesting stage) is another question.
Our intelligence was enough to turn some balding apes into atom-bomb wielders and astronauts over a few thousand generations.
Consider a hypothetical: it is possible to make a semiconductor clone of a human brain, that runs at semiconductor speeds, has semiconductor size-scale, and has a power requirement of the Landauer limit.
This copy would be smaller than a pea, and you'd get to pick on a sliding scale between "same power draw as human but thinks faster than us to the same multiplier that we jog faster than continental drift" or "thinks as fast as we do while using around a few µW of power".
We do not know how to do this. We don't know how far we are from figuring out how to do this. We do know* evolution made our brains, and we do use simulated evolution as a standard technique in machine learning.
But it may well be that just as no human knows how to design a human mind, we find the best AI we can make don't know how to make a better AI, at which point they're stumbling blind in the dark: while evolution is a neat method, it is limited, blind to what the best next step is at any moment.
* well, those of us who are not creationists, at least.
Well at least it seems pretty implausible to me that a machine learning model trained to reproduce human text can in principle generate something that is significantly above human text production ability. If the "pea-sized semiconductor brain" is not a surprisingly shallow problem that you can just solve by interpolating existing research, I don't really see the LLM-approach to AI being the thing that makes something like it happen.
>we do use simulated evolution as a standard technique in machine learning.
Well, not really. For large-scale AI models it's almost exclusively some form of gradient-based non-linear optimization. Genetic algorithms (which is just hill-climbing optimization with extra steps) and genetic programming (which is really cool and not well-understood) do not perform all that well in practice and I'm not aware of any notable applications.
> Well at least it seems pretty implausible to me that a machine learning model trained to reproduce human text can in principle generate something that is significantly above human text production ability.
These are not the only currently-in-use AI models. However, recent news has even this particular category of AI solving multiple previously unsolved Erdős problems.
> Well, not really. For large-scale AI models it's almost exclusively some form of gradient-based non-linear optimization. Genetic algorithms (which is just hill-climbing optimization with extra steps) and genetic programming (which is really cool and not well-understood) do not perform all that well in practice and I'm not aware of any notable applications.
I said "standard technique" rather than "best" for a reason ;)
This is proof-of-possibility: natural selection did it, we know how to mimic that, but we don't know enough to be sure we're doing it with a reward function that will actually give us minds like ours on an interesting timescale with a probability high enough to care about.
Autoregressive pretraining (text/images/video prediction) produces a foundational model. You can look at it as a highly compressed conditional probability distribution of the human brain output. The information-theoretically optimal compression of the data is a program that reproduces functionality of the process that generated the data.
So, it stands to reason (and observations) that such a model captures not only surface statistics of the data, but a part of functionality of the system that generated the data (the human brain for text, physics for video).
Because they depend on whether the rate of improvement of self-improvement outpaces the rate of increase in difficulty or not, and at some points they clearly do - e.g. a lot of skills makes the relative rate of subsequent improvement easier for a while.
It may seem obvious that it can't last, but showing the conditions where it can't still matters.
The point of a modeling exercise like this, which you don’t have to buy, is, first, to generate a simple set of initial conditions that can plausibly explain things we all see or intuit. Likewise, it is obvious that gravity exists, but a simple model that explains where it comes from (in quantum terms) would be a big breakthrough — if it came with plausibly testable implications that could be tested via experiment. But the second part (prediction) comes from the first. The (manipulable) starting conditions need to explain things we see to make subsequent predictions plausible/interesting.
To put it another way, the cost per advancement increases. That’s “as we advance it becomes more difficult to advance”. However, because of the prior advances, you also have more resources to throw at future advancements.
So, then, the question is whether the “profits” on the last advance are enough to pay for the next one. We can define a new term, “affordability”, as what % “profit” you can expect from each advance relative to its cost, telling us whether it becomes relatively easier or harder to continue to advance.
You could argue that there is a capital built up - but even that is difficult as a lot of the knowledge for, eg., building LLMs can not demand rent. Everybody can build their own transformer networks.
> To put it another way, the cost per advancement increases.
This is not true when you don't use capital that demands rent. On the contrary, the cost per advancement is actually decreasing as we develop more knowledge.
LLMs are a cognitive technology (as contrasted to a communication technology). And it will help us tremendously manage knowledge such that you can utilize it better decreasing the cost of advancement.
Whether or not it decreases the costs of advancedment would depend on what is causing advancement to have costs. Some things might be addressable by cognitive technologies, other might not.
If capital doesn't create a return evenutally, why would it invest? (Or did you mean "rent" in the narrow economics definition? But then not sure how that applies here).
Then use batteries as an example. Going on 200+ years now most of the major changes have been an easy swap: chemistry. Lead acid --> nickel cadium --> nickel metal hydride --> lithium ion. Improvements in batteries are hard and the surrounding ecosystem has gotten better. Better BMS and better motors have also made gains to increase the efficiency of the battery. Compounding gains within the ecosystem doesn't truly improve the battery directly, but improves them for implementation and use cases.
All the remaining problems with batteries are a hard solve because it's tradeoff after tradeoff. Lithium sulfur, for example, could be amazing but die after a couple hundred cycles at most [0]. So... If LLMs are so novel, as many continue to claim, why hasn't this been solved? Probably because LLMs are an interpretation of our current understanding of everything. An LLM currently only does what any human can do, albeit in a compressed timeframe, mostly.
I think it follows. LLMs have made some considerable improvements, but use "reasoning" (really just a mechanical, autoregressive, loop: generate tokens --> append tokens to context --> feed the expanded context back into the model --> generate the next token --> repeat) as an example. You can do this by hand as well in a traditional chat volley, but it's slower. So we improved LLMs by putting them in an automatic loop (oversimplification - but at the end of the day holds water).
Until LLMs showcase true external breakthroughs that aren't driven by human guidance (not happening anytime soon) we are in this loop of hacks being used to improve the 80% (the LLM itself). Notable jumps? "Reasoning" and with Mythos-like models we now have "Advanced Reasoning" (by leveraging a more capable looping framework such as an agentic harness).
It seems the rate of advancement has increased over time though. Like from ancient times to 1800s the rate of progress was slow, then basically a relative explosion of progress compressed in 200 years.
Because of access to a giant store of energy. Our advancement has also created a future need to consume more and more energy to fuel increased complexity.
> We have advanced tremendously over the past 200 years
Would most of that have happened if we hadn't found oil, though?
It's difficult to precisely identify how much of progress is owed to intelligence, and how much is simply energy availability. Energy is intrinsically transformative -- the dumbest of organisms can take over the world in a blitz if they can process available energy better than the others, whereas the smartest of entities is going to be ineffective without fuel. Intelligence can, of course, unlock the capability to exploit new energy sources, but there's still an intrinsic physical distribution of it that's outside of anyone's control.
Probably because it's not true. We had shitty neural networks for decades before the recent explosion. That particular branch may be a dead end, but there could be others lurking and waiting for their time.
People have other dreams, and have written about them in science fiction. But science fiction can just handwave away resource problems that exist in real life so that the plot can happen.
Just as the popular "grey goo" nanomachine disaster can easily be shown to be impossible due to resource imbalances and energy shortages, AI recursive self improvements rapidly slows down due to problems with complexity, training data and compute availability (whether due to actual processors or just due to energy demands).
whether or not the singularity would be best thought of as science fiction, it was not presented as such, and has not been taken as such by a great number of people, and has formed the basis of a lot of opinions as to how things will pan out.
It is these opinions that make the conclusion the parent poster supposed should be obvious to everyone not obvious to so many.
In a weak sense, singularities are common and should be expected every so often. In a mathematical sense, a singularity is where a model 'blows up' and neglected terms become part of the dominant balance.
Industrialization hit a point of diminishing returns, but the industrial revolution was nonetheless a 'singularity,' where life afterwards was qualitatively unpredictable to people who lived before. Likewise, agriculture was such a technological singularity to hunter-gatherer ancestors.
I could even make a decent argument that writing and literacy were such a singularity, making inconceivable social organizations routine.
In that weak sense I expect AI to be a singularity, recursive self improvement or no. Life in 2050 may be completely unpredictable to someone who was taken out of time in the year 2000.
The remaining questions are speed and intensity, and both of these questions are related to RSI. If RSI works, then the 'fast takeoff' visions become more plausible where society transforms over months to a few years – at least locally where the enabling technologies have diffused. If not, it might take a couple of decades.
"How is this not obvious to everyone? As we advance it becomes more difficult to advance. "
It's a type of recent change blindness. Because for the last few years there are seemingly impossible breakthroughs every few months. Literally, things everyone said would take 30 or 100 years, happen within months. It is really easy to think this will continue. The new normal. Really, we haven't seen it slow down at all, so why would we expect the miracles to stop all of a sudden?
It seems like accelerating, and decelerating, are both fair game as future directions at this point.
So it is nice that the paper put some weight behind the argument that this could all grind to a halt for a lot of different reasons beyond the hype.
> Literally, things everyone said would take 30 or 100 years, happen within months.
"Everyone"? Plenty of people in the 50s said we'd have sentient humanoid robots before the year 2000. And flying cars. Sure, many people underestimate the rate of change. But techno-optimists have consistently overestimated it, and they still do.
> As we advance it becomes more difficult to advance. You obviously make most advancements around the things that are easiest to improve. Then all the easy things are done.
This isn't some foregone conclusion. It completely depends on the rate at which the intelligence and abilities of the AI increases. If that rate was high enough, then the harder and harder problems would become easier and easier for it.
Actually, evolution seems to show the opposite: The rate of advancement has only sped up, with billions of years between significant changes going to millions, to thousands, to tens and arguably to mere years now.
Having said that, we're probably looking at an S-curve with the physical limits of reality getting in the way in the end.
Reminder that while there are many naysayers who have been on the wrong side of AGI development progress the last decade…
There are two $1T companies who are all-in on RSI internally right now. They are supported by $20T of market cap plowing R&D into their efforts. You can think it’s dumb money at your own peril, however the market rewards intelligent allocation…
The market can also be quick and brutal to punish mistakes, especially when leverage is high. We can be in 1998, but we can be in 1995; you stand to make a ton of money if you know which one we’re in
them having an actual product with huge demand makes them immune
Just like Tesla was immune from all the naysayers which were saying its a highly unprofitable company which will 100% go bankrupt because its economics dont make any sense, and they lost huge amounts of money shorting the stock
Tesla is profitable, but income per share has been very low relative to share price. People who were short Tesla weren't necessarily betting on a bankruptcy, just that shareholders wouldn't put up with the low ROI for much longer. And depending on exactly when they shorted it, they might've actually made a profit.
In contrast, AI companies that are actually unprofitable are dependent on continuously raising additional money to sustain their operations, so a sudden drop in market confidence could become a self-fulfilling prophecy as it makes it more difficult to raise money which makes the business more risky which decreases market confidence in a downward spiral.
An actual product with huge demand is not enough to avert bankruptcy, you also need to serve that demand profitably (like Tesla does).
“The market rewards intelligent allocation” is such a straightforwardly false statement that I can’t believe anyone still says this with a straight face. The last ten years of the US economy have just been scam after scam after scam, and people just keep saying this.
NFTs were worth more than $1 trillion so we know that they were "better" than the Ai efforts of today because “The market rewards intelligent allocation”
Notice: viXra.org only accepts scholarly articles written without AI assistance. Please go to ai.viXra.org to submit new scholarly article written with AI assistance or rxiVerse.org to submit new research article written with or without AI assistance.
69 comments
[ 4.6 ms ] story [ 48.3 ms ] threadWe find that the condition is met if a one-unit increase in AI model capabilities results in at least 15% higher AI R&D pro ductivity.
A rough back-of-the-envelope calculation based on reported AI engineer uplift suggests this return has been around 9% since the launch of coding agents.
This number is below the model-implied threshold, suggesting we are not experiencing a self-sustaining acceleration."
And the source of this data seems to be self-reported productivity gains from surveys: 1.4–2X in METR’s survey of technical workers (Becker, 2026).
A bit flimsy basis but an interesting paper nonetheless.
Imagine having a secretary who could read 1 million records and give you back your answer in 100 microseconds, for just 10 cents an hour. That's Postgres.
So I'd imagine that is R&D can be automated, that everything becomes better and cheaper but we'd all lose our jobs, as secretaries did to postgres. UBI season
Well, that secretary can only answer very specific questions in a rather peculiar format.
> [...] but we'd all lose our jobs, as secretaries did to postgres.
I doubt many secretaries were replaced by postgres.
However, you might like reading about https://en.wikipedia.org/wiki/Unit_record_equipment
The maximal relative share of typists, secretaries, admin assistants et al. in the US workforce was 4% and this apogee was reached in 1980 after ~ 100 years of sustained growth. In 1980, the curve bent and started declining as systematically as it used to rise. Now it is less than half of that. So are other office roles.
https://forklightning.substack.com/p/the-past-present-and-fu...
(Look at the "The Rise and Fall of Office Work" graph in the middle of the page.)
Given the neat correlation with the tech revolution, I'd be surprised if there wasn't a causal relation. Maybe not exactly with Postgres, but with automation in general. Data collection and management does not involve nearly as many people as it once did.
"No, you don't get it, it's me specifically who has a job that's way too special to be automated away!"
Sometimes we don’t choose the cheaper automated product, and instead opt for the “human experience”. I don’t imagine this will change any time soon. Humans like being around humans.
If "humans like being around humans", why does human behavior say otherwise?
People have fewer and fewer relationships, and even fewer close or intimate relationships. "Third places" are in decline, demand-induced - social activity takes place "in person" less and less, and more and more of it moves "online".
If your idea of "human experience" is "close relationship in person" or "group activities offline", and not something like "a friend chat group with memes and games, most of them never met in person", "social media posting", "parasocial relationship with a streamer" or "public Minecraft and Roblox servers", then "human experience" is on decline by just about any metric available.
Those aren't new trends either. They predate things like companion/roleplay AI chatbots - which seem popular with Gen Z and Gen Alpha in particular.
How that new thing shakes out is still unclear, but modern AIs are probably the closest thing to a "human experience" one can get without involving a human at all. So there's even more room for the metrics to fall now - regardless of whether "hanging out in a streamer's chat" counts as "human experience" to you.
Personally I would blame this on smartphones and/or apps, not the people. There's a lot of people who report finding these things addictive, and Meta's getting sued right now by people who have shown that Meta knew the users were upset about being addicted to the platform:
https://news.ycombinator.com/item?id=48817682
Lots of tasks that could be automated with current technology aren't, simply because there's a non-automatable task that requires a baseline labor force (e.g. widget maker gets jammed once in a while, need someone nearby to remove the jammed widget and restart the machine) which then becomes essentially free when used for ancillary tasks (e.g. sealing boxes full of widgets, which also draws attention to drops in widget output volume). And sometimes humans are simply the cheaper option.
No doubt many people will lose their jobs to AI, and others will have to accept wage cuts to compete with the falling cost of automation, but that doesn't necessarily mean the AI is doing the humans' job now, or that none of the original workforce stays.
Perhaps, but there's a thing in economics, the "resource curse": https://en.wikipedia.org/wiki/Resource_curse
I don't have any formal training in economics, so I can't argue the for and against arguments listed in the Wikipedia page, but it is at least possible that having full automation means that the underclass gets left behind and ignored, cut out of all progress forever and without any help from UBI as we cease to be important to those who control the AI.
(Even owning shares in successful AI companies may not help: Tesla doesn't pay dividends, NVIDIA's dividends are tiny).
It's important to recognize that LLMs accelerating development of LLMs does not imply it will lead to self-sustaining acceleration.
Because everyone's thinking around intelligence is incredibly muddled by a variety of factors, and no one is particularly motivated to actually correct anyone's mistaken notions on the matter.
I just wish all the people that claimed to care only about truth would actually care about truth. Feels like society is more that trope where someone says a joke to a crowd and no one hears it except one charismatic person who repeats it and gets all the laughs. In reality it feels like the repeated version of the joke doesn't even make sense, it is just vibes.
That RSI can be bottlenecked? I guess this is obvious to many people. Whether RSI will be bottlenecked (at some not very interesting stage) is another question.
Consider a hypothetical: it is possible to make a semiconductor clone of a human brain, that runs at semiconductor speeds, has semiconductor size-scale, and has a power requirement of the Landauer limit.
This copy would be smaller than a pea, and you'd get to pick on a sliding scale between "same power draw as human but thinks faster than us to the same multiplier that we jog faster than continental drift" or "thinks as fast as we do while using around a few µW of power".
We do not know how to do this. We don't know how far we are from figuring out how to do this. We do know* evolution made our brains, and we do use simulated evolution as a standard technique in machine learning.
But it may well be that just as no human knows how to design a human mind, we find the best AI we can make don't know how to make a better AI, at which point they're stumbling blind in the dark: while evolution is a neat method, it is limited, blind to what the best next step is at any moment.
* well, those of us who are not creationists, at least.
>we do use simulated evolution as a standard technique in machine learning.
Well, not really. For large-scale AI models it's almost exclusively some form of gradient-based non-linear optimization. Genetic algorithms (which is just hill-climbing optimization with extra steps) and genetic programming (which is really cool and not well-understood) do not perform all that well in practice and I'm not aware of any notable applications.
These are not the only currently-in-use AI models. However, recent news has even this particular category of AI solving multiple previously unsolved Erdős problems.
> Well, not really. For large-scale AI models it's almost exclusively some form of gradient-based non-linear optimization. Genetic algorithms (which is just hill-climbing optimization with extra steps) and genetic programming (which is really cool and not well-understood) do not perform all that well in practice and I'm not aware of any notable applications.
I said "standard technique" rather than "best" for a reason ;)
This is proof-of-possibility: natural selection did it, we know how to mimic that, but we don't know enough to be sure we're doing it with a reward function that will actually give us minds like ours on an interesting timescale with a probability high enough to care about.
So, it stands to reason (and observations) that such a model captures not only surface statistics of the data, but a part of functionality of the system that generated the data (the human brain for text, physics for video).
It may seem obvious that it can't last, but showing the conditions where it can't still matters.
I don't think this follows.
We have advanced tremendously over the past 200 years, and we are likely going into a time with rapid advancement again.
With advancement, we also develop tools (eg. Llms) that assist advancing.
So, then, the question is whether the “profits” on the last advance are enough to pay for the next one. We can define a new term, “affordability”, as what % “profit” you can expect from each advance relative to its cost, telling us whether it becomes relatively easier or harder to continue to advance.
> To put it another way, the cost per advancement increases.
This is not true when you don't use capital that demands rent. On the contrary, the cost per advancement is actually decreasing as we develop more knowledge.
LLMs are a cognitive technology (as contrasted to a communication technology). And it will help us tremendously manage knowledge such that you can utilize it better decreasing the cost of advancement.
If capital doesn't create a return evenutally, why would it invest? (Or did you mean "rent" in the narrow economics definition? But then not sure how that applies here).
All the remaining problems with batteries are a hard solve because it's tradeoff after tradeoff. Lithium sulfur, for example, could be amazing but die after a couple hundred cycles at most [0]. So... If LLMs are so novel, as many continue to claim, why hasn't this been solved? Probably because LLMs are an interpretation of our current understanding of everything. An LLM currently only does what any human can do, albeit in a compressed timeframe, mostly.
I think it follows. LLMs have made some considerable improvements, but use "reasoning" (really just a mechanical, autoregressive, loop: generate tokens --> append tokens to context --> feed the expanded context back into the model --> generate the next token --> repeat) as an example. You can do this by hand as well in a traditional chat volley, but it's slower. So we improved LLMs by putting them in an automatic loop (oversimplification - but at the end of the day holds water).
Until LLMs showcase true external breakthroughs that aren't driven by human guidance (not happening anytime soon) we are in this loop of hacks being used to improve the 80% (the LLM itself). Notable jumps? "Reasoning" and with Mythos-like models we now have "Advanced Reasoning" (by leveraging a more capable looping framework such as an agentic harness).
[0] https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.20...
Would most of that have happened if we hadn't found oil, though?
It's difficult to precisely identify how much of progress is owed to intelligence, and how much is simply energy availability. Energy is intrinsically transformative -- the dumbest of organisms can take over the world in a blitz if they can process available energy better than the others, whereas the smartest of entities is going to be ineffective without fuel. Intelligence can, of course, unlock the capability to exploit new energy sources, but there's still an intrinsic physical distribution of it that's outside of anyone's control.
Yes, oil was important back then. Now we can augment with wind, solar, nuclear, etc.
And more technologies will come.
Probably because it's not true. We had shitty neural networks for decades before the recent explosion. That particular branch may be a dead end, but there could be others lurking and waiting for their time.
because people have other ideas https://en.wikipedia.org/wiki/Technological_singularity
Just as the popular "grey goo" nanomachine disaster can easily be shown to be impossible due to resource imbalances and energy shortages, AI recursive self improvements rapidly slows down due to problems with complexity, training data and compute availability (whether due to actual processors or just due to energy demands).
It is these opinions that make the conclusion the parent poster supposed should be obvious to everyone not obvious to so many.
Depends on specifics and how dramatic they're being, given that "mold infestation" can come in grey.
In a weak sense, singularities are common and should be expected every so often. In a mathematical sense, a singularity is where a model 'blows up' and neglected terms become part of the dominant balance.
Industrialization hit a point of diminishing returns, but the industrial revolution was nonetheless a 'singularity,' where life afterwards was qualitatively unpredictable to people who lived before. Likewise, agriculture was such a technological singularity to hunter-gatherer ancestors.
I could even make a decent argument that writing and literacy were such a singularity, making inconceivable social organizations routine.
In that weak sense I expect AI to be a singularity, recursive self improvement or no. Life in 2050 may be completely unpredictable to someone who was taken out of time in the year 2000.
The remaining questions are speed and intensity, and both of these questions are related to RSI. If RSI works, then the 'fast takeoff' visions become more plausible where society transforms over months to a few years – at least locally where the enabling technologies have diffused. If not, it might take a couple of decades.
It's a type of recent change blindness. Because for the last few years there are seemingly impossible breakthroughs every few months. Literally, things everyone said would take 30 or 100 years, happen within months. It is really easy to think this will continue. The new normal. Really, we haven't seen it slow down at all, so why would we expect the miracles to stop all of a sudden?
It seems like accelerating, and decelerating, are both fair game as future directions at this point.
So it is nice that the paper put some weight behind the argument that this could all grind to a halt for a lot of different reasons beyond the hype.
"Everyone"? Plenty of people in the 50s said we'd have sentient humanoid robots before the year 2000. And flying cars. Sure, many people underestimate the rate of change. But techno-optimists have consistently overestimated it, and they still do.
"everyone", being, a typical average of realistic predictions.
In the 90's. I remember people talking about voice recognition will take 50 years. Let alone understanding. Now this is an easy base line feature.
Let alone protein folding.
This isn't some foregone conclusion. It completely depends on the rate at which the intelligence and abilities of the AI increases. If that rate was high enough, then the harder and harder problems would become easier and easier for it.
Having said that, we're probably looking at an S-curve with the physical limits of reality getting in the way in the end.
There are two $1T companies who are all-in on RSI internally right now. They are supported by $20T of market cap plowing R&D into their efforts. You can think it’s dumb money at your own peril, however the market rewards intelligent allocation…
Just like Tesla was immune from all the naysayers which were saying its a highly unprofitable company which will 100% go bankrupt because its economics dont make any sense, and they lost huge amounts of money shorting the stock
In contrast, AI companies that are actually unprofitable are dependent on continuously raising additional money to sustain their operations, so a sudden drop in market confidence could become a self-fulfilling prophecy as it makes it more difficult to raise money which makes the business more risky which decreases market confidence in a downward spiral.
An actual product with huge demand is not enough to avert bankruptcy, you also need to serve that demand profitably (like Tesla does).
if I sell 1 token at $1 and there are a trillion of them minted, that does not make a $1T market cap
but if you want to prove a point against AI with fake data, sure
Space data centres are the panicle of intelligent allocation.