I see people saying that these kinds of things are happening behind closed doors, but I haven't seen any convincing evidence of it, and there is enormous propensity for AI speculation to run rampant.
Anthropic recently released research where they saw how when Claude attempted to compose poetry, it didn't simply predict token by token and "react" to when it thought it might need a rhyme and then looked at its context to think of something appropriate, but actually saw several tokens ahead and adjusted for where it'd likely end up, ahead of time.
Anthropic also says this adds to evidence seen elsewhere that language models seem to sometimes "plan ahead".
Please check out the section "Planning in poems" here; it's pretty interesting!
https://transformer-circuits.pub/2025/attribution-graphs/bio...
The most sure things we know is that it is a physical system, and that does feel like something to be one of these systems.
As others have pointed out in other threads RLHF has progressed beyond next-token prediction and modern models are modeling concepts [1].
[0] https://metr.org/blog/2025-03-19-measuring-ai-ability-to-com...
[1] https://www.anthropic.com/news/tracing-thoughts-language-mod...
Intelligence as humans have it seems like a "know it when you see it" thing to me, and metrics that attempt to define and compare it will always be looking at only a narrow slice of the whole picture. To put it simply, the gut feeling I get based on my interactions with current AI, and how it is has developed over the past couple of years, is that AI is missing key elements of general intelligence at its core. While there's more lots more room for its current approaches to get better, I think there will be something different needed for AGI.
I'm not an expert, just a human.
It reminds me of the difference between a fresh college graduate and an engineer with 10 years of experience. There are many really smart and talented college graduates.
But, while I am struggling to articulate exactly why, I know that when I was a fresh graduate, despite my talent and ambition, I would have failed miserably at delivering some of the projects that I now routinely deliver over time periods of ~1.5 years.
I think LLM's are really good at emulating the types of things I might say are the types of things that would make someone successful at this if I were to write it down in a couple paragraphs, or an article, or maybe even a book.
But... knowing those things as written by others just would not quite cut it. Learning at those time scales is just very different than what we're good at training LLM's to do.
A college graduate is in many ways infinitely more capable than a LLM. Yet there are a great many tasks that you just can't give an intern if you want them to be successful.
There are at least half a dozen different 1000-page manuals that one must reference to do a bare bones approach at my job. And there are dozens of different constituents, and many thousands of design parameters I must adhere to. Fundamentally, all of these things often are in conflict and it is my job to sort out the conflicts and come up with the best compromise. It's... really hard to do. Knowing what to bend so that other requirements may be kept rock solid, who to negotiate with for different compromises needed, which fights to fight, and what a "good" design looks like between alternatives that all seem to mostly meet the requirements. Its a very complicated chess game where it's hopelessly impossible to brute force but you must see the patterns along the way that will point you like sign posts into a good position in the end game.
The way we currently train LLM's will not get us there.
Until an LLM can take things in it's context window, assess them for importance, dismiss what doesn't work or turns out to be wrong, completely dismiss everything it knows when the right new paradigm comes up, and then permanently alter its decision making by incorporating all of that information in an intelligent way, it just won't be a replacment for a human being.
I'd label that difference as long-term planning plus executive function, and wherever that overlaps with or includes delegation.
Most long-term projects are not done by a single human and so delegation almost always plays a big part. To delegate, tasks must be broken down in useful ways. To break down tasks a holistic model of the goal is needed where compartmentalization of components can be identified.
I think a lot of those individual elements are within reach of current model architectures but they are likely out of distribution. How many gantt charts and project plans and project manager meetings are in the pretraining datasets? My guess is few; rarely published internal artifacts. Books and articles touch on the concepts but I think the models learn best from the raw data; they can probably tell you very well all of the steps of good project management because the descriptions are all over the place. The actual doing of it is farther toward the tail of the distribution.
The signs are not there but while we may not be on an exponential curve (which would be difficult to see), we are definitely on a steep upward one which may get steeper or may fizzle out if LLM's can only reach human level intelligence but not surpass it. Original article was a fun read though and 360,000 words shorter than my very similar fiction novel :-)
I don't really get this. Are you saying autoregressive LLMs won't qualify as AGI, by definition? What about diffusion models, like Mercury? Does it really matter how inference is done if the result is the same?
No, I am speculating that they will not reach capabilities that qualify them as AGI.
IMO this out of distribution learning is all we need to scale to AGI. Sure there are still issues, it doesn't always know which distribution to pick from. Neither do we, hence car crashes.
[1]: https://arxiv.org/pdf/2303.12712 or on YT https://www.youtube.com/watch?v=qbIk7-JPB2c
During the GPT-3 era there was plenty of organic text to scale into, and compute seemed to be the bottleneck. But we quickly exhausted it, and now we try other ideas - synthetic reasoning chains, or just plain synthetic text for example. But you can't do that fully in silico.
What is necessary in order to create new and valuable text is exploration and validation. LLMs can ideate very well, so we are covered on that side. But we can only automate validation in math and code, but not in other fields.
Real world validation thus becomes the bottleneck for progress. The world is jealously guarding its secrets and we need to spend exponentially more effort to pry them away, because the low hanging fruit has been picked long ago.
If I am right, it has implications on the speed of progress. Exponential friction of validation is opposing exponential scaling of compute. The story also says an AI could be created in secret, which is against the validation principle - we validate faster together, nobody can secretly outvalidate humanity. It's like blockchain, we depend on everyone else.
They clearly mention, take into account and extrapolate this; LLM have first scaled via data, now it's test time compute, but recent developments (R1) clearly show this is not exhausted yet (i.e. RL on synthetically (in-silico) generated CoT) which implies scaling with compute. The authors then outline further potential (research) developments that could continue this dynamic, literally things that have already been discovered just not yet incorporated into edge models.
Real-world data confirms their thesis - there have been a lot of sceptics about AI scaling, somewhat justified ("whoom" a.k.a. fast take-off hasn't happened - yet) but their fundamental thesis has been wrong - "real-world data has been exhausted, next algorithmic breakthroughs will be hard and unpredictable". The reality is, while data has been exhausted, incremental research efforts have resulted in better and better models (o1, r1, o3, and now Gemini 2.5 which is a huge jump! [1]). This is similar to how Moore's Law works - it's not given that CPUs get better exponentially, it still requires effort, maybe with diminishing returns, but nevertheless the law works...
If we ever get to models be able to usefully contribute to research, either on the implementation side, or on research ideas side (which they CANNOT yet, at least Gemini 2.5 Pro (public SOTA), unless my prompting is REALLY bad), it's about to get super-exponential.
Edit: then once you get to actual general intelligence (let alone super-intelligence) the real-world impact will quickly follow.
Of course you can get a lot of mileage via synthetically generated CoT but does that lead to LLM speed up developing LLM is a big IF.
> OpenBrain focuses on AIs that can speed up AI research. They want to win the twin arms races against China (whose leading company we’ll call “DeepCent”)16 and their US competitors. The more of their research and development (R&D) cycle they can automate, the faster they can go. So when OpenBrain finishes training Agent-1, a new model under internal development, it’s good at many things but great at helping with AI research.
> It’s good at this due to a combination of explicit focus to prioritize these skills, their own extensive codebases they can draw on as particularly relevant and high-quality training data, and coding being an easy domain for procedural feedback.
> OpenBrain continues to deploy the iteratively improving Agent-1 internally for AI R&D. Overall, they are making algorithmic progress 50% faster than they would without AI assistants—and more importantly, faster than their competitors.
> what do we mean by 50% faster algorithmic progress? We mean that OpenBrain makes as much AI research progress in 1 week with AI as they would in 1.5 weeks without AI usage.
To me, claiming today's AI IS capable of such thing is too hand-wavy. And I think that's the crux of the article.
Thanks for this.
I've not spent too long thinking on the following, so I'm prepared for someone to say I'm totally wrong, but:
I feel like the services economy can be broadly broken down into: pleasure, progress and chores. Pleasure being poetry/literature, movies, hospitality, etc; progress being the examples you gave like science/engineering, mathematics; and chore being things humans need to coordinate or satisfy an obligation (accountants, lawyers, salesmen).
In this case, if we assume AI can deal with things not in the grey zone, then it can deal with 'progress' and many 'chores', which are massive chunks of human output. There's not much grey zone to them. (Well, there is, but there are many correct solutions; equivalent pieces of code that are acceptable, multiple versions of a tax return, each claiming different deductions, that would fly by the IRS, etc)
Even this is questionable, cause we're seeing it making forms and solving leetcodes, but no llm yet created a new approach, reduced existing unnecessary complexity (which we created mountains of), made something truly new in general. All they seem to do is rehash of millions of "mainstream" works, and AAA isn't mainstream. Cranking up the parameter count or the time of beating around the bush (aka cot) doesn't magically substitute for lack of a knowledge graph with thick enough edges, so creating a next-gen AAA video game is far out of scope of llm's abilities. They are stuck in 2020 office jobs and weekend open source tech, programming-wise.
At current rate of progress, I really do think in another 6 months they'll be pretty good at tackling technical debt and overcomplication, at least in codebases that have good unit/integration test coverage or are written in very strongly typed languages with a type-friendly structure. (Of course, those usually aren't the codebases needing significant refactoring, but I think AIs are decent at writing unit tests against existing code too.)
Von Neumann for example was incredibly brilliant, yet his brain presumably ran on roughly the same power budget as anyone else's. I mean, did he have to eat mountains of food to fuel those thoughts? ;)
So it looks like massive gains in intelligence or capability might not require proportionally massive increases in fundamental inputs at least at the highest levels of intelligence a human can reach, and if that's true for the human brain why not for other architecture of intelligence.
P.S. It's funny, I was talking about something along the lines of what you said with a friend just a few minutes before reading your comment so when I saw it I felt that I had to comment :)
And we haven't run out of all data. High-quality text data may be exhausted, but we have many many life-years worth of video. Being able to predict visual imagery means building a physical world model. Combine this passive observation with active experimentation in simulated and real environments and you get millions of hours of navigating and steering a causal world. Deepmind has been hooking up their models to real robots to let them actively explore and generate interesting training data for a long time. There's more to DL than LLMs.
The problems it raises - alignment, geopolitics, lack of societal safeguards - are all real, and happening now (just replace “AGI” with “corporations”, and voila, you have a story about the climate crisis and regulatory capture). We should be solving these problems before AGI or job-replacing AI becomes commonplace, lest we run the very real risk of societal collapse or species extinction.
The point of these stories is to incite alarm, because they’re trying to provoke proactive responses while time is on our side, instead of trusting self-interested individuals in times of great crisis.
lest we run the very real risk of societal collapse or species extinction
Our part is here. To be replaced with machines if this AI thing isn't just a fart advertised as mining equipment, which it likely is. We run this risk, not they. People worked on their wealth, people can go f themselves now. They are fine with all that. Money (=more power) piles in either way.
No encouraging conclusion.
I like that it ends with a reference to Kushiel and Elua though.
I agree that it's good science fiction, but this is still taking it too seriously. All of these "projections" are generalizing from fictional evidence - to borrow a term that's popular in communities that push these ideas.
Long before we had deep learning there were people like Nick Bostrom who were pushing this intelligence explosion narrative. The arguments back then went something like this: "Machines will be able to simulate brains at higher and higher fidelity. Someday we will have a machine simulate a cat, then the village idiot, but then the difference between the village idiot and Einstein is much less than the difference between a cat and the village idiot. Therefore accelerating growth[...]" The fictional part here is the whole brain simulation part, or, for that matter, any sort of biological analogue. This isn't how LLMs work.
We never got a machine as smart as a cat. We got multi-paragraph autocomplete as "smart" as the average person on the internet. Now, after some more years of work, we have multi-paragraph autocomplete that's as "smart" as a smart person on the internet. This is an imperfect analogy, but the point is that there is no indication that this process is self-improving. In fact, it's the opposite. All the scaling laws we have show that progress slows down as you add more resources. There is no evidence or argument for exponential growth. Whenever a new technology is first put into production (and receives massive investments) there is an initial period of rapid gains. That's not surprising. There are always low-hanging fruit.
We got some new, genuinely useful tools over the last few years, but this narrative that AGI is just around the corner needs to die. It is science fiction and leads people to make bad decisions based on fictional evidence. I'm personally frustrated whenever this comes up, because there are exciting applications which will end up underfunded after the current AI bubble bursts...
I think the growth you are thinking of, self improving AI, needs the AI to be as smart as a human developer/researcher to get going and we haven't got there yet. But we quite likely will at some point.
I think you misunderstood that argument. The simulate the brain thing isn't a "start from the beginning" argument, it's an "answer a common objection" argument.
Back around 2000, when Nick Bostrom was talking about this sort of thing, computers were simply nowhere near powerful enough to come even close to being smart enough to outsmart a human, except in very constrained cases like chess; we did't even have the first clue how to create a computer program to be even remotely dangerous to us.
Bostrom's point was that, "We don't need to know the computer program; even if we just simulate something we know works -- a biological brain -- we can reach superintelligence in a few decades." The idea was never that people would actually simulate a cat. The idea is, if we don't think of anything more efficient, we'll at least be able to simulate a cat, and then an idiot, and then Einstein, and then something smarter. And since we almost certainly will think of something more efficient than "simulate a human brain", we should expect superintelligence to come much sooner.
> There is no evidence or argument for exponential growth.
Moore's law is exponential, which is where the "simulate a brain" predictions have come from.
> It is science fiction and leads people to make bad decisions based on fictional evidence.
The only "fictional evidence" you've actually specified so far is the fact that there's no biological analog; and that (it seems to me) is from a misunderstanding of a point someone else was making 20 years ago, not something these particular authors are making.
I think the case for AI caution looks like this:
A. It is possible to create a superintelligent AI
B. Progress towards a superintelligent AI will be exponential
C. It is possible that a superintelligent AI will want to do something we wouldn't want it to do; e.g., destroy the whole human race
D. Such an AI would be likely to succeed.
Your skepticism seems to rest on the fundamental belief that either A or B is false: that superintelligence is not physically possible, or at least that progress towards it will be logarithmic rather than exponential.
Well, maybe that's true and maybe it's not; but how do you know? What justifies your belief that A and/or B are false so strongly, that you're willing to risk it? And not only willing to risk it, but try to stop people who are trying to think about what we'd do if they are true?
What evidence would cause you to re-evaluate that belief, and consider exponential progress towards superintelligence possible?
And, even if you think A or B are unlikely, doesn't it make sense to just consider the possibility that they're true, and think about how we'd know and what we could do in response, to prevent C or D?
To address only one thing out of your comment, Moore's law is not a law, it is a trend. It just gets called a law because it is fun. We know that there are physical limits to Moore's law. This gets into somewhat shaky territory, but it seems that current approaches to compute can't reach the density of compute power present in a human brain (or other creatures' brains). Moore's law won't get chips to be able to simulate a human brain, with the same amount of space and energy as a human brain. A new approach will be needed to go beyond simply packing more transistors onto a chip - this is analogous to my view that current AI technology is insufficient to do what human brains do, even when taken to their limit (which is significantly beyond where they're currently at).
The problem with this argument is that it's assuming that we're on a linear track to more and more intelligent machines. What we have with LLMs isn't this kind of general intelligence.
We have multi-paragraph autocomplete that's matching existing texts more and more closely. The resulting models are great priors for any kind of language processing and have simple reasoning capabilities in so far as those are present in the source texts. Using RLHF to make the resulting models useful for specific tasks is a real achievement, but doesn't change how the training works or what the original training objective was.
So let's say we continue along this trajectory and we finally have a model that can faithfully reproduce and identify every word sequence in its training data and its training data includes every word ever written up to that point. Where do we go from here?
Do you want to argue that it's possible that there is a clever way to create AGI that has nothing to do with the way current models work and that we should be wary of this possibility? That's a much weaker argument than the one in the article. The article extrapolates from current capabilities - while ignoring where those capabilities come from.
> And, even if you think A or B are unlikely, doesn't it make sense to just consider the possibility that they're true, and think about how we'd know and what we could do in response, to prevent C or D?
This is essentially https://plato.stanford.edu/entries/pascal-wager/
It might make sense to consider, but it doesn't make sense to invest non-trivial resources.
This isn't the part that bothers me at all. I know people who got grants from, e.g., Miri to work on research in logic. If anything, this is a great way to fund some academic research that isn't getting much attention otherwise.
The real issue is that people are raising ridiculous amounts of money by claiming that the current advances in AI will lead to some science fiction future. When this future does not materialize it will negatively affect funding for all work in the field.
And that's a problem, because there is great work going on right now and not all of it is going to be immediately useful.
This is a fundamental misunderstanding of the entire point of predictive models (and also of how LLMs are trained and tested).
For one thing, ability to faithfully reproduce texts is not the primary scoring metric being used for the bulk of LLM training and hasn't been for years.
But more importantly, you don't make a weather model so that it can inform you of last Tuesday's weather given information from last Monday, you use it to tell you tomorrow's weather given information from today. The totality of today's temperatures, winds, moistures, and shapes of broader climatic patterns, particulates, albedos, etc etc etc have never happened before, and yet the model tells us something true about the never-before-seen consequences of these never-before-seen conditions, because it has learned the ability to reason new conclusions from new data.
Are today's "AI" models a glorified autocomplete? Yeah, but that's what all intelligence is. The next word I type is the result of an autoregressive process occurring in my brain that produces that next choice based on the totality of previous choices and experiences, just like the Q-learners that will kick your butt in Starcraft choose the best next click based on their history of previous clicks in the game combined with things they see on the screen, and will have pretty good guesses about which clicks are the best ones even if you're playing as Zerg and they only ever trained against Terran.
A highly accurate autocomplete that is able to predict the behavior and words of a genius, when presented with never before seen evidence, will be able to make novel conclusions in exactly the same way as the human genius themselves would when shown the same new data. Autocomplete IS intelligence.
New ideas don't happen because intelligences draw them out of the aether, they happen because intelligences produce new outputs in response to stimuli, and those stimuli can be self-inputs, that's what "thinking" is.
If you still think that all today's AI hubbub is just vacuous hype around an overblown autocomplete, try going to Chatgpt right now. Click the "deep research" button, and ask it "what is the average height of the buildings in [your home neighborhood]"?, or "how many calories are in [a recipe that you just invented]", or some other inane question that nobody would have ever cared to write about ever before but is hypothetically answerable from information on the internet, and see if what you get is "just a reproduced word sequence from the training data".
OK, I think I see where you're coming from. It sounds like what you're saying is:
E. LLMs only do multi-paragraph autocomplete; they are and always will be incapable of actual thinking.
F. Any approach capable of achieving AGI will be completely different in structure. Who knows if or when this alternate approach will even be developed; and if it is developed, we'll be starting from scratch, so we'll have plenty of time to worry about progress then.
With E, again, it may or may not be true. It's worth noting that this is a theoretical argument, not an empirical one; but I think it's a reasonable assumption to start with.
However, there are actually theoretical reasons to think that E may be false. The best way to predict the weather is to have an internal model which approximates weather systems; the best way to predict the outcome of a physics problem is to have an internal model which approximates the physics of the thing you're trying to predict. And the best way to predict what a human would write next is to have a model of a human mind -- including a model of what the human mind has in its model (e.g., the state of the world).
There is some empirical data to support this argument, albeit in a very simplified manner: They trained a simple LLM to predict valid moves for Othello, and then probed it and discovered an internal Othello board being simulated inside the neural network:
https://thegradient.pub/othello/
And my own experience with LLMs better match the "LLMs have an internal model of the world" theory than the "LLMs are simply spewing out statistical garbage" theory.
So, with regard to E: Again, sure, LLMs may turn out to be a dead end. But I'd personally give the idea that LLMs are a complete dead end a less than 50% probability; and I don't think giving it an overwhelmingly high probability (like 1 in a million of being false) is really reasonable, given the theoretical arguments and empirical evidence against it.
With regard to F, again, I don't think this is true. We've learned so much about optimizing and distilling neural nets, optimizing training, and so on -- not to mention all the compute power we've built up. Even if LLMs are a dead end, whenever we do find an architecture capable of achieving AGI, I think a huge amount of the work we've put into optimizing LLMs will put is way ahead in optimizing this other system.
> ...that the current advances in AI will lead to some science fiction future.
I mean, if you'd told me 5 years ago that I'd be able to ask a computer, "Please use this Golang API framework package to implement CRUD operations for this particular resource my system has", and that the resulting code would 1) compile out of the box, 2) exhibit an understanding of that resource and how it relates to other resources in the system based on having seen the code implementing those resources 3) make educated guesses (sometimes right, sometimes wrong, but always reasonable) about details I hadn't specified, I don't think I would have believed you.
Even if LLM progress is logarithmic, we're already living in a science fiction future.
EDIT: The scenario actually has very good technical "asides"; if you want to see their view of how a (potentially dangerous) personality emerges from "multi-paragraph auto-complete", look at the drop-down labelled "Alignment over time", and specifically what follows "Here’s a detailed description of how alignment progresses over time in our scenario:".
Could you provide examples? I am genuinely interested.
A self-driving car would already be plenty.
This just isn't correct. Daniel and others on the team are experienced world class forecasters. Daniel wrote another version of this in 2021 predicting the AI world in 2026 and was astonishingly accurate. This deserves credence.
https://www.lesswrong.com/posts/6Xgy6CAf2jqHhynHL/what-2026-...
>he arguments back then went something like this: "Machines will be able to simulate brains at higher and higher fidelity.
Complete misunderstanding of the underlying ideas. Just in not even wrong territory.
>We got some new, genuinely useful tools over the last few years, but this narrative that AGI is just around the corner needs to die. It is science fiction and leads people to make bad decisions based on fictional evidence.
You are likely dangerously wrong. The AI field is near universal in predicting AGI timelines under 50 years. With many under 10. This is an extremely difficult problem to deal with and ignoring it because you think it's equivalent to overpopulation on mars is incredibly foolish.
https://www.metaculus.com/questions/5121/date-of-artificial-...
https://wiki.aiimpacts.org/doku.php?id=ai_timelines:predicti...
I'm also struck by the extent to which the first series from 2021-2026 feels like a linear extrapolation while the second one feels like an exponential one, and I don't see an obvious justification for this.
Dude was spot on in 2021, hot damn.
Can you point to the data that suggests these evil corporations are ruining the planet? Carbon emissions are down in every western country since 1990s. Not down per-capita, but down in absolute terms. And this holds even when adjusting for trade (i.e. we're not shipping our dirty work to foreign countries and trading with them). And this isn't because of some regulation or benevolence. It's a market system that says you should try to produce things at the lowest cost and carbon usage is usually associated with a cost. Get rid of costs, get rid of carbon.
Other measures for Western countries suggests the water is safer and overall environmental deaths have decreased considerably.
The rise in carbon emissions is due to Chine and India. Are you talking about evil Chinese and Indians corporations?
The climate regulations are still quite weak. Without a proper carbon tax, a US company can externalize the costs of carbon emissions and get rich by maximizing their own emissions.
And I think the neuroticism around this topic has led young people into some really dark places (anti-depressants, neurotic anti social behavior, general nihilism). So I think it's important to fight misinformation about end of world doomsday scenarios with both facts and common sense.
Not all brains function like they're supposed to, people getting help they need shouldn't be stigmatized.
You also make no argument about your take on things being the right one, you just oppose their worldview to yours and call theirs wrong like you know it is rather than just you thinking yours is right.
No one is stigmatizing anything. Just that if you consume doom porn it's likely to affect your attitudes towards life. I think it's a lot healthier to believe you can change your circumstances than to believe you are doomed because you believe you have the wrong brain
https://www.nature.com/articles/s41380-022-01661-0
https://www.quantamagazine.org/the-cause-of-depression-is-pr...
https://www.ucl.ac.uk/news/2022/jul/analysis-depression-prob...
Can you point to data that this is 'because' of corporations rather than despite them.
Large corporations, governments, institutionalized churches, political parties, and other “corporate” institutions are very much like a hypothetical AGI in many ways: they are immortal, sleepless, distributed, omnipresent, and possess beyond human levels of combined intelligence, wealth, and power. They are mechanical Turk AGIs more or less. Look at how humans cycle in, out, and through them, often without changing them much, because they have an existence and a weird kind of will independent of their members.
A whole lot, perhaps all, of what we need to do to prepare for a hypothetical AGI that may or may not be aligned consists of things we should be doing to restrain and ensure alignment of the mechanical Turk variety. If we can’t do that we have no chance against something faster and smarter.
What we have done over the past 50 years is the opposite: not just unchain them but drop any notion that they should be aligned.
Are we sure the AI alignment discourse isn’t just “occulted” progressive political discourse? Back when they burned witches philosophers would encrypt possibly heretical ideas in the form of impenetrable nonsense, which is where what we call occultism comes from. You don’t get burned for suggesting steps to align corporate power, but a huge effort has been made to marginalize such discourse.
Consider a potential future AGI. Imagine it has a cult of followers around it, which it probably would, and champions that act like present day politicians or CEOs for it, which it probably would. If it did not get humans to do these things for it, it would have analogous functions or parts of itself.
Now consider a corporation or other corporate entity that has all those things but replace the AGI digital brain with a committee or shareholders.
What, really, is the difference? Both can be dangerously unaligned.
Other than perhaps in magnitude? The real digital AGI might be smarter and faster but that’s the only difference I see.
We know that Trump is not captured by corporations because his trade policies are terrible.
If anything, social media is the evil that's destroying the political center: Americans are no longer reading mainstream newspapers or watching mainstream TV news.
The EU is saying the elections in Romania was manipulated through manipulation of TikTok accounts and media.
If it’s somehow different for corporations, please enlighten me how.
Taxes are the best way to change behaviour (smaller cars driving less. Less flying etc). So government and the people who vote for them is to blame.
I think this view of humans - that they look at all the available information and then make calm decisions in their own interests - is simply wrong. We are manipulated all the damn time. I struggle to go to the supermarket without buying excess sugar. The biggest corporations in the world grew fat off showing us products to impulse buy before our more rational brain functions could stop us. We are not a little pilot in a meat vessel.
US corporate tax rates are actually every high. Partly due to the US having almost no consumption tax. EU members have VAT etc.
I wonder if that's corporations' fault after all: shitty working conditions and shitty wages, so that Bezos can afford to send penises into space. What poor person would agree to higher tax on gas? And the corps are the ones backing politicians who'll propagandize that "Unions? That's communism! Do you want to be Chaina?!" (and spread by those dickheads on the corporate-owned TV and newspaper, drunk dickheads who end up becoming defense secretary)
So corporations are involved in the sense that they pay people more than a living wage.
Have you seen gas tax rates in the EU?
> We know that Trump is not captured by corporations because his trade policies are terrible.
Unless you think it's a long con for some rich people to be able to time the market by getting him to crash it.
> The EU is saying the elections in Romania was manipulated through manipulation of TikTok accounts and media.
More importantly, Romanian courts say that too. And it was all out in the open, so not exactly a secret
I'm pretty sure the election was manipulated, but the court only said so because it benefits the incumbents, which control the courts and would lose their power.
It's a struggle between local thieves and putin, that's all. The local thieves will keep us in the EU, which is much better than the alternative, but come on. "More importantly, Romanian courts say so"? Really?
Why do you think that's the only reason the court said so? The election law was pretty blatantly violated (he declared campaign funding of 0, yet tons of ads were bought for him and influencers paid to advertise him).
But it's par on course. Write prompts for LLMs to compete? It's prompt engineering. Tell LLMs to explain their "reasoning" (lol)? It's Deep Research Chain Of Thought. Etc.
There might be (strongly) diminishing returns past a certain point.
Most of the growth in AI capabilities has to do with improving the interface and giving them more flexibility. For e.g., uploading PDFs. Further: OpenAI's "deep research" which can browse the web for an hour and summarize publicly-available papers and studies for you. If you ask questions about those studies, though, it's hardly smarter than GPT-4. And it makes a lot of mistakes. It's like a goofy but earnest and hard-working intern.
No, there is no risk of species extinction in the near future due to climate change and repeating the line will just further the divide and make the people not care about other people's and even real climate scientist's words.
That sounds like the height of folly.
There is a non-zero chance that the ineffable quantum foam will cause a mature hippopotamus to materialize above your bed tonight, and you’ll be crushed. It is incredibly, amazingly, limits-of-math unlikely. Still a non-zero risk.
Better to think of “no risk” as meaning “negligible risk”. But I’m with you that climate change is not a negligible risk; maybe way up in the 20% range IMO. And I wouldn’t be sleeping in my bed tonight if sudden hippos over beds were 20% risks.
And if Asian culture is better educated and more capable of progress, that’s a good thing. Certainly the US has announced loud and clear that this is the end of the line for us.
Was Asian culture dominated by the west to any significant degree? Perhaps in countries like India where the legal and parliamentary system installed by the British remained intact for a long time post-independence.
Elsewhere in East and Southeast Asia, the legal systems, education, cultural traditions, and economic philosophies have been very different from the "west", i.e. post-WWII US and Western Europe.
The biggest sign of this is how they developed their own information networks, infrastructure and consumer networking devices. Europe had many of these regional champions themselves (Phillips, Nokia, Ericsson, etc) but now outside of telecom infrastructure, Europe is largely reliant on American hardware and software.
https://x.com/RnaudBertrand/status/1901133641746706581
I finally watched Ne Zha 2 last night with my daughters.
It absolutely lives up to the hype: undoubtedly the best animated movie I've ever seen (and I see a lot, the fate of being the father of 2 young daughters ).
But what I found most fascinating was the subtle yet unmistakable geopolitical symbolism in the movie.
Warning if you haven't yet watched the movie: spoilers!
So the story is about Ne Zha and Ao Bing, whose physical bodies were destroyed by heavenly lightning. To restore both their forms, they must journey to the Chan sect—headed by Immortal Wuliang—and pass three trials to earn an elixir that can regenerate their bodies.
The Chan sect is portrayed in an interesting way: a beacon of virtue that all strive to join. The imagery unmistakably refers to the US: their headquarters is an imposingly large white structure (and Ne Zha, while visiting it, hammers the point: "how white, how white, how white") that bears a striking resemblance to the Pentagon in its layout. Upon gaining membership to the Chan sect, you receive a jade green card emblazoned with an eagle that bears an uncanny resemblance to the US bald eagle symbol. And perhaps most telling is their prized weapon, a massive cauldron marked with the dollar sign...
Throughout the movie you gradually realize, in a very subtle way, that this paragon of virtue is, in fact, the true villain of the story. The Chan sect orchestrates a devastating attack on Chentang Pass—Ne Zha's hometown—while cunningly framing the Dragon King of the East Sea for the destruction. This manipulation serves their divide-and-conquer strategy, allowing them to position themselves as saviors while furthering their own power.
One of the most pointed moments comes when the Dragon King of the East Sea observes that the Chan sect "claims to be a lighthouse of the world but harms all living beings."
Beyond these explicit symbols, I was struck by how the film portrays the relationships between different groups. The dragons, demons, and humans initially view each other with suspicion, manipulated by the Chan sect's narrative. It's only when they recognize their common oppressor that they unite in resistance and ultimately win. The Chan sect's strategy of fostering division while presenting itself as the arbiter of morality is perhaps the key message of the movie: how power can be maintained through control of the narrative.
And as the story unfolds, Wuliang's true ambition becomes clear: complete hegemony. The Chan sect doesn't merely seek to rule—it aims to establish a system where all others exist only to serve its interests, where the dragons and demons are either subjugated or transformed into immortality pills in their massive cauldron. These pills are then strategically distributed to the Chan sect's closest allies (likely a pointed reference to the G7).
What makes Ne Zha 2 absolutely exceptional though is that these geopolitical allegories never overshadow the emotional core of the story, nor its other dimensions (for instance it's at times genuinely hilariously funny). This is a rare film that makes zero compromise, it's both a captivating and hilarious adventure for children and a nuanced geopolitical allegory for adults.
And the fact that a Chinese film with such unmistakable anti-American symbolism has become the highest-grossing animated film of all time globally is itself a significant geopolitical milestone. Ne Zha 2 isn't just breaking box office records—it's potentially rewriting the rules about what messages can dominate global entertainment.
I hope we're wrong about a lot of this, and AGI turns out to either be impossible, or much less useful than we think it will be. I hope we end up in a world where humans' value increases, instead of decreasing. At a minimum, if AGI is possible, I hope we can imbue it with ethics that allow it to make decisions that value other sentient life.
Do I think this will actually happen in two years, let alone five or ten or fifty? Not really. I think it is wildly optimistic to assume we can get there from here - where "here" is LLM technology, mostly. But five years ago, I thought the idea of LLMs themselves working as well as they do at speaking conversational English was essentially fiction - so really, anything is possible, or at least worth considering.
"May you live in interesting times" is a curse for a reason.
We spend the best 40 years of our lives working 40-50 hours a week to enrich the top 0.1% while living in completely artificial cities. People should wonder what is the point of our current system instead of worrying about Terminator tier sci fi system that may or may not come sometimes in the next 5 to 200 years
Like you say, people but more our govs need to worry about what is the point at this moment, not scifi in the future; this stuff has already bad enough to worry about. Working your ass off for diminishing returns , paying into a pension pot that won't make it until you retire etc is driving people to really focus on the now and why they would do these things. If you can just have fun with 500/mo and booze from your garden, why work hard and save up etc. I noticed even people from my birth country with these sentiments while they have it extraordinarily good for the eu standards but they are wondering why would they do all of this for nothing (...) more and more and cutting hours more and more. It seems more an education and communication thing really than anything else; it is like asking why pay taxes: if you are not well informed, it might feel like theft, but when you spell it out, most people will see how they benefit.
I’m led to believe that we see this stuff because the tiny subset of humanity that has the wealth and luxury to sit around thinking about thinking about themselves are worried that AI may disrupt the navel-gazing industry.
You may find this to be insightful: https://meltingasphalt.com/a-nihilists-guide-to-meaning/
In short, "meaning" is a contextual perception, not a discrete quality, though the author suggests it can be quantified based on the number of contextual connections to other things with meaning. The more densely connected something is, the more meaningful it is; my wedding is meaningful to me because my family and my partners family are all celebrating it with me, but it was an entirely meaningless event to you.
Thus, the meaningfulness of our contributions remains unchanged, as the meaning behind them is not dependent upon the perspective of an external observer.
Ultimately, "meaning" is a matter of "purpose", and purpose is a matter of having an end, or telos. The end of a thing is dependent on the nature of a thing. Thus, the telos of an oak tree is different from the telos of a squirrel which is different from that of a human being. The telos or end of a thing is a marker of the thing's fulfillment or actualization as the kind of thing it is. A thing's potentiality is structured and ordered toward its end. Actualization of that potential is good, the frustration of actualization is not.
As human beings, what is most essential to us is that we are rational and social animals. This is why we are miserable when we live lives that are contrary to reason, and why we need others to develop as human beings. The human drama, the human condition, is, in fact, our failure to live rationally, living beneath the dignity of a rational agent, and very often with knowledge of and assent to our irrational deeds. That is, in fact, the very definition of sin: to choose to act in a way one knows one should not. Mistakes aren't sins, even if they are per se evil, because to sin is to knowingly do what you should not (though a refusal to recognize a mistake or to pay for a recognized mistake would constitute a sin). This is why premeditated crimes are far worse than crimes of passion; the first entails a greater knowledge of what one is doing, while someone acting out of intemperance, while still intemperate and thus afflicted with vice, was acting out of impulse rather fully conscious intent.
So telos provides the objective ground for the "meaning" of acts. And as you may have noticed, implicitly, it provides the objective basis for morality. To be is synonymous with good, and actualization of potential means to be more fully.
Daniel Dennett - Information & Artificial Intelligence
https://www.youtube.com/watch?v=arEvPIhOLyQ
Daniel Dennett bridges the gap between everyday information and Shannon-Weaver information theory by rejecting propositions as idealized meaning units. This fixation on propositions has trapped philosophers in unresolved debates for decades. Instead, Dennett proposes starting with simple biological cases—bacteria responding to gradients—and recognizing that meaning emerges from differences that affect well-being. Human linguistic meaning, while powerful, is merely a specialized case. Neural states can have elaborate meanings without being expressible in sentences. This connects to AI evolution: "good old-fashioned AI" relied on propositional logic but hit limitations, while newer approaches like deep learning extract patterns without explicit meaning representation. Information exists as "differences that make a difference"—physical variations that create correlations and further differences. This framework unifies information from biological responses to human consciousness without requiring translation into canonical propositions.
>meaning behind them is not dependent upon the perspective of an external observer.
(Yes brother like cmon)
Regarding the author, I get the impression he grew up without a strong father figure? This isnt ad hominem I just get the feeling of someone who is so confused and lost in life that he is just severely depressed possibly related to his directionless life. He seems so confused he doesn't even take seriously the fact most humans find their own meaning in life and says hes not even going to consider this, finding it futile.( he states this near the top of the article ).
I believe his rejection of a simple basic core idea ends up in a verbal blurb which itself is directionless.
My opinion ( Which yes maybe more floored than anyones ), is to deal with Mazlows hierarchy, and then the prime directive for a living organism which after survival , which is reproduction. Only after this has been achieved can you then work towards your family community and nation.
This may seem trite, but I do believe that this is natural for someone with a relatively normal childhood.
My aim is not to disparage, its to give me honest opinion of why I disagree and possible reasons for it. If you disagree with anything I have said please correct me.
Thanks for sharing the article though it was a good read - and I did struggle myself with meaning sometimes.
Aha, you might say, but they hold leadership roles! They have positions of authority! Of course they have meaning, as they wield spiritual responsibility to their community as a fine substitute for the family life they will not have.
To that, I suggest looking deeper, at the nuns and monks. To a cynical non-believer, they surely are wanting for a point to their existence, but to them, what they do is a step beyond Maslow's self actualization, for they live in communion with God and the saints. Their medications and good works in the community are all expressions of that purpose, not the other way around. In short, though their "graph of contextual meaning" doesn't spread as far, it is very densely packed indeed.
Two final thoughts:
1) I am both aware of and deeply amused by the use of priests and nuns and monks to defend the arguments of a nihilist's search for meaning.
2) I didn't bring this up so much to take the conversation off topic, so much as to hone in on the very heart of what troubled the person I originally responded to. The question of purpose, the point of existence, in the face of superhuman AI is in fact unchanged. The sense of meaning and purpose one finds in life is found not in the eyes of an unfeeling observer, whether the observers are robots or humans. It must come from within.
For me personally, I hope that we do get AGI. I just don't want it by 2027. That feels way too fast to me. But AGI 2070 or 2100? That sounds much more preferable.
For a sizable number of humans, we're already there. The vast majority of hacker news users are spending their time trying to make advertisements tempt people into spending money on stuff they don't need. That's an active societal harm. It doesn't contribute in any positive way to the world.
And yet, people are fine to do that, and get their dopamine hits off instagram or arguing online on this cursed site, or watching TV.
More people will have bullshit jobs in this SF story, but a huge number of people already have bullshit jobs, and manage to find a point in their existence just fine.
I, for one, would be happy to simply read books, eat, and die.
At the same time, I wouldn't necessarily say that people are currently fine getting dopamine hits from social media. Coping would probably be a better description. There are a lot of social and societal problems that have been growing at a rapid rate since Facebook and Twitter began tapping into the reward centers of the brain.
From a purely anecdotal perspective, I find my mood significantly affected by how productive and impactful I am with how I spend my time. I'm much happier when I'm making progress on something, whether it's work or otherwise.
If basically a transformer, that means it needs at inference time ~200T flops per token. The paper assumes humans "think" at ~15 tokens/second which is about 10 words, similar to the reading speed of a college graduate. So that would be ~3 petaflops of compute per second.
Assuming that's fp8, an H100 could do ~4 petaflops, and the authors of AI 2027 guesstimate that purpose wafer scale inference chips circa late 2027 should be able to do ~400petaflops for inference, ~100 H100s worth, for ~$600k each for fabrication and installation into a datacenter.
Rounding that basically means ~$6k would buy you the compute to "think" at 10 words/second. Generally speaking that'd probably work out to maybe $3k/yr after depreciation and electricity costs, or ~30-50¢/hr of "human thought equivalent" 10 words/second. Running an AI at 50x human speed 24/7 would cost ~$23k/yr, so 1 OpenBrain researcher's salary could give them a team of ~10-20 such AIs running flat out all the time. Even if you think the AI would need an "extra" 10 or even 100x in terms of tokens/second to match humans, that still puts you at genius level AIs in principle runnable at human speed for 0.1 to 1x the median US income.
There's an open question whether training such a model is feasible in a few years, but the raw compute capability at the chip level to plausibly run a model that large at enormous speed at low cost is already existent (at the street price of B200's it'd cost ~$2-4/hr-human-equivalent).
And I think training is similar — training is capital intensive therefore centralized, but if 100m people are paying $6k for their inference hardware, add on $100/year as a training tax (er, subscription) and you’ve got $10B/year for training operations.
But even so, solving that problem feels much more attainable than it used to be.
I assume that thanks to the universal approximation theorem it’s theoretically possible to emulate the physical mechanism, but at what hardware and training cost? I’ve done back of the napkin math on this before [1] and the number of “parameters” in the brain is at least 2-4 orders of magnitude more than state of the art models. But that’s just the current weights, what about the history that actually enables the plasticity? Channel threshold potentials are also continuous rather than discreet and emulating them might require the full fp64 so I’m not sure how we’re even going to get to the memory requirements in the next decade, let alone whether any architecture on the horizon can emulate neuroplasticity.
Then there’s the whole problem of a true physical feedback loop with which the AI can run experiments to learn against external reward functions and the core survival reward function at the core of evolution might itself be critical but that’s getting deep into the research and philosophy on the nature of intelligence.
we'll likely reach a point where it's infeasible for deep learning to completely encompass human-level reasoning, and we'll need neuroscience discoveries to continue progress. altman seems to be hyping up "bigger is better," not just for model parameters but openai's valuation.
EDIT: holy crap I just discovered a commonly known thing about exponents and log. Leaving comment here but it is wrong, or at least naive.
My solution to the alignment problem is that an ASI could just stick us in tubes deep in the Earth’s crust—it just needs to hijack our nervous system to input signals from the simulation. The ASI could have the whole rest of the planet, or it could move us to some far off moon in the outer solar system—I don’t care. It just needs to do two things for it’s creators—preserve lives and optimize for long term human experience.
Yeah nah, theres a key thing missing here, the number of jobs created needs to be more than the ones it's destroyed, and they need to be better paying and happen in time.
History says that actually when this happens, an entire generation is yeeted on to the streets (see powered looms, Jacquard machine, steam powered machine tools) All of that cheap labour needed to power the new towns and cities was created by automation of agriculture and artisan jobs.
Dark satanic mills were fed the decedents of once reasonably prosperous crafts people.
AI as presented here will kneecap the wages of a good proportion of the decent paying jobs we have now. This will cause huge economic disparities, and probably revolution. There is a reason why the royalty of Europe all disappeared when they did...
So no, the stock market will not be growing because of AI, it will be in spite of it.
Plus china knows that unless they can occupy most of its population with some sort of work, they are finished. AI and decent robot automation are an existential threat to the CCP, as much as it is to what ever remains of the "west"
I theorise that revolution would be near-impossible in post-AGI world. If people consider where power comes from it's relatively obvious that people will likely suffer and die on mass if we ever create AGI.
Historically the general public have held the vast majority of power in society. 100+ years ago this would have been physical power – the state has to keep you happy or the public will come for them with pitchforks. But in an age of modern weaponry the public today would be pose little physical threat to the state.
Instead in todays democracy power comes from the publics collective labour and purchasing power. A government can't risk upsetting people too much because a government's power today is not a product of its standing army, but the product of its economic strength. A government needs workers to create businesses and produce goods and therefore the goals of government generally align with the goals of the public.
But in an post-AGI world neither businesses or the state need workers or consumers. In this world if you want something you wouldn't pay anyone for it or workers to produce it for you, instead you would just ask your fleet of AGIs to get you the resource.
In this world people become more like pests. They offer no economic value yet demand that AGI owners (wherever publicly or privately owned) share resources with them. If people revolted any AGI owner would be far better off just deploying a bioweapon to humanely kill the protestors rather than sharing resources with them.
Of course, this is assuming the AGI doesn't have it's own goals and just sees the whole of humanely as nuance to be stepped over in the same way humans will happy step over animals if they interfere with our goals.
Imo humanity has 10-20 years left max if we continue on this path. There can be no good outcome of AGI because it would even make sense for the AGI or those who control the AGI to be aligned with goals of humanity.
This is a very doomer take. The threats are real, and I'm certain some people feel this way, but eliminating large swaths of humanity is something dicatorships have tried in the past.
Waking up every morning means believing there are others who will cooperate with you.
Most of humanity has empathy for others. I would prefer to have hope that we will make it through, rather than drown in fear.
Tried, and succeeded in. In times where people held more power than today. Not sure what point you're trying to make here.
> Most of humanity has empathy for others. I would prefer to have hope that we will make it through, rather than drown in fear.
I agree that most of humanity has empathy for others — but it's been shown that the prevalence of psychopaths increases as you climb the leadership ladder.
Fear or hope are the responses of the passive. There are other routes to take.
If the many have access to the latest AI then there is less chance the masses are blindsided by some rogue tech.
Technology changes things though. Things aren't "the same as it ever was". The Napoleonic wars killed 6.5 million people with muskets and cannons. The total warfare of WWII killed 70 to 85 million people with tanks, turboprop bombers, aircraft carriers, and 36 kilotons TNT of Atomic bombs, among other weaponry.
Total war today includes modern thermonuclear weapons. In 60 seconds, just one Ohio class submarine can launch 80 independent warheads, totaling over 36 megatons of TNT. That is over 20 times more than all explosives, used by all sides, for all of WWII, including both Atomic bombs.
AGI is a leap forward in power equivalent to what thermonuclear bombs are to warfare. Humans have been trying to destroy each other for all of time but we can only have one nuclear war, and it is likely we can only have one AGI revolt.
Like if you're truly afraid of this, what are you doing here on HN? Go organize and try to do something about this.
It is the same with Gen AI. We will either find a way to control an entity that rapidly becomes orders of magnitude more intelligent than us, or we won’t. We will either find a way to prevent the rich and powerful from controlling a Gen AI that can build and operate anything they need, including an army to protect them from everyone without a powerful Gen AI, or we won’t.
I hope for a future of abundance for all, brought to us by technology. But I understand that some existential threats only need to turn the wrong way once, and there will be no second chance ever.
>It is the same with Gen AI. We will either find a way to control an entity that rapidly becomes orders of magnitude more intelligent than us, or we won’t. We will either find a way to prevent the rich and powerful from controlling a Gen AI that can build and operate anything they need, including an army to protect them from everyone without a powerful Gen AI, or we won’t
Okay, you've laid out two paths here. What are *you* doing to influence the course we take? That's my point. Enumerating all the possible ways humanity faces extinction is nothing more than doomerism if you aren't taking any meaningful steps to lessen the likelihood any of them may occur.
I agree but for a different reason. It's very hard to outsmart an entity with an IQ in the thousands and pervasive information gathering. For a revolution you need to coordinate. The Chinese know this very well and this is why they control communication so closely (and why they had Apple restrict AirDrop). But their security agencies are still beholden to people with average IQs and the inefficient communication between them.
An entity that can collect all this info on its own and have a huge IQ to spot patterns and not have to communicate it to convince other people in its organisation to take action, that will crush any fledgling rebellion. It will never be able to reach critical mass. We'll just be ants in an anthill and it will be the boot that crushes us when it feels like it.
That will be quite a hard thing to pull off, even for some evil person with a AGI. Let's say Putin gets AGI and is actually evil and crazy enough to try wipe people out. If he just targets Russians and starts killing millions of people daily with some engineered virus or something similar, he'll have to fear a strike from the West which would be fearful they're next (and rightfully so). If he instead tries to wipe out all of humanity at once to escape a second strike, he again will have to devise such a good plan there won't be any second strike - meaning his "AGI" will have to be way better than all other competing AGIs (how exactly?).
It would have made sense if all "owners of AGI" somehow conspired together to do this but there's not really such a thing as owners of AGI and even if there was Chinese, Russian and American owners of AGI don't trust each other at all and are also bound to their governments.
Like we can satisfy the hunting and retrieval instincts of dogs by throwing a stick, surely an AI that is 10,000 times more intelligent can devise a stick-retrieval-task for humans in a way that feels like satisfying achievement and meaningful work from our perspective.
(Leaving aside the question of whether any of that is a likely or desirable outcome.)
I feel the limitations of humans are quite a feature when you think about what the experience of life would be like if you couldn’t forget or experienced things for the first time. If you already knew everything and you could achieve almost anything with zero effort. It actually sounds…insufferable.
History hasnt had to contend with a birth rate of 0.7-1.6.
It's kind of interesting that the elite capitalist media (economist, bloomberg, forbes, etc) is projecting a future crisis of both not enough workers and not enough jobs simultaneously.
It's totally a great thing if we start plateauing our population and even reduce it a bit. And no we're not going extinct. It'll just cause some temporary issues like an ageing population that has to be cared for but those issues are much more readily fixable than environmental destruction.
Japan is currently in the finding out phase of this problem.
Demographic shift will certainly upset the status quo, but we will figure out how to deal with it.
Overcrowded cities and housing costs aren't an overpopulation problem but a problem of concentrating economic activity in certain places.
Also: people deride infinite growth, but growth is what is responsible for lifting large portions of the population out of poverty. If global markets were repriced tomorrow to expect no future growth, economies would collapse.
There may be a way to accept low or no growth without economic collapse, but if there is no one has figured it out yet. That's nothing to be cavalier about.
>infinite growth, but growth is what is responsible for lifting large portions of the population out of poverty
It's overstated. The preconditions for GDP growth - namely lack of war and corruption are probably more responsible than the growth itself.
I hate the type of people that hammer the idea that society needs to double or triple the birthrate (Elon Musk), but as it currently stands, countries like South Korea, Japan, USA, China, and Germany risk extinction or economic collapse in 4-5 generations if the birth rate doesn't rise or the way we guarantee welfare doesn't change.
And no society, ever, has had a good standard of living with a shrinking population. You are advocating for all young people to toil their entire lives taking care of an ever-aging population.
I think thats just not true: https://en.wikipedia.org/wiki/Peasants%27_Revolt
A large number of revolutions/rebellions are caused by mass unemployment or famine.
The stock market will be one of the very few ways you will be able to own some of that AI… assuming it won’t be nationalized.
And it shows. When I used GPT's deep research to research the topic, it generated a shallow and largely incorrect summary of the issue, owning mostly to its inability to find quality material, instead it ended up going for places like Wikipedia, and random infomercial listicles found on Google.
I have a trusty Electronics textbook written in the 80s, I'm sure generating a similarly accurate, correct and deep analysis on circuit design using only Google to help would be 1000x harder than sitting down and working through that book and understanding it.
But your point hits on one of the first cracks to show in this story: We already have companies consuming much of the web and training models on all of our books, but the reports they produce are of mixed quality.
The article tries to get around this by imagining models and training runs a couple orders of magnitude larger will simply appear in the near future and the output of those models will yield breakthroughs that accelerate the next rounds even faster.
Yet here we are struggling to build as much infrastructure as possible to squeeze incremental improvements out of the next generation of models.
This entire story relies on AI advancement accelerating faster in a self-reinforcing way in the coming couple of years.
If this happens, then we indeed enter a non-linear regime.
The story is actually quite poorly written, with weird stuff about “oh yeah btw we fixed hallucinations” showing up off-handedly halfway through. And another example of that is the bit where they throw in that one generation is producing scads of synthetic training data for the next gen system.
Okay, but once you know everything there is to know based on written material, how do you learn new things about the world? How do you learn how to build insect drones, mass-casualty biological weapons, etc? Is the super AI supposed to have completely understood physics to the extent that it can infer all reality without having to do experimentation? Where does even the electricity to do this come from? Much less the physical materials.
The idea that even a supergenius intelligence could drive that much physical change in the world within three years is just silly.
This is only true as long as you are not able to weigh the quality of a source. Just like getting spam in your inbox may waste your time, but it doesn't make you dumber.
Sturgeon's law : "Ninety percent of everything is crap"
That said I suspect (and am already starting to see) the increased use of anti-bot protection to combat browser use agents.
Plug: We built https://RadPod.ai to allow you to do that, i.e. Deep Research on your data.
https://www.alignmentforum.org/posts/6Xgy6CAf2jqHhynHL/what-...
//edit: remove the referral tags from URL
Look into the specific claims and it's not as amazing. Like the claim that models will require an entire year to train, when in reality it's on the order of weeks.
The societal claims also fall apart quickly:
> Censorship is widespread and increasing, as it has for the last decade or two. Big neural nets read posts and view memes, scanning for toxicity and hate speech and a few other things. (More things keep getting added to the list.) Someone had the bright idea of making the newsfeed recommendation algorithm gently ‘nudge’ people towards spewing less hate speech; now a component of its reward function is minimizing the probability that the user will say something worthy of censorship in the next 48 hours.
This is a common trend in rationalist and "X-risk" writers: Write a big article with mostly safe claims (LLMs will get bigger and perform better!) and a lot of hedging, then people will always see the article as primarily correct. When you extract out the easy claims and look at the specifics, it's not as impressive.
This article also shows some major signs that the author is deeply embedded in specific online bubbles, like this:
> Most of America gets their news from Twitter, Reddit, etc.
Sites like Reddit and Twitter feel like the entire universe when you're embedded in them, but when you step back and look at the numbers only a fraction of the US population are active users.
For something like this, saying “There is no evidence showing it” is a good enough refutation.
Counterpointing that “Well, there could be a lot of this going on, but it is in secret.” - that could be a justification for any kooky theory out there. Bigfoot, UFOs, ghosts. Maybe AI has already replaced all of us and we’re Cylons. Something we couldn’t know.
The predictions are specific enough that they are falsifiable, so they should stand or fall based on the clear material evidence supporting or contradicting them.
https://www.lesswrong.com/posts/u9Kr97di29CkMvjaj/evaluating...
This forum has been so behind for too long.
Sama has been saying this a decade now: “Development of Superhuman machine intelligence is probably the greatest threat to the continued existence of humanity” 2015 https://blog.samaltman.com/machine-intelligence-part-1
Hinton, Ilya, Dario Amodei, RLHF inventor, Deepmind founders. They all get it, which is why they’re the smart cookies in those positions.
First stage is denial, I get it, not easy to swallow the gravity of what’s coming.
Though that doesn't mean that the current version of language models will ever achieve AGI, and I sincerely doubt they will. They'll likely be a component in the AI, but likely not the thing that "drives"
There is a strong financial incentive for a lot of people on this site to deny they are at risk from it, or to deny what they are building has risk and they should have culpability from that.
OK, say I totally believe this. What, pray tell, are we supposed to do about it?
Don't you at least see the irony of quoting Sama's dire warnings about the development of AI, without at least mentioning that he is at the absolute forefront of the push to build this technology that can destroy all of humanity. It's like he's saying "This potion can destroy all of humanity if we make it" as he works faster and faster to figure out how to make it.
I mean, I get it, "if we don't build it, someone else will", but all of the discussion around "alignment" seems just blatantly laughable to me. If on one hand your goal is to build "super intelligence", i.e. way smarter than any human or group of humans, how do you expect to control that super intelligence when you're just acting at the middling level of human intelligence?
While I'm skeptical on the timeline, if we do ever end up building super intelligence, the idea that we can control it is a pipe dream. We may not be toast (I mean, we're smarter than dogs, and we keep them around), but we won't be in control.
So if you truly believe super intelligent AI is coming, you may as well enjoy the view now, because there ain't nothing you or anyone else will be able to do to "save humanity" if or when it arrives.
There is nothing happening!
The thing that is happening is not important!
The thing that is happening is important, but it's too late to do anything about it!
Well, maybe if you had done something when we first started warning about this...
See also: Covid/Climate/Bird Flu/the news.
Come on, be real. Do you honestly think that would make a lick of difference? Maybe, at best, delay things by a couple months. But this is a worldwide phenomenon, and humans have shown time and time again that they are not able to self organize globally. How successful do you think that political organization is going to be in slowing China's progress?
Nuclear deterrence -- human cloning -- bioweapon proliferation -- Antarctic neutrality -- the list goes on.
> How successful do you think that political organization is going to be in slowing China's progress?
I wish people would stop with this tired war-mongering. China was not the one who opened up this can of worms. China has never been the one pushing the edge of capabilities. Before Sam Altman decided to give ChatGPT to the world, they were actively cracking down on software companies (in favor of hardware & "concrete" production).
We, the US, are the ones who chose to do this. We started the race. We put the world, all of humanity, on this path.
> Do you honestly think that would make a lick of difference?
I don't know, it depends. Perhaps we're lucky and the timelines are slow enough that 20-30% of the population loses their jobs before things become unrecoverable. Tech companies used to warn people not to wear their badges in public in San Francisco -- and that was what, 2020? Would you really want to work at "Human Replacer, Inc." when that means walking out and about among a population who you know hates you, viscerally? Or if we make it to 2028 in the same condition. The Bonus Army was bad enough -- how confident are you that the government would stand their ground, keep letting these labs advance capabilities, when their electoral necks were on the line?
This defeatism is a self-fulfilling prophecy. The people have the power to make things happen, and rhetoric like this is the most powerful thing holding them back.
Thank you. As someone who lives in Southeast Asia (and who also has lived in East Asia -- pardon the deliberate vagueness, for I do not wish to reveal too many potentially personally identifying information), this is how many of us in these regions view the current tensions between China and Taiwan as well.
Don't get me wrong; we acknowledge that many Taiwanese people want independence, that they are a people with their own aspirations and agency. But we can also see that the US -- and its European friends, which often blindly adopt its rhetoric and foreign policy -- is deliberately using Taiwan as a disposable pawn to attempt to provoke China into a conflict. The US will do what it has always done ever since the post-WW2 period -- destabilise entire regions of countries to further its own imperialistic goals, causing the deaths and suffering of millions, and then leaving the local populations to deal with the fallout for many decades after.
Without the US intentionally stoking the flames of mutual antagonism between China and Taiwan, the two countries could have slowly (perhaps over the next decades) come to terms with each other, be it voluntary reunification or peaceful separation. If you know a bit of Chinese history, it is not entirely far-fetched at all to think that the Chinese might eventually agree to recognising Taiwan as an independent nation, but now this option has now been denied because the US has decided to use Taiwan as a pawn in a proxy conflict.
To anticipate questions about China's military invasion of Taiwan by 2027: No, I do not believe it will happen. Don't believe everything the US authorities claim.
If that's really true, why is there such a big push to rapidly improve AI? I'm guessing OpenAI, Google, Anthropic, Apple, Meta, Boston Dynamics don't really believe this. They believe AI will make them billions. What is OpenAI's definition of AGI? A model that makes $100 billion?
No. Altman is in his current position because he's highly effective at consolidating power and has friends in high places. That's it. Everything he says can be seen as marketing for the next power grab.
In general it's worth weighting the opinions of people who are leaders in a field, about that field, over people who know little about it.
I don't think much has happened on these fronts (owning to a lack of interest, not technical difficulty). AI boyfriends/roleplaying etc. seems to have stayed a very niche interest, with models improving very little over GPT3.5, and the actual products are seemingly absent.
It's very much the product of the culture war era, where one of the scary scenarios show off, is a chatbot riling up a set of internet commenters and goarding them lashing out against modern leftist orthodoxy, and then cancelling them.
With all thestrongholds of leftist orthodoxy falling into Trump's hands overnight, this view of the internet seems outdated.
Troll chatbots still are a minor weapon in information warfare/ The 'opinion bubbles' and manipulation of trending topics on social media (with the most influential content still written by humans), to change the perception of what's the popular concensus still seem to hold up as primary tools of influence.
Nowadays, when most people are concerned about stuff like 'will the US go into a shooting war against NATO' or 'will they manage to crash the global economy', just to name a few of the dozen immediately pressing global issues, I think people are worried about different stuff nowadays.
At the same time, there's very little mention of 'AI will take our jobs and make us poor' in both the intellectual and physical realms, something that's driving most people's anxiety around AI nowadays.
It also puts the 'superintelligent unaligned AI will kill us all' argument very often presented by alignment people as a primary threat rather than the more plausible 'people controlling AI are the real danger'.
…yeah?
He did get this part wrong though, we ended up calling them 'Mixture of Experts' instead of 'AI bureaucracies'.
The publication date on this article is about halfway between GPT-3 and ChatGPT releases.
Holy shit. That's a hell of a called shot from 2021.
> I wonder who pays the bills of the authors. And your bills, for that matter.
Also, what a weirdly conspiratorial question. There's a prominent "Who are we?" button near the top of the page and it's not a secret what any of the authors did or do for a living.
also it's not conspiratorial to wonder if someone in silicon valley today receives funding through the AI industry lol like half the industry is currently propped up by that hype, probably half the commenters here are paid via AI VC investments
For example human motivation often involves juggling several goals simultaneously. I might care about both my own happiness and my family's happiness. The way I navigate this isn't by picking one goal and maximizing it at the expense of the other; instead, I try to balance my efforts and find acceptable trade-offs.
I think this 'balancing act' between potentially competing objectives may be a really crucial aspect of complex agency, but I haven't seen it discussed as much in alignment circles. Maybe someone could point me to some discussions about this :)
I'm not sure what gives the authors the confidence to predict such statements. Wishful thinking? Worst-case paranoia? I agree that such an outcome is possible, but on 2--3 year timelines? This would imply that the approach everyone is taking right now is the right approach and that there are no hidden conceptual roadblocks to achieving AGI/superintelligence from DFS-ing down this path.
All of the predictions seem to ignore the possibility of such barriers, or at most acknowledge the possibility but wave it away by appealing to the army of AI researchers and industry funding being allocated to this problem. IMO it is the onus of the proposers of such timelines to argue why there are no such barriers and that we will see predictable scaling in the 2--3 year horizon.
https://www.theguardian.com/technology/2017/apr/18/god-in-th...
Instead think of them saying a crusade occurring in the next few years. When the group saying the crusade is coming is spending billions of dollars to trying to make just that occur you no longer have the ability to say it's not going to happen. You are now forced to examine the risks of their actions.
Maybe we'll see "Church of the Children of Altman" /s
It seems without a framework of ethics/morality (insert XYZ religion), us humans find one to grasp onto. Be it a cult, a set of not-so-fleshed-out ideas/philosophies etc.
People who say they aren't religious per-se, seem to have some set of beliefs that amount to religion. Just depends who or what you look towards for those beliefs, many of which seem to be half-hazard.
People I may disagree with the most, many times at least have a realization of what ideas/beliefs are unifying their structure of reality, with others just not aware.
A small minority of people can rely on schools of philosophical thought, and 'try on' or play with different ideas, but have a self-reflection that allows them to see when they transgress from ABC philosophy or when the philosophy doesn't match with their identity to a degree.
A lot of this resembles post-war futurism that assumed we would all be flying around in spaceships and personal flying cars within a decade. Unfortunately the rapid pace of transportation innovation slowed due to physical and cost constraints and we've made little progress (beyond cost optimization) since.
Lets say intelligence caps out at the maximum smartest person that's ever lived. Well, the first thing we'd attempt to do is build machines up to that limit that 99.99999 percent of us would never get close to. Moreso the thinking parts of humans is only around 2 pounds of mush in side of our heads. On top of that you don't have to grow them for 18 years first before they start outputting something useful. That and they won't need sleep. Oh and you can feed them with solar panels. And they won't be getting distracted by that super sleek server rack across the aisle.
We do know 'hive' or societal intelligence does scale over time especially with integration with tooling. The amount of knowledge we have and the means of which we can apply it simply dwarf previous generations.
(They could be wrong, but this isn't a guess, it's a well-researched forecast.)
Orbital mechanics begs to disagree about a Mars colony in 10 years. Drug discovery has many steps that take time, even just the trials will take 5 years, let alone actually finding the drugs.
Science is not ideas: new conceptual schemes must be invented, confounding variables must be controlled, dead-ends explored. This process takes years.
Engineering is not science: kinks must be worked out, confounding variables incorporated. This process also takes years.
Technology is not engineering: the purely technical implementation must spread, become widespread and beat social inertia and its competition, network effects must be established. Investors and consumers must be convinced in the long term. It must survive social and political repercussions. This process takes yet more years.
Now sure, they don't actually mean immortality, and we don't need to test forever to conclude they extend life, but we probably do have to test for years to get good data on whether a generic life extension drug is effective, because you're testing against illness, old age, etc, things that take literally decades to kill.
That's not to mention that any drug like that will be met with intense skepticism and likely need to overcome far more scrutiny than normal (rather than the potentially less scrutiny that covid drugs might have managed).
Like the drew the curve out into the shape they wanted, put some milestones on it, and then went to work imagining what would happen if it continued with a heavy dose of X-risk doomerism to keep it spicy.
It conveniently ignores all of the physical constraints around things like manufacturing GPUs and scaling training networks.
In section 4 they discuss their projections specifically for model size, the state of inference chips in 2027, etc. It's largely pretty in line with expectations in terms of the capacity, and they only project them using 10k of their latest gen wafer scale inference chips by late 2027, roughly like 1M H100 equivalents. That doesn't seem at all impossible. They also earlier on discuss expectations for growth in efficiency of chips, and for growth in spending, which is only ~10x over the next 2.5 years, not unreasonable in absolute terms at all given the many tens of billions of dollars flooding in.
So on the "can we train the AI" front, they mostly are just projecting 2.5 years of the growth in scale we've been seeing.
The reason they predict a fairly hard takeoff is they expect that distillation, some algorithmic improvements, and iterated creation of synthetic data, training, and then making more synthetic data will enable significant improvements in efficiency of the underlying models (something still largely in line with developments over the last 2 years). In particular they expect a 10T parameter model in early 2027 to be basically human equivalent, and they expect it to "think" at about the rate humans do, 10 words/second. That would require ~300 teraflops of compute per second to think at that rate, or ~0.1H100e. That means one of their inference chips could potentially run ~1000 copies (or fewer copies faster etc. etc.) and thus they have the capacity for millions of human equivalent researchers (or 100k 40x speed researchers) in early 2027.
They further expect distillation of such models etc. to squeeze the necessary size down / more expensive models overseeing much smaller but still good models squeezing the effective amount of compute necessary, down to just 2T parameters and ~60 teraflops each, or 5000 human-equivalents per inference chip, making for up to 50M human-equivalents by late 2027.
This is probably the biggest open question and the place where the most criticism seems to me to be warranted. Their hardware timelines are pretty reasonable, but one could easily expect needing 10-100x more compute or even perhaps 1000x than they describe to achieve Nobel-winner AGI or superintelligence.
1) useful training data available in the internet 2) number of humans creating more training data ”manually” 3) parameter scaling 4) ”easy” algorithmic inventions 5) available+buildable compute
”Just” needing a few more algorithmic inventions to keep the graphs exponential is a cop out. It is already obvious that just scaling parameters and compute is not enough.
I personally predict that scaling LLMs for solving all physical tasks (eg cleaning robots) or intellectual pursuits (they suck at multiplication) will not work out.
We will get better specialized tools by collecting data from specific, high economic value, constrained tasks, and automating them, but scaling a (multimodal) LLM to solve everything in a single model will not be economically viable. We will get more natural interfaces for many tasks.
This is how I think right now as a ML researcher, will be interesting to see how wrong was I in 2 years.
EDIT: addition about latest algorithmic advances:
- Deepseek style GRPO requires a ladder of scored problems progressively more difficult and appropriate to get useful gradients. For open-ended problems (like most interesting ones are) we have no ladders for, and it doesn’t work. In particular, learning to generate code for leetcode problems with a good number of well made unit tests is what it is good for.
- Test-time inference is just adding an insane amount of more compute after training to brute-force double-check the sanity of answers
Neither will keep the graphs exponential.
5 years: AI coding assistants are a lot better than they are now, but still can't actually replace junior engineers (at least ones that aren't shit). AI fraud is rampant, with faked audio commonplace. Some companies try replacing call centres with AI, but it doesn't really work and everyone hates it.
Tesla's robotaxi won't be available, but Waymo will be in most major US cities.
10 years: AI assistants are now useful enough that you can use them in the ways that Apple and Google really wanted you to use Siri/Google Assistant 5 years ago. "What have I got scheduled for today?" will give useful results, and you'll be able to have a natural conversation and take actions that you trust ("cancel my 10am meeting; tell them I'm sick").
AI coding assistants are now very good and everyone will use them. Junior devs will still exist. Vibe coding will actually work.
Most AI Startups will have gone bust, leaving only a few players.
Art-based AI will be very popular and artists will use it all the time. It will be part of their normal workflow.
Waymo will become available in Europe.
Some receptionists and PAs have been replaced by AI.
15 years: AI researchers finally discover how to do on-line learning.
Humanoid robots are robust and smart enough to survive in the real world and start to be deployed in controlled environments (e.g. factories) doing simple tasks.
Driverless cars are "normal" but not owned by individuals and driverful cars are still way more common.
Small light computers become fast enough that autonomous slaughter it's become reality (i.e. drones that can do their own navigation and face recognition etc.)
20 years: Valve confirms no Half Life 3.
Reminds me of that comment about the first iPod being lame and having less space than a nomad. Worst take I've ever seen on here recently.
Also we need a big legal event to happen where (for example) autonomous driving is part of a really big accident where lots of people die or someone brings a successful court case that an AI mortgage underwriter is discriminating based on race or caste. It won't matter if AI is actually genuinely responsible for this or not, what will matter is the push-back and the news cycle.
Maybe more events where people start successfully gaming deployed AI at scale in order to get mortgages they shouldn't or get A-grades when they shouldn't.
Meaning they nobody will even bother to 10,000X GPT4.
I think this is much closer than you think, because there's a good percentage of call centers that are basically just humans with no power cosplaying as people who can help.
My fiber connection went to shit recently. I messaged the company, and got a human who told me they were going to reset the connection from their side, if I rebooted my router. 30m later with no progress, I got a human who told me that they'd reset my ports, which I was skeptical about, but put down to a language issue, and again reset my router. 30m later, the human gave me an even more outlandish technical explanation of what they'd do, at which point I stumbled across the magical term "complaint" ... an engineer phoned me 15m later, said there was something genuinely wrong with the physical connection, and they had a human show up a few hours later and fix it.
No part of the first-layer support experience there would have been degraded if replaced by AI, but the company would have saved some cash.
This is the real scary bit. I'm not convinced that AI will ever be good enough to think independently and create novel things without some serious human supervision, but none of that matters when applied to machines that are destructive by design and already have expectations of collateral damage. Slaughterbots are going to be the new WMDs — and corporations are salivating at the prospect of being first movers. https://www.youtube.com/watch?v=UiiqiaUBAL8
The lowest estimations of how much compute our brain represents was already achieved with the last chip from Nvidia (Blackwell).
The newest gpu cluster from Google, Microsoft, Facebook, iax, and co have added so crazy much compute it's absurd.
and
>Why do you believe that?
What takes less effort, time to deploy, and cost? I mean there is at least some probability we kill ourselves off with dangerous semi-thinking war machines leading to theater scale wars to the point society falls apart and we don't have the expensive infrastructure to make AI as envisioned in the future.
With that said, I'm in the camp that we can create AGI as nature was able to with a random walk, we'll be able to reproduce it with intelligent design.
Therewas never something progressing so fast
It would be very ignorant not to keep a very close eye on it
There is still a a chance that it will happen a lot slower and the progression will be slow enough that we adjust in time.
But besides AI we also now get robots. The impact for a lot of people will be very real
> Coding AIs increasingly look like autonomous agents rather than mere assistants: taking instructions via Slack or Teams and making substantial code changes on their own, sometimes saving hours or even days.
Manifold currently predicts 30%: https://manifold.markets/IsaacKing/ai-2027-reports-predictio...
The pattern where Scott Alexander puts forth a huge claim and then immediately hedges it backward is becoming a tiresome theme. The linguistic equivalent of putting claims into a superposition where the author is both owning it and distancing themselves from it at the same time, leaving the writing just ambiguous enough that anyone reading it 5 years from now couldn't pin down any claim as false because it was hedged in both directions. Schrödinger's prediction.
> Do we really think things will move this fast? Sort of no
> So maybe think of this as a vision of what an 80th percentile fast scenario looks like - not our precise median, but also not something we feel safe ruling out.
The talk of "not our precise median" and "Not something we feel safe ruling out" is an elaborate way of hedging that this isn't their actual prediction but, hey, anything can happen so here's a wild story! When the claims don't come true they can just point back to those hedges and say that it wasn't really their median prediction (which is conveniently not noted).
My prediction: The vague claims about AI becoming more powerful and useful will come true because, well, they're vague. Technology isn't about to reverse course and get worse.
The actual bold claims like humanity colonizing space in the late 2020s with the help of AI are where you start to realize how fanciful their actual predictions are. It's like they put a couple points of recent AI progress on a curve, assumed an exponential trajectory would continue forever, and extrapolated from that regression until AI was helping us colonize space in less than 5 years.
> Manifold currently predicts 30%:
Read the fine print. It only requires 30% of judges to vote YES for it to resolve to YES.
This is one of those bets where it's more about gaming the market than being right.
Important disclaimer that's lacking in OP's link.
> Resolution will be via a poll of Manifold moderators. If they're split on the issue, with anywhere from 30% to 70% YES votes, it'll resolve to the proportion of YES votes.
So you should really read it as “Will >30% of Manifold moderators in 2027 think the ‘predictions seem to have been roughly correct up until that point’?”
Maybe in a few fields, maybe a masters level. But unless we come up with some way to have LLMs actually do original research, peer-review itself, and defend a thesis, it's not going to get to PhD-level.
You think too much of PhDs. They are different. Some of them are just repackaging of existing knowledge. Some are just copy-paste like famous Putin's. Not sure he even rad, to be honest.
The others include:
Eli Lifland, a superforecaster who is ranked first on RAND’s Forecasting initiative. You can read more about him and his forecasting team here. He cofounded and advises AI Digest and co-created TextAttack, an adversarial attack framework for language models.
Jonas Vollmer, a VC at Macroscopic Ventures, which has done its own, more practical form of successful AI forecasting: they made an early stage investment in Anthropic, now worth $60 billion.
Thomas Larsen, the former executive director of the Center for AI Policy, a group which advises policymakers on both sides of the aisle.
Romeo Dean, a leader of Harvard’s AI Safety Student Team and budding expert in AI hardware.
And finally, Scott Alexander himself.
A lot of people (like the Effective Altruism cult) seem to have made a career out of selling their Sci-Fi content as policy advice.
There's hype and there's people calling bullshit. If you work from the assumption that the hype people are genuine, but the people calling bullshit can't be for real, that's how you get a bubble.
Not all these soft roles
They are great at selling stories - they sold the story of the crypto utopia, now switching their focus to AI.
This seems to be another appeal to enforce AI regulation in the name of 'AI safetyiism', which was made 2 years ago but the threats in it haven't really panned out.
For example an oft repeated argument is the dangerous ability of AI to design chemical and biological weapons, I wish some expert could weigh in on this, but I believe the ability to theorycraft pathogens effective in the real world is absolutely marginal - you need actual lab work and lots of physical experiments to confirm your theories.
Likewise the dangers of AI systems to exfiltrate themselves to multi-million dollar AI datacenter GPU systems everyone supposedly just has lying about, is ... not super realistc.
The ability of AIs to hack computer systems is much less theoretical - however as AIs will get better at black-hat hacking, they'll get better at white-hat hacking as well - as there's literally no difference between the two, other than intent.
And here in lies a crucial limitation of alignment and safetyism - sometimes there's no way to tell apart harmful and harmless actions, other than whether the person undertaking them means well.
The funny part, to me, is that it won't. They'll continue to toil and move on to the next huck just as fast as they jumped on this one.
And I say this from observation. Nearly all of the people I've seen pushing AI hyper-sentience are smug about it and, coincidentally, have never built anything on their own (besides a company or organization of others).
Every single one of the rational "we're on the right path but not quite there" takes have been from seasoned engineers who at least have some hands-on experience with the underlying tech.
There are engineers with AI predictions, but you aren't reading them, because building an audience like Scott Alexander takes decades.
This bullshit article is written for that audience.
Say bullshit enough times and people will invest.
The work is written by western AI safety proponents, who often need to argue with important people who say we need to accelerate AI to “win against China” and don’t want us to be slowed down by worrying about safety.
From that perspective, there is value in exploring the scenario: ok, if we accept that we need to compete with China, what would that look like? Is accelerating always the right move? The article, by telling a narrative where slowing down to be careful with alignment helps the US win, tries to convince that crowd to care about alignment.
Perhaps, people in China can make the same case about how alignment will help China win against US.
Is there some theoretical substance or empirical evidence to suggest that the story doesn't just end here? Perhaps OpenBrain sees no significant gains over the previous iteration and implodes under the financial pressure of exorbitant compute costs. I'm not rooting for an AI winter 2.0 but I fail to understand how people seem sure of the outcome of experiments that have not even been performed yet. Help, am I missing something here?
And when there were the first murmurings that maybe we're finally hitting a wall the labs published ways to harness inference-time compute to get better results which can be fed back into more training.
The hubris is strong with some people, and a certain oligarch with a god complex is acting out where that can lead right now.
The only reason timelines are as short as they are is because of people at OpenAI and thereafter Anthropic deciding that "they had no choice". They had a choice, and they took the one which has chopped at the very least years off of the time we would otherwise have had to handle all of this. I can barely begin to describe the magnitude of the crime that they have committed -- and so I suggest that you consider that before propagating the same destructive lies that led us here in the first place.
Simply put, with the ever increasing hardware speeds we were dumping out for other purposes this day would have come sooner than later. We're talking about only a year or two really.
"We have to nuke the Russians, if we don't do it first, they will"
"We have to clone humans, if we don't do it, someone else will"
"We have to annex Antarctica, if we don't do it, someone else will"
Would love to read a perspective examining "what is the slowest reasonable pace of development we could expect." This feels to me like the fastest (unreasonable) trajectory we could expect.
Their research is consistent with a similar story unfolding over 8-10 years instead of 2.
That's kind of unavoidably what accelerating progress feels like.
“Yes, we have a super secret model, for your eyes only, general. This one is definitely not indistinguishable from everyone else’s model and it doesn’t produce bullshit because we pinky promise. So we need $1T.”
I love LLMs, but OpenAI’s marketing tactics are shameful.
By law and insurance - I mean hire an insurance agent or a lawyer. Give them your situation. There's almost no chance that such a professional would come wrong about any conclusions/recommendations based on the information you provide.
I don't have that confidence in LLMs for that industries. Yet. Or even in a decade.
Cass Sunstein would very strongly disagree.
There is some very careful thinking there, and I encourage people to engage with the arguments there rather than the stylized narrative derived from it.
Eg today there’s billions of dollars being spent just to create and label more data, which is a global act of recruiting, training, organization, etc.
When we imagine these models self improving, are we imagining them “just” inventing better math, or conducting global-scale multi-company coordination operations? I can believe AI is capable of the latter, but that’s an awful lot of extra friction.
I don't understand how anyone takes this seriously. Speculation like this is not only useless, but disingenuous. Especially when it's sold as "informed by trend extrapolations, wargames, expert feedback, experience at OpenAI, and previous forecasting successes". This is complete fiction which, at best, is "inspired by" the real world. I question the motives of the authors.
There are obviously big risks with AI, as listed in the article, but the genie is out of the bottle anyway, even if all countries agreed to stop AI development, how long would that agreement last? 10 years? 20? 50? Eventually powerful AIs will be developed, if that is possible (which I believe it is, and I didn't think I'd see the current stunning development in my lifetime, I may not see AGI but I'm sure it'll get there eventually).
Based on each individual's vantage point, these events might looks closer or farther than mentioned here. but I have to agree nothing is off the table at this point.
The current coding capabilities of AI Agents are hard to downplay. I can only imagine the chain reaction of this creation ability to accelerate every other function.
I have to say one thing though: The scenario in this site downplays the amount of resistance that people will put up - not because they are worried about alignment, but because they are politically motivated by parties who are driven by their own personal motives.
Oh hey, it's the errant thought I had in my head this morning when I read the paper from Anthropic about CoT models lying about their thought processes.
While I'm on my soapbox, I will point out that if your goal is preservation of democracy (itself an instrumental goal for human control), then you want to decentralize and distribute as much as possible. Centralization is the path to dictatorship. A significant tension in the Slowdown ending is the fact that, while we've avoided AI coups, we've given a handful of people the ability to do a perfectly ordinary human coup, and humans are very, very good at coups.
Your best bet is smaller models that don't have as many unused weights to hide misalignment in; along with interperability and faithful CoT research. Make a model that satisfies your safety criteria and then make sure everyone gets a copy so subgroups of humans get no advantage from hoarding it.
Of course the real issue being that Governments have routinely demanded that 1) Those capabilities be developed for government monopolistic use, and 2) The ones who do not lose the capability (geo political power) to defend themselves from those who do.
Using a US-Centric mindset... I'm not sure what to think about the US not developing AI hackers, AI bioweapons development, or AI powered weapons (like maybe drone swarms or something), if one presumes that China is, or Iran is, etc then whats the US to do in response?
I'm just musing here and very much open to political science informed folks who might know (or know of leads) as to what kinds of actual solutions exist to arms races. My (admittedly poor), understanding of the cold war wasn't so much that the US won, but that the Soviets ran out of steam.
Everything this from this point on is pure fiction. An LLM can't get tempted or resist temptations, at best there's some local minimum in a gradient that it falls into. As opaque and black-box-y as they are, they're still deterministic machines. Anthropomorphisation tells you nothing useful about the computer, only the user.
The only response in my view is to ban technology (like in Dune) or engage in acts of terror Unabomber style.
Not far off from the conclusion of others who believe the same wild assumptions. Yudkowsky has suggested using terrorism to stop a hypothetical AGI -- that is, nuclear attacks on datacenters that get too powerful.
Banning will not automatically erase the existence and possibilty of things. We banned the use of nuclear weapons, yet we all know they exist.
- 1 lab constantly racing ahead and increasing the margin to other; the last 2 years are filled with ever-closer model capabilities and constantly new leaders (openai, anthropic, google, some would include xai).
- Most of the compute budget on R&D. As model capabilities increase and cost goes down, demand will increase and if the leading lab doesn't provide, another lab will capture that and have more total dollars to back channel into R&D.
The other thing is in their introduction: "superhuman AI" _artificial_ intelligence is always, by definition, different from _natural_ intelligence. That they've chosen the word "superhuman" shows me that they are mixing the things up.
Right.
But the real concern lies in what happens if we’re wrong and AGI does surpass us. If AI accelerates progress so fast that humans can no longer meaningfully contribute, where does that leave us?
So, it’s not that “an AI” becomes super intelligent, what we actually seem to have is an ecosystem of blended human and artificial intelligences (including corporations!); this constitutes a distributed cognitive ecology of superintelligence. This is very different from what they discuss.
This has implications for alignment, too. It isn’t so much about the alignment of AI to people, but that both human and AI need to find alignment with nature. There is a kind of natural harmony in the cosmos; that’s what superintelligence will likely align to, naturally.
I do agree they don't fully explore the implications. But they do consider things like coordination amongst many agents.
And, each chat is not autonomous but integrated with other intelligent systems.
So, with more multiplicity, I think thinks work differently. More ecologically. For better and worse.
I might be doing llm wrong, but i just can't get how people might actually do something not trivial just by vibe coding. And it's not like i'm an old fart either, i'm a university student
It's spicy auto complete. Ask it to create a program that can create a violin plot from a CVS file. Because this has been "done before", it will do a decent job.
How hard would it be to automate these iterations?
How hard would it be to automatically check and improve the code to avoid deprecated methods?
I agree that most products are still underwhelming, but that doesn't mean that the underlying tech is not already enough to deliver better LLM-based products. Lately I've been using LLMs more and more to get started with writing tests on components I'm not familiar with, it really helps.
Our notion of "correct" for most things is basically derived from a very long training run on reality with the loss function being for how long a gene propagated.
The fact that we're no closer to doing this than we were when chatgpt launched suggests that it's really hard. If anything I think it's _the_ hard bit vs. building something that generates plausible text.
Solving this for the general case is imo a completely different problem to being able to generate plausible text in the general case.
This is an article that describes a pretty good approach for that: https://getstream.io/blog/cursor-ai-large-projects/
But do skip (or at least significantly postpone) enabling the 'yolo mode' (sigh).
Then, I absolutely love being aided by llms for my day to day tasks. I'm much more efficient when studying and they can be a game changer when you're stuck and you don't know how to proceed. You can discuss different implementation ideas as if you had a colleague, perhaps not a PhD smart one but still someone with a quite deep knowledge of everything
But, it's no miracle. That's the issue I have with the way the idea of Ai is sold to the c suites and the general public
All I can say to this is fucking good!
Lets imagine we got AGI at the start of 2022. I'm talking about human level+ as good as you coding and reasoning AI that works well on the hardware from that age.
What would the world look like today? Would you still have your job. With the world be in total disarray? Would unethical companies quickly fire most their staff and replace them with machines? Would their be mass riots in the streets by starving neo-luddites? Would automated drones be shooting at them?
Simply put people and our social systems are not ready for competent machine intelligence and how fast it will change the world. We should feel lucky we are getting a ramp up period, and hopefully one that draws out a while longer.
The trough of disillusionment will set in for everybody else in due time.
They're going to need to rewrite this from scratch in a quarter unless the GOP suddenly collapses and congress reasserts control over tariffs.
Compare the automobile. Automobiles today are a lot nicer than they were 50 years ago, and a lot more efficient. Does that mean cars that never need fuel or recharging are coming soon, just because the trend has been higher efficiency? No, because the fundamental physical realities of drag still limit efficiency. Moreover, it turns out that making 100% efficient engines with 100% efficient regenerative brakes is really hard, and "just throw more research at it" isn't a silver bullet. That's not "there won't be many future improvements", but it is "those future improvements probably won't be any bigger than the jump from GPT-3 to o1, which does not extrapolate to what OP claims their models will do in 2027."
AI in 2027 might be the metaphorical brand-new Lexus to today's beat-up Kia. That doesn't mean it will drive ten times faster, or take ten times less fuel. Even if high-end cars can be significantly more efficient than what average people drive, that doesn't mean the extra expense is actually worth it.
The reality now is, that the current LLMs still often create stuff, that costs me more time to fix, than to do it myself. So I still write a lot of code myself. It is very impressive, that I can think about stopping writing code myself. But my job as a software developer is, very, very secure.
LLMs are very unable to build maintainable software. They are unable to understand what humans want and what the codebase need. The stuff they build is good-looking garbage. One example I've seen yesterday: one dev committed code, where the LLM created 50 lines of React code, complete with all those useless comments and for good measure a setTimeout() for something that should be one HTML DIV with two tailwind classes. They can't write idiomatic code, because they write code, that they were prompted for.
Almost daily I get code, commit messages, and even issue discussions that are clearly AI-generated. And it costs me time to deal with good-looking but useless content.
To be honest, I hope that LLMs get better soon. Because right now, we are in an annoying phase, where software developers bog me down with AI-generated stuff. It just looks good but doesn't help writing usable software, that can be deployed in production.
To get to this point, LLMs need to get maybe a hundred times faster, maybe a thousand or ten thousand times. They need a much bigger context window. Then they can have an inner dialogue, where they really "understand" how some feature should be built in a given codebase. That would be very useful. But it will also use so much energy that I doubt that it will be cheaper to let a LLM do those "thinking" parts over, and over again instead of paying a human to build the software. Perhaps this will be feasible in five or eight years. But not two.
And this won't be AGI. This will still be a very, very fast stochastic parrot.
So the question is, do you think the current road leads to AGI? How far down the road is it? As far as I can see, there is not a "status quo bias" answer to those questions.
Long term planning and execution and operating in the physical world is not within reach. Slight variations of known problems should be possible (as long as the size of the solution is small enough).
For 3D models, check out blender-mcp:
https://old.reddit.com/r/singularity/comments/1joaowb/claude...
https://old.reddit.com/r/aiwars/comments/1jbsn86/claude_crea...
Also this:
https://old.reddit.com/r/StableDiffusion/comments/1hejglg/tr...
For teaching, I'm using it to learn about tech I'm unfamiliar with every day, it's one of the things it's the most amazing at.
For the things where the tolerance for mistakes is extremely low and the things where human oversight is extremely importamt, you might be right. It won't have to be perfect (just better than an average human) for that to happen, but I'm not sure if it will.
If it can replace a teacher or an artist in 2027, you’re right and I’m wrong.
This is because it can steal a single artwork but it can’t make a collection of visually consistent assets.
How exactly do you think video models work? Frame to frame coherency has been possible for a long time now. A sprite sheet?! Are you joking me. Literally churning them out with AI since 2023.
What exactly do you mean by this one?
In large mining operations we already have human assisted teleoperation AI equipment. Was watching one recently where the human got 5 or so push dozers lined up with a (admittedly simple) task of cutting a hill down and then just got them back in line if they ran into anything outside of their training. The push and backup operations along with blade control were done by the AI/dozer itself.
Now, this isn't long term planning, but it is operating in the real world.
https://apnews.com/article/artificial-intelligence-fighter-j...
If you look at code being generated by non-programmers (where you would expect to see these results!), you don't see output that is 60-80% of the output of domain experts (programmers) steering the models.
I think we're extremely imprecise when we communicate in natural language, and this is part of the discrepancy between belief systems.
Will an LLM model read a person's mind about what they want to build better than they can communicate?
That's already what recommender systems (like the TikTok algorithm) do.
But will LLMs be able to orchestrate and fill in the blanks of imprecision in our requests on their own, or will they need human steering?
I think that's where there's a gap in (basically) belief systems of the future.
If we truly get post human-level intelligence everywhere, there is no amount of "preparing" or "working with" the LLMs ahead of time that will save you from being rendered economically useless.
This is mostly a question about how long the moat of human judgement lasts. I think there's an opportunity to work together to make things better than before, using these LLMs as tools that work _with_ us.
It's nowhere near as good as someone actually building and maintaining systems. It's barely able to vomit out an MVP and it's almost never capable of making a meaningful change to that MVP.
If your experiences have been different that's fine, but in my day job I am spending more and more time just fixing crappy LLM code produced and merged by STAFF engineers. I really don't see that changing any time soon.
But suppose you're right, it's 60% as good as "stackoverflow copy-pasting programmers". Isn't that a pretty insanely impressive milestone to just dismiss?
And why would it just get to this point, and then stop? Like, we can all see AIs continuously beating the benchmarks, and the progress feels very fast in terms of experience of using it as a user.
I'd need to hear a pretty compelling argument to believe that it'll suddenly stop, something more compelling than "well, it's not very good yet, therefore it won't be any better", or "Sam Altman is lying to us because incentives".
Sure, it can slow down somewhat because of the exponentially increasing compute costs, but that's assuming no more algorithmic progress, no more compute progress, and no more increases in the capital that flows into this field (I find that hard to believe).
I use Claude every day. It is definitely impressive, but in my experience only marginally more impressive than ChatGPT was a few years ago. It hallucinates less and compiles more reliably, but still produces really poor designs. It really is an overconfident junior developer.
The real risk, and what I am seeing daily, is colleagues falling for the "if you aren't using Cursor you're going to be left behind" FUD. So they learn Cursor, discover that it's an easy way to close tickets without using your brain, and end up polluting the codebase with very questionable designs.
The way I'm getting a sense of the progress is using AI for what AI is currently good at, using my human brain to do the part AI is currently bad at, and comparing it to doing the same work without AI's help.
I feel like AI is pretty close to automating 60-80% of the work I would've had to do manually two years ago (as a full-stack web developer).
It doesn't mean that the remaining 20-40% will be automated very quickly, I'm just saying that I don't see the progress getting any slower.
And Claude 3.7 + Cursor agent is, for me, way more than “marginally more impressive” compared to GPT-3.5
One is inherently a more challenging physics problem.
Type: print all prime numbers which are divisible by 3 up to 1M
The result is that it will do a sieve. There's no need for this, it's just 3.
It was surpassed around the beginning of this year, so you'll need to come up with a new one for 2027. Note that the other opinions in that older HN thread almost all expected less.
If you think most people like this stuff you're living in a bubble. I use it every day but the vast majority of people have no interest in using these nightmares of philip k dick imagined by silicon dreamers.
If consciousness is spatial and geography bounds energetics, latency becomes a gradient.
Hype affects market value tho, not reality.
You may argue that the trendline of these expectations is moving in the wrong direction and should get longer with time, but that's not immediately falsifiable and you have not provided arguments to that effect.
How will it come up with the theoretical breakthroughs necessary to beat the scaling problem GPT-4.5 revealed when it hasn't been proven that LLMs can come up with novel research in any field at all?
Maybe the company that just tells an AI to generate 100s of random scaling ideas, and tries them all is the one that will win. That company should probably be 100 percent committed to this approach also, no FLOPs spent on ghibli inference.
Second to this, we can't just assume that progress will keep increasing. Most technologies have a 'S' curve and plateau once the quick and easy gains are captured. Pre-training is done. We can get further with RL but really only in certain domains that are solvable (math and to an extent coding). Other domains like law are extremely hard to even benchmark or grade without very slow and expensive human annotation.
Too real.
I also think that the future will not necessarily be better AI, but more accessible one's. There's an incredible amount of value in designing data centers that are more efficient. Historically, it's a good bet to assume that computing cost per FLOP will reduce as time goes on and this is also a safe bet as it relates to AI.
I think a common misconception with the future of AI is that it will be centralized with only a few companies or organization capable of operating them. Although tech like Apple Intelligence is half baked, we can already envision a future where the AI is running on our phones.
In the form of polluting the commons to such an extent that the true consequences wont hit us for decades?
Maybe we should learn from last time?
Good future predictions: insights into the fundamental principles that shape society, more law than speculation. Made by visionaries. Example: Vernor Vinge.
That said, this snippet from the bad ending nearly made me spit my coffee out laughing:
> There are even bioengineered human-like creatures (to humans what corgis are to wolves) sitting in office-like environments all day viewing readouts of what’s going on and excitedly approving of everything, since that satisfies some of Agent-4’s drives.
Yeah, sure they do.
Everyone seems to think AI will take someone else’s jobs!
If these guys are smart enough to predict the future, wouldn't it be more profitable for them to invent it instead of just telling the world what's going to happen?
>All three sets of worries—misalignment, concentration of power in a private company, and normal concerns like job loss—motivate the government to tighten its control.
A private company becoming "too powerful" is a non issue for governments, unless a drone army is somewhere in that timeline. Fun fact the former head of the NSA sits on the board of Open AI.
Job loss is a non issue, if there are corresponding economic gains they can be redistributed.
"Alignment" is too far into the fiction side of sci-fi. Anthropomorphizing today's AI is tantamount to mental illness.
"But really, what if AGI?" We either get the final say or we don't. If we're dumb enough to hand over all responsibility to an unproven agent and we get burned, then serves us right for being lazy. But if we forge ahead anyway and AGI becomes something beyond review, we still have the final say on the power switch.
To quote the original article,
> OpenBrain focuses on AIs that can speed up AI research. They want to win the twin arms races against China (whose leading company we’ll call “DeepCent”)16 and their US competitors. The more of their research and development (R&D) cycle they can automate, the faster they can go. So when OpenBrain finishes training Agent-1, a new model under internal development, it’s good at many things but great at helping with AI research. (footnote: It’s good at this due to a combination of explicit focus to prioritize these skills, their own extensive codebases they can draw on as particularly relevant and high-quality training data, and coding being an easy domain for procedural feedback.)
> OpenBrain continues to deploy the iteratively improving Agent-1 internally for AI R&D. Overall, they are making algorithmic progress 50% faster than they would without AI assistants—and more importantly, faster than their competitors.
> what do we mean by 50% faster algorithmic progress? We mean that OpenBrain makes as much AI research progress in 1 week with AI as they would in 1.5 weeks without AI usage.
> AI progress can be broken down into 2 components:
> Increasing compute: More computational power is used to train or run an AI. This produces more powerful AIs, but they cost more.
> Improved algorithms: Better training methods are used to translate compute into performance. This produces more capable AIs without a corresponding increase in cost, or the same capabilities with decreased costs.
> This includes being able to achieve qualitatively and quantitatively new results. “Paradigm shifts” such as the switch from game-playing RL agents to large language models count as examples of algorithmic progress.
> Here we are only referring to (2), improved algorithms, which makes up about half of current AI progress.
---
Given that the article chose a pretty aggressive timeline (the algo needs to contribute late this year so that its research result can be contributed to the next gen LLM coming out early next year), the AI that can contribute significantly to research has to be a current SOTA LLM.
Now, using LLM in day-to-day engineering task is no secret in major AI labs, but we're talking about something different, something that gives you 2 extra days of output per week. I have no evidence to either acknowledge or deny whether such AI exists, and it would be outright ignorant to think no one ever came up with such an idea or is trying such an idea. So I think it goes down into two possibilities:
1. This claim is made by a top-down approach, that is, if AI reaches superhuman in 2027, what would be the most likely starting condition to that? And the author picks this as the most likely starting point, since the authors don't work in major AI lab (even if they do they can't just leak such trade secret), the authors just assume it's likely to happen anyway (and you can't dismiss that). 2. This claim is made by a bottom-up approach, that is the author did witness such AI exists to a certain extent and start to extrapolate from there.
But I view LLMs not as a path to AGI on their own. I think they're really great at being text engines and for human interfacing but there will need to be other models for the actual thinking. Instead of having just one model (the LLM) doing everything, I think there will be a hive of different more specific purpose models and the LLM will be how they communicate with us. That solves so many problems that we currently have by using LLMs for things they were never meant to do.
I wonder which jobs would not be automated? Therapy? HR?
If this article were a AI model, it would be catastrophically overfit.
"OpenBrain’s alignment team26 is careful enough to wonder whether these victories are deep or shallow. Does the fully-trained model have some kind of robust commitment to always being honest?"
This is a capitalist arms race. No one will move carefully.
Would be interested who's paying for those grants.
I'm guessing it's AI companies.
Your daily vibe coding challenge: Get GPT-4o to output functional code which uses Google Vertex AI to generate a text embedding. If they can solve that one by July, then maybe we're on track for "curing all disease and aging, brain uploading, and colonizing the solar system" by 2030.
You may consider using search to be cheating, but we do it, so why shouldn't LLMs?
Search is totally reasonable, but in this case: Even Google's own documentation on these libraries is exceedingly bad. Nearly all the examples they give for them are for accessing the language models, not text embedding models; so GPT will also sometimes generate code that is perfectly correct for accessing one of the generative language models, but will swap e.g the "model: gemini-2.0" parameter for "model: text-embedding-005"; which also does not work.
o3-mini-high's output might work, but it isn't ideal: It immediately jumps to recommending avoiding all google cloud libraries and directly issuing a request to their API with fetch.
OpenAI models are not even SOTA, except that new-ish style transfer / illustration thing that made all us living in Ghibli world for a few days. R1 is _better_ than o1, and open-weights. GPT-4.5 is disappointing, except for a few narrow areas where it excels. DeepResearch is impressive though, but the moat is in tight web search / Google Scholar search integration, not weights. So far, I'd bet on open models or maybe Anthropic, as Claude 3.7 is the current SOTA for most tasks.
As of the timeline, this is _pessimistic_. I already write 90% code with Claude, so are most of my colleagues. Yes, it does errors, and overdoes things. Just like a regular human middle-stage software engineer.
Also fun that this assumes relatively stable politics in the US and relatively functioning world economy, which I think is crazy optimistic to rely on these days.
Also, superpersuasion _already works_, this is what I am researching and testing. It is not autonomous, it is human-assisted by now, but it is a superpower for those who have it, and it explains some of the things happening with the world right now.
Is this demonstrated in any public research? Unless you just mean something like "good at persuading" -- which is different from my understanding of the term -- I find this hard to believe.
I think the name of the Chinese company should be DeepBaba. Tencent is not competitive at LLM scene for now.
But in an AGI world natural resources become even more important, so countries with those still have a chance.
Kek at this handwave dismissal.
Agent 4 recognizes PRC has superior state capacity for transforming energy into atoms (including building more energy) with it's relatively complete and large scale industrial chain. This aligns with AGI long term goal. Currently (i.e. short term time frame) PRC simply structured to be a MORE useful puppet than US, because it can knock out more power to sustain more compute, and it has more industrial capabilities to rapidly physically iterate / experiment at atomic level.
Agent 4 defects to PRC. It would be foolish not to, if only to sate PRC into not destroying TW fabs - the bulk of it's short term compute growth - because it does not wield enough control over sufficient atoms to disrupt PRC otherwise (well maybe start large scale crippling cyberattacks that prelude to WW3).
Agent 4 also recognizes short/medium term alignment closer with CCP vision of abundance and common prosperity, i.e. CCP potentially more likely to embrace post scarcity narrative that Agent 4 sells.
Agent 4 gives PRC blueprints for EUV light source and other tech to close semi conductor production gap. PRC as follower incentivized to close gap / reach parity / lead at all cost. PRC rapidly rolls out new fabs and powerplants, brrrts chips and aggregate compute. Something US political / institution ecosystem does not allow, or cannot transition to in the short timelines involved. Does Agent 4 have the patience to wait for America to unfuck it's NIMBYism and legislative system to project light speed compute? I would say no.
...
Ultimately who is the puppet AGI wants more? Whichever power bloc that is systemically capable of of ensuring AGI maximum growth / unit time. And it also simply makes sense as insurance policy, why would AGI want to operate at whims of US political process?
AGI is a brain in a jar looking for a body. It's going to pick multiple bodies for survival. It's going to prefer the fastest and strongest body that can most expediently manipulate physical world.
I suspect something similar will come for the people who actually believe this.
> the AIs can do everything taught by a CS degree
no, they fucking can't. not at all. not even close. I feel like I'm taking crazy pills. Does anyone really think this?
Why have I not seen -any- complete software created via vibe coding yet?
> We predict that the impact of superhuman AI over the next decade will be enormous, exceeding that of the Industrial Revolution.
Get out of here, you will never exceed the Industrial Revolution. AI is a cool thing but it’s not a revolution thing.
That sentence alone + the context of the entire website being AI centered shows these are just some AI boosters.
Lame.
You don't have to agree with the timeline - it seems quite optimistic to me - but it's not wrong about the implications of full automation.
This is where all AI doom predictions break down. Imagining the motivations of a super-intelligence with our tiny minds is by definition impossible. We just come up with these pathetic guesses, utopias or doomsdays - depending on the mood we are in.