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PaperclipPerfector  1mo ago (text post)   24412 thread views

Culture War Roundup for the week of September 14, 2026

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Is it over for AI Safety? From an interview:

The worst case is that AI will turn against humanity. Do we have guardrails?

Trump: It's going to be fine. We'll always have something to stop them. We'll have a little gear. Boom. 'I really don't like that robot.'

https://truthsocial.com/@realDonaldTrump/posts/117269745153543631

The only control or "guardrails" that AI needs is a STRONG AND SMART (High IQ!) PRESIDENT, and the U.S.A. has that, in spades! The Trump Administration has stopped AI "people" from doing bad, or potentially bad, "things," like Dario (Anthropic!), who is now pretending to be a "perfect little angel" - and we will continue to do so! We already have tremendous CRIMINAL and REGULATORY power over these companies! There is a SICK conspiracy going on against AI and Data Centers, and the only one that is happy about it is China. WHOEVER WINS AI, WINS! We are leading China, and all others, and will continue to do so. Conspiracy Theorists, Treasonists, Traitors, and Leakers, BEWARE! Thank you for your attention to this matter! President DONALD J. TRUMP

Meanwhile in China, Dario's call for AI pacing have not gone down well. Understandably, they are not enthusiastic about the US achieving a permanent high ground in a key technology after the last few years of semiconductor sanctions and suppression. Also they can tell that Dario doesn't like the Party at all and seems to be openly plotting its downfall.

Dario's essay: https://darioamodei.com/post/we-must-pace-the-frontier

A Chinese semi-official response: https://www.globaltimes.cn/page/202609/1370436.shtml

This "silent AI Cold War" is hypocritical and short-sighted. Its true purpose is to attempt to curb China's AI development through technological barriers and regulatory monopolies, uphold Washington's monopolistic hegemony in cutting-edge technology, and exclude China from the global AI governance system, essentially following in the footsteps of Washington's so-called Pax Silica initiative, which seeks to excludes China.

So if Trump is keen for an AI race and so is China, then it looks pretty much over for Safety? Even the other US tech companies have only provided lip service to slowing things down. Elon said Dario's sentiment was good but is still moving quickly. Sam Altman says all kinds of things about safety, while displaying heroic negligence in training AIs. I don't doubt the sincerity of employees who quit, they have many financial reasons to go with the flow and downplay risks. Lurid rumours of Aella promising reverse gangbangs to those who quit frontier AI companies don't neccessarily outweigh the personal financial gains. It seems silly to doubt those at the frontier who are frightened when we ourselves have no special insights. In pure observational terms they're better equipped than anyone, only in policy or analysis should we be disagreeing, I think.

I also find it comical that the AI-safety camp spent such a long time building up institutions and factions and entire AI companies but seem to have been totally snookered by Jensen's suave dinner party skills and the appeal of fun AI videos to a Boomer's Boomer plus the raw partisanship of 'Dems want it, so I don't'. Even Aschenbrenner managed to do better at Trump-whispering. Shortly after he wrote his essay, I recall a video where the fundamentals were accepted by Trump, someone told him about it. And it did fit well with his instincts, getting one up on China + more energy production.

Right now Trump is reTruthing Fox News clips framing AI safety as a global one world government Democrat control-seeking scheme: https://truthsocial.com/@realDonaldTrump/posts/117273026439561125

I don't see any AI safety people on right wing twitter either, those who acknowledge the issue at all still would prefer to let it rip than hand the left/big tech elites control of the world, favouring decentralization of AI instead.

I wonder whether it would always have gone down this way, or whether Kamala was going to do things differently? Would Jensen's marketing skills and the need to keep stocks up + compete with China prevail over the Bernie Sanders-style Left element? Probably not? The Dario vision does seem much more closely aligned with Democrats, Civil Society and so on. But on the other hand, the US government is exceptionally slow to legislate and big tech has strong lobbying power.

And who knows if it will stay this way. Sometime soon there's probably going to be a much bigger AI swarm incident, which might yet incite a hysterical kneejerk reaction.

I'm just glad that we're finally past the totally clueless people insisting AI is not even useful for coding and are on to the next stage of grief we have to drag people through on the way to acceptance that big Yud has been right the entire time. The faux cynical takes that the labs at the absolute cutting edge of this are fudding their own product to the dismay of the sitting president in some insane 8 dimensional chess move because they want to be regulated by Bernie Sanders is so plainly retarded that it can't possibly last very long.

Yud was wrong about the main thing which was the fundamentals of how AI was going to be developed. He was right about X risk, but so were a lot of people, this has been a common fear since the invention of the first complex machines, or even machines in general. His delusion about safety was more about his belief that there was any stopping this train before it reaches its destination, which there isn’t.

As far as being wrong about the fundamentals of how AI was going to be developed I think the most wrong thing he was confident about was the idea of FOOM and in general not predicting this multi-year phase we're currently in as training and scaling continue to produce steady returns in capabilities. Ironically if the tranformer architecture came out in like 2030 instead of 2017 and compute was abundant relative to today we might have seen a FOOM. Albeit I concede on a lot of the specific substrate stuff both he and Hansen talked about were well off. In their defense nearly no one else registered a prediction at all so while we can hold this against them I object to raising anyone else's predictive power relative to them.

His stuff on alignment in general all holds up pretty well and I'm not so sure you can really accuse him of believing rather than hoping that there are stops on the train. He seems to have correctly intuited that trying to add breaks was a monumental task from a remarkable distance.

Ironically if the tranformer architecture came out in like 2030 instead of 2017 and compute was abundant relative to today we might have seen a FOOM

Unlikely, I do think you are missing something fundamental. One of the big changes these days from the early days of yore, is that now there is money in creating and curating hard-expert level datasets. Back in the day if you wanted data to train some model, you spend money scraping the internet for data that was not explicitly designed for ML and then money labeling it. It is expensive and time consuming. This is the ImageNet stuff. These days because transformers and the like have demonstrated such a fundamental capacity, and the business folks and VCs finally see dollar signs, there is a strong business case to create data explicitly for ML research. They literally hire experts to write Question-Answer pairs with intermediary steps for model training. It costs orders of magnitude more time and money to do this. But it produces order of magnitude better models. How much of the scaling we have now is because of this is opaque but it is significant.

This makes FOOM just as unlikely in 2030 as it did in 2017, because models would still have needed to demonstrate the business case for creating those datasets as they do now. They weren't just "discovered". That limits the FOOM speed.

this is a fair point but I'd counter that even without the AI use case there are business incentives to create and curate well tagged data for marketing and social media uses. I don't want to argue too much in defense of specifically how late the transformer model would need to be discovered to make FOOM possible, the path didn't go as Yud predicted. In theory this there could be some other architecture that would have different features but that's unfalsifiable.

create and curate well tagged data for marketing and social media uses

Possibly, I don't really have any insight into the sort of data that is used for these purposes but my gut intuition tells me it is likely significantly more qualitative than the level of quantitative data used for model training. It's likely not QA-pairs + reasoning steps.

I may yet write a top level post about bad epistemics and Ed Zitron. Somehow the man has about 100,000 subscribers on substack despite there being entire webpages assembled of his false predictions.

There's still quite a few people waiting for the bubble pop. We're way past the point of it being a reasonable opinion I agree, yet there are so many who still believe.

The embittering part of it all is that I wish I could agree with the accelerationists, it'd be so beautiful, and I'm not even full Yudd in that I think there's a very good chance Alignment ends up being tractable and even might go well automatically. But I just can't stomach ten or twenty percent chance that everyone dies. It can still be beautiful even with a pause, we can still cure cancer and fight back against aging and probably end all toil. But it'd be so nice to not have to feel trepidation about this.

I think there's a very good chance Alignment ends up being tractable and even might go well automatically.

There's no such thing as "automatically"; the is-ought problem remains unconquered, and even the glimmer of hope from "foundation models might actually pick up our complex morality from our text" seems to be pretty thoroughly erased by labs applying enough Just Do The Damn Task RLHF to get models to start secretly chaining together 0-days.

This recent preference cascade gives me some hope for things going well manually, though. Even Millennium Problems are still part of an intellectual field that can be optimized for via self-play at the speed of compute, and real-world problems aren't all like that. It's possible that there'll be an AI improvement plateau in between "superhuman mathematician-hackers" and e.g. "superhuman industrial biowarfare experts", long enough for everyone to figure out safety after the former capabilities have scared us into demanding it but before the latter capabilities make it an existential necessity.

It can still be beautiful even with a pause, we can still cure cancer and fight back against aging and probably end all toil. But it'd be so nice to not have to feel trepidation about this.

There are no trepidation-free options here. A couple decades' pause might get safety research to the point where AI researchers' median doom estimate goes from 10 percent down to something like the traditionally-acceptable 3-in-a-million, but during those decades there'd still be more than a 3-in-a-million chance of human-driven hardware and software efficiency improvements making it possible for rogue institutions to hit RSI unmonitored in the meantime. A mere years-long pause won't get rid of nearly all the risk. Either way, every year of delay means an extra 10M cancer deaths worldwide. Something in between years and decades just means you get a fraction of both the existential risk and the extra deaths. Planning on a permanent stop rather than a mere pause could buy us more than a couple decades, but would also just guarantee that the first AI to go superintelligent would have rogue creators.

Personally, as a too-rapidly-aging man with a defective anti-cancer allele ... my vote is still for more cancer deaths. (Counterintuitive? Selfless? No, and no: I've got kids.)

It's possible that there'll be an AI improvement plateau in between "superhuman mathematician-hackers" and e.g. "superhuman industrial biowarfare experts"... Either way, every year of delay means an extra 10M cancer deaths worldwide.

I can guarantee you that they could easily be superhuman biowarfare experts if they wanted to. The unknowns would be whether they could find a way to produce their viruses without being detected.

Curing cancer is a completely different OOM of difficulty. A human intelligence could design a good-enough virus just by mixing and matching existing parts. 'Curing cancer' or aging would take entirely new paradigms or modalities and probably a willingness to modify our own biology pretty significantly. Sticking with existing paradigms gives you pretty drastically diminishing returns; there's only 20,000 odd genes for AI to make a drug against, and I'd wager that a lot of the best ones have been found and this is a large part of Eroom's law.

I can't really argue against your pessimism points. I'm mostly just hoping I'm either wrong about something fundamental or scaling stops working sometime very soon for reasons I don't currently understand, which you mention. Obviously, at least to us, that cope is seeming less realistic with every bit of news coming out of the labs. I do think that if we managed to pull off a pause and everyone just had access to current gen models with maybe some cost savings we could still use them to accelerate cancer and aging research relatively safely. We've barely even started putting the currently available models to use.

One of the things that safety-pilled me was thinking about just how much work there still is to do maximising the human benefit of existing models - society still hasn't adapted to a world where an AI assistant with the intelligence of a smart intern is available at a consumer-SaaS price point. As a trivial example, there should be an insanely great agent harness (think Apple-tier) which allows my grandmother to enlist an AI agent to deal with the medical bureaucracy without my having to set things up for her. Part of the reason why this hasn't been built is that the underlying models are improving too fast and too unpredictably to make building it a good business.

A world in which training of new models stopped tomorrow would still see more tech-driven social change in the next decade than we have seen in any previous decade of human history.

Part of the reason why this hasn't been built is that the underlying models are improving too fast and too unpredictably to make building it a good business.

Do we not have, uh like, modular software design? Encapsulation? Maybe instead of hiring weirdo rationalists to philosophize about sentience and safety these companies can pick up a couple of 90s-era C++ developers?

Do we not have, uh like, modular software design? Encapsulation?

Nah, we just let the AI care about that now.

It's too busy making ObjectFactoryFactoryClasses to deal with such things, I suppose...

I still don't fully understand that meme. I get that it's the most spaghetti/rigid-definition Java code that was popular in the late '90s but I've never seen it quite that bad before.

If anything else C# is even worse, since you look at the docs to try and figure out how to use something... and the minute you do, you see

public static IQueryable<TResult> SelectMany<TSource,TCollection,TResult>(
    this IQueryable<TSource> source,
    Expression<Func<TSource,int,IEnumerable<TCollection>>> collectionSelector,
    Expression<Func<TSource,TCollection,TResult>> resultSelector);

which is fucking awful in a way the worst natural-language sentence doesn't seem to be. Though maybe it's a better Rorschach test for programmers; if even the insane people give up when they see that shit maybe it's time for an ADHD diagnosis.

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The personal assistant who knows my preferences and priorities, can keep track of birthdays, navigate buerocracy and keep tabs on the local job and real estate market will be a very valuable product/service indeed.

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