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PaperclipPerfector  6d ago (text post)   20297 thread views

Culture War Roundup for the week of August 17, 2026

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Long time listener, first time caller.

The war on AI datacenters (and normal ones as a bit of collateral damage) has been going on for a little while now, with those opposed fiercely fighting a lot of these projects pretty much wherever, whenever. It seems to be one of the most bipartisan issues facing the country right now and those who are pro-datacenter have been effectively politically slaughtered. As of a few days ago though, there seems to have been a small little flash point in the thing. I haven't really seen a thread here about this (although definitely could have missed one), and I'm actually a little surprised at it since I do think this is probably one of the biggest culture wars in America of this year. Anyway, curious thoughts on this, etc

From what I can tell, the flashpoint was yesterday when Axios reported (and later posted) a letter from the Ohio RNC to AI companies effectively calling being pro-datacenter politically radioactive. It kinda exploded at this point over Twitter and later Greg Abbott put a thing out about it yesterday after enacting a pause earlier this month. Basically the only one who didn't respond in a politically obvious way was Trump in a presser who was quoted as saying "if I were the mayor of a town or the governor of a state, and I had a chance to get a big plant in, an AI plant or a data center." I think this quote right here might be the worst strategic blunder since the Iran War. If I were the DNC, the clip would be on every station in the country

The reaction to these things are incredibly understandable. Their argument effectively goes "they use our water and electricity, provide a service to enhance the surveillance state and the interests of Big Tech, their proponents openly admit that they're gonna take your job, they bully communities, and they don't even pay taxes either, all the while poisoning the communities they leech on with pollution and noise." Personally, I'm honestly quite surprised that political radioactivity is the biggest thing that has happened so far. This seems to be an issue that unites people regardless of political party or affiliation...

Prescott a conservative, soft-spoken retired US Bank executive, and Sarah a vivacious and slightly distractible environmental advocate in a neon pink dress [...]

...probably because it's being correctly seen as a existential threat to people's lives and livelihoods. Polling against datacenter construction is incredibly poor, arguably being worse than coal or nuclear power plants (yeah I know, different surveys). I think this, if I were to pin a spark on this (there are many), it's the story of the Memphis xAI datacenter and also the lawsuits against Solon Township to effectively bully them into allowing the project. Communities, seeing what happened there and politically, have somewhat backtracked quite a bit where before they were much more open to it. Some of those that haven't are proactively enacting outright bans

If this culture war has one side, who is the other? Silicon Valley, billionaires, etc, seems to be the obvious answer here.

Both Altman and Amodei screwed up public perception big time, although I don't think it's 100% their fault. I imagine though this is basically because they're put in a double bind. If it isn't absolutely economy ending for every other company, the trillions pumped into their valuation will vaporize faster than you can say "AGI," but people tend to believe (correctly so) when someone threatens them (and have especially checkered pasts as both these guys do), that their threats are genuine, so when those PR puff pieces trickle down to the public at large.

The All In guys have tried to pin this on Amodei in particular, but I think some people just see that as them talking their book

They've tried to label the opposition as "China," but that clearly doesn't make much sense given that the problems are genuine. Cremieux has tried to take up the mantle on the issue, but I think people see his arguments are seen as specious and as relying on theoreticals that don't match observations on the ground, and this has I think backfired

Expect to see more bans and moratoriums happening across the country. Will be interesting to watch. Thoughts?

America is a big country, inhabited by few Americans (in most of its territory). Datacenters are, all told, pretty small things, these aren't wheat fields or even airports. The politics of early singularity is interesting: people refuse the maximalist AGI and human replacement framing for assorted cultural reasons, and instead latch on to any adjacent smear – from the inane water use stuff to semi-legitimate surveillance and energy costs concerns to "they don't create jobs". The outcome is overdetermined. AI is already the main engine of economic growth, and the success at AI development and broad diffusion (at this level of utility, just scaling inference capacity) is deemed a matter of national survival. The popular resistance will be overcome, persuaded to relent, bribed in select locales, or routed around. Maybe Anthropic will finetune a Mythos 2 version specifically for propaganda.

The biggest practical consequence may be the increase in returns to Musk's idea with orbital compute, and of course his ownership of SpaceX. There are no NIMBYs in space. Without all this, it'd have taken maybe 2-3 more years until viability; now, it starts working pretty much after they manage the first Starship land-and-reuse after an orbital insertion.

The politics of early singularity is interesting: people refuse the maximalist AGI and human replacement framing for assorted cultural reasons, and instead latch on to any adjacent smear – from the inane water use stuff to semi-legitimate surveillance and energy costs concerns to "they don't create jobs".

This is probably because you or they won't see AGI or human replacement in your lifetimes, but they will see higher water bills, a surveillance state, higher electricity bills, and job loss. I am not sure what makes you so blasé about predicting bizarre outcomes like «singularity», but it is really odd and out-there, especially given nobody has ever predicted anything properly in the AI space. The systems are too random. Nobody knew GPT 3 would come out and expand in 2022, particularly not you. Nobody knew Opus 5 would be doing what it's doing now in 2026, you have literally no idea what 2030 or 2034 will look like. None.

I don't know why you assume "particularly not you" would have predicted GPT-3. GPT-3 follows from GPT-2 follows from Vaswani et al. Why do you think plenty of intelligent people, including Dario Amodei and Ilya Sutskever, got so agitated when that paper came out and started scrambling for commercialization? Do you believe they were that into machine translation as a business? Of course folks like Hinton, Sutskever's teacher, were running multi-decade research programs premised on connectionism being enough for intelligence in general, not just for some particular capability demo. I was pretty AGI-pilled all of my conscious life, admittedly mostly for shallow sci-fi reasons, and got convinced of inevitable singularity once I saw (and later launched) DeepDream. That degree of quasi-creative flexibility was an unambiguous proof, for me, that we have really grasped the sufficient basic primitive of universal learning. That's the biggest piece that biological life needed to go from insects to humans, and hardware&software cycles are inherently absurdly faster plus capital can grow exponentially, so what exactly could the counterargument even be?

In my opinion, this is "out there" on purely rhetorical grounds – skepticism is a product of incomplete information available in the popular discourse, and skeptics who profess to have some principled objections are continuously humbled and forced into retreat (like Chollet or LeCun, the French are annoying in this way; or hey, remember when Hlynka tried to burst my/others bubble here with some prose about mathematical Truth versus wordcel nonsense? I wonder how he feels about the recent math results from OAI/Ant). The coarse-grained logic is settled since Samuel Butler, the timelines and mechanisms were getting constantly refined, now we have a good idea of the necessary mechanisms and a thicket of more or less cheap ways to compensate for any particular – and still temporary – technological block.

Vernor Vinge, 1993:

I believe that the creation of greater than human intelligence will occur during the next thirty years. (I'll be surprised if this event occurs before 2005 or after 2030.) From the human point of view this change will be a throwing away of all the previous rules, perhaps in the blink of an eye, an exponential runaway beyond any hope of control. I think it's fair to call this event a singularity. It is a point where our models must be discarded and a new reality rules. As we move closer and closer to this point, it will loom vaster and vaster over human affairs till the notion becomes a commonplace. Yet when it finally happens it may still be a great surprise and a greater unknown.

Shane Legg, 2009:

UPDATE 11 April 2009: Note that these predictions do not take into account my apparent bias towards predicting that things will happen faster than they actually do (see previous post). The required compensation for technology events appears to be about 50% more time. Thus if you want the “Shane meta predictor”, then take 2033 as the expected date, perhaps with a standard deviation of 7 years.

Shane Legg, 2011:

I’ve decided to once again leave my prediction for when human level AGI will arrive unchanged. That is, I give it a log-normal distribution with a mean of 2028 and a mode of 2025, under the assumption that nothing crazy happens like a nuclear war. I’d also like to add to this prediction that I expect to see an impressive proto-AGI within the next 8 years. By this I mean a system with basic vision, basic sound processing, basic movement control, and basic language abilities, with all of these things being essentially learnt rather than preprogrammed. It will also be able to solve a range of simple problems, including novel ones.

Kurzweil, according to Google's AI, “predicts that Artificial General Intelligence (AGI) will arrive by 2029. He first made this prediction in his 1999 book The Age of Spiritual Machines, and has maintained it through subsequent books like The Singularity Is Near and The Singularity Is Nearer.”

and in 1988, “pioneering Carnegie Mellon University roboticist Hans Moravec predicted that hardware matching the computing power of the human brain would arrive by the late 2020s, enabling human-level artificial intelligence. He argued that processing capacity, driven by hardware scaling, would naturally unlock general machine intelligence.’’

While nowhere close to these giants, largely on account of my age, I believe I have a pretty decent prediction track record in this field (I have plenty of receipts here, such as talking about DeepSeek when their V1 Coder and LLM came out in late 2023; since then their architecture and much else became the default paradigm in the industry).

Nobody knew Opus 5 would be doing what it's doing now in 2026,

What about Opus 5? It's been programmed since o1-preview at least. Of course the specific product name, timing, costs, benchmark numbers and downstream capability priorities are all unpredictable but trivial. Anthropic surviving and remaining well-resourced enough to compete was very likely, the substance of the rest follows necessarily.

you have literally no idea what 2030 or 2034 will look like. None.

That's fair, that's the predictable part, as Vinge has predicted. But then again, as Yud said, you don't know how specifically a superhuman AI will beat you in chess; you can just safely bet on it doing so. I have a pretty clear idea that at this rate we will have AI doing wildly superhuman knowledge work by 2030. This is the most salient and important part, unlike the specious water use trolling. And thus the real problem people should have with AI, I believe, is still the same I outlined 3 years ago.
I strongly believe there are no remaining valid objections to this big picture. You're welcome to make some.

By this I mean a system with basic vision, basic sound processing, basic movement control, and basic language abilities, with all of these things being essentially learnt rather than preprogrammed.

Seems like we are still missing a few of these bullets, even if we add his 50%?

No, we have such systems, for a certain notion of "basic". It can be built as a simple omnimodal Transformer. Or something like this. It was just underrated how much can be done with language or images alone, without robotic embodiment – these criteria were informed by the assumption that human-level intelligence is more tightly coupled to our mode of existence, and sample efficiency would suffer catastrophically if we just, like, pretrained on a large web corpus.

Liang Wenfeng explicitly says that he won't bother with embodiment because there's a more fundamental problem within the current paradigm:

With the current generation of AI technology, if you can describe a problem very clearly and give it complete context and instructions, it already surpasses humans. But there is a condition, a prerequisite: you give it the complete context and the complete instructions. And this prerequisite is very hard to meet.
For example, today’s meeting: we have a very long and rich context. Perhaps each of us has decades of context. AI doesn’t have that. What AI can have today is the ability, within a limited context, to do better than humans. But it still cannot replace humans. What’s missing is continuous learning. Because humans can continue learning.
… But if AI had continuous learning — if it could, like your employee, come into the company and learn for two months — then it could replace anyone. So the next step is still missing “learning to learn.” We can understand AI development as a staircase. Last year’s step was CoT — the chain of thought. We found that by using chain-of-thought reasoning, we could push intelligence higher. By letting AI think for itself, the ceiling rises and AI can do more. We crossed one step. This year’s step is Agent. We found that with agents, even more things can be done: the range of capabilities expands, and the upper bound of intelligence rises. Why is it a staircase? Because every later step builds on earlier ones. Agent uses CoT, and CoT uses the previous step — the language model. So no step was wasted.
Thus, the development of AI, the direction of intelligence, is traceable. This year’s step is Agent, but Agent will also eventually reach the end of its step. When it solves all solvable problems, it still won’t replace your employees — it will just have reached its ceiling.
Like CoT: after CoT reached its ceiling, it already surpassed the best humans at Olympiad math and programming. But it stopped there; that technology didn’t reach AGI. See, the direction of intelligence is traceable.
… Where we stand now, at the Agent step, we can see the next bottleneck: continuous learning. The next problem to solve is how to make continuous learning work. This is visible, and relatively clear. It’s the obstacle right in front of us. You must cross it, and there must be a way to cross it, but it will take time. After continuous learning, we may arrive at a singularity. That singularity is: when the model can continuously learn, it can do everything humans can do. It will be able to develop its own next version, conduct research itself, and create the next, more advanced AI model. So it will reach a singularity — self-iteration. But this singularity is not truly a singularity; it’s also a gradual process.
The process may be a long, gradual shift, not a sudden mutation. But habitually, people call it a singularity, because earlier prophets thought there would be a singularity. But actually, it’s not a singularity — it’s a continuous process. And after this step, I think the next is embodied intelligence.
This is our speculation — our view of the timeline: first solve learning-to-learn, then reach the self-iterating intelligence singularity, and only then embodied intelligence. After embodied intelligence, AI enters the real world: it can do housework for you, care for the elderly. We think this is an ideal roadmap. Everyone has different views; there is no right or wrong. We just think this roadmap is the easiest. The reason is that each step requires very little that is new. With this roadmap, we don’t have to work overtime. But if the roadmap were reversed — say, embodied intelligence first — then you’d be doing very hard, exhausting labor. We don’t want a roadmap like that. We want to do it the easy way.
If we first solve continuous learning, then the self-iterating singularity, then embodied intelligence, the path is easy. Because later you can use earlier technologies to help develop later ones. After the singularity, embodied intelligence no longer needs to be built by humans — the model itself will produce it. So this answers the question about our long-term goal.

We've made 1 million token context easy and cheap. With LLMs simply greping over a codebase, writing their own memos and using RLM-like tricks it's not hard to have them operate over many millions of tokens continuously. Then there are techniques to compress a given context into a higher-density "cartridge" prefix. Given strong priors from pretraining, this can functionally substitute for most of true continuous learning, in the sense that agents will be able to do long-range tasks well beyond the pretraining distribution. I'm pretty optimistic about the trajectory here. Robots will continue to develop in parallel for a while but that's just because this is "the easy way". We could merge it already.

Or something like this.

Sure, but does that thing talk? It's somewhat reasonable to argue that we have those things individually, but your guy seems to be expecting all of them as a unit?

Also 2011 + (8*1.5) would be 2023, at which point it would have been a much less reasonable argument...

The most promising systems are literally post-trained VLMs, it's really not hard to get them to talk again.

Man, do you really want to quibble about failing to nail 2026 or 2027 from 2011? We could have trained GPT-2 in 2005 if researchers had a bit more taste. Had Americans been less lazy, they wouldn't have needed Alex Krizhevsky to figure out how to use gaming GPUs for training AlexNet in 2012 (prior art was Romanian, in 2011, by the way). Timelines of exponential progress are very sensitive to the exact exponent.

Well his actual prediction (with the 50% caveat) would have been for 2019 -- so yeah, it's not really quibbling to say that he's wrong on the timeline. How wrong exactly is an open question, but that kind of the problem with timelines, yeah?

Timelines of exponential progress are very sensitive to the exact exponent.

Indeed, but the existence of an exponential term is very debatable in this case.

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