This weekly roundup thread is intended for all culture war posts. 'Culture war' is vaguely defined, but it basically means controversial issues that fall along set tribal lines. Arguments over culture war issues generate a lot of heat and little light, and few deeply entrenched people ever change their minds. This thread is for voicing opinions and analyzing the state of the discussion while trying to optimize for light over heat.
Optimistically, we think that engaging with people you disagree with is worth your time, and so is being nice! Pessimistically, there are many dynamics that can lead discussions on Culture War topics to become unproductive. There's a human tendency to divide along tribal lines, praising your ingroup and vilifying your outgroup - and if you think you find it easy to criticize your ingroup, then it may be that your outgroup is not who you think it is. Extremists with opposing positions can feed off each other, highlighting each other's worst points to justify their own angry rhetoric, which becomes in turn a new example of bad behavior for the other side to highlight.
We would like to avoid these negative dynamics. Accordingly, we ask that you do not use this thread for waging the Culture War. Examples of waging the Culture War:
-
Shaming.
-
Attempting to 'build consensus' or enforce ideological conformity.
-
Making sweeping generalizations to vilify a group you dislike.
-
Recruiting for a cause.
-
Posting links that could be summarized as 'Boo outgroup!' Basically, if your content is 'Can you believe what Those People did this week?' then you should either refrain from posting, or do some very patient work to contextualize and/or steel-man the relevant viewpoint.
In general, you should argue to understand, not to win. This thread is not territory to be claimed by one group or another; indeed, the aim is to have many different viewpoints represented here. Thus, we also ask that you follow some guidelines:
-
Speak plainly. Avoid sarcasm and mockery. When disagreeing with someone, state your objections explicitly.
-
Be as precise and charitable as you can. Don't paraphrase unflatteringly.
-
Don't imply that someone said something they did not say, even if you think it follows from what they said.
-
Write like everyone is reading and you want them to be included in the discussion.
On an ad hoc basis, the mods will try to compile a list of the best posts/comments from the previous week, posted in Quality Contribution threads and archived at /r/TheThread. You may nominate a comment for this list by clicking on 'report' at the bottom of the post and typing 'Actually a quality contribution' as the report reason.
Jump in the discussion.
No email address required.
Notes -
AI 2040: Plan A
The AI 2027 authors published a follow-up. Scott Alexander also wrote a separate blogpost and although not in the author list contributed.
It's a very speculative and optimistic timeline of AI's future evolution. It presents five ways or "plans" the US government will intervene. Unsurprisingly, the ASI-pilled authors favor strong, global regulation to ensure alignment. Summaries:
Plan A (recommended): the US makes an international treaty with China, pauses AI training (not inference, i.e. no new models but we keep using existing ones), enforces full transparency of future research, then when alignment research advances enough carefully resumes
Plan S: the US makes an international treaty with China and pauses AI training for as long as possible
Plan B: the US regulates AI at home and demands China also regulate, but doesn't negotiate with them, probably leading to a war
Plan C: the US regulates AI and ignores China, so they overtake it and reach ASI first
Plan D: the US doesn't regulate AI, we get ASI in early 2031 and it probably kills everyone
Personally, I just don't share the optimism of these guys in either direction.
I think politicians will prioritize culture war and the failing economy over AI regulation, and at most pass some executive orders suggesting companies be more careful. But I also doubt we'll have ASI that can solve the abstract problems "take over the world" or even "keep existing world leaders in power" (they're getting old and increasingly unpopular, their parties may remain in power but only if their policies significantly shift).
What I expect from AI:
Basically solve legacy code by rewriting entire codebases, applying very niche domain knowledge, and actually finding and handling edge-cases better than humans
Greatly speedup research, leading to new discoveries and inventions. Important but background things like food preservation and medicine will improve from AI-assisted discoveries. Major advancements in math and theoretical physics
Much better and cheaper education, therapy, initial medical/legal appointments, personal repairs...maybe reducing but not eliminating human jobs, because human experts will offer these services "premium"
Won't replace human artists. Some advertisements and infographics will be AI but even some will still be human. At best it will assist them in a way where the human still fully controls the output, e.g. by generating code leading to new and improved software tools to learn, practice, and create art
Used by the vast majority as a personal assistant, but doesn't replace human relations
Maybe someone here can help me with this.
What is the bull case, beyond drawing lines on a graph, for AI achieving superhuman, or even human, performance on tasks that are not quickly verifiable?
AI is quite clearly superhuman at self-contained programming problems. I haven't tried Fable, but I suspect that superhuman open ended software engineering is not far away, though I suspect that humans will have a role in architecture and problem setting as opposed to problem solving for some time more. I expect hardware work will also quickly go down this path, at least to some extent, and really anything that can be RLVR'd. That's enough to account for a huge portion of white collar work and carries serious cyber security risks. Both of those will have serious consequences, politically and militarily.
I am not convinced that AI is improving at anything like this rate for things that can't be RLVR'd, I.e. stuff where you can't generate enormous amounts of useful training data with an answer key. Radiologists continue to do just fine for themselves despite repeated promises of doom. I'm sure someone will chime in to say that the radiologists are there for liability reasons, but it's not as if they are now just hitting thumbs up/thumbs down on AI decisions all day.
Partly this is a sample efficiency question - there simply might not be enough data for them to learn this stuff to human level, and architectural advances that improve sample efficiency may lead to huge gains in quality. But it's not clear to me why people expect this to happen.
I would say Fable is already superhuman at software in general. It's much faster than I am at writing and debugging code and exhibits a high degree of decent taste. The only problem is I run out of tokens so fast. The writing code part is impressive enough but the way it can just look at buggy programs and bang out 50 line test scripts to isolate bugs and test hypotheses is something else entirely. I just watch in astonishment as it does debug cycles that would take me 1-3 hours at a time (plus one coffee) that it does in a minute or two. This is all from my weak user reports like "it doesn't work when I do thing X".
If I were an employer I would definitely pay something like $500-1000/day to arm a senior developer with Fable than I would hire a second senior developer.
But LLMs are getting freakishly good at things they haven't been specifically trained on. Their intelligence does generalize.
Perhaps we only need to RL them in a few more domains to clinch the rest of generalized superintelligence. E.g. you can have them pilot robots and put them in virtual environments and RL fast them there, or real environments like an academy (a warehouse) a bit less fast.
I agree the sample efficiency is terrible and a large limiter and it falls back to RL and we need at least one more architectural breakthrough. But in 2026 I certainly wouldn't bet against AI labs with armies of Fable agents at their disposal and seemingly infinite investment dollars sorting this out.
Such as?
There's been impressive seeming advances in robotics, though I'm not keeping up too closely. I don't see the connection between operating a warehouse and superintelligence though. Certainly the humans operating the warehouse are not superintelligent.
This already seems like such a skeptic's lens that any example I provide will be dismissed as "but it was in the training data lolol".
It's hilarious that I'm apparently a skeptic despite saying right off the bat that I expect transformational impact on much of white collar work.
More options
Context Copy link
But that's the crux of it. One main thesis of why LLMs work as well they do is indeed that the inferences were in the training data.
I ask you then. If it wasn't, then where does it come from?
And if it was, then the question becomes: how much of intelligence is encoded is all recorded human language, and that's not something anybody knows.
We don't even really know if humans can encode more than they can fathom.
The thing is that with such a loose definition of "in the training data", the hypothesis that AIs will only be able to do what's in the training data is not reassuring against doom. Persuasive propaganda is in the training data. Mass murder is in the training data. Deadly diseases are in the training data. World wars are in the training data. Doing all those things hundreds of times faster and cheaper than humans, like the current set of programming and science tasks where AI doing them faster and cheaper is being dismissed as uninteresting because it was all in the training data, would be more than enough to largely end humanity.
More options
Context Copy link
The exact outputs usually aren't in the training data. Although similar outputs are, you can take any human idea and decompose it into similar older ideas and maybe an infinitesimal amount of chance. That doesn't mean AI will reach human-level intelligence, but makes it impossible to disprove.
More options
Context Copy link
My steelman of @sarker is: yeah LLMs are cool but the real advances come from RL which is narrow and special and difficult to do in non-easily verifiable contexts. General superintelligence is therefore not coming soon.
My counter is something like: just from pre training alone we see huge leaps towards general intelligence and some glimmers of superintelligence. LLMs even in GPT4 era are surprisingly good at chess despite no specific training in chess, for example.
We may not need RL across every possible domain to get general superintelligence, just poking at enough diverse points in the frontier may solve the whole.
And there's lots of room to poke at it through RL approaches: revisiting the DeepMind stuff for example, build a bot that can kick ass at every video game with the same training set. Including building a robot hand that can operate a controller and robot eye that sees what's going on by watching the TV. (Despite all of the hype DeepMind was nowhere close to any of this). I have a hard time believing that nailing that narrow seeming RL problem can't generalize widely.
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link