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 -
It might be worth moving this over into the finance thread, but I am at least partially putting my money where my mouth is.
I'm of the opinion that the current LLM hype is starting to hit the second knee of the S-curve, both financially and technically.
Technically, exponential growth leads to exponential friction, and it looks to me like the real-world improvements in model capabilities are slowing down between generations. Anecdotally, it feels like the models are increasingly fungible and most of the ostensible improvements have come from harnesses, which are regular old software engineering. I think there's something there, but I think LLM tech represents a local maximum. I'm eagerly watching whatever Yann Lecun is cooking up at AMIL, because the general concept of a world model seems to map better to what we think of as "intelligence". His paper on energy models, specifically, is fascinating.
Financially, I think a lot about the "during a gold rush, sell shovels" aphorism. I also think about Buffett and Munger's rules of investing. Meta and Oracle are buying shovels, but using them to dig their own graves, so far as I can tell. I don't think Anthropic and OpenAI are ever going to be able to support their valuations, and per their S-1, xAI has already pretty much given up. If Google dies, it'll be for reasons other than AI spending. Nvidia has a good product and a good moat for now, but various specialized competitors are nipping at their heels while Chinese cards may develop into direct competitors.
In other words, I think the tech is going to continue developing, but I think a lot of the current players are in for a rude reminder of market on market fundamentals by 2H 2027 or so. I know you've bemoaned "financialization" in the past, but at the end or the day the economy is just people buying and selling things, and fuck me if it doesn't seem like some of these companies are trying to act like that's not true.
Where does that leave me? I'm moving down the stack. If the tech is going somewhere, it has to run on real things and interact with the real world eventually. Companies doing physical things are riskier to start than pure software, but they're less likely to get disrupted once they establish themselves. I've largely stopped investing in funds that hold significant amounts of meta, oracle, Tesla (because I think they may absorb SpaceX), and even Nvidia. On the other hand, I'm expanding my positions in funds that hold TSMC, ASML, and Lam Research. I am watching Cerebras, but I won't invest until I better understand how they're using software to get around defects on their enormous chips.
Fair enough, I guess that's a reasonable stance.
It's just that just today I see people online talking about Qwen 3.7 Max:
Are they lying? Was the kernel made up? Maybe Alibaba is massaging the figures to some extent with the exact meaning of what a 10x speedup means in this context, dramatic speedups for just a few tasks being averaged out. Yet we know that other AI models can also do this kind of task, the general idea can't be just a lie. If it's not a lie, then surely this seems like a highly desirable, powerful technology that can substitute for high-end human talent to some extent. GPT5.5's verified mathematical conjectures seem hard to cheat. Kernels and mathematics seem to have real world value, as does whatever Anthropic's been doing with the war in Iran in terms of intelligence, rapid realtime assessment. Hard to get more real-world or frictional than warfare...
Cases like this, and the erdos problems, are exactly where LLMs shine. Problems with clear and unambiguous reward functions that are difficult to hack are perfect use cases. In the Alibaba case, they likely have an extensive set of characterization tests that guarantee consistent behavior. An LLM with a good harness can pound its head against those tests forever while simultaneously measuring the performance as a success metric. It will never get tired and it won't get sick of doing that kind of work.
There's definitely value there, but I don't know how much value. The combination of technical depth and strong guardrails make for a very schizophrenic kind of difficulty. Doing that kind of work is traditionally either the domain of a plucky junior with too much energy, or an insane wizard who claimed a broom closet as his office.
When we've experimented with that kind of optimization work at my employer, it tends to be very expensive, since most of the results come from the absolute tirelessness of the agent. In comparison, how much are you paying your junior? How much are you paying your wizard, and what is he doing if he's not doing that task? Security scans are a similar thing. Line audits aren't hard, but they're hella time consuming. As model costs rise (and they are rising per task completed when you compare any single vendor over time), it might legitimately be cheaper to throw interns at the problem than LLMs.
At least on the software side, I think there's a reasonable chance that what we're seeing is a temporary pop due to a lot of highly verifiable technical debt deadwood finally getting burned out, and that might not be a constant source of demand.
On the war side, I wish I knew more. The sensitive nature of the topic means that all parties are incentivized to obfuscate and dissemble as much as possible. It might legitimately be an ideal case. LLMs do well when you can accept 95% accuracy, and in something like intelligence analysis, 95% accuracy probably has the spooks all but shitting their pants.
More options
Context Copy link
More options
Context Copy link
Surely if you think AI is capping out then you should expect ASICS to be the play. QCOM and the like. I have a hedge in some of those in case scaling doesn't continue as I expect.
They're firmly on my "investigate" list.
More options
Context Copy link
More options
Context Copy link
Astral Codex Ten on this exact topic
More options
Context Copy link
More options
Context Copy link