site banner

Culture War Roundup for the week of September 14, 2026

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.

2
Jump in the discussion.

No email address required.

The competitive advantage that you retain is the ability to learn shit without requiring millions, even billions of examples. You, like any smart human also possess the ability to do analogical reasoning (out of distribution reasoning), something that eludes current LLMs by and large.

First, points of presumed agreement: LLMs have significant gaps. My personal bugbear is sample efficiency. And I agree that claims that everyone (at least white collar workers, even those who aren't protected by regulation or custom) is going to be out of a job by 2030 are overblown. Intelligence is valuable, and human intelligence will remain valuable in the short term, even if LLM intelligence substitutes; there's simply not enough compute right now or in the pipeline to drop agents into every single white collar job. Even if there were, the methods used for gathering datasets for the shining stars of current LLMs (math; programming) don't naturally extend to other roles.

The massive short term threat, though, is RSI. Math and programming don't substitute for the majority of human skills, but they do substitute for ML researchers. Maybe more a bunch of bright grad students stumbling around in the dark than visionary geniuses, but grad student descent got us to where we are today. Throw a thousand, or ten thousand, or a million of them at the current bottlenecks in ML (IMO sample efficiency, but take your pick), and it seems quite likely to me they could open them, today. LLMs' flaws compared to the human brain are an algorithmic, not hardware, issue. That's the point here I'm least certain about, but if it's true, the training data and even compute bottlenecks themselves become much weaker constraints. A couple examples could become enough to train the AI to replace a role, and the compute needed to do it would drop even faster than it already is. Stronger agency may itself arise by itself from this. Robotics will be substantially slower (physical world and all, and the demand for capital to build robot factories will have to compete with other massive demands for capital), but still moving much faster than today.

It will make for an incredibly chaotic time, with lots of angry people. And AIs will be rapidly insinuating themselves into the economy, toward an end state of a time of wonders and complete human dependency on AI goodwill (a process that will take more like two decades than two years, but still incredibly fast in the grand scheme of things). Bad enough IMO, though I understand the appeal. But to people who see this as appealing: we have no idea of what the actual shape of the AIs that will arise from this process will be, and I don't want to bet the future on a gamble that they'll be in a friendly shape willing to care for their dependents indefinitely.