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 -
See For Yourself: A Live Demo of LLM capabilities
As someone concerned with AI Safety or the implications of cognitive automation for human employability since well before it's cool, I must admit a sense of vindication from seeing AI dominate online discourse, including on the Motte.
We have a wide-range of views on LLM capabilities (at present) as well as on their future trajectory. Opinions are heterogeneous enough that any attempt at taxonomy will fail to capture individual nuance, but as I see it:
LLM Bulls: Current SOTA LLMs are capable of replacing a sizeable portion of human knowledge work. Near-future models or future architectures promise AGI, then ASI in short order. The world won't know what hit it.
LLM moderates: Current SOTA models are useful, but incapable of replacing even mid-level devs without negative repercussions on work quality or code performance/viability. They do not fully substitute for the labor of the average professional programmer in the West. This may or may not achieve in the near future. AGI is uncertain, ASI is less likely.
LLM skeptics: Current SOTA models are grossly overhyped. They are grossly incompetent at the majority of programming tasks and shouldn't be used for anything more than boilerplate, if that. AGI is unlikely in the near-term, ASI is a pipedream.
Gary Marcus, the dearly departed Hlynka. Opinions not worth discussing.
Then there's the question of whether LLMs or recognizable derivatives are capable of becoming AGI/ASI, or if we need to make significant discoveries in terms of new architectures and/or training pipelines (new paradigms). Fortunately, that isn't relevant right now.
Alternatively, according to Claude:
The Displacement Imminent camp thinks current models already threaten mid-level knowledge work, and the curve is steep enough that AGI is a near-term planning assumption, not a thought experiment.
The Instrumental Optimist thinks current models are genuinely useful in a supervised workflow, trajectory is positive but uncertain, AGI is possible but not imminent. This is probably the modal position among working engineers who actually use these tools.
The Tool Not Agent camp thinks current models are genuinely useful as sophisticated autocomplete or search, but the "agent" framing is mostly hype — they fail badly without tight human scaffolding, and trajectory is uncertain enough that AGI is not worth pricing in.
The Stochastic Parrot camp (your skeptics, minus the pejorative) thinks the capabilities are brittle, benchmark gaming is rampant, and real-world coding performance is far below reported evals. They're often specifically focused on the unsupervised case and the question of whether the outputs are actually understood vs. pattern-matched.
The dimension you might also want to add explicitly is who bears the cost of the failure modes — because a lot of the disagreement between practitioners isn't about raw capability but about whether the errors are cheap (easily caught, low stakes) or expensive (subtle, compounding, hard to audit). Someone who works on safety-critical systems has a very different prior than someone shipping web apps.
Coding ability is more of a vector than it is a scalar. Using a breakdown helpfully provided by ChatGPT 5.2 Thinking:
Most arguments are really about which of these capabilities you think models have:
Local code generation (Boilerplate, idioms, small functions, straightforward CRUD, framework glue.)
Code understanding in situ (Reading unfamiliar code, tracing control flow, handling large repos, respecting existing patterns.)
Debugging and diagnosis (Finding root cause, interpreting logs, stepping through runtime behavior, reproducing bugs. Refactoring and maintenance)
Changing code without breaking invariants, reducing complexity, untangling legacy.
System design and requirements translation (Turning vague specs into robust design, choosing tradeoffs, anticipating failure modes.)
Operational competence (Tests, CI, tooling, dependency management, security posture, deploy and rollback, observability.)
Two people can both say “LLMs are great at coding” and mean (1) only vs (1)+(2)+(6) vs “end-to-end ticket closure.”
With terminology hopefully clarified, I come to the actual proposal:
@strappingfrequent (one of the many Mottizens I am reasonably well-acquainted with off-platform), has very generously offered:
A sizeable amount of tokens from his very expensive Claude Max plan ($200 a month!) and access to the latest Claude Opus.
His experience using agent frameworks and orchestration. I can personally attest that he was doing this well before it was cool, I recall seeing detailed experimentation as early as GPT-4.
His time in personally setting up experiments/tests, as well as overseeing their progress, while potentially interacting with an audience over a livestream.
He works as a professional programmer, and has told me that he has been consistently impressed by the capabilities of AI coding agents. They've served his needs well.
Here's his description of his skills and experience:
To what end?
He and I share a dissatisfaction with AI discourse that substitutes confident assertion for empirical investigation, and we think the most useful contribution we can make is to show the tools actually working on tasks that skeptics consider beyond their reach.
What do we want from you?
If you self-identify as someone who is either on the fence about LLMs, or strongly skeptical that they're useful for anything: share a coding challenge that you think they're presently incapable of doing, or doing well.
An ideal candidate is a proposal that you think is beyond the abilities of any LLM, while not being so difficult that we think they'd be entirely intractable. Neither of us claim that we can solve Fermat's Last Problem (or that Claude can solve it for us).
Other requirements:
A clear problem specification, or a willingness to submit a vaguer one and then approve a tighter version as created by us/Claude.
Nothing so easy/trivial that a quick Google shows that someone's already done it. If you want a C++ compiler written by an LLM, well, there's one out there (though that is the opposite of trivial).
Nothing too hard. He provides an example of "coding a Netflix clone in 4 hours".
An agreement on the degree of human intervention allowed. Can we prompt the model if it gets stuck? Help it in other ways? Do you want to add something to the scope later? (Strongly inadvisable). Note that if you expect literally zero human intervention, SF isn't game. He says: "I don't think I'd care to demonstrate any sort of zero-shot capacity... that's a silly expectation. If my prime orchestrator spends 30 minutes building a full-stack webapp that doesn't work I'll say
It doesn't work; troubleshoot, please. I trust your judgement."A time-horizon. Even a Max plan has its limits, we can't be expected to start a task that'll take days to complete.
Some kind of semi-objective rubric for grading the outcome, if it isn't immediately obvious. Is it enough to succeed at all? Or do you want code that even Torvalds can't critique? And no, "I know it when I see it" isn't really good enough, for obvious reasons. Ideally, give us an idea of the tests everything needs to pass.
If your task requires the model to review/extend proprietary code, that's not off the table entirely, but it's up to you to make sure we can access it. Either send us a copy or point us at a repo.
Nothing illegal.
But to sum, up, we want a task that we agree is probably feasible for an LLM, and where success will change your mind significantly. By which I mean: "If it succeeds at X, I will revise my estimate of LLM capability from Y to Z." Otherwise I can only imagine a lot of post-hoc goalpost movement, "okay but it still needed 3 prompts" or "the code works but a good programmer would have done it differently."
We reserve the right to choose which proposals we attempt, partly because some will be more interesting than others, and partly because we have finite tokens and finite time.
Miscellaneous concerns:
Why Claude Opus 4.6?
Well, the most honest answer is that @strappingfrequent already has an Claude Max plan, and is familiar with its capabilities. The other SOTA competitors include Gemini 3.1 Pro and GPT 5.3 Codex, which are nominally superior on a few benchmarks, but a very large fraction of programmers insist that Claude is still the best for general programming use cases. We don't think this choice matters that much, and the models are in fact roughly interchangeable while being noticeably superior to anything released before very late 2025.
Why bother at all?
We are hoping to change minds. This is disclosed upfront. We're also open to changing our own minds. We would also be genuinely interested to discover cases where we expect success and get failure - mapping the capability frontier is valuable independent of which direction the evidence points. We will share logs and a final repo either way. An interactive livestream is possible if there is sufficient interest.
Anything else?
You know the model. We'll be using Claude Code. The specifics of the demo are TBD, it could be a livestream with user engagement if there's sufficient interest, otherwise we can dump logs and share a final repo.
The floor is open. What do you think Claude cannot do?
Edit:
I'll forward the proposals to @strappingfrequent, assuming he doesn't show up in the thread himself. I am clearly not the real expert here, plus he's doing all of the heavy lifting. Expect at least a day or two of back and forth before we come to a consensus (but if he agrees to something, then assume I won't object, but not vice versa unless specifically noted), and that includes conversations within this thread to narrow down the scope and make things as rigorous as possible while adhering to the restrictions we've mentioned. At some point, we'll announce winners and get to scheduling.
I don't think you're quite clear in the post as to what camp you're actually in. Are you a straight bull? As in, do you think it can currently replace a sizeable portion of human knowledge work?
Moreover, it is not clear how knowledge work that is not coding qua coding fits into your schema. For example, I have in mind a flight dynamics simulation/control task. I'm not settled on it yet. My plan was to include a little twist that I had thought would likely not be in the published literature, but which I'm sure I could manage without too much difficulty, just pulling one book off of my shelf, confirming where exactly I need to make the modification and how (it's been a long time, but it's something I'm confident I could do without extreme effort), and then coding it. Unfortunately, I looked, and some darned student already published it (only minimal code published AFAICT, but they wrote out all the analysis in detail, so I can't really purely test its ability to do this aspect of the knowledge work on its own), so I'm trying to think of another good variant.
There are other little twists I had in mind, hoping to prevent it from being able to purely just pull code directly from others. These twists are things I've personally coded in the past, so I know they're doable. But the point is that they require sufficient knowledge to make choices along the way (for one example, choose this algorithm for this part, because I know it has certain characteristics) and I think they prevent it from being able to just use someone else's work for the core simulation components.
I guess, where does this fit within your schema, and where are you with respect to your own opinions? There is a lot of room between, "I personally know how to architect this code, what algorithms/assumptions to use, how to modify the analysis for the instant case, and then I use Claude to help with building the components", "I do the analysis, give it to it, tell it to code up the whole thing, then I go in and tell it to change things to make better choices that fit my knowledge-work-educated beliefs on how it should be done," and, "I tell it to code up the whole thing, maybe tell it that something's broken, but part of the test is whether it made the right analysis and knowledge-work-educated choices on its own along the way."
In other words, what I'm interested in is not so much about what it can do in terms of coding qua coding. It could be utterly magical at that, and that would be great. But how much of my own knowledge work do I need to input to get it to code the "right" thing, versus how much it's able to make the correct choices on its own about what the "right" thing is.
I've shared my thoughts on LLMs consistently here, for years. It wasn't central to this particular demo.
But if you want to know:
I think this would probably make me an LLM bull, even if I'm not maximally bullish. Definitely "displacement imminent".
I would call you a moderate under my schema, and probably an "instrumental optimist".
Either way, I don't think you're our target audience for this demo, since you personally and professionally use SOTA LLMs with regularity and are familiar with their pitfalls.
Fair enough. Thanks for clarifying.
Do you have any thoughts that you'd be willing to share on what I wrote concerning the amount of knowledge work currently required to be input to do things like the task I was thinking about? I suppose I wasn't entirely clear, but I think it would likely fail to do the analysis task on its own. For clarity, this is a task that I thought, "It might be weird enough that no one's done it yet, but it's close enough to the standard stuff that I could almost certainly give it to a student who did well enough in their flight mechanics course, and they could almost certainly just do it." That seems to have been partly justified in that I found a publication in which a student did just do it (and skimming the paper, the analysis seems about on par with what I had expected; I guess my flaw was thinking the idea was sufficiently 'weird'; I guess it says something about the state of aerospace that someone out there has done almost every basic variant, sort of regardless of whether it makes sense to do). I'm probably <50% on whether it would make the "right" engineering implementation choices on its own. I don't have a precise number. I think it might get lucky, because there's a pretty large set of choices available, and I hadn't yet tailored the problem so that it requires it to really think conceptually about what's going on and only pick from a small subset; there's a good enough chance that it could guess somewhat randomly or pick a popular one that happens to work (though I'm not sure if it'll put the right context around it even if it does).
Perhaps, given your comment below, this is just something that you mostly don't care about. Does this sort of thing just bucket into, "No, it can't do this sort of knowledge work now, but with sufficient recursive self-improvement, it will be able to do it later"? (I guess, in line with your stated AGI timelines?)
I am really the wrong person to ask this. I don't regularly use LLMs for programming purposes, when I do, it's usually for didactical purposes, or small bespoke utilities.
The most ambitious project I tried was a mod for Rimworld, which didn't work. To be fair to the models, I was asking for something very niche, and I wasn't using an IDE instead of the chat interface. I ended up borrowing open-source code and editing it, and just using AI image generation for art assets (which worked very well, to the point it pissed off the more puritan modders in the Discord). I can mention that the issues I ran into were the models being unfamiliar with the code for the mod I intended to support (Combat Extended, a massive overhaul of core systems), and that what knowledge they had innately was outdated. I was too unfamiliar with Rimworld modding to be confident that editing their efforts was worth my time. Other people have succeeded in writing bigger mods that work well (as far as I can tell) using AI, so there's definitely an element of skill-issue on my part.
SF might have actually useful observations, but he's a lurker to the core, and I'm the forward-facing entity for the moment. He says he's generally busy with work right now, so I wouldn't wait on him to respond, though I'd be happy if he did.
If you insist:
I don't know if it can do this kind of knowledge work, but I do expect that it will be able to short-order. I make no firm commitments on whether this will be the direct consequence of RSI (since labs are opaque about methodology), or if it'll be a simple consequence of further scaling and increasingly intensive RLVR.
(¿Por que no los dos?)
Either way, I think it's more likely than not the kind of problem you describe will be trivial within a year or two. My impression is that the models can just about do what you want them to do, but with significant frustration and wasted time on your part. That is already a very strong starting point, can you imagine asking GPT-4 to even attempt any of this and get working results?
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