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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.
Your Bull and moderate option seems to miss an important middled. We go from ASI imminent to 'useful tool. I want to see a - will likely disrupt the economy and culture and society, regardless of whether AGI or ASI is coming.
Anyway:
This seems extremely, self-servingly narrow and contradictory. We want to show you how much an AI can do, in order to change your mind on it's limits. But please, do only pick something that it can do. This isn't question begging, but something like it.
Anyway, anyway.
How about an 8-bit side-scrolling video game with the relative complexity of Super Mario Brothers 3? If it can go write a full 'feature length' NES game, I'll be quite impressed. (But I'm playing more skeptical than I am)
Or more real world related:
A data replication tool that can move data from a SQL Server to PostGres database. It has to be able to use both time stamp incremental replication or log-based Change Data Capture on selection. You should be able to customize batch size, hard deletes, time-out, and activity on failure. I want a gui that allows me to select tables and ordering to schedule replication intervals, and to select columns on the table. Bonus points if it allows rows filtering conditions or other in-flight transformations.
If it does this latter one, I will beleive that most of IT infrastructure employment is over in 18 months.
I doubt an AI agent's ability to generate a feature length anything that's coherent. Ask an AI to write a novel and it'll fizzle out around 10,000 words in. I'm convinced that the AI assisted smut romance novels that are popular recently are mostly driven by a human gooning while proompting the AI for the next chapter. I doubt that it can be done fully autonomously, those actually fake books that are just words on a page not included of course
I would expect that one of the biggest limitations on long run narrative coherence is time horizon. The doubling time for time horizon is anywhere from 2-7 months.
A typical novel is about 80000 words, so three doublings in length (6-21 months). To be conservative i'll assume novel complexity/task time scales with the square of word count. This is based on each additional word having to mesh with all previous words. This would give 6 doublings or 12-42 months.
I suspect this is an overestimate because complexity probably increases until the climax then begins to drop off.
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To be fair to AI I've fizzled out on a dozen or so stories after writing about 10k words.
I think there might be a hump at a that point where where story idea turns into story and I'm not sure it's easy for most people to pass.
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This is very unlikely to be accepted:
Too subjective to be useful, and far too ambiguous. Who's doing the grading here? How are they assessing "coherence"? How are we blinding things, if not, how do we account for bias?
We strongly prefer actual programming tasks, not creative writing. We could easily ask Claude to write a novel, and it would do it, but then we're back at the issue of grading it properly.
If you want to propose something like this, you need to be as rigorous as @faul_sname up in the thread. At the very least, propose evaluators that aren't you or the two of us, and we can see if it's possible to make this work.
This wasn't meant as a suggestion, just an observation. My suggestion is below.
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