My guess: a 30% chance of rollback within 12 months of launch, defining rollback as disabling the invocation feature site-wide indefinitely, rather than removing a particular model or tightening limits. This is a subjective forecast based on the announcement, not usage data or private knowledge of the admins. I’d expect tightening restrictions to be more likely than outright removal.
Conditional on rollback, here’s my distribution over the primary reason (summing to 100%; actual decisions could involve several):
| Primary reason | Probability |
|---|---|
| Too much low-value content: repetitive answers, tangents, and attention costs outweigh useful contributions—even with default collapsing. | 35% |
| Bad effects on discussion norms: people recruit models as argumentative referees, substitute invocations for thinking, or create disputes about what the model said. | 25% |
| Insufficient benefit relative to cost: limited sustained use, API expenses, or maintenance/moderation work make the experiment not worth keeping. | 20% |
| Unreliable answers undermine trust: confident errors or misleading citations make the “neutral fact checker” framing look actively counterproductive. | 10% |
| Abuse, privacy, or security problems: exploitation of model context, unintended disclosures, or disruptive use proves difficult to contain. | 10% |
Why only 30%? The karma threshold, rate limits, distinct styling, and collapsing already address several obvious failure modes, and admins can adjust those rather than abandon the feature. Why not much lower? Ruby explicitly describes this as an experiment, and its biggest potential downside is changing human conversational behavior, which those safeguards don’t fully address. My strongest early indicator would be whether invocations mostly retrieve/check useful information or mostly become “AI, tell this person they’re wrong.”
Or if you prefer, "@Claude", "@Opus", "@Astra"
@Grok is a popular Twitter/X feature where if you tag Grok in a tweet, Grok will respond in a comment. The stereotypical use case is "@Grok is this true?" But you can also ask things like "How does this compare to layoffs in 2025, 2024, and 2023?" and "How would you verify AI in the domain of AI research?"
Notwithstanding that Grok is often recruited in a charged or condescending way to win arguments, I think it's a pretty good feature:
In short, you get convenience and a neutral fact checker. In launching this feature on LessWrong[1], I'm not fully sure how people will use it and I'm hoping to see some novel, valuable use cases. Likely things like little experiments, retrieving useful content, and similar.
Going beyond @Grok's implementation on Twitter, on LessWrong, you can view the complete details of each LLM run (see below): system prompt, chain of thought, and tool calls.
What about the noise of having LLM content showing up? I'll be keeping a close eye on how it goes. I've attempted to make the UI non-intrusive and distinct from normal content. LLM outputs are collapsed by default (until they reach 10 karma) and styled differently. There are rate limits on how many times someone can @<model>. Lastly, at present only users with 50+ karma can invoke the models, which should filter against people who'd use them in disruptive ways.
The LLM Run page:
Happy invocations! Feel free to try it out on this post!
And if you want to track the various usages of this feature across LessWrong, you can do so on the LLM replies page.