[bryanrasmussen]

I remember there were some studies that this kind of thing was effective a year or so ago, so essentially a lifetime in Model years.

However to me it seems completely reasonable that it would work, because my understanding of what happens is the model interprets what you said as:

Look for a group of people who are considered to be expert growth hackers by the world at large and answer my questions as though they were answering them.

So assuming that there are a set of questions that can best be answered by people that most other people identify as expert growth hackers then yes, I believe assigning a personality in this way should obviously work.

[FeteCommuniste]

I imagined it as kind of a shorthand for "you should be spending my tokens on looking for / addressing issues like X, Y, and Z," where X, Y, and Z are the sorts of things that an expert in [insert domain here] would be likely to care most about.

[bandrami]

At some point we have to just admit we're mass cargo-culting here and that these secret invocations people swear by have the same epistemic value as medieval superstitions.

[FeteCommuniste]

I don't know, I was never one to "assign roles" to AI myself, but if it ends up working for some people in practice, then I guess it might be worth examining why.

[bryanrasmussen]

right, but the thing is how do they know what an expect in [insert domain here] would care about? Obviously by finding content created by

people who claim to be experts in [domain] people who others claim to be experts in [domain]

hopefully valuing membership in group two over membership in group 1.

[code_biologist]

It's been interesting to see how aggressively some reasoning models like to "reason" by analogy. They love to say things like "it's like a CPU" or "it's like a highway", and then they start to make logical leaps based off that rather than just using it for user explanation. Gemini 2.5 and 3.1 Pro have been particularly bad for this type of behavior. Telling models to "speak as though you are a physiologist considering the case with an expert colleague" gets them to "reason" using a more correct linguistic substrate.

The Opus models over the last year doesn't seem as vulnerable to this type of behavior and I've noticed the "identify as expert" prompt tricks aren't as meaningful there.

[xpct]

I propose we move away from the framing of "Model years" - they're standard human research years. Yes, likely more people are working on it, and also working harder, but ever since we acquired a certain amount of compute in the world, many people were able to independently find the same patterns and train models.