Peter McCrory

the most complex sort of high education tasks in our data typically coincide with very sophisticated prompts provided by the human.

So I think in my example that I mentioned before, where I'm asking Claude to do this regression analysis and somewhat sophisticated implementation,

At present, you need a PhD in economics to understand and formulate and have the taste to know how to prompt the model and much less evaluate the output that it produces.

And so there is this question of like,

What's the best way to acquire those skills?

There is evidence on this front that it's not just acquiring the skill, but it's also developing the cognitive endurance.

A shout out to one of my good friends at University of Chicago, Christina Brown, has a nice paper on this front in a developing world educational context.

That's a really great question.

And, you know, maybe I'll have a harder time sort of concretely pointing to something here at Anthropic, but I do believe that it is exactly as you're describing that

to have the taste and discernment of what is good writing, you need to spend a lot of time actually writing.

And so I would encourage someone to not

let Claude take the first pass at a draft.

Like, that's how you develop your voice.

That's how you develop your argument.

That's how you can discern whether the writing that Claude produces is actually of sufficient quality.

And, you know, in previous roles, I've seen examples of where people have made missteps, relying too heavily on sort of language models and, you

developing their own voice.

I think that there's a similar implication for reading papers.

Like you should just sit down and work your way through it.

You know, setting aside the question of AI for like very challenging technical papers, I would have feel a lot of trepidation about just, oh man, like I'm never going to be able to understand that how to do some