I think the skill nowadays is less about prompt engineering and more about figuring out how do you give Claude a hard task that seems a little bit too hard. And then how do you make it possible for Claude to verify its work along the way? And the verification I think is probably the single most important thing that people do not get right
The agile manifesto is the intersection of the ideas of the people in the room. I think there's a lot more to software development than is contained in the manifesto. And I've written books and books and books about what I think those things are.
And so you kind of end up in this this bad equilibrium where everybody kind of knows that it's a bad equilibrium, but like nobody wants to break out. And I I felt like, okay, well, if I just hopefully come out and say like, look guys, let's all recognize that we're in a bad equilibrium and let's move to this different equilibrium where we're we're plotting things with an X-axis
And I think people don't spend nearly enough time thinking about, you know, distribution and figuring out distribution. And that seems to me uh to be a huge differentiator.
It's more that for myself, the process of writing is the way how I figure things out. And figuring things out is really my goal here. So, I'm I'm trying to figure it out in my own heads, and for that I just have to write it myself.
so many times that process has been managed by humans because you're selecting the right candidate build and you're verifying it and you're thinking, you know, you're figuring out cherry picks and then you like rebundle and then you send it out and that doesn't scale if you have one or two or a handful of people trying to make group decisions and do group sense making.
And so I think people are figuring out how to build these things and permitting like I I just like ultimately like permitting and red tape in middle of nowhere Texas or middle of nowhere Wyoming or middle of nowhere like New Mexico is probably a hell of a lot easier than sending stuff into space
reinforcement learning is about understanding your world whereas large language models are about mimicking people doing what people say you should do. They're not about figuring out what to do.
figuring out a document that is actually like sort of all the things that are not platitudes whereas like all the things where someone else like some other company would plausibly take the other side of and but that's but those are the real choices, right? Like if no one would take the other side, it's a platitude.
if the person responds to "what would you do differently" with "nothing," or with non-actionable vague platitudes, it's a sign they may not be great at figuring out how to get better at things over time.
The most difficult analysis to do as an investor is that, is kind of figuring out how wide is the moat, how much at risk is the business to disruption? And we're in, I would say, the greatest period of disruptability in history.
I have reviewed before the effectiveness of peer review at figuring out "what's good" in the context of grant awards, finding that peer review as currently practiced does substantially better than chance at predicting future impact, especially for the most impactful of the papers; but at the same time is is far from perfect, leaving plenty of variance unexplained.
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