Every argument clarity score on this site is built from rows on this page. Each
question and answer was assessed with names hidden, the host's own answers included, on
four things from 1 to 5:
directness (does it answer the question asked), coherence (do the ideas follow),
precision (concrete details and clear references), compression (says a lot per word). The weighted
mix (30/30/25/15) is the exchange score. A person's published score averages their exchange
scores on raw tape only, at least 8 of them, shrunk toward the cohort mean.
Full method →
Answered raw tape
D 5 · C 5 · P 5 · Cm 4 4.85
Q Did you become an entrepreneur after you got laid off?
A No, I ended up, um, eventually making my way to Google. Google at the time was sort of scaling, and it was soaking in all the talent, um, in Silicon Valley, or at least all the entrepreneurial talent, and so, um, I joined there and, um, I, I ended up working on two things. One is I helped buy in the Android team and start a lot of the early mobile efforts. And then second, I worked on some early, um, AI and machine learning for ads targeting related products. So I worked more on the product side. Um, and then after that I left to start a company and, um, Uh, sold that to Twitter, and then I worked at Twitter for a bunch of years, and then started another company, so.
AI assessment note: “No, I ended up, um, eventually making my way to Google.”
Answered raw tape
D 5 · C 5 · P 4 · Cm 4 4.60
Q When you came in, did you guys fix some things there at least?
A We fixed so much stuff. So Twitter was in a state where, um, you mentioned the failable days. Basically what that was is the site kept going down, and they put up a picture of a very cute whale, like a graphic, and that was their product because you couldn't log in or anything because the site was down because they couldn't deal with the traffic. At the same time when we were bought, um, they couldn't, um, what's known as deploy code, they couldn't update the website. And they hadn't been updating it for weeks. They just didn't know how to update the code. And so we went in and we fixed that deploy queue. And that was the first, my team was supposed to go and build all this stuff for the ecosystem and developers.
AI assessment note: “We fixed so much stuff... we went in and we fixed that deploy queue.”
Answered raw tape
D 5 · C 5 · P 4 · Cm 4 4.60
Q Have you seen a lot of more network effect businesses recently? Because network effect is a thing we always talked about a lot in consumer, obviously, and those are giant companies in our world, and there's some enterprise. What are you seeing in network effects these days that are exciting?
A Yeah, I'm seeing less network effects and more scale effects, which are related or overlapping, where as you get more scale, You end up with cost advantages that then allow you to get more scale. You could argue maybe Stripe is in that basket, but there's also more modern versions of that, particularly in energy and other related markets. Um, so I, you know, it's back to a broader question of how do you create defensibility in a business? And there's almost like five or six common patterns of defensibility. Scale effects is one. Network effects is another. Um, creating an ecosystem on top of your platform, like what Salesforce has done is one. Um, there may be long-term contracts and durability of those contracts, which happens, for example, in the medical distribution world. So You know, there's, there's, in the, in the world of business, there's, like, five ways or six ways to create defensibility, and roughly everything falls into those.
AI assessment note: “I'm seeing less network effects and more scale effects”
Answered raw tape
D 5 · C 5 · P 4 · Cm 4 4.60
Q hurt and set back, and things are not being cured because she's being so aggressive and blocking things. Um, like, what, what do you think of her answer about the, about when you talked about that? I think you talked about, like, blocking Meta from acquiring a 30 person company, which seemed a little crazy to me. Do you wish you'd pushed her harder? Like, where are you on this?
A Yeah, so we had, um, a reasonably short interview. I mean, she was generous with her time to do this, right? We had about 30 minutes, and, um, I think there's a lot of standard questions that we asked around M&A and AI and open source and all these topics, and we just didn't have a lot of time to go deep on a lot of things. And honestly, the things I was most interested in, because I actually feel that her stance on small M&A by big tech is very well understood and known, right? It's not like we would have uncovered something new, and I almost viewed those things as the warm-up. The things I wanted to ask about, Were things like, why aren't there young people in government anymore, right? Lena Khan got appointed at, I don't remember, 31, 32.
AI assessment note: “I actually feel that her stance on small M&A by big tech is very well understood”
Answered raw tape
D 4 · C 5 · P 4 · Cm 4 4.30
Q what we're buying. Um, couple more things while we have you on, on AI, uh, and, uh, you know, you have children as, as do I, um, the world's going to probably be very different in 10 or 20 years. Like, like, what are you paying attention to for them? What do we do differently with education? With, with, you know, how does this affect how I raise our kids?
A Yeah, you know, I wonder about that a lot, and I don't have a good answer, and I've asked some of the world's top researchers about this, right? Because I talk, I talk to many of them with some regularity, and I'm like, what, like, what should my kids study? You know, like, what do we do? What's a good thing to know in the future? Um, so that, that, and, you know, the, the positive of this AI wave for this kind of stuff is that eventually each person will have a custom tutor Which is helping them learn really deeply at their own pace, right? And I think AI is perfectly suited for that, and it's gonna be a very exciting world where you have AI really going deep with your kid on different topics, and there's all sorts of research from the eighties that shows that, um, kids that receive one-on-one tutoring, uh, learn dramatically faster.
AI assessment note: “eventually each person will have a custom tutor Which is helping them learn really deeply”
Partly raw tape
D 3 · C 4 · P 3 · Cm 3 3.30
Q Is that staying as my machine somebody else's or do you see the auditor eventually?
A I mean, in the long run, I, I, you know, a couple of years ago, um, I wrote this blog post that I never published. Um, I just never got around to polishing. I just put it out. I wrote this maybe five, six years ago, which is basically positing that there'd be like three eras of mankind in terms of, or three eras of intelligence, right? And the first era is basically people. And the second era is some hybrid era where it's a, uh, you know, if you look at the number of intelligence units or whatever you want to call it, the relative brain power, you'd have a mix of You know, humankind and machines, and then eventually it's gonna be mainly dominated by machines in terms of sheer number of brain equivalents that'll exist out there.
AI assessment note: “eventually it's gonna be mainly dominated by machines”
