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 4 · C 4 · P 4 · Cm 3 3.85
Q Where did you focus at Opendoor where with the benefit of hindsight, you shouldn't have, and how does that impact your mindset?
A Opendoor's had, uh, different variations of, you know, a mortgage product that, that didn't succeed. And I think those were like well articulated, well reasoned decisions. So I think the outcome isn't necessarily what, you know, the company wanted at the time. Um, but I think it's, it's hard to divorce like bad decisions from bad outcomes. So the hindsight becomes Very easy to say, you know, we should have focused elsewhere, but I think there were well reasoned decisions at the time. Um, but I think, uh, you know, focusing on, um, sort of the, the, the, the right to win for sellers is, is, is where a lot of the magic is, is, uh, today. And I think that's, that's the right focus area.
AI assessment note: “different variations of, you know, a mortgage product that, that didn't succeed.”
Answered raw tape
D 4 · C 4 · P 4 · Cm 3 3.85
Q How often should you change OKRs? We were talking about having kind of three to five per team. How do we think about how often we should change them? And how, how should they be communicated to the team?
A Teams should be developing their own OKRs. They should be defining the work that matters the most to the strategy and the top level OKRs that are outlined. A couple of philosophies here. One, generally, you know, the quarterly planning process or whatever, I think that Makes sense. I don't think you should be changing your objectives that frequently, right? Otherwise it's kind of an unfocused strategy. It's unlikely you, uh, officially completed your objective in a quarter, right? And, um, maybe, you know, every year, um, or six months, but I think, um, The, the more nuanced answer is you earn the right to, to set OKRs on longer time horizons by proving you can execute on shorter time horizons. So super early stage companies like having an annual year long OKR process when like it's not clear where you're going to build in three weeks, that doesn't feel like a super worthwhile exercise. And so I think you earn the right to plan on longer time horizons by proving that you can Uh, set and execute over shorter ones.
AI assessment note: “you earn the right to, to set OKRs on longer time horizons by proving”
Partly raw tape
D 3 · C 4 · P 4 · Cm 4 3.70
Q How often should you change OKRs? We were talking about having kind of three to five per team. How do we think about how often we should change them? And how, how should they be communicated to the team?
A Teams should be developing their own OKRs. They should be defining the work that matters the most to the strategy and the top level OKRs that are outlined. A couple of philosophies here. One, generally, you know, the quarterly planning process or whatever, I think that Makes sense. I don't think you should be changing your objectives that frequently, right? Otherwise it's kind of an unfocused strategy. It's unlikely you, uh, officially completed your objective in a quarter, right? And, um, maybe, you know, every year, um, or six months, but I think, um, The, the more nuanced answer is you earn the right to, to set OKRs on longer time horizons by proving you can execute on shorter time horizons. So super early stage companies like having an annual year long OKR process when like it's not clear where you're going to build in three weeks, that doesn't feel like a super worthwhile exercise. And so I think you earn the right to plan on longer time horizons by proving that you can Uh, set and execute over shorter ones.
AI assessment note: “you earn the right to set OKRs on longer time horizons”
Answered raw tape
D 4 · C 4 · P 3 · Cm 3 3.60
Q Where did you focus at Opendoor where with the benefit of hindsight, you shouldn't have, and how does that impact your mindset?
A Opendoor's had, uh, different variations of, you know, a mortgage product that, that didn't succeed. And I think those were like well articulated, well reasoned decisions. So I think the outcome isn't necessarily what, you know, the company wanted at the time. Um, but I think it's, it's hard to divorce like bad decisions from bad outcomes. So the hindsight becomes Very easy to say, you know, we should have focused elsewhere, but I think there were well reasoned decisions at the time. Um, but I think, uh, you know, focusing on, um, sort of the, the, the, the right to win for sellers is, is, is where a lot of the magic is, is, uh, today. And I think that's, that's the right focus area.
AI assessment note: “a mortgage product that, that didn't succeed.”
Partly raw tape
D 3 · C 4 · P 4 · Cm 3 3.55
Q Who is the one to set that OKR of that is what you need to focus on? Is that the CEO? Is that the CPO? Is that the head of product?
A That level where you're talking about that needs to come tops down. Um, and say, this is the, the success metric for the company. And then that cascades to the rest of the org. So for example, in the Uber case, just to extend, you know, trips may just be the metric, right? Like that's the, okay, that, that, that, that matters. Um, but I'm doing Uber pool, right? And so like, okay, I can see that as a, as a product leader for my area, right? Which is not the whole company, obviously for my area and say, okay, how do I ladder to that? Right. And so what ladder, what matters to me Is okay. In this case, it's easy. Uber pool trip count, right? But I can match my OKRs and everyone else can ladder their OKRs to the top level. And so OKRs to me are like cascading trees, right? Where, however, wherever you are in the organization, like it should be layering up to the one or two above that.
AI assessment note: “That level where you're talking about that needs to come tops down.”
Not addressed raw tape
D 2 · C 4 · P 4 · Cm 3 3.25
Q What are your biggest lessons from that time and launching China Pool so efficiently?
A We were launching in China at the same time we were standing up a Chinese, uh, uh, a data center in China, and there was all sorts of technical issues with the day before launch, getting everything to work properly. And we were launching for, uh, in Chengdu, um, uh, which is a city in China, um, that by the way, a city of like, twenty million people that most people at Uber had never heard of. And we were launching for, for rush hour because the product relies heavily on liquidity to, to make Like efficient matches and, um, all that stuff. And so, uh, it wasn't working and it was, you know, nine PM, 10 PM, 11 PM, midnight, one AM. I think I slept 30 minutes on the floor of the Chengdu Uber office. Launched at like five 30 or maybe six AM. Um, and, uh, knock on wood, we were able to figure it out and it, and it sort of worked.
AI assessment note: “knock on wood, we were able to figure it out and it, and it sort of worked.”
