The Exchanges

Q What was the last thing you challenged yourself on?

A I think if you look at my career, I am an emotional group of training. And then I went, did I did, I went to, I was a professor and went into high performance computing, and then I did weather forecasting. Then I went to climate change. I was, uh, is in risk management at Facebook. Then I move to analytics at Facebook and led many of the teams that went to venture capital and did a bunch of different things. And now I'm doing a startup. So I think my journey has been one, which has always been like, Trying to disrupt myself, to be honest with you. And I think the, the biggest challenge now I have is, uh, is, uh, essentially, I think two things. One is growing the company itself. And I think growing a remote team in India and, uh, the growing, the remote team in India had its own challenges, uh, partly because of cultural reasons of India and, uh, the, and the fact that I think, uh, not being able to role model, uh, easily because we were in the U S and they were in India, the rest of the team was in India. And so I think that has been a, ah, that has been a challenge in itself and so on.

AI assessment note: “the biggest challenge now I have is, uh, is, uh, essentially... growing a remote team in India”

Q Do you think pattern recognition and playbooks are good? Or do you think they're misleading? When we look at a pre-AI world versus a post-AI world, a pre-COVID versus a post-COVID, the world changes so much.

A I know, man. I know. I know. I think the, the, the problem, as you point out, the biggest problem for anything with, uh, With your intuition led is bias. It's bias and how the world will change around you. And you, those are the ones that you need to guard against. But honestly, that's the best you got at that point. So if you actually have to make a decision, and I actually think that the cost of not making the decision, in my opinion, is far worse than making a wrong one as long as you can iterate fast. But you're right. You're not going to get it. The intuition is not fail safe for sure. But even data is not fail safe. Data also works under certain assumptions. That you have, and those assumptions can just get ripped off. I can't change. Yeah.

AI assessment note: “honestly, that's the best you got at that point”

Q the other day, and they said, to me, product market fit is when literally monkeys could run the business, and people want it so much that they would still sell really big product volumes. What would it be to you? Other people are like, oh, you're 10 first customers. There's many different variants, and they're not right. It's just different. What's your, like, ah, they have it or they don't?

A I've now, because I worked, especially being at Sequoia and worked at the enterprise and e-commerce and small companies and large companies and all kinds of different companies. I don't have a single answer for this, a single simple answer that will satisfy everyone. But I would basically say it's essentially, uh, if it's a consumer product, people want to come back. They want to keep using the product. They seem to love the product and you're not throwing more money at it, which means that's why I mean by sustainable, it's not like you're throwing huge Amount of marketing dollars to just keep them on. And there is some, they're actually gave, being able to provide a value that other companies don't. Uh, and I think with the product market fit, it doesn't mean the product market fit, you can actually scale it because the moment you start charging, for example, it's a free product. And maybe just the people are only using it because of the free product. The moment you charge it and charge the same amount as a competitor, it may just go away. So it's not that it's guaranteed that you're going to go from I mean, product market fit, scaling product market fit, unit economics, scaling unit economics. I think those are all four different steps in my opinion. So I actually think that it is, ah, you can get to product market fit, but you may not be able to scale it for several reasons.…

AI assessment note: “if it's a consumer product, people want to come back.”

Q the other day, and they said, to me, product market fit is when literally monkeys could run the business, and people want it so much that they would still sell really big product volumes. What would it be to you? Other people are like, oh, you're 10 first customers. There's many different variants, and they're not right. It's just different. What's your, like, ah, they have it or they don't?

A I've now, because I worked, especially being at Sequoia and worked at the enterprise and e-commerce and small companies and large companies and all kinds of different companies. I don't have a single answer for this, a single simple answer that will satisfy everyone. But I would basically say it's essentially, uh, if it's a consumer product, people want to come back. They want to keep using the product. They seem to love the product and you're not throwing more money at it, which means that's why I mean by sustainable, it's not like you're throwing huge Amount of marketing dollars to just keep them on. And there is some, they're actually gave, being able to provide a value that other companies don't. Uh, and I think with the product market fit, it doesn't mean the product market fit, you can actually scale it because the moment you start charging, for example, it's a free product. And maybe just the people are only using it because of the free product. The moment you charge it and charge the same amount as a competitor, it may just go away. So it's not that it's guaranteed that you're going to go from I mean, product market fit, scaling product market fit, unit economics, scaling unit economics. I think those are all four different steps in my opinion. So I actually think that it is, ah, you can get to product market fit, but you may not be able to scale it for several reasons.…

AI assessment note: “I would basically say it's essentially, uh, if it's a consumer product, people want to come back.”

Q Can I ask, what was the toughest situation at Facebook, and how did the preparation that you had before in terms of the character building help you be ready for it?

A I think the hardest part for me was, uh, was, uh, when I was, uh, working on a team, and it Turned out that, uh, that the kind of data that we were actually, uh, providing was people in the organization that want, didn't want to see the truth. And I was trying to be a truth speak truth seeker and say the truth there. And it ended up being that, uh, the senior leadership that did not like what I was saying and I almost got fired for it. And, but that's when I think people like Harvey and Alex at, uh, at Facebook actually backed me up. And, uh, and the trust they, they had in me was, was amazing. And the fact that I didn't actually run away from it and stayed on helped me. And the fact that there were people who were willing to back me up to the health helped me a lot.

AI assessment note: “the senior leadership that did not like what I was saying and I almost got fired”

Q I totally agree with you. Do you think people are destined for a certain stage of a company's life cycle? You've seen many different stages, and I'm just thinking about the slope plateauing. Do you, are certain people destined for certain stages?

A If you're below a certain age, you do, you're not institutionalized. Let's say you're below 27, 30, whatever those ages are, you're probably not institutionalized on until that. I think it's very easy to mold anyone, but beyond a certain age, I think it's so much harder. If you are actually in a certain type of company, you're set in your ways, you have certain ways of thinking. It's that much harder for you to change. I don't think it's impossible. That's not hard, but I would say that if you have developed the growth mindset very early on, which means that even if you're 31, 35 and you've developed a growth mindset very early on, And your mind is nimble and flexible. You can take on almost anything. It's like, how early did you get on your, did you develop a growth mindset? And if you did, I think you can continue to challenge yourself on anything. But if you didn't, I think you need to cast them young.

AI assessment note: “if you have developed the growth mindset very early on, you can take on almost anything”

Q Talk to me about the meditation. I'm just intrigued. It's something that I would like to be getting into. What's your meditation process today and how do you do it?

A Quick story on that. I used to stutter a lot until I was in my ninth grade, um, and back in India. And I used to stutter a lot. And, uh, one of my uncles basically was, uh, in a hypnosis group and he self, he hypnotized me and then taught me self hypnosis and I couldn't finish one sentence. And, uh, and, uh, after hypnotized during the process of hypnosis, which I don't know, But he recorded it on tape and he asked me to say multiple sentences and I had never stuttered. And so he taught me self-hypnosis and I started to hypnotize myself and within six months I stopped stuttering. And then I realized, uh, self-hypnosis was the same as meditation. So I call it meditation now. So I don't even know what form it does. I just, uh, I haven't formally learned it in any form, but I, I just, uh, close my eyes, relax. A lot of times I just may meditate even unconsciously, like, You would be in a, in a flow. So I just go into the flow and I'm go deep into things. I don't even know what I'm doing. And I'm, I actually meditate without knowledge.

AI assessment note: “I just, uh, close my eyes, relax.”

Q I love that how this conversation goes. Can I ask you, going back to the actual talent, how fast do you know when you've made a mishire? Again, you've hired so many people.

A Three things I look for. One is I think you look at Skill gap, knowledge gap, and value slash culture gap. Those are the three things I expect. If somebody is not doing well, performance not doing well, because somebody is not performing, that can be easily founded, more easily founded. If someone is not performing in terms of how they are delivering, you can founder. But not all people who are not performing, uh, will do badly. It may be that they're in the wrong role. Or it may just be that they're deep thinkers. I've seen that too, or they may just not be good at what they're trying to do and so on. So for that, you need to analyze three things. One is, is the problem that they're trying to do is a skill problem. They, they graded Python, but this is a C++ problem. They can't do it. Okay. That's why they're slowing down. Second is like knowledge from, it's a Python problem. They know Python really well, but this space is security and they don't want to send security. So they can't do it really well. Values is like, are they not working hard or any other cultural things that may be, you have to first identify what the problem is. And as soon as you identify it, if you think it is fixable, then you need to put them on the right track and see what happens. So I think identifying whether or not they're performing is an easier problem, which you can actually do, talk to people, n…

AI assessment note: “identifying whether or not they're performing is an easier problem”

Q Can I ask, it's, I mean, I'm a venture investor as well. Um, it's easy to say no, you know, there's always, I don't like the sector, I don't like the size of the market, the time, whatever reason we give, it's harder to say yes and see beauty where others don't. Do you feel that was the right approach?

A So two things. I think if you look at it from a funnel perspective and from the time perspective, if you think about it, if you think about the funnel and saying the investors talk to, you know, uh, let's say 2000 companies in all per year, uh, probably it's more, but let's say it's 2000 to 5000 companies in all. And then you have this bucket of, uh, let's say there are only 50 great companies in all every year that you can even invest into. Then you want to catch those 50. And the thing is that if you make wrong investments, and many of this, by the way, that, that we stopped Sequoia from investing where I think they would have otherwise invested. It was so close to investment. So then you're going to spend so much more companies. So think about every investor. If you have 10 great, 10 companies in your portfolio, every investor has 10, 10 companies in the portfolio. If eight of them are not so good and two are great, they spend all the time on that too, but they can't do anything, but they, they still need to talk to them. If you can make it like out of the 10, you have five good and five bad. You're in so much better place in terms of where you are. So I actually think that it really helps the investors themselves because you know, you do want fewer and fewer bad investors. I actually think that good investments are relatively, I mean, there may be this one great investment …

AI assessment note: “So I actually think that it really helps the investors themselves”

Q and a poker player who wrote a book called Thinking in Bets, which basically talks about kind of decision making, and she has, uh, talks about this theory, and I can't remember what it's called, but it's essentially when good process leads to Not successful outcome or when successful process leads to bad outcome. Uh, how do you think about that? And does that go against the importance of hypothesis?

A In terms of how you do the hypothesis, you need to stress test it, which means you come up with hypothesis and then you need to look at data to validate the hypothesis. So you just can't do a hypothesis and say, this is it and so on. So the way that you, you do this and I'll tell you a quick example that we did, for example, that at, at, at PayPal in fraud, for example, the way we would do that, we found out that, for example, there are bunch of different, uh, I mean, we wanted to reduce fraud, which is the most important metric for, uh, the risk management and probably the most important for even PayPal in terms of the metric that they cared about, one of the most important metrics that they cared about. So what we did was we said, okay, uh, we made a hypothesis and we basically said, Hey, people, if they use the same cookie, Uh, which is a web cookie. And if they, if more than five people use the same cookie on the same day, there's something wrong with it, which basically we're saying that people who are using a single computer, they were taking other people, taking over other people's accounts and do a bunch of different activity on it. And then there is something bad about it. So we basically started the hypothesis and say, Hey, this is wrong. So we go, so then you go into the data and try to validate it and saying, okay, is it really true? And when you go into data, you s…

AI assessment note: “In terms of how you do the hypothesis, you need to stress test it”

Q How often do you change your Northstar metric, Chandra?

A I think a lot of your Northstar metrics changes, uh, you change it Largely because, I mean, for example, when Instagram came along, uh, to Facebook, we still had the MEU as our goal. And so when we had the MEU as a goal, I mean, uh, basically Kevin's system was like, what? We are now in the mobile world. People use the phones all the time. We should be doing, uh, we do DAUs. We don't do MEU. MEUs. It makes sense because essentially people are using the product every single day. Why are you still caught up in the MEU? And that made us start a thing because We were still in the web world, which is fine, but people weren't using the product every day. So in that sense, as the company starts transitioning, let's say from a web world to a mobile world, you need to change. And that is one reason why you would actually change your metric is like move from an MAU goaling metric to a DAU kind of a goaling metric. That's just an example, but I, I, I think the market is one reason. The second reason is actually, uh, the world changing on you itself. And, and, uh, and, and so on. And third, I mean, you may have just picked a bad metric. Sometimes in companies, it's just hard. It takes a lot of iteration to get to the right metric.

AI assessment note: “I think the market is one reason. The second reason is actually, uh, the world”

Q and a poker player who wrote a book called Thinking in Bets, which basically talks about kind of decision making, and she has, uh, talks about this theory, and I can't remember what it's called, but it's essentially when good process leads to Not successful outcome or when successful process leads to bad outcome. Uh, how do you think about that? And does that go against the importance of hypothesis?

A In terms of how you do the hypothesis, you need to stress test it, which means you come up with hypothesis and then you need to look at data to validate the hypothesis. So you just can't do a hypothesis and say, this is it and so on. So the way that you, you do this and I'll tell you a quick example that we did, for example, that at, at, at PayPal in fraud, for example, the way we would do that, we found out that, for example, there are bunch of different, uh, I mean, we wanted to reduce fraud, which is the most important metric for, uh, the risk management and probably the most important for even PayPal in terms of the metric that they cared about, one of the most important metrics that they cared about. So what we did was we said, okay, uh, we made a hypothesis and we basically said, Hey, people, if they use the same cookie, Uh, which is a web cookie. And if they, if more than five people use the same cookie on the same day, there's something wrong with it, which basically we're saying that people who are using a single computer, they were taking other people, taking over other people's accounts and do a bunch of different activity on it. And then there is something bad about it. So we basically started the hypothesis and say, Hey, this is wrong. So we go, so then you go into the data and try to validate it and saying, okay, is it really true? And when you go into data, you s…

AI assessment note: “In terms of how you do the hypothesis, you need to stress test it”

Q How do you calculate impact per capita? If I'm like a founder listening and I'm like, okay, I've got seven people in a marketing team or a growth team. How do I actually do that Chandra?

A It's kind of hard, but I would say when I first joined Facebook and I remember, uh, uh, an engineer, Harry, uh, who worked on the payments team, Told me once the way we think about the impact here is basically, uh, take our market cap, which I think was ten billion then, and they were probably, let's say, and for the math, for the sake of math, I'm just going to say a thousand engineers, I think it is far fewer, but it's just for math. I'm just saying it's a, it's thousand engineers. So that would be like ten million per engineer if I'm doing my math right. So essentially he's saying every single engineer contributes ten million to this. I think it was more like 20 or fifty million, but So every, so every new engineer that comes in and will have to contribute so much. Otherwise, if you can't find something that they can do that can be of that type of impact, don't hire. And I think it's the same sort of mindset that Alex and Harvey and everyone else had, which is like, do not add more people. One thing, if you add more people, what happened is you, the A plus players becomes A and so on. And, and very fast, you need to get to grow very slowly so you can reach equilibriums very slowly and then keep The value of the entire team high. So do not hire very fast. And so that thought was one thing. I would also say that the way you may measure it, yes, it's harder in, in many things, …

AI assessment note: “take our market cap... So that would be like ten million per engineer”

Q senior, which is someone who can, you know, just look at data in isolation and that's fine versus one who ties it to a core decision because of the data that they've seen. That's the difference. So I totally agree. Are there questions in the interview process that you will most frequently revert to? To understand their abilities, their spikes, their skills, as you describe them, a body of skills.

A The more, more and more senior you get. The number one and probably the only thing I care about is their ability to simplify. And, uh, I remember Chris Cox once told me that I asked him, what is the, what is the one single thing that senior people can do? He said, simplify. That's the same thing I have is like, can you actually simplify? When you look at a very senior person, I look at it from, see if they can simplify. Simplify shows clarity of thought. Clarity of thought shows your first principles thinking. So if you have great first principles thinking, you have higher degrees of clarity of thought, which leads to simplification. And so the more senior you are, the kind of skills that I look for is can you actually simplify, which basically means that you can take very complex problems, break it down, and you can actually take on harder and harder problems because you're thinking first principles.

AI assessment note: “The number one and probably the only thing I care about is their ability to simplify.”