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One thing that impressed me most about
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Zuck, and this is true of Bos as well,
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is this is Philip Sue. He's one of the
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few who have been promoted to
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distinguished engineer or IC9 at Meta,
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which is three levels higher than staff.
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And I asked him about everything he
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learned along the way. Someone that
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stands out to you that consistently
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impressed you. John Carmarmac obviously
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legendary, right? Not only is he super
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prolific in coding, he had an ability to
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Oh wow. He also shared about his unique
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demotion. I was eventually releveled
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down from an E9 to an E7. Later he left
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Meta to join OpenAI before it got big
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and shared an interesting perspective on
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why. The reason I joined OpenAI was what
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I'd learned from working at Facebook is
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I would much rather join the market
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leader or nobody at all. And here's why.
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He's one of the guests I was most
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excited to have on. And hopefully you
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see why after listening to the
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conversation. Here is the full
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episode. Thank you, Philip, for for
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joining today. I'm so excited to ask you
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all the questions I have here. I think a
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lot of people are going to get value out
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of it. So, yeah, let's start digging
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into your career. Starting with
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Microsoft. My understanding of your
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career is that you sprinted through
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Microsoft ladder. What do you credit
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your your fast promotions to the
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equivalent of Microsoft's E7 to? I think
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the growth comes from a few things. One
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is just I had several very good managers
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and teammates who I grew a lot from. So
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for instance, when I joined the tablet
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PC team, the team was about 20 people,
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but there were three distinguished
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engineers on that team, which for
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Microsoft terms is like very rare to
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find a team like that stacked with
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talent. So I learned a ton of stuff very
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quickly from people on that team for
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instance. I think another thing is that
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I just worked very long hours is the
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truth of it. Like in my first year at
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Microsoft, I had a sleeping bag in my
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office. I regularly slept at the office.
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I would have an alarm that would wake me
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up at 3:00 a.m. because I was pursuing
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Da Vinci's theory that you can sleep
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four hours, wake up, and then like sleep
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another four hours and wake up. So, I
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would wake up at 3:00 a.m., code a bit,
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and then go back to sleep at 6:00 a.m.
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and then wake up at 10:00 a.m. and and
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keep coding, right? So, I do think a
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thing that I tell people a lot is I
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think there are only three main things
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that contribute to fast career growth.
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One is of course luck. You know, being
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at the right place at the right time.
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you happened to be at uh Stamford when
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Sergey and Larry were working there.
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They happened to pick you to join the
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company, right? So, some of it is luck,
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some of it is talent, you know, like
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people like to say things like, "Oh,
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everyone can be an astronaut." And I
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don't think that that's true. Like the
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harsh truth is, you know, I am never
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going to be a great basketball player,
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right? Like they're just things that I
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cannot do. Um, similarly, someone who is
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not naturally good at public speaking,
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they can always improve to where they're
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passable at public speaking, but they're
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not going to become a Cicero through
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like a lot of practice, right? So, I
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think luck, talent, and the last thing I
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do think is hard work. You know, if you
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are willing to outwork everybody, I
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think who is equally talented and
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equally lucky, you're just going to get
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further. Um, I am not saying that I
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would recommend that to everyone. And in
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fact, I do think that my work life was
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way out of whack for many years there.
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So, I'm not at all saying that this is
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what I recommend people do, but I do
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think that outworking a lot of people
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goes a long
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way. When you first got into the
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industry, it sounds like you were really
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hungry and I I was also, you know, super
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motivated, too. And one thing that I
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wondered though is you know when you
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work so hard it trades off with your
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health and then can eat into your I
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guess productivity throughput um after
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you lived that experience. Do you think
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if your goal was growth at all costs
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that that was actually optimal? Yeah,
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this is a great question. And I think
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there are a lot of people that will
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defend the 40-hour work week with things
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like, you know, when Henry Ford studied
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his people, every additional hour was
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like uh lower in productivity. I think
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one thing people don't like to discuss
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often is that I do believe your
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productivity does go down on a per hour
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basis as you stay up for way too long.
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But I think for many people that curve
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keeps going a long time before it gets
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negative productivity. meaning like an
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additional hour worked is actually
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subtracting from your productivity. So I
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feel like the truth is working 50 60
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hours you will probably get a sum total
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of more done but each incremental hour
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is probably less effective right but I
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think there's one other thing that
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people don't take into account which is
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you know you could say like for instance
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why do surgeons still have residencies
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that have 36 hour shifts right part of
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it is hazing I think part of it is just
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because they did it when they were young
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so they're going to make you do it when
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you're young right but I think another
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part of it that I heard from a surgeon
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is that the biggest quickest way to gain
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experience in surgery is to have a lot
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of surgery hours. So the more you're
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there, the more you experience and if
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you can pack in six years worth of
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experience into four years, then you're
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just going to be a better surgeon coming
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out, right? And so I think although
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nobody wants to be operated on by a
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surgeon who's in his 34th hour, let's
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say, right? Um, at the same time, you
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would like a surgeon who has experienced
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a very wide variety of things. So, I
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think although I wouldn't recommend my
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crazy work hours from when I started
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work, I would say it exposed me to a lot
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more experience than the average junior
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person starting at Microsoft. So, there
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is diminishing returns, but there are
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still returns. And so, if you're a
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growth at all costs career person, you
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should I mean, by that logic, you should
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work more. Of course, there's other
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parts of life. Totally. Like if if you
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were diehard convinced that career was
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the most important thing for you and in
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fact that you were willing to sacrifice
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things like relationships and other
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things like that. I do think the truth
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is working longer hours is going to get
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you there faster. So you grew to the
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equivalent of um you know E7, my
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understanding that's senior staff and
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you said there were three distinguished
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engineer. Is that the equivalent of E9
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or would have been at at Microsoft or
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Yeah, probably something like that. E9,
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it's the equivalent of a vice president
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level and compensation within the
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company. E6 to E7, I I'm familiar with
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that at Meta. Um, when you got that
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promo at Microsoft, was that as a
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manager or as an IC? And what's the
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story behind that promo? Great question.
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That was as a manager. Um, but I think I
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also then became an individual
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contributor at the new level as well. I
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see. What made you decide to switch into
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management
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uh, in the first place?
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I think when I was younger, I was very
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ambitious for myself and I came from a
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culture, you know, I'm ethnically
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Chinese. I came from a culture where
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people really felt like managers were
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more respected and would get further in
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career. And so I think I had the
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somewhat misguided notion that in
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software engineering becoming a manager
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was the next growth step for me. So
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that's largely why I became a manager.
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But in my career I fluctuated between
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managing people and being an individual
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contributor at least six times. And I
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think the main reason for that is I
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really love being an individual
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contributor, but I'm often asked to
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manage if the team needs a temporary
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manager for a while, for instance, or I
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would much rather be the manager of a
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team if it's poorly managed. You know
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what I mean? Uh so my order of
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preference would be be an individual
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contributor, uh be a manager on a team
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because the team doesn't have a better
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manager, right? Like that's my order of
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preference. But in general, I think left
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to my own devices, I'd rather not manage
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people. I see. And in that case where
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you said if it's poorly managed, are you
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saying that there's cases in your career
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where the existing management was
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lacking and you volunteered yourself or
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you're saying someone in your management
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chain said, "Philip, can you please help
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us?" Yeah, both of those things have
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happened before. So for instance in my
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career I've had four separate times when
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a previous manager of mine asked to
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report to me so I became their manager
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basically really and I think that yeah
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uh and so that has happened four times
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and I think part of that is because I've
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learned from great managers you know
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although I don't prefer to be a manager
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I do think there are parts of what I've
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learned that have been useful right and
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so um I have been asked to be a manager
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before um After pursuing trying to
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become a manager early in my career, I
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sort of stopped looking out for those
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opportunities as actively as I did an
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individual contributor opportunity. That
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I've never heard of that before where
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your manager asks to report to you. How
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does that happen? Um well, I think each
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situation is a little different. So on
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some of the situations, they reported to
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me on the same team, right? um on some
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situations uh several years later they
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asked to join my team you know and when
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we had both moved on. So I think that in
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the cases where it happened on the same
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team um one time it was because the
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manager wanted to be an individual
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contributor on the team you know and I
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was a strong individual contributor that
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wanted to be a manager and so it was
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almost like a swap in place right um
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I've had other times several times where
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I joined a different team somewhere else
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and then my manager who liked working
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with me was very happy to work for me as
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well on the new team and so that's been
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the majority of cases at some point you
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get in your career, there's that
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decision point of do you want to
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continue down the icy track or do you
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want to become a manager. What would be
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your your framework of thinking through
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that for for someone who's considering
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that for themselves? Yeah, great
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question. I personalitywise I'm very
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open to new experience. So in general
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I'm going to advocate for trying new
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things, you know. So, I feel like in
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general it can't hurt to try it if you
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think that you might like it or you
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think you might be good at it, but I
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would put a few asterisks on. One is
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like be very sensitive to whether or not
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it locks you into a career you don't
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want. So, for instance, I've told you
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that I've switched back to an IC
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probably six times in my career, right?
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Um, many people cannot make that
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transition, meaning they have become a
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manager. Now, they're a manager of
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managers. they're far away from the
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code. They either are unwilling to take
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a lower level job in order to be an IC,
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which I'm very willing to do, right? Or
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they are unable to meet the expectations
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of their level as a manager as an IC.
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And so I think people have to be very
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careful not to accidentally walk through
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a one-way door on that. For me, I've
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kept that door open two ways because I
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do the switch often. And when I'm
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managing teams, I also love the actual
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coding. So I still try to dive into the
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code whenever I can. And so I've kept
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both sides sort of evergreen. But I
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don't think that's true of many people.
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So like one is be careful that you don't
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walk through a one-way door that you're
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not positive that you want to walk
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through, right? I think another thing
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about that is people often mostly think
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about themselves when they think about
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wanting to be a manager. But what I
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classically tell people is you are ready
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to lead a team when your team would have
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elected you to lead them. Right? Like
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think of it this way. Would you want to
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work for you? That's the key question.
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Would you want to work for you? If the
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answer is not a solid yes, I would be
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thrilled to work for me, right? You
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probably have a few things you can
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improve in yourself to get to that
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point. And people cannot make you the
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lead of anything. Like they can
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nominally make you the lead, but as you
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know in software development, people can
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slow roll things. People can like uh can
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can sand sandbag things. Like there are
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all sorts of ways your team can work
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against you actually. So you need people
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to want to work for you. So I think
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before becoming a manager one key thing
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is take a critical look at like would I
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work for myself and like if not what can
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I improve in myself to make it so that
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people want to work for me. I think when
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you have those improvements it'll be
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clear and then uh people will ask you to
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manage at that point. That was
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interesting you talk about the two-way
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doors because when I think about I made
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a similar decision for myself of you
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know pursuing management versus IC. I
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presumed past a certain point you've
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become more specialized like if I was a
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manager of managers and then I switched
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equivalent which would be principal
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engineer. those jobs are so different
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that I would have thought almost no one
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would be able to keep the door open. Um,
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and I was thinking if I did do switches
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too, I would be, I guess, resetting some
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progress potentially. So, yeah, I'm
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curious how you think about that. Like,
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did did you ever feel like you lost
[13:29] (809.52s)
progress by switching between the two
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tracks?
[13:33] (813.28s)
Yeah. Well, I definitely lost progress
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and intentionally and willingly so. So,
[13:38] (818.80s)
for instance, um when I switched from
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being the site director of Facebook
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London to being an individual
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contributor on Oculus, I was eventually
[13:48] (828.64s)
releveled down from an E9 to an E7. And
[13:52] (832.08s)
that was something that I very much sort
[13:54] (834.64s)
of wanted and I fully acknowledged was
[13:56] (836.96s)
necessary because the skills I could
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demonstrate as a as a site director were
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very different from the skills that were
[14:03] (843.76s)
required to be a good coder on uh on uh
[14:06] (846.56s)
Oculus. And so that was a rele bought
[14:10] (850.32s)
into it. Now many people cannot handle
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that like either for ego reasons like
[14:14] (854.56s)
they feel like they can't handle it or
[14:16] (856.24s)
for compensation reasons like there are
[14:18] (858.32s)
some people whose lifestyles just grow
[14:20] (860.48s)
to fill whatever compensation they're
[14:22] (862.16s)
making right and if they refuse to uh
[14:25] (865.36s)
change their lifestyle you know they
[14:27] (867.12s)
will paint themselves into a corner
[14:28] (868.96s)
where there are increasingly less jobs
[14:31] (871.76s)
they can take because they are unwilling
[14:34] (874.48s)
uh to move. So I do think sometimes that
[14:36] (876.80s)
switch entails that movement and I also
[14:39] (879.36s)
think there are certain times in career
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where that switch is easier and certain
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times where that is harder. So like
[14:44] (884.72s)
here's an interesting dynamic. I think
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early in career like one or two years in
[14:48] (888.96s)
uh let's say you are a software
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developer you want to be a product
[14:51] (891.44s)
manager. I feel like one or two years in
[14:53] (893.36s)
it's easy to make a lateral move. You
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know you're like a a junior software
[14:57] (897.04s)
developer you want to become a junior
[14:58] (898.48s)
product manager. Many people are very
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willing to coach you on that. I think in
[15:02] (902.56s)
the senior, you know, E sort of fiveish
[15:05] (905.60s)
range, I feel like the transition is
[15:08] (908.00s)
very difficult. If you want to go from
[15:09] (909.60s)
an E5 software developer to an E5
[15:12] (912.72s)
product manager, just like that, I think
[15:15] (915.44s)
it will be very difficult to be good
[15:17] (917.84s)
relative to your peers. Unless your
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software development work involved a lot
[15:22] (922.00s)
of product management sort of things,
[15:24] (924.40s)
right? Um, but funny enough, these
[15:26] (926.32s)
things are a diamond shape. Once you're
[15:27] (927.84s)
senior enough, the skills once again
[15:30] (930.08s)
converge. Like what is a really
[15:32] (932.24s)
effective E8 doing? Right? There are
[15:34] (934.96s)
occasionally, of course, the prodigy
[15:36] (936.40s)
coders who are just far more effective
[15:38] (938.24s)
than the average person in producing a
[15:40] (940.40s)
ton of hard code, right? But I think a
[15:44] (944.08s)
lot of very senior individual
[15:45] (945.76s)
contributors, they are actually great at
[15:47] (947.84s)
leading teams, you know, like they have
[15:50] (950.08s)
a vision for technically where the
[15:51] (951.60s)
product needs to go. They understand the
[15:53] (953.12s)
infrastructure and the weak point. They
[15:54] (954.80s)
have a plan for staged migration to the
[15:57] (957.44s)
next better design, right? And people
[15:59] (959.84s)
love working with them because they are
[16:01] (961.52s)
good and compelling at coaching others
[16:03] (963.44s)
and and whatnot. That's a lot of what
[16:05] (965.52s)
makes a great typical sort of E8 um in
[16:08] (968.72s)
in individual contributor in which case
[16:10] (970.96s)
like this diamond shape happens, right?
[16:12] (972.64s)
Which is once you're senior enough, a
[16:14] (974.64s)
lot of the skills once again overlap.
[16:16] (976.56s)
Are you good at communicating with
[16:18] (978.00s)
others? Do you have vision? Do you
[16:20] (980.08s)
understand the business strategy and how
[16:21] (981.68s)
it pertains to the technology you're
[16:23] (983.28s)
building? those things once again become
[16:25] (985.44s)
shared. So I think there's a part in the
[16:27] (987.12s)
middle where it's most difficult to make
[16:28] (988.72s)
the change. Yeah, that is a that's a
[16:30] (990.88s)
really interesting idea and I I see that
[16:34] (994.48s)
as well with the high level archetype I
[16:36] (996.96s)
sees especially the you know tech lead
[16:39] (999.84s)
generalist um the skill set in leading
[16:43] (1003.12s)
an initiative that ad engineers are
[16:46] (1006.08s)
contributing to there's probably a lot
[16:48] (1008.16s)
of leadership skill overlaps with the
[16:50] (1010.56s)
director so I see that um but I I wonder
[16:54] (1014.88s)
though for some of the other archetypes
[16:56] (1016.40s)
like let's say you went deep into the uh
[16:59] (1019.12s)
you're a specialist in AI or something.
[17:02] (1022.48s)
I'm wondering maybe that diamond doesn't
[17:05] (1025.36s)
come back. Yeah, for some archetypes it
[17:07] (1027.68s)
definitely doesn't come back. You know,
[17:09] (1029.60s)
there are some people that are super
[17:10] (1030.88s)
deep like let's say you're into like
[17:13] (1033.20s)
query optimization for SQL queries,
[17:15] (1035.44s)
right? You can go very very deep on
[17:17] (1037.92s)
that. And I think in a way some
[17:19] (1039.84s)
archetypes going that deep on the
[17:21] (1041.68s)
technology might not need to have
[17:24] (1044.08s)
developed all the other skills that are
[17:26] (1046.16s)
related to leading teams and whatnot. it
[17:28] (1048.64s)
and may not be a good uh transition into
[17:31] (1051.36s)
managing. You mentioned that you
[17:33] (1053.76s)
navigated the conversation. You were an
[17:35] (1055.60s)
E9 um and you sought out a demotion to
[17:40] (1060.64s)
E7 and for those who don't know E9 is a
[17:44] (1064.32s)
distinguished engineer and E7 would be a
[17:47] (1067.84s)
senior staff engineer which are both
[17:50] (1070.88s)
crazy high levels. How did you navigate
[17:53] (1073.04s)
that conversation like requesting a
[17:55] (1075.28s)
demotion?
[17:57] (1077.12s)
Yeah. Yeah. This is a great question. I
[17:59] (1079.20s)
was even concerned about it when I was
[18:00] (1080.80s)
first promoted from an E8 to an E9 as
[18:04] (1084.40s)
the site director of London. What I told
[18:07] (1087.12s)
uh Facebook CTO back then, Shrep was,
[18:09] (1089.92s)
you know, I'm very concerned that uh
[18:12] (1092.08s)
because I like being an individual
[18:13] (1093.92s)
contributor, I'm very concerned that I
[18:15] (1095.44s)
cannot maintain this level of
[18:16] (1096.88s)
performance when I want to move back.
[18:18] (1098.88s)
And what he said to me during my
[18:20] (1100.72s)
promotion to E9 was, "Hey, let's worry
[18:22] (1102.64s)
about that problem when it happens. like
[18:24] (1104.80s)
let's not not promote you because you
[18:27] (1107.60s)
think one day you're going to want to go
[18:29] (1109.20s)
back uh to coding. So when I went back
[18:31] (1111.68s)
to coding I very much understood that I
[18:34] (1114.48s)
was at a level that I in no way could
[18:37] (1117.28s)
meet the expectations. And so I think
[18:39] (1119.68s)
under those conditions, it felt honestly
[18:42] (1122.40s)
great for me to ask to be uh moved back
[18:45] (1125.44s)
to E7, which is the level I joined the
[18:48] (1128.00s)
company at like eight years before that,
[18:49] (1129.92s)
you know, um because I knew that I could
[18:52] (1132.64s)
perform at that level and I definitely
[18:55] (1135.04s)
did not want to be compensated more than
[18:57] (1137.12s)
I deserved uh relative to my peers um on
[19:00] (1140.00s)
a team. It feels so much better to be
[19:02] (1142.08s)
performing well at a level than it feels
[19:04] (1144.56s)
to be like barely treading water at a
[19:07] (1147.36s)
level that you feel a lot of imposttor
[19:09] (1149.20s)
syndrome in and that you're not even
[19:10] (1150.96s)
sure that you'll stay long-term
[19:12] (1152.80s)
qualified for. Right? So I actually very
[19:15] (1155.44s)
much welcomed the change. Um but I think
[19:19] (1159.40s)
also many people don't make a change
[19:21] (1161.52s)
that big first of all and second of all
[19:23] (1163.44s)
many people don't want um don't want to
[19:28] (1168.00s)
take all that comes with that right like
[19:30] (1170.16s)
for instance I went from being the site
[19:31] (1171.68s)
director of a site that was probably you
[19:33] (1173.60s)
know four or 500 people um down to
[19:36] (1176.88s)
reporting to a manager who reported to a
[19:39] (1179.12s)
manager who was then part of a
[19:40] (1180.96s)
management of the site right and so like
[19:43] (1183.12s)
you have to have an ego that is very
[19:45] (1185.36s)
willing to say like hey I'm happy to be
[19:47] (1187.60s)
just a member of a team down here um
[19:50] (1190.00s)
doing my own work. And you also have to
[19:51] (1191.76s)
be sensitive to other funny things like
[19:54] (1194.64s)
you have to be careful not to speak up
[19:56] (1196.48s)
about your opinions about all sorts of
[19:58] (1198.08s)
things that you used to have opinions
[19:59] (1199.44s)
about when it came to running the site
[20:00] (1200.88s)
because you need to support the new
[20:02] (1202.32s)
people running the site in being
[20:04] (1204.00s)
successful. Like you need to support the
[20:05] (1205.84s)
new leaders, right? By playing your role
[20:08] (1208.40s)
on the team as opposed to using your
[20:10] (1210.80s)
outsiz influence from the path to get
[20:13] (1213.36s)
things done. And so I think both of
[20:15] (1215.20s)
those things were changes that I was uh
[20:18] (1218.00s)
you know willing to try to learn to
[20:19] (1219.28s)
navigate and I very much welcomed being
[20:21] (1221.28s)
leveled appropriately relative to my
[20:23] (1223.68s)
contributions. Makes sense. And you
[20:26] (1226.72s)
talked about the uh expectations being
[20:29] (1229.92s)
too high for E9. Can you give a high
[20:32] (1232.56s)
level like what what does it generally
[20:34] (1234.32s)
mean for what is an E7 typically doing
[20:37] (1237.20s)
or E8 typically doing or E9 typically
[20:39] (1239.76s)
doing?
[20:41] (1241.44s)
Yeah, great question. And I have to
[20:43] (1243.20s)
caveat my opinion about this relative
[20:45] (1245.52s)
to, you know, I was working at Meta
[20:47] (1247.84s)
starting 15 years ago, right? Um, and so
[20:51] (1251.44s)
my data about how those levels map is
[20:54] (1254.40s)
going to be very coarse grain, right?
[20:57] (1257.04s)
But in general, I think here are a few
[20:58] (1258.72s)
important concepts just when it comes to
[21:00] (1260.72s)
all those levels, right? Is it is about
[21:05] (1265.80s)
the scope of the person's influence. So
[21:09] (1269.52s)
like how big or how long uh or how
[21:14] (1274.00s)
valuable of a project can you
[21:16] (1276.24s)
single-handedly guarantee the delivery
[21:18] (1278.32s)
of? Okay, so like an intern can not even
[21:22] (1282.32s)
guarantee the delivery of a feature
[21:23] (1283.92s)
necessarily, right? Um or they might
[21:26] (1286.08s)
guarantee the delivery of a small
[21:27] (1287.68s)
feature on time, right? A senior
[21:30] (1290.08s)
developer like let's say an E5 E6 in a
[21:32] (1292.88s)
company should be able to deliver the
[21:35] (1295.84s)
work of about 10 people 10 to 15 people
[21:39] (1299.12s)
right if you're regularly influencing
[21:41] (1301.12s)
the work of 10 to 15 people and you can
[21:43] (1303.20s)
reliably deliver things of that scope
[21:45] (1305.60s)
when you are the one in charge for that
[21:47] (1307.52s)
delivery whether it's technical delivery
[21:49] (1309.68s)
or a product feature area or whatever it
[21:52] (1312.16s)
is usually at E7 once again my data is
[21:55] (1315.60s)
probably very stale I'd expect a person
[21:57] (1317.60s)
to regularly impact act the work of
[21:59] (1319.76s)
maybe 50 people, you know. So like um
[22:02] (1322.40s)
you would be a person that is driving
[22:04] (1324.36s)
technical direction for maybe around a
[22:07] (1327.68s)
team of about 50 people. You are
[22:09] (1329.52s)
regularly consulted on key changes on
[22:12] (1332.08s)
that team by the product managers by the
[22:14] (1334.88s)
engineering managers like you are viewed
[22:16] (1336.80s)
as that influential right typically you
[22:19] (1339.52s)
are able to personally deliver projects
[22:21] (1341.76s)
on the scope of like six months. Meaning
[22:23] (1343.84s)
like, hey, if I tell you this is your
[22:25] (1345.68s)
goal and I don't have time to speak to
[22:27] (1347.04s)
you for the next 6 months and I send you
[22:29] (1349.20s)
off into, you know, the wild of coding,
[22:31] (1351.84s)
you should be able to come back in 6
[22:33] (1353.28s)
months with this thing done, right? Like
[22:35] (1355.04s)
that is the scale and the scope of what
[22:36] (1356.96s)
I'd expect a person to personally be
[22:38] (1358.96s)
able to do, right? Um, as you get to
[22:41] (1361.84s)
eight and nine, those things just
[22:43] (1363.84s)
multiply out. And I think there's both a
[22:46] (1366.80s)
quantitative change to it. So meaning
[22:48] (1368.88s)
like I'd expect a director to regularly
[22:51] (1371.20s)
influence probably the work of a hundred
[22:52] (1372.72s)
people, you know, like that that's
[22:55] (1375.28s)
around the scale that I would say seems
[22:58] (1378.16s)
deserving, right? Um on the IC side, as
[23:01] (1381.76s)
you know, people can be IC's that are
[23:04] (1384.08s)
large scope in many different ways. They
[23:06] (1386.32s)
can be large because they're very deep,
[23:07] (1387.84s)
right? They're narrow, but they're very
[23:09] (1389.20s)
deep, right? Uh they're deep in a way
[23:11] (1391.80s)
qualitatively that others they can do
[23:14] (1394.64s)
things others can't do. So like a great
[23:16] (1396.88s)
E8 that's deep qualitatively that is
[23:19] (1399.28s)
deserving of it for instance you cannot
[23:21] (1401.68s)
simply replace him or her with like four
[23:24] (1404.24s)
E6s and expect the same work like there
[23:27] (1407.44s)
there's a level of work at which no
[23:29] (1409.28s)
amount of adding people two levels below
[23:31] (1411.92s)
is going to get the quality of work that
[23:34] (1414.00s)
you need right so there are some jobs
[23:35] (1415.52s)
like that there are some people that are
[23:37] (1417.44s)
very broad so they are a jack of all
[23:40] (1420.80s)
trades this type of person is especially
[23:43] (1423.04s)
useful for nent teams or startup
[23:45] (1425.28s)
startups, right, for small groups
[23:47] (1427.20s)
because generalists are super valuable
[23:49] (1429.04s)
there. And that's also because typically
[23:51] (1431.92s)
you cannot hire three E6s that basically
[23:54] (1434.56s)
can outperform one great generalist E8
[23:57] (1437.36s)
that can like do a little bit of
[23:58] (1438.96s)
everything, right? So there's a
[24:00] (1440.64s)
quantization question there. Um, so
[24:02] (1442.96s)
there's both a quantified difference,
[24:05] (1445.68s)
meaning sides of team influence, but I
[24:07] (1447.44s)
think the qualitative difference is
[24:08] (1448.96s)
very, very important because those
[24:12] (1452.16s)
qualitative differences often cannot be
[24:14] (1454.56s)
articulated well in a bulletoint list
[24:17] (1457.04s)
that your HR team sends you. And in
[24:19] (1459.60s)
fact, if you go and try to do that
[24:21] (1461.28s)
bulletoint list, you'll often be
[24:22] (1462.96s)
disappointed that you're still not at
[24:24] (1464.56s)
the level, and you'll be very confused
[24:26] (1466.08s)
why not. It's because people at those
[24:29] (1469.12s)
levels excel for very different reasons
[24:32] (1472.88s)
and it take the judgment of a person
[24:35] (1475.60s)
above those levels and these are hard
[24:38] (1478.32s)
judgments, right? To decide whether or
[24:40] (1480.96s)
not a person is deserving. So like let's
[24:43] (1483.20s)
say you get promoted from an E8 to an
[24:45] (1485.60s)
E9. When I worked back at the company,
[24:48] (1488.00s)
right, E9 wasn't even a level when I was
[24:50] (1490.88s)
first promoted to E8. Like it didn't
[24:52] (1492.56s)
exist as a concept. Later the company
[24:55] (1495.36s)
like it got hugely important people in
[24:57] (1497.92s)
the company like John Carmarmac as a
[24:59] (1499.68s)
good example as an individual
[25:01] (1501.36s)
contributor right like what would you
[25:02] (1502.88s)
level him at right so the company
[25:05] (1505.12s)
eventually had to invent these levels I
[25:08] (1508.00s)
think there was a period of time when it
[25:09] (1509.52s)
was discussed when I was part of the
[25:11] (1511.84s)
conversation of the first promotions
[25:13] (1513.84s)
into E9 and my memory if my memory
[25:16] (1516.56s)
serves correctly the company was
[25:18] (1518.24s)
probably several thousand people at this
[25:20] (1520.72s)
point and the company was considering
[25:22] (1522.64s)
promoting three engineers into E9, you
[25:26] (1526.40s)
know. Um, and so when you have that
[25:29] (1529.84s)
small of a sample set, it is very hard
[25:31] (1531.84s)
to create a bulleted to-do list of like,
[25:34] (1534.00s)
oh, if you're E8, do these four things
[25:35] (1535.60s)
and then we will like move you to E9.
[25:37] (1537.84s)
Every one of those three people were
[25:39] (1539.28s)
very different in shape. They were
[25:40] (1540.80s)
different archetypes, right? They were
[25:42] (1542.16s)
successful for different reasons. And
[25:44] (1544.32s)
so, I do think that as you get higher up
[25:47] (1547.44s)
in level, it's almost like how you
[25:50] (1550.80s)
become a professor somewhere, right?
[25:53] (1553.04s)
Like it's not like they have a bullet
[25:54] (1554.88s)
list that says you published 10 papers
[25:56] (1556.64s)
over three years in journals of this
[25:58] (1558.40s)
quality and you're guaranteed tenure,
[26:00] (1560.16s)
right? At some level it's all the other
[26:02] (1562.16s)
professors believe you're a professor
[26:04] (1564.48s)
then you're kind of a professor, right?
[26:06] (1566.48s)
Same with these leadership level at like
[26:09] (1569.04s)
eight and above in my opinion a lot of
[26:11] (1571.84s)
it is would other E8 think you are an
[26:14] (1574.52s)
E8? you know, for whatever reasons they
[26:17] (1577.76s)
think that uh and those reasons are
[26:20] (1580.48s)
different for people uh different
[26:22] (1582.64s)
archetypes and they are hard to
[26:24] (1584.40s)
articulate. And so for anybody who's
[26:26] (1586.08s)
looking for a punch card to get there,
[26:28] (1588.40s)
that's why it's hard to describe how to
[26:30] (1590.24s)
do it with that dynamic then I kind of
[26:32] (1592.64s)
wonder if there could be a chicken and
[26:35] (1595.36s)
egg problem. Um so imagine you are a
[26:39] (1599.68s)
excellent E7, but there are no E8s or
[26:43] (1603.20s)
E9s in your vicinity.
[26:45] (1605.96s)
Could you know could you even get that
[26:48] (1608.40s)
qualitative input that says hey you're
[26:50] (1610.80s)
solving problems that you know E7s can't
[26:54] (1614.16s)
because you're just at the top of where
[26:55] (1615.68s)
you are. Yeah totally especially at
[26:58] (1618.24s)
small companies. So that happens all the
[27:00] (1620.16s)
time to people who join small companies
[27:02] (1622.08s)
or weak teams right both things can
[27:04] (1624.64s)
happen. So let's talk about the latter
[27:06] (1626.32s)
first. Uh Microsoft's a huge company.
[27:08] (1628.56s)
You know when I worked there it was
[27:10] (1630.24s)
140,000 people. Now before the layoffs
[27:12] (1632.16s)
it was about 240,000. at 140,000 people.
[27:15] (1635.60s)
This was like 15 years ago, 140,000
[27:18] (1638.08s)
people is more than the populations of
[27:19] (1639.84s)
40 countries in the world. So, you can
[27:22] (1642.08s)
imagine like what that feels like to
[27:23] (1643.92s)
work in a company that's the size of a
[27:25] (1645.76s)
country. Um, in a company that size,
[27:28] (1648.56s)
there are always weak teams. And the
[27:30] (1650.80s)
problem with weak teams is on a weak
[27:32] (1652.72s)
team, you may think you are awesome, but
[27:34] (1654.72s)
that is because you work with very bad
[27:36] (1656.96s)
peers. Okay? So like that will lead to
[27:40] (1660.40s)
funny situations where people get
[27:41] (1661.84s)
overleveled for instance. Um or it'll
[27:44] (1664.16s)
lead to situations where people simply
[27:45] (1665.68s)
do not have someone they can observe
[27:47] (1667.52s)
that they can learn from because they're
[27:49] (1669.28s)
on a weak team. They're like the best.
[27:51] (1671.12s)
It's like how you buy real estate,
[27:52] (1672.80s)
right? You never want to be the most
[27:54] (1674.32s)
expensive house on the block because you
[27:56] (1676.96s)
have no no room to grow up, right? Like
[27:59] (1679.12s)
you you're already at the top. You don't
[28:02] (1682.24s)
want to be the worst house on the block.
[28:03] (1683.84s)
Nobody likes you, right? So you kind of
[28:05] (1685.68s)
want to be the middle house, right? And
[28:07] (1687.60s)
so you should find a block where you're
[28:08] (1688.96s)
the middle house. Um, when it comes to
[28:10] (1690.96s)
those higher levels, I do agree that
[28:12] (1692.96s)
it's very hard to find great examples of
[28:15] (1695.28s)
those people. And furthermore, each of
[28:17] (1697.20s)
those examples excel in different ways
[28:19] (1699.20s)
that might not be well matched to what
[28:20] (1700.80s)
you're good at. Right? So I feel the
[28:23] (1703.76s)
more important thing at those levels is
[28:25] (1705.52s)
not necessarily another person to
[28:27] (1707.20s)
observe, but a great coach that can give
[28:30] (1710.24s)
you feedback. someone who is a decision
[28:33] (1713.52s)
maker in the promotion of let's say an
[28:35] (1715.92s)
eight to a nine and they can tell you
[28:38] (1718.24s)
like hey qualitatively you're way off
[28:41] (1721.44s)
from where we need you to be and then
[28:43] (1723.28s)
you can have a meaningful discussion
[28:45] (1725.04s)
about like oh what things can I do that
[28:47] (1727.76s)
would be more like that right um so I
[28:50] (1730.32s)
think it's much better to get that
[28:51] (1731.68s)
qualitative opinion uh than to
[28:53] (1733.92s)
necessarily find the right people to
[28:55] (1735.52s)
model after so when you got promoted to
[28:58] (1738.44s)
E9 um what was the project or the scope
[29:02] (1742.80s)
just to give people a sense of you know
[29:04] (1744.96s)
what does it look like if something gets
[29:06] (1746.80s)
you to that level.
[29:09] (1749.84s)
Yeah. I had brought 12 people with me to
[29:12] (1752.80s)
start the engineering portion of the
[29:14] (1754.96s)
London office of Meta, right? And so we
[29:17] (1757.52s)
started off pretty small like that and
[29:20] (1760.08s)
over a period of about uh four or five
[29:23] (1763.92s)
years we grew it to a site of probably
[29:26] (1766.64s)
four or 500 people, right? And so
[29:29] (1769.60s)
through that growth, I managed several
[29:32] (1772.00s)
key transitions on the site. So when you
[29:34] (1774.88s)
start off with a site with 12 engineers,
[29:36] (1776.88s)
that's super easy. Like a great
[29:38] (1778.56s)
engineering manager at a level six could
[29:40] (1780.48s)
do a solid job running that, right? But
[29:43] (1783.28s)
as you grow with each order of magnitude
[29:45] (1785.60s)
difference in the size of the team, the
[29:47] (1787.92s)
complexity of the problems becomes much
[29:50] (1790.56s)
harder and the solutions become much
[29:52] (1792.64s)
less black or white. They become always
[29:55] (1795.20s)
kind of gray in some direction. And so I
[29:58] (1798.32s)
think the promotion was primarily
[30:00] (1800.24s)
because I was handling a much wider
[30:02] (1802.96s)
variety of things and much more key
[30:06] (1806.32s)
strategic decisions. Let me give you a
[30:08] (1808.08s)
concrete example. One of the last
[30:10] (1810.00s)
meetings I had as a site leave was I was
[30:13] (1813.04s)
trying to convince the people in Menllo
[30:14] (1814.80s)
Park the number of interns they should
[30:18] (1818.24s)
hire and the headcount they should have
[30:20] (1820.64s)
in uh London this year for this summer
[30:24] (1824.64s)
because I had done the math on the H-1B
[30:27] (1827.28s)
anticipated volume for the following
[30:29] (1829.28s)
year for full-time offers to metriculate
[30:32] (1832.00s)
into Menllo Park. That does that make
[30:34] (1834.40s)
sense? So I was having a conversation 18
[30:36] (1836.56s)
months before the actual thing was going
[30:38] (1838.16s)
to happen and I had used data to derive
[30:40] (1840.80s)
what actions we should take today that
[30:43] (1843.20s)
would only play out 18 months in. Right?
[30:46] (1846.88s)
No one else was thinking about that.
[30:48] (1848.80s)
Right? when I was more junior as an E8
[30:51] (1851.28s)
in the site, I was the first one of all
[30:53] (1853.44s)
of Meta's sites to really look at not
[30:55] (1855.68s)
only ladder levels of all the engineers
[30:58] (1858.08s)
but the pyramid of of of seniority and
[31:00] (1860.96s)
to really think about like hey um do we
[31:03] (1863.52s)
actually uh have the right seniority of
[31:05] (1865.76s)
people? Are we growing people at a
[31:07] (1867.68s)
commensurate engineering like growth
[31:09] (1869.92s)
velocity, right? And so things like that
[31:12] (1872.08s)
which are also much more about strategic
[31:14] (1874.72s)
things than they are about um sort of
[31:17] (1877.44s)
tangible concrete things to deliver. So
[31:19] (1879.52s)
I think there are a variety of those
[31:21] (1881.60s)
sorts of decisions that become larger
[31:23] (1883.12s)
and larger in scope either in the
[31:24] (1884.72s)
breadth of people they impact or in the
[31:26] (1886.88s)
timeline forward in which you're
[31:28] (1888.72s)
expected to think. Is there something
[31:31] (1891.84s)
that you learned that uh really made a
[31:35] (1895.52s)
difference in upholding a strong
[31:37] (1897.52s)
engineering culture? Because you had a
[31:39] (1899.52s)
very unique opportunity to take uh it's
[31:42] (1902.40s)
almost like you started a company within
[31:43] (1903.92s)
a company. What is it that really made a
[31:47] (1907.20s)
difference in making sure that site was
[31:49] (1909.44s)
very strong uh from an engineering
[31:51] (1911.92s)
perspective? Yeah, great question. I
[31:55] (1915.12s)
think one huge help to me before
[31:57] (1917.20s)
starting the site was not only um did I
[31:59] (1919.76s)
observe other sites. So I was the second
[32:01] (1921.76s)
person hired in Meta Seattle and Meta
[32:03] (1923.92s)
Seattle was Meta's first office outside
[32:06] (1926.16s)
of its headquarters back then in Palo
[32:07] (1927.84s)
Alto. So I had seen what it felt like to
[32:10] (1930.48s)
grow from four desks into 120 desks by
[32:13] (1933.68s)
the time I left. And I had learned from
[32:15] (1935.76s)
some of the things we learned from uh
[32:17] (1937.44s)
that were mistakes in how we grew that
[32:19] (1939.12s)
site. Right. But more importantly,
[32:21] (1941.36s)
before I actually started the
[32:23] (1943.04s)
engineering office in London, I
[32:24] (1944.96s)
interviewed site directors from other
[32:27] (1947.28s)
companies at different sites. For
[32:29] (1949.28s)
instance, one super helpful site
[32:31] (1951.28s)
director was the former site director of
[32:33] (1953.36s)
Google London, uh David Singleton, who
[32:35] (1955.52s)
became Stripe's CTO um sometime after
[32:38] (1958.16s)
that. Um he gave me some great advice.
[32:41] (1961.44s)
The best advice of which was that Google
[32:43] (1963.68s)
had experimented with what they call
[32:45] (1965.52s)
landing teams. how many people you bring
[32:47] (1967.28s)
to a site to bootstrap the culture and
[32:49] (1969.52s)
whatnot. Um, our decisions on landing
[32:52] (1972.00s)
teams were influenced by two things. One
[32:53] (1973.84s)
thing was Shrep. Shrep said he wanted
[32:55] (1975.92s)
two full interview loops of people,
[32:58] (1978.32s)
meaning he wanted enough engineers for
[33:01] (1981.12s)
two full loops. Okay, so that estimates
[33:03] (1983.92s)
about 10 to 12 people, right? The thing
[33:06] (1986.16s)
I learned from David Singleton was that
[33:07] (1987.76s)
Google had experimented and Google's
[33:09] (1989.76s)
takeaway was make the landing team
[33:12] (1992.00s)
commit to two years minimum. Now, you'll
[33:14] (1994.56s)
find many companies that approach this
[33:15] (1995.92s)
differently. They'll have a landing team
[33:17] (1997.20s)
show up for the first three months, the
[33:18] (1998.64s)
first six months, and then the site is
[33:20] (2000.24s)
on their own, right? I think that that's
[33:22] (2002.00s)
a mistake because it is very difficult
[33:24] (2004.56s)
to maintain culture in those early
[33:27] (2007.36s)
growth periods where the percentage of
[33:29] (2009.44s)
new people is like commensurate with the
[33:31] (2011.68s)
percentage of old people that have been
[33:32] (2012.96s)
around for like more than 3 months,
[33:34] (2014.48s)
right? And so I brought 12 people with
[33:36] (2016.80s)
me and I asked them to commit for two
[33:38] (2018.80s)
years and that made a humongous
[33:40] (2020.96s)
difference. The other big difference
[33:43] (2023.28s)
culturally with the 12 people I brought
[33:45] (2025.36s)
is I interviewed probably nearly 50
[33:48] (2028.80s)
people in the company that volunteered
[33:50] (2030.56s)
to go and I chose the 12, right? And one
[33:53] (2033.04s)
of the ways I chose it is I interviewed
[33:54] (2034.96s)
not only the people but somewhat of
[33:56] (2036.80s)
their peers and definitely their manager
[33:59] (2039.28s)
when it came to how they were good
[34:01] (2041.76s)
culture carriers for the company. Like
[34:04] (2044.00s)
would this be a good person to add to
[34:06] (2046.24s)
the culture of the site? And I honestly
[34:08] (2048.88s)
really locked out. The 12 people we
[34:10] (2050.64s)
brought with us were amazing were
[34:13] (2053.20s)
amazing people for building and
[34:15] (2055.44s)
maintaining the culture of the company.
[34:17] (2057.76s)
They had been at the company long enough
[34:19] (2059.20s)
to know what its culture was. Right? I
[34:21] (2061.68s)
also asked that each one of them be able
[34:23] (2063.60s)
to bring over a project of some sort
[34:25] (2065.76s)
because you also need someone that's a
[34:27] (2067.44s)
able to lead work, right? And then you
[34:29] (2069.92s)
have the exceptional talents like people
[34:31] (2071.44s)
like Ben Matthews who were um individual
[34:33] (2073.84s)
contributors that were very happy to
[34:35] (2075.76s)
work on whatever the site needed but
[34:37] (2077.68s)
could code the crap out of things, you
[34:39] (2079.44s)
know, was just very very productive and
[34:42] (2082.00s)
very astute when it came to how to hire.
[34:44] (2084.08s)
So I really lucked out with the people
[34:46] (2086.24s)
that we ended up choosing um to build
[34:48] (2088.24s)
the site and so much of it was due to
[34:50] (2090.56s)
learning from others who've done it
[34:51] (2091.92s)
before. I see. And you said that you
[34:55] (2095.20s)
wanted people to commit for two years.
[34:57] (2097.12s)
Does that is that a verbal thing or is
[34:59] (2099.60s)
there a um you know you get some
[35:02] (2102.56s)
additional equity for staying or
[35:04] (2104.48s)
something like that? There's no
[35:06] (2106.48s)
compensation to it. So people got paid
[35:08] (2108.48s)
whatever they got paid before, right?
[35:10] (2110.16s)
And um we didn't make them sign anything
[35:12] (2112.24s)
that said it it was two years, but I
[35:14] (2114.72s)
basically had a serious conversation
[35:16] (2116.16s)
with them of like, hey, I'm only looking
[35:18] (2118.08s)
for people who are committed uh to at
[35:20] (2120.08s)
least being there two years, right? So
[35:21] (2121.84s)
if that doesn't fit in with your life
[35:23] (2123.20s)
plan, this is probably not the job for
[35:24] (2124.64s)
you, right? and and so um those folks uh
[35:29] (2129.20s)
sort of elected self-elected off right
[35:31] (2131.92s)
we had one person who needed to go back
[35:33] (2133.92s)
sooner than their two-year one or two
[35:36] (2136.00s)
people needed to go back sooner than
[35:37] (2137.44s)
their two-year uh time commitment and
[35:39] (2139.52s)
those were understandable reasons and we
[35:41] (2141.36s)
just talked about it when it happened
[35:42] (2142.48s)
and I was very supportive of them
[35:44] (2144.04s)
transitioning but the surprise even to
[35:46] (2146.56s)
me is far more people stayed much longer
[35:49] (2149.12s)
than that in fact from the original
[35:50] (2150.80s)
landing team I want to say probably
[35:52] (2152.16s)
about four people are still in London
[35:54] (2154.00s)
right now 15 years later like they are
[35:55] (2155.84s)
basically British citizens, right? And
[35:57] (2157.84s)
when they joined me in London, that was
[35:59] (2159.52s)
not the plan. They they were not joining
[36:01] (2161.36s)
me because they sneakily thought they
[36:03] (2163.04s)
would become British citizens. Like they
[36:04] (2164.96s)
ended up loving London. They loved the
[36:06] (2166.96s)
office. They stayed committed. Right. So
[36:09] (2169.12s)
I think in the end we also were very
[36:11] (2171.04s)
fortunate to have people that were
[36:12] (2172.56s)
committed like that without any sort of
[36:14] (2174.96s)
real enforcement of it. You mentioned
[36:17] (2177.36s)
that Shrep was your manager at some
[36:19] (2179.92s)
point and I was when I was digging
[36:22] (2182.24s)
through things I saw you had worked with
[36:24] (2184.16s)
Bos as well and I imagine you had some
[36:27] (2187.12s)
proximity to Mark Zuckerberg. Do you
[36:29] (2189.20s)
have any favorite stories working with
[36:31] (2191.04s)
any very curious because they're all so
[36:34] (2194.04s)
legendary? Yeah. Um, you know, I was
[36:37] (2197.04s)
very fortunate in working for a lot of
[36:39] (2199.36s)
great people at Facebook. So, my first
[36:41] (2201.04s)
manager was Bos, right? And Bos
[36:42] (2202.80s)
eventually became uh the CTO. Um my
[36:46] (2206.16s)
manager during the years of leading
[36:47] (2207.84s)
London was Shrep, the then CTO. Uh both
[36:51] (2211.20s)
gave me sort of great advice and
[36:53] (2213.60s)
guidance.
[36:57] (2217.00s)
Um I only talked one-on-one with Zach a
[37:00] (2220.16s)
few times, so I don't really have too
[37:02] (2222.32s)
much insight there other than um he felt
[37:05] (2225.68s)
to me like he led the teams. He felt
[37:10] (2230.16s)
very genuine to me in how he led the
[37:12] (2232.32s)
teams. You know, one thing that
[37:14] (2234.16s)
impressed me most about Zuck is, and
[37:16] (2236.48s)
this is true of Bos as well, is you got
[37:19] (2239.20s)
to remember when I joined the company, I
[37:20] (2240.80s)
was 33 years old, right? The median age
[37:22] (2242.88s)
of the company back then was 27. And so
[37:25] (2245.60s)
I was the oldest guy in the Seattle
[37:27] (2247.76s)
office for the first seven months. Like
[37:30] (2250.40s)
a 33y old was literally the oldest guy,
[37:32] (2252.80s)
right? And so most of the people I
[37:34] (2254.80s)
worked for were actually younger than
[37:36] (2256.32s)
me, right? Uh, but I was very impressed
[37:38] (2258.88s)
with both Bos and Zuck in their ability
[37:42] (2262.80s)
and willingness to personally grow. Like
[37:45] (2265.92s)
in each of those cases, if you talk to
[37:47] (2267.84s)
people that have worked with Bos for
[37:49] (2269.28s)
more than eight or 10 years, right, or
[37:51] (2271.12s)
with Zach for a good number of years,
[37:53] (2273.44s)
they can tell you exactly how they grew
[37:56] (2276.00s)
over time. It was very obvious that they
[37:58] (2278.48s)
were not only open to coaching and
[38:00] (2280.16s)
probably seeking it on their own, right,
[38:02] (2282.08s)
but making a deliberate effort to
[38:04] (2284.24s)
improve. Like small concrete example, if
[38:06] (2286.64s)
you look at how Zuck ran AMAs um when I
[38:09] (2289.28s)
first joined versus even two years into
[38:11] (2291.36s)
my time at uh Facebook where I'd
[38:14] (2294.00s)
randomly guess he was probably like 28
[38:15] (2295.92s)
or or something. He had greatly improved
[38:19] (2299.20s)
how he communicated at AMAs. So like uh
[38:22] (2302.72s)
there were really neat thing. I'm pretty
[38:24] (2304.80s)
sure for instance, small thing but I
[38:26] (2306.40s)
noticed right I'm pretty sure someone
[38:28] (2308.24s)
coached him on how to hold his head when
[38:31] (2311.28s)
he was speaking in public. So when I
[38:33] (2313.04s)
first joined Facebook, um, Zuck's
[38:35] (2315.20s)
natural pose when talking was head
[38:37] (2317.76s)
slightly upward. So he would look
[38:39] (2319.92s)
slightly like this at the crowd, right?
[38:42] (2322.56s)
But through the years, you saw him
[38:44] (2324.16s)
deliberately correct that. It was very
[38:45] (2325.68s)
obvious. He like deliberately corrected
[38:47] (2327.44s)
it to where now he faces the crowd
[38:49] (2329.44s)
straight on, right? And so I was always
[38:51] (2331.52s)
very impressed by the things they were
[38:53] (2333.28s)
very willing to improve in themselves
[38:55] (2335.12s)
and put effort into.
[38:56] (2336.96s)
You mentioned some of the highest level
[38:59] (2339.08s)
IC's. The qualitative feedback from
[39:02] (2342.48s)
people around them is a really important
[39:04] (2344.56s)
aspect. I'm curious from your
[39:07] (2347.00s)
perspective, having worked with a lot of
[39:09] (2349.28s)
Meta's, you know, highest profile
[39:12] (2352.12s)
IC's, is there someone that stands out
[39:14] (2354.72s)
to you that consistently impressed you?
[39:17] (2357.52s)
One person that I worked uh only ever so
[39:21] (2361.52s)
indirectly with so barely with uh John
[39:24] (2364.08s)
Carmarmac obviously legendary right but
[39:26] (2366.48s)
the thing when you work in Oculus that
[39:28] (2368.16s)
you realize is not only is he super
[39:31] (2371.60s)
prolific in coding okay so he produced
[39:34] (2374.32s)
so much code but he had an ability to
[39:37] (2377.60s)
drop into your codebase and product
[39:39] (2379.68s)
after not looking at it for like six
[39:41] (2381.36s)
months and give actual concrete advice
[39:43] (2383.92s)
about what to do like I was working on a
[39:45] (2385.92s)
very tiny product called Oculus 360
[39:48] (2388.16s)
photos. Very simple product. Um, and uh,
[39:51] (2391.60s)
just one random day like John dropped
[39:53] (2393.76s)
into the codebase and he tried it out
[39:55] (2395.36s)
and he like had had insights about like
[39:58] (2398.40s)
the distortion on the corners of the
[40:00] (2400.40s)
photos like when when when you turned
[40:02] (2402.24s)
and he had some performance ideas for
[40:04] (2404.08s)
like, hey, I I think we can speed up
[40:05] (2405.68s)
performance like this. And this is all
[40:07] (2407.20s)
without anybody a asking, right? But b,
[40:10] (2410.16s)
like he said some really smart things
[40:12] (2412.24s)
that um, just no one on the team had
[40:14] (2414.56s)
thought of. So that was impressive, but
[40:16] (2416.32s)
I didn't work with him as directly. A
[40:18] (2418.64s)
great example of an interesting shape
[40:20] (2420.96s)
person, um, Scott Renfro. So I worked
[40:23] (2423.76s)
with him a bit more because he came out
[40:25] (2425.60s)
for a rotation in London for 6 months.
[40:28] (2428.48s)
One thing that you'll find consistently
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is peer feedback about Scott's work on a
[40:33] (2433.76s)
team is super glowing. Like when Scott
[40:37] (2437.68s)
reviews your uh, PR, right? Even if you
[40:40] (2440.72s)
do something very dumb, he will put it
[40:43] (2443.04s)
in a very sensitive way, not to shame
[40:46] (2446.16s)
you or anything. So he'll say it in a
[40:48] (2448.16s)
way that you understand what he's saying
[40:49] (2449.60s)
is like, hey, this thing could be done
[40:51] (2451.60s)
differently, right? But he'll never
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publicly shame you or anything like
[40:54] (2454.96s)
that. If you look at peer feedback for
[40:57] (2457.52s)
him, it is overwhelmingly like Scott
[40:59] (2459.92s)
takes time out of his day to make me
[41:01] (2461.84s)
great. So he was an example where um I
[41:05] (2465.36s)
think his uh seniority was welld
[41:07] (2467.84s)
deserved in the company because it was
[41:09] (2469.68s)
very obvious that having him on the team
[41:12] (2472.96s)
would make your entire team much better.
[41:15] (2475.92s)
So he was a force multiplier for teams
[41:18] (2478.64s)
and later on he led these huge things
[41:20] (2480.56s)
for meta. I'm pretty sure he had
[41:22] (2482.00s)
something to do with GDPR at some level
[41:23] (2483.92s)
and like a few other things like huge
[41:26] (2486.52s)
infrastructurewide like massive changes.
[41:29] (2489.20s)
And so he was capable both personally of
[41:31] (2491.76s)
guaranteeing a huge scope of work,
[41:33] (2493.60s)
right? But he was also a force
[41:35] (2495.68s)
multiplier for the teams he was on. So
[41:37] (2497.52s)
kind of a slam dunk case of someone who
[41:39] (2499.92s)
deserved to be uh rewarded well for that
[41:42] (2502.32s)
work. Coming to the end of your your
[41:45] (2505.04s)
tenure at at Facebook or Yeah, it was
[41:47] (2507.68s)
Facebook at the time. I I remember I
[41:50] (2510.32s)
joined in uh 2018 and I had been there
[41:53] (2513.60s)
for some amount of months and I heard in
[41:56] (2516.88s)
the echoes of workplace that someone had
[42:00] (2520.00s)
bought the entire company coffee for a
[42:02] (2522.64s)
day and you know now I I've read a lot
[42:06] (2526.16s)
of your posts and I know that was you.
[42:08] (2528.48s)
What what made you want to buy everyone
[42:11] (2531.76s)
at the company coffee for a day and
[42:13] (2533.52s)
spend over $23,000?
[42:16] (2536.32s)
Yeah, great question. I'll I'll start
[42:18] (2538.40s)
with the original story which was
[42:20] (2540.72s)
several years into my time at Facebook.
[42:22] (2542.96s)
Uh Facebook had moved into um its
[42:25] (2545.76s)
current location in Menllo Park. Uh
[42:28] (2548.24s)
Hacker Square wasn't fully paved yet.
[42:30] (2550.24s)
Eventually a field coffee opened there.
[42:32] (2552.40s)
And one Christmas I just thought, well,
[42:34] (2554.56s)
let me buy everybody a coffee for
[42:36] (2556.40s)
Christmas, right? So for that Christmas,
[42:39] (2559.20s)
for one day, I had paid for coffee for
[42:42] (2562.08s)
everyone. And I just felt it was such a
[42:45] (2565.04s)
joyous thing to see how happy people
[42:47] (2567.44s)
were to get a free coffee from someone,
[42:49] (2569.36s)
right? And after that day, the CEO of
[42:52] (2572.24s)
Phils had written me to say that his
[42:54] (2574.08s)
employees really enjoyed the the day a
[42:57] (2577.04s)
whole lot because it had never happened
[42:58] (2578.48s)
in their history, like something so
[43:00] (2580.48s)
crazy, right? And so I felt like it was
[43:02] (2582.64s)
a sort of a pay it forward sort of thing
[43:04] (2584.64s)
that made me feel great, that made
[43:06] (2586.16s)
others feel great. When I left the
[43:08] (2588.24s)
company, I really felt like, you know, I
[43:10] (2590.40s)
loved the company so much and the
[43:12] (2592.64s)
company gave me so much over time. Like
[43:15] (2595.36s)
gave me the opportunity to do really
[43:17] (2597.36s)
interesting things, gave me incredible
[43:19] (2599.52s)
coaching, right? Um when I joined the
[43:21] (2601.76s)
company, a brick of money fell on my
[43:23] (2603.20s)
head, right? Like I did not join
[43:24] (2604.80s)
Facebook. People forget when I joined
[43:26] (2606.24s)
Facebook, the big talk was still like
[43:27] (2607.68s)
Friendster was coming back, G+ was going
[43:29] (2609.52s)
to roll over Facebook. Like there was
[43:30] (2610.96s)
all sorts of talk. Uh and eventually
[43:33] (2613.20s)
like Facebook would never make money on
[43:34] (2614.88s)
mobile. like there was a lot of like
[43:36] (2616.40s)
rock throwing, right? And so when I
[43:38] (2618.56s)
joined the company, I had uh no
[43:41] (2621.12s)
expectations for what would happen. I
[43:43] (2623.36s)
had left Microsoft um after working at
[43:45] (2625.76s)
Microsoft 12 years. The stock had gone
[43:47] (2627.92s)
up 15 cent in those 12 years, you know.
[43:50] (2630.40s)
So I had um sold my 78s of unvested like
[43:54] (2634.64s)
remaining shares for $1,600 and I went
[43:57] (2637.28s)
to the company store and bought like an
[43:58] (2638.72s)
Xbox 360, a driving wheel and fortza 3
[44:01] (2641.12s)
and I was thrilled, right? So, the fact
[44:03] (2643.68s)
that a brick of money hit my head, not
[44:05] (2645.44s)
by my own design, but really just by
[44:07] (2647.52s)
being lucky, right? Um, made me so
[44:10] (2650.64s)
grateful, you know, what I did when I
[44:13] (2653.68s)
first when Facebook IPOed, I gave 100%
[44:17] (2657.12s)
of what I made from the IPO to charity,
[44:19] (2659.44s)
you know, cuz I felt like it was not a
[44:22] (2662.00s)
thing that I deserved, honestly. And I
[44:24] (2664.96s)
don't mean to poo poo myself. Like all I
[44:26] (2666.80s)
mean is like I was lucky enough to be
[44:28] (2668.80s)
born in a time frame where my interest
[44:31] (2671.36s)
in computer science actually paid
[44:32] (2672.72s)
something versus if I worked for a
[44:34] (2674.16s)
blacksmith 400 years ago, right? Um and
[44:37] (2677.04s)
I happened to have migrated to America,
[44:39] (2679.68s)
land of opportunity, right? So I felt
[44:41] (2681.44s)
super lucky. And so what I wanted to do
[44:43] (2683.52s)
was given the great experience at Phils
[44:45] (2685.44s)
that first time with Christmas when I
[44:47] (2687.60s)
left the company, I felt like I want to
[44:49] (2689.60s)
give something back, you know, for all
[44:51] (2691.84s)
the things the company has done for me.
[44:53] (2693.52s)
And so what I did was I coordinated with
[44:56] (2696.32s)
the major sites that had employees. So
[44:58] (2698.72s)
back then it was probably five different
[45:00] (2700.08s)
sites I thought of coordinating with and
[45:02] (2702.16s)
I just called the individual owners of
[45:03] (2703.92s)
the coffee shops in those places uh and
[45:06] (2706.96s)
arranged a way to pay for coffee for a
[45:09] (2709.36s)
day and it was super fun and really
[45:11] (2711.68s)
rewarding to do and I'm really glad that
[45:14] (2714.16s)
I did it. Yeah, it I mean it definitely
[45:16] (2716.48s)
left an impression. I think everyone me
[45:18] (2718.96s)
and all my friends were kind of new
[45:20] (2720.40s)
grads at the time and you know it was
[45:22] (2722.48s)
kind of this uh fun little thing. I I
[45:25] (2725.60s)
don't remember if it was attached to
[45:27] (2727.52s)
your name at the time. I think we just
[45:29] (2729.20s)
thought it was someone nice at Facebook
[45:31] (2731.60s)
was paying. So, you know, it was
[45:33] (2733.76s)
appreciated for sure. So, when you
[45:36] (2736.08s)
decided to leave Facebook, I'm curious
[45:38] (2738.08s)
because it sounds like everything was
[45:39] (2739.92s)
going quite well. What What was your
[45:42] (2742.32s)
rationale for leaving? Yeah, great
[45:44] (2744.64s)
question. The biggest thing on my mind
[45:46] (2746.72s)
back then was I was very concerned about
[45:48] (2748.72s)
the rising income gap in America. And so
[45:51] (2751.44s)
I wanted to start a BC Corp to bridge
[45:53] (2753.52s)
the rising income gap. I had a few ideas
[45:55] (2755.84s)
for how to do that. Uh back then in
[45:58] (2758.92s)
2018 I was also like this doesn't sound
[46:01] (2761.76s)
revolutionary now but back then I was
[46:04] (2764.24s)
complaining to co-workers that I think
[46:06] (2766.16s)
we're going to experience technical
[46:07] (2767.72s)
unemployment you know and back then that
[46:09] (2769.76s)
term hadn't even really come around but
[46:11] (2771.52s)
I I was convinced that robots would
[46:13] (2773.04s)
replace people and that our society
[46:14] (2774.96s)
needed to change quickly to adapt to
[46:16] (2776.88s)
that and so most of my co-workers
[46:19] (2779.28s)
disagreed but I felt very concerned
[46:21] (2781.12s)
about it so I thought I should actually
[46:23] (2783.20s)
do something like start a BC Corp to try
[46:26] (2786.08s)
to do that. And so one of the main
[46:28] (2788.00s)
reasons that I stepped down was that I
[46:29] (2789.92s)
felt like Facebook had grown to a point
[46:33] (2793.28s)
where um it would be fine honestly
[46:36] (2796.08s)
without me like plenty more than fine.
[46:38] (2798.08s)
It was doing really well. Um and I was
[46:40] (2800.32s)
concerned about this thing happening to
[46:42] (2802.00s)
America. I think another thing if I were
[46:44] (2804.56s)
to evaluate myself having left the
[46:47] (2807.04s)
company at the same level as when I
[46:48] (2808.64s)
joined, right, is I feel like I was much
[46:52] (2812.48s)
more useful as an individual contributor
[46:54] (2814.88s)
when Facebook was small. When I joined
[46:57] (2817.12s)
as the second person hired in Seattle,
[46:58] (2818.72s)
Facebook had 500 engineers globally at
[47:01] (2821.04s)
that point, right? Um but that meant
[47:03] (2823.76s)
that I along with two other engineers
[47:06] (2826.32s)
could ship the entire video calling
[47:08] (2828.32s)
feature you know from nothing to
[47:10] (2830.08s)
shipping um in a period of probably six
[47:12] (2832.48s)
months right um I'm a generalist not a
[47:15] (2835.04s)
specialist so I was also able to like
[47:17] (2837.84s)
help with the negotiation of things like
[47:20] (2840.00s)
what does the Skype contract say I
[47:22] (2842.48s)
helped uh negotiate and hire and manage
[47:25] (2845.12s)
the contractors that were testing on it
[47:26] (2846.96s)
so I was capable of doing a very wide
[47:29] (2849.12s)
variety of things eight years after that
[47:31] (2851.84s)
time the company had grown to I mean
[47:34] (2854.80s)
just thousands of engineers right um the
[47:36] (2856.96s)
company had grown to a point where I
[47:39] (2859.04s)
think a generalist like me is much less
[47:42] (2862.24s)
useful you know than specialists and so
[47:44] (2864.48s)
I also felt that my unique value
[47:47] (2867.28s)
contribution was no longer the same you
[47:49] (2869.20s)
know I I I was a very generic coder um
[47:52] (2872.16s)
and I wasn't contributing anything that
[47:54] (2874.08s)
was exceptional honestly uh and so I
[47:56] (2876.56s)
also felt like I was much less effective
[47:58] (2878.64s)
for the company would you say that's
[48:00] (2880.64s)
generally True. So, you know, taking it
[48:03] (2883.44s)
out of big tech, imagine someone wants
[48:06] (2886.40s)
to work at startups. Would you say the
[48:08] (2888.24s)
generalist skill set is the way to go?
[48:11] (2891.04s)
And, you know, specialists can have an
[48:12] (2892.96s)
edge at the largest companies. Yeah,
[48:15] (2895.04s)
that that's my intuition is that most
[48:16] (2896.96s)
small companies value generalists more
[48:18] (2898.80s)
than specialists. Like, every small
[48:20] (2900.88s)
company can use definitely one
[48:22] (2902.32s)
specialist. Like, no matter what domain
[48:24] (2904.16s)
you're in, right, you probably want at
[48:25] (2905.52s)
least one person who really knows that
[48:27] (2907.12s)
that domain super well, right? But I
[48:29] (2909.52s)
think most of the times what you deal
[48:31] (2911.44s)
with in small companies and is
[48:33] (2913.04s)
quantization effects. Like if your
[48:35] (2915.20s)
company is large enough, you can hire a
[48:37] (2917.12s)
PR comms person who's full-time job 40
[48:40] (2920.00s)
hours a week is to handle comms, right?
[48:42] (2922.72s)
But before you're that large, it's very
[48:45] (2925.44s)
difficult to hire a part-time three
[48:47] (2927.60s)
hours a week comms person, right? And so
[48:49] (2929.84s)
you kind of tap some product manager on
[48:52] (2932.40s)
your team that's pretty good with
[48:53] (2933.52s)
communication and you ask them to handle
[48:55] (2935.20s)
it for a while, right? So because of
[48:56] (2936.64s)
these quantization effects, I do think
[48:58] (2938.64s)
generalists are more purposeable in
[49:01] (2941.12s)
those cases of fractionalization, right?
[49:03] (2943.20s)
You can fractionalize a generalist um to
[49:06] (2946.00s)
a bunch of different roles, but
[49:07] (2947.44s)
eventually a company gets so large that
[49:09] (2949.52s)
each uh fraction of work that needs to
[49:11] (2951.52s)
be done is probably uh you know
[49:13] (2953.76s)
warranting one or more employees
[49:15] (2955.36s)
full-time on it. In which case having
[49:17] (2957.44s)
the generalist is kind of like a lot of
[49:19] (2959.12s)
wasted energy, right? Because they are
[49:20] (2960.80s)
only you know only 15% of their skill
[49:23] (2963.36s)
set pertains to the actual work you're
[49:24] (2964.96s)
doing.
[49:25] (2965.88s)
So at some point once you left uh
[49:29] (2969.12s)
Facebook and then you pursued um the BC
[49:32] (2972.16s)
Corp, I saw that you did you came back
[49:34] (2974.88s)
to the industry for a little bit to join
[49:37] (2977.72s)
OpenAI. Um I'm curious what's the the
[49:41] (2981.52s)
story behind you coming back to
[49:43] (2983.76s)
industry. Sometime after quitting
[49:46] (2986.48s)
Facebook, I ended up starting a local
[49:49] (2989.04s)
nonprofit in Seattle called Outere that
[49:51] (2991.20s)
builds free software for global health,
[49:53] (2993.76s)
you know, and this was funded by the
[49:55] (2995.36s)
Gates Foundation. Um, and the software
[49:57] (2997.44s)
is deployed, I think, in Nigeria, Kenya,
[49:59] (2999.36s)
and South Africa, and it helps with
[50:01] (3001.84s)
assisting um, uh, rapid testing for
[50:04] (3004.64s)
malaria. Okay. Um, that work was simply
[50:07] (3007.52s)
because I didn't expect to have a grant
[50:09] (3009.84s)
from the Gates Foundation. I felt very
[50:11] (3011.76s)
fortunate and lucky to have been chosen
[50:13] (3013.68s)
to get a grant for that and I felt like
[50:16] (3016.16s)
building some free software for global
[50:17] (3017.84s)
health seemed like a great idea and so I
[50:20] (3020.08s)
really enjoyed that work. Um after that
[50:23] (3023.52s)
I took a break for a while where I was
[50:25] (3025.44s)
unemployed and I think part of that
[50:28] (3028.08s)
during that time I had started using a
[50:29] (3029.92s)
lot more of GitHub copilot in VS Code
[50:32] (3032.32s)
back then trying to evangelize to
[50:34] (3034.08s)
friends that hey this AI thing like it's
[50:36] (3036.16s)
it's useful but I had a hard time
[50:37] (3037.84s)
convincing people to join back then uh
[50:39] (3039.92s)
to pay for it. Um and so when AI began
[50:43] (3043.52s)
to really take off, you know, sometime
[50:45] (3045.68s)
in the time frame of maybe chat GPT3,
[50:48] (3048.32s)
five or four, um I felt like, wow, this
[50:51] (3051.68s)
is this is a huge transition point in
[50:55] (3055.04s)
the industry. I feel like I missed two
[50:57] (3057.36s)
major transition points because I was at
[50:59] (3059.36s)
Microsoft. First of all, I missed the
[51:00] (3060.80s)
whole.com thing because Microsoft as a
[51:02] (3062.64s)
whole missed the internet revolution,
[51:04] (3064.40s)
right? And so that was a massive thing.
[51:06] (3066.48s)
The second thing was the mobile
[51:07] (3067.76s)
revolution. Microsoft largely missed
[51:09] (3069.76s)
that as well. And I was at Microsoft
[51:11] (3071.52s)
when that happened. And so I felt very
[51:13] (3073.76s)
fortunate to have caught the social
[51:15] (3075.20s)
network revolution, right, which was a
[51:17] (3077.12s)
huge transition point in humanity. But I
[51:19] (3079.68s)
increasingly felt like AI would
[51:22] (3082.00s)
potentially be as big or maybe bigger
[51:24] (3084.88s)
than the internet's impact on humanity.
[51:27] (3087.52s)
Because from a universe perspective, we
[51:29] (3089.76s)
might be the first species to create
[51:31] (3091.36s)
something smarter than us. And from like
[51:33] (3093.76s)
an evolutionary perspective, that's just
[51:35] (3095.84s)
really weird. Like that goes second
[51:37] (3097.36s)
order like once that happens, right? is
[51:39] (3099.44s)
like what is actually happening in the
[51:41] (3101.36s)
universe right we might answer the firmy
[51:43] (3103.84s)
question right and so I felt like this
[51:47] (3107.12s)
transition was something I didn't want
[51:48] (3108.48s)
to miss the reason I joined open AI was
[51:51] (3111.52s)
what I learned from working at Facebook
[51:53] (3113.44s)
is I would much rather join the market
[51:55] (3115.44s)
leader or nobody at all and here's why I
[51:59] (3119.20s)
think the market leader has room to
[52:01] (3121.68s)
experiment because it can afford to fail
[52:04] (3124.16s)
at a few things this is what I
[52:05] (3125.84s)
discovered at Facebook with a big enough
[52:07] (3127.84s)
market lead you can take some brave big
[52:10] (3130.08s)
shots because like if two things fail
[52:12] (3132.16s)
and Facebook had some big colossal
[52:13] (3133.84s)
failures too, right? But you have enough
[52:16] (3136.16s)
of a lead to do it. Usually the market
[52:18] (3138.64s)
follower like Google+ as a good example,
[52:20] (3140.80s)
right? Usually the market follower their
[52:23] (3143.28s)
formula has to be match all of the
[52:25] (3145.68s)
leaders checkboxes and have one unique
[52:28] (3148.32s)
thing you think is going to be
[52:29] (3149.60s)
differentiating. In the case of Google+,
[52:31] (3151.60s)
the thing was circles. This idea that
[52:33] (3153.28s)
you might want to manage circles of
[52:34] (3154.80s)
friends that are different. Okay? But to
[52:37] (3157.28s)
have a social network, you had to do all
[52:38] (3158.72s)
the basics that every other social
[52:40] (3160.08s)
network did, right? So, I feel like
[52:42] (3162.88s)
especially number TWs, number threes
[52:44] (3164.72s)
might might be able to do crazy hail
[52:46] (3166.56s)
Marys, but number TW's, the problem with
[52:48] (3168.48s)
number TWs is their whole schedule and
[52:51] (3171.36s)
timeline is dictated by the leader.
[52:53] (3173.36s)
Like, they have to be fast follow,
[52:55] (3175.04s)
right? Um whereas number one gets to
[52:57] (3177.44s)
blow a lot of time on crazy things and
[52:59] (3179.52s)
and I love the craziness. So that's why
[53:02] (3182.32s)
when I look to join AI, I only applied
[53:05] (3185.20s)
to o to open AAI. My plan was I either
[53:08] (3188.08s)
joined that or I'm still going to write
[53:09] (3189.52s)
my own software. I see. And when you
[53:12] (3192.24s)
joined OpenAI, how did it compare to to
[53:16] (3196.00s)
uh Facebook?
[53:18] (3198.08s)
Great question. I had joined Facebook at
[53:20] (3200.24s)
a different stage in its growth. When I
[53:21] (3201.76s)
joined Facebook, it was 500 engineers,
[53:23] (3203.84s)
right? when I joined Open AI, I want to
[53:26] (3206.16s)
say it was about 120 engineers. So like
[53:28] (3208.64s)
qualitatively that is very different. Um
[53:31] (3211.28s)
so with that caveat in mind, um it is
[53:34] (3214.40s)
really the highest talent density of any
[53:36] (3216.80s)
group of engineers I've worked with. And
[53:38] (3218.80s)
so it was really amazing and that after
[53:42] (3222.56s)
when I shifted from Microsoft to
[53:44] (3224.24s)
Facebook, I felt like Facebook was a
[53:46] (3226.16s)
huge jump in quality of engineer on
[53:48] (3228.56s)
average of the person that I worked with
[53:50] (3230.72s)
and there are just amazing people.
[53:53] (3233.84s)
especially in early Facebook, it was
[53:56] (3236.00s)
like full of amazing people. Um, I felt
[53:58] (3238.64s)
like OpenAI was even a notch above that.
[54:01] (3241.84s)
And so, um, it was really amazing the
[54:04] (3244.40s)
people I got a chance to work with, uh,
[54:06] (3246.32s)
and to learn from. What are the things
[54:08] (3248.72s)
that made you see how amazing it was?
[54:11] (3251.92s)
Was it the technical abilities or
[54:14] (3254.32s)
something else that really stood out to
[54:15] (3255.92s)
you? Yeah. Uh, people had exceptional
[54:19] (3259.60s)
technical capabilities. I I would say
[54:22] (3262.16s)
one thing that Facebook always had a lot
[54:24] (3264.96s)
of that I believe OpenAI is going to get
[54:27] (3267.44s)
increasingly better at is I think
[54:29] (3269.52s)
Facebook always focused a lot on
[54:31] (3271.36s)
product. You know, like what is the
[54:33] (3273.28s)
product? Why do people use it? And I
[54:35] (3275.20s)
think there's a way in which like
[54:37] (3277.52s)
because Facebook did not honestly have
[54:40] (3280.48s)
much magic sauce beyond a network effect
[54:42] (3282.72s)
and a great product people love using,
[54:44] (3284.56s)
right? Um it causes you to work on it.
[54:46] (3286.72s)
Whereas I would say in the early chat
[54:49] (3289.36s)
GPT days when there were no viable
[54:51] (3291.76s)
competitors, right? You kind of could
[54:54] (3294.24s)
have a lousy product, people use it
[54:56] (3296.00s)
anyway, right? And because like you you
[54:58] (3298.32s)
might remember like a year or two ago,
[55:00] (3300.00s)
it's very common for like every fourth
[55:02] (3302.64s)
conversation thread to have an error
[55:04] (3304.24s)
like a server error and then you like
[55:05] (3305.92s)
reload the thread and you rerun it,
[55:07] (3307.60s)
right? You can get away with that when
[55:09] (3309.44s)
nobody else is producing anything near
[55:11] (3311.52s)
as compelling. But I think as
[55:13] (3313.28s)
competition heats up, people will begin
[55:15] (3315.68s)
to see that ultimately products are what
[55:18] (3318.48s)
make things, not pure research, but
[55:20] (3320.96s)
actual research shipping in compelling
[55:23] (3323.12s)
products is what makes for success.
[55:26] (3326.64s)
Yeah. Yeah, definitely. I I agree. The
[55:29] (3329.52s)
early days of Chat GPT, it was uh so
[55:33] (3333.20s)
special that you you put up with it and
[55:36] (3336.32s)
now people are catching up of it. Um,
[55:39] (3339.36s)
one thing I wanted to ask you and I
[55:40] (3340.96s)
think this is because I, you know, am a
[55:43] (3343.52s)
fan of your writing and I've read so
[55:45] (3345.36s)
much of it. What I've seen is that your
[55:48] (3348.16s)
your writing ability is a superpower of
[55:51] (3351.36s)
yours and I know writing is important
[55:54] (3354.00s)
for software engineers. It gets you a
[55:55] (3355.92s)
lot of leverage. I'm curious how how did
[55:59] (3359.20s)
you develop that skill? Were you were
[56:00] (3360.96s)
you writing from a young age or is that
[56:02] (3362.64s)
something you developed at Microsoft?
[56:04] (3364.80s)
Um, yeah. What tips do you have when it
[56:06] (3366.40s)
comes to writing? I was bad at writing
[56:08] (3368.96s)
at a young age. The main reason that I
[56:11] (3371.12s)
was bad at writing when I was uh even
[56:14] (3374.24s)
through college is I very much did not
[56:16] (3376.64s)
respect the value of good communication.
[56:19] (3379.04s)
Like I came from a culture that
[56:20] (3380.96s)
respected getting A's and everything and
[56:23] (3383.28s)
like I felt like there were hard
[56:25] (3385.36s)
sciences and then everything else was
[56:26] (3386.96s)
flop. So if you made me take history, if
[56:28] (3388.96s)
you made me take English, I felt like
[56:30] (3390.64s)
this was a waste of time. These are
[56:32] (3392.24s)
subjects with no right answer. it's all
[56:34] (3394.08s)
like people's opinions, right? And so I
[56:35] (3395.76s)
had a very sort of um low attitude
[56:38] (3398.88s)
toward the soft skills and so I did not
[56:41] (3401.36s)
respect the soft skills and so I didn't
[56:43] (3403.28s)
invest in them. So I was a bad writer,
[56:45] (3405.40s)
right? Over time I think I lucked out in
[56:48] (3408.96s)
that when I started work at Microsoft um
[56:52] (3412.48s)
in some of the spare time I had, I just
[56:55] (3415.12s)
started rereading some of the classics,
[56:56] (3416.96s)
you know, and these are things that in
[56:58] (3418.24s)
high school when assigned I I really
[57:00] (3420.00s)
hated them, right? the these are all the
[57:01] (3421.92s)
classic like Hemingways and then Great
[57:03] (3423.68s)
Gatsby and like stuff like this. And I
[57:06] (3426.00s)
started to see these books in a
[57:07] (3427.68s)
different light. Like I was a bit older
[57:09] (3429.52s)
so I could appreciate more of what was
[57:10] (3430.96s)
said in it. And I also started to really
[57:13] (3433.36s)
develop a love of the language, right? I
[57:15] (3435.60s)
think it was Virginia Wolf that said um
[57:17] (3437.68s)
to write well you need to read well. And
[57:20] (3440.00s)
so I think first of all is you got to
[57:21] (3441.44s)
read well, right? And so the types of
[57:23] (3443.52s)
sentences that someone like David Foster
[57:25] (3445.60s)
Wallace DFW can put out, the types of
[57:28] (3448.16s)
sentences um that he can put out are
[57:31] (3451.28s)
amazing. If you read Alice Monroe short
[57:33] (3453.44s)
stories, right? The language is
[57:35] (3455.04s)
beautiful. Pilgrim at Tinker Creek, like
[57:37] (3457.36s)
there all sorts of amazing books where
[57:39] (3459.36s)
when you read it, you feel like, wow,
[57:41] (3461.12s)
the language here is exceptional. So I
[57:43] (3463.60s)
then started to really respect that that
[57:45] (3465.28s)
was a skill, you know, and that was a
[57:46] (3466.80s)
valuable skill, every bit as valuable as
[57:48] (3468.64s)
a hard skill, like a science skill. And
[57:51] (3471.04s)
so then I started working at it. And a
[57:52] (3472.96s)
lot of writing better, I think, is not
[57:54] (3474.48s)
only reading well, but really being
[57:56] (3476.72s)
willing to rewrite multiple times. So
[58:00] (3480.16s)
many of the posts that you've seen me
[58:02] (3482.48s)
make, I have rewritten multiple times
[58:04] (3484.96s)
before posting them, right? And so being
[58:07] (3487.36s)
willing to have some things um on the,
[58:10] (3490.32s)
you know, cutting room floor, right? Uh
[58:12] (3492.40s)
is also um pretty important. Even now
[58:15] (3495.76s)
when I read people's subf facts for
[58:17] (3497.44s)
instance, I once in a while do notice
[58:20] (3500.08s)
something and I try to remember it like
[58:21] (3501.92s)
oh this way of saying something
[58:24] (3504.16s)
rhetorically was very clever. So I'm
[58:26] (3506.32s)
going to try to remember that this is
[58:27] (3507.76s)
one way you can communicate an idea. Um
[58:30] (3510.56s)
and so I think that has also helped but
[58:33] (3513.12s)
most of all I think was my willingness
[58:35] (3515.36s)
to not disrespect the skill and to
[58:37] (3517.52s)
actually see it as an essential skill
[58:40] (3520.00s)
for uh even for scientists for technical
[58:43] (3523.12s)
people I think is hugely important. Yeah
[58:46] (3526.08s)
definitely and what what you said about
[58:48] (3528.32s)
growing up and not respecting the softer
[58:51] (3531.92s)
subjects definitely resonates. I have
[58:54] (3534.40s)
very similar value system when I was
[58:56] (3536.80s)
growing up and now I see the value of
[58:59] (3539.36s)
writing and so that's why I'm kind of
[59:01] (3541.04s)
into it. Um some of the stuff that you
[59:04] (3544.24s)
wrote too um for instance your essay and
[59:06] (3546.72s)
I guess this is only only people at Meta
[59:08] (3548.96s)
could see it but the your essay as good
[59:11] (3551.84s)
as it gets um your the creativity to
[59:15] (3555.84s)
kind of jump around and give a
[59:19] (3559.32s)
nonchronological narrative I thought was
[59:21] (3561.76s)
really cool. you're jumping around your
[59:23] (3563.68s)
life and just getting the feel of a
[59:26] (3566.72s)
bunch of things and some of the things
[59:28] (3568.16s)
that you communicate are not it's not
[59:30] (3570.24s)
even a full, you know, narrative bit,
[59:32] (3572.88s)
but it somehow gets the idea across. I
[59:35] (3575.84s)
really liked that. And um I'm curious,
[59:38] (3578.08s)
do you have an editor when you were
[59:39] (3579.76s)
writing or that's all from you
[59:41] (3581.88s)
continuously reading and rereading?
[59:44] (3584.68s)
Yeah, that thing was a moment of
[59:47] (3587.44s)
inspiration where I thought how do I
[59:50] (3590.40s)
tell this story, you know, like what is
[59:53] (3593.12s)
a compelling way to tell this story? And
[59:55] (3595.84s)
most often like the compelling way is
[59:58] (3598.40s)
chronologically actually because that's
[60:00] (3600.08s)
the way that makes sense, right? Okay.
[60:01] (3601.84s)
But some stories I think work better out
[60:04] (3604.64s)
of chronological order because you see a
[60:07] (3607.28s)
different arc that's developing in a
[60:08] (3608.96s)
person's life, an arc that's actually
[60:11] (3611.12s)
like threaded, right? And so that's what
[60:13] (3613.44s)
I felt for that uh particular story.
[60:16] (3616.16s)
I've not had an editor for any of those,
[60:18] (3618.00s)
but one place where I did hire an
[60:19] (3619.76s)
editor. Um it it was great. I hired a
[60:22] (3622.16s)
lady named Rebecca Ambrose to edit my
[60:24] (3624.80s)
podcast, Peak Salvation. And the big
[60:27] (3627.28s)
thing about that is I knew I wanted to
[60:28] (3628.88s)
do a miniseries and I wanted the words
[60:31] (3631.68s)
to be well structured. I wanted the
[60:33] (3633.60s)
story to be told well and I knew that a
[60:35] (3635.84s)
professional would do a much better job
[60:37] (3637.52s)
at editing. And there I learned a ton
[60:39] (3639.84s)
from Rebecca. Like if you listen to
[60:41] (3641.68s)
those episodes, you can tell that oh,
[60:43] (3643.68s)
someone who works in the profession has
[60:45] (3645.52s)
has like given feedback on this, right?
[60:47] (3647.92s)
And so I have gotten feedback in those
[60:50] (3650.32s)
cases, but nothing ever that I've posted
[60:52] (3652.40s)
professionally was edited by another
[60:54] (3654.56s)
person. I see. Yeah, your essays are
[60:58] (3658.32s)
legendary. I think I sometimes I go back
[61:00] (3660.80s)
to visit them and I'll see they're still
[61:03] (3663.36s)
alive. People are commenting saying,
[61:04] (3664.96s)
"Oh, I just found this. This is great."
[61:06] (3666.56s)
Or, "Oh, you should reread this." Big
[61:09] (3669.12s)
fan. So glad you put those out. Thank
[61:11] (3671.12s)
you. And um the the great by the way the
[61:14] (3674.24s)
great post explorer I I it was alive as
[61:18] (3678.24s)
of maybe twoish years ago. I think
[61:20] (3680.72s)
someone took it down at this point. I
[61:22] (3682.48s)
tried to before we did this I was like
[61:24] (3684.40s)
looking for it. I'm curious how did that
[61:26] (3686.88s)
side project come along? Yeah. So I
[61:29] (3689.20s)
wrote a thing inside the company
[61:30] (3690.80s)
Facebook called the great post explorer
[61:32] (3692.72s)
which was meant to like rank and expose
[61:35] (3695.84s)
people to some of the most commented on
[61:38] (3698.00s)
or the most reacted to posts of all time
[61:40] (3700.32s)
within the internet of Facebook. Right?
[61:43] (3703.04s)
The main reason I wrote that was I
[61:45] (3705.04s)
benefited from discovering
[61:47] (3707.16s)
serendipitously great things written by
[61:49] (3709.28s)
people who came before me in Facebook
[61:51] (3711.60s)
that I wish were somehow cataloged
[61:53] (3713.44s)
somewhere and not only cataloged but
[61:55] (3715.28s)
like crowdsourced in how they were
[61:57] (3717.36s)
categorized because I wanted to be able
[61:58] (3718.96s)
to navigate it by categories, right? And
[62:01] (3721.52s)
so I built that thing with that hope.
[62:04] (3724.00s)
And so the whole purpose of it was the
[62:06] (3726.80s)
realization that um you know Slack has a
[62:09] (3729.44s)
worst case of this but but Facebook has
[62:11] (3731.20s)
this which is like posts fade temporally
[62:14] (3734.48s)
but in my opinion posts values the value
[62:17] (3737.84s)
of a post is often temporal but in some
[62:20] (3740.80s)
the cases of some posts the value is not
[62:23] (3743.28s)
temporal like this is just like some
[62:25] (3745.20s)
podcasts you know are evergreen and some
[62:26] (3746.96s)
podcasts talk about current news and so
[62:29] (3749.36s)
to me I wrote the great posts explorer
[62:32] (3752.00s)
as a as a chance for people to discover
[62:35] (3755.36s)
some amazingly timeless things that
[62:37] (3757.52s)
people have posted in the past. Part of
[62:40] (3760.08s)
writing well is reading quality stuff.
[62:42] (3762.96s)
And that's also what made me want to go
[62:45] (3765.12s)
deep in there cuz you essentially
[62:47] (3767.04s)
created a curated set of quality posts
[62:51] (3771.28s)
where all of Meta was helping you
[62:54] (3774.08s)
curate. And so yeah, I loved that when
[62:56] (3776.32s)
it was up. Um I'm not sure what happened
[62:58] (3778.64s)
to it. Coming to the end, I just want to
[63:02] (3782.00s)
ask you a few reflections on your career
[63:04] (3784.88s)
that might be helpful for some people.
[63:07] (3787.04s)
So, the first thing I wanted to ask was
[63:09] (3789.60s)
when you were young, you prided yourself
[63:12] (3792.88s)
on fast execution speed. That was
[63:14] (3794.96s)
something that set you apart, at least
[63:16] (3796.56s)
from I tell your your writing, and then
[63:18] (3798.88s)
later you were more of a leader. You
[63:21] (3801.12s)
slowed down in the execution. I'm
[63:23] (3803.28s)
curious. Do you do you find that
[63:27] (3807.28s)
software engineering is like being an
[63:30] (3810.56s)
athlete from the perspective of you have
[63:33] (3813.76s)
some performance peak at I don't know
[63:36] (3816.64s)
exactly what for an athlete it's
[63:38] (3818.24s)
probably late 20s or early 30s and then
[63:41] (3821.20s)
there's a performance decay or or do you
[63:45] (3825.44s)
think that you know you continue to
[63:47] (3827.12s)
develop experience in software
[63:48] (3828.56s)
engineering doesn't have that cap?
[63:51] (3831.16s)
Yeah, I feel like you can get more and
[63:53] (3833.60s)
more valuable over time. Now everybody
[63:55] (3835.60s)
has an asmtote and everybody's curve is
[63:57] (3837.92s)
probably asmmptoic meaning like you
[64:00] (3840.72s)
probably have a phase in your career
[64:02] (3842.56s)
where growth is easy and then everybody
[64:04] (3844.80s)
has a final level right um everybody's
[64:07] (3847.36s)
final level is different by the way
[64:08] (3848.88s)
right and and so as you approach that
[64:11] (3851.28s)
you should feel like the growth is
[64:13] (3853.28s)
slowing however just as with work hours
[64:15] (3855.92s)
beyond 50 or 60 right I believe each
[64:18] (3858.48s)
incremental growth can still be growth
[64:20] (3860.16s)
so like I don't think you have to stay
[64:22] (3862.00s)
fixed and you're a flatline forever Um,
[64:24] (3864.88s)
I think here's the thing. When I was
[64:26] (3866.48s)
young, I would outrun everybody, but I
[64:29] (3869.36s)
was running serpentine basically, right?
[64:32] (3872.72s)
Um, as I've gotten more experience, the
[64:35] (3875.76s)
idea is to jog in a straight line,
[64:37] (3877.92s)
right? And how does a senior experienced
[64:40] (3880.08s)
person jog in a straight line? Part of
[64:41] (3881.92s)
it is an experienced person usually gets
[64:44] (3884.24s)
society sense for when things aren't
[64:46] (3886.32s)
headed the right direction and they
[64:47] (3887.92s)
either don't pursue that thing or they
[64:50] (3890.40s)
convince that team to stop that early.
[64:52] (3892.40s)
like they basically save an entire
[64:54] (3894.08s)
team's worth of time because they're
[64:55] (3895.36s)
like, "Hey, I feel like we're heading
[64:57] (3897.12s)
this direction." I've seen this in the
[64:58] (3898.88s)
past. One of the things that can happen
[65:00] (3900.24s)
is this concrete example. I had a friend
[65:02] (3902.40s)
who worked at Microsoft who was telling
[65:04] (3904.32s)
me that the product he was working on,
[65:06] (3906.56s)
the vice president of the organization
[65:08] (3908.64s)
met with them every week to triage the
[65:10] (3910.72s)
bugs towards shipping. And I told him,
[65:13] (3913.28s)
look, if your VP is in triage meetings
[65:16] (3916.16s)
on a weekly basis so that you can ship
[65:18] (3918.24s)
on time, your project is nowhere near
[65:20] (3920.24s)
shipping on time. like this thing is in
[65:22] (3922.00s)
a huge flame ball like this is a huge
[65:24] (3924.24s)
problem right I don't know how people
[65:26] (3926.64s)
that experience can live through that
[65:28] (3928.08s)
and not see you know how obvious it was
[65:30] (3930.64s)
to me that that thing is like a bad sign
[65:33] (3933.76s)
right this is a tell that your project
[65:35] (3935.28s)
is not on track um but I think you
[65:37] (3937.84s)
develop that sense of like oh this is
[65:40] (3940.00s)
worth investing in this is not worth
[65:41] (3941.76s)
investing in I think the other way you
[65:44] (3944.40s)
grow your scope over time is I think you
[65:47] (3947.04s)
begin to
[65:48] (3948.36s)
value everybody running in the same
[65:51] (3951.52s)
direction even if the direction is 3°
[65:54] (3954.24s)
off ideal. You know, when I was young, I
[65:56] (3956.80s)
had this idealism to me. So, I felt like
[65:58] (3958.56s)
there was a right direction to go and
[66:00] (3960.24s)
like two degrees off was like wrong, you
[66:02] (3962.72s)
know. But the problem is a whole team
[66:05] (3965.12s)
pulling 98 degrees correct versus like
[66:07] (3967.84s)
half the team pulling 100% correct and
[66:09] (3969.76s)
the other half debating them on like
[66:11] (3971.28s)
which is correct. I think the team
[66:13] (3973.12s)
pulling together is going to get
[66:14] (3974.48s)
further, you know, and like that was
[66:16] (3976.08s)
wisdom over time of saying like even
[66:18] (3978.72s)
though I don't agree with a 100% of what
[66:20] (3980.88s)
this is what Amazon means by disagree
[66:22] (3982.80s)
and commit, right? Is like there are
[66:24] (3984.96s)
times you need to disagree and commit
[66:27] (3987.92s)
and like that is valuable in itself. And
[66:30] (3990.96s)
so I think some of that wisdom I I
[66:33] (3993.76s)
continue to develop. But when it comes
[66:35] (3995.44s)
to for instance a young person's ability
[66:38] (3998.24s)
to outwork me is definitely true now.
[66:40] (4000.40s)
Like I do not have the physical stamina
[66:42] (4002.16s)
I had when I was 24. If you woke me up
[66:44] (4004.80s)
at 3:00 a.m. today to start coding, I
[66:46] (4006.96s)
would code very slowly at 3:00 a.m. Like
[66:49] (4009.04s)
when I was 23, if you woke me up at 3,
[66:51] (4011.44s)
it was no problem. I I I would just get
[66:53] (4013.60s)
going, right? So I do think that some
[66:55] (4015.92s)
things get weaker and hopefully if we're
[66:58] (4018.00s)
growing like other things get stronger.
[67:00] (4020.16s)
At the beginning of your career, it
[67:01] (4021.60s)
sounds like you you were kind of
[67:03] (4023.12s)
leaprogging people to some extent. you
[67:05] (4025.12s)
became a very young manager and
[67:07] (4027.76s)
management is often seen as a a
[67:10] (4030.72s)
profession where your reports need to
[67:13] (4033.44s)
respect you and there might be some
[67:15] (4035.28s)
unusual dynamic because of the age
[67:17] (4037.44s)
difference. Let's say you have older
[67:19] (4039.20s)
people reporting to you. I'm curious how
[67:21] (4041.36s)
did you win the respect of these people
[67:23] (4043.92s)
as such a young manager?
[67:27] (4047.04s)
Yeah, this was difficult and I think
[67:29] (4049.20s)
that I both lacked humility when I was
[67:31] (4051.44s)
younger and I was overly ambitious for
[67:34] (4054.08s)
myself. And so as a concrete example, I
[67:37] (4057.28s)
would not have worked for my 24 year old
[67:39] (4059.28s)
self, you know, but I was so ambitious
[67:42] (4062.08s)
that I was one of the team's strongest
[67:44] (4064.16s)
engineers. So when I asked to manage
[67:45] (4065.84s)
people, people were like, well, he was a
[67:47] (4067.04s)
good engineer. Like let's not, you know,
[67:48] (4068.56s)
make him unhappy. Let's like have him
[67:50] (4070.00s)
manage people. Right? In that first
[67:52] (4072.32s)
year, I had to as a 24 year old, I had
[67:55] (4075.12s)
to fire a 40-year-old and that was very
[67:58] (4078.08s)
difficult. Like firing people never gets
[68:00] (4080.32s)
easier. Like hopefully like hopefully
[68:02] (4082.00s)
you aren't like the eye of Sauron and
[68:03] (4083.52s)
you delight in firing people. But like
[68:06] (4086.00s)
that was exceptionally difficult and it
[68:09] (4089.04s)
was in my opinion made more difficult by
[68:11] (4091.60s)
my self-awareness of just how junior I
[68:14] (4094.64s)
was relative to this person. You know
[68:16] (4096.40s)
what I mean? And so um now he did
[68:19] (4099.92s)
deserve to be fired. He was not
[68:21] (4101.44s)
performing to the level of expectations
[68:23] (4103.76s)
that we had of him, right? But he was
[68:26] (4106.24s)
also a person that was two levels above
[68:28] (4108.00s)
what my level was and I was managing
[68:29] (4109.68s)
him, right? And so like I was too
[68:32] (4112.16s)
immature to know how to manage people
[68:34] (4114.48s)
more experienced than me. Like that's
[68:36] (4116.24s)
its own sort of maturity is how do you
[68:38] (4118.16s)
manage someone who's a higher level than
[68:39] (4119.84s)
you are, you know, like how do you even
[68:41] (4121.92s)
do that successfully is a tricky thing,
[68:43] (4123.92s)
right? So I did a very poor job of
[68:46] (4126.12s)
managing and so I was a a pretty um bad
[68:50] (4130.52s)
manager but I was so obsessed with
[68:53] (4133.76s)
growth that I was also a huge sponge for
[68:56] (4136.32s)
learning. So I was constantly getting
[68:58] (4138.16s)
mentors to grow in some area. I would
[69:00] (4140.40s)
ask for feedback like how can I do this
[69:02] (4142.16s)
better? I would read a lot of
[69:04] (4144.24s)
non-fiction about things like managing
[69:06] (4146.48s)
teams about what conversations like all
[69:09] (4149.12s)
sorts of things right. So I was doing a
[69:10] (4150.80s)
lot of self-improvement over time. So I
[69:12] (4152.80s)
do think that I grew over time but I was
[69:15] (4155.92s)
always like the start was pretty rough
[69:18] (4158.72s)
and and I've had a lot of things where
[69:20] (4160.40s)
I've managed poorly. Even in my last
[69:22] (4162.48s)
year of managing uh people you know I
[69:25] (4165.04s)
made some huge mistakes. So for instance
[69:28] (4168.24s)
uh one of my biggest weaknesses when
[69:30] (4170.08s)
managing people is I am very bad at
[69:32] (4172.00s)
giving feedback you know. So like I um
[69:36] (4176.56s)
pull punches when I give feedback. I
[69:38] (4178.88s)
resist giving feedback because I hate
[69:40] (4180.64s)
the the the awkward feeling of
[69:42] (4182.56s)
confrontation, right? And so I resist
[69:44] (4184.48s)
giving feedback. When I give the
[69:46] (4186.32s)
feedback, I try to downplay how
[69:48] (4188.56s)
important it actually is and so people
[69:50] (4190.16s)
don't understand how serious it is. And
[69:52] (4192.24s)
so I continue to be bad at doing that. I
[69:55] (4195.04s)
think over the years I've gone from very
[69:57] (4197.20s)
bad to like pathable, you know, but that
[69:59] (4199.76s)
is not an area where I expect to become
[70:02] (4202.16s)
uh, you know, excellent. Whereas I do
[70:04] (4204.48s)
think I am much better at motivating
[70:06] (4206.32s)
action. So I think when I lead a team,
[70:08] (4208.40s)
whether I'm a manager on a team or an
[70:10] (4210.24s)
I'm an individual contributor, you know,
[70:12] (4212.32s)
I only join teams that I believe in and
[70:14] (4214.16s)
when I believe in something, I'm very
[70:15] (4215.52s)
passionate about it. And I think
[70:16] (4216.80s)
communicating that passion and that
[70:18] (4218.40s)
vision excites people to do the work. So
[70:20] (4220.56s)
I think I am good at that. Uh but there
[70:23] (4223.12s)
still are a lot of things to to improve.
[70:26] (4226.00s)
We talked a little bit about generalist
[70:28] (4228.08s)
versus specialists and sounds like you
[70:30] (4230.40s)
you're you identify as a generalist. I'm
[70:32] (4232.88s)
curious how did that play out over your
[70:35] (4235.36s)
career? And let's say there's some new
[70:37] (4237.68s)
grad or someone earlier in their career,
[70:39] (4239.92s)
they're they're thinking, should I
[70:41] (4241.52s)
become a specialist or a generalist? How
[70:44] (4244.08s)
would you make that decision? Yeah, that
[70:47] (4247.36s)
I think is a tricky decision depending
[70:49] (4249.20s)
on how well the person knows themselves.
[70:51] (4251.36s)
So there's the occasional exceptional
[70:53] (4253.44s)
person like these prodigies in chess for
[70:55] (4255.92s)
instance, right? They will have been a
[70:57] (4257.68s)
prodigy by the time they're eight or
[70:59] (4259.12s)
nine years old and so they're obviously
[71:01] (4261.36s)
fit to play chess. that person should
[71:03] (4263.60s)
specialize because that's like
[71:05] (4265.48s)
unnaturally unique talent, right? Most
[71:08] (4268.16s)
people aren't like that. Most people do
[71:09] (4269.76s)
not like from age seven have a solid
[71:12] (4272.64s)
like thing of like I am very good at
[71:14] (4274.16s)
this. I should do this, right? And so
[71:16] (4276.32s)
for most people, I would caution against
[71:18] (4278.48s)
early binding too much to a specialty.
[71:20] (4280.96s)
You know, there there are two dangers to
[71:22] (4282.88s)
binding too early. One is how do you
[71:24] (4284.96s)
know you're a specialist and not a
[71:26] (4286.16s)
generalist without being a generalist?
[71:27] (4287.60s)
Right? So that that's part one. Part two
[71:29] (4289.76s)
is you have an idiot soant risk, right?
[71:32] (4292.72s)
Is like you bound so early to a
[71:35] (4295.32s)
specialization like Erdish, a famous
[71:37] (4297.52s)
mathematician, um even as an adult did
[71:40] (4300.08s)
not know how to cut his own grapefruit
[71:41] (4301.92s)
with a knife. So his mom would cut his
[71:44] (4304.00s)
grapefruit, okay? But this was an
[71:45] (4305.92s)
amazing mathematician that published
[71:47] (4307.84s)
tons of papers, right? But he
[71:49] (4309.44s)
specialized so early he can't even cut
[71:51] (4311.12s)
fruit. Okay? So like that to me feels
[71:53] (4313.20s)
like over specialization. But he did
[71:55] (4315.04s)
some amazing work in mathematics, right?
[71:57] (4317.12s)
So I feel like if you are a 22 23
[72:00] (4320.00s)
starting your career, I would in general
[72:02] (4322.16s)
encourage at least dabble in a few
[72:04] (4324.00s)
things before you like die hard commit,
[72:06] (4326.72s)
one last risk to committing to a
[72:08] (4328.56s)
specialty way early in your career is
[72:10] (4330.88s)
what if that specialty is going away,
[72:13] (4333.84s)
you know, especially in the age of AI,
[72:16] (4336.24s)
this is something to think about. It's
[72:18] (4338.00s)
like if you join some company and you're
[72:20] (4340.72s)
diehard committed to like technology A,
[72:24] (4344.56s)
right? um what if in three years that
[72:26] (4346.80s)
thing becomes irrelevant and that's all
[72:28] (4348.64s)
you know you know you're like the cobalt
[72:31] (4351.04s)
person like hoping like Y2K happens
[72:33] (4353.28s)
again right is because cobalt's not used
[72:35] (4355.04s)
anywhere but that's your specialty so I
[72:37] (4357.92s)
think specializations come with that
[72:39] (4359.84s)
danger that generalists don't suffer
[72:42] (4362.24s)
that danger I guess the last question
[72:44] (4364.80s)
that I'm curious about is after this
[72:48] (4368.00s)
career that you've had if you were able
[72:50] (4370.48s)
to go back to the beginning when you
[72:53] (4373.20s)
just entered the industry
[72:54] (4374.80s)
and you give yourself some advice based
[72:57] (4377.12s)
on everything that you know today, what
[72:59] (4379.44s)
would that advice be?
[73:02] (4382.56s)
Yeah, great question. I have a few
[73:04] (4384.64s)
thoughts that I share. One, Roy Disney
[73:07] (4387.20s)
said, "Decisions are easy when your
[73:09] (4389.20s)
values are clear to you." Right?
[73:11] (4391.44s)
Decisions for me a lot of times were
[73:13] (4393.68s)
hard because I didn't have clear values.
[73:16] (4396.00s)
If you know exactly where you're going,
[73:17] (4397.92s)
decisions toward getting there become a
[73:19] (4399.76s)
lot easier. So, one advice I would give
[73:21] (4401.92s)
my younger self is spend a little more
[73:23] (4403.84s)
time thinking about what you actually
[73:25] (4405.76s)
want, right, before you commit to doing
[73:28] (4408.16s)
these things. I think another thing is I
[73:32] (4412.08s)
often feel like I was the dog that
[73:34] (4414.88s)
caught the car, you know, like I was
[73:37] (4417.52s)
convinced when I joined Microsoft that
[73:39] (4419.20s)
what I wanted to be was a dev manager.
[73:41] (4421.20s)
Okay? And so for my first eight years or
[73:43] (4423.12s)
so, I would take any job that would get
[73:44] (4424.80s)
me a step toward that. So, one,
[73:47] (4427.28s)
decisions were easy then cuz it was
[73:49] (4429.04s)
like, well, does this job get me a step
[73:50] (4430.64s)
closer? Yes, it does. Then I'll take it,
[73:52] (4432.16s)
right? But once I caught that car, you
[73:55] (4435.04s)
know, once I hit that level, once I
[73:56] (4436.88s)
recognize that, hey, E7 might be my
[73:59] (4439.28s)
terminal level. This might be the
[74:00] (4440.96s)
highest I ever get in my career. Um, the
[74:05] (4445.28s)
problem with peaking early, you know,
[74:06] (4446.88s)
because I hit that level when I was
[74:08] (4448.64s)
probably, I don't know, 20, you know, 30
[74:11] (4451.20s)
years old or or something like this,
[74:12] (4452.64s)
right?
[74:13] (4453.88s)
Um, the problem is you're like a child
[74:16] (4456.80s)
actor. Like the question is what are you
[74:18] (4458.24s)
going to do with the rest of your life?
[74:19] (4459.44s)
Like if your whole plan is acting and if
[74:21] (4461.52s)
nobody hires you for acting, you're
[74:23] (4463.12s)
going to be in a world of hurt. Like you
[74:24] (4464.64s)
you are not going to enjoy the next 40
[74:26] (4466.64s)
years of work. And so for me, I I was
[74:29] (4469.60s)
the dog that caught that car. And I had
[74:31] (4471.68s)
no mental model for like once I became
[74:33] (4473.68s)
the death manager, which I did become
[74:35] (4475.36s)
right at a very young age. Once I became
[74:38] (4478.00s)
a level 67 and I became that at a
[74:40] (4480.48s)
relatively young age, it was sort of
[74:43] (4483.56s)
well like what happens next? And I
[74:46] (4486.32s)
really went through a period of pretty
[74:48] (4488.32s)
serious depression because I felt like I
[74:50] (4490.32s)
had lost a purpose to life. Like life
[74:52] (4492.72s)
seemed to have so much meaning and
[74:54] (4494.56s)
direction when I had a clear goal. But I
[74:56] (4496.56s)
caught the car, right? And then it was
[74:58] (4498.24s)
like what to do. So the other thing I
[75:00] (4500.16s)
would caution my young self about is to
[75:03] (4503.68s)
just be sure you actually want the thing
[75:05] (4505.60s)
you want. You know what I mean? Like one
[75:08] (4508.08s)
thing I tell people now is, you know,
[75:09] (4509.84s)
would you want to be Warren Buffett? You
[75:11] (4511.68s)
know, so many people love the billions
[75:13] (4513.92s)
of dollars, but he also in is in his
[75:16] (4516.64s)
late 90s. Like would you want a few
[75:19] (4519.44s)
billion dollars and be in your late 90s?
[75:21] (4521.52s)
Like is that worth it? Is that what you
[75:23] (4523.28s)
want? Like this is an interesting
[75:24] (4524.64s)
question, right? So, I think for me now,
[75:27] (4527.52s)
I think a lot more about um am I even
[75:30] (4530.32s)
going a direction I want to be at when I
[75:32] (4532.56s)
get there, right? One final thought that
[75:35] (4535.12s)
I'll leave with you with the sleeping
[75:36] (4536.56s)
bag business is sometimes you bend the
[75:39] (4539.44s)
read, sometimes you break the read,
[75:41] (4541.24s)
right? Sometimes things break and they
[75:44] (4544.32s)
aren't fixable. So, when I had sleeping
[75:46] (4546.88s)
bags in my office, uh my fiance came out
[75:49] (4549.84s)
to Seattle to visit me during that time.
[75:52] (4552.80s)
When she visited me in Seattle from
[75:54] (4554.32s)
Maryland, during that time, I would see
[75:56] (4556.96s)
her every evening at 11 p.m. when work
[76:00] (4560.56s)
ended. That was when I would go see her.
[76:03] (4563.36s)
So, basically, I got to eat like a late
[76:05] (4565.92s)
Denny's dinner with her from 11:00 p.m.
[76:07] (4567.68s)
to like midnight. And that was my plan
[76:10] (4570.00s)
for my visiting fiance, right? I am very
[76:13] (4573.52s)
fortunate and lucky that she is my wife
[76:16] (4576.64s)
now. But anybody I tell that story to,
[76:20] (4580.00s)
you could bet nine out of 10 times
[76:22] (4582.08s)
that's the story that ends with that's
[76:23] (4583.76s)
how I got the engagement ring back,
[76:25] (4585.44s)
right? Is how that story ends, right? I
[76:28] (4588.00s)
did not realize that some reads bend and
[76:30] (4590.48s)
some reads break and I was clearly
[76:32] (4592.72s)
bending something to an extreme that
[76:35] (4595.84s)
most things would have broken, right?
[76:38] (4598.00s)
And so I do think that in one's life,
[76:40] (4600.40s)
back to your point of like what should a
[76:41] (4601.92s)
diehard person who only cares about
[76:43] (4603.68s)
career do? like what is the absolute
[76:45] (4605.76s)
fastest way to like get there, right? I
[76:48] (4608.00s)
would say a be sure that's what you
[76:50] (4610.88s)
want, right? Don't be the dog that
[76:52] (4612.48s)
caught that thing and then you regret
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catching the thing, right? So, a be sure
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that's really what you want. And part B
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like, you know, be sure you're com
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comfortable with other things breaking,
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you know, because like that is what it
[77:05] (4625.52s)
will take to get there if that's truly
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what you want. So my advice to my
[77:08] (4628.72s)
younger self about that period of time
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would have been, you know, in net
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getting to level 67, getting to an E7
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when you're 30 versus 38 in the big arc
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doesn't make any difference. Like beyond
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38, I still have 30 years of work to go,
[77:24] (4644.24s)
right? So it's like how fast do I want
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to be at my terminal level? Like what's
[77:27] (4647.84s)
the real plan there versus can I keep a
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healthy relationship with my spouse,
[77:33] (4653.04s)
with my kids, right? That's important.
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And so that's what I would advise back
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then. Yeah. It's funny because I I think
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maybe this is the type of advice where
[77:43] (4663.12s)
looking back it's obvious, but I wonder
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if you right now could talk to that
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Philip that was grinding, you know, at
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26 or 27. Would you even accept the
[77:55] (4675.56s)
advice? It's hard to say. I feel like a
[77:58] (4678.24s)
lot of people who are fully focused may
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not. Well, I feel like it's easy to say.
[78:03] (4683.60s)
I feel like for me the answer would have
[78:04] (4684.96s)
been no, I would not have accepted the
[78:06] (4686.64s)
advice. Because here's the thing, like
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everybody tells you money doesn't buy
[78:11] (4691.76s)
happiness, right? Like when when someone
[78:14] (4694.32s)
with $3 million tells you, trust me,
[78:16] (4696.24s)
money will not buy buy you happiness. If
[78:17] (4697.92s)
you're an unhappy person now, you're
[78:19] (4699.36s)
going to be an unhappy person that can
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spend a lot of money uh you know, 10
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years hence, right? But nobody believes
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it, right? Like everybody thinks, "Oh
[78:27] (4707.44s)
yeah, yeah, yeah, but when I'm wealthy,
[78:29] (4709.36s)
I'm going to be happy, right?" Everybody
[78:31] (4711.12s)
thinks they're the exception to that
[78:32] (4712.40s)
rule. So I agree with you completely
[78:34] (4714.48s)
like advice feels good to give because I
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feel like maybe I'm saving someone from
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something but in reality 90% of the
[78:41] (4721.56s)
times you have to be ready to receive
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the advice for the advice to have any
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impact and I was at a maturity level
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that was nowhere near ready nowhere near
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ready uh to receive that advice. So I
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would not have benefited from my own
[78:54] (4734.16s)
advice for sure. Well yeah Philip thank
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you so much for this. I mean, I was
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really looking forward to this
[79:00] (4740.88s)
conversation and I learned so much. I
[79:03] (4743.92s)
uh, you know, really appreciate your
[79:05] (4745.52s)
time. This is this is awesome. I'm going
[79:07] (4747.20s)
to polish this up, make it really good
[79:09] (4749.20s)
for others so they can enjoy it, too. Is
[79:11] (4751.92s)
there anything that you wanted to shout
[79:13] (4753.84s)
out to the audience? Maybe we can
[79:15] (4755.76s)
redirect them to something that you're
[79:17] (4757.44s)
working on. One of the things I might
[79:20] (4760.24s)
recommend just because I'm still
[79:21] (4761.84s)
concerned about technical unemployment
[79:23] (4763.76s)
is people might give my podcast
[79:25] (4765.76s)
miniseries Peak Salvation a spin. It was
[79:28] (4768.32s)
about my time working at Amazon's
[79:30] (4770.16s)
flagship warehouse over peak season from
[79:32] (4772.64s)
Black Friday to Christmas. Right? I feel
[79:34] (4774.96s)
like in there I was able to explore a
[79:37] (4777.28s)
lot more of my concerns around
[79:38] (4778.64s)
automation, what it means to society as
[79:40] (4780.96s)
a whole, the income gap in America and
[79:43] (4783.44s)
its rise. Right? I do think that as
[79:46] (4786.08s)
Americans, we need to get together and
[79:48] (4788.08s)
like figure out how we help the average
[79:50] (4790.40s)
person through this change. I think as
[79:53] (4793.28s)
you know in technology, the change is
[79:55] (4795.68s)
going to be massive. it's going to come
[79:57] (4797.60s)
sooner than people expect and then very
[80:00] (4800.32s)
bad things can happen if we don't help
[80:02] (4802.16s)
the whole society adapt to it. So I
[80:04] (4804.40s)
would encourage people to like think
[80:06] (4806.00s)
about that maybe give the podcast a
[80:07] (4807.76s)
listen which might give you different
[80:09] (4809.36s)
perspectives about it. Um and hopefully
[80:12] (4812.32s)
you know figure out a way to contribute
[80:13] (4813.76s)
to making that better. I will put that
[80:15] (4815.92s)
in the show notes. So if you're
[80:17] (4817.12s)
interested in listening to that take a
[80:19] (4819.12s)
look at the show notes. I'll put it in
[80:20] (4820.48s)
there. Cool. Okay. Well thanks so much
[80:22] (4822.80s)
today Ryan. This was great. Oh yeah. I I
[80:25] (4825.68s)
mean thank you. you're the one that I I
[80:27] (4827.76s)
want to thank. This is really great.
[80:29] (4829.52s)
I've been wanting to have this
[80:31] (4831.28s)
conversation for a while. So, really
[80:33] (4833.04s)
really appreciate your time. Yeah. And I
[80:35] (4835.28s)
appreciate the effort that you put into
[80:36] (4836.80s)
making these valuable to other people.
[80:38] (4838.32s)
So, thank you for putting that
[80:39] (4839.36s)
investment