[00:00] (0.00s)
I have a feeling WhatsApp was not
[00:01] (1.24s)
exactly a standard [clears throat]
[00:02] (2.68s)
>> So, we didn't have code reviews. But,
[00:05] (5.16s)
the only time I got my code reviewed was
[00:07] (7.80s)
the first time I made a comment.
[00:09] (9.60s)
>> And you said that Jan said no a lot.
[00:12] (12.64s)
>> 99% of the time he was saying no. All
[00:15] (15.40s)
the cool features were missing in my
[00:17] (17.72s)
mind, but that was by design because we
[00:19] (19.88s)
really wanted to prioritize again the
[00:21] (21.76s)
quality of a grandma in a remote town
[00:24] (24.36s)
being able to use our app at any given
[00:26] (26.44s)
time. Scrum, Agile with a capital A,
[00:29] (29.00s)
TDD. Did you use any of these at
[00:30] (30.40s)
WhatsApp?
[00:30] (30.88s)
>> I'm surprised to hear they thought they
[00:32] (32.52s)
were shipping faster because of it. We
[00:34] (34.40s)
didn't use any of it.
[00:35] (35.48s)
>> So, you were break even. Yeah, that $1
[00:37] (37.96s)
was enough to pay for the server cost,
[00:40] (40.56s)
the salaries, and the SMS code every
[00:44] (44.54s)
>> [music]
[00:47] (47.76s)
>> Jean Lee was engineer number 19 at
[00:49] (49.56s)
WhatsApp. She joined when hardly anyone
[00:51] (51.96s)
in the US had heard of it, saw it
[00:53] (53.71s)
[music] grow to 450 million users, and
[00:56] (56.36s)
was sitting at her desk with
[00:57] (57.60s)
noise-canceling headphones on when news
[00:59] (59.72s)
broke that Facebook bought them for 19
[01:01] (61.56s)
billion dollars. In today's
[01:03] (63.00s)
conversation, we discuss [music] how
[01:04] (64.76s)
WhatsApp built natively eight different
[01:06] (66.56s)
platforms with a team of 30 engineers.
[01:09] (69.28s)
Why the founder said no to almost every
[01:11] (71.20s)
feature request for years. How
[01:13] (73.16s)
WhatsApp's team operated with no code
[01:15] (75.16s)
reviews, no stand-ups, no sprint
[01:16] (76.92s)
planning, [music] and many more. If you
[01:18] (78.84s)
want to understand how a tiny team with
[01:20] (80.72s)
almost no process built one of the most
[01:22] (82.80s)
successful products in history, and what
[01:24] (84.56s)
today's AI-native startups can still
[01:26] (86.20s)
learn from them, this episode is for
[01:27] (87.88s)
you. This episode is presented by
[01:29] (89.48s)
Statsig, [music]
[01:30] (90.28s)
the unified platform for flags,
[01:31] (91.80s)
analytics, experiments, and more. Check
[01:33] (93.96s)
out the show notes to learn more about
[01:35] (95.20s)
them and our other season sponsors,
[01:37] (97.16s)
Sonar and WorkOS.
[01:39] (99.36s)
Jean, welcome to the podcast. It is
[01:41] (101.92s)
amazing to to meet you. You have quite
[01:44] (104.52s)
the story, early engineer at at
[01:47] (107.32s)
WhatsApp. But, before we get into
[01:48] (108.64s)
WhatsApp, how did you get into tech?
[01:50] (110.88s)
I've always been a small-town girl. My
[01:52] (112.64s)
dad was an OG hipster. He was really
[01:54] (114.56s)
into brewing beer. So, he decided to get
[01:56] (116.92s)
a PhD in beer.
[01:58] (118.92s)
In beer? Yeah. In brewing. In brewing.
[02:02] (122.32s)
So, I moved to San Francisco in 1999,
[02:04] (124.44s)
and that's when I got really exposed to
[02:06] (126.44s)
all the different tech roles, like
[02:08] (128.40s)
growing up, I didn't really even think
[02:10] (130.80s)
about engineering as a job. Um of
[02:13] (133.52s)
course, I used computers, and I thought
[02:15] (135.20s)
it was really cool to be able to use
[02:16] (136.68s)
Yahoo and search things online, but
[02:19] (139.28s)
beyond that, my first exposure to
[02:21] (141.92s)
Silicon Valley and tech came from living
[02:24] (144.52s)
here. I got to meet a lot of people who
[02:26] (146.20s)
work in tech. I dabbled around with
[02:28] (148.48s)
coding when I was a teenager, but not
[02:30] (150.52s)
too seriously. But, I did think it was
[02:32] (152.72s)
really cool that you can just write a
[02:34] (154.64s)
few lines, and they will just do things
[02:37] (157.04s)
for you over and over and over. It was
[02:38] (158.96s)
almost magical. I I loved the feeling of
[02:41] (161.20s)
creating something that that actually
[02:42] (162.68s)
runs, um and debugging something and
[02:45] (165.56s)
fixing it, and it runs again. That that
[02:47] (167.72s)
was really joyous, and I didn't really
[02:50] (170.52s)
get into like super into coding until I
[02:52] (172.60s)
went to college, but one of the reasons
[02:54] (174.72s)
why I decided I wanted to go into coding
[02:57] (177.48s)
was I talked to different people. So, I
[03:00] (180.28s)
thought maybe I want to be a designer,
[03:01] (181.68s)
maybe I want to be an architect, maybe I
[03:03] (183.24s)
want to be an engineer. And I talked to
[03:04] (184.68s)
different adults who work in the in the
[03:06] (186.80s)
industry. After talking to a lot of
[03:08] (188.60s)
adults, I realized people who are in
[03:10] (190.52s)
tech were the only ones who were really
[03:12] (192.84s)
excited about their jobs. So, in Silicon
[03:15] (195.32s)
Valley, when you ask people, like tell
[03:17] (197.16s)
me about your work, people are often
[03:19] (199.36s)
very hopeful for the future and very
[03:21] (201.36s)
proud of what they're building. Compared
[03:23] (203.92s)
to when you ask adults that I spoke
[03:26] (206.24s)
with, they were
[03:27] (207.56s)
not so encouraging. They're like, "Oh,
[03:28] (208.92s)
don't become an architect. Don't become
[03:30] (210.88s)
a designer."
[03:32] (212.32s)
So, that that was one of the influences
[03:34] (214.56s)
for me early on. I studied computer
[03:36] (216.56s)
science at USC, and one of my first
[03:38] (218.92s)
internships, actual like coding
[03:40] (220.92s)
internships, was at a small company. It
[03:44] (224.84s)
a three-person startup started by one of
[03:47] (227.56s)
the new grads from USC. And you'll
[03:50] (230.24s)
probably understand it was a video
[03:52] (232.76s)
sharing website.
[03:55] (235.15s)
>> [laughter]
[03:55] (235.72s)
>> But, it was not like YouTube, but there
[03:57] (237.96s)
were so many versions of YouTube back in
[04:00] (240.36s)
the days before what YouTube was
[04:02] (242.24s)
dominant, right? So, you probably
[04:03] (243.72s)
remember dozens of these video sharing
[04:05] (245.92s)
platforms.
[04:06] (246.88s)
>> And one of the issues of having so many
[04:08] (248.84s)
options is that you have to be visiting
[04:10] (250.96s)
12 different sites to search for any
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things. So, we had a website where you
[04:15] (255.00s)
can aggregate all the different types of
[04:17] (257.16s)
videos from different sources. Which is
[04:19] (259.36s)
actually kind of funny because lately
[04:21] (261.00s)
I've been seeing a lot of AI platforms
[04:23] (263.88s)
where you can just switch between the
[04:25] (265.24s)
models. Very similar to that. Yeah. How
[04:28] (268.16s)
did you get into IBM? I really loved
[04:30] (270.92s)
working for a small three-person startup
[04:34] (274.04s)
because I got to work with engineers We
[04:38] (278.28s)
had engineers overseas in China. So, I
[04:40] (280.68s)
got to work with them. I got to also do
[04:42] (282.84s)
a little bit of coding myself, but I was
[04:45] (285.32s)
coming up with the design docs like the
[04:47] (287.88s)
the features list and I was calling a
[04:49] (289.64s)
lot of the shots and I could also
[04:51] (291.56s)
directly see the impact of my code
[04:53] (293.84s)
immediately on the website. And I
[04:55] (295.72s)
thought that type of ownership and speed
[04:58] (298.76s)
and the visibility was really exciting
[05:00] (300.52s)
that I get to see the the impact of my
[05:03] (303.40s)
work immediately.
[05:05] (305.36s)
But, one thing I wish I had was a little
[05:08] (308.64s)
bit more mentorship because we were all
[05:10] (310.68s)
new grads and in college I felt like we
[05:13] (313.36s)
were just
[05:14] (314.64s)
shooting things to see what sticks.
[05:17] (317.28s)
And I thought maybe for my first job out
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of school, I would like a little bit
[05:21] (321.04s)
more mentorship and training and I
[05:23] (323.20s)
started looking at more bigger
[05:24] (324.68s)
companies, more traditional companies
[05:26] (326.52s)
and that's how I ended up at At the time
[05:29] (329.20s)
it was literally the biggest company in
[05:31] (331.68s)
the US. At what point did you decide
[05:33] (333.56s)
that you wanted to leave or try out
[05:35] (335.20s)
something else? Or did you even decide
[05:36] (336.44s)
or something just came up? One of the
[05:37] (337.68s)
reasons why I wanted to go to a more
[05:39] (339.68s)
traditional company with more structure
[05:41] (341.44s)
was so that I could get more mentorship
[05:43] (343.20s)
and training and IBM was excellent for
[05:45] (345.96s)
that. There were so many veterans, they
[05:48] (348.12s)
had so much experience, and they were
[05:49] (349.68s)
willing to share with me because they
[05:51] (351.80s)
were 20, 30 years ahead of me, right?
[05:54] (354.40s)
But, one thing I really missed was the
[05:57] (357.68s)
small team environment. It was just so
[06:00] (360.24s)
big. There was
[06:01] (361.64s)
a lot of meetings, a lot of process, and
[06:03] (363.88s)
I I missed seeing the impact of my work.
[06:06] (366.32s)
I couldn't quite understand how my work
[06:09] (369.08s)
was contributing to the overall company.
[06:11] (371.40s)
So, then I decided to take some time off
[06:13] (373.96s)
and explore and have some fun. Yeah, and
[06:17] (377.36s)
on what time was this? What year was
[06:18] (378.84s)
this? So, I started working in 2007, and
[06:21] (381.48s)
I left by 2009, which was actually in
[06:24] (384.92s)
retrospect, I was really brave because
[06:26] (386.84s)
it was in the midst of economic
[06:28] (388.60s)
downturn. My thought process at the time
[06:30] (390.72s)
was I was only 22 or three, and I
[06:33] (393.56s)
figured even if I take a year off,
[06:36] (396.36s)
I can still catch up, which I did. And
[06:39] (399.04s)
what what what happened from there? How
[06:40] (400.60s)
did you eventually get to WhatsApp? That
[06:42] (402.72s)
was years later, right? Yeah, so I took
[06:45] (405.48s)
some time off to try out different like
[06:49] (409.08s)
classes. I took a lot of classes. I did
[06:51] (411.28s)
a little bit of Now nowadays you call it
[06:53] (413.92s)
the gig work, but I I did [laughter] all
[06:55] (415.68s)
kinds of work. So, whatever I needed to,
[06:58] (418.72s)
you know, make a living
[07:00] (420.44s)
um while
[07:02] (422.28s)
taking all these classes and exploring
[07:04] (424.04s)
and really finding out what like what
[07:05] (425.84s)
kind of environment or what kind of
[07:07] (427.60s)
career do I envision for myself. And
[07:09] (429.92s)
after I took those time off, I decided
[07:12] (432.52s)
that I want to go back to Silicon
[07:14] (434.08s)
Valley, but this time I do for a
[07:16] (436.72s)
startup, but maybe with people who are a
[07:19] (439.52s)
little bit more experienced, maybe not
[07:21] (441.56s)
new grads, and maybe not a three-person
[07:23] (443.80s)
startup, but a little bit more stable
[07:25] (445.80s)
startup where I can possibly get both
[07:28] (448.08s)
the the autonomy and the the impact of
[07:31] (451.28s)
the work, but also a little bit more
[07:33] (453.88s)
mentoring because I was still in my 20s.
[07:36] (456.64s)
Okay, so how did you find this startup,
[07:38] (458.48s)
which of course happened to be WhatsApp?
[07:40] (460.60s)
In 2012,
[07:42] (462.60s)
WhatsApp was still early. They started
[07:45] (465.08s)
in 2009 and they did still have a lot of
[07:47] (467.68s)
users, but they're mostly in Europe and
[07:50] (470.32s)
in India. Um they were not very known in
[07:53] (473.40s)
America. Were you a WhatsApp user back
[07:55] (475.76s)
then? I was not, but my my wife and her
[07:58] (478.12s)
friends were or or back then my you know
[08:00] (480.16s)
my my my girlfriend, but so some of my
[08:02] (482.04s)
friends were using it on and off. It was
[08:03] (483.76s)
kind of starting to be big in Europe. It
[08:05] (485.76s)
wasn't as massive just yet.
[08:07] (487.36s)
>> Exactly. Um I was lucky because I
[08:09] (489.72s)
actually lived in New York for a little
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bit before moving here and a lot of
[08:14] (494.04s)
people in New York were using it because
[08:15] (495.72s)
it's an international hub. So I I had
[08:18] (498.52s)
used a prod- product in the past and I
[08:20] (500.76s)
saw the job posting on LinkedIn.
[08:23] (503.40s)
And then you applied?
[08:24] (504.76s)
What was the interview like? I don't
[08:26] (506.48s)
think we did any leak code until way way
[08:29] (509.36s)
later until when we started hiring
[08:31] (511.96s)
interns and new grads.
[08:34] (514.24s)
Most of the interviews were talking
[08:37] (517.96s)
I I guess you can call it system design
[08:40] (520.20s)
interviews. We would talk about how
[08:42] (522.16s)
would you design this? How would you
[08:43] (523.76s)
design that? Like tell me about your
[08:46] (526.00s)
past experience building this product.
[08:48] (528.52s)
And I recall talking to Jan about
[08:51] (531.96s)
different messaging apps.
[08:54] (534.00s)
And being Korean, I told him a lot about
[08:56] (536.16s)
KakaoTalk and how it worked. Yeah, that
[08:58] (538.40s)
was my interview. Just like that you you
[09:00] (540.52s)
got an offer. I guess it's a startup,
[09:02] (542.08s)
right? Things move fast. Like but I
[09:04] (544.24s)
assume it must have been quick
[09:05] (545.16s)
turnaround offer and then you have to
[09:07] (547.04s)
decide, right? How did you decide that
[09:09] (549.08s)
you're going to join this
[09:10] (550.76s)
relatively unknown startup that is
[09:12] (552.64s)
building some cool messaging that you
[09:14] (554.16s)
kind of thought was cool, but there
[09:16] (556.00s)
wasn't much information about that. In
[09:17] (557.64s)
fact, their Glassdoor rating at the time
[09:19] (559.88s)
I remember had a one star. It had one
[09:21] (561.84s)
review, one star someone saying, "Oh, I
[09:23] (563.52s)
don't like working here." or he Who
[09:25] (565.52s)
knows if that was even a real employee,
[09:27] (567.08s)
but that was their Glassdoor. Oh, that's
[09:29] (569.40s)
so interesting. I don't remember looking
[09:31] (571.16s)
up I must have looked up Glassdoor, but
[09:33] (573.32s)
like I was really lucky because I
[09:35] (575.68s)
actually had another offer from a
[09:37] (577.24s)
different company, but they were a
[09:39] (579.92s)
little bit slow. One company was taking
[09:42] (582.12s)
weeks to get you an offer letter.
[09:44] (584.00s)
Another founder closed the deal in
[09:45] (585.52s)
person the very next day.
[09:47] (587.72s)
Speed matters and not just in hiring.
[09:50] (590.00s)
This leads us nicely to our season
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[09:59] (599.68s)
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[10:21] (621.44s)
let's get back to Jean and how that
[10:23] (623.00s)
other company could not get her written
[10:24] (624.64s)
offer as quickly as WhatsApp did.
[10:26] (626.96s)
It was not a startup, and they said,
[10:28] (628.88s)
"Oh, hey, like you have my verbal offer.
[10:31] (631.00s)
I am going to give you a written offer
[10:34] (634.40s)
But then it took them a while, and
[10:36] (636.44s)
meanwhile
[10:38] (638.08s)
um Jan called me a few days later after
[10:41] (641.24s)
the interview, and he said, "Come into
[10:43] (643.56s)
the office right like today or
[10:45] (645.68s)
tomorrow." Yeah. And then he asked me,
[10:48] (648.68s)
"What would it take for you to take the
[10:50] (650.60s)
offer right now?"
[10:52] (652.88s)
Love it. What did you say? I mean, I
[10:54] (654.88s)
wasn't looking for that much. I mean, I
[10:56] (656.92s)
was in my 20s, so I just told them, "Oh,
[10:59] (659.16s)
like a few things I would like to have,
[11:00] (660.88s)
then sure, I'll take the offer, and I
[11:03] (663.28s)
saw and signed the offer the following
[11:05] (665.64s)
And I did actually hear back from the
[11:08] (668.00s)
other company
[11:09] (669.56s)
on the first day I started WhatsApp.
[11:11] (671.28s)
They called me, and I was like, "Oh, I
[11:12] (672.88s)
just started a new company." Hey, is
[11:15] (675.08s)
that's it with startup if you move
[11:16] (676.80s)
faster, or otherwise don't be surprised.
[11:19] (679.12s)
So, you were engineer or you were
[11:20] (680.52s)
employee number 19 at WhatsApp, right?
[11:22] (682.80s)
Was engineer number 19.
[11:24] (684.44s)
>> Engineer number 19 at at at WhatsApp.
[11:26] (686.56s)
And you told me something really
[11:27] (687.88s)
interesting that you were the youngest
[11:29] (689.36s)
person even though you were like by this
[11:30] (690.96s)
time at your mid-mid 20s or or so. I
[11:33] (693.68s)
thought about that. So, I recall there
[11:36] (696.16s)
were about four of us under the age of
[11:41] (701.00s)
So, I was not the youngest, but there
[11:42] (702.92s)
were two people who were new grads. And
[11:45] (705.96s)
then myself and one other person who
[11:48] (708.44s)
were in our late 20s. But the other like
[11:51] (711.28s)
15 or so people above 30 at the start of
[11:53] (713.76s)
which is kind of unheard. What what why
[11:55] (715.84s)
do you think this was? This is so
[11:57] (717.32s)
interesting. That is true.
[12:00] (720.12s)
Is it still rare nowadays? Like
[12:02] (722.84s)
Good question. I I think these days it
[12:05] (725.04s)
might not be as rare, by the way. I
[12:07] (727.44s)
think so because I think I read some
[12:09] (729.48s)
kind of statistics from investors that
[12:12] (732.48s)
actually when they look at the success
[12:14] (734.36s)
rates of startups, they found that older
[12:17] (737.64s)
founders tend to do better.
[12:20] (740.04s)
Yeah. And and then WhatsApp, I guess you
[12:21] (741.92s)
know, like Jan and and Brian, they they
[12:24] (744.28s)
started this at like mid-30s or so after
[12:27] (747.24s)
they spent like more than a decade
[12:29] (749.08s)
working at Yahoo and other places.
[12:31] (751.04s)
>> Exactly.
[12:31] (751.76s)
Yep. So, I I guess they must have been
[12:33] (753.04s)
able to hire like their network whatnot.
[12:34] (754.96s)
Yeah, the first 10 or so engineers, a
[12:37] (757.52s)
lot of them came from Yahoo.
[12:40] (760.52s)
Some came from Europe.
[12:42] (762.60s)
You mentioned the story when Jan reached
[12:44] (764.48s)
out to you. Jan used to do that. He
[12:46] (766.60s)
would just look up who is the expert in
[12:48] (768.48s)
this field and reach out to people and
[12:50] (770.76s)
we had a lot of contractors in Europe.
[12:53] (773.28s)
And then we had some like mostly from
[12:56] (776.24s)
personal connection like from Stanford
[12:58] (778.04s)
because Brian went to Stanford. And then
[12:59] (779.76s)
we had some referrals from Sequoia
[13:01] (781.60s)
because they invested in WhatsApp. It is
[13:04] (784.56s)
just fascinating cuz the way we
[13:05] (785.88s)
connected actually is is both of us know
[13:08] (788.56s)
Jan. I mean, you've worked with him, but
[13:10] (790.36s)
I I had an email in my inbox from him, I
[13:12] (792.76s)
think 6 months before you joined
[13:14] (794.16s)
WhatsApp where I got a message from him
[13:16] (796.00s)
and saying, "Hey, I I built a Windows
[13:18] (798.20s)
phone app at the time together with my
[13:19] (799.76s)
brother called Cocktail Flow." And it
[13:21] (801.32s)
was a beautiful Windows phone app and it
[13:23] (803.52s)
was labeled career opportunity. So, what
[13:25] (805.64s)
you're saying is there's a
[13:27] (807.56s)
alternative timeline where if I said
[13:29] (809.76s)
like yes, I'm interested which in
[13:31] (811.16s)
hindsight if a founder reaches out, you
[13:32] (812.88s)
probably should at least talk to them.
[13:34] (814.56s)
Don't make the mistake that I did which
[13:35] (815.84s)
is just saying like I'm sorry, I'm busy.
[13:37] (817.88s)
If I might have been a contractor from
[13:39] (819.68s)
Europe. So, like this sounds like that
[13:41] (821.32s)
that was a strategy and that was a smart
[13:42] (822.48s)
strategy. Yeah, we had many contractors
[13:45] (825.08s)
in Europe and they were all very
[13:46] (826.88s)
experienced people. They were basically
[13:48] (828.84s)
managing themselves. We had people all
[13:51] (831.24s)
over the world working with us. What was
[13:53] (833.24s)
the tech stack like at WhatsApp?
[13:55] (835.80s)
Before Jean walks us through one of the
[13:57] (837.40s)
most unusual tech stacks in startup
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history, we're talking about eight
[14:00] (840.80s)
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era. And with this, let's get back to
[15:31] (931.20s)
Gene and all the different tech stacks
[15:32] (932.68s)
that WhatsApp had.
[15:34] (934.40s)
We were actually pretty unique. I I
[15:36] (936.12s)
don't think any startup ever really does
[15:38] (938.56s)
this, but we had seven different stacks.
[15:41] (941.80s)
We had I actually looked it up because
[15:43] (943.92s)
it's hard to count them all.
[15:45] (945.68s)
We had of course everybody has iPhone
[15:47] (947.60s)
and Android, but we also had BlackBerry
[15:50] (950.40s)
and Windows Phone, which is also pretty
[15:51] (951.88s)
common, but we also had
[15:54] (954.24s)
uh Nokia S40 S60. We had a thing called
[15:57] (957.76s)
KaiOS for a while, but not for a long
[16:00] (960.60s)
time, and we had the web client. So,
[16:02] (962.52s)
it's actually eight. So, so you have of
[16:04] (964.88s)
course, you know, we know that iOS is
[16:06] (966.88s)
Objective-C, Android was Java back in
[16:09] (969.28s)
the day, and then all of these like the
[16:11] (971.12s)
the BlackBerry, the Nokia, they all had
[16:13] (973.04s)
I think Nokia was Symbian C++. They all
[16:15] (975.32s)
had like their own different language.
[16:17] (977.92s)
And then we've not talked about the back
[16:19] (979.04s)
end, right?
[16:19] (979.56s)
>> Mhm, and the back end was Erlang.
[16:21] (981.64s)
Erlang. Can you tell us about Erlang?
[16:23] (983.88s)
Because this that is one of the most
[16:26] (986.04s)
exotic tech stack. I've heard Erlang in
[16:28] (988.40s)
telecommunications context at Ericsson
[16:31] (991.24s)
again in in Europe. It is popular with
[16:33] (993.52s)
the telcos, but startup-wise, I'm not
[16:36] (996.36s)
sure I heard anyone else use Erlang. You
[16:38] (998.76s)
might be right. They do have a Erlang
[16:41] (1001.12s)
conference. I think it's called Erlang
[16:42] (1002.72s)
Factory. There's a really great talk by
[16:45] (1005.20s)
one of our engineers, Rick Reed, if
[16:47] (1007.64s)
you're interested in learning more about
[16:49] (1009.04s)
it, but
[16:49] (1009.56s)
>> Or or we'll put it in the show notes
[16:50] (1010.52s)
below.
[16:50] (1010.76s)
>> Yeah, I'm I'm pretty sure that it's
[16:52] (1012.00s)
still on YouTube. I haven't looked at
[16:53] (1013.36s)
recently, but uh he gave a really great
[16:55] (1015.32s)
talk about why they started working with
[16:58] (1018.04s)
Erlang and then it was the perfect
[16:59] (1019.20s)
choice, and he he describes it as um
[17:02] (1022.48s)
trying to maintain the engine of an
[17:05] (1025.60s)
airplane while it's flying 24/7.
[17:09] (1029.04s)
Because if you imagine like WhatsApp is
[17:11] (1031.00s)
so international, we can't take a break,
[17:13] (1033.16s)
right? We have to continuously keep
[17:15] (1035.28s)
running, and it's always busy. If
[17:17] (1037.72s)
someone's it's 8:00 a.m. somewhere in
[17:19] (1039.68s)
the world, right? And Erlang was a
[17:21] (1041.96s)
really robust
[17:24] (1044.04s)
language that it was really good at
[17:25] (1045.96s)
concurrencies.
[17:27] (1047.40s)
And they stumbled upon it because they
[17:29] (1049.64s)
were using this other tool that happened
[17:31] (1051.32s)
to use Erlang and [clears throat]
[17:32] (1052.68s)
decided this is the perfect language.
[17:34] (1054.84s)
And I guess at the core of WhatsApp,
[17:37] (1057.00s)
what was the core engineering challenge?
[17:38] (1058.60s)
Was it like so many messages being kind
[17:40] (1060.64s)
of coming in needing to be seated out
[17:43] (1063.12s)
and sent to different, you know,
[17:44] (1064.64s)
platforms? platforms. Yeah, that was one
[17:46] (1066.64s)
of the main challenges. Like for
[17:47] (1067.84s)
example, for New Year's or Christmas.
[17:50] (1070.68s)
Because everyone's saying "Happy New
[17:52] (1072.36s)
Year" at the exact same moment, and that
[17:54] (1074.44s)
was always our big biggest challenges
[17:57] (1077.16s)
every year and we would celebrate "Hey,
[17:58] (1078.96s)
we didn't we didn't go down after New
[18:01] (1081.12s)
Year's."
[18:02] (1082.28s)
So the the interesting thing about the
[18:03] (1083.84s)
seven different mobile platforms
[18:05] (1085.40s)
specifically is the conventional wisdom
[18:07] (1087.84s)
wisdom before and after has been like
[18:09] (1089.96s)
look, if you want to support all those
[18:11] (1091.52s)
platforms, don't be silly. Do
[18:13] (1093.84s)
cross-platform either build your own
[18:15] (1095.68s)
layer that is cross-platform or use, you
[18:18] (1098.28s)
know, there's all sorts of frameworks.
[18:20] (1100.12s)
Why did WhatsApp not do this? Do you
[18:21] (1101.68s)
remember the discussions of like why why
[18:24] (1104.16s)
hire seven including some really hard to
[18:26] (1106.48s)
hire people like for Nokia and Symbian
[18:28] (1108.44s)
and you mentioned the contractors in
[18:30] (1110.12s)
Europe. I mean, it sounds a bit of a
[18:31] (1111.16s)
nightmare. Why? And so Jan used to
[18:33] (1113.16s)
always say, "I want
[18:35] (1115.76s)
a grandma in a remote countryside to be
[18:38] (1118.36s)
able to use our app." So what does that
[18:41] (1121.64s)
They may not have the newest iPhone, the
[18:44] (1124.08s)
shiniest phone with the biggest memory,
[18:46] (1126.24s)
right? In the countryside where a
[18:48] (1128.32s)
grandma is using it, you need the app to
[18:50] (1130.40s)
be lightweight. You need it to work on
[18:53] (1133.32s)
any kind of device.
[18:55] (1135.20s)
And you need the app to be simple.
[18:57] (1137.76s)
So those were our
[18:59] (1139.92s)
goals and priorities and that's the
[19:03] (1143.00s)
thought process that went into our
[19:04] (1144.48s)
decision to build seven different
[19:06] (1146.56s)
platforms. And then inside WhatsApp, how
[19:09] (1149.00s)
did you get things done? Do you remember
[19:10] (1150.96s)
like how a project got done or what was
[19:13] (1153.40s)
the concept of projects and kind of what
[19:15] (1155.04s)
engineering processes people might have
[19:16] (1156.68s)
followed especially, you know, later you
[19:18] (1158.24s)
worked at that meta compared to like how
[19:20] (1160.84s)
you know, like more kind of you know,
[19:22] (1162.04s)
standard startups work cuz I have a
[19:24] (1164.28s)
feeling WhatsApp was not exactly a
[19:25] (1165.72s)
standard startup, was it?
[19:27] (1167.92s)
Not really.
[19:30] (1170.44s)
even meta compared to other big tech
[19:33] (1173.68s)
especially when I was at meta was pretty
[19:36] (1176.12s)
scrappy. I like not so much on writing
[19:39] (1179.16s)
documents for example. So the move fast
[19:42] (1182.00s)
and break things model kind of allowed
[19:44] (1184.64s)
them to be a little bit more lean in
[19:47] (1187.12s)
terms of their process um at least while
[19:49] (1189.84s)
I was there. But WhatsApp was like the
[19:52] (1192.96s)
ultimate lean company. By the time we
[19:55] (1195.08s)
were acquired we only had 20 something
[19:57] (1197.60s)
engineers so under 30 people serving 450
[20:01] (1201.24s)
million monthly active users.
[20:03] (1203.88s)
So we didn't have code reviews. The only
[20:07] (1207.24s)
time I got my code reviewed was the
[20:09] (1209.88s)
first time I made a commit Brian asked
[20:12] (1212.72s)
to take a look at it before I committed
[20:14] (1214.72s)
it and he asked me a bunch of questions
[20:17] (1217.04s)
which I had to think through a lot like
[20:19] (1219.28s)
a kind of like a coding interview. But
[20:21] (1221.88s)
that that was it. After first time we
[20:24] (1224.20s)
didn't really have a formal code review,
[20:26] (1226.04s)
but I mean people read the get commits
[20:28] (1228.96s)
because there's only 30 engineers so you
[20:30] (1230.80s)
can read other people's code and they
[20:32] (1232.80s)
would discuss it on the WhatsApp groups.
[20:34] (1234.76s)
So everyone was trusted all engineers
[20:37] (1237.28s)
that they just pushed their code to they
[20:39] (1239.60s)
merged it into production, pushed it to
[20:41] (1241.16s)
production without a manager review and
[20:44] (1244.00s)
it was trusted that you know, they would
[20:45] (1245.64s)
ask if they were unsure or something
[20:47] (1247.56s)
like that.
[20:48] (1248.12s)
>> Exactly.
[20:49] (1249.48s)
Okay, and it worked. It worked. What
[20:52] (1252.00s)
about the release process? Like if if if
[20:54] (1254.28s)
you tell me 450 million people, the
[20:56] (1256.04s)
first thing I'm going to say is like,
[20:57] (1257.36s)
okay, did you do canarying? Did you do
[20:59] (1259.52s)
feature flagging? Did you do
[21:00] (1260.68s)
experiments? Did you do you know, what
[21:02] (1262.76s)
kind of safety nets did you have, right?
[21:04] (1264.88s)
We didn't do much of that but we were
[21:07] (1267.16s)
really big on dog fooding. So, every
[21:09] (1269.56s)
time we were about to do a release, we
[21:12] (1272.16s)
would all internally use it ourselves.
[21:16] (1276.00s)
Jan, I think he might still say it on
[21:18] (1278.24s)
his LinkedIn. If you look up Jan, he's
[21:22] (1282.04s)
said just quality engineer. His title,
[21:24] (1284.80s)
when he messaged me cuz I didn't know he
[21:26] (1286.00s)
was CEO, it said chief QA officer.
[21:30] (1290.36s)
>> [laughter]
[21:30] (1290.68s)
>> And I didn't know what that meant. I
[21:32] (1292.08s)
thought it was some sort of weird joke
[21:34] (1294.32s)
uh from the outside.
[21:36] (1296.64s)
So, now it makes sense. So, he he he he
[21:38] (1298.68s)
was going around. He was making sure
[21:40] (1300.24s)
that it worked. He would try to break
[21:42] (1302.68s)
things as much as he can. And then if he
[21:46] (1306.00s)
finds a bug, he will like really try to
[21:48] (1308.48s)
break it, and then he'll come to and
[21:50] (1310.16s)
say, "Hey, like I found this bug." And
[21:52] (1312.52s)
you also said that Jan said no a lot. He
[21:55] (1315.88s)
did say no almost as I recall 99% of the
[22:00] (1320.28s)
time he was saying no.
[22:02] (1322.28s)
Which I thought as a again as a young
[22:04] (1324.28s)
engineer, I was very confused because
[22:06] (1326.16s)
when you look at all these other apps,
[22:07] (1327.68s)
there were like dozen different
[22:09] (1329.40s)
messaging apps at the time. Like WeChat
[22:11] (1331.56s)
is notorious for having everything,
[22:13] (1333.76s)
right? They have so many features. And I
[22:15] (1335.64s)
was so confused like why don't we build
[22:17] (1337.88s)
all these features? They these are the
[22:19] (1339.76s)
newest coolest things that we should
[22:21] (1341.32s)
have because at the time when I joined,
[22:23] (1343.28s)
we didn't have groups. We launched
[22:26] (1346.16s)
groups shortly after I joined. We didn't
[22:29] (1349.00s)
have voice calls, video calls. We didn't
[22:31] (1351.28s)
have any of these no stories, you know,
[22:33] (1353.40s)
all the cool features were missing in my
[22:35] (1355.88s)
mind, but that was by design because we
[22:38] (1358.32s)
really wanted to prioritize again the
[22:40] (1360.40s)
quality of a grandma in a remote town
[22:43] (1363.36s)
being able to use our app at any given
[22:45] (1365.84s)
time. WhatsApp held back features for
[22:48] (1368.00s)
years until they were absolutely sure
[22:50] (1370.20s)
about quality. They worked on video
[22:52] (1372.32s)
calling long before they shipped it.
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You watch what happens. Not just that it
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crashed, but what did it do to the
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With this, let's get back to how Jean
[24:00] (1440.20s)
and the WhatsApp team ship quality code
[24:01] (1441.92s)
with close to zero formal processes.
[24:04] (1444.52s)
So, it it sounds like WhatsApp had very,
[24:06] (1446.56s)
very little process. This was very, very
[24:08] (1448.88s)
interesting because when I worked at
[24:11] (1451.80s)
at the same time as you joined WhatsApp,
[24:13] (1453.52s)
and and I also joined in 2013, I joined
[24:15] (1455.68s)
Skype, and you joined WhatsApp in 2012.
[24:18] (1458.16s)
Skype was very proud that they sent
[24:20] (1460.36s)
everyone to a Scrum training. I was a
[24:22] (1462.16s)
Scrum Master, other people were Scrum
[24:23] (1463.68s)
Masters. So,
[24:24] (1464.84s)
here we were with all the Scrum, all the
[24:26] (1466.72s)
consultants, all the everything, and
[24:29] (1469.40s)
WhatsApp had defeated us with like
[24:31] (1471.72s)
a lot smaller team, and and no no Scrum,
[24:34] (1474.60s)
no TDD, no Agile.
[24:36] (1476.68s)
>> at Skype? 1,000 engineers.
[24:38] (1478.68s)
>> Wow. That was a lot of people. Yep. I
[24:41] (1481.28s)
mean, when you have 1,000 people, you
[24:42] (1482.84s)
kind of need these.
[24:44] (1484.60s)
Yeah, and and in all fairness, like, for
[24:46] (1486.16s)
example, one thing that this whole Scrum
[24:47] (1487.80s)
thing solved for a little bit is we had
[24:50] (1490.04s)
more more than 100 teams, and everyone
[24:51] (1491.96s)
was working on different things, and
[24:53] (1493.44s)
because of all this organization, we we
[24:55] (1495.68s)
had a prioritized list of which teams
[24:57] (1497.48s)
are the most important and those got all
[24:59] (1499.52s)
the support. So, I I guess one lesson
[25:01] (1501.36s)
might be that when you're just big it's
[25:02] (1502.64s)
just so much harder to move fast and a
[25:05] (1505.64s)
small team can outcompete you. Yeah, it
[25:07] (1507.68s)
just takes a long time even just to
[25:09] (1509.80s)
communicate with everyone. Being inside
[25:11] (1511.80s)
of WhatsApp, how did it feel to see this
[25:14] (1514.24s)
massive growth not in your team size but
[25:16] (1516.40s)
but in the product usage, the you know,
[25:18] (1518.64s)
the people, the media, the feedback? We
[25:21] (1521.80s)
didn't have much media.
[25:24] (1524.04s)
Like nobody knew about WhatsApp. One
[25:25] (1525.88s)
interesting thing you told me about the
[25:27] (1527.16s)
office is you had countdown this place.
[25:28] (1528.96s)
Can you tell me about them? What were
[25:30] (1530.16s)
these? What did they display? Yeah, so
[25:32] (1532.16s)
you you asked me a lot about metrics and
[25:34] (1534.16s)
I think the really the only metrics we
[25:36] (1536.56s)
track like we didn't really pay too much
[25:38] (1538.36s)
attention to media or
[25:40] (1540.68s)
Skype's usage numbers or other messaging
[25:43] (1543.32s)
apps usage numbers, but the one metric
[25:45] (1545.44s)
we counted down was number of days like
[25:48] (1548.84s)
X number of days since the last outage.
[25:53] (1553.00s)
No pressure.
[25:54] (1554.33s)
>> [laughter]
[25:55] (1555.44s)
>> Well, the numbers had to go up over
[25:57] (1557.48s)
time. Maybe that helped to have it
[25:59] (1559.88s)
visibly there. And when an outage
[26:01] (1561.68s)
happened, do you remember what happened
[26:03] (1563.60s)
after because these days in the tech
[26:06] (1566.12s)
industry it's all about blameless
[26:07] (1567.52s)
postmortems. If an outage happens, you
[26:10] (1570.04s)
know, we first mitigated then we get
[26:11] (1571.56s)
together then we write a document where
[26:13] (1573.08s)
we try really hard to not say who push
[26:15] (1575.92s)
caused this but we come up with why the
[26:17] (1577.80s)
system is like this and so on. How did
[26:19] (1579.96s)
you go about like dealing with outages
[26:22] (1582.48s)
and also following up and ensuring that
[26:25] (1585.16s)
they don't they won't happen again?
[26:26] (1586.60s)
Today I know they did these discussions
[26:29] (1589.28s)
in the server group chats, but I wasn't
[26:31] (1591.72s)
in the server group chats so I can't
[26:34] (1594.04s)
really say for sure. I mean, for sure we
[26:36] (1596.04s)
did not have documentations. It sounds
[26:38] (1598.48s)
like a lot of things were pretty simple.
[26:40] (1600.72s)
You talk with people, if you have a
[26:42] (1602.16s)
problem you try to fix it.
[26:44] (1604.08s)
Don't over document things for no reason
[26:47] (1607.08s)
and it seemed to just work. And and then
[26:49] (1609.40s)
have the key thing like if I guess if
[26:51] (1611.00s)
you put out days since since outage,
[26:52] (1612.76s)
people will know like, okay, I should do
[26:54] (1614.44s)
what I can to not have an outage. And
[26:56] (1616.44s)
everybody knew exactly who was working
[26:58] (1618.56s)
on what, so we didn't have to blame
[27:01] (1621.24s)
anyone. Everyone just knew. WhatsApp was
[27:03] (1623.84s)
a massive massive massive success. What
[27:05] (1625.64s)
do you think made it so successful in
[27:07] (1627.64s)
the early years and in the
[27:09] (1629.96s)
especially for for for the product
[27:11] (1631.64s)
itself, you know, you've seen Kakao, you
[27:13] (1633.76s)
were you were aware of the some of the
[27:15] (1635.44s)
competing messaging app. What did
[27:16] (1636.76s)
WhatsApp do that others did not? There
[27:18] (1638.96s)
is a little bit of the network effect.
[27:22] (1642.24s)
If you It's like the thing about
[27:23] (1643.72s)
messaging app is that if you use it, you
[27:26] (1646.08s)
need your friends to use it, and if your
[27:27] (1647.68s)
friends use it, you need to use it. And
[27:29] (1649.40s)
WhatsApp was the first to be on the
[27:32] (1652.80s)
market. That certainly helped, but there
[27:35] (1655.72s)
was a lot of competition. But again, I
[27:38] (1658.04s)
think um a lot of other apps and
[27:40] (1660.56s)
messaging apps were chasing features.
[27:43] (1663.28s)
Thinking about adding the the shiniest
[27:45] (1665.64s)
newest features.
[27:47] (1667.16s)
Whereas WhatsApp was very intentional.
[27:49] (1669.08s)
They actually worked on video calling
[27:51] (1671.08s)
for a very long time. We were probably
[27:53] (1673.72s)
working on it by the time you joined
[27:55] (1675.16s)
Skype when your founder said, "We have
[27:57] (1677.56s)
video." Um we were working on it, but we
[27:59] (1679.60s)
just didn't launch it until much later
[28:01] (1681.28s)
when we were actually like 100% sure
[28:04] (1684.28s)
about the quality of the the feature.
[28:06] (1686.72s)
So, we often held onto features until we
[28:10] (1690.36s)
felt really sure before launching them.
[28:13] (1693.96s)
Interesting, cuz that is a little bit of
[28:15] (1695.72s)
a different than the conventional
[28:16] (1696.80s)
advice, which is if you're a startup,
[28:18] (1698.52s)
launch early, get feedback, improve it,
[28:21] (1701.24s)
and iterate it. Sounds like you did the
[28:22] (1702.80s)
opposite. It's It's like polish it and
[28:25] (1705.28s)
then do when you have full conviction.
[28:26] (1706.84s)
Yeah, we did use it internally.
[28:28] (1708.52s)
Internally, we used the voice and the
[28:30] (1710.56s)
video calling features with our
[28:32] (1712.28s)
families. So, we had like a list, okay,
[28:34] (1714.96s)
like I have family members, these are
[28:36] (1716.84s)
all my my parents and my brother and
[28:38] (1718.80s)
sisters numbers. Let's enable it for
[28:41] (1721.00s)
this beta group, and we used it for a
[28:43] (1723.40s)
very long time before we launched it
[28:45] (1725.40s)
with the public. Two years into working
[28:47] (1727.20s)
at WhatsApp
[28:48] (1728.52s)
in 2014, Facebook announces their
[28:50] (1730.24s)
biggest ever acquisition, WhatsApp for
[28:52] (1732.24s)
19 billion dollars. What do you remember
[28:54] (1734.56s)
of this time? How unexpected was it and
[28:57] (1737.48s)
and what what what kind of feelings what
[28:58] (1738.88s)
kind of emotions went through you and
[29:00] (1740.24s)
the team around you?
[29:01] (1741.68s)
I actually journaled soon after the
[29:03] (1743.96s)
acquisition, so I looked at my journal
[29:06] (1746.24s)
around this time 2014. So, it's been
[29:08] (1748.56s)
over 10 years. But, I looked at my
[29:10] (1750.48s)
journal and I remember I was coding. I
[29:13] (1753.40s)
had this Spotify playlist with
[29:16] (1756.40s)
noise-canceling headphones. I had this
[29:18] (1758.96s)
playlist called let me think. This is
[29:21] (1761.04s)
the one I I listen to when I want to
[29:22] (1762.92s)
focus.
[29:24] (1764.36s)
And again, like we were in a pretty
[29:26] (1766.04s)
small office where I can see everything.
[29:28] (1768.24s)
I was sitting in pretty central
[29:30] (1770.76s)
location, so I I could see people
[29:33] (1773.40s)
bustling and hustling, which which was a
[29:35] (1775.56s)
little bit weird, but I tried to tune it
[29:37] (1777.04s)
out so I can code.
[29:38] (1778.35s)
>> [laughter]
[29:39] (1779.80s)
>> But then, from the side I saw um Neeraj,
[29:43] (1783.16s)
who was the head of business at the
[29:44] (1784.68s)
time. He was just like waving his arms.
[29:47] (1787.40s)
He's He's a pretty tall guy, so I could
[29:49] (1789.12s)
see it. He's like like stop whatever
[29:51] (1791.56s)
you're working on right now.
[29:53] (1793.60s)
Come into the
[29:55] (1795.84s)
we had one meeting room. Come in.
[29:57] (1797.99s)
>> [laughter]
[29:59] (1799.04s)
>> Come into the meeting room. And I was
[30:01] (1801.08s)
like, what is happening? Like we never
[30:03] (1803.76s)
have meetings. Like we we never
[30:05] (1805.86s)
>> [laughter]
[30:06] (1806.24s)
>> So, you didn't have meetings? I mean, we
[30:08] (1808.08s)
we have scheduled meetings every now and
[30:10] (1810.12s)
then, but we rarely have like we we have
[30:12] (1812.36s)
never had unscheduled meetings. And we
[30:14] (1814.96s)
rarely have meetings at all.
[30:17] (1817.08s)
So, I was confused and I I dropped
[30:20] (1820.00s)
whatever I was working on and I went
[30:21] (1821.80s)
into the conference room. And then they
[30:23] (1823.72s)
asked like turn off your phones.
[30:26] (1826.00s)
WhatsApp, turn off your phone. That
[30:27] (1827.68s)
That's kind of weird, right? And I
[30:29] (1829.28s)
thought, oh my gosh, what's happening?
[30:30] (1830.72s)
Like did we go out of business?
[30:33] (1833.37s)
>> [laughter]
[30:34] (1834.96s)
>> That was one thought. I thought, are we
[30:38] (1838.16s)
getting another raise of fund like round
[30:40] (1840.52s)
of funding? Like a new investor coming
[30:43] (1843.24s)
on board. It can't be that we sold the
[30:45] (1845.72s)
company because Jan used to say he will
[30:47] (1847.80s)
never sell the company.
[30:49] (1849.76s)
He used to actually say selling your
[30:51] (1851.52s)
company is like selling your baby.
[30:54] (1854.96s)
And I remember we were waiting for quite
[30:56] (1856.80s)
a while because there was one person
[30:58] (1858.12s)
missing. Oh. And it turns out she was
[31:00] (1860.96s)
getting her eyebrows done.
[31:02] (1862.84s)
>> [laughter]
[31:03] (1863.76s)
>> With your phone, like by the way. Yeah,
[31:06] (1866.00s)
she came she came after the
[31:07] (1867.80s)
announcement, but the news was about to
[31:09] (1869.60s)
hit the public and they wanted to tell
[31:12] (1872.16s)
us before the news hit. And
[31:16] (1876.00s)
I I noticed that Jan and Brian were
[31:17] (1877.92s)
making this
[31:19] (1879.20s)
what face and I couldn't tell what it
[31:21] (1881.84s)
was. And then they made the announcement
[31:24] (1884.72s)
WhatsApp has been acquired by Facebook
[31:27] (1887.08s)
for $19 billion and I'm I realized oh,
[31:30] (1890.16s)
that was them trying to hide their
[31:32] (1892.48s)
excitement.
[31:34] (1894.24s)
That was the face.
[31:35] (1895.20s)
>> kind of smiley but not smiley.
[31:38] (1898.21s)
>> [laughter]
[31:38] (1898.64s)
>> And that that was a really exciting
[31:40] (1900.20s)
moment and I I kind of zoned out for a
[31:42] (1902.20s)
little bit because I was trying to
[31:43] (1903.64s)
remember hey, like how many shares did I
[31:46] (1906.92s)
Like they were again it was my first
[31:49] (1909.00s)
startup ever. I didn't even negotiate my
[31:51] (1911.24s)
equity and I honestly couldn't remember
[31:53] (1913.16s)
how much equity I had and I was trying
[31:55] (1915.80s)
to think how much is a billion dollars?
[31:57] (1917.76s)
It seems like a lot of money.
[31:59] (1919.76s)
And how much is like 1% of 19 billion? I
[32:02] (1922.44s)
couldn't do the math. And I I remember
[32:04] (1924.44s)
sitting there thinking like trying to do
[32:06] (1926.04s)
the math. And then I thought, you know,
[32:09] (1929.32s)
no matter how the math works, I think
[32:11] (1931.68s)
one thing is clear. I'm going to be
[32:14] (1934.89s)
>> [laughter]
[32:15] (1935.36s)
>> And then Zuckerberg walked in.
[32:17] (1937.52s)
It's it's Zuckerberg walked in, sat in a
[32:19] (1939.32s)
meeting.
[32:19] (1939.84s)
>> Yeah. Wow. And then you had like a Q&A
[32:22] (1942.60s)
or something.
[32:23] (1943.64s)
>> We did. We did. Yeah. What what kind of
[32:25] (1945.40s)
questions can you ask at that at this
[32:27] (1947.20s)
point? Or what kind of questions did
[32:28] (1948.24s)
people ask? There was a a mix of
[32:30] (1950.28s)
excitement and nervousness, right? Um,
[32:32] (1952.88s)
are we going to have to change
[32:34] (1954.60s)
everything like because I think a lot of
[32:37] (1957.48s)
the engineers were more experienced and
[32:40] (1960.68s)
they talked about how when Yahoo
[32:42] (1962.88s)
acquired companies, they changed 100%
[32:46] (1966.44s)
and lost the
[32:48] (1968.96s)
What is it? The essence of the business.
[32:51] (1971.40s)
So there there were a lot of questions
[32:52] (1972.96s)
around that and
[32:54] (1974.72s)
Mark is actually very charismatic in
[32:56] (1976.64s)
person and he I thought he had great
[32:58] (1978.76s)
answers at the time. He made sure
[33:01] (1981.60s)
everyone feels assured that nothing's
[33:04] (1984.12s)
going to change and he will try to
[33:06] (1986.84s)
maintain it as much as possible. At
[33:09] (1989.08s)
least that was the messaging at the
[33:10] (1990.52s)
time. Clearly this this was an
[33:13] (1993.84s)
amazing exit and to this day it's not
[33:16] (1996.56s)
really been repeated. May- maybe a few
[33:18] (1998.80s)
companies might have come close but
[33:20] (2000.20s)
definitely not with with such a such a
[33:21] (2001.72s)
small team. How did you and and and your
[33:24] (2004.52s)
colleagues deal with the fact that wow,
[33:26] (2006.40s)
you've just got an amazing financial
[33:28] (2008.64s)
exit but I guess the company kind of
[33:30] (2010.12s)
continues inside of Meta like
[33:33] (2013.72s)
it it seems seems like you know, two
[33:35] (2015.64s)
things at the same time like okay, I
[33:37] (2017.04s)
have this like amazing financial exit
[33:39] (2019.32s)
but there's also work. How how do you
[33:41] (2021.68s)
balance? How did you balance? How do you
[33:42] (2022.96s)
decide what next? That's twofold.
[33:47] (2027.08s)
So the the finance side in terms of that
[33:49] (2029.92s)
aspect, we actually got a lot of
[33:51] (2031.88s)
support. Our business person organized
[33:55] (2035.56s)
many meetings with like the accountants
[34:00] (2040.04s)
even a financial advisor. We invited a a
[34:03] (2043.24s)
professor who was a founder of
[34:05] (2045.04s)
Wealthfront and he gave us
[34:07] (2047.32s)
an hour of uh finance advice and he
[34:10] (2050.12s)
recommended books. Um I read the Random
[34:13] (2053.36s)
Walk Down Wall Street, which is a great
[34:15] (2055.64s)
book. I recommend that people read it if
[34:18] (2058.60s)
you're interested in financial
[34:19] (2059.88s)
management and I read several other
[34:21] (2061.84s)
books to really educate myself to be
[34:25] (2065.12s)
able to
[34:26] (2066.84s)
manage this new wealth that I I came
[34:29] (2069.32s)
across as a young 29-year-old. Yeah.
[34:32] (2072.64s)
What changed in the day-to-day once you
[34:34] (2074.40s)
officially became part of Facebook? Did
[34:36] (2076.00s)
you have to move offices? Did, you know,
[34:38] (2078.32s)
did you get a new title added to like
[34:41] (2081.04s)
the the meta org chart, that kind of
[34:42] (2082.92s)
stuff? The changes were very slow in the
[34:45] (2085.88s)
beginning. We didn't even move into the
[34:48] (2088.40s)
meta, or at the time was called
[34:50] (2090.28s)
Facebook, headquarters, Menlo Park,
[34:53] (2093.08s)
until at least a couple years after the
[34:55] (2095.72s)
acquisition. So, in the beginning,
[34:57] (2097.88s)
everything was same as usual. We still
[35:00] (2100.80s)
had our old office. Oh, we did actually
[35:03] (2103.32s)
move to a little bit nicer office, a
[35:05] (2105.52s)
slightly bigger office.
[35:07] (2107.68s)
But other than that, it was business as
[35:10] (2110.04s)
usual. It was Jan and Brian. And we were
[35:13] (2113.88s)
hiring, but not, you know, at our
[35:16] (2116.52s)
similar like slow, steady
[35:21] (2121.96s)
and I think not until when we actually
[35:24] (2124.36s)
moved into the Facebook office, we
[35:27] (2127.52s)
started seeing a little bit more um
[35:30] (2130.20s)
cultural influence and merging. Like, we
[35:32] (2132.76s)
started using
[35:34] (2134.76s)
their like HR services, their recruiting
[35:37] (2137.32s)
services, and things like that. But it
[35:39] (2139.28s)
was a very gradual change over time.
[35:41] (2141.96s)
And then when WhatsApp became part of
[35:43] (2143.84s)
Facebook, as I understand it it it's
[35:45] (2145.88s)
still is even to this day its own
[35:47] (2147.32s)
organization like inside of Facebook. I
[35:49] (2149.84s)
understand there's organizations like
[35:51] (2151.28s)
Messenger or like there's the Facebook
[35:54] (2154.24s)
group etc. So, like did WhatsApp remain
[35:56] (2156.00s)
its own kind of organization a little
[35:57] (2157.60s)
bit shielded from the rest of Facebook?
[35:59] (2159.52s)
We had our own area. Yeah, or your own
[36:02] (2162.92s)
>> And in the beginning, we even had like
[36:05] (2165.72s)
our our own chairs and our own
[36:08] (2168.84s)
whatever like walls and decorations that
[36:10] (2170.84s)
we were using, we brought them all over.
[36:13] (2173.32s)
But over time, you know, there was more
[36:15] (2175.60s)
and more mixing. After the acquisition,
[36:18] (2178.76s)
how did you start to hire more people?
[36:21] (2181.40s)
How did the projects change? Did things
[36:23] (2183.28s)
become more ambitious? Did you start to
[36:24] (2184.84s)
add more features? Cuz clearly like you
[36:26] (2186.64s)
were about 30 of you and then few in a
[36:28] (2188.88s)
few years there was hundreds of people
[36:30] (2190.28s)
working on WhatsApp these days it must
[36:31] (2191.76s)
be thousands of people. And like what
[36:33] (2193.80s)
would those people like what were new
[36:35] (2195.84s)
work came up cuz again originally
[36:37] (2197.44s)
WhatsApp was so minimalist, right? And
[36:39] (2199.12s)
kind of so scrappy. I guess we were
[36:41] (2201.08s)
choosing to be small not that there was
[36:43] (2203.52s)
not enough work for us to do, right? So
[36:46] (2206.20s)
one of the reasons why we also tried to
[36:48] (2208.28s)
remain small was actually Brian and Jan
[36:51] (2211.04s)
did not want to raise too much money.
[36:53] (2213.44s)
And it actually cost a lot of money to
[36:55] (2215.60s)
serve so many users. You have to pay for
[36:57] (2217.64s)
the servers.
[36:59] (2219.12s)
You have to pay for the SMS registration
[37:01] (2221.40s)
codes. Every year
[37:03] (2223.76s)
Jan and Brian would do uh all hands
[37:06] (2226.24s)
meeting. So we did have meetings. Once a
[37:09] (2229.76s)
Uh and Brian was very transparent. He
[37:13] (2233.24s)
would walk through our
[37:15] (2235.64s)
earnings and expenses. Ooh, interesting.
[37:19] (2239.36s)
>> well I had a lot of information around
[37:21] (2241.48s)
it. So the three main buckets of our
[37:23] (2243.72s)
spending was server cost was about a
[37:26] (2246.56s)
third and then about a third on salaries
[37:29] (2249.48s)
for the engineers mostly. And then a
[37:32] (2252.08s)
third uh the rest was for the SMS fee.
[37:35] (2255.40s)
The when you try to register you get
[37:37] (2257.32s)
that code and we have to pay that 10
[37:39] (2259.80s)
cents or whatever how much it cost to
[37:41] (2261.56s)
send that international messaging.
[37:44] (2264.28s)
Uh those numbers I mean they add up when
[37:46] (2266.80s)
you have millions of people using your
[37:49] (2269.36s)
app. So they actually didn't want to
[37:51] (2271.44s)
grow too fast because it gets very
[37:53] (2273.68s)
expensive. WhatsApp was free for the
[37:55] (2275.92s)
first year and then after that WhatsApp
[37:58] (2278.96s)
was charging $1 for every year.
[38:02] (2282.60s)
But they were only using it in certain
[38:04] (2284.76s)
countries really to suppress growth
[38:07] (2287.32s)
because they didn't want to grow too
[38:08] (2288.40s)
fast. Fascinating. Cuz I I remember in
[38:11] (2291.64s)
in in Europe and in the US there was
[38:13] (2293.52s)
this $1 cost which I think people were
[38:15] (2295.92s)
like yeah, well
[38:17] (2297.24s)
whatever. I don't think we realized that
[38:19] (2299.24s)
that this was a growth suppression
[38:20] (2300.36s)
tactic. Fascinating. And then when
[38:22] (2302.20s)
Facebook acquired I guess they got rid
[38:23] (2303.92s)
of it. Yeah, Facebook said we don't need
[38:26] (2306.40s)
the dollar. We can grow as much as we
[38:29] (2309.16s)
can because they had the funding for it.
[38:31] (2311.20s)
>> growth just did it did it speed up? Do
[38:33] (2313.16s)
you remember? It did, yeah. Incredible
[38:35] (2315.28s)
detail. You're using payment to slow
[38:37] (2317.48s)
down growth. A lesser known detail about
[38:39] (2319.96s)
the $1
[38:41] (2321.40s)
is that that $1 was enough to pay for
[38:45] (2325.36s)
all of these, the server cost, the
[38:47] (2327.20s)
salaries, and the SMS code. Per per
[38:49] (2329.76s)
year. So, you were roughly break even.
[38:52] (2332.12s)
Break even. We did have funding from
[38:54] (2334.64s)
Sequoia, but we never touched that
[38:56] (2336.24s)
money.
[38:57] (2337.28s)
Incredible. Yeah, Brian explained it as
[39:00] (2340.24s)
how his dad was a business owner and
[39:03] (2343.36s)
they would wake up in the middle of of
[39:05] (2345.00s)
the night worried, "What if I cannot pay
[39:07] (2347.48s)
the the salaries for the employees
[39:09] (2349.60s)
tomorrow?"
[39:10] (2350.92s)
And he he explained that he took the
[39:13] (2353.88s)
funding from Sequoia as like a backup.
[39:16] (2356.76s)
And I think it was $8 million of funding
[39:18] (2358.48s)
if I recall, if I looked at the looked
[39:20] (2360.20s)
at the backup. Yeah, so we never touched
[39:22] (2362.48s)
that money. The $1 paid for everything.
[39:25] (2365.16s)
And it slowed down growth enough to be
[39:26] (2366.48s)
manageable. Yeah. When you joined
[39:28] (2368.68s)
Facebook, what what title did you get
[39:31] (2371.60s)
and how did your career change? So, the
[39:33] (2373.60s)
thing about Facebook is that everyone's
[39:36] (2376.40s)
actually software engineer. I'm pretty
[39:38] (2378.44s)
sure they still don't have titles. They
[39:40] (2380.76s)
don't have titles, but they have levels.
[39:42] (2382.04s)
What what what level did you come in at?
[39:44] (2384.20s)
So, being one of the five youngest
[39:46] (2386.88s)
people, I got I got leveled as a junior
[39:49] (2389.32s)
engineer. No, you did not.
[39:52] (2392.28s)
L3 or L4? L3 L3, yeah.
[39:56] (2396.44s)
>> to like climb climb all over again. Oh
[39:58] (2398.92s)
my gosh, that that must have been a bit
[40:00] (2400.40s)
awkward. I was not too happy about it,
[40:02] (2402.64s)
but what's the alternative? Do I want to
[40:04] (2404.48s)
give up vesting the rest of the shares?
[40:07] (2407.76s)
And eventually I got promoted. But that
[40:10] (2410.08s)
was within WhatsApp, so you got promoted
[40:11] (2411.88s)
pretty quickly. How many times did you
[40:13] (2413.28s)
get promoted there? A few times. I mean,
[40:15] (2415.68s)
I eventually became an engineering
[40:17] (2417.60s)
manager. And then, as you became an
[40:19] (2419.40s)
engineering manager, at some point you
[40:21] (2421.36s)
decided to help and start a new office
[40:23] (2423.88s)
in London.
[40:25] (2425.20s)
How did that decision come, and how did
[40:27] (2427.72s)
you go about it?
[40:29] (2429.44s)
That was actually an ask from
[40:33] (2433.24s)
Facebook headquarters. So, they said,
[40:35] (2435.16s)
"Hey, like, we're actually running out
[40:36] (2436.84s)
of space in Menlo Park. And also,
[40:39] (2439.76s)
WhatsApp is so big in Europe, so why not
[40:41] (2441.68s)
have a presence there? It'll be much
[40:43] (2443.68s)
easier to hire engineers because
[40:46] (2446.56s)
everybody actually uses WhatsApp. So,
[40:48] (2448.48s)
let's let's start a new office there,
[40:50] (2450.16s)
and we didn't have that many engineering
[40:52] (2452.40s)
managers, right? I was very lucky
[40:54] (2454.40s)
because
[40:55] (2455.40s)
I got asked to go along with couple
[40:57] (2457.68s)
other engineering managers, and all
[40:59] (2459.68s)
three of us actually became managers
[41:01] (2461.56s)
around the same time. We actually even
[41:03] (2463.36s)
trained together. So, we were relatively
[41:05] (2465.52s)
new managers when we got asked to go
[41:07] (2467.40s)
there, but I think we were the only ones
[41:09] (2469.16s)
who could go because, you know, people
[41:10] (2470.88s)
have children, and they have to think
[41:12] (2472.76s)
about school, and they they couldn't go.
[41:14] (2474.80s)
I remember one, the director that I was
[41:17] (2477.04s)
working with, he couldn't go because his
[41:18] (2478.88s)
wife says she doesn't want to move with
[41:20] (2480.96s)
the children. It It makes perfect sense.
[41:23] (2483.52s)
You arrived in London, you landed with
[41:25] (2485.08s)
these two or three other engineering
[41:26] (2486.84s)
managers.
[41:28] (2488.04s)
How did you start to grow the office?
[41:30] (2490.28s)
From a practical perspective, what can I
[41:31] (2491.92s)
imagine? Like, you know, like, how did
[41:33] (2493.56s)
you start hiring or
[41:35] (2495.64s)
leasing space? Or what are the other
[41:37] (2497.00s)
things that you had to do that, you
[41:39] (2499.16s)
know, like, were maybe a little bit
[41:40] (2500.32s)
unexpected for you? A lot of the
[41:42] (2502.20s)
logistical part was taken care of for us
[41:44] (2504.72s)
because Facebook already had an office
[41:46] (2506.72s)
there, so we kind of moved in.
[41:48] (2508.68s)
>> [laughter]
[41:49] (2509.04s)
>> We got our own section. And it wasn't
[41:51] (2511.80s)
big because at the time, again, we had a
[41:54] (2514.00s)
lot of contractors in Europe, so we had
[41:56] (2516.28s)
one contractor already in England.
[41:59] (2519.36s)
So, we turned we
[42:01] (2521.16s)
converted them full-time, and then we
[42:02] (2522.96s)
had one in Scotland. We also converted
[42:05] (2525.44s)
him full-time, so he would commute from
[42:07] (2527.40s)
Scotland every now and then. So, we had
[42:09] (2529.80s)
two engineers plus three managers, and
[42:12] (2532.28s)
we started hiring there.
[42:14] (2534.36s)
I think the hiring part was something
[42:17] (2537.40s)
that took longer to set up. We worked
[42:20] (2540.00s)
very closely with the Facebook hiring
[42:22] (2542.08s)
team, which was really great that we
[42:24] (2544.04s)
already had people who were familiar
[42:26] (2546.24s)
with the the local recruiting logistics
[42:29] (2549.72s)
there. So,
[42:31] (2551.00s)
one thing we focused on a lot was really
[42:33] (2553.44s)
letting engineers know, "Hey, WhatsApp
[42:35] (2555.32s)
is hiring in Europe now. Come apply."
[42:37] (2557.76s)
Because we were hiring from all over
[42:39] (2559.28s)
Europe and also a lot from India.
[42:41] (2561.84s)
Do you feel it was easier to hire for
[42:43] (2563.88s)
WhatsApp in Europe just because people
[42:45] (2565.44s)
knew about it? Did you get more
[42:46] (2566.60s)
excitement, more applicants? 100%.
[42:49] (2569.59s)
>> [laughter]
[42:50] (2570.08s)
>> You wouldn't believe. Like, I used to do
[42:52] (2572.00s)
a lot of university recruiting and when
[42:54] (2574.80s)
I used to go to Stanford, maybe 2013,
[42:59] (2579.12s)
like anytime before the acquisition,
[43:01] (2581.36s)
I would say, "Hey, like the people would
[43:03] (2583.32s)
come up to the booth." And I would say,
[43:04] (2584.68s)
"Hey, do you want to give me your
[43:05] (2585.56s)
resume?" And they would be like,
[43:07] (2587.56s)
"Tell me about your company first."
[43:10] (2590.55s)
>> [laughter]
[43:11] (2591.36s)
>> Because they have they have never heard
[43:12] (2592.40s)
of WhatsApp. What is this company? I'm
[43:14] (2594.08s)
not even going to give you my resume.
[43:15] (2595.68s)
>> only have one resume. I have only 20 of
[43:17] (2597.00s)
these.
[43:17] (2597.36s)
>> Exactly. [laughter]
[43:19] (2599.64s)
Uh versus in Europe, people were
[43:21] (2601.80s)
actually excited to talk to us. What
[43:23] (2603.88s)
were the good and bad things of working
[43:26] (2606.36s)
in what basically is a remote office?
[43:28] (2608.52s)
Like, yes, London was a big office, but
[43:30] (2610.48s)
HQ was in California, Menlo Park. That's
[43:33] (2613.84s)
8 hours of time zone difference, a lot
[43:35] (2615.68s)
less overlap. There's probably some good
[43:37] (2617.24s)
things about this and some downsides. It
[43:40] (2620.12s)
helped because
[43:41] (2621.84s)
the three of us were from Menlo Park and
[43:44] (2624.68s)
we actually had great relationships with
[43:47] (2627.60s)
other teams and other engineers and
[43:49] (2629.52s)
other managers. And we also traveled
[43:52] (2632.32s)
back to Menlo Park every quarter and
[43:54] (2634.68s)
then we had the leadership from Menlo
[43:56] (2636.64s)
Park also travel to London
[43:59] (2639.32s)
almost every quarter. So, there was a
[44:01] (2641.12s)
lot of back and forth um to really
[44:03] (2643.88s)
strengthen the relationship in the
[44:05] (2645.64s)
beginning. Your your growth went to like
[44:07] (2647.96s)
being, I guess, the one of the
[44:10] (2650.20s)
most junior people in WhatsApp, which is
[44:12] (2652.52s)
crazy to say cuz you were experienced as
[44:14] (2654.08s)
well, but then you were also L3 in
[44:16] (2656.16s)
Facebook, which I still cannot believe.
[44:18] (2658.12s)
But you you you went and became a
[44:19] (2659.76s)
manager. What pushed you to actually
[44:22] (2662.36s)
say, "I actually want to try to manage
[44:23] (2663.80s)
people?"
[44:25] (2665.52s)
I actually never asked for it myself.
[44:27] (2667.68s)
Someone on my team begged my manager,
[44:31] (2671.44s)
"Hey, can I please report to Jean?" And
[44:33] (2673.80s)
that's how I became a manager. Wow.
[44:36] (2676.44s)
Okay. What do you think this this person
[44:37] (2677.92s)
saw in you that they they wanted to
[44:39] (2679.40s)
report you when you were not a manager?
[44:41] (2681.16s)
I was the tech lead, so I was already
[44:43] (2683.20s)
managing the project. So, it was sort of
[44:45] (2685.44s)
a natural transition for me. And when
[44:47] (2687.56s)
you become a manager, what parts of the
[44:48] (2688.96s)
job came naturally to you and what parts
[44:51] (2691.20s)
were hard that you had to learn or get
[44:53] (2693.44s)
mentorship for? You know, I started
[44:55] (2695.60s)
reading books. I love reading books.
[44:57] (2697.96s)
Whenever there's a new challenge, I like
[45:00] (2700.44s)
to read, learn, and research. There
[45:02] (2702.84s)
actually at the time weren't a lot of
[45:05] (2705.32s)
courses on how to become a manager. And
[45:08] (2708.80s)
not a lot of books. Like, I still don't
[45:10] (2710.56s)
think there are too many books about how
[45:12] (2712.16s)
to become a manager. There's a little
[45:13] (2713.56s)
bit more now. There there's like three
[45:15] (2715.00s)
or four good ones, but but they all came
[45:16] (2716.88s)
out after like 2015 or 2016. Yeah, the
[45:19] (2719.96s)
the resources were pretty
[45:22] (2722.32s)
limited, but I I did what I can to read
[45:25] (2725.28s)
as much as I can about leadership and I
[45:28] (2728.36s)
think I read actually a lot about
[45:30] (2730.44s)
communication and psychology. There's
[45:33] (2733.48s)
several books like I love the book
[45:36] (2736.00s)
Surrounded by Idiots. Have you read that
[45:38] (2738.04s)
book? It talks about the the DISC
[45:40] (2740.76s)
personality, the different types of
[45:42] (2742.64s)
personalities. And I try to really
[45:44] (2744.60s)
understand like what motivates people,
[45:47] (2747.12s)
how do you communicate with people in a
[45:49] (2749.28s)
in a way that makes sense to the other
[45:51] (2751.80s)
person. And also I reflected personally
[45:54] (2754.84s)
like what were some good managers and
[45:57] (2757.04s)
bad manager in my experience because you
[46:00] (2760.24s)
hear the saying that people don't leave
[46:02] (2762.08s)
companies, they leave managers, right?
[46:04] (2764.48s)
Your manager can really break or make
[46:06] (2766.36s)
your career. And they can make your life
[46:08] (2768.80s)
miserable if you're, you know, matched
[46:10] (2770.80s)
with someone you don't vibe with. What
[46:13] (2773.56s)
are the traits that you found like as
[46:15] (2775.60s)
you recalled, what were things you said
[46:16] (2776.92s)
like I think this makes a good manager,
[46:18] (2778.68s)
I want to do more of that, and I think
[46:20] (2780.72s)
these were terrible managers or bad
[46:22] (2782.24s)
managers and I want to avoid doing that.
[46:24] (2784.00s)
Do you remember some things that stuck
[46:25] (2785.32s)
out? Yeah, I tried to really understand
[46:28] (2788.72s)
each individual person. So, for example,
[46:31] (2791.04s)
like one person that I had on my team
[46:33] (2793.80s)
really loves going deep into problems or
[46:37] (2797.84s)
debugging and finding out how to improve
[46:40] (2800.76s)
things, right? Whereas another person
[46:43] (2803.00s)
really loves building new features.
[46:45] (2805.36s)
And you cannot ask this person who loves
[46:48] (2808.12s)
to build new features to go debug 10
[46:50] (2810.52s)
bugs, and that person will go nuts,
[46:52] (2812.48s)
right? And then I want person who was
[46:54] (2814.64s)
really good at
[46:56] (2816.08s)
uh building new features was not so
[46:58] (2818.00s)
great at mentoring new colleagues. So, I
[47:00] (2820.68s)
try to really look for their strengths
[47:03] (2823.48s)
and of course you also want to set them
[47:05] (2825.80s)
up for challenges so they can learn as
[47:08] (2828.08s)
well, but you want to balance them out.
[47:09] (2829.84s)
So, I I try to really understand by
[47:12] (2832.64s)
asking them a lot of questions to
[47:14] (2834.52s)
understand like how do they want to be
[47:16] (2836.40s)
challenged? When do they feel excited
[47:18] (2838.56s)
about their work? Or what are the things
[47:20] (2840.80s)
that they're really good at? What are
[47:22] (2842.16s)
the things they want to improve on? So,
[47:24] (2844.20s)
I spent a lot of time really talking to
[47:26] (2846.20s)
them. As a manager, you were part of
[47:28] (2848.48s)
calibration meetings, right? Now that
[47:31] (2851.12s)
you're not at not at WhatsApp, not at
[47:33] (2853.08s)
Meta, can we talk honestly about what
[47:36] (2856.12s)
are those meetings like?
[47:39] (2859.00s)
Uh you know, what are maybe the the good
[47:40] (2860.68s)
things about them? How how can you
[47:42] (2862.16s)
prepare? And what's the kind of reality?
[47:44] (2864.24s)
Cuz I feel outside of a small group of
[47:46] (2866.24s)
managers who are in there, it's not many
[47:48] (2868.68s)
people know like how how these things
[47:50] (2870.52s)
go. So, people number one biggest
[47:52] (2872.84s)
mistake people make is they think your
[47:55] (2875.60s)
manager is the one giving you a
[47:58] (2878.68s)
promotion or a salary boost. Like as a
[48:01] (2881.44s)
manager, middle manager, right? Like I
[48:03] (2883.64s)
have no authority to give you a
[48:05] (2885.96s)
promotion. You have no budget. Typically
[48:08] (2888.60s)
directors have a discretionary budget
[48:10] (2890.52s)
and sometimes to be able to give a
[48:12] (2892.40s)
reward, but not even a promotion they
[48:14] (2894.12s)
even they cannot give, right? Right. And
[48:16] (2896.88s)
um the bonuses are tied to your
[48:19] (2899.28s)
performance review, right? So at Meta,
[48:21] (2901.52s)
for every level there's exact
[48:23] (2903.20s)
percentages lined up by the comp team.
[48:26] (2906.32s)
Like I have no control over it. The only
[48:28] (2908.28s)
control I have is I think of myself as
[48:31] (2911.12s)
the lawyer representing my clients.
[48:33] (2913.88s)
Wow, [laughter] yeah. I'm making a case
[48:36] (2916.20s)
for them. Yeah. Why they deserve to get
[48:39] (2919.64s)
a certain performance review rating or a
[48:42] (2922.28s)
promotion. And obviously like I want my
[48:45] (2925.04s)
clients to do well. I want my team to
[48:47] (2927.68s)
get, you know, the recognition that they
[48:50] (2930.16s)
deserve because I know they worked hard.
[48:53] (2933.08s)
But it's not up to me. All the other
[48:55] (2935.36s)
managers also have to agree. That is the
[48:58] (2938.84s)
the nature of performance reviews. And
[49:01] (2941.96s)
being specific on a performance review,
[49:05] (2945.28s)
who were the people that you saw, the
[49:07] (2947.24s)
engineers who got these high performance
[49:09] (2949.04s)
reviews from this committee? What kind
[49:11] (2951.16s)
of taxes did you see? What were there
[49:13] (2953.04s)
things were like, well, some managers
[49:14] (2954.44s)
kind of like, you know, politics where
[49:16] (2956.24s)
they kind of like they're calling in
[49:17] (2957.84s)
favors for each other and pushing
[49:19] (2959.20s)
someone up or or was it mostly
[49:21] (2961.00s)
meritocracy meaning this engineer was
[49:23] (2963.72s)
actually doing great work that a lot of
[49:25] (2965.72s)
managers saw and they just naturally
[49:27] (2967.96s)
agreed that, you know, this person who's
[49:29] (2969.56s)
on on Jean's team is actually they
[49:32] (2972.84s)
should be above my great person and I
[49:35] (2975.00s)
kind of agree with that. Cuz cuz there's
[49:37] (2977.00s)
bucketing, right? Like let's say there's
[49:38] (2978.04s)
bucketing, you're going to have buckets
[49:39] (2979.92s)
and you you need to put like, I don't
[49:41] (2981.28s)
know, X people in the top bucket, middle
[49:43] (2983.40s)
bucket, bottom bucket, and so on.
[49:45] (2985.72s)
Yeah, when I was coaching engineers, so
[49:49] (2989.12s)
I learned that different companies have
[49:51] (2991.56s)
different ways of self-promotion.
[49:54] (2994.88s)
So, like for example, I heard some
[49:57] (2997.04s)
companies use emails. Like, they send
[49:59] (2999.60s)
mass emails every time they do a new
[50:01] (3001.52s)
release or launch.
[50:03] (3003.12s)
Or like at WhatsApp, we use WhatsApp
[50:06] (3006.36s)
groups for everything. But at Facebook,
[50:08] (3008.60s)
they used Facebook Workplace, which is
[50:10] (3010.56s)
like Facebook groups where you have a
[50:12] (3012.68s)
group for team or your org and you're
[50:15] (3015.40s)
like everything has a different group.
[50:18] (3018.20s)
And I noticed as I'm representing my
[50:21] (3021.68s)
clients
[50:23] (3023.00s)
during performance reviews, the people
[50:24] (3024.92s)
who post the most often, who have the
[50:27] (3027.16s)
most visibility,
[50:28] (3028.96s)
usually get the easiest consensus.
[50:33] (3033.16s)
Because it's just like all very natural.
[50:35] (3035.60s)
Like, if I have no clue what you worked
[50:37] (3037.88s)
on and your manager tells me you're
[50:40] (3040.24s)
great, maybe, but how would I know? I
[50:42] (3042.56s)
don't I don't know anything about you.
[50:44] (3044.12s)
So, it I'm less likely to be inclined to
[50:47] (3047.44s)
agree with your manager. Maybe your
[50:49] (3049.16s)
manager's right, but I don't know.
[50:51] (3051.64s)
Whereas, if you have been actively
[50:54] (3054.16s)
posting and telling me indirectly or
[50:56] (3056.76s)
directly what type of work you have done
[50:59] (3059.48s)
and what type of impact that has made
[51:01] (3061.64s)
and what are the lessons that you've
[51:03] (3063.08s)
learned and what type of people you work
[51:05] (3065.04s)
with, then I already know, "Oh, okay."
[51:07] (3067.24s)
Like, when your manager tells me you're
[51:09] (3069.00s)
ready, then I I say, I say, "Yeah."
[51:11] (3071.48s)
>> in turn we'll this was actually like
[51:13] (3073.48s)
it's it's more than just groups. It was
[51:14] (3074.92s)
like this Facebook feed where, you know,
[51:16] (3076.88s)
like it's a bit like LinkedIn, right?
[51:18] (3078.24s)
Just to make it so so you see these
[51:20] (3080.16s)
posts come across the company and
[51:21] (3081.60s)
sometimes you'll put it like and what
[51:23] (3083.32s)
you're saying is like if you've seen
[51:24] (3084.92s)
this post from this engineer on some
[51:26] (3086.52s)
other team saying, "Oh, we've launched
[51:27] (3087.80s)
this feature. Here's an interesting
[51:29] (3089.40s)
thing we've learned that we're using for
[51:31] (3091.16s)
Facebook." And I hit like,
[51:33] (3093.04s)
I now remember it. And then when
[51:34] (3094.60s)
performance review comes, I go, "Oh, I
[51:36] (3096.36s)
remember that person. They wrote that."
[51:38] (3098.20s)
Exactly. And I might even have some
[51:40] (3100.24s)
questions, right? Maybe like if your
[51:42] (3102.04s)
manager tells me, I might be like,
[51:43] (3103.60s)
"Well, what about this? What about
[51:45] (3105.00s)
that?" But if you make a post, I can
[51:47] (3107.04s)
just ask you directly through the
[51:48] (3108.32s)
comments, right? There's a lot of
[51:50] (3110.04s)
engagement happening in the comments.
[51:51] (3111.88s)
So, I might ask, "Have you thought about
[51:53] (3113.48s)
this other thing? Have you thought about
[51:54] (3114.80s)
this thing?" And you might give me
[51:56] (3116.16s)
answers, and I think, "Oh, okay. Yeah,
[51:57] (3117.80s)
he's thought about it. He's really
[51:59] (3119.12s)
good." It's amusing because it sounds
[52:00] (3120.72s)
like simplifying a little bit, but to be
[52:02] (3122.64s)
successful at Facebook, you need to also
[52:04] (3124.48s)
be good inside of the Facebook app and
[52:07] (3127.20s)
and do interesting work and and not hide
[52:09] (3129.64s)
it. Actually, make it visible. Mhm.
[52:12] (3132.16s)
That's interesting. Now, stepping up a
[52:14] (3134.12s)
step back, uh and you were a manager at
[52:16] (3136.08s)
Facebook, you saw a lot of engineers.
[52:18] (3138.20s)
Outside of the performance you had and
[52:19] (3139.76s)
people posting about it, what traits did
[52:22] (3142.48s)
the the best engineers that you remember
[52:25] (3145.00s)
share? Like, what made them so good? I I
[52:27] (3147.72s)
struggle with this question a little bit
[52:29] (3149.44s)
because there's a difference between
[52:32] (3152.28s)
Like, how do you measure skill?
[52:35] (3155.44s)
How do you measure what a good engineer
[52:37] (3157.64s)
is? Is a good engineer someone who can
[52:40] (3160.68s)
bang out new features? Is a good
[52:42] (3162.72s)
engineer someone who can design a
[52:45] (3165.28s)
complicated system? Is a good engineer
[52:47] (3167.60s)
someone who can communicate all of this
[52:49] (3169.76s)
and explain it to non-technical people?
[52:52] (3172.72s)
I I struggle a little bit with the
[52:54] (3174.48s)
definition of a good engineer because I
[52:57] (3177.00s)
can have a definition of a good
[52:58] (3178.20s)
engineer, but
[52:59] (3179.64s)
it may be different for every culture. A
[53:01] (3181.80s)
different company might have different
[53:03] (3183.16s)
definitions. A good one. At Facebook,
[53:05] (3185.88s)
what was the definition? I remember that
[53:07] (3187.96s)
a lot of it went down to just a very
[53:09] (3189.48s)
simple characteristic, impact, right?
[53:12] (3192.60s)
Definitely. And I think the way like
[53:14] (3194.84s)
there are many ways to measure impact,
[53:17] (3197.00s)
and definitely at Facebook
[53:19] (3199.20s)
their way of measuring impact was
[53:20] (3200.88s)
through these posts.
[53:22] (3202.68s)
If I know about your work
[53:24] (3204.72s)
and you tell me you have impact, and I
[53:26] (3206.84s)
agree that's impact. So, going back to
[53:29] (3209.76s)
when you were in London office and and
[53:31] (3211.12s)
startup grow, at what point did the
[53:32] (3212.76s)
London office start to feel less of a
[53:35] (3215.44s)
startup a scrappy startup and more of a
[53:38] (3218.08s)
big tech? I remember a time
[53:41] (3221.36s)
after about a year and half or so, I
[53:44] (3224.08s)
realized
[53:45] (3225.48s)
I don't know who that person is or I
[53:46] (3226.96s)
don't know their name.
[53:49] (3229.28s)
That was a turning point. Mhm.
[53:51] (3231.56s)
And at what point did you actually start
[53:53] (3233.16s)
to think of leaving Facebook?
[53:56] (3236.64s)
I I think I really enjoyed the intimate
[53:58] (3238.88s)
environment. So, I appreciate being able
[54:01] (3241.16s)
to like at WhatsApp with 30 engineers, I
[54:04] (3244.84s)
knew everyone's names, I knew where
[54:06] (3246.80s)
everybody lived, I knew their spouses
[54:08] (3248.56s)
and their children and their dogs'
[54:09] (3249.88s)
names, right?
[54:11] (3251.46s)
>> [laughter]
[54:12] (3252.04s)
>> I really like that type of intimate
[54:13] (3253.96s)
environment. Um we still hang out and we
[54:16] (3256.40s)
have a pretty
[54:18] (3258.24s)
strong bond. And I feel like when when I
[54:20] (3260.48s)
even when I don't even know this
[54:22] (3262.24s)
person's name, I I just feel less
[54:25] (3265.56s)
connected. Yeah. So, so what was this
[54:28] (3268.12s)
the point where you decided that maybe
[54:29] (3269.84s)
it's time for you to leave and do
[54:31] (3271.12s)
something else? Also, okay, I was
[54:34] (3274.48s)
um in London on a contract. So, I had a
[54:36] (3276.96s)
2-year contract. They said, "Hey, like
[54:39] (3279.28s)
go start this office." And then once the
[54:41] (3281.76s)
contract ended, I had the option to
[54:44] (3284.24s)
either stay there to continue working in
[54:47] (3287.28s)
the London office or I could come back
[54:50] (3290.16s)
to Menlo Park.
[54:52] (3292.00s)
But then at that point I had been
[54:53] (3293.32s)
working there for 8 years and I honestly
[54:55] (3295.84s)
I think I was pretty burned out. I'm the
[54:57] (3297.84s)
type of personality who likes to get
[55:00] (3300.52s)
like A+
[55:02] (3302.08s)
on everything I do every single time.
[55:05] (3305.28s)
So, it was pretty uh tiring after 8
[55:07] (3307.28s)
years. I needed a break. Yeah. And
[55:10] (3310.36s)
when you left WhatsApp, what did you
[55:11] (3311.96s)
decide to do?
[55:13] (3313.84s)
What I say WhatsApp, but it was Facebook
[55:15] (3315.60s)
at that point.
[55:16] (3316.28s)
>> Yeah.
[55:17] (3317.24s)
Um I actually because I know my
[55:19] (3319.52s)
personality, I don't take breaks.
[55:22] (3322.20s)
>> [laughter]
[55:23] (3323.72s)
>> So, I actually had a goal. It's so
[55:26] (3326.20s)
simple, but I said, "I will do nothing
[55:29] (3329.00s)
for the next 6 months. I'm going to
[55:31] (3331.00s)
challenge myself to do nothing for 6
[55:34] (3334.08s)
months." Did you manage?
[55:35] (3335.96s)
I did it. I did it. I did read a lot. I
[55:38] (3338.80s)
exercise. I went on long walks. I did
[55:42] (3342.00s)
multiple meditation retreats. But that
[55:44] (3344.52s)
that was my challenge to myself to not
[55:46] (3346.92s)
work for 6 months. So, after 6 months of
[55:49] (3349.68s)
successfully doing nothing, after
[55:51] (3351.28s)
setting yourself that goal, what did you
[55:53] (3353.84s)
do to figure out what next? So,
[55:55] (3355.44s)
initially I thought maybe I want to go
[55:58] (3358.20s)
start a new company or join another
[56:00] (3360.44s)
startup because I like working. I love
[56:03] (3363.64s)
building things. So, I decided okay, I'm
[56:06] (3366.12s)
going to start talking to other founders
[56:08] (3368.64s)
or people who are hiring or people who
[56:10] (3370.60s)
are looking to start a new company. So,
[56:12] (3372.48s)
I I actually talked to 100 founders. I
[56:15] (3375.48s)
have a spreadsheet. Wow. To really see
[56:18] (3378.12s)
like is there any interesting
[56:19] (3379.32s)
opportunities that I might feel
[56:20] (3380.88s)
passionate about joining or building.
[56:23] (3383.56s)
And then after talking to 100 startups,
[56:25] (3385.48s)
I realized I wasn't really passionate
[56:27] (3387.68s)
about joining any of them.
[56:29] (3389.84s)
And I thought like what would I feel
[56:31] (3391.72s)
more passionate about and what was the
[56:33] (3393.56s)
thing that I liked the most about
[56:35] (3395.16s)
working at WhatsApp for the past 8
[56:37] (3397.24s)
years. And I realized I actually really
[56:39] (3399.72s)
liked being a manager because I felt
[56:41] (3401.48s)
like I was creating a culture of like
[56:44] (3404.72s)
support so that other people can really
[56:48] (3408.56s)
be learning and thriving and you know,
[56:51] (3411.44s)
be able to do things freely without
[56:54] (3414.16s)
people breathing down your neck. Or
[56:55] (3415.56s)
there are many things that make for a
[56:57] (3417.48s)
happy career, but I found it really um
[57:00] (3420.56s)
gratifying to be able to find that from
[57:03] (3423.20s)
each person and really try to help them
[57:05] (3425.24s)
out and create whatever that is. It
[57:07] (3427.72s)
might be different for different people
[57:09] (3429.28s)
and trying to unblock them so they can
[57:10] (3430.96s)
really flourish. And I thought well, if
[57:13] (3433.56s)
that's what I really want to do, I don't
[57:15] (3435.16s)
have to start a new company. I'll just
[57:17] (3437.72s)
do that part. So, I started exploring
[57:20] (3440.24s)
like mentoring people. Um I did a little
[57:23] (3443.16s)
bit of coaching. I don't do anymore and
[57:25] (3445.32s)
making videos on YouTube, writing, um
[57:28] (3448.12s)
all of that to see how how would I
[57:31] (3451.84s)
find the best way to support other
[57:33] (3453.28s)
people?
[57:34] (3454.32s)
And on on YouTube and on LinkedIn you
[57:36] (3456.08s)
have been sharing a lot of your
[57:37] (3457.40s)
learnings, your observations. What what
[57:39] (3459.44s)
pushed you to to just start sharing way
[57:42] (3462.20s)
more of that than before? Like I I think
[57:44] (3464.12s)
you started to do this publicly after
[57:45] (3465.60s)
you left Facebook. I was actually
[57:47] (3467.28s)
writing a blog about this.
[57:49] (3469.57s)
>> [laughter]
[57:50] (3470.28s)
>> So I actually just hit 100k subscribers
[57:53] (3473.76s)
on YouTube like last week. Thank you. Um
[57:57] (3477.16s)
and I was reflecting
[57:59] (3479.24s)
I almost gave up doing YouTube because
[58:03] (3483.60s)
I was really not comfortable being seen
[58:05] (3485.60s)
in public. And I I I've been thinking a
[58:08] (3488.00s)
lot about this like my grandma's from
[58:10] (3490.12s)
North Korea. She escaped during the war
[58:13] (3493.36s)
and in that culture like you are you do
[58:16] (3496.60s)
not speak publicly.
[58:19] (3499.80s)
you don't want to be seen because it's
[58:21] (3501.76s)
dangerous. And I think there's
[58:24] (3504.16s)
generations of that still kind of
[58:26] (3506.24s)
installed in me. The the fear of
[58:28] (3508.56s)
speaking up is real. I felt really
[58:31] (3511.24s)
uncomfortable. So I almost stopped doing
[58:32] (3512.84s)
YouTube.
[58:33] (3513.88s)
Uh once one of my videos went viral from
[58:36] (3516.56s)
early on and I felt really
[58:37] (3517.84s)
uncomfortable. But luckily I was talking
[58:40] (3520.20s)
to a mentor of mine and she said, "Hey,
[58:43] (3523.12s)
it's okay to do something that you enjoy
[58:45] (3525.56s)
doing. Just give it a shot." So then I I
[58:48] (3528.48s)
stuck with it. I'm so glad I did.
[58:50] (3530.88s)
Speaking of the thing that is happening
[58:52] (3532.84s)
of course right now AI. You you spoke
[58:55] (3535.24s)
about this on on your YouTube channel as
[58:56] (3536.80s)
well. But from your your vantage point,
[58:59] (3539.92s)
how is AI changing how engineers work,
[59:03] (3543.00s)
how managers work? I do find it really
[59:05] (3545.24s)
interesting how with AI we're seeing
[59:09] (3549.20s)
smaller teams emerge. I know that a lot
[59:12] (3552.80s)
of teams are saying, "Well, we're small
[59:14] (3554.72s)
because of AI."
[59:16] (3556.36s)
But I wonder if it's independent from
[59:19] (3559.40s)
AI. When you're small, you're just more
[59:21] (3561.40s)
efficient. Because WhatsApp did not use
[59:26] (3566.52s)
But we were efficient because we were
[59:28] (3568.92s)
small.
[59:30] (3570.20s)
And I almost feel that
[59:32] (3572.24s)
even today I can't cannot really point
[59:35] (3575.80s)
to too many teams
[59:37] (3577.68s)
that are as small as WhatsApp and have
[59:39] (3579.84s)
that kind of impact. Maybe Anthropic
[59:41] (3581.80s)
might come to mind, but I think even
[59:43] (3583.20s)
they're bigger.
[59:44] (3584.40s)
So I I wonder if if there is a
[59:46] (3586.96s)
maybe just
[59:48] (3588.08s)
going back to basics with all of this.
[59:49] (3589.92s)
Maybe AI allows to do the way most
[59:53] (3593.24s)
companies would have wished they
[59:54] (3594.64s)
operated. Yeah, and I think there's also
[59:57] (3597.00s)
a shift in the mindset. Like I remember
[59:59] (3599.36s)
back in the days
[60:01] (3601.08s)
people when you go to networking events,
[60:03] (3603.08s)
people would brag about, "Oh, like we've
[60:05] (3605.12s)
hired like a thousand new engineers or
[60:08] (3608.36s)
we're growing at X times bigger." And
[60:10] (3610.76s)
that was like a point of brag. And
[60:12] (3612.96s)
investors also thought that was a good
[60:15] (3615.16s)
thing. You need to grow. You need to
[60:16] (3616.76s)
hire more engineer. That was a sign of
[60:18] (3618.88s)
healthy engineering environment. Whereas
[60:21] (3621.56s)
nowadays
[60:23] (3623.20s)
investors actually think smaller is
[60:25] (3625.76s)
better, right? Like they don't
[60:27] (3627.08s)
necessarily push you to hire more
[60:28] (3628.96s)
people. And I think as a byproduct of
[60:32] (3632.40s)
hiring less people and staying lean,
[60:34] (3634.56s)
they have found this new found
[60:36] (3636.64s)
efficiency and they happen to equate it
[60:38] (3638.76s)
with AI. Although AI I think it's clear
[60:41] (3641.64s)
it makes engineers a lot more efficient
[60:43] (3643.88s)
in well
[60:45] (3645.24s)
we think it makes them efficient because
[60:46] (3646.72s)
it can generate a lot of code. You can
[60:48] (3648.48s)
work on more things parallel as
[60:49] (3649.68s)
happening with agents.
[60:51] (3651.48s)
How are you seeing the role of software
[60:53] (3653.16s)
engineers change and also the role of
[60:54] (3654.60s)
engineering managers? Yeah, I mean I
[60:56] (3656.56s)
love AI tools. I use it every day as a
[60:59] (3659.60s)
thought partner. I
[61:01] (3661.68s)
I often ask ChatGPT, "Hey, like be my
[61:04] (3664.68s)
executive coach or be a Harvard-trained
[61:08] (3668.00s)
futurist and then help me find the next
[61:11] (3671.12s)
trends." Or you know, there are there
[61:12] (3672.56s)
are various ways of really using AI to
[61:15] (3675.16s)
its full potential. I feel like
[61:16] (3676.92s)
engineering management is less affected
[61:20] (3680.12s)
by AI because it it requires a lot of
[61:23] (3683.00s)
like people to people like asking
[61:25] (3685.04s)
questions and learning about your
[61:26] (3686.40s)
engineers.
[61:27] (3687.68s)
AI can maybe help you with that, but I
[61:29] (3689.80s)
don't see AI replacing that part.
[61:33] (3693.72s)
But again, because the teams are much
[61:35] (3695.68s)
smaller, if you are the type of
[61:37] (3697.36s)
engineering manager who was doing a lot
[61:39] (3699.24s)
of these like OKR and process and
[61:42] (3702.08s)
writing documentation, a lot of that
[61:44] (3704.36s)
part is going to be gone. And I'm kind
[61:46] (3706.68s)
of glad it it will be gone because I I
[61:49] (3709.08s)
don't think it's really necessary. Yeah,
[61:51] (3711.24s)
for example, a lot of performance
[61:52] (3712.36s)
management of you know, gathering the
[61:53] (3713.64s)
impact, it can probably be done by
[61:55] (3715.52s)
asking agents to gather all these
[61:57] (3717.64s)
things. I remember as an engineering
[61:58] (3718.64s)
manager, I used to go through
[62:00] (3720.60s)
gathering all the work that my engineers
[62:02] (3722.40s)
have done. So on the calibration
[62:04] (3724.52s)
meeting, I could fairly represent them.
[62:06] (3726.00s)
And then turns out that the managers who
[62:07] (3727.64s)
showed up without doing that, I had an
[62:09] (3729.12s)
advantage. But that was not fair for the
[62:11] (3731.00s)
engineers, by the way, right? Maybe
[62:12] (3732.89s)
[clears throat] I will get rid of this
[62:13] (3733.88s)
advantage. Yeah, AI will do a lot of the
[62:16] (3736.76s)
the grunt work, more tedious work that
[62:19] (3739.28s)
maybe engineering managers or even
[62:21] (3741.04s)
software engineers had to do manually
[62:22] (3742.80s)
back in the days. Like we had an
[62:25] (3745.84s)
engineer who was just there to add
[62:27] (3747.60s)
comments.
[62:29] (3749.32s)
And that is something AI can do really
[62:31] (3751.08s)
well. If you had to give career advice
[62:33] (3753.08s)
to a new grad who says, "I would like to
[62:35] (3755.04s)
build a durable career in software
[62:37] (3757.84s)
engineering in this kind of AI native
[62:39] (3759.72s)
world, what would you suggest they focus
[62:43] (3763.24s)
I say foundations.
[62:45] (3765.76s)
You know, tools come and go, languages
[62:48] (3768.20s)
come and go, but foundations don't go
[62:50] (3770.36s)
anywhere. We mentioned that WhatsApp
[62:52] (3772.28s)
that's
[62:53] (3773.48s)
WhatsApp was very small, very efficient.
[62:55] (3775.36s)
What do you think today's AI
[62:58] (3778.68s)
or like AI native startups could still
[63:00] (3780.60s)
learn from WhatsApp? That made WhatsApp
[63:02] (3782.64s)
successful and it probably helped them
[63:04] (3784.64s)
as well. I think of AI so like we went
[63:07] (3787.44s)
through several trends. Like when I
[63:09] (3789.68s)
first got my first internship I ever had
[63:12] (3792.04s)
was a video sharing website and I've
[63:13] (3793.56s)
seen how there were dozens of video
[63:15] (3795.68s)
sharing websites and how the ecosystem
[63:18] (3798.76s)
changed over time. And then I saw what
[63:21] (3801.64s)
what WhatsApp there were dozens of other
[63:23] (3803.44s)
messaging app competitors and how that
[63:26] (3806.56s)
kind of settled down over time. I think
[63:28] (3808.68s)
we're living through something similar.
[63:30] (3810.04s)
There are so many new AI startups and
[63:33] (3813.16s)
new tools and so easy to get distracted
[63:36] (3816.32s)
by all the different options and it can
[63:38] (3818.32s)
feel quite overwhelming. There are too
[63:39] (3819.92s)
many options and you can feel the
[63:42] (3822.32s)
decision paralysis. But really again, go
[63:45] (3825.12s)
back to the core foundation. Think about
[63:47] (3827.64s)
like if you're builder, think about what
[63:49] (3829.16s)
you're building, why you're building. If
[63:50] (3830.96s)
you're learning, think about why you're
[63:52] (3832.40s)
learning, what you want to learn. And if
[63:54] (3834.36s)
you have clear goals of what where you
[63:56] (3836.36s)
want to go, it will really ground you
[63:58] (3838.16s)
because otherwise you're just going to
[63:59] (3839.60s)
be all over the place and you might work
[64:01] (3841.36s)
really hard and end up nowhere. Did I
[64:03] (3843.72s)
understand correctly that you're saying
[64:05] (3845.12s)
that WhatsApp was successful because
[64:08] (3848.12s)
the goal was clear? Jan said no to the
[64:10] (3850.52s)
distractions and all all the ideas, but
[64:12] (3852.88s)
was very thorough in watching, whereas
[64:14] (3854.40s)
all the other competitors, even all the
[64:16] (3856.00s)
messaging apps, they got distracted
[64:18] (3858.32s)
building, "Oh, let's do like all this
[64:20] (3860.04s)
cool video feature. Let's do stories.
[64:21] (3861.52s)
Let's do all of these things." They got
[64:22] (3862.68s)
some traction and they did a lot of
[64:24] (3864.60s)
these things, but WhatsApp was very good
[64:26] (3866.76s)
at doing the core thing well and then
[64:29] (3869.20s)
slowly adding things that
[64:31] (3871.80s)
were value added. Is that a fair
[64:33] (3873.28s)
summary? Yeah. And also I noticed this
[64:36] (3876.04s)
when I started advising and coaching uh
[64:38] (3878.44s)
startup founders as well. And also for
[64:40] (3880.48s)
any engineers who want to join new
[64:42] (3882.92s)
startups, this is great way to evaluate
[64:45] (3885.56s)
new founders. Like some founders, if
[64:47] (3887.60s)
you're the opposite of Jan and say no to
[64:50] (3890.24s)
things,
[64:51] (3891.48s)
um the I call it removing distractions,
[64:53] (3893.60s)
right? You're prioritizing ruthlessly.
[64:56] (3896.64s)
If you're the opposite of that, imagine
[64:58] (3898.44s)
what type of startup you end up. You say
[65:00] (3900.20s)
yes to everything. Maybe it might feel
[65:02] (3902.52s)
really nice as a 20-something-year-old
[65:04] (3904.72s)
if I were to go back in time and I go to
[65:06] (3906.84s)
the founder with all my great ideas and
[65:08] (3908.96s)
he says, "That's a great idea, Jean.
[65:10] (3910.88s)
Let's build it." But imagine like he
[65:12] (3912.68s)
said that to every single idea that I
[65:14] (3914.44s)
had, the company will be all over the
[65:16] (3916.88s)
place. In terms of the long-term growth,
[65:20] (3920.40s)
it's not a very ideal situation. So,
[65:22] (3922.72s)
looking back, what are some kind of like
[65:24] (3924.84s)
pre-AI or not as modern practices that
[65:28] (3928.68s)
you did at WhatsApp that were really
[65:29] (3929.80s)
good that today's very modern AI native
[65:32] (3932.68s)
teams or whoever could benefit from?
[65:34] (3934.76s)
Yeah, uh several things come to my mind.
[65:37] (3937.16s)
I think one of it is
[65:39] (3939.64s)
by having lean teams, you get several
[65:41] (3941.92s)
benefits. You get to remove a lot of the
[65:44] (3944.52s)
distractions in process, and through
[65:47] (3947.20s)
that you get two really incredible
[65:49] (3949.44s)
benefits, which is ownership and the
[65:53] (3953.16s)
the really like the freedom to build
[65:55] (3955.64s)
things.
[65:57] (3957.28s)
Right? Because Jan was always like Jan
[66:00] (3960.12s)
and Brian were always very specific
[66:03] (3963.72s)
what we're building.
[66:06] (3966.08s)
But, how we're building it was up to
[66:09] (3969.04s)
debate, right? I had mentioned earlier
[66:10] (3970.88s)
that the only time
[66:12] (3972.68s)
we did a actual code review was the
[66:14] (3974.64s)
first time I
[66:16] (3976.00s)
made my Git commit, uh Brian reviewed my
[66:18] (3978.56s)
code and asked me a bunch of questions.
[66:20] (3980.04s)
So, Jan and Brian were both like so
[66:22] (3982.64s)
technically adept, they were really
[66:24] (3984.24s)
excellent at doing this. They would ask,
[66:26] (3986.44s)
"We're trying to achieve this. Like,
[66:27] (3987.72s)
what is the problem here? Or what is the
[66:29] (3989.80s)
best way to solve this issue? What are
[66:32] (3992.72s)
like different ways we can approach
[66:34] (3994.68s)
this? Tell me." So, so do I understand
[66:37] (3997.12s)
correctly that of course the small teams
[66:39] (3999.12s)
helped with a lot of things, but then
[66:40] (4000.88s)
having the founders push people they
[66:44] (4004.44s)
hire, especially early on, they almost
[66:46] (4006.68s)
like push them to excellence, right? Is
[66:48] (4008.80s)
it fair to say that by Brian doing that
[66:51] (4011.00s)
super detailed code review with you the
[66:52] (4012.68s)
first time, it it upped your game, and
[66:55] (4015.64s)
later he didn't even have to do
[66:56] (4016.96s)
anything, right?
[66:58] (4018.48s)
Yeah, and there's like multi-fold,
[67:00] (4020.84s)
right? Like, one is to really challenge
[67:02] (4022.76s)
me to think critically, and then I took
[67:06] (4026.16s)
I learned a lot just from that
[67:07] (4027.68s)
conversation. And then also like from
[67:10] (4030.20s)
then on he never checked my code again.
[67:12] (4032.20s)
So I know I am responsible, right? And I
[67:16] (4036.04s)
do believe when you give
[67:18] (4038.00s)
responsibilities to people, people will
[67:20] (4040.44s)
step up. May not everyone.
[67:22] (4042.71s)
>> [laughter]
[67:23] (4043.56s)
>> But most people will. But I think this
[67:25] (4045.08s)
might be a bit underrated. I I wonder if
[67:26] (4046.76s)
we've had a little bit of too
[67:27] (4047.84s)
over-babying
[67:29] (4049.52s)
of engineers. I I remember for a long
[67:31] (4051.60s)
time there was a talk in the you know,
[67:33] (4053.84s)
in the past 5 to 10 years and the as
[67:36] (4056.28s)
engineering managers like, "Well, I have
[67:37] (4057.76s)
a new grad. It will take them months to
[67:40] (4060.00s)
onboard. I need to assign them a mentor
[67:42] (4062.20s)
for at least 6 months, maybe even a
[67:44] (4064.28s)
year." And were we over-babying these
[67:46] (4066.92s)
very capable adults? You know, they're
[67:48] (4068.48s)
adults, right? It even if even if
[67:50] (4070.20s)
they're 18, but they're typically
[67:51] (4071.36s)
20-something because they came out of
[67:53] (4073.32s)
college and they're hungry and they're
[67:54] (4074.68s)
ambitious and
[67:56] (4076.40s)
maybe we don't need to do this much of
[67:58] (4078.80s)
Always. Yeah, I think as long as you
[68:01] (4081.16s)
hire smart people, it's kind of like a
[68:03] (4083.44s)
mold, right? If you make a mold too
[68:05] (4085.44s)
small, that's that's the limit of how
[68:08] (4088.52s)
far they will grow. Yeah, if the mold is
[68:10] (4090.80s)
too small, you have to throw away a lot
[68:12] (4092.80s)
of things that could have made excellent
[68:15] (4095.36s)
material. And finally, you're a reader.
[68:18] (4098.52s)
What are some books that you would
[68:19] (4099.64s)
recommend for software engineers or
[68:22] (4102.00s)
people wanting to grow professionally or
[68:24] (4104.00s)
in a personal sense? I love reading
[68:25] (4105.88s)
books. I did
[68:27] (4107.68s)
So while I I challenged myself to do
[68:29] (4109.88s)
nothing, I actually read I I actually
[68:32] (4112.40s)
took a year, but I did read 100 books
[68:34] (4114.56s)
during that time. That was my doing
[68:36] (4116.40s)
nothing. Anyways, it kind of depends on
[68:39] (4119.20s)
what your goals are, but you gave me
[68:40] (4120.72s)
some specific things like for your
[68:43] (4123.00s)
career, I think for me what was really
[68:44] (4124.80s)
helpful was
[68:46] (4126.56s)
What Color Is Your Parachute?
[68:49] (4129.32s)
That helped me really understand my
[68:51] (4131.04s)
strengths and my goals and priorities in
[68:54] (4134.04s)
my career and life. I mentioned the book
[68:56] (4136.20s)
Surrounded by Idiots. I know the title's
[68:58] (4138.04s)
kind of funny, but it's an excellent
[68:59] (4139.76s)
book if you want to learn more about how
[69:01] (4141.92s)
to really communicate and work with
[69:03] (4143.56s)
different people. If you want to
[69:04] (4144.88s)
understand finance, I mentioned earlier
[69:06] (4146.88s)
the Random Walk Down Wall Street. It's a
[69:09] (4149.16s)
great book for understanding how to
[69:10] (4150.96s)
manage your money. Yeah, I I would
[69:12] (4152.64s)
recommend those books to start with.
[69:14] (4154.12s)
>> Any fiction books? Hunger Games was one
[69:16] (4156.36s)
of my favorite books. I I read the whole
[69:18] (4158.24s)
series. I read it as well and I
[69:21] (4161.08s)
I almost like the I like the movies as
[69:23] (4163.12s)
well, but I love the books.
[69:24] (4164.28s)
>> Yeah, yeah. I love the story of like
[69:27] (4167.20s)
this woman overcoming her challenges.
[69:29] (4169.56s)
And everyone else and winning in the
[69:31] (4171.04s)
end. Several times.
[69:33] (4173.05s)
>> [laughter]
[69:33] (4173.76s)
>> Well, Jean, thank you so much.
[69:35] (4175.80s)
Yeah, thank you. Thank you for having me
[69:37] (4177.32s)
on the channel. This was a great
[69:38] (4178.64s)
conversation.
[69:39] (4179.44s)
>> Yeah.
[69:40] (4180.12s)
I hope you enjoyed this rare
[69:40] (4180.96s)
conversation with Jean. One thing that
[69:42] (4182.84s)
stuck with me was Jean's point about why
[69:44] (4184.88s)
WhatsApp had almost no process and why
[69:47] (4187.40s)
it worked. Processes exist for audits,
[69:49] (4189.80s)
for accountability, and for tracking who
[69:51] (4191.52s)
did what. But when you have 30 people
[69:54] (4194.08s)
and everyone can see what everyone else
[69:55] (4195.68s)
is working on, you don't really need a
[69:57] (4197.40s)
paper trail. You just walk over and
[69:59] (4199.12s)
talk. This is a good reminder that most
[70:01] (4201.04s)
processes exist to solve problems that
[70:03] (4203.00s)
are created by scale [music]
[70:04] (4204.44s)
and not by the work itself. found the
[70:07] (4207.28s)
Skype contrast really surprising.
[70:09] (4209.08s)
[music] A thousand engineers, Scrum
[70:10] (4210.72s)
certifications, two-week sprints, and a
[70:12] (4212.52s)
dedicated Scrum Master for every team. I
[70:14] (4214.72s)
was one of them at Skype. And WhatsApp
[70:16] (4216.88s)
with 30 people and zero formal
[70:18] (4218.56s)
methodology was shipping faster and
[70:20] (4220.56s)
growing faster on every metric [music]
[70:22] (4222.20s)
that mattered. This is a much-needed
[70:24] (4224.04s)
reminder that organizational discipline
[70:26] (4226.04s)
and actual shipping speed are just not
[70:28] (4228.28s)
the same thing. And I was in the middle
[70:30] (4230.28s)
of this at Skype and Jean was at in the
[70:32] (4232.48s)
middle of it in WhatsApp. [music]
[70:33] (4233.76s)
Finally, it was interesting, as a former
[70:35] (4235.76s)
manager, to hear how Jean described
[70:37] (4237.36s)
performance reviews as a manager
[70:38] (4238.96s)
herself. She described herself as a
[70:41] (4241.08s)
lawyer representing her clients. As in,
[70:43] (4243.20s)
she doesn't control the promotion, she
[70:44] (4244.88s)
just makes the case. And the engineers
[70:47] (4247.08s)
who had the easiest time getting
[70:48] (4248.24s)
promoted were not necessarily the best
[70:50] (4250.16s)
engineers. They were the ones who made
[70:52] (4252.12s)
their work visible. They posted about
[70:53] (4253.84s)
their launches in the internal Facebook
[70:56] (4256.00s)
workplace. They engaged in comments,
[70:57] (4257.88s)
answer questions publicly, and the
[70:59] (4259.36s)
managers in those calibration rooms are
[71:00] (4260.96s)
making decisions about people that they
[71:02] (4262.88s)
might have never worked with directly.
[71:04] (4264.68s)
So, visibility is not just vanity.
[71:06] (4266.72s)
[music] It's how the system inside
[71:08] (4268.96s)
larger companies actually works. This is
[71:10] (4270.92s)
an uncomfortable truth, but I think
[71:12] (4272.68s)
every engineer at a big company needs to
[71:14] (4274.44s)
hear it. If you've enjoyed this podcast,
[71:16] (4276.28s)
please do subscribe on your favorite
[71:17] (4277.52s)
podcast platform and on YouTube. A
[71:19] (4279.76s)
special thank you if you also leave a
[71:21] (4281.00s)
rating [music] on the show. Thanks, and
[71:22] (4282.92s)
see you in the next one.