[00:00] (0.16s)
have not heard any other companies where
[00:01] (1.76s)
the largest outages are taken so
[00:03] (3.84s)
seriously by everyone the whole
[00:05] (5.44s)
leadership chain operational excellence
[00:07] (7.84s)
and dives deep and all these kind of
[00:09] (9.52s)
things tie in together and there's this
[00:11] (11.20s)
culture which you know sometimes people
[00:12] (12.80s)
complain about like new external hires
[00:14] (14.40s)
sometimes Amazon has had these pockets
[00:16] (16.00s)
of a bunch of VPs hired from a different
[00:17] (17.84s)
company or they don't join the on call
[00:19] (19.84s)
don't join this F1 and it's like oh my
[00:21] (21.68s)
god these bad external people who don't
[00:23] (23.36s)
understand how we do things but I would
[00:24] (24.72s)
say yeah the longtime Amazonians like we
[00:26] (26.32s)
would sometimes have for example in AWS
[00:28] (28.24s)
you'd have the engineers mirrors call
[00:30] (30.00s)
where they're all discussing fixes and
[00:31] (31.68s)
then sort of separately like there's
[00:33] (33.04s)
also the VP SVP call of like give us the
[00:35] (35.60s)
updates, what's the current status and
[00:36] (36.96s)
some of this is a scale thing, right?
[00:38] (38.24s)
Like Netflix is down in like half the
[00:40] (40.24s)
country because there's some global
[00:41] (41.68s)
networking outage and we're like talking
[00:43] (43.52s)
to the backbone providers trying to get
[00:45] (45.60s)
them to fix things faster. We're telling
[00:46] (46.96s)
them exactly where the backbone broke
[00:48] (48.32s)
because our alerts are much better than
[00:49] (49.84s)
theirs. I was shocked sometimes that the
[00:51] (51.68s)
connections that you have and the data
[00:53] (53.36s)
you have sometimes are so
[00:54] (54.80s)
mind-bogglingly big. What is it like to
[00:57] (57.20s)
work at Amazon as a software engineer?
[00:59] (59.36s)
Dave Anderson was an engineering manager
[01:01] (61.04s)
and director of engineering at Amazon
[01:02] (62.48s)
for more than 12 years. He's now retired
[01:04] (64.88s)
and so can speak with more candid than
[01:06] (66.64s)
usual. In our conversation, we cover
[01:09] (69.44s)
engineer and manager career levels at
[01:11] (71.28s)
Amazon from L4 to L10, the interview
[01:13] (73.92s)
process for engineers and Amazon's
[01:15] (75.52s)
unique bar raiser interview, Amazon's
[01:17] (77.60s)
infamous PIP/underperformer target and
[01:20] (80.24s)
how performance management actually
[01:21] (81.92s)
plays out on call and incident
[01:23] (83.92s)
management inside Amazon and many more
[01:26] (86.16s)
topics. If you're interested in
[01:27] (87.92s)
understanding how Amazon works
[01:29] (89.28s)
differently than most big tech companies
[01:31] (91.12s)
and why startups often have success in
[01:32] (92.96s)
hiring engineers and engineering leaders
[01:34] (94.48s)
from Amazon, this episode is for you. If
[01:36] (96.96s)
you enjoy the show, please subscribe to
[01:38] (98.32s)
the podcast on any podcast platform and
[01:40] (100.24s)
on YouTube.
[01:42] (102.00s)
Dave, welcome to the podcast. Thanks.
[01:45] (105.04s)
Glad to be here. So, you've you've
[01:47] (107.20s)
worked at Amazon as an engineering
[01:49] (109.36s)
manager as a how is Amazon called? SDM.
[01:52] (112.64s)
SDM. Yeah. Yeah. I started off as an SDM
[01:54] (114.96s)
and then went up that engineering
[01:56] (116.64s)
management chain. You were you're a
[01:59] (119.36s)
software development manager. Before we
[02:02] (122.08s)
get into what it's like to work as a dev
[02:04] (124.48s)
manager there, you you manage and work
[02:07] (127.28s)
with a lot of software engineers, what
[02:09] (129.60s)
is it like to be a software developer
[02:11] (131.60s)
and what levels does Amazon have for for
[02:13] (133.92s)
devs and also for managers and how do
[02:15] (135.76s)
they overlap? Yeah, the the the numbers
[02:18] (138.72s)
overlap except for um so when you get
[02:21] (141.12s)
hired out of college like the the entry
[02:22] (142.80s)
level that lasts like a year and a half
[02:24] (144.48s)
to sometimes more years uh is level
[02:27] (147.68s)
four. That's for whatever reason we
[02:29] (149.52s)
start at level four for engineers. Uh
[02:31] (151.52s)
then there's level five which is like
[02:33] (153.28s)
the beginnings of middle level engineer
[02:35] (155.04s)
which equates to the first level of SDM.
[02:39] (159.12s)
Uh Amazon technically has SDM at level
[02:42] (162.08s)
five and six. Level five is more rare
[02:44] (164.32s)
and it's more for you know you almost
[02:46] (166.08s)
want to call it junior except no one
[02:47] (167.20s)
would want to call it junior SDM because
[02:48] (168.64s)
it'd be a little insulting. Um that's
[02:50] (170.72s)
actually where I started. Um so there's
[02:53] (173.12s)
level five and then level six is what
[02:55] (175.04s)
you might what called a senior software
[02:57] (177.12s)
engineer uh or a software development
[02:59] (179.68s)
manager uh which is like the software
[03:02] (182.72s)
development manager level six is the
[03:04] (184.24s)
most common management position where
[03:06] (186.56s)
you manage a two pizza team you you own
[03:09] (189.44s)
your space and uh you usually don't have
[03:11] (191.68s)
managers underneath you uh and that's
[03:14] (194.16s)
senior software engineer which is where
[03:15] (195.68s)
I would say a lot of software
[03:17] (197.52s)
engineering careers end as in that's a
[03:20] (200.00s)
senior position you're uh you could uh
[03:23] (203.44s)
almost equate it to like a level six
[03:25] (205.44s)
manager has a level six engineer in a
[03:27] (207.84s)
perfect perfectly designed team which is
[03:29] (209.92s)
a level six engineer would sort of be
[03:31] (211.44s)
the head engineer for a team and then uh
[03:34] (214.80s)
you hit level seven at Amazon which is
[03:36] (216.72s)
principal software engineer uh and they
[03:40] (220.40s)
would theoretically be over a group of
[03:42] (222.64s)
teams the lead you know sort of
[03:44] (224.96s)
directing you know and as you could
[03:46] (226.80s)
imagine as you get to like larger groups
[03:49] (229.04s)
you get to more broad ownership. So, uh
[03:54] (234.24s)
a principal engineer would wouldn't be
[03:55] (235.92s)
necessarily focused on one system
[03:57] (237.36s)
because you're theoretically leading
[03:59] (239.04s)
across multiple teams. So, you get more
[04:00] (240.64s)
and more architecture, you know, bigger
[04:02] (242.08s)
scale designs, things like that. Um and
[04:04] (244.48s)
that equates to level seven manager,
[04:06] (246.56s)
which is senior software development
[04:08] (248.32s)
manager, uh which would be a manager of
[04:10] (250.64s)
managers, the first level where you're
[04:12] (252.40s)
really managing multiple teams. Then you
[04:14] (254.64s)
get to level eight which is uh uh senior
[04:17] (257.68s)
principal engineer uh which is just more
[04:21] (261.52s)
senior bigger orgs bigger scope etc
[04:24] (264.64s)
which equates to director for uh
[04:26] (266.72s)
development
[04:27] (267.88s)
management and then you get up to level
[04:30] (270.00s)
10 because there's no nine for whatever
[04:32] (272.48s)
reason. Um I don't know if I ever got a
[04:34] (274.64s)
straight answer on why there's no nine.
[04:36] (276.00s)
It was like you know this idea of like a
[04:37] (277.36s)
senior director position that never got
[04:38] (278.96s)
created. Um but anyway, you jump up to
[04:41] (281.28s)
10 and you get to distinguish engineer
[04:43] (283.04s)
or uh VP. Um which is sort of, you know,
[04:46] (286.88s)
the pinnacle of where any of us normal
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humans would get to. This episode is
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brought to you by work OS. If you're
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credits every month. That is m o
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d.com/pragmatic. Why do you think it
[06:25] (385.44s)
starts with L4? This is so strange
[06:27] (387.28s)
because at Google, Uber, I think even
[06:31] (391.36s)
potentially Stripe, it all starts at L3.
[06:33] (393.84s)
It's also for a historic reason, but L4
[06:36] (396.48s)
at most companies is is like the
[06:38] (398.00s)
mid-level engineer. And here at Amazon,
[06:39] (399.60s)
it's the entry- level one. And I I don't
[06:41] (401.68s)
even know why like it. It's not like we
[06:43] (403.60s)
don't have different pay scales. So So I
[06:45] (405.60s)
I'm pretty sure if you get to the FC's
[06:47] (407.92s)
that I I don't remember the levels for
[06:49] (409.68s)
the FC workers that there's like one,
[06:51] (411.28s)
two, and three, and like you know that
[06:53] (413.28s)
their leveling uh fulfillment center.
[06:56] (416.08s)
Sorry. Fulfillment centers. So when you
[06:57] (417.36s)
get to the fulfillment centers, of which
[06:58] (418.64s)
I've had very little to do in my career,
[07:00] (420.64s)
so like, you know, once in a while I
[07:02] (422.24s)
touch on something. So I'm pretty sure
[07:03] (423.68s)
that their levels start lower. Um,
[07:07] (427.12s)
and if you get to a few weird positions
[07:10] (430.16s)
like uh QA was a position which I don't
[07:13] (433.52s)
know exists anymore. It existed and then
[07:16] (436.00s)
didn't and then existed again and
[07:17] (437.36s)
didn't. So I don't know what the current
[07:18] (438.72s)
status is. It was a quality assurance
[07:20] (440.48s)
tester which is like a non not like QAE
[07:23] (443.12s)
which is quality assurance engineer. uh
[07:25] (445.04s)
they would be level three. So there's
[07:26] (446.88s)
like this weird quirks where you'd touch
[07:28] (448.88s)
level three once in a while, but for the
[07:30] (450.24s)
most part, yeah, Amazon just starts off
[07:31] (451.92s)
at level four. Um no idea why they
[07:34] (454.08s)
decided their numbering schemes needs to
[07:35] (455.68s)
start there. And how easy or or
[07:38] (458.64s)
difficult are promotions? I think you
[07:41] (461.12s)
kind of alluded a little bit to that
[07:42] (462.48s)
when you said that a lot of people get
[07:44] (464.64s)
stuck at L6, which is the senior level.
[07:47] (467.20s)
Yeah, I would I would say the like four
[07:49] (469.44s)
to five for engineers isn't too bad at
[07:52] (472.48s)
all or or or for just about any
[07:54] (474.00s)
position. I mean, you know, there's
[07:55] (475.04s)
level fours for uh some other roles. Um
[07:58] (478.08s)
but, uh I would say that's not too hard.
[08:00] (480.24s)
If you're competent, you complete your
[08:02] (482.00s)
projects on time, you hit like two years
[08:04] (484.24s)
in or so on and most teams and they're
[08:06] (486.48s)
just going to end up promoting you.
[08:07] (487.76s)
Like, hey, look, they because the idea
[08:09] (489.20s)
of like a level four is sort of like you
[08:12] (492.16s)
need assistance. You know, imagine
[08:13] (493.20s)
hiring someone straight out of college.
[08:14] (494.48s)
Like there's very few college hires who
[08:17] (497.28s)
are competent enough to be handed a
[08:19] (499.52s)
project and actually work on it. It's
[08:21] (501.12s)
just just think about how you were in
[08:22] (502.96s)
college. like you you finished, are you
[08:24] (504.32s)
really ready to work on like large scale
[08:25] (505.84s)
work now? Like someone's sitting over
[08:27] (507.28s)
your shoulder helping you, explaining
[08:28] (508.96s)
what you're doing wrong, like don't
[08:30] (510.48s)
forget to test before you push. Um so
[08:34] (514.32s)
level five is sort of like you can be
[08:36] (516.00s)
trusted to work on a project, not a huge
[08:37] (517.76s)
project, but a decent sized project at
[08:40] (520.40s)
the start of level five at least um on
[08:42] (522.48s)
your own. So hopefully after a year and
[08:45] (525.28s)
a half or two years of work, you've
[08:46] (526.64s)
repeatedly demonstrated that you can
[08:47] (527.84s)
work on your own. So you're promoted to
[08:49] (529.20s)
level five. Past that you get to a lot
[08:51] (531.60s)
harder. So there's this very detailed
[08:54] (534.00s)
document of the criteria for what's the
[08:57] (537.20s)
difference between level four to five,
[08:58] (538.88s)
five to six, six to seven for both for
[09:00] (540.80s)
every position at Amazon actually like
[09:02] (542.40s)
there's a big archive in SharePoint last
[09:04] (544.80s)
I saw um which was uh just the detailed
[09:08] (548.88s)
of like at level five you and it
[09:12] (552.00s)
describes it in fairly good detail. Um I
[09:14] (554.80s)
mean hand wavy as you could imagine in
[09:16] (556.48s)
terms of uh a a project with the scope
[09:19] (559.76s)
of a team, a project with the scope of
[09:21] (561.52s)
multiple teams. Yeah. Um so those are
[09:24] (564.72s)
actually what's mostly used when
[09:26] (566.56s)
managers uh go to write promotions. So
[09:29] (569.16s)
Amazon's in comparison to a lot of
[09:31] (571.28s)
companies. Amazon has a very document
[09:33] (573.20s)
culture. I mean for for everything
[09:35] (575.76s)
including projects, the famous six
[09:37] (577.92s)
pagers, right? Yeah. Yeah. And so you
[09:40] (580.16s)
write a famous six-pager for promotions
[09:42] (582.08s)
as well. And so uh oh even for
[09:44] (584.00s)
promotions for even for promotions and
[09:45] (585.92s)
it's actually um I would say one of the
[09:48] (588.64s)
aspects that's the hardest for managers
[09:50] (590.40s)
and one of the big differentiators
[09:52] (592.08s)
between a having a good manager and not
[09:53] (593.92s)
is and one of the reasons you want a
[09:56] (596.08s)
manager who can write is that your
[09:58] (598.96s)
promotion document is the thing that
[10:01] (601.04s)
gets you promoted or not. Um I just
[10:03] (603.60s)
contrast that quickly with I I worked at
[10:05] (605.28s)
face Facebook for a bit meta now. Um,
[10:08] (608.96s)
and I remember being in OR there and a
[10:11] (611.28s)
manager said like, "Oh, my engineer is
[10:12] (612.72s)
so good. They really need to be promoted
[10:13] (613.84s)
to level six." And a couple people said,
[10:15] (615.12s)
"Yeah, yeah. Are you sure?" Like, "Yeah,
[10:16] (616.56s)
I remember. I heard they were good."
[10:18] (618.08s)
Like, "Yeah, they're good." Okay, sounds
[10:19] (619.76s)
good. Moving on. And flabbergasted. I
[10:22] (622.56s)
was just like, my mind was blown. I'm
[10:24] (624.16s)
like, are you kidding me? Um because at
[10:26] (626.24s)
Amazon, what you you have is this um
[10:28] (628.48s)
document which has like a lot of
[10:29] (629.76s)
required fields where you have to
[10:31] (631.84s)
explain
[10:33] (633.52s)
um like write this big narrative. First
[10:36] (636.88s)
of all, there's some like normal fields
[10:38] (638.56s)
like how long is their time in position?
[10:40] (640.56s)
Uh which again is like a criteria
[10:42] (642.40s)
they'll look at and say like oh only 13
[10:44] (644.88s)
months that's not very long. Are you
[10:46] (646.16s)
really sure you can proponent them that
[10:47] (647.28s)
fast? Like this it's a very uh um it's a
[10:51] (651.04s)
hard process to get through let's say
[10:53] (653.52s)
but then you have to write this
[10:54] (654.40s)
narrative multi-page narrative
[10:55] (655.60s)
explaining how they meet the criteria of
[10:57] (657.52s)
the next level. So you know everyone has
[10:59] (659.44s)
the doc of here's what a level six
[11:01] (661.52s)
engineer does. You have to be able to,
[11:02] (662.96s)
you know, read through that, understand
[11:04] (664.16s)
what the the criteria is, and then you
[11:06] (666.32s)
have a narrative where you explain how
[11:08] (668.56s)
they've done that thing at the next
[11:11] (671.16s)
repeatedly. And then you need quite a
[11:14] (674.24s)
few people at the next level or higher
[11:16] (676.72s)
uh saying that they believe you have met
[11:18] (678.56s)
the criteria for the next level. And
[11:20] (680.24s)
they usually have to give some anecdotes
[11:21] (681.60s)
of why they're pretty convinced that you
[11:24] (684.00s)
should be promoted. And
[11:26] (686.20s)
so level four to five is pretty darn
[11:28] (688.72s)
easy. again like you say they completed
[11:30] (690.56s)
this project, this project and this
[11:31] (691.84s)
project and then a few level five
[11:33] (693.44s)
engineers will write down saying, "Yeah,
[11:34] (694.88s)
yeah, totally cool. Thumbs up." Uh by
[11:37] (697.28s)
the time you get to like level seven to
[11:39] (699.12s)
level eight, like it is serious amounts
[11:41] (701.76s)
of documentation, lot you know, years
[11:43] (703.84s)
worth of project completion. You have to
[11:45] (705.92s)
have a lot of VPs and directors on their
[11:48] (708.72s)
dock writing out narrative blocks of
[11:50] (710.96s)
text explaining all the things they've
[11:52] (712.48s)
done with you. Um, and so I would say
[11:55] (715.28s)
that each level you go up, it gets I
[11:58] (718.32s)
would probably say exponentially harder
[11:59] (719.92s)
to get promoted, which is why four to
[12:01] (721.84s)
five is not a big deal. 5 to six is a
[12:03] (723.60s)
pretty big promotion. Six to seven is
[12:06] (726.56s)
super damn hard. Like it's already
[12:08] (728.48s)
hitting like levels of um slightly less
[12:11] (731.52s)
bad for management, which I can explain
[12:13] (733.60s)
in a second why, but um principles have
[12:15] (735.60s)
like additional hurdles they have to get
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past sort of unfairly in my mind. um uh
[12:20] (740.96s)
like technical assessments of exactly
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what they've done by multiple principles
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who seem to have incentive to say no.
[12:27] (747.20s)
It's it's really tough. Um and so uh um
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and I would
[12:33] (753.08s)
say in ways that makes it uh have the
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wrong incentives for managers. One of
[12:38] (758.80s)
the big things for managers is how big
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is their team? Because if you're a level
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if you're a level six manager and over
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time your team scope has grown and you
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eventually got some more managers
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reporting to you and then they get more
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engineers and then you start getting
[12:51] (771.44s)
more managers more engineers and someone
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looks at your team and says uh this dude
[12:56] (776.56s)
has 55 people reporting to him and he's
[12:59] (779.92s)
level six like that makes no sense. We
[13:01] (781.52s)
really need to promote them. So, uh,
[13:03] (783.52s)
managers had this sort of artificial
[13:05] (785.08s)
pressure and incentive to get their
[13:07] (787.28s)
teams larger, which is until recently
[13:09] (789.36s)
was one of the big things almost
[13:10] (790.88s)
everyone did was like a huge part of
[13:13] (793.04s)
your job as a manager to grow your
[13:14] (794.80s)
career was just grow your scope, figure
[13:16] (796.64s)
out more projects, more teams, more
[13:18] (798.80s)
everything else. You could take on Yeah.
[13:20] (800.08s)
more initiative. You can almost
[13:21] (801.12s)
guarantee a promotion if you just grow
[13:22] (802.56s)
your team enough. Yeah.
[13:24] (804.72s)
Incentives are are an interesting
[13:26] (806.40s)
things. Yeah. Taking a step back, we
[13:29] (809.76s)
talked about what it's like to be inside
[13:31] (811.52s)
Amazon and, you know, get get hired in
[13:33] (813.36s)
there. How do you get hired as as an
[13:35] (815.20s)
engineer and and what what is the
[13:37] (817.12s)
interview process? Is it the pretty
[13:38] (818.72s)
standard, you know, the ones we know
[13:40] (820.56s)
what Google and and Meta does, the you
[13:42] (822.56s)
know, algorithical coding interview,
[13:44] (824.96s)
etc. Or are there some unique things for
[13:48] (828.00s)
Amazon? For the most part, I would say
[13:51] (831.04s)
it was very very similar.
[13:53] (833.64s)
Um, in fact, I would say at least when I
[13:56] (836.40s)
went to Facebook, I was there for about
[13:58] (838.32s)
a week and I started getting added to
[13:59] (839.76s)
interview loops because they heard I was
[14:00] (840.72s)
a bar razer at Amazon, which I'll
[14:02] (842.56s)
explain bar razer in a second for anyone
[14:04] (844.08s)
who doesn't know. Um, but I got added to
[14:06] (846.40s)
the loops and I was like, "Oh my god,
[14:07] (847.76s)
like this is the exact same thing."
[14:08] (848.88s)
Like, everyone's doing the same thing.
[14:10] (850.32s)
It's extremely similar. Uh, at least the
[14:12] (852.64s)
groups I was in, we were I was just
[14:14] (854.16s)
added to the loops. It was exactly the
[14:15] (855.52s)
same. Nothing was different.
[14:17] (857.16s)
Um so for the most part with some
[14:20] (860.32s)
exceptions uh interview loops are you
[14:22] (862.96s)
have five people uh you do a phone
[14:25] (865.12s)
screen they they just sort of check
[14:26] (866.80s)
whatever they feel like checking to make
[14:28] (868.08s)
sure that you're like a vaguely
[14:29] (869.68s)
competent candidate and then you have an
[14:31] (871.36s)
interview loop of of usually five people
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of which four is usually on the team or
[14:37] (877.36s)
or closely related to the team and then
[14:39] (879.36s)
one person's the bar raiser who is a
[14:42] (882.08s)
essentially a third party person who can
[14:46] (886.80s)
a higher decision and they also are
[14:50] (890.00s)
running the loop as in um you're you're
[14:53] (893.20s)
putting someone who's very experienced
[14:54] (894.64s)
interviewer on the loop to make sure
[14:56] (896.00s)
that everything goes well, everyone
[14:57] (897.44s)
interviews well, that you the loop is
[14:58] (898.96s)
set up correctly and they have the
[15:00] (900.56s)
ability to say no. Um is like their
[15:03] (903.84s)
special power and so but they cannot say
[15:06] (906.48s)
yes, right? So they're not the hiring
[15:07] (907.92s)
who says yes? Is it the hiring manager?
[15:09] (909.60s)
So a yes requires a yes from the hiring
[15:11] (911.68s)
manager and the bar raiser. So it's like
[15:13] (913.52s)
a a combo decision. So either one of
[15:15] (915.44s)
them can say no and it doesn't happen.
[15:17] (917.04s)
Now in reality once you're there long
[15:19] (919.84s)
enough there's lots of ways to get
[15:21] (921.04s)
around that as both the not not as a
[15:23] (923.12s)
hiring manager. You can't really get
[15:24] (924.16s)
around the bar raiser but um as a bar
[15:26] (926.24s)
raiser a few times I've had hiring
[15:27] (927.36s)
managers saying no. I'm like that's
[15:28] (928.64s)
cool. I'll just pass them off to my
[15:29] (929.84s)
friend here as a hiring
[15:31] (931.96s)
manager like like dude quick quick
[15:34] (934.48s)
before anyone else gets them. Um because
[15:36] (936.80s)
sometimes hiring managers have weird
[15:38] (938.80s)
opinions but um yeah for the most part
[15:40] (940.72s)
it has to be yes from both the hiring
[15:42] (942.08s)
manager and the bar raiser. And then
[15:44] (944.32s)
like I I think we can all imagine you
[15:46] (946.88s)
know the interview you're going through
[15:48] (948.00s)
as as a candidate correct me if I'm
[15:50] (950.40s)
wrong but you know you apply to Amazon
[15:52] (952.00s)
you have the first screening you're
[15:54] (954.08s)
you're through you're you're put on to
[15:56] (956.00s)
maybe you have a technical pre-screening
[15:58] (958.32s)
but then you have the on-site or you
[16:00] (960.48s)
know it might be virtual but you'll
[16:02] (962.00s)
probably have coding architecture hiring
[16:04] (964.24s)
manager may maybe multiples of some of
[16:06] (966.16s)
these you have all these interviews
[16:08] (968.88s)
which we talked with all these people
[16:10] (970.72s)
you probably think you did great on some
[16:12] (972.40s)
you did on terrible some what happens
[16:14] (974.72s)
behind the scenes? How does a debrief
[16:16] (976.64s)
happen? So, the loop is set up and um
[16:20] (980.16s)
there's at no point Amazon's very bad at
[16:22] (982.80s)
consistency across all of Amazon. So,
[16:24] (984.72s)
everything every just about everything I
[16:26] (986.40s)
say has a grain of salt. The only thing
[16:27] (987.84s)
is not is like yes, bar raisers are
[16:29] (989.44s)
always included, but other than that,
[16:30] (990.88s)
like I don't know how many times I've
[16:32] (992.24s)
said this is the way you do things and
[16:33] (993.92s)
someone else is like not at all, you're
[16:35] (995.20s)
totally wrong. Which is funny because
[16:36] (996.72s)
like we're both wrong. It's just um I
[16:40] (1000.24s)
worked in a good number of groups so I
[16:41] (1001.84s)
have fair confidence that I've been in
[16:44] (1004.40s)
you know like five six six groups at
[16:46] (1006.80s)
Amazon so like very different or so. So
[16:48] (1008.72s)
I have a pretty good idea of what's
[16:49] (1009.84s)
consistent. Um you set up a loop with
[16:52] (1012.96s)
for engineers for example you would have
[16:54] (1014.64s)
usually like two coding
[16:56] (1016.44s)
interviews a design interview if it's a
[16:58] (1018.80s)
senior engineer you'd add an
[16:59] (1019.92s)
architecture interview and then um at
[17:02] (1022.00s)
least one leadership leadership
[17:04] (1024.00s)
principle style interview which is like
[17:05] (1025.60s)
checking your leadership skills. Um,
[17:07] (1027.68s)
level four interviews are sometimes a
[17:09] (1029.36s)
little different because they have no
[17:10] (1030.48s)
leadership because they're college hires
[17:12] (1032.40s)
and like it's a little bit of a waste of
[17:14] (1034.96s)
time to spend much time asking them
[17:16] (1036.56s)
about times they've had conflicts with
[17:18] (1038.32s)
co-workers. Like, well, they've had no
[17:19] (1039.68s)
co-workers. They they lived in a dorm.
[17:21] (1041.36s)
Like, I've heard some really weird
[17:22] (1042.56s)
stories when I try to ask those kind of
[17:23] (1043.92s)
questions. Um, yeah, this one time my my
[17:28] (1048.24s)
uh my roommate was was staying up really
[17:31] (1051.12s)
late at night and I had a test in the
[17:32] (1052.32s)
morning. I'm like, "Okay, maybe we just
[17:33] (1053.60s)
move on." Okay. Um so, uh in normal
[17:37] (1057.04s)
interviews with experienced people, you
[17:38] (1058.72s)
you have all these things. Everyone
[17:40] (1060.24s)
takes detailed notes on both the
[17:41] (1061.68s)
questions they ask and the answers they
[17:44] (1064.16s)
give and your interpretation. Like
[17:45] (1065.52s)
there's three parts of a good interview
[17:47] (1067.20s)
notes. And everyone takes notes. You you
[17:49] (1069.76s)
interview independently. You shouldn't
[17:51] (1071.44s)
be talking in between the interviews.
[17:52] (1072.80s)
Like each interviewer does their thing.
[17:55] (1075.04s)
And you take these notes. Then you get
[17:56] (1076.96s)
into what we have as a debrief, a a
[17:59] (1079.28s)
discussion about the candidate. So the
[18:01] (1081.52s)
raiser leads that meeting. You start off
[18:03] (1083.36s)
the meeting for the most part. You start
[18:04] (1084.64s)
it off by saying, "Okay, everyone read."
[18:06] (1086.72s)
So you all start reading and you read
[18:10] (1090.32s)
typical across Amazon, right? A lot of
[18:12] (1092.16s)
meetings. Yeah. Amazon's document
[18:13] (1093.60s)
culture here. It's like you start off
[18:14] (1094.96s)
and you say, "Everyone read." And you're
[18:16] (1096.00s)
going to spend like if you have a
[18:18] (1098.08s)
depending on how long the debrief is,
[18:19] (1099.60s)
half an hour, hour. Like you sit there
[18:21] (1101.28s)
and you read, you read the questions,
[18:22] (1102.72s)
you read the candidates's answers, you
[18:24] (1104.40s)
read the interpretation of those
[18:25] (1105.60s)
answers. Um, and then how the rest of it
[18:30] (1110.00s)
proceeds is very much up to the bar
[18:31] (1111.84s)
raiser. like their org and like how they
[18:33] (1113.84s)
tend to do things. Um I usually would
[18:36] (1116.24s)
say like uh some version of now that
[18:38] (1118.64s)
you've read all the feedback are you
[18:41] (1121.28s)
change because oh sorry part of the uh
[18:43] (1123.08s)
interview notes is what is your vote for
[18:45] (1125.76s)
higher or not hire and so I would sort
[18:48] (1128.48s)
of carefully go around and say like what
[18:50] (1130.48s)
are your current thoughts on higher or
[18:51] (1131.92s)
no hire? Um not like are you I would
[18:54] (1134.32s)
usually not say are you going to change
[18:55] (1135.36s)
your mind because people are reluctant
[18:56] (1136.56s)
to change their mind. I like to say just
[18:58] (1138.24s)
like now what are your current thoughts?
[19:00] (1140.08s)
You've read all the feedback. Um, and
[19:02] (1142.08s)
you sort of get an idea of like where is
[19:03] (1143.92s)
the room uh landing. In I would probably
[19:07] (1147.44s)
say like 90% of the cases it's not all
[19:10] (1150.32s)
yes or all no. I would say almost always
[19:12] (1152.24s)
there's at least one no and one yes even
[19:14] (1154.32s)
on the worst or best candidates. And so
[19:17] (1157.52s)
you preferably then go through and try
[19:20] (1160.16s)
to pressure test both directions of like
[19:22] (1162.32s)
what happens if we don't hire this
[19:23] (1163.68s)
person? Like are we missing something
[19:24] (1164.80s)
really good or what happens if we hire
[19:26] (1166.72s)
this person? What's the big risk that
[19:28] (1168.08s)
we're overlooking? And um the bar
[19:30] (1170.96s)
raiser's main job is to make sure you
[19:32] (1172.64s)
have a really good discussion that you
[19:34] (1174.40s)
don't overly obsess with one little bit
[19:36] (1176.56s)
of feedback. You know, sometimes people
[19:38] (1178.48s)
are like, "Oh my god, I hated that
[19:39] (1179.76s)
answer. Yeah, we need to say no." And
[19:41] (1181.52s)
okay, that was one sentence given by the
[19:44] (1184.64s)
candidate for one of the interviewers
[19:46] (1186.32s)
like let's try not to obsess over that
[19:48] (1188.32s)
too much. But um uh in fact like as what
[19:52] (1192.24s)
you said was um sometimes you almost
[19:54] (1194.80s)
always you'll have a good interview and
[19:56] (1196.80s)
a bad interview. like that's extremely
[19:59] (1199.20s)
common. And so hopefully what the bar
[20:02] (1202.08s)
raiser is doing is making sure you have
[20:03] (1203.60s)
a good discussion over like everything
[20:06] (1206.00s)
seen and and one of the reasons in fact
[20:07] (1207.68s)
what we do to coding interviews almost
[20:09] (1209.84s)
always is because it gives you more
[20:12] (1212.40s)
chances to have good answers. I don't
[20:14] (1214.16s)
know how many times I've heard oh yeah
[20:16] (1216.08s)
they couldn't code their way out of
[20:17] (1217.04s)
paper bag and the other person says they
[20:18] (1218.64s)
did just fine for me and now you have a
[20:20] (1220.56s)
good discussion like why do we get two
[20:23] (1223.44s)
very different answers? What exactly did
[20:25] (1225.12s)
they put as their answer for the other
[20:26] (1226.48s)
person? let's compare them, let's take a
[20:28] (1228.08s)
look, you know, what kind of hints did
[20:30] (1230.00s)
you give, what kind of hints did you not
[20:31] (1231.60s)
give? You know, uh, and you just try to
[20:34] (1234.24s)
arrive at the idea of like, should we
[20:35] (1235.44s)
have them or not?
[20:37] (1237.20s)
So, is it fair to say that the bar
[20:38] (1238.96s)
raiser tries to, you know, make the the
[20:42] (1242.84s)
debrief a bit more fair? You know, even
[20:46] (1246.24s)
out things, compensate for less
[20:48] (1248.24s)
experienced interviewers and and maybe
[20:49] (1249.92s)
some of their biases given the bar
[20:51] (1251.76s)
raiser is it's both an experienced
[20:53] (1253.76s)
interviewer, but is is there like a
[20:55] (1255.20s)
special like training or or or like how
[20:57] (1257.36s)
do you become a bar raiser? Yeah, for
[20:59] (1259.04s)
the most part. Um, so
[21:01] (1261.00s)
you again orwide like there's a lot of
[21:04] (1264.40s)
differences across Amazon. For the most
[21:06] (1266.16s)
part, it's like once you've had a 100
[21:08] (1268.96s)
plus interviews, sometimes 50 you can
[21:11] (1271.60s)
start training, but like usually 100ish
[21:13] (1273.36s)
interviews is when you get to um train
[21:15] (1275.92s)
as a bar raiser. And for the most part,
[21:17] (1277.20s)
the training is you watch a bar raiser
[21:20] (1280.00s)
do their thing for a while and then you
[21:22] (1282.48s)
start to do the bar razor thing while
[21:23] (1283.76s)
they watch and then criticize you behind
[21:25] (1285.12s)
your, you know, later on. um criticize.
[21:28] (1288.24s)
Uh and that can last anywhere from like
[21:31] (1291.76s)
10 interviews to I've I've literally
[21:33] (1293.84s)
I've seen 50 or plus more of like they
[21:36] (1296.00s)
keep watching them and saying they're
[21:37] (1297.12s)
still not ready to do this on their own.
[21:38] (1298.80s)
Um depending on how picky the bar raiser
[21:40] (1300.64s)
is and or how bad the person in training
[21:42] (1302.80s)
is. Um and uh but for the most part I
[21:46] (1306.00s)
would say in a lot of my
[21:48] (1308.52s)
interviews I had more time interviewing
[21:51] (1311.68s)
than everyone else combined. And so a
[21:54] (1314.08s)
lot, you know, a lot of the bar raisers
[21:56] (1316.24s)
do have 400, 500, 600 interviews under
[21:59] (1319.04s)
their belt. And a lot of the
[22:00] (1320.60s)
interviewers have done 10, 15, 20. Like,
[22:04] (1324.32s)
so, so you are not just like a bit more
[22:07] (1327.60s)
trusted, you are wildly more experienced
[22:09] (1329.52s)
than other people on the loop. And so
[22:11] (1331.92s)
very frequently you're both a sort of a
[22:14] (1334.00s)
louder voice in the room in terms of
[22:15] (1335.52s)
people can just trust your opinion on
[22:16] (1336.88s)
these things but
[22:18] (1338.52s)
also I will look at their question the c
[22:21] (1341.60s)
I don't know how many times I've seen
[22:22] (1342.64s)
like the question the candidates's
[22:24] (1344.08s)
answer and then their interpretation I
[22:26] (1346.00s)
think is wrong. I'm like that's actually
[22:27] (1347.84s)
a pretty good answer. I can see where
[22:29] (1349.12s)
they were going with this and you're
[22:30] (1350.24s)
like absolutely wrong and I'm thinking
[22:32] (1352.00s)
that's actually not wrong. It's not it's
[22:34] (1354.16s)
an interesting you know it wasn't the
[22:35] (1355.84s)
approach you liked but I actually see
[22:37] (1357.68s)
where they were going with that and I
[22:38] (1358.80s)
think it was fine. So, um I would say
[22:41] (1361.28s)
that that's sort of where the bar raiser
[22:43] (1363.36s)
like thing comes in is like trying to
[22:45] (1365.60s)
like you said sort of fair like a fair
[22:48] (1368.00s)
evaluation of a candidate. Um because
[22:50] (1370.72s)
we're not trying to hire people who have
[22:52] (1372.32s)
the exact answer you have like that you
[22:54] (1374.56s)
know they want to hear. What you want to
[22:55] (1375.76s)
hire is people who can come up with good
[22:57] (1377.84s)
answers as they work here for many
[22:59] (1379.12s)
years. And so um you know one of the
[23:02] (1382.08s)
things you're looking for for example is
[23:03] (1383.52s)
more of like growth uh you know the
[23:06] (1386.24s)
possibility of growth like is this
[23:07] (1387.68s)
person willing to hear things you know
[23:09] (1389.28s)
sometimes it'll be in the first
[23:11] (1391.04s)
interview they said this by the last
[23:12] (1392.72s)
interview they said this other thing I'm
[23:13] (1393.92s)
like that's interesting like they
[23:15] (1395.04s)
already pivoting their answer sort of
[23:16] (1396.56s)
recognizing what we want to hear that's
[23:19] (1399.12s)
totally fine with me like if they
[23:20] (1400.56s)
recognize ownership is sort of a key
[23:22] (1402.32s)
leadership principle and they sort of
[23:23] (1403.92s)
switch their answers to demonstrate more
[23:25] (1405.52s)
ownership great like that's sort of what
[23:28] (1408.00s)
you want is uh people who and change
[23:29] (1409.52s)
their mind over time. Awesome. So once
[23:33] (1413.04s)
you you get hired and and you start in
[23:34] (1414.96s)
inside of Amazon back in your day, what
[23:37] (1417.84s)
was the the kind of tooling stack? And I
[23:40] (1420.00s)
know it's changing these days because
[23:41] (1421.36s)
I'm hearing more and more of it. It's
[23:42] (1422.72s)
pretty much AWS which is which is super
[23:44] (1424.96s)
unique across all of the companies by
[23:46] (1426.56s)
the way like you know Google doesn't use
[23:48] (1428.16s)
GCP internally and Microsoft is starting
[23:50] (1430.88s)
to use Azure a little bit but not nearly
[23:52] (1432.48s)
as much and you know Meta has their own
[23:54] (1434.16s)
you know thing. Uh so yeah, I would say
[23:56] (1436.56s)
it changed it was changing over time and
[23:58] (1438.56s)
it dep it was actually one of the
[24:00] (1440.56s)
interesting things you know like I was
[24:01] (1441.84s)
mentioning before of people would say
[24:03] (1443.20s)
like you're totally wrong like no one
[24:04] (1444.88s)
used that. Um there were homegrown like
[24:09] (1449.04s)
uh web hosting stuff that we used in um
[24:12] (1452.56s)
marketplace back in the which was like
[24:14] (1454.96s)
originally was Groupa and then was
[24:16] (1456.64s)
multiple versions of of new web hosting
[24:18] (1458.88s)
platforms that that teams were building
[24:20] (1460.80s)
on. And you get over to AWS and they're
[24:22] (1462.96s)
like well obviously everyone's using all
[24:24] (1464.24s)
AWS systems now to build things. And I
[24:26] (1466.40s)
thought it was sort of funny because
[24:27] (1467.28s)
it's like obviously we're all using this
[24:28] (1468.72s)
and like a lot of teams weren't yet. Um
[24:32] (1472.08s)
then you move over to uh IDOs and
[24:34] (1474.24s)
devices and like the mobile stacks and
[24:36] (1476.00s)
that was pretty much using straight up
[24:37] (1477.84s)
like the the development tools that need
[24:41] (1481.12s)
to be used, you know, by Apple and by um
[24:44] (1484.40s)
uh Google in terms of like the Android
[24:46] (1486.08s)
development system and stuff. And so it
[24:48] (1488.00s)
it was highly dependent on what stack
[24:51] (1491.52s)
you're building on. Is it like a
[24:53] (1493.20s)
homegrown web hosting platform or are we
[24:55] (1495.44s)
building, you know, AWS tools or are we
[24:57] (1497.68s)
building devices? And
[25:00] (1500.12s)
um and that's one of the reasons like
[25:02] (1502.64s)
when we talk about like a job I didn't
[25:04] (1504.72s)
mention of the bar racers is also
[25:08] (1508.20s)
um for example I I've seen people
[25:11] (1511.12s)
complain that a candidate only coded in
[25:13] (1513.52s)
Python for their answers like and
[25:16] (1516.96s)
definitely a bar raiser answer for that
[25:18] (1518.96s)
is we don't care what language they code
[25:20] (1520.76s)
in because we every time you change jobs
[25:25] (1525.20s)
you're frequently going to use different
[25:26] (1526.48s)
tools. like it's just there's just such
[25:28] (1528.96s)
a wide selection of tools and again
[25:30] (1530.56s)
there's some homegrown some AWS and you
[25:33] (1533.44s)
want people to pivot to different tools
[25:34] (1534.96s)
you want them to be able to say sure
[25:36] (1536.56s)
I'll flip to Cotlin or something for
[25:38] (1538.56s)
coding because why not this team does it
[25:40] (1540.88s)
um or or you know this software stack
[25:43] (1543.68s)
we've decided to switch and you want
[25:46] (1546.56s)
them for the most part we want engineers
[25:48] (1548.56s)
to be funible to be able to switch over
[25:50] (1550.00s)
and you know you move over to um we had
[25:52] (1552.96s)
some like not C++ but C code inside of
[25:55] (1555.68s)
one of the AWS teams I was on, for
[25:57] (1557.44s)
example. And which as a side note was
[25:59] (1559.68s)
sort of funny because I think I knew
[26:02] (1562.16s)
more C than a lot of the engineers
[26:03] (1563.84s)
because I'm old enough to like that's
[26:05] (1565.20s)
what I was learning in college. Oh yeah.
[26:06] (1566.96s)
And so I remember them saying something
[26:08] (1568.64s)
about like stupid memory management and
[26:10] (1570.24s)
I'm like wait C like cool let me I
[26:13] (1573.28s)
haven't looked at the code in a while.
[26:15] (1575.04s)
That's not a SDM thing at Amazon. Um but
[26:17] (1577.36s)
I'm going to look at this like I'm sort
[26:19] (1579.12s)
of curious because um like you're
[26:21] (1581.52s)
suddenly flipping back into my
[26:23] (1583.12s)
wheelhouse now that you're looking at
[26:24] (1584.24s)
really old code. One thing I've kind of
[26:26] (1586.40s)
heard a few horror stories about Amazon
[26:28] (1588.80s)
is on call and how brutal uh it can be.
[26:32] (1592.72s)
Like what is on call like? Uh how is it
[26:35] (1595.04s)
organized? How important is it? And I
[26:37] (1597.52s)
think we all know or most of us will
[26:39] (1599.44s)
know about Amazon's customer obsession.
[26:41] (1601.28s)
How does that translate onto on call?
[26:43] (1603.92s)
Yeah, I I think it's a super interesting
[26:46] (1606.16s)
and also I would say one of the more
[26:47] (1607.52s)
interesting differences between like
[26:49] (1609.12s)
Facebook and Amazon. Um, so at least in
[26:53] (1613.20s)
the teams I was on at Meta, like there's
[26:54] (1614.96s)
no emergency support. It's, you know,
[26:56] (1616.96s)
there's a completely separate team that
[26:58] (1618.72s)
was dealing with emergencies. I think
[27:00] (1620.08s)
that's true of Google as well. Um, at
[27:02] (1622.08s)
Amazon, it was like it's a core aspect
[27:04] (1624.96s)
of most teams way that they operate is
[27:08] (1628.24s)
if you wrote the code, you're also
[27:09] (1629.84s)
supporting it in production. And not
[27:11] (1631.28s)
just like when we talk about supporting,
[27:13] (1633.04s)
we're talking about literally you're the
[27:14] (1634.88s)
one deciding which fleets host your code
[27:17] (1637.60s)
and which data centers and how
[27:18] (1638.96s)
distributed do you need to be and how
[27:21] (1641.12s)
are you sharding your database and all
[27:22] (1642.64s)
this kind of stuff. It's very, you know,
[27:24] (1644.96s)
the ownership stack is is or the
[27:27] (1647.52s)
ownership leadership principle comes
[27:28] (1648.80s)
into play on a lot of this stuff. But
[27:30] (1650.56s)
that also means that your team in most
[27:33] (1653.36s)
cases sets up a rotation where there's
[27:35] (1655.80s)
someone responding to emergencies in
[27:38] (1658.80s)
real time when emergencies happen.
[27:41] (1661.72s)
And the the philosophy behind it is if
[27:46] (1666.72s)
there's problems with your software, you
[27:48] (1668.56s)
want the problems to land on the person
[27:51] (1671.68s)
people who know it best. And the pain of
[27:56] (1676.48s)
bad software lands on the people who
[27:58] (1678.64s)
have the responsibility to fix it. And
[28:00] (1680.32s)
so there's this when done well, and
[28:04] (1684.24s)
there's a couple caveats there. When
[28:05] (1685.52s)
done well, I really appreciate it. Like
[28:07] (1687.84s)
as a manager of a team, I've been paged
[28:09] (1689.84s)
in way too many times at 1 or 2 a.m.
[28:12] (1692.80s)
because there was a problem. But
[28:15] (1695.24s)
also I believe that managed like you
[28:19] (1699.44s)
know you go into work 2 days later after
[28:22] (1702.08s)
a couple big emergency nights and we say
[28:24] (1704.16s)
okay we're stopping all feature
[28:26] (1706.00s)
developments and we're fixing these
[28:27] (1707.20s)
problems. Right? That's that's like the
[28:29] (1709.04s)
the done well part is it's we are not
[28:31] (1711.84s)
patching this. We are not setting it to
[28:33] (1713.36s)
reboot every night to to fix it. We're
[28:35] (1715.60s)
not letting people get paged at 2 a.m.
[28:37] (1717.36s)
because that's
[28:38] (1718.36s)
ridiculous. So we're going to fix it
[28:40] (1720.64s)
right now. We're going to rewrite this
[28:41] (1721.76s)
code. Whatever. Um, I would say like
[28:44] (1724.12s)
contrast in the negative way. There were
[28:46] (1726.72s)
some systems that Facebook I remember
[28:48] (1728.00s)
people talking about like, "Oh, they've
[28:49] (1729.04s)
been having problems for months." Like
[28:50] (1730.32s)
they're just restarting this thing
[28:51] (1731.68s)
constantly. Some point in time we'll get
[28:53] (1733.60s)
someone to fix it. And I was like,
[28:54] (1734.56s)
"That's interesting because at least the
[28:56] (1736.48s)
teams I ran, we would have fixed that
[28:58] (1738.56s)
thing right away because it would like
[29:00] (1740.96s)
you would lose your mind if your system
[29:02] (1742.48s)
was always having errors like this." Um,
[29:05] (1745.04s)
on the other side, like the the poorly
[29:07] (1747.28s)
run teams where you hear like it's been
[29:09] (1749.36s)
nine months now and everyone is paged
[29:11] (1751.36s)
two or three times a night. Anytime
[29:12] (1752.88s)
they're on rotation, they never sleep
[29:14] (1754.80s)
because they're getting paged every
[29:16] (1756.16s)
single night. That's just a nightmare.
[29:18] (1758.16s)
Like that's just not okay. Things are
[29:20] (1760.00s)
broken there. Like that's that's a
[29:21] (1761.36s)
management problem. Um, I think more
[29:23] (1763.44s)
than anything. So yeah, so the rotations
[29:26] (1766.64s)
usually on most teams, let's say you
[29:27] (1767.92s)
have a team of like seven people,
[29:29] (1769.68s)
seven's a good maybe eight. uh eight
[29:32] (1772.24s)
people. You usually have someone go on
[29:34] (1774.56s)
rotation for a week. And for that week,
[29:37] (1777.36s)
you're responsible for responding to
[29:38] (1778.64s)
emergencies. On most of my teams, that
[29:41] (1781.44s)
would be, let's just say like two times
[29:44] (1784.32s)
in that week, you would be paged
[29:46] (1786.16s)
sometime off hours. A lot of times it's
[29:48] (1788.24s)
9:00 p.m. Uh frequently it's when code
[29:50] (1790.72s)
gets pushed. So if you are pushing your
[29:53] (1793.20s)
code at 10 p.m., which is a common
[29:54] (1794.64s)
enough time of like, hey, we have a
[29:56] (1796.00s)
downtime in traffic. Let's push the code
[29:57] (1797.44s)
and make sure it works. The pager goes
[30:00] (1800.52s)
Um, it's not a big deal. I think when
[30:03] (1803.36s)
you have a team having issues and you're
[30:05] (1805.12s)
getting paid two or three times every
[30:07] (1807.28s)
night, that's just a nightmare. Um, but
[30:09] (1809.68s)
again, most of the time that was fixed
[30:11] (1811.68s)
pretty quickly. Like the engineers
[30:12] (1812.96s)
aren't okay with it. Hopefully the
[30:14] (1814.16s)
manager is not okay with it and then um
[30:16] (1816.80s)
you get it resolved. Yeah. Well, I I
[30:19] (1819.28s)
think there's always a there's always a
[30:21] (1821.04s)
downside of being too close to the
[30:22] (1822.40s)
problems, right? Because totally you do
[30:24] (1824.32s)
have to be responsive. But if it's set
[30:26] (1826.96s)
up in a way that you're actually paged
[30:28] (1828.56s)
for things that truly matter and matter
[30:30] (1830.80s)
to like let's say customers, you're
[30:32] (1832.32s)
you're now going to fix it faster. I I
[30:34] (1834.40s)
think you know we've all used software
[30:36] (1836.40s)
where like there were some annoying bugs
[30:38] (1838.48s)
and you can tell when it's there like
[30:40] (1840.00s)
months later that that is not paging
[30:42] (1842.08s)
anyone inside of they might not even
[30:44] (1844.32s)
know about it or people with and I think
[30:46] (1846.08s)
it's like can you tie responsibility for
[30:49] (1849.12s)
your schedule like in terms of like what
[30:51] (1851.12s)
are you working on next? tie that to the
[30:53] (1853.44s)
responsibility of responding to problems
[30:55] (1855.04s)
and tie that to the responsibility of
[30:56] (1856.48s)
having a good customer obsession and
[30:58] (1858.48s)
that's where it's done well right um as
[31:01] (1861.04s)
I I sometimes have to remind people over
[31:03] (1863.36s)
and over again like I'd hear from an
[31:04] (1864.72s)
engineer like oh yeah that was such a
[31:06] (1866.08s)
hard on call last week and I wasn't
[31:07] (1867.84s)
paying attention as a manager or
[31:09] (1869.28s)
something or maybe I'm the senior
[31:10] (1870.72s)
manager
[31:12] (1872.20s)
um and I'd be like what what do you mean
[31:14] (1874.64s)
like last week and I have to go like
[31:16] (1876.16s)
quickly run a report because uh usually
[31:18] (1878.16s)
manager is not paged into everything
[31:19] (1879.52s)
unless it escalates like if they can't
[31:20] (1880.96s)
fix it within 10 minutes, then I get
[31:22] (1882.72s)
paged or something. Um, and they say,
[31:24] (1884.72s)
"Oh, yeah, we got paged eight times last
[31:26] (1886.24s)
week." And I'm like, "Well, what you
[31:27] (1887.68s)
know, what did you have to fix?" They
[31:28] (1888.64s)
say, "Oh, we haven't had a chance to fix
[31:29] (1889.76s)
it yet." And you're like, "Oh my god."
[31:31] (1891.04s)
Like, dude, why are we working on
[31:33] (1893.12s)
anything else if you're getting paid
[31:34] (1894.32s)
seven times in a week? Like, that's not
[31:35] (1895.76s)
okay. Um, so I usually would say like
[31:39] (1899.04s)
you should only be okay with being on an
[31:40] (1900.80s)
on call rotation if you also have the
[31:43] (1903.04s)
capability of of saying that this is the
[31:45] (1905.36s)
next most important thing we're all
[31:46] (1906.48s)
going to work on.
[31:47] (1907.88s)
So I think it's not fair when you put
[31:50] (1910.64s)
responsibility on people if you don't
[31:52] (1912.32s)
give them the authority to to also
[31:55] (1915.92s)
redirect your resources. You know, those
[31:57] (1917.84s)
things have to come together. And so um
[32:00] (1920.96s)
I would say most teams at Amazon had
[32:03] (1923.52s)
that. I would and certainly there were
[32:05] (1925.44s)
teams where they felt like were helpless
[32:06] (1926.80s)
and just had a terrible time of it. Um
[32:09] (1929.44s)
but well-run teams you would say like
[32:12] (1932.32s)
they're not going to be okay with again
[32:14] (1934.56s)
a a bad operational situation. And then
[32:17] (1937.20s)
when outages happen, what is the
[32:19] (1939.28s)
categorization? I I think there's
[32:20] (1940.80s)
different levels between the severity of
[32:22] (1942.64s)
of the outage, right? So like a page
[32:24] (1944.64s)
comes in, someone decides the on call
[32:26] (1946.88s)
decides that it's if it's an outage and
[32:29] (1949.04s)
then they have to assign a level to it.
[32:31] (1951.68s)
How does that work? Yeah, it and it was
[32:33] (1953.44s)
different across teams a little bit of
[32:35] (1955.44s)
like what the exact severity meant again
[32:38] (1958.00s)
because Amazon hates consistency in all
[32:40] (1960.72s)
ways and for the most part. I love the
[32:42] (1962.40s)
lack of consistency because you can just
[32:43] (1963.76s)
invent things and just tell. One of my
[32:45] (1965.92s)
favorite things as a side note was uh
[32:48] (1968.16s)
someone would say like, you know, that
[32:49] (1969.84s)
they're doing something and I don't like
[32:50] (1970.88s)
it, right? And I would say I would
[32:52] (1972.64s)
quickly go edit our wiki and and write
[32:55] (1975.44s)
in a rule against whatever they just did
[32:57] (1977.04s)
and then say like as you can see here in
[32:59] (1979.36s)
this document, that's not allowed. And
[33:01] (1981.28s)
then they would look at the wiki and
[33:02] (1982.32s)
say, "Well, that's true." Okay. And I'm
[33:04] (1984.16s)
like, I was never called on to the fact
[33:06] (1986.72s)
that I just edited like the rule set or
[33:08] (1988.88s)
the, you know, instructions or the
[33:10] (1990.16s)
step-by-step whatever that I just like
[33:11] (1991.68s)
edited and then showed them the the
[33:13] (1993.12s)
instructions. It was great how people
[33:14] (1994.40s)
would just believe these things. Um,
[33:16] (1996.72s)
that was like a barra razor thing, too.
[33:18] (1998.08s)
It's like that's not allowed. And I am
[33:20] (2000.32s)
the bar raiser, so I would know these
[33:21] (2001.68s)
things. They're like, "Oh, okay. Cool.
[33:24] (2004.24s)
Smart hack." Oh, man. Yeah. With
[33:26] (2006.88s)
confidence, you can get past almost
[33:28] (2008.32s)
everything if you need to. Uh, but yeah.
[33:30] (2010.56s)
So, um, most of the time in most teams
[33:33] (2013.36s)
there's severity one through five. And
[33:35] (2015.28s)
five means we're never going to do fix
[33:36] (2016.88s)
anything because it's not doesn't
[33:38] (2018.80s)
doesn't matter at all. Most things are
[33:40] (2020.00s)
like level three, which is um it's a
[33:43] (2023.12s)
problem. You should work on it someday.
[33:44] (2024.72s)
Uh severity two is severity two and one
[33:47] (2027.28s)
are the ones that would like page
[33:48] (2028.64s)
people, make people pay attention, make
[33:50] (2030.32s)
their phones freak out. Um severity 2
[33:52] (2032.80s)
and one are just emergencies. And for
[33:54] (2034.32s)
the most part, severity 2 is what we're
[33:55] (2035.84s)
used for most things. Severity one is
[33:57] (2037.28s)
like companywide emergency is sort of um
[34:00] (2040.80s)
uh you know, sev one. It's a it's was
[34:03] (2043.20s)
usually like the phrasing for it. um in
[34:05] (2045.84s)
certain groups like uh let's say AWS in
[34:09] (2049.12s)
in SEV 2 is there's a system problem and
[34:12] (2052.24s)
we need to fix it and you'll probably
[34:13] (2053.60s)
let your manager know SE one is usually
[34:16] (2056.24s)
like this is where I um so for a while I
[34:18] (2058.88s)
was on the encroation of manager you get
[34:20] (2060.56s)
paged in instantly for SE ones it was
[34:22] (2062.40s)
like you would have a big less you could
[34:24] (2064.64s)
set up a you know a distribution list
[34:26] (2066.48s)
where a whole bunch of people would get
[34:27] (2067.52s)
alerted for your SE one including maybe
[34:29] (2069.28s)
your VP maybe your SVP um for for AWS
[34:33] (2073.20s)
style SE ones that was frequently like I
[34:34] (2074.96s)
was writing to Jasse right away with a
[34:36] (2076.64s)
list of other SVPs and VPs on the list
[34:38] (2078.72s)
saying we have an AWS outage like blah
[34:40] (2080.64s)
blah blah. So I actually have a really
[34:42] (2082.32s)
interesting story and one of the reasons
[34:43] (2083.84s)
I I asked this we had a senior director
[34:46] (2086.88s)
join from Amazon. He was a senior
[34:48] (2088.72s)
director there and he became the senior
[34:50] (2090.24s)
director of my of my group. Amazon
[34:53] (2093.44s)
doesn't have senior directors. That's
[34:54] (2094.56s)
that level nine thing that doesn't
[34:55] (2095.68s)
exist. Okay. Well, I'm not maybe a
[34:58] (2098.80s)
director. Anyway, people like to invent
[35:00] (2100.32s)
their own titles or or or maybe it was a
[35:02] (2102.48s)
director who became a senior director,
[35:03] (2103.92s)
but he was senior director, which meant
[35:05] (2105.36s)
that he had about like 300 engineers in
[35:07] (2107.92s)
his group and we we had an outage. So,
[35:10] (2110.88s)
my team was payments and the way Uber
[35:13] (2113.36s)
did it. So, Amazon SE1 was L5 like first
[35:16] (2116.32s)
L1 was the smallest and L3 is internal.
[35:19] (2119.12s)
L4 is like, you know, it's just the
[35:21] (2121.84s)
opposite. So, L5 is the highest out is
[35:24] (2124.24s)
is when it's actually companywide. So it
[35:26] (2126.32s)
impacts the whole product. We're
[35:28] (2128.00s)
actually losing revenue or something
[35:29] (2129.28s)
like that. And then there's like levels
[35:31] (2131.28s)
there's like low, high, medium or high.
[35:33] (2133.52s)
And my team pretty frequently had an L5
[35:36] (2136.32s)
low outage which meant that payments
[35:38] (2138.16s)
were down in one part of the world.
[35:40] (2140.40s)
Typically we had a third party issue.
[35:43] (2143.04s)
Let's say um one of the payment methods
[35:45] (2145.44s)
in India just stopped working because
[35:47] (2147.28s)
the thing had an outage there. And it it
[35:49] (2149.76s)
was pretty routine for us. like this is
[35:51] (2151.60s)
something that we couldn't do anything
[35:52] (2152.88s)
about beyond alert the uh you know the
[35:55] (2155.60s)
third party and so we were just having
[35:58] (2158.24s)
one of these things and we always have a
[35:59] (2159.68s)
call it's kind of routine like
[36:00] (2160.80s)
everyone's just sitting there bored
[36:02] (2162.00s)
saying okay have you guys page the third
[36:04] (2164.00s)
party yeah we page suddenly my the this
[36:07] (2167.68s)
new director is in the call saying all
[36:10] (2170.48s)
right what's the situation can I help
[36:12] (2172.72s)
anyone here and I'm like it's like Jeff
[36:15] (2175.52s)
what are you doing here he's like it's
[36:16] (2176.96s)
it's a this is this is L5 right it's the
[36:18] (2178.96s)
highest one right like it's
[36:21] (2181.20s)
I was like, "Yeah, it is." He's like,
[36:22] (2182.80s)
"Okay, I'm here. Use me. How can put out
[36:25] (2185.76s)
the fire?" So then I learned that on at
[36:28] (2188.72s)
Amazon VPs jump onto SE one. And we were
[36:31] (2191.28s)
just having really casual like for us
[36:33] (2193.20s)
like an off five wasn't really like
[36:35] (2195.28s)
this. We just knew this was just our
[36:37] (2197.12s)
regular Ali because we had to follow we
[36:38] (2198.96s)
we couldn't do anything about it. But it
[36:40] (2200.56s)
just made me realize how Amazon's taking
[36:42] (2202.96s)
it way more seriously. And this this
[36:44] (2204.72s)
senior like jumped out of some important
[36:46] (2206.80s)
meeting to go there and for me it was
[36:48] (2208.24s)
just like whoa. like this is this is new
[36:50] (2210.48s)
for me. Yeah. So it it just shows I it
[36:53] (2213.68s)
gave me a sense of how serious Amazon
[36:56] (2216.56s)
seems and I have not seen any other
[36:58] (2218.16s)
companies or have not heard any other
[36:59] (2219.60s)
companies where the the the largest
[37:02] (2222.32s)
outages are taken so seriously by by
[37:05] (2225.12s)
everyone the whole man the whole
[37:06] (2226.48s)
leadership chain. So I think that's you
[37:08] (2228.16s)
know just it's a bit of a story there.
[37:10] (2230.40s)
Operational excellence and dives deep
[37:13] (2233.12s)
and all these kind of things tie in
[37:14] (2234.80s)
together. So, and and there's this
[37:17] (2237.24s)
culture which you know sometimes people
[37:19] (2239.36s)
complain about like new external hires.
[37:21] (2241.04s)
Sometimes Amazon has had these pockets
[37:22] (2242.64s)
of like uh you know a bunch of VPs hired
[37:26] (2246.40s)
from a different company where where
[37:27] (2247.92s)
they they don't join the on call you
[37:29] (2249.76s)
know don't join this F1 and it's like
[37:31] (2251.44s)
this like oh my god like these these bad
[37:34] (2254.32s)
external people who don't understand how
[37:35] (2255.92s)
we do things. Um, but I would say yeah,
[37:37] (2257.76s)
the longtime Amazonians like we would
[37:39] (2259.20s)
sometimes have for example in AWS you'd
[37:41] (2261.28s)
have the engineers call where they're
[37:44] (2264.00s)
all discussing fixes and then sort of
[37:46] (2266.40s)
separately like there's also the VP SVP
[37:48] (2268.72s)
call of like give us the updates what's
[37:50] (2270.32s)
the current status because and some of
[37:53] (2273.12s)
this is a scale thing right like Netflix
[37:55] (2275.20s)
is down like we're sitting there and
[37:56] (2276.88s)
like Netflix is down in like half the
[37:58] (2278.56s)
country because there's some global
[38:00] (2280.08s)
networking outage and we're like trying
[38:01] (2281.88s)
to also Amazon scale you know we're
[38:04] (2284.56s)
we're talking to the backbone providers
[38:07] (2287.12s)
trying to get them to fix things faster.
[38:08] (2288.64s)
We're telling them exactly where the
[38:09] (2289.76s)
backbone broke because our alerts are
[38:12] (2292.00s)
much better than theirs. Like there I
[38:14] (2294.24s)
was shocked sometimes at like uh um the
[38:17] (2297.76s)
connections that you have and the data
[38:19] (2299.52s)
you have sometimes are so
[38:21] (2301.00s)
mind-bogglingly big. You know, it's like
[38:23] (2303.12s)
you you see the news articles coming out
[38:24] (2304.64s)
and it's like like we know about the
[38:27] (2307.28s)
backbone outage in whatever place
[38:29] (2309.04s)
because we can see exactly where it is
[38:30] (2310.40s)
in the network based on all of our
[38:32] (2312.08s)
alerts and alarms and all that kind of
[38:33] (2313.44s)
stuff. But yeah, it was very expected
[38:36] (2316.56s)
that depending on the scale of your
[38:39] (2319.12s)
outage or issue going on that your VP
[38:41] (2321.12s)
would be joining in the call if not not
[38:43] (2323.68s)
usually to run it unless it's really
[38:45] (2325.76s)
important but definitely like um
[38:49] (2329.68s)
what you want and which is what you
[38:51] (2331.44s)
usually have is we need X done and you
[38:54] (2334.32s)
need it done right now. And that's the
[38:57] (2337.52s)
kind of thing a VP or director, whoever,
[38:59] (2339.20s)
you know, whoever the really senior
[39:00] (2340.56s)
person is is around for is um like, oh,
[39:03] (2343.60s)
okay, you need, you know, we need a the
[39:05] (2345.60s)
kind of thing that would come up
[39:06] (2346.48s)
sometimes is like, oh my god, we need
[39:08] (2348.48s)
100 extra servers right now. You know,
[39:10] (2350.48s)
these big beefy things. We need them
[39:12] (2352.00s)
provisioned immediately in Dublin. And
[39:14] (2354.88s)
they say, okay, we're, you know, I'm I'm
[39:16] (2356.40s)
calling that, you know, the head of EC2
[39:18] (2358.24s)
right now on the phone and we're going
[39:19] (2359.28s)
to get that, you know, some engineer to
[39:21] (2361.20s)
allocate those to us in the next about
[39:22] (2362.72s)
two minutes. And so boom boom boom they
[39:24] (2364.32s)
get this thing done and a bunch of
[39:25] (2365.36s)
servers are turning on um
[39:28] (2368.52s)
because the person can do the direct
[39:30] (2370.96s)
call and just say I need this right now
[39:32] (2372.24s)
and they say got it. So I think that
[39:34] (2374.80s)
that's useful. I mean it it sounds to me
[39:36] (2376.80s)
if you you know like this might sound a
[39:38] (2378.40s)
bit like cheeky but if if you get it to
[39:39] (2379.84s)
Amazon and usually being on an outage
[39:42] (2382.64s)
you don't really want to be on an outage
[39:43] (2383.84s)
as an engineer but if you're in one of
[39:46] (2386.16s)
these outages and it already happened
[39:48] (2388.16s)
it's kind of an awesome opportunity.
[39:50] (2390.40s)
Where else would you be able to even
[39:52] (2392.64s)
experience this? I have heard from I I
[39:55] (2395.44s)
believe like if you're talking about
[39:57] (2397.12s)
like um building skills in terms of like
[40:00] (2400.52s)
running running software, not like just
[40:02] (2402.88s)
writing software, but like running
[40:04] (2404.48s)
software, right? If you want to go to a
[40:05] (2405.68s)
startup and run software, um you hire
[40:08] (2408.96s)
like if you get an Amazon engineer who's
[40:10] (2410.72s)
been in the thick of things sometimes,
[40:12] (2412.00s)
it's like you're not just good at
[40:13] (2413.60s)
writing software, but you've seen what
[40:16] (2416.56s)
happened. like Twilio calls you know I
[40:20] (2420.00s)
got on the phone with you know the CTO
[40:22] (2422.20s)
Twilio company you know it's like we're
[40:24] (2424.72s)
having this issue it's in this specific
[40:26] (2426.40s)
region and we're like we have engineers
[40:28] (2428.08s)
in the room you know we're talking to
[40:29] (2429.68s)
them on the phone in a separate phone
[40:31] (2431.04s)
call from the engineers talking about
[40:32] (2432.32s)
whatever test they're running on
[40:33] (2433.60s)
whatever trace routes and figuring
[40:34] (2434.96s)
things out and like the skills you build
[40:38] (2438.24s)
in terms of product and what can we ask
[40:39] (2439.92s)
the customer to do can we ask them to
[40:41] (2441.36s)
test something can we get access to
[40:42] (2442.72s)
their host so we can try something out
[40:44] (2444.80s)
um it's it's such a cool experience I
[40:46] (2446.88s)
think it sucks that it's happening at 3
[40:48] (2448.88s)
a.m. sometimes, but I would say most of
[40:51] (2451.68s)
the time it's not because it usually
[40:53] (2453.36s)
problems happen when you push
[40:54] (2454.92s)
code. Just the way things work. Uh uh
[40:58] (2458.48s)
funny thing of like the amount of pages
[41:00] (2460.08s)
that happen when Amazon historically did
[41:02] (2462.24s)
lock down sometime around Thanksgiving
[41:03] (2463.92s)
time. You would just stop pushing code
[41:05] (2465.20s)
changes out in this exponential drop off
[41:07] (2467.84s)
in pages because changes are what breaks
[41:10] (2470.80s)
things in the world. Um, but yeah, I
[41:13] (2473.20s)
think the experience I think you you
[41:15] (2475.36s)
talk two years later about like, oh, we
[41:16] (2476.88s)
had such a good time on the team
[41:17] (2477.92s)
together, didn't we? You always talk
[41:19] (2479.12s)
about the outages and stuff because that
[41:20] (2480.40s)
was the exciting times at work. Yeah.
[41:22] (2482.88s)
Yeah. One interesting thing about Amazon
[41:26] (2486.08s)
that's that's also just Amazon was
[41:28] (2488.00s)
famous for and people get surprised for
[41:29] (2489.36s)
is the frugality. Yeah. Can can you tell
[41:31] (2491.92s)
me what you know what what engineers
[41:34] (2494.72s)
experience or what did engineers
[41:36] (2496.08s)
complain to you who joined from like
[41:38] (2498.24s)
companies that were a bit more generous?
[41:40] (2500.08s)
You know, like I I worked at Uber. I
[41:41] (2501.44s)
visited friends at Google Meta. You have
[41:43] (2503.28s)
like these like, you know, just kind of
[41:44] (2504.56s)
standard perks like like lunch uh at at
[41:47] (2507.84s)
I think at Uber we had like these
[41:49] (2509.36s)
vending machines. You could you could
[41:50] (2510.80s)
get like whatever like mice free stuff
[41:52] (2512.64s)
from the vending machines. Yeah. Yeah.
[41:53] (2513.84s)
Our vending machines hardware,
[41:56] (2516.32s)
you know, they got a profit from our
[41:57] (2517.76s)
vending machines. It wasn't even like
[41:59] (2519.36s)
cost. Oh my god. Yeah. Um Yeah. No, no.
[42:02] (2522.24s)
Uh I think
[42:04] (2524.04s)
Amazon and there's there is some
[42:06] (2526.24s)
interesting points that um sometimes
[42:08] (2528.08s)
we've had these arguments of like oh my
[42:09] (2529.44s)
god like the amount of money that you
[42:10] (2530.80s)
pay engineers why can't you be a little
[42:12] (2532.32s)
bit more generous and some of it comes
[42:14] (2534.16s)
down to fridity like the stupid frugal
[42:17] (2537.44s)
thing. Um this is like just this
[42:19] (2539.36s)
ridiculous stupid level of frugality. Um
[42:22] (2542.48s)
but sometimes they do there is
[42:24] (2544.40s)
interesting explanations. Um because
[42:26] (2546.08s)
Amazon has fulfillment centers and we
[42:28] (2548.16s)
have the majority of Amazon's workers
[42:31] (2551.08s)
are very very low margin per person uh
[42:34] (2554.88s)
you can't afford to and then they talk
[42:36] (2556.88s)
about for I think legal and other
[42:38] (2558.96s)
reasons that they can't offer different
[42:40] (2560.96s)
benefits and different things perks to
[42:44] (2564.08s)
corporate workers versus fulfillment
[42:45] (2565.84s)
workers whether or not that's legal
[42:47] (2567.04s)
whether or not that's just like now
[42:48] (2568.64s)
we're at risk for unions forming across
[42:50] (2570.72s)
the world because they're giving unfair
[42:52] (2572.16s)
benefits to corporate workers. Um you
[42:54] (2574.24s)
look at something like uh 401k match for
[42:56] (2576.16s)
example was something that you know came
[42:57] (2577.68s)
up was like couldn't we just do a you
[42:59] (2579.52s)
know double the match do a you know 100%
[43:01] (2581.68s)
of whatever and like yeah we could for
[43:03] (2583.28s)
engineers but we definitely can't afford
[43:05] (2585.44s)
that for FC workers because our margin
[43:07] (2587.28s)
there is like
[43:08] (2588.44s)
0.5%. If we give them any more money
[43:10] (2590.72s)
we're losing money on every item Amazon
[43:12] (2592.96s)
sells. Like we have to be very very
[43:14] (2594.08s)
careful. It's super expensive to have
[43:16] (2596.08s)
million workers. I mean for good reason
[43:18] (2598.72s)
like it's just expensive. So, uh, when
[43:21] (2601.76s)
you try to give, you know, try to give
[43:23] (2603.12s)
comparable benefits to corporate workers
[43:25] (2605.12s)
and you have these issues. Um, but then
[43:27] (2607.44s)
you get to just how Amazon operates is
[43:29] (2609.84s)
unless there's a really damn good reason
[43:31] (2611.28s)
to spend the money, we don't do it. And
[43:33] (2613.12s)
that's the same thing for resources. I
[43:35] (2615.12s)
think if you had to
[43:38] (2618.12s)
choose, I guess as a business owner, a
[43:41] (2621.04s)
person who wants a company to be
[43:42] (2622.24s)
successful, you would definitely want
[43:43] (2623.76s)
your company to be frugal. Like that the
[43:45] (2625.36s)
amount of money wasted when I was at
[43:46] (2626.96s)
Facebook, it just blew my mind. I mean,
[43:48] (2628.92s)
just some things are great. It's very
[43:51] (2631.52s)
relaxing to get free food. It's very and
[43:53] (2633.52s)
no big deal. Like, in comparison to the
[43:55] (2635.12s)
amount you're paid, like you calculate
[43:56] (2636.72s)
it out and it's like, okay, what you
[43:59] (2639.20s)
know, a few thousand dollar a year spent
[44:01] (2641.28s)
per person on food compared to their
[44:03] (2643.44s)
salary is nothing, so why not do it? Um,
[44:06] (2646.24s)
but then you get to just like the stupid
[44:09] (2649.20s)
levels of waste of like building
[44:10] (2650.64s)
products that no one needs or something
[44:12] (2652.16s)
like that. And at Amazon, I kept be
[44:13] (2653.84s)
like, "Oh my god, we would never I
[44:15] (2655.36s)
remember saying once like, "This project
[44:16] (2656.72s)
looks like a duplicate of this other
[44:18] (2658.16s)
project. Shouldn't we shut it down?"
[44:19] (2659.12s)
When I was at Facebook, shouldn't we
[44:20] (2660.56s)
shut it down? And they looked at me like
[44:21] (2661.76s)
I was crazy and like, "Why would we shut
[44:22] (2662.88s)
it down?" I'm like, "Well, cuz we're
[44:23] (2663.84s)
wasting money." And they're like, "So we
[44:25] (2665.12s)
have billions. Like what? What?" Like it
[44:26] (2666.64s)
was just kind of like confusion over
[44:28] (2668.40s)
like, "Why would we not build something
[44:30] (2670.08s)
pointlessly just for fun?"
[44:32] (2672.60s)
And very deep in Amazon's culture is
[44:35] (2675.36s)
this like we do things efficiently. Now
[44:37] (2677.92s)
sometime sometimes you're doing things
[44:39] (2679.68s)
efficiently to launch something faster.
[44:42] (2682.00s)
So you'll rewrite someone's code because
[44:43] (2683.36s)
it's faster than integrating with theirs
[44:44] (2684.88s)
or something like you know you're you're
[44:46] (2686.64s)
always balancing speed of building
[44:48] (2688.88s)
versus efficiency of resources and all
[44:50] (2690.64s)
that kind of stuff. But I think
[44:53] (2693.24s)
um if your if your answer is always
[44:56] (2696.32s)
unless there's a strong reason to do it,
[44:57] (2697.84s)
let's not do it leads to like at least
[45:00] (2700.16s)
the first half of my tenure at Amazon
[45:02] (2702.48s)
engineers would get like one monitor of
[45:04] (2704.32s)
only 16 in or something ridiculous. And
[45:07] (2707.20s)
um like literally and it was I mean it
[45:09] (2709.76s)
was both hilarious but also frustrating
[45:11] (2711.44s)
was like an intern would leave and
[45:13] (2713.52s)
before the IT group came up to take
[45:15] (2715.36s)
their equipment it was like the sharks
[45:17] (2717.52s)
would swim in all the engineers would
[45:18] (2718.88s)
run over and take their equipment, take
[45:20] (2720.24s)
their extra keyboard, take their monitor
[45:21] (2721.68s)
and stuff because they wanted two
[45:23] (2723.44s)
monitors and the only way to get it is
[45:24] (2724.80s)
to steal someone else's.
[45:27] (2727.36s)
That was stupidity but oh my god it was
[45:30] (2730.08s)
so stupid. But I think it is good
[45:32] (2732.08s)
context like just understanding where
[45:33] (2733.84s)
the company is coming from and that it
[45:35] (2735.28s)
is a unique set up in the sense that
[45:37] (2737.36s)
there are so many fulfillment center
[45:38] (2738.96s)
workers which most companies do not have
[45:41] (2741.04s)
and and they don't have this. So
[45:44] (2744.64s)
yeah, and and fix things. I mean, data
[45:47] (2747.28s)
driven. I've been on teams where they
[45:48] (2748.64s)
wrote six pager explaining why they
[45:50] (2750.16s)
needed, you know, the top-of-the-line
[45:51] (2751.60s)
Mac Pros instead of the middlegrade Mac
[45:53] (2753.60s)
Pros and they explained the, you know,
[45:55] (2755.12s)
productivity benefits and someone said,
[45:56] (2756.56s)
"Okay, now now you've explained it."
[45:58] (2758.16s)
Like finally someone came up with an
[45:59] (2759.44s)
actual math reason, not just like we
[46:01] (2761.04s)
would like it and boom, everyone got
[46:02] (2762.96s)
their stuff and we moved on. But
[46:05] (2765.08s)
um I would say like if you had to choose
[46:07] (2767.76s)
between frugality and not frugality,
[46:10] (2770.08s)
you'd want frugality. It just makes
[46:11] (2771.52s)
things operate better when you spend
[46:13] (2773.36s)
money on the things that need to be
[46:14] (2774.48s)
spent on and not waste time uh
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na.com/pragmatic for $1,000 off. Now,
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one thing when I did my Amazon deep dive
[47:24] (2844.88s)
on on Amazon's engineering culture, one
[47:26] (2846.56s)
kind of one of the most negative things
[47:28] (2848.08s)
that came up was Amazon's so-called UR
[47:32] (2852.48s)
target, unregulated attrition target,
[47:34] (2854.64s)
which if rumors have it that every year
[47:37] (2857.44s)
about 6% of staff is expected to leave
[47:40] (2860.72s)
as unregulated attrition and what this
[47:43] (2863.04s)
results in is basically uh focus plans,
[47:46] (2866.88s)
which which is what comes before PIP and
[47:48] (2868.64s)
and there's pips and and the people I
[47:50] (2870.72s)
again I have friends at Amazon and many
[47:52] (2872.88s)
of them mentioned that either they were
[47:54] (2874.24s)
on a PIP or they had a someone else be
[47:56] (2876.56s)
be on a PIP. There there is a bit of a
[47:59] (2879.60s)
you know scare uncertainty especially
[48:01] (2881.36s)
for for people entering Amazon. You were
[48:03] (2883.60s)
on the other side of this. You were a
[48:05] (2885.04s)
manager plus you clearly you were also
[48:07] (2887.40s)
like maybe sub subject to this on your
[48:09] (2889.92s)
end. How scary is this and how does it
[48:12] (2892.72s)
play out in in reality? I would say for
[48:16] (2896.40s)
most people it wasn't scary and it like
[48:20] (2900.48s)
so you know at various times it was
[48:23] (2903.04s)
between six to 10% of people had to be
[48:25] (2905.44s)
it and exact exactly what's measured was
[48:27] (2907.76s)
a little different over time of like
[48:29] (2909.04s)
whether or not you were like 6 to 10%
[48:30] (2910.72s)
had to be rated poorly or 6 to 10% had
[48:32] (2912.72s)
to be put into like coaching or whatever
[48:36] (2916.00s)
or 6 to 10% had to be fired. um
[48:38] (2918.96s)
sometimes different levels of goals in
[48:40] (2920.88s)
terms of like the cascading level of
[48:42] (2922.40s)
like as you're managing people out
[48:43] (2923.68s)
there's goals to hit.
[48:45] (2925.88s)
Um but I would
[48:49] (2929.32s)
say it wasn't scary for the vast
[48:51] (2931.76s)
majority of people because you're
[48:53] (2933.68s)
looking ahead. You're thinking about how
[48:55] (2935.12s)
do I get promoted? How do I, you know,
[48:57] (2937.28s)
do these projects well so I can get
[48:58] (2938.88s)
ahead in my career. I don't think you're
[49:01] (2941.36s)
I don't think most people are thinking
[49:02] (2942.88s)
I'm probably in the bottom 10% of all my
[49:04] (2944.72s)
peers, right?
[49:05] (2945.92s)
I think most people don't think that
[49:07] (2947.28s)
you're not worried about being the
[49:08] (2948.32s)
bottom 10%. And I would say until it
[49:10] (2950.48s)
pops up, right? You move to a new team,
[49:12] (2952.08s)
your manager doesn't like you, and you
[49:13] (2953.44s)
think about like, huh, my manager
[49:15] (2955.12s)
doesn't like me and seems to like all my
[49:16] (2956.72s)
peers. Suddenly, it becomes very scary
[49:19] (2959.44s)
because I I've actually heard this from
[49:21] (2961.60s)
people I know of the same story that I
[49:23] (2963.92s)
they've been with Amazon for some time,
[49:26] (2966.08s)
great, either a new manager came or they
[49:28] (2968.64s)
moved to a new team and that's when
[49:30] (2970.88s)
things became, you know, I actually had
[49:32] (2972.80s)
a friend who was put on a pip. He
[49:34] (2974.24s)
thought it was really unfair. He was
[49:35] (2975.28s)
there for 4 years. He actually made it
[49:36] (2976.56s)
out of it, but he he kind of became
[49:38] (2978.16s)
bitter, especially if she was his
[49:39] (2979.28s)
manager. Almost never happens. Yeah.
[49:40] (2980.56s)
Yeah. Uh he he actually turned it
[49:41] (2981.92s)
around, but yeah. Still, yeah, I think
[49:44] (2984.16s)
there's a few a few edge cases where
[49:46] (2986.56s)
people can make it out of pips. And
[49:47] (2987.92s)
mostly that I would say the vast
[49:49] (2989.84s)
majority of people who get out of those
[49:51] (2991.04s)
is something like a college
[49:53] (2993.64s)
hire. And you know, this is just a
[49:56] (2996.00s)
common thing. I I make fun of college
[49:57] (2997.52s)
hires because of like how much they move
[49:59] (2999.04s)
from kids to adult over time, right?
[50:00] (3000.64s)
Like you're you're in college and you're
[50:02] (3002.00s)
a kid and then you become an adult. how
[50:04] (3004.16s)
many times they get hired and like
[50:05] (3005.84s)
they're three months into their job and
[50:07] (3007.44s)
I see them posting on Instagram at 4 in
[50:09] (3009.84s)
the morning and then the next day they
[50:11] (3011.28s)
don't show up to work and they're like,
[50:12] (3012.40s)
"I was sick." I'm like, "Yeah, I bet you
[50:13] (3013.84s)
were sick." Like alcohol poisoning is
[50:16] (3016.08s)
definitely an illness.
[50:17] (3017.96s)
Um, like that's kids, right? Like it's
[50:20] (3020.80s)
like, "Dude, you haven't done any work
[50:22] (3022.24s)
in two weeks and you're out late and you
[50:24] (3024.00s)
show up to work at 1 p.m. Like then then
[50:27] (3027.20s)
you can get a pit that you can get out
[50:28] (3028.48s)
of because they decide to buckle down
[50:29] (3029.92s)
and actually do their work. Yep. you let
[50:32] (3032.08s)
them back out because you think they're
[50:33] (3033.12s)
competent. They're just need to grow up.
[50:35] (3035.64s)
Um, but if you think about like the the
[50:38] (3038.48s)
incentives, right? You're you're running
[50:41] (3041.24s)
team. I have 50 people working for me.
[50:45] (3045.20s)
Uh, because usually like the the
[50:46] (3046.64s)
criteria of that like six to 10% is only
[50:48] (3048.56s)
if you're in a big enough or work. So,
[50:49] (3049.68s)
let's say I have 50 people. So, now the
[50:51] (3051.28s)
criteria hits me because usually it's 50
[50:53] (3053.04s)
or or so when you start to have these
[50:54] (3054.88s)
expectations. Um, if I need to rate 10%
[50:57] (3057.92s)
of my team poorly, let's say, and I have
[51:00] (3060.96s)
50 people, that means I need to find
[51:02] (3062.32s)
five people by the time the review
[51:04] (3064.56s)
period rolls around. I need to be like
[51:06] (3066.24s)
considering who's not doing great. Um,
[51:08] (3068.80s)
review period is every six months, every
[51:10] (3070.56s)
12 months. 12 months. Yeah. 12 months.
[51:12] (3072.72s)
Yeah. Yeah. And it again like at various
[51:15] (3075.28s)
times we had checkpoints, six month
[51:16] (3076.72s)
checkpoints. Sometimes HR would care
[51:18] (3078.16s)
that you're flagging people. Sometimes
[51:19] (3079.52s)
it's like a a
[51:21] (3081.72s)
um for the most part, especially when
[51:23] (3083.84s)
you're talking about like firing people,
[51:24] (3084.88s)
it would be like by the end of the year,
[51:26] (3086.16s)
you need to have fired 65 people. And
[51:28] (3088.08s)
sometimes it's like, well, we already
[51:29] (3089.68s)
did four along the year because of
[51:31] (3091.20s)
various things that happened. So the
[51:33] (3093.68s)
fifth one is the only one we're looking
[51:34] (3094.96s)
for. And so you're going through the
[51:35] (3095.92s)
review period with your 50 people and
[51:37] (3097.20s)
you need to find one person. It's not a
[51:38] (3098.64s)
big deal, right? You talk to that would
[51:40] (3100.80s)
probably be like seven or eight managers
[51:42] (3102.64s)
at least. one of them has some employee
[51:44] (3104.64s)
who's not doing great or one of your
[51:45] (3105.92s)
managers is annoying as hell and so you
[51:47] (3107.60s)
like no you're okay. Um but uh um I
[51:53] (3113.12s)
would say it it becomes an issue when
[51:56] (3116.96s)
your team especially if your team is
[51:58] (3118.56s)
more stable like if you're constantly
[51:59] (3119.92s)
hiring people like there's always going
[52:01] (3121.28s)
to be new blood someone wasn't
[52:02] (3122.80s)
interviewed well someone's just like not
[52:04] (3124.16s)
at all suited to be here and that makes
[52:05] (3125.52s)
it easy. when your team's more stable
[52:07] (3127.20s)
and you've been for the most part with
[52:08] (3128.96s)
the same people for two years and you're
[52:10] (3130.40s)
running into yet another review cycle
[52:11] (3131.76s)
and you're like, "Oh my god, like we
[52:13] (3133.20s)
already got rid of everyone who was not
[52:14] (3134.64s)
doing great. Everyone on my team is
[52:15] (3135.84s)
doing well." Now you end up in
[52:17] (3137.12s)
frustrating situations. Um, and it most
[52:20] (3140.96s)
of the time I would say it was it was
[52:22] (3142.96s)
not a big deal and wasn't that stressful
[52:24] (3144.80s)
because uh it again those those
[52:27] (3147.92s)
expectations don't come into play until
[52:29] (3149.52s)
you have a bigger organization. So like
[52:31] (3151.12s)
you're a dev manager and you'd say no,
[52:32] (3152.56s)
everyone on my team's good and as long
[52:33] (3153.92s)
as you have backbone, you're okay with
[52:35] (3155.28s)
that. And then you get to be a senior
[52:37] (3157.92s)
manager and you have 47
[52:39] (3159.96s)
people. The criteria doesn't apply to
[52:42] (3162.08s)
you still. Now you start to have like,
[52:45] (3165.04s)
you know, boy, you have 47 people, you
[52:46] (3166.80s)
have no one underperforming. Are you
[52:48] (3168.32s)
sure? Like 47 people. Like now you start
[52:50] (3170.56s)
to get to the point where your manager
[52:51] (3171.76s)
sits down with you and says, let's go
[52:53] (3173.04s)
through and tell me the bottom five
[52:54] (3174.88s)
people of your or why they don't deserve
[52:56] (3176.88s)
to be rated as least effective. Um, and
[53:00] (3180.24s)
then you get to bigger orgs and that's
[53:01] (3181.44s)
where you get this weird like the
[53:02] (3182.72s)
organizational pressure of like now you
[53:04] (3184.32s)
hit your director. You know, I have 150
[53:07] (3187.04s)
people reporting to me. I need to come
[53:08] (3188.64s)
up with the I don't know 11 people who
[53:11] (3191.68s)
are in coaching by the end of the year
[53:13] (3193.04s)
or something. And like now I sit down
[53:14] (3194.72s)
with my managers and say, "Okay guys,
[53:16] (3196.16s)
like we have eight. I need
[53:17] (3197.96s)
11." Like sorry, we we have to talk
[53:21] (3201.76s)
through all the people who are like on
[53:24] (3204.00s)
that borderline and explain why they're
[53:25] (3205.68s)
not whatever at this level. um because
[53:28] (3208.40s)
for the most part they they were very
[53:29] (3209.84s)
very hard asses on making sure that you
[53:31] (3211.60s)
hit these um the the goals. And then
[53:34] (3214.96s)
from a software engineer perspective,
[53:36] (3216.40s)
you know, you're kind of like blissfully
[53:37] (3217.76s)
unaware. You're kind of working away.
[53:39] (3219.60s)
Let's imagine that you know like one day
[53:42] (3222.16s)
your manager probably had this like
[53:43] (3223.84s)
discussion with their director or you
[53:45] (3225.68s)
know their manager and they identified
[53:48] (3228.00s)
you as as someone.
[53:50] (3230.24s)
Usually when when this happens you know
[53:52] (3232.72s)
like you kind of in in your group you
[53:54] (3234.88s)
circle down on like all right this is
[53:56] (3236.16s)
this person these are the people who are
[53:57] (3237.76s)
underperforming. Yep. Is is there also a
[54:02] (3242.60s)
where there is like reasoning given of
[54:05] (3245.04s)
why so that the manager can go back and
[54:07] (3247.04s)
say like all right here's here's the
[54:08] (3248.64s)
difference here's why you're not meeting
[54:10] (3250.52s)
expectations or is it just more like
[54:12] (3252.56s)
well you know you're you're b you're
[54:13] (3253.76s)
bottom of the pile like sorry here's a
[54:17] (3257.28s)
So, so I guess the first thing to say is
[54:19] (3259.36s)
like there there's often
[54:22] (3262.12s)
a definition issue and even even with
[54:25] (3265.04s)
the managers sometimes like boy this is
[54:26] (3266.80s)
where managers screw up the most often
[54:28] (3268.24s)
and you almost need like a bar razor for
[54:29] (3269.92s)
HR processes.
[54:32] (3272.28s)
Um the description of that lowest rating
[54:35] (3275.92s)
and the description of letting people go
[54:38] (3278.16s)
is all about not not meeting
[54:40] (3280.96s)
expectations. It is least effective. And
[54:43] (3283.44s)
so the argument is we explain to people
[54:45] (3285.20s)
is if we have a hundred people we want
[54:48] (3288.24s)
let's just let's just you know pick an
[54:49] (3289.76s)
easy number you know the bottom 5% five
[54:51] (3291.84s)
people out of that hundred we want the
[54:53] (3293.76s)
least effective of them it doesn't mean
[54:56] (3296.00s)
not effective it doesn't mean that
[54:57] (3297.36s)
they're not doing their job but if you
[54:59] (3299.28s)
pick the bottom five people and say
[55:01] (3301.36s)
would you rather keep them or would you
[55:04] (3304.44s)
rather interview and find someone who
[55:06] (3306.80s)
might be just this amazing awesome hire
[55:09] (3309.12s)
and most of the time you would say well
[55:11] (3311.68s)
I mean the bottom five out of a 100
[55:14] (3314.48s)
people aren't great. Like they might be
[55:16] (3316.24s)
okay at their job comparatively, right?
[55:18] (3318.56s)
Comparatively, like they're they're a
[55:20] (3320.24s)
fine, let's just say, engineer, right?
[55:22] (3322.00s)
They're they're a fine engineer. Most
[55:23] (3323.76s)
people get that work done in a week and
[55:26] (3326.00s)
it takes them two weeks because they're
[55:28] (3328.00s)
slower. Like it's just like they're not
[55:29] (3329.92s)
great. If their manager is doing well,
[55:32] (3332.24s)
their manager has been telling them this
[55:33] (3333.52s)
all along is like, "Hey, do you notice
[55:35] (3335.12s)
that your work is getting done slower
[55:36] (3336.64s)
than everyone else on the team?" Right?
[55:38] (3338.00s)
Because again, this is the bottom five
[55:39] (3339.28s)
of a hundred. Like they're slower than
[55:41] (3341.04s)
everyone else. they're making more
[55:42] (3342.88s)
mistakes, like whatever it is that makes
[55:44] (3344.56s)
them at the bottom. Hopefully, they've
[55:46] (3346.32s)
been getting feedback along the way. And
[55:48] (3348.00s)
then in this discussion that inevitably
[55:50] (3350.56s)
happened, right, you have all the
[55:51] (3351.76s)
managers in a room with their senior
[55:53] (3353.36s)
manager, maybe with the director, and
[55:54] (3354.72s)
you're having detailed discussions. And
[55:56] (3356.24s)
we're saying, "Okay, their productivity
[55:58] (3358.24s)
is lower. They make a lot of mistakes.
[55:59] (3359.92s)
their peers complain a lot about them
[56:01] (3361.76s)
because almost always there's like a lot
[56:03] (3363.44s)
of peer like I really don't want to work
[56:06] (3366.00s)
with Dave on this one because boy Dave
[56:08] (3368.32s)
always is slower and you have to do most
[56:09] (3369.92s)
of the work for Dave right so you have
[56:13] (3373.04s)
this collaborative discussion with
[56:15] (3375.60s)
whatever feedback you have available to
[56:17] (3377.20s)
say okay definitely Dave has to be in
[56:18] (3378.96s)
that bottom
[56:20] (3380.20s)
five now hopefully again the manager of
[56:23] (3383.68s)
Dave has been giving him feedback
[56:25] (3385.20s)
throughout the year and and maybe this
[56:27] (3387.20s)
happens not at the very end of the year
[56:28] (3388.48s)
like preferably it's not just like all
[56:29] (3389.92s)
of a sudden wham everyone gets
[56:31] (3391.20s)
surprised. Um but if it has happened
[56:33] (3393.84s)
then yes you you the manager should be
[56:35] (3395.84s)
able to sit down and say you are being
[56:38] (3398.40s)
rated as least effective or you're being
[56:40] (3400.08s)
put into coaching or whatever you know
[56:41] (3401.44s)
whatever it is the step is because
[56:44] (3404.16s)
here's the the you know the situation
[56:47] (3407.36s)
you know you in the last four
[56:49] (3409.48s)
sprints you know specifically in the
[56:51] (3411.68s)
last four sprints you have missed your
[56:53] (3413.20s)
you know your uh projects haven't been
[56:56] (3416.16s)
completed like we thought would be
[56:57] (3417.28s)
completed in the sprint every single
[56:58] (3418.48s)
sprint for the last four sprints we've
[57:00] (3420.00s)
already talked about this Um the issue
[57:02] (3422.24s)
usually comes up is if the manager has
[57:03] (3423.60s)
been saying you're awesome, you're
[57:04] (3424.56s)
awesome, you're awesome, and then
[57:05] (3425.44s)
suddenly like terribly sorry, you're in
[57:06] (3426.96s)
coaching now because you're not awesome.
[57:08] (3428.48s)
And that I've actually heard quite a bit
[57:11] (3431.28s)
that the problem that some some of the
[57:14] (3434.24s)
people that I talked with who were in
[57:15] (3435.52s)
the situation, their manager was new or
[57:17] (3437.36s)
did not understand and some of those
[57:19] (3439.52s)
things. But this this does make sense.
[57:22] (3442.24s)
In some ways, it feels to me, correct me
[57:25] (3445.04s)
if I'm wrong, but this feels somewhat
[57:26] (3446.40s)
similar to Netflix in the sense of, you
[57:28] (3448.24s)
know, Netflix is also very open about
[57:29] (3449.92s)
having the keeper test where they re
[57:32] (3452.96s)
regular re-evaluate people. And they're
[57:34] (3454.96s)
not saying you're not meeting
[57:36] (3456.16s)
expectations. They're just like, well,
[57:37] (3457.76s)
if if someone is not, they actually do
[57:39] (3459.68s)
the other way around. They're saying if
[57:40] (3460.96s)
someone is not worth fighting for
[57:42] (3462.56s)
keeping, y we we don't necessarily want
[57:44] (3464.48s)
to retain them. In fact, we might let
[57:45] (3465.84s)
them go. And they're they communicate
[57:47] (3467.60s)
this up front. Whereas with Amazon, if I
[57:49] (3469.60s)
understand, they're saying you're the
[57:50] (3470.56s)
least effective, which again, people got
[57:52] (3472.72s)
into Amazon, it's it's it's hard to get
[57:54] (3474.32s)
into. They're typically good engineers.
[57:56] (3476.00s)
They might they might be awesome at a
[57:58] (3478.00s)
lot of places, but Amazon says, "Okay,
[58:00] (3480.80s)
let's you know, we we want to this is
[58:04] (3484.00s)
this is their performance philosophy."
[58:06] (3486.08s)
Yeah. Yeah. And I think a good number of
[58:09] (3489.12s)
the times if you talk to someone and say
[58:11] (3491.24s)
like you know someone who's getting this
[58:13] (3493.52s)
kind of feedback and they're saying but
[58:14] (3494.72s)
I did complete that eventually or
[58:16] (3496.16s)
something like that and say but do you
[58:17] (3497.96s)
agree that every person on the team
[58:20] (3500.64s)
would have gotten that done faster than
[58:22] (3502.08s)
you? Like you know it's well yes. is
[58:24] (3504.72s)
like okay so at least you would agree
[58:26] (3506.40s)
out of your 11 peers on the team that
[58:28] (3508.24s)
you are the slowest engineer like you
[58:30] (3510.32s)
know it's like I think people understand
[58:33] (3513.36s)
one of the hard things is just like the
[58:35] (3515.36s)
difference between least effective which
[58:36] (3516.96s)
is just like hey at the bottom of the
[58:38] (3518.48s)
pile you're all you're going to be at
[58:39] (3519.68s)
risk if you if you think if you look
[58:41] (3521.04s)
around the room and you're thinking yep
[58:42] (3522.32s)
I'm the worst one here that's not a
[58:43] (3523.76s)
great situation to ever be in um it's
[58:46] (3526.88s)
just never safe and at Amazon it's
[58:49] (3529.44s)
definitely not safe at some other
[58:51] (3531.28s)
companies where they just might you know
[58:52] (3532.72s)
do layoffs once every four years you
[58:54] (3534.32s)
might be safe for quite a while but
[58:55] (3535.44s)
Amazon has this sort of regular cycle
[58:57] (3537.12s)
you know well also I feel some companies
[59:00] (3540.24s)
and I I wrote about this I I I feel I
[59:02] (3542.32s)
sense some companies are are starting to
[59:04] (3544.16s)
become a bit more like Amazon so
[59:05] (3545.76s)
Facebook has done 5% performance based
[59:08] (3548.56s)
layoff for uh this time I think Stripe
[59:11] (3551.44s)
was doing something similar so
[59:13] (3553.20s)
previously I I think we saw there was
[59:15] (3555.20s)
these kind of golden years if you will
[59:17] (3557.20s)
you know when the job market was really
[59:18] (3558.56s)
good Amazon kept doing this and and
[59:20] (3560.16s)
Amazon was really the kind of the bad
[59:22] (3562.16s)
company if you will the only one who
[59:23] (3563.44s)
were doing besides Netflix. Yeah. And it
[59:26] (3566.88s)
seems to me Amazon hasn't changed
[59:28] (3568.08s)
anything, but some of the other
[59:29] (3569.36s)
companies are doing so. Maybe as
[59:30] (3570.80s)
software engineers, we should accept
[59:32] (3572.40s)
that, you know, like some of these top
[59:33] (3573.60s)
companies in terms of brand
[59:34] (3574.96s)
compensation, Yeah. they're they're
[59:36] (3576.80s)
going to be tougher places to to be at
[59:38] (3578.64s)
and and, you know, they'll just keep
[59:40] (3580.80s)
pushing people to to to do better and
[59:43] (3583.04s)
they'll keep comparing people with one
[59:44] (3584.40s)
another. Yeah. And in in terms of like
[59:47] (3587.60s)
as an individual if you're joining as an
[59:49] (3589.76s)
engineer or or manager, you know, anyone
[59:52] (3592.80s)
um I think one of the things to keep in
[59:54] (3594.56s)
mind in terms of like ego and like how
[59:56] (3596.56s)
does it feel to be told all of a sudden
[59:58] (3598.16s)
like you're not doing well.
[60:00] (3600.36s)
Um I guess partially is like awareness
[60:03] (3603.12s)
of I would actually say as a manager
[60:04] (3604.96s)
even like 50% of that performance
[60:06] (3606.96s)
frequently is your relationship with
[60:09] (3609.04s)
your manager and like your team how well
[60:12] (3612.00s)
you fit in with the team with your peers
[60:13] (3613.36s)
with your manager. So many times I've
[60:15] (3615.84s)
had someone who was either doing amazing
[60:18] (3618.56s)
on one team, they move to the next team
[60:20] (3620.00s)
and they're like actually not doing well
[60:21] (3621.28s)
at all or someone who was not doing well
[60:23] (3623.68s)
escapes to another team before they get
[60:25] (3625.28s)
fired and they do well. Like they, you
[60:27] (3627.84s)
know, I've had people who I was planning
[60:29] (3629.68s)
on firing who managed to transfer off my
[60:31] (3631.60s)
team in time and they ended up getting
[60:33] (3633.44s)
promoted someday. And the same thing,
[60:34] (3634.64s)
I've had people who said like, "I'm
[60:36] (3636.64s)
about to be
[60:37] (3637.64s)
fired. Uh, I'm pretty sure that it's
[60:40] (3640.08s)
coming towards me. Can I get off the
[60:42] (3642.40s)
This is like my my sneaky recommendation
[60:44] (3644.56s)
for anyone is like if you start to hear
[60:46] (3646.64s)
performance feedback whatsoever from
[60:48] (3648.32s)
your management chain. If you have any
[60:49] (3649.68s)
opportunity at all, get off your team
[60:50] (3650.96s)
fast as possible. Like that's that's the
[60:53] (3653.76s)
uh as we frequently say it's like unfair
[60:55] (3655.52s)
thing that managers know is if your
[60:57] (3657.12s)
manager says, you know, that wasn't so
[60:59] (3659.20s)
great what you did recently. And he's
[61:00] (3660.40s)
like, "Okay, well, switching teams like
[61:02] (3662.08s)
fast as I can." Because uh um most
[61:05] (3665.36s)
managers won't. Now, if you trust your
[61:07] (3667.36s)
manager, they might be actually just
[61:08] (3668.56s)
giving you honest feedback, which you'd
[61:09] (3669.84s)
like to be able to receive. But for the
[61:12] (3672.08s)
most part, if you've been working for
[61:13] (3673.20s)
someone for 3 years and suddenly they
[61:15] (3675.04s)
start giving you performance feedback,
[61:16] (3676.32s)
that's a really bad sign. Um if you run
[61:19] (3679.12s)
for the hills fast enough, it's possible
[61:20] (3680.56s)
you'll get away before they flag you in
[61:22] (3682.16s)
the system as is non-transferable. Yeah.
[61:24] (3684.08s)
Yeah, and I guess it's just worth
[61:26] (3686.40s)
keeping in mind that for you people who
[61:28] (3688.00s)
have not worked that these are companies
[61:29] (3689.52s)
internal transfers are possible and you
[61:32] (3692.00s)
do want to make take advantage of when
[61:34] (3694.56s)
when it makes sense. It's still better
[61:35] (3695.76s)
to do or easier to do internal transfer
[61:38] (3698.00s)
than to start a new job search process
[61:40] (3700.40s)
to start you know like your network will
[61:42] (3702.64s)
if you change companies you'll have to
[61:44] (3704.64s)
start to rebuild your network and so on.
[61:46] (3706.96s)
Yeah. I and I think again it frequently
[61:49] (3709.92s)
is not that you're a bad manager or a
[61:52] (3712.32s)
bad software engineer, a bad project
[61:53] (3713.68s)
manager or something. It's just, you
[61:55] (3715.04s)
know, sometimes it doesn't work out with
[61:56] (3716.16s)
you and your manager. And I think the
[61:58] (3718.24s)
same thing like if you're like, "Boy, I
[61:59] (3719.76s)
don't get along well with my manager,
[62:01] (3721.28s)
but I like this team." Like that's
[62:03] (3723.04s)
that's not the safest thing to to to put
[62:05] (3725.44s)
up with. I've heard people saying that
[62:07] (3727.12s)
like, "My manager doesn't like me, but I
[62:08] (3728.96s)
really like what we're working on." I'm
[62:10] (3730.16s)
like, "Dude, get off the team." Like if
[62:12] (3732.00s)
you want to keep your job, get off the
[62:13] (3733.36s)
team. I feel Amazon might might might
[62:15] (3735.44s)
make some of these kind of you know
[62:17] (3737.12s)
universal truths a bit like more
[62:19] (3739.68s)
emphasized because I remember like for
[62:21] (3741.52s)
example I I worked at Microsoft at a
[62:23] (3743.68s)
time where Microsoft just didn't let
[62:25] (3745.52s)
people go and there were people who were
[62:27] (3747.44s)
who were doing really bad like like we
[62:29] (3749.36s)
knew like they were doing bad work but
[62:32] (3752.24s)
for some reason management didn't want
[62:34] (3754.48s)
to let anyone go so they were dead
[62:36] (3756.64s)
weight but if and and by the way when
[62:39] (3759.12s)
you know like actually when this policy
[62:42] (3762.16s)
I wasn't there when it changed But I
[62:43] (3763.44s)
heard heard when I changed they were one
[62:44] (3764.72s)
of the first ones to let go and no one
[62:46] (3766.08s)
was surprised. But the point is like
[62:47] (3767.76s)
it's this is usually how companies work
[62:50] (3770.64s)
in like either normal times or when when
[62:53] (3773.68s)
times are a bit tougher. Right now it is
[62:55] (3775.44s)
a bit tougher and it's just good to
[62:56] (3776.72s)
prepare for that. So like speaking for
[62:58] (3778.40s)
myself I was always paranoid and in most
[63:00] (3780.80s)
most of my companies especially when I
[63:02] (3782.24s)
joined the first like six to 12 months
[63:03] (3783.76s)
of I assumed I probably was not doing
[63:05] (3785.76s)
that good. So I tried to do my best and
[63:08] (3788.56s)
you know hope that it was enough. over
[63:10] (3790.24s)
time you could become more comfortable.
[63:11] (3791.68s)
But as you said, I was as a manager as
[63:14] (3794.00s)
well, if you're not getting with your
[63:15] (3795.44s)
manager, what you said, like like you
[63:17] (3797.28s)
need to switch managers, like try to fix
[63:18] (3798.96s)
it if you can, but if you can't,
[63:20] (3800.88s)
sometimes there's just not much you can
[63:23] (3803.04s)
do. Yeah. I think
[63:25] (3805.40s)
that there's like the the mistake that
[63:28] (3808.08s)
people will sometimes make is like my
[63:29] (3809.84s)
manager doesn't influence my job that
[63:31] (3811.60s)
much because I can work independently
[63:32] (3812.88s)
or, you know, I don't need to figure
[63:34] (3814.56s)
this out with my manager because I can,
[63:37] (3817.20s)
you know, work with my peers or I have
[63:38] (3818.48s)
this great engineer on my team. I can
[63:39] (3819.76s)
work with. Um, but especially at a place
[63:42] (3822.64s)
like Amazon, like your manager is
[63:44] (3824.32s)
responsible for your rating, which is
[63:47] (3827.04s)
like tied directly into comp. Like
[63:48] (3828.80s)
they're definitely responsible for your
[63:50] (3830.32s)
promotion. Like you're never ever going
[63:51] (3831.76s)
to get promoted if your manager doesn't
[63:53] (3833.20s)
like you. And uh, every year at least,
[63:56] (3836.80s)
your manager needs to pick out a number
[63:58] (3838.16s)
of people who are not doing great. And
[64:00] (3840.72s)
so if you're not getting along with your
[64:02] (3842.48s)
manager, you need to fix it or you need
[64:03] (3843.60s)
to move on to another team. Like that's
[64:04] (3844.88s)
just a a a solid thing at Amazon. and
[64:08] (3848.56s)
and and there are bad managers. I think
[64:10] (3850.24s)
like half the time someone gets fired,
[64:11] (3851.52s)
it's probably because the manager is
[64:12] (3852.72s)
like, you know, it's a relationship
[64:15] (3855.28s)
issue. And I guess it's it's fair to say
[64:17] (3857.04s)
the this is specific to Amazon and maybe
[64:19] (3859.36s)
at some other companies you can get by.
[64:21] (3861.52s)
But I think it's fair to say the other
[64:22] (3862.88s)
thing as well, right? If you have a
[64:24] (3864.16s)
great manager, someone where you really
[64:26] (3866.76s)
click, they have your back, you've got
[64:29] (3869.28s)
your back, you work well together, y it
[64:31] (3871.68s)
might be even worth follow, you know,
[64:33] (3873.12s)
like depending on the situation. But
[64:34] (3874.72s)
often times I hear people following
[64:36] (3876.32s)
their managers to other companies, you
[64:38] (3878.40s)
know, they join a year later, they reach
[64:40] (3880.00s)
out, do you want to have a coffee? Guess
[64:42] (3882.08s)
what? They're going to be their their
[64:43] (3883.68s)
right hand at that company as well. I
[64:45] (3885.76s)
actually know people who have risen with
[64:47] (3887.28s)
and you know these good managers usually
[64:49] (3889.36s)
Oh yeah, not always but but often times
[64:50] (3890.88s)
they're good at you know get getting
[64:52] (3892.96s)
ahead especially because they they do
[64:54] (3894.40s)
bring some of their team. So if if you
[64:56] (3896.80s)
know or if you have a good manager or if
[64:58] (3898.80s)
you had in the past just hit them up
[65:00] (3900.80s)
again. Uh it it's rare like it's not
[65:03] (3903.28s)
that easy. Oh, totally that if you if
[65:06] (3906.24s)
you've had no managers with that you
[65:07] (3907.92s)
have a good good enough relationship
[65:09] (3909.28s)
where you want to go move with them then
[65:11] (3911.04s)
you've either had really unfortunate
[65:12] (3912.96s)
choice of managers or maybe you're
[65:14] (3914.32s)
having the issue with the the
[65:15] (3915.96s)
relationship you know sometimes it's you
[65:18] (3918.08s)
not them but uh yeah know I've heard
[65:20] (3920.40s)
before from managers especially as you
[65:22] (3922.00s)
get higher level is like if they don't
[65:23] (3923.60s)
pull in a number of people into their or
[65:26] (3926.32s)
soon as they transfer over and like
[65:27] (3927.52s)
there's not a list of people ready to
[65:28] (3928.88s)
transfer over then maybe there's an
[65:30] (3930.80s)
issue with that person because you want
[65:32] (3932.40s)
them to say how like every time I moved
[65:35] (3935.28s)
groups I would frequently start off with
[65:36] (3936.72s)
how many open roles am I going to have
[65:38] (3938.16s)
because I have a number of people who I
[65:39] (3939.88s)
want you know who will be happy to come
[65:42] (3942.56s)
over and join and take all those open
[65:44] (3944.16s)
positions um and that's what you expect
[65:47] (3947.04s)
that's what you you should have and
[65:48] (3948.56s)
especially within Amazon that's a
[65:49] (3949.84s)
totally expected thing is um the person
[65:52] (3952.32s)
comes over and then suddenly like all
[65:53] (3953.84s)
the spots fill up with the people who
[65:55] (3955.12s)
have followed them for the last three or
[65:56] (3956.80s)
you know org moves and that's just yeah
[65:58] (3958.80s)
and it's a good way to shuffle um I
[66:01] (3961.28s)
think it's healthy especially you at
[66:03] (3963.68s)
companies like Amazon where you want
[66:05] (3965.12s)
fresh blood and new ideas, new thoughts
[66:07] (3967.04s)
like it's a good way of you know orgs
[66:09] (3969.36s)
sort of shuffle around you know your VP
[66:11] (3971.12s)
leaves and suddenly half your management
[66:12] (3972.80s)
team leaves like that's a good thing
[66:14] (3974.24s)
that means the VP was liked by some
[66:15] (3975.76s)
people they move off to somewhere else
[66:17] (3977.52s)
to bring some new ideas in and then you
[66:19] (3979.44s)
get a whole fresh swath of of new hires
[66:22] (3982.96s)
so I'm getting a sense that Amazon and
[66:25] (3985.84s)
obviously I got that sense when I was
[66:27] (3987.44s)
refreshing Amazon but talking to you
[66:29] (3989.44s)
even more so that Amazon is just not not
[66:31] (3991.60s)
a typical what you would expect a large
[66:33] (3993.76s)
company to be. And one thing I've
[66:35] (3995.68s)
noticed that I think underscores this is
[66:38] (3998.08s)
when startups hire from large tech
[66:40] (4000.20s)
companies. When I'm talking with
[66:41] (4001.92s)
founders or people who have hired in the
[66:43] (4003.76s)
past or engineers who work with them,
[66:45] (4005.12s)
there's a lot of eye rolling like gh we
[66:46] (4006.72s)
hired that person from Google and it
[66:48] (4008.40s)
didn't really work out at a small
[66:50] (4010.00s)
company or or meta or some other
[66:53] (4013.00s)
companies. One thing that I rarely hear
[66:55] (4015.92s)
is that this Amazon hire didn't work
[66:58] (4018.24s)
out. In fact, I see so many Amazon
[67:00] (4020.32s)
people go to, you know, either large
[67:02] (4022.80s)
tech companies and then they like some
[67:05] (4025.36s)
that even have like maybe a shinier
[67:06] (4026.96s)
brand like Google, Meta, OpenAI, etc.,
[67:09] (4029.84s)
and they do pretty well there, but they
[67:11] (4031.44s)
also go to startups and they do pretty
[67:12] (4032.88s)
well there as as employees, not just as
[67:15] (4035.76s)
founders. Why do you think this is I
[67:18] (4038.32s)
mean, I think we've already touched on a
[67:19] (4039.60s)
few things why it could be because
[67:21] (4041.36s)
Amazon feels a bit like a scrappy
[67:23] (4043.68s)
startup even though it's a big company.
[67:25] (4045.76s)
I mean, corporatewide is not a scrappy
[67:28] (4048.56s)
startup. It's a big old behemoth that
[67:30] (4050.40s)
can't turn, you know, to save its life.
[67:32] (4052.32s)
Uh, so,
[67:35] (4055.12s)
not that I'm biased or anything, but
[67:36] (4056.56s)
sometimes like HR policies and stuff
[67:38] (4058.08s)
like, you know, with a few Amazonwide
[67:39] (4059.92s)
things, it's like good luck freaking
[67:41] (4061.52s)
changing anything. It's like bang my
[67:43] (4063.20s)
head against the wall so many times
[67:44] (4064.64s)
trying to get things changed.
[67:47] (4067.08s)
Um, so corporatewide they're not, but
[67:49] (4069.52s)
when you get down to a dev team, like I
[67:51] (4071.40s)
I loved the fact of like almost
[67:54] (4074.80s)
everything is controllable at the lowest
[67:57] (4077.76s)
possible level. And so, um, you know,
[68:01] (4081.28s)
everything from stupid changes people
[68:03] (4083.44s)
can make or awesome changes they can
[68:04] (4084.80s)
make that you have a lot of control at
[68:06] (4086.48s)
every individual dev team can pick their
[68:09] (4089.12s)
process, can pick their coding language,
[68:11] (4091.20s)
can pick how they're deploying their
[68:12] (4092.40s)
code, like what tools they're going to
[68:13] (4093.68s)
use. And sometimes it becomes stupid
[68:16] (4096.40s)
like it's just like what the there was a
[68:18] (4098.72s)
team that built their whole um very
[68:21] (4101.20s)
important middleware like service out of
[68:24] (4104.72s)
lisp and it's like what are they
[68:27] (4107.12s)
thinking like it's why I mean is sure
[68:31] (4111.16s)
but no one else knows the damn language
[68:34] (4114.64s)
like no one else has written anything in
[68:36] (4116.00s)
Lisbon like no one knows what you're
[68:38] (4118.16s)
doing. Um, and I think it was like two
[68:40] (4120.56s)
engineers on the team had this great
[68:41] (4121.84s)
idea and they wrote the whole damn
[68:43] (4123.12s)
service and like great, they built the
[68:44] (4124.72s)
service and they all transferred off the
[68:46] (4126.00s)
team and then they had to rewrite the
[68:47] (4127.68s)
code because no one knew how to support
[68:48] (4128.88s)
it and it was this nightmare. But they
[68:51] (4131.20s)
could, right? And thankfully no one came
[68:53] (4133.52s)
up with an Amazonwide policy saying
[68:54] (4134.96s)
you're not allowed to write anything
[68:56] (4136.32s)
except if it's in Java or something. Um,
[68:59] (4139.44s)
so teams would regularly build stuff in
[69:01] (4141.92s)
whatever language they want, in whatever
[69:03] (4143.36s)
tool set they want, at whatever pace
[69:04] (4144.80s)
they want. They can do agile, they can
[69:06] (4146.72s)
do waterfall, they can do scrum or um
[69:09] (4149.36s)
they can not do sprints, they can I
[69:12] (4152.80s)
liked the fact that unless there's a
[69:14] (4154.56s)
strong strong strong reason for
[69:16] (4156.16s)
something to be dictated by Amazon or VP
[69:19] (4159.20s)
or director whatever else for the most
[69:21] (4161.04s)
part the culture
[69:22] (4162.52s)
was you can't tell me what to do like in
[69:25] (4165.76s)
a good way as in like um you know the
[69:28] (4168.16s)
director would say something like why
[69:29] (4169.36s)
don't once in a while you'd hear someone
[69:30] (4170.96s)
say something like why don't we line up
[69:32] (4172.08s)
all the sprints and everyone just starts
[69:33] (4173.36s)
stomping their feet saying hell No, you
[69:35] (4175.52s)
can't. They want to do a three-week
[69:37] (4177.04s)
sprint, but we like four-week sprints or
[69:39] (4179.20s)
whatever it is. And it's like um I use
[69:41] (4181.68s)
that as an example because that did
[69:43] (4183.04s)
happen a couple times where like someone
[69:45] (4185.04s)
thought like, you know what, we should
[69:46] (4186.08s)
do is line up all the sprints across our
[69:48] (4188.08s)
organization and all the engineers are
[69:50] (4190.00s)
like, "Oh, that does sound a good line."
[69:51] (4191.28s)
Bear as a dove. Yeah. And I appreciate
[69:53] (4193.92s)
like I I really like that culture of
[69:55] (4195.88s)
like someone says we should all flip to
[69:58] (4198.24s)
Macs and like no, I like my Windows
[69:59] (4199.68s)
laptop. Why? Because I do. Or like
[70:01] (4201.12s)
someone else has a Linux laptop. It's
[70:02] (4202.40s)
like, well, I'm just going to do that
[70:03] (4203.36s)
because I feel like it. And um unless
[70:06] (4206.56s)
there's a reason to dictate it, like a
[70:08] (4208.24s)
really really strong reason, the default
[70:09] (4209.92s)
answer is just everything goes to the
[70:11] (4211.60s)
lowest level. So again, like you're on
[70:14] (4214.00s)
call because it's your software and it
[70:15] (4215.52s)
it feels in most cases like you're in a
[70:19] (4219.20s)
sevenperson startup or a 10 person
[70:20] (4220.80s)
startup because like you picked your
[70:22] (4222.80s)
language, you picked your your uh how
[70:25] (4225.92s)
you deploy it, you picked how you QA it,
[70:28] (4228.08s)
you picked, you know, how you monitor
[70:30] (4230.16s)
it, you you set your own monitors and
[70:31] (4231.84s)
alarms. It's not like there's some
[70:33] (4233.12s)
global Amazon click the alarm button
[70:34] (4234.96s)
like, oh god, no. Like you have to make
[70:36] (4236.32s)
all this stuff up and people make it up
[70:37] (4237.84s)
wrong, but you learn, right? you I think
[70:40] (4240.96s)
there's a lot of skills like uh
[70:44] (4244.24s)
you know I can't name the exact gaps but
[70:46] (4246.00s)
sometimes you you know I would talk to
[70:47] (4247.28s)
the Facebook engineers and like they've
[70:48] (4248.80s)
never dealt with an emergency of like
[70:51] (4251.76s)
how fast you know it's like where are
[70:53] (4253.04s)
alarm you know where are that was like
[70:55] (4255.12s)
an example was I said something about
[70:56] (4256.40s)
like where are your metrics and stuff
[70:58] (4258.48s)
like that for this system and they're
[70:59] (4259.68s)
like I don't know I'll have to go look
[71:01] (4261.12s)
for them and I'm like blew my mind
[71:02] (4262.96s)
because if you're an Amazon engineer you
[71:04] (4264.80s)
have a freaking hot link on your browser
[71:07] (4267.28s)
right because you have to click the
[71:08] (4268.40s)
button really fast when there's an
[71:10] (4270.32s)
emergency and see all your alarms and
[71:12] (4272.24s)
they better be on like one one big
[71:14] (4274.00s)
dashboard with the alarms that you know
[71:15] (4275.36s)
about and the metrics you know about
[71:16] (4276.88s)
because you support this system and you
[71:19] (4279.12s)
know exactly how everything works and
[71:20] (4280.40s)
you know that your memory usage is
[71:22] (4282.24s)
higher than usual so what the heck is
[71:23] (4283.76s)
going on and so um I think it just
[71:27] (4287.28s)
builds a lot of skills at that like
[71:28] (4288.48s)
really low level of like we own the
[71:30] (4290.00s)
whole stack we own the product we you
[71:31] (4291.68s)
know frequently like you're you're
[71:32] (4292.88s)
making product decisions because there's
[71:34] (4294.16s)
not enough product managers for your
[71:35] (4295.68s)
space or things like that so on the
[71:37] (4297.60s)
frugality side of things for example
[71:39] (4299.28s)
Well, so I'm sensing that you're like as
[71:41] (4301.44s)
an Amazon engineer, you're actually
[71:42] (4302.80s)
really close to, you know, the the
[71:44] (4304.80s)
metal, the work. You're in a small team.
[71:47] (4307.28s)
You you have a lot of ownership, your
[71:49] (4309.04s)
your whole team. You have to wear
[71:50] (4310.80s)
multiple hats. You you cannot afford to
[71:52] (4312.88s)
like, you know, like buy all sorts like
[71:54] (4314.88s)
use budget cuz Amazon doesn't want you
[71:56] (4316.80s)
to. plus that's not my problem is
[71:59] (4319.04s)
something that you know that's one of
[72:00] (4320.16s)
the quotes in the leadership principle
[72:01] (4321.44s)
is like you you can't say like that's a
[72:03] (4323.84s)
product decision and like go la I don't
[72:06] (4326.72s)
want to make a decision like no dude
[72:08] (4328.08s)
like sorry you're an engineer I know but
[72:09] (4329.92s)
you have to make this product decision
[72:11] (4331.12s)
now and then you also need to listen to
[72:13] (4333.36s)
customers and stay close to them which
[72:15] (4335.36s)
is already I guess like you know like a
[72:17] (4337.36s)
a startup founder would like say like
[72:19] (4339.44s)
these are my ideal folks plus on top of
[72:22] (4342.40s)
it when you leave any big company Google
[72:25] (4345.60s)
Meta uh uh maybe even Microsoft, you're
[72:28] (4348.56s)
used to internal tools that do not exist
[72:30] (4350.64s)
outside of the world. But with Amazon,
[72:32] (4352.24s)
especially these days, you're using AWS.
[72:34] (4354.88s)
Guess which one is the most popular?
[72:36] (4356.56s)
Yeah. you're lucky service. I would say
[72:38] (4358.64s)
both lucky but also I definitely heard
[72:41] (4361.04s)
teams at Amazon switching to publicly
[72:44] (4364.08s)
available tools in part because they say
[72:46] (4366.08s)
I wanted to learn this like this is a
[72:48] (4368.08s)
popular tool these days which is again
[72:49] (4369.36s)
is like one of those big advantages of a
[72:51] (4371.24s)
a a system like Amazon where you can
[72:53] (4373.68s)
just choose to use whatever you want is
[72:55] (4375.20s)
someone saying the hot new way of doing
[72:57] (4377.44s)
Android development is X so we're going
[72:59] (4379.28s)
to do that for our for this next project
[73:01] (4381.20s)
because we you know us collectively on
[73:04] (4384.00s)
this team want to learn that tool so
[73:05] (4385.52s)
we're gonna use it like that's pretty
[73:06] (4386.96s)
awesome like
[73:08] (4388.08s)
Such a good way of keeping on top of
[73:09] (4389.60s)
tech is being able to say we're just
[73:11] (4391.12s)
going to use whatever we want and we've
[73:12] (4392.96s)
read a lot about this thing. It looks
[73:14] (4394.16s)
like it's popular so we're going to try
[73:15] (4395.28s)
it out. Yeah, this is awesome. I mean my
[73:18] (4398.40s)
my view changed a little bit about
[73:20] (4400.64s)
Amazon because it just seems like unlike
[73:23] (4403.36s)
some big tech jobs that you know you can
[73:25] (4405.84s)
you either go to big tech or become a
[73:27] (4407.36s)
founder. This Amazon does open multiple
[73:29] (4409.36s)
paths. You can keep going on in larger
[73:31] (4411.04s)
tech companies you can you know found a
[73:32] (4412.96s)
startup because you've kind of seen how
[73:34] (4414.16s)
it's done. You can join a startup. You
[73:35] (4415.84s)
can join a scale up. Pretty cool. Yeah.
[73:38] (4418.24s)
And I think I think the the downside
[73:40] (4420.80s)
which I would say absolutely exists at
[73:43] (4423.28s)
Amazon is related to the like
[73:46] (4426.48s)
independence thing of like everyone can
[73:48] (4428.00s)
do whatever they want is uh imagine the
[73:50] (4430.72s)
the manager who says we're going to be
[73:52] (4432.88s)
working on these projects. No, we can't
[73:54] (4434.96s)
go do that operational excellence. We're
[73:56] (4436.56s)
just going to put two people on call and
[73:57] (4437.92s)
we'll get paid 17 times a week. And like
[74:00] (4440.96s)
there's not the uber Amazon policy
[74:03] (4443.92s)
holder who says like you manager are
[74:05] (4445.60s)
doing it wrong. you need to be fired
[74:07] (4447.12s)
right now. Like, so so you do end up
[74:09] (4449.92s)
with these pockets of just absolute
[74:11] (4451.60s)
horribleness because people can do
[74:13] (4453.04s)
whatever they want. And so once in a
[74:14] (4454.88s)
while you talk to someone and you hear
[74:16] (4456.16s)
about what it's like on their team. And
[74:17] (4457.44s)
I'm like, that sounds horrific. Like I
[74:19] (4459.20s)
can't believe your team sucks so much.
[74:21] (4461.52s)
And so when someone says, you know,
[74:23] (4463.60s)
you'll see on Reddit or something about
[74:24] (4464.96s)
like, you know, oh my god, don't go to
[74:26] (4466.48s)
Amazon. Everyone leaves after 6 months.
[74:28] (4468.48s)
They don't. Like the actual stats are
[74:30] (4470.96s)
not at all. But in some pockets they do.
[74:32] (4472.64s)
But in some pockets they do. Absolutely.
[74:34] (4474.96s)
I have met teams that had no no one like
[74:38] (4478.08s)
13 engineers on one team. No one had a
[74:40] (4480.64s)
tenure more than 12 months because they
[74:42] (4482.24s)
all left. They were all leaving. People
[74:44] (4484.48s)
would join the team, realize how
[74:45] (4485.76s)
terrible it was and they transfer off
[74:47] (4487.12s)
and it just like just pop on, pop off,
[74:49] (4489.28s)
pop on, pop off because the team was
[74:50] (4490.72s)
terrible, the manager was bad, their
[74:52] (4492.24s)
senior manager wasn't paying attention.
[74:53] (4493.76s)
Like there are these pockets of
[74:55] (4495.04s)
terribleness. And so again on the like
[74:58] (4498.32s)
if you're in a bad spot, move somewhere
[74:59] (4499.60s)
else. Internal transfers are great. And
[75:01] (4501.12s)
I assure you like I was in six orgs. I
[75:04] (4504.88s)
had a bad manager I guess sort of like
[75:07] (4507.44s)
two bad managers. They were both fired
[75:09] (4509.80s)
like everywhere else was great. Like and
[75:13] (4513.04s)
those places became great after I had
[75:15] (4515.20s)
new managers. And so I had great
[75:17] (4517.28s)
managers. We had great orgs, great
[75:18] (4518.96s)
peers. And you would just see pockets of
[75:20] (4520.80s)
terribleness here or there. That's
[75:22] (4522.56s)
almost like the inevitable downside of
[75:24] (4524.92s)
having thousands of little startups
[75:27] (4527.60s)
around the way is like once in a while
[75:28] (4528.96s)
you end up with a really bad situation.
[75:31] (4531.20s)
I know you had a short at Facebook as
[75:33] (4533.12s)
well but in total how long did you spend
[75:34] (4534.80s)
at Amazon? I think it was a little over
[75:37] (4537.52s)
12 years uh at Amazon little Amazon a
[75:40] (4540.96s)
bit of Facebook afterwards you had a
[75:42] (4542.56s)
little stint. I took a leave of absence
[75:44] (4544.16s)
in the like towards the end I took a
[75:45] (4545.92s)
leave um uh but yeah and then you worked
[75:49] (4549.60s)
at at Basil Academy for a little bit as
[75:51] (4551.92s)
well but then you made a decision to
[75:54] (4554.64s)
retire. What what and you're you're
[75:56] (4556.80s)
still young. You don't look 65 to me. So
[76:00] (4560.40s)
how did you decide on retiring and and
[76:03] (4563.60s)
you know like what did it take to build
[76:06] (4566.08s)
up a kind of financial independence to
[76:08] (4568.24s)
be able to do this and what do you do
[76:10] (4570.16s)
right now? So yeah. Yeah. So many years
[76:13] (4573.28s)
ago, I have no idea how I got so lucky
[76:15] (4575.44s)
that I stumbled on the idea of financial
[76:17] (4577.20s)
independence and read about a bunch
[76:18] (4578.64s)
about it and learned math uh you know
[76:20] (4580.72s)
like financial math in terms of like
[76:22] (4582.80s)
withdrawal rates and investment things
[76:24] (4584.48s)
and um index funds and stuff. And so
[76:27] (4587.68s)
many many years ago and in my 30s or
[76:31] (4591.24s)
whatever, yeah, I guess that's a while
[76:33] (4593.04s)
ago. Uh I I early 30s, let's say, uh I
[76:36] (4596.80s)
realized like how much money I would
[76:38] (4598.72s)
need and like keep an eye on our
[76:39] (4599.92s)
expenses every year and stuff like that.
[76:41] (4601.12s)
And so, uh, Amazon pays well, stocks go
[76:45] (4605.04s)
up, like over time. If you're making
[76:48] (4608.00s)
tech salary, you don't
[76:51] (4611.68s)
They did. They used to back in the day.
[76:53] (4613.68s)
Back in my day, stocks actually they
[76:56] (4616.00s)
Yeah. Yeah. Hopefully they will again
[76:57] (4617.52s)
someday.
[76:59] (4619.32s)
Um, and I would say almost like as for
[77:02] (4622.32s)
tech workers, it's like keeping an eye
[77:03] (4623.44s)
on your expenses is probably more
[77:04] (4624.72s)
important than keeping an eye on your
[77:05] (4625.92s)
income because you're probably making
[77:07] (4627.12s)
enough to retire if you don't spend a
[77:08] (4628.64s)
lot. Um, and so thankfully my wife and I
[77:11] (4631.12s)
don't and it comes down to that kind of
[77:13] (4633.28s)
thing. And so a good number of years ago
[77:16] (4636.24s)
I built a lot I mean I don't know eight
[77:18] (4638.32s)
years ago I built a spreadsheet and I
[77:19] (4639.60s)
could tell about when we would have
[77:20] (4640.72s)
enough money where I wouldn't need to
[77:21] (4641.68s)
work anymore. And that was my position
[77:23] (4643.84s)
when I was getting promoted to director
[77:25] (4645.84s)
at Amazon. I could see like essentially
[77:28] (4648.48s)
like my next vestings make us way past
[77:31] (4651.04s)
my safety margin and so I don't need to
[77:32] (4652.72s)
work anymore.
[77:34] (4654.12s)
Um, so there's just like that that
[77:36] (4656.56s)
fundamental like what am I going to do
[77:38] (4658.16s)
with my life when I don't need to when I
[77:39] (4659.84s)
don't need to work. I can keep working.
[77:41] (4661.20s)
I enjoy working like some some aspects
[77:42] (4662.80s)
of it. Like some I don't like waking up
[77:44] (4664.24s)
in the morning and commuting sucks. But
[77:46] (4666.40s)
um I I really appreciate sometimes like
[77:49] (4669.36s)
walk hanging out with smart people and
[77:51] (4671.04s)
talking to them or or doing creative
[77:52] (4672.64s)
things. So Bezos Academy poached me out
[77:55] (4675.52s)
of Amazon at a convenient time when I
[77:57] (4677.20s)
didn't need to work anymore and I was
[77:58] (4678.40s)
having a little bit of that existential
[77:59] (4679.92s)
crisis of like what do I do next with my
[78:01] (4681.68s)
life? Um, so they poached me. I worked
[78:04] (4684.00s)
there for a bit and then ended up
[78:06] (4686.24s)
deciding that that's not like the right
[78:07] (4687.92s)
kind of job for me. Um, and so I
[78:10] (4690.64s)
thought, well, like my dream since I was
[78:13] (4693.92s)
young was to write a book, you know,
[78:16] (4696.16s)
actually like fiction book. I want to
[78:18] (4698.00s)
write sci-fi or fantasy about dragons
[78:20] (4700.08s)
and stuff, but how do you force yourself
[78:22] (4702.96s)
to write? Because I've been wanting to
[78:24] (4704.24s)
write for years and I have like
[78:26] (4706.08s)
literally 20 stories of like 50 pages
[78:30] (4710.00s)
each. Um, and so I thought, well, how do
[78:32] (4712.08s)
you force yourself to write regularly?
[78:33] (4713.36s)
Newsletter is an obvious answer. So, I'd
[78:36] (4716.28s)
written years before while I was at
[78:38] (4718.40s)
Amazon, I'd written a few very popular
[78:40] (4720.00s)
articles on LinkedIn that just sort of
[78:42] (4722.32s)
blew up. Um, one of them was one, the
[78:44] (4724.72s)
main one, in fact, was like the Amazon
[78:46] (4726.16s)
leadership principles article that a lot
[78:47] (4727.68s)
of people know about. Y uh where I'd
[78:50] (4730.40s)
written an article or sorry, I'd written
[78:52] (4732.96s)
an email that I would forward to people
[78:55] (4735.12s)
when friends or family or relatives or
[78:56] (4736.96s)
whatever else said, "Hey, I want to
[78:58] (4738.08s)
interview at Amazon." I would forward
[78:59] (4739.20s)
them this email that essentially said,
[79:00] (4740.56s)
"Here's what you need to know about
[79:02] (4742.00s)
interviewing at Amazon." So, I turned
[79:04] (4744.16s)
that into an article on LinkedIn. And it
[79:05] (4745.76s)
became wildly popular. And so, I
[79:08] (4748.00s)
thought, well, why don't I do that more?
[79:09] (4749.44s)
Like, that's sort of fun. I I I'll do a
[79:11] (4751.28s)
newsletter for a bit. I I start a
[79:13] (4753.32s)
newsletter and like by the second or
[79:15] (4755.68s)
third article, I had like thousands of
[79:17] (4757.20s)
subscribers. And I was like, holy
[79:18] (4758.64s)
smokes. Okay. Well, I need to write a
[79:20] (4760.72s)
paid newsletter is what I need to do
[79:22] (4762.08s)
just for fun. And this was literally
[79:23] (4763.52s)
like I just see the tools where I can
[79:26] (4766.16s)
click paid and say like you could pay
[79:27] (4767.84s)
and like how much should they pay me? I
[79:29] (4769.12s)
don't know, $5. Um, so I just pushed
[79:31] (4771.28s)
some buttons said I I thought I'll just
[79:33] (4773.20s)
write two articles a week then. So
[79:35] (4775.28s)
things have changed over time, but in
[79:36] (4776.64s)
general it just blew up and for a while
[79:39] (4779.52s)
it was I'm going to write a new article.
[79:41] (4781.68s)
Scarlet Inc., right? This is Scarlet
[79:43] (4783.36s)
Inc. Yeah. So the short thing for
[79:45] (4785.04s)
Scarlet Inc. is I got that uh uh domain
[79:48] (4788.32s)
a long time ago when I thought like I
[79:49] (4789.84s)
want to do some kind of writing, a blog,
[79:51] (4791.28s)
a newsletter, a a book, something. Um I
[79:54] (4794.16s)
wanted red ink because I always like the
[79:56] (4796.16s)
red pens at Amazon when I mark up
[79:57] (4797.68s)
documents, you know, document culture,
[79:59] (4799.12s)
right? You sit there like with a piece
[80:00] (4800.80s)
of paper in front of you and I'd always
[80:02] (4802.48s)
use the red pens because I could see it
[80:04] (4804.00s)
better against the uh uh black
[80:06] (4806.16s)
printouts. And so I thought red ink, but
[80:08] (4808.32s)
red ink was taken and it was really
[80:09] (4809.68s)
expensive to buy the domain. So I
[80:11] (4811.20s)
thought scarlet, it's even more
[80:12] (4812.88s)
literary.
[80:14] (4814.40s)
Scarlet ink. It sounds really fancy. So,
[80:16] (4816.16s)
um, so I got Scarlet Inc. I wrote the
[80:18] (4818.16s)
Scarlet Inc. newsletter. It was really
[80:19] (4819.68s)
fun. Um, and I was writing for a bit and
[80:22] (4822.56s)
I kept think I kept saying and thinking
[80:24] (4824.40s)
like I'm I'm just sort of on a sobatical
[80:26] (4826.16s)
where I'm writing and then I'll figure
[80:28] (4828.08s)
out what I want to do for a job like do
[80:30] (4830.00s)
I want to take another nonprofit job? Do
[80:31] (4831.84s)
I want to go to a startup? Do I want to
[80:33] (4833.04s)
go to, I don't know, Google or Microsoft
[80:34] (4834.96s)
or something? And I did a couple small
[80:37] (4837.36s)
interview like I talked to some people
[80:38] (4838.40s)
at Google. I talked to some people at
[80:39] (4839.76s)
other companies and it was like me. But
[80:42] (4842.16s)
the newsletter kept growing and then
[80:43] (4843.76s)
it's you start to get to the point of
[80:45] (4845.32s)
like but why would I go to a company
[80:48] (4848.80s)
again when I'm making so like I don't
[80:52] (4852.72s)
know a couple years a year and a half
[80:54] (4854.08s)
ago two years ago I sort of crossed
[80:55] (4855.68s)
where we were making in my newsletter
[80:58] (4858.00s)
more than we were spending and so like I
[81:01] (4861.12s)
haven't actually withdrawn any of my
[81:02] (4862.56s)
retirement money withdrawn any from the
[81:04] (4864.88s)
I mean it's only grown because I don't
[81:06] (4866.80s)
need to touch it because I have this
[81:07] (4867.92s)
newsletter going and then it gets to
[81:09] (4869.20s)
this weird point of like why would I go
[81:11] (4871.68s)
when I'm writing a newsletter that
[81:12] (4872.96s)
already pays me a salary and I'm not
[81:15] (4875.12s)
spending all that much time on it. I'm
[81:16] (4876.40s)
not commuting. I'm sitting in my little
[81:18] (4878.16s)
office
[81:19] (4879.00s)
here writing one artic I end up writing
[81:21] (4881.92s)
like one article a
[81:23] (4883.40s)
week. I enjoy writing. I sit down. I
[81:26] (4886.08s)
write for a few hours. Sometimes I'll
[81:27] (4887.68s)
write over a period of a few days. Like
[81:29] (4889.36s)
I'll just, you know, sit down for a
[81:30] (4890.64s)
couple hours, write something,
[81:31] (4891.60s)
brainstorm. I'll be on a walk and I'll
[81:34] (4894.16s)
text myself an idea of something I could
[81:35] (4895.76s)
write about.
[81:37] (4897.08s)
Um, and so you sort of like the idea of
[81:39] (4899.92s)
getting a job fades away when you
[81:41] (4901.28s)
realize you have so many other things
[81:42] (4902.40s)
you can do in life.
[81:43] (4903.96s)
Uh, sitting in the office and being
[81:46] (4906.32s)
forced to do it for 40 plus hours a week
[81:48] (4908.08s)
doesn't sound quite appealing. So sounds
[81:51] (4911.04s)
like you you built up really good
[81:52] (4912.64s)
savings as a mix of just working in
[81:54] (4914.60s)
Becktech being at a good time where
[81:57] (4917.04s)
where you know like there were stocks
[81:58] (4918.56s)
awarded where where yeah back when stock
[82:00] (4920.96s)
it was appreciation but you also read
[82:03] (4923.12s)
upon financial independence the planning
[82:05] (4925.76s)
you actually started to make a plan and
[82:08] (4928.00s)
then there was this point where you
[82:09] (4929.36s)
crossed the boundary where like okay you
[82:11] (4931.84s)
are financially stable and you could
[82:13] (4933.36s)
afford not to work you kind of explored
[82:16] (4936.08s)
some things different things you know
[82:17] (4937.84s)
like the the nonprofit job at at the the
[82:21] (4941.88s)
academy whether you should work at other
[82:24] (4944.08s)
companies and then you just did
[82:25] (4945.44s)
something that kind of like just worked
[82:27] (4947.96s)
out. Sounds like because you didn't Is
[82:30] (4950.48s)
it fair to say that maybe it worked out
[82:32] (4952.00s)
cuz you just didn't really have the
[82:33] (4953.20s)
pressure? I mean if this didn't if if
[82:35] (4955.12s)
this like once a week newsletter didn't
[82:37] (4957.04s)
work out, you probably would try
[82:38] (4958.80s)
something else, right? Because you you
[82:40] (4960.08s)
have no pressure right now. Yeah. If you
[82:41] (4961.92s)
imagine. Yeah. And that's it's I don't
[82:44] (4964.48s)
know if it's responsible for the
[82:46] (4966.16s)
newsletter working out. The fact that I
[82:47] (4967.68s)
didn't need it. I mean, if I really
[82:49] (4969.52s)
wanted to make it work, that probably
[82:50] (4970.80s)
would have worked. Also, I think a
[82:52] (4972.48s)
little bit of what might have happened
[82:53] (4973.52s)
was um if you're not trying to impress
[82:56] (4976.72s)
people, you your work product for
[82:58] (4978.64s)
creative things is probably a bit better
[83:00] (4980.40s)
because I'm not I wasn't and I I don't
[83:02] (4982.96s)
try to appeal to like what do people
[83:04] (4984.56s)
really want to hear? Like what's the hot
[83:06] (4986.40s)
topic? Like I I've only written about AI
[83:08] (4988.64s)
like once or twice. Like I think only
[83:10] (4990.56s)
once like real an article about AI,
[83:12] (4992.40s)
which is mostly saying like now it's all
[83:14] (4994.16s)
overblown. Um which I still believe in.
[83:16] (4996.88s)
Um, but uh
[83:19] (4999.56s)
uh I think that for the most part it's
[83:23] (5003.52s)
like you're writing about the things
[83:24] (5004.72s)
that you think are interesting or what
[83:25] (5005.84s)
someone else has talked to you about and
[83:26] (5006.88s)
you're like, "Oh, that was such a cool
[83:28] (5008.00s)
thing." Um, I did some I did actually
[83:30] (5010.24s)
some co like one-on-one coaching for a
[83:31] (5011.84s)
while, too. Like that was like one of
[83:33] (5013.52s)
those side things once I started writing
[83:34] (5014.72s)
and I actually I think when we first met
[83:37] (5017.12s)
you, you were still doing a little bit
[83:38] (5018.80s)
of that. Yeah. Yeah. Yeah. So, I was
[83:40] (5020.80s)
doing hourly coaching and then I my wife
[83:43] (5023.04s)
and I argued about it cuz it was like I
[83:45] (5025.04s)
was making a lot of money doing hourly
[83:46] (5026.72s)
coaching and uh I kept raising my rates
[83:50] (5030.00s)
and she's like, "Stop it. You're not
[83:51] (5031.12s)
going to get any more work." I'm like,
[83:52] (5032.00s)
"Well, I know cuz I don't want to be
[83:53] (5033.52s)
doing this anymore." So, I kept raising
[83:55] (5035.12s)
the rates trying to send people away and
[83:56] (5036.56s)
then I started feeling guilty cuz I was
[83:58] (5038.32s)
making such an huge amount of money per
[84:00] (5040.24s)
hour and people were still signing up
[84:02] (5042.16s)
for it. But then you start to have this
[84:03] (5043.44s)
like performance pressure of like, am I
[84:05] (5045.60s)
worth that many, you know, that much
[84:07] (5047.44s)
money? like, "Oh my gosh." Like, "Holy
[84:09] (5049.36s)
crap." So, uh, uh, I lowered the rates a
[84:12] (5052.00s)
little bit and then people were still
[84:12] (5052.96s)
buying and I I ended up just turning it
[84:14] (5054.64s)
off. I just told everyone like, "Sorry,
[84:16] (5056.48s)
I'm I'm not getting joy out of it
[84:18] (5058.56s)
anymore." And I don't know why I'm doing
[84:19] (5059.92s)
it. It's it's a weird existential
[84:21] (5061.68s)
problem to run into when you like
[84:23] (5063.12s)
realize that money incremental money
[84:25] (5065.60s)
won't change anything other than like a
[84:27] (5067.36s)
nicer car or something which is like
[84:30] (5070.80s)
like fundamentally something like if you
[84:32] (5072.96s)
if you feel that way like this is how
[84:35] (5075.60s)
you reach financial independence is not
[84:37] (5077.20s)
to keep growing your spending because a
[84:39] (5079.84s)
lot of people be like I got a raise so I
[84:41] (5081.92s)
spend more. Yeah. If you stop that from
[84:44] (5084.56s)
happening at the right time then as your
[84:46] (5086.48s)
career improves and you make more and
[84:48] (5088.00s)
more money the only thing it's doing is
[84:49] (5089.76s)
increasing your savings rate and that's
[84:51] (5091.76s)
how if you have a good tech job um over
[84:54] (5094.64s)
time like it you know you can afford
[84:56] (5096.56s)
things and save 15% and the next time
[84:58] (5098.48s)
you know your next raise you can save
[85:00] (5100.08s)
20% of your salary and then 25 and then
[85:01] (5101.84s)
40 and then you know by the end we were
[85:03] (5103.92s)
my wife was working at Amazon which is
[85:05] (5105.52s)
partially how this happened. I mean, she
[85:07] (5107.20s)
she retired a couple years before me,
[85:08] (5108.80s)
but you know, we had two tech workers.
[85:10] (5110.80s)
We were saving like 85%, I think, of our
[85:13] (5113.60s)
income. Like, it you can retire pretty
[85:16] (5116.08s)
damn fast if you're saving 85% of what
[85:17] (5117.84s)
you make. Um, and then where did you
[85:20] (5120.00s)
learn about financial independence or or
[85:22] (5122.56s)
for people who are thinking about like,
[85:24] (5124.00s)
okay, I'm actually inspired. I'd like to
[85:26] (5126.24s)
learn more. Where did you start? And do
[85:27] (5127.68s)
you have some pointers of books,
[85:29] (5129.52s)
resources?
[85:31] (5131.12s)
Yeah, the internet. Um, I would say, uh,
[85:33] (5133.68s)
yeah, Mr. money mustache and he doesn't
[85:36] (5136.48s)
write as much anymore but you could
[85:38] (5138.08s)
definitely he has some pretty fantastic
[85:39] (5139.92s)
archives. The Mr. Money mustache is a
[85:41] (5141.92s)
blog you know newsletter. Um Mad
[85:44] (5144.96s)
Scientist was another one who also he's
[85:46] (5146.96s)
been retired for years. It's sort of
[85:48] (5148.24s)
funny like you these financial
[85:50] (5150.64s)
independence newsletters in particular
[85:52] (5152.08s)
are just sort of hilarious because
[85:54] (5154.08s)
they'll be like I have you know $10,000
[85:57] (5157.36s)
and I want to retire in five years. And
[85:58] (5158.96s)
so they write like mad for five years
[86:00] (5160.72s)
and they're like I've made it. like I
[86:02] (5162.32s)
think I've made it and then you notice
[86:04] (5164.08s)
like the newsletters just sort of peters
[86:05] (5165.60s)
out and stops like they'll write once
[86:06] (5166.88s)
every two months because they're busy
[86:09] (5169.12s)
with their life and like the the driving
[86:11] (5171.60s)
that's what they wanted to do. It was
[86:13] (5173.04s)
their thing. So uh almost no active
[86:15] (5175.44s)
newsletter exists that was from the time
[86:17] (5177.44s)
of which I originally read about it.
[86:20] (5180.20s)
Um uh but yeah, Mr. Money Mustache, Mad
[86:23] (5183.12s)
Fientist um are the biggest ones that I
[86:26] (5186.56s)
read about at the time. But um there
[86:29] (5189.12s)
there's a good selection of of um
[86:32] (5192.00s)
actually if you hold on one second.
[86:33] (5193.76s)
There's
[86:34] (5194.84s)
a I was going to look up the book. Uh I
[86:38] (5198.48s)
I can look it up right now. There there
[86:39] (5199.84s)
there's a a book that I recommend on um
[86:42] (5202.40s)
on my website. In fact, you can set it
[86:45] (5205.20s)
over and then we'll put it in the show
[86:46] (5206.56s)
notes after show notes. Yeah. Yeah. This
[86:48] (5208.56s)
was really fun. Yeah. Both about
[86:50] (5210.96s)
software engineering and a little bit on
[86:53] (5213.20s)
management. We didn't go into too much
[86:55] (5215.20s)
detail this time, but perhaps on a on a
[86:56] (5216.96s)
follow-up one. Yeah. Well, thanks very
[86:59] (5219.28s)
much for for being here. This was fun.
[87:00] (5220.88s)
Oh, thanks for having me. It's been fun.
[87:02] (5222.40s)
I enjoy uh uh chatting on these topics.
[87:04] (5224.72s)
I hope you enjoyed this deep dive into
[87:06] (5226.16s)
how engineers at Amazon work, as well as
[87:08] (5228.48s)
how working at a large tech company like
[87:10] (5230.08s)
Amazon could help getting to early
[87:12] (5232.08s)
retirement like Dave has done. So,
[87:14] (5234.24s)
thanks to Dave for a great conversation.
[87:16] (5236.32s)
You can read more from him on his
[87:17] (5237.92s)
newsletter at scarleting.com and you can
[87:20] (5240.40s)
find him on social media as linked in
[87:22] (5242.16s)
the show notes below. We previously did
[87:24] (5244.08s)
a more detailed deep dive on the
[87:25] (5245.44s)
engineering culture at Amazon. Check it
[87:27] (5247.36s)
out linked in the show notes. If you've
[87:29] (5249.28s)
enjoyed this podcast, please do
[87:30] (5250.72s)
subscribe on your favorite podcast
[87:32] (5252.16s)
platform and on YouTube. This helps more
[87:34] (5254.48s)
people discover the podcast and a
[87:36] (5256.24s)
special thank you if you leave a rating.
[87:38] (5258.00s)
Thanks and see you in the next one.