[00:00] (0.16s)
Think about the area under the curve of
[00:02] (2.24s)
utility that you could contribute to
[00:03] (3.92s)
society and everything else is
[00:06] (6.48s)
similacrim. It is not real. When you
[00:09] (9.12s)
think about SBF, when you think about
[00:10] (10.96s)
therronos, when you think about the
[00:12] (12.32s)
things that truly disgrace us as people
[00:16] (16.16s)
who create technology, when you peel
[00:18] (18.56s)
back a little bit, you realize there's
[00:20] (20.72s)
nothing. It was a [ __ ] lie. I don't
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want that for us. people outside of this
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room, the world at large looks at tech
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and they hate us sometimes because those
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are the people who represent us. And I
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say, "Not for me. They don't represent
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[Applause]
[00:43] (43.12s)
Welcome to another episode of the Light
[00:44] (44.80s)
Cone. This time we're doing it live.
[00:47] (47.04s)
We're not used to doing it in front of a
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studio audience. So, we thought we would
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uh start off with a controversial topic.
[00:53] (53.60s)
This is something that uh a bunch of
[00:55] (55.44s)
people who are at this conference uh
[00:58] (58.40s)
have been I don't know just talking
[01:00] (60.24s)
about coming to us to talk about. Uh is
[01:03] (63.04s)
this the last window to get rich? Are
[01:05] (65.76s)
you worried about this? Are you guys
[01:07] (67.12s)
worried about this? Is this the end of
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capitalism? What's what's happening?
[01:11] (71.04s)
Be like no
[01:12] (72.08s)
money going to stop to exist with EGI.
[01:14] (74.48s)
they they won't they won't admit to it
[01:16] (76.48s)
but in private conversations this is one
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of the topics that certainly we've been
[01:20] (80.40s)
debating.
[01:21] (81.20s)
Yeah. what you know why is this coming
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up actually
[01:24] (84.48s)
seems like at least when we speak to
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people who are applying to IC who are
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kind of like members of the audience
[01:30] (90.56s)
there's a real sense of uncertainty
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created by AI right now right like the
[01:34] (94.64s)
thing it's like the sense of will the
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jobs that we thought would be there be
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available and if we're not um if they're
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not like kind of what do we do and if
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we're not sort of if we don't have real
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ownership in something that's like
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valuable and growing like what will we
[01:50] (110.80s)
be left with That seems to be the thing
[01:52] (112.56s)
that comes up a lot.
[01:53] (113.52s)
I had dinner with some undergrads who
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are here last night and they were saying
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that this is one of the things that
[01:58] (118.80s)
people are talking about a lot on
[02:00] (120.24s)
college campuses. It's like, hey, the
[02:02] (122.16s)
AI's gotten really good at programming
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now. Um, what's going to happen to all
[02:06] (126.96s)
the programming jobs? Like it used to be
[02:08] (128.96s)
the case that if you were a CS major,
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there is a very clear path to like a
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very stable like upper middle class
[02:16] (136.08s)
background where you get like a good
[02:17] (137.52s)
stable job as a as a programmer. Um but
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like are those jobs still going to be
[02:22] (142.08s)
here in 10 years? Like Yeah.
[02:24] (144.16s)
Yeah. Like my my parents were really
[02:26] (146.40s)
proud when I uh you know graduating I
[02:28] (148.88s)
you know got my degree and then I got my
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job at Microsoft and I was a level 59 PM
[02:35] (155.52s)
uh you know lowest of the low but I had
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health insurance and my parents were
[02:38] (158.96s)
really really proud of me. And you know,
[02:41] (161.44s)
one of the fears, frankly, like that
[02:43] (163.52s)
we're hearing uh and it's sort of, you
[02:45] (165.92s)
know, coming out in the numbers is that
[02:48] (168.32s)
will there actually be jobs? You I think
[02:51] (171.12s)
it's a tricky thing right now with the
[02:53] (173.52s)
advent of intelligence. You know, some
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of the simplest things that people rely
[02:57] (177.20s)
on entry level people right out of
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college for, uh they're not hiring as
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many of them anymore. And you know the
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craziest stat I think this came out of
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uh uh the New York Fed in February of
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this year. Um computer science majors uh
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you know obviously this is not the
[03:15] (195.36s)
people in this room. This is just like
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out of like you know a normal
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distribution of all computer science
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majors. 6.1% in unemployment in February
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of this year. Art history in contrast
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was only 3.0%. Wait, you're saying that
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the unemployment rate of art history
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majors is lower than the unemployment
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rate of CS majors?
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Unbelievably, but that's what the stat
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We're talking about, you know, the the
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median, which you know, you guys are so
[03:48] (228.08s)
so many standard deviations above. Don't
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That's concerning, right?
[03:53] (233.04s)
Yeah. But like but like this this role
[03:55] (235.12s)
of like like level 59, you know,
[03:57] (237.52s)
engineer at Microsoft used to be this
[03:59] (239.92s)
like super stable job. If you do that
[04:01] (241.92s)
job, all the adults in your life will be
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like good job. Like you make the you
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made the safe choice, the prudent
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choice. But like one of the things I've
[04:09] (249.04s)
been I've been noodling a lot. It's like
[04:11] (251.12s)
is that actually the safe choice? Like
[04:12] (252.88s)
is it possible that the world has become
[04:15] (255.60s)
inverted and like the career path that
[04:17] (257.92s)
seemed to be like the lowest risk, most
[04:19] (259.84s)
safe path might not be anymore?
[04:23] (263.04s)
Yeah, I think like one thing that's
[04:24] (264.48s)
going to be interesting with this
[04:25] (265.36s)
audience is that there's one theory
[04:26] (266.96s)
there's um Brian Kaplan has this theory
[04:29] (269.52s)
on education. I think it's Brian Kaplan
[04:30] (270.88s)
at least. It's like where um it's
[04:32] (272.64s)
basically all about it's credentiing but
[04:34] (274.72s)
it's actually a very specific thing
[04:36] (276.64s)
that's being credentialed. It's like
[04:38] (278.56s)
what colleges are credentiing to
[04:40] (280.56s)
employers is that um
[04:43] (283.44s)
these people graduate our program which
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means that they can like show up to a
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place on time and like perform a series
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of instructions and you know not do too
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many drugs and like kind of like make it
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through which is like the kind of people
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you want to hire like they'll turn up
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they'll do the job and
[04:59] (299.60s)
if if you're a Microsoft
[05:00] (300.80s)
Yeah. Yeah. like fang like I think like
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any company at scale starts like that's
[05:05] (305.12s)
actually what they are hiring is like
[05:06] (306.56s)
you went to a good college which means
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that you can like do things reliably and
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follow instructions well it's pretty
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clear now in the AI world that like the
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AI is very good at following
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instructions and it's probably going to
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be hard for humans to compete with the
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AI on just like following instructions
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reliably in which case people here need
[05:23] (323.92s)
to think about what are they going to
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get out of their college experience that
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goes beyond just kind of showing up,
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passing the test, following the
[05:32] (332.88s)
instructions really, really well. Like
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it's going to require how do you know to
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do things yourself and how do you have
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like agency and independence? Um cuz
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that's actually the stuff that's going
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to matter in like I think a post AI
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And I think the thing that happened is
[05:46] (346.96s)
uh Dar and I went on this college tour
[05:48] (348.88s)
as well and what's happening is that a
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lot of the CS curriculum is actually
[05:54] (354.88s)
quite outdated. Like how many of you in
[05:57] (357.20s)
the audience if you're still in in in
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college do your courses even allow use
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of a cursor?
[06:05] (365.20s)
Yeah. How how many like forbid you to
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use cursor and like vibe coding tools in
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your CS classes?
[06:10] (370.16s)
Oh yeah. Way more hands.
[06:11] (371.44s)
Yeah. Yeah.
[06:13] (373.36s)
And this is the future and those are the
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kinds of skills that are now
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they're quite literally prohibiting the
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students from learning the tools that
[06:22] (382.16s)
they are going to need in the future.
[06:24] (384.48s)
It's crazy. It's like Google
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when the internet first came out um a
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lot of teachers would say you're not
[06:29] (389.76s)
allowed to use Google
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totally
[06:31] (391.76s)
which is unfathomable today
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and I think to Har's point a lot of the
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most crack students as we were meeting
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them and all these we had we had some
[06:41] (401.04s)
some events that were hosted they had
[06:44] (404.56s)
this um the sense of talking on the side
[06:47] (407.44s)
and working on a lot of side projects to
[06:49] (409.20s)
your point harsh of having a lot of
[06:50] (410.72s)
agency you learn a lot more in the
[06:53] (413.12s)
process of building a lot of projects on
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the side rather than at school. How many
[06:57] (417.44s)
of you had learned way more on
[06:58] (418.72s)
independent projects than at school?
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All right. We picked the right people.
[07:05] (425.36s)
What do you guys think is the answer to
[07:06] (426.48s)
Gary's question? Is this the last window
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to get rich?
[07:08] (428.88s)
Being intellectually honest. One of our
[07:10] (430.64s)
sort of colleagues, Paul Buhight,
[07:12] (432.16s)
pointed this out where it's there's
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probably a flaw in the logic
[07:14] (434.80s)
potentially. like if this is actually
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the last window to make money and get
[07:18] (438.88s)
rich, then you're basically implying
[07:20] (440.32s)
that, you know, like the we're going to
[07:21] (441.84s)
get some definition of AGI or ASI or
[07:24] (444.08s)
whatever you want to call it. Um that's
[07:25] (445.68s)
like a necessary condition for that to
[07:27] (447.20s)
be true. Like in which case we're
[07:29] (449.20s)
probably going to have like
[07:31] (451.52s)
bigger like there's going to be a lot
[07:32] (452.96s)
more going on than just figuring out
[07:34] (454.40s)
like how to make this like human money.
[07:36] (456.00s)
I think that's a concept that Paul talks
[07:37] (457.60s)
a lot about is that in a world where the
[07:39] (459.44s)
machines can do everything that's better
[07:41] (461.28s)
than humans like what value will they
[07:43] (463.44s)
even be in human money in which case why
[07:46] (466.24s)
does it matter that you're going to race
[07:47] (467.52s)
to accumulate like the human money now
[07:50] (470.40s)
the game itself might change the you
[07:52] (472.88s)
know you sort of you grow up you go to
[07:55] (475.28s)
college you graduate you go work you get
[07:57] (477.68s)
a job you buy a house you have a
[07:59] (479.36s)
mortgage all this stuff and then um you
[08:02] (482.96s)
know one of the weirder things that uh I
[08:05] (485.52s)
see people critique San Francisco about
[08:07] (487.60s)
is somehow this belief that San
[08:09] (489.84s)
Francisco itself is about like
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credential maxing which um I don't I
[08:14] (494.88s)
mean I the the part of San Francisco I
[08:17] (497.44s)
want to spend time with is like not
[08:19] (499.04s)
really about that but I can see the
[08:21] (501.12s)
critique
[08:22] (502.00s)
in general I don't think people do their
[08:24] (504.08s)
best work out of fear like you do it out
[08:26] (506.48s)
of like more positive motivations cuz
[08:28] (508.16s)
you're excited about stuff and so I
[08:30] (510.80s)
don't think my advice to anyone here
[08:32] (512.40s)
would be you should drop out of college
[08:35] (515.44s)
and work on an AI startup because it's
[08:37] (517.60s)
going to be your last chance to make
[08:39] (519.28s)
money before I don't know like the event
[08:41] (521.60s)
horizon hits us. Um I do think something
[08:44] (524.80s)
that's interesting to note is just like
[08:47] (527.28s)
the how quickly like AI startups can
[08:50] (530.40s)
grow is definitely something we've
[08:51] (531.76s)
talked about but if you think about um
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something I've been thinking about
[08:55] (535.44s)
recently like I all of us actually when
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we were in college like um we would
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you'd always have speakers come back
[09:01] (541.92s)
like startup founders who were like a
[09:03] (543.84s)
year or two out of college come back and
[09:05] (545.36s)
speak and you hear from them and I kind
[09:07] (547.20s)
of remember that the milestone to hit
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when you were like a year or two out of
[09:10] (550.96s)
college having worked on your startup
[09:12] (552.72s)
was like raising a series A round of
[09:14] (554.64s)
funding and we'd have like the Dropbox
[09:16] (556.32s)
founder come back and say like yeah I
[09:18] (558.08s)
raised like a series A and it's like
[09:19] (559.52s)
really really cool and then it sort of
[09:21] (561.76s)
became okay well actually like maybe a
[09:24] (564.00s)
couple years out of college you could
[09:25] (565.20s)
raise like a series B or a series C. If
[09:27] (567.92s)
you fast forward today, it's like you've
[09:29] (569.44s)
got the cursor founder a couple of years
[09:31] (571.12s)
out of college coming back with a 10
[09:32] (572.64s)
billion dollar company. Like it's like
[09:34] (574.32s)
the the the pace
[09:36] (576.24s)
order magnitude.
[09:37] (577.04s)
Yeah. It's like the orders of magnitude
[09:38] (578.56s)
difference, right? So I actually think a
[09:40] (580.08s)
far more exciting reason to think about
[09:43] (583.28s)
um you know should I start a company or
[09:45] (585.44s)
should I join like one of these fast
[09:46] (586.72s)
growing companies is like the time like
[09:49] (589.44s)
how much you can get done a year or two
[09:51] (591.76s)
like out of college is orders of
[09:54] (594.00s)
magnitudes higher than it was even a few
[09:55] (595.68s)
years ago. And I think that's like for a
[09:58] (598.08s)
certain type of person a very like
[10:00] (600.00s)
exciting motivating factor.
[10:02] (602.80s)
I like that a lot hard that I think
[10:04] (604.32s)
that's why Sam said at the beginning of
[10:06] (606.16s)
the of the event yesterday, this is the
[10:07] (607.76s)
best [ __ ] time in history to start a
[10:09] (609.28s)
company.
[10:10] (610.40s)
Well, the interesting thing about
[10:12] (612.00s)
credential maxing andor what's happening
[10:14] (614.16s)
now is that raising a series A is a
[10:16] (616.72s)
credential that kind of gets bestowed by
[10:19] (619.52s)
a fancy VC uh you know driving a Ferrari
[10:23] (623.60s)
down Sand Hill Road or something, right?
[10:25] (625.36s)
like that's something that's external to
[10:27] (627.68s)
outcomes and often it's you know really
[10:30] (630.56s)
like the shooting of a a starting line
[10:32] (632.88s)
gun as opposed to like something to
[10:34] (634.56s)
celebrate in and of itself. The really
[10:36] (636.48s)
big difference today is that the very
[10:38] (638.72s)
best companies that we get to see
[10:40] (640.16s)
day-to-day, they're like, I don't know,
[10:42] (642.40s)
five people, 10 people. Uh I think all
[10:45] (645.04s)
you know, each of us on stage and all of
[10:46] (646.64s)
the YC partners are sort of collecting
[10:48] (648.96s)
uh incredible startups that we get to
[10:51] (651.44s)
work with that went from zero to 10
[10:53] (653.76s)
million, 12 million a year revenue. Like
[10:56] (656.08s)
that's net revenue. Like it just goes in
[10:58] (658.48s)
the bank. So you basically get the
[11:00] (660.56s)
equivalent of an entire series A and
[11:04] (664.00s)
instead of this fake credential thing
[11:06] (666.80s)
where some fancy person on Twitter with
[11:09] (669.44s)
lots of followers, you know, blesses you
[11:12] (672.32s)
and suddenly like all these people, you
[11:14] (674.24s)
know, TechCrunch says like the new
[11:16] (676.32s)
hottest founder and you know what, like
[11:18] (678.64s)
those are all external things that are
[11:20] (680.32s)
not actually connected to real business
[11:22] (682.56s)
or having an impact on anyone. It's
[11:24] (684.96s)
fake, right? It's the fake credential.
[11:26] (686.80s)
And then the cool thing now is that that
[11:29] (689.36s)
is actually very directly being replaced
[11:32] (692.40s)
by people making things that you know
[11:35] (695.60s)
people not only really need but they're
[11:37] (697.28s)
willing to pay a lot of money for.
[11:39] (699.60s)
This is like a very good point sort of
[11:41] (701.68s)
instead of uh taking the leap out of
[11:44] (704.72s)
fear that this is the only time taking
[11:47] (707.76s)
it from a from a place of approaching
[11:49] (709.84s)
and really going after something where
[11:52] (712.72s)
this is really an exciting time to be a
[11:55] (715.04s)
builder. We've seen crazy growth unlike
[11:58] (718.80s)
anything only possible with right now
[12:01] (721.52s)
with AI like all these companies that we
[12:03] (723.36s)
work with zero to 12 million in 12
[12:06] (726.00s)
months. The cursor story where they went
[12:07] (727.92s)
zero to one in one year the next one to
[12:11] (731.44s)
This is like unprecedented in tech
[12:13] (733.76s)
history
[12:14] (734.24s)
for B2B SAS companies. It didn't used to
[12:16] (736.00s)
be the case that B2B SAS companies were
[12:17] (737.60s)
the ones that had hyperrowth. Like there
[12:19] (739.44s)
were some like consumer social companies
[12:21] (741.04s)
that got hyperrowth but like B2B SAS
[12:23] (743.28s)
used to be this like you know plotting
[12:26] (746.16s)
slow growing like kind of thing. Now
[12:29] (749.76s)
there's this weird inversion. It's the
[12:31] (751.20s)
B2B SAS companies that are the like the
[12:32] (752.96s)
hyperrowth one.
[12:34] (754.48s)
I think what we're saying is a lot of
[12:35] (755.76s)
times is uh founders who are at living
[12:39] (759.12s)
in the future at the cutting edge who
[12:40] (760.88s)
are winning here because you have to
[12:42] (762.88s)
sort of build the taste to build
[12:44] (764.32s)
something good and you don't get taught
[12:46] (766.16s)
some of those things in school. I know
[12:47] (767.84s)
that like on that front like something
[12:49] (769.36s)
very specifically we're seeing is that
[12:51] (771.36s)
to build any products you you always
[12:53] (773.60s)
need some combination of like domain
[12:55] (775.28s)
expertise which is really just like
[12:56] (776.96s)
understanding your customer really well
[12:58] (778.40s)
and understanding the space you're
[12:59] (779.60s)
building in and understanding the market
[13:01] (781.68s)
really well and then technical expertise
[13:03] (783.44s)
to actually build the product and it
[13:05] (785.12s)
feels like preAI thing shifted where um
[13:09] (789.60s)
sort of the technical expertise wasn't
[13:11] (791.68s)
that important because it was most of
[13:13] (793.68s)
the software was like web software and
[13:15] (795.20s)
it became fairly straightforward to
[13:16] (796.80s)
build web software and actually all the
[13:18] (798.88s)
value was in how much like domain
[13:20] (800.32s)
expertise do you have? Like do you have
[13:22] (802.16s)
relationships with the customers you're
[13:23] (803.52s)
going after? Um do you have some edge on
[13:25] (805.68s)
how to sell to them because everyone
[13:27] (807.28s)
you're selling to is already got like 10
[13:29] (809.52s)
roughly equivalent products being sold
[13:31] (811.44s)
to them. Uh, and that actually made it
[13:34] (814.00s)
quite hard, I think, for college
[13:35] (815.36s)
students to be able to like go and
[13:37] (817.92s)
compete for like you can't like compete
[13:40] (820.08s)
on compete with Salesforce for like a
[13:41] (821.76s)
CRM or go build like the best
[13:44] (824.56s)
appointment booking software for
[13:46] (826.56s)
healthcare practices. Like all of these
[13:48] (828.40s)
things were just very saturated. And now
[13:50] (830.72s)
I think what we're seeing is with AI,
[13:52] (832.16s)
there's this promise of hey, like this
[13:53] (833.44s)
is more than software. Like this can do
[13:54] (834.88s)
like the work of people. It's like
[13:57] (837.20s)
magic. But like it's actually quite hard
[13:59] (839.44s)
to do that reliably. And so there's been
[14:01] (841.44s)
this flip of where the technical
[14:03] (843.60s)
expertise is now actually really like
[14:05] (845.44s)
the missing piece for a lot of these
[14:07] (847.44s)
things. Um, and we consistently see at
[14:09] (849.84s)
least in YC that college students are
[14:12] (852.32s)
actually at the forefront of this stuff.
[14:13] (853.76s)
Like actually understanding how to use
[14:15] (855.60s)
the models and how to squeeze the
[14:17] (857.68s)
performance consistently out of the
[14:19] (859.12s)
models is something that even like you
[14:21] (861.36s)
know PhDs and people are really
[14:23] (863.04s)
experienced don't get. I think maybe
[14:24] (864.72s)
that's why Elon had that sort of look
[14:26] (866.16s)
yesterday when he was talking about
[14:27] (867.28s)
researchers versus engineers. It's like
[14:30] (870.00s)
it's actually in the engineering and
[14:31] (871.76s)
it's like working on the projects and
[14:33] (873.52s)
like building real things is where you
[14:36] (876.24s)
get the expertise.
[14:37] (877.52s)
Yeah. I had a lot of college students
[14:39] (879.36s)
ask me over the last two days like hey I
[14:42] (882.16s)
don't have domain expertise in any
[14:43] (883.92s)
particular area cuz like I haven't
[14:45] (885.36s)
worked in industry that much like what
[14:47] (887.52s)
idea should I work on and like how do I
[14:50] (890.48s)
basically like how do how do I get
[14:52] (892.24s)
enough domain expertise to like do
[14:53] (893.60s)
something interesting. All right. Well,
[14:55] (895.28s)
what advice would you have for folks in
[14:56] (896.80s)
that position, Harge, based on on that
[14:58] (898.88s)
insight?
[14:59] (899.36s)
I think Gary's got like a great point on
[15:01] (901.12s)
this. Um, it's basically like become
[15:03] (903.60s)
like a forward deployed engineer, right?
[15:05] (905.84s)
Yeah. Just I mean go undercover, I
[15:08] (908.64s)
guess. Like go go go and figure out what
[15:11] (911.04s)
people actually need and um yeah, there
[15:14] (914.32s)
are just too many examples of
[15:15] (915.92s)
billion-dollar uh startups that we got
[15:17] (917.84s)
to see. I mean, I always think about
[15:19] (919.28s)
Flexport. you know, here's this guy who
[15:21] (921.92s)
literally became one of the top
[15:23] (923.76s)
importers of medical hot tubs. Like, I
[15:26] (926.24s)
don't think anyone wakes up, you know,
[15:28] (928.32s)
and graduates and decides like, hey, I
[15:30] (930.56s)
really need to become one of the
[15:31] (931.76s)
foremost, you know, import exporters of
[15:34] (934.56s)
uh of medical hot tubs. But, you know,
[15:36] (936.32s)
he did it. He they they did they also um
[15:38] (938.56s)
I think were one of the first e the
[15:40] (940.16s)
biggest ebike importer. But then you
[15:42] (942.48s)
know basically being in weird parts in
[15:44] (944.80s)
the economy um caused them to understand
[15:48] (948.24s)
just things that that uh the the other
[15:50] (950.64s)
person you know the sort of thousand
[15:52] (952.88s)
10,000 other people who want to start
[15:54] (954.72s)
startups like they didn't have that
[15:56] (956.32s)
knowledge and so sort of your ability
[15:58] (958.80s)
your you know if you're here like your
[16:00] (960.48s)
inherent ability already is like one
[16:02] (962.96s)
part of the ven diagram and then the
[16:05] (965.60s)
other part is just something weird. It's
[16:08] (968.72s)
literally just like where does your
[16:10] (970.56s)
interest come from? I'm like I'm really
[16:12] (972.16s)
taken by to what degree both open AAI
[16:14] (974.64s)
and SpaceX for instance were uh you know
[16:18] (978.24s)
the genesis came from like interest and
[16:21] (981.68s)
a hunch and just like not really any
[16:25] (985.12s)
commercial intent and yet you know
[16:27] (987.44s)
coming out the other side uh that was
[16:29] (989.92s)
enough to attract the smartest people in
[16:31] (991.92s)
the world attract capital and then
[16:34] (994.56s)
really create you know the most enduring
[16:37] (997.36s)
businesses in the world.
[16:38] (998.96s)
Yeah. And the other thing that I've seen
[16:40] (1000.08s)
that's pretty cool is just I've just
[16:41] (1001.76s)
seen a lot of college students go from
[16:43] (1003.68s)
having like no domain expertise in an
[16:46] (1006.24s)
area to being like total experts in like
[16:48] (1008.96s)
a month or two at YC. And I think people
[16:51] (1011.52s)
maybe don't give themselves enough
[16:53] (1013.60s)
credit for how quickly you can become an
[16:55] (1015.68s)
expert in something if you're just smart
[16:57] (1017.44s)
and you learn fast and you just make
[16:58] (1018.96s)
like a concerted effort.
[17:00] (1020.16s)
I think the door is more open now than
[17:01] (1021.92s)
ever. Like you kind of go back to Yeah.
[17:04] (1024.40s)
in a world where like um any domain
[17:07] (1027.20s)
let's make like you know if you're
[17:08] (1028.32s)
trying to build software for dentists is
[17:10] (1030.40s)
a random example pre AI it was just like
[17:13] (1033.36s)
people were being pitched with so many
[17:14] (1034.72s)
different software products that they
[17:16] (1036.24s)
weren't actually that receptive to like
[17:18] (1038.24s)
some college students promising some
[17:19] (1039.92s)
software and wanted to come like learn
[17:21] (1041.52s)
and like work in the office and
[17:22] (1042.96s)
understand like how it works like got
[17:24] (1044.96s)
like 20 software vendors all like um
[17:27] (1047.92s)
telling me the same thing but now
[17:29] (1049.84s)
because like AI has captured the m like
[17:32] (1052.16s)
the imagination of everybody everyone
[17:34] (1054.00s)
like wants to know what's possible and
[17:36] (1056.16s)
are consistently underwhelmed by what
[17:38] (1058.48s)
like the established software companies
[17:40] (1060.96s)
can offer them, but they're open to like
[17:42] (1062.96s)
college students just coming in and like
[17:44] (1064.88s)
well because the college students are
[17:46] (1066.08s)
selling them pure magic. So I had three
[17:48] (1068.24s)
founders in the last batch actually that
[17:49] (1069.68s)
are building quite literally like AI
[17:51] (1071.04s)
agents for dentists. None of them like I
[17:53] (1073.44s)
think their only experience with
[17:54] (1074.32s)
dentists is they went to a dentist
[17:58] (1078.56s)
and but it was exactly what you said
[18:00] (1080.64s)
Harj like they're literally selling
[18:02] (1082.64s)
these dentists like magic in a bottle
[18:04] (1084.88s)
and so like of course the dentist will
[18:06] (1086.56s)
spend their time because if it works
[18:08] (1088.24s)
it's like just incredible for their
[18:09] (1089.76s)
business
[18:10] (1090.08s)
which kind of just comes back to the
[18:11] (1091.36s)
agency thing cuz it's like the thing
[18:13] (1093.20s)
like in order to build these products in
[18:15] (1095.60s)
order to go out and like build like the
[18:17] (1097.36s)
future big companies you kind of just
[18:18] (1098.88s)
have to have the agency to be like ah
[18:20] (1100.56s)
yeah like I'm actually going to go do
[18:22] (1102.00s)
the like undercover agent or um fully
[18:25] (1105.52s)
deployed engineer and I'm just going to
[18:27] (1107.04s)
go like camp out in like someone's
[18:29] (1109.52s)
office and just see how they do their
[18:31] (1111.04s)
jobs and learn how to do it and learn
[18:32] (1112.56s)
how to like build it with AI.
[18:34] (1114.32s)
What about some um pitfalls like things
[18:36] (1116.40s)
that would prevent people from
[18:38] (1118.32s)
exercising their agency or exposing
[18:41] (1121.28s)
themselves to you know the real economy?
[18:44] (1124.96s)
I think one of the thing that keeps
[18:46] (1126.32s)
coming back to my mind is having a lot
[18:48] (1128.80s)
of these conversations with uh recent
[18:51] (1131.28s)
grads or college students. I think
[18:53] (1133.60s)
there's this arc of um a lot of you
[18:55] (1135.84s)
trying to figure out what to do with
[18:57] (1137.44s)
your life and through most of your life
[19:00] (1140.08s)
you've been conditioned to kind of just
[19:02] (1142.00s)
pass test, study for the exam, do the
[19:05] (1145.60s)
homework and it's sort of like all these
[19:08] (1148.00s)
uh very constrained boxes that you have
[19:10] (1150.40s)
to check and then you treat startups or
[19:13] (1153.20s)
your next jobs sort of like another test
[19:15] (1155.60s)
or exam that a lot of the rules are
[19:18] (1158.24s)
predetermined and you just have to go
[19:20] (1160.08s)
check the boxes. But that's the complete
[19:22] (1162.88s)
wrong mental model for it. Because the
[19:25] (1165.76s)
problem is that when you go after
[19:28] (1168.96s)
building and tackling a big problem, it
[19:32] (1172.24s)
is a open wide space. There's no rules.
[19:34] (1174.88s)
You get to create it. I mean the good
[19:36] (1176.48s)
thing about startups is plus and
[19:38] (1178.56s)
minuses. You have agency to design what
[19:42] (1182.40s)
you're going to go after. Set your goals
[19:44] (1184.48s)
instead of like some authority figure to
[19:46] (1186.56s)
like oh you need to do this this and
[19:47] (1187.92s)
that. And we get asked questions like,
[19:49] (1189.60s)
"Oh, what should I look like in order to
[19:52] (1192.00s)
raise money?" That is such a student
[19:54] (1194.00s)
question. Sort of like there's some sort
[19:55] (1195.84s)
of bar like by some higher power. Guess
[19:58] (1198.64s)
what? There's no adults in the room. Is
[20:00] (1200.88s)
you. You're in control and you get to
[20:03] (1203.44s)
design those rules and you can go as
[20:05] (1205.36s)
fast as possible. You don't have to have
[20:07] (1207.44s)
like, oh, we have to do this, this, and
[20:09] (1209.44s)
this and check the marks and get there.
[20:11] (1211.84s)
Is really you design it. You're in
[20:14] (1214.40s)
control. I think there are two very
[20:16] (1216.48s)
dangerous uh forms of like credentialism
[20:19] (1219.84s)
that you create for yourself that we see
[20:22] (1222.40s)
that actually like we'd really like to
[20:24] (1224.00s)
warn you guys about. Uh one is I mean I
[20:26] (1226.48s)
think we already talked about like
[20:27] (1227.92s)
making raising money from investors like
[20:30] (1230.56s)
somehow the the biggest goal I mean
[20:32] (1232.32s)
including us by the way. It's like,
[20:34] (1234.64s)
you know, that we're just like people to
[20:37] (1237.04s)
help you and we think we can help you a
[20:38] (1238.80s)
lot, but like once you turn that into
[20:41] (1241.60s)
like sort of uh the idol that you have
[20:44] (1244.72s)
to achieve, then that's just missing the
[20:47] (1247.36s)
whole point. And I, you know, I think
[20:49] (1249.04s)
that that's quite dangerous. Um, the
[20:51] (1251.60s)
other thing that we we're kind of
[20:52] (1252.96s)
concerned about is there are like
[20:54] (1254.24s)
entrepreneurship programs at some of
[20:56] (1256.00s)
your campuses. Uh, some of them might
[20:58] (1258.00s)
take you to wild exotic places for
[21:00] (1260.48s)
retreats. We're not going to name them,
[21:02] (1262.72s)
but like in full transparency, I'm very
[21:05] (1265.20s)
worried about them because what we're
[21:07] (1267.20s)
coming we're coming to understand is
[21:09] (1269.28s)
they are teaching you to lie. And that
[21:12] (1272.16s)
is at a moment when literally all of
[21:15] (1275.68s)
software is changing and that software
[21:17] (1277.84s)
is the most empowering thing in the
[21:19] (1279.52s)
world. Why do you have to lie? I
[21:21] (1281.68s)
understand in a world of like
[21:24] (1284.16s)
contracting capability, in a world where
[21:26] (1286.80s)
there's less money, where there's, you
[21:28] (1288.80s)
know, fewer and fewer jobs, I kind of
[21:30] (1290.48s)
get it. It's very zero sum. We're at the
[21:32] (1292.56s)
most open like sort of abundanceoriented
[21:36] (1296.40s)
like mindset thing that is happening
[21:38] (1298.16s)
right now. Like literally everyone here
[21:40] (1300.32s)
is hyper hypermpowered. You don't have
[21:42] (1302.64s)
to play by those old rules anymore. You
[21:45] (1305.04s)
don't have to lie to investors. You
[21:46] (1306.72s)
don't have to like fake it till you make
[21:48] (1308.32s)
it. Like you know I worry that some of
[21:50] (1310.40s)
these programs are just literally trying
[21:51] (1311.92s)
to teach people to become more uh you
[21:55] (1315.44s)
know SPFs and therronoses and that's
[21:57] (1317.84s)
like you know that's a waste of time.
[21:59] (1319.76s)
like and you're gonna go to jail.
[22:04] (1324.72s)
also a lot of these entrepreneurship
[22:06] (1326.08s)
programs they do what Diana said which
[22:07] (1327.68s)
is like entrepreneurship programs
[22:09] (1329.12s)
especially ones that are not started not
[22:10] (1330.80s)
run by founders you know all of us were
[22:12] (1332.80s)
were were startup founders they they
[22:15] (1335.04s)
basically teach entrepreneurship like it
[22:17] (1337.84s)
was a course like like it was just a
[22:20] (1340.16s)
series of tests to pass a series of
[22:21] (1341.92s)
check boxes to to check. Um, anytime you
[22:25] (1345.12s)
try to bottle up entrepreneurship and
[22:26] (1346.64s)
like teach it as a college course,
[22:28] (1348.08s)
that's kind of what you end up with is
[22:29] (1349.60s)
like basically like a a sort of like
[22:32] (1352.40s)
cheap faximile of entrepreneurship where
[22:34] (1354.56s)
like they teach you to like, you know,
[22:36] (1356.80s)
follow a particular method or a
[22:38] (1358.48s)
particular practice and that's just not
[22:39] (1359.76s)
what startups are actually like.
[22:41] (1361.04s)
I just think about that Jay-Z line is
[22:42] (1362.72s)
like everybody want to tell you how to
[22:44] (1364.00s)
do it, they never did it.
[22:46] (1366.08s)
True. A riff on that I'm curious to get
[22:47] (1367.92s)
people's opinions on. Uh, maybe
[22:49] (1369.20s)
especially Gary actually. Um, something
[22:51] (1371.76s)
that is clearly different I feel about
[22:53] (1373.84s)
the age we live in today versus say 10
[22:56] (1376.00s)
years ago is just social media and using
[22:58] (1378.40s)
social media as a way to um,
[23:01] (1381.20s)
like amplify your message and your
[23:03] (1383.28s)
brand. This is actually something came
[23:04] (1384.40s)
up at dinner last night. is how much in
[23:06] (1386.96s)
the early stages when you're building a
[23:08] (1388.56s)
product like should you focus in on kind
[23:10] (1390.72s)
of like building the product and going
[23:12] (1392.64s)
one by one to get users all of the kind
[23:14] (1394.88s)
of like traditional startup advice
[23:17] (1397.12s)
versus trying to cultivate sort of like
[23:20] (1400.32s)
a following or a brand um or attention
[23:23] (1403.76s)
like online and like you know 10 years
[23:26] (1406.24s)
spend thousands of dollars on a video.
[23:27] (1407.92s)
Yeah. Yeah. just like higher production
[23:29] (1409.68s)
launch videos and like lots of following
[23:31] (1411.76s)
like lots of followers on Twitter or X
[23:33] (1413.52s)
and um
[23:35] (1415.28s)
I certainly I think it's more confusing
[23:37] (1417.60s)
now because that wasn't even an option
[23:39] (1419.20s)
before and you definitely see people
[23:42] (1422.00s)
succeeding at the getting the online
[23:44] (1424.08s)
like attention and people talking about
[23:46] (1426.08s)
like the company
[23:47] (1427.52s)
what do they call it aura farming is
[23:51] (1431.36s)
we got lost maybe that's the the phrase
[23:53] (1433.20s)
yeah it is like aura farming I guess
[23:55] (1435.20s)
yeah I'm curious what you think Gary
[23:57] (1437.04s)
all I care about is what's real and what
[23:59] (1439.52s)
you can, you know, touch and see and
[24:01] (1441.28s)
feel and, you know, think about the area
[24:03] (1443.28s)
under the curve of utility that you
[24:05] (1445.36s)
could contribute to society. And you can
[24:07] (1447.52s)
always just look at that as ground truth
[24:09] (1449.52s)
and everything else is similacra. It is
[24:12] (1452.96s)
not real. It is like media. It is fake.
[24:15] (1455.92s)
It is a credential. It is a thing that
[24:17] (1457.84s)
represents something. And yet like if
[24:20] (1460.16s)
you look deeper into it, it's nothing.
[24:22] (1462.56s)
Like there's nothing. When you think
[24:24] (1464.00s)
about SPF, when you think about
[24:25] (1465.60s)
Theronos, when you think about the
[24:27] (1467.04s)
things that truly disgrace us as people
[24:30] (1470.88s)
who create technology, when you when you
[24:33] (1473.84s)
peel back a little bit, you realize
[24:36] (1476.00s)
there's nothing. This was just
[24:37] (1477.44s)
simulacum. It was a [ __ ] lie. I don't
[24:40] (1480.08s)
want that for us. Like, you know, people
[24:42] (1482.16s)
outside of this room, the world at large
[24:44] (1484.88s)
looks at tech and they hate us sometimes
[24:48] (1488.00s)
because those are the people who
[24:49] (1489.60s)
represent us.
[24:50] (1490.64s)
And I say, "Not for me. They don't
[24:52] (1492.40s)
represent us.
[24:59] (1499.04s)
So, I think that's a no on social media.
[25:01] (1501.84s)
I mean, I think social media is really
[25:03] (1503.28s)
great. I mean, I'm clearly extremely
[25:05] (1505.04s)
addicted to it and it's done some really
[25:06] (1506.96s)
great things for me, some terrible
[25:08] (1508.32s)
things, too. But, um, I, you know, I
[25:11] (1511.12s)
think that, you know, you do have to
[25:12] (1512.96s)
tell your story. Like, I think one of
[25:15] (1515.04s)
the more important things that is the
[25:16] (1516.56s)
gift is that you can tell your own
[25:18] (1518.88s)
story. like in fact that you have to
[25:21] (1521.36s)
like the second you rely on someone else
[25:23] (1523.44s)
to tell your story it's going to be
[25:25] (1525.28s)
great great great and then when you
[25:26] (1526.80s)
don't have that voice like someone's
[25:28] (1528.64s)
going to take that you know there's the
[25:30] (1530.40s)
world wants to you know the only thing
[25:32] (1532.56s)
it loves more than like you know a uh a
[25:35] (1535.52s)
story of like of becoming is one of
[25:38] (1538.00s)
unbecoming and uh if you don't have that
[25:40] (1540.48s)
voice and you can't go direct um you
[25:43] (1543.12s)
know they're going to do that to you and
[25:44] (1544.80s)
so better to start right like you know I
[25:47] (1547.60s)
I think working backwards the thing was
[25:49] (1549.68s)
most helpful for me, you know, obviously
[25:51] (1551.68s)
for my startup, but we try to encourage
[25:53] (1553.44s)
all of our startups to do this is you
[25:55] (1555.60s)
can sort of work backwards from the
[25:57] (1557.60s)
outcome that you want. Like uh I
[26:00] (1560.56s)
actually think Apple does this really
[26:02] (1562.32s)
really well. Like they don't commit to
[26:04] (1564.24s)
doing a feature until they have, you
[26:06] (1566.40s)
know, a product manager who says this is
[26:08] (1568.56s)
who it's for and this is the the problem
[26:10] (1570.64s)
it's going to solve, right? And you can
[26:12] (1572.48s)
actually turn that into going direct.
[26:14] (1574.64s)
So, you know, let's say you have a one
[26:16] (1576.48s)
week or twoe sprint. It's all too easy
[26:18] (1578.64s)
to just say, look, like here's my bug
[26:21] (1581.20s)
list and here's my backlog and I'm going
[26:23] (1583.12s)
to fix these 10 bugs. But a much more
[26:25] (1585.44s)
powerful version of this is let's work
[26:27] (1587.68s)
backwards from what I want to put on X.
[26:30] (1590.00s)
I'm going to make a very simple Loom
[26:32] (1592.08s)
video showing off a feed of strength, a
[26:34] (1594.72s)
thing that I really want to share that I
[26:36] (1596.80s)
know our team can do. And then working
[26:39] (1599.28s)
backwards from that, the next two weeks,
[26:41] (1601.12s)
that's all we're going to do. Like you
[26:42] (1602.64s)
could storyboard it. It's like it's
[26:44] (1604.16s)
going to do this, right? you can work
[26:46] (1606.24s)
like basically at that point media and
[26:48] (1608.80s)
PM and design can be all the same thing
[26:51] (1611.92s)
you know it's connected to users it's
[26:53] (1613.84s)
connected to communication it's
[26:55] (1615.52s)
connected to what your product will do
[26:57] (1617.68s)
for people and then you build it and you
[26:59] (1619.76s)
can just basically rinse and repeat like
[27:01] (1621.36s)
that if you can do two week sprints of
[27:03] (1623.52s)
working backwards from a really powerful
[27:06] (1626.08s)
not flashy uh loom video of what you did
[27:09] (1629.36s)
in the last two weeks you can do this
[27:11] (1631.04s)
all for each other and we can create a
[27:12] (1632.88s)
culture that is not about lash but about
[27:15] (1635.60s)
substance.
[27:20] (1640.32s)
We got a question over here.
[27:22] (1642.80s)
I'm a big fan of Gary and Jared. So
[27:25] (1645.36s)
Jared actually inspired me for my
[27:28] (1648.88s)
for my startup,
[27:30] (1650.80s)
but I just wanted some advice because
[27:32] (1652.80s)
I'm kind of going through a dilemma
[27:34] (1654.08s)
right now. So I've been working on a
[27:36] (1656.40s)
startup for the past month and I'm I'm
[27:38] (1658.16s)
going to my third year of uni um for
[27:40] (1660.24s)
context. But um so yesterday at one of
[27:44] (1664.08s)
the afterp parties which I'm not going
[27:45] (1665.36s)
to disclose the name cuz you guys are
[27:46] (1666.96s)
probably going to apply to it but um uh
[27:49] (1669.44s)
I was pretty much pitched my idea to one
[27:51] (1671.28s)
of the founders and he basically said
[27:53] (1673.60s)
like you know drop out of school and
[27:55] (1675.52s)
come work for me u move to San
[27:57] (1677.36s)
Francisco. So, um I'm really stuck
[28:01] (1681.04s)
between like what do I want to do? Like
[28:02] (1682.96s)
what's the right choice? Like do I
[28:04] (1684.56s)
continue university and then go to uh
[28:07] (1687.20s)
San Francisco and then you know grind
[28:08] (1688.64s)
the startup life or do I drop out now
[28:11] (1691.60s)
cuz I'm already halfway done. Like you
[28:13] (1693.20s)
know I'm not like almost finished with
[28:14] (1694.64s)
college or anything. I'm already halfway
[28:15] (1695.68s)
there. So do I drop out and just work
[28:18] (1698.24s)
and then just move on from there?
[28:20] (1700.40s)
I mean I think the most important thing
[28:21] (1701.76s)
is do you trust them and is it actually
[28:24] (1704.32s)
a good startup? So, which is uh kind of
[28:27] (1707.12s)
a hard question to answer like like
[28:30] (1710.24s)
this, but like what you know,
[28:31] (1711.84s)
if it's one of ours, it must be a good
[28:33] (1713.12s)
startup,
[28:33] (1713.52s)
right?
[28:34] (1714.00s)
Is it a YC startup?
[28:35] (1715.92s)
Oh, you should probably do it. No, I
[28:38] (1718.48s)
mean more seriously, I mean, I don't
[28:40] (1720.08s)
know. How would you like when you think
[28:42] (1722.72s)
about like where someone should go? Like
[28:45] (1725.12s)
what would you say?
[28:46] (1726.24s)
Good answer.
[28:47] (1727.04s)
I'd add one one third criteria, Gary. Uh
[28:49] (1729.60s)
I I dropped out of college to do Y
[28:51] (1731.52s)
Combinator and do a startup. I think in
[28:53] (1733.92s)
addition to the two ones that Gary said,
[28:55] (1735.60s)
the third one for you to consider is
[28:57] (1737.04s)
like do you really like being in
[28:59] (1739.52s)
college, which is like obvious, but like
[29:01] (1741.44s)
actually Yeah. Okay. Like like I I think
[29:04] (1744.72s)
I think like like Har was saying earlier
[29:07] (1747.20s)
when college students are thinking about
[29:08] (1748.88s)
dropping out and they're making it in a
[29:10] (1750.72s)
fear they're making a fear-based
[29:13] (1753.28s)
decision. They have FOMO about startups
[29:15] (1755.28s)
happening in San Francisco. Maybe their
[29:17] (1757.36s)
friend dropped out and they're like
[29:18] (1758.96s)
worried that they're going to miss out.
[29:20] (1760.24s)
I don't know that those are the best
[29:21] (1761.84s)
decisions. Like some of those people
[29:23] (1763.36s)
actually do end up regretting it versus
[29:25] (1765.44s)
like when I dropped out of college. I
[29:27] (1767.36s)
don't out of college because I was bored
[29:28] (1768.64s)
of college. Like I'd done three years of
[29:30] (1770.16s)
college. I felt like I had gotten out of
[29:31] (1771.84s)
it what I wanted from the experience.
[29:33] (1773.52s)
And I was just a lot more excited to
[29:35] (1775.20s)
like build real technology for real
[29:37] (1777.04s)
people. And I felt that regardless of
[29:39] (1779.60s)
whether the startup that I was working
[29:41] (1781.28s)
on succeeded or not, I wouldn't regret
[29:43] (1783.44s)
leaving college because I was just kind
[29:45] (1785.20s)
of ready to move on to the next stage of
[29:46] (1786.88s)
my life. So I think if you can if you if
[29:48] (1788.88s)
you can honestly feel that way then
[29:52] (1792.00s)
maybe it does make sense for you to drop
[29:53] (1793.60s)
I think if you feel you're done
[29:55] (1795.68s)
exploring living alternative life paths
[29:59] (1799.12s)
what I mean by that like you tried an
[30:01] (1801.52s)
internship working at a big tech company
[30:03] (1803.68s)
you tried an internship at a startup.
[30:05] (1805.36s)
You tried another internship to start a
[30:07] (1807.28s)
company or you tried another one doing
[30:08] (1808.56s)
research. If you feel you fulfill the
[30:11] (1811.04s)
chessboard and the land of what your
[30:14] (1814.08s)
life could be and you explore
[30:15] (1815.44s)
everything, I think it's fine. But if
[30:18] (1818.00s)
you still have a bit of a inkling, it's
[30:19] (1819.68s)
like, oh, maybe I want to try what doing
[30:22] (1822.24s)
re research is like, I think maybe not
[30:24] (1824.72s)
yet. But if you're super sure you want
[30:26] (1826.88s)
to have a career in tech or startups,
[30:30] (1830.16s)
then maybe it's fine. To to Jar's point,
[30:32] (1832.16s)
he's like you already did that life
[30:35] (1835.68s)
alternate life path. I feel like I got
[30:38] (1838.00s)
lucky and then because I ended up at
[30:40] (1840.40s)
Palunteer and these things that ended up
[30:42] (1842.00s)
being super successful but in the moment
[30:43] (1843.84s)
like you know I could have just got you
[30:45] (1845.76s)
know Palanteer could have been a bad
[30:47] (1847.28s)
startup actually and uh I didn't even
[30:50] (1850.08s)
think about it. So like thinking back on
[30:51] (1851.92s)
what I should have been thinking of when
[30:53] (1853.44s)
I was you know 22 23 it's actually
[30:56] (1856.48s)
really important to try to be at the
[30:59] (1859.28s)
most dominant places actually. I mean
[31:01] (1861.84s)
the power law for startups is so intense
[31:04] (1864.08s)
that if you're going to go work at a
[31:05] (1865.68s)
startup I do actually think that you
[31:07] (1867.28s)
should try to go work at
[31:08] (1868.40s)
at a really good startup really good
[31:10] (1870.24s)
startup totally
[31:10] (1870.88s)
like objectively you should make
[31:12] (1872.24s)
literally a spreadsheet you should you
[31:14] (1874.16s)
know go down and like evaluate it the
[31:16] (1876.96s)
way uh an investor would and then the
[31:19] (1879.84s)
difference is the investor has a
[31:21] (1881.12s)
portfolio and you just have one life
[31:23] (1883.36s)
and likewise when you start a startup
[31:25] (1885.28s)
you should not try to start a startup
[31:26] (1886.96s)
just to be the median startup the median
[31:28] (1888.80s)
startup is dead like you actually if If
[31:30] (1890.96s)
you're going to do it, you need to be
[31:32] (1892.48s)
like the, you know, you need to work at
[31:34] (1894.00s)
superlative places with superlative
[31:36] (1896.24s)
people. And then that's the only way
[31:38] (1898.24s)
that like good things happen. And you I
[31:41] (1901.28s)
I got lucky. I feel like I, you know,
[31:43] (1903.20s)
later in my life, I became much more of
[31:45] (1905.20s)
a heat-seeking missile. Like, you know,
[31:47] (1907.36s)
I think that that's why I was drawn to
[31:49] (1909.44s)
YC itself is like this is a place that
[31:51] (1911.44s)
has an energy that I've never
[31:53] (1913.20s)
experienced even at Stanford or even at
[31:55] (1915.68s)
Palunteer that I just wanted to be here.
[31:58] (1918.08s)
And I became much more of a heat seeker
[31:59] (1919.92s)
seeking missile for like uh like that
[32:02] (1922.40s)
type of like this is going to be huge.
[32:04] (1924.48s)
But um I wish you know at 22 I lucked
[32:06] (1926.96s)
out you know like my friends went to
[32:09] (1929.28s)
college with he they started a company
[32:11] (1931.52s)
with Peter Teal. So that was just very
[32:13] (1933.52s)
lucky. That's Diana's thing is uh you
[32:16] (1936.56s)
know she likes to fund lucky startups.
[32:19] (1939.44s)
just get lucky. And I think you get a
[32:21] (1941.04s)
lot more lucky by being in San
[32:22] (1942.16s)
Francisco.
[32:23] (1943.12s)
And working around really smart people.
[32:24] (1944.96s)
Um, I want to ask Gary, you talked about
[32:26] (1946.96s)
like that level 59 job at Microsoft and
[32:29] (1949.52s)
like your parents are proud, you have
[32:30] (1950.64s)
health insurance. Um, and at some point
[32:32] (1952.48s)
you have to decide, okay, I'm done with
[32:34] (1954.08s)
this. I want to go start a company. And
[32:35] (1955.52s)
like you work at something, uh, maybe at
[32:37] (1957.52s)
night, you know, after work, you can't
[32:38] (1958.88s)
really like go out on LinkedIn and
[32:40] (1960.16s)
advertise it cuz like your boss would
[32:41] (1961.52s)
see it. But at what point do you say, I
[32:43] (1963.20s)
have enough here. I can really go quit
[32:45] (1965.12s)
my job, you know, start spending down my
[32:47] (1967.36s)
savings and go go do something.
[32:49] (1969.28s)
You're much more responsible than I am.
[32:51] (1971.20s)
like I had, you know, $50,000 in credit
[32:53] (1973.52s)
card debt and I had the nicest apartment
[32:55] (1975.92s)
in Queen Anne and I bought a brand new
[32:58] (1978.32s)
Honda and it was very stupid and so I
[33:01] (1981.12s)
had to go get a job and like you know I
[33:03] (1983.36s)
I couldn't start a startup. I like you
[33:05] (1985.36s)
know waited I had I needed my friends to
[33:07] (1987.12s)
pull me out of that situation. So I mean
[33:10] (1990.24s)
I think you want I don't know at least
[33:12] (1992.24s)
six maybe nine months minimum of like
[33:14] (1994.96s)
just you know being able to live on
[33:16] (1996.72s)
ramen in the cheapest possible way. Uh
[33:19] (1999.12s)
and then at that point like the money in
[33:20] (2000.88s)
your bank is like just capital that you
[33:22] (2002.72s)
think of. And so that's probably what I
[33:25] (2005.12s)
would want. And then the other thing is
[33:27] (2007.20s)
uh I would try to bring on I would want
[33:29] (2009.12s)
to work with the smartest possible
[33:30] (2010.96s)
people. Like I know this is a big
[33:33] (2013.04s)
internet debate. It's like do you need a
[33:34] (2014.88s)
co-founder? Honestly like if it's your
[33:36] (2016.72s)
first startup
[33:38] (2018.80s)
I I wouldn't I would never start like my
[33:41] (2021.28s)
second or third startup. Sure I could do
[33:43] (2023.28s)
it like alone. I you know have
[33:44] (2024.88s)
connections. I know who to hire like all
[33:46] (2026.88s)
this stuff. If it were my first, I would
[33:48] (2028.88s)
not try to do it alone because there's
[33:50] (2030.64s)
just too much going on. There's like the
[33:52] (2032.80s)
the gradient of things that you need to
[33:54] (2034.72s)
learn is too wide and you need to go you
[33:57] (2037.60s)
need to go together.
[33:58] (2038.80s)
Yeah. And in my experience for people
[34:00] (2040.32s)
who are your age who have like already
[34:01] (2041.92s)
graduated college or working at some
[34:03] (2043.28s)
company, that's the biggest limiting
[34:05] (2045.20s)
factor in practice for them actually
[34:07] (2047.12s)
doing a company is like they and their
[34:09] (2049.68s)
co-founder both need to be willing to
[34:11] (2051.44s)
quit their jobs and go in on a startup
[34:13] (2053.60s)
at the same time. It's it's just like a
[34:15] (2055.44s)
timing problem and like co-founders are
[34:17] (2057.84s)
hard to find and it's hard to make that
[34:19] (2059.84s)
timing line up. So my advice would be
[34:21] (2061.68s)
like if you have if you and your co- if
[34:23] (2063.52s)
that does line up for you and you and
[34:25] (2065.04s)
your co-founder are both in a point in
[34:26] (2066.72s)
your life where you're like able to quit
[34:28] (2068.72s)
your jobs and go all in, you should
[34:30] (2070.56s)
probably just do it because it literally
[34:32] (2072.32s)
might not ever happen again. It's
[34:33] (2073.60s)
actually that hard to do.
[34:34] (2074.80s)
Hey guys. Well, thank first of all thank
[34:36] (2076.64s)
you for for the event. My voice is a bit
[34:38] (2078.56s)
cracked because of talking too much over
[34:41] (2081.04s)
the over the past few days. Um, we're
[34:43] (2083.36s)
actually talking to the CEO of Straa uh
[34:45] (2085.36s)
yesterday at one of the afterparties and
[34:47] (2087.20s)
he mentioned uh that they started being
[34:49] (2089.36s)
a very niche startup. Um, and I've taken
[34:51] (2091.76s)
a look at all the Y cominator startups
[34:53] (2093.52s)
over the years and it looks like uh
[34:54] (2094.80s)
they're getting increasingly niche. Uh,
[34:56] (2096.40s)
so I was just wondering like what do you
[34:58] (2098.00s)
think like what's your take on on on
[35:00] (2100.00s)
being super niche at the beginning and
[35:02] (2102.24s)
then expanding and how do you know like
[35:04] (2104.96s)
how niche you have to be um in the
[35:07] (2107.04s)
beginning? being niche at the start has
[35:08] (2108.96s)
actually always been like the recipe to
[35:11] (2111.92s)
succeed. Like even in the YC world kind
[35:14] (2114.56s)
like the current biggest company by
[35:16] (2116.96s)
market cap is Airbnb. Um and Airbnb was
[35:20] (2120.16s)
like the definition of niche when it
[35:21] (2121.76s)
started. Like it was literally airbeds
[35:24] (2124.56s)
in people's living rooms, right?
[35:26] (2126.24s)
During conferences.
[35:27] (2127.04s)
Yeah. During conferences. So like I'm
[35:29] (2129.44s)
not sure it can get more niche than
[35:31] (2131.20s)
Democratic conferences.
[35:32] (2132.24s)
Yeah. Democratic conferences. Yeah. like
[35:35] (2135.12s)
um and obviously it turns out that that
[35:37] (2137.04s)
expanded into just being this like
[35:38] (2138.80s)
monster company that's taking over all
[35:40] (2140.32s)
of travel. Um but there's even less
[35:42] (2142.24s)
obvious examples of this where people
[35:43] (2143.92s)
don't realize that things that seem huge
[35:46] (2146.32s)
now were niche at the beginning. Stripe
[35:48] (2148.72s)
actually in a sense obviously it's
[35:50] (2150.80s)
payments of course as a big market but
[35:52] (2152.80s)
actually when they first started it was
[35:54] (2154.80s)
like um an API for developers and the
[35:58] (2158.48s)
only thing that differentiated it from
[35:59] (2159.84s)
Brainree was that you could take
[36:01] (2161.60s)
payments instantly. Um, and people
[36:03] (2163.68s)
actually didn't think that that was much
[36:04] (2164.88s)
of a wedge. It was like, oh, okay, sure,
[36:06] (2166.40s)
if I'm working on a weekend project,
[36:07] (2167.84s)
I'll care a lot about that. But like big
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businesses aren't going to care about
[36:11] (2171.28s)
that. They're fine to wait two weeks to
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get like their merchant account. And so,
[36:15] (2175.52s)
it's always been the case that niches
[36:17] (2177.44s)
have been the right way to start
[36:19] (2179.04s)
actually and to dominate a niche and
[36:21] (2181.44s)
find ways to like expand into like
[36:23] (2183.84s)
adjacent markets and grow into a big
[36:26] (2186.16s)
company, I think, is like the recipe.
[36:28] (2188.32s)
And Brian Chesy quotes a lot of some of
[36:31] (2191.52s)
the best advice he got from PG during
[36:33] (2193.84s)
the batch was to really hone in to find
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10 people that love your product much
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better than I don't know 100 randos. And
[36:43] (2203.52s)
a lot of companies start like that. You
[36:46] (2206.08s)
want to find those maximalist users that
[36:49] (2209.36s)
really obsess with you and you iterate
[36:52] (2212.32s)
on those. I mean Coinbase was also very
[36:54] (2214.80s)
niche. Yeah, Coinbase was classic niche
[36:56] (2216.64s)
because um crypto itself was small and
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fringe and even within that they were
[37:01] (2221.68s)
building for what people thought was a
[37:03] (2223.04s)
non-existent market essentially. It was
[37:04] (2224.88s)
just like regular people who wanted a
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nice user interface to like buy and hold
[37:09] (2229.76s)
Bitcoin and it was
[37:11] (2231.12s)
oxyon regular people.
[37:12] (2232.88s)
Exactly. It was like based Yeah. The
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conventional wisdom was is just people
[37:15] (2235.68s)
who want to use it to like launder money
[37:17] (2237.20s)
and buy drugs and it's like okay no
[37:18] (2238.80s)
there's like other um use cases for it.
[37:21] (2241.28s)
Um, but with AI more than ever, by the
[37:24] (2244.08s)
way, like I think that niche is the way
[37:26] (2246.08s)
to go because no one really knows how
[37:28] (2248.00s)
big the markets are. And it does seem
[37:30] (2250.56s)
like things that seem like they were
[37:32] (2252.08s)
niche before AI, you can get people to
[37:34] (2254.24s)
pay you a lot lot more money for because
[37:36] (2256.00s)
they're not just buying software,
[37:37] (2257.20s)
they're buying like work from you. And
[37:39] (2259.60s)
so find the niche you're really
[37:41] (2261.28s)
interested in, optimize for your like
[37:43] (2263.92s)
passion and interest in it. And just
[37:45] (2265.36s)
pull on that thread. This is like
[37:47] (2267.12s)
actually a really powerful moment
[37:48] (2268.48s)
because literally you have 130, you
[37:50] (2270.96s)
know, I think of uh 03 as basically
[37:53] (2273.28s)
about 130 IQ. Maybe 03 Pro can be even
[37:56] (2276.32s)
smarter than that. Um when I really
[37:58] (2278.48s)
think about that, it's like, oh yeah,
[38:00] (2280.00s)
like a lot of the people who I've ever
[38:01] (2281.44s)
hired in my lifetime are like, yeah, 03
[38:04] (2284.72s)
is smarter than that person now. So, and
[38:06] (2286.88s)
then you can basically take that and um
[38:10] (2290.64s)
you know connect it to the proprietary
[38:13] (2293.20s)
data systems of almost any niche. And
[38:16] (2296.08s)
the more weird and unlikely for someone
[38:18] (2298.96s)
like someone in this room to know about
[38:20] (2300.56s)
it, the more likely that will be a
[38:22] (2302.32s)
durable enough moat that you can get a
[38:24] (2304.80s)
foot, you know, you can basically get
[38:26] (2306.32s)
you can wedge you yourself in there and
[38:29] (2309.28s)
then basically all you need is a wedge
[38:31] (2311.76s)
and then you just basically expand that
[38:33] (2313.52s)
wedge until you have the pie. Thank you
[38:35] (2315.84s)
guys so much for coming out. Awesome
[38:37] (2317.92s)
stuff.
[38:40] (2320.41s)
[Applause]
[38:43] (2323.98s)
[Music]