WEBVTT
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Big ideas require us to think differently
about failure, learning, and creativity.
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My name is Matt Abrahams and I
teach strategic communication at
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Stanford Graduate School of Business.
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Welcome to this special Rethinks episode
of Think Fast, Talk Smart, the podcast.
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My conversation with Astro Teller
changed the way I think about what
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it takes to pursue ambitious ideas.
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Astro shared so many insights about
learning from failure, fostering
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creativity, and making big ideas
understandable that I wanted to
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bring his advice back for all of you.
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And this time, we're sharing the
extended version of our conversation.
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Enjoy.
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The best communication is
architected for understanding.
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My name is Matt Abrahams, and I
teach Strategic Communication at
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Stanford Graduate School of Business.
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Welcome to Think Fast,
Talk Smart, the podcast.
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Today, I am really excited
to speak with Astro Teller.
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Astro is a computer scientist,
entrepreneur, and inventor.
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He serves as captain of moonshots
at X, Alphabet's Moonshot Factory,
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where he oversees audacious
high-impact technology projects.
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He has written two novels and the
nonfiction book, Sacred Cows: The
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Truth About Divorce and Marriage,
that he co-wrote with his wife.
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He's also a new podcaster,
hosting the really fun and
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insightful The Moonshot Podcast.
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Welcome, Astro.
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I am super excited to
be chatting with you.
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I've been excited ever
since we arranged this.
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Thank you for being here.
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Thanks for having me.
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Excellent.
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Shall we get started?
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Yeah, let's do it.
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To begin, I'd love for you to define for
our audience what you mean by a moonshot.
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Sure.
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So we think of this as the
blueprint for moonshots.
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And in order for us to have
something that counts as a moonshot,
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it has to have three components.
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One, there has to be a huge
problem with the world that you
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can name and you want to solve.
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Number two, there has to be some kind
of radical proposed solution that
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however unlikely it is you could make
that thing, this is like a science
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fiction sounding product or service.
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We could agree ahead of time if
you could make it, it would resolve
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that huge problem with the world.
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And then three, there has to be some
kind of breakthrough technology that
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gives us at least a glimmer of a hope
that we could make that science fiction
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sounding product or service that would
solve that huge problem with the world.
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Once you have those three
things, we're not done.
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That means you have a
moonshot story hypothesis.
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You have a testable hypothesis.
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And from there, the question is, how
quickly can we verify that you're wrong
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so we can move on to the next idea?
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Because anything that fits those three
criteria that I just named, exciting
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as they are, it is exactly because it
is so unlikely to work that we have to
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be constantly pursuing the reality that
each one of them is likely to be wrong.
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So the goal is actually to come
up with these audacious ideas
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and strivings, and then to as
quickly as possible negate them.
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Yeah, of course, we want any
particular moonshot we come up
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with to turn out to be a once in a
generation opportunity for the world.
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But since most of them aren't, wanting
to win, wanting to get it to be right
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each time leads to sadness after sadness,
and denial kicks in that slows up the
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efficiency of being the learning machine
that we would aspire to be, verifying
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which of the many that we've started
is in fact worth doubling down on.
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You said several things there
I want to dive deeper into.
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So you see this as learning, and
it's a learning machine, as you
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said, which leads me to wonder, is
there a particular type of mindset
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that you try to bring about in your
organization that looks at it this way?
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Because many people don't
think of projects this way.
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They start a project, they want it
to be successful, not let's start
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a project and figure out all the
reasons why it won't be successful.
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Can you talk a little
bit about that mindset?
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Sure.
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Let me give you one or two examples.
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I mean, the first is, if you were
working at X, I would say, "Can we
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pre-agree before you've come up with
something and then fallen in love with
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it, that if it's a 1 in 100 chance of
working, it has a 99% chance of not
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working?" Which means that if you tie
your sense of self-worth to getting
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a yes, you're just lying to yourself.
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You think that you're the 1% and you're
always gonna win, but this can't be Lake
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Wobegon, where we're all above average.
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So that's one way to help put
some intellectual guardrails
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around what we're about to go do.
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Here's another way of doing it.
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Think of the last really hard
thing that you and a team did.
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I'd ask each of your
listeners to think about that.
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Now imagine you lose all the hardware,
all of the software, anything except
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what's in your and your team's heads.
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How long, once you've succeeded,
would it take for you to go back
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and rebuild the solution, having
already verified what it is?
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Most people say somewhere between
5 and 20% of the time that it took.
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And for moonshots, it's
much closer to 5% than 20%.
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Let's call it 10% on average.
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What are we gonna call
that other 90% of the work?
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It's learning.
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Most of the work isn't
the making of stuff.
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It's the learning what to make.
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And the more you're shooting over the
horizon, which is what a moonshot is,
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the more of the journey is exploration,
not the settling that happens afterwards.
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So if you agree conceptually,
intellectually, that we're on a
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learning journey, then scary as it
is to be wrong, we need to focus on
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the moments where we learn something.
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And when do you learn something?
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You learn something when you have a model
about the world, and then in some way you
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get some data that tells you you're wrong.
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You learn nothing when you're right.
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At best, you deepen grooves in your brain.
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If you're wrong, whatever else
we wanna call it, that feels bad.
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That's a failure of a kind.
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If you hate failure, you will emotionally
avoid that moment, which means
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you're emotionally avoiding learning.
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If we just agreed we're gonna spend
90 plus percent of our time learning,
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then we need to de-stigmatize
failure so we can have our learning
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loops be as tight as possible.
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So a moonshot mindset is really
about seeing learning as the goal.
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How do you inculcate and support
that, and how do you produce
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anything if, for many companies,
productivity is what's the goal?
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So how do you produce the amazing things
that you've done and help people see
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learning as the goal, not productivity?
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It's very hard to prevent
people from chasing progress.
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Even at X, there's a decent amount
of it, even though I'm constantly
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going around trying to stamp it out
and redirect people towards learning.
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So it's not hard for the end result
of a lot of learning to turn into
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something very valuable that looks
ultimately like great progress.
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During the journey, the thing that is
hardest to fight is you feel, especially
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once you've done some learning and
you've found something that works, it's
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so hard for you as an X-er not to feel
like, "I found it. I'm right. Now just
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let me build it, and then I'll give it
to the world and we're gonna be great."
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And I just have to say, we, the leadership
at X, have to say over and over again, "I
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know it feels like you've found it, and
you know more about the teleporter, if
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that's what you're working on, than I do.
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But I'm telling you from induction,
having watched this hundreds of
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times now, you're not correct yet.
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You have a lot to learn, and what you
think is the answer isn't the answer.
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It's not that I don't
want you to be right.
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It's just my experience tells me you have
a lot more to learn, so I need you to
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stay in learning mode, keep your humility
and your curiosity really high for
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another two or three years." It's helping
people to do that that's really hard.
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I find it really interesting that
humility and curiosity are what
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keep the flames going for learning.
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How do you capture the learning
across the different teams?
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We had Amy Edmondson on, and she talks
a lot about the right kind of failure
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and learning from the failure and
the processes you can put in place.
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What are some of the best practices
you've implemented so if somebody learns
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something, that learning is cascaded so
others can benefit from that learning?
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There's a bunch of different ways.
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The truth is, some of it is just
institutional knowledge, and that's
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sort of a background radiation
that's very real at X. There are
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lots of things that we capture.
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We actually have a document called
headwinds and tailwinds to remind
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us of some of these learnings.
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These kinds of things
tend to be tailwinds.
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In our experience, these kinds
of things tend to be headwinds.
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That doesn't mean you can't ignore one of
them if you want to or if you think it's
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worth it, but these are good reminders
from the past that we've learned.
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We have maybe 20% of the people who
work at X aren't on one of the projects.
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They're in these central teams.
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So this is like finance, legal, public
policy, some business development, a
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lot of sort of hardware prototyping.
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And what happens is they go over
here and they help the teleporter
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team, and then they go over here
and they help the time machine team.
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And so they act as a kind of vector,
transplanting interesting ideas and
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learnings from one team to another.
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So even if those teams don't talk to
each other very much, because they share
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these central teams, the central teams
can move good ideas back and forth.
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So you actually have a structural
element where there are people spoke
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and hub, and the, those folks in the
hub bring those learnings across.
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I find that really helpful for
other people to think about in their
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organizations how they might do that.
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Yeah.
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It is hub and spoke from a help
perspective, but it is not as the concept
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or the visualization of hub and spoke
might imply, a highly top-down process.
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Discovery is mostly a bottoms-up process.
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It's very hard to dictate, so you
have to create some structure and
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guardrails, but then let people
be pretty free range within those
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guardrails, or they won't ultimately
find things that are unexpected.
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So you have to have just enough boundary
setting to keep things moving forward, but
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not so much that people can't be creative.
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And we've seen that a lot.
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We've talked a lot about improvisation
and how that mindset helps.
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In, in improv, they have some rules, and
that's what allows for that creativity.
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Right.
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Let me give you an example.
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Obviously, we have to have a
performance management system.
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It'd be weird if we didn't.
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The temptation across the whole world,
so including at the Moonshot Factory, is
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to have our performance management system
be one that rewards people for outcomes.
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You rang the bell, you got the million
dollars or the big deal you signed.
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Good for you, bonus,
promotion, whatever that is.
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That is the death of radical innovation,
because if I'm asking you to do things
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that have a one in 100 chance of success,
and then you get rewarded on the basis
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of a yes, of a success, you will very
quickly learn to pretend you're doing
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radical innovation, but find ways to get
the safety of like, "Okay, maybe it's not
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as radical, maybe it's not as innovative,
but I'll dress it up so it looks like
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that, and I'm pretty sure I can make
this work even if it's not that exciting.
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And so I'm gonna focus on this
thing because I know I'm only
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gonna get rewarded when I get a
yes." All humans will do that.
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And so the performance management system
we have directs people back towards
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the habits, not towards the outcomes.
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What are the kinds of things like humility
and curiosity that tend to have wild
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success as a long-term side effect?
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And then you have to trust, and this is a
big, scary trust fall, that those outcomes
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will happen when you focus on the habits.
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And you have good
evidence that will happen.
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It's harder from a management point of
view to measure those kind of things
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versus did you ring the bell or not?
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That's a very binary, easy thing to do.
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So you have to have more flexibility and
openness as managers, I guess, as well.
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Yeah, and this is a struggle to this
day at X. It is so much harder to train
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managers to hold people accountable to
habits rather than to outcomes, that
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they routinely will try to find ways to
hack the system so that they can reward
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people for outcomes, or they'll gripe
about their performance management system.
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I get it.
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And that's an example where we're
trying to create some guardrails, let
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them use some creativity within those
guardrails, but also make sure that
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they're pointed, in this case, towards
the habits rather than the outcomes.
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I really like this idea of rewarding
people for habits, not for outcomes.
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I really wanna dive deeper into
this notion of failing fast and
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looking how to kill projects.
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Can you share a little bit about that?
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And you do some kind of celebration,
the Dia de los Muertos, that I'd
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love to hear a bit about as well.
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Sure.
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We have a lot of traditions.
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That's one that we, we haven't done in
a few years, but we did it for 7 or 8
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years, the Day of the Dead celebration.
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In order to help people feel good,
or at least not avoid failures at X,
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we have to help them destigmatize it.
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In order to destigmatize it, you can get
at that in a bunch of different ways.
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But some of it is, it is sad to
have something that you worked
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on for a long time need to stop.
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And we set up a remembrance party.
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So the Day of the Dead is a very
long-standing tradition for remembering
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people who've gone from our lives.
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And so by doing that and sort of
respecting that aspect of the tradition,
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but adding to it the sadness we
have about people who've just left
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X, they're not dead, they've just
left X. We're remembering projects
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that were really touching to us.
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We wish they could have made it, but
for a variety of reasons they didn't.
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It allows us the catharsis and the
grieving, after which we can move on
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from it a lot more productively than
if we're holding on to that sadness,
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and often then projecting anger at
whoever made us stop doing that.
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What I find really interesting in
what you said is you're really taking
00:14:03.555 --> 00:14:08.022
care not just of the act, but also
the emotion that people feel about it.
00:14:08.028 --> 00:14:11.355
So you worked hard on this thing, and
maybe it didn't go where you wanted
00:14:11.357 --> 00:14:14.578
it But you have an emotion and an
attachment to that, and you're allowing
00:14:14.578 --> 00:14:18.197
people to recognize it and express
it in a way to hopefully move on.
00:14:18.525 --> 00:14:23.220
Even in a widget factory, humans aren't
robots, and it doesn't go super well
00:14:23.221 --> 00:14:24.774
trying to treat them like robots.
00:14:24.872 --> 00:14:30.268
But the more you want people to show
up in ways that are creative, that get
00:14:30.269 --> 00:14:34.797
the most out of themselves, where they
bring the most interesting parts of
00:14:34.797 --> 00:14:39.565
themselves to work and combine them in
an important unexpected alchemy that
00:14:39.567 --> 00:14:43.791
turns into something like self-driving
cars over time, the more you have
00:14:43.793 --> 00:14:47.749
to recognize the things that hold
them back are mostly here in their
00:14:47.756 --> 00:14:49.718
hearts and down here in their guts.
00:14:50.063 --> 00:14:51.169
Their heads are fine.
00:14:51.547 --> 00:14:55.228
The number of people in any
organization who need to leave because
00:14:55.231 --> 00:14:57.660
they're incompetent is very low.
00:14:58.202 --> 00:15:03.317
The number of people who don't work out
for other reasons, most of those reasons
00:15:03.473 --> 00:15:08.130
are style reasons, their egos are too
large or too brittle or whatever that is.
00:15:08.290 --> 00:15:11.313
That's all about their
hearts and their guts.
00:15:11.324 --> 00:15:12.596
It's not about their heads.
00:15:12.860 --> 00:15:13.665
Absolutely.
00:15:14.214 --> 00:15:15.343
I want to take a step back.
00:15:15.343 --> 00:15:18.170
We haven't actually heard
from you an example or two of
00:15:18.170 --> 00:15:19.399
what you guys have created.
00:15:19.399 --> 00:15:22.349
Can you share one moonshot that
was successful so we can have
00:15:22.351 --> 00:15:24.583
an appreciation of the different
types of things you work on?
00:15:24.944 --> 00:15:26.987
Let me give a few examples.
00:15:27.335 --> 00:15:32.622
Google Brain, which is one of the
places that caused the explosion of
00:15:32.624 --> 00:15:36.788
industrialized machine learning that is
now the sort of hot topic in the world,
00:15:37.106 --> 00:15:38.960
that came from X. That's an example.
00:15:39.358 --> 00:15:43.879
Uh, the self-driving cars, now called
Waymo, those came from X. Uh, Wing, the
00:15:43.880 --> 00:15:50.355
drones for package delivery, which are
actually doing almost as well as Waymo,
00:15:50.464 --> 00:15:54.871
and people haven't caught on that it's
going to be as big a deal, that came from
00:15:54.872 --> 00:15:58.964
X. And I will give some more examples,
but let me use Waymo as an example.
00:15:59.107 --> 00:16:01.419
So this is now 15 years ago.
00:16:01.838 --> 00:16:03.068
Huge problem with the world.
00:16:03.603 --> 00:16:07.451
More than a million people a
year die in car accidents in the
00:16:07.451 --> 00:16:09.265
world because of human error.
00:16:09.339 --> 00:16:13.752
More than a trillion dollars is
wasted between sitting in traffic
00:16:13.913 --> 00:16:17.300
and all of the costs of the
accidents, even leaving death aside.
00:16:17.554 --> 00:16:22.156
Between a million lives and a trillion
dollars, that's a problem worth fixing.
00:16:22.356 --> 00:16:28.116
Radical proposed solution: what if, just
like we got to the place with elevators
00:16:28.154 --> 00:16:33.108
where we realized you didn't have to have
a human driving the elevator, you could
00:16:33.108 --> 00:16:39.972
just trust the elevator to drive itself,
we can get behind the idea that if they
00:16:39.996 --> 00:16:44.529
could drive themselves, we could get to
the place where these metal boxes, like
00:16:44.529 --> 00:16:49.833
an elevator, take the passengers where
they need to go, and you just push a
00:16:49.833 --> 00:16:54.300
button and say, "Here's where I want to
go," and it takes you there much safer.
00:16:54.570 --> 00:16:58.432
And there's all kinds of benefits,
including maybe particularly the lives
00:16:58.432 --> 00:17:04.588
that are being saved, because those metal
boxes can now take themselves there much,
00:17:04.604 --> 00:17:06.475
much safer than a human driving the car.
00:17:07.241 --> 00:17:09.236
So that's the radical proposed solution.
00:17:09.589 --> 00:17:15.926
And then there was a set of, at the time,
new technologies, how to coordinate LiDAR,
00:17:16.069 --> 00:17:20.521
radar, and cameras around a vehicle.
00:17:20.833 --> 00:17:26.477
There'd been some evidence before we
started on what is now called Waymo from
00:17:26.477 --> 00:17:34.483
the DARPA Grand Challenges, the first of
which in 2006 was won by Stanford here.
00:17:34.748 --> 00:17:36.284
That car was called Stanley.
00:17:36.612 --> 00:17:39.462
And DARPA, just so people understand,
is something that the US federal
00:17:39.466 --> 00:17:43.383
government, it's a funding source
for creative ideas that can be
00:17:43.384 --> 00:17:44.974
used for defense and other things.
00:17:44.985 --> 00:17:45.563
Exactly.
00:17:45.563 --> 00:17:48.400
And so there was this worldwide,
or at least countrywide, grand
00:17:48.400 --> 00:17:51.587
challenge that was announced about
cars that could drive themselves.
00:17:51.792 --> 00:17:56.688
And while even in the second time
it was run, there were three groups
00:17:56.690 --> 00:17:59.091
that completed the 150 miles.
00:17:59.265 --> 00:18:00.195
They were out in a desert.
00:18:00.225 --> 00:18:04.563
It was 1/1000th of the difficulty
of what Waymo currently has to do,
00:18:05.070 --> 00:18:08.485
but it was enough evidence that
maybe, just maybe, it was time.
00:18:08.870 --> 00:18:12.331
I love that example, and many
people listening perhaps have
00:18:12.331 --> 00:18:13.924
not seen a Waymo or been in one.
00:18:13.947 --> 00:18:17.873
I have, and where I live is where
a lot of them were tested, so
00:18:17.874 --> 00:18:18.832
I'm a little more used to it.
00:18:19.098 --> 00:18:22.320
It is freaky to be in the back
of a car that has no driver, but
00:18:22.321 --> 00:18:24.473
it is absolutely cool as well.
00:18:24.533 --> 00:18:29.234
And I assume that you are seeing some
of the goals realized, like more safe
00:18:29.444 --> 00:18:31.571
arrival, fewer accidents, et cetera.
00:18:31.690 --> 00:18:36.030
Yeah, the evidence we have is that
the Waymo cars are at least an
00:18:36.041 --> 00:18:41.138
order of magnitude safer than human
drivers in similar situations.
00:18:41.340 --> 00:18:46.708
And of course, every time a Waymo
car learns anything, all of the
00:18:46.711 --> 00:18:50.864
cars get that learning, so it's
a one-way ratchet of safety.
00:18:51.357 --> 00:18:55.208
I have been in San Francisco, where you
literally see a fleet of them driving
00:18:55.210 --> 00:18:56.905
people around, and it's very cool.
00:18:57.273 --> 00:19:00.590
I want to come to something
that you just did.
00:19:00.724 --> 00:19:04.352
You just told us not just about
Waymo, but you told us a story.
00:19:04.885 --> 00:19:08.937
How important is storytelling in
defining and realizing moonshots?
00:19:09.341 --> 00:19:13.917
I think storytelling is phenomenally
important, but I watch people
00:19:13.952 --> 00:19:16.793
at the Moonshot Factory get a
little bit confused about this.
00:19:17.051 --> 00:19:21.253
At least when I say storytelling,
I don't primarily mean marketing
00:19:21.409 --> 00:19:23.099
or just getting someone excited.
00:19:23.407 --> 00:19:28.803
I mean, if you worked at X and you're
proposing that we start a moonshot for
00:19:28.803 --> 00:19:34.225
teleportation, let's say, I need from
you an architecture of understanding.
00:19:34.684 --> 00:19:36.585
Where are we trying to go?
00:19:36.947 --> 00:19:40.649
What makes us think we might be able
to get from where we are to there?
00:19:40.999 --> 00:19:43.343
And how should we interpret
things along the way?
00:19:43.346 --> 00:19:46.713
What's that set of lily
pads that you can envision?
00:19:47.103 --> 00:19:47.899
You'll be wrong.
00:19:47.928 --> 00:19:51.944
I'm fine with the fact that you're
wrong, but if you can't paint that
00:19:51.945 --> 00:19:57.050
picture so that we have a hypothesis to
test, it's very hard to get behind the
00:19:57.072 --> 00:20:02.081
idea that the upside of getting there
will be worth the risks if you can't
00:20:02.091 --> 00:20:07.557
even paint an idealized picture of how
we would get there in a controlled way
00:20:07.572 --> 00:20:11.164
where the costs aren't out of control,
where nobody gets hurt, et cetera.
00:20:11.549 --> 00:20:15.044
The idea of an architecture of
understanding is what a story provides,
00:20:15.057 --> 00:20:18.331
and the outcome of the story for you
that's important is a hypothesis that
00:20:18.331 --> 00:20:23.161
can be tested, and that's a really unique
way, I think, of looking at storytelling.
00:20:23.649 --> 00:20:25.389
You are very good at telling stories.
00:20:25.462 --> 00:20:29.699
I'm wondering, do you reflect often
on how to tell stories better?
00:20:29.761 --> 00:20:33.536
I noted in the Waymo story one
tool you seem to use is analogies.
00:20:33.803 --> 00:20:36.966
You talked about self-driving
car being like an elevator, and
00:20:36.966 --> 00:20:38.607
that makes it very accessible.
00:20:38.611 --> 00:20:40.803
I might not have been in a
self-driving car, but I've
00:20:40.803 --> 00:20:42.120
certainly been in an elevator.
00:20:42.359 --> 00:20:45.341
Talk to me about your own internal
process for the stories you tell.
00:20:45.580 --> 00:20:46.324
What do you think about?
00:20:46.843 --> 00:20:52.239
I think I understand the world mostly
through mechanisms and call it the
00:20:52.425 --> 00:20:54.835
gross high-level systems thinking.
00:20:54.836 --> 00:20:59.589
That's just how I understand the world,
which makes it easy for me to find two
00:20:59.591 --> 00:21:04.890
things that feel related because they have
similar mechanisms, like I just did in
00:21:04.890 --> 00:21:07.220
this example between Waymo and elevators.
00:21:07.571 --> 00:21:09.941
Now, I can sometimes get caught.
00:21:09.956 --> 00:21:12.415
Let me tell you an
example where I was wrong.
00:21:12.714 --> 00:21:15.687
I mean, I wasn't intellectually wrong,
but I was wrong as a storyteller.
00:21:16.228 --> 00:21:21.839
For quite a long time, I used as an
analogy to help people understand about
00:21:21.861 --> 00:21:26.549
what we're trying to do at X. And I
would say, "We are not the gamblers of
00:21:26.550 --> 00:21:30.764
innovation. We are working really hard
to be the card counters of innovation."
00:21:30.929 --> 00:21:34.322
If we go back to that conversation
we just had about habits versus
00:21:34.349 --> 00:21:38.725
outcomes, whether you win or not,
let the statistics take care of that.
00:21:39.157 --> 00:21:43.348
I want you to focus on playing the
odds, those are the habits, correctly.
00:21:43.351 --> 00:21:45.720
So you can see the analogy
with card counting.
00:21:45.915 --> 00:21:49.932
Only I've discovered more recently
that a lot of people would nod along
00:21:49.933 --> 00:21:54.072
with me when I said card counting and
used that as an analogy to what we're
00:21:54.074 --> 00:21:58.966
trying to do to systematize radical
innovation at the Moonshot Factory, only
00:21:58.966 --> 00:22:02.699
to discover more recently, most people
don't really understand card counting.
00:22:03.101 --> 00:22:05.886
Am I right that that's a good analogy?
00:22:06.064 --> 00:22:06.658
Maybe.
00:22:06.795 --> 00:22:11.610
But as a storyteller, I'm failing if I'm
using analogy that doesn't really connect
00:22:11.610 --> 00:22:14.995
with people because they don't understand
the mechanism of, of card counting.
00:22:15.410 --> 00:22:18.455
So whatever tools you use to tell
a good story, you have to make
00:22:18.455 --> 00:22:20.454
sure that they're doing their job.
00:22:20.531 --> 00:22:24.145
And that required you to do a fair
amount of reflection because most
00:22:24.147 --> 00:22:27.073
people just tell a story and move
on to the next story rather than
00:22:27.074 --> 00:22:28.869
reflecting, is it really working?
00:22:28.878 --> 00:22:32.945
So that reflective piece is important and
not surprising because a lot of what it
00:22:32.945 --> 00:22:34.840
sounds like you do is reflect and learn.
00:22:35.262 --> 00:22:37.603
Can we dive deeper into creativity?
00:22:37.628 --> 00:22:42.687
You've given us some ideas, focus
on habits, failure is essential.
00:22:43.117 --> 00:22:46.742
Are there other things you have
found really help spark and
00:22:46.890 --> 00:22:50.403
not just spark creativity, but
foster it and keep it growing?
00:22:50.745 --> 00:22:56.707
One of the things that people struggle
with is the reality that creativity's
00:22:56.719 --> 00:23:00.176
wellspring is almost entirely humor.
00:23:00.471 --> 00:23:05.866
Being serious and being creative are
nearly impossible to do at the same time.
00:23:06.455 --> 00:23:14.532
Creativity is a willingness to scramble
your understanding in some maybe
00:23:14.678 --> 00:23:20.351
random or not random way And that means
there's a looseness that has to happen.
00:23:20.427 --> 00:23:24.662
The idea of seriousness
is a tightening up.
00:23:24.857 --> 00:23:27.411
It is an organizational imperative.
00:23:27.411 --> 00:23:29.171
That's what the idea of serious is.
00:23:29.549 --> 00:23:34.367
So you hear from people, "Are you
playing or are you working?" Because
00:23:34.369 --> 00:23:39.758
we think fundamentally that work is
serious and that the alternate when
00:23:39.758 --> 00:23:42.364
we're having fun, that's not work.
00:23:42.675 --> 00:23:47.023
But creativity, as I just described,
only happens in the play state.
00:23:47.422 --> 00:23:54.038
And so constantly trying to inculcate
a sense of wonder, silliness,
00:23:54.319 --> 00:24:00.977
experimentation without concern
for looking silly, for example,
00:24:00.981 --> 00:24:06.213
looking dumb, is absolutely required
in order to be really creative.
00:24:06.671 --> 00:24:10.250
And yet people resist
that push for playfulness.
00:24:10.542 --> 00:24:15.194
So I find that's one of the things that
we have to keep coming back to at X is
00:24:15.446 --> 00:24:20.880
I'm not asking for silliness because I
disrespect the journey that we're on.
00:24:21.356 --> 00:24:28.603
I'm demanding silliness and playfulness
because the journey we're on is so serious
00:24:29.088 --> 00:24:35.681
in its goal and can only be seriously
pursued through a non-serious attitude.
00:24:36.071 --> 00:24:39.726
What are some things that you
specifically do to bring silliness
00:24:39.727 --> 00:24:41.505
and playfulness into work?
00:24:41.875 --> 00:24:44.526
Well, as we were talking
before we started, I live
00:24:44.545 --> 00:24:45.872
on my rollerblades at work.
00:24:46.092 --> 00:24:48.824
One, it's just fun for me, frankly.
00:24:49.134 --> 00:24:51.201
Two, I get around a little bit faster.
00:24:51.202 --> 00:24:53.258
I probably save eight
or nine minutes a day.
00:24:53.258 --> 00:24:54.555
It's a fairly big building.
00:24:54.920 --> 00:25:00.322
But three, it sends a signal to people
not to take me too seriously, not
00:25:00.322 --> 00:25:04.102
to take themselves too seriously,
not to take the work too seriously.
00:25:04.229 --> 00:25:09.073
And when they see me doing that, I don't
need them to wear rollerblades, but
00:25:09.142 --> 00:25:14.933
unconsciously it's one of 100 signals
that we send each other at the Moonshot
00:25:14.942 --> 00:25:20.421
Factory to keep ourselves from tightening
up into the rigidity of seriousness.
00:25:20.790 --> 00:25:24.079
When you interview people to come on
and be part of the X team, give me an
00:25:24.080 --> 00:25:27.514
example of an interview question you
would ask that helps you get insight
00:25:27.515 --> 00:25:32.713
into if this person will fit into the
culture and perhaps help prepare them
00:25:32.724 --> 00:25:34.014
for what the culture would be like.
00:25:34.407 --> 00:25:38.886
I mean, one of the main interview
questions I ask, but it's not a trick,
00:25:39.313 --> 00:25:46.222
so you can't prep for it really, is
if you come to work at X and you don't
00:25:46.505 --> 00:25:49.146
really understand what we're trying
to do, 'cause you just think it's
00:25:49.148 --> 00:25:52.945
cool from the outside, like watching
someone jump out of an airplane, but
00:25:52.945 --> 00:25:56.815
when I have you at the door of the
airplane and I'm asking you to jump, it
00:25:56.818 --> 00:25:58.686
might not seem like your jam anymore.
00:25:58.940 --> 00:26:03.507
And I reserve the right, we reserve the
right, to torture you if you're at X and
00:26:03.508 --> 00:26:06.974
you're not actually down for the thing
we're trying to do, for those habits.
00:26:07.061 --> 00:26:08.805
So this is not an interview.
00:26:08.964 --> 00:26:11.142
You and I should be failing fast together.
00:26:11.503 --> 00:26:14.815
If we can possibly discover
that you're not right, that's
00:26:14.815 --> 00:26:18.247
definitely in my best interest, but
it's also in your best interest.
00:26:18.362 --> 00:26:22.998
And I know some part of you wants
to believe that you guys, your job
00:26:23.010 --> 00:26:24.797
here is to trick me into hiring.
00:26:25.004 --> 00:26:27.229
But look at me, I am serious.
00:26:27.256 --> 00:26:29.197
I will torture you if you do that.
00:26:29.504 --> 00:26:31.857
You will not be happy on
the other side of this.
00:26:31.967 --> 00:26:34.091
You and I want the same
thing in this moment.
00:26:34.285 --> 00:26:40.078
So let's spend the next 45 minutes
trying to fail fast here, verifying
00:26:40.338 --> 00:26:42.902
why you won't be happy at X.
00:26:43.253 --> 00:26:46.666
Wow, that's like an experiential
interview on steroids, where it's,
00:26:46.723 --> 00:26:50.543
you wanna have that experience of
failing, and you wanna be comfortable
00:26:50.564 --> 00:26:51.766
that that person is okay with it.
00:26:51.838 --> 00:26:54.708
Number one, if somebody isn't comfortable
doing that with me, then they're
00:26:54.737 --> 00:26:58.589
probably not gonna fit at X. But if
they are comfortable doing that with
00:26:58.590 --> 00:27:01.960
me, then I'm getting what I want, which
is for us to have a real conversation
00:27:01.967 --> 00:27:03.795
about why it might not work out.
00:27:04.178 --> 00:27:08.294
I can only imagine people come away
feeling that experience was special
00:27:08.295 --> 00:27:11.713
and unique, and you probably end up
bringing someone on who works out.
00:27:11.851 --> 00:27:13.610
Your success rate is much higher, likely.
00:27:13.962 --> 00:27:15.039
It is quite high.
00:27:15.351 --> 00:27:17.017
It's certainly not 100%.
00:27:17.272 --> 00:27:20.784
I would also say that our success
rate is as good as it is partly
00:27:20.785 --> 00:27:22.617
because we're very flexible.
00:27:22.736 --> 00:27:28.004
If I was hiring you to come on, I would
say, "I do not have a perfectly shaped job
00:27:28.012 --> 00:27:32.724
for you here." You might suspect what it
is, but let's be honest, if you're coming
00:27:32.724 --> 00:27:37.010
in, I'm gonna be running naked and so
are you, and we're gonna basically invent
00:27:37.018 --> 00:27:41.143
your job on your way in and probably for
the first year or two you're with us.
00:27:41.506 --> 00:27:44.774
And if that's not your jam, please don't
come, because all of X is like that.
00:27:44.774 --> 00:27:46.126
It's just gonna drive you crazy.
00:27:46.714 --> 00:27:50.718
But if you're up for that, we have
a better chance of winning because I
00:27:50.723 --> 00:27:54.510
don't have to be right ahead of time
about exactly what your superpowers are.
00:27:54.655 --> 00:27:57.178
We can design a job
around your superpowers.
00:27:57.628 --> 00:28:00.321
It sounds like that's a perfect
example of what you talked
00:28:00.330 --> 00:28:03.612
about earlier about setting some
boundaries, but not being rigid.
00:28:03.657 --> 00:28:04.809
Allow that flexibility.
00:28:05.290 --> 00:28:09.007
So you've added podcast host to your
list of titles and accomplishments.
00:28:09.154 --> 00:28:13.213
What do you think about the hosting gig,
and what motivated you to start the show?
00:28:13.216 --> 00:28:14.438
I listen and I love it.
00:28:14.451 --> 00:28:15.206
I've learned a lot.
00:28:15.220 --> 00:28:18.036
It's really cool for me to be
in person with you after having
00:28:18.036 --> 00:28:19.542
listened to you in my headset.
00:28:19.779 --> 00:28:20.634
What brought it about?
00:28:20.999 --> 00:28:24.260
First, if people are interested,
they can just look up Moonshot
00:28:24.270 --> 00:28:26.208
Factory, Moonshot Podcast.
00:28:26.714 --> 00:28:30.934
We were turning 15 years old, and at
least one of the things was we're trying
00:28:30.936 --> 00:28:36.110
to get a little bit less secret, and
we wanted to start exposing some of how
00:28:36.110 --> 00:28:41.072
we work and what we've done, what we've
learned, partly because we want to empower
00:28:41.138 --> 00:28:43.381
other people to take their own moonshots.
00:28:44.090 --> 00:28:48.597
And giving them that learning
opportunity hopefully gives them a boost.
00:28:48.907 --> 00:28:54.353
Rather than me just blah blah blah-ing
about moonshot taking, what we've done is
00:28:54.354 --> 00:28:59.468
we've sliced it up into about 50-minute
episodes, 10 of them in the first season,
00:28:59.737 --> 00:29:04.554
where we've given people who've come
through the Moonshot Factory a chance to
00:29:04.555 --> 00:29:06.869
tell their stories, what they've learned.
00:29:06.872 --> 00:29:09.321
So you get to hear in the
first person what it was like
00:29:09.343 --> 00:29:11.051
to be taking those moonshots.
00:29:11.501 --> 00:29:16.558
And these are some people who are now
luminaries in their field, Sebastian Thrun
00:29:16.568 --> 00:29:19.092
or Andrew Ng or Jeff Dean and others.
00:29:19.391 --> 00:29:21.543
And how did we get to
something like Google Brain?
00:29:21.554 --> 00:29:25.257
How did we get to something like
Waymo, the self-driving cars?
00:29:25.770 --> 00:29:30.386
And when you hear from them the mistakes
they made or those initial things that
00:29:30.402 --> 00:29:35.141
gave them the faith to start, that,
that seed crystal that they thought
00:29:35.141 --> 00:29:39.930
was worth building on, I hope it then
helps other people not only understand
00:29:39.982 --> 00:29:44.003
us and the moonshots we take, but
inspire them to go do some of their own.
00:29:44.224 --> 00:29:47.329
It's very inspirational, and
it's really cool to hear in their
00:29:47.330 --> 00:29:48.796
own words what they've done.
00:29:49.180 --> 00:29:50.598
And there are learnings that come.
00:29:50.625 --> 00:29:54.056
I have listened and thought
differently about ways I team with
00:29:54.073 --> 00:29:57.561
people and the ways that I think
about the decisions I need to make.
00:29:58.129 --> 00:30:01.023
So I'm excited that you're doing it,
and I'm excited to listen to season two.
00:30:02.298 --> 00:30:04.856
Before we end, I ask all
my guests three questions.
00:30:04.857 --> 00:30:07.311
One I create just for you, and
then the other two are similar.
00:30:07.311 --> 00:30:07.920
Are you up for that?
00:30:08.023 --> 00:30:08.392
Sure.
00:30:08.842 --> 00:30:11.171
I'd be very curious, what's a
current moonshot you're working
00:30:11.173 --> 00:30:12.747
on that has you really excited?
00:30:13.171 --> 00:30:14.835
We started this about eight years ago.
00:30:14.835 --> 00:30:17.088
This is very typical of
the moonshots we take.
00:30:17.288 --> 00:30:20.885
They seem crazy when we start them,
and then at least occasionally, much
00:30:20.885 --> 00:30:24.271
later, they turn out to be in the
right place at the right time, is
00:30:24.271 --> 00:30:26.689
a moonshot for the electric grid.
00:30:26.901 --> 00:30:31.885
It turns out that everyone would like to
make software that helps the electric grid
00:30:31.886 --> 00:30:34.780
get better, including us, and you can't.
00:30:34.981 --> 00:30:41.901
You can't until somebody has a
circuit diagram for the grid itself.
00:30:42.289 --> 00:30:47.003
If you don't have a digital twin for the
grid, where every wire is, where every
00:30:47.003 --> 00:30:51.830
inverter is, where every transformer
is, no software on top of that will help
00:30:51.831 --> 00:30:56.811
you manage what you have or plan for
the future or optimize it in real time.
00:30:57.214 --> 00:31:03.555
And so what our team, Tapestry, did
over the last seven years was build out
00:31:03.771 --> 00:31:09.334
the tools for taking in a wide variety
of data, including the very noisy and
00:31:09.335 --> 00:31:13.038
imperfect data that grid operators
have about their own grid, 'cause they
00:31:13.039 --> 00:31:15.054
don't have a map of their own grid.
00:31:15.300 --> 00:31:20.832
And then using lots of different sources,
Street View cars, drone data, satellite
00:31:20.832 --> 00:31:27.210
data, lots of different inputs, we can
then use induction and deduction to do
00:31:27.214 --> 00:31:33.577
the detective work and back out the exact
grid that a particular grid operator has.
00:31:33.841 --> 00:31:38.108
And then we can help them manage
the grid they have, plan for
00:31:38.109 --> 00:31:41.280
the future of their grid, and
optimize their grid in real time.
00:31:41.834 --> 00:31:44.540
So it's as if you've decoded
the genome for the grid, and
00:31:44.540 --> 00:31:45.811
now we can do some work with it.
00:31:45.880 --> 00:31:49.094
That sounds very useful and
hopefully very beneficial.
00:31:49.732 --> 00:31:52.909
Question number two, who is a
communicator that you admire and why?
00:31:53.530 --> 00:31:57.976
I enjoy giving public talks,
and so oration has always
00:31:57.977 --> 00:31:59.395
been very interesting to me.
00:31:59.596 --> 00:32:05.178
And I have watched a wide variety of
public speakers, not because I'm going
00:32:05.179 --> 00:32:10.905
to be any one of them, but to understand
how each of their patterns functions.
00:32:11.178 --> 00:32:13.806
You know, Martin Luther
King Jr., great speaker.
00:32:13.967 --> 00:32:17.045
His preacher style, there are
things you can learn from that.
00:32:17.727 --> 00:32:20.163
James Baldwin, amazing speaker.
00:32:20.447 --> 00:32:21.535
Not a preacher.
00:32:22.114 --> 00:32:27.253
Somewhat professorial, but with
a calm angriness because of the
00:32:27.255 --> 00:32:29.225
specifics of the life that he led.
00:32:29.757 --> 00:32:32.450
Barack Obama, very professorial.
00:32:32.869 --> 00:32:36.964
My grandfather, Edward Teller,
actually was another great public
00:32:36.964 --> 00:32:40.466
speaker, and I learn a lot from
watching each of these people.
00:32:40.995 --> 00:32:44.795
Yeah, they each bring a really interesting
approach that you can then synthesize.
00:32:44.806 --> 00:32:47.558
And by the way, your TED Talk is
one that everybody should listen to
00:32:47.559 --> 00:32:50.786
because you deliver it very well and
it's very exciting to learn from.
00:32:51.177 --> 00:32:54.680
Final question, what are the first
three ingredients that go into a
00:32:54.680 --> 00:32:56.817
successful communication recipe?
00:32:57.365 --> 00:33:01.346
I personally don't love the
start with a personal story.
00:33:01.597 --> 00:33:07.189
I would encourage people to
get real, as in honest, get
00:33:07.194 --> 00:33:10.796
specific, and you can be human.
00:33:11.155 --> 00:33:18.760
So when we tell a story, like in our
podcast, we use visuals from the field,
00:33:19.192 --> 00:33:21.324
from the actual people who are doing it.
00:33:21.550 --> 00:33:26.243
We explain what we were trying
to do and how where we actually
00:33:26.244 --> 00:33:28.624
ended up wasn't that, getting real.
00:33:28.748 --> 00:33:34.168
We show people how we harvest value
even from being wrong, and can have
00:33:34.171 --> 00:33:35.830
fun in the process of learning.
00:33:36.311 --> 00:33:41.166
And when you get a cycle of those three
things, while that doesn't feel like Mary
00:33:41.168 --> 00:33:46.950
Jane in Idaho, start of the sort of New
York Times article, it is deeply human.
00:33:46.950 --> 00:33:49.957
You can feel what it feels like
for these people to go through it.
00:33:50.396 --> 00:33:54.085
It's exciting, it's meaningful,
you can see yourself in it, it's
00:33:54.104 --> 00:33:55.942
memorable because of those things.
00:33:56.221 --> 00:33:57.480
So that's how we try to do it.
00:33:57.736 --> 00:34:01.414
Be real, be specific, be human,
and all of that allows you
00:34:01.416 --> 00:34:02.584
to connect through emotion.
00:34:02.752 --> 00:34:04.426
And I appreciate that recipe.
00:34:04.701 --> 00:34:06.254
And I appreciate this conversation, Astro.
00:34:06.255 --> 00:34:07.346
This was fantastic.
00:34:07.356 --> 00:34:10.655
You've talked about architecting to
understanding, and you certainly did
00:34:10.656 --> 00:34:14.500
that for us, and I hope all of us can
build these habits and reward ourselves
00:34:14.500 --> 00:34:16.186
for these habits and not just outcomes.
00:34:16.187 --> 00:34:16.985
Thank you for your time.
00:34:17.183 --> 00:34:17.524
Thank you.
00:34:17.525 --> 00:34:18.315
That was a lot of fun.
00:34:19.712 --> 00:34:21.867
Thank you for joining us
for another episode of Think
00:34:21.869 --> 00:34:23.891
Fast, Talk Smart, the podcast.
00:34:24.257 --> 00:34:26.011
It was great learning from Astro.
00:34:26.148 --> 00:34:29.918
Among the many ideas he shared, the one
that stands out most to me is that being
00:34:29.920 --> 00:34:32.010
wrong isn't something we should avoid.
00:34:32.300 --> 00:34:35.734
It's often where the most important
learnings happen, and embracing it
00:34:35.755 --> 00:34:38.071
can help us move forward faster.
00:34:38.610 --> 00:34:42.326
To learn more about creativity and
innovation, please listen to episode 70
00:34:42.580 --> 00:34:45.975
and 120 with Jeremy Utley and Tina Seelig.
00:00:03.105 --> 00:00:08.246
Big ideas require us to think differently
about failure, learning, and creativity.
00:00:08.560 --> 00:00:11.497
My name is Matt Abrahams and I
teach strategic communication at
00:00:11.498 --> 00:00:13.309
Stanford Graduate School of Business.
00:00:13.598 --> 00:00:18.748
Welcome to this special Rethinks episode
of Think Fast, Talk Smart, the podcast.
00:00:19.645 --> 00:00:22.872
My conversation with Astro Teller
changed the way I think about what
00:00:22.874 --> 00:00:25.415
it takes to pursue ambitious ideas.
00:00:25.988 --> 00:00:29.525
Astro shared so many insights about
learning from failure, fostering
00:00:29.526 --> 00:00:33.021
creativity, and making big ideas
understandable that I wanted to
00:00:33.021 --> 00:00:35.085
bring his advice back for all of you.
00:00:35.593 --> 00:00:39.072
And this time, we're sharing the
extended version of our conversation.
00:00:39.426 --> 00:00:40.182
Enjoy.
00:00:41.623 --> 00:00:44.938
The best communication is
architected for understanding.
00:00:45.149 --> 00:00:48.393
My name is Matt Abrahams, and I
teach Strategic Communication at
00:00:48.394 --> 00:00:50.194
Stanford Graduate School of Business.
00:00:50.449 --> 00:00:53.221
Welcome to Think Fast,
Talk Smart, the podcast.
00:00:54.153 --> 00:00:57.047
Today, I am really excited
to speak with Astro Teller.
00:00:57.471 --> 00:01:00.920
Astro is a computer scientist,
entrepreneur, and inventor.
00:01:01.153 --> 00:01:05.740
He serves as captain of moonshots
at X, Alphabet's Moonshot Factory,
00:01:05.979 --> 00:01:09.565
where he oversees audacious
high-impact technology projects.
00:01:09.949 --> 00:01:13.602
He has written two novels and the
nonfiction book, Sacred Cows: The
00:01:13.602 --> 00:01:16.887
Truth About Divorce and Marriage,
that he co-wrote with his wife.
00:01:17.368 --> 00:01:20.794
He's also a new podcaster,
hosting the really fun and
00:01:20.795 --> 00:01:23.342
insightful The Moonshot Podcast.
00:01:23.770 --> 00:01:24.618
Welcome, Astro.
00:01:25.082 --> 00:01:27.178
I am super excited to
be chatting with you.
00:01:27.178 --> 00:01:29.131
I've been excited ever
since we arranged this.
00:01:29.137 --> 00:01:29.993
Thank you for being here.
00:01:30.196 --> 00:01:30.967
Thanks for having me.
00:01:31.000 --> 00:01:31.467
Excellent.
00:01:31.509 --> 00:01:32.195
Shall we get started?
00:01:32.231 --> 00:01:32.893
Yeah, let's do it.
00:01:33.112 --> 00:01:37.610
To begin, I'd love for you to define for
our audience what you mean by a moonshot.
00:01:37.868 --> 00:01:38.356
Sure.
00:01:38.431 --> 00:01:41.170
So we think of this as the
blueprint for moonshots.
00:01:41.208 --> 00:01:45.070
And in order for us to have
something that counts as a moonshot,
00:01:45.422 --> 00:01:46.993
it has to have three components.
00:01:47.317 --> 00:01:50.484
One, there has to be a huge
problem with the world that you
00:01:50.484 --> 00:01:51.824
can name and you want to solve.
00:01:52.211 --> 00:01:56.679
Number two, there has to be some kind
of radical proposed solution that
00:01:56.751 --> 00:02:00.698
however unlikely it is you could make
that thing, this is like a science
00:02:00.698 --> 00:02:02.645
fiction sounding product or service.
00:02:02.922 --> 00:02:06.186
We could agree ahead of time if
you could make it, it would resolve
00:02:06.198 --> 00:02:07.583
that huge problem with the world.
00:02:08.073 --> 00:02:11.733
And then three, there has to be some
kind of breakthrough technology that
00:02:11.733 --> 00:02:16.490
gives us at least a glimmer of a hope
that we could make that science fiction
00:02:16.491 --> 00:02:19.559
sounding product or service that would
solve that huge problem with the world.
00:02:20.069 --> 00:02:23.026
Once you have those three
things, we're not done.
00:02:23.258 --> 00:02:27.348
That means you have a
moonshot story hypothesis.
00:02:27.497 --> 00:02:29.459
You have a testable hypothesis.
00:02:29.632 --> 00:02:35.103
And from there, the question is, how
quickly can we verify that you're wrong
00:02:35.706 --> 00:02:37.698
so we can move on to the next idea?
00:02:38.013 --> 00:02:43.894
Because anything that fits those three
criteria that I just named, exciting
00:02:43.897 --> 00:02:50.275
as they are, it is exactly because it
is so unlikely to work that we have to
00:02:50.291 --> 00:02:56.269
be constantly pursuing the reality that
each one of them is likely to be wrong.
00:02:56.823 --> 00:03:00.803
So the goal is actually to come
up with these audacious ideas
00:03:00.803 --> 00:03:04.707
and strivings, and then to as
quickly as possible negate them.
00:03:05.245 --> 00:03:08.739
Yeah, of course, we want any
particular moonshot we come up
00:03:08.742 --> 00:03:13.144
with to turn out to be a once in a
generation opportunity for the world.
00:03:13.394 --> 00:03:18.847
But since most of them aren't, wanting
to win, wanting to get it to be right
00:03:19.027 --> 00:03:25.801
each time leads to sadness after sadness,
and denial kicks in that slows up the
00:03:25.802 --> 00:03:31.221
efficiency of being the learning machine
that we would aspire to be, verifying
00:03:31.233 --> 00:03:35.541
which of the many that we've started
is in fact worth doubling down on.
00:03:35.797 --> 00:03:38.286
You said several things there
I want to dive deeper into.
00:03:38.286 --> 00:03:42.886
So you see this as learning, and
it's a learning machine, as you
00:03:42.892 --> 00:03:46.897
said, which leads me to wonder, is
there a particular type of mindset
00:03:46.911 --> 00:03:50.713
that you try to bring about in your
organization that looks at it this way?
00:03:50.714 --> 00:03:52.688
Because many people don't
think of projects this way.
00:03:52.690 --> 00:03:55.108
They start a project, they want it
to be successful, not let's start
00:03:55.108 --> 00:03:57.722
a project and figure out all the
reasons why it won't be successful.
00:03:57.831 --> 00:03:59.529
Can you talk a little
bit about that mindset?
00:03:59.755 --> 00:04:00.071
Sure.
00:04:00.072 --> 00:04:01.564
Let me give you one or two examples.
00:04:01.849 --> 00:04:05.783
I mean, the first is, if you were
working at X, I would say, "Can we
00:04:05.783 --> 00:04:08.835
pre-agree before you've come up with
something and then fallen in love with
00:04:08.838 --> 00:04:13.861
it, that if it's a 1 in 100 chance of
working, it has a 99% chance of not
00:04:13.863 --> 00:04:19.645
working?" Which means that if you tie
your sense of self-worth to getting
00:04:19.647 --> 00:04:21.962
a yes, you're just lying to yourself.
00:04:22.376 --> 00:04:26.306
You think that you're the 1% and you're
always gonna win, but this can't be Lake
00:04:26.322 --> 00:04:28.653
Wobegon, where we're all above average.
00:04:28.669 --> 00:04:32.772
So that's one way to help put
some intellectual guardrails
00:04:32.775 --> 00:04:34.240
around what we're about to go do.
00:04:34.577 --> 00:04:35.868
Here's another way of doing it.
00:04:36.409 --> 00:04:39.732
Think of the last really hard
thing that you and a team did.
00:04:39.892 --> 00:04:41.811
I'd ask each of your
listeners to think about that.
00:04:42.489 --> 00:04:47.798
Now imagine you lose all the hardware,
all of the software, anything except
00:04:47.798 --> 00:04:49.774
what's in your and your team's heads.
00:04:50.018 --> 00:04:53.650
How long, once you've succeeded,
would it take for you to go back
00:04:53.668 --> 00:04:57.339
and rebuild the solution, having
already verified what it is?
00:04:57.651 --> 00:05:01.775
Most people say somewhere between
5 and 20% of the time that it took.
00:05:01.969 --> 00:05:05.245
And for moonshots, it's
much closer to 5% than 20%.
00:05:05.961 --> 00:05:07.714
Let's call it 10% on average.
00:05:08.069 --> 00:05:10.596
What are we gonna call
that other 90% of the work?
00:05:10.833 --> 00:05:11.663
It's learning.
00:05:11.970 --> 00:05:14.801
Most of the work isn't
the making of stuff.
00:05:14.861 --> 00:05:16.849
It's the learning what to make.
00:05:17.352 --> 00:05:21.136
And the more you're shooting over the
horizon, which is what a moonshot is,
00:05:21.473 --> 00:05:27.897
the more of the journey is exploration,
not the settling that happens afterwards.
00:05:28.252 --> 00:05:32.898
So if you agree conceptually,
intellectually, that we're on a
00:05:32.898 --> 00:05:39.112
learning journey, then scary as it
is to be wrong, we need to focus on
00:05:39.115 --> 00:05:40.588
the moments where we learn something.
00:05:40.792 --> 00:05:42.169
And when do you learn something?
00:05:42.400 --> 00:05:46.209
You learn something when you have a model
about the world, and then in some way you
00:05:46.209 --> 00:05:48.201
get some data that tells you you're wrong.
00:05:48.546 --> 00:05:49.981
You learn nothing when you're right.
00:05:50.066 --> 00:05:52.148
At best, you deepen grooves in your brain.
00:05:52.460 --> 00:05:55.880
If you're wrong, whatever else
we wanna call it, that feels bad.
00:05:55.880 --> 00:05:57.253
That's a failure of a kind.
00:05:57.483 --> 00:06:02.479
If you hate failure, you will emotionally
avoid that moment, which means
00:06:02.481 --> 00:06:04.195
you're emotionally avoiding learning.
00:06:04.205 --> 00:06:08.374
If we just agreed we're gonna spend
90 plus percent of our time learning,
00:06:09.003 --> 00:06:13.527
then we need to de-stigmatize
failure so we can have our learning
00:06:13.527 --> 00:06:15.214
loops be as tight as possible.
00:06:15.540 --> 00:06:19.069
So a moonshot mindset is really
about seeing learning as the goal.
00:06:19.207 --> 00:06:23.626
How do you inculcate and support
that, and how do you produce
00:06:23.681 --> 00:06:27.451
anything if, for many companies,
productivity is what's the goal?
00:06:27.461 --> 00:06:32.551
So how do you produce the amazing things
that you've done and help people see
00:06:32.551 --> 00:06:34.443
learning as the goal, not productivity?
00:06:34.860 --> 00:06:38.097
It's very hard to prevent
people from chasing progress.
00:06:38.571 --> 00:06:42.010
Even at X, there's a decent amount
of it, even though I'm constantly
00:06:42.010 --> 00:06:45.395
going around trying to stamp it out
and redirect people towards learning.
00:06:45.534 --> 00:06:52.012
So it's not hard for the end result
of a lot of learning to turn into
00:06:52.012 --> 00:06:55.515
something very valuable that looks
ultimately like great progress.
00:06:55.867 --> 00:07:02.047
During the journey, the thing that is
hardest to fight is you feel, especially
00:07:02.056 --> 00:07:05.321
once you've done some learning and
you've found something that works, it's
00:07:05.321 --> 00:07:10.830
so hard for you as an X-er not to feel
like, "I found it. I'm right. Now just
00:07:10.830 --> 00:07:13.554
let me build it, and then I'll give it
to the world and we're gonna be great."
00:07:13.925 --> 00:07:18.155
And I just have to say, we, the leadership
at X, have to say over and over again, "I
00:07:18.157 --> 00:07:21.974
know it feels like you've found it, and
you know more about the teleporter, if
00:07:21.975 --> 00:07:23.662
that's what you're working on, than I do.
00:07:23.747 --> 00:07:27.495
But I'm telling you from induction,
having watched this hundreds of
00:07:27.496 --> 00:07:30.059
times now, you're not correct yet.
00:07:30.177 --> 00:07:34.052
You have a lot to learn, and what you
think is the answer isn't the answer.
00:07:34.219 --> 00:07:35.982
It's not that I don't
want you to be right.
00:07:36.025 --> 00:07:39.968
It's just my experience tells me you have
a lot more to learn, so I need you to
00:07:39.971 --> 00:07:44.768
stay in learning mode, keep your humility
and your curiosity really high for
00:07:44.768 --> 00:07:49.691
another two or three years." It's helping
people to do that that's really hard.
00:07:50.330 --> 00:07:53.568
I find it really interesting that
humility and curiosity are what
00:07:53.652 --> 00:07:56.020
keep the flames going for learning.
00:07:56.467 --> 00:07:59.614
How do you capture the learning
across the different teams?
00:07:59.960 --> 00:08:03.693
We had Amy Edmondson on, and she talks
a lot about the right kind of failure
00:08:03.798 --> 00:08:07.003
and learning from the failure and
the processes you can put in place.
00:08:07.272 --> 00:08:10.513
What are some of the best practices
you've implemented so if somebody learns
00:08:10.522 --> 00:08:14.311
something, that learning is cascaded so
others can benefit from that learning?
00:08:14.711 --> 00:08:16.029
There's a bunch of different ways.
00:08:16.135 --> 00:08:19.606
The truth is, some of it is just
institutional knowledge, and that's
00:08:19.606 --> 00:08:23.422
sort of a background radiation
that's very real at X. There are
00:08:23.422 --> 00:08:25.188
lots of things that we capture.
00:08:25.534 --> 00:08:29.680
We actually have a document called
headwinds and tailwinds to remind
00:08:29.680 --> 00:08:31.460
us of some of these learnings.
00:08:31.868 --> 00:08:34.340
These kinds of things
tend to be tailwinds.
00:08:34.351 --> 00:08:37.303
In our experience, these kinds
of things tend to be headwinds.
00:08:37.465 --> 00:08:42.054
That doesn't mean you can't ignore one of
them if you want to or if you think it's
00:08:42.059 --> 00:08:45.757
worth it, but these are good reminders
from the past that we've learned.
00:08:46.125 --> 00:08:51.778
We have maybe 20% of the people who
work at X aren't on one of the projects.
00:08:51.957 --> 00:08:53.540
They're in these central teams.
00:08:53.780 --> 00:08:58.569
So this is like finance, legal, public
policy, some business development, a
00:08:58.569 --> 00:09:00.650
lot of sort of hardware prototyping.
00:09:01.004 --> 00:09:04.165
And what happens is they go over
here and they help the teleporter
00:09:04.461 --> 00:09:08.185
team, and then they go over here
and they help the time machine team.
00:09:08.693 --> 00:09:14.398
And so they act as a kind of vector,
transplanting interesting ideas and
00:09:14.401 --> 00:09:16.415
learnings from one team to another.
00:09:16.519 --> 00:09:20.858
So even if those teams don't talk to
each other very much, because they share
00:09:20.872 --> 00:09:25.178
these central teams, the central teams
can move good ideas back and forth.
00:09:25.684 --> 00:09:28.203
So you actually have a structural
element where there are people spoke
00:09:28.204 --> 00:09:31.670
and hub, and the, those folks in the
hub bring those learnings across.
00:09:31.712 --> 00:09:34.747
I find that really helpful for
other people to think about in their
00:09:34.747 --> 00:09:36.563
organizations how they might do that.
00:09:36.716 --> 00:09:36.999
Yeah.
00:09:37.004 --> 00:09:42.894
It is hub and spoke from a help
perspective, but it is not as the concept
00:09:42.895 --> 00:09:48.071
or the visualization of hub and spoke
might imply, a highly top-down process.
00:09:48.381 --> 00:09:51.545
Discovery is mostly a bottoms-up process.
00:09:51.545 --> 00:09:56.140
It's very hard to dictate, so you
have to create some structure and
00:09:56.143 --> 00:10:00.421
guardrails, but then let people
be pretty free range within those
00:10:00.447 --> 00:10:04.314
guardrails, or they won't ultimately
find things that are unexpected.
00:10:04.624 --> 00:10:09.291
So you have to have just enough boundary
setting to keep things moving forward, but
00:10:09.291 --> 00:10:11.464
not so much that people can't be creative.
00:10:11.510 --> 00:10:12.615
And we've seen that a lot.
00:10:12.616 --> 00:10:15.492
We've talked a lot about improvisation
and how that mindset helps.
00:10:15.492 --> 00:10:19.438
In, in improv, they have some rules, and
that's what allows for that creativity.
00:10:19.608 --> 00:10:19.843
Right.
00:10:19.844 --> 00:10:20.948
Let me give you an example.
00:10:21.321 --> 00:10:24.139
Obviously, we have to have a
performance management system.
00:10:24.139 --> 00:10:25.244
It'd be weird if we didn't.
00:10:25.406 --> 00:10:30.024
The temptation across the whole world,
so including at the Moonshot Factory, is
00:10:30.024 --> 00:10:35.063
to have our performance management system
be one that rewards people for outcomes.
00:10:35.674 --> 00:10:40.176
You rang the bell, you got the million
dollars or the big deal you signed.
00:10:40.351 --> 00:10:42.850
Good for you, bonus,
promotion, whatever that is.
00:10:43.186 --> 00:10:48.289
That is the death of radical innovation,
because if I'm asking you to do things
00:10:48.345 --> 00:10:53.091
that have a one in 100 chance of success,
and then you get rewarded on the basis
00:10:53.120 --> 00:10:58.826
of a yes, of a success, you will very
quickly learn to pretend you're doing
00:10:58.828 --> 00:11:03.685
radical innovation, but find ways to get
the safety of like, "Okay, maybe it's not
00:11:03.686 --> 00:11:07.571
as radical, maybe it's not as innovative,
but I'll dress it up so it looks like
00:11:07.572 --> 00:11:11.186
that, and I'm pretty sure I can make
this work even if it's not that exciting.
00:11:11.650 --> 00:11:15.283
And so I'm gonna focus on this
thing because I know I'm only
00:11:15.283 --> 00:11:18.824
gonna get rewarded when I get a
yes." All humans will do that.
00:11:19.052 --> 00:11:24.465
And so the performance management system
we have directs people back towards
00:11:24.465 --> 00:11:27.323
the habits, not towards the outcomes.
00:11:27.432 --> 00:11:34.628
What are the kinds of things like humility
and curiosity that tend to have wild
00:11:34.630 --> 00:11:37.746
success as a long-term side effect?
00:11:38.329 --> 00:11:42.771
And then you have to trust, and this is a
big, scary trust fall, that those outcomes
00:11:42.773 --> 00:11:45.114
will happen when you focus on the habits.
00:11:45.408 --> 00:11:47.259
And you have good
evidence that will happen.
00:11:47.423 --> 00:11:50.958
It's harder from a management point of
view to measure those kind of things
00:11:50.965 --> 00:11:52.766
versus did you ring the bell or not?
00:11:52.784 --> 00:11:54.692
That's a very binary, easy thing to do.
00:11:54.693 --> 00:11:59.439
So you have to have more flexibility and
openness as managers, I guess, as well.
00:11:59.521 --> 00:12:04.843
Yeah, and this is a struggle to this
day at X. It is so much harder to train
00:12:04.913 --> 00:12:09.841
managers to hold people accountable to
habits rather than to outcomes, that
00:12:09.842 --> 00:12:14.317
they routinely will try to find ways to
hack the system so that they can reward
00:12:14.318 --> 00:12:18.194
people for outcomes, or they'll gripe
about their performance management system.
00:12:18.528 --> 00:12:19.106
I get it.
00:12:19.564 --> 00:12:23.826
And that's an example where we're
trying to create some guardrails, let
00:12:23.827 --> 00:12:28.170
them use some creativity within those
guardrails, but also make sure that
00:12:28.170 --> 00:12:31.746
they're pointed, in this case, towards
the habits rather than the outcomes.
00:12:32.098 --> 00:12:36.086
I really like this idea of rewarding
people for habits, not for outcomes.
00:12:36.420 --> 00:12:39.861
I really wanna dive deeper into
this notion of failing fast and
00:12:39.863 --> 00:12:41.571
looking how to kill projects.
00:12:41.584 --> 00:12:42.919
Can you share a little bit about that?
00:12:42.919 --> 00:12:47.309
And you do some kind of celebration,
the Dia de los Muertos, that I'd
00:12:47.310 --> 00:12:48.615
love to hear a bit about as well.
00:12:48.909 --> 00:12:49.313
Sure.
00:12:49.348 --> 00:12:50.752
We have a lot of traditions.
00:12:50.778 --> 00:12:54.554
That's one that we, we haven't done in
a few years, but we did it for 7 or 8
00:12:54.554 --> 00:12:56.688
years, the Day of the Dead celebration.
00:12:57.016 --> 00:13:04.210
In order to help people feel good,
or at least not avoid failures at X,
00:13:04.427 --> 00:13:07.637
we have to help them destigmatize it.
00:13:07.652 --> 00:13:12.040
In order to destigmatize it, you can get
at that in a bunch of different ways.
00:13:12.059 --> 00:13:15.625
But some of it is, it is sad to
have something that you worked
00:13:15.625 --> 00:13:17.689
on for a long time need to stop.
00:13:17.829 --> 00:13:21.516
And we set up a remembrance party.
00:13:21.681 --> 00:13:27.091
So the Day of the Dead is a very
long-standing tradition for remembering
00:13:27.093 --> 00:13:28.956
people who've gone from our lives.
00:13:29.229 --> 00:13:34.062
And so by doing that and sort of
respecting that aspect of the tradition,
00:13:34.063 --> 00:13:38.395
but adding to it the sadness we
have about people who've just left
00:13:38.399 --> 00:13:42.024
X, they're not dead, they've just
left X. We're remembering projects
00:13:42.025 --> 00:13:44.116
that were really touching to us.
00:13:44.116 --> 00:13:47.252
We wish they could have made it, but
for a variety of reasons they didn't.
00:13:47.483 --> 00:13:51.977
It allows us the catharsis and the
grieving, after which we can move on
00:13:51.997 --> 00:13:55.858
from it a lot more productively than
if we're holding on to that sadness,
00:13:56.020 --> 00:14:00.356
and often then projecting anger at
whoever made us stop doing that.
00:14:00.573 --> 00:14:03.553
What I find really interesting in
what you said is you're really taking
00:14:03.555 --> 00:14:08.022
care not just of the act, but also
the emotion that people feel about it.
00:14:08.028 --> 00:14:11.355
So you worked hard on this thing, and
maybe it didn't go where you wanted
00:14:11.357 --> 00:14:14.578
it But you have an emotion and an
attachment to that, and you're allowing
00:14:14.578 --> 00:14:18.197
people to recognize it and express
it in a way to hopefully move on.
00:14:18.525 --> 00:14:23.220
Even in a widget factory, humans aren't
robots, and it doesn't go super well
00:14:23.221 --> 00:14:24.774
trying to treat them like robots.
00:14:24.872 --> 00:14:30.268
But the more you want people to show
up in ways that are creative, that get
00:14:30.269 --> 00:14:34.797
the most out of themselves, where they
bring the most interesting parts of
00:14:34.797 --> 00:14:39.565
themselves to work and combine them in
an important unexpected alchemy that
00:14:39.567 --> 00:14:43.791
turns into something like self-driving
cars over time, the more you have
00:14:43.793 --> 00:14:47.749
to recognize the things that hold
them back are mostly here in their
00:14:47.756 --> 00:14:49.718
hearts and down here in their guts.
00:14:50.063 --> 00:14:51.169
Their heads are fine.
00:14:51.547 --> 00:14:55.228
The number of people in any
organization who need to leave because
00:14:55.231 --> 00:14:57.660
they're incompetent is very low.
00:14:58.202 --> 00:15:03.317
The number of people who don't work out
for other reasons, most of those reasons
00:15:03.473 --> 00:15:08.130
are style reasons, their egos are too
large or too brittle or whatever that is.
00:15:08.290 --> 00:15:11.313
That's all about their
hearts and their guts.
00:15:11.324 --> 00:15:12.596
It's not about their heads.
00:15:12.860 --> 00:15:13.665
Absolutely.
00:15:14.214 --> 00:15:15.343
I want to take a step back.
00:15:15.343 --> 00:15:18.170
We haven't actually heard
from you an example or two of
00:15:18.170 --> 00:15:19.399
what you guys have created.
00:15:19.399 --> 00:15:22.349
Can you share one moonshot that
was successful so we can have
00:15:22.351 --> 00:15:24.583
an appreciation of the different
types of things you work on?
00:15:24.944 --> 00:15:26.987
Let me give a few examples.
00:15:27.335 --> 00:15:32.622
Google Brain, which is one of the
places that caused the explosion of
00:15:32.624 --> 00:15:36.788
industrialized machine learning that is
now the sort of hot topic in the world,
00:15:37.106 --> 00:15:38.960
that came from X. That's an example.
00:15:39.358 --> 00:15:43.879
Uh, the self-driving cars, now called
Waymo, those came from X. Uh, Wing, the
00:15:43.880 --> 00:15:50.355
drones for package delivery, which are
actually doing almost as well as Waymo,
00:15:50.464 --> 00:15:54.871
and people haven't caught on that it's
going to be as big a deal, that came from
00:15:54.872 --> 00:15:58.964
X. And I will give some more examples,
but let me use Waymo as an example.
00:15:59.107 --> 00:16:01.419
So this is now 15 years ago.
00:16:01.838 --> 00:16:03.068
Huge problem with the world.
00:16:03.603 --> 00:16:07.451
More than a million people a
year die in car accidents in the
00:16:07.451 --> 00:16:09.265
world because of human error.
00:16:09.339 --> 00:16:13.752
More than a trillion dollars is
wasted between sitting in traffic
00:16:13.913 --> 00:16:17.300
and all of the costs of the
accidents, even leaving death aside.
00:16:17.554 --> 00:16:22.156
Between a million lives and a trillion
dollars, that's a problem worth fixing.
00:16:22.356 --> 00:16:28.116
Radical proposed solution: what if, just
like we got to the place with elevators
00:16:28.154 --> 00:16:33.108
where we realized you didn't have to have
a human driving the elevator, you could
00:16:33.108 --> 00:16:39.972
just trust the elevator to drive itself,
we can get behind the idea that if they
00:16:39.996 --> 00:16:44.529
could drive themselves, we could get to
the place where these metal boxes, like
00:16:44.529 --> 00:16:49.833
an elevator, take the passengers where
they need to go, and you just push a
00:16:49.833 --> 00:16:54.300
button and say, "Here's where I want to
go," and it takes you there much safer.
00:16:54.570 --> 00:16:58.432
And there's all kinds of benefits,
including maybe particularly the lives
00:16:58.432 --> 00:17:04.588
that are being saved, because those metal
boxes can now take themselves there much,
00:17:04.604 --> 00:17:06.475
much safer than a human driving the car.
00:17:07.241 --> 00:17:09.236
So that's the radical proposed solution.
00:17:09.589 --> 00:17:15.926
And then there was a set of, at the time,
new technologies, how to coordinate LiDAR,
00:17:16.069 --> 00:17:20.521
radar, and cameras around a vehicle.
00:17:20.833 --> 00:17:26.477
There'd been some evidence before we
started on what is now called Waymo from
00:17:26.477 --> 00:17:34.483
the DARPA Grand Challenges, the first of
which in 2006 was won by Stanford here.
00:17:34.748 --> 00:17:36.284
That car was called Stanley.
00:17:36.612 --> 00:17:39.462
And DARPA, just so people understand,
is something that the US federal
00:17:39.466 --> 00:17:43.383
government, it's a funding source
for creative ideas that can be
00:17:43.384 --> 00:17:44.974
used for defense and other things.
00:17:44.985 --> 00:17:45.563
Exactly.
00:17:45.563 --> 00:17:48.400
And so there was this worldwide,
or at least countrywide, grand
00:17:48.400 --> 00:17:51.587
challenge that was announced about
cars that could drive themselves.
00:17:51.792 --> 00:17:56.688
And while even in the second time
it was run, there were three groups
00:17:56.690 --> 00:17:59.091
that completed the 150 miles.
00:17:59.265 --> 00:18:00.195
They were out in a desert.
00:18:00.225 --> 00:18:04.563
It was 1/1000th of the difficulty
of what Waymo currently has to do,
00:18:05.070 --> 00:18:08.485
but it was enough evidence that
maybe, just maybe, it was time.
00:18:08.870 --> 00:18:12.331
I love that example, and many
people listening perhaps have
00:18:12.331 --> 00:18:13.924
not seen a Waymo or been in one.
00:18:13.947 --> 00:18:17.873
I have, and where I live is where
a lot of them were tested, so
00:18:17.874 --> 00:18:18.832
I'm a little more used to it.
00:18:19.098 --> 00:18:22.320
It is freaky to be in the back
of a car that has no driver, but
00:18:22.321 --> 00:18:24.473
it is absolutely cool as well.
00:18:24.533 --> 00:18:29.234
And I assume that you are seeing some
of the goals realized, like more safe
00:18:29.444 --> 00:18:31.571
arrival, fewer accidents, et cetera.
00:18:31.690 --> 00:18:36.030
Yeah, the evidence we have is that
the Waymo cars are at least an
00:18:36.041 --> 00:18:41.138
order of magnitude safer than human
drivers in similar situations.
00:18:41.340 --> 00:18:46.708
And of course, every time a Waymo
car learns anything, all of the
00:18:46.711 --> 00:18:50.864
cars get that learning, so it's
a one-way ratchet of safety.
00:18:51.357 --> 00:18:55.208
I have been in San Francisco, where you
literally see a fleet of them driving
00:18:55.210 --> 00:18:56.905
people around, and it's very cool.
00:18:57.273 --> 00:19:00.590
I want to come to something
that you just did.
00:19:00.724 --> 00:19:04.352
You just told us not just about
Waymo, but you told us a story.
00:19:04.885 --> 00:19:08.937
How important is storytelling in
defining and realizing moonshots?
00:19:09.341 --> 00:19:13.917
I think storytelling is phenomenally
important, but I watch people
00:19:13.952 --> 00:19:16.793
at the Moonshot Factory get a
little bit confused about this.
00:19:17.051 --> 00:19:21.253
At least when I say storytelling,
I don't primarily mean marketing
00:19:21.409 --> 00:19:23.099
or just getting someone excited.
00:19:23.407 --> 00:19:28.803
I mean, if you worked at X and you're
proposing that we start a moonshot for
00:19:28.803 --> 00:19:34.225
teleportation, let's say, I need from
you an architecture of understanding.
00:19:34.684 --> 00:19:36.585
Where are we trying to go?
00:19:36.947 --> 00:19:40.649
What makes us think we might be able
to get from where we are to there?
00:19:40.999 --> 00:19:43.343
And how should we interpret
things along the way?
00:19:43.346 --> 00:19:46.713
What's that set of lily
pads that you can envision?
00:19:47.103 --> 00:19:47.899
You'll be wrong.
00:19:47.928 --> 00:19:51.944
I'm fine with the fact that you're
wrong, but if you can't paint that
00:19:51.945 --> 00:19:57.050
picture so that we have a hypothesis to
test, it's very hard to get behind the
00:19:57.072 --> 00:20:02.081
idea that the upside of getting there
will be worth the risks if you can't
00:20:02.091 --> 00:20:07.557
even paint an idealized picture of how
we would get there in a controlled way
00:20:07.572 --> 00:20:11.164
where the costs aren't out of control,
where nobody gets hurt, et cetera.
00:20:11.549 --> 00:20:15.044
The idea of an architecture of
understanding is what a story provides,
00:20:15.057 --> 00:20:18.331
and the outcome of the story for you
that's important is a hypothesis that
00:20:18.331 --> 00:20:23.161
can be tested, and that's a really unique
way, I think, of looking at storytelling.
00:20:23.649 --> 00:20:25.389
You are very good at telling stories.
00:20:25.462 --> 00:20:29.699
I'm wondering, do you reflect often
on how to tell stories better?
00:20:29.761 --> 00:20:33.536
I noted in the Waymo story one
tool you seem to use is analogies.
00:20:33.803 --> 00:20:36.966
You talked about self-driving
car being like an elevator, and
00:20:36.966 --> 00:20:38.607
that makes it very accessible.
00:20:38.611 --> 00:20:40.803
I might not have been in a
self-driving car, but I've
00:20:40.803 --> 00:20:42.120
certainly been in an elevator.
00:20:42.359 --> 00:20:45.341
Talk to me about your own internal
process for the stories you tell.
00:20:45.580 --> 00:20:46.324
What do you think about?
00:20:46.843 --> 00:20:52.239
I think I understand the world mostly
through mechanisms and call it the
00:20:52.425 --> 00:20:54.835
gross high-level systems thinking.
00:20:54.836 --> 00:20:59.589
That's just how I understand the world,
which makes it easy for me to find two
00:20:59.591 --> 00:21:04.890
things that feel related because they have
similar mechanisms, like I just did in
00:21:04.890 --> 00:21:07.220
this example between Waymo and elevators.
00:21:07.571 --> 00:21:09.941
Now, I can sometimes get caught.
00:21:09.956 --> 00:21:12.415
Let me tell you an
example where I was wrong.
00:21:12.714 --> 00:21:15.687
I mean, I wasn't intellectually wrong,
but I was wrong as a storyteller.
00:21:16.228 --> 00:21:21.839
For quite a long time, I used as an
analogy to help people understand about
00:21:21.861 --> 00:21:26.549
what we're trying to do at X. And I
would say, "We are not the gamblers of
00:21:26.550 --> 00:21:30.764
innovation. We are working really hard
to be the card counters of innovation."
00:21:30.929 --> 00:21:34.322
If we go back to that conversation
we just had about habits versus
00:21:34.349 --> 00:21:38.725
outcomes, whether you win or not,
let the statistics take care of that.
00:21:39.157 --> 00:21:43.348
I want you to focus on playing the
odds, those are the habits, correctly.
00:21:43.351 --> 00:21:45.720
So you can see the analogy
with card counting.
00:21:45.915 --> 00:21:49.932
Only I've discovered more recently
that a lot of people would nod along
00:21:49.933 --> 00:21:54.072
with me when I said card counting and
used that as an analogy to what we're
00:21:54.074 --> 00:21:58.966
trying to do to systematize radical
innovation at the Moonshot Factory, only
00:21:58.966 --> 00:22:02.699
to discover more recently, most people
don't really understand card counting.
00:22:03.101 --> 00:22:05.886
Am I right that that's a good analogy?
00:22:06.064 --> 00:22:06.658
Maybe.
00:22:06.795 --> 00:22:11.610
But as a storyteller, I'm failing if I'm
using analogy that doesn't really connect
00:22:11.610 --> 00:22:14.995
with people because they don't understand
the mechanism of, of card counting.
00:22:15.410 --> 00:22:18.455
So whatever tools you use to tell
a good story, you have to make
00:22:18.455 --> 00:22:20.454
sure that they're doing their job.
00:22:20.531 --> 00:22:24.145
And that required you to do a fair
amount of reflection because most
00:22:24.147 --> 00:22:27.073
people just tell a story and move
on to the next story rather than
00:22:27.074 --> 00:22:28.869
reflecting, is it really working?
00:22:28.878 --> 00:22:32.945
So that reflective piece is important and
not surprising because a lot of what it
00:22:32.945 --> 00:22:34.840
sounds like you do is reflect and learn.
00:22:35.262 --> 00:22:37.603
Can we dive deeper into creativity?
00:22:37.628 --> 00:22:42.687
You've given us some ideas, focus
on habits, failure is essential.
00:22:43.117 --> 00:22:46.742
Are there other things you have
found really help spark and
00:22:46.890 --> 00:22:50.403
not just spark creativity, but
foster it and keep it growing?
00:22:50.745 --> 00:22:56.707
One of the things that people struggle
with is the reality that creativity's
00:22:56.719 --> 00:23:00.176
wellspring is almost entirely humor.
00:23:00.471 --> 00:23:05.866
Being serious and being creative are
nearly impossible to do at the same time.
00:23:06.455 --> 00:23:14.532
Creativity is a willingness to scramble
your understanding in some maybe
00:23:14.678 --> 00:23:20.351
random or not random way And that means
there's a looseness that has to happen.
00:23:20.427 --> 00:23:24.662
The idea of seriousness
is a tightening up.
00:23:24.857 --> 00:23:27.411
It is an organizational imperative.
00:23:27.411 --> 00:23:29.171
That's what the idea of serious is.
00:23:29.549 --> 00:23:34.367
So you hear from people, "Are you
playing or are you working?" Because
00:23:34.369 --> 00:23:39.758
we think fundamentally that work is
serious and that the alternate when
00:23:39.758 --> 00:23:42.364
we're having fun, that's not work.
00:23:42.675 --> 00:23:47.023
But creativity, as I just described,
only happens in the play state.
00:23:47.422 --> 00:23:54.038
And so constantly trying to inculcate
a sense of wonder, silliness,
00:23:54.319 --> 00:24:00.977
experimentation without concern
for looking silly, for example,
00:24:00.981 --> 00:24:06.213
looking dumb, is absolutely required
in order to be really creative.
00:24:06.671 --> 00:24:10.250
And yet people resist
that push for playfulness.
00:24:10.542 --> 00:24:15.194
So I find that's one of the things that
we have to keep coming back to at X is
00:24:15.446 --> 00:24:20.880
I'm not asking for silliness because I
disrespect the journey that we're on.
00:24:21.356 --> 00:24:28.603
I'm demanding silliness and playfulness
because the journey we're on is so serious
00:24:29.088 --> 00:24:35.681
in its goal and can only be seriously
pursued through a non-serious attitude.
00:24:36.071 --> 00:24:39.726
What are some things that you
specifically do to bring silliness
00:24:39.727 --> 00:24:41.505
and playfulness into work?
00:24:41.875 --> 00:24:44.526
Well, as we were talking
before we started, I live
00:24:44.545 --> 00:24:45.872
on my rollerblades at work.
00:24:46.092 --> 00:24:48.824
One, it's just fun for me, frankly.
00:24:49.134 --> 00:24:51.201
Two, I get around a little bit faster.
00:24:51.202 --> 00:24:53.258
I probably save eight
or nine minutes a day.
00:24:53.258 --> 00:24:54.555
It's a fairly big building.
00:24:54.920 --> 00:25:00.322
But three, it sends a signal to people
not to take me too seriously, not
00:25:00.322 --> 00:25:04.102
to take themselves too seriously,
not to take the work too seriously.
00:25:04.229 --> 00:25:09.073
And when they see me doing that, I don't
need them to wear rollerblades, but
00:25:09.142 --> 00:25:14.933
unconsciously it's one of 100 signals
that we send each other at the Moonshot
00:25:14.942 --> 00:25:20.421
Factory to keep ourselves from tightening
up into the rigidity of seriousness.
00:25:20.790 --> 00:25:24.079
When you interview people to come on
and be part of the X team, give me an
00:25:24.080 --> 00:25:27.514
example of an interview question you
would ask that helps you get insight
00:25:27.515 --> 00:25:32.713
into if this person will fit into the
culture and perhaps help prepare them
00:25:32.724 --> 00:25:34.014
for what the culture would be like.
00:25:34.407 --> 00:25:38.886
I mean, one of the main interview
questions I ask, but it's not a trick,
00:25:39.313 --> 00:25:46.222
so you can't prep for it really, is
if you come to work at X and you don't
00:25:46.505 --> 00:25:49.146
really understand what we're trying
to do, 'cause you just think it's
00:25:49.148 --> 00:25:52.945
cool from the outside, like watching
someone jump out of an airplane, but
00:25:52.945 --> 00:25:56.815
when I have you at the door of the
airplane and I'm asking you to jump, it
00:25:56.818 --> 00:25:58.686
might not seem like your jam anymore.
00:25:58.940 --> 00:26:03.507
And I reserve the right, we reserve the
right, to torture you if you're at X and
00:26:03.508 --> 00:26:06.974
you're not actually down for the thing
we're trying to do, for those habits.
00:26:07.061 --> 00:26:08.805
So this is not an interview.
00:26:08.964 --> 00:26:11.142
You and I should be failing fast together.
00:26:11.503 --> 00:26:14.815
If we can possibly discover
that you're not right, that's
00:26:14.815 --> 00:26:18.247
definitely in my best interest, but
it's also in your best interest.
00:26:18.362 --> 00:26:22.998
And I know some part of you wants
to believe that you guys, your job
00:26:23.010 --> 00:26:24.797
here is to trick me into hiring.
00:26:25.004 --> 00:26:27.229
But look at me, I am serious.
00:26:27.256 --> 00:26:29.197
I will torture you if you do that.
00:26:29.504 --> 00:26:31.857
You will not be happy on
the other side of this.
00:26:31.967 --> 00:26:34.091
You and I want the same
thing in this moment.
00:26:34.285 --> 00:26:40.078
So let's spend the next 45 minutes
trying to fail fast here, verifying
00:26:40.338 --> 00:26:42.902
why you won't be happy at X.
00:26:43.253 --> 00:26:46.666
Wow, that's like an experiential
interview on steroids, where it's,
00:26:46.723 --> 00:26:50.543
you wanna have that experience of
failing, and you wanna be comfortable
00:26:50.564 --> 00:26:51.766
that that person is okay with it.
00:26:51.838 --> 00:26:54.708
Number one, if somebody isn't comfortable
doing that with me, then they're
00:26:54.737 --> 00:26:58.589
probably not gonna fit at X. But if
they are comfortable doing that with
00:26:58.590 --> 00:27:01.960
me, then I'm getting what I want, which
is for us to have a real conversation
00:27:01.967 --> 00:27:03.795
about why it might not work out.
00:27:04.178 --> 00:27:08.294
I can only imagine people come away
feeling that experience was special
00:27:08.295 --> 00:27:11.713
and unique, and you probably end up
bringing someone on who works out.
00:27:11.851 --> 00:27:13.610
Your success rate is much higher, likely.
00:27:13.962 --> 00:27:15.039
It is quite high.
00:27:15.351 --> 00:27:17.017
It's certainly not 100%.
00:27:17.272 --> 00:27:20.784
I would also say that our success
rate is as good as it is partly
00:27:20.785 --> 00:27:22.617
because we're very flexible.
00:27:22.736 --> 00:27:28.004
If I was hiring you to come on, I would
say, "I do not have a perfectly shaped job
00:27:28.012 --> 00:27:32.724
for you here." You might suspect what it
is, but let's be honest, if you're coming
00:27:32.724 --> 00:27:37.010
in, I'm gonna be running naked and so
are you, and we're gonna basically invent
00:27:37.018 --> 00:27:41.143
your job on your way in and probably for
the first year or two you're with us.
00:27:41.506 --> 00:27:44.774
And if that's not your jam, please don't
come, because all of X is like that.
00:27:44.774 --> 00:27:46.126
It's just gonna drive you crazy.
00:27:46.714 --> 00:27:50.718
But if you're up for that, we have
a better chance of winning because I
00:27:50.723 --> 00:27:54.510
don't have to be right ahead of time
about exactly what your superpowers are.
00:27:54.655 --> 00:27:57.178
We can design a job
around your superpowers.
00:27:57.628 --> 00:28:00.321
It sounds like that's a perfect
example of what you talked
00:28:00.330 --> 00:28:03.612
about earlier about setting some
boundaries, but not being rigid.
00:28:03.657 --> 00:28:04.809
Allow that flexibility.
00:28:05.290 --> 00:28:09.007
So you've added podcast host to your
list of titles and accomplishments.
00:28:09.154 --> 00:28:13.213
What do you think about the hosting gig,
and what motivated you to start the show?
00:28:13.216 --> 00:28:14.438
I listen and I love it.
00:28:14.451 --> 00:28:15.206
I've learned a lot.
00:28:15.220 --> 00:28:18.036
It's really cool for me to be
in person with you after having
00:28:18.036 --> 00:28:19.542
listened to you in my headset.
00:28:19.779 --> 00:28:20.634
What brought it about?
00:28:20.999 --> 00:28:24.260
First, if people are interested,
they can just look up Moonshot
00:28:24.270 --> 00:28:26.208
Factory, Moonshot Podcast.
00:28:26.714 --> 00:28:30.934
We were turning 15 years old, and at
least one of the things was we're trying
00:28:30.936 --> 00:28:36.110
to get a little bit less secret, and
we wanted to start exposing some of how
00:28:36.110 --> 00:28:41.072
we work and what we've done, what we've
learned, partly because we want to empower
00:28:41.138 --> 00:28:43.381
other people to take their own moonshots.
00:28:44.090 --> 00:28:48.597
And giving them that learning
opportunity hopefully gives them a boost.
00:28:48.907 --> 00:28:54.353
Rather than me just blah blah blah-ing
about moonshot taking, what we've done is
00:28:54.354 --> 00:28:59.468
we've sliced it up into about 50-minute
episodes, 10 of them in the first season,
00:28:59.737 --> 00:29:04.554
where we've given people who've come
through the Moonshot Factory a chance to
00:29:04.555 --> 00:29:06.869
tell their stories, what they've learned.
00:29:06.872 --> 00:29:09.321
So you get to hear in the
first person what it was like
00:29:09.343 --> 00:29:11.051
to be taking those moonshots.
00:29:11.501 --> 00:29:16.558
And these are some people who are now
luminaries in their field, Sebastian Thrun
00:29:16.568 --> 00:29:19.092
or Andrew Ng or Jeff Dean and others.
00:29:19.391 --> 00:29:21.543
And how did we get to
something like Google Brain?
00:29:21.554 --> 00:29:25.257
How did we get to something like
Waymo, the self-driving cars?
00:29:25.770 --> 00:29:30.386
And when you hear from them the mistakes
they made or those initial things that
00:29:30.402 --> 00:29:35.141
gave them the faith to start, that,
that seed crystal that they thought
00:29:35.141 --> 00:29:39.930
was worth building on, I hope it then
helps other people not only understand
00:29:39.982 --> 00:29:44.003
us and the moonshots we take, but
inspire them to go do some of their own.
00:29:44.224 --> 00:29:47.329
It's very inspirational, and
it's really cool to hear in their
00:29:47.330 --> 00:29:48.796
own words what they've done.
00:29:49.180 --> 00:29:50.598
And there are learnings that come.
00:29:50.625 --> 00:29:54.056
I have listened and thought
differently about ways I team with
00:29:54.073 --> 00:29:57.561
people and the ways that I think
about the decisions I need to make.
00:29:58.129 --> 00:30:01.023
So I'm excited that you're doing it,
and I'm excited to listen to season two.
00:30:02.298 --> 00:30:04.856
Before we end, I ask all
my guests three questions.
00:30:04.857 --> 00:30:07.311
One I create just for you, and
then the other two are similar.
00:30:07.311 --> 00:30:07.920
Are you up for that?
00:30:08.023 --> 00:30:08.392
Sure.
00:30:08.842 --> 00:30:11.171
I'd be very curious, what's a
current moonshot you're working
00:30:11.173 --> 00:30:12.747
on that has you really excited?
00:30:13.171 --> 00:30:14.835
We started this about eight years ago.
00:30:14.835 --> 00:30:17.088
This is very typical of
the moonshots we take.
00:30:17.288 --> 00:30:20.885
They seem crazy when we start them,
and then at least occasionally, much
00:30:20.885 --> 00:30:24.271
later, they turn out to be in the
right place at the right time, is
00:30:24.271 --> 00:30:26.689
a moonshot for the electric grid.
00:30:26.901 --> 00:30:31.885
It turns out that everyone would like to
make software that helps the electric grid
00:30:31.886 --> 00:30:34.780
get better, including us, and you can't.
00:30:34.981 --> 00:30:41.901
You can't until somebody has a
circuit diagram for the grid itself.
00:30:42.289 --> 00:30:47.003
If you don't have a digital twin for the
grid, where every wire is, where every
00:30:47.003 --> 00:30:51.830
inverter is, where every transformer
is, no software on top of that will help
00:30:51.831 --> 00:30:56.811
you manage what you have or plan for
the future or optimize it in real time.
00:30:57.214 --> 00:31:03.555
And so what our team, Tapestry, did
over the last seven years was build out
00:31:03.771 --> 00:31:09.334
the tools for taking in a wide variety
of data, including the very noisy and
00:31:09.335 --> 00:31:13.038
imperfect data that grid operators
have about their own grid, 'cause they
00:31:13.039 --> 00:31:15.054
don't have a map of their own grid.
00:31:15.300 --> 00:31:20.832
And then using lots of different sources,
Street View cars, drone data, satellite
00:31:20.832 --> 00:31:27.210
data, lots of different inputs, we can
then use induction and deduction to do
00:31:27.214 --> 00:31:33.577
the detective work and back out the exact
grid that a particular grid operator has.
00:31:33.841 --> 00:31:38.108
And then we can help them manage
the grid they have, plan for
00:31:38.109 --> 00:31:41.280
the future of their grid, and
optimize their grid in real time.
00:31:41.834 --> 00:31:44.540
So it's as if you've decoded
the genome for the grid, and
00:31:44.540 --> 00:31:45.811
now we can do some work with it.
00:31:45.880 --> 00:31:49.094
That sounds very useful and
hopefully very beneficial.
00:31:49.732 --> 00:31:52.909
Question number two, who is a
communicator that you admire and why?
00:31:53.530 --> 00:31:57.976
I enjoy giving public talks,
and so oration has always
00:31:57.977 --> 00:31:59.395
been very interesting to me.
00:31:59.596 --> 00:32:05.178
And I have watched a wide variety of
public speakers, not because I'm going
00:32:05.179 --> 00:32:10.905
to be any one of them, but to understand
how each of their patterns functions.
00:32:11.178 --> 00:32:13.806
You know, Martin Luther
King Jr., great speaker.
00:32:13.967 --> 00:32:17.045
His preacher style, there are
things you can learn from that.
00:32:17.727 --> 00:32:20.163
James Baldwin, amazing speaker.
00:32:20.447 --> 00:32:21.535
Not a preacher.
00:32:22.114 --> 00:32:27.253
Somewhat professorial, but with
a calm angriness because of the
00:32:27.255 --> 00:32:29.225
specifics of the life that he led.
00:32:29.757 --> 00:32:32.450
Barack Obama, very professorial.
00:32:32.869 --> 00:32:36.964
My grandfather, Edward Teller,
actually was another great public
00:32:36.964 --> 00:32:40.466
speaker, and I learn a lot from
watching each of these people.
00:32:40.995 --> 00:32:44.795
Yeah, they each bring a really interesting
approach that you can then synthesize.
00:32:44.806 --> 00:32:47.558
And by the way, your TED Talk is
one that everybody should listen to
00:32:47.559 --> 00:32:50.786
because you deliver it very well and
it's very exciting to learn from.
00:32:51.177 --> 00:32:54.680
Final question, what are the first
three ingredients that go into a
00:32:54.680 --> 00:32:56.817
successful communication recipe?
00:32:57.365 --> 00:33:01.346
I personally don't love the
start with a personal story.
00:33:01.597 --> 00:33:07.189
I would encourage people to
get real, as in honest, get
00:33:07.194 --> 00:33:10.796
specific, and you can be human.
00:33:11.155 --> 00:33:18.760
So when we tell a story, like in our
podcast, we use visuals from the field,
00:33:19.192 --> 00:33:21.324
from the actual people who are doing it.
00:33:21.550 --> 00:33:26.243
We explain what we were trying
to do and how where we actually
00:33:26.244 --> 00:33:28.624
ended up wasn't that, getting real.
00:33:28.748 --> 00:33:34.168
We show people how we harvest value
even from being wrong, and can have
00:33:34.171 --> 00:33:35.830
fun in the process of learning.
00:33:36.311 --> 00:33:41.166
And when you get a cycle of those three
things, while that doesn't feel like Mary
00:33:41.168 --> 00:33:46.950
Jane in Idaho, start of the sort of New
York Times article, it is deeply human.
00:33:46.950 --> 00:33:49.957
You can feel what it feels like
for these people to go through it.
00:33:50.396 --> 00:33:54.085
It's exciting, it's meaningful,
you can see yourself in it, it's
00:33:54.104 --> 00:33:55.942
memorable because of those things.
00:33:56.221 --> 00:33:57.480
So that's how we try to do it.
00:33:57.736 --> 00:34:01.414
Be real, be specific, be human,
and all of that allows you
00:34:01.416 --> 00:34:02.584
to connect through emotion.
00:34:02.752 --> 00:34:04.426
And I appreciate that recipe.
00:34:04.701 --> 00:34:06.254
And I appreciate this conversation, Astro.
00:34:06.255 --> 00:34:07.346
This was fantastic.
00:34:07.356 --> 00:34:10.655
You've talked about architecting to
understanding, and you certainly did
00:34:10.656 --> 00:34:14.500
that for us, and I hope all of us can
build these habits and reward ourselves
00:34:14.500 --> 00:34:16.186
for these habits and not just outcomes.
00:34:16.187 --> 00:34:16.985
Thank you for your time.
00:34:17.183 --> 00:34:17.524
Thank you.
00:34:17.525 --> 00:34:18.315
That was a lot of fun.
00:34:19.712 --> 00:34:21.867
Thank you for joining us
for another episode of Think
00:34:21.869 --> 00:34:23.891
Fast, Talk Smart, the podcast.
00:34:24.257 --> 00:34:26.011
It was great learning from Astro.
00:34:26.148 --> 00:34:29.918
Among the many ideas he shared, the one
that stands out most to me is that being
00:34:29.920 --> 00:34:32.010
wrong isn't something we should avoid.
00:34:32.300 --> 00:34:35.734
It's often where the most important
learnings happen, and embracing it
00:34:35.755 --> 00:34:38.071
can help us move forward faster.
00:34:38.610 --> 00:34:42.326
To learn more about creativity and
innovation, please listen to episode 70
00:34:42.580 --> 00:34:45.975
and 120 with Jeremy Utley and Tina Seelig.
