332. Rethinks: Learn Faster and Fail Smarter

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.