so it wouldn't come out warped or delaminated.

And they were making a little progress, but not much.

And finally, one night he broke down and asked the team who spec this part.

I want to talk to them.

And he happened to be standing next to the battery team.

And so they said, oh, this wasn't us.

This is the ride dynamics team.

depending on how you count, five or seven that are worth more than $50 billion.

Tell me a little bit about how that works.

So I, I have teams literally go to a wall and they make...

They take a pile of sticky notes, and they put a sticky on the wall for every step in a process.

And, and then underneath those process steps, there are sub-process steps with sticky notes.

So we got a, we got a wall where we can see the whole process, the whole, maybe the whole customer journey of, of buying a garage door and everything that needs to happen.

You gotta go out to the house, you gotta measure, you gotta, uh, you gotta determine specs, uh, you choose styles, whatever.

Uh, you order, you set up the appointment, you load the truck, team goes out, does the install.

How do you change the norm?

What about the regulatory side of this? Is this something that you think is many, many years in the future, given just a regulatory appetite to have hovering cars, especially the speeds you're describing?

Yeah, I think, you know, Ferrari has a significant business of cars that are not street legal.

They are sold to enthusiasts and can only be used on a track.

And I think you're going to see the first version or maybe versions of the Roadster potentially not being street legal.

I'm sure they'll have a street legal version.

But in terms of the hovering and thrusting version, I'm pretty sure that won't be street legal anymore.

And what they'll do is they'll wait for the regulators to catch up or they may push the regulatory framework a bit to get it to accelerate.

Have you been impressed, though, with municipalities' willingness to sort of embrace some of this stuff?

That, those are weird numbers, are they not?

I think, Carl, we're seeing the beginning of a tectonic shift in the auto mar- uh, manufacturers.

Uh, and that is you're gonna see China battling the North Americans.

Uh, and the Europeans are behind, uh, kind of in two fronts.

The Europeans are behind in manufacturing costs.

Uh, they're behind in technology.

And both of those have really put the European market, uh, for the European manufacturers' market in a, uh, in a really tight spot.

W- what is the timeframe here? How long is it gonna take, and how deep of a job is this to turn it around? Is it just a product reset, or is there something sort of more that needs to change culturally, um, to get this company growing again?

Yeah.

So a little bit of part of this hack of concentrating on the one or two things that matter in your business and getting deeply involved in them on a weekly basis means your teams are bringing their A game to you on those issues on a weekly basis, and that means you are a weekly compounding advantage versus your competition who's not doing that.

So this isn't micromanagement really at all.

This is leading on the core issues and being deeply informed, uh, and so you're not surprised.

And I watch CEOs who don't do this, who show up on earnings calls on a quarterly basis saying, "I got surprised by X." These ki- this kind of leadership style doesn't get surprised.

Um, they are in the, they're into the core issues, and they're course correcting too on a weekly basis.

So if something's going wrong, they don't find out about it 30 days later or a quarter later by the time it's affecting earnings.

Mm.

Could you tell us this story?

Yeah.

I, uh, was talking with Elon about joining Tesla, and I kept saying to him, like, "Um, I think you need a big company g-guy to come in." And he said, "No, that's exactly what I don't need.

I need a fellow entrepreneur." And I said, "Well, I wanna make sure I can be helpful to you, so, like, what's your biggest problem right now?" And he said, "We have a demand problem." And I said, "Okay, like, frame that for me.

They're a public company, so what's your commitment to the street this quarter?" He said, "Roughly twelve thousand cars." And I said, "How you doing so far?" It was about a month into the quarter.

He says, "It's a month into the quarter, and we've sold, like, twenty-five hundred cars, short of three thousand cars." Like, "Oh, now I understand your demand problem.

You're gonna miss your quarter.

Well, so could you maybe walk us through a couple examples of, all right, grab an existential issue and then work through the steps?

Um, does product market fit equals, does product market fit equal personal alignment?

Well, I'll, let me break down the algorithm first, then we can get into product market fit.

So the algorithm basically says start by questioning all the assumptions and ask whether this is a, uh, this is a requirement of law, of physics, of safety.

If not, maybe toss it aside.

So now you're down to, uh, okay, now I've, I've gotten rid of a bunch of requirements that a lot of people assume are there.

Now I gotta develop a new process.

And so the second step of the algorithm is develop a new process.

Hm.

[laughs]

Totally.

And now that you see this unleashed, I'm like, "Oh, this is what we were trying to, trying to do three decades ago." So, um, so I'm super excited by it, largely because every technical revolution and breakthrough like this that has happened in history creates enormous opportunities for entrepreneurs.

I cannot name a technical revolution that's happened that's resulted in less GDP and less jobs.

I can't.

Like, it just doesn't work that way.

Um, and there's a lot of hand-wringing, though, at the beginning of every technological revolution because humans are really good at seeing the first order effect, which is the job destruction, but they're not good at seeing the second order effect, which is the job creation that happens by entrepreneurs on the back end.

What do you see as the kinda first generation businesses that you're excited about, and what the second order effect might be?

Hmm.

The original team at Amazon didn't automate the distribution centers we see today.

[chuckles] Uh, they actually went and bought books.

They, they put up an order site.

They would take an order.

They would go buy the book from a local bookseller, put it in a box, ship it, so that they learned the distribution side of the business before they automated anything.

The founders of DoorDash, who were CS majors at Stanford, put up a PDF of restaurant menus with a phone number at the bottom, and the phone number rang in their dorm room, and then they would order the food from the restaurant, go pick it up, and deliver it so that they could, they could simplify the process.

Hmm.

So what was your first, you know, your first piece of business to kind of get that company to $20 billion?

The first thing, like when he and I were getting to know each other, I was trying to figure out, could I be helpful, useful? And so I went to like, I was traveling a lot for the business that I had just started.

And I went to eight different Tesla stores and took a test drive.

And each store gave them a different email address so they wouldn't like catch on who I was, what I was doing.

But this super crazy thing happened.

Like I did eight test drives and that's supposed to be like the pinnacle of the sales process.

But nobody called me back.

Go ahead.

So I guess what you're hearing is you're actually more confident in that further out bet.

Yeah, and it may not be further out.

Like I think in both of these businesses, autonomous cars and robots, these are two businesses where we're behind China.

And so if you want to see the movie and watch the movie first, you can see this movie playing out already in China.

So China has... autonomous cars on the roads in all of their major cities.

And you can see what that's done to the ride share business in terms of share.

You can also see humanoid robots in action across the economy in China, from factories to people's homes.

How do you think about this playbook then in this era of AI where that is literally what people are trying to do?

Mm.

So he determines the two top issues that are existential to the business.

And so at Tesla right now, that's autonomy and it's robotics.

He shows up every Tuesday, and all he's working on is autonomy and robotics, and meeting with those engineering teams.

And so every week, those engineering teams bring their A game, and they make forward progress on a weekly basis because they're presenting to the CEO, who is expecting to see that forward progress.

Mary Barra now does that at GM.

She meets weekly with the key engineering teams and, and moving this weekly pace forward.

Stock has done, not done a lot, and growth has ground to a halt in the US.

Mm-hmm.

And, and the second...

Well, the third pillar of that is then that they subsidize 100 market entrants to come in, and then they let evolutionary biology take, take its place.

Uh, and may the best person win.

And so they get down to the top three or five competitors that are winning.

So in this case it's like BYD, Geely, NIO.

And they say to those competitors, "Now we're gonna consolidate all of the capacity that we've created with these 100 companies under you three.

Wow.

Yeah

... was they have this genius ability to be able to price a ride for what you're willing to pay for it and what they can deliver it for at a moment in time, in a street corner, in a weather condition, whatever.

Super sophisticated.

And so they have these pricing engines that can determine what people are willing to pay.

And every product company I've been a part of or sat on the board of doesn't have that capability.

So Lululemon set their legging prices at $108 like 12 years ago, and it had...

They don't know why, and it hasn't changed.

Mm-hmm.

What is the first step of that algorithm, and how did it shape the way Tesla was built?

This algorithm got developed over time, basically through the mistakes that we had made, and we did a lot of time riffing and reflecting on mistakes that we've made.

And the first step of the algorithm comes from a number of experiences, and that is question every requirement.

Ask if those requirements are a requirement of law, of physics or safety, and ask for the name of the person who came up with the requirement so you can go interrogate whether that is really true or not.

We were riffing one day on digital sales, and we had limited amount of money.

We could only open so many stores.

We'd opened several hundred around the world.

Can you share more of what that looked like in the design of Tesla cars and how Mattel toy cars with just a top and bottom piece became a kind of inspiration?