Former Tesla president on The 5 Step Algorithm Behind Tesla, SpaceX, and Radical Innovation (#294)

Love him or hate him, Elon Musk has built companies like Tesla and SpaceX that introduced a radically different way of building cars and rockets. There's a story about Elon that I love. Early on he flew to Russia, hoping to buy a rocket. The engineers there laughed at him. On the flight home, he started breaking down the cost of a rocket, material by material. He realized that the physical components of a rocket cost only about two percent of the total cost.

The rest, administrative costs, bureaucracy, and layers of inefficiency. That insight helped spark the idea that it eventually became SpaceX. Elon says the thinking behind companies like Tesla and SpaceX follows a formula he calls the algorithm. So what is that algorithm and what might happen if more of us started thinking? Hi everyone, I'm Lynn Thoman and this is three Scientists. Each episode ends with three key takeaways. Today I'm excited to be joined by John McNeil.

John has spent his career building and scaling companies. Before joining Tesla, he founded and sold six startups. So he knows first hand what it takes to turn bold ideas into real business. At Tesla he served as president, working closely with Elon Musk. Today, John is a venture investor and the author of the wonderful book, The Algorithm. where he lays out the thinking behind Tesla and SpaceX. He also explores how that approach to solving problems can apply far beyond cars and rockets.

John, it's great to have you on the show. Thank you for joining Three Takeaways today. Thanks for having me.

It is my pleasure. John Elon talks about his five-step algorithm for building things. 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 a limited amount of money. We could only open so many stores, we'd open several hundred around the world.

And then we started s to brainstorm could we sell a hundred thousand dollar product online site unseen? Like how would we do that? And one of the things we always concentrated on was the friction that we would put between ourselves and the customer. And friction online is measured in clicks.

And Elon asked me, like, how many clicks does it take to buy a Tesla? And I happen to know. I said sixty-four. And he said, just for fun, just for kicks, pull out the Domino's app. Let's figure out how many clicks it takes to buy a pizza. And it's about ten. He's like Let's get it down to 10. And I said, well, 44 of the 60 clicks are in one document, and that is the loan release document. And it's because those loan documents are like dozens of pages long.

But let me figure out if there's a way around that. And so I went and questioned the requirement of every paragraph that was in a loan document. We had a great member of our legal team go through this with me and for me. But he came back and he said, You wouldn't believe this. Almost the entire loan document is not a requirement of law or regulators.

It's well-meaning corporate attorneys who are trying to protect their bank, but none of this stuff matters. So I went back to the next week's brainstorming session with Elon. I'm like, you know what? What I just heard was twelve pages of loan docs that everybody assumes are required aren't required. It can be done in one paragraph.

So I went and talked to like ten different banks. They all slammed the door in my face. And then we finally got to a bank in Minneapolis, US Bank, and they said, We'll do it. We'll do a one click loan.

And we got forty-four clicks like eliminated immediately because we questioned a loan doc. Like who would be crazy enough to question paragraphs in loan docs? But we are crazy enough to do this sort of thing. So that's first step in the algorithm. If you're gonna have a breakthrough It gets a lot easier if you remove requirements that aren't real.

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? Elon gave us this challenge, could we take fifty percent of the cost out of building a car? So he wouldn't ask for five percent, he wouldn't ask for ten percent, he'd ask for something ridiculous because it takes you to another level of thinking. So the way a factory is laid out is

They're often more than a mile long and they're kind of long rectangles. And you can go to the halfway point. the fifty yard line of a car factory, and you can look to your right and you've got hundreds of robots building the skeleton of the car. It's called the body shop typically. And you can look to your left and you've got thousands of people hanging parts on that skeleton. And that's called general assembly.

Doug Field, who is head of engineering and I, walked out on the scaffolding that sits above the floor so we could like start to brainstorm about how we could take 50% of the cost of the car out. And we were doing this because we'd been to China and we'd seen how cost effective China was. And quite frankly, we were scared to death. And so this wasn't just like an exercise.

out of thin air. It was an exercise we believed in long term survival. So Doug and I are out there looking at the factory and we look to our right and we see all these robots building the skeleton of the car. And then Doug's like, I got an idea. So he comes back the next day and we're in a conference room and he rolls a matchbox car across the conference table and says, Here's the idea. And like, what a toy car, what what is this?

And he said, Well, in the body shop where we're building the skeleton of the car, it's all robots welding about three hundred parts together. That's not the way matchbox cars are built. They're casted. Somebody pours liquid metal into a mold and a car comes out. What if we could cast the cars? And I knew enough about the physics to say, Doug, the reason you can't cast cars is kind of obvious.

You can't pour molten metal the size of a car into a mold and have the mold not melt and have the pressure of that situation not like explode a factory. Like it's there's a reason why Matchbox can do that'cause they're the size of our thumb, but you can't do that with bigger metal pieces. He's like, I know, but I think it can be solved. And so he turns to me and says, Do you have anything we can melt?

I'm like, Yeah, I got a bunch of scratch and dent rims in the factory that we reject and we put'em out behind the factory and a recycler comes and picks'em up every week. We can melt those. He's like, we're gonna get a few engineers and we're gonna start to melt these wheels, and then we're gonna take that molten metal and we're gonna pour it into small molds first.

figure out how to do small molds and then we'll figure out how to make those molds bigger and bigger and bigger. So eventually they figured out how to cast half of a car skeleton. But it's all because Doug had this like insight and then pursued the insight in small steps. And we failed and blew up stuff and it wasn't linear, but we eventually got there. So now when you look at a Tesla car factory, whether you go to Austin, Texas or Berlin or Shanghai.

you see that there is no body shop anymore. Half the factory's gone because there are no robots that are welding two or three hundred parts together. because two parts come together to make that car. And we couldn't have foreseen a second order benefit, but the first order benefit is you remove a ton of complexity and cost. The second order benefit was

When you're welding two or three hundred parts together, the skeleton never quite aligns. And so you have to really do a lot of work to get the doors that you're going to hang on that skeleton to align and the windows and the seals and all kinds of stuff. When you have two parts that are cast and you put them together,

Everything fits every time. And so all of a sudden the doors fit, the windows fit, the gaps are right. Doug literally changed car manufacturing. And now, eight years later, every car manufacturer in the world wants to get their hands on casting. But they can't. It's really hard to do. So Tesla has built this compounded advantage over time with that one challenge. I take fifty percent of the cost of the car out. Not to mention many fewer repair issues and problems.

Exactly. And all the manufacturing issues that come with those three hundred pieces not aligning very well.

Something else that stands out that Elon doesn't hire people who've worked in the auto industry or in the space industry. Why is that? Is it because they carry too many assumptions about how things have to be done? He hires orthogonally.

So what he means by that is he hires people that might have some related insight but have never really worked directly either on the problem or maybe in the industry before. And it's not a hundred percent. There are exceptions to the rule for sure. But for the most part, nobody came from the industry. And the reason for that is he didn't want you coming in with a preconceived notion of how the industry worked, could work, what was possible, what wasn't possible.

And that turns out to be a hell of an advantage when you get a fresh set of eyes. And or people that just don't know enough not to be crazy enough to like consider other solutions. And Jim Farley at Ford just talked about this over the past couple of weeks. He He described a tear down of a Tesla that they did and they tore down their leading EV, the uh the Mustang E. And he said, All of a sudden, when you start to tear down vehicles next to each other, you start to realize, oh

These people had no car experience. Therefore, they weren't biased to do things that car people would do. And the example we gave is. The nervous system of a car is called the wiring harness and it's literally hundreds of pounds of wire that gets strung around the car so different things work like your headlights and your music and your seat and the AC, et cetera. When they did the teardown, they realized that the Tesla wiring harness weighed 76 pounds less than the Ford wiring harness.

That's a very big deal because that's basically half a human you have to carry around in the car. And so that really affects the car's range. And Farley said, I knew why it happened because car people at Ford never questioned pulling weight out of the wiring harness.

So they would just call the supply chain people and say, I need a wiring harness and they would order one up. Whereas the Tesla people were like, No, like to get range out of the car, like we gotta completely rethink the way this wiring harness works. And so they had completely redesigned the wiring harness. To save weight. And Farley said that is the example of why like having non car people involved makes sense because these people had come from building phones and laptops.

where the weight really matters. And so they thought about like how to be super efficient with the electronics they were designing. And that's just one small example of how orthogonal thinkers can be really productive.

John, you had weekly meetings with Elon every Tuesday. Did knowing you and the team had to report progress directly to him light a fire under the entire organization? What were those meetings actually like? So there are a couple of kinds of meetings when it comes to Tesla.

And I do think this aspect that you're pointing to, Lynn, is the I think this is the thing that people will come to understand and probably write about decades from now in terms of what makes him such an effective leader of fast moving companies. He picks like the one or two things that are existential to the company and then he only works on those and they become the focus of his time at the company. For example, right now at Tesla, that is autonomy and robotics.

So he'll show up and he's only working on those two issues. And the teams that are working on those issues have to report weekly progress to him. And if you're a team meeting with the CEO, you do not bring your B game, you bring your A game. And if something's going south, he knows every week.

It allows him to keep momentum up. And so Elon can allocate capital where it's needed super quickly because he's seeing it firsthand. It's not coming through reports or presentation decks. He's seeing it firsthand. One thing you've noted is that Elon can sometimes just sit there in silence during meetings. What's going on in those moments? He's basically processing the problem. He is almost like a computer. He's taking inputs, processing those inputs and really deeply thinking about them.

And he's not worried about the awkwardness of silence in that situation like a lot of us would be. For him, he's gotta just stop and be quiet to process and he knows that about himself. And so you get used to these moments where he like he literally is just trying to devise the next step based on the inputs that he's just heard.

Elon is famous for setting targets that seem wildly ambitious. Was that deliberate setting goals so big that the only way to get there was to question every underlying assumption? Yeah, exactly. Like you don't question every underlying assumption if you have to grow the business two or three percent. In our case, we were doubling the business every eight months. So we're going from two billion in sales. In thirty months, we're at twenty billion in sales. So we 10x'd it.

So if you're asking teams to double every eight months, they can't think incrementally. They have to think quantum leap. And so the way you're setting goals actually then determines how your team is going to be thinking about achieving those goals. And although incrementalism is important, he really wanted quantum in the big, big levers of the business.

One of Elon's rules is to simplify everything and delete every possible step in a process. How did that play out at Tesla when somebody buys a car? We tried to delete as many steps in that process of car buying as possible. Like anybody that's bought a car really doesn't say it's a lovely experience. They go back and say that was like worse than going to the dentist.

'Cause I had so many steps, so much paper to fill out, going back and forth on pricing, et cetera. So we literally deleted every step in that process we could. We eliminated haggling. We had one price for everybody, including us in company. Like we didn't get a discount on the cars. We pay what you pay. We eliminated loan docs, lease docs, because we got down to a single paragraph.

We automated the whole licensing process. So when you showed up and got your car, there was actually a license plate on it. We just innovated all the way through that process and basically lined up every step in the process. They're paying you for a car. So let's eliminate everything that is not building the car because that's the only thing we get paid for. And the rest of it is all administrative overhead and junk that the customer doesn't see, doesn't care about, isn't gonna pay for.

And when you do that, when you map your process and you circle the stuff the customer actually pays you for, turns out it's very few things in their mind they're paying you for. And that's the mentality that we use as we go to eliminate and delete everything that's not in the customer's economic equation. At one point Tesla did something that sounded almost crazy. It built an entire assembly line run by humans instead of machines. Why did Tesla do that?

Because we'd learn too many times when you automate first, it is almost impossible to get the process right. And we'd literally just been through this. We had tried to create an alien dreadnought, the machine that creates the machine, and we had built a factory digitally and designed all the machines digitally. And when that factory was built, that line was built, it didn't work. And we realized that we had violated this principle.

that we weren't gonna violate again and that is automate first. We said we c you can only automate last and By that we mean you gotta do things manually, kind of third step of the algorithm. You run a process manually. And you get it really, really efficient and then you speed it up manually to show all the warts. You add speed. And then finally you're at a process that works.

And so we did this in the model three. We had to abandon this automated line that didn't work. And the company needed the cash from model three or else we weren't gonna survive. So we had to quickly get model threes out the door. And so a tent was built in the factory parking lot and an assembly line was built in the tent by hand. And we started to build these things by hand so we could figure out the most efficient way then to automate and then eventually build the automated line.

And we started to produce a hundred cars a week and then two hundred and then four hundred and then five hundred on our way to five thousand. And it literally saved the company to do that. So step one, question requirement. Step two, you delete steps. Step three, now we're gonna run the process manually. We're gonna get that as sufficient as we can, then we're gonna speed it up with a lot of cycle time, so we'll try to double through.

And then lastly you automate, because if you automate first, it often doesn't work. So the algorithm was developed really in response to this mistake of over automating the p model three factory.

Stepping back, after working with Elon as president of Tesla, what's the biggest lesson you took away about building companies? I think how unfair the advantage is that comes from world class team members. He has a very high bar for hiring. And what I learned was not compromising on talent up front.

means that you build a world class team and that world class team is like an unfair advantage because they could they can do so much so fast at such a high quality level. The ability to him to attract engineering talent and executive talent. is a key to his success because he does attract just unbelievably world class people around him. John, what are the three takeaways you'd like to leave the audience with today?

I think number one, anybody can drive innovation and drive product breakthroughs and process breakthroughs by applying the algorithm. Takeaway number two is I think it's really important for you to use your own product because you start to realize the holes in that product if you use it on a daily basis. And I'm surprised how many companies don't use their own products.

And then takeaway number three is set very ambitious goals because you change the way people think about the problem if you're asking them to make a quantum, almost impossible change versus an incremental change. John, this has been wonderful. Thank you so much. I really enjoyed your book, The Algorithm. And I have to say, I also drive a Tesla, which I really appreciate. Oh, thank you. I'm glad.

If you're enjoying the podcast, and I really hope you are, please review us on Apple Podcasts or Spotify or wherever you get your podcasts. It really helps get the word out. If you're interested, you can also sign up for the three-tech. Takeaways newsletter at threetakaways.com, where you can also listen to previous episodes. You can also follow I'm Lynn Thoman and this is the first one. Thanks for listening.