Motley Fool Money - The Secret to Out-Innovating the Competition: Inside the Tesla Playbook

Episode Date: April 5, 2026

What’s the secret to out-innovating the competition? Former Tesla President Jon McNeill joins the show to discuss his new book, The Algorithm: The Hypergrowth Formula that Transformed Tesla, Lululemon, General Motors and SpaceX. Motley Fool analyst Rachel Warren talks with McNeill about the five-step formula for achieving hypergrowth, the hidden metric every investor should track, and the AI revolution. Host: Rachel Warren Guest: Jon McNeill  Producer: Bart Shannon, Mac Greer  Advertisements are sponsored content and provided for informational purposes only. The Motley Fool and its affiliates (collectively, "TMF") do not endorse, recommend, or verify the accuracy or completeness of the statements made within advertisements. TMF is not involved in the offer, sale, or solicitation of any securities advertised herein and makes no representations regarding the suitability, or risks associated with any investment opportunity presented. Investors should conduct their own due diligence and consult with legal, tax, and financial advisors before making any investment decisions. TMF assumes no responsibility for any losses or damages arising from this advertisement. We’re committed to transparency: All personal opinions in advertisements from Fools are their own. The product advertised in this episode was loaned to TMF and was returned after a test period or the product advertised in this episode was purchased by TMF. Advertiser has paid for the sponsorship of this episode. Learn more about your ad choices. Visit megaphone.fm/adchoices

I wanted people to see, like, what was behind the scenes driving the success of Tesla, who seems to out-innovate their competitors again and again and again. And I wanted people to see that that could be done by frontline people, everyday people. You didn't need to be Elon Musk to do this. That was John McNeill, former president of Tesla, and author of the new book, The Algorithm, The Hypergrowth Formula that transformed Tesla, Lulu Lemon, General Motors, and SpaceX. I'm Motleyful producer Matt Greer. Motleyful analyst Rachel Warren recently talked with McNeil about that formula and about why you

don't have to be Elon Musk to out-innovate the competition. Enjoy. Hello everyone and welcome back to Motleyful Conversations. I'm Motleyful analyst Rachel Warren and today I'm excited to welcome John McNeil to the show. John currently serves as the CEO and co-founder of DVX Ventures. Previously, John served as president at Tesla. Following his time at Tesla, he joined Lyft as COO, where he played a pivotal role in doubling the company's revenues, helping to take the company public. John currently serves on the boards of numerous companies,

including General Motors, Lul Lemon, and Stash. In his upcoming book, The Algorithm, the Hypergrowth Formula that transformed Tesla, Lulu, General Motors, and SpaceX, John shares the behind-the-scenes look at how iconic companies scale and outlines a playbook for leaders to drive sustained growth and impact. John, welcome to the show. Thanks. Nice to be here. Excited to talk with you today. One of the things that's really interesting, and I want to start off talking about your book, you've codified this formula that has been applied at a range of these companies that I noted called the algorithm. So I'd love it if you could walk us through

your book and its themes, but really also talk about those steps of the algorithm in order and what it means. for companies. Yeah, this is the the algorithm is really the operating system I would describe it that got invented at Tesla really through making mistakes. We made a lot of mistakes that we would do post-mortems and say, how the heck did we get here? And then we would develop a principle to not do that in the future. So that's that's the heart of the beginning of the framework. And the framework then was used once we developed it to give everybody in the company a framework from which to innovate, really on a weekly basis. And the reason I wrote the book was I wanted people to see, like, what was behind the scenes

driving the success of Tesla, who seems to out-innovate their competitors again and again and again. And I wanted people to see that that could be done by frontline people, everyday people. You didn't need to be Elon must to do this, which is why the framework was developed, because Elon can't be everywhere all the time. And so we had literally thousands of people that were driving innovation all over the world who are following this framework. So the framework to the heart of your question is basically five steps and then I identify three secret ingredients that really make it work. But the five steps are question everything at the beginning, question the requirements that you've been given because you want to not build a business around a bad set of assumptions or you don't

want to build a process around a bad set of assumptions. So I'll give you an example of that from the financial market. So my firm today, we invent companies. We start companies ourselves and grow them and scale them. We took a look at the ETF market and looked at like super cycle ETFs and found that the top holdings of almost every ETF in the super cycle space was Amazon, Google, Microsoft, Nvidia, and Apple. You can buy that as an investor for two bips. That's called the Mag 7 or the S&P 500. You can buy that product. You don't need a super cycle product at 75Bips doing the same thing. But we step back and started to question the requirements of why that was, why

ETFs were built this way. And the first thing you get is a requirement that everybody who builds an ETF follows, and that is they build ETFs by market cap weightings. And if you want to do like an AI infrastructure ETF, and you're going to follow the market-waiting assumption that the whole industry follows. You drop Nvidia into that ETF, and it overwhelms every other stock because its market cap is so high. But if you take a different approach, so we started to say, is it a requirement to formulate ETFs based on market cap? The answer is no.

It's not a requirement of law. It's not a requirement of, it's just a requirement of, that has been evolved over time because that's the way everybody does it. And we said, well, that doesn't give investors exposure to profit pools. And there's a lot that goes into like an AI data center that's not Nvidia. And it turns out there are 60 public stocks that go into an AI data center. And if you weight those public stocks by their contribution to the AI data center, you get a very different looking ETF that actually exposes investors to the actual profit pools. And this is kind of a unique situation where we've got,

five years of commitments by the hyperscalers to AI data center builds. We know what the purchase orders look like. We know what's going into those so that we can backwards engineer that and say, which stocks are going to benefit. And it turns out when you create an ETF and you question the requirement for market cap weightings and you say, is there a better way, you come out the other end with a answer that says, yeah, there is a better way. And that is in this case to wait by profit contribution.

And so we developed an AI infrastructure ETF based on profit contribution, not market cap. The industry told us we were crazy. Nobody does it this way. We said fine, because we had back tested it to know that we beat the stuffing out of every other AI infrastructure ETF on the market. We introduced that in December 25, so just over a year ago. and it is over the past 12 months the top-performing AI infrastructure ETF in the market. You would have been up more than 80% if you'd bought it on day one. Because it turns out that it reflects the profit pools that are getting created.

And as the market wakes up to these individual stocks that are being helped by AI Data Center builds, the value gets created in those stocks and our ETF takes advantage of that. But that's an example of the very first step of the algorithm, which is question everything. Because when you start to question things, you start to discover there are these assumptions that people operate under that are really self-limiting. And if you step out of those self-limiting assumptions, you can create an innovation that creates a lot of value and breaks new ground. And so that's the first step of the algorithm is question everything, basically.

When Johan Rawl received the letter on Christmas Day 1776, he put it away. way to read later. Maybe he thought it was a season's greeting and wanted to save it for the fireside. But what it actually was was a warning, delivered to the Hessian colonel, letting him know that General George Washington was crossing the Delaware and would soon attack his forces. The next day, when Rawl lost the Battle of Trenton and died from two colonial Boxing Day musket balls, the letter was found, unopened in his vest pockets. As someone with 15,000 unread emails in his inbox, I feel like there's a lesson there. Oh well, this is the Constant, a history of getting things wrong. I'm Mark Chrysler. Every episode, we look at the bad ideas, mistakes, and accidents

that misshaped our world. Find us at Constantpodcast.com or wherever you get your podcasts. I was going to say, can you walk us through the remaining four? Yeah. So then the next one is a second step in simplification. And simplification is hard. Most people don't do it. There's that Mark Twain quote that I would have written you a shorter letter if I'd just taken the time. It's because simplifying is hard. It takes a lot of work. So the first step of removing false assumptions and requirements is a step to simplify. The second step in the algorithm is the second step of simplification, and that is delete every step you can in a process. And you probably haven't deleted enough until you've had to add some back. So delete ruthlessly.

And then the third step of the algorithm is once you've got the simplified process, now you manually run that process. And this has got to sound crazy coming from somebody who spent their entire career in tech and hard tech, not to automate. But we tell our teams, go manual because all the best businesses were built manually first. The original team in Amazon didn't automate the distribution centers we see today. they actually went and bought books. 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 simplify the process. And we tell our teams, do what Amazon did, do what DoorDash did, do it manually first.

Because when you do, you learn firsthand all the opportunities to simplify. So that's the third step of the algorithm, run the process manually. Fourth step is now speeded up. So put cycle time constraints around it, because when you speed something up, it exposes all the quality flaws and process flaws. And so now you're further refining. And then once you've got this process optimized with speed, the fifth step is automate last,

which again sounds totally counterintuitive coming from a tech firm, a place like Tesla. But we just learned this lesson so many times where we would automate first. And when you automate a bad process, all you do is speed up the time to a bad answer or a bad outcome. And so we learned over time to automate last. And probably the sharpest example that happened in Model 3 when we were trying to design a factory that was the most automated factory in the world. You might remember like Elon talking about the alien dreadnought and the machine that would make the machine. There was an attempt to design a factory completely digitally before it was done manually, before the process was actually run in the real world. And when that happened, when the machines were installed, you could see that, oh my God,

huge mistakes were made. Like there wasn't enough room in between the machines for humans to get in and maintain them. So you couldn't even maintain the machines. And that automated line never worked. And that was close to a billion dollar mistake. And when we did the post-mortem on that and said, how did we get ourselves into this situation where we had to build a tent in the parking lot to build cars manually to actually save the company with cash flow that was desperately needed? How did that happened, it was because we automated first, not last. And so we learned these principles by making the mistakes along the way. And that was a doozy of one that led to that last point. During your tenure at Tesla, you saw the company go from $2 billion to $20 billion in 30 months

in the early years of the Model 3 launch. And I wonder, was there a specific algorithm breakthrough that moved the Model 3 from sort of those production doldrums to a cash flow machine was that driven by superior product, a superior operating system, or both, kind of to apply your framework there? Kind of both. Like we had to, we had to start to apply the algorithm to each problem we were facing, whether that was how to do service in a business that's like doubling every eight months with an installed base that's doubling every eight months. Anything from service to how the car was built, to how the car was sourced, to how the car was designed. Like, we took this We took this framework and gave it to teams.

So an example that was we were selling cars faster than we ever had in our history, and we couldn't build service centers fast enough to service the cars. And so we got a small team together that ran our service business. And actually, the team was the team that ran our Palo Alto Service Center because it happened to be just down the road from headquarters, so we could work with them really quickly and iteratively. And so we went to the manager of that site and said, can you figure out how many cars you can fix before the customers,

done with their cup of coffee? And he's like, yeah, why? And we said, well, because if you can do that, so a customer can finish their cup of coffee, say, in 20 minutes. If you can fix a car in 20 minutes, we may not need a building for that. And if we don't need a building, we may be able to do service in a way that is never been done in the industry before, and that's mobily in customers' driveways or their offices. And the guy said, yeah, John's accepted. I'm up for it. So worked on that problem for like a month.

He came back a month later and said, you guys have to come see this. So he went down the hill, and what we saw was a parking lot that was buzzing. And the parking lot had three lanes that cars would go down. A customer was met at the entrance to the parking lot, by our senior tech, so like the senior surgeon in a hospital. And that senior tech would ask them for the symptoms of what was wrong with their car. And because that senior tech had seen everything, they knew whether that car was going to be a small, medium, or large repair. And those were the three lines that the car went down in the parking lot.

So the smalls and mediums could be fixed in the parking lot and the largest had to go inside. And it turns out that 80% of the cars didn't have to go inside. So we didn't need a building. And if we didn't need a building, we could fix those cars anywhere, not just in a parking lot of the service center. We could fix them in the customer's driveway or home office or in their parking lot of their office where they went to work. And so questioning the requirement first office, do we have to do repairs in a building? The entire industry believes that dealerships have enormous service centers that they've built. Midas and Miningy and others have built enormous footprints of buildings to fix cars.

We questioned the requirement of do you need a building to fix a car? The answer was no. We ran the process manually in a parking lot to get it working. Then we took a couple of hundred model X's that had been returned on Lemon Law returns, stripped out the insides, put tooling on the inside, put technicians in those. And it turned out we answered the question, could a technician be more productive if they didn't come to a building, or at least as productive? And the answer was, yeah, we found out that they could.

So we started to deploy these just in San Francisco and the Bay Area, and customers were blown away that we would come and fix their car in their driveway, like Magic Elves. We tried to make it fun. We put espresso machines in these model Lexus so you can get an espresso while your car was being fixed, and just had a lot of bunch of fun with it. And then the last step was we automated all the process around scheduling and parts of ordering and all this stuff. And it fundamentally changed the way that automotive service is done.

And still, car manufacturers cannot figure out how to do this. And they could go try to copy. But I think until they ran the process manually within their own system, they probably wouldn't optimize it for themselves. But that's an example of how a service team, just by using this framework, completely innovated in service. And that didn't involve Elon Musk at all. That was really just really diligent people on the front lines,

following this framework to invent something new. Something you pointed out, and something I also took away from your book, was this idea that really the speed of innovation is such a key factor. So I think it kind of begs the question, does a company with a shorter cycle time tend to have a more durable moat than one with just a strong brand? And how does this apply to areas outside of tech? Yeah, I think one of the things that the Japanese taught us and Toyota taught us

was this metric that Toyota executives follow, which is cash velocity through a business. We all learn to evaluate investments based on market cap kind of metrics, like EBITDA multiples, revenue multiples, which have growth metrics built into them. But what the Japanese do is they say, really, the measure of how good you are as a leader is how fast cash moves through your business. And so an example of that is when I started a Tesla, it was roughly 14 days for us to take a pile of aluminum on one end of the factory and turn it into a car on the other end of the factory. And Toyota at that time could do that in about four days. They could take a pile of aluminum and a Lexus would come out the other end of the factory or a Toyota or a Scion in four days.

Took us 14 days, took us four days. So what does that mean? That means Toyota can do the same amount of business. with a third less working capital than we could. Let me just let that sink in. Speed of cycle time and speed of cash mean that you need far less working capital than your competitors.

And if you need far less working capital, you have a balance sheet advantage, you have a liquidity advantage versus your competition. And that's why cycle time of cash or speed of cash really, really matter. it's really hard to measure that. And so that hasn't become a measure that is available to the street or to investors. Right.

But it's absolutely critical if you want to assess how good you are versus your competition. It's almost like the 40-year-a-dash from businesses. Like, what's your time? Well, can a culture of speed be retrofitted into a slower growth company or is it something that's more of a DNA-level trait? I think it's a DNA-level trait. It is, you know, there's this old adage, it's hard to, it's hard to, it's hard to to make a sprinter out of a marathoner because the sports are so different, right?

And that's because the muscles get toned up and trained to go 26.2 miles versus 100 yards or 100 meters. And I think it's just hard to take a business that has been a business that hasn't been measured on cycle time or has been a slower moving business and speed it up. Not impossible, but just like you've got to know what you're getting into if you're biting off that challenge. We've talked a lot about how the algorithm applies to tech sectors, but I want to lean a little bit more into its application to other sectors outside of tech. I mentioned earlier on the boards of GM, Lulu Lemon. So how does one apply the algorithm to say, you know, a century old automaker versus a high growth retail powerhouse?

And how can we as retail investors sort of view those elements when we're evaluating investments? I think the first clear evidence of this is top-line growth. So you look for top-line growth. But then you've got to achieve that top-line growth with discipline, and that discipline is reflected in gross margin. That's the first discipline indicator that you get is, are they doing this really well? And then third, you get operating expense leverage over time because you're not adding headcount for every new dollar revenue that comes in. And so you're getting operating expense leverage and that comes down to EBIT and cash flow, operating cash flow. So like take invidia for a second. You know, they've got the biggest market cap because they're generating sort of high two digits, three digit growth.

so incredible growth at that scale, add a close to an 80% gross margin. So they're showing operating discipline and the ability to continue to command both price and efficiency out of their organization, price out of their customer efficiency out of their organization. And then they're also getting operating leverage. So they're kind of three for three. So as an investor, I'd look at that and say, I have, I have industry leading top line growth. I have industry leading gross margin.

And I have industry leading operating cash flow. That looks like three reasons to say yes to that stock, depending, again, on how far it's run past its peers and how much upside there is. But that's the first indicator that, yeah, this is something you probably should consider investing in. Just a couple more questions for you. One, looking ahead to the next decade, what are some industries or sectors even that you think are best position for an algorithm style disruption? I think wealth management. It befuddles me today that consumers still have to put together their own estate plans and tax plans,

and those are disconnected from their investing plans and often lead to adverse outcomes because they're disconnected. and there are not a thousand ways to construct a portfolio for a person at a certain age and a certain stage of life. And I do think the wealth management industry, which has largely been a sales-driven approach of going and getting clients and assets, is going to change or you could say it in a more negative way, it's going to get disrupted. because there's an ability to deliver more sophisticated outcomes that are coordinated for customers

and much higher value for customers. And so that's one where we have our eye on it. And we're going to try to maybe play in that space and figure out how to do that too. Because there's a trillion dollar transfer that's happening between generations over the next 10 years. And that's going to create a bunch of wealth management opportunities in this current kind of manual bespoke process. isn't going to serve that kind of capital well. Is there a sector or industry that looking ahead to the next five to ten years excites you the most? That's one that does in terms of the opportunity for it.

I think AI is one of the first technical revolutions that's happening in affecting white-collar work first, not blue-collar work. Every other technical revolution is essentially affected blue-collar work first. And so I think we look across white-collar sectors for all. opportunities because this is a unique moment in time where there's going to be disruption and value creation happening at scale in white collar. And so there are a bunch of industries that excite us as we look with that kind of filter on. One last question. If someone listening or watching this could only take away one or two algorithm metrics to really track the health

of their long-term holdings, what should those be? Health of their long-term holdings. I would look In terms of metrics in those companies, I would look at cash velocities of cycle time and the ability to expand margins over time, both operating margins and bottom line margins. That tells you whether management team is really good at what they do. Fantastic. John, it's been so great to talk with you today. I wish we had more time. Those who are listening or watching, check out John's book, The Algorithm, the Hypergrowth Formula, the Transform Tesla, Llemon. General Motors and SpaceX. It's a fantastic read. John, thanks so much for joining me today.

Thanks, Rachel. As always, people on the program may have interest in the stocks they talk about, and the Motley Fool may have formal recommendations for or against. So don't buy ourselves stocks based solely on what you hear. All personal finance content follows Motley Fool editorial standards and is not approved by advertisers. Advertisements are sponsored content and provided for informational purposes only. To see our full advertising disclosure, please check out our show notes. For the Motley Full Money team, I'm Matt Greer. Thanks for listening, and we will see you tomorrow.