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JP Morgan has teams to support you at every stage of your growth. As former founders who have worked in local markets themselves, the bankers at JP Morgan bring deep sector knowledge and lived experience to their clients. JP Morgan can help you navigate complexity with confidence, backed by real world insights and an entrepreneurial perspective. Get what your startup needs now@jpmorgan.com growwithoutlimits. JP Morgan is the bank of the innovation economy for Tuesday, June 30, it's Brew Markets Daily and I'm Ann Berry. Tesla, Amazon, General Motors, why do they seem to move faster than others and what is it that they're doing differently? Well, John McNeil, former president of Tesla, says it comes down to a very specific process. In March, I sat down with John to unpack his Five Step Framework for Operational Success, the foundation of his recent book the Algorithm. It draws on his time working alongside Elon Musk at Tesla. And in that, John explains how Elon's habits, like taking part in weekly operating meetings, drove performance at speed inside one of the world's most closely watched businesses. We also discuss John's board seats at General Motors and Lululemon and the lessons he's gathered from decades of operational experience. Well, this was one of my favorite conversations so far this year, so let's listen back to my discussion with John McNeil, former President of Tesla.
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So good, so good, so good.
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New summer arrivals are at Nordstrom Rack stores. Now get ready to save big with up to 60 off brands like Rag and Bone, Levi's, Adidas and free people. Join the NordicLub to unlock exclusive discounts. Shop new arrivals first and more. Plus, buy online and pick up at your favorite Rack store for free. Great brands, great prices. That's why you rack. I am absolutely delighted to have here John McNeil, author of the Algorithm. And we'll go into your background and I want to hear, John, from your own words, the different experiences that got you to write this book. But I just want to frame this because folks may not know I was CEO of a manufacturing business and I'm very involved in one now as executive chair. I love manufacturing. I love the tangibility of it and I love the problem solving of it. And getting a team to stare what's right in front of them. Yeah, acknowledge what's right in front of them and dig into why there could be a creative way to solve. So I would just say I tore through your book. I flagged all these pages. It's dog eared and every word and it resonated with me and I don't Actually say that about a lot of books.
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B
Thank you.
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A
Thank you. Yeah. Give us. Quickly, John, get. Give us a sort of 90 seconds of your bio.
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B
All right. I grew up in a rural area of America where you had to work as a little kid on farms.
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A
Where was that?
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B
Nebraska. Rural Nebraska. And I got lucky. I went to school in Chicago, and then the first place I worked was Bain Consulting when they were launching Bain Capital. So I spent two years at Bain Consulting, and then I got pulled further and further towards Bain Capital. And the folks that were working in Bain Capital at the time said, we think you're an entrepreneur. Why don't you hop out and we'll back you.
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A
You got promoted to customer.
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B
I got promoted to customer. Exactly. So I ended up starting scaling and selling to public company six companies in a row. And then through a crazy series of events, got introduced to Elon and joined Tesla when It was about 2 billion in revenue. And 30 months later, it was 20 billion in revenue. It was a crazy ride. And it wasn't software. So we had to make the factories, make the supply chains, the delivery systems, all that fun stuff that you know and love and I do. And then I was asked to come help take Lyft public. So we doubled revenue at Lyft, took it public, and then I've jumped out to start a firm where we start companies from scratch.
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A
You've gone full circle.
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B
Full circle, right back to where I started. Yeah.
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A
That's amazing. And it was a throwaway comment. I just want you to clarify it. When you said, through a crazy circumstance, you got introduced to Elon Musk. Would you mind clarifying who introduced you?
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B
Yeah. So Sheryl Sandberg introduced me because Elon was chasing her to be. To consider being his president. And she said, I'm an ad person. I'm not a manufacturer, but I know a guy.
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A
You know the guy.
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B
So let me introduce you to this guy. And he and I hit it off. But thanks to Sheryl for kicking off that opportunity.
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A
Well, walk us through, John. Just to set the stage for everybody the five steps of the algorithm.
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B
Yeah. So this is basically an approach to simplification, and it's something that we learned through making a bunch of mistakes and reviewing those mistakes and saying, we don't ever want to do that again. So it is, first of all, you question every assumption and question every requirement that somebody's handed you. Once you rip those assumptions and requirements away, now you're down to something more simple. And now you can really map the current delivery process or creation process and rip out every step that is unnecessary. And now you've simplified a second level. Once you've done that, you can optimize that and really start to run the process manually. Then you apply speed to that because there's this legend, good, fast or cheap, Pick two. Turns out you have to have all three. And if you add speed, a quality process will deliver speed and low cost. And at the very last step. This sounds weird for a technologist to say automate last because automation is like concrete that just sets the current process in stone. So you better have it as good as it can be before you do that.
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A
I want to start with that last step. It feels counterintuitive, but I'll explain why there's step five, which is automate last.
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B
Yeah.
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A
And the reason I bring that up is your book and you just said here started really on the premise of what you've seen in manufacturing.
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B
Yeah.
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A
But this idea of automate loss. I just want to read this. Page 107. You provide these stories of several companies that we think of as digitally native.
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B
Totally.
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A
And you actually describe how actually they were really labor intensive to get off the ground. Amazon and Doordash talk about what those two companies did.
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B
Yeah. So what they, what they teach the startup world and what they teach all of us who run companies or teams or products is start manual first. Because when you start manual, you get to know what the, what the, what the hurdles are and what, what optimal could look like. So the, the, the original Amazon team put up a webpage where you could order books, but they didn't have any fulfillment on the back end, so they would literally take your order down the street. They would buy a book at retail, come back, box it up and ship it to you. So they could start to map the process. And then doord of another example. 5Cs majors at Stanford. You think the first thing they would do is hit the keyboard and automate. But instead they throw up a PDF of restaurant menus with a phone number at the bottom and the phone number rang in their dorm room and they would take the order, order the food from the restaurant, go pick it up and deliver it so that they could start to manually build a process that they could optimize before they automated it.
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A
So today there's this idea that AI can help us shortcut so much.
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B
Oh yeah.
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A
Do you think that there's a risk that it's going to be too tempting for people to say, forget the whole. That is yesteryear, what you just described.
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B
Totally.
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We're going to AI first Do you think there's an attitude shift that we've seen come about because of the technology change?
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B
I do. Because people now want to just automate like crazy. But if you automate a bad process, all you do is get to the bad answer faster. So it's really dumb thing to do. So what you want is a really good process, as good as you can make it, and then put AI over it to optimize it.
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A
And so just talk again on this last step because I'm going to go full circle. When you saw Tesla Automate first, talk about what happened as a result of that and why it led you to this conclusion.
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B
Yeah, so maybe our most public face plant on automating first was the Model 3 production line. And you might remember like 2018, there was this talk of the alien Dreadnought and create the machine that makes the machine. And it was going to be the most advanced manufacturing facility in the world. What they did was they took this approach of creating a digital twin, so fully digital environment and design a factory in a fully digital environment. And several of us on the team were queasy about this because that often doesn't work because you can't predict everything that happens in a real world environment. And when the factory started to be installed, I took a walk down the line and I could see these machines were like spaced six inches apart. And I grabbed one of the engineers in Elon and said, hey, these are machines, they have to be calibrated and maintained like every shift. How are you going to get humans into a 6 inch space? And they got, oh my God, yeah. And I said, yeah, oh my God. And that was like a small example of sort of everything got screwed up with Model 3 production and we couldn't get those cars out. And this was critical because we were operating the company on 90 days of cash. So we needed to get those, get that cash flow going.
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A
And you were public at the time?
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B
We're public at the time. And so we literally ended up building a tent in the parking lot and building these cars manually. And we started building 50 cars a week, then 100 cars a week, then 500 cars a week. And when we went and reviewed, like, what the heck happened? What was the root cause? The root cause was we automated something before we really run it at capacity manually. And we said, we're never going to do this again. And so that's where the fifth stage of the algorithm came from, was like, we are not doing this again and not putting ourselves through it or nor the company through it.
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A
It's interesting that something could have gone so wrong at that. That's a critical step when you've done other things that you so right. So let's go back to step one, which is question area of requirement. Could you talk about the moment where Tesla really challenged this notion that to be present in China to manufacture, you needed to allow the Chinese government effectively to take an ownership stake? Talk about how you figured out how not to do that.
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B
Yeah. So Elon would always ask these questions like is it a requirement of law? Is it a requirement of physics? So when we're questioning requirements, he' Is it a requirement of physics? Is it a requirement of safety? Is it a requirement of law? And so we're talking about the China market, which is really important if you're a car manufacturer. Here in the US we produce 15 million cars a year. In China they produce 30 million, so they're twice our size. So if you're a car manufacturer, big market to get into. And as we sat with our China teams and talked through how we could possibly enter, they said, hey, you gotta know one thing. Every Western company has to have a joint venture and the profits are shared. 50, 50. And we said, well that's, that takes the American or the Chinese market down by half from a cash flow standpoint, makes it equal to the American market. That's a lot less interesting. Is that a requirement of law? And they came back and said, no, it's not written down anywhere in the law. We said, is it a requirement of regulation? They said, no, this has been just cultural the norm.
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A
So the norm is the requirement.
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B
And so we said, all right, Elon, then he got a twinkle in his eye and he turned to me and he said, why don't you go negotiate the first Western joint venture without economic sharing?
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A
How's your Mandarin? How did that go?
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B
I got better. I studied it a lot. I had to give a 10 minute speech once in Mandarin. That was like the hardest thing I've ever done. Maybe it was so hard. But we negotiated for 18 months knowing that this wasn't a requirement of law or physics. And then we put a little bit of momentum on our side. Every five years China comes out with an economic plan for five industries they want to enter and then eventually dominate. And their five year economic plan at that time was batteries, electric vehicles, autonomy, ASIC chips, et cetera. And so we lined right up with that economic strategy and therefore we could negotiate, I think a little bit differently than a lot of Western companies had. But I don't think any Western company started with this notion that, hey, maybe this is a flexible thing, maybe it's not a requirement.
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A
And it was clever. You let them keep ownership of the land.
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B
They have the land.
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A
Yeah, but you get the four walls.
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B
But we got full control of the economics of the venture, which is great.
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A
Well, I thought that was a fantastic story and one that lots of different companies, again, not specific to manufacturing, can learn from. Which, again, what I think is the beauty of the book. Let's talk about step two. Delete every possible step in a process. And there's a question you ask there, which is, which steps does a customer not pay you for?
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B
Right.
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A
I love that.
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B
Yeah.
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So talk about your favorite example of where you identified where customers weren't ponying up or at least not conscious.
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B
So literally, we still do this. We go put stickies on a sticky notes on a wall for every step in the process. And one of the things we did, I asked the team to do, is circle every step the customer pays for. Because there's most of the stuff that happens inside a company customers aren't paying you for.
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A
Not explicitly. They're paying through your margin, right?
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B
Yeah. But they're not paying you for your change orders, timesheets, your accounting systems, they're not paying you for any of that. So then that becomes like a question mark, like, should we be doing that? And that's the test. If they're not willing to pay us for it, should we be doing it? Because we were trying to double margin, so we didn't want to assume that all this stuff was just baked into the margin. We wanted to really, like, hone every process down to the bare requirements. So when you ask that tough question, does the customer pay you for this? A lot of the stuff falls out. And so we would delete all of it and then make any of those steps beg to get back into the process, basically. And so some things, like, we would pull out and the whole process would break without them, like a purchase order. But you kind of have to like go through this exercise because it reveals like 75 or 80% of the motion that we put people through is waste. Yeah.
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The corollary of that is the customer pays you for a problem you solve for them. Exactly. Like the value you bring. And you also talk about how it's important for companies to really think about what the definition of a problem is and make sure it's the customers and not what you hope it is.
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Yeah.
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As you look at all the companies out there and I know you've got a Venture fund will come to that. Who do you look at and say, I, John, have never been involved with that business, but I can see outside in they're doing it really well.
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B
Yeah, I would say Amazon is so it's just removed so much friction from the process, they make it easy for you to. For me, that's my first shopping destination and it's a few clicks and I'm done. And so that to me is a great example of a business that's put customers at the center and just said let's remove every chunk of friction we can to get this buying process so smooth and effective that it just becomes a natural extension. And today that's a natural extension for me. It's a company I really admire.
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A
Let's take a break and when we come back, more of my conversation with John McNeil. This episode is brought to you by Google Chrome. You think you know a browser, but Gemini and Chrome, that's new. It can help you with practically anything on the web, like restoring a vintage motorcycle from a 50 page restoration block or finally break down that long article you've had open for weeks. Gemini and Chrome is here for it, ready to make anything online make sense. There's no place like Chrome. Check responses, set up required compatibility and availability various 18 when it's time to scale your business, it's time for Shopify. Get everything you need to grow the way you want, like all the way away. Stack more sales with the best converting checkout on the planet. Track your cha chings from every channel right in one spot and turn real time reporting into big time opportunities. Take your business to a whole new level. Switch to Shopify. Start your free trial today. Now let's listen back to the rest of my conversation with John McNeil, former president of Tesla. You also found a way, and you talk about this in the book of making sure that those you worked with tried to define what customers would pay you for if you weren't providing it yet.
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B
Yes, exactly.
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A
And auto insurance was one of those. Yeah, talk to us about that.
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B
Well, we looked at like what you pay for sort of in and around your car and you pay for fueling and you pay for insurance and you pay for maintenance basically. And we said what if that was just in one simple payment because today like I have to like make a bunch of payments to get that stuff done. And so we started to experiment in a quiet place in the world in Asia, so we could just like if we were screwing up, there wasn't a bunch of press hopping on it. And what we did was we embedded, we combined the car payment and the insurance payment and the maintenance sort of all in one. So we'd say this is your all in one car payment for the month. And the Asian customers just went gaga over, got a ton of traction. And then we said, well, if this works at scale, this is kind of a Warren Buffett move. Cause we're going to get two things. We're going to get the float from the insurance revenue.
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A
That's the cash that comes in.
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That's the cash that comes in before. And you get to invest it before there's ever a claim. And the second thing is we have information on how these people drive and we know what it costs to fix these cars. So we have an asymmetric data advantage versus other insurers so that we can price this stuff to make it really attractive, because insurers are pricing with a lot of uncertainty and we had less uncertainty. And so it turned out we could bundle that together. And consumers were really attracted to that.
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And now it's a very important part
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of Tesla's cash flow. Exactly.
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A
Just to finish that thought, step three is to simplify and optimize, just to go back to this. And step four, accelerate cycle time. And John, you, you also wrote in the book something which I found very interesting. You joined the board of General Motors 2022, and you write, I'm going to read this out in style. The contrast between the CEO Mary Barra and Elon Musk could hardly be starker. Yet in many ways, their approaches are similar. I actually found that sort of gobsmacking. Just explain what you meant by that for folks.
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B
Well, I think when people study Elon 10 or 20 years from now and then step away from a lot of the noise and say, how did he become such an effective industrialist? I think part of the answer is he's involved weekly on the core engineering decisions in his businesses. And I noticed Mary was doing this weekly, too. And so I asked Mary, you're doing this at gm. You're, you're in the core engineering meetings on a weekly basis. Why? She said, because I'm the capital allocator. I have a CEO, the capital. I have to figure out where that capital goes and if it's being used effectively. And there's no better way for me to do that than to be in the room with the engineers on the key products. And I know exactly what our hurdles are. I know exactly what our pace is, I know exactly what our market potential is, and I can become a much better capital Allocator. And so she's. Her operating cycle looks a lot like Elon's on a weekly basis. But they are very different leadership styles, for sure.
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A
It's interesting because there's this sort of preconception that once you're in as big of a seat as Mary Barra is in, there's one point of view which would be, oh, is that micromanaging? Because she's so in the weeds. But to hear it called a capital allocator is extraordinary. How were General Motors employees responding? How did they embrace that? Or did they. Or was it weird for them?
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B
I think it's like it's. People don't bring their B game to meet with the CEO. They bring their A game. 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 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. 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. This kind of leadership style doesn't get surprised. 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, they're finding out about it nearly real time. And then can react the agility that
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A
comes with that competitive advantage. Let's shift gears and talk about you as a capital allocator.
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B
All right.
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If you don't mind. You have your venture fund, DVX Ventures, and DV stands for Delta V, which is the change in velocity to break free of gravity or shift trajectory, which is what the algorithm is aspiring.
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B
Exactly.
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A
So I spend a little time, a little bit of time, digging into some of the investments you've been making. And there's one. Just because our audience are engaged in the public markets, I want to talk to you about your ETF business. Yes. Mind?
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B
Yeah.
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A
I love this because you tell the story in your book about how you're on cnbc.
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B
Right.
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And you were asked how folks should get exposure to electrification. Yeah. And you sort of threw out that ETFs.
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B
Yeah.
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A
But you didn't actually love your answer when you look back. What. Tell us about what happened.
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B
All right, so, so I got in the car after the interview and I started to look at the ETFs that were on the market for electrification. So I get in the car, I open up the largest, the biggest market cap ETF in electrification. And the top five holdings are Amazon, Google, Microsoft, Nvidia, and Apple. Yeah, now I know what the bill of materials looks like for the electrical grid. There is none of that in the electrical grid. And I also know what the bill of materials looks like for an electric car. There's none of that in an electric car. There's no Microsoft in an electric car. We can't put a $20,000 Nvidia chip in an electric car. None of that stuff. And so my first reaction was, okay, people are paying 75 bips for the Mag 7. That's uncool.
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A
Paying the fee to buy the ETF.
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B
Exactly. And so that's uncool if people are marketing this really expensive product for something people can get for a lot less. But the second thing that was uncool about it was the stocks that were in these ETFs had nothing to do with the theme. And so therefore, if you wanted as an investor to get profit exposure to, say, electrification or AI infrastructure, you buy these funds and you're not getting what you're paying for. And to date, every ETF has been built on a market cap weighted basis. So you take the market caps of the stocks that are in this basket, say for AI infrastructure, if you drop Nvidia into that basket, it has such a large market cap, it overwhelms the entire basket of stocks value. And we said, that's kind of dumb because that's not really the way profit pools get created. So the next call I made, I called a guy that used to run manufacturing at Tesla, and he at the time was running data center buildouts at Google. And I said, could you send me the bill of material for an AI data center? He said, yeah, why? I said, because I have a feeling that 80% of the dollars that you're paying out don't go into Nvidia. He's like, yeah, only 8%. Why? I said, well, if you construct an ETF in this space with Nvidia in it, it looks like they get 80% of the dollars. He said, they don't. They only get 8 to 10%. I said, what's the rest? He said, unsexy stuff like power electronics, substrates, heating and cooling systems. Networking, high bandwidth memory, like all this stuff that we are talking about a lot today. But this was two years ago. So we built the first ETF that is not market cap weighted, it's actually weighted on a bill of material basis. And the idea is you give investors exposure to these profit pools. And so we took that AI data center bill of material. Turns out there's 60 public stocks, 30 US stocks, 30 international. We built an index and back tested it and it blew away all of the competitive ETFs. So we launched it last December. So December 25th and over the past year, it's the top performing AI infrastructure ETF on the market. Trades under ticker AIs on the NASDAQ. If you put a dollar into it January 1st last year, it's up 94%. If you put a dollar into it Jan. 1st this year, it's up over 30%. It's just a superior way to show investors and give investors exposure to these profit pools that are getting created beyond the headline stocks, essentially.
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A
And this is to be banned on Vista shares. So I went over to Vista shares and I dug in and I have your fact sheet on the artificial intelligence super cycle. The thing that's interesting. Yes. When I look at your top 10 holdings, that does include for example, SK Hynix, which doesn't get as much coverage to your point, versus some others you've got right there. So kudos to you. The other thing I just want to touch on here is I look at your team.
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B
Yeah.
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A
At Vista shares.
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B
Yeah.
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A
And I look at your team at dvx. I see a lot of familiar names from your book because clearly folks have followed you over from Tesla and. Right. And what's fascinating to me is your investment team is predominantly operational people.
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B
Exactly right.
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A
They're not. They're not money people.
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B
Exactly.
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A
You've one or two, of course. And that difference in perspective leads me to my next question, which is generally when it comes to the talent you surround yourself with. Yeah. When you find people who can implement the algorithm, what kinds of attributes do those people have?
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B
They usually have two things. They have a fire in the belly. So they're motivated in a special way to make a difference and have impact. They're builders and they're typically highly intelligent.
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A
There's different forms of intelligence.
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B
Totally.
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A
So which kinds of intelligence are you talking about?
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B
Mainly process intelligent and customer intelligence. So they've got IQ and EQ both, but the EQ is really focused on a customer. Like what would the customer really want here?
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A
Well, that's curiosity.
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B
Yeah, It's a form of curiosity and it's also a form of putting yourself into your target customer's shoes.
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A
Empathy.
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B
Yeah. So that empathy, you said it is a big, big piece of this. So they're curious, they're empathetic, they've got a process orientation and they're builders. And those type of people really excel at the kind of work we do.
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A
So. And they often go across different businesses.
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B
Yes.
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A
And so you reference Walter Isaacson, who's a phenomenal writer.
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B
Amazing.
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A
Wrote a fantastic endorsement for your book. Yeah, and I did read Walter's book. Elon. Elon Musk.
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B
Yeah.
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A
And he describes how Elon has a habit of taking talent with those kinds of attributes, maybe from SpaceX, and then dropping them into Tesla occasionally and then dropping them into X. Is that something you do? Do you SWAT team like that as well?
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B
We do, because like he. One of the things I learned from him was this notion of orthogonal hires. And what that means is you hire people in who have no direct experience. So like I came in to President Roll of Tesla. I'd never worked in auto manufacturing. I didn't, I didn't have a single clue about how you distributed cars. And he didn't want people around the table with preconceived notions. He wanted people who were going to think on a first principles basis. And what I found is like really, really good operators. People who are wired this way, sort of building their builders and operators. They can go into a lot of different business contexts and be successful. And so you see that with our team. Like, their resumes are all over the place. Somebody ran revenue at Burberry and then ran revenue at Tesla and is now starting businesses with us. Somebody that ran a big chunk of operations at GE and then ran operations at Tesla turns out to be one of our most creative company creators. So it's these folks have, I think, special genetics that then sets them up to do a lot of different things.
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A
That's interesting. So many people default to. What have they done that. Super cookie cutters. Exactly. To fit what's in front of them. I have two final questions for you. They're both a little cheeky.
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B
All right.
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A
But I'll ask you anyway.
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B
All right.
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A
And this, my second to last question, also is born of Walter's book. You are described in pretty glowing terms in the book, so you come out looking pretty good in Walter's book. There is one moment, though, and this was when he was talking to Elon Musk about your departure from ESLA from Tesla and Elon Musk describes you there as being too reluctant to fire people.
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B
Yeah.
- [27:17]
A
Was that true?
- [27:18]
B
I think in this particular situation that Walter was describing, yeah. Like a guy got fired on the spot who had nothing to do with the problem that, that Elon was ticked off about that day, and that was unfair. And both the co founder JB Straubel and I said that's not right. And Elon disagreed. It's his prerogative to disagree. But in that case, yeah, like, I would hesitate, but I don't hesitate to fire people who aren't performing. And, and, but I, but I do have a different trigger than Elon does. And so it's a fair thing for him to say, but I would still disagree today.
- [27:57]
A
So my second cheeky, my last cheeky question, and also my last question is sort of in the vein of firing people. You are on the board of Lululemon.
- [28:04]
B
Yeah.
- [28:05]
A
And the founder, Chip Wilson, has been very vocal in public about saying the board has not moved, frankly, to fire CEOs who have not been performing. I'd love to ask you to comment. I suspect I know what your answer is going to be, but is that an example where the board did not move quickly enough?
- [28:22]
B
So a board is a collective decision, obviously, but the, the, the CEO of Lululemon, who's just left, tripled the business. So as an owner and a significant owner, you can look at the stock returns over time. It's in a different place than it was. The stock was trading in the 50s when that CEO arrived and trading in, say, 170 last month. So that is upside that was created there. But I do think that as a rule, the core job of a board is to hire and fire the CEO. And that has to be kind of front and center in discussions. And I found that board members who come from an operating background move at a faster pace than board members who come from a finance background. And I don't know why that is, but it just is.
- [29:11]
A
I think once you've been there and you've seen actually, you can get through it.
- [29:14]
B
That's right.
- [29:15]
A
It doesn't have to be that scary. It does change your perspective. The founder of Lululemon, Chip Wilson, has criticized the brand for having, quote, lost its soul. And I guess the last subpart of this question is, can too much algorithm cost a business its soul?
- [29:30]
B
I think, like, you do have to have an eye on the soul and the vibe and the sort of overall culture that you want to create with your team and your customer. And when teams get too oriented around financial returns and especially short term financial metrics. The Soul can leak. And so I do keep an eye out for our companies. And when I'm on board, if the team starts to really focus on short term financial metrics, that's a trigger for me to say my spidey sense is going up, that we may need to put some more emphasis here and start to ask more questions around product and soul.
- [30:10]
A
John McNeil, author of the Algorithm, founder of DVX Ventures thank you very, very much for joining.
- [30:15]
B
Thank you man.
- [30:16]
A
Good to have you.
- [30:16]
B
Yeah, great to be with you.
- [30:17]
A
Well, huge thanks to John McNeil for joining us. And I do recommend his book the Algorithm. By the way, I've sent copies of it to those that I work with in the manufacturer, manufacturing arena and also other businesses too. It's not just specific to that one. Lots to learn for now. That's all for today's brew Markets daily. You want to get your backyard summer ready, but you don't want to break the bank. Wayfair gets it. Planning on dining al fresco or relaxing poolside? Wayfair has everything you need to prep your space. Shop now and save up to 70% off during Wayfair's 4th of July clearance. Score huge deals on outdoor furniture, area rugs and more. We're just talking thousands of products for every style and budget, plus surprise flash deals. July 6th don't wait. Shop Wayfair's 4th of July clearance now through July 6th at Wayfair.com Wayfair Every style, every home.