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Speaker 1: This is Master's in Business with Barry Ridholts on Bloomberg Radio.

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Speaker 1: This week on Masters in Business on Bloomberg Radio, I

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Speaker 1: have a really interesting guest. His name is Jeff Magian Calda.

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Speaker 1: He is the co founder and CEO of Financial Engines.

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Speaker 1: A reader and a fan of some of our early

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Speaker 1: earlier podcasts had written in and said, hey, you know

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Speaker 1: you should interview you should interview this guy, Jeff Magian

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Speaker 1: Calda of Financial Engines. Are you familiar with them? And

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Speaker 1: I said yes, I actually am very familiar with them.

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Speaker 1: Their public company, uh, they run a hundred plus billion

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Speaker 1: dollars in assets, and Bill Sharp, the Nobel Laureate economist,

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Speaker 1: is essentially uh, the founder and the guy who put

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Speaker 1: the original idea together. And I said, I would love

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Speaker 1: to interview Jeff. I don't know him. I don't know

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Speaker 1: how to reach him. I don't know his his information

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Speaker 1: is in public. How do I how do I find

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Speaker 1: this guy? And the reader said, well, I know them.

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Speaker 1: Let me make an introduction. And that was about six

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Speaker 1: months ago. He Jeff lives in Palo Alto. He's on

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Speaker 1: the West Coast. He's not in New York all that often.

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Speaker 1: And when I pinned him in made the offer of Hey,

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Speaker 1: you should come on the show. Here's who we've had on,

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Speaker 1: here's what it's about. He said, Um, that would be interesting,

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Speaker 1: but I'm I'm rarely in New York. Two weeks ago,

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Speaker 1: I said, Hey, any chance you're in New York anytime soon.

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Speaker 1: He's like, yeah, I'm in New York on on the

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Speaker 1: week of the of the first of the month. Let's

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Speaker 1: let's see what we can schedule. So here we are.

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Speaker 1: It was really a fascinating conversation and I think you'll

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Speaker 1: really enjoy it. So, with no further ado, here is

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Speaker 1: my conversation with Jeff Magian Calda, co founder and former

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Speaker 1: CEO of Financial Engines. This is Masters in Business with

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Speaker 1: Barry Ridholts on Bloomberg Radio. This week on Masters in

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Speaker 1: Business on Bloomberg Radio, I have Jeff maggian Calda. He

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Speaker 1: is the founding CEO of Financial Engines. Now, you, as

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Speaker 1: a lay person, may not have heard of Financial Engines,

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Speaker 1: but it's really an interesting company with a fascinating history. Uh.

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Speaker 1: They are currently the largest r I A in the country,

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Speaker 1: managing over a hundred billion dollars in assets. They were

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Speaker 1: founded by Nobel Laureate Bill Sharp. Uh, we'll talk a

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Speaker 1: little bit about Bill later. And Jeff was tapped by

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Speaker 1: the three leading investors of the firm, literally right out

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Speaker 1: of his Stanford NBA program to take what was then

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Speaker 1: a free website offering suggestions for asset allocation models and

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Speaker 1: turn it into the powerhouse that it's become today. Jeff,

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Speaker 1: welcome to Bloomberg. Thank you glad to be here. So

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Speaker 1: let's let's talk a little bit about that history, because

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Speaker 1: it's so it's so amazing. Bill Sharp essentially created a

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Speaker 1: free website. Hey, I developed the capital asset pricing model,

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Speaker 1: eventually winning the Nobel Price for that. And here's the

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Speaker 1: output of that work. You could play with this for

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Speaker 1: your retirement accounts. It's free on the site. How did

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Speaker 1: that morph into what's now with publicly traded company? Well so,

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Speaker 1: so Bill, he's he's a genius and he is very

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Speaker 1: well meaning. He's always wanted to use the work he's

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Speaker 1: done to help people. Um. He has also been a

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Speaker 1: total gear head and sort of hacker since the early days.

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Speaker 1: He wrote one of the first compilers for the Basic

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Speaker 1: programming language back in the day, and so he had

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Speaker 1: his own son's sparkstation as which is his own web server.

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Speaker 1: At the time, this is the beginning of the internet.

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Speaker 1: No one, no one had personal computers in right, and

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Speaker 1: he has like a note on the darknet or something

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Speaker 1: and uh, and so he thought, you know what, I

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Speaker 1: can write some simulation software and some some optimizers and

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Speaker 1: I can make this available because I mean he saw

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Speaker 1: early on that the demographics of the baby boomers, uh,

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Speaker 1: and the shift away from defined benefit plans towards four

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Speaker 1: O one case was going to mean a lot of

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Speaker 1: people had a new responsibility to invest, but but really

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Speaker 1: didn't get access to very good advice, so didn't have

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Speaker 1: the skills, didn't have access to people who can help

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Speaker 1: them in so many for one case, are under a

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Speaker 1: hundred thousand dollars. Nobody is really going to take that

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Speaker 1: as a as a client. That just doesn't work. So

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Speaker 1: so what did he do with that? So he started

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Speaker 1: a website and back in the days where they used

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Speaker 1: to index popularity, it was actually a pretty popular site.

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Speaker 1: But you know, among the people using the internet the time,

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Speaker 1: which were kind of the more actively technically, it was

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Speaker 1: much more of a crowd. It was very much else.

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Speaker 1: So his buddy Joe grin Fest, lawyer at the law school,

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Speaker 1: former SEC commissioner. Um. He said, Bill, if you really

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Speaker 1: want to make a difference, you gotta start a company.

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Speaker 1: Bill was not really interested in starting a company. He's like, look,

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Speaker 1: I want to do something good for the world. I

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Speaker 1: don't really want to charge money for it. And Joe said,

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Speaker 1: if you want people to really promote the idea and

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Speaker 1: create a good product that people can really use, you

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Speaker 1: got to start a company. So so you're saying Bill Sharp,

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Speaker 1: noble laureate, kind of a wonky academic. Is that the description?

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Speaker 1: Why does that not surprise? Yeah? Yeah he he uh

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Speaker 1: his his his his pursuit of sort of the intellectual breakthroughs,

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Speaker 1: and this pursuit of doing good in the world definitely

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Speaker 1: trumps his pursuit for financial gain. Not not uncommon. You

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Speaker 1: look at people like Bob Schiller or Jeremy Siegel, or

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Speaker 1: when when I spoke with Siegel, it was he was

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Speaker 1: amazed that he made any money because that wasn't what

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Speaker 1: he ever expected. He expected to just toil away and

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Speaker 1: academic obscurity and have a satisfying life. It sounds like

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Speaker 1: Bill is Sharp is similar very much so. So so

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Speaker 1: you're tapped. You're literally twenty seven years old, just graduating, Uh,

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Speaker 1: Stanford NBA program. What's it like when a couple of

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Speaker 1: heavy hitters like Sharp and Grunfest and who's the third

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Speaker 1: Craig Johnson. Craig Johnson also not no lightweight. Um says, hey, kid, come,

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Speaker 1: we got an idea for you. How do you how

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Speaker 1: do you respond to that? Well? So, so I was

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Speaker 1: at the time, I had I had worked for McKinsey

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Speaker 1: over the summer. I had taken a full time job.

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Speaker 1: So I my wife had spent the signing bonus on

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Speaker 1: new furniture, and we had two kids. We were weigh

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Speaker 1: in debt and the big sum of money comes and

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Speaker 1: what did the economists say when you get a windfall,

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Speaker 1: the propensity to spend is like one you like, just

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Speaker 1: spend it? And uh. So she did and um and

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Speaker 1: the account and uh And I had been approached by

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Speaker 1: our professor at the business school, h. Robert Bergelman. He

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Speaker 1: at the time, I think he still teaches, And he

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Speaker 1: taught a class with Andy Grove, who was at the

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Speaker 1: time the CEO of Intel on kind of strategy and

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Speaker 1: information technology. And they had no cases on the internet.

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Speaker 1: And so as an undergrad. I was an English and

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Speaker 1: economics major, so I knew about statistics and quant stuff.

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Speaker 1: I knew how to write, and they said, would you

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Speaker 1: be a case writer and write a bunch of cases

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Speaker 1: on the internet. So I said that would be fun

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Speaker 1: to do. I called McKensy. I said, well, you know,

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Speaker 1: I'll becoming in January. And then it was during the

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Speaker 1: time that I was writing cases that I got this

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Speaker 1: call from Joe grin Fast, who I had worked with

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Speaker 1: previously at at a litigation consulting firm. And I was like,

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Speaker 1: Bill Sharp was my hero. It was amazing because when

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Speaker 1: I graduated in he had received the Nobel Prize the

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Speaker 1: year before, and he was my graduation speaker. And I

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Speaker 1: just thought this guy was the most amazing guy in

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Speaker 1: the world. And I love the elegance of his model.

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Speaker 1: I thought his overall philosophies. I totally bought into it.

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Speaker 1: You know, generally speaking, he can't beat the market, so

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Speaker 1: go for a low costs. I thought that was great.

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Speaker 1: I think by the way, that's managed to catch on

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Speaker 1: quite a bit. It seems to have pretty well among

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Speaker 1: the the investing public. So so so I was really

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Speaker 1: kind of stuck, and I was like, well, I'm kind

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Speaker 1: of tied up right now. And and I talked to

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Speaker 1: my wife, who's you know, PhD and evolutionary biology. She's like, Jeff,

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Speaker 1: you have to do this. This is a once in

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Speaker 1: a lifetime thing. You have to do this. We'll figure

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Speaker 1: out the the money and I we'll figure that stuff

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Speaker 1: out later. And uh and so I'll like, you're totally right.

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Speaker 1: I mean, how often do you get to work not

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Speaker 1: only with the Nobel prisminer, but with like a hero

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Speaker 1: of yours? Uh And and so I sort of took

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Speaker 1: the plunge, not having any idea what I was gonna do.

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Speaker 1: I'm Barry Ridhults. You're listening to Masters in Business on

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Speaker 1: Bloomberg Radio. My guest this week Jeff Magian Calda. He

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Speaker 1: is the co founder and former CEO of Financial Engines,

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Speaker 1: the country's largest r I A, which manages over a

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Speaker 1: hundred billion dollars in four one K money. And I

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Speaker 1: want to talk about how you guys moved into that space.

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Speaker 1: But first, there's this great little story about you playing

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Speaker 1: Monopoly with your wife and you decided to let's run

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Speaker 1: a million simulations of Monopoly to figure out what the

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Speaker 1: ideal move is for each property and what have you.

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Speaker 1: Essentially high frequency trading the monopoly board, a little bit

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Speaker 1: of cheating, and your wife, instead of being angry, was actually, oh,

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Speaker 1: you should do something. Yeah, how did that about? So?

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Speaker 1: So so Monopoly is a is a really fun game.

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Speaker 1: It's mostly about trading, but but a lot of it

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Speaker 1: has to do with the probability is landing on certain spaces,

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Speaker 1: and so you know, it's it's a game with pretty

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Speaker 1: simple rules, right, you roll the dice, you roll doubles,

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Speaker 1: you go again. But if you do it three times

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Speaker 1: and you go to jail and then you pull certain

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Speaker 1: cards advanced to St Charles, blah blah blah blah. They're

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Speaker 1: very simple rules, but it's really hard to get a

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Speaker 1: distribution of the likelihood that you land on any property.

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Speaker 1: And so I was like, well, look, the value of

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Speaker 1: a property is going to be based on the probability

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Speaker 1: that you land on it, plus the rents that you

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Speaker 1: get of something if you have a house or whatever,

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Speaker 1: and then you have to look at the cost of

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Speaker 1: building those houses. But I just want to know what's

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Speaker 1: the probability of landing on every space on on the

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Speaker 1: on the on the board and turns out what computers

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Speaker 1: is pretty if you have a game with simple rules,

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Speaker 1: even if it's a very complex set of distributions. At

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Speaker 1: the end, you just play the game millions of times

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Speaker 1: and you just see like how often did you land there?

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Speaker 1: So my wife didn't know I was doing in this.

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Speaker 1: I had actually create a little cheat sheet and uh,

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Speaker 1: I was using it to kind of assess the odds

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Speaker 1: that I buy a proper trade of proper collections of properties.

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Speaker 1: And it was basically just a sheet that showed the

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Speaker 1: probably beloy of landing on any space. And it turns

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Speaker 1: out certain spaces are way more likely to be landed

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Speaker 1: on than other space. So what are the greens the oranges?

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Speaker 1: What are the greens? Are bad? Greens are bad and

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Speaker 1: the oranges best the best. Yeah, because people go to

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Speaker 1: jail all the time, and if you play they come around.

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Speaker 1: That's right, you end up in jail. You've got ten

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Speaker 1: spots free parking if you play crafts what number comes

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Speaker 1: up the most? Seven? Seven? Right, Well, the other two

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Speaker 1: are going to be six and eight, and those are

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Speaker 1: the oranges. So and that basically gets there. Going to

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Speaker 1: jail a lot and rolling anything six to nine is

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Speaker 1: pretty good for the oranges. So so how did that

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Speaker 1: then lead to the idea of, hey, we could run

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Speaker 1: analyzes and come up with the best probability for people's

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Speaker 1: retirement portfolios as opposed to letting them just randomly pick

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Speaker 1: whatever fun manager is hot that much. Well, so it

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Speaker 1: only helped me get the job by convincing Bill Sharp

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Speaker 1: I knew something about money Carlo simulation. He had been

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Speaker 1: doing this for the defined benefit pension managers for a

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Speaker 1: long time because they're trying to figure out on the

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Speaker 1: pension side, how much do we have to fund these

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Speaker 1: pensions and how should we invest to have you know,

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Speaker 1: certain probability of being able to pay these pensions? When

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Speaker 1: are our people all looking for the greatest return with

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Speaker 1: the least amount of risk and just end up in

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Speaker 1: the fat part of that car Well, so so that's

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Speaker 1: kind of how you set your portfolio. But then you

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Speaker 1: even have to say how much money are we likely

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Speaker 1: to have thirty years from now if we invested a

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Speaker 1: certain way, And that's really about simulating, well, what if

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Speaker 1: the markets do well, what if they too poorly? What

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Speaker 1: if interest rates are high or lower? Inflation does this

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Speaker 1: or that, or you know the spread between you know,

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Speaker 1: a small cap and and MidCap stocks varies. Sharp described

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Speaker 1: it as a thirty variable dimensional analysis. Yeah, and so

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Speaker 1: we would. So so he was already doing this for

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Speaker 1: pension funds to kind of get a sense for like

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Speaker 1: how much is this pension gonna be worth twenty years

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Speaker 1: from now, and he wanted to do the same things

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Speaker 1: for individual to answer the bay is a question like

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Speaker 1: are you going to have enough money? Well, it's an

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Speaker 1: easy question. It's a pretty hard answer because you don't

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Speaker 1: know what the future holds. It doesn't mean you throw

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Speaker 1: up your hands you say I have I have absolutely

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Speaker 1: no idea. It's a little bit like predicting other things

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Speaker 1: that are stochastic or uncertain, where you say, we don't

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Speaker 1: have a perfect idea, but we could put a range

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Speaker 1: around it. You know, if you're all in in very

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Speaker 1: very vaulatile stocks, a couple of stocks, it's gonna be

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Speaker 1: a very wide range that probably goes down to almost

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Speaker 1: zero in a bankruptcy situation. If you're in money market

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Speaker 1: funds of treasuries, it's gonna be a very tight range.

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Speaker 1: And anywhere in between you're gonna have a higher sort

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Speaker 1: of expected outcome, but a bigger range depending on how

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Speaker 1: much risk you having that porfile. So the monopoly thing

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Speaker 1: was was was useful because when Bill Sharp said, hey,

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Speaker 1: you know, we're gonna be doing a lot of simulation

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Speaker 1: and a lot of optimization. What do you know about that?

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Speaker 1: I said, well, you have already kind of used it

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Speaker 1: to my game previously in life. Although you know, my

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Speaker 1: my wife didn't didn't take kindly when she found out

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Speaker 1: that cheat sheet. That's that's great. So so now I'm

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Speaker 1: trying to follow the path of this. So this is

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Speaker 1: a free website. And then of eventually Grundfest and Johnson say, Bill,

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Speaker 1: what are you doing? This is a there's a business here.

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Speaker 1: You don't have to do the unpleasant. You don't have

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Speaker 1: to do the firing and hiring. Let's get a guy

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Speaker 1: who's never done that to do right. But here your

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Speaker 1: your chairman emeritus. You're the nobel laureate. You're gonna be.

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Speaker 1: We'll trot you out when we have big clients that

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Speaker 1: we want to razzle dazzle, and I'm based on your

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Speaker 1: client list, so you don't. Essentially, it's not like individuals

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Speaker 1: come to financial engines and say take my four oh

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Speaker 1: one K. It's companies like Ford and other giant employers

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Speaker 1: who say, we're not gonna play this crazy game. We're

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Speaker 1: just gonna do this simply and and crank uh crank

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Speaker 1: out one of Bill's simulations, and we'll use that for

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Speaker 1: or a couple of options for our clients. So so,

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Speaker 1: but how do you get from A to B? How

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Speaker 1: do you get from a free website run by a

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Speaker 1: quirky Nobel laureate to all right, this is a real

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Speaker 1: business with a hundred billion in assets, a public company

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Speaker 1: and generating drives and millions of dollars on revenue. If

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Speaker 1: I had boiled down to kind of one thing, I

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Speaker 1: would say hardcore learning and iteration, which which sounds a

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Speaker 1: lot like failure. And you know, if we had given up,

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Speaker 1: it would have been failure. But it was kind of

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Speaker 1: close to failure a lot of times. So what were

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Speaker 1: the first few What what what are the first few versions

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Speaker 1: of this look like? So the first thing we tried

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Speaker 1: to do is and I thought it was a pretty

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Speaker 1: decent idea. The Department of labor At just issues from

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Speaker 1: regulations saying that if an employer hired someone who did

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Speaker 1: not give specific recommendations, just asset allocation advice, it wouldn't

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Speaker 1: be advice. It would stay on this fiduciary safe harbor

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Speaker 1: and they could do without worried being worried that would

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Speaker 1: be constituting advice. And and our original thought was, let's

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Speaker 1: be an education company. Let's actually just just give education.

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Speaker 1: Let's not be an advisor, let's not give advice. And

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Speaker 1: what kind of sell as an employee benefit? Kind of

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Speaker 1: financial literacy? And so I read up a big business plan.

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Speaker 1: This is my big document. I'm excited to go raise money.

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Speaker 1: I go out to walk to uh To to sand

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Speaker 1: Hill Road and all the vcs and they're like, this

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Speaker 1: is a stupid idea, and we're not going to fund

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Speaker 1: an education company. Uh. They didn't really say what do

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Speaker 1: They're just like, you know, tell me when you get

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Speaker 1: a better idea. And a couple of guys at work,

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Speaker 1: and there are only five of us, but two of

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Speaker 1: the guys are like, yeah, this is so stupid. We

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Speaker 1: shouldn't be just doing education. We should become an investment visor,

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Speaker 1: because that's how you can charge of some percentage and

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Speaker 1: get paid on the assets under management and surreptitiously work

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Speaker 1: the education. Yes, and and and what we what we

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Speaker 1: really did have a sense for And so I agree

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Speaker 1: with them. I didn't want to kind of take on

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Speaker 1: that headache of becoming an advisor, but the fact that

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Speaker 1: the matters most people want to know what to do

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Speaker 1: and kind of hey, you're getting warmer, you're getting colder.

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Speaker 1: Study a little bit of this now, they're like, just

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Speaker 1: tell me what I should do, what fund should I buy?

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Speaker 1: What find should I sell? In order to really do

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Speaker 1: a good job with that, you gotta be an investment visor.

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Speaker 1: I'm Barry Rihults. You're listening to Masters in Business on

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Speaker 1: Bloomberg Radio. My guest this week is co founder and

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Speaker 1: former CEO Jeff Agian, called of Financial Engines. They are

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Speaker 1: the largest r i A in the United States, managing

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Speaker 1: over a hundred billion dollars in four oh one k money.

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Speaker 1: And I've described, by the way, whenever I described financial

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Speaker 1: engines to people, because so many people have never heard

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Speaker 1: of you. You're not selling into the retail market. You

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Speaker 1: sell essentially into corporate and institutional markets. Financial Engines, I've

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Speaker 1: I've never heard of them. Well, really, they were the

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Speaker 1: original robo advisor. They started doing this before one case

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Speaker 1: we started, so it's almost twenty years twenty years old,

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Speaker 1: and essentially you were using software and algorithms to drive

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Speaker 1: investing decisions, long before all the cool kids started doing

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Speaker 1: Oh yeah, I mean there was no JavaScript. I mean

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Speaker 1: web servers were brand new job and didn't even exist yet,

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Speaker 1: and so there was a whole different world out there.

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Speaker 1: And when we first did this, you it took about

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Speaker 1: fifteen minutes to download our software. Uh when Java finally

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Speaker 1: came out and and people were using dial up modems.

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Speaker 1: So it's kind of a problem that the technology really

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Speaker 1: wasn't there yet when we were starting, not not a

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Speaker 1: whole lot of graphic interface and a lot of interesting

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Speaker 1: web flash before Java, before Flash before all the things

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Speaker 1: we take for granted on the internet. Yeah. Yeah, So

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Speaker 1: let's talk about I don't even know if I could

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Speaker 1: call them your competitors, although they have talked about moving

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Speaker 1: into the four O one case based. There are a

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Speaker 1: dozen or so companies, the probably the two best known

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Speaker 1: or wealth Fronts and Betterment. What are these other robo

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Speaker 1: advisors doing right? What are they getting wrong? It's hard

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Speaker 1: to say for sure. I mean, I clearly have a

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Speaker 1: lot of biases because I've been in the industry for

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Speaker 1: a long time, and obviously we we compete, um, but

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Speaker 1: you know what, I what I think they're doing right

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Speaker 1: is that they recognized a really big need, which is

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Speaker 1: that more and more Americans do need help. And there's

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Speaker 1: a lot of inefficiency ease, and a lot of conflicts

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Speaker 1: and a lot of poor quality when it comes to

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Speaker 1: individual Americans getting advice, and so I think there is

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Speaker 1: a big need. I think that they are also kind

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Speaker 1: of carving up different sorts of spaces. Well Front is

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Speaker 1: going after kind of the the younger millennial, generally tech

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Speaker 1: person who made a lot of money. They're focused a

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Speaker 1: lot on tax loss harvesting, betterment, has a few different

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Speaker 1: distribution channels, including going through r A s not through

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Speaker 1: corporate are big thing. You know has been going through

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Speaker 1: our A. So we last time and I'm not the

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Speaker 1: company anymore, so I just kind of read the press

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Speaker 1: releases and the earnings reports when they come out. But

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Speaker 1: company now has more than a trillion dollars in four

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Speaker 1: win K plans that have hired them and managing over

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Speaker 1: a hundred and fifteen billion in a u M, so

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Speaker 1: that that's a pretty big company. It's the largest independent

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Speaker 1: area in the country and a big part of the

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Speaker 1: success has been making it really easy and safe for

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Speaker 1: an individual to say, yeah, I want you to be

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Speaker 1: my advisor. And by going through the workplace, we get

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Speaker 1: the trusted introduction from the from the company, keeps acquisition

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Speaker 1: costs low and because we manage your four O OK,

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Speaker 1: you don't need to sign any paperwork, you don't need

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Speaker 1: to move any money, you don't need to figure any

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Speaker 1: it's just like, yeah, please do this. I think without

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Speaker 1: that trust to introduction and not having to move money,

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Speaker 1: it gets very difficult. So I know our unit economics

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Speaker 1: quite well, and they were very they were very compelling.

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Speaker 1: It's a very profitable company. I think it would be

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Speaker 1: really really difficult to do this without a some sort

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Speaker 1: of secret sauce on the acquisition side, because acquisition costs

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Speaker 1: are huge to say the least. So let's ask the

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Speaker 1: flip side of of the question. So, what's the role

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Speaker 1: of a live human in providing financial advice or financial

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Speaker 1: planning either as an adjunct to or above and beyond

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Speaker 1: the al GOO driven allocation strategy? And so so this

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Speaker 1: is something that took us a long time to figure out.

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Speaker 1: We started as an education software company that didn't work

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Speaker 1: at all. Then we went to advice, but we the

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Speaker 1: tagline was your personal online advice. But this was your

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Speaker 1: personal online advisor. Our whole thing was we didn't want

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Speaker 1: to have our own advisors. They cost too much. You know.

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Speaker 1: We talked to customers and they said, it's great thing

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Speaker 1: you're giving me a tool. I don't know how to

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Speaker 1: use the tool. I think I'm gonna misuse the tool.

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Speaker 1: So he said, well, what if there was an advisor

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Speaker 1: you could talk to anytime you want who could actually

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Speaker 1: manage your portfolio for you. Now, actually, what we weren't

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Speaker 1: saying is it's all the same technology. It's a it's

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Speaker 1: an advisor center with Series sixty five license advisors out

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Speaker 1: in Phoenix. Their their financial enges employees. But they're just

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Speaker 1: basically using our software in a call center, but talking

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Speaker 1: to people and making sure that those people feel comfortable

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Speaker 1: about the decisions that they're making. It's the behavioral side,

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Speaker 1: not the investing, absolutely is. So what we found is

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Speaker 1: that at least for baby boomers, where there's a lot

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Speaker 1: of the five trillions and four oh win K accounts

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Speaker 1: are with people who are older than fifty. I think

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Speaker 1: last time I checked it was sixty seven percent of

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Speaker 1: the four K asks are held by people fifty and older.

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Speaker 1: Give me that number again, sixty seven of four oh

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Speaker 1: one k assets. Sure, that would make sense. You warned more.

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Speaker 1: As you get older, you can contribute more, absolutely, and

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Speaker 1: you start thinking about it more because you're getting close

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Speaker 1: to retirement. So when you've saved all your life and

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Speaker 1: now the question is how you gonna invest that money,

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Speaker 1: you really want to make sure that you're not screwing

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Speaker 1: something up, and at least for that age, cohort talking

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Speaker 1: to someone to make sure, like, you know what I'm

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Speaker 1: saying right, you know what you got the data right?

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Speaker 1: I want to make sure I understand what you're doing.

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Speaker 1: There's just an interest in knowing, alright, someone is accountable

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Speaker 1: and they know how to use these these tools in

429
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Speaker 1: this technology. So I'm not going to get it wrong.

430
00:21:41,359 --> 00:21:44,200
Speaker 1: You're listening to Masters and Business on Bloomberg Radio. My

431
00:21:44,400 --> 00:21:48,080
Speaker 1: special guest this week is former CEO and co founder

432
00:21:48,119 --> 00:21:53,360
Speaker 1: of Financial Engines Jeff Nagian Calda. And you got to

433
00:21:53,359 --> 00:21:58,480
Speaker 1: work with a hero of yours. You graduate Stanford, the

434
00:21:58,600 --> 00:22:04,119
Speaker 1: NBA program, what you was at undergrad was alright, so

435
00:22:04,160 --> 00:22:06,600
Speaker 1: your Stanford undergrad and then you stay so you really

436
00:22:06,600 --> 00:22:09,320
Speaker 1: like that party. I like and I have two of

437
00:22:09,320 --> 00:22:12,399
Speaker 1: my three daughters go there. Uh and my wife got

438
00:22:12,440 --> 00:22:14,080
Speaker 1: a couple of degrees and that's where we met. So

439
00:22:14,080 --> 00:22:17,159
Speaker 1: so Stanford has been uh home away from home for

440
00:22:17,200 --> 00:22:19,840
Speaker 1: you basically, so you should talk to them about their

441
00:22:19,840 --> 00:22:24,800
Speaker 1: in down terrible and could use your your assistance. Um.

442
00:22:24,880 --> 00:22:29,560
Speaker 1: But you go to your graduation for your m b A.

443
00:22:29,880 --> 00:22:35,000
Speaker 1: And the commencement speaker is Bill Sharp, Nobel Laureate, and

444
00:22:35,040 --> 00:22:39,600
Speaker 1: you're entranced. What was it like after that experience to

445
00:22:39,680 --> 00:22:43,520
Speaker 1: get a phone call essentially from Bill Sharp? Hey, kid,

446
00:22:43,560 --> 00:22:46,320
Speaker 1: I got ah, I have an idea. I'd like to

447
00:22:46,400 --> 00:22:49,359
Speaker 1: have you helped me build this into a business. Yeah. Well,

448
00:22:49,600 --> 00:22:51,879
Speaker 1: so he spoke at my undergrad graduation. I was an

449
00:22:51,880 --> 00:22:55,080
Speaker 1: econ major, but I always thought that he was just

450
00:22:56,119 --> 00:22:58,800
Speaker 1: a magnificent mind. I was like, this guy is super smart,

451
00:22:58,840 --> 00:23:00,600
Speaker 1: and I happened to think he could that he was

452
00:23:00,640 --> 00:23:02,720
Speaker 1: saying a lot of things that could help people. I

453
00:23:02,760 --> 00:23:06,639
Speaker 1: didn't ever meet him until he interviewed me, and I

454
00:23:06,680 --> 00:23:09,359
Speaker 1: was shaking up my boots. I mean, I'm like, I'm

455
00:23:09,400 --> 00:23:12,240
Speaker 1: not worthy, but he was really down to earth. It

456
00:23:12,359 --> 00:23:14,800
Speaker 1: was interesting. He wanted to make sure that this is

457
00:23:14,840 --> 00:23:17,640
Speaker 1: that says a lot about Bill Sharp. He quickly said,

458
00:23:17,680 --> 00:23:19,479
Speaker 1: you know, are you smart enough in terms of the

459
00:23:19,520 --> 00:23:21,960
Speaker 1: economic stuff, But he really wanted to do you know

460
00:23:22,000 --> 00:23:26,880
Speaker 1: how to program? I'm like yeah, and and when real

461
00:23:26,920 --> 00:23:31,200
Speaker 1: programming scripts like you could really you could like like

462
00:23:31,200 --> 00:23:34,359
Speaker 1: like real programming now literally he wrote in C plus

463
00:23:34,359 --> 00:23:37,280
Speaker 1: plus and he wrote an assembly, so he this guy

464
00:23:37,359 --> 00:23:42,080
Speaker 1: is like a really serious old school so I couldn't

465
00:23:42,080 --> 00:23:45,240
Speaker 1: do that. But I was mostly I was mostly programming

466
00:23:45,280 --> 00:23:47,639
Speaker 1: sort of Pascal and kind of lightweight stuff. I mean,

467
00:23:47,680 --> 00:23:50,320
Speaker 1: I'm not really a hacker, but I programmed enough stuff

468
00:23:50,359 --> 00:23:52,000
Speaker 1: to kind of know the basics. And then he said,

469
00:23:52,680 --> 00:23:56,640
Speaker 1: what do you know about graphic arts? And I go, uh, well,

470
00:23:56,680 --> 00:23:58,679
Speaker 1: I used to have a graphic arts business. Because I

471
00:23:58,760 --> 00:24:01,960
Speaker 1: used to I learned how to use it Adobe Illustrator

472
00:24:02,080 --> 00:24:04,960
Speaker 1: back in the back. Still it's still around. And he said,

473
00:24:04,960 --> 00:24:06,679
Speaker 1: you know, one of the most important things about this

474
00:24:06,720 --> 00:24:09,360
Speaker 1: business is going to be figuring out how to communicate

475
00:24:09,400 --> 00:24:12,520
Speaker 1: these principles to people so they can understand it. And

476
00:24:12,560 --> 00:24:15,080
Speaker 1: if you if you're not able to communicate, you know

477
00:24:15,119 --> 00:24:17,800
Speaker 1: we're gonna have it. It's gonna be tough. So for him,

478
00:24:17,840 --> 00:24:19,560
Speaker 1: it was kind of checked the box on the on

479
00:24:19,600 --> 00:24:22,800
Speaker 1: the math and economics, but really Let's make sure we

480
00:24:22,840 --> 00:24:24,960
Speaker 1: get someone who can solve the hard problem, which is

481
00:24:24,960 --> 00:24:28,280
Speaker 1: helping people understand it, making it simple, easy to understand

482
00:24:28,520 --> 00:24:31,800
Speaker 1: visual graphics. Are you know a picture does health is

483
00:24:31,840 --> 00:24:34,880
Speaker 1: worth a thousand words? And if you can communicate this dry,

484
00:24:35,000 --> 00:24:37,720
Speaker 1: complicated stuff in a way that makes sense to people,

485
00:24:38,000 --> 00:24:40,200
Speaker 1: you're two thirds of the way that. He was also

486
00:24:40,320 --> 00:24:44,000
Speaker 1: really clear that Jeff, I don't want to run this business.

487
00:24:44,200 --> 00:24:45,920
Speaker 1: You're going to run it, which means you're gonna deal

488
00:24:45,920 --> 00:24:48,399
Speaker 1: with the headaches. He didn't say, I want to spend

489
00:24:48,400 --> 00:24:50,840
Speaker 1: all the time doing the research, and he was involved

490
00:24:50,880 --> 00:24:54,600
Speaker 1: every day in actually writing the early code, helping us

491
00:24:54,640 --> 00:24:57,000
Speaker 1: with the algorithms. It was amazing, It was amazing, he was.

492
00:24:57,320 --> 00:24:59,359
Speaker 1: Was it a little surreal working with that close with

493
00:24:59,400 --> 00:25:01,400
Speaker 1: someone like him? Tell you what's awesome? Because you're trying

494
00:25:01,440 --> 00:25:04,080
Speaker 1: to recruit a team of finance people and you're like,

495
00:25:04,480 --> 00:25:07,199
Speaker 1: how'd you like to sit next to Bill Sharp working

496
00:25:07,200 --> 00:25:08,920
Speaker 1: on something that we think is gonna change the world.

497
00:25:08,960 --> 00:25:11,480
Speaker 1: I mean, it wasn't hard to hire talent when you've

498
00:25:11,520 --> 00:25:14,119
Speaker 1: got Bill Sharp there. So let me jump ahead to

499
00:25:14,160 --> 00:25:16,600
Speaker 1: a question, because there was a quote of yours that

500
00:25:16,680 --> 00:25:20,240
Speaker 1: I really liked, and I'm gonna paraphrase it. You mentioned,

501
00:25:20,720 --> 00:25:23,240
Speaker 1: you know, when you're building a company, there are three

502
00:25:23,400 --> 00:25:28,120
Speaker 1: key elements that a CEO has to focus on, strategy, culture,

503
00:25:28,200 --> 00:25:32,480
Speaker 1: and hires, and you just basically, uh, touched on on

504
00:25:32,560 --> 00:25:35,440
Speaker 1: the higher side. So before we get the strategy and culture.

505
00:25:35,960 --> 00:25:40,760
Speaker 1: So you're you're recruiting from a pool of Stanford and

506
00:25:40,800 --> 00:25:44,960
Speaker 1: other top schools. You're right there in Silicon Valley. How

507
00:25:45,000 --> 00:25:48,800
Speaker 1: difficult was it finding the right people and then convincing

508
00:25:48,840 --> 00:25:52,560
Speaker 1: them to come sit with you and Bill? I mean, honestly,

509
00:25:52,760 --> 00:25:54,879
Speaker 1: it wasn't that hard. I didn't think it was. I mean,

510
00:25:54,880 --> 00:25:57,120
Speaker 1: I'd like to say it was really hard. Nowadays it's

511
00:25:57,200 --> 00:25:59,919
Speaker 1: much harder. There's there's so much money and people. I

512
00:26:00,040 --> 00:26:03,119
Speaker 1: think we're realizing the value of talent, so much competition,

513
00:26:03,119 --> 00:26:05,800
Speaker 1: and there's a huge competitionists. But the kinds of Bill

514
00:26:05,960 --> 00:26:09,920
Speaker 1: here we're sitting in Bloomberg, this is testament to how

515
00:26:09,960 --> 00:26:11,720
Speaker 1: you have to compete for talent. I mean, you have

516
00:26:11,760 --> 00:26:14,840
Speaker 1: to have really compelling reasons for people to join your company.

517
00:26:15,000 --> 00:26:17,720
Speaker 1: You asked me about the food segment. When you walked

518
00:26:17,720 --> 00:26:20,120
Speaker 1: in this food space on the sixth floor, you want

519
00:26:20,119 --> 00:26:22,280
Speaker 1: to hire somebody, they walk by, they're like, Wow, this

520
00:26:22,320 --> 00:26:25,439
Speaker 1: place is for real. It's just one of those parks

521
00:26:25,480 --> 00:26:27,880
Speaker 1: that people see and say, oh, I think I could

522
00:26:27,880 --> 00:26:31,280
Speaker 1: play with you guys exactly back in the day though, right,

523
00:26:31,359 --> 00:26:33,240
Speaker 1: So I had raised a lot of money. That was good.

524
00:26:33,359 --> 00:26:36,399
Speaker 1: The Internet was really just ramping up. I think Netscape

525
00:26:36,440 --> 00:26:40,440
Speaker 1: went public, it was right at the beginning, and so

526
00:26:40,720 --> 00:26:43,560
Speaker 1: people were hungry for this, and we had the VC money,

527
00:26:43,600 --> 00:26:45,800
Speaker 1: we had a big idea, we had a Nobel Prize winner.

528
00:26:45,800 --> 00:26:48,679
Speaker 1: We were in Palo Alto, and you wouldn't have been

529
00:26:48,680 --> 00:26:51,080
Speaker 1: that difficult to do it was. It wasn't hard to

530
00:26:51,119 --> 00:26:54,040
Speaker 1: get the early team on board. And so now let's

531
00:26:54,040 --> 00:26:58,400
Speaker 1: talk about the other two elements you mentioned, strategy and culture. Yeah.

532
00:26:58,600 --> 00:27:02,560
Speaker 1: Culture is the one that I find fascinating because it's

533
00:27:02,720 --> 00:27:06,720
Speaker 1: very easy to have a culture go astray. It's very

534
00:27:06,760 --> 00:27:09,959
Speaker 1: easy to lose sight on those core values. How do

535
00:27:10,000 --> 00:27:13,440
Speaker 1: you focus on building a culture at a company? Yeah, well,

536
00:27:13,560 --> 00:27:18,879
Speaker 1: i'd say I'd say that it starts with the leadership team.

537
00:27:18,920 --> 00:27:21,000
Speaker 1: I mean, it's easier if you're just like, well, what's

538
00:27:21,000 --> 00:27:24,960
Speaker 1: the personality of the leaders because it's if there's a

539
00:27:25,000 --> 00:27:27,399
Speaker 1: big mismatch between what you want the culture to be

540
00:27:27,480 --> 00:27:29,159
Speaker 1: in the in the way they actually act and the

541
00:27:29,240 --> 00:27:33,040
Speaker 1: leadership team acts, then it's gonna ben it's not authentic

542
00:27:33,080 --> 00:27:35,480
Speaker 1: that people will be like, yeah, that's really that's our culture.

543
00:27:35,520 --> 00:27:38,359
Speaker 1: Uh huh um. So a lot of it really comes

544
00:27:38,440 --> 00:27:41,280
Speaker 1: the culture. So the way I usually think about it is,

545
00:27:41,320 --> 00:27:44,000
Speaker 1: you know, first of all, get the strategy right and

546
00:27:44,000 --> 00:27:47,119
Speaker 1: and the strategy is really about where do you choose

547
00:27:47,119 --> 00:27:49,200
Speaker 1: to focus your energy in a way that you can

548
00:27:49,240 --> 00:27:52,400
Speaker 1: win a big opportunity. And if you win, you can

549
00:27:52,480 --> 00:27:54,760
Speaker 1: keep winning because you have some advantage that that other

550
00:27:54,760 --> 00:27:57,560
Speaker 1: people can't compete away. And I think if you don't,

551
00:27:57,600 --> 00:28:00,000
Speaker 1: if you don't pick your battles properly, you can execut

552
00:28:00,480 --> 00:28:02,919
Speaker 1: incredibly well. You have a great team, but you just

553
00:28:02,960 --> 00:28:05,879
Speaker 1: want to have a valuable business, and it's winning the

554
00:28:05,920 --> 00:28:08,960
Speaker 1: business that doesn't matter isn't as important as winning the

555
00:28:08,960 --> 00:28:10,840
Speaker 1: business that does matter, right, And I hear a lot

556
00:28:10,920 --> 00:28:14,960
Speaker 1: of people say, oh, you know, execution beat strategy every day. Well,

557
00:28:15,040 --> 00:28:16,600
Speaker 1: I would say, well, it kind of depends. If you

558
00:28:16,680 --> 00:28:19,239
Speaker 1: have a good strategy, then you're totally right. But if

559
00:28:19,280 --> 00:28:23,080
Speaker 1: you're really executing well on a poor strategy, it doesn't

560
00:28:23,080 --> 00:28:25,119
Speaker 1: really make much difference. In my view. Is no, you

561
00:28:25,480 --> 00:28:27,640
Speaker 1: got to get the strategy right, and yes, that's definitely

562
00:28:27,640 --> 00:28:29,240
Speaker 1: not all you got to execute. And how do you

563
00:28:29,280 --> 00:28:31,880
Speaker 1: execute well? First you get the leadership team in place,

564
00:28:31,920 --> 00:28:34,119
Speaker 1: because if if you're as a CEO of your people

565
00:28:34,160 --> 00:28:37,200
Speaker 1: aren't good, you can't go and hire everybody else. I mean,

566
00:28:37,480 --> 00:28:39,560
Speaker 1: you've got to really get the talent and the tone

567
00:28:39,600 --> 00:28:43,120
Speaker 1: from the top, starting with that core group is really important.

568
00:28:43,160 --> 00:28:45,760
Speaker 1: And then that just reflects across them. That's where the

569
00:28:45,760 --> 00:28:48,440
Speaker 1: culture come exactly. And then what you do is you

570
00:28:48,520 --> 00:28:51,080
Speaker 1: kind of say, and of course you want to be

571
00:28:51,120 --> 00:28:53,560
Speaker 1: explicit about the types of people you're bringing into that

572
00:28:53,600 --> 00:28:55,640
Speaker 1: you can reinforce a certain kind of culture. There's certain

573
00:28:55,640 --> 00:28:58,000
Speaker 1: people who might have the talent, but they just don't

574
00:28:58,080 --> 00:29:01,480
Speaker 1: have the personality or sort of the the way you

575
00:29:01,520 --> 00:29:03,840
Speaker 1: want it to feel working with them and say you say,

576
00:29:03,920 --> 00:29:05,960
Speaker 1: you know, you're not really consistent with our culture. You

577
00:29:06,000 --> 00:29:07,920
Speaker 1: don't say it out loud, but you started your screening

578
00:29:07,920 --> 00:29:10,600
Speaker 1: people as you interview and you're like, this is this

579
00:29:10,680 --> 00:29:13,440
Speaker 1: is not gonna be uh, this is not having you

580
00:29:13,480 --> 00:29:16,120
Speaker 1: as an exact might not set the kind of tone

581
00:29:16,120 --> 00:29:19,240
Speaker 1: that we want to um. And culture is really tricky,

582
00:29:19,440 --> 00:29:23,040
Speaker 1: largely because it is such an outgrowth of the earlier people,

583
00:29:23,120 --> 00:29:26,240
Speaker 1: and and and the senior people and and so um.

584
00:29:26,280 --> 00:29:27,920
Speaker 1: You know, I never I did not get it perfect,

585
00:29:27,920 --> 00:29:29,760
Speaker 1: for sure, and with something I was always trying to

586
00:29:29,840 --> 00:29:33,360
Speaker 1: learn more about, but at least intentionally being aware of

587
00:29:33,400 --> 00:29:35,720
Speaker 1: what your culture is and your what your culture is

588
00:29:35,720 --> 00:29:37,840
Speaker 1: is what your people basically say that it is you.

589
00:29:37,840 --> 00:29:40,040
Speaker 1: You can't say, as a CEO, this is my culture.

590
00:29:40,440 --> 00:29:42,760
Speaker 1: You ask your people what's it like to work here?

591
00:29:42,800 --> 00:29:45,200
Speaker 1: They'll tell you what the culture is. And then if

592
00:29:45,200 --> 00:29:46,800
Speaker 1: you want it to be different, you guys say, well,

593
00:29:46,840 --> 00:29:49,520
Speaker 1: how do we have to change as leaders to create

594
00:29:49,520 --> 00:29:51,880
Speaker 1: a different environment for the for the company. So you

595
00:29:51,920 --> 00:29:53,680
Speaker 1: said you made a lot of mistakes you learned on

596
00:29:53,720 --> 00:29:56,320
Speaker 1: the job, but when you look at this company, you

597
00:29:56,320 --> 00:29:59,560
Speaker 1: were there for a CEO or co founder or I

598
00:29:59,600 --> 00:30:01,440
Speaker 1: don't know if you actually had any of the titles

599
00:30:01,440 --> 00:30:04,320
Speaker 1: other than I. Well, when you're the first employee, you

600
00:30:04,320 --> 00:30:06,840
Speaker 1: can call yourself what I said, I'll be the president CEO.

601
00:30:07,040 --> 00:30:11,240
Speaker 1: But basically, the three co founders or co investors said,

602
00:30:11,280 --> 00:30:14,320
Speaker 1: you get a business model together, you get vcs to invest,

603
00:30:14,720 --> 00:30:17,200
Speaker 1: you can become CEO and let's see where it goes.

604
00:30:17,520 --> 00:30:20,560
Speaker 1: At what point did you realize, hey, this is working

605
00:30:20,600 --> 00:30:24,320
Speaker 1: this this business model. We're attracting assets, we're winning business,

606
00:30:24,600 --> 00:30:26,520
Speaker 1: We're heading in the right direction. When did you first

607
00:30:26,520 --> 00:30:29,680
Speaker 1: get that glimmer? Well, the glimmer kind of came and

608
00:30:29,720 --> 00:30:31,680
Speaker 1: went and came and went and came and went a

609
00:30:31,680 --> 00:30:35,160
Speaker 1: lot of times before I really knew, knew and well,

610
00:30:35,200 --> 00:30:37,280
Speaker 1: in the beginning, you have to be saying, well, I'm

611
00:30:37,320 --> 00:30:40,800
Speaker 1: working with Bill Sharp, this is an amazing idea. I

612
00:30:40,840 --> 00:30:43,440
Speaker 1: love the concept. Let's see what happens. But at what

613
00:30:43,520 --> 00:30:45,760
Speaker 1: point did you say, Hey, we have a billion dollars,

614
00:30:45,800 --> 00:30:48,480
Speaker 1: we have ten billion, Hey we have like where did?

615
00:30:48,560 --> 00:30:51,080
Speaker 1: How long did it take from start? I'm asking this

616
00:30:51,160 --> 00:30:54,719
Speaker 1: for my own personal selfish questions because I'm fascinated by this.

617
00:30:55,480 --> 00:30:58,520
Speaker 1: It's a startup, it's got no real assets other than

618
00:30:58,800 --> 00:31:01,640
Speaker 1: the people in the until actual capital and then at

619
00:31:01,720 --> 00:31:05,680
Speaker 1: some point down the road it's a multibillion dollar asset manager.

620
00:31:05,720 --> 00:31:07,680
Speaker 1: How long did it take from it was from A

621
00:31:07,800 --> 00:31:10,280
Speaker 1: to B. We started in nineties six, and I would

622
00:31:10,280 --> 00:31:14,880
Speaker 1: say that I had a very very strong conviction that

623
00:31:14,920 --> 00:31:19,880
Speaker 1: we were going to be very successful in December of

624
00:31:19,920 --> 00:31:23,160
Speaker 1: two thousand four, So eight years that's that's a long time.

625
00:31:23,200 --> 00:31:26,600
Speaker 1: So by two thousand you managing assets at that point,

626
00:31:26,640 --> 00:31:29,400
Speaker 1: so you still know we're offering the online tool. In

627
00:31:29,440 --> 00:31:31,760
Speaker 1: two thousand and two, two thousand three, we had like

628
00:31:31,760 --> 00:31:34,120
Speaker 1: a thirty million dollar business. We were cash flow break

629
00:31:34,200 --> 00:31:36,920
Speaker 1: even by many accounts. You'd say, this company is doing

630
00:31:36,960 --> 00:31:38,880
Speaker 1: pretty well. But you know, I was like, we're not

631
00:31:38,960 --> 00:31:42,480
Speaker 1: growing fast enough and it's just too hard. So Jeff,

632
00:31:42,520 --> 00:31:45,400
Speaker 1: if people want to find you or your writings, how

633
00:31:45,440 --> 00:31:48,400
Speaker 1: do they track you down? I would just say, don't

634
00:31:48,400 --> 00:31:51,160
Speaker 1: worry about my writing, go to Financial Engines, all right,

635
00:31:51,200 --> 00:31:55,160
Speaker 1: I'm not the CEO. I I stepped down in January

636
00:31:55,280 --> 00:31:58,360
Speaker 1: of this year of this year, after grooming a successor

637
00:31:58,440 --> 00:32:00,800
Speaker 1: over the course of two years. Yeah. Well, I and

638
00:32:00,840 --> 00:32:02,960
Speaker 1: I had hired Larry from Fidelity is one of the

639
00:32:03,000 --> 00:32:05,200
Speaker 1: top guys on the r I A space, and I

640
00:32:05,200 --> 00:32:07,120
Speaker 1: has been that. He had been there for thirteen years,

641
00:32:07,120 --> 00:32:10,800
Speaker 1: so he knows financial Engines inside and out. Um, but

642
00:32:10,920 --> 00:32:13,280
Speaker 1: I would say, you know, and by the way, with

643
00:32:13,320 --> 00:32:16,920
Speaker 1: the robo advisors, there's so much advice that is so

644
00:32:17,080 --> 00:32:19,920
Speaker 1: much better and so much less expensive than what's been

645
00:32:19,960 --> 00:32:23,720
Speaker 1: out there. I mean, consumers have a huge number of choices.

646
00:32:24,400 --> 00:32:27,480
Speaker 1: You know. I love financial Engines partly because I know

647
00:32:27,520 --> 00:32:30,240
Speaker 1: how it's built, and you know that's that's what's managing

648
00:32:30,280 --> 00:32:33,880
Speaker 1: my money right now personally. UM, but a lot of

649
00:32:34,000 --> 00:32:35,720
Speaker 1: a lot of integrity into that. I think there are

650
00:32:35,720 --> 00:32:38,160
Speaker 1: a lot of other firms. Vanguard's doing something great. Schwab's

651
00:32:38,200 --> 00:32:42,320
Speaker 1: got some good portfolios that are being managed at low cost.

652
00:32:42,520 --> 00:32:44,680
Speaker 1: I think the newer advisors are good. There's just a

653
00:32:44,680 --> 00:32:47,120
Speaker 1: lot of good advice out there. We've been speaking with

654
00:32:47,200 --> 00:32:50,600
Speaker 1: Jeff Nanjian Kalda, the co founder and former CEO of

655
00:32:50,640 --> 00:32:55,120
Speaker 1: Financial Engines. If you enjoy this conversation, be sure check

656
00:32:55,160 --> 00:32:57,960
Speaker 1: out our podcast extras, where we keep the tape rolling

657
00:32:57,960 --> 00:33:02,120
Speaker 1: and continue chatting about also of fascinating stuff. Be sure

658
00:33:02,120 --> 00:33:05,680
Speaker 1: and check out my daily column on Bloomberg View dot com.

659
00:33:05,800 --> 00:33:09,480
Speaker 1: Follow me on Twitter at rid Halts. I'm Barry rid Halts.

660
00:33:09,480 --> 00:33:12,640
Speaker 1: You've been listening to Masters in Business on Bloomberg Radio.

661
00:33:12,920 --> 00:33:15,320
Speaker 1: All right, this is the podcast portion and and that's

662
00:33:15,320 --> 00:33:17,160
Speaker 1: the part of the show where I throw my arms

663
00:33:17,160 --> 00:33:20,200
Speaker 1: out and say let's uh, let's let's roll up with

664
00:33:20,280 --> 00:33:24,160
Speaker 1: sleeves and go over the questions we missed and talk about, um,

665
00:33:24,200 --> 00:33:26,840
Speaker 1: some other interesting stuff. By the way, Jeff, I've been

666
00:33:26,880 --> 00:33:30,680
Speaker 1: really looking forward to having this conversation because I followed

667
00:33:30,720 --> 00:33:35,200
Speaker 1: financial engines at least for the past five years or so. UM,

668
00:33:35,280 --> 00:33:38,160
Speaker 1: we never got to talk about when taking them public

669
00:33:38,240 --> 00:33:41,480
Speaker 1: with that experience was like and we never There's a

670
00:33:41,480 --> 00:33:44,200
Speaker 1: whole run of stuff we haven't gotten to. So so

671
00:33:44,280 --> 00:33:49,760
Speaker 1: let's jump right into that. UM first, let's see what

672
00:33:49,920 --> 00:33:55,400
Speaker 1: questions I missed. We know about that. UM So, the

673
00:33:55,440 --> 00:33:58,800
Speaker 1: pivot to four on one case, the to running the assets,

674
00:33:59,000 --> 00:34:01,200
Speaker 1: it was two thousand walk that was two thousand four.

675
00:34:01,280 --> 00:34:03,240
Speaker 1: Now it took us. We were working on it a

676
00:34:03,320 --> 00:34:06,200
Speaker 1: year before we launched it, and we had done it

677
00:34:06,280 --> 00:34:09,040
Speaker 1: was that before or after the customer surveys when they

678
00:34:09,080 --> 00:34:12,239
Speaker 1: said you guys do it? It was after, so it

679
00:34:12,320 --> 00:34:14,719
Speaker 1: was like, Wow, there might be a totally new type

680
00:34:14,719 --> 00:34:17,759
Speaker 1: of business opportunity. And and then we created a little

681
00:34:17,800 --> 00:34:20,839
Speaker 1: prototype and we did a fake survey. One of our

682
00:34:20,920 --> 00:34:23,680
Speaker 1: cust o our corporate customers was really great because we

683
00:34:23,719 --> 00:34:25,840
Speaker 1: needed to figure out what people really sign up for this,

684
00:34:25,920 --> 00:34:27,640
Speaker 1: but we didn't want to build it until we knew.

685
00:34:28,560 --> 00:34:31,319
Speaker 1: And they let us do a survey where basically said

686
00:34:31,360 --> 00:34:33,400
Speaker 1: do you want to sign up for this? And little

687
00:34:33,440 --> 00:34:35,279
Speaker 1: tiny print on the back of it said, by the way,

688
00:34:35,320 --> 00:34:38,319
Speaker 1: you know it's not yet available. Then they signed up,

689
00:34:38,400 --> 00:34:41,439
Speaker 1: and then we called them and big numbers, and then

690
00:34:41,520 --> 00:34:43,960
Speaker 1: and then we called them back, and we literally called

691
00:34:44,000 --> 00:34:46,240
Speaker 1: each one and said, thank you very much for your interest,

692
00:34:46,360 --> 00:34:47,840
Speaker 1: and we're working on it and we'll let you know

693
00:34:47,840 --> 00:34:50,600
Speaker 1: when it's ready. But we already kind of knew from

694
00:34:50,640 --> 00:34:54,680
Speaker 1: a pretty big test that people wanted this. And I

695
00:34:54,719 --> 00:34:57,239
Speaker 1: went to the board. We had about fifteen million dollars left.

696
00:34:57,320 --> 00:34:59,919
Speaker 1: Have that hundred million I talked about. I said, guys,

697
00:35:00,040 --> 00:35:02,520
Speaker 1: we let's go all in here. This is it. We've

698
00:35:02,520 --> 00:35:05,479
Speaker 1: got enough evidence that we've shown a prototype. People seem

699
00:35:05,560 --> 00:35:08,480
Speaker 1: to want it. They're responding to sort of direct mail enrollment.

700
00:35:08,800 --> 00:35:11,840
Speaker 1: Our our our corporate distribution partners want their employees to

701
00:35:11,880 --> 00:35:14,440
Speaker 1: have this. This is it, Let's go. It was It

702
00:35:14,520 --> 00:35:16,719
Speaker 1: was sort of it was about the company and a

703
00:35:16,840 --> 00:35:20,120
Speaker 1: freaking work. Sometimes you have to bet the company. Sometimes

704
00:35:20,160 --> 00:35:22,399
Speaker 1: you have to say, hey, what we've been doing isn't

705
00:35:22,400 --> 00:35:25,200
Speaker 1: generating the revenue, or it's not the business model that

706
00:35:25,960 --> 00:35:29,120
Speaker 1: we think the idea is worthy of. Here's something that

707
00:35:29,200 --> 00:35:32,200
Speaker 1: we can actually monetize in a way that's that's both

708
00:35:32,239 --> 00:35:36,600
Speaker 1: true to the underlying philosophy and productive as a company. Yeah,

709
00:35:36,640 --> 00:35:38,120
Speaker 1: and and and a big part of it too, is

710
00:35:38,160 --> 00:35:41,000
Speaker 1: we we felt that we've done enough tests to make

711
00:35:41,040 --> 00:35:43,000
Speaker 1: it a smart bet. It wasn't a sure bet, but

712
00:35:43,040 --> 00:35:45,320
Speaker 1: it was a smart bet because we we've shown the prototype,

713
00:35:45,360 --> 00:35:48,080
Speaker 1: we talked to customers, We've done this or enrollment test

714
00:35:48,120 --> 00:35:50,400
Speaker 1: to fairit with the acquisition costs would be. So we

715
00:35:50,560 --> 00:35:52,080
Speaker 1: kind of had a lot of indications that this was

716
00:35:52,080 --> 00:35:54,319
Speaker 1: gonna work. What was your degree of confidence in that?

717
00:35:55,200 --> 00:35:58,520
Speaker 1: I mean, honestly, it was okay. So this wasn't like, hey,

718
00:35:58,560 --> 00:36:01,479
Speaker 1: we're rolling the dice. Let's let's hope that snake eyes

719
00:36:01,520 --> 00:36:04,440
Speaker 1: doesn't coming up. It's like, hey, this is really based

720
00:36:04,440 --> 00:36:06,279
Speaker 1: on all the evidence in front of us, based on

721
00:36:06,680 --> 00:36:09,840
Speaker 1: the success of the prior business model, which was so so,

722
00:36:10,040 --> 00:36:12,719
Speaker 1: based on what our clients are telling us, both the

723
00:36:12,840 --> 00:36:16,719
Speaker 1: corporate clients and their employees, this looks like really the

724
00:36:16,760 --> 00:36:18,840
Speaker 1: way to go when nobody else is really yeah. And

725
00:36:18,840 --> 00:36:21,400
Speaker 1: now what was tough is I had I had a

726
00:36:21,440 --> 00:36:24,480
Speaker 1: company where we had very low turnover. So the employees

727
00:36:24,520 --> 00:36:27,479
Speaker 1: had been with me and the other senior management team

728
00:36:27,840 --> 00:36:31,080
Speaker 1: for the journey, and after your fourth or fifth pivot,

729
00:36:31,400 --> 00:36:34,440
Speaker 1: they're like yeah, right, yeah, here we go again. And

730
00:36:34,560 --> 00:36:36,920
Speaker 1: so it was a little bit tough, and I had

731
00:36:37,239 --> 00:36:39,399
Speaker 1: we had an all hands meeting where I said, look, yeah,

732
00:36:39,400 --> 00:36:41,359
Speaker 1: this is what we're doing. A lot of people are

733
00:36:41,400 --> 00:36:43,520
Speaker 1: very enthusiastic. I showed the data. We kind of we went,

734
00:36:43,719 --> 00:36:45,560
Speaker 1: we talked to our customers, etcetera. Listen, if you could

735
00:36:45,560 --> 00:36:48,400
Speaker 1: sell your employees, you can sell anybody. There were a

736
00:36:48,400 --> 00:36:50,960
Speaker 1: lot of people with their arms crossed, basically saying I'll

737
00:36:51,000 --> 00:36:53,640
Speaker 1: believe it when I see it. And I said, I said, guys,

738
00:36:54,440 --> 00:36:56,239
Speaker 1: if you're on the sideline, I'll give you like a

739
00:36:56,280 --> 00:36:58,719
Speaker 1: week and and no, no fault. I mean, if you

740
00:36:58,760 --> 00:37:02,520
Speaker 1: decide you don't believe, then you should find something else,

741
00:37:02,560 --> 00:37:05,680
Speaker 1: because this is what we're doing. And I totally get

742
00:37:05,840 --> 00:37:07,600
Speaker 1: why you wouldn't believe because we tried so many. Things

743
00:37:07,640 --> 00:37:10,840
Speaker 1: that happened were the really five previous significant pivots, the

744
00:37:11,000 --> 00:37:14,480
Speaker 1: different business there there was. There was There was the

745
00:37:14,600 --> 00:37:17,640
Speaker 1: education at first, the quick pivot to become an online

746
00:37:17,680 --> 00:37:21,520
Speaker 1: advisor within the workplace. I raised all that money. We

747
00:37:21,560 --> 00:37:22,920
Speaker 1: had a big deal with A O. L to go

748
00:37:23,040 --> 00:37:25,320
Speaker 1: B two C. There was this. We were one of

749
00:37:25,360 --> 00:37:28,120
Speaker 1: the first freemium models. You could get your forecast for free,

750
00:37:28,560 --> 00:37:32,080
Speaker 1: but then pay for advice that didn't work. Uh, then

751
00:37:32,120 --> 00:37:34,960
Speaker 1: we did this uh, this business, which was we called

752
00:37:34,960 --> 00:37:38,560
Speaker 1: the enterprise business, was trying to create a workstation for

753
00:37:38,640 --> 00:37:41,600
Speaker 1: our as to use. Then we had something we call

754
00:37:41,680 --> 00:37:43,920
Speaker 1: the Advice Server where we were going to sell it

755
00:37:44,000 --> 00:37:46,840
Speaker 1: to the big wirehouses and say, look, here's a servert

756
00:37:46,840 --> 00:37:50,120
Speaker 1: that's gonna give automated advice within your firm. And then

757
00:37:50,160 --> 00:37:53,719
Speaker 1: finally we did manage accounts, and manage accounts was the

758
00:37:54,280 --> 00:37:59,359
Speaker 1: one was the magic button. That's that's amazing. UM. I'm

759
00:37:59,360 --> 00:38:04,000
Speaker 1: gonna save the conversation about target date funds and fiduciaries later,

760
00:38:04,080 --> 00:38:07,719
Speaker 1: but I definitely, uh, I definitely want to come back

761
00:38:07,760 --> 00:38:12,040
Speaker 1: to that. We're done with this question. UM. We briefly

762
00:38:12,080 --> 00:38:15,800
Speaker 1: touched upon Vanguard before. So there are the non Silicon

763
00:38:15,920 --> 00:38:19,319
Speaker 1: Valley startups like Schwab and Vanguard who are rolling out

764
00:38:19,360 --> 00:38:23,480
Speaker 1: their own UM robo advisor. Schwab Vanguard is really fascinating

765
00:38:23,680 --> 00:38:27,720
Speaker 1: because they launched it with essentially no fanfare and they

766
00:38:27,880 --> 00:38:32,799
Speaker 1: took a bunch of older UM advisory accounts and basically said, okay,

767
00:38:32,840 --> 00:38:35,759
Speaker 1: you guys are now I'll go automatic driven. And it

768
00:38:35,840 --> 00:38:37,880
Speaker 1: was five billion, and then it was fifteen billion, and

769
00:38:37,920 --> 00:38:42,800
Speaker 1: now it's like thirty billion plus. UM. I've spoken with them, UM,

770
00:38:42,840 --> 00:38:46,400
Speaker 1: including Bill McNab was on the show. But that's gonna

771
00:38:46,440 --> 00:38:49,040
Speaker 1: be a hundred billion dollars and in no time at all,

772
00:38:49,280 --> 00:38:51,239
Speaker 1: no doubt about it. What what are your thoughts so

773
00:38:51,440 --> 00:38:56,600
Speaker 1: within financial engines, UM, portfolios, what asset classes, what sort

774
00:38:56,640 --> 00:38:59,120
Speaker 1: of UM is it? Vanguard d f A, like, who

775
00:38:59,120 --> 00:39:01,839
Speaker 1: are you? Who you holding in those accounts? Yeah, well,

776
00:39:02,320 --> 00:39:05,560
Speaker 1: so we're managing the last quarter I think it was

777
00:39:05,560 --> 00:39:08,960
Speaker 1: like a hundred fifteen billion. We only use the investments

778
00:39:08,960 --> 00:39:11,680
Speaker 1: that are available in four O wind K plans, so

779
00:39:11,719 --> 00:39:14,520
Speaker 1: it tends to be mutual funds and a handful of ets. Well,

780
00:39:14,600 --> 00:39:16,920
Speaker 1: what's interesting is that the large part of the market,

781
00:39:17,120 --> 00:39:20,040
Speaker 1: it's it's not many mutual funds and and no E

782
00:39:20,120 --> 00:39:23,520
Speaker 1: t f s. They're almost all separately managed accounts. And

783
00:39:23,560 --> 00:39:26,640
Speaker 1: when you start working with ibm s and forwards, UH

784
00:39:26,920 --> 00:39:30,880
Speaker 1: and hallmarks, you're talking single digit basis points in many cases.

785
00:39:31,239 --> 00:39:33,480
Speaker 1: So here's what's kind of amazing. The meaning you're charging

786
00:39:33,920 --> 00:39:38,239
Speaker 1: us less than a fifth of a percent, less than

787
00:39:39,120 --> 00:39:41,360
Speaker 1: So if you look at the economics of that hundred

788
00:39:41,440 --> 00:39:44,799
Speaker 1: fifteen billion that we're managing, what you find is that

789
00:39:44,880 --> 00:39:47,200
Speaker 1: the underlying fund fee. So that kind of the expense

790
00:39:47,320 --> 00:39:51,080
Speaker 1: ratio of those funds that we're managing, it's about fifteen

791
00:39:51,080 --> 00:39:56,000
Speaker 1: basis points considered fairly low, considered low. That's Vanguard territory.

792
00:39:56,560 --> 00:39:59,279
Speaker 1: And if you look at the cost of our services,

793
00:39:59,360 --> 00:40:01,640
Speaker 1: after all the A discounts and everything, this is this

794
00:40:01,719 --> 00:40:03,360
Speaker 1: is not the cost of financial engines. Is what the

795
00:40:03,400 --> 00:40:08,080
Speaker 1: employee actually ends up paying. It's about thirty seven basis points.

796
00:40:08,800 --> 00:40:12,680
Speaker 1: So seven plus fifteen is for the for the whole

797
00:40:12,719 --> 00:40:15,280
Speaker 1: innch a lot of talk to an advisor retirement planning.

798
00:40:15,400 --> 00:40:18,200
Speaker 1: Not a terrible price at all, and that's actually much

799
00:40:18,280 --> 00:40:20,680
Speaker 1: cheaper than what we see from a lot of places

800
00:40:21,040 --> 00:40:24,640
Speaker 1: we see. So whenever we we run an st management firm,

801
00:40:24,719 --> 00:40:27,120
Speaker 1: whenever we look at a four oh one K comes

802
00:40:27,120 --> 00:40:30,160
Speaker 1: in and I'm talking about some pretty big entities that

803
00:40:30,200 --> 00:40:33,759
Speaker 1: are fairly famous that you would think would have access

804
00:40:33,840 --> 00:40:37,799
Speaker 1: to whoever they want. We look at these portfolios just

805
00:40:38,480 --> 00:40:44,200
Speaker 1: festooned with active, high fee funds that have all underperformed

806
00:40:44,200 --> 00:40:47,160
Speaker 1: their benchmark. And then there's the Custonian fee and the

807
00:40:47,200 --> 00:40:50,719
Speaker 1: reporting fee and the advisor fee and all in it's

808
00:40:50,800 --> 00:40:56,319
Speaker 1: three d basis points. It's astonishingly ridiculous and it's a

809
00:40:56,440 --> 00:41:00,000
Speaker 1: terrible drag on long term returns totally. The good news

810
00:41:00,080 --> 00:41:02,240
Speaker 1: is is if you look at the kind of weighted

811
00:41:02,280 --> 00:41:05,200
Speaker 1: average expense ratio of four O n K assets, given

812
00:41:05,239 --> 00:41:08,359
Speaker 1: that over six of the assets are held in the

813
00:41:08,400 --> 00:41:11,520
Speaker 1: top point four percent of companies, so the found Fortune

814
00:41:11,560 --> 00:41:14,399
Speaker 1: one thousand, that's where almost over half the assets are.

815
00:41:14,680 --> 00:41:17,760
Speaker 1: Those guys do a really good job of getting nice,

816
00:41:17,960 --> 00:41:21,680
Speaker 1: well managed, low cost funds um so, so at least

817
00:41:21,719 --> 00:41:24,480
Speaker 1: on average dollar weighted, most of the four on K

818
00:41:24,600 --> 00:41:28,320
Speaker 1: money is is sort of is sort of good investment options.

819
00:41:28,600 --> 00:41:30,520
Speaker 1: And what you find is the d O L and

820
00:41:30,520 --> 00:41:34,359
Speaker 1: the regulators are really pushing that down market. So it's

821
00:41:34,480 --> 00:41:35,920
Speaker 1: it's gonna be a while till you get to the

822
00:41:35,920 --> 00:41:38,600
Speaker 1: smaller plans, but there's definitely gonna be a trickled down.

823
00:41:38,600 --> 00:41:41,839
Speaker 1: And I think Americans need to know if you work

824
00:41:41,920 --> 00:41:44,920
Speaker 1: for a Fortune one thou company, your four o wn

825
00:41:45,000 --> 00:41:47,240
Speaker 1: K options are probably your four o w K plans

826
00:41:47,280 --> 00:41:51,560
Speaker 1: like the best investment. You're getting a match, you're getting

827
00:41:51,560 --> 00:41:53,879
Speaker 1: all kinds of advice for very low costs, and your

828
00:41:53,920 --> 00:41:56,719
Speaker 1: funds are probably the cheapest you're going to see anywhere. Plus,

829
00:41:57,520 --> 00:41:59,440
Speaker 1: so for you guys, what does that mean? You're filled

830
00:41:59,480 --> 00:42:03,960
Speaker 1: with guard or dimensional or fidelity? Like who's in your

831
00:42:04,400 --> 00:42:06,840
Speaker 1: I know it varies from company to company does, but

832
00:42:06,880 --> 00:42:09,239
Speaker 1: I'm gonna bet there's a bias towards a handful of

833
00:42:09,280 --> 00:42:13,960
Speaker 1: it's about half actively managed, half indexed. And if you

834
00:42:14,000 --> 00:42:17,440
Speaker 1: look at the actively managed guys, they're not very expensive.

835
00:42:17,480 --> 00:42:22,040
Speaker 1: They're they're they're really good lower cost actively managed funds.

836
00:42:22,120 --> 00:42:25,160
Speaker 1: But what happens is if you have a fund, uh,

837
00:42:25,360 --> 00:42:28,200
Speaker 1: in a lineup of funds that maybe cost fifteen basis points,

838
00:42:28,400 --> 00:42:31,839
Speaker 1: if one cost sev in the scheme of things, it's

839
00:42:31,880 --> 00:42:34,640
Speaker 1: not it's not going to make the cut. Uh, you

840
00:42:34,680 --> 00:42:37,319
Speaker 1: guys carve that out. Well, they're still is in the plan,

841
00:42:37,400 --> 00:42:39,319
Speaker 1: but we just don't put in any of our portfolios.

842
00:42:39,360 --> 00:42:44,600
Speaker 1: Got it? The differential price is cost too much? Thumb. Also,

843
00:42:45,080 --> 00:42:47,600
Speaker 1: you know, there was a great morning Star research note

844
00:42:47,600 --> 00:42:50,040
Speaker 1: which I'm sure they rude the day they put this out,

845
00:42:50,440 --> 00:42:53,480
Speaker 1: which is so morning Star came to fame for by

846
00:42:53,520 --> 00:42:57,040
Speaker 1: the way, their second behind you in terms of managing

847
00:42:57,080 --> 00:42:59,279
Speaker 1: four oh one k properties. Because everyone thinks they're the

848
00:43:00,200 --> 00:43:03,359
Speaker 1: mutual fund expert, but their claim to fame is their

849
00:43:03,400 --> 00:43:05,640
Speaker 1: five star fund rating system and then one of the

850
00:43:05,719 --> 00:43:08,200
Speaker 1: researchers put out a piece that said, if you don't

851
00:43:08,200 --> 00:43:11,680
Speaker 1: know anything about a company of fund, or a mutual fund,

852
00:43:11,760 --> 00:43:15,840
Speaker 1: or even whatever type of fund it is, if you

853
00:43:15,920 --> 00:43:19,279
Speaker 1: did nothing else but pick the cheapest funds over the

854
00:43:19,280 --> 00:43:23,160
Speaker 1: long haul, that's gonna outperform everything else, which sort of

855
00:43:23,440 --> 00:43:26,919
Speaker 1: was a problem because they're selling, hey, our five star

856
00:43:27,040 --> 00:43:30,680
Speaker 1: rating system is great, and their own research is essentially saying, yeah, yeah,

857
00:43:30,680 --> 00:43:33,160
Speaker 1: the rating system is great, just by the cheapest fund.

858
00:43:33,520 --> 00:43:35,759
Speaker 1: So I I don't blame you guys for saying, hey,

859
00:43:35,760 --> 00:43:38,640
Speaker 1: we're not gonna put one of these expensive now every

860
00:43:38,640 --> 00:43:42,640
Speaker 1: now and then. There's an asset class, the actively managed

861
00:43:42,680 --> 00:43:47,239
Speaker 1: mortgage back um fixed income funds. There are no inexpensive

862
00:43:47,320 --> 00:43:50,200
Speaker 1: versions of those, and you can't do it passively. You

863
00:43:50,280 --> 00:43:53,680
Speaker 1: have to have some degree of active due to quality

864
00:43:53,680 --> 00:43:56,840
Speaker 1: concerns and how that turns over. But for just about

865
00:43:56,840 --> 00:44:01,000
Speaker 1: everything else, cheapest is invariably the best, which is really

866
00:44:01,040 --> 00:44:03,399
Speaker 1: a difficult message for people to wrap their heads around.

867
00:44:03,440 --> 00:44:06,160
Speaker 1: You would think that would be really easy to grasp,

868
00:44:06,520 --> 00:44:09,560
Speaker 1: but people use price as a as a quality signal

869
00:44:10,400 --> 00:44:13,320
Speaker 1: when it isn't always a quality signal. I totally agree

870
00:44:13,320 --> 00:44:16,600
Speaker 1: with you, all right, so that's the end of my diatribe.

871
00:44:16,760 --> 00:44:21,160
Speaker 1: Let's um, let's talk a little bit about your gap year.

872
00:44:21,320 --> 00:44:24,879
Speaker 1: This is your gap year. So you you stepped down

873
00:44:24,880 --> 00:44:29,680
Speaker 1: officially in January after grooming a success sor um and

874
00:44:29,760 --> 00:44:32,480
Speaker 1: that successor is now running the shop. And you said

875
00:44:32,960 --> 00:44:35,200
Speaker 1: you're gonna do nothing for a year, because you were

876
00:44:35,239 --> 00:44:38,960
Speaker 1: there for what eighteen years? So you said You're just

877
00:44:38,960 --> 00:44:40,920
Speaker 1: gonna kick back for a year. And I stayed on

878
00:44:40,960 --> 00:44:43,440
Speaker 1: as an an advisor to the company for six months,

879
00:44:43,440 --> 00:44:46,879
Speaker 1: so that officially finished on June. But you're right. I said,

880
00:44:46,880 --> 00:44:49,839
Speaker 1: I'm going to take a year and just pull pull,

881
00:44:50,040 --> 00:44:52,239
Speaker 1: pull back. And you know, a lot of what I

882
00:44:52,280 --> 00:44:54,360
Speaker 1: was interested in doing was a combination of learning and

883
00:44:55,040 --> 00:44:58,080
Speaker 1: having fun, and so I thought one of the things

884
00:44:58,120 --> 00:45:00,239
Speaker 1: I'd like to learn that I just haven't had time

885
00:45:00,239 --> 00:45:03,480
Speaker 1: to learn. I always wanted to sail in the ocean.

886
00:45:03,520 --> 00:45:05,120
Speaker 1: I sailed on lakes as a kid. I wanted to

887
00:45:05,160 --> 00:45:07,319
Speaker 1: sail on the ocean, so I didn't I've been doing

888
00:45:07,320 --> 00:45:10,080
Speaker 1: a lot of sailing and getting certified as as a sailor.

889
00:45:10,719 --> 00:45:12,799
Speaker 1: I want to always want to learn, not just how

890
00:45:12,840 --> 00:45:14,840
Speaker 1: to play the piano. I wanted to learn music. I

891
00:45:14,880 --> 00:45:19,080
Speaker 1: want to say, I learned music like I believed, and

892
00:45:19,120 --> 00:45:21,960
Speaker 1: I'm just super excited into it. I mean, here I

893
00:45:21,960 --> 00:45:25,839
Speaker 1: am at forty six like learning about the basic patterns

894
00:45:25,840 --> 00:45:30,480
Speaker 1: and structures that underlying music. This is pitch and harmonics

895
00:45:30,600 --> 00:45:34,040
Speaker 1: and residents and rhythmic patterns and things, and so music

896
00:45:34,080 --> 00:45:37,560
Speaker 1: theory is really interesting. And and now when I listen

897
00:45:37,600 --> 00:45:40,239
Speaker 1: to songs, it's like, oh, you know, there there are

898
00:45:40,280 --> 00:45:43,680
Speaker 1: some deep patterns that once you kind of understand how

899
00:45:43,719 --> 00:45:46,360
Speaker 1: these things are built, it's almost like a puzzle that

900
00:45:46,360 --> 00:45:48,200
Speaker 1: you can unlock to say, what's that corporate? What key

901
00:45:48,280 --> 00:45:50,200
Speaker 1: is it? And what's the core progression? What are some

902
00:45:50,280 --> 00:45:52,840
Speaker 1: of the surprises the way there, the way they're breaking

903
00:45:52,880 --> 00:45:55,560
Speaker 1: the rules are are refraining a chorus? And so it's

904
00:45:55,600 --> 00:45:57,680
Speaker 1: like a way, a new way for me at least

905
00:45:57,680 --> 00:46:00,360
Speaker 1: to understand music, which has been really fun. I have

906
00:46:00,440 --> 00:46:02,560
Speaker 1: a book for you. I'm sure you've either read it

907
00:46:02,640 --> 00:46:04,920
Speaker 1: or heard of it, and if you haven't, I'm gonna

908
00:46:05,120 --> 00:46:11,799
Speaker 1: rock your world. Good godal escher bach Um. Essentially, it's

909
00:46:11,840 --> 00:46:16,640
Speaker 1: about the concept of repeating patterns showing up in mathematics,

910
00:46:17,239 --> 00:46:20,920
Speaker 1: art and music. And it's not just fractals. It's just

911
00:46:21,040 --> 00:46:25,319
Speaker 1: when you look at a box um piano concerto. It's

912
00:46:25,320 --> 00:46:28,080
Speaker 1: a series of and if you've ever heard like kat

913
00:46:28,239 --> 00:46:31,000
Speaker 1: tun still do something with the you know, the the

914
00:46:31,040 --> 00:46:34,680
Speaker 1: recording box whereh will play and loop it over and

915
00:46:34,760 --> 00:46:38,520
Speaker 1: over again. It's the same basic concept in a symphony.

916
00:46:38,560 --> 00:46:41,960
Speaker 1: It's just explosive. I recall reading that in college and

917
00:46:42,040 --> 00:46:45,760
Speaker 1: just pulling my hair on my head, going, it can't

918
00:46:45,760 --> 00:46:48,080
Speaker 1: be just that everything is a pattern? Is that? Is

919
00:46:48,120 --> 00:46:51,400
Speaker 1: that true? Everything is just repeating fractal forever And it

920
00:46:51,400 --> 00:46:54,000
Speaker 1: turns out some things are some things aren't. But if

921
00:46:54,080 --> 00:46:58,279
Speaker 1: you like that idea of of the concept Um Douglas Hofstatter,

922
00:46:58,360 --> 00:47:01,560
Speaker 1: I think is the is the I'm pulling these names

923
00:47:01,560 --> 00:47:04,200
Speaker 1: out is a hard years ago. He wrote one other

924
00:47:04,239 --> 00:47:06,200
Speaker 1: book that I think UM did really well, and I

925
00:47:06,760 --> 00:47:10,160
Speaker 1: um that I can't access, but this if you like

926
00:47:10,320 --> 00:47:12,680
Speaker 1: that sort of thing. I just remember saying, wow, this

927
00:47:12,719 --> 00:47:15,160
Speaker 1: book is amazing. Oh yeah. I mean I like to

928
00:47:15,239 --> 00:47:18,359
Speaker 1: understand how things work, and music is such a part

929
00:47:18,440 --> 00:47:20,279
Speaker 1: of culture. I just I love music too, but I

930
00:47:20,320 --> 00:47:22,520
Speaker 1: never really understand, like, how does this stuff really work?

931
00:47:22,760 --> 00:47:25,560
Speaker 1: And now I'm starting, I'm starting to discover it, and

932
00:47:25,600 --> 00:47:28,680
Speaker 1: I have ideas about how it changes your brain and

933
00:47:28,680 --> 00:47:30,920
Speaker 1: and and how even when you have these patterns, you know,

934
00:47:30,960 --> 00:47:32,480
Speaker 1: a lot of what it is I think is is

935
00:47:32,520 --> 00:47:35,520
Speaker 1: the song and also the antecedents of the song, songs

936
00:47:35,520 --> 00:47:37,440
Speaker 1: that sound like that song, the genre that came from

937
00:47:37,640 --> 00:47:40,520
Speaker 1: it primes your brain to expect patterns, and the early

938
00:47:40,600 --> 00:47:43,120
Speaker 1: part of a song that sets up patterns. And then

939
00:47:43,120 --> 00:47:47,160
Speaker 1: there's this dance between rewarding you with the familiar, recurring

940
00:47:47,200 --> 00:47:49,440
Speaker 1: pattern but then surprising you by breaking the pattern a

941
00:47:49,440 --> 00:47:52,080
Speaker 1: little bit. And there's this there's this dance of like

942
00:47:52,480 --> 00:47:54,520
Speaker 1: giving you the pattern and then not giving you the

943
00:47:54,520 --> 00:47:57,959
Speaker 1: pattern that I think sucks you winto the music. That's

944
00:47:58,000 --> 00:48:01,400
Speaker 1: that's fascinating. There was the other book if you like music.

945
00:48:01,920 --> 00:48:05,080
Speaker 1: David Byrne, former frontman of The Talking has a new

946
00:48:05,080 --> 00:48:07,960
Speaker 1: book on I think it's called How Music Works or

947
00:48:08,000 --> 00:48:11,360
Speaker 1: something like that, and it's he you're these are both

948
00:48:11,440 --> 00:48:14,759
Speaker 1: right in exactly what you're talking about. So so other

949
00:48:14,840 --> 00:48:17,480
Speaker 1: things that My my wife, you know, when she knew

950
00:48:17,480 --> 00:48:19,239
Speaker 1: that I was going to be stepping down, she said,

951
00:48:19,280 --> 00:48:22,840
Speaker 1: I gotta get you to a Buddhist retreat. Okay, I said, okay,

952
00:48:23,160 --> 00:48:26,000
Speaker 1: she excuse she's done. These are these silent retreats that

953
00:48:26,000 --> 00:48:28,200
Speaker 1: that one goes on and how long are you silent?

954
00:48:28,320 --> 00:48:32,640
Speaker 1: I was silent for three days and it was you know,

955
00:48:32,960 --> 00:48:37,879
Speaker 1: it was it was, it was definitely. You know. My

956
00:48:37,960 --> 00:48:40,799
Speaker 1: view of it is sort of a form of brain management.

957
00:48:41,280 --> 00:48:44,000
Speaker 1: It's like, can you get your brain in a certain

958
00:48:44,040 --> 00:48:47,880
Speaker 1: mode where it's it's not worrying about the past, and

959
00:48:47,880 --> 00:48:50,080
Speaker 1: it's not worrying about the future. It's just kind of

960
00:48:50,120 --> 00:48:53,239
Speaker 1: tuned in. And it's not like metaphysical or anything. It's

961
00:48:53,239 --> 00:48:56,520
Speaker 1: actually quite basic. It's like, can you just be noticing

962
00:48:57,080 --> 00:48:59,480
Speaker 1: the things that are happening here, now here and now?

963
00:49:00,320 --> 00:49:02,520
Speaker 1: And what's kind of amazing about it? With some pretty

964
00:49:02,520 --> 00:49:06,800
Speaker 1: simple little practices, your brain gets into a different state

965
00:49:06,960 --> 00:49:08,879
Speaker 1: and you do you kind of feel this sense sense

966
00:49:08,880 --> 00:49:11,840
Speaker 1: of freedom and and it creates it sort of creates

967
00:49:11,840 --> 00:49:14,799
Speaker 1: a lightness that that is sometimes hard to find in

968
00:49:14,800 --> 00:49:18,080
Speaker 1: a busy life. You know, there's a tendency, especially in

969
00:49:18,160 --> 00:49:22,239
Speaker 1: today's between Twitter and email and everything else, that people

970
00:49:22,280 --> 00:49:25,040
Speaker 1: are just playing tennis and hitting the ball back constantly,

971
00:49:25,040 --> 00:49:27,400
Speaker 1: and sometimes you have to step back and say, I

972
00:49:27,440 --> 00:49:29,799
Speaker 1: just want to exist and and be a little I

973
00:49:29,800 --> 00:49:33,640
Speaker 1: hate to turn mindful or mindfulness, but sometimes you can't

974
00:49:33,680 --> 00:49:36,920
Speaker 1: just be. Stimulus response stimulus response. Sometimes you have to

975
00:49:37,000 --> 00:49:39,520
Speaker 1: let me think about stuff for a minute and not

976
00:49:40,000 --> 00:49:42,560
Speaker 1: be wrapped up in that world. That sounds like that

977
00:49:42,640 --> 00:49:44,719
Speaker 1: was a fun experience, And that's kind of what I'm

978
00:49:44,719 --> 00:49:46,640
Speaker 1: trying to do with this whole gap year. It's just

979
00:49:46,920 --> 00:49:51,759
Speaker 1: is have a year filled with experiences that can that

980
00:49:51,800 --> 00:49:55,040
Speaker 1: can kind of break my brain, like I haven't seen

981
00:49:55,120 --> 00:49:57,000
Speaker 1: this before. This is a new way to think about

982
00:49:57,040 --> 00:49:59,120
Speaker 1: the world. This is a new way to think about myself.

983
00:49:59,160 --> 00:50:00,920
Speaker 1: This is a new way to think about my marriage,

984
00:50:00,960 --> 00:50:03,000
Speaker 1: This is a new way to think about you know,

985
00:50:03,200 --> 00:50:05,799
Speaker 1: team depression. We're having a lot of problems out in

986
00:50:05,800 --> 00:50:09,400
Speaker 1: Palo Alto with with kids under a lot of pressure,

987
00:50:09,680 --> 00:50:12,560
Speaker 1: getting depressed and committing suicide. There was just a big

988
00:50:12,640 --> 00:50:14,920
Speaker 1: article in any of the Times, the Worlster Regeneral about

989
00:50:15,320 --> 00:50:18,040
Speaker 1: it's not the big urban areas, it's small town suburbia

990
00:50:18,080 --> 00:50:21,759
Speaker 1: and rural areas that have seen suicide rate spike. So

991
00:50:21,760 --> 00:50:24,480
Speaker 1: so you know, this has been happening close to our community,

992
00:50:24,800 --> 00:50:27,759
Speaker 1: and and I wanted to understand because you know, when

993
00:50:27,800 --> 00:50:30,920
Speaker 1: this happens, clearly the community reacts and there are a

994
00:50:30,960 --> 00:50:33,920
Speaker 1: lot of people who say, uh, it's it's this thing,

995
00:50:34,000 --> 00:50:36,040
Speaker 1: It's this one thing that's doing it, and it's never

996
00:50:36,160 --> 00:50:37,799
Speaker 1: just one thing. There's never one thing. But then there

997
00:50:37,800 --> 00:50:40,560
Speaker 1: are people say it's kind of everything, which means it's

998
00:50:40,600 --> 00:50:42,319
Speaker 1: almost kind of nothing, because like, what are you gonna

999
00:50:42,320 --> 00:50:44,319
Speaker 1: do about everything? I'm like, no, no, it's not one

1000
00:50:44,360 --> 00:50:46,839
Speaker 1: thing and it's not everything. There's there's got to be

1001
00:50:46,920 --> 00:50:50,240
Speaker 1: some system of effects that are causing this to happen

1002
00:50:50,280 --> 00:50:52,920
Speaker 1: at an unusual rate here in this community. And I

1003
00:50:52,920 --> 00:50:54,399
Speaker 1: don't know if I have the answer, but I kind

1004
00:50:54,400 --> 00:50:56,400
Speaker 1: of developed a model for how to think about it. Right, Well,

1005
00:50:56,440 --> 00:50:58,160
Speaker 1: if you want to be rational, you have no future

1006
00:50:58,200 --> 00:51:02,239
Speaker 1: in politics, So forget about that. Um so let's talk

1007
00:51:02,280 --> 00:51:05,600
Speaker 1: a little bit about going public. Yes, alright, that was

1008
00:51:05,640 --> 00:51:08,839
Speaker 1: on my list. We didn't get to so oh four

1009
00:51:08,960 --> 00:51:11,200
Speaker 1: or five oh six. You're ramping up a Um what

1010
00:51:11,320 --> 00:51:14,080
Speaker 1: point does someone say, hey, we could use some more

1011
00:51:14,360 --> 00:51:18,040
Speaker 1: uh use some more capital. I know, let's let's make

1012
00:51:18,080 --> 00:51:21,799
Speaker 1: our vcs happy and take the company uh to Wall Street. Well,

1013
00:51:22,000 --> 00:51:24,600
Speaker 1: at first, the first time people said that was in

1014
00:51:26,920 --> 00:51:30,600
Speaker 1: when we had five thousand dollars of revenue. Uh so

1015
00:51:30,640 --> 00:51:33,160
Speaker 1: you would have been worth nine or ten billion dollars exactly.

1016
00:51:33,480 --> 00:51:36,919
Speaker 1: That's it's all about eyeballs. I raised around. It wasn't

1017
00:51:36,920 --> 00:51:38,920
Speaker 1: we didn't go public, but it was gonna get the

1018
00:51:39,239 --> 00:51:42,160
Speaker 1: idea was. We raised around in Novembers. Our series is

1019
00:51:42,200 --> 00:51:44,360
Speaker 1: eighty five million. We had five hundred thousand dollars of revenue.

1020
00:51:44,400 --> 00:51:46,560
Speaker 1: We were valued at three five million, which at the

1021
00:51:46,600 --> 00:51:49,719
Speaker 1: time was a massive valuation. Right now nowadays it's kind

1022
00:51:49,719 --> 00:51:53,240
Speaker 1: of like, well that's that's a And we were planning

1023
00:51:53,440 --> 00:51:57,200
Speaker 1: on going public within six months, and we didn't. We

1024
00:51:57,239 --> 00:51:59,359
Speaker 1: didn't have a good revenue, we didn't have a good

1025
00:51:59,360 --> 00:52:03,200
Speaker 1: business moment. You were lucky that the dot com crash happened. You.

1026
00:52:03,440 --> 00:52:05,880
Speaker 1: We drafted the S one and just never filed it.

1027
00:52:06,200 --> 00:52:08,279
Speaker 1: Really we had we had a draft that would have

1028
00:52:08,280 --> 00:52:10,319
Speaker 1: been a disaster. It would it would have been we

1029
00:52:10,320 --> 00:52:14,240
Speaker 1: we had a draft written in We created another draft

1030
00:52:14,280 --> 00:52:17,359
Speaker 1: in November two thousand seven into two thousand eight. So

1031
00:52:17,400 --> 00:52:20,520
Speaker 1: your timing is phenomenally freaking precipitate market crash. I was

1032
00:52:20,520 --> 00:52:22,440
Speaker 1: gonna say, every S one, let me know, let me

1033
00:52:22,440 --> 00:52:23,920
Speaker 1: know the next S one you're doing, so I could

1034
00:52:23,920 --> 00:52:27,280
Speaker 1: get Then the third time was okay. We had managed accounts,

1035
00:52:27,320 --> 00:52:29,400
Speaker 1: we had weather two thousand and eight. Really well, we

1036
00:52:29,480 --> 00:52:32,200
Speaker 1: kind of grew right through it, and we were like,

1037
00:52:32,239 --> 00:52:35,120
Speaker 1: now now we really this is good. You won public.

1038
00:52:37,040 --> 00:52:39,239
Speaker 1: So what was the valuation when you went public? You know,

1039
00:52:39,280 --> 00:52:44,600
Speaker 1: we were valued at seven are millions something like. Okay,

1040
00:52:44,640 --> 00:52:47,839
Speaker 1: so fairly modest. What was the process like going public?

1041
00:52:47,920 --> 00:52:50,600
Speaker 1: Who were the underwrite the underwrise Goldman Sachs and then

1042
00:52:50,880 --> 00:52:54,560
Speaker 1: a handful of others, but Goldman kind of lad the deal. Um.

1043
00:52:54,880 --> 00:52:57,520
Speaker 1: I frankly loved it really well. You had a good

1044
00:52:57,520 --> 00:53:00,359
Speaker 1: experience across the board in the bell with the New

1045
00:53:00,440 --> 00:53:03,600
Speaker 1: York I did all that stuff, and you know, we

1046
00:53:03,640 --> 00:53:06,080
Speaker 1: had been working on this model with enough pivots that

1047
00:53:06,800 --> 00:53:10,080
Speaker 1: my board was always saying and I didn't really heat

1048
00:53:10,320 --> 00:53:12,400
Speaker 1: heat them well enough. Luckily I was prevented from doing it.

1049
00:53:12,440 --> 00:53:14,520
Speaker 1: But they're like, you gotta have a predictable business model

1050
00:53:14,560 --> 00:53:16,879
Speaker 1: before you go public. Gotta I'm like, yeah, yeah, yeah,

1051
00:53:16,880 --> 00:53:22,120
Speaker 1: thats okay. Whatever makes sense. Well, predictable, predictable revenue streaming totally,

1052
00:53:22,120 --> 00:53:25,560
Speaker 1: because when we finally went out, we had an incredibly stable,

1053
00:53:25,600 --> 00:53:29,239
Speaker 1: predictable business model. And so I wrote our first kind

1054
00:53:29,239 --> 00:53:32,680
Speaker 1: of earnings announcement, kind of the quarterly call, in kind

1055
00:53:32,719 --> 00:53:35,360
Speaker 1: of almost mad libs format. I wrote the story with

1056
00:53:35,400 --> 00:53:38,160
Speaker 1: a bunch of blanks, and I said, we're just gonna

1057
00:53:38,200 --> 00:53:40,520
Speaker 1: fill in the blank every quarter. And because this is

1058
00:53:40,520 --> 00:53:43,960
Speaker 1: a long term demographic bet this is not zig zags.

1059
00:53:44,040 --> 00:53:45,680
Speaker 1: We're done with our ziggig and zag. And we think

1060
00:53:45,680 --> 00:53:48,480
Speaker 1: we found something that should last for quite should create

1061
00:53:48,760 --> 00:53:51,799
Speaker 1: growth and profitability for many, many years. And so every

1062
00:53:51,880 --> 00:53:54,000
Speaker 1: quarter we pop in the new numbers, we tell the

1063
00:53:54,040 --> 00:53:56,120
Speaker 1: same story with a little bit of nuance, but not

1064
00:53:56,200 --> 00:53:58,520
Speaker 1: a lot and and and I think that's what a

1065
00:53:58,560 --> 00:54:04,560
Speaker 1: great business can do, is is have periods of sustained, predictable,

1066
00:54:04,680 --> 00:54:08,520
Speaker 1: scalable revenue growth and profitability punctuated by whoop's got a

1067
00:54:08,600 --> 00:54:11,680
Speaker 1: zig zagagan because the world changes, right, I've meant to

1068
00:54:11,719 --> 00:54:15,040
Speaker 1: ask you before I forgot. I love the name financial engines.

1069
00:54:15,200 --> 00:54:18,080
Speaker 1: Who came up with that? I think it was either

1070
00:54:18,200 --> 00:54:20,439
Speaker 1: Joe or Bill? It was named before I got there.

1071
00:54:20,600 --> 00:54:24,040
Speaker 1: It's it's a terrific name. And if if you if

1072
00:54:24,080 --> 00:54:25,640
Speaker 1: you hear it, you kind of scratch your head and

1073
00:54:25,680 --> 00:54:28,960
Speaker 1: then as you look under the hood, no pun intended you. Oh,

1074
00:54:29,000 --> 00:54:31,200
Speaker 1: of course, this makes perfect sense. This is the engine

1075
00:54:31,200 --> 00:54:34,239
Speaker 1: that drives the allocation. And yeah, that makes that makes

1076
00:54:34,280 --> 00:54:37,799
Speaker 1: perfect sense. Um, I only have you for another ten

1077
00:54:37,880 --> 00:54:40,480
Speaker 1: or fifteen minutes. So let me get to some of

1078
00:54:40,520 --> 00:54:45,360
Speaker 1: my favorite questions that I asked all of my guests. Um,

1079
00:54:45,360 --> 00:54:49,320
Speaker 1: and this is right here. I did the gap question,

1080
00:54:49,360 --> 00:54:52,080
Speaker 1: so we'll get rid of that. Oh, before we do that,

1081
00:54:52,160 --> 00:54:54,680
Speaker 1: I have to just ask your opinion on on my

1082
00:54:55,120 --> 00:54:58,319
Speaker 1: So you've experienced vcs, you know it's like to raise money.

1083
00:54:58,360 --> 00:55:02,120
Speaker 1: I have a pet theory that the current group of

1084
00:55:02,280 --> 00:55:06,480
Speaker 1: robo advisers have pulled off one of the greatest scams

1085
00:55:06,520 --> 00:55:10,520
Speaker 1: on vcs ever, because essentially they created this business that

1086
00:55:10,600 --> 00:55:14,120
Speaker 1: has there's nothing proprietary, there's nothing unique, there's no barriers

1087
00:55:14,160 --> 00:55:17,160
Speaker 1: to entry, and existing players can basically say, yeah, we'll

1088
00:55:17,200 --> 00:55:20,439
Speaker 1: do the exact same thing. But they've raised a couple

1089
00:55:20,480 --> 00:55:24,200
Speaker 1: of hundred million dollars in VC cash in order to

1090
00:55:24,239 --> 00:55:26,520
Speaker 1: generate three or four million dollars a year in revenue,

1091
00:55:27,080 --> 00:55:30,120
Speaker 1: and essentially said, all I need is ninety million dollars

1092
00:55:30,200 --> 00:55:33,120
Speaker 1: to fund on our I A and I'll charge twenty

1093
00:55:33,200 --> 00:55:35,680
Speaker 1: five bases points and if we never go public or

1094
00:55:35,719 --> 00:55:38,719
Speaker 1: we never get brought out, that's the VC's headache. How

1095
00:55:38,800 --> 00:55:43,080
Speaker 1: wildly wrong is that statement? How cynical and incorrect is that? Well?

1096
00:55:43,200 --> 00:55:49,680
Speaker 1: So um uh so My view is we raised a

1097
00:55:49,760 --> 00:55:53,839
Speaker 1: hundred and fifty million, and you know, every time I did,

1098
00:55:54,040 --> 00:55:57,320
Speaker 1: I really believed that even though the last zigg and

1099
00:55:57,360 --> 00:55:59,840
Speaker 1: zag didn't quite work, we were going to get the

1100
00:56:00,080 --> 00:56:02,360
Speaker 1: one and you eventually did and we eventually did, and

1101
00:56:02,400 --> 00:56:04,440
Speaker 1: so so it's it's really hard to say and this

1102
00:56:04,480 --> 00:56:06,439
Speaker 1: is what they was. It's easy to say, oh, yeah,

1103
00:56:06,480 --> 00:56:08,760
Speaker 1: it's all about the two. There's zigs and zags coming

1104
00:56:08,840 --> 00:56:11,839
Speaker 1: with these guys and to justify it or not or not.

1105
00:56:11,920 --> 00:56:13,680
Speaker 1: And so I wouldn't totally rule it out. But but

1106
00:56:13,760 --> 00:56:16,040
Speaker 1: you but you have to ask yourself a question, which

1107
00:56:16,120 --> 00:56:21,120
Speaker 1: is how well are you on track to discovering how

1108
00:56:21,160 --> 00:56:23,560
Speaker 1: to see the big opportunity? And is the big opportunity

1109
00:56:23,560 --> 00:56:25,960
Speaker 1: getting bigger or is it receiving or getting more crowded.

1110
00:56:26,160 --> 00:56:29,080
Speaker 1: That's fascinating And when we were there, there just weren't

1111
00:56:29,080 --> 00:56:31,319
Speaker 1: many players in the space known was in the four

1112
00:56:31,360 --> 00:56:33,279
Speaker 1: oh one k. We believe there was a way to

1113
00:56:33,280 --> 00:56:35,239
Speaker 1: reach a lot of people in assets. We believe that

1114
00:56:35,280 --> 00:56:37,279
Speaker 1: we could get very long. We had a lot of

1115
00:56:37,440 --> 00:56:40,400
Speaker 1: a lot of Uh, you had it easier than the

1116
00:56:40,440 --> 00:56:43,879
Speaker 1: current crop. It's much harder for them. It's it's more competitive,

1117
00:56:44,160 --> 00:56:46,719
Speaker 1: much more difficult, So I wouldn't I wouldn't rule it out,

1118
00:56:47,960 --> 00:56:50,960
Speaker 1: but it's it is not. This business is not easy,

1119
00:56:51,080 --> 00:56:53,000
Speaker 1: and I think it's only gotten far more difficult with

1120
00:56:53,000 --> 00:56:54,719
Speaker 1: the new competition and some of them, what is a

1121
00:56:54,840 --> 00:56:58,200
Speaker 1: personal capital was just bought by somebody I think black

1122
00:56:58,280 --> 00:57:02,680
Speaker 1: Future Advisor, Future Advisor, Black one and and for a number.

1123
00:57:02,760 --> 00:57:04,480
Speaker 1: I'm like, gee, I would have thought black Rock could

1124
00:57:04,520 --> 00:57:07,160
Speaker 1: have built their own for that, But obviously they saw

1125
00:57:07,239 --> 00:57:09,520
Speaker 1: something that that no one else did. And we talked

1126
00:57:09,560 --> 00:57:11,520
Speaker 1: earlier about the talent right there. There are a lot

1127
00:57:11,520 --> 00:57:14,799
Speaker 1: of companies with with so so or non existent business malls,

1128
00:57:14,840 --> 00:57:17,920
Speaker 1: but with talented people, we've got some pretty good technology

1129
00:57:17,920 --> 00:57:19,800
Speaker 1: where you know, it might be worth it for a

1130
00:57:19,920 --> 00:57:22,240
Speaker 1: year head start to market with the talented team, that

1131
00:57:22,280 --> 00:57:24,640
Speaker 1: might be able to move faster than you could higher

1132
00:57:24,840 --> 00:57:27,280
Speaker 1: is that phrase? Alright? Let me get to before they

1133
00:57:27,360 --> 00:57:29,160
Speaker 1: kick us out of the studios. Let me get to

1134
00:57:29,240 --> 00:57:33,840
Speaker 1: some of my favorite questions that we ask everybody. Um, So,

1135
00:57:34,440 --> 00:57:38,040
Speaker 1: your early mentors, aside from Nobel laureate Bill Black, Bill

1136
00:57:38,200 --> 00:57:46,280
Speaker 1: uh Sharp, who was your early mentors? You know? Uh?

1137
00:57:46,640 --> 00:57:49,360
Speaker 1: Before I started financial engines, I did consulting and I

1138
00:57:49,400 --> 00:57:51,720
Speaker 1: was a student and so it's all kind of you know,

1139
00:57:51,800 --> 00:57:54,080
Speaker 1: do it yourself, be a smart person and try to

1140
00:57:54,080 --> 00:57:57,000
Speaker 1: get the right answer. And that was that. What really

1141
00:57:57,040 --> 00:57:59,640
Speaker 1: became hard is managing people and figure out how do

1142
00:57:59,640 --> 00:58:02,000
Speaker 1: you build and work with a team of people. And

1143
00:58:02,080 --> 00:58:04,040
Speaker 1: I had some I had a lot of learning to do.

1144
00:58:04,920 --> 00:58:06,960
Speaker 1: I really think my biggest mentor in terms of like

1145
00:58:06,960 --> 00:58:08,760
Speaker 1: who gave me the most advice, it was actually my

1146
00:58:08,800 --> 00:58:12,520
Speaker 1: team telling me what I was doing wrong. They're like, look,

1147
00:58:12,520 --> 00:58:14,320
Speaker 1: I'm not sure exactly how to do it the right way,

1148
00:58:14,360 --> 00:58:16,280
Speaker 1: but I don't like what you're doing. This isn't really

1149
00:58:16,320 --> 00:58:18,520
Speaker 1: working for the company for me, And and a lot

1150
00:58:18,560 --> 00:58:20,240
Speaker 1: of it for me was like and I started this,

1151
00:58:20,320 --> 00:58:22,080
Speaker 1: I was like, look, I have so much to learn.

1152
00:58:22,640 --> 00:58:24,720
Speaker 1: I'm going to take feedback from anywhere I can get it.

1153
00:58:24,760 --> 00:58:27,760
Speaker 1: And my team was really candid with me about what

1154
00:58:27,920 --> 00:58:29,960
Speaker 1: we need to change in the business, what what I

1155
00:58:30,000 --> 00:58:31,960
Speaker 1: needed to change in my management style, you know, what

1156
00:58:32,000 --> 00:58:33,760
Speaker 1: we need to change in the product. That's got to

1157
00:58:33,800 --> 00:58:38,120
Speaker 1: be a challenging um way to to start out a company. Yeah,

1158
00:58:38,120 --> 00:58:40,520
Speaker 1: if if, I would say if if, if you're an

1159
00:58:40,640 --> 00:58:44,280
Speaker 1: entrepreneur and you don't like feedback, then you're in the

1160
00:58:44,320 --> 00:58:47,440
Speaker 1: wrong job. That's that's a that's a fair statement. Let

1161
00:58:47,560 --> 00:58:49,960
Speaker 1: I mentioned a couple of books earlier. What are are

1162
00:58:50,040 --> 00:58:54,000
Speaker 1: some of your favorite nonfiction books? Nonfiction books? You know.

1163
00:58:54,040 --> 00:58:58,320
Speaker 1: I'll just when I recently read is called Sapiens, which

1164
00:58:58,400 --> 00:59:01,720
Speaker 1: is sort of a history of Homo sapiens. I just

1165
00:59:02,000 --> 00:59:04,680
Speaker 1: got that as exult from somebody. It's so funny, it

1166
00:59:04,960 --> 00:59:07,920
Speaker 1: is worth reading, and it is it is an epic

1167
00:59:08,160 --> 00:59:10,760
Speaker 1: kind of view of literally, how do we compete against

1168
00:59:10,760 --> 00:59:13,600
Speaker 1: Homo or gaster and Homo fororensis? I mean, when there

1169
00:59:13,640 --> 00:59:17,080
Speaker 1: were multiple Homo species out there, you know, Holmo sapiens

1170
00:59:17,080 --> 00:59:20,320
Speaker 1: was only one of a few species nine or seven.

1171
00:59:20,680 --> 00:59:23,680
Speaker 1: So the book I just finished was called Last ape Standing,

1172
00:59:24,360 --> 00:59:27,360
Speaker 1: and it's the same. Hey, these are the twenty seven

1173
00:59:27,400 --> 00:59:31,760
Speaker 1: or twenty nine species that were contemporaries and competitors to

1174
00:59:31,840 --> 00:59:34,160
Speaker 1: Homo sapiens. And then and it goes and it goes

1175
00:59:34,200 --> 00:59:37,000
Speaker 1: through sort of cultivation of fire and technology. It goes

1176
00:59:37,040 --> 00:59:39,160
Speaker 1: through the spread of religion, the worle that religious place,

1177
00:59:39,400 --> 00:59:45,240
Speaker 1: wonderful treatment of polytheistic religions, monotheistic religions, naturalist religions, and

1178
00:59:45,280 --> 00:59:47,760
Speaker 1: then it moves forward to talk a bit about happiness,

1179
00:59:47,840 --> 00:59:49,880
Speaker 1: which is like was surprising to me, But I think

1180
00:59:49,880 --> 00:59:51,840
Speaker 1: it really goes like, why are what are we even

1181
00:59:51,880 --> 00:59:54,480
Speaker 1: trying to achieve here? And then it kind of goes

1182
00:59:54,520 --> 00:59:56,720
Speaker 1: through what's the future of the of the of the

1183
00:59:56,760 --> 01:00:00,200
Speaker 1: species Homo sapiens, and it talks about cyborgs and are

1184
01:00:00,240 --> 01:00:03,320
Speaker 1: jacking into people's brains and and there's gonna be some

1185
01:00:04,160 --> 01:00:12,360
Speaker 1: crazy stuff coming. It's is gonna be absolutely wild, so

1186
01:00:12,480 --> 01:00:15,480
Speaker 1: not quite dystopian, not quite the negative things we've see

1187
01:00:15,480 --> 01:00:17,800
Speaker 1: in some sci fi films, or does it do we

1188
01:00:17,840 --> 01:00:20,000
Speaker 1: have the potential for that if we're not, you know,

1189
01:00:20,040 --> 01:00:22,320
Speaker 1: I mean what I talked about, what's happening in Palo Alto.

1190
01:00:23,200 --> 01:00:25,560
Speaker 1: When you get kids, you're a teenagers jumping in front

1191
01:00:25,560 --> 01:00:29,440
Speaker 1: of trains, there's definitely something dystopian going on. And I

1192
01:00:29,560 --> 01:00:33,040
Speaker 1: think the technology. I I predict that in five or

1193
01:00:33,080 --> 01:00:35,560
Speaker 1: ten years, we're going to look back on our kids

1194
01:00:35,640 --> 01:00:39,240
Speaker 1: and their use of seven screens and social media as

1195
01:00:39,280 --> 01:00:43,640
Speaker 1: one of the most foolish, um hazardous thing that young

1196
01:00:43,720 --> 01:00:46,040
Speaker 1: people could be spending that much time doing, just like

1197
01:00:46,080 --> 01:00:48,320
Speaker 1: eating fast food every day or so we'll say, look,

1198
01:00:48,360 --> 01:00:51,200
Speaker 1: it's it's gonna be part of our culture. But but

1199
01:00:51,200 --> 01:00:53,240
Speaker 1: but it could be harmful if use the wrong way

1200
01:00:53,320 --> 01:00:55,360
Speaker 1: or too much, and so there's got to be more

1201
01:00:55,400 --> 01:00:58,120
Speaker 1: guidelines on what's healthy and what's not with the consumption.

1202
01:00:58,320 --> 01:01:00,959
Speaker 1: My wife's a teacher, and she he says, even about

1203
01:01:00,960 --> 01:01:03,560
Speaker 1: the kids who are she goes, they're not cooked yet.

1204
01:01:03,600 --> 01:01:06,120
Speaker 1: There's still still a work in progress, and you can't

1205
01:01:06,120 --> 01:01:09,160
Speaker 1: expect them to make the best decisions with all these

1206
01:01:09,240 --> 01:01:12,440
Speaker 1: terrible influences in there, not having the tools to know

1207
01:01:12,480 --> 01:01:14,400
Speaker 1: how to deal with that totally. So, so you're on

1208
01:01:14,440 --> 01:01:16,840
Speaker 1: the same same So we so we we shut down

1209
01:01:16,880 --> 01:01:20,760
Speaker 1: our WiFi every night from eleven to seven because I

1210
01:01:20,800 --> 01:01:22,880
Speaker 1: don't know how else to make sure that that my

1211
01:01:23,000 --> 01:01:25,200
Speaker 1: daughter gets at least has a chance to get eight

1212
01:01:25,240 --> 01:01:28,600
Speaker 1: hours of sleep. That that that's pretty interesting. Um, so

1213
01:01:28,760 --> 01:01:31,560
Speaker 1: what advice? Speaking of young people? So, what advice would

1214
01:01:31,560 --> 01:01:34,800
Speaker 1: you give to a millennial or a recent college graduate

1215
01:01:34,880 --> 01:01:38,680
Speaker 1: who was interested in finance, someone just coming out of school? Now,

1216
01:01:38,720 --> 01:01:42,800
Speaker 1: what what would you say to them? I would say,

1217
01:01:42,960 --> 01:01:46,000
Speaker 1: I'd say a couple of things. The more generic thing

1218
01:01:46,000 --> 01:01:50,000
Speaker 1: I'd say is, um, the world is changing at a

1219
01:01:50,040 --> 01:01:54,520
Speaker 1: faster and faster rate, and watch out because technology and

1220
01:01:54,600 --> 01:01:57,200
Speaker 1: kind of machine learning, artificial intelligence is going to totally

1221
01:01:57,280 --> 01:02:01,800
Speaker 1: change the landscape. So the best shot you have if

1222
01:02:01,840 --> 01:02:05,400
Speaker 1: you want to become become you know, financially independent, which

1223
01:02:05,440 --> 01:02:07,000
Speaker 1: just means like be able to put food on the

1224
01:02:07,000 --> 01:02:09,120
Speaker 1: table and have a house. I'm not talking about being

1225
01:02:09,160 --> 01:02:12,160
Speaker 1: like rich. You're gonna have to have some skills. They're

1226
01:02:12,160 --> 01:02:14,840
Speaker 1: gonna be able to survive lots of twists and turns

1227
01:02:14,880 --> 01:02:17,320
Speaker 1: in terms of how technology changes the way we create

1228
01:02:17,320 --> 01:02:20,560
Speaker 1: an ad value in society. So your ability to learn

1229
01:02:20,800 --> 01:02:23,240
Speaker 1: and keep learning, I say that's the number one thing.

1230
01:02:23,640 --> 01:02:27,200
Speaker 1: Stay agile, stay curious, and make sure that you're always

1231
01:02:27,280 --> 01:02:30,439
Speaker 1: investing in learning the next thing, because if you stay

1232
01:02:30,480 --> 01:02:32,960
Speaker 1: on one thing, the rug's gonna get pulled out from

1233
01:02:32,960 --> 01:02:35,720
Speaker 1: money world will pass you by. With respect to finance,

1234
01:02:35,760 --> 01:02:40,000
Speaker 1: I'd say be very wary before going into asset management,

1235
01:02:40,120 --> 01:02:42,320
Speaker 1: at least in terms of kind of large cap equities.

1236
01:02:42,400 --> 01:02:45,200
Speaker 1: It's the game is not the same as it used

1237
01:02:45,200 --> 01:02:48,200
Speaker 1: to be and the economics are fundamentally I think the

1238
01:02:48,360 --> 01:02:50,800
Speaker 1: change forever. We we had Mario ga Belly last week,

1239
01:02:51,000 --> 01:02:54,160
Speaker 1: he essentially said the same thing. Would not recommend kids

1240
01:02:54,240 --> 01:02:57,600
Speaker 1: jump into that unless they really felt they had no choice.

1241
01:02:57,760 --> 01:03:01,080
Speaker 1: Last question, So you've been an entrepreneur, You've been in

1242
01:03:01,120 --> 01:03:03,800
Speaker 1: the world to finance. You've been doing this for twenty years.

1243
01:03:04,320 --> 01:03:06,680
Speaker 1: What do you know today that you wish you knew

1244
01:03:06,680 --> 01:03:12,600
Speaker 1: when you began twenty years ago? Uh? You know, I

1245
01:03:12,640 --> 01:03:16,920
Speaker 1: guess partly because I expected to have no idea what

1246
01:03:16,960 --> 01:03:19,120
Speaker 1: I was doing. Uh, and I had to learn a lot.

1247
01:03:19,160 --> 01:03:21,000
Speaker 1: I was I was kind of a blank sheet. I

1248
01:03:21,000 --> 01:03:27,160
Speaker 1: didn't have a lot of preconceptions. Um. And I think,

1249
01:03:27,200 --> 01:03:28,800
Speaker 1: and I also think to myself, like, what would I

1250
01:03:28,800 --> 01:03:33,120
Speaker 1: do differently if I could do it again? You know,

1251
01:03:33,240 --> 01:03:35,600
Speaker 1: I'm glad I had kids when I was twenty two

1252
01:03:35,920 --> 01:03:37,880
Speaker 1: because one of the things you said, I think is

1253
01:03:37,960 --> 01:03:41,360
Speaker 1: really important. If you really want to solve a problem,

1254
01:03:41,640 --> 01:03:43,320
Speaker 1: I think that the best way to do it is

1255
01:03:43,360 --> 01:03:46,520
Speaker 1: to go deep, deep, deep, like really focus on it

1256
01:03:46,560 --> 01:03:49,840
Speaker 1: and bring as much talent to bearn as possible. But

1257
01:03:49,920 --> 01:03:51,600
Speaker 1: sometimes you don't solve it right away, and you need

1258
01:03:51,640 --> 01:03:54,480
Speaker 1: to pull back and let your mind work on the

1259
01:03:54,520 --> 01:03:57,040
Speaker 1: problem when you're not specifically thinking about the problem. But

1260
01:03:57,760 --> 01:04:01,160
Speaker 1: I think optimal problem solving is the ability to stay

1261
01:04:01,160 --> 01:04:03,720
Speaker 1: focused on a problem and recruit other people to work

1262
01:04:03,720 --> 01:04:06,320
Speaker 1: on the problem for a period of intense focus, then

1263
01:04:06,400 --> 01:04:09,320
Speaker 1: back off of it, then come back to it, then

1264
01:04:09,360 --> 01:04:11,360
Speaker 1: back off it. And sort of put the pieces together

1265
01:04:11,440 --> 01:04:13,600
Speaker 1: kind of don't always right. There's another book by the way,

1266
01:04:13,600 --> 01:04:17,520
Speaker 1: we're just thinking fast and slow, which which which I love.

1267
01:04:17,600 --> 01:04:21,200
Speaker 1: And it's not the blink your guts right, it's like no,

1268
01:04:21,400 --> 01:04:24,040
Speaker 1: in some things usually where there's an instinct to mate

1269
01:04:24,160 --> 01:04:27,080
Speaker 1: or survive, your instincts are quite good, right that they've

1270
01:04:27,080 --> 01:04:30,040
Speaker 1: done a good job keeping you alive on the savannah.

1271
01:04:30,080 --> 01:04:33,880
Speaker 1: But we're using them for to to borrow the pharmaceutical phrase,

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01:04:34,160 --> 01:04:36,440
Speaker 1: it's off label. It's what we're using our brains for

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01:04:36,560 --> 01:04:39,000
Speaker 1: is not what they would design for. And so trying

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01:04:39,000 --> 01:04:43,200
Speaker 1: to intentionally structure the way you think, I think it's valuable.

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01:04:43,240 --> 01:04:46,640
Speaker 1: So so I think I might have gone fast. I'm

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01:04:46,640 --> 01:04:48,479
Speaker 1: happy I had kids because they pulled me back away

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01:04:48,520 --> 01:04:52,720
Speaker 1: from the problem. Honestly, I kind of wish that I

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01:04:52,760 --> 01:04:57,240
Speaker 1: could have not been so worried about failing all the time.

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01:04:58,640 --> 01:05:00,360
Speaker 1: Maybe I would have failed if I weren't so worried

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01:05:00,360 --> 01:05:02,040
Speaker 1: about it. But it took it took a lot of

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01:05:02,040 --> 01:05:04,960
Speaker 1: the joy out of it, right. That's fascinating. Jeff, thank

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01:05:05,000 --> 01:05:06,840
Speaker 1: you so much for spending so much time with us.

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01:05:06,880 --> 01:05:10,400
Speaker 1: I think this was really fascinating, and it's an aspect

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01:05:10,600 --> 01:05:13,440
Speaker 1: of of investing in finance that I think most people

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01:05:13,880 --> 01:05:16,920
Speaker 1: are not familiar with. UM. For those of you who

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01:05:16,960 --> 01:05:22,120
Speaker 1: want more information, you could go to Financial Engines dot com. UM.

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01:05:22,160 --> 01:05:25,600
Speaker 1: You mentioned social you on Twitter at all? I'm actually

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01:05:25,600 --> 01:05:27,840
Speaker 1: I am not on Twitter, not on Twitter, and so

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01:05:27,920 --> 01:05:29,920
Speaker 1: this is a whole year of note Twitter. This is

1290
01:05:29,960 --> 01:05:31,760
Speaker 1: a whole decade of not Twitter good. I don't know

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01:05:31,760 --> 01:05:33,960
Speaker 1: how long this is gonna last, so we'll see what

1292
01:05:34,040 --> 01:05:37,760
Speaker 1: happens again. If you enjoy this conversations, be sure and

1293
01:05:37,800 --> 01:05:39,960
Speaker 1: look up an Inch or down an Inch on iTunes

1294
01:05:40,000 --> 01:05:43,320
Speaker 1: and you could see the rest of our interviews. Check

1295
01:05:43,360 --> 01:05:46,160
Speaker 1: out my daily blog at Dholtz dot com and on

1296
01:05:46,200 --> 01:05:49,439
Speaker 1: Bloomberg View dot com. I want to thank my head

1297
01:05:49,520 --> 01:05:54,680
Speaker 1: research Michael bat Nick and my engineer and producer Charlie Volmer.

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01:05:55,080 --> 01:05:58,240
Speaker 1: You've been listening to Masters in Business on Bloomberg Radio.