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Speaker 1: You're listening to Math and Magic, a production at iHeartRadio.

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Speaker 2: We have to innovate, we have to keep pushing the

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Speaker 2: envelope to build our audience and really be relevant. And

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Speaker 2: the only way that you'll be able to innovate and

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Speaker 2: provide those services and build value is to know who

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Speaker 2: they are what they do in a real way.

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Speaker 3: Hi, I'm Bob Pittman, and welcome to this episode of

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Speaker 3: Math and Magic Stories from the Frontiers of Marketing. Today,

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Speaker 3: we have an honest to god magician. He began his

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Speaker 3: career at what was a revolutionary tech company in its day.

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Speaker 3: Wang Labs had a few startups that hit, was a

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Speaker 3: hugely important player in the incredible success of AOL in

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Speaker 3: the nineties. Continued his tech investing while building a Washington,

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Speaker 3: DC based sports powerhouse centered on the Washington Capitals and

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Speaker 3: the Wizards. He's the former vice chair of AOL. He's

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Speaker 3: the founder and CEO of Monumental Sports and Entertainment, and

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Speaker 3: he's a co founder of the investment group Revolution Growth.

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Speaker 3: Ted Leonsis ted is Brooklyn, born, raised there and in Massachusetts.

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Speaker 3: He's a Georgetown grad and tech pioneer. He's known for

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Speaker 3: seeing things early and in a way others can't. He

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Speaker 3: cares deeply about people and communities and always finds a

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Speaker 3: way to put them first and all he does. He's

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Speaker 3: the former mayor of a small Florida town. He has

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Speaker 3: a wicked sense of humor, big heart, and a perpetual smile.

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Speaker 3: He's been a friend for almost thirty years, and I

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Speaker 3: have to say he is also the person who talked

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Speaker 3: me into going to AOL just as our big ride began,

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Speaker 3: So I owe him a big one for that.

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Speaker 4: Ted, welcome, Thank you, Bob.

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Speaker 2: It's very sweet of you, and I missed everything about

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Speaker 2: your voice and leadership, and you shouldn't be shy to

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Speaker 2: say that. I reported to you and I learned a

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Speaker 2: lot from you back in the day, so it's wonderful

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Speaker 2: to be on your podcast.

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Speaker 3: Well, you're very nice, Ted. I always thought of us

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Speaker 3: as partners, and boy, did you contribute a lot. Before

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Speaker 3: we jump into that meat though, I want to do

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Speaker 3: you in sixty seconds. You ready to go yep? Do

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Speaker 3: you prefer cats or dogs? Dogs, VITYL or CDs, Vinyl

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Speaker 3: FaceTime or.

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Speaker 4: Call oh face to face, hugging.

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Speaker 3: DC or Brooklyn DC, Slow and steady or pedal to

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Speaker 3: the metal, both early riser or night out.

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Speaker 4: Oh, I'm early rise.

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Speaker 2: I get all my best work done between five and

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Speaker 2: seven am.

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Speaker 3: Movies at the theater are a streaming movie, streaming crypto

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Speaker 3: or dollars dollars Elvis or David Bowie Elvis.

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Speaker 4: Me and Elvis had the same birthday.

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Speaker 3: Guilty pleasure.

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Speaker 4: I am spoiling the shit out.

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Speaker 2: Of my grandchildren.

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Speaker 3: Okay, let's jump into the meat. You have an uncanny

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Speaker 3: ability to see around corners, computers, new media, online, e sports,

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Speaker 3: and on and on. Let's start with the topic of

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Speaker 3: the minute AI friend or foe and how's it going

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Speaker 3: to change our lives in business?

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Speaker 2: Oh, it's totally faux right now, and I'm horrified with

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Speaker 2: the answers that I'm hearing from. You would think socially

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Speaker 2: responsible CEOs. So you know, we used to talk about

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Speaker 2: early in the computer industry, you know, garbage in, garbage out,

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Speaker 2: And so you know, these machines in the cloud that

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Speaker 2: are doing machine learning, the input that they're getting, it's

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Speaker 2: bad info.

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Speaker 4: You can't tell the sentiment if you will.

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Speaker 2: That's on social media and Reddit and all of the

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Speaker 2: rivers of data that's at scale, and so we will

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Speaker 2: get very, very bad answers, because it's holding a mirror

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Speaker 2: up to ourselves, but it's not our real self. We're

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Speaker 2: holding a mirror up to this faux personality, this faux

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Speaker 2: knowledge base. When you're seeing and you hear a CEO

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Speaker 2: say the machines lined, they call it hallucinations. But the

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Speaker 2: machines line and we don't know why. It's like, hey,

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Speaker 2: full stop till you can figure out why.

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Speaker 3: So with this view of AI, in our day in

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Speaker 3: taking the Internet to the mass market, it was really

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Speaker 3: AOL versus Microsoft today in the AI battleground, and it

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Speaker 3: is a battle. Who do you think are the big competitors?

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Speaker 3: If you're sort of watching saying, wow, we need to

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Speaker 3: watch this, who are the two companies? Are there more

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Speaker 3: that are really in the middle of this that we

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Speaker 3: have to look to provide that kind of guidance.

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Speaker 2: Well, like every industry, you'll have the big incumbents. Certainly

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Speaker 2: here Microsoft, which has a multi trillion dollar market cap

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Speaker 2: and more cash than the United States does right now,

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Speaker 2: they will be formidable. Apple certainly will be late to

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Speaker 2: the game, but they'll be formidable. Obviously, Alphabet and Google

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Speaker 2: they'll be big and important, and then you'll have startups

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Speaker 2: But where I see a positive difference is that we

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Speaker 2: always thought in our generation that there would be some

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Speaker 2: fantastic verticalization and where you could go deeper in a

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Speaker 2: vertical with a solution. You know, we thought vertical search

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Speaker 2: one day would be important. I'm an investor in a

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Speaker 2: company called tempest Temus, and it is a AI company.

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Speaker 2: It uses a power plant now mostly from Google and

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Speaker 2: alphabet but you wouldn't know it as a doctor, a

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Speaker 2: healthcare provider, or a consumer, and it is noble. It

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Speaker 2: is using the power of machine learning all of the

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Speaker 2: data that's out there to help families answer a question.

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Speaker 4: Your wife gets diagnosed.

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Speaker 2: With stage three cancer and she would go to the

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Speaker 2: doctor and they would take a sell and say you've

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Speaker 2: got cancer, and then they would tell you, hey, there

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Speaker 2: was a study in the New England Journal of Medicine

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Speaker 2: of fifteen people and based on that and my experience

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Speaker 2: as the doctor, it's her genetics, family history.

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Speaker 4: Here's what you should be doing.

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Speaker 2: So AI in that kind of setting where you can

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Speaker 2: provide at scale data with deep genomic information, to me

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Speaker 2: is a positive on AI. If we can take that

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Speaker 2: high road, use a higher calling around the tools. I

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Speaker 2: think we're going to be in good shape, and I

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Speaker 2: think that will only come from deep verticalization where companies

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Speaker 2: are saying this isn't a business. We're using this technology

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Speaker 2: for research and to help doctors to find better ways.

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Speaker 2: And yes it'll help save money, Yes it'll help activate

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Speaker 2: more efficiency. But we should be using the technology to

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Speaker 2: answer big questions, not fill the algorithms up with junk.

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Speaker 3: So let's take AI sports. You got into team ownership

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Speaker 3: in the late nineties, and I'm sure technologies have affected

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Speaker 3: your business. What are the new opportunities with the tech

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Speaker 3: today and especially with AI. Is this going to select

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Speaker 3: your players now?

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Speaker 2: Well, that's really interesting because when you know, I came

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Speaker 2: into sports in nineteen ninety nine, everyone lived on Fax

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Speaker 2: machines and we were the first team. At least we

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Speaker 2: gave everyone personal computers. I gave everyone email addresses. It

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Speaker 2: was basic connectivity. But then we started to say, hey,

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Speaker 2: this is a platform to launch a lot of new technology,

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Speaker 2: and then if it takes hold here, it can be

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Speaker 2: nested if you will, within sports and arenas because we

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Speaker 2: touched so many people, and then move into general media.

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Speaker 2: And so the first big investments that were being made

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Speaker 2: were selecting of teams for coaching the players, and the

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Speaker 2: amount of work that was done in high speed cameras

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Speaker 2: and being able to create these heat maps on where

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Speaker 2: was the optimal place for the player to take the shot.

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Speaker 2: When you were playing defense, we would pixelate the floor.

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Speaker 2: And I remember once Kobe Bryant were playing the Lakers

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Speaker 2: and he was going to get the end of game

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Speaker 2: shot and was, well, if you can move Kobe two

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Speaker 2: pixels to the left, he shoots thirty eight percent. If

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Speaker 2: he's those two pixels to the right, he's forty four percent.

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Speaker 4: All you can do.

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Speaker 2: Is get him to the suboptimized place and where the

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Speaker 2: most transparent of providing that data to fans, to coaches,

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Speaker 2: to competitors, and now to sports betters.

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Speaker 4: Right, they have access to.

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Speaker 2: More data, certainly than a Wall Street trader has in

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Speaker 2: trying to.

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Speaker 4: Pick whether you should short a stock.

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Speaker 2: Right, So I looked at sports as being the most

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Speaker 2: data rich side on the product.

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Speaker 4: And now it's moved into marketing.

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Speaker 2: We have a database and monumental sports and entertainment that's

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Speaker 2: closing in on four million active records where we really

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Speaker 2: know who's coming into the building, how long have they

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Speaker 2: been a customer, what have they spent, how do they

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Speaker 2: renew who do they give their tickets to, how far

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Speaker 2: do they travel? What are they're viewing habits? Are they

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Speaker 2: streaming now? Are they still watching games on cable? How

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Speaker 2: important is radio? One thing I'll say, radio and digital

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Speaker 2: radio and podcasting has become incredibly powerful and on trend again.

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Speaker 4: We have to innovate.

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Speaker 2: We have to keep pushing the envelope to build our

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Speaker 2: audience and really be relevant. And the only way that

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Speaker 2: you'll be able to innovate and provide those services and

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Speaker 2: build value is to know who they are, what they

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Speaker 2: do in a real way, not a superficial way, which

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Speaker 2: is what's happened on the internet. But I think it's

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Speaker 2: lost its efficacy and deep verticalization. Really understanding your customer,

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Speaker 2: really understanding how to connect all the way through all

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Speaker 2: of the ways that they live their life on the

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Speaker 2: net is what the challenge for the next set of

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Speaker 2: marketers will.

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Speaker 4: Be in our industry.

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Speaker 3: Well, okay, we've covered the business. I mean it's fascinating

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Speaker 3: how you've applied all you knew from technology to sports.

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Speaker 3: I want to jump for a minute to you with

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Speaker 3: a personal matter. One of the most interesting things we

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Speaker 3: talked about when we first met, was this list you

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Speaker 3: had one hundred and one things you wanted to do

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Speaker 3: before you die. Tell us about the genesis of that list.

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Speaker 3: And by the way, I also want to know how

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Speaker 3: many of those one hundred and one have you achieved?

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Speaker 4: Thank you.

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Speaker 2: It's you know, deeply personal and everyone will have their reckonings.

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Speaker 2: Everyone will have something very disruptive happen in their life

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Speaker 2: and times, and it's how you react to that. And

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Speaker 2: a lot of times the disruption ends up being a

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Speaker 2: grade advantage if you take it in and process it

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Speaker 2: in a positive way. So for me, you know, I

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Speaker 2: was a poor kid growing up. My father was a waiter.

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Speaker 2: It was an immigrant family. We grew up in Lowell, Massachusetts,

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Speaker 2: working in mills. And then my dad went into the

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Speaker 2: service and married later in life and was a waiter

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Speaker 2: and we lived in a.

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Speaker 4: Rental apartment in Brooklyn, and.

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Speaker 2: I'm an only child. They taught me to work hard,

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Speaker 2: get good grades. If you get good grades, you'll get

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Speaker 2: into a good school. If you get in good school

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Speaker 2: and you get good grades, you'll get a good job

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Speaker 2: and you'll make a lot of money. And so I

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Speaker 2: followed that Mantra. I went to Georgetown University and had

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Speaker 2: some academic success and got out of college and worked

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Speaker 2: at a tech company as you mentioned, Wang Laboratories, and

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Speaker 2: then I had the entrepreneurial bug and I started my

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Speaker 2: first company. I was lucky and maybe clever, and I

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Speaker 2: sold that company two years after I started for sixty

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Speaker 2: million dollars, and Bob I declared victory.

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Speaker 4: And how society tells you to the clear victory.

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Speaker 2: Ends up being false social responsibility. In false victory, you

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Speaker 2: buy stuff. You're very popular with people who were using

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Speaker 2: you as their platform for success. And so I kind

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Speaker 2: of lost my way. And then I got on the

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Speaker 2: wrong airplane. I had a reckoning. The plane was going down.

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Speaker 2: I was alone on the plane, and I started praying.

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Speaker 2: Seemed like the right thing to do. And I started

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Speaker 2: laughing because I was praying to God, saying, you know,

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Speaker 2: please let me get through this.

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Speaker 4: I don't want to die.

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Speaker 2: And my first thought was if there was a God listening,

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Speaker 2: they say, oh, now you need me. You're in deep

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Speaker 2: angst and you think I'm here to help you. So

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Speaker 2: I reverted back to type. I said, all right, let's

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Speaker 2: cut a deal. If I live.

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Speaker 4: Through this, I promise I will leave more than I take.

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Speaker 4: How's that?

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Speaker 2: Obviously we made it through, but I've always been well,

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Speaker 2: a deal's a deal. How will I now pay that

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Speaker 2: debt back? I made this list of one hundred and

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Speaker 2: one things to do before I die, so that I

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Speaker 2: could say an end of my life. Could I look

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Speaker 2: back and metric and score? How did I do it?

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Speaker 2: As I matured and I look at the list, I

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Speaker 2: go some of the things that I thought was a

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Speaker 2: good way of keeping score ended up not being as

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Speaker 2: important as I thought.

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Speaker 4: But Bob, and you'll remember, you were kind of there.

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Speaker 2: I had put on my list on a sports team

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Speaker 2: win a championship, and if I hadn't have written that down.

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Speaker 2: When I was approach to buy the teams, I initially

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Speaker 2: said no. I said, I'm working at AOL, I have

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Speaker 2: two young children. There's no way I could own a team.

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Speaker 2: And I came home and Lynn said to me, what's new?

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Speaker 2: What happened today? And I said, oh, this guy approached

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Speaker 2: me wanted to buy teams. And she said what did

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Speaker 2: you say?

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Speaker 4: And I said, I can't engaged at the company. We

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Speaker 4: have young kids.

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Speaker 2: And Lynn said to me, hey, we've been together for

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Speaker 2: a long time. We'd be together forever. What if you

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Speaker 2: get ninety nine of the one hundred and one thing's

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Speaker 2: done and you don't buy a team and you don't

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Speaker 2: win a championship, and so well, this is why I

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Speaker 2: love you.

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Speaker 4: Yeah, I'm going to go and.

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Speaker 2: I bought the team and we've won championships. But it's

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Speaker 2: also been like the best financial decision as a family

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Speaker 2: we could have made.

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Speaker 4: So I'm now at like ninety four.

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Speaker 2: I wrote this list in nineteen eighty six, and back

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Speaker 2: then I said I want to go into outer space.

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Speaker 2: That seemed crazy. Right now I go, oh, I'm definitely

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Speaker 2: gonna get that one checked off, right, I mean, Richard

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Speaker 2: Branson's called me, Bezos has called me, and I'm sure

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Speaker 2: I will get into outer space, and I'm clicking them off.

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Speaker 2: But it also was will I be happy and self

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Speaker 2: actualized at the end of the journey, And some of

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Speaker 2: the things on the list weren't going to make me happy,

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Speaker 2: And so I've matured, if you will, And I don't

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Speaker 2: think I have to get all one hundred and one done,

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Speaker 2: But you know, big important things like falling in love,

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Speaker 2: getting married, and having children, having grandchildren, living a healthy life,

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Speaker 2: giving back.

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Speaker 4: Those are the things that I think will be most important.

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Speaker 4: At the end of the day.

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Speaker 3: You touched on your childhood and growing up and what

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Speaker 3: it meant to you. If I remember my story right,

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Speaker 3: I think your high school guidance counselor told you you

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Speaker 3: weren't college material and tried to steer you to a

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Speaker 3: vocational school. Obviously that didn't sit well with you. You

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Speaker 3: went onto Georgetown, and I think that really set you

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Speaker 3: on the track of technology and got you into tech.

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Speaker 3: Can you tell us a little bit more about that story,

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Speaker 3: because I do think that probably was a seminal moment

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Speaker 3: for ted Leonsis and where you wound up.

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Speaker 2: Yeah, I probably created the first algorithmic based liberal arts

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Speaker 2: program ever and it was powered by a Jesuit priest

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Speaker 2: who was my mentor.

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Speaker 4: I had to write a thesis.

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Speaker 2: And I was a little bit lazy and said what

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Speaker 2: are you going to write about? And I said, I'll

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Speaker 2: come up with the idea over the weekend. So I

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Speaker 2: went to the library and I was with some buddies

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Speaker 2: and I said, all right, I'm going to find the shortest,

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Speaker 2: easiest book to read in the library. I found Old

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Speaker 2: Man in the Seat and it's a novella. If you

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Speaker 2: will and I read in like an hour and a half.

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Speaker 2: And on Monday I went to see my mentor, father,

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Speaker 2: Joseph Dirkin, Society of Jesus, and he said, okay, great,

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Speaker 2: tell me what you thought of the book. And I

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Speaker 2: gave him like an annotated book report, and he said,

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Speaker 2: I'm so disappointed in you. There's so much going on

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Speaker 2: in this book with this author at the time it

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Speaker 2: was published. You're better than this. And he challenged me

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Speaker 2: to be more of a critical thinker.

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Speaker 4: So I didn't know anything about Hemingway.

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Speaker 2: There was no Internet back then, so I literally and

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Speaker 2: got a bunch.

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Speaker 4: Of his books.

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Speaker 2: And the second book I read, Across the River and

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Speaker 2: Into the Trees, was nothing like Old Man in the Sea,

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Speaker 2: And as I started to do research, Hemingway was a

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Speaker 2: journalist Esquire Magazine, New Yorker Magazine, and then he became

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Speaker 2: a writer and Old Man in the Sea was published

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Speaker 2: in the fifties. He won a Pulit surprise, he ended

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Speaker 2: up taking his life. And I just had this idea

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Speaker 2: that I think Ernest Hemingway wrote this book earlier in

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Speaker 2: his career and he needed the money, and you know,

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Speaker 2: jazz was becoming popular and Jack Kerouac and Allen Ginsburg,

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Speaker 2: and he went back to his journalistic style. And Father

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Speaker 2: Dirk and said, well, great, how are you going to

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Speaker 2: prove it? And I said, I'll call his wife. Oh,

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Speaker 2: I'll try and talk to the publisher. And Father Dirkin,

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Speaker 2: to his credit, said why don't we use a computer?

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Speaker 2: And I said, a computer like in the movies. Like.

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Speaker 2: We had one computer on campus in the registrar's office.

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Speaker 2: It was IBM three stixy and it spit out for

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Speaker 2: students what classes you would take. So he introduced me

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Speaker 2: to a linguistics major. He introduced me to someone who

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Speaker 2: worked in the computer mis department, and we went to

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Speaker 2: dinner and we came up with the idea of I

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Speaker 2: would have to type in the first five thousand lines

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Speaker 2: of a book and articles from the fifties, from the forties.

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Speaker 4: From the thirties.

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Speaker 2: And then the linguistics major she came up with these

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Speaker 2: metrics words, person sentences, sentences per paragraph, pronoun references. There

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Speaker 2: were seventeen measures, and then the programmer wrote this code

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Speaker 2: to basically answer the question. Based on this algorithm, when

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Speaker 2: was Old Man in the Sea as a control written?

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Speaker 4: And it said the book was written in.

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Speaker 2: The thirties, not in nineteen fifty two, and it was

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Speaker 2: mind blowing to us. And you know, I wrote a

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Speaker 2: college thesis. But it showed how a computer and software

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Speaker 2: and algorithms could find essence of truth in the data

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Speaker 2: and show you something that you never knew happened.

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Speaker 4: And it was truly for me.

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Speaker 2: Ah moment, and it powered pretty much everything for the

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Speaker 2: rest of my life and career.

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Speaker 4: Made a movie in a similar fashion in that.

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Speaker 2: I didn't have anything to read Bob and I started

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Speaker 2: reading the Last thirty Days of the New York Times

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Speaker 2: from a bookstore in Saint Bart's and I read an

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Speaker 2: obituary about an author named Iris Chang and she wrote

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Speaker 2: a book called.

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Speaker 4: The Rape of Name King.

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Speaker 2: And when I got home, I went on Amazon and

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Speaker 2: I said, I'm going to buy the book, and their

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Speaker 2: simple little algorithm said, oh.

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Speaker 4: If you like this book, then you like these books.

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Speaker 2: And there was a starburst that said the Forgotten Holocaust

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Speaker 2: and it was basically behind the Chinese Communist Wall. This

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Speaker 2: was in nineteen thirty four. I was at AOL, I

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Speaker 2: had some contacts. I worked with the people at CIA,

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Speaker 2: and I made a film called Nang King and it

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Speaker 2: won Best at Open Sundayands, it.

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Speaker 4: Went on HBO.

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Speaker 2: It won the Emmy Award for Best Documentary. And it

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Speaker 2: all came from reading an obituary and an algorithm telling

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Speaker 2: me there's more to this story than this just this

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Speaker 2: one book, and that turned me into making films.

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Speaker 3: More mathem magic. Right after this quick break, welcome back

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Speaker 3: to Mathemagic. Let's hear more from my conversation with Ted

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Speaker 3: leonsis so Ted, you make mundane things sound exciting about

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Speaker 3: adding a twist, and I want to tell a story

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Speaker 3: on you. When I got to AOL and we were

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Speaker 3: in this whole process of how can we make this

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Speaker 3: company profitable? I was laying out to our group of

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Speaker 3: senior leaders, tying it to what the stock price could

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Speaker 3: be because they all were equity holders if we achieved

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Speaker 3: those earnings levels, and I start think it's mathematical, it's

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Speaker 3: this and this and this, and you could see people

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Speaker 3: sort of glazing over some skepticism. You suddenly amped up

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Speaker 3: the discussion by declaring to the room that Bob, I'm

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Speaker 3: going to give you my house if we hit that

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Speaker 3: stock price. Suddenly the room was on fire. And by

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Speaker 3: the way, when we walked out of the room, you

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Speaker 3: sort of gave me that ted le Ounce's smile and goes,

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Speaker 3: I'll be delighted to give you by house hip. The

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Speaker 3: stock hits that price, and you knew exactly what you

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Speaker 3: were doing. I've seen you use that technique other times,

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Speaker 3: and I know you're using it in your business. How

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Speaker 3: do you think about that and what's the benefit of it?

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Speaker 2: Well, I think that people want to have romance in

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Speaker 2: their life. People want to have hopes and dreams. And

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Speaker 2: my beef if you will after we acquire Time Warner

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Speaker 2: was we put out a company goal of ten billion

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Speaker 2: dollars of EBITA and with a billion dollars of synergy

400
00:26:32,040 --> 00:26:33,600
Speaker 2: EBAA savings.

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Speaker 4: And I remember.

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Speaker 2: Saying, do you think a mom or a dad is

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Speaker 2: going to work late, work over the weekend and come

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00:26:45,640 --> 00:26:51,199
Speaker 2: home and have spouse or partners say what you do today?

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Speaker 4: And it's I contributed to our synergies and EBITDA.

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Speaker 2: It's like, no, people want best, they want higher calling, right,

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00:27:02,520 --> 00:27:06,760
Speaker 2: but AOL was at its best. We said, we're out

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00:27:06,840 --> 00:27:12,639
Speaker 2: to create a medium that is more valuable than the

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00:27:12,760 --> 00:27:18,560
Speaker 2: media before it radio, television, and we kind of meant

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00:27:18,720 --> 00:27:25,440
Speaker 2: that because the higher calling of the Internet of interactivity

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00:27:25,720 --> 00:27:30,159
Speaker 2: and scale and faster and cheaper till it's free.

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Speaker 4: Was very enabling, very empowering, very.

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Speaker 2: Connected to people's DNA of we want to be social,

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Speaker 2: we want to belong to something.

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Speaker 4: And you know why you and I made.

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Speaker 2: A great team was you were the best business person

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Speaker 2: I had ever been exposed to. But my gift was

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Speaker 2: to be able to say, how do we motivate people

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Speaker 2: and what will become their story and narrative when they

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Speaker 2: go home. You've got to be able to have both

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Speaker 2: in balance to build great businesses.

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Speaker 3: As an observer, one of your superpowers has been people.

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Speaker 3: You spot people. But I want to ask you a

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Speaker 3: question about that. When you spot these special people and

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Speaker 3: you've got a long list of those folks you've found,

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Speaker 3: do you treat them differently? Do you manage them differently

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Speaker 3: than you do everybody else?

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Speaker 4: For me on the people's skills, a lot of it

429
00:28:33,000 --> 00:28:39,080
Speaker 4: comes from my family. The ethnic family around your dinner

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Speaker 4: table will rip each other and needle each other and

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Speaker 4: fight and have real diversity. But then when you leave

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Speaker 4: the dinner table, you know.

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Speaker 2: I'd have friends over from college who'd come and think,

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Speaker 2: you guys hate each other. My family, we don't even

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00:29:00,560 --> 00:29:03,400
Speaker 2: talk at dinner, right, or we don't even have dinner together,

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00:29:03,960 --> 00:29:08,760
Speaker 2: But they wouldn't realize. No, this is how we share

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00:29:08,880 --> 00:29:12,400
Speaker 2: our emotions, our thoughts, and then when we leave the

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00:29:12,520 --> 00:29:17,600
Speaker 2: dinner table whereas tight as can be. So that's always

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Speaker 2: driven me. I want companies. I want executives that aren't

440
00:29:25,440 --> 00:29:30,160
Speaker 2: afraid to speak truth, to give you a different point

441
00:29:30,240 --> 00:29:35,400
Speaker 2: of view. And once we get into that cadence, then

442
00:29:35,720 --> 00:29:39,160
Speaker 2: we can have a great relationship. And the relationships are

443
00:29:39,160 --> 00:29:42,760
Speaker 2: going to be filled with ups and downs. One of

444
00:29:42,800 --> 00:29:47,360
Speaker 2: my business partners, Peter Gouber, the Great Peter Gouber's and

445
00:29:47,520 --> 00:29:51,280
Speaker 2: is it's the youngest eighty year old on the planet

446
00:29:51,880 --> 00:29:54,880
Speaker 2: and the greatest guy ever. We once did a deal.

447
00:29:55,440 --> 00:29:58,160
Speaker 2: We didn't realize we were competing with each other for

448
00:29:58,240 --> 00:30:00,720
Speaker 2: the deal, and then we I found out at the

449
00:30:00,800 --> 00:30:04,960
Speaker 2: last minute he won the deal, and I went to

450
00:30:05,040 --> 00:30:07,480
Speaker 2: him and said, wow, I had no idea you were

451
00:30:07,520 --> 00:30:10,479
Speaker 2: bidding on this and I wanted to do it, and

452
00:30:10,520 --> 00:30:12,280
Speaker 2: he said, hey, I love you.

453
00:30:13,840 --> 00:30:17,160
Speaker 4: Let's split it fifty to fifty.

454
00:30:18,920 --> 00:30:22,640
Speaker 2: I'm not going to make any big on it, any profit,

455
00:30:23,360 --> 00:30:28,320
Speaker 2: and Ted will laugh together, will cry together, but we'll

456
00:30:28,360 --> 00:30:29,760
Speaker 2: be in it together.

457
00:30:29,680 --> 00:30:33,520
Speaker 4: And it'll probably be more successful because I'm not that smart.

458
00:30:33,560 --> 00:30:38,160
Speaker 2: You're not that smart anymore, but together we probably do well.

459
00:30:38,200 --> 00:30:40,520
Speaker 4: And that was such genius.

460
00:30:41,120 --> 00:30:45,080
Speaker 2: The company we bought Team Liquid, which is one of

461
00:30:45,120 --> 00:30:49,680
Speaker 2: the most valuable esports teams. We made investments in Epic

462
00:30:49,800 --> 00:30:56,000
Speaker 2: Games and the Antic and the companies called Axiomatic, and

463
00:30:56,040 --> 00:30:57,000
Speaker 2: it's a big hit.

464
00:30:57,200 --> 00:30:59,680
Speaker 4: And his instinct was really right.

465
00:31:00,040 --> 00:31:03,160
Speaker 2: This notion of we'll laugh together, we'll cry together, and

466
00:31:03,240 --> 00:31:07,960
Speaker 2: together we're going to do a better job really proved true.

467
00:31:08,560 --> 00:31:13,480
Speaker 2: That was like the ultimate people person and it worked.

468
00:31:13,960 --> 00:31:17,080
Speaker 2: And so you know, I've always believed that be a

469
00:31:17,120 --> 00:31:19,959
Speaker 2: people person, do the right thing in the right way,

470
00:31:20,200 --> 00:31:21,760
Speaker 2: and make a ton of money.

471
00:31:22,080 --> 00:31:25,040
Speaker 3: Right, let's go to some advice before we wrap up.

472
00:31:25,080 --> 00:31:27,920
Speaker 3: I want to do one piece of advice. If you

473
00:31:27,920 --> 00:31:30,840
Speaker 3: could go back in time and give your twenty one

474
00:31:30,920 --> 00:31:33,240
Speaker 3: year old self some advice, what would that advice be?

475
00:31:34,000 --> 00:31:36,320
Speaker 4: Slow down a little bit.

476
00:31:37,280 --> 00:31:43,040
Speaker 2: I mean, I really got driven by accomplishment, and this

477
00:31:44,040 --> 00:31:53,680
Speaker 2: relentlessness and pace sometimes let you lose a great experience

478
00:31:53,720 --> 00:31:55,880
Speaker 2: because you're just so busy, right. I mean you used

479
00:31:55,880 --> 00:31:58,400
Speaker 2: to say you want something done, give it to the

480
00:31:58,440 --> 00:32:02,160
Speaker 2: busiest person. There's a lot of truth to that. But

481
00:32:02,200 --> 00:32:07,080
Speaker 2: the price you pay is you'll miss a moment with

482
00:32:07,160 --> 00:32:11,640
Speaker 2: your grandchild, you'll miss a moment with your boyfriend or girlfriend,

483
00:32:11,680 --> 00:32:15,880
Speaker 2: you'll miss a sunset. As you grow older, you realize

484
00:32:15,920 --> 00:32:19,120
Speaker 2: that was a big price to pay, and so my

485
00:32:19,280 --> 00:32:23,360
Speaker 2: advice would be stay on that fast track, but it's

486
00:32:23,400 --> 00:32:24,920
Speaker 2: okay to take the step off.

487
00:32:25,560 --> 00:32:28,560
Speaker 3: We wrap up each episode of Math and Magic with

488
00:32:28,680 --> 00:32:30,880
Speaker 3: a shout out to the greats on the math side

489
00:32:30,880 --> 00:32:34,920
Speaker 3: of the business analytical view and the magic side. This year, Creativity,

490
00:32:35,440 --> 00:32:38,560
Speaker 3: you've seen so many people. I got to ask you

491
00:32:38,680 --> 00:32:40,280
Speaker 3: for the shout outs. Who would you give the shout

492
00:32:40,280 --> 00:32:42,600
Speaker 3: out to? For the math person and for the magic person?

493
00:32:43,240 --> 00:32:46,560
Speaker 4: You know, for the math person, I am going to.

494
00:32:48,360 --> 00:32:56,160
Speaker 2: Go out of the box because his reputation somehow got besmirched.

495
00:32:56,240 --> 00:33:02,959
Speaker 2: In nineteen seventy seven, job out of college, I was

496
00:33:03,040 --> 00:33:08,600
Speaker 2: asked to find a speaker for our group. And I

497
00:33:08,640 --> 00:33:12,840
Speaker 2: had read an article and then went and met a

498
00:33:12,880 --> 00:33:18,640
Speaker 2: man da named doctor Minski, who worked at the MIT

499
00:33:18,920 --> 00:33:23,360
Speaker 2: Lab and was considered a father of AI.

500
00:33:24,000 --> 00:33:27,120
Speaker 4: And he came and gave a speech as.

501
00:33:26,960 --> 00:33:30,520
Speaker 2: A prop He wanted a piano, and he started his

502
00:33:30,680 --> 00:33:36,240
Speaker 2: speech by sitting at the piano and he was a

503
00:33:36,520 --> 00:33:39,000
Speaker 2: trained like concert pianist.

504
00:33:39,320 --> 00:33:44,560
Speaker 4: He played piano beautifully and it was shocking. It was

505
00:33:44,720 --> 00:33:46,560
Speaker 4: like out of context.

506
00:33:46,960 --> 00:33:51,000
Speaker 2: And then he went into his speech and said, we'll

507
00:33:51,160 --> 00:33:57,320
Speaker 2: know when the machines are important and have arrived when

508
00:33:57,320 --> 00:34:01,600
Speaker 2: they can do that, because every time I play, I'm

509
00:34:01,840 --> 00:34:07,520
Speaker 2: feeling the music, I'm sentient. And then he talked about

510
00:34:07,520 --> 00:34:11,080
Speaker 2: the formula and the algorithms, and boy, that really had

511
00:34:11,120 --> 00:34:11,920
Speaker 2: an effect on me.

512
00:34:13,440 --> 00:34:15,480
Speaker 4: I would say, you know.

513
00:34:15,480 --> 00:34:19,040
Speaker 2: An obvious person with Steve Jobs, and you know we

514
00:34:19,160 --> 00:34:25,720
Speaker 2: knew Steve. I grew up with Steve. His gift was saying,

515
00:34:26,200 --> 00:34:29,239
Speaker 2: you know why is Apple the most valuable company in

516
00:34:29,280 --> 00:34:37,760
Speaker 2: the world. Apple is where liberal arts and engineering come together.

517
00:34:38,440 --> 00:34:41,719
Speaker 4: And he was able to take.

518
00:34:43,040 --> 00:34:48,799
Speaker 5: Art and science and be able to express it and

519
00:34:49,320 --> 00:34:54,799
Speaker 5: obviously what's ended up being the most important piece of

520
00:34:54,920 --> 00:34:57,239
Speaker 5: technology of our lifetime.

521
00:34:58,080 --> 00:34:58,680
Speaker 4: Time on the.

522
00:34:58,640 --> 00:35:03,200
Speaker 3: Iphoneed You are a magnificent human being, a great friend.

523
00:35:03,640 --> 00:35:05,840
Speaker 3: I think you are, by the way, destined to be

524
00:35:05,960 --> 00:35:10,239
Speaker 3: this creative, genius and successful business person. You shared a

525
00:35:10,280 --> 00:35:15,719
Speaker 3: birthday with Elvis, David Bowie and even Stephen Hawking, so

526
00:35:16,480 --> 00:35:18,800
Speaker 3: must have been something in the stars. Thanks for sharing

527
00:35:18,840 --> 00:35:24,920
Speaker 3: your stories today and thanks for decades of friendship. Here

528
00:35:24,960 --> 00:35:27,000
Speaker 3: are a few things I picked up from my conversation

529
00:35:27,080 --> 00:35:31,680
Speaker 3: with Ted. One build value with data. Today businesses have

530
00:35:31,840 --> 00:35:34,840
Speaker 3: access to more data than ever before, but don't rely

531
00:35:34,920 --> 00:35:37,880
Speaker 3: on just that. Connect the dots to get a deeper

532
00:35:37,920 --> 00:35:42,359
Speaker 3: picture of your audience and provide them with services they value. Two.

533
00:35:42,880 --> 00:35:45,840
Speaker 3: Make a list of your priorities. After a close brush

534
00:35:45,880 --> 00:35:48,360
Speaker 3: with death, Ted made a list of one hundred and

535
00:35:48,400 --> 00:35:51,520
Speaker 3: one things he wants to do before he dies. Had

536
00:35:51,560 --> 00:35:54,960
Speaker 3: he not narrowed down these priorities, he wouldn't have achieved

537
00:35:55,120 --> 00:35:58,360
Speaker 3: some of his biggest dreams and opportunities. Take time to

538
00:35:58,360 --> 00:36:01,560
Speaker 3: reflect on your own goals, so when opportunity knocks, you

539
00:36:01,640 --> 00:36:02,319
Speaker 3: know what you want.

540
00:36:02,760 --> 00:36:03,280
Speaker 4: Three.

541
00:36:03,520 --> 00:36:07,360
Speaker 3: Remember what motivates people. Ted is the ultimate team builder.

542
00:36:07,560 --> 00:36:09,759
Speaker 3: He knows people are excited when they get to work

543
00:36:09,760 --> 00:36:14,239
Speaker 3: together to build something visionary and purposeful. Create opportunities for

544
00:36:14,360 --> 00:36:17,960
Speaker 3: your team to work on something that inspires them. I'm

545
00:36:17,960 --> 00:36:19,600
Speaker 3: Bob Pittman. Thanks for listening.

546
00:36:29,080 --> 00:36:31,840
Speaker 1: That's it for today's episode. Thanks so much for listening

547
00:36:31,840 --> 00:36:34,919
Speaker 1: to Math and Magic, a production of iHeartRadio. The show

548
00:36:34,960 --> 00:36:38,440
Speaker 1: is hosted by Bob Pittman. Special thanks to Sydney Rosenbloom

549
00:36:38,520 --> 00:36:41,200
Speaker 1: for booking and wrangling our wonderful talent, which is no

550
00:36:41,239 --> 00:36:46,319
Speaker 1: small feat our editor Emily Meronoff, our engineers Jessica Crincicch

551
00:36:46,360 --> 00:36:50,440
Speaker 1: and Bahid Fraser, our executive producers nickki Etoor and Ali Perry,

552
00:36:50,920 --> 00:36:54,600
Speaker 1: and of course Gail Raoul, Eric Angel Noel and everyone

553
00:36:54,600 --> 00:36:58,319
Speaker 1: who helped bring this show to your ears. Until next time,