June 8, 2023 • 37 mins
Tech pioneer Ted Leonsis is never one to shy away from a challenge. He’s an uncanny trend spotter, from his beginnings at companies like Wang Laboratories and AOL to his current role as Founder and CEO of Monumental Sports and Entertainment. He’s also the ambitious co-founder of investment group Revolution Growth and even did a stint as mayor of a small Florida town. But before college, Ted was a self-described “lazy” student who would just get by. So, what changed? In this conversation, Ted tells Bob about the monumental event that changed the trajectory of his life and ignited his passion for technology. He also explains how a near death experience propelled him to create a list of 101 things to do before he died – a list that guides and motivates him in all aspects of his life to this day.
See omnystudio.com/listener for privacy information.
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:01):
You're listening to Math and Magic, a production at iHeartRadio.
Speaker 2 (00:07):
We have to innovate, we have to keep pushing the
envelope to build our audience and really be relevant. And
the only way that you'll be able to innovate and
provide those services and build value is to know who
they are what they do in a real way.
Speaker 3 (00:30):
Hi, I'm Bob Pittman, and welcome to this episode of
Math and Magic Stories from the Frontiers of Marketing. Today,
we have an honest to god magician. He began his
career at what was a revolutionary tech company in its day.
Wang Labs had a few startups that hit, was a
hugely important player in the incredible success of AOL in
the nineties. Continued his tech investing while building a Washington,
(00:54):
DC based sports powerhouse centered on the Washington Capitals and
the Wizards. He's the former vice chair of AOL. He's
the founder and CEO of Monumental Sports and Entertainment, and
he's a co founder of the investment group Revolution Growth.
Ted Leonsis ted is Brooklyn, born, raised there and in Massachusetts.
He's a Georgetown grad and tech pioneer. He's known for
(01:18):
seeing things early and in a way others can't. He
cares deeply about people and communities and always finds a
way to put them first and all he does. He's
the former mayor of a small Florida town. He has
a wicked sense of humor, big heart, and a perpetual smile.
He's been a friend for almost thirty years, and I
have to say he is also the person who talked
(01:40):
me into going to AOL just as our big ride began,
So I owe him a big one for that.
Speaker 4 (01:46):
Ted, welcome, Thank you, Bob.
Speaker 2 (01:47):
It's very sweet of you, and I missed everything about
your voice and leadership, and you shouldn't be shy to
say that. I reported to you and I learned a
lot from you back in the day, so it's wonderful
to be on your podcast.
Speaker 3 (02:04):
Well, you're very nice, Ted. I always thought of us
as partners, and boy, did you contribute a lot. Before
we jump into that meat though, I want to do
you in sixty seconds. You ready to go yep? Do
you prefer cats or dogs? Dogs, VITYL or CDs, Vinyl
FaceTime or.
Speaker 4 (02:22):
Call oh face to face, hugging.
Speaker 3 (02:25):
DC or Brooklyn DC, Slow and steady or pedal to
the metal, both early riser or night out.
Speaker 4 (02:32):
Oh, I'm early rise.
Speaker 2 (02:33):
I get all my best work done between five and
seven am.
Speaker 3 (02:37):
Movies at the theater are a streaming movie, streaming crypto
or dollars dollars Elvis or David Bowie Elvis.
Speaker 4 (02:46):
Me and Elvis had the same birthday.
Speaker 3 (02:48):
Guilty pleasure.
Speaker 4 (02:49):
I am spoiling the shit out.
Speaker 2 (02:51):
Of my grandchildren.
Speaker 3 (02:54):
Okay, let's jump into the meat. You have an uncanny
ability to see around corners, computers, new media, online, e sports,
and on and on. Let's start with the topic of
the minute AI friend or foe and how's it going
to change our lives in business?
Speaker 2 (03:10):
Oh, it's totally faux right now, and I'm horrified with
the answers that I'm hearing from. You would think socially
responsible CEOs. So you know, we used to talk about
early in the computer industry, you know, garbage in, garbage out,
(03:32):
And so you know, these machines in the cloud that
are doing machine learning, the input that they're getting, it's
bad info.
Speaker 4 (03:43):
You can't tell the sentiment if you will.
Speaker 2 (03:46):
That's on social media and Reddit and all of the
rivers of data that's at scale, and so we will
get very, very bad answers, because it's holding a mirror
up to ourselves, but it's not our real self. We're
(04:08):
holding a mirror up to this faux personality, this faux
knowledge base. When you're seeing and you hear a CEO
say the machines lined, they call it hallucinations. But the
machines line and we don't know why. It's like, hey,
(04:31):
full stop till you can figure out why.
Speaker 3 (04:35):
So with this view of AI, in our day in
taking the Internet to the mass market, it was really
AOL versus Microsoft today in the AI battleground, and it
is a battle. Who do you think are the big competitors?
If you're sort of watching saying, wow, we need to
watch this, who are the two companies? Are there more
(04:58):
that are really in the middle of this that we
have to look to provide that kind of guidance.
Speaker 2 (05:02):
Well, like every industry, you'll have the big incumbents. Certainly
here Microsoft, which has a multi trillion dollar market cap
and more cash than the United States does right now,
they will be formidable. Apple certainly will be late to
the game, but they'll be formidable. Obviously, Alphabet and Google
(05:27):
they'll be big and important, and then you'll have startups
But where I see a positive difference is that we
always thought in our generation that there would be some
fantastic verticalization and where you could go deeper in a
(05:50):
vertical with a solution. You know, we thought vertical search
one day would be important. I'm an investor in a
company called tempest Temus, and it is a AI company.
It uses a power plant now mostly from Google and
(06:10):
alphabet but you wouldn't know it as a doctor, a
healthcare provider, or a consumer, and it is noble. It
is using the power of machine learning all of the
data that's out there to help families answer a question.
Speaker 4 (06:33):
Your wife gets diagnosed.
Speaker 2 (06:35):
With stage three cancer and she would go to the
doctor and they would take a sell and say you've
got cancer, and then they would tell you, hey, there
was a study in the New England Journal of Medicine
of fifteen people and based on that and my experience
(06:57):
as the doctor, it's her genetics, family history.
Speaker 4 (07:01):
Here's what you should be doing.
Speaker 2 (07:05):
So AI in that kind of setting where you can
provide at scale data with deep genomic information, to me
is a positive on AI. If we can take that
high road, use a higher calling around the tools. I
(07:28):
think we're going to be in good shape, and I
think that will only come from deep verticalization where companies
are saying this isn't a business. We're using this technology
for research and to help doctors to find better ways.
(07:49):
And yes it'll help save money, Yes it'll help activate
more efficiency. But we should be using the technology to
answer big questions, not fill the algorithms up with junk.
Speaker 3 (08:04):
So let's take AI sports. You got into team ownership
in the late nineties, and I'm sure technologies have affected
your business. What are the new opportunities with the tech
today and especially with AI. Is this going to select
your players now?
Speaker 2 (08:18):
Well, that's really interesting because when you know, I came
into sports in nineteen ninety nine, everyone lived on Fax
machines and we were the first team. At least we
gave everyone personal computers. I gave everyone email addresses. It
was basic connectivity. But then we started to say, hey,
(08:42):
this is a platform to launch a lot of new technology,
and then if it takes hold here, it can be
nested if you will, within sports and arenas because we
touched so many people, and then move into general media.
(09:02):
And so the first big investments that were being made
were selecting of teams for coaching the players, and the
amount of work that was done in high speed cameras
and being able to create these heat maps on where
was the optimal place for the player to take the shot.
(09:24):
When you were playing defense, we would pixelate the floor.
And I remember once Kobe Bryant were playing the Lakers
and he was going to get the end of game
shot and was, well, if you can move Kobe two
pixels to the left, he shoots thirty eight percent. If
he's those two pixels to the right, he's forty four percent.
Speaker 4 (09:47):
All you can do.
Speaker 2 (09:49):
Is get him to the suboptimized place and where the
most transparent of providing that data to fans, to coaches,
to competitors, and now to sports betters.
Speaker 4 (10:06):
Right, they have access to.
Speaker 2 (10:08):
More data, certainly than a Wall Street trader has in
trying to.
Speaker 4 (10:14):
Pick whether you should short a stock.
Speaker 2 (10:17):
Right, So I looked at sports as being the most
data rich side on the product.
Speaker 4 (10:23):
And now it's moved into marketing.
Speaker 2 (10:26):
We have a database and monumental sports and entertainment that's
closing in on four million active records where we really
know who's coming into the building, how long have they
been a customer, what have they spent, how do they
renew who do they give their tickets to, how far
(10:49):
do they travel? What are they're viewing habits? Are they
streaming now? Are they still watching games on cable? How
important is radio? One thing I'll say, radio and digital
radio and podcasting has become incredibly powerful and on trend again.
Speaker 4 (11:11):
We have to innovate.
Speaker 2 (11:13):
We have to keep pushing the envelope to build our
audience and really be relevant. And the only way that
you'll be able to innovate and provide those services and
build value is to know who they are, what they
do in a real way, not a superficial way, which
(11:33):
is what's happened on the internet. But I think it's
lost its efficacy and deep verticalization. Really understanding your customer,
really understanding how to connect all the way through all
of the ways that they live their life on the
net is what the challenge for the next set of
(11:55):
marketers will.
Speaker 4 (11:56):
Be in our industry.
Speaker 3 (11:58):
Well, okay, we've covered the business. I mean it's fascinating
how you've applied all you knew from technology to sports.
I want to jump for a minute to you with
a personal matter. One of the most interesting things we
talked about when we first met, was this list you
had one hundred and one things you wanted to do
before you die. Tell us about the genesis of that list.
(12:20):
And by the way, I also want to know how
many of those one hundred and one have you achieved?
Speaker 4 (12:27):
Thank you.
Speaker 2 (12:27):
It's you know, deeply personal and everyone will have their reckonings.
Everyone will have something very disruptive happen in their life
and times, and it's how you react to that. And
a lot of times the disruption ends up being a
(12:50):
grade advantage if you take it in and process it
in a positive way. So for me, you know, I
was a poor kid growing up. My father was a waiter.
It was an immigrant family. We grew up in Lowell, Massachusetts,
working in mills. And then my dad went into the
service and married later in life and was a waiter
(13:12):
and we lived in a.
Speaker 4 (13:13):
Rental apartment in Brooklyn, and.
Speaker 2 (13:15):
I'm an only child. They taught me to work hard,
get good grades. If you get good grades, you'll get
into a good school. If you get in good school
and you get good grades, you'll get a good job
and you'll make a lot of money. And so I
followed that Mantra. I went to Georgetown University and had
some academic success and got out of college and worked
(13:39):
at a tech company as you mentioned, Wang Laboratories, and
then I had the entrepreneurial bug and I started my
first company. I was lucky and maybe clever, and I
sold that company two years after I started for sixty
million dollars, and Bob I declared victory.
Speaker 4 (13:57):
And how society tells you to the clear victory.
Speaker 2 (14:01):
Ends up being false social responsibility. In false victory, you
buy stuff. You're very popular with people who were using
you as their platform for success. And so I kind
of lost my way. And then I got on the
wrong airplane. I had a reckoning. The plane was going down.
(14:24):
I was alone on the plane, and I started praying.
Seemed like the right thing to do. And I started
laughing because I was praying to God, saying, you know,
please let me get through this.
Speaker 4 (14:37):
I don't want to die.
Speaker 2 (14:39):
And my first thought was if there was a God listening,
they say, oh, now you need me. You're in deep
angst and you think I'm here to help you. So
I reverted back to type. I said, all right, let's
cut a deal. If I live.
Speaker 4 (14:56):
Through this, I promise I will leave more than I take.
How's that?
Speaker 2 (15:01):
Obviously we made it through, but I've always been well,
a deal's a deal. How will I now pay that
debt back? I made this list of one hundred and
one things to do before I die, so that I
could say an end of my life. Could I look
(15:25):
back and metric and score? How did I do it?
As I matured and I look at the list, I
go some of the things that I thought was a
good way of keeping score ended up not being as
important as I thought.
Speaker 4 (15:47):
But Bob, and you'll remember, you were kind of there.
Speaker 2 (15:52):
I had put on my list on a sports team
win a championship, and if I hadn't have written that down.
When I was approach to buy the teams, I initially
said no. I said, I'm working at AOL, I have
two young children. There's no way I could own a team.
(16:12):
And I came home and Lynn said to me, what's new?
What happened today? And I said, oh, this guy approached
me wanted to buy teams. And she said what did
you say?
Speaker 4 (16:22):
And I said, I can't engaged at the company. We
have young kids.
Speaker 2 (16:26):
And Lynn said to me, hey, we've been together for
a long time. We'd be together forever. What if you
get ninety nine of the one hundred and one thing's
done and you don't buy a team and you don't
win a championship, and so well, this is why I
love you.
Speaker 4 (16:44):
Yeah, I'm going to go and.
Speaker 2 (16:46):
I bought the team and we've won championships. But it's
also been like the best financial decision as a family
we could have made.
Speaker 4 (16:55):
So I'm now at like ninety four.
Speaker 2 (16:58):
I wrote this list in nineteen eighty six, and back
then I said I want to go into outer space.
That seemed crazy. Right now I go, oh, I'm definitely
gonna get that one checked off, right, I mean, Richard
Branson's called me, Bezos has called me, and I'm sure
(17:19):
I will get into outer space, and I'm clicking them off.
But it also was will I be happy and self
actualized at the end of the journey, And some of
the things on the list weren't going to make me happy,
(17:39):
And so I've matured, if you will, And I don't
think I have to get all one hundred and one done,
But you know, big important things like falling in love,
getting married, and having children, having grandchildren, living a healthy life,
giving back.
Speaker 4 (17:56):
Those are the things that I think will be most important.
At the end of the day.
Speaker 3 (18:02):
You touched on your childhood and growing up and what
it meant to you. If I remember my story right,
I think your high school guidance counselor told you you
weren't college material and tried to steer you to a
vocational school. Obviously that didn't sit well with you. You
went onto Georgetown, and I think that really set you
(18:22):
on the track of technology and got you into tech.
Can you tell us a little bit more about that story,
because I do think that probably was a seminal moment
for ted Leonsis and where you wound up.
Speaker 2 (18:35):
Yeah, I probably created the first algorithmic based liberal arts
program ever and it was powered by a Jesuit priest
who was my mentor.
Speaker 4 (18:54):
I had to write a thesis.
Speaker 2 (18:56):
And I was a little bit lazy and said what
are you going to write about? And I said, I'll
come up with the idea over the weekend. So I
went to the library and I was with some buddies
and I said, all right, I'm going to find the shortest,
easiest book to read in the library. I found Old
(19:18):
Man in the Seat and it's a novella. If you
will and I read in like an hour and a half.
And on Monday I went to see my mentor, father,
Joseph Dirkin, Society of Jesus, and he said, okay, great,
tell me what you thought of the book. And I
gave him like an annotated book report, and he said,
(19:38):
I'm so disappointed in you. There's so much going on
in this book with this author at the time it
was published. You're better than this. And he challenged me
to be more of a critical thinker.
Speaker 4 (19:54):
So I didn't know anything about Hemingway.
Speaker 2 (19:56):
There was no Internet back then, so I literally and
got a bunch.
Speaker 4 (20:01):
Of his books.
Speaker 2 (20:02):
And the second book I read, Across the River and
Into the Trees, was nothing like Old Man in the Sea,
And as I started to do research, Hemingway was a
journalist Esquire Magazine, New Yorker Magazine, and then he became
a writer and Old Man in the Sea was published
(20:23):
in the fifties. He won a Pulit surprise, he ended
up taking his life. And I just had this idea
that I think Ernest Hemingway wrote this book earlier in
his career and he needed the money, and you know,
jazz was becoming popular and Jack Kerouac and Allen Ginsburg,
(20:46):
and he went back to his journalistic style. And Father
Dirk and said, well, great, how are you going to
prove it? And I said, I'll call his wife. Oh,
I'll try and talk to the publisher. And Father Dirkin,
to his credit, said why don't we use a computer?
And I said, a computer like in the movies. Like.
(21:12):
We had one computer on campus in the registrar's office.
It was IBM three stixy and it spit out for
students what classes you would take. So he introduced me
to a linguistics major. He introduced me to someone who
(21:32):
worked in the computer mis department, and we went to
dinner and we came up with the idea of I
would have to type in the first five thousand lines
of a book and articles from the fifties, from the forties.
Speaker 4 (21:51):
From the thirties.
Speaker 2 (21:53):
And then the linguistics major she came up with these
metrics words, person sentences, sentences per paragraph, pronoun references. There
were seventeen measures, and then the programmer wrote this code
to basically answer the question. Based on this algorithm, when
(22:19):
was Old Man in the Sea as a control written?
Speaker 4 (22:23):
And it said the book was written in.
Speaker 2 (22:25):
The thirties, not in nineteen fifty two, and it was
mind blowing to us. And you know, I wrote a
college thesis. But it showed how a computer and software
and algorithms could find essence of truth in the data
(22:49):
and show you something that you never knew happened.
Speaker 4 (22:56):
And it was truly for me.
Speaker 2 (22:59):
Ah moment, and it powered pretty much everything for the
rest of my life and career.
Speaker 4 (23:09):
Made a movie in a similar fashion in that.
Speaker 2 (23:14):
I didn't have anything to read Bob and I started
reading the Last thirty Days of the New York Times
from a bookstore in Saint Bart's and I read an
obituary about an author named Iris Chang and she wrote
a book called.
Speaker 4 (23:31):
The Rape of Name King.
Speaker 2 (23:33):
And when I got home, I went on Amazon and
I said, I'm going to buy the book, and their
simple little algorithm said, oh.
Speaker 4 (23:43):
If you like this book, then you like these books.
Speaker 2 (23:48):
And there was a starburst that said the Forgotten Holocaust
and it was basically behind the Chinese Communist Wall. This
was in nineteen thirty four. I was at AOL, I
had some contacts. I worked with the people at CIA,
and I made a film called Nang King and it
(24:10):
won Best at Open Sundayands, it.
Speaker 4 (24:13):
Went on HBO.
Speaker 2 (24:15):
It won the Emmy Award for Best Documentary. And it
all came from reading an obituary and an algorithm telling
me there's more to this story than this just this
one book, and that turned me into making films.
Speaker 3 (24:39):
More mathem magic. Right after this quick break, welcome back
to Mathemagic. Let's hear more from my conversation with Ted
leonsis so Ted, you make mundane things sound exciting about
(25:00):
adding a twist, and I want to tell a story
on you. When I got to AOL and we were
in this whole process of how can we make this
company profitable? I was laying out to our group of
senior leaders, tying it to what the stock price could
be because they all were equity holders if we achieved
those earnings levels, and I start think it's mathematical, it's
(25:22):
this and this and this, and you could see people
sort of glazing over some skepticism. You suddenly amped up
the discussion by declaring to the room that Bob, I'm
going to give you my house if we hit that
stock price. Suddenly the room was on fire. And by
the way, when we walked out of the room, you
sort of gave me that ted le Ounce's smile and goes,
(25:43):
I'll be delighted to give you by house hip. The
stock hits that price, and you knew exactly what you
were doing. I've seen you use that technique other times,
and I know you're using it in your business. How
do you think about that and what's the benefit of it?
Speaker 2 (26:00):
Well, I think that people want to have romance in
their life. People want to have hopes and dreams. And
my beef if you will after we acquire Time Warner
was we put out a company goal of ten billion
(26:24):
dollars of EBITA and with a billion dollars of synergy
EBAA savings.
Speaker 4 (26:34):
And I remember.
Speaker 2 (26:35):
Saying, do you think a mom or a dad is
going to work late, work over the weekend and come
home and have spouse or partners say what you do today?
Speaker 4 (26:51):
And it's I contributed to our synergies and EBITDA.
Speaker 2 (26:57):
It's like, no, people want best, they want higher calling, right,
but AOL was at its best. We said, we're out
to create a medium that is more valuable than the
media before it radio, television, and we kind of meant
(27:18):
that because the higher calling of the Internet of interactivity
and scale and faster and cheaper till it's free.
Speaker 4 (27:31):
Was very enabling, very empowering, very.
Speaker 2 (27:36):
Connected to people's DNA of we want to be social,
we want to belong to something.
Speaker 4 (27:43):
And you know why you and I made.
Speaker 2 (27:46):
A great team was you were the best business person
I had ever been exposed to. But my gift was
to be able to say, how do we motivate people
and what will become their story and narrative when they
go home. You've got to be able to have both
(28:09):
in balance to build great businesses.
Speaker 3 (28:11):
As an observer, one of your superpowers has been people.
You spot people. But I want to ask you a
question about that. When you spot these special people and
you've got a long list of those folks you've found,
do you treat them differently? Do you manage them differently
than you do everybody else?
Speaker 4 (28:29):
For me on the people's skills, a lot of it
comes from my family. The ethnic family around your dinner
table will rip each other and needle each other and
fight and have real diversity. But then when you leave
(28:50):
the dinner table, you know.
Speaker 2 (28:51):
I'd have friends over from college who'd come and think,
you guys hate each other. My family, we don't even
talk at dinner, right, or we don't even have dinner together,
But they wouldn't realize. No, this is how we share
our emotions, our thoughts, and then when we leave the
(29:12):
dinner table whereas tight as can be. So that's always
driven me. I want companies. I want executives that aren't
afraid to speak truth, to give you a different point
of view. And once we get into that cadence, then
(29:35):
we can have a great relationship. And the relationships are
going to be filled with ups and downs. One of
my business partners, Peter Gouber, the Great Peter Gouber's and
is it's the youngest eighty year old on the planet
and the greatest guy ever. We once did a deal.
We didn't realize we were competing with each other for
(29:58):
the deal, and then we I found out at the
last minute he won the deal, and I went to
him and said, wow, I had no idea you were
bidding on this and I wanted to do it, and
he said, hey, I love you.
Speaker 4 (30:13):
Let's split it fifty to fifty.
Speaker 2 (30:18):
I'm not going to make any big on it, any profit,
and Ted will laugh together, will cry together, but we'll
be in it together.
Speaker 4 (30:29):
And it'll probably be more successful because I'm not that smart.
Speaker 2 (30:33):
You're not that smart anymore, but together we probably do well.
Speaker 4 (30:38):
And that was such genius.
Speaker 2 (30:41):
The company we bought Team Liquid, which is one of
the most valuable esports teams. We made investments in Epic
Games and the Antic and the companies called Axiomatic, and
it's a big hit.
Speaker 4 (30:57):
And his instinct was really right.
Speaker 2 (31:00):
This notion of we'll laugh together, we'll cry together, and
together we're going to do a better job really proved true.
That was like the ultimate people person and it worked.
And so you know, I've always believed that be a
people person, do the right thing in the right way,
(31:20):
and make a ton of money.
Speaker 3 (31:22):
Right, let's go to some advice before we wrap up.
I want to do one piece of advice. If you
could go back in time and give your twenty one
year old self some advice, what would that advice be?
Speaker 4 (31:34):
Slow down a little bit.
Speaker 2 (31:37):
I mean, I really got driven by accomplishment, and this
relentlessness and pace sometimes let you lose a great experience
because you're just so busy, right. I mean you used
to say you want something done, give it to the
(31:58):
busiest person. There's a lot of truth to that. But
the price you pay is you'll miss a moment with
your grandchild, you'll miss a moment with your boyfriend or girlfriend,
you'll miss a sunset. As you grow older, you realize
that was a big price to pay, and so my
(32:19):
advice would be stay on that fast track, but it's
okay to take the step off.
Speaker 3 (32:25):
We wrap up each episode of Math and Magic with
a shout out to the greats on the math side
of the business analytical view and the magic side. This year, Creativity,
you've seen so many people. I got to ask you
for the shout outs. Who would you give the shout
out to? For the math person and for the magic person?
Speaker 4 (32:43):
You know, for the math person, I am going to.
Speaker 2 (32:48):
Go out of the box because his reputation somehow got besmirched.
In nineteen seventy seven, job out of college, I was
asked to find a speaker for our group. And I
(33:08):
had read an article and then went and met a
man da named doctor Minski, who worked at the MIT
Lab and was considered a father of AI.
Speaker 4 (33:24):
And he came and gave a speech as.
Speaker 2 (33:26):
A prop He wanted a piano, and he started his
speech by sitting at the piano and he was a
trained like concert pianist.
Speaker 4 (33:39):
He played piano beautifully and it was shocking. It was
like out of context.
Speaker 2 (33:46):
And then he went into his speech and said, we'll
know when the machines are important and have arrived when
they can do that, because every time I play, I'm
feeling the music, I'm sentient. And then he talked about
(34:07):
the formula and the algorithms, and boy, that really had
an effect on me.
Speaker 4 (34:13):
I would say, you know.
Speaker 2 (34:15):
An obvious person with Steve Jobs, and you know we
knew Steve. I grew up with Steve. His gift was saying,
you know why is Apple the most valuable company in
the world. Apple is where liberal arts and engineering come together.
Speaker 4 (34:38):
And he was able to take.
Speaker 5 (34:43):
Art and science and be able to express it and
obviously what's ended up being the most important piece of
technology of our lifetime.
Speaker 4 (34:58):
Time on the.
Speaker 3 (34:58):
Iphoneed You are a magnificent human being, a great friend.
I think you are, by the way, destined to be
this creative, genius and successful business person. You shared a
birthday with Elvis, David Bowie and even Stephen Hawking, so
must have been something in the stars. Thanks for sharing
(35:18):
your stories today and thanks for decades of friendship. Here
are a few things I picked up from my conversation
with Ted. One build value with data. Today businesses have
access to more data than ever before, but don't rely
on just that. Connect the dots to get a deeper
picture of your audience and provide them with services they value. Two.
(35:42):
Make a list of your priorities. After a close brush
with death, Ted made a list of one hundred and
one things he wants to do before he dies. Had
he not narrowed down these priorities, he wouldn't have achieved
some of his biggest dreams and opportunities. Take time to
reflect on your own goals, so when opportunity knocks, you
know what you want.
Speaker 4 (36:02):
Three.
Speaker 3 (36:03):
Remember what motivates people. Ted is the ultimate team builder.
He knows people are excited when they get to work
together to build something visionary and purposeful. Create opportunities for
your team to work on something that inspires them. I'm
Bob Pittman. Thanks for listening.
Speaker 1 (36:29):
That's it for today's episode. Thanks so much for listening
to Math and Magic, a production of iHeartRadio. The show
is hosted by Bob Pittman. Special thanks to Sydney Rosenbloom
for booking and wrangling our wonderful talent, which is no
small feat our editor Emily Meronoff, our engineers Jessica Crincicch
and Bahid Fraser, our executive producers nickki Etoor and Ali Perry,
(36:50):
and of course Gail Raoul, Eric Angel Noel and everyone
who helped bring this show to your ears. Until next time,
You're listening to Math and Magic, a production at iHeartRadio.
Speaker 2 (00:07):
We have to innovate, we have to keep pushing the
envelope to build our audience and really be relevant. And
the only way that you'll be able to innovate and
provide those services and build value is to know who
they are what they do in a real way.
Speaker 3 (00:30):
Hi, I'm Bob Pittman, and welcome to this episode of
Math and Magic Stories from the Frontiers of Marketing. Today,
we have an honest to god magician. He began his
career at what was a revolutionary tech company in its day.
Wang Labs had a few startups that hit, was a
hugely important player in the incredible success of AOL in
the nineties. Continued his tech investing while building a Washington,
(00:54):
DC based sports powerhouse centered on the Washington Capitals and
the Wizards. He's the former vice chair of AOL. He's
the founder and CEO of Monumental Sports and Entertainment, and
he's a co founder of the investment group Revolution Growth.
Ted Leonsis ted is Brooklyn, born, raised there and in Massachusetts.
He's a Georgetown grad and tech pioneer. He's known for
(01:18):
seeing things early and in a way others can't. He
cares deeply about people and communities and always finds a
way to put them first and all he does. He's
the former mayor of a small Florida town. He has
a wicked sense of humor, big heart, and a perpetual smile.
He's been a friend for almost thirty years, and I
have to say he is also the person who talked
(01:40):
me into going to AOL just as our big ride began,
So I owe him a big one for that.
Speaker 4 (01:46):
Ted, welcome, Thank you, Bob.
Speaker 2 (01:47):
It's very sweet of you, and I missed everything about
your voice and leadership, and you shouldn't be shy to
say that. I reported to you and I learned a
lot from you back in the day, so it's wonderful
to be on your podcast.
Speaker 3 (02:04):
Well, you're very nice, Ted. I always thought of us
as partners, and boy, did you contribute a lot. Before
we jump into that meat though, I want to do
you in sixty seconds. You ready to go yep? Do
you prefer cats or dogs? Dogs, VITYL or CDs, Vinyl
FaceTime or.
Speaker 4 (02:22):
Call oh face to face, hugging.
Speaker 3 (02:25):
DC or Brooklyn DC, Slow and steady or pedal to
the metal, both early riser or night out.
Speaker 4 (02:32):
Oh, I'm early rise.
Speaker 2 (02:33):
I get all my best work done between five and
seven am.
Speaker 3 (02:37):
Movies at the theater are a streaming movie, streaming crypto
or dollars dollars Elvis or David Bowie Elvis.
Speaker 4 (02:46):
Me and Elvis had the same birthday.
Speaker 3 (02:48):
Guilty pleasure.
Speaker 4 (02:49):
I am spoiling the shit out.
Speaker 2 (02:51):
Of my grandchildren.
Speaker 3 (02:54):
Okay, let's jump into the meat. You have an uncanny
ability to see around corners, computers, new media, online, e sports,
and on and on. Let's start with the topic of
the minute AI friend or foe and how's it going
to change our lives in business?
Speaker 2 (03:10):
Oh, it's totally faux right now, and I'm horrified with
the answers that I'm hearing from. You would think socially
responsible CEOs. So you know, we used to talk about
early in the computer industry, you know, garbage in, garbage out,
(03:32):
And so you know, these machines in the cloud that
are doing machine learning, the input that they're getting, it's
bad info.
Speaker 4 (03:43):
You can't tell the sentiment if you will.
Speaker 2 (03:46):
That's on social media and Reddit and all of the
rivers of data that's at scale, and so we will
get very, very bad answers, because it's holding a mirror
up to ourselves, but it's not our real self. We're
(04:08):
holding a mirror up to this faux personality, this faux
knowledge base. When you're seeing and you hear a CEO
say the machines lined, they call it hallucinations. But the
machines line and we don't know why. It's like, hey,
(04:31):
full stop till you can figure out why.
Speaker 3 (04:35):
So with this view of AI, in our day in
taking the Internet to the mass market, it was really
AOL versus Microsoft today in the AI battleground, and it
is a battle. Who do you think are the big competitors?
If you're sort of watching saying, wow, we need to
watch this, who are the two companies? Are there more
(04:58):
that are really in the middle of this that we
have to look to provide that kind of guidance.
Speaker 2 (05:02):
Well, like every industry, you'll have the big incumbents. Certainly
here Microsoft, which has a multi trillion dollar market cap
and more cash than the United States does right now,
they will be formidable. Apple certainly will be late to
the game, but they'll be formidable. Obviously, Alphabet and Google
(05:27):
they'll be big and important, and then you'll have startups
But where I see a positive difference is that we
always thought in our generation that there would be some
fantastic verticalization and where you could go deeper in a
(05:50):
vertical with a solution. You know, we thought vertical search
one day would be important. I'm an investor in a
company called tempest Temus, and it is a AI company.
It uses a power plant now mostly from Google and
(06:10):
alphabet but you wouldn't know it as a doctor, a
healthcare provider, or a consumer, and it is noble. It
is using the power of machine learning all of the
data that's out there to help families answer a question.
Speaker 4 (06:33):
Your wife gets diagnosed.
Speaker 2 (06:35):
With stage three cancer and she would go to the
doctor and they would take a sell and say you've
got cancer, and then they would tell you, hey, there
was a study in the New England Journal of Medicine
of fifteen people and based on that and my experience
(06:57):
as the doctor, it's her genetics, family history.
Speaker 4 (07:01):
Here's what you should be doing.
Speaker 2 (07:05):
So AI in that kind of setting where you can
provide at scale data with deep genomic information, to me
is a positive on AI. If we can take that
high road, use a higher calling around the tools. I
(07:28):
think we're going to be in good shape, and I
think that will only come from deep verticalization where companies
are saying this isn't a business. We're using this technology
for research and to help doctors to find better ways.
(07:49):
And yes it'll help save money, Yes it'll help activate
more efficiency. But we should be using the technology to
answer big questions, not fill the algorithms up with junk.
Speaker 3 (08:04):
So let's take AI sports. You got into team ownership
in the late nineties, and I'm sure technologies have affected
your business. What are the new opportunities with the tech
today and especially with AI. Is this going to select
your players now?
Speaker 2 (08:18):
Well, that's really interesting because when you know, I came
into sports in nineteen ninety nine, everyone lived on Fax
machines and we were the first team. At least we
gave everyone personal computers. I gave everyone email addresses. It
was basic connectivity. But then we started to say, hey,
(08:42):
this is a platform to launch a lot of new technology,
and then if it takes hold here, it can be
nested if you will, within sports and arenas because we
touched so many people, and then move into general media.
(09:02):
And so the first big investments that were being made
were selecting of teams for coaching the players, and the
amount of work that was done in high speed cameras
and being able to create these heat maps on where
was the optimal place for the player to take the shot.
(09:24):
When you were playing defense, we would pixelate the floor.
And I remember once Kobe Bryant were playing the Lakers
and he was going to get the end of game
shot and was, well, if you can move Kobe two
pixels to the left, he shoots thirty eight percent. If
he's those two pixels to the right, he's forty four percent.
Speaker 4 (09:47):
All you can do.
Speaker 2 (09:49):
Is get him to the suboptimized place and where the
most transparent of providing that data to fans, to coaches,
to competitors, and now to sports betters.
Speaker 4 (10:06):
Right, they have access to.
Speaker 2 (10:08):
More data, certainly than a Wall Street trader has in
trying to.
Speaker 4 (10:14):
Pick whether you should short a stock.
Speaker 2 (10:17):
Right, So I looked at sports as being the most
data rich side on the product.
Speaker 4 (10:23):
And now it's moved into marketing.
Speaker 2 (10:26):
We have a database and monumental sports and entertainment that's
closing in on four million active records where we really
know who's coming into the building, how long have they
been a customer, what have they spent, how do they
renew who do they give their tickets to, how far
(10:49):
do they travel? What are they're viewing habits? Are they
streaming now? Are they still watching games on cable? How
important is radio? One thing I'll say, radio and digital
radio and podcasting has become incredibly powerful and on trend again.
Speaker 4 (11:11):
We have to innovate.
Speaker 2 (11:13):
We have to keep pushing the envelope to build our
audience and really be relevant. And the only way that
you'll be able to innovate and provide those services and
build value is to know who they are, what they
do in a real way, not a superficial way, which
(11:33):
is what's happened on the internet. But I think it's
lost its efficacy and deep verticalization. Really understanding your customer,
really understanding how to connect all the way through all
of the ways that they live their life on the
net is what the challenge for the next set of
(11:55):
marketers will.
Speaker 4 (11:56):
Be in our industry.
Speaker 3 (11:58):
Well, okay, we've covered the business. I mean it's fascinating
how you've applied all you knew from technology to sports.
I want to jump for a minute to you with
a personal matter. One of the most interesting things we
talked about when we first met, was this list you
had one hundred and one things you wanted to do
before you die. Tell us about the genesis of that list.
(12:20):
And by the way, I also want to know how
many of those one hundred and one have you achieved?
Speaker 4 (12:27):
Thank you.
Speaker 2 (12:27):
It's you know, deeply personal and everyone will have their reckonings.
Everyone will have something very disruptive happen in their life
and times, and it's how you react to that. And
a lot of times the disruption ends up being a
(12:50):
grade advantage if you take it in and process it
in a positive way. So for me, you know, I
was a poor kid growing up. My father was a waiter.
It was an immigrant family. We grew up in Lowell, Massachusetts,
working in mills. And then my dad went into the
service and married later in life and was a waiter
(13:12):
and we lived in a.
Speaker 4 (13:13):
Rental apartment in Brooklyn, and.
Speaker 2 (13:15):
I'm an only child. They taught me to work hard,
get good grades. If you get good grades, you'll get
into a good school. If you get in good school
and you get good grades, you'll get a good job
and you'll make a lot of money. And so I
followed that Mantra. I went to Georgetown University and had
some academic success and got out of college and worked
(13:39):
at a tech company as you mentioned, Wang Laboratories, and
then I had the entrepreneurial bug and I started my
first company. I was lucky and maybe clever, and I
sold that company two years after I started for sixty
million dollars, and Bob I declared victory.
Speaker 4 (13:57):
And how society tells you to the clear victory.
Speaker 2 (14:01):
Ends up being false social responsibility. In false victory, you
buy stuff. You're very popular with people who were using
you as their platform for success. And so I kind
of lost my way. And then I got on the
wrong airplane. I had a reckoning. The plane was going down.
(14:24):
I was alone on the plane, and I started praying.
Seemed like the right thing to do. And I started
laughing because I was praying to God, saying, you know,
please let me get through this.
Speaker 4 (14:37):
I don't want to die.
Speaker 2 (14:39):
And my first thought was if there was a God listening,
they say, oh, now you need me. You're in deep
angst and you think I'm here to help you. So
I reverted back to type. I said, all right, let's
cut a deal. If I live.
Speaker 4 (14:56):
Through this, I promise I will leave more than I take.
How's that?
Speaker 2 (15:01):
Obviously we made it through, but I've always been well,
a deal's a deal. How will I now pay that
debt back? I made this list of one hundred and
one things to do before I die, so that I
could say an end of my life. Could I look
(15:25):
back and metric and score? How did I do it?
As I matured and I look at the list, I
go some of the things that I thought was a
good way of keeping score ended up not being as
important as I thought.
Speaker 4 (15:47):
But Bob, and you'll remember, you were kind of there.
Speaker 2 (15:52):
I had put on my list on a sports team
win a championship, and if I hadn't have written that down.
When I was approach to buy the teams, I initially
said no. I said, I'm working at AOL, I have
two young children. There's no way I could own a team.
(16:12):
And I came home and Lynn said to me, what's new?
What happened today? And I said, oh, this guy approached
me wanted to buy teams. And she said what did
you say?
Speaker 4 (16:22):
And I said, I can't engaged at the company. We
have young kids.
Speaker 2 (16:26):
And Lynn said to me, hey, we've been together for
a long time. We'd be together forever. What if you
get ninety nine of the one hundred and one thing's
done and you don't buy a team and you don't
win a championship, and so well, this is why I
love you.
Speaker 4 (16:44):
Yeah, I'm going to go and.
Speaker 2 (16:46):
I bought the team and we've won championships. But it's
also been like the best financial decision as a family
we could have made.
Speaker 4 (16:55):
So I'm now at like ninety four.
Speaker 2 (16:58):
I wrote this list in nineteen eighty six, and back
then I said I want to go into outer space.
That seemed crazy. Right now I go, oh, I'm definitely
gonna get that one checked off, right, I mean, Richard
Branson's called me, Bezos has called me, and I'm sure
(17:19):
I will get into outer space, and I'm clicking them off.
But it also was will I be happy and self
actualized at the end of the journey, And some of
the things on the list weren't going to make me happy,
(17:39):
And so I've matured, if you will, And I don't
think I have to get all one hundred and one done,
But you know, big important things like falling in love,
getting married, and having children, having grandchildren, living a healthy life,
giving back.
Speaker 4 (17:56):
Those are the things that I think will be most important.
At the end of the day.
Speaker 3 (18:02):
You touched on your childhood and growing up and what
it meant to you. If I remember my story right,
I think your high school guidance counselor told you you
weren't college material and tried to steer you to a
vocational school. Obviously that didn't sit well with you. You
went onto Georgetown, and I think that really set you
(18:22):
on the track of technology and got you into tech.
Can you tell us a little bit more about that story,
because I do think that probably was a seminal moment
for ted Leonsis and where you wound up.
Speaker 2 (18:35):
Yeah, I probably created the first algorithmic based liberal arts
program ever and it was powered by a Jesuit priest
who was my mentor.
Speaker 4 (18:54):
I had to write a thesis.
Speaker 2 (18:56):
And I was a little bit lazy and said what
are you going to write about? And I said, I'll
come up with the idea over the weekend. So I
went to the library and I was with some buddies
and I said, all right, I'm going to find the shortest,
easiest book to read in the library. I found Old
(19:18):
Man in the Seat and it's a novella. If you
will and I read in like an hour and a half.
And on Monday I went to see my mentor, father,
Joseph Dirkin, Society of Jesus, and he said, okay, great,
tell me what you thought of the book. And I
gave him like an annotated book report, and he said,
(19:38):
I'm so disappointed in you. There's so much going on
in this book with this author at the time it
was published. You're better than this. And he challenged me
to be more of a critical thinker.
Speaker 4 (19:54):
So I didn't know anything about Hemingway.
Speaker 2 (19:56):
There was no Internet back then, so I literally and
got a bunch.
Speaker 4 (20:01):
Of his books.
Speaker 2 (20:02):
And the second book I read, Across the River and
Into the Trees, was nothing like Old Man in the Sea,
And as I started to do research, Hemingway was a
journalist Esquire Magazine, New Yorker Magazine, and then he became
a writer and Old Man in the Sea was published
(20:23):
in the fifties. He won a Pulit surprise, he ended
up taking his life. And I just had this idea
that I think Ernest Hemingway wrote this book earlier in
his career and he needed the money, and you know,
jazz was becoming popular and Jack Kerouac and Allen Ginsburg,
(20:46):
and he went back to his journalistic style. And Father
Dirk and said, well, great, how are you going to
prove it? And I said, I'll call his wife. Oh,
I'll try and talk to the publisher. And Father Dirkin,
to his credit, said why don't we use a computer?
And I said, a computer like in the movies. Like.
(21:12):
We had one computer on campus in the registrar's office.
It was IBM three stixy and it spit out for
students what classes you would take. So he introduced me
to a linguistics major. He introduced me to someone who
(21:32):
worked in the computer mis department, and we went to
dinner and we came up with the idea of I
would have to type in the first five thousand lines
of a book and articles from the fifties, from the forties.
Speaker 4 (21:51):
From the thirties.
Speaker 2 (21:53):
And then the linguistics major she came up with these
metrics words, person sentences, sentences per paragraph, pronoun references. There
were seventeen measures, and then the programmer wrote this code
to basically answer the question. Based on this algorithm, when
(22:19):
was Old Man in the Sea as a control written?
Speaker 4 (22:23):
And it said the book was written in.
Speaker 2 (22:25):
The thirties, not in nineteen fifty two, and it was
mind blowing to us. And you know, I wrote a
college thesis. But it showed how a computer and software
and algorithms could find essence of truth in the data
(22:49):
and show you something that you never knew happened.
Speaker 4 (22:56):
And it was truly for me.
Speaker 2 (22:59):
Ah moment, and it powered pretty much everything for the
rest of my life and career.
Speaker 4 (23:09):
Made a movie in a similar fashion in that.
Speaker 2 (23:14):
I didn't have anything to read Bob and I started
reading the Last thirty Days of the New York Times
from a bookstore in Saint Bart's and I read an
obituary about an author named Iris Chang and she wrote
a book called.
Speaker 4 (23:31):
The Rape of Name King.
Speaker 2 (23:33):
And when I got home, I went on Amazon and
I said, I'm going to buy the book, and their
simple little algorithm said, oh.
Speaker 4 (23:43):
If you like this book, then you like these books.
Speaker 2 (23:48):
And there was a starburst that said the Forgotten Holocaust
and it was basically behind the Chinese Communist Wall. This
was in nineteen thirty four. I was at AOL, I
had some contacts. I worked with the people at CIA,
and I made a film called Nang King and it
(24:10):
won Best at Open Sundayands, it.
Speaker 4 (24:13):
Went on HBO.
Speaker 2 (24:15):
It won the Emmy Award for Best Documentary. And it
all came from reading an obituary and an algorithm telling
me there's more to this story than this just this
one book, and that turned me into making films.
Speaker 3 (24:39):
More mathem magic. Right after this quick break, welcome back
to Mathemagic. Let's hear more from my conversation with Ted
leonsis so Ted, you make mundane things sound exciting about
(25:00):
adding a twist, and I want to tell a story
on you. When I got to AOL and we were
in this whole process of how can we make this
company profitable? I was laying out to our group of
senior leaders, tying it to what the stock price could
be because they all were equity holders if we achieved
those earnings levels, and I start think it's mathematical, it's
(25:22):
this and this and this, and you could see people
sort of glazing over some skepticism. You suddenly amped up
the discussion by declaring to the room that Bob, I'm
going to give you my house if we hit that
stock price. Suddenly the room was on fire. And by
the way, when we walked out of the room, you
sort of gave me that ted le Ounce's smile and goes,
(25:43):
I'll be delighted to give you by house hip. The
stock hits that price, and you knew exactly what you
were doing. I've seen you use that technique other times,
and I know you're using it in your business. How
do you think about that and what's the benefit of it?
Speaker 2 (26:00):
Well, I think that people want to have romance in
their life. People want to have hopes and dreams. And
my beef if you will after we acquire Time Warner
was we put out a company goal of ten billion
(26:24):
dollars of EBITA and with a billion dollars of synergy
EBAA savings.
Speaker 4 (26:34):
And I remember.
Speaker 2 (26:35):
Saying, do you think a mom or a dad is
going to work late, work over the weekend and come
home and have spouse or partners say what you do today?
Speaker 4 (26:51):
And it's I contributed to our synergies and EBITDA.
Speaker 2 (26:57):
It's like, no, people want best, they want higher calling, right,
but AOL was at its best. We said, we're out
to create a medium that is more valuable than the
media before it radio, television, and we kind of meant
(27:18):
that because the higher calling of the Internet of interactivity
and scale and faster and cheaper till it's free.
Speaker 4 (27:31):
Was very enabling, very empowering, very.
Speaker 2 (27:36):
Connected to people's DNA of we want to be social,
we want to belong to something.
Speaker 4 (27:43):
And you know why you and I made.
Speaker 2 (27:46):
A great team was you were the best business person
I had ever been exposed to. But my gift was
to be able to say, how do we motivate people
and what will become their story and narrative when they
go home. You've got to be able to have both
(28:09):
in balance to build great businesses.
Speaker 3 (28:11):
As an observer, one of your superpowers has been people.
You spot people. But I want to ask you a
question about that. When you spot these special people and
you've got a long list of those folks you've found,
do you treat them differently? Do you manage them differently
than you do everybody else?
Speaker 4 (28:29):
For me on the people's skills, a lot of it
comes from my family. The ethnic family around your dinner
table will rip each other and needle each other and
fight and have real diversity. But then when you leave
(28:50):
the dinner table, you know.
Speaker 2 (28:51):
I'd have friends over from college who'd come and think,
you guys hate each other. My family, we don't even
talk at dinner, right, or we don't even have dinner together,
But they wouldn't realize. No, this is how we share
our emotions, our thoughts, and then when we leave the
(29:12):
dinner table whereas tight as can be. So that's always
driven me. I want companies. I want executives that aren't
afraid to speak truth, to give you a different point
of view. And once we get into that cadence, then
(29:35):
we can have a great relationship. And the relationships are
going to be filled with ups and downs. One of
my business partners, Peter Gouber, the Great Peter Gouber's and
is it's the youngest eighty year old on the planet
and the greatest guy ever. We once did a deal.
We didn't realize we were competing with each other for
(29:58):
the deal, and then we I found out at the
last minute he won the deal, and I went to
him and said, wow, I had no idea you were
bidding on this and I wanted to do it, and
he said, hey, I love you.
Speaker 4 (30:13):
Let's split it fifty to fifty.
Speaker 2 (30:18):
I'm not going to make any big on it, any profit,
and Ted will laugh together, will cry together, but we'll
be in it together.
Speaker 4 (30:29):
And it'll probably be more successful because I'm not that smart.
Speaker 2 (30:33):
You're not that smart anymore, but together we probably do well.
Speaker 4 (30:38):
And that was such genius.
Speaker 2 (30:41):
The company we bought Team Liquid, which is one of
the most valuable esports teams. We made investments in Epic
Games and the Antic and the companies called Axiomatic, and
it's a big hit.
Speaker 4 (30:57):
And his instinct was really right.
Speaker 2 (31:00):
This notion of we'll laugh together, we'll cry together, and
together we're going to do a better job really proved true.
That was like the ultimate people person and it worked.
And so you know, I've always believed that be a
people person, do the right thing in the right way,
(31:20):
and make a ton of money.
Speaker 3 (31:22):
Right, let's go to some advice before we wrap up.
I want to do one piece of advice. If you
could go back in time and give your twenty one
year old self some advice, what would that advice be?
Speaker 4 (31:34):
Slow down a little bit.
Speaker 2 (31:37):
I mean, I really got driven by accomplishment, and this
relentlessness and pace sometimes let you lose a great experience
because you're just so busy, right. I mean you used
to say you want something done, give it to the
(31:58):
busiest person. There's a lot of truth to that. But
the price you pay is you'll miss a moment with
your grandchild, you'll miss a moment with your boyfriend or girlfriend,
you'll miss a sunset. As you grow older, you realize
that was a big price to pay, and so my
(32:19):
advice would be stay on that fast track, but it's
okay to take the step off.
Speaker 3 (32:25):
We wrap up each episode of Math and Magic with
a shout out to the greats on the math side
of the business analytical view and the magic side. This year, Creativity,
you've seen so many people. I got to ask you
for the shout outs. Who would you give the shout
out to? For the math person and for the magic person?
Speaker 4 (32:43):
You know, for the math person, I am going to.
Speaker 2 (32:48):
Go out of the box because his reputation somehow got besmirched.
In nineteen seventy seven, job out of college, I was
asked to find a speaker for our group. And I
(33:08):
had read an article and then went and met a
man da named doctor Minski, who worked at the MIT
Lab and was considered a father of AI.
Speaker 4 (33:24):
And he came and gave a speech as.
Speaker 2 (33:26):
A prop He wanted a piano, and he started his
speech by sitting at the piano and he was a
trained like concert pianist.
Speaker 4 (33:39):
He played piano beautifully and it was shocking. It was
like out of context.
Speaker 2 (33:46):
And then he went into his speech and said, we'll
know when the machines are important and have arrived when
they can do that, because every time I play, I'm
feeling the music, I'm sentient. And then he talked about
(34:07):
the formula and the algorithms, and boy, that really had
an effect on me.
Speaker 4 (34:13):
I would say, you know.
Speaker 2 (34:15):
An obvious person with Steve Jobs, and you know we
knew Steve. I grew up with Steve. His gift was saying,
you know why is Apple the most valuable company in
the world. Apple is where liberal arts and engineering come together.
Speaker 4 (34:38):
And he was able to take.
Speaker 5 (34:43):
Art and science and be able to express it and
obviously what's ended up being the most important piece of
technology of our lifetime.
Speaker 4 (34:58):
Time on the.
Speaker 3 (34:58):
Iphoneed You are a magnificent human being, a great friend.
I think you are, by the way, destined to be
this creative, genius and successful business person. You shared a
birthday with Elvis, David Bowie and even Stephen Hawking, so
must have been something in the stars. Thanks for sharing
(35:18):
your stories today and thanks for decades of friendship. Here
are a few things I picked up from my conversation
with Ted. One build value with data. Today businesses have
access to more data than ever before, but don't rely
on just that. Connect the dots to get a deeper
picture of your audience and provide them with services they value. Two.
(35:42):
Make a list of your priorities. After a close brush
with death, Ted made a list of one hundred and
one things he wants to do before he dies. Had
he not narrowed down these priorities, he wouldn't have achieved
some of his biggest dreams and opportunities. Take time to
reflect on your own goals, so when opportunity knocks, you
know what you want.
Speaker 4 (36:02):
Three.
Speaker 3 (36:03):
Remember what motivates people. Ted is the ultimate team builder.
He knows people are excited when they get to work
together to build something visionary and purposeful. Create opportunities for
your team to work on something that inspires them. I'm
Bob Pittman. Thanks for listening.
Speaker 1 (36:29):
That's it for today's episode. Thanks so much for listening
to Math and Magic, a production of iHeartRadio. The show
is hosted by Bob Pittman. Special thanks to Sydney Rosenbloom
for booking and wrangling our wonderful talent, which is no
small feat our editor Emily Meronoff, our engineers Jessica Crincicch
and Bahid Fraser, our executive producers nickki Etoor and Ali Perry,
(36:50):
and of course Gail Raoul, Eric Angel Noel and everyone
who helped bring this show to your ears. Until next time,
Host
Bob Pittman
Popular Podcasts
40s and Free Agents: NFL Draft Season
Daniel Jeremiah of Move the Sticks and Gregg Rosenthal of NFL Daily join forces to break down every team's needs this offseason.
Dateline NBC
Current and classic episodes, featuring compelling true-crime mysteries, powerful documentaries and in-depth investigations. Follow now to get the latest episodes of Dateline NBC completely free, or subscribe to Dateline Premium for ad-free listening and exclusive bonus content: DatelinePremium.com
The Bobby Bones Show
Listen to 'The Bobby Bones Show' by downloading the daily full replay.