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Speaker 1: Okay, we're gonna jump right into it. So, Rob, what

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Speaker 1: did your father do for a living? What did my

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Speaker 1: father do? From My father had a small printing company

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Speaker 1: in New York called Flair Printing, which was on eight West,

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Speaker 1: just off Fifth and Uh. It was a business he

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Speaker 1: started in nineteen sixty two years before I was born.

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Speaker 1: And he did it my whole adult kid, my whole

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Speaker 1: life growing up New York up until about nine. And

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Speaker 1: you grew up in Manhattan. So I grew up in Yonkers,

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Speaker 1: two blocks outside of the Bronx. I was born in

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Speaker 1: Mount Sina Hospital, so I was born in Manhattan, but

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Speaker 1: I grew up in basically Yonkers. But I know you

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Speaker 1: went to the same school as Jason Flam Right, Jason

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Speaker 1: flam and I were both Filston class of seventy nine. Um,

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Speaker 1: it's a it's a true story. I met Jason uh

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Speaker 1: freshman year that Fieldston has two feeder schools, a grammar

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Speaker 1: school called Filston Lower, which is the one I went to,

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Speaker 1: and know the grammar school called Ethical in Midtown Manhattan,

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Speaker 1: which is what Jason went to. I think. So Jason

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Speaker 1: I first met in seventh grade in Mrs Lesher's home

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Speaker 1: room and I remembered ovidly because one of the first

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Speaker 1: days of school, Jason walked in with a T shirt. Uh,

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Speaker 1: this is nineteen seventy three, I think, with a T

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Speaker 1: shirt of one pig on top of another one said

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Speaker 1: Macon Bacon and Mrs Lesher made Jason go home and

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Speaker 1: get a different shirt. So he was a troublemaker from

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Speaker 1: day one. Were you friendly with Jason? Then? Jason and

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Speaker 1: I were always like, uh, friendly, but not best buddies.

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Speaker 1: We were in different circles. Um and Uh I was

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Speaker 1: like Jason. Uh. I've learned subsequently as we've become much

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Speaker 1: closer as adults. Part of wide and see him a

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Speaker 1: lot of part of high school, and he spent a

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Speaker 1: lot of time at the track, which I think he's

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Speaker 1: talked about subsequently. So he, Jason spent the latter part

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Speaker 1: of his high school years uh, in a very different

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Speaker 1: arc than I spent on mine. Uh. And we reconnected

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Speaker 1: later in life. Okay, so were you a science kid

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Speaker 1: from day one? I was more of a math kid

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Speaker 1: than a science kid. I was one of these kids

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Speaker 1: who just numbers. I always made sense to me. I

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Speaker 1: grew up a baseball fan and to me, like baseball

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Speaker 1: with statistics, so like literal would would study way before

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Speaker 1: saber metrics or way before any of this stuff became popular.

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Speaker 1: I would know everybody's batting average to the fourth decimal point.

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Speaker 1: I just numbers kind of made sense to me, and

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Speaker 1: I was like a numbers kid. I also liked media.

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Speaker 1: I mean my one of my favorite things to do

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Speaker 1: was listening to w ABC radio as a kid. I

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Speaker 1: just felt like radio was like this window to the

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Speaker 1: whole wide universe. This is you know. I didn't at

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Speaker 1: the time, I didn't have a shortwave radio, and it

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Speaker 1: was obviously way before other stuff ended up doing. But

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Speaker 1: I was like media, especially music started radio station when

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Speaker 1: I was in high school. So I was the fact

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Speaker 1: that my life ended up connecting to music, even though

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Speaker 1: I have very, very poor skills as a performer. I

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Speaker 1: played cello for two years in piano for a couple

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Speaker 1: of years, and I sucked at both of them. But

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Speaker 1: I love music, and music and math were two of

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Speaker 1: my passions growing up. And was that encouraged in your household? Uh?

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Speaker 1: Yet both were? Um? I mean my uh my my

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Speaker 1: sisters two years older, and she was a really good

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Speaker 1: pianist and music good musician. She went to Oberlin the

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Speaker 1: lulib Arts part, but she spent a lot of time

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Speaker 1: with her buddies in the conservatory. She went to music camp.

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Speaker 1: So she's quite a good musician, or was quite a

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Speaker 1: good musician, probably still is. Uh and uh. I was

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Speaker 1: like the math kid, and so we kind of each

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Speaker 1: gave each other our own turfs kind of deal. But yeah,

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Speaker 1: my parents were like they were both the first and

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Speaker 1: their families to go to college. They were very much

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Speaker 1: focused on pushing us in academic directions. But in a

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Speaker 1: Jewish family, well, my mom's Catholic, my dad's Jewish. Really

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Speaker 1: we raised Jewish. I was great double majored and guilt

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Speaker 1: um and uh uh so I, well, the woman you're

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Speaker 1: married to now she Jewish. I've actually done multiple religions.

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Speaker 1: I've buried at jew and I've married a Catholic woman,

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Speaker 1: so I've kind of haven't done Buddhists yet. But okay,

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Speaker 1: but but I am uh my current partner grew up Catholic,

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Speaker 1: so yes, I've gotten back to Catholicism. But you can

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Speaker 1: only marry what I'm told. Um, although the Pube's kind

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Speaker 1: of changed that, I think. But but anyway, the Uh,

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Speaker 1: the role of religion. We had a Christmas tree, which

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Speaker 1: was kind of almost a cultural thing, but we were

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Speaker 1: mostly My mom was from California, so I didn't really

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Speaker 1: know as closely my the Catholic side of the family.

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Speaker 1: I knew the Jewish relaity was my dad's brother lived

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Speaker 1: in Long Island and we would do holidays with him,

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Speaker 1: So I was in Fieldson is probably like a third Jewish,

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Speaker 1: so I was in touch with kind of both sides

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Speaker 1: of my heritage. Okay, you have I have to ask

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Speaker 1: them if your mother was from California, did your father

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Speaker 1: meet her? It's actually an awesome story. So my dad

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Speaker 1: and my mom met at a folk dancing place, uh

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Speaker 1: in nineteen fifty three or nine four, I think, Uh.

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Speaker 1: And my dad and his best friend, Bob weren't back,

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Speaker 1: and my mom and her her best friend, uh Felice,

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Speaker 1: both went to this place, and uh, three lifelong couples

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Speaker 1: were formed that night. So my mom and my dad met,

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Speaker 1: Felice met her husband Roger, and Bob and his wife

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Speaker 1: Audrey all the same night. So I don't know it

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Speaker 1: was in the tequila there that night, but something magical, uh,

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Speaker 1: and they all married for life, which was just pretty

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Speaker 1: amazing and what was folk dancing? Did they ever tell

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Speaker 1: you what actually went trance fired at this venue? There

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Speaker 1: were no videotapes at the time, so there's very subjective

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Speaker 1: recollections of it, but it was. It was a meaningful night,

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Speaker 1: obviously for all six of them. Uh and relatively you know,

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Speaker 1: my parents were married over fifty years until my dad

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Speaker 1: passed away five years ago, and so they had a

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Speaker 1: great long run out of it. Okay, So if you're

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Speaker 1: born in nineteen sixty two, you're in high school when

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Speaker 1: the Apple two comes out? Were you an adherent of

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Speaker 1: computers in high school? I was, but not through uh

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Speaker 1: not through PCs. I was a little before and a

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Speaker 1: little after in a funny way. So my first computer

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Speaker 1: was a a programmable t I fifty eight computer, which

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Speaker 1: was not a computer at all. I was a calculator

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Speaker 1: with two hundred forty steps in it, so you literally

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Speaker 1: you had to learn how to really efficient code. That

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Speaker 1: was the first UH computing device that I owned. Uh

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Speaker 1: And and you could program it to do sort of

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Speaker 1: simulation kinds of things that had a little display. I

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Speaker 1: couldn't afford the expensive one with magnetic cards, but I

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Speaker 1: was able to save my money from shelving stone, raking

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Speaker 1: leaves and other stuff to where I got the printer

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Speaker 1: so I could actually save my programs and type them

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Speaker 1: back in meticulously, all two and forty instructions of them.

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Speaker 1: Then my senior in high school, Fieldston started time sharing

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Speaker 1: on another computer down the street at the school called

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Speaker 1: Riverdale UM. And so that was when I got first

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Speaker 1: got into any sort of serious interactive programming. Uh. It

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Speaker 1: was a PDP eleven thirty four. There ran an obscure

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Speaker 1: operating system called Ristus for any nerds out there, UM.

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Speaker 1: And the biggest thing that I did there was I

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Speaker 1: wrote a game that simulated running a presidential election called

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Speaker 1: pres and it became very popular, and it leveraged that popularity,

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Speaker 1: and I put a trojan horse in it that gave

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Speaker 1: me system access to the entire computer system. Okay, and

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Speaker 1: did you utilize that access to do bad or good?

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Speaker 1: I utilized it for because back in the day, hacker

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Speaker 1: meant something different than it means now. Hacker meant somebody

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Speaker 1: who had the curiosity, the pioneer spirit. Stephen Levi's book

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Speaker 1: Hacker describes this, and it was about like climbing the mountain.

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Speaker 1: The digital version of the Mountain. So I never did

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Speaker 1: anything malicious. I would basically there were there were fun

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Speaker 1: games on the system that you could play if you

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Speaker 1: had access to them, I would do that. Uh. So

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Speaker 1: the worst you could say is that I stole some

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Speaker 1: CPU cycles um from the from the Riverdale computer. I

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Speaker 1: did it with a buddy. I won't say his name,

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Speaker 1: but he ended up being our professor at m I

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Speaker 1: t so and uh a very accomplished person in his

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Speaker 1: own right. So apparently we didn't We were not too

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Speaker 1: too bad, but but we had fun and I learned

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Speaker 1: a lot about programming computers. Uh and uh, I can

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Speaker 1: tell you a lot about bad computer operating system security

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Speaker 1: and trojan horses because the case did not have robust security.

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Speaker 1: So that computer terminal that you had access to the

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Speaker 1: main frame or mini computer whatever it was, that was

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Speaker 1: at school, that was in high school. That was my

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Speaker 1: that was my first experience. My my first experience in

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Speaker 1: owning our computer was in one when the IBM PC

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Speaker 1: came out. But that was I was alarading. Okay, but

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Speaker 1: before your college. So when you're at Fieldston, would you

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Speaker 1: stay after school to use that computer it was it?

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Speaker 1: Would you track your relationship with a computer in school

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Speaker 1: somewhat similarly to what Bill Gates says his experience was.

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Speaker 1: So I didn't in beak between between the program blow

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Speaker 1: calculator that I owned and my access to the the

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Speaker 1: PDP eleven thirty four through a time sharing terminal. I

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Speaker 1: um I got. I don't know if I got my

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Speaker 1: full ten thousand hours, and but I definitely got a

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Speaker 1: decent amount of computing experience. We also, by the way

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Speaker 1: we had it, we had a PDP I forgot. My

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Speaker 1: whole Sky school had a PDP eight before the PDP

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Speaker 1: eleven thirty four, which had a one tin bawd teletype,

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Speaker 1: you know, the old like you know, something out of

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Speaker 1: two thousand and one, with the with the he's clanging

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Speaker 1: and the uh, the loud teletype. Uh. The only thing

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Speaker 1: about that was the computer would reboot when you plug

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Speaker 1: the coffee pot in. So it really wasn't that robust

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Speaker 1: a computer. So I dinked out with that a little bit.

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Speaker 1: But it was really the the my own calculator and

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Speaker 1: the other one where my main computing experence. And you know,

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Speaker 1: by I'm a little bit older than you. I was

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Speaker 1: born in nineteen fifty three, and then people are younger.

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Speaker 1: But there was really class division in my high school,

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Speaker 1: but it was much larger than your high school. When

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Speaker 1: you're pursuing your love of computers, does that make you

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Speaker 1: an ostracized nerd or does that include you in the class?

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Speaker 1: So this I had different things I did. So when

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Speaker 1: I before I started this, I started a radio station

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Speaker 1: in my high school. Uh, and that was that was fun.

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Speaker 1: I just did because I loved radio. Actually when I

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Speaker 1: went when I when I was twelve, I went to

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Speaker 1: a summer camp and my camp counselor was a a

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Speaker 1: d J at w N y C. I think it

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Speaker 1: was the City College, uh station, And I thought it

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Speaker 1: was was it was that a classical station back then? Uh? No, No,

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Speaker 1: this was it was like an all format station. Was

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Speaker 1: that it was a SID No, no, w CC, it

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Speaker 1: was a it was the City College in New York station.

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Speaker 1: So not even I see c C and Y or

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Speaker 1: something like that. And so I went and visited his

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Speaker 1: radio station, uh that next fall, and I thought it

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Speaker 1: was the coolest thing in the world. So I got

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Speaker 1: my we we ended up setting up a intercom radio

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Speaker 1: station in high school. When I did that, when I

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Speaker 1: was like a sophomore junior. How much techno? Well know,

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Speaker 1: how did that know? It was more of the inspiration.

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Speaker 1: It was more of the inspiration. It wasn't super technical.

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Speaker 1: I mean it was literally we didn't get it. We

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Speaker 1: didn't get a low watching license. We just literally wired

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Speaker 1: the building from the UH from the when we were

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Speaker 1: into the gymnasium in the cafeteria. So it was basically

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Speaker 1: doing radio over wired lines, basically what I ended up

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Speaker 1: doing twenty years later when we started real networks, but

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Speaker 1: not used. The Internet didn't exist then, so we just

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Speaker 1: wired it ourselves. Okay, so you end up going to Yale.

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Speaker 1: Where else did you apply to college? I actually only

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Speaker 1: applied to two places. I applied to Yale and a

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Speaker 1: little college and Iowa called Grinnell. And the reason I

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Speaker 1: applied to Grinnell was um I thought after being growing

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Speaker 1: you know, follow all the whole sort of snobby elitist

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Speaker 1: kind of stuff that you associate with the Northeast and

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Speaker 1: the I vs. UH. One of the inventors, one of

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Speaker 1: the inventor of the integrated circle, one of the early

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Speaker 1: people in Intel, was a Grinnell graduate and so in

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Speaker 1: the late seventies, Grinnell had one of the best funded

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Speaker 1: computer science facilities of any small liberal arts college in

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Speaker 1: the country. UH and I had. I went and visited Grinnell,

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Speaker 1: had a lovely time, was super fun. But I made

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Speaker 1: the purely pragmatic decision that if I hated Yale and

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Speaker 1: with open arms U. Whereas if I went the other

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Speaker 1: tried to get back into Yelle, they'd flipped me the bird.

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Speaker 1: So I figured the right thing to do was to

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Speaker 1: pick the pick the I like them both, and I

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Speaker 1: picked the one that seemed like if I didn't like it,

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Speaker 1: I could twitch to the other one. And UH and

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Speaker 1: I ended up having a great time. So you did

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Speaker 1: have a good experience in college. Fantastic I was. It

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Speaker 1: was and I actually by the time I went there,

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Speaker 1: I felt really good about it. My UH sister, who

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Speaker 1: was two years older, a couple of her one of

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Speaker 1: her friends going to Yale, and went up and hung

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Speaker 1: out with them, and it was like it was a

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Speaker 1: revelation to find a place where like being a nerd

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Speaker 1: was kind of cool, at least not as ostracized as

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Speaker 1: at Ostracigo Strong. It was not in the cultural mainstream

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Speaker 1: in my high school to be a nerd like it was,

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Speaker 1: but whereas college, it was a college full and nerds,

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Speaker 1: so it felt they were different kinds of nerds, not

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Speaker 1: all computer nerds, some or math nerds to different things. Uh.

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Speaker 1: The other thing was helpful is so in I was

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Speaker 1: good at math, but there was a kid named Paul

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Speaker 1: Quintus who was the best in math in my school,

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Speaker 1: and there were a few of us. Jason was before

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Speaker 1: he started becoming a stoner, was super smart at math.

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Speaker 1: Who there are a few of us who were like

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Speaker 1: vibed for second best in math. So I never had

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Speaker 1: the image that I had to become a pure mathematician.

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Speaker 1: But then at Yale, I started, Okay, I'm gonna see

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Speaker 1: what I can do. So I started in the early

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Speaker 1: Concentration math program, which was like the hardest program. Had

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Speaker 1: to take the AP and all that stuff to get in,

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Speaker 1: and it started out with about fifteen people in it,

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Speaker 1: and after two weeks everybody had dropped out except for

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Speaker 1: me and five guys who knew each other from math

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Speaker 1: camp um, which I had not gone to. Uh, So

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Speaker 1: I was nerdy, but not uber nerdy, I guess and

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Speaker 1: uh And it was really a revelation for me because

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Speaker 1: I was clearly the worst of the six of us UM.

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Speaker 1: And it was a really good thing to learn that, uh,

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Speaker 1: you know, at the age of still seventeen, I think,

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Speaker 1: and then turn eighteen yet that I would not be

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Speaker 1: hurting society by not being a theoretical mathematician because there

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Speaker 1: were five people in the room with me who were

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Speaker 1: far better theory mathematicians and me just in that one

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Speaker 1: room in one class at Yale. So it was a

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Speaker 1: liberation for me that I could go do other stuff

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Speaker 1: that built on the fact that I was pretty good

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Speaker 1: at math without having to go to the purest route,

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Speaker 1: which of course academia almost always steers you towards, like

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Speaker 1: if you're really, really good, you do the purest form

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Speaker 1: of something, and then if you're you know, if you're

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Speaker 1: not able to cut cut it in the purest thing,

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Speaker 1: you can do something more philistine like being business. So

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Speaker 1: for me, I got that UM feedback early on, and

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Speaker 1: it was quite liberating for me. Okay, so you wake

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Speaker 1: up one day and you say you're not gonna be

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Speaker 1: the head of the class in math. What are your

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Speaker 1: other interests in college? So it was a combine a

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Speaker 1: bunch of things. I was always sitting media, but in

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Speaker 1: college I did it by getting involved in the student newspaper,

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Speaker 1: the All Daily News. I got involved in student activism,

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Speaker 1: and I was pretty politically active because night was when

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Speaker 1: Reagan came in. There was a lot of you know,

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Speaker 1: saber rattling going on. I didn't like that very much.

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Speaker 1: So I got I was sort of a student activist

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Speaker 1: in a bunch of anti military, anti militaristic ninety military

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Speaker 1: excuse me, anti militaristic, anti jingoistic, uh, you know, anti

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Speaker 1: Reagan kinds of things, uh. In college, and I throw that.

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Speaker 1: I got involved with writing editorials in the student newspaper,

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Speaker 1: and a bunch of these friends are like, hey, we

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Speaker 1: like you would you've wrote a column for us UM

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Speaker 1: And so I wrote a column for two or three

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Speaker 1: years in college called What's Left? Uh. And then the

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Speaker 1: funny thing about it is the guy who wrote the

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Speaker 1: column right of Way has actually become a good friend

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Speaker 1: of mine, a guy named David from So David David,

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Speaker 1: and I wrote, I didn't know that David from David

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Speaker 1: and David was like the point counterpoint of this. Uh

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Speaker 1: you know, And so I would I wrote the left

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Speaker 1: wing column, he wrote the right wing column. I would

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Speaker 1: think I wrote one that was a kind of a

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Speaker 1: headline that was a little nasty, as I recall, it

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Speaker 1: was from colon distortions, misrespsentations and lies. Um. But we've

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Speaker 1: since buried the hatchet on that. And well he's also

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Speaker 1: moved left. Well he he's moved. He's moved sensible at least,

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Speaker 1: I would say. And he and I when I got

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Speaker 1: involved many years later and a bunch of stuff around

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Speaker 1: Putin and Trump. Uh, David was one of the firs

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Speaker 1: as people I talked about, and he ran along piece

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Speaker 1: in Atlantic about it. And so we've stayed, we've our

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Speaker 1: lives have intertwined in a funny way. In college, we

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Speaker 1: were pretty pretty different views. And then he I think

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Speaker 1: you have to doing penance for the whole access of

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Speaker 1: evil thing, because he's famously one of the person I

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Speaker 1: think he's the person who coined that phrase. Uh he Uh,

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Speaker 1: he's now obviously changed his views of that part of

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Speaker 1: the Republican Party. Anyway, So you say you got your

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Speaker 1: first computer at nineteen and IBM PC what was the

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Speaker 1: inspiration for that? So luck? Luck always plays a role

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Speaker 1: in life. So in the summer of eighty one, I

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Speaker 1: went to work for IBM uh in Pokeepsie, New York,

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Speaker 1: who was a little slower. So you're this was this

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Speaker 1: is right after freshman year of college, after sophomore year,

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Speaker 1: after sophomore years. So how did you even get that gig?

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Speaker 1: It's actually interesting thing. So I I did computer programming

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Speaker 1: while I was I was. I ended up doing a

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Speaker 1: computer science degree and then a master degree in economics.

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Speaker 1: So I was sort of sprattling between those two fields. Okay, wait, wait, wait,

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Speaker 1: just you you start with math in N one to

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Speaker 1: what agree? Yale? Is there a computer science program? Y'all

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Speaker 1: had a good but small computer science program. It was

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Speaker 1: not It's not as large as certainly uh m I

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Speaker 1: T s or one of the other clothes. But it

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Speaker 1: had some really good professors. Uh a few, one of

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Speaker 1: the pioneers of early program language of Alan Perliss, who's

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Speaker 1: since passed away. Some really interesting people in there. But

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Speaker 1: it was a small program. I don't know. There were

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Speaker 1: maybe fifteen of US majors, maybe a little more than

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Speaker 1: that um and so I was doing computer science stuff

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Speaker 1: and then I was doing economics. And one of the

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Speaker 1: virtues of having done advanced math is you can start

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Speaker 1: doing advanced economics earlier, because basically the only difference, as

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Speaker 1: I can see between the graduate and the undergraduate economic

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Speaker 1: classes is the graduate economic classes assumed calculus in economics.

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Speaker 1: So I was able to kind of did you have

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Speaker 1: any vision of who would what you wanted to do

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Speaker 1: or be? I think I knew I wanted to do

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Speaker 1: something at the intersection of technology, media and politics, but

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Speaker 1: what the manifestation of that would be, and the fact

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Speaker 1: that it ended up involving more technology and media professionally

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Speaker 1: and more politics as a as an advocation rather than

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Speaker 1: a vocation, uh was It's sort of kind of how

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Speaker 1: it evolved. Part of it is when you do computer stuff,

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Speaker 1: you can get decent summer jobs that pay reasonably well.

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Speaker 1: And like so, I gotta I applied for a summer

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Speaker 1: job at IBM in the summer of eighty one, and

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Speaker 1: because one of the jobs they had at school involved

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Speaker 1: doing programming on an IBM main faw also my first

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Speaker 1: paid job. I worked at n y U the summer

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Speaker 1: after by senior in high school working on their computer.

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Speaker 1: So I did that my and doing what on their

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Speaker 1: computers it was for it was a computer for the

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Speaker 1: admissions department. So I was just doing regularly, regular random

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Speaker 1: programming assignments, you know, job running reports for a basic

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Speaker 1: for mini computer h on that the n y U

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Speaker 1: Admissions department had as their in house. Okay, when you

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Speaker 1: were at Fieldston, was there a teacher who taught you

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Speaker 1: this stuff? That we were all self taught? It was

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Speaker 1: a computer science. There were no teachers, so I taught.

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Speaker 1: I taught myself programming. But you know, if you know

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Speaker 1: math and you're you know, technically uh inclined, you know,

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Speaker 1: I didn't find learning programming was that hard. So my

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Speaker 1: first actual course in programming was freshman year in college probably.

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Speaker 1: And what was the language then? Uh? We learned everything

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Speaker 1: from you know, C that was before C plus plus

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Speaker 1: C two, A p L which was it's kind of

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Speaker 1: obscure sy mode language LISP, which was a natural language programming.

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Speaker 1: I did some Burgram for trand, which was more for

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Speaker 1: these applied jobs. Basic is obviously the presidential election simulators

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Speaker 1: slash Trudgan Horse was written in Basic, uh, which was

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Speaker 1: Basic was probably the first higher level language I wrote in. Okay,

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Speaker 1: So what is your gig after your sophomore year at IBM?

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Speaker 1: So I worked at IBM in a really boring job

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Speaker 1: in Pokeepsie, New York. UH in a group that wrote

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Speaker 1: IBM had a language called j c L that was

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Speaker 1: a language to batch program computers. And I came in

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Speaker 1: just I just missed having to work on punch cards.

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Speaker 1: I'm really lucky in that. But this was They had

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Speaker 1: a language called Jewel. I don't know, I remember that

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Speaker 1: because it's a prett obscure language that generated j c L.

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Speaker 1: So my job was to write programs in Jewel UH

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Speaker 1: that we were used for some set of maintenance programs

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Speaker 1: for the for the Maiden Rand group. The reason it

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Speaker 1: wasn't super boring is one of the guys in my

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Speaker 1: group that I was in in Pokeepsie, New York, happened

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Speaker 1: to be part of the task force that was working

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Speaker 1: on the IBM PC. So I got I get there

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Speaker 1: in June. Who getting lucky is better than good all

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Speaker 1: the time? I get there in June, or like Marylyn June,

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Speaker 1: and there about a month the IBM PC gets announced

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Speaker 1: and asked this guy Jim, and he knows everything was

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Speaker 1: to know about it. So he tells me all about it,

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Speaker 1: and I get super excited. I was never an Apple

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Speaker 1: two kid, as I mentioned that kind of sort of

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Speaker 1: generationally didn't quite line up with that. But I thought

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Speaker 1: the ibm PC seemed like it was going to be

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Speaker 1: a really good thing, uh in terms of like taking

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Speaker 1: the PC industry to the next level. And you can

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Speaker 1: say legitimizing and famously, Steve Jobs and Apple once ran

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Speaker 1: an ad you know what they said that people say,

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Speaker 1: the ibm PC is going to legitimize personal computing. Well

430
00:19:52,800 --> 00:19:55,360
Speaker 1: the bastards say welcome. So that was a famous ad

431
00:19:55,359 --> 00:19:58,879
Speaker 1: Apple ran. But I but it did have that vibe,

432
00:19:58,880 --> 00:20:00,480
Speaker 1: that feel and the culture. And this is going back,

433
00:20:00,680 --> 00:20:04,320
Speaker 1: you know, thirty seven years now. And so I, after

434
00:20:04,359 --> 00:20:07,080
Speaker 1: I finished my job summer job, with a bunch of buddies,

435
00:20:07,320 --> 00:20:10,640
Speaker 1: decided to start a little software company, which I called

436
00:20:10,680 --> 00:20:15,119
Speaker 1: Ivy Research, to make games for the ibm PC. So, okay,

437
00:20:15,160 --> 00:20:17,840
Speaker 1: so the ibm PC, I've already forgotten. It was introduced

438
00:20:17,840 --> 00:20:20,240
Speaker 1: exactly when it came out. It was just announced in

439
00:20:20,240 --> 00:20:22,560
Speaker 1: the summer of eighty one and at first became available

440
00:20:22,560 --> 00:20:25,200
Speaker 1: early in the fall Okay, so when you start this

441
00:20:25,320 --> 00:20:28,080
Speaker 1: ivy company, are you saying, oh, this will be fun

442
00:20:28,200 --> 00:20:32,119
Speaker 1: or you see dreams of riches a little bit of both,

443
00:20:32,200 --> 00:20:35,439
Speaker 1: but really more it'll be fun and we'll learn a lot,

444
00:20:35,920 --> 00:20:39,520
Speaker 1: uh and maybe it will become something. So basically bought

445
00:20:39,560 --> 00:20:43,520
Speaker 1: one PC, which I think I used most of my

446
00:20:43,520 --> 00:20:45,760
Speaker 1: summer job money to buy. I think one of the

447
00:20:45,760 --> 00:20:48,080
Speaker 1: other guys bought the printer or something like that. UM

448
00:20:48,119 --> 00:20:50,400
Speaker 1: in the in the we couldn't really we there were

449
00:20:50,440 --> 00:20:52,600
Speaker 1: originally half a dozen of us that got excited about it.

450
00:20:52,760 --> 00:20:54,639
Speaker 1: There were three of us who ended up working on

451
00:20:54,680 --> 00:20:57,000
Speaker 1: it full time. So my summer job in the summer

452
00:20:57,000 --> 00:20:59,640
Speaker 1: of eight two other than you a little political organizing

453
00:20:59,680 --> 00:21:02,320
Speaker 1: which I could talk about, was to write games for

454
00:21:02,359 --> 00:21:06,240
Speaker 1: the IBM PC UM and the game. We wrote two games.

455
00:21:06,240 --> 00:21:09,400
Speaker 1: My friends was a better programmer than me. Uh so

456
00:21:09,440 --> 00:21:12,000
Speaker 1: he wrote the libraries for both of them, and I

457
00:21:12,040 --> 00:21:14,399
Speaker 1: wrote one of them. Um he wrote the other. They

458
00:21:14,400 --> 00:21:18,520
Speaker 1: were called SLINKs and Viper and SLINKs is basically you

459
00:21:18,520 --> 00:21:20,800
Speaker 1: know the old snake game on the Nokia candybar phone.

460
00:21:21,000 --> 00:21:22,920
Speaker 1: It was that game, and it may have been the

461
00:21:22,960 --> 00:21:25,120
Speaker 1: first incarnation of it. I've never heard of an older one,

462
00:21:25,440 --> 00:21:27,200
Speaker 1: but it was the notion that you basically you run

463
00:21:27,200 --> 00:21:30,360
Speaker 1: around a maze and the longer that you you pick

464
00:21:30,480 --> 00:21:33,040
Speaker 1: up tokens, and the more tokens you get, the longer

465
00:21:33,080 --> 00:21:34,800
Speaker 1: the snake gets. And the goal is to get the

466
00:21:34,800 --> 00:21:37,800
Speaker 1: snake could be as long as possible without tripping into yourself.

467
00:21:37,960 --> 00:21:40,520
Speaker 1: It's fun game. And that was one of our games.

468
00:21:40,920 --> 00:21:43,320
Speaker 1: And uh we sold them nationally through a chain of

469
00:21:43,320 --> 00:21:47,280
Speaker 1: computer stores called computer Land. UH and so I mentioned

470
00:21:47,320 --> 00:21:50,280
Speaker 1: all these old defunct names. Um and if you do

471
00:21:50,400 --> 00:21:52,920
Speaker 1: the math on it, the three of us made enough

472
00:21:52,960 --> 00:21:55,240
Speaker 1: money to make it a good summer job, but not

473
00:21:55,359 --> 00:21:58,439
Speaker 1: a life changing event. So it was like that was

474
00:21:58,720 --> 00:22:00,840
Speaker 1: that was our we It's the summer of eight t

475
00:22:00,880 --> 00:22:02,320
Speaker 1: two and then a little bit into the fall, and

476
00:22:02,400 --> 00:22:04,600
Speaker 1: then I got busy getting ready to graduate and finishing

477
00:22:04,600 --> 00:22:10,800
Speaker 1: with my school work. We'll take a quick break and

478
00:22:10,920 --> 00:22:13,439
Speaker 1: come back with more of my conversation with CEO of

479
00:22:13,480 --> 00:22:17,200
Speaker 1: Real Networks, Rob Glazer, recorded live at the Music Media

480
00:22:17,240 --> 00:22:21,840
Speaker 1: Summit in Santa Barbara, California. This week. I'm speaking with

481
00:22:21,960 --> 00:22:25,200
Speaker 1: Real Networks Rob Glazer. Over the last couple of months,

482
00:22:25,240 --> 00:22:27,760
Speaker 1: I've interviewed Tony Hawk and the head of music for

483
00:22:27,800 --> 00:22:30,919
Speaker 1: YouTube and Google Lee or Cone. Be the first to

484
00:22:30,960 --> 00:22:34,560
Speaker 1: hear next week's episode by subscribing to the podcast I'm

485
00:22:34,600 --> 00:22:38,879
Speaker 1: tuned in, Apple Podcast or your podcast Apple Choice. While

486
00:22:38,880 --> 00:22:43,160
Speaker 1: you're there, please rate and review the podcast. Okay, let's

487
00:22:43,160 --> 00:22:47,840
Speaker 1: get back to my conversation with Rob Glazer. Okay, So

488
00:22:48,520 --> 00:22:52,159
Speaker 1: you have the experience with the IBM PC, you graduate,

489
00:22:52,240 --> 00:22:54,639
Speaker 1: and then you go to graduate school. No. No, I

490
00:22:54,680 --> 00:22:57,520
Speaker 1: was lucky because of my head certain math. I was

491
00:22:57,600 --> 00:23:00,440
Speaker 1: able to do the master's degree at an economic concurrent

492
00:23:00,480 --> 00:23:02,560
Speaker 1: with the bachelor green computer science. So I was able

493
00:23:02,600 --> 00:23:04,639
Speaker 1: to do all that in four years. And what was

494
00:23:04,680 --> 00:23:08,720
Speaker 1: your thinking getting a master's in economics? So I thought

495
00:23:08,760 --> 00:23:10,240
Speaker 1: about whether or not I wanted to go get a

496
00:23:10,280 --> 00:23:15,199
Speaker 1: PhD in economics, and I didn't know. I loved the

497
00:23:15,560 --> 00:23:19,280
Speaker 1: questions that economics addresses. How do we create a just society? Uh?

498
00:23:19,320 --> 00:23:22,040
Speaker 1: You know, how do you what? What's the source of wealth? Uh?

499
00:23:22,080 --> 00:23:24,200
Speaker 1: You know, how? What's the fairest way to distribute things?

500
00:23:24,200 --> 00:23:26,560
Speaker 1: What's the right how to markets work? But I didn't

501
00:23:26,600 --> 00:23:30,560
Speaker 1: end up liking the methodology that academia uses in economics.

502
00:23:30,600 --> 00:23:33,480
Speaker 1: It seemed very abstract and a historical by and large,

503
00:23:33,800 --> 00:23:36,120
Speaker 1: So I really by the time I graduated, I thought,

504
00:23:36,119 --> 00:23:37,720
Speaker 1: I'm just gonna go out in the world. But what

505
00:23:38,000 --> 00:23:41,400
Speaker 1: my master's thesis was on the job market for academic economists,

506
00:23:41,880 --> 00:23:44,840
Speaker 1: because I thought, how can I It's a market, right,

507
00:23:45,080 --> 00:23:48,000
Speaker 1: And I thought, how can I uh learned about what

508
00:23:48,080 --> 00:23:50,520
Speaker 1: it's like to be an academic economist. I know, I'll

509
00:23:50,560 --> 00:23:52,800
Speaker 1: write a paper about them. So I did a survey

510
00:23:52,840 --> 00:23:55,720
Speaker 1: about how the market worked and how the structural market worked. Um,

511
00:23:56,040 --> 00:23:57,600
Speaker 1: and by then I was clear I didn't want to

512
00:23:57,640 --> 00:23:59,840
Speaker 1: go do that. Uh. So I thought about what I

513
00:23:59,880 --> 00:24:02,320
Speaker 1: was going to do next. Uh And I so, what

514
00:24:02,359 --> 00:24:05,560
Speaker 1: did you determine? What was the market for academic It's

515
00:24:05,600 --> 00:24:07,760
Speaker 1: a very self referential market, in other words, as a

516
00:24:07,800 --> 00:24:11,879
Speaker 1: market where reputation carries a huge amount of weight. And

517
00:24:11,920 --> 00:24:15,840
Speaker 1: you look at the feedback loop of which graduate programs

518
00:24:15,960 --> 00:24:19,040
Speaker 1: place people in which academic institution. Now this was, you know,

519
00:24:19,119 --> 00:24:22,359
Speaker 1: thirty five years ago, so it may have changed, although

520
00:24:22,359 --> 00:24:25,440
Speaker 1: I doubt it that. Uh. You know, over a long

521
00:24:25,440 --> 00:24:27,399
Speaker 1: period of time. You might say the cream risers at

522
00:24:27,440 --> 00:24:32,000
Speaker 1: the top. But insofar as the best people uh go

523
00:24:32,040 --> 00:24:34,520
Speaker 1: to the best schools, it's fine. But it tends to

524
00:24:34,560 --> 00:24:38,240
Speaker 1: provide a lot of sort of feedback around orthodoxy where

525
00:24:38,280 --> 00:24:41,960
Speaker 1: the best professor's best students get the best jobs as

526
00:24:41,960 --> 00:24:45,040
Speaker 1: sort of the pattern that emerged, which is okay. At

527
00:24:45,080 --> 00:24:48,080
Speaker 1: this late date, there's a huge division between the left

528
00:24:48,080 --> 00:24:50,000
Speaker 1: and the right read Paul Krugman the New York Times

529
00:24:50,119 --> 00:24:52,520
Speaker 1: is one thing, and we have all the economists backing

530
00:24:52,560 --> 00:24:55,919
Speaker 1: up the right wing. What's your viewpoint on this, on

531
00:24:55,920 --> 00:24:59,840
Speaker 1: on the degree to which economics it can be used

532
00:24:59,840 --> 00:25:02,920
Speaker 1: as justification? Right now, we're in a it's a very

533
00:25:02,920 --> 00:25:06,600
Speaker 1: weird world because we're in such a a world where

534
00:25:06,600 --> 00:25:09,520
Speaker 1: there's the division is not between the academic left and

535
00:25:09,520 --> 00:25:13,640
Speaker 1: the academic right. The it's between people who are scienced

536
00:25:13,680 --> 00:25:16,320
Speaker 1: and fact based and people who are know nothings. So

537
00:25:16,760 --> 00:25:18,720
Speaker 1: you know nothings in the old American condition of nothing.

538
00:25:18,840 --> 00:25:23,520
Speaker 1: So when you look at the degree to which the

539
00:25:24,359 --> 00:25:27,679
Speaker 1: tax cut, for instance, is being justified on academic on

540
00:25:27,760 --> 00:25:30,800
Speaker 1: academic economic grounds, that's not really the case anymore. You

541
00:25:30,800 --> 00:25:33,439
Speaker 1: look at the deficits, right, the fact that the right

542
00:25:33,440 --> 00:25:35,760
Speaker 1: wing said they hate deficits, but they have now passed

543
00:25:35,800 --> 00:25:37,560
Speaker 1: the tax bill that's going to blow up the deficit

544
00:25:37,840 --> 00:25:39,639
Speaker 1: to a higher level than it's ever been before, at

545
00:25:39,640 --> 00:25:41,840
Speaker 1: a faster rate than you've ever seen outside of an

546
00:25:41,880 --> 00:25:45,480
Speaker 1: economic depression. Ever, so right now, I actually don't really

547
00:25:45,480 --> 00:25:47,760
Speaker 1: think when you look at what's going on, um in

548
00:25:47,840 --> 00:25:52,800
Speaker 1: the economy, Uh, we were was a strong economy before Trump.

549
00:25:52,840 --> 00:25:55,200
Speaker 1: They've you know, added a little bit of uh of

550
00:25:55,480 --> 00:25:58,280
Speaker 1: of you know, put some more sugar in the in

551
00:25:58,359 --> 00:26:00,240
Speaker 1: the in the coffee to make it even sweet, or

552
00:26:00,520 --> 00:26:03,280
Speaker 1: uh not doing a way that's sustainable. I don't think

553
00:26:03,320 --> 00:26:05,480
Speaker 1: most academic conicts would say that that's what you should

554
00:26:05,520 --> 00:26:08,199
Speaker 1: have done. But people who like low tax rates for

555
00:26:08,240 --> 00:26:11,760
Speaker 1: corporations are very happy and staying on the same point

556
00:26:11,760 --> 00:26:14,600
Speaker 1: of know nothings in fact base. Is there any hope

557
00:26:14,600 --> 00:26:18,600
Speaker 1: for America both in terms of the top level arguing

558
00:26:18,640 --> 00:26:21,800
Speaker 1: and the people you know in the hinder lands moving

559
00:26:21,840 --> 00:26:25,000
Speaker 1: back towards facts. Are we on this divergent path and

560
00:26:24,960 --> 00:26:27,800
Speaker 1: it will sustain? I think it could go either way.

561
00:26:27,840 --> 00:26:30,879
Speaker 1: I mean I'm pretty uh generally an optimist, but in

562
00:26:30,920 --> 00:26:34,359
Speaker 1: the present time, Um, you know, you've always had political

563
00:26:34,359 --> 00:26:36,560
Speaker 1: division in this country, you know. But and I didn't

564
00:26:36,560 --> 00:26:39,480
Speaker 1: like Reagan very much, although in hindsight, the worst thing

565
00:26:39,520 --> 00:26:42,120
Speaker 1: he did in terms of creating a huge military conflict

566
00:26:42,359 --> 00:26:46,000
Speaker 1: was invading Grenada, So you know that was a pretty Uh.

567
00:26:46,160 --> 00:26:47,879
Speaker 1: You know, that was a pretty mild thing to do.

568
00:26:48,040 --> 00:26:49,840
Speaker 1: You know, you knocked over a few palm trees, and

569
00:26:50,080 --> 00:26:52,639
Speaker 1: you know, maybe maybe one or two people got their

570
00:26:52,640 --> 00:26:55,719
Speaker 1: noses broken or something. But compared to you know, some

571
00:26:55,800 --> 00:26:57,520
Speaker 1: of the really bad worst we've had since then, it

572
00:26:57,600 --> 00:27:01,240
Speaker 1: wasn't that pernicious. Uh. You know then you uh uh

573
00:27:01,280 --> 00:27:05,040
Speaker 1: George H. W. Bush, who you know, did some really

574
00:27:05,119 --> 00:27:08,959
Speaker 1: horrible things, but uh, ultimately the system worked and the

575
00:27:08,960 --> 00:27:12,360
Speaker 1: pendulum swung back. Uh. And then with Obama, I think

576
00:27:12,400 --> 00:27:15,560
Speaker 1: what happened was there was a lot of people who

577
00:27:15,640 --> 00:27:19,480
Speaker 1: got freaked out by the fact that this uh, the

578
00:27:19,480 --> 00:27:23,240
Speaker 1: the the change, the visual change, and the of having

579
00:27:23,840 --> 00:27:28,160
Speaker 1: an African American president, which in my view was a

580
00:27:28,200 --> 00:27:31,240
Speaker 1: sign of society being a fundamentally healthy place and a

581
00:27:31,320 --> 00:27:33,040
Speaker 1: very positive thing. And I still think he's the best

582
00:27:33,040 --> 00:27:35,919
Speaker 1: president of my lifetime. Uh, But but that there were

583
00:27:35,920 --> 00:27:37,880
Speaker 1: a lot of people who really freaked out about that,

584
00:27:38,240 --> 00:27:40,199
Speaker 1: and that created an environment when you look at they

585
00:27:40,200 --> 00:27:43,240
Speaker 1: were freaked out about what the fact that somebody who

586
00:27:43,280 --> 00:27:45,800
Speaker 1: doesn't look like them got to be president, and they

587
00:27:45,680 --> 00:27:48,840
Speaker 1: that's you know, you whether whether it's because there was

588
00:27:48,960 --> 00:27:50,679
Speaker 1: there's all these studies that are coming out now that

589
00:27:51,080 --> 00:27:53,439
Speaker 1: status anxiety was a huge motivator of a lot of

590
00:27:53,440 --> 00:27:56,640
Speaker 1: the Trump supporters, and the status anxiety has an economic

591
00:27:56,680 --> 00:27:58,720
Speaker 1: element to it, But also want to explain what you

592
00:27:59,000 --> 00:28:02,800
Speaker 1: for my audience, what lot of anxiety as well. Basically,

593
00:28:02,800 --> 00:28:05,480
Speaker 1: the people are afraid that they're they're not going to

594
00:28:05,520 --> 00:28:08,560
Speaker 1: be able to maintain their life situation, be it financially

595
00:28:08,680 --> 00:28:11,760
Speaker 1: or be its social prestiges, and that that those kinds

596
00:28:11,800 --> 00:28:14,119
Speaker 1: of fears get played on in all kinds of different

597
00:28:14,160 --> 00:28:18,320
Speaker 1: ways by uh, uh you know, by by people who

598
00:28:18,359 --> 00:28:22,160
Speaker 1: are you know, fearmongers or uh you know, various kinds

599
00:28:22,200 --> 00:28:25,800
Speaker 1: of uh whistle dog whistleblowers, And a lot of what

600
00:28:25,880 --> 00:28:28,560
Speaker 1: happened with Trump has been a lot of that sort

601
00:28:28,600 --> 00:28:32,200
Speaker 1: of uh rhetoric, in my view, combined with the fact

602
00:28:32,240 --> 00:28:35,080
Speaker 1: that when the economic recovery happened, you know, there's all

603
00:28:35,080 --> 00:28:37,280
Speaker 1: these things that happened with two that's sixteen, but it

604
00:28:37,359 --> 00:28:39,600
Speaker 1: was when the economic recovery happened, it happened on the

605
00:28:39,640 --> 00:28:41,600
Speaker 1: coast very well, and it happened in the middle country,

606
00:28:42,120 --> 00:28:45,280
Speaker 1: uh much much less comprehensively, and a lot of people

607
00:28:45,600 --> 00:28:50,040
Speaker 1: that had solid middle class lives with union jobs, they

608
00:28:50,040 --> 00:28:54,640
Speaker 1: don't have those union jobs anymore, and ironically, the the

609
00:28:54,800 --> 00:28:57,120
Speaker 1: economy is getting gutted in a way that makes it

610
00:28:57,240 --> 00:28:59,760
Speaker 1: much much harder for people who aren't college educated and

611
00:28:59,840 --> 00:29:03,160
Speaker 1: or living in you know, the wealthier coastal cities to

612
00:29:03,160 --> 00:29:06,880
Speaker 1: to to make it. And that the that created an

613
00:29:06,920 --> 00:29:10,080
Speaker 1: environment where people who were chingo istic are people who

614
00:29:10,080 --> 00:29:14,960
Speaker 1: are willing to manipulate the truth uh to uh sort

615
00:29:14,960 --> 00:29:18,800
Speaker 1: of create uh boogey men are able to do that. Okay,

616
00:29:18,840 --> 00:29:21,160
Speaker 1: So you graduate from college, you decide you don't want

617
00:29:21,160 --> 00:29:24,280
Speaker 1: to be an academic economists't want to be an economists

618
00:29:24,280 --> 00:29:26,120
Speaker 1: at all. So what do you do? Uh? Well, I

619
00:29:26,160 --> 00:29:27,920
Speaker 1: had had two or three different job choices, and they

620
00:29:27,920 --> 00:29:30,200
Speaker 1: were pretty interesting. I could have stayed with a little

621
00:29:30,240 --> 00:29:33,920
Speaker 1: ivy research company that had made it made those games,

622
00:29:33,920 --> 00:29:36,240
Speaker 1: and we were working on some consulting projects that turned

623
00:29:36,280 --> 00:29:39,560
Speaker 1: into some interesting things. My friends Steve stayed with that,

624
00:29:40,280 --> 00:29:41,800
Speaker 1: but I decided, I do I want to do something

625
00:29:41,840 --> 00:29:44,240
Speaker 1: where I could learn a lot more about scale um

626
00:29:44,640 --> 00:29:46,239
Speaker 1: and so I had a couple of job offers at

627
00:29:46,240 --> 00:29:48,480
Speaker 1: Hewlett Packard, which I thought was I didn't want to

628
00:29:48,480 --> 00:29:50,680
Speaker 1: go to IBM. I thought it was too bureaucratic and

629
00:29:50,840 --> 00:29:55,200
Speaker 1: to organized around like people who's you know, in intensity

630
00:29:55,280 --> 00:29:58,040
Speaker 1: level and in intellect and like, we're more sort of

631
00:29:58,080 --> 00:30:00,720
Speaker 1: within one standard deviation of the norm in a place

632
00:30:00,720 --> 00:30:02,360
Speaker 1: where if you really want to throw yourself into it,

633
00:30:02,400 --> 00:30:04,520
Speaker 1: you could have a huge impact. And IBM is a

634
00:30:04,520 --> 00:30:06,800
Speaker 1: is a great company in a lot of ways, but

635
00:30:06,840 --> 00:30:08,960
Speaker 1: at that point it was it had gotten pretty big

636
00:30:08,960 --> 00:30:12,800
Speaker 1: and bureaucratic um so Hewlett Packard had a couple of

637
00:30:12,800 --> 00:30:14,960
Speaker 1: opportunities there. I thought it was an interesting place. And

638
00:30:15,000 --> 00:30:18,960
Speaker 1: then Uh, Steve Bomber, uh and Paul Allen went to

639
00:30:19,000 --> 00:30:22,160
Speaker 1: Mike went to Yale and and and inteviewed people from Microsoft.

640
00:30:22,520 --> 00:30:23,880
Speaker 1: And I knew a little bit about the I knew

641
00:30:23,880 --> 00:30:25,640
Speaker 1: more about the PC than the average college kid in

642
00:30:26,920 --> 00:30:29,800
Speaker 1: three so interven with them and they, you know a

643
00:30:29,840 --> 00:30:32,200
Speaker 1: little bit slower. Did you know them previously? You know,

644
00:30:32,240 --> 00:30:34,760
Speaker 1: I knew, I knew who Microsoft was, but they just

645
00:30:35,000 --> 00:30:37,280
Speaker 1: they came to campus. One of the things Microsoft did

646
00:30:37,280 --> 00:30:40,160
Speaker 1: well and why it's scaled up, is they did uh

647
00:30:40,320 --> 00:30:43,040
Speaker 1: college recruiting very early in Microsoft cycle. At the time,

648
00:30:43,080 --> 00:30:47,560
Speaker 1: Microsoft was about two fifty people, and they started recruiting

649
00:30:47,600 --> 00:30:52,400
Speaker 1: on at least some college campuses starting in like so

650
00:30:52,680 --> 00:30:55,760
Speaker 1: by that time Microsoft was coming to Yale, and Microsoft

651
00:30:55,800 --> 00:30:58,920
Speaker 1: ended up hiring me in six or seven of my classmates.

652
00:30:59,360 --> 00:31:01,720
Speaker 1: Uh for uh with the class of eight three And

653
00:31:01,800 --> 00:31:03,800
Speaker 1: what do you think? And you moved to Seattle, right?

654
00:31:03,840 --> 00:31:05,920
Speaker 1: I did? Yes? And what was that for someone who

655
00:31:05,920 --> 00:31:08,560
Speaker 1: basically was at East Coast boy, Well, my mom was

656
00:31:08,600 --> 00:31:12,680
Speaker 1: from California and we visited the Barrier bunch. Uh and Uh,

657
00:31:12,720 --> 00:31:14,280
Speaker 1: So I thought, if you'd asked me, I thought, maybe

658
00:31:14,320 --> 00:31:16,760
Speaker 1: I'll end up in California. Uh. And all I knew

659
00:31:16,760 --> 00:31:18,720
Speaker 1: about Seattle was I'd read Ball four when I was

660
00:31:18,720 --> 00:31:22,080
Speaker 1: a kid, which was Jim Bountain's baseball book, primarily about

661
00:31:22,080 --> 00:31:23,520
Speaker 1: the year that he was with the Seattle Pilots in

662
00:31:23,600 --> 00:31:26,160
Speaker 1: nine nine. So I knew that Seattle was a team.

663
00:31:26,240 --> 00:31:30,040
Speaker 1: Was he was a city that hadn't lost a baseball team. Fortunately,

664
00:31:30,080 --> 00:31:31,440
Speaker 1: by the time I got through in a D three,

665
00:31:31,480 --> 00:31:34,200
Speaker 1: they had a new one because the Mariners started in

666
00:31:34,200 --> 00:31:37,520
Speaker 1: seventy seven as part of an antitrust settlement for the

667
00:31:37,560 --> 00:31:42,120
Speaker 1: Pilots moving to Milwaukee. So the the uh the head

668
00:31:42,120 --> 00:31:44,600
Speaker 1: of baseball team. So that was a check. It was

669
00:31:44,640 --> 00:31:47,120
Speaker 1: close to the West Coast. My parents ended up retiring

670
00:31:47,120 --> 00:31:49,760
Speaker 1: to northern California a couple years after we were out there,

671
00:31:49,800 --> 00:31:51,760
Speaker 1: so that was sort of a check. And it was

672
00:31:51,800 --> 00:31:54,960
Speaker 1: honestly that when everybody in a Microsoft was incredibly smart,

673
00:31:55,000 --> 00:31:58,080
Speaker 1: incredibly hardcore, and I thought, well, this is this place.

674
00:31:58,120 --> 00:31:59,600
Speaker 1: Seems like it's got a lot of people that I

675
00:31:59,640 --> 00:32:01,360
Speaker 1: want to work with and I can learn from, and

676
00:32:01,400 --> 00:32:02,640
Speaker 1: I'll do it for a couple of years. Un Look,

677
00:32:02,680 --> 00:32:05,479
Speaker 1: it's boring and ended up being ten years. Okay. When

678
00:32:05,560 --> 00:32:07,360
Speaker 1: you went to work there, it wasn't public yet, No,

679
00:32:07,520 --> 00:32:09,480
Speaker 1: it was. I was getting lucky but better than good.

680
00:32:09,640 --> 00:32:12,120
Speaker 1: So I got there in eighty three. Uh, and Microsoft

681
00:32:12,160 --> 00:32:15,800
Speaker 1: went public in eighty six. Okay, And what was your

682
00:32:15,840 --> 00:32:18,800
Speaker 1: gig at Microsoft? Well, I worked in basically three parts

683
00:32:18,840 --> 00:32:21,280
Speaker 1: of the company. Was always met banking products because that's

684
00:32:21,320 --> 00:32:24,160
Speaker 1: my passion. The first four years I worked in the

685
00:32:24,240 --> 00:32:28,400
Speaker 1: applications products. Uh, I was involved with the I didn't

686
00:32:28,400 --> 00:32:31,000
Speaker 1: create Microsoft Word, but when I got involved with word,

687
00:32:31,040 --> 00:32:33,800
Speaker 1: word was like number five and word processing and got

688
00:32:33,840 --> 00:32:35,640
Speaker 1: it up to number two at the time, right behind

689
00:32:35,640 --> 00:32:38,640
Speaker 1: Word Perfect, and we're kind of getting getting strong with them.

690
00:32:38,640 --> 00:32:40,840
Speaker 1: And then I ran what it's called program management for

691
00:32:40,840 --> 00:32:43,920
Speaker 1: all the applications products, which is like the connective tissue

692
00:32:43,960 --> 00:32:47,240
Speaker 1: between the marketers on the one hand and the engineers

693
00:32:47,280 --> 00:32:48,840
Speaker 1: and the other. And since I had sort of a

694
00:32:48,920 --> 00:32:51,840
Speaker 1: dual background, I was able to kind of play hopefully

695
00:32:52,000 --> 00:32:54,720
Speaker 1: a contructive role in spanning the two. So that was

696
00:32:54,760 --> 00:32:57,880
Speaker 1: my first four years, and then my next to three

697
00:32:57,960 --> 00:33:00,680
Speaker 1: years was working on networking products because I was always

698
00:33:00,680 --> 00:33:03,960
Speaker 1: interested in communication, right, so networking is about understanding the

699
00:33:03,960 --> 00:33:06,840
Speaker 1: plumbing and the infrastructure. So that was the least successful

700
00:33:06,880 --> 00:33:09,480
Speaker 1: thing I worked on because at the time Microsoft was

701
00:33:09,520 --> 00:33:12,840
Speaker 1: betting heavily on an operating system called OS two and

702
00:33:13,040 --> 00:33:16,400
Speaker 1: which was a joint venture of form between Microsoft and IBM,

703
00:33:16,440 --> 00:33:19,520
Speaker 1: and Microsoft decided to build all its networking products on

704
00:33:19,560 --> 00:33:23,000
Speaker 1: the OS two platform. UM and UH. So I did

705
00:33:23,000 --> 00:33:24,560
Speaker 1: it for a couple of years, worked with some great people.

706
00:33:24,600 --> 00:33:26,680
Speaker 1: It was a fantastic lesson to me of how when

707
00:33:26,680 --> 00:33:30,400
Speaker 1: you have a great team, great products, and a bad strategy,

708
00:33:30,480 --> 00:33:33,280
Speaker 1: you fail right. So UM a lot of the people

709
00:33:33,320 --> 00:33:36,560
Speaker 1: I work with ended up in different capacities, having senior

710
00:33:36,640 --> 00:33:38,440
Speaker 1: jobs in Microsoft for a number of years and doing

711
00:33:38,480 --> 00:33:40,760
Speaker 1: cool things in the industry. But our products weren't that

712
00:33:40,800 --> 00:33:45,040
Speaker 1: successful where the generally were failures because OS two was

713
00:33:45,080 --> 00:33:49,040
Speaker 1: not a viable platform to build UH networking servers on.

714
00:33:49,080 --> 00:33:52,040
Speaker 1: Top of Microsoft ended up being very successful in networking

715
00:33:52,240 --> 00:33:54,520
Speaker 1: when it moved from OS to to an operating system

716
00:33:54,520 --> 00:33:57,640
Speaker 1: called Windows NT, which was the next generation. But but

717
00:33:57,760 --> 00:34:00,160
Speaker 1: that was after I left that group, so did for

718
00:34:00,200 --> 00:34:02,240
Speaker 1: a couple of years, and then I worked in something

719
00:34:02,320 --> 00:34:05,120
Speaker 1: we called Multimedia and Consumer Systems, which is the last

720
00:34:05,440 --> 00:34:07,920
Speaker 1: three or four years there, which was basically about figuring

721
00:34:07,960 --> 00:34:11,359
Speaker 1: out how to add media technology into Windows and then

722
00:34:11,360 --> 00:34:14,000
Speaker 1: starting working on the next sort of advanced digital to

723
00:34:14,120 --> 00:34:16,759
Speaker 1: some of the early sort of proto digital devices. So

724
00:34:16,840 --> 00:34:18,680
Speaker 1: that was my last three or four years there, and

725
00:34:18,920 --> 00:34:21,799
Speaker 1: I ended up it was a fantastic tenure run. I

726
00:34:21,880 --> 00:34:25,040
Speaker 1: learned a ton uh and made made some lifelong friends

727
00:34:25,200 --> 00:34:29,080
Speaker 1: and uh couldn't and financially it was obviously a very

728
00:34:29,120 --> 00:34:33,000
Speaker 1: advantageous time to be there. So financially, you know, if

729
00:34:33,040 --> 00:34:35,960
Speaker 1: you read the literature, you did very well. It was

730
00:34:36,000 --> 00:34:39,279
Speaker 1: that primarily getting in early or your success in the

731
00:34:39,320 --> 00:34:43,520
Speaker 1: operation or well, yeah, I mean it was the right place,

732
00:34:43,640 --> 00:34:45,560
Speaker 1: right time. And then I did work my butt off

733
00:34:45,680 --> 00:34:48,640
Speaker 1: and worked very closely with you know, Gates and Bomber

734
00:34:48,680 --> 00:34:51,040
Speaker 1: and these other guys and uh, and they I think

735
00:34:51,040 --> 00:34:54,839
Speaker 1: they viewed me as somebody who was hardcore like you know,

736
00:34:55,200 --> 00:34:57,319
Speaker 1: I would work six seven days a week without not like,

737
00:34:57,360 --> 00:34:59,120
Speaker 1: without worrying about it because I I loved what I

738
00:34:59,160 --> 00:35:01,000
Speaker 1: was doing. It was was like it was I didn't

739
00:35:01,040 --> 00:35:03,799
Speaker 1: have any family or kids at the time. So classic thing.

740
00:35:03,800 --> 00:35:05,480
Speaker 1: You know, you're in your twenty you throw yourself into

741
00:35:05,480 --> 00:35:07,480
Speaker 1: your career and had a great time with it. So

742
00:35:07,560 --> 00:35:09,839
Speaker 1: it worked out. It worked out very well to me.

743
00:35:10,040 --> 00:35:12,000
Speaker 1: And uh, you know, at the end of the ten years,

744
00:35:12,040 --> 00:35:14,000
Speaker 1: when I looked up and you know, said, wow, this

745
00:35:14,040 --> 00:35:16,279
Speaker 1: has been a decade and pull up my pull my

746
00:35:16,320 --> 00:35:19,040
Speaker 1: pariscope up, I was ready to do something else. But

747
00:35:19,080 --> 00:35:22,040
Speaker 1: I was incredibly happy for the experience. I okay, So

748
00:35:22,480 --> 00:35:26,120
Speaker 1: what came first in terms of leaving this the concept

749
00:35:26,200 --> 00:35:29,000
Speaker 1: that you were done and you've fulfilled your mission at Microsoft,

750
00:35:29,200 --> 00:35:31,160
Speaker 1: or that you wanted to do real networks. Well, I

751
00:35:31,200 --> 00:35:33,120
Speaker 1: didn't have the idea for real network So I remember

752
00:35:33,160 --> 00:35:37,320
Speaker 1: in the spring of nineties three, so I'm what thirty one, uh,

753
00:35:37,360 --> 00:35:39,960
Speaker 1: and uh Gates comes to me and says, I want

754
00:35:40,000 --> 00:35:42,600
Speaker 1: to reorganize some things, and I say, great, let's re

755
00:35:42,719 --> 00:35:45,200
Speaker 1: organize me out of a job. Um, which I don't

756
00:35:45,200 --> 00:35:48,440
Speaker 1: think what he expected because but it was really was

757
00:35:48,520 --> 00:35:50,440
Speaker 1: how I was feeling I was. I'd been working so

758
00:35:50,520 --> 00:35:53,239
Speaker 1: hard loving it, but I knew that I wouldn't really

759
00:35:53,280 --> 00:35:55,359
Speaker 1: be able to step back and think about the next

760
00:35:55,360 --> 00:35:57,360
Speaker 1: thing until I had some breathing room. I had to

761
00:35:57,680 --> 00:36:00,239
Speaker 1: create some separation. Just go back when you have sets

762
00:36:00,239 --> 00:36:02,239
Speaker 1: off the way you tell the story, you really were

763
00:36:02,239 --> 00:36:05,120
Speaker 1: not a programmer. No. I wrote a very little code

764
00:36:05,120 --> 00:36:07,520
Speaker 1: that ended up in Microsoft products. I was a technical

765
00:36:07,680 --> 00:36:11,319
Speaker 1: enough person to help to work with the engineers UH,

766
00:36:11,360 --> 00:36:14,280
Speaker 1: and a marketing business or in enough person to understand

767
00:36:14,360 --> 00:36:17,080
Speaker 1: to go talk to customers, try to understand what the

768
00:36:17,120 --> 00:36:19,960
Speaker 1: market wanted. And I was thought of myself as adding

769
00:36:20,040 --> 00:36:23,239
Speaker 1: value at that intersection between those two. Okay, so you

770
00:36:23,280 --> 00:36:26,360
Speaker 1: wanted some breathing room, so you said, Bill, let's reorganize

771
00:36:26,360 --> 00:36:28,520
Speaker 1: me out of a job. Right? And how did that

772
00:36:28,560 --> 00:36:32,480
Speaker 1: go down? So we had three meetings on the topic. UM.

773
00:36:32,600 --> 00:36:35,640
Speaker 1: The first meeting, he yelled at me, this is the

774
00:36:35,719 --> 00:36:39,080
Speaker 1: most irrational thing, This is the stupidest thing. Why you're

775
00:36:39,080 --> 00:36:40,520
Speaker 1: doing this, Why do you want to do this? Go

776
00:36:40,600 --> 00:36:42,160
Speaker 1: talk to this guy and go to talk to that guy.

777
00:36:42,320 --> 00:36:46,560
Speaker 1: So fine, I did. That second meeting was basically the same. UH.

778
00:36:46,680 --> 00:36:48,839
Speaker 1: Then the third meeting happened in about five minutes into

779
00:36:48,880 --> 00:36:51,239
Speaker 1: the third meeting two star, I looked at him and

780
00:36:51,280 --> 00:36:54,120
Speaker 1: I said, is this making you feel better? And he

781
00:36:54,160 --> 00:36:55,799
Speaker 1: looked at me like I was crazy, because why would

782
00:36:55,800 --> 00:36:57,560
Speaker 1: I say that? And I said, because it is making

783
00:36:57,560 --> 00:36:59,520
Speaker 1: you feel better, you should keep doing it, but it's

784
00:36:59,520 --> 00:37:02,720
Speaker 1: not making me feel better and it's not changing my mind. Um,

785
00:37:02,719 --> 00:37:05,280
Speaker 1: And to his credit, he stopped cold, and we proceeded

786
00:37:05,320 --> 00:37:09,440
Speaker 1: to have a very rational conversation about you know, uh,

787
00:37:09,760 --> 00:37:12,319
Speaker 1: how we make a good transition, how what we do.

788
00:37:12,640 --> 00:37:14,680
Speaker 1: I ended up technically doing a leave of absence, so

789
00:37:14,719 --> 00:37:17,120
Speaker 1: I spent six months traveling around the world and do

790
00:37:17,200 --> 00:37:19,160
Speaker 1: another taking up. Actually I took a class down and

791
00:37:19,160 --> 00:37:21,759
Speaker 1: maybe I go to PhD, which I decided not to do.

792
00:37:22,280 --> 00:37:25,080
Speaker 1: Uh and uh. Then I did some consulting from Microsoft

793
00:37:25,280 --> 00:37:27,839
Speaker 1: on a few topics that are related to the one

794
00:37:27,920 --> 00:37:31,080
Speaker 1: related to what the intersection of the Internet and Microsoft

795
00:37:31,120 --> 00:37:34,680
Speaker 1: strategy should be. One related to the future. There's this

796
00:37:34,760 --> 00:37:37,640
Speaker 1: joint venture Microsoft was brewing called cable Soft, and I

797
00:37:37,680 --> 00:37:39,759
Speaker 1: consulted with Bill on whether he should do the joint

798
00:37:39,800 --> 00:37:42,080
Speaker 1: venture with the two big cable companies to try to

799
00:37:42,120 --> 00:37:45,960
Speaker 1: create the dominant operating system for cable TV set top boxes.

800
00:37:46,200 --> 00:37:48,440
Speaker 1: So I did a little consulting at the back end

801
00:37:48,480 --> 00:37:50,600
Speaker 1: of that. Uh and at the time I was already

802
00:37:50,600 --> 00:37:53,480
Speaker 1: brewing the idea for what became Real Networks. But it

803
00:37:53,520 --> 00:37:57,239
Speaker 1: was really during that that summer and fall of of

804
00:37:57,520 --> 00:38:01,799
Speaker 1: two thousands of other uh three, that I was figuring

805
00:38:01,840 --> 00:38:03,759
Speaker 1: out what I was gonna do next. And when you

806
00:38:03,840 --> 00:38:06,560
Speaker 1: left Microsoft, did you have enough money to never work again?

807
00:38:07,480 --> 00:38:11,080
Speaker 1: You know? If I was a monk? Sure, okay, I

808
00:38:11,120 --> 00:38:14,200
Speaker 1: got my answer. But but I figured, like I was

809
00:38:14,239 --> 00:38:18,920
Speaker 1: thirty one, uh, I think I Uh, I knew I

810
00:38:18,920 --> 00:38:20,880
Speaker 1: had a lot of ambitions and things I was interested

811
00:38:20,880 --> 00:38:22,719
Speaker 1: in doing, and ended up putting a bunch of the

812
00:38:22,719 --> 00:38:25,359
Speaker 1: money into the company that became Real Network. So uh

813
00:38:25,400 --> 00:38:26,960
Speaker 1: it worked out just fine. So how did you come

814
00:38:27,000 --> 00:38:29,279
Speaker 1: up with the idea for Real Networks? Well? I had

815
00:38:29,320 --> 00:38:32,120
Speaker 1: two ideas, actually, uh in the summer of ninety three.

816
00:38:32,800 --> 00:38:35,560
Speaker 1: One was focused on this notion of set top boxes

817
00:38:35,600 --> 00:38:38,760
Speaker 1: and cable and television as his starians and this stuff.

818
00:38:38,880 --> 00:38:41,239
Speaker 1: Remember all the cable companies were talking about the five

819
00:38:41,239 --> 00:38:43,879
Speaker 1: inter channel universe that time. Warner had their big trial

820
00:38:43,880 --> 00:38:46,760
Speaker 1: in Orlando, Florida. They were reinventing, They're going to reinvent

821
00:38:46,840 --> 00:38:51,080
Speaker 1: television uh, t c I, the other big cable company

822
00:38:51,200 --> 00:38:53,399
Speaker 1: had their own tiles, and telcos had them. So we're

823
00:38:53,440 --> 00:38:55,319
Speaker 1: like tele A TV, which was the thing that Howard

824
00:38:55,320 --> 00:38:57,759
Speaker 1: Stringer was involved in. They're on these different trials. So

825
00:38:57,800 --> 00:39:00,680
Speaker 1: I thought, when maybe I'll make technology gene products for

826
00:39:00,719 --> 00:39:04,319
Speaker 1: those And then at the same time, I thought, well,

827
00:39:04,360 --> 00:39:07,520
Speaker 1: maybe there's something to this online communications world. I've been

828
00:39:07,520 --> 00:39:09,520
Speaker 1: an online geek. One of the products that created a

829
00:39:09,560 --> 00:39:13,680
Speaker 1: Microsoft was an online product called Microsoft Access, before the database,

830
00:39:13,719 --> 00:39:16,720
Speaker 1: Microsoft Access, the first Micronofic Action was a communications product

831
00:39:16,800 --> 00:39:19,640
Speaker 1: that was not that successful, but I'd work closely with

832
00:39:20,000 --> 00:39:23,239
Speaker 1: trying to trying to create a consistent user interface behind

833
00:39:23,239 --> 00:39:25,600
Speaker 1: CompuServe and a o L and the products and all

834
00:39:25,640 --> 00:39:28,160
Speaker 1: these services. So I was very smilar with the online world.

835
00:39:28,680 --> 00:39:31,279
Speaker 1: And I got lucky again. A good friend of mine

836
00:39:31,360 --> 00:39:33,480
Speaker 1: named Mitch kap Poor asked me to get involved in

837
00:39:33,520 --> 00:39:36,719
Speaker 1: a nonprofit he started, Well, that was Lotus one, two three, right.

838
00:39:36,800 --> 00:39:39,840
Speaker 1: I got to know Mitch through various interact with Microsoft.

839
00:39:40,080 --> 00:39:42,719
Speaker 1: Mitch was somebody and is somebody who cares about the

840
00:39:42,760 --> 00:39:46,480
Speaker 1: intersection between technology and society. And Mitch is one of

841
00:39:46,480 --> 00:39:49,840
Speaker 1: these people who's always understand stuff. Five years before anybody

842
00:39:49,840 --> 00:39:54,360
Speaker 1: else does both, both strategically and really more almost like, uh,

843
00:39:54,400 --> 00:39:57,720
Speaker 1: you know, conceptually. And Mitch was convinced that the Internet

844
00:39:57,760 --> 00:40:00,279
Speaker 1: was going to be a huge deal cyberspace was most

845
00:40:00,320 --> 00:40:02,800
Speaker 1: people called it at the time, and he created this

846
00:40:02,880 --> 00:40:05,960
Speaker 1: organization called the Electronic Frontier Foundation that was very focused

847
00:40:06,000 --> 00:40:09,319
Speaker 1: on issues of civil liberty and community in cyberspace. So

848
00:40:09,440 --> 00:40:12,279
Speaker 1: Mitch asked me, and the time that I was I

849
00:40:12,280 --> 00:40:14,120
Speaker 1: was technically to leave, but I was I wasn't gonna

850
00:40:14,120 --> 00:40:16,719
Speaker 1: go back to Microsoft. So in this late spring early

851
00:40:16,719 --> 00:40:19,000
Speaker 1: summer of nine three, he said, come to a board

852
00:40:19,040 --> 00:40:21,839
Speaker 1: meeting of the Electronic Frontier Foundation, And so I went

853
00:40:21,880 --> 00:40:23,880
Speaker 1: to one of the board meetings in Austin, Texas, I

854
00:40:23,920 --> 00:40:26,520
Speaker 1: think it was, and I met and one of the

855
00:40:26,520 --> 00:40:28,759
Speaker 1: guys there was this guy named Dave Farber, who really

856
00:40:28,840 --> 00:40:31,319
Speaker 1: was one of the fathers in the Internet, was telling everyone, Hey,

857
00:40:31,360 --> 00:40:33,799
Speaker 1: you gotta try this Mosaic thing. So that's when I

858
00:40:33,800 --> 00:40:36,600
Speaker 1: first saw the Mosaic web browser in a very early

859
00:40:36,680 --> 00:40:40,080
Speaker 1: form that Mark Andresen and his band of of geeks

860
00:40:40,120 --> 00:40:43,640
Speaker 1: at University of Illinois Champagne ORBANDA were creating. So I

861
00:40:43,680 --> 00:40:47,480
Speaker 1: downloaded Mosaic, I got an I S D n dial

862
00:40:47,520 --> 00:40:50,520
Speaker 1: in line to my home in Seattle. UH, And I

863
00:40:50,560 --> 00:40:52,440
Speaker 1: just all of a sudden saw, wow, this is the future.

864
00:40:52,880 --> 00:40:54,600
Speaker 1: Because you would look at the web, you look at

865
00:40:54,640 --> 00:40:56,760
Speaker 1: the web browser, and they they had a web page

866
00:40:56,800 --> 00:41:00,000
Speaker 1: called What's New with NCSA mosaic And at first there

867
00:41:00,080 --> 00:41:02,880
Speaker 1: be like one or two new web pages a week.

868
00:41:03,480 --> 00:41:05,759
Speaker 1: Then there'd be like six new ones a week. Then

869
00:41:05,800 --> 00:41:08,279
Speaker 1: there'd be like three a day. And it literally felt

870
00:41:08,320 --> 00:41:10,680
Speaker 1: like I was looking at a Petrie dish where the

871
00:41:11,520 --> 00:41:14,920
Speaker 1: sales were multiplying rapidly. And it was clear that as

872
00:41:14,960 --> 00:41:18,120
Speaker 1: the summer of ninety three progressed and got into the

873
00:41:18,120 --> 00:41:19,960
Speaker 1: fall of ninety three, that there was a huge thing

874
00:41:20,040 --> 00:41:26,000
Speaker 1: happening UH that was going to be like a a

875
00:41:26,040 --> 00:41:29,960
Speaker 1: huge cultural phenomenon. And I had been very interested in media,

876
00:41:30,040 --> 00:41:32,440
Speaker 1: of course, and had done a bunch of staff rounding

877
00:41:32,520 --> 00:41:37,520
Speaker 1: around video and audio Codex in my world and Multimedia Microsoft.

878
00:41:37,840 --> 00:41:39,400
Speaker 1: So I had the idea, what if I tried to

879
00:41:39,480 --> 00:41:43,040
Speaker 1: do what Mosaic is doing for text and still graphics,

880
00:41:43,239 --> 00:41:44,560
Speaker 1: what if we tried to do the same thing for

881
00:41:44,600 --> 00:41:47,400
Speaker 1: audio and then video? Uh? And that's why what the

882
00:41:47,440 --> 00:41:50,440
Speaker 1: idea for real networks ended up being, and ended up

883
00:41:50,480 --> 00:41:54,080
Speaker 1: incorporating in February nine and creating the first prototype in

884
00:41:54,120 --> 00:41:58,560
Speaker 1: the summer of ninety four UH, and then launching in April. Okay,

885
00:41:58,680 --> 00:42:02,600
Speaker 1: and did you raise money of Real Networks? Yes? What

886
00:42:02,719 --> 00:42:05,280
Speaker 1: I did was when I started, I took a million

887
00:42:05,280 --> 00:42:08,040
Speaker 1: dollars of my Microsoft crop steak, which was a high

888
00:42:08,040 --> 00:42:10,319
Speaker 1: percentage of it, but not a you know, I wasn't

889
00:42:10,320 --> 00:42:12,040
Speaker 1: I didn't have to sell any houses or anything, or

890
00:42:12,280 --> 00:42:14,920
Speaker 1: my one house. But I took a million dollars, put

891
00:42:14,960 --> 00:42:17,200
Speaker 1: it into the company and told the four of the

892
00:42:17,320 --> 00:42:19,759
Speaker 1: people I hired, we've got this much money to get

893
00:42:19,800 --> 00:42:21,279
Speaker 1: to the next phase. So I was very lucky that

894
00:42:21,320 --> 00:42:23,359
Speaker 1: I didn't have to go raise money. Then my friend

895
00:42:23,440 --> 00:42:25,839
Speaker 1: Mitch kape Poor got excited about what I was doing,

896
00:42:26,480 --> 00:42:30,280
Speaker 1: and in the fall of ninety four window of ninety

897
00:42:30,920 --> 00:42:33,640
Speaker 1: Mitch made about a two million dollar investment into Real

898
00:42:33,680 --> 00:42:37,120
Speaker 1: Networks UM. And then we launched in UH in April nine.

899
00:42:37,960 --> 00:42:41,440
Speaker 1: So I was able to bootstrap it with some of

900
00:42:41,480 --> 00:42:44,960
Speaker 1: my grub steak from Microsoft. Mitch, because you know, Mitch

901
00:42:45,000 --> 00:42:46,799
Speaker 1: and I became close through the involved with you have

902
00:42:46,800 --> 00:42:49,720
Speaker 1: have Mitch made the original Angeal investment. Then we launched.

903
00:42:49,760 --> 00:42:52,799
Speaker 1: Then we took our first venture round in the fall

904
00:42:52,840 --> 00:42:55,120
Speaker 1: of ninety five from a great firm called Excel and

905
00:42:55,160 --> 00:42:58,040
Speaker 1: a guy named Jim Bryer who's uh, you know, maybe

906
00:42:58,040 --> 00:43:00,600
Speaker 1: on the second most famous investment, but invested years later

907
00:43:00,640 --> 00:43:02,279
Speaker 1: in this thing called Facebook. That worked out pretty well

908
00:43:02,280 --> 00:43:04,719
Speaker 1: for him to Okay, when you're starting real network, so

909
00:43:04,800 --> 00:43:08,120
Speaker 1: many people are working there. So when we started, obviously

910
00:43:08,160 --> 00:43:11,359
Speaker 1: it was just me UM, and then I cobbled together

911
00:43:11,400 --> 00:43:14,640
Speaker 1: a team of three guys, only one of whom became

912
00:43:14,680 --> 00:43:17,719
Speaker 1: a full time employee UH to help build a prototype

913
00:43:17,760 --> 00:43:21,160
Speaker 1: for real report became Real Audio UM, and then by

914
00:43:21,200 --> 00:43:25,279
Speaker 1: the time we launched UH in April of ninety we

915
00:43:25,400 --> 00:43:29,040
Speaker 1: probably had fifteen employees something like that. UM. One of

916
00:43:29,080 --> 00:43:31,200
Speaker 1: the key guys I got was an named Phil Barrett,

917
00:43:31,440 --> 00:43:34,760
Speaker 1: who was a key engineer and injuring leader in Windows

918
00:43:35,239 --> 00:43:37,440
Speaker 1: UH and was one of the unsung heroes of the

919
00:43:37,480 --> 00:43:40,719
Speaker 1: success of Windows UH. Windows. If people know the history,

920
00:43:40,719 --> 00:43:43,480
Speaker 1: Microsoft had a very long gestation period and it was

921
00:43:43,520 --> 00:43:46,640
Speaker 1: basically a near failure, and Phil got involved in creating

922
00:43:46,640 --> 00:43:49,680
Speaker 1: the first useful version of Windows called Windows three D six,

923
00:43:49,920 --> 00:43:52,440
Speaker 1: which allows you to run multiple doss apps on a

924
00:43:52,480 --> 00:43:54,920
Speaker 1: three D six until three D six chip computer and

925
00:43:54,960 --> 00:43:57,120
Speaker 1: it was really useful. And then he and a couple

926
00:43:57,160 --> 00:43:59,959
Speaker 1: of his guys created a version of Windows that saw

927
00:44:00,000 --> 00:44:03,640
Speaker 1: of the memory problem that Windows has. Windows was really

928
00:44:03,719 --> 00:44:06,680
Speaker 1: bad with using memory because the PC had a the

929
00:44:06,719 --> 00:44:08,719
Speaker 1: Intel chip had a kind of a funny segmented memory

930
00:44:08,800 --> 00:44:11,200
Speaker 1: architecture that made it hard. And they fixed that and

931
00:44:11,239 --> 00:44:13,480
Speaker 1: they created a core of what became Windows three. So

932
00:44:13,600 --> 00:44:16,560
Speaker 1: Phil was the the unsung hero behind that, and I

933
00:44:16,600 --> 00:44:19,240
Speaker 1: got him to come in not technically as my co founder,

934
00:44:19,480 --> 00:44:22,000
Speaker 1: but as the technical leader of the company in the

935
00:44:22,080 --> 00:44:25,200
Speaker 1: fall of ninety four. And then he Phil led the

936
00:44:25,239 --> 00:44:27,399
Speaker 1: team that launched Real Audio, and so I said about

937
00:44:27,440 --> 00:44:33,480
Speaker 1: a fifteen people when we launched. You're listening to my

938
00:44:33,520 --> 00:44:37,479
Speaker 1: conversation with CEO of Real Networks, Rob Glazer, recorded live

939
00:44:37,560 --> 00:44:41,880
Speaker 1: at the Music Media Summit in Santa Barbara, California. I

940
00:44:41,880 --> 00:44:44,319
Speaker 1: hope you're enjoying listening to this episode of The Bob

941
00:44:44,440 --> 00:44:47,040
Speaker 1: Left That's podcast. If you want to listen to sound

942
00:44:47,080 --> 00:44:49,880
Speaker 1: bites from the interviews and see some of my guests,

943
00:44:50,160 --> 00:44:54,000
Speaker 1: check it out and at tune in or Twitter, Facebook,

944
00:44:54,080 --> 00:44:57,800
Speaker 1: and Instagram. Now more of my chat with the CEO

945
00:44:57,840 --> 00:45:01,839
Speaker 1: of Real Networks, Rob Glazer on The Bob Left sets podcast.

946
00:45:03,360 --> 00:45:06,160
Speaker 1: So if you go to the late nineties, real Player

947
00:45:06,320 --> 00:45:09,480
Speaker 1: is the standard. But if one were to pull back

948
00:45:09,680 --> 00:45:12,040
Speaker 1: and look at the war, one would say, you may

949
00:45:12,120 --> 00:45:14,120
Speaker 1: or may not agree. That's what I'm asking you, that

950
00:45:14,239 --> 00:45:17,359
Speaker 1: Real Player ultimately lost the war other than China, which

951
00:45:17,400 --> 00:45:20,840
Speaker 1: we'll talk about in a minute. True, what did you

952
00:45:20,960 --> 00:45:24,799
Speaker 1: learn there? Uh? Well, we had a great run, and

953
00:45:24,840 --> 00:45:28,080
Speaker 1: of course I'm back running the company now, so we're

954
00:45:28,239 --> 00:45:30,120
Speaker 1: we've lived to fight another day, and I think we

955
00:45:30,120 --> 00:45:32,200
Speaker 1: have another renaissance coming up, which we can talk about.

956
00:45:32,239 --> 00:45:34,080
Speaker 1: But one of the things I learned is that when

957
00:45:34,120 --> 00:45:40,000
Speaker 1: you have a huge competitor that threatened is threatened by you. Um.

958
00:45:40,160 --> 00:45:42,960
Speaker 1: You can try to do anything you can to avoid

959
00:45:42,960 --> 00:45:45,080
Speaker 1: a head on fight with that competitor, but the end

960
00:45:45,120 --> 00:45:46,799
Speaker 1: of the day, you just gotta assume it's coming, and

961
00:45:46,840 --> 00:45:49,839
Speaker 1: you've got to scale up massively to do that. So

962
00:45:50,200 --> 00:45:52,239
Speaker 1: what happened is the following. So we got out there

963
00:45:52,520 --> 00:45:55,120
Speaker 1: with real Audio in ninety five or video, and ninety

964
00:45:55,160 --> 00:45:59,239
Speaker 1: seven became the de facto market standard, uh for you know,

965
00:45:59,320 --> 00:46:03,480
Speaker 1: for a number different use cases of streaming audio streaming video.

966
00:46:03,719 --> 00:46:05,680
Speaker 1: We can talk about the music connection. Later we started

967
00:46:05,719 --> 00:46:09,840
Speaker 1: dabbling music, UM and then Microsoft decided that both we

968
00:46:10,200 --> 00:46:14,040
Speaker 1: and we us Netscape and Java or existential threats to Microsoft.

969
00:46:14,080 --> 00:46:17,520
Speaker 1: So they decided to do whatever they could to beat us,

970
00:46:18,000 --> 00:46:20,680
Speaker 1: and UM they had a huge work chest of money

971
00:46:20,719 --> 00:46:24,560
Speaker 1: to do it. UH, they had willingness to use their

972
00:46:24,560 --> 00:46:27,880
Speaker 1: power relationship with PC manufacturers in any way they needed to,

973
00:46:28,400 --> 00:46:32,480
Speaker 1: and so they moved and moved to potentially get PC

974
00:46:32,600 --> 00:46:37,080
Speaker 1: manufacturers to ship their software and not ship our software,

975
00:46:37,280 --> 00:46:39,960
Speaker 1: and to get media companies to use their software and

976
00:46:40,040 --> 00:46:42,960
Speaker 1: not use our software. In the case of the computer manufacturers,

977
00:46:43,120 --> 00:46:46,239
Speaker 1: they basically had a program where let's say the price

978
00:46:46,239 --> 00:46:49,200
Speaker 1: of Windows license vis a hundred dollars UM. And but

979
00:46:49,239 --> 00:46:51,560
Speaker 1: they have a co op program where if you ship

980
00:46:51,600 --> 00:46:54,480
Speaker 1: Windows in the configuration they want, it only costs you sixty.

981
00:46:55,320 --> 00:46:58,000
Speaker 1: So let's say you sell a million PCs, So that's

982
00:46:58,000 --> 00:47:00,120
Speaker 1: a that's a forty million dollar difference in what you

983
00:47:00,120 --> 00:47:02,360
Speaker 1: have to pay a year, and it's forty million dollars

984
00:47:02,360 --> 00:47:04,719
Speaker 1: of pure margin or a low margin business. So all

985
00:47:04,760 --> 00:47:06,799
Speaker 1: you have to do is what Microsoft tells you. So

986
00:47:06,840 --> 00:47:09,759
Speaker 1: that's very powerful. So Microsoft was able to leverage the

987
00:47:09,760 --> 00:47:13,560
Speaker 1: power of their monopoly to squeeze US out UH to

988
00:47:13,920 --> 00:47:17,080
Speaker 1: at the time squeeze the Netscape browser out and and

989
00:47:17,200 --> 00:47:20,040
Speaker 1: uhh force Netscape to basically sell to a o l

990
00:47:20,480 --> 00:47:24,759
Speaker 1: um uh and uh to uh also mess around with

991
00:47:24,840 --> 00:47:27,400
Speaker 1: Java and job and Java script is complicated history, but

992
00:47:27,760 --> 00:47:32,719
Speaker 1: so so we got basically squeezed. And the rational thing

993
00:47:32,760 --> 00:47:34,560
Speaker 1: for us to have done at the time, if what

994
00:47:34,680 --> 00:47:36,600
Speaker 1: we wanted to do was take our chips and go home,

995
00:47:37,000 --> 00:47:40,120
Speaker 1: was to sell to the highest bidder uh make it

996
00:47:40,200 --> 00:47:43,560
Speaker 1: their problem or sell to someone at such larger scale

997
00:47:43,840 --> 00:47:47,000
Speaker 1: that they could fight the fight on with with us

998
00:47:47,040 --> 00:47:49,600
Speaker 1: as a tool or a piece of it. UM. But

999
00:47:49,680 --> 00:47:52,080
Speaker 1: I kind of. I mean's a few things reason why

1000
00:47:52,080 --> 00:47:53,840
Speaker 1: I didn't do that. One is I actually wanted to

1001
00:47:53,880 --> 00:47:55,560
Speaker 1: try and build a company for long term. I didn't

1002
00:47:55,600 --> 00:47:58,120
Speaker 1: want to just flip it and sell it. Um. The

1003
00:47:58,120 --> 00:48:01,640
Speaker 1: second was the UM you know a lot of things

1004
00:48:01,680 --> 00:48:05,239
Speaker 1: that happened concur with that. This is now around the

1005
00:48:05,320 --> 00:48:08,240
Speaker 1: until the had the the the the the Internet bubble

1006
00:48:08,239 --> 00:48:11,200
Speaker 1: bursting at the same time. So you have the telecom

1007
00:48:11,200 --> 00:48:14,040
Speaker 1: bubble burst and the Internet bubble bursting, and this assault

1008
00:48:14,040 --> 00:48:18,879
Speaker 1: from Microsoft against and a trust uh violations that turned

1009
00:48:18,920 --> 00:48:22,120
Speaker 1: out UM and uh and they and those those three

1010
00:48:22,120 --> 00:48:25,120
Speaker 1: things had a very negative impact on us. We ultimately

1011
00:48:25,200 --> 00:48:27,799
Speaker 1: ended up uh not only surviving, but in a star

1012
00:48:27,800 --> 00:48:31,440
Speaker 1: way prevailing. We won. Ah, we started winning some and

1013
00:48:31,560 --> 00:48:34,480
Speaker 1: a trust legal cases against Microsoft, and they ended up

1014
00:48:34,480 --> 00:48:37,439
Speaker 1: settling with us for about seven and fifty million dollars. Uh.

1015
00:48:37,480 --> 00:48:40,080
Speaker 1: So we got a decent outcome from that standpoint. From

1016
00:48:40,080 --> 00:48:43,920
Speaker 1: the standpoint of shareholders, um but um, but we ended

1017
00:48:44,000 --> 00:48:46,640
Speaker 1: up also pivoting the company and reinventing the company in

1018
00:48:46,680 --> 00:48:48,560
Speaker 1: a few different ways. But you know, if I had

1019
00:48:48,560 --> 00:48:51,480
Speaker 1: it to do over again, I would probably have found

1020
00:48:51,920 --> 00:48:55,239
Speaker 1: the safe given that we didn't have the resources ourselves

1021
00:48:55,680 --> 00:48:58,799
Speaker 1: to compete head on with that attack. Even though we're

1022
00:48:58,800 --> 00:49:03,120
Speaker 1: able to win legal settlement, they basically uh compensated us

1023
00:49:03,160 --> 00:49:04,919
Speaker 1: for the lost business, but they didn't replace the lost

1024
00:49:04,960 --> 00:49:07,760
Speaker 1: business except in China. More than that in a minute.

1025
00:49:08,520 --> 00:49:12,719
Speaker 1: But they um but they If we found the right

1026
00:49:12,760 --> 00:49:15,000
Speaker 1: safe port in a storm, that could have been interesting.

1027
00:49:15,120 --> 00:49:16,640
Speaker 1: It's not clear who they would have been like in

1028
00:49:16,680 --> 00:49:20,040
Speaker 1: two thousand, who would have who would have been the player? Obviously,

1029
00:49:20,080 --> 00:49:21,960
Speaker 1: net Escape went to a o L. They got some

1030
00:49:22,000 --> 00:49:24,919
Speaker 1: money from a share oulder standpoint, but the browser didn't

1031
00:49:24,960 --> 00:49:27,360
Speaker 1: live in a o L, didn't even ultimately end up living.

1032
00:49:27,360 --> 00:49:29,839
Speaker 1: And now they get folded into a Verizon. So it's

1033
00:49:29,960 --> 00:49:33,080
Speaker 1: it's not obvious the I mean, you know, it depends

1034
00:49:33,120 --> 00:49:34,440
Speaker 1: on you're playing the game, but playing the game to

1035
00:49:34,440 --> 00:49:36,719
Speaker 1: make the most cash. The thing that Netscape did, or

1036
00:49:36,719 --> 00:49:39,399
Speaker 1: that Mark Huban did, where Mark sold his broadcast dot

1037
00:49:39,440 --> 00:49:42,880
Speaker 1: com business to Yahoo for four billion dollars and you know,

1038
00:49:42,960 --> 00:49:44,640
Speaker 1: took the money and did other stuff with it. You

1039
00:49:44,680 --> 00:49:46,480
Speaker 1: could have done that, but I was was trying to

1040
00:49:46,480 --> 00:49:49,319
Speaker 1: build something for the longer term. So when did you

1041
00:49:49,360 --> 00:49:53,000
Speaker 1: buy the Pro Bowlers Tour? So, okay, that's kind of

1042
00:49:53,000 --> 00:49:55,080
Speaker 1: a weird one, but it's true. So my friend Chris

1043
00:49:55,120 --> 00:49:57,960
Speaker 1: Peters was a Microsoft buddy of mine who got really

1044
00:49:58,000 --> 00:50:00,759
Speaker 1: into bowling. Uh. Chris is a much better bowler than me.

1045
00:50:01,360 --> 00:50:04,759
Speaker 1: Uh he He approached me in a third friend of ours,

1046
00:50:04,800 --> 00:50:07,840
Speaker 1: Mike Slade, uh more than ten years ago, I have

1047
00:50:07,840 --> 00:50:11,600
Speaker 1: to remember how longer it was, probably sixteen seventeen years ago,

1048
00:50:11,880 --> 00:50:15,200
Speaker 1: and said, hey, I can buy the p b a

1049
00:50:14,560 --> 00:50:18,160
Speaker 1: uh at a bankruptcy or near bankruptcy that's been mismanaged.

1050
00:50:18,360 --> 00:50:20,240
Speaker 1: And I was bowling was one of my favorite sports

1051
00:50:20,280 --> 00:50:22,360
Speaker 1: as a kid. I was in a bowling league. Um,

1052
00:50:22,400 --> 00:50:24,719
Speaker 1: I am a mediocre bowler, but I enjoy it to

1053
00:50:24,719 --> 00:50:26,360
Speaker 1: this day. And you have a bully at a bowling

1054
00:50:26,400 --> 00:50:29,600
Speaker 1: lane in your house. I I Uh, I used to

1055
00:50:29,600 --> 00:50:32,200
Speaker 1: have a bowling lane in my office. Yes, we and

1056
00:50:32,280 --> 00:50:34,560
Speaker 1: Real Network's office in where we were for a decade,

1057
00:50:34,560 --> 00:50:36,239
Speaker 1: we had a bowling lane. When we moved to the

1058
00:50:36,400 --> 00:50:37,920
Speaker 1: to a top floor of a building, we didn't get

1059
00:50:37,960 --> 00:50:39,680
Speaker 1: a bowling lane there, but we did have a bowling

1060
00:50:39,719 --> 00:50:41,200
Speaker 1: We did a pair of bowling lanes in the in

1061
00:50:41,239 --> 00:50:43,759
Speaker 1: the building uh that we had and uh in a

1062
00:50:43,800 --> 00:50:46,360
Speaker 1: neighborhood called Belltown in Seattle, and that was fun. So

1063
00:50:46,400 --> 00:50:49,319
Speaker 1: I love bowling. Uh, and I thought, okay, let's do

1064
00:50:49,400 --> 00:50:53,080
Speaker 1: this and uh Chris ended up bowing out about five

1065
00:50:53,160 --> 00:50:55,239
Speaker 1: or six years ago. So now Mike and I are

1066
00:50:55,239 --> 00:50:56,719
Speaker 1: the owners of it. We got a really good guy

1067
00:50:56,760 --> 00:50:59,319
Speaker 1: named Tom Clark who runs it. Um if anybody wants

1068
00:50:59,320 --> 00:51:01,480
Speaker 1: to buy the p b A, I'm willing to sell it.

1069
00:51:01,719 --> 00:51:05,320
Speaker 1: But it's it makes a little money. Uh, it's uh,

1070
00:51:05,360 --> 00:51:07,080
Speaker 1: it's nice to be a steward or something that you

1071
00:51:07,120 --> 00:51:10,320
Speaker 1: care about. The bowlers are all great human beings, uh

1072
00:51:10,440 --> 00:51:13,680
Speaker 1: and uh but most of them are great human beings.

1073
00:51:13,800 --> 00:51:16,800
Speaker 1: And uh, you know, I'm happy that we're able to

1074
00:51:16,840 --> 00:51:18,680
Speaker 1: sustain a sport that is sort of at the center

1075
00:51:18,680 --> 00:51:21,759
Speaker 1: of Americana, And is there any hope for bowling or

1076
00:51:21,800 --> 00:51:25,400
Speaker 1: is it going to go extinct? Uh? Well, bowling itself

1077
00:51:25,440 --> 00:51:27,600
Speaker 1: has never even more popular in terms of like, you know,

1078
00:51:27,719 --> 00:51:31,080
Speaker 1: how many people your bowl bowling leagues have had to

1079
00:51:31,080 --> 00:51:34,760
Speaker 1: reinvent themselves because there's describe a book called Bowling Alone,

1080
00:51:35,440 --> 00:51:37,800
Speaker 1: which he sent me a copy of for sort of

1081
00:51:37,800 --> 00:51:41,200
Speaker 1: obvious reasons and talked him once or twice how bowling

1082
00:51:41,320 --> 00:51:44,920
Speaker 1: was tied to the industrial period where everyone worked the

1083
00:51:44,960 --> 00:51:47,959
Speaker 1: same shift and left the shift and then went bowld

1084
00:51:48,000 --> 00:51:50,080
Speaker 1: at night, you know, sort of Lavern and Shirley style,

1085
00:51:50,520 --> 00:51:53,480
Speaker 1: And that culture doesn't really exist anymore. We have a

1086
00:51:53,640 --> 00:51:57,440
Speaker 1: much more everybody's on their own schedule, much more scattered,

1087
00:51:57,760 --> 00:52:01,239
Speaker 1: both in more and more families both both parents work,

1088
00:52:01,600 --> 00:52:04,480
Speaker 1: so you have much less rhythm around that. So there

1089
00:52:04,560 --> 00:52:07,200
Speaker 1: is a core industry of corp like four million people

1090
00:52:07,200 --> 00:52:09,440
Speaker 1: you're bowling bowling leagues, but it's down from whatever ten

1091
00:52:09,480 --> 00:52:11,440
Speaker 1: million used to be, But the number of people who

1092
00:52:11,520 --> 00:52:14,680
Speaker 1: bowl recreationally is up higher. You just bowling has been reinvented.

1093
00:52:14,800 --> 00:52:18,400
Speaker 1: So how you actually connect professional bowling into that world

1094
00:52:18,440 --> 00:52:21,279
Speaker 1: where you have a lot of casual bowlers, but you

1095
00:52:21,320 --> 00:52:23,720
Speaker 1: don't have as many sort of league bowls who identify

1096
00:52:23,800 --> 00:52:26,239
Speaker 1: with bowling as their sport. Sort of an interesting thing

1097
00:52:26,280 --> 00:52:29,359
Speaker 1: to do. But what we've managed to, you know, the

1098
00:52:29,400 --> 00:52:31,440
Speaker 1: p b A, we've been on, We've had a long

1099
00:52:31,520 --> 00:52:34,120
Speaker 1: run on ESPN. We're just moving to uh Fox. I

1100
00:52:34,120 --> 00:52:36,440
Speaker 1: think that kind announced. I hope I'm not jumping the

1101
00:52:36,440 --> 00:52:38,520
Speaker 1: gun on that one. I don't think I am uh

1102
00:52:38,560 --> 00:52:40,840
Speaker 1: and uh and so I think we're gonna have a

1103
00:52:40,880 --> 00:52:43,479
Speaker 1: good you know, next next run with the bowling um

1104
00:52:43,520 --> 00:52:46,440
Speaker 1: And it's a it's it's a funding. I only get

1105
00:52:46,440 --> 00:52:48,799
Speaker 1: to like one or two tournaments a year. I wish

1106
00:52:48,840 --> 00:52:50,360
Speaker 1: I get to more of but I have a pretty

1107
00:52:50,360 --> 00:52:53,560
Speaker 1: busy day job in between that and family. Okay, so

1108
00:52:54,040 --> 00:52:58,960
Speaker 1: you're into two thousand. There's a steria revolution in the

1109
00:52:59,040 --> 00:53:02,719
Speaker 1: music business, and abster you have real networks, you ultimately

1110
00:53:02,760 --> 00:53:06,080
Speaker 1: decide to go in that business, right, What is your

1111
00:53:06,160 --> 00:53:09,560
Speaker 1: thinking there? Well, I believe that the end of the

1112
00:53:09,640 --> 00:53:12,200
Speaker 1: day there would be a legal streaming industry. I didn't

1113
00:53:12,239 --> 00:53:14,239
Speaker 1: know all the fits and starts in the twist of

1114
00:53:14,239 --> 00:53:17,160
Speaker 1: the road, but I had the belief that all this

1115
00:53:17,719 --> 00:53:21,480
Speaker 1: illegal uh you know, uh pirate stuff was going to

1116
00:53:21,560 --> 00:53:23,480
Speaker 1: be a phase the industry would go through and the

1117
00:53:23,480 --> 00:53:25,680
Speaker 1: industry would figure it out. So I started having conversations

1118
00:53:25,680 --> 00:53:27,920
Speaker 1: with people the music industry about this as early as

1119
00:53:27,960 --> 00:53:31,600
Speaker 1: like ninety five. I remember meeting Charlie Koppelman, who was

1120
00:53:31,640 --> 00:53:34,160
Speaker 1: when he was running a M. I remember meeting, you know,

1121
00:53:34,719 --> 00:53:38,839
Speaker 1: senior folks UH in UH multiple labels. Nothing really came

1122
00:53:38,880 --> 00:53:42,240
Speaker 1: with those discussions. I remember, you know, having a conversation

1123
00:53:42,320 --> 00:53:45,000
Speaker 1: with Al Teller, who was running MC at the time,

1124
00:53:45,280 --> 00:53:47,040
Speaker 1: and we're in the middle of the conversation and then

1125
00:53:47,440 --> 00:53:50,960
Speaker 1: um uh then Edgar Brompan bought m C A and

1126
00:53:51,000 --> 00:53:52,799
Speaker 1: it was clear there's gonna be a new sheriff in town.

1127
00:53:53,080 --> 00:53:54,680
Speaker 1: So that there's a lot of a lot of that

1128
00:53:54,719 --> 00:53:58,200
Speaker 1: happened a few times. So then um what happened was

1129
00:53:59,120 --> 00:54:05,000
Speaker 1: UH sony UH and Universal decided to come together to

1130
00:54:05,120 --> 00:54:07,680
Speaker 1: create a joint venture that ended up being first was

1131
00:54:07,680 --> 00:54:10,040
Speaker 1: called Duet and it'd be called press called press Play,

1132
00:54:10,200 --> 00:54:13,120
Speaker 1: and UM we talked to them about using our technology

1133
00:54:13,120 --> 00:54:14,759
Speaker 1: and they kind of wanted to build their own, so

1134
00:54:14,800 --> 00:54:17,439
Speaker 1: we put together a coalition of the three other UH

1135
00:54:17,640 --> 00:54:20,640
Speaker 1: majors at the time, but there was E M I Bertlesman,

1136
00:54:20,719 --> 00:54:24,239
Speaker 1: causin Ian BMG hadn't merged yet. UH and Warner and

1137
00:54:24,280 --> 00:54:25,600
Speaker 1: we also had a O L in there because it

1138
00:54:25,600 --> 00:54:27,600
Speaker 1: was after the all time Warner and we created a

1139
00:54:27,680 --> 00:54:32,080
Speaker 1: joint venture called music Net and it was basically it

1140
00:54:32,120 --> 00:54:35,440
Speaker 1: was the other three majors and they had the two majors,

1141
00:54:35,480 --> 00:54:38,200
Speaker 1: so the two biggest so UH they had about forty

1142
00:54:38,239 --> 00:54:40,440
Speaker 1: percent of the content. We had about forty percent. There

1143
00:54:40,480 --> 00:54:43,240
Speaker 1: was about there were indies that were licensed to both

1144
00:54:43,680 --> 00:54:46,279
Speaker 1: and it was clear that that wasn't gonna work. It

1145
00:54:46,360 --> 00:54:49,040
Speaker 1: was just one of the series of tactical maneuvers to

1146
00:54:49,200 --> 00:54:52,840
Speaker 1: create UM UH an opening for everything to get licensed

1147
00:54:52,920 --> 00:54:56,319
Speaker 1: everybody and UM and so then what happened was there

1148
00:54:56,360 --> 00:54:59,239
Speaker 1: was a little company UH that product called Rhapsody. The

1149
00:54:59,400 --> 00:55:01,799
Speaker 1: product was called Listen to Coming Listen dot Com. And

1150
00:55:01,880 --> 00:55:04,880
Speaker 1: they ended up being like the Checkpoint Charlie of if

1151
00:55:04,880 --> 00:55:07,760
Speaker 1: you remember the in the Cold War that exchanged prisoners,

1152
00:55:08,160 --> 00:55:10,640
Speaker 1: Checkpoint Charlie and Berlin right the line between East Berlin

1153
00:55:10,680 --> 00:55:13,600
Speaker 1: and West Berlin, and Rhapsody was the Checkpoint Charlie. And

1154
00:55:13,600 --> 00:55:16,440
Speaker 1: that they were the first company that all five majors

1155
00:55:16,680 --> 00:55:19,000
Speaker 1: would license to because they wouldn't they didn't license to

1156
00:55:19,040 --> 00:55:21,239
Speaker 1: each other yet because the lawyers were freaked out about that.

1157
00:55:21,600 --> 00:55:25,160
Speaker 1: But by creating sort of standard licenses that Rhapsody could have,

1158
00:55:25,239 --> 00:55:29,399
Speaker 1: Rhapsody became the first company that had licenses from all

1159
00:55:29,440 --> 00:55:33,719
Speaker 1: five majors. At the time we owned of music Net

1160
00:55:33,719 --> 00:55:35,759
Speaker 1: and A During fence with the labels and the board

1161
00:55:35,800 --> 00:55:38,160
Speaker 1: meetings were the most painful things. They were like the

1162
00:55:38,280 --> 00:55:40,440
Speaker 1: u N in terms of like everyone shows up with

1163
00:55:40,480 --> 00:55:42,680
Speaker 1: all these lawyers. The lawyers are making sure there's no

1164
00:55:42,760 --> 00:55:47,000
Speaker 1: ended trust collusion. Meanwhile nothing gets done. So uh. So

1165
00:55:47,200 --> 00:55:51,280
Speaker 1: we had an opportunity to basically to buy Rhapsody because

1166
00:55:51,280 --> 00:55:53,840
Speaker 1: they were running out of money. Uh, and we believed

1167
00:55:53,840 --> 00:55:57,400
Speaker 1: in the space, so we basically bought Rhapsody and sold

1168
00:55:57,400 --> 00:56:00,840
Speaker 1: our stake in music Net. So we sort of switched

1169
00:56:00,880 --> 00:56:05,000
Speaker 1: horses until the license got cleared. And then basically at

1170
00:56:05,000 --> 00:56:07,759
Speaker 1: the time we bought raps and you had fifty subscribers

1171
00:56:07,840 --> 00:56:11,120
Speaker 1: or something, and then we wrote it up as our vehicle.

1172
00:56:11,600 --> 00:56:14,439
Speaker 1: Uh and you know it's still going. We actually merged

1173
00:56:14,440 --> 00:56:16,400
Speaker 1: with Napster and now we use the name Napster. We

1174
00:56:16,520 --> 00:56:19,960
Speaker 1: got you know, to an appallion subscribers whatever it is. Uh.

1175
00:56:20,000 --> 00:56:24,160
Speaker 1: We are we're uh we've resized the staff so it generates,

1176
00:56:24,239 --> 00:56:27,360
Speaker 1: so it's even a positive. But we're obviously we're not

1177
00:56:27,400 --> 00:56:29,680
Speaker 1: Spotify and we're not Apple. So there were so many

1178
00:56:29,760 --> 00:56:32,200
Speaker 1: lessons there which we can talk about about why that

1179
00:56:32,280 --> 00:56:34,200
Speaker 1: ended up. You know, we still have a business that's

1180
00:56:34,200 --> 00:56:37,680
Speaker 1: got you know, uh, it's you know, if you if

1181
00:56:37,719 --> 00:56:40,920
Speaker 1: you step back from the you know, from Spotify and

1182
00:56:41,000 --> 00:56:44,960
Speaker 1: obviously Apple were the largest, uh independent company in the space,

1183
00:56:45,320 --> 00:56:48,600
Speaker 1: but you know, a huge order of magnitude lower than

1184
00:56:48,880 --> 00:56:51,160
Speaker 1: than the two big guys, and you know where that

1185
00:56:51,160 --> 00:56:53,840
Speaker 1: goes is an interesting question. But learned a lot of

1186
00:56:53,840 --> 00:56:56,440
Speaker 1: things along the way. So isn't the real business being

1187
00:56:56,440 --> 00:57:00,759
Speaker 1: a white labeled service now licensing your technology the third party? Uh?

1188
00:57:00,840 --> 00:57:03,040
Speaker 1: So the Naster business is a combination of white label

1189
00:57:03,080 --> 00:57:05,920
Speaker 1: and co branded. It depends what partners want. So like

1190
00:57:05,960 --> 00:57:08,120
Speaker 1: for instance, the case of I Heart Radio, I heard

1191
00:57:08,200 --> 00:57:10,600
Speaker 1: music and we power there on demand. I Heart wants

1192
00:57:10,600 --> 00:57:13,120
Speaker 1: to be the brand and it says powered by Napster,

1193
00:57:13,440 --> 00:57:16,120
Speaker 1: but you don't necessarie it prominently. Some our other partners

1194
00:57:16,200 --> 00:57:19,000
Speaker 1: use an Abster brand more prominently like and uh and

1195
00:57:19,040 --> 00:57:21,560
Speaker 1: so it just depends. Like in Brazil, uh, we have

1196
00:57:22,240 --> 00:57:25,760
Speaker 1: Vivo Music. Uh, it was the largest whereless carrying Brazil,

1197
00:57:25,840 --> 00:57:28,680
Speaker 1: powered by Napster. Uh So we use different brands and

1198
00:57:28,720 --> 00:57:31,360
Speaker 1: different parts of the world. We have a director consumer business,

1199
00:57:31,360 --> 00:57:33,600
Speaker 1: but it's it's not it's the minority of the business.

1200
00:57:33,680 --> 00:57:37,439
Speaker 1: It's no longer the main business. So you ultimately walk

1201
00:57:37,520 --> 00:57:40,520
Speaker 1: away from real networks to become a VC. What's the

1202
00:57:40,560 --> 00:57:44,000
Speaker 1: thinking there, Well, well, these things always, these life changes

1203
00:57:44,000 --> 00:57:47,800
Speaker 1: always happen through accombination of personal reasons and professional reasons.

1204
00:57:48,440 --> 00:57:52,640
Speaker 1: I have three fantastic kids, twins who are almost twelve,

1205
00:57:53,000 --> 00:57:55,520
Speaker 1: and the little guy called little Guy is almost eight.

1206
00:57:55,840 --> 00:57:59,480
Speaker 1: Um Um, I've said this publicly, so I'm not speaking

1207
00:57:59,480 --> 00:58:02,360
Speaker 1: at the school. In their mom Oh I'm still get

1208
00:58:02,360 --> 00:58:05,360
Speaker 1: along with very well, had really bad postpart and depression. Um.

1209
00:58:05,400 --> 00:58:07,720
Speaker 1: Like you know, she wanted kids every but as much

1210
00:58:07,760 --> 00:58:10,160
Speaker 1: as I did, and she is a fantastic mom, but

1211
00:58:10,200 --> 00:58:12,760
Speaker 1: she went through a really rough period. So my switching

1212
00:58:12,800 --> 00:58:15,600
Speaker 1: gears and focusing on being a dad for about three

1213
00:58:15,680 --> 00:58:17,760
Speaker 1: years was one of the best things I ever did

1214
00:58:17,800 --> 00:58:20,040
Speaker 1: in my life. It probably wasn't the best thing for

1215
00:58:20,040 --> 00:58:23,640
Speaker 1: Real Networks because the we tried to other people into

1216
00:58:23,640 --> 00:58:25,080
Speaker 1: the company who were kind of like the drummer and

1217
00:58:25,120 --> 00:58:29,000
Speaker 1: spinal tap unfortunately if that's the music ANALYSI people know.

1218
00:58:29,920 --> 00:58:34,000
Speaker 1: But it was appropriate for me personally to to really

1219
00:58:34,200 --> 00:58:36,960
Speaker 1: go deep in in family. And my kids are doing great,

1220
00:58:37,320 --> 00:58:40,440
Speaker 1: uh great partner, Uh, my ex is doing super well.

1221
00:58:40,480 --> 00:58:41,960
Speaker 1: The kids go back and forth in the two and

1222
00:58:42,320 --> 00:58:43,800
Speaker 1: I don't know that we could have gotten there if

1223
00:58:43,840 --> 00:58:45,960
Speaker 1: I had stayed, you know, full time. A real networks

1224
00:58:45,960 --> 00:58:50,120
Speaker 1: the whole way through. And did you I mean, were

1225
00:58:50,120 --> 00:58:53,000
Speaker 1: you in basically a house dad or were you a

1226
00:58:53,080 --> 00:58:55,600
Speaker 1: VC during that period? So I was this thing called

1227
00:58:55,640 --> 00:58:59,920
Speaker 1: a venture partner, which I it's a little bit like

1228
00:59:00,080 --> 00:59:05,360
Speaker 1: the p massuseatists I wrote on on economists. Um, if

1229
00:59:05,360 --> 00:59:08,200
Speaker 1: you want to be a VC, or think if you

1230
00:59:08,240 --> 00:59:10,000
Speaker 1: think you might want to be a VC, if you

1231
00:59:10,040 --> 00:59:12,320
Speaker 1: go be a venture partner at a firm, you learn

1232
00:59:12,360 --> 00:59:14,600
Speaker 1: a lot. So Excel is an incredible firm. You know

1233
00:59:14,640 --> 00:59:17,880
Speaker 1: they invested you know I mentioned us, but they Facebook

1234
00:59:17,920 --> 00:59:20,680
Speaker 1: is probably their signature huge investment. They've had many other

1235
00:59:20,760 --> 00:59:23,760
Speaker 1: successes over the years. You can go look at their website. Um.

1236
00:59:24,000 --> 00:59:25,200
Speaker 1: And so what I would do is I would go

1237
00:59:25,240 --> 00:59:27,880
Speaker 1: down to board to partner meetings once or twice a month,

1238
00:59:28,200 --> 00:59:30,720
Speaker 1: and I would KiB its uh and give them feedback.

1239
00:59:30,760 --> 00:59:33,560
Speaker 1: I probably made three or four investments with them. I

1240
00:59:33,640 --> 00:59:35,080
Speaker 1: probably made, you know, in the period when I was

1241
00:59:35,120 --> 00:59:38,840
Speaker 1: actually investing, I probably made fifty investments, UM, maybe a

1242
00:59:38,920 --> 00:59:40,560
Speaker 1: quarter of them with Excel on a quarter of them

1243
00:59:40,680 --> 00:59:43,040
Speaker 1: either on my own or with other venture enities. I

1244
00:59:43,200 --> 00:59:46,160
Speaker 1: ended up deciding that I'm not wired to be a

1245
00:59:46,160 --> 00:59:49,400
Speaker 1: good VC. UM. I like angel investing, which is very

1246
00:59:49,440 --> 00:59:52,640
Speaker 1: different than a VC. But when you're a VC, uh,

1247
00:59:52,680 --> 00:59:55,840
Speaker 1: it's this weird mix of you've got a partner partnership,

1248
00:59:55,880 --> 00:59:59,320
Speaker 1: you are running shared money, but you're each individual responsible

1249
00:59:59,320 --> 01:00:01,600
Speaker 1: for a deal. And if the chemistry is great with

1250
01:00:01,640 --> 01:00:04,520
Speaker 1: the partners, I'm sure it can be great, but it's

1251
01:00:04,600 --> 01:00:08,080
Speaker 1: very easy for these partnerships to fracture, depending on like

1252
01:00:08,440 --> 01:00:10,440
Speaker 1: you know, which deals work, which deals don't work, who

1253
01:00:10,440 --> 01:00:12,360
Speaker 1: gets credit for them, who doesn't get credit for them?

1254
01:00:12,840 --> 01:00:15,800
Speaker 1: And Uh. To me, I'm much I much prefer to

1255
01:00:15,840 --> 01:00:18,680
Speaker 1: do individual angel investing, which I do very little of

1256
01:00:18,720 --> 01:00:20,360
Speaker 1: nowadays because I'm too busy. I do a little bit

1257
01:00:20,360 --> 01:00:23,960
Speaker 1: of it. Uh. I prefer that to being part of

1258
01:00:24,000 --> 01:00:27,160
Speaker 1: a professional venture fund. But I only know that because

1259
01:00:27,200 --> 01:00:29,560
Speaker 1: I spent a couple of years sort of sitting around

1260
01:00:29,600 --> 01:00:31,400
Speaker 1: the table with the Excel guys as they did their thing.

1261
01:00:31,960 --> 01:00:34,840
Speaker 1: So at this late date, what are the main products

1262
01:00:34,960 --> 01:00:38,000
Speaker 1: of Real Networks and where's the company going. So Real

1263
01:00:38,040 --> 01:00:41,760
Speaker 1: Networks has four major growth initiatives going forward. Um and

1264
01:00:41,800 --> 01:00:43,200
Speaker 1: you can say it's a late date, but for some

1265
01:00:43,240 --> 01:00:46,800
Speaker 1: of these initiatives is actually super early. Um we one

1266
01:00:46,880 --> 01:00:49,120
Speaker 1: is a consumer initiative that's actually in the games business.

1267
01:00:49,120 --> 01:00:51,120
Speaker 1: We we had a games business that we spun up

1268
01:00:51,120 --> 01:00:53,360
Speaker 1: in the nineties. Uh. It did well for a while

1269
01:00:53,360 --> 01:00:55,040
Speaker 1: and then it lay follow and then I brought back

1270
01:00:55,320 --> 01:00:58,080
Speaker 1: to the entrepreneurs that helped create the European business. It's

1271
01:00:58,080 --> 01:01:01,200
Speaker 1: called game House. It's actually East in the Netherlands, and

1272
01:01:01,240 --> 01:01:05,000
Speaker 1: it's at the intersection of of casual gameplay and storytelling.

1273
01:01:05,320 --> 01:01:07,760
Speaker 1: So we have now about two dozen of what we

1274
01:01:07,800 --> 01:01:10,440
Speaker 1: call these game House original stories, and we're in the

1275
01:01:10,440 --> 01:01:13,360
Speaker 1: process of launching a subscription product around them. We for

1276
01:01:13,400 --> 01:01:14,960
Speaker 1: the first couple years, we didn't have enough to make

1277
01:01:14,960 --> 01:01:17,840
Speaker 1: a subscription, so we sold them individually, built a decent

1278
01:01:17,880 --> 01:01:20,680
Speaker 1: business on it. It's about it's about million dollar business

1279
01:01:20,720 --> 01:01:23,920
Speaker 1: something like that. But now with subscriptions, were pretty optimistic

1280
01:01:24,080 --> 01:01:26,640
Speaker 1: that we can grow it significantly. And the team doing

1281
01:01:26,680 --> 01:01:29,840
Speaker 1: that kind of rut Computers and Rakusen's are the two

1282
01:01:29,840 --> 01:01:33,240
Speaker 1: guys who are in a jointly. They're fantastic entrepreneurs. They're uh,

1283
01:01:33,480 --> 01:01:35,560
Speaker 1: they kind of both do both, but Eric is more

1284
01:01:35,600 --> 01:01:37,840
Speaker 1: of the creative spark and Rutgerry is more of the

1285
01:01:37,840 --> 01:01:40,240
Speaker 1: business guy. Although they both do both and they probably

1286
01:01:40,600 --> 01:01:44,080
Speaker 1: um blanched my pigeonholing him in that way, but they

1287
01:01:44,080 --> 01:01:46,920
Speaker 1: work together collaboratively. They do a fantastic job. I love

1288
01:01:47,000 --> 01:01:49,800
Speaker 1: working with them, and Uh in that business, I think

1289
01:01:50,160 --> 01:01:52,680
Speaker 1: I'm very optimistic about its future as a subscription business.

1290
01:01:53,120 --> 01:01:55,400
Speaker 1: Then we have three B two B products, which is

1291
01:01:55,720 --> 01:01:57,680
Speaker 1: something real has always had a foot in. Both we

1292
01:01:57,760 --> 01:02:00,760
Speaker 1: make technology, we love consumers. Um. The one that when

1293
01:02:00,760 --> 01:02:03,480
Speaker 1: I said, remember China, the one that's the most obscure,

1294
01:02:04,160 --> 01:02:07,080
Speaker 1: but it's true, is we have a robust business of

1295
01:02:07,160 --> 01:02:11,640
Speaker 1: licensing video compression technology in China. Uh and uh we

1296
01:02:11,800 --> 01:02:14,480
Speaker 1: when I came back, it was clear that what happened

1297
01:02:14,480 --> 01:02:17,160
Speaker 1: is and I'll explain why when Microsoft displaced us with

1298
01:02:17,200 --> 01:02:18,640
Speaker 1: this thing I told you about where they got to

1299
01:02:18,680 --> 01:02:21,200
Speaker 1: PC manufacturers and they say you only have to pay

1300
01:02:21,280 --> 01:02:23,720
Speaker 1: us sixty dollars per windows if you do what we

1301
01:02:23,760 --> 01:02:27,160
Speaker 1: say a hundred dollars. Otherwise, the Chinese manufacturers companies like

1302
01:02:27,320 --> 01:02:29,920
Speaker 1: Legend which became Lenovo, or a great Wall or some

1303
01:02:29,960 --> 01:02:33,160
Speaker 1: of these others. They basically told Microsoft, you know, I

1304
01:02:33,160 --> 01:02:36,080
Speaker 1: only shipped a hundred thousand PCs this year, here's your money.

1305
01:02:36,120 --> 01:02:38,240
Speaker 1: And when Microsoft said, no, we think you shift a million,

1306
01:02:38,440 --> 01:02:41,800
Speaker 1: They're like, you know, maybe only ship fifty. So so

1307
01:02:42,200 --> 01:02:45,640
Speaker 1: the Microsoft had less leverage in the Chinese market circuit

1308
01:02:45,640 --> 01:02:49,440
Speaker 1: two thousand to use this tactic of using their marketing

1309
01:02:49,480 --> 01:02:51,680
Speaker 1: car program to push us out of the market. So

1310
01:02:51,760 --> 01:02:54,040
Speaker 1: we never got displaced in the Chinese market. So our

1311
01:02:54,120 --> 01:02:57,040
Speaker 1: video format, which is called r M v B continuous

1312
01:02:57,040 --> 01:03:00,400
Speaker 1: to be very popular. So about three years ago three

1313
01:03:00,400 --> 01:03:02,440
Speaker 1: four years ago, I spot up a team where commissioned

1314
01:03:02,440 --> 01:03:04,240
Speaker 1: the team to spun up to build a next generation

1315
01:03:04,320 --> 01:03:06,680
Speaker 1: video technology. We put half the R and D team

1316
01:03:06,680 --> 01:03:08,840
Speaker 1: in Seattle, half in Beijing, and we have a next

1317
01:03:08,840 --> 01:03:13,360
Speaker 1: generation video technology called r MHD that is doing very

1318
01:03:13,360 --> 01:03:15,640
Speaker 1: well in China. We have a partnership with one of

1319
01:03:15,640 --> 01:03:18,480
Speaker 1: the three largest broadcasters in China called c I the

1320
01:03:18,600 --> 01:03:23,600
Speaker 1: end UH their affiliate just launched uh in uh in

1321
01:03:23,640 --> 01:03:26,000
Speaker 1: the in the first phase of of launch in China.

1322
01:03:26,320 --> 01:03:28,600
Speaker 1: So that business is going to do very well basically

1323
01:03:28,680 --> 01:03:32,760
Speaker 1: licensing video technology. That's next generation technology. It's better than

1324
01:03:32,800 --> 01:03:35,560
Speaker 1: the next generation m PEG. It's very competitive with the

1325
01:03:35,600 --> 01:03:38,240
Speaker 1: best in the world. Whether we bring that technology from

1326
01:03:38,320 --> 01:03:40,480
Speaker 1: China the rest of the world, we'll see. It's something

1327
01:03:40,520 --> 01:03:41,760
Speaker 1: we want to do, but we've got to get into

1328
01:03:41,760 --> 01:03:44,280
Speaker 1: the chip sets. We've got to build out the infrastructure first.

1329
01:03:44,480 --> 01:03:47,720
Speaker 1: So those are two businesses. The third business is actually

1330
01:03:47,840 --> 01:03:52,120
Speaker 1: very obscure, but it's very interesting. It's around messaging. UH.

1331
01:03:52,240 --> 01:03:54,840
Speaker 1: Do you know how much like robotcalling and spam you're

1332
01:03:54,840 --> 01:03:57,800
Speaker 1: getting on your phone. Uh. We built a product called

1333
01:03:57,880 --> 01:04:01,560
Speaker 1: Context that's designed to cut down on TECHT spamming and

1334
01:04:01,560 --> 01:04:05,320
Speaker 1: then also providing a way for message UH providers to

1335
01:04:05,480 --> 01:04:08,800
Speaker 1: classify their messages to basically create more structure on them

1336
01:04:08,960 --> 01:04:10,520
Speaker 1: a lot of stuff that's done an email and not

1337
01:04:10,560 --> 01:04:12,960
Speaker 1: done in texting. The reason we've gotten that business is

1338
01:04:13,000 --> 01:04:16,480
Speaker 1: we have a related business that is the powers what's

1339
01:04:16,520 --> 01:04:18,720
Speaker 1: called inter carry messaging. So if you're on Verizon on

1340
01:04:18,840 --> 01:04:21,120
Speaker 1: an A, T and T and I send you a text,

1341
01:04:21,360 --> 01:04:24,240
Speaker 1: it goes over our inter carry messaging software. So we

1342
01:04:24,280 --> 01:04:27,680
Speaker 1: have the software that is the highly reliable but invisible

1343
01:04:27,960 --> 01:04:30,280
Speaker 1: layer that about a billion and a half message today

1344
01:04:30,280 --> 01:04:33,080
Speaker 1: travel over. So that team used that expertise to build

1345
01:04:33,120 --> 01:04:35,760
Speaker 1: this next product, which is called Context and that's just

1346
01:04:35,960 --> 01:04:38,160
Speaker 1: rolling out. And then the fourth, which may be the

1347
01:04:38,200 --> 01:04:41,240
Speaker 1: coolest of them all, is a computer vision platform called

1348
01:04:41,280 --> 01:04:44,040
Speaker 1: real c V, which leverages all of our expertise and

1349
01:04:44,120 --> 01:04:49,000
Speaker 1: video technology to do phenomenal face recognition. We haven't yet

1350
01:04:49,040 --> 01:04:51,480
Speaker 1: described what markets are going after that, but we've got

1351
01:04:51,520 --> 01:04:54,400
Speaker 1: some fantastic ideas that we think are gonna go are

1352
01:04:54,440 --> 01:04:58,479
Speaker 1: gonna make people really happy to have computer based face

1353
01:04:58,560 --> 01:05:02,400
Speaker 1: recognition be applied, not in creepy situations, but in situations

1354
01:05:02,400 --> 01:05:04,960
Speaker 1: where it's actually good for society to have people know

1355
01:05:05,240 --> 01:05:09,560
Speaker 1: who's who. Okay, you're also very heavenly political. If one

1356
01:05:09,640 --> 01:05:13,000
Speaker 1: follows your Twitter feed, most of it is politics. You

1357
01:05:13,160 --> 01:05:17,920
Speaker 1: invested in Air America, So what was your thinking of

1358
01:05:17,960 --> 01:05:21,800
Speaker 1: the Air America and where are you relative to politics today? Well,

1359
01:05:21,840 --> 01:05:23,480
Speaker 1: so the reason I got involved in America, and that

1360
01:05:23,520 --> 01:05:27,280
Speaker 1: goes back over a decade, UH is UH I was

1361
01:05:27,440 --> 01:05:31,520
Speaker 1: very frustrated by the rise of UH Fox News and

1362
01:05:31,640 --> 01:05:36,160
Speaker 1: the the right media right that there was no longer

1363
01:05:36,880 --> 01:05:39,880
Speaker 1: UH any kind of There was not any kind of

1364
01:05:40,480 --> 01:05:42,840
Speaker 1: balance because you had the mainstream media which is very

1365
01:05:42,880 --> 01:05:46,880
Speaker 1: much covers the horse rays tries to have journalistic ethics, etcetera.

1366
01:05:46,920 --> 01:05:48,960
Speaker 1: And then you had Fox operating as kind of a

1367
01:05:49,360 --> 01:05:53,280
Speaker 1: right word magnet that was not operating within journalistic bounds

1368
01:05:53,280 --> 01:05:56,200
Speaker 1: in my view even back then, but was sort of

1369
01:05:56,640 --> 01:06:00,320
Speaker 1: being uh like talk radio and television. Uh also had

1370
01:06:00,320 --> 01:06:02,680
Speaker 1: a lot of right wing talk radio. So the question

1371
01:06:02,760 --> 01:06:04,760
Speaker 1: was why can't why why can't there be a progressive

1372
01:06:04,800 --> 01:06:08,800
Speaker 1: alternative just to rebalance society. So I got to know, uh,

1373
01:06:08,920 --> 01:06:11,800
Speaker 1: somebody who may talk about in a minute, Al Franken

1374
01:06:12,240 --> 01:06:16,040
Speaker 1: Uh through some people I knew, uh, and Al got

1375
01:06:16,080 --> 01:06:19,040
Speaker 1: was interested in getting involved in this fledgling in Gold America,

1376
01:06:19,360 --> 01:06:21,560
Speaker 1: and he asked me for advice, and I said, well, all,

1377
01:06:21,600 --> 01:06:22,959
Speaker 1: I think you should do it. I think it's gonna

1378
01:06:23,000 --> 01:06:25,280
Speaker 1: I think you'll be fantastic. You know, Al had already

1379
01:06:25,560 --> 01:06:28,200
Speaker 1: uh you know, he'd done Saturdayright Live, he'd written some

1380
01:06:28,200 --> 01:06:30,680
Speaker 1: books that were very compelling, and I thought, Alex somebody

1381
01:06:30,680 --> 01:06:34,440
Speaker 1: who could could really have a big impact. Um and uh,

1382
01:06:34,760 --> 01:06:36,400
Speaker 1: but I said, I don't know about the people that

1383
01:06:36,480 --> 01:06:37,919
Speaker 1: the people are backing yet. I don't know if it's

1384
01:06:37,960 --> 01:06:41,120
Speaker 1: the resources anyway, So through Al I allowed myself to

1385
01:06:41,120 --> 01:06:43,880
Speaker 1: get sucked in and became for a while the chairman

1386
01:06:43,920 --> 01:06:46,320
Speaker 1: of this thing. Um. And it was very it was

1387
01:06:46,360 --> 01:06:50,200
Speaker 1: financially not very robust. Uh it was. But we you know,

1388
01:06:50,240 --> 01:06:52,720
Speaker 1: there's this there's this old expression, right, you know, the

1389
01:06:52,720 --> 01:06:56,000
Speaker 1: patient operations was successful, but the patient died. Um. That's

1390
01:06:56,080 --> 01:06:58,919
Speaker 1: kind of like what happened with Their America because through

1391
01:06:59,000 --> 01:07:02,640
Speaker 1: Air America we created something that had critical mass around

1392
01:07:02,680 --> 01:07:05,440
Speaker 1: progressive media. Rachel Maddow came from Air America. She was

1393
01:07:05,440 --> 01:07:08,080
Speaker 1: one of our up and coming stars. Uh. And and

1394
01:07:08,320 --> 01:07:10,960
Speaker 1: obviously Al played a huge role in it. Uh. You

1395
01:07:10,960 --> 01:07:12,200
Speaker 1: know you you there are there are There are a

1396
01:07:12,200 --> 01:07:14,160
Speaker 1: few other folks that are less famous that came up

1397
01:07:14,200 --> 01:07:17,280
Speaker 1: to the ranks there. The whole way MSNBC started came

1398
01:07:17,320 --> 01:07:20,560
Speaker 1: out of the um, the sort of the ashes, if

1399
01:07:20,600 --> 01:07:23,360
Speaker 1: you will, of Air America. So it was not a

1400
01:07:23,440 --> 01:07:26,040
Speaker 1: good use of capital from the standpoint of making money,

1401
01:07:26,040 --> 01:07:27,680
Speaker 1: but I kind of knew that. But I think it

1402
01:07:27,720 --> 01:07:30,480
Speaker 1: did play a catalytic roll. Um. It took a while,

1403
01:07:30,840 --> 01:07:32,959
Speaker 1: uh in two thousand fours when it started, it didn't

1404
01:07:32,960 --> 01:07:36,240
Speaker 1: move the meter enough to change the outcome there. But

1405
01:07:36,520 --> 01:07:40,760
Speaker 1: over time I think we do have more you know,

1406
01:07:40,840 --> 01:07:44,400
Speaker 1: be it Huving to Post, be it, the MSNBC more

1407
01:07:44,440 --> 01:07:48,479
Speaker 1: outlets that are that give voice to progressive ideas and value.

1408
01:07:48,480 --> 01:07:51,200
Speaker 1: So I'm not involved it anymore, but I'm glad I

1409
01:07:51,240 --> 01:07:53,440
Speaker 1: did it, even though it was from a cash on

1410
01:07:53,520 --> 01:07:56,120
Speaker 1: cash standpoint, is a bad investment. Okay, So the big

1411
01:07:56,160 --> 01:08:00,120
Speaker 1: issue today and technology is privacy. So what's your a

1412
01:08:00,160 --> 01:08:03,320
Speaker 1: point on what's going on there with privacy and data? Uh?

1413
01:08:03,680 --> 01:08:07,560
Speaker 1: I think that the reckoning that's happening now is important

1414
01:08:07,880 --> 01:08:13,560
Speaker 1: and has to happen. I think that, uh remember Facebook's

1415
01:08:14,120 --> 01:08:18,360
Speaker 1: used to have their slogan move fast and break stuff. UM,

1416
01:08:18,439 --> 01:08:21,040
Speaker 1: I don't think they meant to break democracy, but I

1417
01:08:21,040 --> 01:08:25,160
Speaker 1: think that's largely what happened. Uh And and and it

1418
01:08:25,280 --> 01:08:30,120
Speaker 1: happened because it became too easy for bad actors. Probably

1419
01:08:30,160 --> 01:08:32,840
Speaker 1: Cambridge Analytics could be one of those bad actors, uh

1420
01:08:32,960 --> 01:08:37,519
Speaker 1: to scoop up huge amounts of data on people UH

1421
01:08:37,560 --> 01:08:42,120
Speaker 1: that that can be used to uh both target uh

1422
01:08:42,160 --> 01:08:45,880
Speaker 1: them and influence them uh in all kinds of pernicious ways.

1423
01:08:46,000 --> 01:08:49,200
Speaker 1: And so so from my standpoint, we need to have

1424
01:08:49,360 --> 01:08:54,800
Speaker 1: much more rigorous uh laws and enforcement of those laws

1425
01:08:54,840 --> 01:08:58,200
Speaker 1: around data privacy. Uh. And and you know, for instance,

1426
01:08:58,240 --> 01:09:01,280
Speaker 1: this this new European Ladge GDPR, which I think it

1427
01:09:01,280 --> 01:09:03,960
Speaker 1: becomes effective like amount a month from now, I think

1428
01:09:03,960 --> 01:09:06,160
Speaker 1: the middle end of May. I think it's a huge

1429
01:09:06,200 --> 01:09:08,880
Speaker 1: step forward in the right direction. What I'm seeing is

1430
01:09:08,920 --> 01:09:11,960
Speaker 1: a lot of companies are actually used because Europe is

1431
01:09:12,000 --> 01:09:14,400
Speaker 1: such a big part of their business. Whatever policies they're

1432
01:09:14,400 --> 01:09:18,120
Speaker 1: putting in place for um uh for Europe, they're putting

1433
01:09:18,120 --> 01:09:20,240
Speaker 1: in place globally. But I think the U s should

1434
01:09:20,360 --> 01:09:23,800
Speaker 1: should have its own GDPR law not rely on the Europeans, right,

1435
01:09:23,840 --> 01:09:26,160
Speaker 1: you know, we would We wouldn't say, well, the Europeans

1436
01:09:26,200 --> 01:09:28,200
Speaker 1: are taken care of air traffic safety, we don't need

1437
01:09:28,240 --> 01:09:30,200
Speaker 1: our own air traffic safety standards. Of course we do,

1438
01:09:30,439 --> 01:09:32,599
Speaker 1: and maybe they'll be the same as Europe, maybe they'll

1439
01:09:32,600 --> 01:09:34,519
Speaker 1: be a little different or something, but we should have

1440
01:09:34,560 --> 01:09:38,720
Speaker 1: our own equivalent GDPR in the US. Absolutely agree with

1441
01:09:38,760 --> 01:09:43,360
Speaker 1: that in terms of what it would mean in practice.

1442
01:09:43,640 --> 01:09:46,760
Speaker 1: I still think most people would would be willing to

1443
01:09:48,680 --> 01:09:52,479
Speaker 1: uh use these social media services uh and would use

1444
01:09:52,520 --> 01:09:55,240
Speaker 1: the ad supported versions of them. The approach that I've

1445
01:09:55,280 --> 01:09:57,759
Speaker 1: taken is I don't think I've had I think every

1446
01:09:57,760 --> 01:10:01,679
Speaker 1: post I've ever posted on Facebook, I've basically put Twitter

1447
01:10:01,760 --> 01:10:04,080
Speaker 1: Twitter rolls on it. I assume that it's public because

1448
01:10:04,080 --> 01:10:07,160
Speaker 1: my assumption is anything I post, if I share it

1449
01:10:07,200 --> 01:10:10,000
Speaker 1: with fifty friends, I've shared it with the world anyway.

1450
01:10:10,360 --> 01:10:12,479
Speaker 1: So I don't post pictures of my kids on social

1451
01:10:12,520 --> 01:10:15,800
Speaker 1: media just as a matter of personal preference, personal truth,

1452
01:10:15,840 --> 01:10:17,800
Speaker 1: and privacy. If I want to send pictures of my kids,

1453
01:10:17,800 --> 01:10:20,080
Speaker 1: I email to my friends and I trust that they're

1454
01:10:20,080 --> 01:10:23,080
Speaker 1: not gonna you know, Poston social media. Um, anything I

1455
01:10:23,120 --> 01:10:25,599
Speaker 1: post publicly assume you know, I'm gonna have to live

1456
01:10:25,640 --> 01:10:27,160
Speaker 1: with it twenty years from now. If I said something

1457
01:10:27,360 --> 01:10:29,600
Speaker 1: obnoxious about Trump, and you know, Trump gets to know

1458
01:10:29,640 --> 01:10:33,560
Speaker 1: about prize or something, gonna have to eat crow, that's possible. Um,

1459
01:10:33,680 --> 01:10:37,360
Speaker 1: But but I you know, it's uh, my my assumption

1460
01:10:37,479 --> 01:10:41,360
Speaker 1: was and with this anything. So my daughter is now eleven, UM,

1461
01:10:41,439 --> 01:10:43,519
Speaker 1: and she rides a school bus because she now goes

1462
01:10:43,560 --> 01:10:46,800
Speaker 1: to a Catholic girls school that she has to take

1463
01:10:46,800 --> 01:10:49,920
Speaker 1: a bus to go to. UM and uh, it's fantastical,

1464
01:10:50,000 --> 01:10:52,519
Speaker 1: great for her. But now she needed a phone. So

1465
01:10:52,760 --> 01:10:54,320
Speaker 1: she was like, well, Dad, can I have an iPhone?

1466
01:10:54,680 --> 01:10:56,519
Speaker 1: She's eleven years old. I said no, So I got

1467
01:10:56,520 --> 01:10:59,400
Speaker 1: her a five year old LG flip phone with a

1468
01:10:59,479 --> 01:11:02,240
Speaker 1: text keep word when you can buy an eBay by

1469
01:11:02,280 --> 01:11:05,400
Speaker 1: the way, um, And she's mad at me about this.

1470
01:11:05,400 --> 01:11:07,960
Speaker 1: This is like, this is like the biggest like and

1471
01:11:08,160 --> 01:11:11,200
Speaker 1: so she she's like, Dad, all my friends have iPhones.

1472
01:11:12,080 --> 01:11:15,000
Speaker 1: And I said, what about Andy's daughter? And he's a

1473
01:11:15,000 --> 01:11:17,519
Speaker 1: guy worked with a Microsoft, and I know that Andy

1474
01:11:17,600 --> 01:11:20,639
Speaker 1: is also vigilant about this stuff. And she said, well,

1475
01:11:20,680 --> 01:11:23,479
Speaker 1: Andy's dad won't even let his Naomi get a flip

1476
01:11:23,479 --> 01:11:25,920
Speaker 1: phone for two more years. And I said, why don't

1477
01:11:25,920 --> 01:11:27,519
Speaker 1: we go to lunch with Andy and his daughter and

1478
01:11:27,560 --> 01:11:29,960
Speaker 1: talk about this? And I said, no, no no, Dad, we

1479
01:11:29,960 --> 01:11:32,800
Speaker 1: don't need to do that. So my point is that

1480
01:11:33,240 --> 01:11:36,000
Speaker 1: a lot of what makes how to make social media

1481
01:11:36,479 --> 01:11:40,320
Speaker 1: healthy for kids and not unhealthy is actually parents understanding

1482
01:11:40,320 --> 01:11:43,120
Speaker 1: that there are choices. The choice isn't give him an

1483
01:11:43,120 --> 01:11:45,360
Speaker 1: iPhone or not. You know, yeah, you gotta go to

1484
01:11:45,400 --> 01:11:47,240
Speaker 1: let your way to find a five year old flip phone.

1485
01:11:47,680 --> 01:11:50,120
Speaker 1: But it works just fine. She can text and call us,

1486
01:11:50,360 --> 01:11:51,920
Speaker 1: which is all at the age of eleven I want

1487
01:11:51,960 --> 01:11:54,000
Speaker 1: her to do. When she's thirteen. I'm sure she'll have

1488
01:11:54,040 --> 01:11:57,000
Speaker 1: an iPhone. We'll probably have policies on you know that

1489
01:11:57,040 --> 01:11:58,400
Speaker 1: she has to show us all of her text. I'm

1490
01:11:58,439 --> 01:12:01,680
Speaker 1: sure she'll get around them in some ways, but it

1491
01:12:01,840 --> 01:12:05,479
Speaker 1: to me, Um, the pernicioners of social media comes with

1492
01:12:05,560 --> 01:12:09,080
Speaker 1: the interaction between the fact that most people don't understand

1493
01:12:09,080 --> 01:12:12,360
Speaker 1: what this stuff is and the fact that the large

1494
01:12:12,360 --> 01:12:15,599
Speaker 1: scale companies running it we're too willing to take advantage

1495
01:12:15,600 --> 01:12:18,000
Speaker 1: of that naivete. So I don't think the solution is

1496
01:12:18,040 --> 01:12:20,599
Speaker 1: no social media. I think the solution is better regulation

1497
01:12:20,640 --> 01:12:28,280
Speaker 1: and better education. We'll plause here for a brief moment

1498
01:12:28,320 --> 01:12:31,759
Speaker 1: and get right back to CEO of Real Networks, Rob Glazer.

1499
01:12:33,520 --> 01:12:35,760
Speaker 1: As you know, I'm a writer and you can read

1500
01:12:35,840 --> 01:12:38,920
Speaker 1: my work at left sets dot com. In addition of

1501
01:12:39,040 --> 01:12:41,920
Speaker 1: reading my commentary on music tech in the world at large.

1502
01:12:42,160 --> 01:12:44,400
Speaker 1: There'll be the first to find out when we've published

1503
01:12:44,400 --> 01:12:47,200
Speaker 1: a new podcast. Go to left sets dot com and

1504
01:12:47,320 --> 01:12:51,120
Speaker 1: sign up for the news left Now More with CEO

1505
01:12:51,160 --> 01:12:54,160
Speaker 1: of Real Networks, Rob Glazer, recorded live with the Music

1506
01:12:54,200 --> 01:12:59,600
Speaker 1: Media Summit in Santa Barbara, California. Okay, it's time for

1507
01:12:59,720 --> 01:13:02,679
Speaker 1: question and from the audience, raise your hand. Jim has

1508
01:13:02,680 --> 01:13:06,439
Speaker 1: a microphone, and please talk into the microphone and ask

1509
01:13:06,439 --> 01:13:08,799
Speaker 1: a question as opposed to the basic a statement. Okay,

1510
01:13:09,080 --> 01:13:14,320
Speaker 1: before the first question, Um, quick story. So, when we

1511
01:13:14,320 --> 01:13:18,559
Speaker 1: were about to reach out to um Rob about coming here,

1512
01:13:19,240 --> 01:13:21,599
Speaker 1: I spelled his name with a Z and I get

1513
01:13:21,640 --> 01:13:26,160
Speaker 1: this note from Bob, you know, chastising me, like, dude,

1514
01:13:26,200 --> 01:13:29,440
Speaker 1: you've got to spell people's names correctly if you're gonna.

1515
01:13:29,560 --> 01:13:33,519
Speaker 1: And so then Rob gets here today and and uh

1516
01:13:33,720 --> 01:13:36,120
Speaker 1: looks at his name tag and it has two essays.

1517
01:13:38,280 --> 01:13:41,600
Speaker 1: So I have to apologize for that and take ownership

1518
01:13:41,680 --> 01:13:44,040
Speaker 1: of it. And I don't know how it happened. Well,

1519
01:13:44,200 --> 01:13:46,240
Speaker 1: I'll tell you a story about my daughter, which um

1520
01:13:46,560 --> 01:13:48,120
Speaker 1: since I don't show a pictures of the internet. So

1521
01:13:48,439 --> 01:13:50,719
Speaker 1: her name is Eva E v A. And it turns

1522
01:13:50,760 --> 01:13:53,439
Speaker 1: out that in the current generation that's a moderately popular

1523
01:13:53,479 --> 01:13:56,280
Speaker 1: in but apparently because of Reef Witherspoon, Ava a Via

1524
01:13:56,439 --> 01:13:58,200
Speaker 1: is like one of the five most popular girl names.

1525
01:13:58,600 --> 01:14:01,120
Speaker 1: So about five years ago, when she was six years old,

1526
01:14:01,320 --> 01:14:03,320
Speaker 1: she goes through T S A and the T S

1527
01:14:03,360 --> 01:14:08,519
Speaker 1: A guy looks at her and says Ava Glazer, and

1528
01:14:08,600 --> 01:14:10,400
Speaker 1: she looks back at him. T s A guy you

1529
01:14:10,400 --> 01:14:13,080
Speaker 1: know she doesn't know, noting its assassin, she says, actually

1530
01:14:13,160 --> 01:14:16,880
Speaker 1: it's Eva, and the T S A Gui apparently isn't

1531
01:14:16,920 --> 01:14:19,120
Speaker 1: used to being sassed by six year old kids. Uh

1532
01:14:19,280 --> 01:14:21,000
Speaker 1: hands her back the passport and in a kind of

1533
01:14:21,000 --> 01:14:26,360
Speaker 1: exaggerated way says, excuse me, Eva, And then her reply is,

1534
01:14:26,560 --> 01:14:33,519
Speaker 1: actually it's a common mistake. So it's actually a common mistake.

1535
01:14:36,360 --> 01:14:39,920
Speaker 1: I have a couple of technical questions, So tracking all

1536
01:14:39,960 --> 01:14:44,519
Speaker 1: the video technology but you've been doing since where do

1537
01:14:44,560 --> 01:14:46,800
Speaker 1: you where do you think the technology is moving with

1538
01:14:47,040 --> 01:14:49,400
Speaker 1: like AI and like fake app and all that stuff

1539
01:14:49,439 --> 01:14:51,040
Speaker 1: that's going on, and how big a problem is that

1540
01:14:51,080 --> 01:14:53,200
Speaker 1: going to be? And then following up on that with

1541
01:14:53,320 --> 01:14:59,080
Speaker 1: AI and the Sergey brand founder's letter about saying responsibilities

1542
01:14:59,080 --> 01:15:01,599
Speaker 1: in AI? Where you see AI and what's your feeling

1543
01:15:01,600 --> 01:15:03,600
Speaker 1: on that? Well, the first one is easier than the

1544
01:15:03,600 --> 01:15:05,599
Speaker 1: second one because the second one is like so open ended,

1545
01:15:05,680 --> 01:15:07,439
Speaker 1: you know, you get to the you know, the Elon

1546
01:15:07,520 --> 01:15:11,280
Speaker 1: Mesk debates with everybody that stuff and Sergey Uh. So

1547
01:15:11,560 --> 01:15:13,519
Speaker 1: there's gonna be a really interesting convergence in the next

1548
01:15:13,560 --> 01:15:18,400
Speaker 1: five years between uh C g UH computer graphics and

1549
01:15:18,520 --> 01:15:22,280
Speaker 1: natural video. We already see it today with augmented reality

1550
01:15:22,280 --> 01:15:24,240
Speaker 1: because you think about what it augumented reality is. It's

1551
01:15:24,560 --> 01:15:27,640
Speaker 1: it's reality where you take a video and you superimpose

1552
01:15:27,720 --> 01:15:30,920
Speaker 1: in some way something that looks very lifelike that is

1553
01:15:30,920 --> 01:15:35,720
Speaker 1: not actually in the scene, and so augmented reality is

1554
01:15:35,760 --> 01:15:38,559
Speaker 1: incredibly powerful, you know, with lots of utility for it,

1555
01:15:38,640 --> 01:15:40,920
Speaker 1: but also means that you can no longer trust the

1556
01:15:40,960 --> 01:15:44,160
Speaker 1: authenticity of an image or an experience. Uh. And that's

1557
01:15:44,160 --> 01:15:45,840
Speaker 1: been going from for a while. I mean people have

1558
01:15:45,880 --> 01:15:48,880
Speaker 1: been uh, you know, retouching photos for years, you know,

1559
01:15:48,960 --> 01:15:51,400
Speaker 1: the whole You can argue that you know, uh, you know,

1560
01:15:51,800 --> 01:15:54,760
Speaker 1: People Magazine and the like are our pure examples of

1561
01:15:54,760 --> 01:15:57,360
Speaker 1: the fact you can't trust to photograph, uh, you know,

1562
01:15:57,400 --> 01:15:59,800
Speaker 1: where the images they show of people looking for more

1563
01:16:00,000 --> 01:16:03,040
Speaker 1: itiful than they ever could look or have looked. But

1564
01:16:03,080 --> 01:16:05,000
Speaker 1: it's gonna get worse, and it's gonna apply to video.

1565
01:16:05,920 --> 01:16:07,880
Speaker 1: I think we'll probably have to come up with some

1566
01:16:09,080 --> 01:16:13,560
Speaker 1: authentication techniques on video that involve some combination of triangulation

1567
01:16:13,560 --> 01:16:16,880
Speaker 1: and water marking. And by triangulation, I mean literally when

1568
01:16:16,960 --> 01:16:19,439
Speaker 1: you get the same image with the same time code

1569
01:16:19,800 --> 01:16:23,040
Speaker 1: from multiple cameras and multiple advantage points, and then you

1570
01:16:23,040 --> 01:16:26,240
Speaker 1: you have those water marked by some kind of trust authority,

1571
01:16:26,320 --> 01:16:28,360
Speaker 1: so you know that that's the image of what really

1572
01:16:28,400 --> 01:16:32,840
Speaker 1: happened and the uh and that's that whole how you

1573
01:16:32,920 --> 01:16:38,519
Speaker 1: create um trust authorities that people respect for factual things.

1574
01:16:38,960 --> 01:16:41,320
Speaker 1: Is a huge problem you have already in in you know,

1575
01:16:41,360 --> 01:16:43,639
Speaker 1: in text and quote unquote fake news, and it's gonna

1576
01:16:43,640 --> 01:16:46,760
Speaker 1: get even worse as this uh, you know, connection between

1577
01:16:46,760 --> 01:16:49,799
Speaker 1: computer generated video and natural video gets deeper and deeper.

1578
01:16:49,840 --> 01:16:52,920
Speaker 1: But hopefully we'll solve that problem because you know, and

1579
01:16:53,080 --> 01:16:55,200
Speaker 1: and we're living in weird times, right because at the

1580
01:16:55,240 --> 01:16:58,240
Speaker 1: same time, there's all this forensic stuff that gets that

1581
01:16:58,240 --> 01:17:00,960
Speaker 1: that gets done, like the Golden a killer got caught

1582
01:17:01,240 --> 01:17:04,519
Speaker 1: because of consumer DNA websites that were used by law

1583
01:17:04,640 --> 01:17:08,160
Speaker 1: enforcement to track relatives of the guy. So that's a

1584
01:17:08,200 --> 01:17:11,679
Speaker 1: case where data was used to solve a crime. Um

1585
01:17:11,720 --> 01:17:14,760
Speaker 1: that where you actually were able to combine gumshoe work

1586
01:17:14,840 --> 01:17:17,240
Speaker 1: with data. And you know, he'll go to a trial

1587
01:17:17,320 --> 01:17:19,840
Speaker 1: and you know, he's guilty, instantly proven guilty, but it

1588
01:17:19,960 --> 01:17:23,160
Speaker 1: sounds like he's probably the guy. Uh, And they got

1589
01:17:23,240 --> 01:17:26,320
Speaker 1: him through science that authentically figured out who he was

1590
01:17:26,360 --> 01:17:28,599
Speaker 1: based on you know, DNA from thirty years ago they

1591
01:17:28,600 --> 01:17:31,519
Speaker 1: got put in a vault somewhere. So there's cross currents here.

1592
01:17:32,080 --> 01:17:33,600
Speaker 1: But I do think for video, we're going to have

1593
01:17:33,680 --> 01:17:38,240
Speaker 1: to create strategies for validating authentic video rather than just

1594
01:17:38,439 --> 01:17:41,559
Speaker 1: looks realistic to me, because sometimes it might be just

1595
01:17:41,600 --> 01:17:46,439
Speaker 1: a brilliant fake. On the second point about ai UH,

1596
01:17:46,800 --> 01:17:50,240
Speaker 1: there's no question that there are certain categories of jobs

1597
01:17:50,280 --> 01:17:53,240
Speaker 1: that will be massively disrupted by a i UH. Like

1598
01:17:53,360 --> 01:17:56,960
Speaker 1: let's take like a radiologist, for instance. The best radiologists

1599
01:17:57,360 --> 01:18:02,639
Speaker 1: can find things that that mediocre radiologists can't find, and

1600
01:18:03,000 --> 01:18:05,720
Speaker 1: the best AI system can probably find things that the

1601
01:18:05,760 --> 01:18:09,639
Speaker 1: best radiologists can't find. Is that good for society? Yes,

1602
01:18:09,680 --> 01:18:13,200
Speaker 1: it's good to have better radiology. Is it good for radiologists?

1603
01:18:13,960 --> 01:18:16,200
Speaker 1: Probably the best ones will still do fine. Probably the

1604
01:18:16,240 --> 01:18:19,040
Speaker 1: mediocre ones will will find that in their job being

1605
01:18:19,040 --> 01:18:21,200
Speaker 1: outsourced to India, the job got outsourced to a computer.

1606
01:18:21,640 --> 01:18:25,360
Speaker 1: So there's gonna be economic disruption around from AI, for sure.

1607
01:18:26,120 --> 01:18:28,080
Speaker 1: I don't know that. I take the view that you

1608
01:18:28,080 --> 01:18:29,479
Speaker 1: know we're gonna have the problem that you had in

1609
01:18:29,520 --> 01:18:32,040
Speaker 1: two thousand one with how where they're going to take over.

1610
01:18:32,439 --> 01:18:35,040
Speaker 1: I think that's a little bit of a stretch. But

1611
01:18:35,120 --> 01:18:37,320
Speaker 1: I think the economic disruption that's going to come from

1612
01:18:37,960 --> 01:18:40,519
Speaker 1: AI is gonna be huge. Um, But I think it

1613
01:18:40,520 --> 01:18:43,160
Speaker 1: takes longer than you think. Like, for instance, I'm personally

1614
01:18:43,240 --> 01:18:45,920
Speaker 1: quite bearish on self driving cars. I think it's not

1615
01:18:45,960 --> 01:18:50,320
Speaker 1: gonna be self driving cars as a phenomenon replacing taxi drivers.

1616
01:18:50,680 --> 01:18:55,880
Speaker 1: Uh won't happen uh before Let's say, um, and that's

1617
01:18:55,880 --> 01:18:58,479
Speaker 1: a bearer's few compared to some. There'll be scenarios where

1618
01:18:58,520 --> 01:19:01,479
Speaker 1: augmentation is good, Like, for instance, you're driving and you

1619
01:19:01,520 --> 01:19:03,800
Speaker 1: start to fall asleep. It'll save a bunch of lives

1620
01:19:03,800 --> 01:19:07,200
Speaker 1: in that case. Uh, that's a good thing. Um, Somewhat

1621
01:19:07,280 --> 01:19:09,719
Speaker 1: somewhat that will be maybe because it notices your braiding

1622
01:19:09,760 --> 01:19:12,080
Speaker 1: pattern and it wakes you up by by you know,

1623
01:19:12,320 --> 01:19:14,840
Speaker 1: giving you a little bit of shake. Maybe it'll be

1624
01:19:14,840 --> 01:19:17,200
Speaker 1: because it prevents you from u steering into a rail

1625
01:19:17,520 --> 01:19:20,200
Speaker 1: and then it wakes you up. But pure that's augmentation.

1626
01:19:20,280 --> 01:19:23,879
Speaker 1: So I'm bullish on that big picture. I'm a technology optimist.

1627
01:19:23,920 --> 01:19:27,000
Speaker 1: I mean I called uh my company Progressive Networks before

1628
01:19:27,000 --> 01:19:29,280
Speaker 1: we ended up changing the real networks foundation is the

1629
01:19:29,280 --> 01:19:31,919
Speaker 1: Glazer Progress Foundation. I believe in the idea of progress,

1630
01:19:32,240 --> 01:19:34,000
Speaker 1: but I don't think it's an unguided missile. I think

1631
01:19:34,000 --> 01:19:36,439
Speaker 1: you've got to make very conscious social choices on how

1632
01:19:36,680 --> 01:19:42,240
Speaker 1: you use the technology. Question over here, Um, two things.

1633
01:19:42,680 --> 01:19:45,919
Speaker 1: One is recently on I think it was on CNN.

1634
01:19:46,439 --> 01:19:50,000
Speaker 1: They were showing some augmented video of Obama and but

1635
01:19:50,040 --> 01:19:53,479
Speaker 1: they were interviewing a company that actually has software to

1636
01:19:53,520 --> 01:19:57,839
Speaker 1: detect that. So maybe there's a future with those companies

1637
01:19:57,880 --> 01:20:02,040
Speaker 1: and embedding their software. But secondly, I don't know if

1638
01:20:02,080 --> 01:20:04,640
Speaker 1: anyone else has this question, But you worked for Microsoft

1639
01:20:04,680 --> 01:20:08,680
Speaker 1: all those years when you had to deal with them

1640
01:20:08,800 --> 01:20:14,439
Speaker 1: um trying to basically bury you, didn't you at any

1641
01:20:14,479 --> 01:20:18,320
Speaker 1: point reach out to them and see if we could

1642
01:20:18,360 --> 01:20:20,800
Speaker 1: make a deal? Or because I didn't, hang I did

1643
01:20:20,840 --> 01:20:23,320
Speaker 1: I skipped over that story. I mean, Bob asked amazing questions,

1644
01:20:23,320 --> 01:20:25,320
Speaker 1: but you can only cover so much stuff. So in

1645
01:20:25,800 --> 01:20:28,920
Speaker 1: what happened in ninety and remember the years, right? So

1646
01:20:30,200 --> 01:20:34,400
Speaker 1: uh in nine seven we came out with real real

1647
01:20:34,479 --> 01:20:39,120
Speaker 1: video and then uh uh later that year or was it,

1648
01:20:41,400 --> 01:20:43,960
Speaker 1: we heard that Microsoft was about to buy our biggest competitor,

1649
01:20:44,000 --> 01:20:47,120
Speaker 1: coming called the Extreme. Uh, and so we we went

1650
01:20:47,160 --> 01:20:49,760
Speaker 1: to Microsoft. We said, rather than buying the Extreme, why

1651
01:20:49,800 --> 01:20:51,519
Speaker 1: don't we do a deal with you? And so we

1652
01:20:51,560 --> 01:20:53,760
Speaker 1: did a licensing deal. When we sold Microsoft ten percent

1653
01:20:53,800 --> 01:20:57,080
Speaker 1: of our company, licensed them our technology, gave them an

1654
01:20:57,120 --> 01:20:59,519
Speaker 1: opportunity to buy another ten percent of our company and

1655
01:20:59,560 --> 01:21:02,479
Speaker 1: gave it comortunity buy another license to US in order

1656
01:21:02,560 --> 01:21:04,880
Speaker 1: to create a counter common roadmap. And that was the

1657
01:21:04,920 --> 01:21:08,080
Speaker 1: deal done in good faith with the very senior people

1658
01:21:08,080 --> 01:21:11,000
Speaker 1: to Microsoft. What happened then was just one of those

1659
01:21:11,040 --> 01:21:13,040
Speaker 1: unfortunate things, and we had a plan B for it.

1660
01:21:13,439 --> 01:21:16,880
Speaker 1: But what happened was Microsoft gave the responsibility of that

1661
01:21:17,840 --> 01:21:20,479
Speaker 1: deal to the guys whose job previously was to compete

1662
01:21:20,520 --> 01:21:23,200
Speaker 1: with us, and they hated our guts UM, and so

1663
01:21:23,280 --> 01:21:24,880
Speaker 1: they did everything they could to try to put us

1664
01:21:24,880 --> 01:21:27,679
Speaker 1: out of business by they took the software, the license

1665
01:21:27,720 --> 01:21:30,280
Speaker 1: from US and they gave it away for free, trying

1666
01:21:30,320 --> 01:21:33,840
Speaker 1: to create our server business UM. And we got around

1667
01:21:33,880 --> 01:21:36,560
Speaker 1: that by coming out with a new version of the

1668
01:21:36,560 --> 01:21:38,960
Speaker 1: product three months later than meant they were giving away

1669
01:21:39,000 --> 01:21:41,800
Speaker 1: day old milk for free UM. And so there was

1670
01:21:41,840 --> 01:21:44,479
Speaker 1: a lot of back and forth and maneuvering. UH. We

1671
01:21:44,520 --> 01:21:46,400
Speaker 1: took a shot at trying to be aligned with them.

1672
01:21:46,920 --> 01:21:49,280
Speaker 1: We meant it sincerely. We weren't time, We were not

1673
01:21:49,320 --> 01:21:51,760
Speaker 1: trying to get into the PC operating system business. We

1674
01:21:51,760 --> 01:21:54,320
Speaker 1: weren't trying to be a hostile competitor. There's but we

1675
01:21:54,360 --> 01:21:57,799
Speaker 1: didn't We did not manage to UH get the team

1676
01:21:57,840 --> 01:22:01,200
Speaker 1: that received the deal licensing deal to feel good about it.

1677
01:22:01,680 --> 01:22:03,880
Speaker 1: That's life. So we took our best shot at trying

1678
01:22:03,920 --> 01:22:05,760
Speaker 1: to trying to stay a line with them. Uh. And

1679
01:22:05,800 --> 01:22:08,240
Speaker 1: then years later, we actually didn't end up initiating the

1680
01:22:08,280 --> 01:22:10,519
Speaker 1: lawsuit until three or four years later because I kind

1681
01:22:10,520 --> 01:22:12,720
Speaker 1: of wanted us to stabilize the company. And you know,

1682
01:22:12,760 --> 01:22:14,760
Speaker 1: with these suits workers, you can look back three or

1683
01:22:14,800 --> 01:22:17,920
Speaker 1: four years. So we waited until we were stable and

1684
01:22:18,000 --> 01:22:19,840
Speaker 1: we're still in a look back period, and that's when

1685
01:22:19,840 --> 01:22:23,360
Speaker 1: we started the lawsuit and we settled the lawsuit. I

1686
01:22:23,400 --> 01:22:27,080
Speaker 1: sat down with Gates and we negotiated the final negotiation.

1687
01:22:27,479 --> 01:22:29,920
Speaker 1: Some very senior people on both sides were involved, so

1688
01:22:29,960 --> 01:22:32,160
Speaker 1: we ended up settling without going to court. Uh. We

1689
01:22:32,360 --> 01:22:34,760
Speaker 1: we started in court, we didn't finish in court. Um

1690
01:22:35,360 --> 01:22:36,920
Speaker 1: we Uh. We settled, you know, a year or two

1691
01:22:36,960 --> 01:22:39,519
Speaker 1: into the lawsuit. Uh, and got a good outcome and

1692
01:22:39,560 --> 01:22:46,240
Speaker 1: moved on. Okay, any Jim, you have the microphone? Okay? Hi? UM,

1693
01:22:46,320 --> 01:22:48,800
Speaker 1: My name is Vicky Nahman and I actually worked at

1694
01:22:48,880 --> 01:22:52,880
Speaker 1: music Net back in the day and uh, just as

1695
01:22:52,880 --> 01:22:55,880
Speaker 1: side anecdote, one of the first meetings I had was

1696
01:22:55,960 --> 01:22:59,320
Speaker 1: with Warner Music wanting to license US tracks at four

1697
01:22:59,360 --> 01:23:02,360
Speaker 1: dollars and d five cents wholesale, and that that was

1698
01:23:02,400 --> 01:23:04,160
Speaker 1: a yeah, that was a point where I thought, Okay,

1699
01:23:04,200 --> 01:23:06,439
Speaker 1: this is going to take a while. UM. But my

1700
01:23:06,520 --> 01:23:09,600
Speaker 1: question for you is is in there's a school of

1701
01:23:09,640 --> 01:23:12,080
Speaker 1: thought that there's only one Apple, there's only one Google,

1702
01:23:12,160 --> 01:23:16,519
Speaker 1: there's only one Facebook. UM. In music, do you what

1703
01:23:16,560 --> 01:23:19,639
Speaker 1: are your thoughts about there's only one Spotify, there's only

1704
01:23:19,680 --> 01:23:23,640
Speaker 1: one streaming service? Um. Right now we have a commoditization

1705
01:23:23,720 --> 01:23:26,640
Speaker 1: of all of the catalogs and I'm just interested in

1706
01:23:26,880 --> 01:23:31,120
Speaker 1: what your view is in in the landscape for music. Well,

1707
01:23:31,520 --> 01:23:34,600
Speaker 1: I would say the following. Uh, Spotify has done an

1708
01:23:34,600 --> 01:23:37,439
Speaker 1: incredible job and a huge respect for Daniel and folks

1709
01:23:37,439 --> 01:23:40,280
Speaker 1: like Toroy with her yesterday, what they've done, they were

1710
01:23:40,280 --> 01:23:42,759
Speaker 1: able to succeed in a particular context and maybe useful

1711
01:23:42,760 --> 01:23:44,519
Speaker 1: for this group to explain that. I thought maybe Bob

1712
01:23:44,520 --> 01:23:47,800
Speaker 1: would ask about this. Spotify I was able to get

1713
01:23:47,840 --> 01:23:51,639
Speaker 1: to scale because they started in Europe for a very

1714
01:23:51,680 --> 01:23:55,759
Speaker 1: particular reason. So we uh, we launched in the US

1715
01:23:56,160 --> 01:23:58,280
Speaker 1: between US which was rapture at the time, Napster, a

1716
01:23:58,280 --> 01:24:01,080
Speaker 1: few others, we had over a million subscribe verse. We

1717
01:24:01,080 --> 01:24:03,000
Speaker 1: we did a teeny bit of every in Europe, but

1718
01:24:03,040 --> 01:24:05,360
Speaker 1: not a much much. In the early days, Europe was

1719
01:24:05,439 --> 01:24:08,160
Speaker 1: very expensive because you had a license, county, back country,

1720
01:24:08,200 --> 01:24:10,519
Speaker 1: and of course every country has its own repertoire, where

1721
01:24:10,560 --> 01:24:13,519
Speaker 1: sixtiescent of the repertoire in most countries is local language,

1722
01:24:13,520 --> 01:24:15,760
Speaker 1: local culture. So it was much more expensive to launch

1723
01:24:15,800 --> 01:24:19,240
Speaker 1: in Europe at the time that Spotify launched Europe. If

1724
01:24:19,240 --> 01:24:22,040
Speaker 1: you guys had a million paid subscribers Europe, maybe at

1725
01:24:22,040 --> 01:24:24,760
Speaker 1: a hundred thousand, it was tiny. So Spotify was able

1726
01:24:24,800 --> 01:24:26,479
Speaker 1: to go to the majors and get a much more

1727
01:24:26,520 --> 01:24:30,280
Speaker 1: flexible deal for their free to premium, for their freemu

1728
01:24:30,360 --> 01:24:31,960
Speaker 1: offer than we could never get in the US. We

1729
01:24:32,000 --> 01:24:34,200
Speaker 1: begged the labels for that deal. The best deal we

1730
01:24:34,200 --> 01:24:35,840
Speaker 1: can get from the U S labels was what we

1731
01:24:35,880 --> 01:24:38,840
Speaker 1: called rapswenty five, which was a non subscriber could listen

1732
01:24:38,880 --> 01:24:40,760
Speaker 1: to twenty five streams a month and then we have

1733
01:24:40,840 --> 01:24:43,160
Speaker 1: to pay. And that turned out maybe we were allows

1734
01:24:43,200 --> 01:24:45,519
Speaker 1: you marketers, but I think that was much harder to

1735
01:24:45,600 --> 01:24:48,719
Speaker 1: market than the than the than the Spotify pre product.

1736
01:24:48,920 --> 01:24:51,479
Speaker 1: So Spotify launched and got two critical mass in Europe

1737
01:24:51,960 --> 01:24:54,439
Speaker 1: with a free offer that was much much more robust

1738
01:24:54,680 --> 01:24:56,920
Speaker 1: than any free offer that that we were able to

1739
01:24:56,920 --> 01:24:59,080
Speaker 1: get the U S labels too right now. Then Spotify

1740
01:24:59,120 --> 01:25:01,519
Speaker 1: came to the US and Spotify already had the momentum

1741
01:25:01,520 --> 01:25:03,720
Speaker 1: of Europe and they basically said to the majors, we

1742
01:25:03,760 --> 01:25:05,720
Speaker 1: want to do the same deal in the US, and

1743
01:25:05,760 --> 01:25:07,679
Speaker 1: it took two years to get the deal done because

1744
01:25:07,720 --> 01:25:10,559
Speaker 1: the major's kept saying no. Finally, Daniel raised a hundred

1745
01:25:10,560 --> 01:25:13,160
Speaker 1: million dollars, basically paid it to the labels and he

1746
01:25:13,280 --> 01:25:17,080
Speaker 1: got a pretty similar form of that deal. Good for him,

1747
01:25:17,120 --> 01:25:20,440
Speaker 1: incredible for him. But that's a huge reason why Spotify

1748
01:25:20,560 --> 01:25:24,360
Speaker 1: became the king of the industry was because it had

1749
01:25:24,360 --> 01:25:26,559
Speaker 1: the bootstrap in Europe and by the time it got

1750
01:25:26,640 --> 01:25:30,760
Speaker 1: into the US, it had the financial structure to raise

1751
01:25:30,800 --> 01:25:33,559
Speaker 1: a hundred million dollars like a billion dollar valuation or something.

1752
01:25:33,760 --> 01:25:36,280
Speaker 1: So as tempers in the company ended up, obviously, they've

1753
01:25:36,280 --> 01:25:38,680
Speaker 1: they've created a lot of economic values since then. They

1754
01:25:38,720 --> 01:25:40,479
Speaker 1: don't they know the cash flow yet, but they have

1755
01:25:40,560 --> 01:25:44,959
Speaker 1: the economic value in terms of what happens next UH

1756
01:25:45,160 --> 01:25:47,240
Speaker 1: visa to be Spotify and the labels. I think the

1757
01:25:47,240 --> 01:25:50,479
Speaker 1: big question is what happens to the current day tont

1758
01:25:50,600 --> 01:25:53,439
Speaker 1: right Spotify, it's been widely reported. I have no proprietary

1759
01:25:53,439 --> 01:25:55,680
Speaker 1: information on this. Has contractually agreed they're not going to

1760
01:25:55,720 --> 01:25:58,960
Speaker 1: be a labeled right, so they they can only go

1761
01:25:59,080 --> 01:26:02,320
Speaker 1: so far. They can negotiate margins, try and get more profitability,

1762
01:26:02,360 --> 01:26:05,120
Speaker 1: but at the end of the day, their their biggest

1763
01:26:05,120 --> 01:26:08,000
Speaker 1: power as a distributor is to leverage that into being

1764
01:26:08,000 --> 01:26:12,000
Speaker 1: a label. And how that plays out is to me,

1765
01:26:12,160 --> 01:26:14,880
Speaker 1: the unwritten story of the next five years, which is

1766
01:26:14,920 --> 01:26:19,080
Speaker 1: real Universal and Sony and and and Warner, which are

1767
01:26:19,080 --> 01:26:21,439
Speaker 1: the three big labels and that control a huge amount

1768
01:26:21,439 --> 01:26:26,360
Speaker 1: of catalog end up getting um uh, I don't know

1769
01:26:26,360 --> 01:26:30,320
Speaker 1: what the right world would be uh squeezed by Spotify

1770
01:26:30,640 --> 01:26:33,600
Speaker 1: needing to have more control of repertoire. Now Spotify has

1771
01:26:33,600 --> 01:26:35,920
Speaker 1: tried to branch out video, trying to branch out of

1772
01:26:36,000 --> 01:26:39,439
Speaker 1: Jason areas, but in so far Spotify stays an independent company.

1773
01:26:39,760 --> 01:26:42,639
Speaker 1: They have to make a move like that somehow sometime

1774
01:26:42,680 --> 01:26:45,800
Speaker 1: in the next five years, uh, or they're ultimately their

1775
01:26:45,800 --> 01:26:48,280
Speaker 1: model is going to be compromised unless they sell to

1776
01:26:48,320 --> 01:26:50,880
Speaker 1: somebody else. That's a massive scale. So you know, there's

1777
01:26:50,920 --> 01:26:53,920
Speaker 1: all these things that haven't played out yet. If I

1778
01:26:53,960 --> 01:26:55,599
Speaker 1: knew the answer to that question, I know the answer

1779
01:26:55,640 --> 01:26:58,960
Speaker 1: to your question, which is what happens next? Right, you know,

1780
01:26:58,960 --> 01:27:01,880
Speaker 1: you've got Spotify have any million change whatever numbatory I

1781
01:27:01,920 --> 01:27:04,200
Speaker 1: told you yesterday, Apple at forty and change or whatever

1782
01:27:04,200 --> 01:27:06,960
Speaker 1: their lettuce number is, uh, and then you know everybody

1783
01:27:07,040 --> 01:27:09,080
Speaker 1: is much much lower than that. Amazon a sort of

1784
01:27:09,080 --> 01:27:10,800
Speaker 1: a wild card because they have a hundred million prime

1785
01:27:10,880 --> 01:27:14,639
Speaker 1: users and they've got a catalog, you know, mediocre product

1786
01:27:14,680 --> 01:27:16,800
Speaker 1: for them, and then they've got a premium product that

1787
01:27:16,840 --> 01:27:20,040
Speaker 1: has many, many fewer users, but it's it's very stratified.

1788
01:27:20,640 --> 01:27:22,639
Speaker 1: And if I were a major, I would view that

1789
01:27:22,880 --> 01:27:25,840
Speaker 1: as a very very dangerous, unstable place to be for

1790
01:27:25,880 --> 01:27:27,400
Speaker 1: the reason that I just described. So if I was

1791
01:27:27,439 --> 01:27:29,759
Speaker 1: a major, I'd be doing everything it could. It's ethnically

1792
01:27:29,840 --> 01:27:31,960
Speaker 1: legal to make sure there are three or four other

1793
01:27:32,040 --> 01:27:35,320
Speaker 1: scale competitors so that when Spotify comes to you for

1794
01:27:35,400 --> 01:27:37,800
Speaker 1: the next renewal cycle and wants to jam, you wants

1795
01:27:37,840 --> 01:27:39,160
Speaker 1: to get out of this, we can't be a labeled

1796
01:27:39,160 --> 01:27:42,920
Speaker 1: through whatever. You'd have the ability to walk away. But

1797
01:27:42,960 --> 01:27:45,200
Speaker 1: if there's a certain point, if Spotify is half of

1798
01:27:45,240 --> 01:27:48,400
Speaker 1: the major's revenue, that's going to be unbelievably hard for

1799
01:27:48,439 --> 01:27:50,160
Speaker 1: them to do. And we're getting to that point. I

1800
01:27:50,160 --> 01:27:52,320
Speaker 1: don't know what the math is today, but Spotify is

1801
01:27:52,320 --> 01:27:54,960
Speaker 1: obviously a majority there streaming revenue, and streaming is a

1802
01:27:55,000 --> 01:27:57,280
Speaker 1: majority of their revenue, so it doesn't take to more

1803
01:27:57,560 --> 01:27:59,960
Speaker 1: much for Spotify'd literally be a majority of their revenue

1804
01:28:00,000 --> 01:28:02,400
Speaker 1: the whole, in which case the labels, you know, the

1805
01:28:02,400 --> 01:28:04,559
Speaker 1: power Dinamic and shift. So if I were a label,

1806
01:28:04,840 --> 01:28:06,880
Speaker 1: I would be trying to encourage as many people as

1807
01:28:06,920 --> 01:28:09,880
Speaker 1: possible to create as much diversity as possible. We have

1808
01:28:09,960 --> 01:28:13,920
Speaker 1: that conversation we meaning Napster with Sony, with Warner, with Universal.

1809
01:28:14,280 --> 01:28:16,479
Speaker 1: They get it, but then there's a question of how

1810
01:28:16,520 --> 01:28:21,680
Speaker 1: you activate it. Okay, Uh, but I'm aware of your

1811
01:28:21,720 --> 01:28:26,320
Speaker 1: interest in what's happened and what happened in VSA V

1812
01:28:26,479 --> 01:28:31,120
Speaker 1: Russia and uh their involvement in our elections. Uh looking

1813
01:28:31,120 --> 01:28:35,920
Speaker 1: forward to what do you anticipate the things that uh

1814
01:28:36,200 --> 01:28:38,240
Speaker 1: we ought to be, you know, looking out for an

1815
01:28:38,280 --> 01:28:43,120
Speaker 1: anticipating vs V their chicanery. Uh well, I'll explain what

1816
01:28:43,160 --> 01:28:45,080
Speaker 1: you what you said for the post. So in the

1817
01:28:45,120 --> 01:28:48,080
Speaker 1: summer of sixteen, I started a website called putin Trump

1818
01:28:48,120 --> 01:28:50,320
Speaker 1: dot org um. And it's not because I have a

1819
01:28:50,360 --> 01:28:53,040
Speaker 1: crystal ball. Uh, It's because I was just pattern matching,

1820
01:28:53,040 --> 01:28:55,360
Speaker 1: which is one of the things that I do right,

1821
01:28:55,760 --> 01:28:57,280
Speaker 1: and it was clear to me that there was something

1822
01:28:57,320 --> 01:29:01,559
Speaker 1: really weird happening when the Republican convence and soften in

1823
01:29:01,600 --> 01:29:05,880
Speaker 1: their in their um, you know, in their uh, their platform,

1824
01:29:06,120 --> 01:29:11,040
Speaker 1: they softened their program to be anti Ukraine and pro Russia,

1825
01:29:11,360 --> 01:29:15,040
Speaker 1: whereas the Republican Party had been the hardercore ones, particularly

1826
01:29:15,080 --> 01:29:17,880
Speaker 1: when Mitt Romney is running the hardercore anti Russia party.

1827
01:29:17,880 --> 01:29:20,040
Speaker 1: So there was something weird going on there. There's all

1828
01:29:20,040 --> 01:29:22,639
Speaker 1: this stuff about uh, you know, all of the uh,

1829
01:29:23,120 --> 01:29:26,280
Speaker 1: the money laundering ish things that happened with high end

1830
01:29:26,320 --> 01:29:29,320
Speaker 1: real estate and Trump that has been reported on UH.

1831
01:29:29,360 --> 01:29:32,680
Speaker 1: And I didn't know anything about tapes of of of

1832
01:29:32,800 --> 01:29:34,800
Speaker 1: random things that happened in Moscow, any of that stuff.

1833
01:29:34,920 --> 01:29:36,800
Speaker 1: I didn't know anything about the I heard rumors about

1834
01:29:36,880 --> 01:29:40,080
Speaker 1: the what what ended up, uh you know, uh being

1835
01:29:40,120 --> 01:29:44,200
Speaker 1: the Steel Documents steal dossier in the fall of sixteen

1836
01:29:44,240 --> 01:29:46,160
Speaker 1: after we launched the website, but I didn't anything about

1837
01:29:46,160 --> 01:29:48,400
Speaker 1: it at the time we launched it. UM. But it

1838
01:29:48,439 --> 01:29:50,360
Speaker 1: was clear that there was something weird going on here,

1839
01:29:50,560 --> 01:29:51,960
Speaker 1: and I wanted to just shine a light on it.

1840
01:29:52,000 --> 01:29:54,720
Speaker 1: So we created a website. UH didn't put a huge

1841
01:29:54,720 --> 01:29:57,160
Speaker 1: amount of money behind it, but publicized it a bit,

1842
01:29:57,439 --> 01:29:59,200
Speaker 1: got some attention for it. But at the end of

1843
01:29:59,200 --> 01:30:02,479
Speaker 1: the day, UH, there were so many other massive trends

1844
01:30:02,680 --> 01:30:06,240
Speaker 1: that were driving the election of Trump over Hillary. Looking

1845
01:30:06,280 --> 01:30:09,320
Speaker 1: to do thousand eighteen, there's no question in my mind

1846
01:30:09,800 --> 01:30:13,360
Speaker 1: that there is going to be a lot of of

1847
01:30:13,360 --> 01:30:20,600
Speaker 1: of effort by the Russian government to influence the elections

1848
01:30:20,920 --> 01:30:24,879
Speaker 1: where they can. They're gonna have one disadvantage relative to sixteen,

1849
01:30:25,160 --> 01:30:28,040
Speaker 1: which is there's not you can't write the same national,

1850
01:30:28,600 --> 01:30:32,080
Speaker 1: national fake news and fake social media postings that you

1851
01:30:32,120 --> 01:30:35,400
Speaker 1: couldn't do thousand sixteen. A because hopefully the facebooks and

1852
01:30:35,400 --> 01:30:37,519
Speaker 1: the twitters of the World War vigilant and be because

1853
01:30:37,560 --> 01:30:40,320
Speaker 1: it won't be one national issue. What what wins that

1854
01:30:40,400 --> 01:30:43,800
Speaker 1: swing state congressional district in Ohio will be specific to

1855
01:30:43,800 --> 01:30:46,200
Speaker 1: two Ohio candidates. The most of us have ever heard

1856
01:30:46,200 --> 01:30:48,439
Speaker 1: of the same thing, So it'll be district by district.

1857
01:30:48,720 --> 01:30:52,440
Speaker 1: But I do think that who wins Congress in ten

1858
01:30:52,760 --> 01:30:55,439
Speaker 1: is the binary issue for the next two years. You know,

1859
01:30:55,520 --> 01:30:58,759
Speaker 1: if Democrats win control of the House representatives, they're gonna

1860
01:30:58,760 --> 01:31:03,879
Speaker 1: be robust UH hearings. UH there's gonna be a robust process,

1861
01:31:03,960 --> 01:31:08,400
Speaker 1: and I think it's highly likely than Trump will be impeached. Now,

1862
01:31:08,439 --> 01:31:12,240
Speaker 1: the Democrats are afraid to say that because they don't properly.

1863
01:31:12,320 --> 01:31:13,880
Speaker 1: I think they don't want to make it sound like

1864
01:31:13,920 --> 01:31:15,920
Speaker 1: they're not going to be data driven. I say that

1865
01:31:15,960 --> 01:31:18,559
Speaker 1: because I think that what the data would expose between

1866
01:31:18,600 --> 01:31:22,040
Speaker 1: the Muller axis UM and the uh, you know, the

1867
01:31:22,560 --> 01:31:25,280
Speaker 1: Michael Cohen sleazy stuff and who knows what else, is

1868
01:31:25,320 --> 01:31:28,639
Speaker 1: that Trump has committed a series of acts that are

1869
01:31:28,760 --> 01:31:31,680
Speaker 1: impeachable offenses. But we have we don't have all the

1870
01:31:31,720 --> 01:31:33,840
Speaker 1: data yet, we only we have certain things that are

1871
01:31:34,640 --> 01:31:36,920
Speaker 1: suggestive of that. But I think when we get the data,

1872
01:31:36,960 --> 01:31:39,560
Speaker 1: we'll see what happens. My personal belief is when we

1873
01:31:39,600 --> 01:31:42,080
Speaker 1: get the data, it will demonstrate the need to impeach him,

1874
01:31:42,280 --> 01:31:44,599
Speaker 1: and that that should then happen. Then there's no question

1875
01:31:44,600 --> 01:31:46,759
Speaker 1: would be what happens in the Senate and does Trump

1876
01:31:47,640 --> 01:31:50,599
Speaker 1: cave then or does he stay in fight. Different people

1877
01:31:50,600 --> 01:31:52,600
Speaker 1: of different views of that. I don't think it's a

1878
01:31:52,600 --> 01:31:56,599
Speaker 1: foreker conclusion either way. If you study seventy three seventy four, UM,

1879
01:31:56,760 --> 01:31:59,280
Speaker 1: you know it was the smoking gun tapes that ultimately

1880
01:31:59,360 --> 01:32:03,160
Speaker 1: cause Republic in the Senate to flip and made Nixon resigned. Whereas,

1881
01:32:03,200 --> 01:32:08,360
Speaker 1: of course in in in nine with the Lewinsky stuff, Uh,

1882
01:32:08,479 --> 01:32:11,160
Speaker 1: it was an idea stuff. It was clear that Clinton

1883
01:32:11,160 --> 01:32:15,960
Speaker 1: had perjured himself over a blowjob, which was wrong. I

1884
01:32:15,960 --> 01:32:18,240
Speaker 1: think you can have an interesting debate about whether that's

1885
01:32:18,240 --> 01:32:20,040
Speaker 1: an apeche of offense or not. But I don't. I

1886
01:32:20,040 --> 01:32:22,760
Speaker 1: think the Democrats didn't feel under political pressure to call

1887
01:32:22,800 --> 01:32:25,320
Speaker 1: it in a petual offense, and therefore he finished his

1888
01:32:25,400 --> 01:32:27,240
Speaker 1: termat So I don't know how it's going to go

1889
01:32:27,600 --> 01:32:29,720
Speaker 1: beyond that, but I think the eighteen election will be

1890
01:32:29,800 --> 01:32:32,559
Speaker 1: the uh, you know, the limit. If the Democrats don't

1891
01:32:32,560 --> 01:32:34,280
Speaker 1: win the House an eighteen, we'll just have two more

1892
01:32:34,360 --> 01:32:36,160
Speaker 1: years like the last two years, the less year and

1893
01:32:36,160 --> 01:32:40,479
Speaker 1: a half. Okay. I have another political minded question, and

1894
01:32:40,520 --> 01:32:45,040
Speaker 1: that's if there if the right has uh media and

1895
01:32:45,080 --> 01:32:47,120
Speaker 1: they have media scale with Fox News, and then they

1896
01:32:47,200 --> 01:32:49,800
Speaker 1: have the biggest you know, tweeter of all time that's

1897
01:32:49,840 --> 01:32:54,720
Speaker 1: really dominating the headlines. Based on your experience with Air America, Um,

1898
01:32:55,320 --> 01:32:58,400
Speaker 1: what can the left do to create a media engine,

1899
01:32:58,520 --> 01:33:03,040
Speaker 1: communications strategy move forward that can rival what the right

1900
01:33:03,200 --> 01:33:06,599
Speaker 1: is doing at scale and to communicate what the demos

1901
01:33:06,600 --> 01:33:10,120
Speaker 1: about or what the left represents. And then also how

1902
01:33:10,200 --> 01:33:13,080
Speaker 1: does that intersect with technology? Is it like what the

1903
01:33:13,160 --> 01:33:15,800
Speaker 1: crooked media guys are doing, but just at scale. And

1904
01:33:16,160 --> 01:33:17,720
Speaker 1: if you were to say I'm gonna I'm gonna launch

1905
01:33:17,720 --> 01:33:21,760
Speaker 1: you America for what would that company look like and

1906
01:33:22,000 --> 01:33:26,439
Speaker 1: what would that communication strategy be? Well, UM, I have

1907
01:33:26,600 --> 01:33:30,680
Speaker 1: thought about conceptually, but not operationally, what it would be. UM.

1908
01:33:31,080 --> 01:33:33,760
Speaker 1: I think it actually if you look at the fact

1909
01:33:33,840 --> 01:33:38,080
Speaker 1: that Trump's popularity is still below UM, I think it's

1910
01:33:38,120 --> 01:33:43,599
Speaker 1: all about marshaling electoral critical mass from people who want

1911
01:33:43,640 --> 01:33:45,560
Speaker 1: our country to work where it's supposed to work. So

1912
01:33:45,640 --> 01:33:47,880
Speaker 1: I don't think the right thing to do is to

1913
01:33:48,200 --> 01:33:55,400
Speaker 1: fight UM their boisterous UH propaganda and UH name calling

1914
01:33:55,640 --> 01:33:58,600
Speaker 1: and frequently lies with the same. I think the right

1915
01:33:58,640 --> 01:34:01,599
Speaker 1: thing to do is to appeal to anybody that thinks

1916
01:34:01,680 --> 01:34:05,760
Speaker 1: that our democracy is too important to allow truths to

1917
01:34:05,840 --> 01:34:09,400
Speaker 1: become a subjective thing, that our societal cohesion is too

1918
01:34:09,439 --> 01:34:14,000
Speaker 1: important to allow uh facts to be irrelevant. And if

1919
01:34:14,080 --> 01:34:16,160
Speaker 1: you just say, look, this is just about what the

1920
01:34:16,240 --> 01:34:19,680
Speaker 1: facts are, right, you know, climate change, there's there's an

1921
01:34:19,840 --> 01:34:24,040
Speaker 1: incoutabal scientific consensus around this. That's a fact. Um. The

1922
01:34:24,840 --> 01:34:27,160
Speaker 1: the data on what weather is happening. That's a fact.

1923
01:34:27,439 --> 01:34:29,720
Speaker 1: And yes, it's unfortunate for people in the abstract, in

1924
01:34:29,760 --> 01:34:32,559
Speaker 1: the extractive industries that those facts are there, but those

1925
01:34:32,560 --> 01:34:35,479
Speaker 1: are facts. So I think if we just literally um

1926
01:34:35,720 --> 01:34:41,000
Speaker 1: stick to the facts and being unyielding about raising those

1927
01:34:41,080 --> 01:34:43,439
Speaker 1: facts in the space, in the face of name calling,

1928
01:34:43,800 --> 01:34:45,920
Speaker 1: in the face of about hominem attacks, in the face

1929
01:34:45,960 --> 01:34:48,479
Speaker 1: of whatever else, I think that's the greatest chance to

1930
01:34:49,000 --> 01:34:54,720
Speaker 1: restore some balance to the political discourse and to the

1931
01:34:54,920 --> 01:34:57,840
Speaker 1: text and balances. I mean the big tragedy of TWI

1932
01:34:58,000 --> 01:35:01,040
Speaker 1: sixteen West and just Trump's election, but the fact that

1933
01:35:01,520 --> 01:35:03,360
Speaker 1: one of the one of the other branches of government's

1934
01:35:03,400 --> 01:35:06,720
Speaker 1: supposed to be a check and balance has completely abdicated

1935
01:35:06,760 --> 01:35:10,719
Speaker 1: that responsibility. And it's advocate the responsibility because the politics

1936
01:35:10,920 --> 01:35:13,800
Speaker 1: for the for Republicans are that they feel like if

1937
01:35:13,840 --> 01:35:16,400
Speaker 1: they don't, they'll get primary and they'll lose, and if

1938
01:35:16,439 --> 01:35:19,360
Speaker 1: they do, they'll get to keep playing ball. So that's

1939
01:35:19,479 --> 01:35:22,280
Speaker 1: it's unfortunate that that that is the dynamic we have.

1940
01:35:22,840 --> 01:35:25,519
Speaker 1: But the only solution to that dynamic is to is

1941
01:35:25,560 --> 01:35:29,280
Speaker 1: to not have them being the have the levels of power.

1942
01:35:29,600 --> 01:35:31,519
Speaker 1: And to do that we've got to persuade a lot

1943
01:35:31,560 --> 01:35:34,000
Speaker 1: of independence that that change has to happen. And if

1944
01:35:34,040 --> 01:35:46,160
Speaker 1: we do that, then I think that will build on itself. Okay, rights,

1945
01:35:46,200 --> 01:35:51,840
Speaker 1: The question was about music and VR. Um. I am

1946
01:35:53,240 --> 01:35:58,920
Speaker 1: mediu embarrassed on VR because the ergonomics of using a

1947
01:35:59,040 --> 01:36:03,960
Speaker 1: VR headset and an experience are not Uh. It's so

1948
01:36:04,160 --> 01:36:08,040
Speaker 1: immersive that it is not doesn't fit in a wide

1949
01:36:08,120 --> 01:36:11,160
Speaker 1: enough swatch of life. Everyone got excited about three D

1950
01:36:11,800 --> 01:36:13,880
Speaker 1: because you could go to watch James Cameron movie in

1951
01:36:13,880 --> 01:36:16,920
Speaker 1: a movie theater, but that's an intended immersive experience where

1952
01:36:16,960 --> 01:36:19,799
Speaker 1: for two hours you're focusing on you're going to a location,

1953
01:36:19,880 --> 01:36:24,080
Speaker 1: you're watching it home. Three D didn't work because people

1954
01:36:24,160 --> 01:36:26,519
Speaker 1: don't watch glass put glasses on to watch movies at

1955
01:36:26,560 --> 01:36:30,519
Speaker 1: home because they're watching in such a range of different ways. So,

1956
01:36:31,160 --> 01:36:33,200
Speaker 1: you know, and lets until you have the equivalent of

1957
01:36:33,320 --> 01:36:36,880
Speaker 1: direct view VR. Uh, I think it's going to be

1958
01:36:37,000 --> 01:36:40,640
Speaker 1: a more of a niche thing, uh than than people realize.

1959
01:36:40,680 --> 01:36:44,519
Speaker 1: So do I think that? Um? Uh? There are ways

1960
01:36:44,560 --> 01:36:49,840
Speaker 1: to do much more immersive experiences with live music shows. Absolutely,

1961
01:36:49,920 --> 01:36:52,080
Speaker 1: and you know there's a there's there's lots of different

1962
01:36:52,479 --> 01:36:55,080
Speaker 1: uh techniques for doing that. Do I think people are

1963
01:36:55,080 --> 01:36:58,760
Speaker 1: gonna put VR headsets on to watch concerts. It makes

1964
01:36:58,800 --> 01:37:01,720
Speaker 1: for a great demo. But know, the headset fatigue thing

1965
01:37:01,800 --> 01:37:05,640
Speaker 1: is a real phenomenon, and uh, you know, the the

1966
01:37:05,840 --> 01:37:09,879
Speaker 1: ergonomics of using these devices have to be uh compelling.

1967
01:37:10,520 --> 01:37:13,639
Speaker 1: Uh and uh you know I I one of one

1968
01:37:13,680 --> 01:37:16,639
Speaker 1: of the Angel investments that I've done that I'm proudest

1969
01:37:16,800 --> 01:37:18,639
Speaker 1: in the last couple of years is a company called

1970
01:37:18,680 --> 01:37:22,600
Speaker 1: Control Labs, which Stephen Lee wrote the nice write up

1971
01:37:22,640 --> 01:37:24,960
Speaker 1: of in Wired magazine to bringian kind of Thomas Weird,

1972
01:37:25,000 --> 01:37:27,200
Speaker 1: who I've known for for many years, and his his

1973
01:37:27,400 --> 01:37:33,080
Speaker 1: his band of of neurobiologists, and they've created a technology

1974
01:37:33,240 --> 01:37:36,040
Speaker 1: letch of an arm band that you basically think of

1975
01:37:36,080 --> 01:37:39,080
Speaker 1: it as like an Apple Watch or a you know,

1976
01:37:39,280 --> 01:37:41,760
Speaker 1: a next generation fitbit where the Apple where the band

1977
01:37:41,840 --> 01:37:46,880
Speaker 1: can can literally detect individual electron signals, uh, neurological signals

1978
01:37:47,200 --> 01:37:49,800
Speaker 1: from your brain to your fingers, so you it can

1979
01:37:49,880 --> 01:37:52,360
Speaker 1: tell what you're doing here with your individual fingers with

1980
01:37:52,680 --> 01:37:55,479
Speaker 1: a band that sits under wrist something like that, where

1981
01:37:55,520 --> 01:37:57,360
Speaker 1: the technology is not in base, you don't have to

1982
01:37:57,400 --> 01:37:59,360
Speaker 1: wear a big data glove. I think it's going a

1983
01:37:59,439 --> 01:38:02,400
Speaker 1: huge import for the input side of all this this

1984
01:38:02,640 --> 01:38:04,720
Speaker 1: a r VR staff and we need the equivalent of

1985
01:38:04,720 --> 01:38:06,559
Speaker 1: the output side of that where you can get an

1986
01:38:06,560 --> 01:38:09,840
Speaker 1: immersive experience without having to wear an immersive headset. And

1987
01:38:09,960 --> 01:38:12,920
Speaker 1: I think I think the a R plus path is

1988
01:38:12,960 --> 01:38:14,920
Speaker 1: probably a better path for achieving that in my view

1989
01:38:15,280 --> 01:38:17,960
Speaker 1: than the than the immersive headset experience, which make for

1990
01:38:18,000 --> 01:38:21,120
Speaker 1: incredible demos, but I don't think people really want to

1991
01:38:21,160 --> 01:38:24,240
Speaker 1: be in those. Thank gear for that long. Okay, let

1992
01:38:24,280 --> 01:38:25,840
Speaker 1: that be a lesson. You don't have to be limited

1993
01:38:25,880 --> 01:38:29,959
Speaker 1: to one vertical Rob as we've owned the Pro Bowlers

1994
01:38:30,040 --> 01:38:34,640
Speaker 1: tour is involved in music, technology and politics. Thanks so

1995
01:38:34,800 --> 01:38:37,439
Speaker 1: much for being here and sharing your ideas of the audiences.

1996
01:38:43,600 --> 01:38:46,080
Speaker 1: That wraps up this week's episode of the Bob left

1997
01:38:46,080 --> 01:38:49,080
Speaker 1: Sets podcast, recorded live at the Music Media Summit in

1998
01:38:49,120 --> 01:38:52,840
Speaker 1: Santa Barbara, California and produced by tune In. I hope

1999
01:38:52,880 --> 01:38:55,760
Speaker 1: you enjoyed this conversation with CEO of Real Networks, Rob

2000
01:38:55,800 --> 01:38:58,800
Speaker 1: Glazer and his interactions with Asia and his history with

2001
01:38:58,920 --> 01:39:01,800
Speaker 1: Bill Gates and micro us off. What did you think

2002
01:39:01,840 --> 01:39:04,400
Speaker 1: of the interview? Emailed me at Bob at left Sex

2003
01:39:04,479 --> 01:39:13,080
Speaker 1: dot com. Until next time, I'm Bob left Sex. Can

2004
01:39:13,479 --> 01:39:22,040
Speaker 1: think of and me reas don't know exactly why must

2005
01:39:22,200 --> 01:39:25,000
Speaker 1: be its out of theseas