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Predictable: Legends - David “Scottilicious” Bensoussan · Predictable with Stu Burguiere

• 0:00
  Welcome
• 0:01
  to Predictable with Stu.
• 0:02
  We've got a very special episode planned for you today.
• 0:05
  I wanna tell you first though about our subscription.
• 0:07
  We just launched this yesterday.
• 0:09
  Uh,
• 0:09
  PredictableShow.com.
• 0:10
  It's a paid tier of our subscription,
• 0:13
  uh,
• 0:13
  that,
• 0:13
  uh,
• 0:13
  it's gonna have all sorts of additional information,
• 0:15
  deeper dives,
• 0:16
  all sorts of content that we can have together,
• 0:18
  I'll show you where,
• 0:19
  you know,
• 0:19
  what I'm trading,
• 0:20
  what I'm doing.
• 0:21
  In the markets and uh we'll learn from each other.
• 0:23
  It's a really cool community.
• 0:24
  I'd love for you to join it.
• 0:25
  It's at predictableshow.com.
• 0:28
  Big show today.
• 0:29
  Joining us in just a couple of minutes,
• 0:31
  David Ben Susan.
• 0:32
  He's also known as Scottylicious.
• 0:34
  He is a legend of prediction.
• 0:36
  Market trading.
• 0:37
  He's uh over $1.5 million
• 0:40
  on polymarket in profits.
• 0:42
  Look,
• 0:42
  we promised you expert analysis on prediction markets.
• 0:45
  It doesn't get any more expert than this.
• 0:47
  Uh,
• 0:47
  so don't go anywhere.
• 0:49
  Join us
• 0:50
  at predictableshow.com.
• 1:07
  Welcome to Predictable with Stu.
• 1:09
  Today we are kicking off a brand new series highlighting some of the biggest names,
• 1:14
  the most successful traders and investors in the prediction market space.
• 1:19
  If you're serious about getting into prediction markets yourself,
• 1:22
  or even just kind of fascinated by the industry,
• 1:24
  wanna learn more about it,
• 1:25
  how it works,
• 1:25
  who's uh in these,
• 1:27
  in these spaces,
• 1:27
  and who's the one,
• 1:28
  who are the people that are really leading it,
• 1:30
  you don't wanna miss these conversations.
• 1:32
  We're calling it Predictable
• 1:34
  Legends.
• 1:43
  Joining me now for our inaugural edition
• 1:45
  of Predictable Legends is David Ben Soussan.
• 1:48
  He's better known in the prediction market space as Scottylicious.
• 1:52
  He's a prediction market trader for Team Scottylicious,
• 1:55
  and,
• 1:55
  uh,
• 1:55
  you can follow him on X under the handle
• 1:58
  at Scott on Polly.
• 2:00
  Be sure to follow him there.
• 2:01
  David,
• 2:02
  really appreciate you taking the time.
• 2:03
  Thank you so much for joining us.
• 2:06
  My pleasure,
• 2:06
  Stu.
• 2:07
  Thank you so much for having me.
• 2:08
  Yeah,
• 2:08
  it's really great to talk to you.
• 2:10
  Let me,
• 2:10
  let me start you with a very,
• 2:11
  very basic question cause I was,
• 2:13
  I was,
• 2:13
  I was going through the research and doing this.
• 2:15
  I don't know the answer to this question.
• 2:17
  Uh,
• 2:17
  I asked AI,
• 2:19
  uh,
• 2:19
  it did not have a good answer.
• 2:20
  So your name is David Ben Susan.
• 2:22
  Everyone knows you as Scottylicious,
• 2:24
  uh,
• 2:24
  at least if you're,
• 2:25
  uh,
• 2:25
  in the prediction market space.
• 2:27
  Uh,
• 2:28
  AI was convinced that the reason for this is because you're,
• 2:31
  it was a play on your name,
• 2:32
  which was Scott,
• 2:34
  and you were naming it after Bootylicious,
• 2:36
  uh,
• 2:36
  the song.
• 2:37
  I don't know if that's accurate.
• 2:39
  I,
• 2:39
  I'm pretty sure it may have hallucinated that.
• 2:42
  Why is the name Scottylicious?
• 2:43
  Where did it come from?
• 2:45
  OK,
• 2:45
  so
• 2:46
  AI is wrong.
• 2:48
  Um,
• 2:50
  do you have,
• 2:51
  do you have an assistant
• 2:53
  just out of curiosity,
• 2:54
  I mean,
• 2:54
  you do.
• 2:54
  I,
• 2:54
  I think you have a few,
• 2:55
  right?
• 2:56
  I have thousands of minions,
• 2:58
  uh,
• 2:58
  David,
• 2:59
  all over the place.
• 3:01
  So,
• 3:02
  um,
• 3:03
  I don't know
• 3:05
  of.
• 3:06
  Who
• 3:08
  will have their overseas family invest in your prediction market business
• 3:12
  and clip your nose hairs before an interview with Stoop.
• 3:17
  Her name is Scotty,
• 3:18
  and she's,
• 3:20
  uh,
• 3:20
  scot terrific.
• 3:21
  She's Scott-licious.
• 3:23
  She's
• 3:24
  everything.
• 3:24
  I don't know how I ended up taking her name and using it for polymarket,
• 3:28
  but it probably had to do with the structure of the business.
• 3:30
  And um you are the first
• 3:33
  uh in the media to now know where the name Scottish just came from.
• 3:37
  We're breaking news just seconds into the interview.
• 3:39
  I love this.
• 3:40
  Uh,
• 3:40
  so there you go.
• 3:41
  wow,
• 3:41
  that's,
• 3:41
  that's a fascinating.
• 3:42
  It's a fascinating story,
• 3:44
  uh,
• 3:44
  because it's so,
• 3:44
  I,
• 3:45
  I did actually initially when I saw you thought your name was Scott,
• 3:48
  and I was like,
• 3:48
  oh wait a minute,
• 3:48
  this is David.
• 3:50
  Uh,
• 3:50
  he is a,
• 3:50
  a,
• 3:51
  a massive trade.
• 3:51
  If you don't know David,
• 3:52
  uh,
• 3:53
  from,
• 3:53
  uh,
• 3:53
  his,
• 3:54
  uh,
• 3:54
  trades on polymarket,
• 3:55
  I,
• 3:55
  I can't wait for you to hear the stories behind them because
• 3:59
  You know,
• 4:00
  I think there's some people out there who are doing this in a way,
• 4:03
  uh,
• 4:03
  where
• 4:04
  they're running numbers,
• 4:05
  they've got bots out there trading for them,
• 4:07
  they're taking slivers of pennies and moving them
• 4:10
  back and forth over and over again.
• 4:12
  That's not the way that you've done this.
• 4:15
  You have had a massive amount of success
• 4:17
  as you've gone through the prediction market space.
• 4:19
  Can you kind of take us back to the beginning?
• 4:21
  How did you even come,
• 4:22
  how did you fall into this world,
• 4:24
  David?
• 4:25
  OK,
• 4:26
  that's a great question,
• 4:27
  Stu.
• 4:27
  Um,
• 4:29
  so I was
• 4:31
  I identified
• 4:33
  Bitcoin really early,
• 4:34
  I think in 2011,
• 4:36
  um,
• 4:37
  as an investment,
• 4:38
  and somehow through
• 4:40
  crypto,
• 4:41
  I got to know of polymarket's existence back in 2020.
• 4:45
  And um,
• 4:47
  There was a lot of easy money to make about 10% in two weeks,
• 4:51
  2 or 3 weeks.
• 4:53
  Some people apparently thought that
• 4:55
  after Joe Biden had already been inaugurated,
• 4:58
  Trump was somehow gonna come back.
• 5:01
  Um,
• 5:01
  I saw that that wasn't gonna happen,
• 5:03
  and it's not every day that you get a near certain um
• 5:07
  possibility of making 10% in a month.
• 5:09
  So,
• 5:11
  I made a quick $40,000 or so around that,
• 5:15
  and then I thought,
• 5:15
  well,
• 5:16
  you know what,
• 5:16
  is this gambling?
• 5:17
  Is this not gambling?
• 5:18
  I don't really know,
• 5:18
  and I was busy with other pursuits,
• 5:20
  so I left it alone
• 5:22
  until um
• 5:23
  until I found myself in uh France taking care of my dad in 2023,
• 5:29
  24,
• 5:30
  205,
• 5:30
  2023,
• 5:31
  2024.
• 5:32
  And um I wasn't able to do my usual business,
• 5:36
  and
• 5:36
  this was,
• 5:37
  you know,
• 5:38
  very easy to do from anywhere
• 5:40
  and um
• 5:41
  just started having a lot of success starting with the Mexican election,
• 5:45
  which,
• 5:45
  which was very obviously
• 5:47
  gonna go to Shine Bomb,
• 5:48
  made a quick 8% in two weeks on that.
• 5:51
  And then I convinced myself that it wasn't gambling and
• 5:54
  So far it's not.
• 5:55
  So hopefully that continues to be the case and
• 5:59
  keep having successes.
• 6:00
  Yeah,
• 6:00
  we,
• 6:01
  we should talk about that maybe a little bit later,
• 6:02
  but I always think to myself,
• 6:03
  my answer to that every time someone says the gambling thing is like,
• 6:05
  I don't know,
• 6:06
  whenever I gamble,
• 6:06
  I'm gonna lose.
• 6:07
  I'm constantly losing whenever I try to gamble.
• 6:10
  That's not what happens with these,
• 6:12
  so it's,
• 6:12
  it's got to be different in some way.
• 6:13
  Um,
• 6:14
  you know,
• 6:15
  there's one thing of like dabbling,
• 6:16
  as you said you did in 2020.
• 6:18
  By the way,
• 6:18
  that was like the glory days,
• 6:20
  uh,
• 6:20
  that,
• 6:21
  that was that period where there was just a lot of like,
• 6:24
  you know,
• 6:25
  unfortunate rumors and speculation that somehow Donald Trump
• 6:27
  was gonna resume the office in like April.
• 6:31
  Like there's no constitutional path for that to even
• 6:34
  happen,
• 6:34
  even if you wanted it to happen.
• 6:36
  Uh,
• 6:36
  that was,
• 6:36
  that was a fun time.
• 6:37
  I,
• 6:37
  I,
• 6:37
  I like that as well.
• 6:39
  But like that felt,
• 6:40
  you know,
• 6:40
  that was a unique opportunity,
• 6:42
  um.
• 6:43
  Looking at it from that perspective and thinking,
• 6:45
  OK,
• 6:45
  well,
• 6:46
  this is something I can dabble in,
• 6:48
  as opposed to this can be a career,
• 6:49
  this can be,
• 6:50
  you know,
• 6:50
  the main focus of my time as far as,
• 6:54
  as,
• 6:54
  as far as work goes.
• 6:56
  How did you make that transition?
• 7:00
  Uh,
• 7:00
  the transition was just through
• 7:04
  repeated gains and,
• 7:05
  and,
• 7:05
  um,
• 7:06
  so after the Mexican election,
• 7:09
  a couple of months after that,
• 7:11
  uh,
• 7:11
  there were rumors that Joe Biden was going to resign the presidency,
• 7:16
  and,
• 7:16
  um,
• 7:17
  logically speaking,
• 7:18
  this is a man who fought tooth and nail
• 7:22
  to not,
• 7:23
  uh,
• 7:23
  give up the candidacy,
• 7:25
  right?
• 7:25
  So there's a big difference between the candidacy and the presidency.
• 7:29
  And I think I made 15% in two weeks,
• 7:32
  uh,
• 7:33
  saying no to that.
• 7:34
  So just the idea that you would have,
• 7:36
  you know,
• 7:37
  these no-brainer opportunities
• 7:40
  where you could make those kinds of returns so rapidly,
• 7:44
  uh,
• 7:44
  I was also able to predict that Kamala
• 7:46
  Harris was going to be the Democratic candidate,
• 7:48
  which,
• 7:48
  which was,
• 7:49
  uh,
• 7:50
  a lot of that and a lot of the insight on that analysis.
• 7:54
  Um,
• 7:55
  came from watching various news sources,
• 7:57
  but one of the,
• 7:59
  it's,
• 7:59
  it's,
• 8:00
  it's hard to,
• 8:01
  you know,
• 8:01
  it's hard to tell how the brain works,
• 8:02
  right?
• 8:03
  Do you make decisions and then seek out news to support it,
• 8:06
  or do you grab your news sources and figure out which
• 8:10
  ones make the most sense and then make a decision?
• 8:12
  In this case,
• 8:13
  I think I had already made
• 8:15
  the prediction that um Kamala Harris was gonna be the nominee,
• 8:19
  but then you saw guys like James Carville,
• 8:21
  for instance,
• 8:22
  um,
• 8:23
  On the Democratic side,
• 8:24
  really
• 8:25
  insisting that Joe Biden had to drop out.
• 8:28
  And once,
• 8:29
  you know,
• 8:29
  you,
• 8:29
  if,
• 8:29
  if you're familiar with James Carville,
• 8:31
  he was a main advisor to Bill Clinton,
• 8:34
  uh,
• 8:34
  former Marine Corps guy,
• 8:36
  centrist Democrat,
• 8:37
  very establishment.
• 8:39
  And once guys like that started saying it,
• 8:41
  it became,
• 8:42
  the writing was kind of on the wall to me.
• 8:44
  And um I went to
• 8:47
  um
• 8:48
  I went to grad school for political science.
• 8:51
  I did all the coursework for a PhD even though I dropped out with a master's.
• 8:54
  And so I have a lot of
• 8:56
  background political knowledge that's really helpful for a
• 8:58
  lot of these geopolitical and and other events.
• 9:01
  And uh
• 9:02
  also kind of an entrepreneurial
• 9:04
  entrepreneurial mindset,
• 9:05
  which is probably why I dropped out of grad school in the first place.
• 9:08
  Um,
• 9:09
  and I think it's,
• 9:10
  it's,
• 9:10
  you have a lot of very knowledgeable people who know a lot about politics,
• 9:14
  and you have a lot of entrepreneurs who don't know anything about politics,
• 9:17
  but
• 9:18
  I think I'm lucky that I had both of those characteristics,
• 9:21
  and,
• 9:21
  and I finally found a way to capitalize on all the political knowledge.
• 9:25
  I never thought it would happen,
• 9:26
  but
• 9:27
  I know most people,
• 9:28
  most people go and they,
• 9:29
  they do that and then they wind up,
• 9:31
  you know,
• 9:31
  owning a restaurant or something.
• 9:33
  It's good that you were able to take advantage of this,
• 9:35
  uh,
• 9:35
  education in,
• 9:36
  in that way.
• 9:36
  And I think like your,
• 9:38
  what's fascinates me about your makeup,
• 9:40
  uh,
• 9:41
  when we're talking about trading.
• 9:43
  Is
• 9:43
  in a way,
• 9:44
  prediction markets are really good at giving an outline as to what
• 9:47
  might happen because it's combining and it's the wisdom of crowds,
• 9:50
  right?
• 9:50
  It's combining
• 9:51
  lots of people,
• 9:53
  different,
• 9:53
  you know,
• 9:54
  with different knowledge,
• 9:55
  different knowledge base,
• 9:56
  and,
• 9:56
  and that all coming together in one number.
• 9:59
  Your approach here,
• 10:00
  you,
• 10:00
  you,
• 10:00
  cause you kinda have,
• 10:01
  you have a base
• 10:03
  in a,
• 10:03
  in a,
• 10:03
  in a financial investment world.
• 10:04
  You talked about some of your crypto,
• 10:06
  uh,
• 10:06
  stuff.
• 10:07
  You have that base in politics.
• 10:09
  And you seem to have a,
• 10:10
  a really just good sense at seeing,
• 10:13
  uh,
• 10:13
  individuals incentives,
• 10:14
  right?
• 10:15
  Like the Biden thing,
• 10:16
  you can't look at a,
• 10:17
  a,
• 10:17
  a stat sheet,
• 10:18
  you can't,
• 10:19
  you can't look at,
• 10:20
  you know,
• 10:20
  past voting patterns and figure out whether Joe Biden's gonna resign or not.
• 10:24
  But it's just a way of like taking
• 10:25
  all of that different knowledge from all those disparate sources
• 10:28
  that,
• 10:29
  you know,
• 10:29
  you've been able to acquire over your life and,
• 10:30
  and kind of combining that together in the
• 10:32
  same way that maybe a prediction market does.
• 10:35
  I is that kind of what you do,
• 10:36
  or,
• 10:36
  or is there a deeper sort of,
• 10:38
  are you diving into all sorts of,
• 10:40
  you know,
• 10:40
  computer,
• 10:41
  you know,
• 10:41
  models to try to figure this stuff out?
• 10:45
  I don't use,
• 10:45
  no,
• 10:46
  you,
• 10:46
  I,
• 10:46
  I think you have a good
• 10:48
  take on it,
• 10:48
  Stu.
• 10:48
  I don't really use computer models,
• 10:51
  um.
• 10:52
  When I was in junior high school,
• 10:54
  I would.
• 10:56
  Read college-level psychology textbooks.
• 10:59
  I've always kind of had an interest in how human beings behave and,
• 11:02
  and how the human mind works.
• 11:04
  And in um
• 11:06
  a recurring theme is these charismatic personalities.
• 11:10
  Uh,
• 11:11
  I mean,
• 11:11
  you saw that with,
• 11:13
  I made,
• 11:13
  I did really well with um
• 11:16
  Graham Plattner getting the Democratic nomination in Maine.
• 11:19
  That was a hairy one
• 11:21
  because,
• 11:21
  you know,
• 11:22
  I,
• 11:22
  I could have
• 11:23
  easily lost it.
• 11:25
  But the guy has charisma,
• 11:26
  you know,
• 11:27
  whether you agree,
• 11:27
  disagree with his platform,
• 11:29
  what have you,
• 11:30
  there's no,
• 11:31
  no,
• 11:31
  uh,
• 11:32
  debating that the guy has charisma.
• 11:34
  And
• 11:35
  there was also an instance where,
• 11:37
  um,
• 11:38
  There was the Kanye coin um uh prediction
• 11:42
  whether he was gonna come out really quickly with a,
• 11:45
  with a coin.
• 11:47
  And uh one of my favorite
• 11:50
  traders
• 11:51
  uh by the name of A News
• 11:53
  made,
• 11:54
  he,
• 11:54
  he basically wrote down what I was thinking in,
• 11:57
  in one of the um
• 11:59
  chat room Discords that we,
• 12:00
  that we follow.
• 12:02
  And he said,
• 12:03
  uh,
• 12:05
  What did he say exactly?
• 12:07
  You know what,
• 12:07
  I don't,
• 12:07
  I don't even remember what he said,
• 12:08
  but
• 12:09
  it,
• 12:10
  it
• 12:11
  mirrored what I said,
• 12:12
  and
• 12:13
  he's,
• 12:13
  he's,
• 12:14
  he's a friend,
• 12:14
  so I'm not saying this in any way to disparage him,
• 12:17
  but
• 12:18
  the bet,
• 12:19
  the,
• 12:19
  the prediction was very,
• 12:20
  very volatile.
• 12:21
  It was all over the place.
• 12:22
  I think I might have bought in like around 60 cents,
• 12:25
  and then it went all the way down to 20 cents,
• 12:27
  and then it goes back up to 50 cents and then back down to 25.
• 12:30
  And I think
• 12:31
  by virtue of my age,
• 12:32
  I'm in my,
• 12:33
  you know,
• 12:33
  mid,
• 12:34
  mid to late 40s,
• 12:35
  uh,
• 12:36
  I've invested in across different asset categories.
• 12:39
  And when you have conviction,
• 12:41
  you,
• 12:41
  you,
• 12:41
  you know,
• 12:41
  unless,
• 12:42
  unless the,
• 12:43
  the,
• 12:43
  um,
• 12:45
  news and the circumstances on the ground change,
• 12:48
  you keep your conviction.
• 12:49
  If new data comes out,
• 12:51
  then,
• 12:51
  you know,
• 12:52
  you can,
• 12:52
  you can assess things and,
• 12:54
  and,
• 12:54
  um,
• 12:55
  change your,
• 12:55
  your prediction,
• 12:56
  but
• 12:57
  In this case,
• 12:58
  um,
• 12:59
  no new data had come out.
• 13:00
  It was just a price battle and the price was all over the place,
• 13:03
  and I thought,
• 13:03
  well,
• 13:03
  there's no new news,
• 13:04
  why would I change my prediction?
• 13:06
  And I know
• 13:08
  I've dealt with a lot of eccentric people in my life,
• 13:10
  uh.
• 13:12
  I have a line of business where
• 13:14
  um I help uh nonprofit organizations that that uh
• 13:20
  uh help out homeless people,
• 13:22
  formerly incarcerated folks,
• 13:23
  um,
• 13:24
  find properties to rent out.
• 13:26
  And so I've come across all sorts of,
• 13:28
  I mean,
• 13:28
  you have,
• 13:29
  you know,
• 13:30
  If you're working for a nonprofit,
• 13:32
  dealing with very difficult people all day,
• 13:35
  there's a chance you might have a slightly eccentric personality and,
• 13:38
  and
• 13:38
  And that's fine.
• 13:39
  There's a lot of people with eccentric personalities in the business world too,
• 13:42
  and I've dealt with a lot of them,
• 13:43
  and so I kind of assessed Kanye's personality,
• 13:46
  and I thought,
• 13:47
  you know,
• 13:48
  he seems to me kind of indecisive.
• 13:50
  This doesn't seem like a guy who's gonna
• 13:51
  make an immediate decision about something like this,
• 13:54
  given also given all the potential regulations,
• 13:56
  and he's kind of a paranoid dude,
• 13:58
  like if you look at some of his postings,
• 14:00
  they're not exactly happy go lucky.
• 14:02
  And so,
• 14:03
  yeah,
• 14:04
  I mean,
• 14:04
  assessing,
• 14:05
  assessing human behavior,
• 14:07
  and
• 14:07
  it's hard to quantify.
• 14:08
  I mean,
• 14:09
  what are you gonna do?
• 14:09
  You're gonna look up,
• 14:10
  you know,
• 14:12
  100 studies on how bipolar people behave.
• 14:15
  No,
• 14:15
  you just have your life experience and you kinda
• 14:18
  catch the vibe and,
• 14:19
  and oftentimes it's,
• 14:21
  it's correct.
• 14:23
  Um,
• 14:23
  let me,
• 14:24
  uh,
• 14:24
  drill down on one trade that you made that I think highlights this a little bit.
• 14:28
  Uh,
• 14:30
  it's about knowing
• 14:31
  the person and not thinking about it necessarily from your perspective,
• 14:34
  but thinking about it from their perspective,
• 14:35
  like how are they considering,
• 14:37
  uh,
• 14:37
  the situation and what's important to them.
• 14:40
  I think it's your,
• 14:41
  at least what I was looking at,
• 14:42
  the,
• 14:42
  the biggest trade you've ever made,
• 14:44
  which was at least the most money on the line,
• 14:47
  which was you,
• 14:48
  you had over $1.4 million.
• 14:53
  Onto whether
• 14:54
  Vladimir Zelensky
• 14:56
  would wear a suit.
• 14:58
  And I looked at that and I thought I would be,
• 15:01
  every time this guy appeared in public,
• 15:03
  I would be terrified
• 15:04
  to see
• 15:05
  if he walks out there that one time,
• 15:08
  uh,
• 15:08
  as he's he's straightening a tie.
• 15:10
  Um,
• 15:11
  what made you decide to go so big into that market and did you feel that stress?
• 15:16
  Did you feel that risk?
• 15:19
  So,
• 15:20
  I'll,
• 15:21
  I'll share
• 15:22
  with you a little bit of the technique.
• 15:24
  Um,
• 15:25
  the first million dollars
• 15:27
  in profits,
• 15:28
  I took on an entrepreneurial mindset,
• 15:31
  and I thought,
• 15:31
  you know what,
• 15:32
  I'm gonna make a million dollars doing this,
• 15:34
  and I've been right,
• 15:35
  and I'm probably gonna continue to be right.
• 15:37
  And if I end up going back to zero,
• 15:38
  you know what,
• 15:39
  at least I tried,
• 15:39
  and,
• 15:40
  and,
• 15:40
  uh,
• 15:41
  so I didn't have,
• 15:42
  uh,
• 15:43
  uh,
• 15:44
  the,
• 15:44
  my risk parameters were not what they are now.
• 15:47
  Uh,
• 15:48
  what happened with the Zelensky suit thing was.
• 15:53
  There had been,
• 15:54
  if I,
• 15:54
  if I remember correctly,
• 15:55
  there had been a prior so market,
• 15:58
  and there was this battle on OMA.
• 16:00
  OMA is the regulatory mechanism for polymarket when there's a dispute.
• 16:04
  And
• 16:05
  I was,
• 16:06
  I had analyzed these personalities on OMA
• 16:08
  and their arguments,
• 16:10
  and I was very,
• 16:12
  very confident
• 16:14
  in how
• 16:15
  that market was going to pan out,
• 16:17
  uh,
• 16:18
  you know,
• 16:19
  you have,
• 16:19
  you have.
• 16:21
  A lot of,
• 16:21
  unfortunately,
• 16:22
  human nature is such that there's a lot of followers.
• 16:24
  There aren't that many leaders,
• 16:26
  and there are,
• 16:27
  and this applies also to prediction markets.
• 16:29
  So,
• 16:29
  you have some people
• 16:31
  on the UMA resolution mechanism
• 16:33
  that people almost follow blindly,
• 16:36
  and,
• 16:36
  and once they make a certain determination,
• 16:38
  the people will follow.
• 16:40
  So,
• 16:41
  Uh,
• 16:41
  I think I had a really good reading on that,
• 16:44
  and,
• 16:44
  and it wasn't,
• 16:45
  you know,
• 16:45
  it wasn't just a luck thing that,
• 16:47
  oh,
• 16:47
  you know,
• 16:48
  this is a suit or not a suit,
• 16:49
  uh,
• 16:50
  although there was a little bit of analysis on,
• 16:52
  on,
• 16:52
  you know,
• 16:53
  whether it was a suit,
• 16:54
  but it was mainly a reading of the dispute
• 16:56
  resolution mechanism.
• 16:57
  And I'll tell you,
• 16:58
  Stu,
• 16:59
  for a long time,
• 17:00
  a lot of people People didn't even know about that,
• 17:03
  about that mechanism.
• 17:04
  A lot of people didn't even realize that it existed,
• 17:06
  and that was,
• 17:07
  that was the earlier period of,
• 17:08
  of,
• 17:09
  uh,
• 17:10
  my time on polymarket,
• 17:11
  and it was just a really crazy time.
• 17:12
  Yeah,
• 17:13
  that sounds like it.
• 17:14
  I,
• 17:14
  I,
• 17:14
  I know,
• 17:14
  and it's a little bit different than,
• 17:16
  you know,
• 17:16
  maybe if you,
• 17:17
  you know,
• 17:17
  Calci or even polymarket US it does not operate that same way.
• 17:21
  Uh,
• 17:21
  that mechanism is somewhat unique,
• 17:23
  at least to poly-market,
• 17:25
  uh,
• 17:25
  Global.
• 17:26
  Um,
• 17:26
  and it's,
• 17:27
  it's a fascinating thing.
• 17:28
  If you can dive into there and find how one of these things is gonna turn out,
• 17:32
  you could do very,
• 17:33
  very well.
• 17:33
  Um,
• 17:34
  I want to take a quick break here,
• 17:35
  uh,
• 17:35
  David,
• 17:36
  I wanna come back on the other side.
• 17:37
  I have so many questions with you.
• 17:38
  This is such a fascinating conversation.
• 17:40
  You've,
• 17:40
  uh,
• 17:40
  you've been through so many of these wars,
• 17:42
  and I,
• 17:42
  I,
• 17:42
  I can't wait to keep asking you about them.
• 17:44
  Um,
• 17:44
  I wanna come back in a second.
• 17:46
  We'll have more with David Ben Susani,
• 17:48
  and I,
• 17:48
  I know him as Scottylicious.
• 17:49
  Lots to,
• 17:50
  lots to cover here,
• 17:51
  and I wanna make sure we get to all of it back in a second.
• 18:06
  Back on Predictable with Stu.
• 18:08
  It's our first episode of Predictable Legends,
• 18:10
  and we have the one,
• 18:11
  the only Scottylicious,
• 18:13
  uh,
• 18:13
  with us,
• 18:13
  David Ben Susan,
• 18:15
  joins us,
• 18:15
  uh,
• 18:15
  and,
• 18:16
  uh,
• 18:16
  we've been talking about,
• 18:17
  uh,
• 18:18
  you know,
• 18:18
  the experience you've had,
• 18:19
  David,
• 18:19
  through this,
• 18:20
  and I,
• 18:20
  I would love to drill down if you don't mind into
• 18:22
  some of the ways you think about
• 18:24
  these markets
• 18:25
  specifically.
• 18:26
  Uh,
• 18:26
  you know,
• 18:26
  you,
• 18:27
  you,
• 18:27
  you were,
• 18:27
  if I remember right,
• 18:28
  uh,
• 18:29
  pretty big on the,
• 18:29
  uh,
• 18:30
  Spencer Pratt no position.
• 18:33
  Um,
• 18:33
  in,
• 18:34
  uh,
• 18:34
  in LA,
• 18:35
  uh,
• 18:35
  and this was one,
• 18:36
  you know,
• 18:37
  we were on that as well.
• 18:39
  You know,
• 18:39
  I,
• 18:39
  you know,
• 18:40
  as I'm,
• 18:40
  I'm,
• 18:40
  I lean more on the conservative side of things,
• 18:42
  I would have loved to see Spencer Brown win that election.
• 18:45
  He had some great AI ads.
• 18:46
  There's lots of hype about it,
• 18:48
  but that's not how elections are won and lost.
• 18:51
  And,
• 18:51
  and I,
• 18:52
  I thought this was a fascinating example
• 18:54
  of how the media lets people down all the time.
• 18:57
  There was so much hype that this was gonna be
• 18:59
  a close call and who knows what could happen,
• 19:01
  and he might win and he's,
• 19:03
  and it's like,
• 19:03
  when you look at the fundamentals of the race,
• 19:05
  it looked to me
• 19:07
  to be an impossibility that he would be able to actually win,
• 19:11
  uh,
• 19:11
  the race.
• 19:12
  Is that how you approached it and,
• 19:14
  and tell me your thinking of that race,
• 19:15
  and I know I think you're still active on Nitia Rahman as well.
• 19:20
  Correct.
• 19:21
  Um,
• 19:22
  I,
• 19:22
  like you,
• 19:22
  I thought it was an impossibility that he would win.
• 19:25
  I mean,
• 19:26
  uh,
• 19:26
  I think the Republican registration in LA is on the order of 15 or 16%.
• 19:32
  Um,
• 19:33
  there's a lot of,
• 19:34
  there was a lot of displeasure with,
• 19:36
  uh,
• 19:36
  Karen Bass about the fires and what have you.
• 19:40
  Um,
• 19:41
  I think the odds,
• 19:43
  you know,
• 19:43
  if,
• 19:43
  if you went on X,
• 19:45
  you saw that a lot of Spencer
• 19:47
  Spratt,
• 19:48
  uh,
• 19:48
  supporters didn't even live in California,
• 19:51
  um,
• 19:52
  and a lot of people.
• 19:55
  are not necessarily familiar with how the city of Los Angeles is configured.
• 20:01
  Um,
• 20:01
  I have some familiarity with LA and
• 20:05
  areas that,
• 20:06
  that one might think would vote
• 20:08
  for Spencer Pratt,
• 20:09
  like for instance,
• 20:10
  uh,
• 20:11
  Beverly Hills,
• 20:13
  um,
• 20:14
  Maybe,
• 20:15
  you know,
• 20:15
  certain parts of Santa Monica that were close to the,
• 20:18
  close to the fires,
• 20:18
  even though Santa Monica is very liberal,
• 20:20
  but
• 20:20
  you have,
• 20:20
  you have a lot of little cities that aren't even part of the city of Los Angeles,
• 20:25
  uh,
• 20:26
  that,
• 20:27
  that wouldn't be counted,
• 20:28
  obviously in the vote totals for,
• 20:29
  for mayor of LA.
• 20:31
  And you also,
• 20:33
  um,
• 20:34
  have a,
• 20:34
  uh,
• 20:35
  LA is not a city that's friendly towards Trump.
• 20:38
  Um,
• 20:38
  you had the,
• 20:39
  uh,
• 20:40
  ICE raids in Los Angeles that were,
• 20:43
  uh,
• 20:44
  You know,
• 20:44
  even,
• 20:45
  even I have some business contacts in Los Angeles,
• 20:47
  in the real estate world with builders and so on and so forth.
• 20:51
  And
• 20:52
  some of them were originally uh Trump supporters,
• 20:56
  and
• 20:57
  I can name at least a few who uh
• 21:00
  who live,
• 21:01
  who reside in Los Angeles and no longer support Trump because of the ICE raids.
• 21:05
  So,
• 21:06
  uh,
• 21:07
  because,
• 21:07
  you know,
• 21:08
  some of them had employees that they may have had for,
• 21:10
  for,
• 21:11
  you know,
• 21:11
  a long time.
• 21:13
  And without going into the legality of it and what have you,
• 21:15
  I mean,
• 21:16
  everybody's gonna have,
• 21:17
  people are gonna have different takes on that,
• 21:19
  but
• 21:19
  they viewed,
• 21:20
  they viewed it in their words as inhuman.
• 21:23
  And um
• 21:25
  the fact that Spencer Pratt
• 21:27
  didn't make a bigger effort to separate himself from Trump,
• 21:31
  I think
• 21:32
  at the end of the day in a city like Los Angeles did him in,
• 21:35
  um,
• 21:36
  there was a Republican guy by the name of Rick Caruso who ran for mayor
• 21:42
  and came pretty close.
• 21:44
  I mean,
• 21:44
  he got within 5 or 10% points,
• 21:46
  which for a guy who used to be a Republican in LA is,
• 21:49
  is really a great showing.
• 21:50
  And a lot of people were saying,
• 21:52
  you know,
• 21:52
  if Caruso had had ran again,
• 21:55
  he probably would have made it.
• 21:57
  But,
• 21:58
  you know,
• 21:58
  you have Caruso is a successful businessman,
• 22:02
  billionaire.
• 22:02
  He built some of the landmark uh
• 22:05
  shopping centers in Los Angeles that have like a really
• 22:08
  unique characteristics to them and that are very pedestrian friendly.
• 22:12
  On the other hand,
• 22:14
  Spencer Pratt,
• 22:15
  you know,
• 22:15
  he's a reality TV star.
• 22:18
  Uh,
• 22:19
  never had any experience running
• 22:21
  large
• 22:22
  organizations,
• 22:23
  uh,
• 22:24
  certainly no political experience.
• 22:26
  Um,
• 22:27
  so,
• 22:28
  a lot of the centrist
• 22:30
  folks who might be willing to,
• 22:32
  who are,
• 22:32
  you know,
• 22:33
  registered Democrats,
• 22:35
  but might be willing to vote for a Republican in the right circumstance,
• 22:38
  like,
• 22:39
  um,
• 22:40
  uh,
• 22:41
  like a person like Car Russo,
• 22:42
  they,
• 22:43
  they
• 22:44
  Pratt was a non-starter to them.
• 22:45
  Hm,
• 22:46
  yeah,
• 22:46
  it was,
• 22:46
  it was a fascinating,
• 22:47
  uh,
• 22:47
  race to watch.
• 22:48
  Do you think,
• 22:49
  uh,
• 22:49
  I just quickly,
• 22:50
  I,
• 22:50
  I,
• 22:50
  you know,
• 22:51
  we can maybe get,
• 22:51
  I wanted to get into some of your current positions here in a minute,
• 22:53
  but
• 22:54
  Nitia Rahman is,
• 22:55
  uh,
• 22:55
  uh,
• 22:55
  you know,
• 22:56
  still in the race.
• 22:56
  You still have a position with her,
• 22:59
  uh,
• 22:59
  you know,
• 22:59
  in some ways because of what we've seen with the,
• 23:01
  the,
• 23:01
  the Democratic Socialists and,
• 23:03
  and the way that they have been victorious in
• 23:05
  a lot of these local races across the country,
• 23:08
  I'm a bit surprised that she's still hanging around 37,
• 23:11
  38%.
• 23:12
  Uh,
• 23:13
  are you still active in that market,
• 23:15
  and,
• 23:15
  and do you,
• 23:15
  are you,
• 23:16
  do you see an upside there,
• 23:17
  or do you,
• 23:18
  or what's your feeling on it?
• 23:21
  I don't know if she can win.
• 23:22
  Uh,
• 23:23
  Batts is a force to be reckoned with,
• 23:25
  with,
• 23:25
  uh,
• 23:26
  the unions and,
• 23:27
  uh,
• 23:27
  city employee union in,
• 23:29
  in LA.
• 23:30
  And uh
• 23:33
  Nita Raman seems to lack a little bit
• 23:36
  of charisma.
• 23:38
  So it's
• 23:39
  probably not something I'm gonna put hundreds of thousands of dollars on.
• 23:43
  I think the uh I think she should probably be in the 40s,
• 23:45
  at least the low 40s,
• 23:46
  so it does seem,
• 23:47
  she does seem
• 23:48
  uh inexpensive given the DSA
• 23:51
  uh takeover of a lot of,
• 23:53
  a lot of blue cities.
• 23:55
  But
• 23:56
  Uh,
• 23:56
  it's,
• 23:57
  it's worth noting
• 23:58
  that
• 23:59
  Karen Bass is also a democratic socialist.
• 24:02
  So it's not like you have a centrist republic,
• 24:04
  centrist,
• 24:05
  pardon me,
• 24:06
  centrist Democrat with Karen Bass
• 24:08
  and this extreme,
• 24:09
  you know,
• 24:10
  person.
• 24:11
  Who's going against her.
• 24:13
  They're both very left wing.
• 24:15
  Um,
• 24:16
  surprisingly,
• 24:16
  Nitia Raman had some really good proposals for small business
• 24:20
  to cut out a lot of bureaucracy in Los Angeles,
• 24:22
  and Karen Bass is known as a,
• 24:24
  as a,
• 24:25
  as a bureaucrat.
• 24:26
  So,
• 24:27
  there,
• 24:28
  there are,
• 24:28
  uh,
• 24:29
  variables that,
• 24:30
  that aren't necessarily ide ideologically driven in this race
• 24:34
  that may come to affect things.
• 24:36
  Uh,
• 24:37
  I'm gonna keep an open mind on this one,
• 24:39
  and I may,
• 24:40
  I may add or decrease the position depending on how things
• 24:43
  go in the next few weeks and a few months,
• 24:45
  and I,
• 24:45
  I'm,
• 24:46
  but I'm definitely not aping into it right now.
• 24:48
  It's,
• 24:48
  it's gonna be a close call,
• 24:49
  I think.
• 24:50
  Mm,
• 24:50
  uh,
• 24:52
  kind of thinking about this race a little bit and just generally,
• 24:55
  uh.
• 24:56
  One of the areas that I really like,
• 24:59
  uh,
• 24:59
  when it comes to these prediction marks,
• 25:00
  especially as it ties to elections,
• 25:02
  is finding
• 25:04
  these positions that,
• 25:06
  at least to me,
• 25:07
  kind of seem like sure things,
• 25:08
  you know,
• 25:08
  when you're saying Spencer Pratt,
• 25:09
  he's in the,
• 25:10
  in the mid-twenties,
• 25:11
  so you,
• 25:11
  you're getting out of no position in like 75%.
• 25:15
  To me,
• 25:15
  that seemed like more like a 95,
• 25:16
  98% um possibility.
• 25:20
  That range to me is just a,
• 25:22
  it's a really sexy range,
• 25:23
  right?
• 25:23
  Like those 70,
• 25:24
  80% chances.
• 25:26
  Uh,
• 25:26
  and the,
• 25:27
  the possibility of taking those up to 100.
• 25:30
  I feel like there's in some ways almost weakness in the markets right around there.
• 25:34
  Do,
• 25:34
  do you,
• 25:35
  is that,
• 25:35
  am I making that up or does that seem rational to you?
• 25:39
  No,
• 25:39
  it seems,
• 25:40
  it seems perfectly rational,
• 25:43
  and also,
• 25:44
  um,
• 25:45
  I feel like politics and geopolitics are very different
• 25:48
  per se than sports.
• 25:50
  Uh,
• 25:50
  a sports event,
• 25:51
  you know,
• 25:51
  one in like in,
• 25:53
  in soccer,
• 25:54
  one penalty,
• 25:55
  uh,
• 25:56
  11 lucky shot can really make a difference,
• 25:59
  and
• 26:01
  Uh,
• 26:02
  the one exception in in politics,
• 26:04
  that's usually not going to happen.
• 26:06
  Now,
• 26:07
  you have a very
• 26:08
  Strong exception to that
• 26:11
  with
• 26:12
  sex scandals for Democrats.
• 26:14
  Those are the things,
• 26:16
  the,
• 26:16
  you know,
• 26:16
  like what happened with Swalwell and then later Graham Plattner.
• 26:19
  Those are the types of events that can come out of nowhere,
• 26:23
  especially on the more,
• 26:24
  in my opinion,
• 26:25
  more on the Democratic side than on the Republican side for whatever reason,
• 26:29
  uh,
• 26:30
  but
• 26:30
  my,
• 26:31
  my take on it is that with politics.
• 26:34
  For something to go from like 85 to 45,
• 26:38
  a lot more things have to change than in a sports match.
• 26:42
  Is that,
• 26:42
  does that
• 26:43
  sort of make sense?
• 26:44
  Yeah,
• 26:45
  that does make sense.
• 26:45
  I,
• 26:45
  I think you're right.
• 26:46
  It does seem like the sports markets are so efficient too,
• 26:49
  at least,
• 26:50
  especially when it's around,
• 26:51
  when you're talking around a game or development in the game.
• 26:53
  There's so,
• 26:54
  so much math,
• 26:55
  so,
• 26:56
  you know,
• 26:56
  such a large sample size,
• 26:58
  right?
• 26:58
  Like you can really,
• 26:59
  you could see those things move like crazy,
• 27:01
  very difficult,
• 27:03
  uh,
• 27:03
  to beat.
• 27:04
  Um,
• 27:05
  let me,
• 27:05
  uh,
• 27:06
  let me ask you,
• 27:06
  let me move on to something a little bit,
• 27:08
  uh,
• 27:09
  different here.
• 27:11
  When we're talking about,
• 27:13
  um,
• 27:14
  uh,
• 27:15
  analyzing a race,
• 27:16
  right?
• 27:16
  Like we just talked about it kind of nerded out basically through data,
• 27:19
  right,
• 27:20
  about how,
• 27:21
  uh,
• 27:21
  you know,
• 27:21
  what would happen to a Spencer Pratt in,
• 27:23
  in Los Angeles,
• 27:24
  just the numbers were really stacked against him,
• 27:26
  no matter how good of a candidate he was.
• 27:28
  You also have taken another approach here,
• 27:31
  and I,
• 27:31
  I,
• 27:32
  I've seen you,
• 27:32
  you talk about it a little bit
• 27:34
  when it comes to foreign elections,
• 27:36
  of really
• 27:37
  getting on the ground,
• 27:38
  like actually going to these countries
• 27:41
  and talking to everyday people.
• 27:43
  Is this something that's really valuable?
• 27:46
  Do you add a lot of perspective?
• 27:47
  Do you understand the race
• 27:49
  better by being there?
• 27:51
  Or is this just one of those things that you can kind
• 27:52
  of be an internet warrior and just take it from a distance?
• 27:56
  You know,
• 27:57
  Stu,
• 27:57
  I was hoping you'd ask this question actually,
• 28:00
  because uh back in December,
• 28:01
  I took a trip to Portugal
• 28:03
  with my cousin who's uh a film guy,
• 28:06
  and the guy uh I had,
• 28:08
  I had started to take on as a risk manager for Team Scottallicious,
• 28:13
  and we went to Portugal,
• 28:15
  and we sought out,
• 28:16
  I had a,
• 28:17
  I had one friend already in Portugal,
• 28:19
  but we sought out two other guys who were ridiculously politically knowledgeable,
• 28:22
  all of them just young,
• 28:23
  brilliant guys.
• 28:25
  And uh who kind of gave us a lay of the land.
• 28:28
  And we actually went and interviewed
• 28:30
  um
• 28:32
  around 100 people,
• 28:33
  and
• 28:34
  the,
• 28:35
  the results of the,
• 28:36
  the film,
• 28:37
  the film results of the interview ended up being
• 28:39
  published by Will Ward in the Poly Market Oracle,
• 28:42
  which was really cool.
• 28:43
  And when we did our interviews,
• 28:46
  um,
• 28:47
  we noticed that the front runner,
• 28:49
  Marcus Mendez,
• 28:51
  his support had no depth.
• 28:53
  So we would ask people,
• 28:54
  You know,
• 28:55
  oh,
• 28:55
  you're a Marcus Mendez supporter,
• 28:56
  that's great.
• 28:57
  So,
• 28:58
  what do you think about him?
• 28:59
  And they were like,
• 28:59
  uh,
• 29:00
  I think he'll probably win,
• 29:02
  you know,
• 29:03
  he's,
• 29:03
  he's,
• 29:04
  he's an all right guy.
• 29:05
  Whereas you would ask the young people
• 29:08
  specifically about Cotrim,
• 29:09
  and they're like,
• 29:10
  oh,
• 29:10
  he's anti uh bureaucracy,
• 29:13
  he wants to give us young people more freedom,
• 29:15
  and he's just an amazing guy,
• 29:17
  he's an entrepreneur.
• 29:19
  Um,
• 29:20
  and then you would ask,
• 29:21
  uh,
• 29:21
  the older folks about the socialist candidate,
• 29:23
  and they're like,
• 29:24
  oh,
• 29:24
  he's gonna take care of us.
• 29:25
  We,
• 29:25
  we really need to keep our pensions.
• 29:26
  I mean,
• 29:27
  affordability has gone through the roof,
• 29:29
  and,
• 29:29
  you know,
• 29:30
  without him,
• 29:30
  the whole,
• 29:31
  the whole structure of,
• 29:32
  of,
• 29:33
  uh,
• 29:33
  free healthcare and,
• 29:34
  and support for us old people would,
• 29:36
  would get dismantled.
• 29:38
  But the Mendez guy,
• 29:39
  uh,
• 29:41
  just by people's tone of voices,
• 29:42
  and,
• 29:43
  and we started betting against him,
• 29:45
  uh,
• 29:46
  due to these interviews.
• 29:48
  And we
• 29:50
  just
• 29:50
  ended up making around $80,000 based on,
• 29:54
  on,
• 29:54
  uh,
• 29:54
  something that I believe,
• 29:56
  I believe AI cannot capture
• 29:59
  tone of voice,
• 29:59
  and neither can a survey,
• 30:01
  and we were able to somehow do that,
• 30:04
  um,
• 30:04
  and it,
• 30:05
  and it worked,
• 30:06
  uh,
• 30:07
  it worked really well
• 30:08
  in,
• 30:08
  in that instance,
• 30:09
  and,
• 30:10
  and,
• 30:10
  uh,
• 30:11
  I think,
• 30:11
  uh,
• 30:12
  I think there's other instances where it could work,
• 30:14
  maybe countries that are smaller,
• 30:16
  you know,
• 30:16
  I,
• 30:16
  I don't know if it would work,
• 30:17
  for instance,
• 30:18
  in Brazil,
• 30:18
  which is,
• 30:19
  which isn't a huge country,
• 30:20
  but,
• 30:21
  but there is definitely something to be said for that technique.
• 30:24
  Mm,
• 30:24
  it's really fascinating,
• 30:25
  uh,
• 30:26
  how you could dive into that look,
• 30:27
  look,
• 30:27
  I mean,
• 30:28
  you know this almost like from going around to when you visit a city,
• 30:31
  right?
• 30:31
  If you,
• 30:32
  you go to a city and you,
• 30:33
  you can go on
• 30:34
  Yelp,
• 30:35
  and you could get some information about the city,
• 30:37
  but if you go there and you talk to the locals and you say,
• 30:39
  hey,
• 30:40
  where's the best place to get Mexican food?
• 30:42
  You're gonna get a totally different place probably and a place
• 30:45
  that you're gonna remember probably for the rest of your life.
• 30:47
  That is uh kind of the strategy there.
• 30:49
  It's,
• 30:49
  it's a,
• 30:50
  it's a fascinating look.
• 30:51
  Do you,
• 30:51
  do you worry about
• 30:53
  Do you worry about taking too much out of the anecdotal though?
• 30:55
  I mean,
• 30:55
  if you're talking to,
• 30:56
  you know,
• 30:57
  50,
• 30:57
  100 people,
• 30:58
  you might not have that perspective.
• 31:00
  Are you,
• 31:00
  are you worried about getting lost in,
• 31:02
  in a bunch of,
• 31:03
  you know,
• 31:03
  stories or vibes and,
• 31:05
  and,
• 31:05
  and missing out on something big?
• 31:08
  Uh,
• 31:09
  you know,
• 31:09
  that,
• 31:09
  that can happen.
• 31:10
  Um,
• 31:11
  I will,
• 31:11
  I'll share with you that
• 31:13
  over that trip,
• 31:14
  we
• 31:15
  looked at how different regions,
• 31:17
  so even though we weren't able to scour Portugal and interview like 2000 people,
• 31:21
  uh,
• 31:22
  we did talk to people from different regions,
• 31:24
  and it was also,
• 31:27
  I mean,
• 31:29
  It was kind of,
• 31:31
  older folks were making fun of his height.
• 31:33
  We,
• 31:33
  there was a really cool scene where we ran into a bunch of old folk
• 31:37
  poker players,
• 31:38
  they were playing,
• 31:39
  you know,
• 31:40
  playing poker in the park,
• 31:42
  and they were like,
• 31:42
  oh yeah,
• 31:43
  Mendez,
• 31:43
  Shorty,
• 31:44
  yeah,
• 31:44
  we know him,
• 31:45
  and they just started laughing.
• 31:46
  And so,
• 31:48
  And I,
• 31:48
  we did talk to people from,
• 31:50
  we,
• 31:50
  we,
• 31:51
  one big thing I like to do is,
• 31:54
  I like to ask people,
• 31:55
  uh,
• 31:56
  not only how they're voting,
• 31:56
  but how their friends and family are voting.
• 31:58
  And they may have friends and family in different regions.
• 32:00
  And so,
• 32:01
  we would get the phone numbers of people in other regions,
• 32:04
  and,
• 32:04
  and we would call them and say,
• 32:05
  hey,
• 32:06
  how are the people around you thinking about voting?
• 32:08
  So,
• 32:08
  it wasn't just limited to,
• 32:10
  to a few.
• 32:11
  Uh,
• 32:12
  to a few cities,
• 32:13
  but,
• 32:13
  you know,
• 32:13
  you're absolutely right.
• 32:14
  I mean,
• 32:15
  I think,
• 32:16
  I think,
• 32:16
  uh,
• 32:17
  uh,
• 32:18
  combining what we do
• 32:20
  with a very,
• 32:21
  very solid quant
• 32:23
  would result in a,
• 32:24
  in an unstoppable,
• 32:26
  uh,
• 32:26
  team.
• 32:28
  But we're,
• 32:28
  you know,
• 32:28
  on our,
• 32:29
  on our end,
• 32:29
  we're working on it.
• 32:31
  Um,
• 32:32
  that's in essence
• 32:33
  why it's called Team Scottallicious.
• 32:35
  I have,
• 32:36
  I have,
• 32:36
  you know,
• 32:36
  I ask a lot of people's opinions on various bets,
• 32:39
  and sometimes I,
• 32:40
  I defer to their expertise.
• 32:42
  Uh,
• 32:44
  so it,
• 32:44
  it's,
• 32:45
  it's a concern that you're,
• 32:46
  you're correct to identify that.
• 32:48
  All right,
• 32:48
  I'm gonna take,
• 32:49
  uh,
• 32:49
  one more break.
• 32:49
  We'll be back in a second,
• 32:50
  more with,
• 32:51
  uh,
• 32:51
  David Ben Sousan,
• 32:52
  of course,
• 32:52
  Scottylicious.
• 32:53
  Uh,
• 32:53
  you can follow him at Scott on Polly,
• 32:56
  uh,
• 32:56
  on,
• 32:56
  uh,
• 32:56
  X,
• 32:57
  more in just a second.
• 33:06
  Welcome
• 33:07
  to Predictable with Stu.
• 33:08
  We've got a little more time with David Ben Soussan,
• 33:11
  AKA Scott Elicious,
• 33:12
  uh,
• 33:12
  as part of our
• 33:13
  Predictable Legends series,
• 33:15
  The Legends of trading,
• 33:16
  the Legends of Prediction Markets,
• 33:18
  and,
• 33:18
  uh,
• 33:18
  Scott Elicious is with us now.
• 33:20
  Um,
• 33:20
  David,
• 33:21
  I,
• 33:21
  I know,
• 33:21
  uh,
• 33:22
  I,
• 33:22
  you know,
• 33:22
  I talk about prediction markets all the time.
• 33:24
  I love them.
• 33:25
  We've been,
• 33:25
  I've been doing them for a long time.
• 33:27
  Uh,
• 33:27
  and I take in a lot of media.
• 33:29
  I do a lot of interviews about them as I know you do as well.
• 33:32
  And I've come up with 3 questions that apparently I am legally required to ask you
• 33:36
  when it comes to prediction markets.
• 33:38
  So,
• 33:38
  a little rapid-fire round for you here.
• 33:40
  You can just take as much time as you'd like to on these 3 basic questions.
• 33:43
  Number 1,
• 33:45
  prediction markets.
• 33:46
  Are they
• 33:46
  gambling?
• 33:50
  Not if you do it the right way.
• 33:51
  Uh,
• 33:52
  you,
• 33:53
  I mean,
• 33:53
  obviously if you do a ton of research and you have an edge in certain markets,
• 33:56
  it's not gambling.
• 33:57
  It's more like playing chess,
• 33:59
  or maybe some people,
• 34:00
  um,
• 34:01
  compare it to playing poker at a in it
• 34:04
  with you being an ace in mathematics and playing
• 34:06
  with people who can't even add and subtract.
• 34:09
  So I,
• 34:10
  I think it can be gambling if,
• 34:11
  if it's not done the right way,
• 34:12
  but if it is,
• 34:13
  it's not gambling.
• 34:14
  Yeah,
• 34:14
  and that's one of the things we've talked about on the show a lot.
• 34:16
  It's like.
• 34:17
  You know,
• 34:17
  I,
• 34:17
  I feel like some of the promotional stuff that I see around prediction markets is,
• 34:21
  you know,
• 34:22
  around the exciting things and the hype,
• 34:24
  uh,
• 34:25
  you know,
• 34:25
  and this is why we,
• 34:26
  you know,
• 34:26
  the colloquial term of,
• 34:28
  of using the word betting,
• 34:29
  you know,
• 34:29
  you,
• 34:30
  you bet,
• 34:30
  you can bet on a stock,
• 34:31
  you could bet on,
• 34:32
  uh,
• 34:32
  you know,
• 34:32
  any outcome of anything,
• 34:34
  uh,
• 34:34
  but
• 34:34
  we try to use investing as much as we can because I,
• 34:37
  I feel like when you think about it that way,
• 34:39
  you can approach it in a,
• 34:40
  in a more sane,
• 34:41
  balanced,
• 34:42
  sober manner,
• 34:43
  and if you do that,
• 34:45
  You'll be one of the people who do well on prediction markets.
• 34:47
  If you go in it and try to do,
• 34:49
  you know,
• 34:49
  2017 parlays,
• 34:50
  you know,
• 34:51
  like,
• 34:51
  like,
• 34:51
  you know,
• 34:51
  yeah,
• 34:52
  that,
• 34:52
  that's gonna feel a lot like gambling probably to you.
• 34:54
  So at least that's uh,
• 34:55
  that's how I think about it.
• 34:56
  Um,
• 34:57
  let me go to,
• 34:57
  uh,
• 34:58
  question number two of the legally required prediction market questions.
• 35:01
  Should insider trading be legal and is it a problem?
• 35:07
  Man,
• 35:07
  that's a tough one,
• 35:08
  too.
• 35:08
  Um,
• 35:09
  it all depends on your philosophy,
• 35:11
  and one of the things that makes us,
• 35:13
  uh,
• 35:14
  according to,
• 35:14
  I'm reading actually a really good book,
• 35:16
  uh,
• 35:19
  On,
• 35:20
  uh,
• 35:20
  success,
• 35:21
  I think it's called Super forecasters,
• 35:23
  and they identified
• 35:24
  one of the characteristics of a successful,
• 35:27
  um,
• 35:28
  forecaster
• 35:29
  is open-mindedness.
• 35:31
  So open-mindedness and being self-critical
• 35:34
  and being open to the idea that you're
• 35:37
  Uh,
• 35:38
  way of looking at things,
• 35:39
  which in my view encompasses ideology
• 35:41
  may not always be correct.
• 35:43
  So,
• 35:44
  for me,
• 35:44
  that's a bit of an ideological question.
• 35:47
  Um,
• 35:48
  I think it's
• 35:49
  it's up to society,
• 35:50
  you know,
• 35:51
  society needs to determine.
• 35:52
  Is,
• 35:53
  is the information itself
• 35:55
  so valuable that even if it's insider info,
• 35:58
  uh,
• 35:59
  it's worth being used,
• 36:00
  or there should,
• 36:01
  should there be some parameters,
• 36:03
  and there's pros and cons which are probably too lengthy for,
• 36:06
  for the length of this question,
• 36:07
  so I'm gonna stay neutral on that one.
• 36:09
  All right,
• 36:10
  uh,
• 36:10
  and the final one here,
• 36:12
  maybe encompasses everything we've talked about today.
• 36:14
  Are prediction markets
• 36:16
  good for society?
• 36:17
  Is there utility in them?
• 36:23
  There's definitely utility in them,
• 36:25
  because I believe that they have a role as a truth-seeking mechanism.
• 36:30
  Um,
• 36:30
  I recently read a,
• 36:32
  a really interesting book on anarchy by a guy named David Graber,
• 36:37
  and
• 36:38
  in,
• 36:39
  in anarchy,
• 36:41
  Um,
• 36:42
  believe it or not,
• 36:42
  it's not just,
• 36:43
  you know,
• 36:44
  free for all,
• 36:44
  I can take your property and your wife and whatever else I want.
• 36:47
  That's not really how,
• 36:48
  how,
• 36:49
  in,
• 36:49
  in David Graber's idea,
• 36:51
  that's not how anarchy works.
• 36:53
  Um,
• 36:53
  how anarchy works for him is people
• 36:56
  with completely different ideologies and different opinions
• 36:58
  get together
• 37:00
  and they make a plan of action on a specific item
• 37:03
  without having to all agree on how they're going to live their lives.
• 37:06
  So I realized
• 37:08
  That's kind of um
• 37:10
  tangential,
• 37:12
  but to me,
• 37:13
  certain aspects of prediction markets,
• 37:15
  especially the geopolitical ones,
• 37:17
  can be good for society because
• 37:19
  they're a way of agreeing on a truth.
• 37:21
  And I mean,
• 37:22
  you know how polarized things are out there uh in in the United States and in Europe,
• 37:27
  and in a lot of places,
• 37:28
  you have people who don't even
• 37:29
  agree on
• 37:30
  on basic worldviews.
• 37:32
  But with prediction markets,
• 37:34
  you can say,
• 37:34
  OK,
• 37:35
  this really happened.
• 37:37
  Or it didn't happen.
• 37:38
  Not to say that they're perfect,
• 37:39
  OK?
• 37:40
  I mean,
• 37:40
  Calhi has,
• 37:41
  has a,
• 37:41
  a rule-making body,
• 37:43
  and they make the rules,
• 37:44
  and polymarket
• 37:45
  is focusing a little bit more on rules than they used to,
• 37:48
  and they have UMA,
• 37:49
  and
• 37:50
  none of these are perfect,
• 37:51
  but
• 37:51
  the net benefit in the geopolitical realm,
• 37:54
  I think is,
• 37:54
  is very positive.
• 37:56
  Mm,
• 37:56
  uh,
• 37:57
  it's,
• 37:57
  it's,
• 37:57
  uh,
• 37:58
  it's a fascinating conversation,
• 37:59
  and I think,
• 37:59
  you know,
• 38:00
  it'll be interesting to see how,
• 38:01
  you know,
• 38:02
  America and the world.
• 38:03
  As these things become more prominent,
• 38:05
  as people are more and more people are on them.
• 38:08
  You know,
• 38:08
  kind of tussle with those questions because I think,
• 38:11
  you know,
• 38:11
  in a,
• 38:12
  in a world where people are looking at
• 38:14
  these as responsible instruments and interesting financial innovations.
• 38:18
  I,
• 38:18
  I think there's a lot of,
• 38:20
  a lot of positive,
• 38:20
  a lot of room to run here with prediction markets,
• 38:22
  and,
• 38:23
  and it'll be interesting to see if that's the direction we go or if,
• 38:26
  if we do what we do with most other things and just screw it all up.
• 38:32
  Yeah,
• 38:32
  well,
• 38:32
  it's,
• 38:32
  it's one of the few,
• 38:34
  few,
• 38:34
  uh,
• 38:35
  instances where you'll have the,
• 38:36
  the anarchists agree with,
• 38:38
  with the hardcore like Friedrich Hayek economists,
• 38:41
  and they'll be on the same page about that.
• 38:42
  So,
• 38:43
  yeah,
• 38:43
  it's gonna be fascinating to watch.
• 38:45
  Uh,
• 38:45
  so David Ben Sussan,
• 38:46
  we,
• 38:46
  I appreciate you taking all this time,
• 38:48
  uh,
• 38:48
  of course,
• 38:48
  known as Scottylicious,
• 38:49
  uh,
• 38:50
  on,
• 38:50
  uh,
• 38:50
  on Poly Market,
• 38:51
  and,
• 38:52
  uh,
• 38:52
  you can see him at Scott on Poly
• 38:54
  on X.
• 38:54
  So this is a,
• 38:55
  a predictable legends.
• 38:55
  We're gonna do this from time to time and bring in
• 38:57
  great people who really understand this stuff better than anybody.
• 39:00
  Um,
• 39:00
  uh,
• 39:01
  David,
• 39:01
  if you have a second,
• 39:02
  I'd love to hold you out for a few questions.
• 39:04
  We have some questions from our subscribers.
• 39:05
  Uh,
• 39:06
  they would love to ask you as well.
• 39:08
  Do you have a couple more minutes to hang on?
• 39:10
  Absolutely,
• 39:10
  and,
• 39:11
  and thank you,
• 39:11
  Stu,
• 39:11
  so much for having me.
• 39:12
  This has been a pleasure,
• 39:13
  a very fun interview.
• 39:14
  Oh yeah,
• 39:14
  it's,
• 39:15
  it's been great to talk to you.
• 39:16
  I,
• 39:16
  I honestly could spend so much more time asking you a million other questions,
• 39:19
  but
• 39:20
  I appreciate you taking this time.
• 39:21
  If you go over to predictableShow.com,
• 39:23
  if you're a paid subscriber,
• 39:24
  you get to access uh these additional questions.
• 39:26
  We're gonna try to dive,
• 39:27
  uh,
• 39:28
  even deeper into this stuff so you can get
• 39:30
  not only the trading insight,
• 39:31
  I mean,
• 39:31
  I,
• 39:31
  you know,
• 39:32
  I'm
• 39:33
  Uh,
• 39:33
  I'm,
• 39:33
  I'm writing stuff down.
• 39:34
  Uh,
• 39:34
  I,
• 39:34
  we may have new constitutional amendments for
• 39:36
  how we trade after this interview because
• 39:38
  we're learning so much from this.
• 39:40
  Uh,
• 39:40
  it's a great conversation.
• 39:41
  We're gonna try to have as many of these as we can.
• 39:43
  Uh,
• 39:43
  that's all the time we have for the show though tonight.
• 39:45
  We'll see you tomorrow.
• 39:46
  We'll have another episode of Predictable with Stu
• 39:48
  and join us over at predictableshow.com.
• 39:51
  Be part of the community.
• 39:52
  It's a great group of people who have
• 39:54
  really interesting questions and insights on all these markets.
• 39:56
  So,
• 39:57
  thanks so much for taking the time.
• 39:58
  Thanks so much to David,
• 39:59
  and we'll see you tomorrow.