SAM RANSBOTHAM:
How can AI help bring new ideas and products
to market in industries where risk aversion is rampant?
Find out today when we talk with Kartik Hosanagar, professor
at the Wharton School and founder of Jumpcut,
a startup helping previously undiscovered talent
produce movies and tv.
Welcome to Me, Myself, and AI, a podcast
on artificial intelligence in business.
Each episode, we introduce you to someone innovating with AI.
I'm Sam Ransbotham, professor of information systems
at Boston College.
I'm also the guest editor for the AI and Business Strategy
Big Ideas program at MIT Sloan Management Review.
SHERVIN KHODABANDEH: And I'm Shervin Khodabandeh,
senior partner with BCG, and I colead BCG's AI practice
in North America.
Together, MIT SMR and BCG have been
researching AI for five years, interviewing hundreds
of practitioners and surveying thousands
of companies on what it takes to build and to deploy and scale
AI capabilities and really transform
the way organizations operate.
SAM RANSBOTHAM: Today, we're talking with Kartik Hosanagar.
He's a professor at The Wharton School and founder
and CEO of Jumpcut.
Kartik, thanks for joining us.
Welcome.
KARTIK HOSANAGAR: Thanks for having me, Sam and Shervin.
SAM RANSBOTHAM: So this is a bit of a different interview
for me, because I've known Kartik
for years in academic circles.
But Kartik, your latest venture is Jumpcut.
I think the idea basically is to help
surface new and fresh stories for Hollywood.
Can you tell us a little bit about that?
KARTIK HOSANAGAR: My new startup is called Jumpcut,
as you mentioned.
What we are doing is essentially trying
to create a new data-driven studio that's
reimagining the way films and TV shows
are developed, with the specific goal
of elevating fresh new voices.
The context here that got me interested in this is,
Hollywood has historically been an old boys' club --
a few execs making decisions on what movies get made,
who's in those movies, [and] at what budgets.
All of these [are] just pretty much based on gut
and relationships -- who knows who.
There are costs [to] this kind of decision-making.
There's the economic cost; Hollywood has historically had
a very poor batting average.
There's the social cost; by just about any measure,
Hollywood has not been a particularly inclusive
industry.
And then there's the cost to audiences.
So, what we're trying to do is break
that mold and use data to make more objective, better
decisions.
But ultimately, by doing that, we
can assess storytellers and stories on their merit
as opposed to who is connected to whom or just [the]
gut feelings of a few people.
So that's how we're trying to democratize
Hollywood using data.
SAM RANSBOTHAM: I mean, maybe your world
is slightly different than mine, but no movie execs
are in my classes.
So how did you end up with this idea,
and how did you end up with Jumpcut?
KARTIK HOSANAGAR: It's interesting --
no movie execs in my classes either, Sam.
SAM RANSBOTHAM: Do you know that I
went to high school with Julia Roberts, though?
I mean, there's a connection there.
But anyway, sorry.
Keep going.
KARTIK HOSANAGAR: Well, I hope you are still connected
to your high school --
SAM RANSBOTHAM: No, unfortunately;
this is pre-social media, and I made lots of tragic mistakes
in that era, but anyway.
Sad tale for another time.
KARTIK HOSANAGAR: Right, right.
I look forward to catching up on that story sometime.
Coming back to your question: What got me interested in this?
I've always had an interest in content and storytelling
and films.
I'm actually an amateur filmmaker.
So back when I was a newly tenured professor,
and also, I didn't have kids, it was an interesting combination
where I had my weekends to myself,
so I would go make short films and just put them out
on YouTube.
It was really just a fun hobby of mine.
In fact, during my first sabbatical from Wharton,
I wrote a screenplay.
I flew into Mumbai, met with a bunch of film producers
there, and pitched my screenplay.
A number of them liked it.
But the common response from many of them
was, "We really like this, but how
do we take a bet on a completely new writer/director?"
One of them said, "I'll buy your script,
but I can't have you direct it.
You may be good, but I just can't have you direct it,
because I can't go to financiers and to actors
and ask them to take a bet on a new voice."
So my response back then was, "Fair enough; makes sense.
I realize why you can't do it."
But at the same time, I didn't want to give my script away.
So I came back.
Then my sabbatical was over, so back to Wharton --
back to teaching and all of that.
But over the years, I've met with so many writers
and directors who have shared similar perspectives.
There's a friend of mine who told me
it took him 15 years to break into the industry.
And he's a successful writer/director.
Fifteen years for his first opportunity.
And I hear this so many times.
In recent years, we see really successful movies or shows
like Get Out or Stranger Things come
from very unexpected places, so I've always
been fascinated by this space.
Initially it was a hobby, but one fine day I kind of felt
like the problem I'm seeing is something
my skills and data might have some relevance to use to solve.
SHERVIN KHODABANDEH: It's actually quite interesting
because there are several angles to the story here.
One is the equity angle and the social angle,
and giving those who deserve and have the merit
the voice that they might otherwise not get.
And then there is the more business, data-driven angle,
which takes my mind to Moneyball in baseball, or underwriting --
giving credit to folks who don't have a credit history
because they emigrate to this country and, but for the fact
that they've just arrived here, they would be perfect in terms
of the profile and the background and the education --
and being able to actually assess talent based on a bunch
of attributes and features and sort of de-risk the very
executives that say, "How do I take a bet on you,
because I don't know anything about you?"
KARTIK HOSANAGAR: Yeah, absolutely.
And you used a very interesting word here.
You talked about de-risking.
You talked about credit applications,
and there are people who are applying,
and how do we de-risk them?
A lot of this is really about risk.
If you look at Hollywood, one of the big things I've realized
is, Hollywood would like to take chances.
They understand the problems of inequity.
They understand the industry has not been inclusive.
But everyone's worried about the risk issue.
What was interesting for me was, a lot of execs in Hollywood
told me, "Well, the reason everyone's
so risk averse is, if you do a movie with Brad Pitt in it
and it doesn't work out, you won't lose
your job for making that bet.
But if you take a bet on something new [and] unexpected,
and that doesn't work out, you'll
have some explaining to do: 'Why did you make this bet?
This is not the kind of bet we've made in the past.'" And I
remember at some point I even read a statistic,
which was on movies, and it said like 74%
of the highest-grossing movies are all sequels or adaptations
of existing IP [intellectual property].
So there's no one willing to take a chance on something new.
Everything is a sequel.
As an outsider, I was wondering, why
is it that it has to be a bestselling
book or a comic book for something to get made?
Why can't it be an original screenplay or an original idea?
And it came back to, "Oh, yes, because [the] IP
has been de-risked."
But the insight that I had was that IP isn't
the only way of de-risking.
There are other ways to de-risk as well.
So that's essentially how we approached it.
And there's really three pieces, and I'll mention each
at a high level, and then you'll tell me
if there's any of them that's worth going deeper into.
One is, first of all, how do we discover
stories and storytellers?
The classic Hollywood approach is that studios get submissions
of scripts from agents, and agents
are representing talent and people who
are outside of the system; they don't
have a godfather who'd connect them
or don't already have the connections.
So the first thing we had to solve
for is how do we find stories and storytellers that
are outside of the system?
So for that, one of the things we do
is, our algorithms are assessing content as well as creators
on various platforms like YouTube or Reddit,
or even storytelling platforms like Wattpad and others.
And what we're doing in those platforms --
I'll use YouTube as an example.
If a screenwriter or if a director
has created short films and has posted those short films that
are already resonating with audiences,
then we try and discover them.
So the idea is to find people where they are.
That's whatever country, whatever platform you're
in, and go discover them there.
And essentially, the algorithms and analytics
are trying to analyze the content
to look for high production value, which
you can infer from the frames and images in the videos,
for example.
You're looking for strong storytelling
and strong audience response, which
you can infer from the kinds of comments
that are elicited in response to these videos or even tech
stories.
You can short-list a set of stories or storytellers
for our creative team to look at.
So it's not AI making the decision,
but it's AI making humans efficient,
because if I had a creative team that
had to scour through YouTube to find
the needle in the haystack, I would
need a massive team and several years
to comb through 250,000 short films on YouTube.
SHERVIN KHODABANDEH: Or just my two kids.
KARTIK HOSANAGAR: [Laughs.] Well, that's true.
That works, too.
Except I think they will just send me Minecraft
videos or something like that.
SHERVIN KHODABANDEH: Given how many hours of YouTube
they watch.
But I digress.
Sorry, go ahead.
SAM RANSBOTHAM: Let's interrupt there.
I mean, that's the opposite of the Moneyball problem
that Shervin mentioned.
Because with Moneyball, you had the people,
and it's about figuring out which one was good.
You're more about expanding that search space.
KARTIK HOSANAGAR: Before we can say, "Among the stories,
which is the one to bet on?" -- before we can do that,
we want to know, "Is the pool of people and stories
we're looking at the right pool?
Or is that the complete pool?"
If we don't expand the pool, we are not
solving the inclusion problem.
We work with agents.
We work with established writers as well.
But in addition, we're actively seeking out new people
and not waiting for them to get discovered by an agent
and the agent then forwards them to us, but instead
[we're] finding them where they are.
SAM RANSBOTHAM: So that was one.
I'm sorry.
You're going on with two and three here.
KARTIK HOSANAGAR: Yeah, yeah.
And the second is, again, how do we de-risk them?
Once we find somebody we ask them, "Hey, do you
have a story for us?"
And they don't have one -- they have 15.
And then we hear them or we read them,
and we get excited with a few.
We want to figure out, how do we bring in some objectivity
to this?
And that's where data comes in.
And some of it is backward looking,
which is a classic machine learning
kind of approach, which is, "Let's look at data
on what's been doing well."
And that could be what movies are doing well
but also what kinds of stories are trending.
It could even be search queries and looking
at where is the cultural zeitgeist, where
are people going.
And [we try to] understand which of the stories that we have
are stories that we think audiences will respond to.
But that's not everything, because I
think as long as we are backward looking
and we're looking at what's worked,
it is a fundamentally conservative approach,
because we'll do more and more of what's worked in the past.
SAM RANSBOTHAM: It's the sequel problem.
KARTIK HOSANAGAR: The sequel problem.
We'll be stuck there.
So the other question that we are
trying to solve for is, "How do we go take a bet on something
that's never been done before and there
is no historical success there?"
That's where we bring in ideas as from AB testing
and experimentation.
We interact with lots of audiences
online, where we pitch stories, and we're
having multiple stories compete with each other
and see which ones people are gravitating toward.
We pair the classic machine learning
based on past data with digital experimentation.
And we're running these experiments
and then figuring out not just which stories we like,
but also sometimes questions, and this is all very
hypothesis driven.
And it actually also creates very interesting breakthroughs.
Like, one of the shows we're working on --
it's with a very senior writer, in this instance --
he had an interesting sci-fi story,
and it's a very high-budget show.
He came to us saying, "If I have to sell this,
it has to be the case that this is a four-quadrant show.
Because of the budget, it can't be
that this has a niche audience.
Can your data prove that this is a four-quadrant show?"
And we said, "OK, let's test it out."
And we tested it, and it tested off the charts.
I went back to him and I said, "It's
almost a four-quadrant show.
It's testing off the charts --
Gen Z, Gen X, millennials.
Doesn't matter what the age group; they're responding."
Sci-fi, drama, all these different audiences:
People who are into sci-fi are responding,
but also people who are not into sci-fi or into drama,
they're responding.
People in the U.S. are responding.
People outside and in international markets
are responding.
So all that's great, but women aren't responding.
And I said, "Well, this is what's going on."
And his first reaction was, "Oh, we
can't go with that kind of data to the buyers,
because that hurts the show."
And that was our initial reaction,
which was to be defensive about it: Can we hide that data?
Can we not show it, and so on?
And then, as we are talking, it's
like, "Why are women not responding?
What's going wrong?"
We have this show, there's three main characters in it.
There's a woman in there.
There's two male characters.
To simplify it, I'll just say there's the good guy,
there's the bad guy, and then there's the woman in the show.
As we talked about it, we realized, "Look,
the woman in the show doesn't seem to have enough agency.
She has no motivation.
The female character is serving the motivations
of the male characters."
And he realized that he's approaching it
with this mindset and he's not thought hard
about what is driving her.
And then he reimagined the female character.
I'm getting into the weeds, but now the female character
is a genetically CRISPR kind of female, modified,
and she's got superpowers or special powers and so on.
And suddenly, when we tested the new version,
women were responding to it.
Now the idea improved.
This isn't like soulless data telling you
that you need to insert a chase sequence on page 13
to increase the audience.
This is hypotheses and asking, "What is my story missing?"
and really improving creative decisions [that are]
very much human led but data informed.
SHERVIN KHODABANDEH: This is quite exciting.
What I particularly like about it, Kartik,
is you're breaking into the last standing
bastion of judgment-driven decision-making, right?
I mean, if you think about 20 years ago,
[to] the loan officer making a judgment on who's going to get
the loan, and then fast-forward to 10 years ago,
where it's the retailer [who] decides what price he or she
should charge, or how he should stock up the shelves --
all of those industries have been completely revolutionized
with data and analytics, and they're making actually
data-driven decisions, and they're doing test and learn,
and they course-correct.
And we're seeing a lot of that in entertainment,
but still a lot of the studios are very much judgment driven.
And I think this is very interesting,
because it's the beginning of the beginning
for this industry.
So hats off to you for doing that.
KARTIK HOSANAGAR: Well, yeah, thanks for saying that.
I mean, I'm super excited to apply data to a setting
where people are the most skeptical of
"Should data be used here?"
While we are finding value in this,
I will also say that Hollywood has enough and more skeptics
with regard to whether data should play a role.
And sometimes, there is [even this]
misconception of what data is.
And sometimes, it is, "Oh, there's
an AI system making all the decisions,
and I no longer have a role to play."
I think there's a lot of room for subjectivity,
but "How can data be a useful tool?"
is how we're looking at it.
And hopefully in five, 10 years, we
can establish a track record of what data can do.
And that might change the mindset here
in the whole industry.
I did see a lot of that reaction,
saying, "Oh, data in creative has no role."
But I saw another kind of reaction,
and that was interesting.
I had some people in the industry saying,
"This is inevitable, and Netflix is already
starting some of this stuff.
We are forced by Netflix to get into this.
So we have to start doing it, and we
may as well partner with people like
you to understand this game."
So in fact, one of them told me -- and this is quite simple,
intuitive.
This is a producer who told me that when his show came
on Netflix, within days -- first of all, within a day or two --
Netflix was able to give him feedback, of course,
on how they thought his entire first season would play out.
By the end of Month 1, they were already
in conversations about Season 2, because Netflix
was able to project out what things would look like.
Apparently, he was also given information
about which characters audiences are most interested in.
They wanted to kill one of the characters early in Season 2,
and they were told, "No, you cannot do this.
That's your main character."
So this producer was telling me, "Yes, I mean,
Netflix is already pushing us to do some of this stuff."
The difference is, Netflix's data is coming in
after the show is released, and Jumpcut
is trying to bring in data before the show is created.
But I think some of them see this
as being inevitable because of companies like Netflix
and are happy to embrace our approach.
SAM RANSBOTHAM: I was a bit surprised,
because obviously your book from a couple of years ago
was about how algorithms, I think in your words,
are shaping our lives.
But this seems to be one area that algorithms are really
not shaping our lives.
So how are you getting this constituency of people
to pay attention to data and algorithms that
have historically not?
That's got to be not just selling a story;
you're also having to sell an approach.
KARTIK HOSANAGAR: So in the book,
which is called A Human's Guide to Machine Intelligence ,
I documented several ways in which AI is shaping our lives,
as Sam was just mentioning, and included some examples
in the movie world.
So, for example, I talk about how on Netflix, algorithms
are driving a lot of our consumption.
There was a paper by data scientists at Netflix
that said something like 80% of viewing hours on Netflix
originated from algorithmic recommendations.
So it's pretty clear it is shaping our lives, even
in a setting like the movies.
But I think while it's shaping our lives --
certainly in terms of how we consume content --
how content is created, that side of the business,
hasn't really evolved.
So the supply side still looks the same,
even though the demand has completely
been reshaped by the market.
SHERVIN KHODABANDEH: To me, there [are] a lot of analogs
in what you're doing here to the evolution of data-driven
decision-making -- not replacing [the] human,
but basically creating a smarter, more effective,
or efficient human.
And there is exactly the same thing
in retail and in personalization and in marketing.
Like I said, it feels to me that entertainment and media
has been sort of the last bastion of it.
I think, Kartik, as you said, a lot of it
is risk rather than anything else.
And I think in another book of yours,
you alluded to the chicken-and-egg problem:
If you don't have data on the talent,
how do you make those decisions without some level
of experimentation and some level of data?
And then, of course, there are disruptors like Netflix
who are forcing everybody to become more data driven
as a way of surviving.
I wonder whether studios could survive 10 years from now
without making a major step that way.
KARTIK HOSANAGAR: Right.
I would think that the time has come.
In fact, maybe the time was yesterday
for something like this, which is of course why
I went and started Jumpcut.
I think it's inevitable, because we know that human judgment has
its big share of problems.
Obviously, human judgment is also great in many ways,
but we have our biases; we're colored by them
in ways we don't realize.
Having a tool that can free us from those, I think --
it's a no-brainer that we should be embracing them.
And yeah, the time has come to do this
in fields that were unexpected.
I think sports is not, again, a place where I would have
guessed there would be early adopters,
but clearly there have been, and they've
shown that the Moneyball kind of approach works.
What you really need is to integrate data
into decision-making along the way.
You have a deep understanding of data to know when to lean on it
but also when to question it, and you
have a strong creative point of view,
and you bring the two together.
So you've got to almost create it [from the] ground up,
which is why we said we're not a data insights vendor.
We're a company that's creating really
a new kind of business that brings
data and creative together.
SHERVIN KHODABANDEH: You have to really close the loop,
because otherwise you have the problem
that I think a lot of early adopters
of data-driven decision-making fell into,
which is they said, "Well, data is
our future competitive advantage.
Let's acquire as much of it as possible and put it in some
Hadoop cluster somewhere, only to know we can't do anything
with it because we haven't thought through --"
SAM RANSBOTHAM: But they've got it, they've got it.
SHERVIN KHODABANDEH: They have the data, right?
And they're like the number of companies that have done just
that 10 years ago, 15 years ago, even doing it now --
they're like, "We'll get all the data.
Once we have it all, we'll figure out what to do with it."
But as you're saying, sometimes it's
about doing so much more with the data
you already have by connecting it
to the business process or the creative process
and connecting the endpoints.
SAM RANSBOTHAM: Kartik, we really
enjoyed both your background and how you're applying this
into your perspective on algorithms, and the difference
that algorithms can make in terms of exploring our search
space I think is fascinating.
But also those parts two and three
were pretty fascinating too, about how to integrate
that with creative.
We appreciate you taking the time to talk with us.
KARTIK HOSANAGAR: My pleasure.
Thank you for having me.
SAM RANSBOTHAM: In our next episode,
we speak with Sarah Karthigan about how
she's helping ExxonMobil use AI for self-healing process
improvement across business units.
Please join us.
ALLISON RYDER: Thanks for listening
to Me, Myself, and AI.
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