Knowledge Is Power: It’s Time to Learn The Story Behind the Numbers

Danny Wasserman:
This is Reveal: The Revenue Intelligence Podcast, here to help go-to-market leaders do one thing: Stop guessing. If you’re ready to unlock reality and reach your full potential, then this show is for you. I’m Danny Wasserman, coming to you from the Gong Studios. Howdy, howdy, howdy. Danny, the Rev, Wasserman, back to you for what will be one of the podcast’s most historic recordings, and I don’t say that with any shred of hyperbole. When we did this episode, it was actually transcontinental. We had three people in the studio, albeit remote, yours truly coming to you from Europe, in Dublin, specifically; our next guest, stateside; and our third guest over in Israel.

Perhaps you’re starting to connect the dots for who’s in the room, but let me tell you a little bit about why I think this was one of the more unforgettable exchanges we had in the Gong Studios, starting with our first guest. This individual, as you’ll hear in my opening bit for the podcast, has set the standard for what it means to be not just a financial journalist, but an actual journalist of all topics at hand, that challenge conventional wisdom. And his ability to string together an absolutely riveting story at a topic that we ordinarily overlook has set him apart. A numerous New York Times bestselling author who has then had multiple pieces converted into absolutely legendary blockbuster movies. We’re talking about The Blind Side, we’re talking about Moneyball, we’re talking about The Big Short. Folks, how the heck did we get Michael Lewis on the podcast?

Well, let me tell you a little bit about why. There’s a history between Michael, who also happens to host his own podcast, and Gong. He interviewed Gong’s CEO and Co-founder Amit Bendov, who really has done everything to embody one of the organization’s founding precepts, challenge conventional wisdom, and how he has led Gong over the last eight years to totally unearth new insights for how go-to-market teams can coach and train their sellers, their leaders, and apply those insights by capturing what is exchanged between sellers and customers, to product, to marketing. And the list of teams influenced by this technology go on and on. What I like so much about this exchange, which is really driven by Amit, who’s asking all the questions of Michael, is that we begin to understand that, in Michael’s experience, he finds that people are more attracted to stories than facts in isolation.

And what he describes storytelling as is the combination of, yes, using facts alongside personal experience and our own bias as human beings. And in thinking, well, what are the stories that Michael has told that are really most unforgettable? Let’s take Moneyball as an example. Billy Beane, who leads the Oakland A’s against all odds to a historic season by applying data and deep scientific analysis to how they strategically compete against larger clubs with much bigger payrolls.

Danny Wasserman:
What Michael tells us is that, if you’re going to be bold and daring to do something new for the first time, you need to anticipate that, in challenging the status quo, in undermining the way things have been done for years, if not decades or generations, you need to anticipate that you’re going to encounter resistance and objection. And, in boldly taking on that risk, you need to be armed with the fortitude, the audacity, and sometimes, yes, the data, to fight back. Yes, again, because data is such a powerful antidote against preconceived notions, yes, it’s critical that we come into this new approach to doing something with data, but as we talked about before, storytelling is not singularly a list of facts, but it’s how we contextualize those facts alongside personal experience.

I have no idea what I’m doing in this room with Amit and Michael. I’m grossly out of my depth. So what I’ll tell you is this, it’s one for the books. I hope you enjoy it. And with that said, DJ, spin that.

Danny Wasserman:
Over a 34-year career with his first piece of literature, Liar’s Poker, published in 1989, to his most recent piece, uncovering the history of Sam Bankman-Fried and the collapse of FTX. We’ve got someone who, in between those bookends, has put out numerous bestsellers that went on to become blockbusters, including but not limited to Moneyball, The Blind Side, and of course, The Big Short. Along the way, he hosts his own podcast, which leads me to our next guest who was a guest on this individual’s podcast a few years ago. We’ve got Michael Lewis, who a few years ago interviewed the CEO and co-founder of Gong, Amit Bendov.

Amit Bendov:
Well, thank you, Danny, and a pleasure to be here with you, Michael. It’s a real honor. Thanks for joining us today.

Michael Lewis:
Well, I love the conversation we had a few years ago, so I’m looking forward to it.

Amit Bendov:
Yes, I’m excited on so many levels. First, you’re one of the first to recognize Gong, and I remember you called it a Moneyball for sales, which, even before we met, first, I watched the movie probably six or seven times, and I really found connection because when I started researching before starting Gong, one of the things I noticed, if you go to Amazon and you search for books on sales, there are like 13,000 of them last time I checked, but none of them is actually based on data. It’s what people think, and the quote that is always tattooed in my brain: “You don’t have a crystal ball.” People are saying stuff and they’re advising, but without actually knowing, and the ability to connect data and facts to make informed decisions has been huge for us. So I’m super excited and couldn’t be more honored.

Michael Lewis:
Well, I mean, there’s a reason for this. Until the age of data, people didn’t really have the ability to, or even the inclination to apply the scientific method to all sorts of things, and that’s what you’re doing with sales, and I thought it was riveting. You came up in the season of our podcast where we were addressing the power of coaching and new forms that coaching had taken, but you could have just as easily come up in the following season of the podcast, which was about experts. One of the reasons the coaching was so powerful is it was a new form of expertise taking shape where you could actually tell people how many questions should you ask in a conversation or should you curse when you’re trying to sell something.

I thought of it as the Moneyball for sales because you were doing, in your sphere, what Billy Beane had been doing in baseball, which was creating new knowledge. And the power, that’s incredible, and also just interesting. Sometimes the new knowledge sort of confirms the old wisdom and sometimes it challenges it, but at least you have a starting point for a different kind of conversation.

Amit Bendov:
Exactly. I mean, the common theme, I think at least like a few of your books, if I go like Premonition or Moneyball and even Big Short, is that there’s lots of objections to evidence and data, even though it’s right in front of you, that not everybody accepts. Why do you think that is?

Michael Lewis:
So there’s a five-hour answer to this question, and there’s a two-minute answer to this question. I’m going to give you the two-minute answer to the question. I think the two-minute answer to the question is we don’t actually think in data and statistics, we think in stories, and that our minds are governed by narratives, and the narratives are infected by various biases. Those narratives are very hard to shake once they’re there. And if you present someone with a fact that contradicts the narrative they have in their head, they’re either hostile to it or it just doesn’t fit. It doesn’t fit the way, if you took a piece to one puzzle and tried to jam it into another puzzle. It’s like there’s no place for it. The way we process reality is through story, not through data and not through analysis. Human beings are not naturally statistical machines.

Michael Lewis:
Your fellow Israelis, Danny Kahneman and Amos Tversky, I wrote a book about this, The Undoing Project, spent the better part of their careers showing the ways that people, even when they were given a problem to which there was a statistically correct and solvable… it was a solvable problem and there was a statistically correct answer, defaulted to some story rather than the statistically correct answer. So people do that over and over. Now, it’s forgivable for all sorts of reasons, but it’s not until fairly recently was it easy to gather, access, analyze data, essentially do the statistics in places, so that story was all you had. But now we have more, and so there’s this collision that’s going on in all these different human activities. I wrote Moneyball 20 years ago. It’s why it’s still in the air, because that collision keeps happening in places you just would never expect it to happen.

Amit Bendov:
Right. It’s interesting because a narrative in stories has been with us for hundreds of thousands of years at least, and data is a fairly new phenomenon, or at least data at scale and used in day-to-day. What do you recommend, let’s say if I work in an organization and I have strong evidence and believe in a direction, but I’m fighting a narrative, that’s someone that just refuses to see? What’s the best way to get someone to see what they refuse to see?

Michael Lewis:
It’s really hard. There’s not a simple answer to this. I mean, let’s continue with the Moneyball analogy. This is how complicated it is that, even after, even when the boss of the organization has bought in to, “Our decision-making is going to be driven by data, not by story, not by the intuitive judgment of our scouts.” And even when the organization has succeeded beyond wildest expectations using these methods, the people in the organization still resist the methods.

So if there were a simple answer to that question, the Oakland A’s would not have been having fights within the Oakland A’s five years into their experiment with using data instead of scout judgment to make a lot of big decisions. I don’t actually think there’s some easy answer to this. In fact, I do think that some people just are comfortable with the database discussion and some people just aren’t, and the people who aren’t, in the case of Moneyball and the Oakland A’s, the only way that you brought them around to your way of thinking was by shouting at them. Billy Beane was imposing reason with violence, which is a really weird thing to do. So if you said, “Okay, Michael, please give me the next-best answer, there’s no perfect answer to this,” I would say that really the only thing that beats story is another story. And so you’ve got to take the data and weave it into a story. You can’t just present the data, use it to tell a different story, and that story might trump the first story.

Amit Bendov:
I mean, I’m curious what you’ve run across. You are presenting to people who think, you say there are 13,000 books on sales, and just like baseball, lots of people who are very good salespeople or lots of people who think they know what the best way to sell is, you walk in and you say, “Actually, no, you should be talking less and listening more,” or “You should be asking more questions,” or “You should not be cursing,” or whatever it is you’re saying to them, what happens then? You must have arguments, right?

Amit Bendov:
Absolutely. We actually showed data and tried to keep it in a very simple way, not like a huge philosophy. But one thing, here’s a fact, here’s the data with and without kind of control group, and with, it’s hard to ignore. There are actually people like that. There are still people who think that sales is an art. You know who wrote The Art of the Deal, the US president. I said, “No, art actually belongs in a museum.” We use the word art to describe things that we don’t fully understand.

Michael Lewis:
Correct.

Amit Bendov:
It’s not a lot different from magic or witchcraft. And our mission is to chip in and out every time and carve out a piece of data.

Michael Lewis:
To make sales less of an art…

Amit Bendov:
Yes.

Michael Lewis:
…and more of a science.

Amit Bendov:
More of a science. Most things are explainable. It’s just stuff that we don’t know yet, but we will one day. And I also think it’s part of our education system, our kids learning stuff that isn’t relevant. But the basic things about data, just probabilities, how do we understand a survey? I mean, there are a lot of fake news right now. People are using surveys, how to really understand what’s being asked, how to interpret the data. When the iPod first came out, people were saying, “Oh, Steve Jobs cut a deal with Elton John, because the random shuffle always plays Elton John,” and they don’t know that actually that’s through randomness. Through randomness, you could get five times Elton John. So they created a human narrative of randomness.

Michael Lewis:
We aren’t intuitively statistical.

Amit Bendov:
Yes.

Michael Lewis:
Right. That’s right. That creates all sorts of problems when a statistical-based understanding walks into the room.

Amit Bendov:
But if we teach the kids at school, this is an age where they can learn, and this is something that’s so useful in day-to-day just for understanding risk, to weighing different options. That’s such a useful skill that we’re not teaching. He also, when we spoke, it was about coaching and expertise. Now, there’s obviously the resistance to data that a lot of people just don’t understand or it doesn’t fit their narrative. But there’s also sometimes opposition to experts. You think it’s kind of the same thing, or there’s a nuance to that?

Michael Lewis:
I think the resistance to a statistical understanding of a matter is one category of resistance to experts. Across society, there’s a rise of a different kind of expert. Simple example would be Nate Silver in political analysis, going back to 2008 when using better analysis, better data, he made sensational predictions about what was going to happen in not just the presidential election, but all the congressional elections. And overnight, FiveThirtyEight’s born. Everybody’s like, “Oh, my God. He knows something that the political pundits on TV don’t know.” But, also, he faced enormous hostility and resistance, and it still does to this day.

Where the resistance comes from, it is Nate Silver’s expertise in that moment is perceived, not unreasonably, as an assault on experience, that all of a sudden the people who are being paid to be pundits on cable news are made to feel like they don’t know what they’re talking… He’s saying they don’t know what they’re talking about. So that war breaks out. It’s a form of the Moneyball wars. That’s just one form the resistance takes. There are other forms, though. Here’s a really great way to dramatize that and another form of the resistance to expertise. If you talk to ancient weathermen, like people who’ve been on the television for 45 years, they will tell you there’s a mystery in their lives.

And it’s that, if you go back 45 years ago, they actually knew very little. Their predictions were not great, not much better than going outside and looking up at the sky and then coming back in and saying, “It’s sunny and probably will stay sunny for a little bit.” And over time, weather prediction has gotten much, much more sophisticated. It’s unbelievable, actually. If you went back, I don’t know, 50 years and said, “Guess what they’re going to be able to do in 50 years?” People will say, “No way. They’re not going to be able to, with degrees of assigned degrees of uncertainty, be able to tell you what the weather’s going to be three days from now.” People would be just very skeptical. The weatherman will tell you that back in the day when they knew very little, they were treated with great respect.

And that, as they’ve learned more and more, they’re delivering more and more actual knowledge to their customers, to their listeners. But the listeners are more and more angry about and skeptical of them, and more and more inclined to jump on them when they get something wrong. So it’s sort of the curse of understanding, like in your world, in sales. Let’s say you get to the point where you’re quite precise about what people can do, but you get one thing wrong, you’re going to go from, “Oh, my God, he’s actually introduced some knowledge to us,” where you are now. “Oh, my God, just tell our people not to curse until the customer curses first.” These are great insights. To, “Oh, Christ, they screwed up. Gong screwed up. There’s a mistake in Gong.”

So it’s like we’re holding them to such higher standards because they’ve actually gotten so much better. Expertise has gotten better across lots of dimensions. There’s another aspect to this, and I don’t want to just monologue about this, I can go on forever. But it’s that a lot of experts are being put into the position of referees, basketball, sports refs. And the refereeing position is just increasingly under attack because of just general, I don’t mean just political polarization, but polarization, this tendency in the culture for people to tribe up, take sides, anchor in whatever opinion they have, whether it’s they root for this team rather than that team or that politician rather than this politician or two sides of a war. Everything has become a religious dispute.

And so a lot of experts find themselves in the middle of these disputes, and both sides have an interest in eroding the authority of the expert in any given instance. So it’s a curious situation we’re in. Broadly speaking, experts get better and better. And, broadly speaking, most people probably think they’re worse and worse and trust them less and less.

Amit Bendov:
Right. It’s a combination. There are two trends. I connect with what you said about holding experts to higher standards. If you think of AI, take a look at self-driving cars.

Michael Lewis:
Yep.

Amit Bendov:
One Tesla crashes and then it gets a front page in all the newspapers. Now people crash a lot more.

Michael Lewis:
Yes.

Amit Bendov:
We’re worse drivers than the self-driving cars. But if you look for perfection, it’s hard to accept that something that you expect to work better actually messes up once. And second is the wealth of misinformation or disinformation that’s available on social media today that actually has a polarizing and almost creates more ignorance than knowledge.

Michael Lewis:
Well, certainly because there’s a constituency for every argument. There’s some constituency that’s hostile to self-driving cars, and the minute the self-driving driving car screws up, or it’s more likely they’re hostile to Elon Musk. And so they can amplify this thing. But the background facts are that a million people a year die in automobile accidents around the world. And it’s just like that’s the air we breathe. That’s an extraordinary example of this phenomenon.

There’s another thing going on, and in my most recent book, Going Infinite, the book I wrote about Sam Bankman-Fried, it was a dystopic form of this thing. But one of the things that emerges from the whole Moneyball approach to the world is a kind of maybe unintentional, but nevertheless unignorable denigration of experience that, if the data’s just going to give you the answers, who cares what you know from your lifetime in baseball? That you’ve spent 50 years in the minor leagues and playing and you have all this felt knowledge and intuition, and along comes a guy with a laptop and he says, “None of that matters. Here’s the real answer.”

Sam Bankman-Fried was an extreme case of this and took it in all kinds of wrong directions. But one way to think about the way he went through the world, and he really did go through the world this way, is thinking that nobody’s experience actually had anything to teach him, that everything could be reduced to numbers and probability and an expected value calculation, that as a result, the adults who walked into his life, into the room, and tried to exercise any kind of influence over him were dismissed and eventually just not ever let into the room. So there is this other thing going on, a collision between people with experience and people without experience and the people without experience using the rise of data and analytics as a kind of status move. And we saw where this ended up with FTX and Sam Bankman-Fried.

This is an observation that there is a thing going on right now, and I wonder if you see it in your life that, yes, better data, better analytics, asking questions of the data in an intelligent way leads you to actual insights about how to sell something. Nevertheless, you would not claim that you’ve learned everything there is to know. And there are these people out there who are unbelievable salespeople, quite possibly they know things you have not divined from the data, and there are things to learn from their experience, even if they don’t know quite how to put it, even if they don’t know how to exactly replicate themselves. And I think we ignore those people at our peril. Human experience is very complicated. The human mind is very complicated. There are things to learn from people who might themselves not have a whole lot of time for data and analytics.

Amit Bendov:
Right. It’s tricky to balance these things, and I always think about when I hire people, there’s obviously advantages to hire someone very experienced because they’ve been there, done that. They know what they could get in. But there’s also an advantage in hiring someone who doesn’t have the experience, that don’t know that they can’t defy gravity.

Michael Lewis:
Yes.

Amit Bendov:
They’ll try and it might be successful. It’s good to get a good mix. And I totally get it, that the data itself doesn’t tell the story. You need to understand the world. And it reminds me of a quote, a scene from Good Will Hunting, and I think Robin Williams towards the end says like, “Well, you know what the fresco on a Sistine Chapel looks like, but you never smelled the paint.” It’s like all is from the books. There is a real world and you need to connect both.

Danny Wasserman:
Telling a story about your brand is a great way to get people interested and involved with what you’re doing. As we heard from Michael, our brains are hardwired to stick to and react to stories. Well, Marketing Words blog in 2023 found that 55% of customers are more likely to remember a story than a laundry list of facts. Think about it. Aren’t most social interactions involving the recounting of personal stories that help deepen, if not create, relationships between individuals? Yeah, it’s because those stories are more engaging than hard numbers.

And again, we’re not here to poo-poo those facts. We need those as well. But as it turns out, Search Engine Watch found that stories can increase conversion rates by up to 30%. Not only that, we are drawn to this type of dialogue. Rain Group found that sales reps who use storytelling are 22% more likely to close the deal. So yes, whether it’s personal or commercial relationships, it’s been proven that stories, alongside facts, help convert. Let’s get back to Michael and Amit and hear a little bit more about the intersection between storytelling and business.

Amit Bendov:
I’ve got to ask you, I’m sure everybody’s curious. I mean, you’re a prolific writer and I don’t know how you do all of that and not just write, but create great work, one after the other. First, how do you get everything done? What’s your productivity secret that you’re willing to share? And second, we here at Gong, we try to hone our craft, become better, that we’re better every time. How do you improve?

Michael Lewis:
Well, that’s an interesting question. How do I get everything done? I think of myself as kind of lazy and unreliable. Unless I have a deadline, I’m capable of doing nothing. I just finished a book tour last week. I don’t have a whole lot going on. I don’t really owe anybody anything, and this is a very dangerous period in my life in that I really am capable of just screwing around for months. I don’t feel internal pressure to produce something. What will happen and what has just happened over and over again is I will collide with something that really interests me. I’ll start to noodle on it. I’ll start to learn about it. I’ll eventually call my publisher and they’ll eventually say, “Well, when will you get this to us?” And I’ll eventually give them a date. And for whatever reason, that date has this huge effect on my life. I’ve never missed a deadline, just never done it. It’s just some sort of internal rule.

So the minute that date is in place, I’m in a state of panic. Then I become extremely productive. I try to delay the arrival of that date because a lot of useful stuff happens when you don’t have pressure. But then a lot of useful stuff happens when you do have pressure. Because you’re sitting in Israel, Amos Tversky and Danny Kahneman are all of a sudden salient to me in a big way. But apropos of deadlines and productivity, Amos Tversky had this line that occurs to me whenever I have a deadline, but it was in a different context. He had this habit of being very ruthless with his time, and if he walked into a situation, decided this was not worth his time, he would just walk out. So he would walk out of dinner parties before the salad was finished just because… It was rude but that was just how he was. And someone asked him once, “How do you do that? What do you say?”

And he says, “I found that if I just get up and start walking, it’s amazing how my mind generates the words I need to give them an excuse for why I’m walking.” And I do feel deadlines do this for me. The minute I start walking towards the deadline, the stuff just kind of comes out of me. How do I improve? This is largely by feel. I do think about that, though. This is what it is, that I do try or think about when I’m deciding what I’m going to write about: Will this allow me to hit notes I’ve never hit before? So it isn’t so much, “Oh, I’m just going to get better.” It’s, “I’m going to be different.” I try not to write the same. I don’t want to write the same book twice.

There was a sequel to Moneyball that I was supposed to write. I sold it as two books, and the minute I realized this was just the same thing all over again, after two years of work, I walked away from it. So I would say that’s my big thing is that I avoid doing the same thing twice. It may not look that way to you. It might look to you like I’m just singing the same song over and over, but in my mind, the books are all different from one another. They’re presenting different challenges.

Michael Lewis:
One other thing about improving in my writing is what I would call cross-training. In addition to writing the books you read, I write podcasts. The podcast I did with you was scripted. I actually had to write it out and perform it, and I write film and TV scripts. The film and TV scripts are exercising this muscle that you’ve got to visualize everything and you’ve got to compress everything. So you’ve got to see pictures in your head, because that’s the information that the audience has, the pictures in their head, and there’s a lot of words that are unnecessary when you know they have a picture to go with it. And with the podcast, it’s directed to the ear as opposed to the eye, and the ear is a different kind of instrument than the eye. The ear is more emotional. People hear tone. It’s easier to make people laugh, make them cry if you’re going through their ear than through their eye. Easier to deliver complicated explanations through the eye.

If I tried to explain a collateralized debt obligation here on this podcast, it would be a catastrophe, but on the page, you can kind of do it. But working those muscles, that writing for the ear and also writing for that visualization mechanism bleeds back into my books. I can see it makes a difference. So that’s another way I improve. The third way is actually process. It’s realizing that, which I did years ago, that if I read my work aloud, the printed work, I’ll catch stuff that I wouldn’t catch if I didn’t read it aloud. So that’s adding things to the process. There you go.

Amit Bendov:
Absolutely. No, super helpful. I’m sure everybody would appreciate it.

Amit Bendov:
So you wrote a lot about the data evidence and experts, and now we’re entering the era of AI, which uses both. You have an expert that is either easy to agree with or easy to disagree with, kind of both. Do you have any thoughts? Where is this going and what’s the next chapter?

Michael Lewis:
Where artificial intelligence is going?

Amit Bendov:
Well, more of the impact on our world.

Michael Lewis:
I’m an ignoramus, so you’re getting the ignoramus’s view of AI. It’s like natural stupidity trying to get its mind around artificial intelligence. But the first thing I did when we all at once started to pay attention to this when ChatGPT-3.0 came out is I asked it to write the first chapter in a book about Sam Bankman-Fried by Michael Lewis, in my style, and it generated something, and it was so bad in so many ways that it was… The phenomenon was the famous dog walking on its hind legs analogy. The wonder isn’t that it does it well, but is that it does it at all. Yes, it was amazing it even tried, but it was so bad. Even the idea of the book, the only information it had was the information that was on the web. It just felt preposterous. So it was like, really? Is this something we’re going to actually start to worry about replacing us? But then I watched my son use it for his homework, and it was encouraged. It wasn’t sneaking around. It was like, maybe you do your first draft this way.

Michael Lewis:
At that level, when you’re just summarizing existing material, it was surprisingly useful. So my first thought was, well, that’s what it’s going to do. In the first instance, it’s going to replace lots of really boring jobs, like tasks that people don’t really want to do, and they don’t want to do it because the human being is not adding all that much to it, like summarizing a book you read. It’s going to free up life for more interesting things or for total inactivity. I’m sure it’ll get better. I don’t know. And of course, what everybody says, it’s going to get better very fast, and we’re going to be shocked by how much better it gets.

As it gets better, I assume the low-hanging fruit, it’ll start gobbling up fruit higher a little higher up in the tree. Slightly less boring jobs or tasks will be replaced by this thing. When it gets to me, which I’m interpreting this question as, how is it going to affect me? I kind of think never, and I’ll tell you why I kind of think never. Maybe 100 years from now. But if I were to write a book, most of which you could just find on the web, that would be a very bad book. It would be so unlike any book I’ve ever written.

What I do is go find stuff. That’s my job. In fact, what AI is, if you think about it this way, it’s a wonderful affirmation of the importance of reporting. Or put it another way, the importance of research and searching on your own and going out into the world and talking to people and using your senses to organize the world in a new way. Because at the end of my process, when I sit down to write a book, most of the stuff I’m writing, you couldn’t find it on Google. Google wasn’t in the meeting I was in or didn’t watch the scene I watched or didn’t observe this little interaction between two people that is incredibly revealing. And so, if it’s not there already, AI can’t do anything with it.

Which isn’t to say that AI might not try to take my book the day after it comes out and put it out of business. It may have a commercial effect, but the challenge to what I do, it doesn’t feel like a threat. It feels like, in some way, maybe one day it’ll be slightly useful to me as like a research assistant.

Amit Bendov:
Absolutely. Yeah. It’s not great with the creative stuff. I think where it could—you spoke earlier about the narrative. Narrative, we can think of a blank page that has a lot of dots. And narrative, I’m connecting it almost like connecting the dots and I’m telling this story, but there are different ways to do it that it could show us how to do it.

Michael Lewis:
Yeah. No, totally. I mean, it’s funny you say that. The way that it finds new ways to play chess. It never occurred to a human being that you could play chess, those moves would work. It can try every possible version of storytelling and something might turn up that no human being had conceived of.

Amit Bendov:
Absolutely.

Michael Lewis:
I’ll be impressed when it does that. It hasn’t done it yet, but that’s right. That might happen.

Amit Bendov:
Well, the chess is where it’s doing it, but it’s definitely doing it on data as well. I’m pretty sure. We’re getting close to our time, but anything you can share about your next project?

Michael Lewis:
At this very moment, I’m on my way to meetings in Los Angeles to talk about a TV show that I sold to Apple. It’s a drama. It’s a fictionalization of an article I wrote in Vanity Fair years ago about the man who invented the market for Cuban baseball players. He was a human bridge over which the first Cubans escaped and played in the early ’90s and played professional baseball. They didn’t think they could, and he persuaded them they were good enough. It’s like a wonderful American story, and the question is… I’ve written the script. I’m about to write a summary of the show, and in the next month, I’ll know whether they’re going to do it or not, and if they do it, it’s probably the next couple of years of my life. Other than that, I have half a dozen very beginnings of ideas that might be books, but I’ve got to wait because if I’m running a TV show, I don’t have time to write a book. Something will walk in. Something always does.

Amit Bendov:
Well, we’ll keep our fingers crossed and we’ll call Tim Cook to get the deal done.

Michael Lewis:
I hope when you’re in San Francisco, it would occur to you to just shoot me a note and we can go grab dinner sometime.

Amit Bendov:
I would absolutely love it.

Michael Lewis:
I would love to do that. I mean, you are going to keep learning stuff.

Amit Bendov:
Yes.

Michael Lewis:
You’re like an ongoing story and I would like to not be too far away from it.

Amit Bendov:
All right. It’s a deal.

Michael Lewis:
All right.

Amit Bendov:
Michael, thank you so much. Danny, great having you. And thank you to our audience. That was a great episode.

Danny Wasserman:
Thanks so much for listening to this episode of Reveal. If you want more resources on how revenue intelligence can help you create high-performing sales teams, then head on over to Gong.io. And if you like what you heard, well, go ahead, give us that five-star review on Apple Podcasts, Spotify, or wherever you may listen.