Latest / The Innovation Show / Rita McGrath & Sangeet Paul Choudary — Rule Makers, Rule Takers and Structural Uncertainty

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Rita McGrath & Sangeet Paul Choudary — Rule Makers, Rule Takers and Structural Uncertainty · The Innovation Show

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Conventional strategy used to have specific rules.
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When the game was largely about occupying markets in the physical
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world and dominating over companies doing the same thing you were, how we
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used to think about competition, , you duked it out with existing structures.
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Now, with the advent of a digital world, AI, and dematerialization, you win by
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changing the structures themselves.
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You could even say reshuffling them.
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One of today's guests wrote the book The End of Competitive Advantage,
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which highlighted the birth of transient advantage, a business
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strategy stating that companies' edges are very short-lived.
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Our other guest wrote a book called Reshuffle: Who Wins When
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AI Restacks the Knowledge Economy.
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So kind of like Reese's Pieces, I mashed these two things together, and it makes
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me wonder, are we seeing the end of transient advantage, or are we seeing
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a doubling down of certain advantages?
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So before we attack those questions, let me first welcome not only
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friends of the show, but two people who have become friends of my own.
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First, a lady who never sits still.
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She just brought into the world a grandchild, and now she's bringing
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in a new child and a new book, and she doesn't sit on the laurels of her
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massive success and her hit books.
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She's just written a new book, which we'll hear a little bit about today.
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Welcome to the show, as always, Rita McGrath.
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Pleasure to be here, Aidan
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Always a pleasure, Rita.
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And a gent who recently won the Thinkers50 Award for strategy for
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his work on platforms and then his hit book, Reshuffle, that
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many of us have studied over time.
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And he will give us a sneak peek, not only into his new book, not Reshuffle
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Part Two, but the man who calls The Innovation Show, the idea sommelier, the
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fine wine himself, Sangeet Paul Choudary.
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Welcome to the show.
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Thank you, Aidan.
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So good to be back
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It's great to have you, man.
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And I looked everywhere for white gloves to treat you two fine wines
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with the respect you deserve.
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But I thought we'd begin with some definitions.
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I thought we'd take a little lesson from your book, Rita, which I wish I
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had 10 years ago, The Entrepreneurial Mindset, where you talk about
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the idea of an assumptions map.
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So not, let's not let any assumptions go astray and assume
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people are on the same page.
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So let's give a whirlwind history of what got us here.
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Rita, you've talked before about the theory of the growth of the
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firm, the late Edith Penrose.
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Your, the, the current collaborator of yours, Coletta Perez, her role,
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her work plays an important role in understanding long- longer term cycles.
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So I'd love you to give us a whirlwind history of what got us to
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today, and then maybe where we are today, and then what's reshuffling
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it later, Sangeet can talk about
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I'll try to make it concise.
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So if you think about the field of strategy, it had, it has its
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intellectual roots in a thing called industrial economics.
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And industrial economics makes two assumptions that I would argue and
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believe Sangeet would agree, in today's environment are just kind of absurd.
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The first assumption is that there is such a thing as an industry with
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clearly defined boundaries, and that the most significant competition you'll
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face comes from within that industry.
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And the second assumption is that the normal state of things is equilibrium.
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Because if you're an economist, you love equilibrium.
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You can derive all your fancy formulas from that.
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if you relax those two assumptions, what that creates is a very different world.
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And so if I were to go all the way back to Edith Penrose, who was very
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interested in how firms actually grow, and unlike most economists
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of her time, she actually talked to people and studied what firms did.
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And what she articulated was that firms would a success, they'd accumulate
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some s- some, slack resources, and then entrepreneurially minded managers
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would go off and look for new areas to deploy those resources, and
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that was what caused firms to grow.
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And that firms themselves were unique in that their paths were
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path-dependent and idiosyncratic.
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So even if you wanted to copy a firm, you'd gone on that same journey, it
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would be very difficult for you to do so.
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So moving forward, what I started to look at back in the '90s was this
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phenomena of transient advantage.
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And my first book, Entrepreneurial Mindset, was about, well, what do you
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do when advantages don't last and when the most significant competitors you
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face are not even in your industry?
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And so that book was how do we teach companies to behave
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like habitual entrepreneurs.
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next book was Market Busters, which was where do we look for opportunities?
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Then Discovery Driven Growth was where do we, , how do we make all this systematic?
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End of Competitive Advantage, as you said, Aidan, was really about this transient
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advantage phenomena and how we need to have a new playbook for strategy.
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And then the most recent one, Seeing Around Corners, , was, well, if
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advantages are transient, what are the signals in the environment you
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need to be paying attention to?
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Now, the new one is really looking at what I believe to be this massive
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transition, and I, I think, Sangeet, you, you agree as well, which is
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we're really going from this, system of physical things, mass production,
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suburbs and appliances and, you know, basically a, a robust middle class, to
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a system which has yet to be defined.
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We know some of what its contours are, but we know that the mass market
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paradigm is not gonna be the one that w- works for this future system.
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So Carlota Perez is a, is a researcher, an economic historian some people
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call her, and she studied this and said, "You know, we've been down
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this path before with capitalism."
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And what happens is an old regime, and we, in our, in our situation,
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the mass market phenomena loses its ability to continue to drive growth.
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Capital goes looking for the next big thing.
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When it thinks it finds it, you know, capital goes flowing and creating
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a bubble, , or a series of bubbles.
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But anyway, what happens is eventually the bubble comes to an end.
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Capital, which has been gaily operating in kind of a casino
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environment, sort of sobers up.
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And if we play our cards right, we, can get, , capital aligned with production,
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we can reduce income inequality, and we get to what Carlota calls a golden age.
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And she's argued we've done this five times already before.
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So we both, Carlota and I both believe that we're at this turning point.
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It's, and it has all the hallmarks of a classic turning point: massive
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inequality you know, populism, people feeling rightly, you know, we played
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by the rules and the, the rules changed on us, and we feel we're being treated
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unfairly, and, and, and, and, and.
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And on top of all that, now the substrate of what we're doing is creating digital
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inputs, which are general purpose.
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So Carlota was talking about GPT long before we knew about AI.
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But as oil was cheap and ubiquitous and everywhere in the previous regime, today
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data, information, and intelligence are that in our current regime.
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So that kind of brings us to where we are.
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the concept of capabilities is an important one as well.
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Maybe before we come to Sangeet, the systems thinker himself, maybe we'll talk
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about that because capabilities will open up later in the conversation, the idea of
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playing to different rules, being a rule maker, being a rule taker, what arena
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you play in as well, which is something that you talk about a lot in your work
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So I define a corporate capability or competence as equivalent to a
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human skill, so typing, for example.
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And when you first start, you're not gonna be very good, and then as you
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gain practice and get better, eventually you get to the point where it's so
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natural you don't even realize you're, you're making an effort to type.
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So a capability in a corporate context is the ability to increasingly
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achieve what you set out to.
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in my own dissertation, what I, what I studied was how,
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where these things come from.
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So it starts off with understanding what's driving what, which would be
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sort of discovery-driven planning.
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How are you converting assumptions to knowledge?
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Then you build a team that gets better and better over time.
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Now, in the beginning, they're gonna be terrible because they don't
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know what they're doing, literally.
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As these things start to work together, you start to see the emergence of
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new competencies or new capabilities, some of which are distinctive, and
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some of those distinctive competencies can create a competitive advantage.
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And Sangeet, I would love a different word.
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I don't, I 'cause it's not advantage over competition.
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It's gaining rents from the environment that, that you alone
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can access, and I don't know how to describe that succinctly.
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But anyway to me, a competitive advantage has three necessary conditions.
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So you have to get the market enthusiastic about it, so somebody's
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gotta be willing to pay you for this.
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You've gotta get your company enthusiastic about this, and we have this myth in our
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heads that something that's beloved by the market is gonna be beloved by your firm.
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No, not necessarily.
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Your sister divisions may not be set up to support and endorse you.
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And then you've gotta have some kind of competitive insulation, otherwise
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people will copy that's the, the growth path of a competitive advantage.
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Sangeet opens you up perfectly because recently I had the amazing
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honor to host Carlos Baldwin and Kim Clark on the book Design Rules.
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And what I, what I really learned about it, also with the work of Rebecca
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Henderson, was that you create those capabilities that Rita talked about.
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You're terrible at them.
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Then you create the firm based on those capabilities, and you inadvertently
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create s- silos inside the company, and then that informs all future
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products going out of the company because they become kind of a mirror
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of the organizational setup, and they need to be reshuffled in an age of AI.
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Or not that they need to be, but they have to be.
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They have no choice as the organization.
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So I, I always see your work as a, a systems thinker in action, and actually
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the growth of your work through platforms as well to where you are today.
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But I thought it was important to talk about something that's a huge
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shift from a physical world, where we created physical products, to a
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digital world, which is the idea of a network effect on a digital platform.
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I'd love before you get stuck in on where you see strategy is today and
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that idea of rule maker, rule taker, to describe to us the idea of a network
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effect and what a digital platform is
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Yeah, absolutely.
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So my, my first body of work was focused on explaining digital
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platforms and network effects.
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But really what we saw with digital platforms back in you know, 2010s and
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what we are seeing with AI right now, there's something underl- underlying
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both of them which is very similar, and is that you have a shift in technology
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because of which previous bottlenecks get removed, you start seeing a fundamental
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restructuring , of the landscape.
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I won't even use the term industry to Rita's point.
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You start seeing a restructuring of the system.
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And so the last time around the most interesting set of shifts that brought
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this together was the convergence of the smartphone data and cloud capabilities
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and to some extent, the social graph.
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All these four things coming together gave us what we now
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think of as the platform economy.
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Now, the reason that's interesting is that before that convergence happened,
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the shift to online had already happened quite some time back, but
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the mental models we had all through were very much channel-based mental
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models or artifact-based mental models.
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We were focused on transforming the artifact from the store to the
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website, from offline to online, and we even looked at, say, a
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Netflix versus Blockbuster as well.
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Netflix is going online; Blockbuster was offline.
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Fundamentally, we were missing the fact all through that when technological shifts
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play out, they allow a fundamentally new way to restructure the system.
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I'll just give a simple example which happened even before the, the smartphones.
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So N- take Netflix as an example, right?
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Both Blockbuster and Netflix were delivering DVDs offline
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and through physical logistics.
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But what really separated the two was the fact that Netflix had a data-driven
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inventory management system which allowed it to predict demand at national scale.
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Because as customers were queuing which DVDs they wanted to get access
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to, they were giving the signals to Netflix in terms of where they
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could move DVDs across the country.
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And what that helped Netflix do was it allowed them to convert
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what was previously a local market, because Blockbuster, you had
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local supply serving local demand.
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Even if the movie was available 40 miles away, you would not
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drive all the way to get it.
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You were still s- being served by stores in your vicinity.
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Netflix, because it could move inventory at national scale, was able to serve
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local demand with national supply.
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And it's simply impossible for a local demand, local supply
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economic model to compete with that for a variety of reasons, right?
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And that, that essentially is what enabled Netflix to, compete or, to,
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fundamentally create a model that Blockbuster simply could not copy.
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It could copy the removal of late fees, it could copy going online.
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It could simply not copy this model of serving local demand
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with national inventory.
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Now, the reason all of these things are important is because when we started
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seeing the convergence of smartphone, social, cloud data, we started getting
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the ability to create global markets which could be orchestrated using a central
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brain, which is what data allowed, and which could have trust embedded in them,
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thanks to social technologies improving.
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And so that led to the rise of companies like Airbnb, Uber, and so on.
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And what we saw as the dominant model across all of this was that the firm
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was no longer creating products and services and selling it to the customer
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in the traditional linear model, which is what I call the pipeline model.
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The firm now was creating the rules and the tools for the market to come
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together and transact with each other.
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But the other thing that, the firm was doing was that it was changing
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the distinction between who was a rule taker and who was a rule maker.
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Traditionally, the regulator was the rule maker, and companies competing
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in the market were the rule takers.
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And the structures within which they competed, the industry structures
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that we're talking about, used to be very fixed, very clearly defined.
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And what started happening first with the smartphone, because
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a-a-and with data, because with both of these things, you could now
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easily cross industry boundaries.
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The smartphone allowed the customer journey to cross
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different industry boundaries.
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Data allowed capabilities to cross different industry boundaries.
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And because of that, you now had this convergence where the rule making of
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these new arenas was up for grabs.
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And so that created this, the distinction between who could make the
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rules and who could take the rules.
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So you had Uber, Airbnb, Google, Facebook, all of these companies emerging as
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rule makers, eventually the platform companies, and everybody participating
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in their ecosystems were the rule takers.
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That brings us to this idea of network effects.
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You know, the i- fundamental idea of network effects is that once you provide
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the tools and the rules, because you are making the rules, others are taking
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them, and you organize demand and supply around yourself, you start benefiting
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from network effects, not because you've aggregated the market, but because
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you aggregated them in a way that they subscribe to your rules, and hence the
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more they engage with your governance mechanism, the more they play by your
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rules, the more value gets created on that basis, the more it becomes difficult
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for somebody else to displace you.
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And that is essentially what we saw with the rise of platform business models.
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Beautiful.
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Beautiful job.
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I wanna just double click on one thing you said there, because it
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speaks to what Rita calls an arena.
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So what arena do you play in?
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And, , it's why I love doing this work, I think the work that we do is
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to give people a new set of lenses.
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Our mutual friend, Scott Anthony, told me that Clayton Christensen's wife,
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Christine, used to joke with him that his, his glasses had an S-Curve built
16:31
into them and, and his disruptive innovation model was in there, and
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he saw everything through that lens.
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And it stuck with me as this concept that when we give an organization a
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new lens through the work that you do, that we all do in this field,
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they can see advantages that they have sitting right in front of them.
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And the reason I mention that is sometimes organizations, lots of which you've worked
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with, don't see the oil, to Rita's point, that could be data sitting right in front
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of them, some capability that they've, they've built, that if they see it through
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the new lens, they could unlock it.
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And I think bringing them this work and bringing them these
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new concepts, the books that you write, is a way to unlock that.
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And Rita, I'd love you both to share an example of when you've seen that in
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play, because I really wanna make this relevant to people to go, "Oh, that's
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not just talking about Airbnb and Uber."
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Airbnb, A- Airbnb was a physical product in the first place, and
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eventually either they realized or some consultant or some- something they
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read unlocked a new way of seeing their v- their business, and it became a
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platform, or they saw it as a platform.
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I'd love you to share that i- as an encouragement for people to
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see that this is relevant to you and so much so in this age of AI.
17:46
Oh, absolutely.
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Before I go there though Sangeet I was at an investor conference not too
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long ago, and there was a, you know, an investment advisor there who said
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he had always bet against Netflix.
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And we all looked at him in horror and we said, "Why would you bet
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against Netflix? They're so great."
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He said, "I never thought the movie studios would be stupid enough to provide
18:05
them with all that content." I think it really reinforces your point about where
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the choke points are, because if you look at it very narrowly, right, what the
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movie studios were all saying was, "Whoa, all this new revenue from, you know,
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old libraries of content which we are not monetizing anymore," because they've
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already gone through their life cycle.
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They were in the movies, then they were on TV, then they were
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on reruns, then they were… And they're just sitting there now.
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, We're not monetizing them.
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And Netflix "Oh, here's a whole new revenue stream if you license them to
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us." And this guy was saying, "I can't believe anybody would be so stupid
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to allow this competitor to emerge in a market, with that would eventually
18:40
destabilize that power transition."
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something I learned from you was then, Netflix didn't rest on their laurels.
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They saw the threat, and they went, "Actually, we need our own competitive
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advantage here, which is our own content."
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to make it really concrete, I run a course at Columbia called Leading Strategic
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Growth and Change, and as part of that course each participant prepares what
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we call a personal case, which is some situation or dilemma or problem that
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they're facing, and they write it out.
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And then we put them in small groups with w- e- every day they, they work with their
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groups on making progress on their case.
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Now, they may not get to a solution, but almost always they have their assumptions
19:18
challenged, they think about whether they defined the problem accurately or not.
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And one of the participants in that course was a guy who was in
19:25
the seed and fertilizer business.
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S- sensible enough, right?
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And his, the problem he came into the course with was, well, how do
19:32
I improve, you know, better, better seed and fertilizer combinations?
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And, and was wrestling with it, and about halfway through the course,
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the light bulb went off in his head, and he said, "Wait a minute.
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I'm not in the seed and fertilizer business.
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I'm actually in the information business, because through the business
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of selling these seeds and fertilizers, I have all this information about the
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quality of the soil, and which plants respond best, and how best to deal with
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weeds, and that information actually has huge value to local townships,
20:00
and environmental regulators, and people that are concerned about, you
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know, are we overusing the soil?"
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And, you know, all kinds of things.
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And he left the case, and you'd be very proud of this, Sangeet.
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He left the case with, the course, with the idea of creating indeed
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a platform on another example is the Leading Hotels of the World.
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Same, same sort of scenario.
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They came into my course, and the original idea was, well,
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how do we make this collection?
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And these are unique individual hotel properties.
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And Ted Tang, who was in my course, said, "Oh, what we really need to
20:33
create is a platform for these hotels so that there's this commonality
20:37
that a guest would experience even though they're all very different."
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And so their their tagline became, "Every kind of one of a kind," which I
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thought was just so interesting, right?
20:47
Rita, I hope you got some contra on both of those for the garden and for
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as an educator, you kind of we, we live on the reflected glory, right, Aidan?
20:57
Sangeet, speaking to that, one of the cases we spoke about recently was
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John Deere as a, a great example that to extend the idea of the garden.
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But I'd love you to share one that you've seen.
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Again, just to encourage people to see what they do and the capabilities
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they've built through a different lens
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Yeah, I think, one thing I'd like to call out is that Very often discount
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the value of a lens and look for silver bullets, and this was my experience
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with this whole platform journey, right?
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A lot of companies would call me and they wanted to figure out if they were building
21:31
a platform or not, and if they were building a platform, how is it different
21:34
from one of the big tech platforms?
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And so it was all about we've set this box as the winning box, and we need to
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figure out how we fit inside that box.
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And the question is really, you're not looking for a silver bullet.
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You're looking for a new way to navigate an uncertain landscape.
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you're looking to understand if the landscape is uncertain, how do I read it?
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And what are the few points at which I apply pressure which
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then works in my favor, right?
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That's what you're essentially looking at.
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What, what kind of gives me that, that leverage?
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And if I go back to the Netflix example very briefly, what Netflix has done all
22:10
through and which is why anybody who's bet against them has struggled, it's
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they've always looked for what is the new choke point that needs to be released.
22:19
another example is Tesla, right?
22:21
Tesla's North Star was always, this is an emerging industry.
22:25
We need to get to maximum driving range, in order to get to that driving
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range, we have to work with capabilities that are evolving very rapidly.
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So we need to figure out which capability is the bottleneck right now.
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Once we release that, what's the next one?
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And so if you look at Tesla's s-sort of strategy, if you will, right?
22:44
During a time when everybody was talking about openness, platforms, et cetera, they
22:48
were vertically integrated because when capabilities are rapidly evolving, only
22:52
way to guarantee end user performance is to work across the full chain and figure
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out where the bottleneck is, attack it.
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Battery performance, go for it, attack it.
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Charger performance, go for it, attack it.
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Charger rollout, software, it's the integrated set that you have to attack.
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So the reason I'm saying all of this is when I was talk-- when I
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used to talk about platforms in the, in the 2010s this was a question
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that used to keep coming my way.
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If everybody's doing platforms, why is Tesla doing vertical integration?
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So the point, again, is there are no silver bullets.
23:26
You have to figure out what is the landscape you're playing in, what
23:30
is re-required to play successfully in that landscape, and given the
23:34
capability set that you have and you have access to, what is the best way in
23:38
which you can orient yourself towards what that landscape has to offer?
23:42
So that's, that's really key a-and you, you mentioned the John Deere example.
23:46
I mean, there are many other cases I would say which are, quite interesting.
23:53
A client or a company that I worked with over the years which is quite
23:57
interesting is this Norwegian newspaper company called Schibsted so
24:02
Schibsted Media Group they came from the traditional newspaper business.
24:07
And then when newspapers got unbundled, so the traditional bundle
24:11
used to be content classifieds and advertising, and Google unbundled it,
24:16
aggregated all the advertising towards itself, content became commodity.
24:19
So they decided, "Let's double down on classifieds." Pretty soon with
24:23
social, mobile, cloud, everything I talked about, the platform economy
24:26
came in and they realized pure play classifieds doesn't work.
24:29
We need marketplaces, so they moved into marketplaces, created,
24:33
a common ID and a common payments mechanism across all of that.
24:36
And now some of the questions that they're looking at is, well, in the platform
24:41
economy, there was the idea of a network effect, and if you were running a real
24:46
estate marketplace or if you were running say, a used car marketplace, you had
24:50
the benefit of the local network effect.
24:52
So think again th- about the Blockbuster versus Netflix example.
24:56
A real estate marketplace, a used car marketplace, these are all local
24:59
network effect models, so it's very difficult for the global player to
25:03
come in and get into your business.
25:05
So Zillow might dominate in the US, but they won't easily come into Norway.
25:09
All of those marketplaces are having a Netflix moment because a horizontal
25:14
AI model trained in the US can come in and become a buyer-side agent
25:20
real estate transactions, on mobility transactions in a European market.
25:25
And so the traditional marketplace model unbundles, the network effect
25:29
is no longer strong enough, and AI or, you know, a horizontal AI player
25:35
can either get trained in one market, move to others, or get trained
25:38
in one vertical, move to others.
25:40
As long as it represents the buyer side well, it is able to represent their
25:44
participation across multiple markets.
25:46
So again, the point I'm trying to make is this is an example of a
25:49
traditional business, 150 years old or whatever, constantly looking at
25:54
new lenses, and that's what we need.
25:56
We can't say, "Well, network effects are the way to go," and then suddenly
26:00
AI comes in and you're still applying the network effects model because now
26:03
you have a buyer-side agent coming in and disrupting the network effect model.
26:07
So that's the key point.
26:09
Always look for what is the right lens with which you should be looking
26:13
at the uncertainty you are facing.
26:15
And you know, I mean in business more than anything else, all models
26:19
are wrong, but some are useful.
26:20
So even if, you know we have this conversation and there are points
26:25
where Rita and I may agree, and there are points where we may not
26:27
agree as much, it's because all models are wrong and some are useful.
26:30
And I believe both the models that we bring here are useful in their own ways,
26:34
and you have to figure out what's the model you should be applying to look at
26:37
the uncertainty you are dealing with.
26:38
So I think that's really what a lot of companies looking for
26:41
silver bullets tend to miss.
26:43
It's not so much this is a big tech thing, we can't do it.
26:46
It's more we've been looking for a silver bullet, it didn't work, and
26:50
now we're going the other direction saying that this does not work for us.
26:53
So that's the wrong way to approach innovation.
26:56
I love that you said that.
26:57
And, you know, the, the, the silly arguments you often see about my
27:02
framework, deliberate, emergent, whatever it might be, are often
27:06
just the, the same person looking at the exact same thing with the lens
27:09
that they have available to them.
27:10
And it's ignorance by exclusion of the other framework half the time.
27:16
That's one of the reasons I love doing the show.
27:18
I love learning wi- witnesses, as you guys know.
27:20
But I also think it's so important to just have that empathy, to go,
27:24
"Okay, well, what are you seeing, and how are you d- defining that?"
27:27
'Cause the definition makes a, a huge difference to what you see.
27:31
And I wanna come to the definition of uncertainty itself, because
27:35
you talk about the definition of uncertainty a firm is facing, Sangeet.
27:38
But before I do, just on this idea of rule makers, rule takers, and regulation, I was
27:44
thinking about this as you were speaking.
27:46
And Rita, I know you, you again, work that you've done, and you've
27:49
collaborated with Carlotta Perez.
27:51
if I'm a regulator, on a country level, and I go, "This firm is too big
27:57
to fail, I need to bail it out," or the opposite, is that we don't have
28:02
enough GDP coming in, the rules are changing, we have to deregulate in
28:06
this area, like an Uber, for example.
28:08
We have to let an Uber in, so we have to actually change
28:11
how rules are done, et cetera.
28:12
There's a pressure, call it an environmental pressure, on
28:16
governments to change the rules as well as we go into this.
28:20
And they too lack the lenses to see what's actually right in front of them.
28:25
And they have some gold right there.
28:27
They might see it as trash, but it's treasure.
28:30
And I'd love you to maybe share what, what shift you see there.
28:33
So, 'cause a lot of people in regulation listen to the show, and I'd love you
28:36
to share your perspectives to them
28:38
If you have a policy role, I would love to be in touch with you,
28:42
whatever level of policy you're at, because we are in this seismic shift.
28:47
So we are going from a world where energy was cheap and materials were
28:53
cheap and plentiful and trade was fairly frictionless and, you know, there were,
28:58
there were a known set of, of rules and we're going into a world where really
29:03
for our own good energy should be expensive, materials should be expensive.
29:08
there should be greater frictions between moving stuff around.
29:11
So a couple of quick examples.
29:13
Right now we as a species s-s-- we spend more calories moving food around the
29:20
world than we do actually consuming it.
29:22
So just to take a simple example, there are fisheries off of Norway
29:25
that catch the fish, freeze it, send it to China where it gets deboned and
29:30
then it gets sent back to markets, like in the Nordics and in France.
29:33
That's insane from a perspective of, you know, you wanna
29:37
conserve energy and calories.
29:39
That's just nuts.
29:39
But right now we have a set of rules that, that make that economically attractive.
29:44
So I think as politicians we really have to think about what's the directionality
29:49
that we want to give capital?
29:50
Because capital by and of itself, it's like a river, you know, it's
29:52
gonna flow wherever the, you know tells it to and wherever gravity goes.
29:58
So it by itself does not have a moral imagination.
30:01
So as a policy maker, what we ought to do, right?
30:04
Is think about how do we create more broadly shared prosperity.
30:08
Because if all the billionaires control the wealth, we know from history that
30:13
creates social instability, populism and potentially far worse things.
30:18
And we muffed it at the end of the '20s.
30:20
What happened in the '20s was there was this incredible advance in
30:24
manufacturing capability so we had actual mass market capability but
30:28
there was no corresponding demand.
30:30
People didn't have money.
30:32
And so you had all this excess capacity and, and at the same time incredible
30:37
poverty and deprivation and it wasn't until we had Roosevelt's New Deal, the
30:42
Second World War and a group of people very intelligently sat down and said,
30:46
"What kind of rules do we need so that we don't repeat the '30s or break them?"
30:50
And out of that came the world order we currently are very familiar with.
30:54
So the Bretton Woods Agreement, the Marshall Plan which some people
30:57
have described as the, the biggest bribe ever offered in history but
31:00
what that did was it rebuilt Europe.
31:02
It created demand for American goods and it created a fairly stable social bloc.
31:06
You had the IMF, World Bank.
31:09
I mean this whole structure that came out of-- that was all designed
31:12
and I think people forget that.
31:13
You know, it's what's the old line from Hamilton, right?
31:16
"I wanna be in the room where it happens not just assume that it happened."
31:20
so policy makers think about what would it take to make a lifestyle
31:25
that's less asset intensive okay?
31:27
would it take to make energy actually reflect the true costs of burning?
31:32
What would it take to you know- be kinder to labor and harsher
31:38
on pure investment returns.
31:40
And those kinds of questions are the things I really encourage
31:43
policymakers to think about.
31:45
So, you know, my expertise really is on corporations.
31:47
A little bit on public policy, but, but I think there's a, a bunch of
31:50
really rich conversations to be had.
31:52
And what we know made that so successful in War period was it
31:58
was government stepping up, but it was also business stepping up.
32:01
It was ordinary people putting pressure on moving certain
32:05
things to work a certain way.
32:06
And so the answer for the demand question was let's make the suburbs happen, right?
32:10
And then when people can afford a house, let's create the 30-year mortgage.
32:14
Let's build the highway infrastructure that means then we'll need cars, and
32:17
then those houses all have people in them, so they're gonna need appliances.
32:20
And it was just this really big creation of demand together with
32:24
a broadening a, a reduction in income inequality and a broadening
32:29
of payments into ordinary people.
32:31
Now, the parallel, right?
32:33
So back then, the, these leaders said to themselves, "You know, we don't
32:37
wanna repeat the '30s, and there are gonna be literally millions of men who
32:42
are going to be demobilized, and we have just spent the last four years
32:45
teaching them how to kill people. This is probably not a good situation if we
32:49
don't have anything for them to do." So there was a real sense of urgency,
32:53
and I think what you're seeing now is the beginning of that sense as well.
32:57
You've got, large numbers of people feeling very hard done by.
33:00
You're starting to see, which sad indicator, you're starting to see
33:05
violence against business leaders.
33:08
know, I mean, Sam Altman just had a Molotov cocktail thrown on his porch.
33:12
I mean, you don't have to be a genius to figure out that a large number
33:15
of people feel that business is not working in their best interests.
33:19
You had the murder of a healthcare CEO on, in broad daylight in, in New York.
33:24
So, and business leaders and the government leaders need
33:26
to kind of pay attention to things that maybe they're not…
33:30
You know, back to your idea of lenses Aidan, maybe they're not
33:33
normally thinking about this, that people are telling them very, very
33:36
clearly this system is not working
33:39
Thank you for saying that, Rita.
33:40
And also, as an Irish person, thank you for your work on Irish policy that
33:44
we're benefiting from today, but is absolutely transient and needs an update.
33:50
That pol- those policies needs an update as well.
33:53
, Before we come to Sangeet on the same point for policies makers and what
33:56
you're seeing, Sangeet, what you're seeing the really advanced thinkers out
33:59
there from a country level are doing, I thought about what you said, Rita, about
34:04
the idea of build the infrastructure.
34:07
So in, in Ireland, we had a lot of our infrastructure built by
34:10
overseas companies, so there were public-private partnerships.
34:13
, And it… I, I always see it as a metaphor for business, that Clay
34:18
Christensen used to talk about this idea of the Ship of Theseus, of about
34:21
outsourcing, like ASUS and Dell.
34:24
Don't outsource too much because you won't have anything left and that you'll
34:27
give them all the competitive advantage.
34:28
But that giving away of the crown jewels, it's such an important aspect
34:33
as well from a metaphorical concept.
34:35
Sangeet, maybe you'll share what you're seeing out there and the
34:39
threats you're seeing as well.
34:40
, riffing off what you just said about outsourcing, because one of the
34:46
issues with this whole question around outsourcing was that the easier lens
34:52
to use around outsourcing was, is this operationally something that should
34:55
be outsourced or can be outsourced?
34:58
The, the more important lens should have been, is this strategically something
35:02
that should be outsourced in the sense that if I outsource this, will I enable
35:06
the creation of capabilities outside?
35:08
And y-we've sort of seen that with Apple in China and all of those
35:11
things in, in recent cases where even the best companies fall prey
35:16
to over-outsourcing capabilities and building rival capabilities externally.
35:21
And, you know, all of this sort of comes back to looking at , your
35:25
competitive system the system in which you are playing - with the right lens.
35:29
Because going back to this point about the world we created in the latter half
35:34
of the previous century, which was stable for a period of 40, 50 years or so, the
35:39
post-war all the way through globalization till digital happened, if you will.
35:45
It was a fairly well-structured world with clear boundaries to industries,
35:52
clear boundaries in terms of where you could meet the customer, w- how
35:56
you could reach the customer, a- and how the customer could interact
35:59
with the business, and so on.
36:01
assets applied to which industries.
36:03
W- there was a lot of asset specificity where- whenever there was assets around
36:08
which you could create advantage.
36:10
And in, in this in you know, in this scenario, the, the dominant
36:15
uncertainty that firms faced was what I think of as operational uncertainty.
36:19
It was the rules of the game were fixed.
36:21
It was uncertainty within that.
36:23
So you were playing the same game, but, you know, demand would fluctuate,
36:27
and you would have supply shocks, but fundamentally, the way your
36:32
business worked or, or more broadly, the, the, the, the phrase I would
36:36
just use is rules of the game.
36:37
The rules of the game were very fixed, right?
36:40
Today, we're increasingly confronting structural uncertainty.
36:44
This happened with the shift to digital and then to the platform
36:48
economy, but even more so with AI.
36:50
The, the big difference between what happened with platforms 15 years
36:53
back versus what's happening with AI right now is that you are, you know,
36:58
the, the impact of technology is playing out not just at the level of
37:04
structured interfaces between firms.
37:06
It's playing out in workflows inside firms, in unstructured interfaces like,
37:12
you know, negotiations and contracts and conversations between firms.
37:16
All of those things which were traditionally beyond the impact of
37:20
technology are now getting restructured.
37:22
So, for instance, if you deploy a sales agent to negotiate with your customer,
37:27
or if you run procurement where an agent is working on your behalf, you
37:32
have converted a previously tacit, unstructured interaction into a highly
37:38
structured learnable interaction, which changes where the value of the interaction
37:43
accrues, whether to the procurement manager at that point or to an AI tool
37:46
provider somewhere else or to some other player's learning loop, and so on, right?
37:51
And so that is a, a, a really profound shift that we haven't
37:55
yet come to terms with.
37:56
But whether it's with what happened with, you know, platforms or what's happening
38:01
with AI right now, what's common across all of this is that we're increasingly
38:06
confronted with structural uncertainty.
38:08
And structural uncertainty, very simply put, is that the
38:11
game itself is in question.
38:13
The playing field is in question.
38:14
You're no longer working within the rules of the game.
38:17
You're no longer playing with uncertainty within the game.
38:20
The game itself is uncertain.
38:21
And so that is where The real advantage, and this is the key idea of my next book,
38:27
which I call term "Unfair Advantage," the real source of advantage comes by
38:32
setting the new playing field, defining the new playing field, and defining the
38:36
game within it in a way that everybody has to follow and start playing your game.
38:41
An example to illustrate this is what happened with Reliance, which
38:45
is an oil and gas company in India, moving into telecom with Reliance Jio.
38:50
So Reliance moved into Reliance Jio with this technological shift that
38:55
happened when India went from 3G to 4G.
38:58
They were the first mover, invested in 4G infrastructure, and overnight
39:02
they commoditized voice and data.
39:05
They made it almost free, and through that, amassed 400 million users over
39:10
a period of three to four years.
39:12
Now, the reason that was so interesting was that the traditional
39:16
telco model was invest a lot in infrastructure and make money on voice
39:19
and data, and suddenly Reliance had commoditized the cash cow, right?
39:24
Now the telcos who were competing or the incumbent telcos, Reliance is also
39:29
a 100-plus-year-old company or, it's a very traditional company, if you will.
39:32
So it's not an AI native startup coming from s- somewhere and doing all of this.
39:36
But they were thinking in terms of structural uncertainty.
39:39
The telcos that saw this, they thought of this in terms of operational uncertainty.
39:44
They felt, well, the game is supposed to be the same, and now -- Reliance
39:47
is basically starting a price war.
39:49
So they started engaging in a price war, which was fundamentally misreading
39:53
the whole game, because what Reliance was trying to do was commoditize
39:57
data and voice, through that amass an audience, and move that audience
40:01
into higher value-added services over which you own the central choke point
40:06
because all of them are coming to you through commoditized data and voice.
40:09
And That essentially moved the industry from some 17 or 20 players to really
40:16
two or three big players overnight because everybody who read that as
40:20
playing the same old game but running a price war, all of them went out
40:23
of business or had to consolidate.
40:26
And a few rivals like Airtel, who initially read it wrong, but then
40:30
realized what the real game was and then changed their game to, play
40:33
the structural game as well, they succeeded and they've played on.
40:36
So that's a really interesting example of you're, no longer in
40:40
the traditional telco industry.
40:41
You fundamentally changed the game.
40:43
You fundamentally changed the playing field because you're no
40:45
longer just selling voice and data.
40:47
You're going into e-commerce, you're going into content, you're now competing with
40:50
Netflix, Amazon, and, , Facebook, Amazon, Google, everybody's investing in you.
40:55
So -- you've changed both the playing field and the game at the same time.
40:58
And so that's the, distinction of what I call a rule maker versus a rule taker.
41:03
Rule takers are constantly moving from transient advantage to transient
41:07
advantage to transient advantage because they're taking the rules of
41:10
anybody who's willing to set them.
41:13
That's the rule takers, but the rule makers have the ability to corner a
41:18
certain arena, capture that, and then keep coding there or keep expanding beyond that
41:24
as long as they continue making the rules.
41:26
But, you know, as, as we see with it's not traditional competition anymore, but
41:31
at the same time, today's rule makers can become tomorrow's rule takers as well.
41:35
So it's constant reinvention.
41:37
You know, Google seeing OpenAI coming up, rule maker had to take rules for
41:41
some time and now is trying to figure out how to make the new rules as well.
41:44
So that's really the distinction that I feel most companies miss, which really
41:49
explains a lot of what's happening today.
41:51
One of
41:51
the fun stories to me is how Reliance Jio got going, and apparent- I'm told that
41:57
the daughter of the founder had been in New York and was attending university
42:03
there, and came back to India and said, "Why is our service so awful compared
42:08
to what we've got in other parts of the
42:10
Right
42:12
Right.
42:12
Which I thought was a very human aspect to that story.
42:15
I'm sure it would have triggered over there.
42:16
Yeah.
42:18
Rita, from your perspective there, if, if you're gonna understand rule-making,
42:22
rule-taking, firstly you have to a- almost unbundle yourself and understand
42:26
your capabilities, where, where you've built capabilities itself, which i-
42:30
it's kind of like an inventory of sorts.
42:33
And one of the questions I wanted to ask you both was this, was I, I sh- and
42:38
the way I'm gonna do this is actually gonna illustrate with a, a brilliant
42:42
image I use in my own workshops, which, which is the, the following.
42:45
So you can see it on the screen there.
42:47
For those just listening, 'cause most of the show are listeners, it's a giraffe as
42:51
an artist painting a woman who's wearing an orange hat, and the artist has created
42:58
a brilliant version of what they can see.
43:01
The giraffe is the artist.
43:02
They're looking head down onto the woman, which is, all they've painted is
43:06
essentially an orange dot on the page.
43:08
Now, the woman's gonna be disappointed when she sees that, kind of like when
43:11
you go to Paris and you get your portrait painted and it's terrible for 10 euro.
43:16
But from your own perspective, if you were to look at your work through
43:22
the lens of someone else and say, "This is where people challenge my
43:26
work," how would you articulate that?
43:29
So Rita, your work, say, for example, competitive advantage, then to transient
43:33
advantage, and some people would go, "No, no, there, there's moats to be had,"
43:38
or, "There's a, a long-term advantage to be had," where would you see that?
43:43
And then Sangeet, I'm gonna come to you, yourself.
43:45
So this is what I'm saying, that I, I see a lot of this kind of people
43:50
arguing their perspective, which is perfectly right from your perspective.
43:54
But I'd love you to, in a way, if you had somebody challenging
43:58
it, to go, "Here's what they challenge and here's what I say."
44:01
Roger Martin and I, debate this all the time.
44:04
And we actually had a series in HBR where I think Roger's article was s-
44:09
entitled something like, competitive advantages, habitual advantages last
44:14
forever, and my counterpoint to that was old habits die hard, but they do die.
44:20
And so there's a whole group of people who are still very wedded to the
44:24
traditional frameworks of strategy.
44:25
So, there are people even today who will say Michael Porter's Five Forces
44:28
is the way to understand where you s- where you are, in competition.
44:33
So for those that don't know it, it basically what Michael
44:36
Porter did was brilliant, right?
44:37
What he said was, "If you want to understand how a company's gonna
44:40
make big fat profits you need to create the company equivalent of a
44:45
monopoly." So what you want is power over your buyers and suppliers.
44:48
You want to be unique so there are no substitutes.
44:51
You want low levels of rivalry, and you want high barriers to entry, right?
44:55
Well, Sangeet, tell me, is Google, Apple, Samsung buyers,
45:01
suppliers, rivals, substitutes?
45:03
All of the above.
45:04
So to me, that framework it operates very well in a specific set of
45:10
boundary conditions, and more and more of our economy has nothing to
45:13
do with those boundary conditions.
45:14
So you know, my, my argument with folks is we're really looking at, if you look
45:21
at the, the, the, the balance sheets of most large companies in 1975, you would
45:27
have had something like 93% of the assets on those books would have been tangible
45:31
assets, so plant, property, equipment, inventory, you know, you name it.
45:34
If you took the same slice of companies in our economy today and, and looked at it in
45:38
2026 it, it would be completely flipped.
45:41
It would be something like 93, 94% intangible, so intellectual property,
45:46
software patents you know, things that you cannot drop on your foot.
45:50
And so if we're talking about strategy designed for an era in which it was
45:54
things you could drop on your foot versus strategy today, which is about
45:58
network effects and value from connection and, you know, things that used to be
46:03
physical things being replaced completely.
46:06
I'll give you a, a concrete example.
46:08
So there's a company in Israel called Bina AI, and they've developed this application
46:14
which can use a phone to get vital signs.
46:19
So in the past, if you wanted to get, say, life insurance you would go to a doctor.
46:25
You'd get blood drawn.
46:25
The blood would go to a lab.
46:27
The lab would analyze it using chemicals.
46:28
The chemicals would give a certain reading.
46:30
Then somebody expert would read it.
46:31
They'd say what it was.
46:32
Then the results would go to an actuary who would then give you a price and
46:35
say, "Okay, this is how much it's gonna cost us, you know, cost you
46:39
for, for us to insure you," right?
46:40
All that, that's the way it used to be.
46:42
Bina, you start your application on the phone.
46:46
get to the point where you need to submit your vital signs.
46:48
What you literally do is you hold the phone up to your face and you circle your
46:51
face a few times with the the light from the phone doing just under under your
46:57
skin analysis of your vital signs, and I think they're up to 15 vital signs.
47:01
Then no actuary, the, the results of that thing go to an AI built into the app it
47:07
runs through its calculations, which it's been taught to do, and it gives you a
47:10
price right then and there in two seconds.
47:11
So no blood draw, no lab, no chemicals, no reading, no actuary.
47:16
All that disappears.
47:18
So when people say to me, "Oh, you know, five forces," you tell me how that's
47:21
even relevant in an example like that.
47:23
It's, it's, you know, it's a completely different solution
47:26
to basically the same problem.
47:28
So I, you know, I always try to understand when people criticize
47:32
my work, which I'm always open to.
47:33
You know, you can always learn something.
47:35
I try to really understand what boundary conditions they're coming
47:38
from, and very often they're just operating with a very different
47:41
frame of reference than I would be
47:43
and that's why I found Sangeet, your definition of the whole idea
47:47
of being comfortable with being uncomfortable, but it depends on
47:50
what uncomfortable means to you
47:52
Mm-hmm.
47:53
an organization.
47:54
I think that's a huge, it's a huge challenge in life when you come
47:57
across arguments all the time.
47:58
Like, my wife just thinks I take way too many risks, and I'm
48:01
like, "Um, what's the big deal?"
48:03
But we have totally different settings of what comfort and what risk is,
48:08
and that has a huge determination on how you see things and how you
48:12
measure things and how you go forward.
48:13
Sangeet, the same question to you.
48:15
That idea of when you're looking from perspective, where do people come at
48:19
you in a way, and maybe the, the most common way, because somebody always does
48:25
Yeah.
48:26
Yeah, absolutely.
48:26
So I think, there are two or three things . First is that I, I welcome other
48:32
perspectives just from the perspective of all models are wrong, but some are useful.
48:36
So let's start with that, and then let's try to figure out
48:40
what are your assumptions what are your first principles?
48:44
What are the first principles based on which you are creating your model?
48:47
Because the problem is that people argue using models, and those models,
48:52
to Rita's point, have s- assumptions underneath which are not very explicit.
48:59
And so the models, emit answers like, "This is right, that is wrong.
49:03
This is the way to think about it."
49:04
But if you really have a more fundamental conversation about what the assumptions
49:09
are underneath, what-- and you reason it out through first principles, that,
49:13
that gives a much richer conversation.
49:15
And so , coming in and disagreeing is open to doing that, we've always figured
49:20
out a way to identify where we are seeing things in a fundamentally different way.
49:26
It's difficult to argue out the models.
49:27
It's much easier to figure out from first principles what you
49:30
are looking at differently, right?
49:33
, But the, the third thing that I want to say is probably the most important,
49:36
and that is this, that a lot of the, the criticism on a model on what the
49:46
receiver understands of the model, not on, you know, underst-- They
49:50
don't go into figuring out what the f- what the principles underneath are,
49:54
why the model was created that way.
49:55
And that, that happens because there's a lot of silver bullet thinking going on.
49:59
So you'd use you know, take the idea of a platform or take the idea of a reshuffle.
50:05
All of these are packaged concepts because packaged
50:07
concepts are easier to understand.
50:09
There's a specific boundary.
50:10
It can fit in a book.
50:11
It can fit in a keynote.
50:12
And so people then get wedded to as absorbing that package concept.
50:19
But what's important is both understanding what the assumptions are and much
50:22
more importantly, understanding what the dynamics are, because the package
50:26
concept only contains the statics.
50:28
I'll just talk about, you know, two ways to think about dynamics, which
50:31
are really important today, right?
50:33
So first, I talked about structural uncertainty outside
50:36
in what's happening to the firm.
50:38
If the playing field was stable, as was traditionally the case, then
50:42
the assumptions of the playing field and how you get advantage and which
50:46
positions are advantageous versus not, where can a distributor have more power
50:50
versus a supplier have more power, all of that was very straightforward.
50:54
But if the playing field is the object of strategy, and if every firm
50:58
is then pursuing actions that are changing the state of the playing
51:02
field, that means that the dynamics are more important than the statics.
51:05
You have to constantly understand and monitor how the rules of competition
51:10
are changing in response to the f- to the moves that individual
51:14
firms are actively taking.
51:16
So it's no longer power of buyers, bargaining power of suppliers,
51:20
substitutes, none of that matters.
51:22
What matters is who's changing, who's making what moves, and
51:26
how is that changing the playing field that you are operating in.
51:30
It's no longer bilateral power.
51:32
It's working on the playing field and the playing field
51:35
putting pressure back on you.
51:37
So understanding that is dynamics.
51:39
I'll take another example of dynamics, which is more inside out, right?
51:43
It's very easy to say that, "Hey That's an AI-native firm, and
51:48
this is not an AI-native firm.
51:49
This firm has adopted AI, this has not.
51:51
But if you really think about what AI does to a firm, it creates a
51:55
constantly evolving capability set.
51:58
The reason I say that is because there are three vectors through which
52:01
this constant evolution happens.
52:03
First, AI capability in itself constantly evolves because with every new model,
52:08
we're extending what that capability can do, a certain capability, right?
52:13
Then the second thing is that there's an adoption and diffusion element to
52:17
it, which is the extent to which it is being adopted in the firm and becoming
52:21
a usable capability for the firm.
52:23
So even if Anthropic or OpenAI launch new capabilities, they become
52:28
usable inside the firm only once they're adopted in a certain way.
52:31
And the third is, as these capabilities get adopted, people who are using AI or
52:38
who are in the firm, they constantly try to rethink what they should be doing,
52:42
and so they're evolving the capabilities that they can offer to the firm.
52:46
if you really think about the firm, there's a dynamic playing field because
52:51
everybody is making a different choice and impacting the field, and there's a dynamic
52:55
capability set because it's constantly evolving with this constant sh- constantly
53:00
shifting division of what humans do, what machines do, and how everybody
53:03
is reimagining their work around it.
53:05
And so the goal of the firm is to fig-figure out how to
53:09
marry, marry those two things.
53:10
And if you think of those in terms of statics, where you say, "Okay, we need
53:13
an AI strategy, and you know what? We need a strategy for horizontal
53:18
AI coming in, and we need a strategy for our s- our suppliers coming into
53:23
our business," those are all statics.
53:25
And so a lot of the criticism to any model happens because in a world which should
53:31
be understood through dynamics, people are arguing it on the basis of statics.
53:34
That's been my experience.
53:36
Man, you remind me of the Heraclitus quote that, "No one ever steps in the
53:39
same river twice, for it's not the same river and they're not the same man or
53:43
woman." But I always think about this as the way the world continually changes,
53:48
where it's almost like jumping into a moving train, and inside the moving
53:53
train there's a conveyor belt, and you're trying to land on one of those
53:57
wobble boards to strengthen your ankle.
53:59
And the train is on a boat that's going down a waterfall.
54:03
So everything's moving, and it's not all moving in the same direction, either.
54:07
But one of the things just both of you alluded to is this, is that y- you talked
54:12
about the individual there, Sangeet.
54:14
The individual in the age of AI, and I see this as a real threat, I wrote about this
54:20
today, is that we're also using AI to try and understand what to do in an age of AI.
54:28
And by not actually going through the struggle of the work, and I, by that
54:31
mean reading the papers, reading the books, studying the work, we lose that
54:36
ability, and the organization loses that ability, and there's gonna be very few
54:39
people who actually can attend to actually understand this work, and we're gonna
54:44
outsource all that thinking to the LLMs.
54:46
And there's nobody actually making the strategy anymore.
54:49
And it's a real danger because already I find it really hard to understand how a
54:54
CEO or a strategist with- inside a company is getting the time to actually study
55:00
work, to understand all these different frames, to understand all these different
55:03
lenses, to see things differently.
55:05
Because if they have to do that and they still have to deliver and they still
55:09
have to lead people and develop people, where do they get the time to learn?
55:13
And time needs to be carved out for the learning.
55:16
And in times like, Rita, I, I saw you recently wrote, wrote about the MIT
55:21
Sloan review being shut down, when we need it probably most in the world.
55:26
Th- like, this is a real challenge, and I just thought we'd share a thought from
55:30
you both on that challenge that we're seeing on a human level, regardless
55:34
of strategy, the ability to attend and to actually do the strategy work.
55:39
I think that's, really a question of discipline and prioritization.
55:43
Work will expand to occupy all the time you allocate to it.
55:47
And so if I give somebody half an hour to solve a problem or
55:50
have a negotiation or do a thing, it'll get done in half an hour.
55:53
If I give them two hours, it'll take the two hours.
55:55
So I think the first issue that I see in companies is just very poor
56:02
Prioritization and clarity around what's on the agenda and what's not.
56:07
So it's like peanut butter, right?
56:08
It's a little bit of everything all at once.
56:10
Where-- And so this argument that I'm making about strategic centering is
56:14
it should really inform your agenda.
56:16
And I've said for years that if something's important to you, so let's
56:19
say it's innovation or it's strategy or it's whatever, I wanna be able to
56:23
look at your agenda, which is what's on your meeting list, how you spend your
56:27
time, who you're with in meetings, and I wanna see that visibly represented
56:30
as item number one, two, or three.
56:32
So if you start from there and say, "This is not a nice to have that I'll squeeze
56:37
in when other pressures me," but if you say, "This is actually-- this is the
56:42
most important thing I do." For example, Vas Narasimhan, who's the CEO Novartis
56:47
has a great phrase which I really love.
56:49
He said he sees his primary job as directing the energy of the organization.
56:53
Like-- And he's a scientist and he's an enormously capable person.
56:57
But that, that leadership role that he's got is directing
57:00
the organizational energy.
57:01
So that, that, that's one.
57:03
The other thing that I think is, is true is we, we're, we just take on a lot of
57:10
stuff, and we're not judicious about what we can stop doing and what we what,
57:15
what we believe to be important or not.
57:18
So, you know, a friend of mine, Sharon Price John, who was the
57:21
CEO of Build-A-Bear Workshop and is now taking on that role at
57:25
Carter's has this great phrase.
57:27
She calls it SDSS times two.
57:29
And the first SDSS is stop doing stupid stuff.
57:33
You know?
57:34
And if you think about it, over time, any organization accumulates stuff,
57:39
you know, habits, processes, approvals.
57:42
I read somewhere that, that at Harvard, they're, they're doing this
57:45
exercise right now, and that it w- took sixty-seven steps to hire somebody.
57:50
You know, sixty-seven completely different processes to hire somebody.
57:52
Well, so get rid of that stuff, so that'll clear a lot of bandwidth.
57:56
And then the second SDSS is start doing smart stuff.
57:58
But until you get the deck cleared of the stupid stuff that's no longer
58:02
relevant, you're not gonna have the bandwidth to do the new things.
58:04
So I think that's a, just a, a point of discipline, you know?
58:07
And making choices is hard.
58:09
But hey, if you got the top job, that's what you're getting paid for.
58:13
And Sangeet, what's your take?
58:14
Yeah.
58:15
You know, I, I, I'm going to complement what Rita shared.
58:19
I'm going to look at it from a different angle of what has it-- what has,
58:24
what has been my personal experience sort of thinking about my work as,
58:28
you know uh, I've started using AI.
58:30
So One of the key things that I believe is important is to make a distinction
58:36
between are you working for the model is-- or is the model working for you, right?
58:42
I mean, at the extreme, it's the distinction that I make between an
58:45
Uber data scientist and a driver Uber driver, where the Uber data
58:48
scientist is, you know, structuring the algorithm and the Uber driver is
58:53
essentially working for the algorithm.
58:54
But you know, in a different way, it applies to a lot of
58:57
knowledge work that we do.
58:58
We, we sort of collapse all of it in this fuzzy word called augmentation.
59:03
Just because you use the tool doesn't mean you're using it right.
59:06
so the way I think about it is that if most of my time is spent in generating
59:13
AI and then correcting and fixing it and making it usable for myself,
59:17
I'm really working for the model.
59:18
I'm not making the model work for me.
59:21
And in order for… What that has then helped me, you know,
59:27
forced me to think about is: how do I make the model work for me?
59:29
How do I provide it sufficient context?
59:31
How do I think about creating a framework and a structure where I don't have to
59:36
keep providing it context every time?
59:38
And there have been different ways of going about it.
59:40
You know, I've worked on creating a knowledge graph that brings together
59:43
all of my research links it to other research that's interesting, and then
59:48
I always start the model by creating the knowledge graph first before
59:51
I start looking at any problems.
59:54
So it starts with my lens.
59:56
Otherwise, you know, if it's generic answers that I'm retrofitting into my lens
1:00:00
that's, that's just a lot of wasted work.
1:00:03
So, so that's one thing.
1:00:04
You know, I'm constantly looking at, am I working for the model
1:00:07
or is the model working for me?
1:00:08
And that's something that's happening a lot of professions today.
1:00:12
You know, take the example of translation.
1:00:14
The moment you think large language models, you think that translators
1:00:18
are out of a job, and clearly both the translation companies
1:00:21
and their clients think that way.
1:00:23
So what's happening with translators is that translators are stuck verifying
1:00:27
and correcting AI translation, whereas very often it's faster for them to
1:00:33
translate the document themselves.
1:00:37
they are not just spending double the time doing this, they're also being paid half
1:00:41
the money to do this because everybody assumes that AI is doing all of the work.
1:00:45
But translation is not just mapping of language, it's understanding of
1:00:47
context, it's understanding of risk, what should be managed in what way.
1:00:51
And if if the translator is fixing you know AI output, the
1:00:57
easy part has been done by AI.
1:00:58
The harder part for which their neck is on the line is what they have to
1:01:02
do, but now they have to do it in a workflow that is not familiar to them.
1:01:05
So that's the distinction of, you know, working for the model
1:01:07
versus the model working for you.
1:01:09
The other piece that is, is you know, I've kind of always tried to think about
1:01:15
is, A- and again what you wrote in your article, there's an-- resonates right?
1:01:21
So always thinking about Am I just being fluent because I've got enough
1:01:28
output to deal with and to work with, and some slides are looking good
1:01:32
because Claude code designed them?
1:01:33
Or do I actually understand this in a way that I can tear that apart and
1:01:39
rethink about it in different ways?
1:01:41
And again new way of thinking about how you provide context to the
1:01:46
model has helped me figure that out.
1:01:48
The, the final point is, you know, going back to versus dynamics.
1:01:52
If I'm talking about reshuffle and I'm going to be going and giving answers
1:01:58
that are statics, which is, "Here's how I believe your industry is-- you
1:02:03
know, things are playing out, and here's what, what this means for you."
1:02:06
By the time I do the analysis and I present it two weeks later the
1:02:11
playing field has changed again.
1:02:12
So clearly that is not the right way to do it.
1:02:15
So what I, what I'm trying to do right now, what I'm-- I'm, I mean, it's still an
1:02:19
experiment, but what I'm hoping to do is to move the work from statics to dynamics.
1:02:26
So I'm working on creating an index where essentially use AI as a monitoring
1:02:31
mechanism where, you know, agents can constantly figure out what are the
1:02:35
moves that companies are making on a daily basis, bring them back in.
1:02:39
But what I do is I curate the central lens in a deep way, where every move
1:02:44
informs the lens and the lens informs how the move should be evaluated.
1:02:49
So yeah, that's been my, my big personal project this year that I've been
1:02:53
working on and I'm, I'm, I'm hoping to bring it out in the next two months.
1:02:57
So that's-- you know, those are some ways that I'm thinking about how do
1:03:01
you preserve your edge in an age where just the tools are getting better,
1:03:08
but the way people think the tools are getting better is actually outpacing
1:03:11
how, good the tools are because people just assume that everything is being
1:03:15
solved by the tool, but it's not.
1:03:16
And speaking of which, Rita, you've just written another brilliant
1:03:20
article for Harvard Business Review.
1:03:22
I'll link to that because you continually go into the mind gym,
1:03:25
just like Sangeet alluded to.
1:03:28
For both of you, where is the best place to find you?
1:03:30
Rita, where is the best place people can find your writing, your books, et cetera?
1:03:34
ritamcgrath.com will sort of be the central hub.
1:03:37
I have a regular podcast, as you know, Aidan, and publish regular articles
1:03:43
on Substack and Medium and on LinkedIn
1:03:46
And I'll link to those.
1:03:47
And Sangeet, for people that wanna find you, where's the best place?
1:03:51
Yeah, you can go to reshufflebook.com and platforms.substack.com for my newsletter
1:03:57
It's always a pleasure.
1:03:58
I always learn wi- witnesses with you two guys.
1:04:01
It's been an absolute pleasure learning from you today.
1:04:04
Rita McGrath and Sangeet Paul Choudary, thank you for joining us
1:04:08
Thanks
1:04:09
Thank you so much
1:04:10
Thanks as always to our sponsor, Kyndryl, who runs and reimagines the
1:04:14
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1:04:32
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See you next week