← Infinite Loops
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- [00:00]
A
Everybody wants to believe that the game is changing for others, but not for themselves, that there's something special about what they're doing. They've thought, they've figured the rules of the game and now they're seeing the game change. If you really care about your work, if you really care about getting better and doing better, you should fundamentally, you know, you should assume that everything you know is going to be taken away and you have to rethink what that is going to look like. You have to figure out how to get to the new game, because winning the wrong game is actually losing.
- [00:31]
B
Well, hello everyone, it's Jim o' Shaughnessy with yet another Infinite Loops. I am delighted to welcome back Sangeet Chowdhury, the best selling author of Platform Revolution and the one that I really want to talk about, the newer Reshuffle who wins when the AI restacks the knowledge economy. Welcome back.
- [00:55]
A
Thank you, Jim. So glad to be here.
- [00:56]
B
You know, I have certain guests. When I see them on my schedule, I get excited and I so enjoy talking to you. But for people who might not be familiar with your work, let's do a little bit of a recap of your thesis so that we can level set for our listeners and watchers to understand where you think we're going.
- [01:24]
A
Absolutely. The key thesis that I talk about in Reshuffle is that when we think about the impact of AI on knowledge work, we often tend to think of AI in the same way we've thought of previous forms of mechanization, which have typically impacted relatively more codified standardized workflow, if you will. And AI is fundamentally different because work that was previously considered tacit and bound inside human cognition can now be broken down and can be taken over by machines in a way that was previously not possible. And so one of my key points is that the traditional way of looking at the impact of mechanization on traditional forms of work does not apply when we think about the impact of AI on tacit knowledge work. Now, what I mean by tacit work over here is fundamentally this, that let's say you take the example of solving any market analysis problem, if you will, there is no fully codified workflow. You sort of make up your workflow as you get into the problem, you define it, you learn from the information you're gathering. And so it's difficult to explicitly codify the workflow, the number of iterations, what good looks like. And that's why traditional forms of mechanization did not apply to tacit work. But today AI does. And you can see that with the market analysis example, again, you can get a really good market analysis report using claude, which is getting better every few months. And so my key point is that when we looked at previous forms of mechanization, we used to think about the impact primarily in terms of automation or augmentation, because you were taking a predefined way of doing work, you were looking at different steps in that predefined way of doing work, and you were inserting a machine at specific steps. And it was either automating that step or that task, or augmenting the human on that task. And a lot of us are still taking the same model when we're thinking about AI and applying that same model over here. And that is what I call the task centric, automation based way of looking at AI. The real value or the real story that we're missing in that process is that when previously known, modularizable, non definable forms of work suddenly become redefined, remodularized in fundamentally new ways, the structure of work changes. Who performs which work changes. Take the example of consulting. The pressure on consulting firms is not simply from model providers. The pressure is also from the fact that model providers now change the division of work between what customers could have done in the past and can do now versus what consulting firms can provide them. So when customers take more of the work, model providers absorb some of the other work. The consulting firm in between has to rethink not just what to do with the work that remains, but what new work to go towards. And so the real idea of reshuffle is that what AI does is it unbundles our traditional work structures, whether it's a workflow, whether it's our organization. These are specific configurations, what I broadly call bundles of performing work. And it unbundles all of that. It redistributes what machines can do and how humans can reorganize what they want to do based on improving machine capabilities. And with that, it now creates the possibility for fundamentally new structures, new workflows, new organizations, new industrial boundaries to emerge. So the real impact of AI will not be in speeding up today's work. It will be in reconfiguring everything that supports work, which is our workflows, our jobs, our organizations, our value chains. All of those are poised for reconfiguration. And that's the real idea of reshuffle. When AI restacks the entire knowledge economy, the game will change, and hence the winners and losers will change as well. So that's the whole Idea of who wins when AI restacks the knowledge economy.
- [05:31]
B
Yeah. And we are very simpatico on that idea. I think that many people are asking the wrong questions and they're framing it wrong. And I think one of the reasons they're framing it wrong is because they're captured by their priors. They're captured by the old workflows, the hierarchy, the meetings, the departments, the job descriptions. I started thinking about this a while back and as you know, called my little thing the Great Reshuffle. And what I saw coming, and it really is changing the routing AI is really going to change the way things get routed to different nodes. And in a way, if. And there's going to be problems. There's always problems. But it could create what I would call a new distributed value kind of mesh network. If a CEO, an earnest CEO who really wanted to understand what was going on, hired you as a consultant, what would you tell him about how he or she should work to design maybe from first principles around the new coordination that AI will allow?
- [06:51]
A
Yeah. So the place where I would start looking at it is how do you think about, you know, strategy? Today, the typical way you think about strategy is that you have to think about it in terms of answering two questions, where to play and how to win. Right. What's the playing field going to look like and how do you think about the winning game? The first thing that I would talk about is that the playing field within which your firm is playing is fundamentally changing. I'll give a few different examples. If you are, say, in the knowledge services industry, professional services industry, clearly a playing field was structured around the assumption that access to certain forms of knowledge work was expensive, it was proprietary, and hence you could hoard it internally and license it or charge for access to it. So when access to certain forms of knowledge work suddenly becomes very cheap, because AI does perform certain forms of knowledge work within limitations, of course, but it dramatically reduces the cost of accessing certain forms of knowledge work. How does that then change what is differentiated versus what is commoditizable in your industry logic? Or take it all the way away from professional services to a very physical industry like material sciences and plastics and compounding. Traditionally, the cost of compounding was that if you had to identify or discover new compounds, you had to go into the lab and run a lot of tests and come out with compounds. Today, AI can generate new compounds from scratch. And so the value then shifts to how you match the compound to the process, to the demand profile. Because alongside that, the number of Use cases at the front end are also dramatically expanding. So as the supply of compounds increases and the demand for different use cases increases, how do you match the two things? Really well, that's where the new constraint sits. So the first thing that you would want to look at is if you're a CEO, you have to follow the scarcity because you can compete on the basis of what's scarce, not what's becoming abundant. Because what's becoming abundant is going to get commoditized. Margins associated with that particular activity are going to get compressed. And so you need to think of where does my new scarcity lie and how can I create defensible business models around owning that scarcity? So that's on the playing field side. At the same time you have to think about AI strategically in terms of your organization. Because today a lot of CEOs are just focused on we need to get AI adopted, we need to roll this out, we need to get teams on board. And I think all of that is a good starting point. But it is only that what we should really be thinking about is that as AI gets adopted in our organizations, there are three things that are changing simultaneously. One, the capabilities themselves are changing because Claude OpenAI all of these are improving on a daily basis. And so with every new release, what the machine can do and what humans can do changes. And so the capabilities from a pure play model perspective are changing. Which of those capabilities actually get adopted in value? Creating workflows in the organization are constantly changing as people learn how to adopt the technology. And as people adopt the technology, how they change their own work is constantly changing. So if you're a CEO, the biggest thing you need to think about today is how do you manage this constantly evolving capability set? Because you've never had such a constantly evolving capability set. You had a top down capability set where you said we're going to hire these people, we're going to map them into these skills and then we're going to keep training them on this basis so that they we can the skills we have access to constantly change. But you've kind of managed how the capabilities evolved. But as you deploy AI in the organization, it's going to evolve not just the machine capabilities, but how they're adopted and more importantly how your people are changing their work around it and how they are discovering new capabilities or moving to new things. That's going to change what you can do with this evolving capability set. So there are two things over here. One, the nature of differentiation and competitive advantage in the playing Field is changing to the capability set with which you can innovate from inside is changing. And it's really at the intersection of these two things, given the fact that these two things are changing for every company across industries. So your competitors are not going to look the way your competitors used to look like in the past. Because once certain forms of knowledge work again that were protected by your industry boundary become available outside your industry boundary, new competitors can come into your industry where they could not have in the past. And so I'll just give a very simple example of that. You know, think of what's happening to customer support. Traditionally, customer support was something you performed in a contact center. And so it was a very closely bound industry boundary in terms of who runs a contact center, what kind of tools are provided to them. Today, customer support can be performed by AI agents alongside human agents. And so you have companies like Microsoft Salesforce completely outside those industry boundaries now entering that support industry because they can access the capability of providing support without having to invest in training humans to build that capability from scratch. And so when you think of these aspects, the fact that previously scarce capabilities are becoming commoditized ways of differentiating are changing. Who can get access to your playing field and can enter your playing field is fundamentally changing. And also your capability set internally is changing. That creates a very volatile and dynamic competitive playing set both in terms of what you can do and where you can play. And I think understanding and resolving that is the key challenge for CEOs today.
- [12:55]
B
And another thing I think we face, the authors of the book, the Weirdest People in the World. I don't know if you're familiar with the book, but they make the argument that cultural lag is a real thing and that it takes humanity a while to adopt to a new way of doing things. And I think that's true. But I also think it provides a asymmetric arbitrage opportunity. And the asymmetric arbitrage opportunity is are you going to be willing to move to a truly AI first in everything corporate or institutional structure, rapidly. And if you are, it's my thesis that you're going to be able to eat the lunch of the players using the old playbooks. Is that one of the reasons why you see the current top of the hierarchy? Let's look at movies, books, a variety of things. They're not happy at all. And yet, much like the movie industry, they tried to suppress VHS for years and years and years. They spent millions of dollars, they lobbied against it, and of course they lost. And Then the wonderful irony is VHS sales and other type sales ended up being a much larger part of their revenue. The thing that they fought all that time. Do you see something like that happening with traditional Dominan companies today versus the upstarts?
- [14:33]
A
Yeah, I think so. And I think it's that particular element of company native to a new technology versus a company migrating to a new technology. And the arbitrage opportunity for the company that's native to it is not entirely new. What's new is the rate of change or the speed at which this is happening, which confers a compounding advantage to the native player versus the migrant player, if you will. Right. And if I were to sort of qualify this, take a different technological shift, take the shift from desktop to cloud, which happened 15 years back, you had a company like Adobe which moved to the cloud very successfully. There are Harvard Business School case studies about how well Adobe managed that shift, how well its CEO moved. You know, the CFO agreed to move from licensing to subscriptions. And yet it struggles today. It struggled ever since to compete with the native player, which was Figma. And the reason it struggled to compete with Figma was because Figma was architecturally organized around the properties of the cloud. Adobe was using the cloud only as a channel. Figma was using it to redesign or reimagine the product architecture, the offering architecture, the workload supporting it around the capabilities of the cloud. And if I just give a simple example over there, Adobe was built on a file centric architecture. So you had to create the file and then in the past you would send it over email. Now you could be on the cloud and you could have others coming in and commenting on the file, but it was still centered on the file centric architecture and the file was tied to the design division. And so the file centric architecture preserved a certain logic. Figma is created because it's cloud hosted. It's created on an element centric architecture, which means that every design element that the organization uses is managed in a central library. It can be reused across design files. And so design files are very often reconstructed using new elements and common elements across the organization. Now there are two important unlocks because of this, and this shows the difference between the AI, you know, the native player versus the migrant. The first is that when you manage design elements at an organization wide library, you've moved the value from design only to design and governance. Governance no longer sits inside the minds of designers. That's what was happening within Adobe. Now it's managed organizationally, so the budgets you can access are fundamentally different. And when you can represent design in terms of a cloud hosted graph and not a designed file, that allows multiple different players, you know, marketing, sales, product, all of them, to work alongside the designer on the same file without, you know, forcing the designer to keep ensuring that it is well validated, it's well verified, because all that verification is happening centrally, that validation is happening centrally. So my point over there is that figma realized that design was not an isolated activity, it was, it was an activity that fed many workflows across the organization. And the cloud natively allowed that workflow spanning to happen across the organization. And no matter what Adobe does, it cannot move in that direction. Ironically, figma is now facing the same issue because figma is sort of an AI migrant. It's got this nice file structure, element based, but then anthropic, for instance, can infer connections between elements, can identify fundamentally new ways in which design files are being manipulated. It can do a lot more with the entire design system that a migrant like figma cannot. And so we kind of see this cycle repeatedly where when a new technology comes in, if you have the opportunity to rethink the logic of the product, the workflow, your customer's workflow, your organization, everything around this new technology, and the starting point of that is if you can really define what the new unit of value is. If you look at figma versus Adobe, Adobe, the unit of value was the file figma, it was the element. So you could do everything at the level of the element. Even if you opened up APIs for the ecosystem, your partners could be the same, but with Adobe they could only work on the file. With figma they could work at the level of the element and integrate at that level. So it fundamentally changes how companies organize, how they collaborate, how they compete. And that is why there's a very, there's a huge arbitrage opportunity for players who are built ground up with the new technology. There is a significant opportunity for players who are migrating as long as they have several structural constraints that do not simply get wiped out. So just as an example, in professional services you could be a copywriter at one end, you could be an audit and tax consultant at the other end. As a copywriter, you don't necessarily own structural constraints, whereas as an audit or tax consultant, you have the right of final sign off, which is regulatory protected. And so there's a structural constraint that even if the work gets taken over or commoditized by AI, you are able to hold on to the value at Your end. So it's important to know under what conditions incumbents can retain value because of the structural constraints under what conditions the AI native player can fundamentally impose a game that the incumbent simply cannot play. And so all of those factors become very important when we think about who has that or arbitrage opportunity.
- [20:37]
B
And one of the things this is my view, I'm not attributing it to you, but I think one of the reasons we're seeing all of the, what I view as desperate attempts for regulatory capture on the big frontier model designers and providers is because they understand that if they can't get regulatory capture, if they can't make the legal aspect of who gets to do what right, then the playing field is truly a different playing field because you're not going to be able to be saved through the traditional things like copyright, things like all of the various legal procedures and protocols that we have built up over long periods of time. They're all under attack. Right?
- [21:36]
A
Yeah. So I think there are two different issues. And let me know if I'm approaching this the way you had intended it. When we think of copyright and other issues around value captured by AI models, there's this element of ring fencing what was previously a public good and extracting it the way you would a private good. And AI enables you to create a transformation value chain where the end product is completely untraceable back to the input. And copyright law was structured in an era or was built in an era which did not assume that kind of transformation. And that is essentially because of that it's difficult to protect what's happening over here. And this is again, this is not my idea. This comes from a well known book called Power and Progress. This has sort of played out over the centuries where every new technological shift or many new technological shifts have created the opportunities to extract value by ring fencing a public good and making it private by, you know, essentially the regulation being stuck to the previous model, whereas the technology imposed a fundamentally new model. So in one way that's what we're seeing over here. The other thing that's interesting in terms of how, you know, anthropic and others, the model providers in general are calling for regulation is because they also, I believe that, you know, that they realize that the American models and the Chinese models are playing a fundamentally different game. In the American model game, the model is sort of the end. And so that is where value is supposed to be captured in the long run. And in the Chinese model, I believe that the model is the complement that has to be commoditized in order to capture value at the complementary layers, which, especially when we think of physical AI and new production systems, the value there to be captured is in the energy system, it's in the production system on top of the model, which is the physical production system, robotics, etc. And the overall coordination system in terms of how you build fundamentally new value chains, you know, fusing sort of the knowledge value chain and the physical value chain. So that is, I think that is also, you know, the fact that it's a fundamentally different race and a fundamentally different game that the Chinese model providers are playing is also playing or is also driving a lot of this concern around regulation.
- [24:14]
B
And which, which model, the Chinese version or the American version? Which do you think wins?
- [24:21]
A
I think it's fundamentally different games. Right. So essentially if we believe that eventually the knowledge value chain and the, I would say the physical execution value chain are not two distinct value chains. The way we've been talking about digital and physical for the last, for the initial part of the digital era. So when the digital era started, we always talked about digital and physical value chain separately and eventually we realized that they are not really separate. You essentially create value by bringing the two things together. And I think the same thing when it comes to AI, there's the knowledge work value chain and then there's the physical work value chain which converge. And I believe that the way the model providers in, in the US in general are looking at it is purely in terms of what AI does to the knowledge work value chain, which is a fair way to look at it because traditionally most value in today's industries have moved to knowledge work. You know, even if you look at, you know, any, any industry, if you look at mining, I took the example of plastics, the physical work gets fully commoditized, the value moves to the knowledge work. But if you look at the Chinese model in general, the two things are not fundamentally distinct. I take the example of Shane in the book, which kind of clarifies this really well, because if you think of Shane, the fast fashion player, the reason it's interesting is because it creates a learning architecture that brings the physical and the knowledge value chains together. It constantly senses new ideas and new trends that are coming up in social media and then it converts it into a design using a network of designer that converts it into a small batch of production tested in the market. And with all of that, it's constantly training which batches work and hence how its physical production system, its design system, how all of that should work together. And so this idea of creating a learning architecture that spans both the knowledge work and physical execution is, I believe, where things are headed. But because in the west we've largely assumed that the physical side of things is commoditized, we tend to see the impact of AI only within the knowledge work value chain.
- [26:37]
B
Yeah. And you know, I should note that we also have a publishing company. And so we're very concerned about copyright and are looking for new ways that the technology itself can unlock to preserve the author's right to their work, to preserve the filmmakers right to their films, et cetera. I think that that is going to be like really important topic because you can't cut the creators out. You have to come up with a protocol that works with the new technology. Right. Because when everything is infinite, if you can digitize everything and the end product looks nothing like the inputs that created that end product, you're going to get a lot of very unhappy people. So I think that this is not just a business question, this is a societal question. Because another question that I would have for you is I sort of think that distributed value networks, mesh networks, where there is no central authority, like so, for example, China had the Gutenberg technology way before good old Gutenberg came up with it. But it had a very centralized command and control government that suppressed it because the emperor wasn't interested in people printing things that might be critical of the emperor. Whereas Gutenberg worked because Germany was not unified. It was a collection of city states competing with each other. Right. And so the China had the tech first, but Gutenberg was operating under a distributed network type protocol. The protocol won. Right. And I think another one of your points is that the coordination layer is going to be vitally important to determining who's the winner and who's the loser. If you could explain that a little bit, because you're much better at it than me.
- [28:51]
A
No, for sure. I think, you know, this is one of the things that I felt people were missing about what AI can do. And so the way I typically think of it is that when we think of the impact of AI on, you know, the underlying economics of what's improving, we tend to think about two effects. Predictive AI is collapsing the cost of prediction. Generative AI is collapsing the cost of generation. And hence what happens when the cost of something collapses is it gets done more, it gets done in many more places, and hence it then unlocks second and third order effects because those previous constraints associated with those activities go away. Those things get done much more. The Third thing that these two framings miss is that the cost of translation or coordination is also going down with AI. And what I mean by cost of translation over here is that when a foundation model is trained on the world's knowledge, it can translate between different domains in a way that previous technologies could not. Previously, if you had to translate between different domains, you had to either rely on bespoke integrations, or you had to rely on some form of power structure that was willing to invest in, in doing that translation so that it could then extract rents from it. So it could be a broker or an intermediary, or it could just be what we call platforms, where they specify the interface, manage some form of the mapping, and then once they get sufficient traction, they force the ecosystem to follow the standard. And so the translation happens through that mechanism. And so essentially what, what all that leads to is that the only way to coordinate multiple actors. There were two primary ways to coordinate multiple actors. You either had to get the actors to agree and get to consensus. And the example I take is the shipping container. You know, once trains, trucks and ships agreed to as the same form format of the container, logistics got unlocked globally. So it's coordination with consensus or its coordination through enforcement and power, which is what say, you know, a Facebook or an Amazon or any of these large platforms do, initially heavily funded by venture capital, which allows them to gain scale and then they enforce the coordination. And my point with AI is that it allows coordination without consensus because it collapses this cost of translation. Now, the reason that's important is today's power structures are structured around proprietary formats, proprietary ways of doing things, proprietary workflows, proprietary interfaces. And essentially those power structures create walled gardens. So if you take any industry, whoever owns the choke point, in some industries it could be the R and D choke point, and other industries it could be the distribution choke point. But whoever owns the choke point enforces the standard and the schema and the way of working on everybody else. What AI can do is you can take outputs at every step of a value chain and you can learn from it and translate it between different walled gardens in the same industrial system, if you will. The example I give is that of construction. So in the construction industry, you have players like Autodesk, Bentley with different design formats, and then you have that's on the architecture and design side. Then there's downstream construction and engineering side. And all of these tools and technologies don't talk to each other. And the players who are providing these tools create their own islands or walled gardens. And what we're seeing now with new AI native construction players coming in, is that all of them, none of them are trying to do design faster. Nobody is attacking Autodesk front, straight up. They're trying to dismantle the Autodesk logic by creating these translation layers where essentially they then tell the user, no matter where your different teams are working and in which formats, we'll help you see a unified view of the project. And through that, we'll create a coordination without consensus layer. And once enough demand moves in that direction to start using this AI layer to make decisions, they would then want to flip it and enforce the interfaces on the various format providers. So that's the idea of a coordination without consensus, where you can dismantle today's power structures and create an alternate way to span the different formats. And I think, you know, the new power structures that it yields could be a combination of protocols, because completely open protocols, because you need different formats to work together, but then a different form of concentration at different points. It could be just one node that you capture, it could be a combination of different layers that you capture, but it would be different from today's power structure. That is where, going back to your point about protocols, I think that's a fundamental reshuffle pattern that we'll see as well, this idea of enabling coordination without consensus and then breaking down today's power structure, creating alternate structures going forward.
- [34:15]
B
Yeah, that's where I got really intrigued in that part of your book, because I agree wholeheartedly. But if you study history, it's really nothing new, right? By that I mean the distributed value architectures and protocols, like the example I always use is the Rothschilds, right? The Rothschilds were five nodes without a central authority.
- [34:42]
A
Right.
- [34:42]
B
The father sent each of his five sons to the capitals of Europe of the 19th century, the financial capitals, and they had an amazing sub layer of courier network, right? So they had rented boats, they had carrier pigeons, they had ciphers that only they knew. But it was not routed to a central authority to make decisions. It was routed to the brother who could take the greatest advantage of it. And it's an apocryphal story. I think historians go back and forth on this, but because of this topology that the Rothschilds had, that was their key advantage over others. They had a better information network than government information networks at the time. And of course, the canon story is Nathan Rothschild, through this courier network, knew that the Brits won the battle against Napoleon. And then he took advantage of human psychology, went into the stock exchange and sold British gilts or bonds. Everyone thought Rothschild knows things ahead of other people, so everyone just rushed to sell. And of course, he had his agents buying from the plunging prices and they literally controlled finance in 19th century Europe until the telegraph destroyed their advantage. Right. I see AI in a very similar role here. Right. The idea that I think it's hard to articulate just how fundamentally transformational a technology this is because you're going to be touching a lot of third rails. Right. And that's going to be messy and it's going to be a very difficult fight. What about the idea of measurement capture? You know, you don't have to control the entire network if you can control what gets counted could AI itself as the new router, if you will, of all this information, could we see a system where that just gets copied and copied and copied and everyone's using the same routing system? Or am I off base with that?
- [37:19]
A
No, that's an interesting thought. I mean, there are a few complementary thoughts I have to that one is that we're clearly moving in a direction where firms are not going to compete on the basis of outputs. And at the risk of using the cliche outputs to outcomes, everybody's talking about it, but what does that mean? And I think that has something to do with what we're talking about in terms of measurement capture as well. Because we've optimized firms around creating the best output, creating the most output, scaling that output. We don't necessarily know what it means to optimize against outcomes. And I believe that optimizing against outcomes is about owning a proprietary learning architecture which helps you own the outcome better than every other firm and hence optimize your entire internal production architecture to improve that outcome better than anybody else. And so owning that architecture, what I mean by owning that learning architecture, when we think about learning, after 10, 15 years of digital technologies, we still think about learning as what happens on the customer interface. Personalization, that the system knows more about you, it's learning about you. We've not necessarily seen the learning to the same extent across the entire production architecture. And chain is one of those examples where because it's a proprietary architecture, you are fine tuning every element of that architecture. What should be designed, which kinds of design elements work well with which kinds of production factories, which production factories work well with which markets. You're fine tuning every, you know, what I would call every coupling in the value chain in terms of what works well with what. And in new value chains, if you look at something like electric vehicles, you do have that learning architecture because you're simultaneously fine tuning what the battery can do and what your charger can do and what your charging network should look like, and what, and hence what the customer experience looks like as a combination of these three things. So there's a coupling between battery capabilities, charger capabilities, charging network spread and so on. And we haven't, I would say over the last 50 years, we've constantly moved away from inefficient vertical integration to modularization, first without sourcing, then with cloud capabilities, whichever way you think about it, but constant modularization, decoupling, making things more flexible and plug and play. But when you move away from outputs to outcomes, you need to own the entire production system in order to guarantee the best outcome. So we'll see a new form of reintegration happening which will be structured around defining what should be counted as the right measurement at every stage in the value chain towards ensuring that you are able to guarantee the best outcomes to the end customer. So that's the way I would think about the measurement capture as being the ability to win the outcome game is determined by your ability to own the learning architecture, which is determined by your ability to dictate what should be measured at every stage, because that's what guarantees the end outcome in the best possible way. You know, there's a. I'll just throw a related idea against it as well. There's a recent HBI article that I just wrote which came out last week, where I essentially make the point that we, we've traditionally seen work and learning and skilling and, you know, all of these things as decoupled in the sense that you go and you perform your work, then you go somewhere else to learn, and then you come back and perform your work. With AI, what's happening is for the first time we're working with a technology that's constantly working alongside us. And hence it's also measuring how we are working, it's transforming how it behaves so that it can give us highly personalized feedback based on how we're working. And so we have created this, we are moving in this direction where learning, measurement, all of that is going to happen in the flow of work. And hence which kind of measurements matter and what learning path they take you individually down is also going to be determined by AI within that. So I don't know if that directly addresses.
- [41:59]
B
Yeah, actually I'm looking at a question because I believe pretty deeply in this distributed value chain, right? Where historically, if you look at all wealth creation, not extraction, but creation, it happens under these circumstances. Right. The Greek city states all the way up. I mean, for politics and philosophy and all of that. And it's not just business, it's societal, it's political, it's everything. The Gutenberg example. And your answer, it was this, of I asked you this question that I've written down. I'm going to read it because you answered it in very much the way that I am looking at the world. And I say if AI is the new coordination layer and the distributed architecture, the historical engine of value creation is the highest value produced by controlling the layer or designing a layer that can't be controlled. And what you've just said is you've got to create a proprietary layer of knowledge, reinforcement, learning, all of that. So I think it sounds like we're both directionally thinking the same way, right?
- [43:13]
A
Yeah. And I would agree. I think when I use the term learning architecture, two things that I try to reinforce because people have preconceived notions of what learning means based on how we've used the term in the last 15 years when it comes to business models. And the first is that people just assume that learning happens in the market, not across the production system. And I think that's going to be truly unique and new with AI. And that is why again, I believe the Chinese model of betting on commoditizing the model and capturing value from the production system and the energy system and the coordination system on top of the physical knowledge production system is where value is going to be. So that learning architecture piece is something that I believe is not fully understood to the same extent in the Western paradigm of looking at technology. And the second piece is essentially that the other reason I use the term architecture is because when we use the term layer and I'm just trying to refine what we're just going back and forth on, when we use the term layer. The term layer is borrowed from the past several years of modularization that we've gone down where we've assumed that certain layers are going to be commoditized, certain layers are going to be. And I believe that's going to continue happening. That's just how value migration works. But in the past it was possible to be just one layer and not do anything else, or at least be visible as one layer, even if you had adjacent control positions and adjacent points. Classy example being Amazon has prime as the visible layer, but it has that price matching algorithm that goes at the back end which helps it retain the prime position. But you know, if I take the Tesla and electric vehicle example. Again, it's not just what we call a layer today, but it's the couplings between layers and a proprietary new learning architecture that helps you understand how those couplings behave. As the technologies at each of those layers are improving rapidly, owning those couplings is where advantage accrues to. So I think those are the two reasons why I stress the idea of the learning architecture in a way that people have not used it with digital technology at least.
- [45:40]
B
Yeah. And again, we are aligned because we think about it at the architectural level as well. We started o' Shaughnessy Ventures to be AI first in every aspect of everything it does. And you know, if you're 66 years old, that requires a lot of open mindedness about the way things are going to get done. Luckily, I'm very open minded about these things. And I definitely think that you are absolutely right that people are not thinking big enough in many circumstances. Because I think you're spot on about thinking at the layer level. That's a whole different conversation from the architecture level and the ability for learning to happen in the workflow to be reinforced and all of that. That change is the way everything is done. And my concern is, you know, again, back to cultural lag. We have been socialized to believe that, you know, I'm going to get this job and this is what I'm going to do and this is how I'm going to be judged by my outputs and, you know, the whole deal. Right, right. And that is not what the future institutions look like to me at all.
- [46:58]
A
No.
- [46:59]
B
And like, I don't think that there's necessarily a sense of urgency, those of us who were early to machine learning and seeing what it could unlock. I hear a lot of people saying, oh, we got to get going, we're late, we're late. I think in the grand scheme of things, we're still pretty early. Am I off base? Do you think I'm right there?
- [47:24]
A
I think we're at that phase where we've seen the new capabilities that are emerging. We've seen how that could lead to new firms, new architectures. We have not yet seen all possible architectures in all possible contexts through which the capabilities can be used to create advantage. So in a way, what I mean by that is that if you think of, you know, any of the previous shifts, and I think the shift to AI is very foundational. It's not simply, you know, something that can be related to, based on a shift to electricity, if you will. I would say that it's more foundational. It's probably similar to a shift to, you know, agriculture or wood or minvuls,
- [48:18]
B
because that's the argument I've been making. I'm saying don't think electricity, think agriculture.
- [48:26]
A
Exactly right. Because the fundamental input into the value chain is changing. The previous input used to be very different and the entire new, a fundamentally new value chain has to come on top of it. So both agriculture and minerals are two things that I would look at. Most of our industrial production mindset assumes the input as given and assumes the production system as the object of action. But now that we have a new input, we have to rethink what the new production system should be. And I don't think we are thinking about that. And this brings me again back to the China point where they are working on both the energy as a new input and AI as a new input into fundamentally new production systems. I think it's less a point of whether China is right or whether the US way of looking at AI is right. It's more about we're still very, very early. We sort of just discovered agriculture, we have figured out how to make the ground more fertile, more arable and so on. We haven't yet figured out that we're going to make clothes with cotton at some point. And we are too far from that. We're too far from that whole value chain at this point.
- [49:43]
B
Yes, and again, I agree completely. And I think the thing I'm struggling with as well though is this notion of like if you have this distributed, non centralized mesh network protocol, right, A viable mesh should be forkable, it should be auditable, it should be hard to fake and hard to capture. So what happens if AI becomes the ultimate mesh capturer? And by that I mean we have the centralized model that we're using in America, we have the commodity model that the Chinese are pursuing. It'll probably be a combination of both. Right, but what happens if, if the AI is getting smarter and smarter and smarter, are they also compressing towards a middle? That's a criticism I often hear about AI that it doesn't do unless you specifically say, hey, I want to look way out in the tail. What it does is it compresses all of the knowledge into a standardized common ground.
- [51:03]
A
Right.
- [51:04]
B
I'm being inarticulate about this, but the idea of is it the ultimate enabler of these hard to fork, auditable, hard to fake and hard to capture mesh networks? Which I kind of think it is, but I've been thinking a lot and this is what I was looking forward about in getting your view because I like to think I might be wrong. What happens if I'm wrong?
- [51:30]
A
So I'm going to again, offer a complementary perspective over here. And I think what I'm saying and what you're saying, if you bring that together, we could have an interesting theory over here. So let's take the creative value chain again, right? It assumes that there's a certain creative process that's valuable and the object of action in that creative process is the output that has to be created, whether it's a book or a song or whatever that is in the process of create of that, of working on that object of action, there's a significant level of creative waste it is willing to deal with, which is not going anywhere. It just goes out as creative waste. It does not have any place in that value chain, right? So it's all the unwritten drafts, all the messy notes, etc. And I believe that if value chains are moving from output to outcome, the creative exhaust is going to be more valuable than the object of action, because the creative exhaust is where the learning architecture sits. And hence I believe that everybody who has built a life thinking about perfecting the output should now go back to thinking about what do I do with the creative exhaust. And I wonder whether the protocol, you know, plays out most interestingly at the level of the creative exhaust, where my creative exhaust, what I waste, can be refurbished and reused by somebody else who it perfectly applies to, who is at a certain, you know, point in their own creative exploration. I'll try to give a couple of examples to illustrate that and also share, you know, how I'm thinking about my own work with that context. But the other thing. So there are two points that I'm trying to make over there. One is that if we believe that the input is fundamentally changing, which it is, which is what is scaring the creative industries. If we keep focusing on the creative object as the output, as the focus of production and the focus of improvement and all of that, if we stay focused on the output centric model of creative work, I have a feeling that's a losing game. When we are moving in the direction of where does this output move into, what problems does it solve? How can I think about that outcome and create an architecture, a learning architecture, to solve that particular thing? It's easier to think about that when we think about, you know, electric vehicles, because we're saying, okay, whether it's battery technology or charging technology or charging network, all of these are working towards one outcome, driving range. So you have to keep improving them. You don't improve the, you know, only the vehicle. You improve every component that was previously assumed as given in order to get to the outcome. And the learning architecture is where the advantage is. It's difficult to apply that directly to creative work today because we don't necessarily see. Think. Think of it in the same way. But, you know, I'll try to, you know, share what I've been thinking about in terms of my work in case it helps us, you know, A, brings it closer home to something I'm applying this to, but B, helps us see if there's a way to bridge it back to creative work. So when I am writing books, there's. For every word that I write in a book, there's probably 10 to 20 words that I've written in wasted drafts, but there's probably 200 words that I've captured in notes or more, 500 words that I've captured in notes that are very valuable but are never seeing the light of day because they're only helping me improve, they're only helping me get to the output. Right. And so over the past 15 years or 16 years now, I've captured everything that I've read, every thought that I've had, every idea that I've connected. I've captured them in a tool called Workflowy, which is sort of like, you know, something before notion and roam came in. It was a way to organize your ideas and do it in a bit of a hierarchical but flexible way. But I. I essentially curated that over the years, every day I would sit on it, put in what I was reading, put in what I was thinking, connecting those ideas, and then every so often, when I was in a curatorial mood, I would go into it and move ideas around based on how I think things connected. And every year at the end of the year, I would take a week off to just play with the workflow for the year and see how it connected with previous years and so on. So that was just me geeking out on something that I enjoy doing anyway. So far, I've not been able to do anything with it. But now I've been thinking about, you know, why should I be looking only at the book? I should be looking at all these ideas that have been there at the back end. So what I've done is I've taken all of that and I've created a knowledge graph which links all of those ideas and concepts together. And now that the underlying knowledge graph has been created, it's really Powerful because it can tell me which conceptual gaps the ideas have, because I just never read that. And so it's able to identify what I should be looking at next. It's able to identify, you know, what's unique about that contribution, and it's able to show it to me. To, for my curation on, you know, whether a certain thing should be added to the knowledge graph or not, I can take the same knowledge graph and I can run an entire case study of something that's happening in a certain industry through it. And it calibrates the knowledge graph to say these concepts are playing out in this format, in this timeframe, in this industry. And I'm having a lot of fun with it because I'm constantly learning where the gaps in my learning are. But it's also opening a whole range of ways in which what I had previously seen as exhaust and waste and had never seen the light of day, all of that is being converted into a knowledge graph that can be converted into something that's usable and productizable and, you know, interesting for different stakeholders downstream. And so that's the way I've been thinking about it, that if you think of the creative process or the production process in any knowledge work, for that, for that matter, the value that corresponds to the learning architecture lies in these learning iterations that lie scattered all over the place, not in the finalized output. And I've always felt that the finalized output was more of slicing and dicing and providing a certain narrative. But the narrative is only one way of bringing together all those ideas. So if I bring, you know, take back my unbundling, rebundling thesis, the narrative of the book is a bundle. If you unbundle it, there are many concepts, they can be rebundled in many ways. But then because the book has a certain constraint that forced that narrative, whether it's attention span, whether it's storytelling, that constraint prevented many other components from entering that bundle. So how do you then reorganize all of those components? And that's where a knowledge graph, which can then be transformed into many different products, becomes even more interesting. So that's been my way of thinking about it. And I mean, this is something that I always think about in terms of where, you know, my own work is going. But in general, you know, I think some version of this applies to the knowledge economy and knowledge work in general.
- [58:55]
B
Again, listeners and viewers are going to get bored because we're just going to agree on everything. I think that you are absolutely right. The repurposing of I call it. The analogy I use is what was left on the cutting room floor. Correct. And I am a prodigious note taker. And for many, many years over the last 45, it's been by hand.
- [59:21]
A
Right.
- [59:22]
B
And so, but we're digitizing all of this and some of the things that I'm seeing are kind of like, I would have never. Even though it's in my own notes, I would have never thought about it in this particular new combination. And it is, for me at least, it is incredibly exciting and incredibly generative because the AI's ability to go in and say, dude, you've been saying the same thing for the last 45 years. How come you never acted on that? That jives with your ability, you can rec combine and then the whole new product lines, whole new ideas, whole new books, whole new ways of learning come out of that.
- [60:14]
A
Exactly.
- [60:15]
B
But let's. Okay, so we're both very lucky in that we have the ability to have access. We have our own hardware installation, our own multimodal AI. At osv, you obviously have access to all of the frontier models. You probably have your own bespoke models as well. What advice can we give just the listener out there who doesn't have the access, the total access, but some access. How can they start to change their own path? Especially I'm thinking of younger people. I think the days of the I'm going to go get my degree in X, Y or Z are painfully antiquated. What would your advice be? I'll hire you for a second here as a consultant to our younger listeners. Where would you point them to be able to maximize their ability to thrive in this new economy?
- [61:20]
A
Okay, so I think the larger question over here has different answers. When you think about younger audience versus people like me who are in their 40s or even people who are in their 30s who have had a certain run which gives them not so much, you know, yes, a certain buffer to experiment with, but more importantly, it has helped them cultivate judgment. Right. It has helped them understand what good looks like. What. And it has helped them see what output to outcome looks like. Because even though you don't measure outcome, you sort of know what the outcome you're looking for and you tacitly understand the outcome and so you fine tune the output to get there. So.
- [62:07]
B
So
- [62:09]
A
I want to come to the younger part, but to the.
- [62:14]
B
Actually, you know what? You're going the right place. Start with the people like yourself who are in their 40s or even 50s. They have this long history that they've habituated in themselves, and they're going to need to be able to change that. So let's address those folks first, but then get to the younger people who might not have to to avoid all the bad habits that we've developed.
- [62:40]
A
Absolutely. I think that there are two very, very different scenarios between the two. There's people who have been through it. I think the biggest thing to unlearn is that they were told a story and then they saw that story play out, and they now believe in that story. So what? The youngsters don't have to worry about to the same extent. They had to worry about a lot of things, but not this. The youngsters already don't believe in the story they have been told because they're seeing it's not working. But. But those who have been through some sort of a career for 10, 15, 20, 25 years, they were told the story. They saw the story play out. They've thought they've figured the rules of the game, and now they're seeing the game change. So the first thing is that everybody wants to believe that the game is changing for others, but not for themselves. That there's something special about what they're doing and not about what they're doing, but how they mastered the game, that it cannot be taken from them. And I think that is one of the first challenges that, you know, anybody with experience has. I think I've gone through that cycle as well in thinking that I've mastered a certain game. And now, you know, even if AI can do certain things, I should just keep getting better at my game. And it takes, you know, it takes a lot of unlearning questioning, some level of, you know, I would say shock to your system and some level of a lot of reflection for you to come to a point where you realize that, well, it's all good if the game is not changing and if I'm special. But shouldn't I be preparing for the reality where the game is changing? I'm not special. And if that doesn't come through, that's fine. It was just insurance money. But, you know, I should be preparing for that so that I'm not caught unawares. Right. So I think that flip does not necessarily happen for everyone. That flip happens in different ways for different people. Some people might, you know, realize they've sort of come to an end of the road of the previous game and then realize that it's just not worth moving towards a new game. They're at a place where they want to Stop playing games. That's possible. Another, you know, group might be, we've come to the end of the road. But you know what? I can see four more years of playing this game and I'm going to double down and make as much as I can with playing this game and I'm going to stop playing the game after that. So those are very viable scenarios and those are, you know, the classic board level gotchas, if you will. Every time I speak to a board, the underlying question in the room is, you know, I'm 65 or 75 or whatever, and I'm going to be around in this board seat for three more years and in any board for five more years. Should I really care about AI? If you don't know when is when your specific prediction is going to play out, yes, it's going to play out, but are the two years or 10 years? And so should I then care about it? So all those are very viable responses as well. But if you're somebody who has taken pride in their craft, if you're somebody who's always tried to improve at their game because of the, you know, your internal ambition and to see the results of that ambition, and you're not just, you know, trying to stay in the game just to stay in the game, I think you should take a hard look at what does. What is the new game that's going to be played. You should just assume that the game is going to change. If you really care about your work, if you really care about getting better and doing better, you should fundamentally, you know, you should assume that everything you know is going to be taken away and you have to rethink what that is going to look like. Maybe it's going to happen in two years, maybe in five years, but instead of betting on the timeline, I would just bet on the outcome and figure out how to make the most of the lag period while betting on the outcome. Right. So that's how I would think about people in their 40s, 30s, whatever. Unlearn what you felt you knew. Get over the fact that you felt you had figured out the game and that your game will not change, but it's actually going to change. So get over that and start afresh. I think for the youngsters, I think it's a lot more challenging because. And over here, I'll say it's not just those who are young, but anybody who's sort of been structurally locked out of an upwardly mobile knowledge economy career and who's been constantly trying to work their Way back into it, whether they got kicked out of a job and entered the services industry or became an Uber driver or whatever, and they now see that path being locked out even more. So I think in general, people who are getting into what would have traditionally been called an upward graph of a knowledge work career, they, and especially for those who are young, they don't necessarily, they won't necessarily have the on ramp that allows them to build judgment because the friction of working with an untrained human in certain cases will be too high compared to the lack of friction of working with a sufficiently trained, highly trained AI, if you will. And because of that, firms are going to make choices where they simply don't go down a path where they need it. And so a lot of those on ramps that helped you fine tune your judgment, helped you learn from people who had judgment, all of that goes away. So it's very easy to say AI will take away, you know, will do the execution. Humans will detain judgment. But while there are many issues with that, the bigger issue is who will have that judgment. And there's going to be a significant portion of humanity who might not even be allowed the opportunity to develop that judgment and hence have a unique edge, if you will. I don't have this very good advice to give to people who are young. I think the younger you are, the better off you are in a way, because you're sort of learning that this is happening. I think the, the transition generation which went into College in 2020, was stuck home instead of being in at university because of COVID and then came out of COVID to see ChatGPT launch. I think they are the ones who that transition generation has it most difficult in a way because they've been told a certain story and they've sort of worked their first 12 years of schooling, if you will, towards getting into that story and suddenly they've seen that whole story break apart. I think if you're much earlier on, it's an important time for both the students and their parents to realize that the traditional way of optimizing for outputs, optimizing for measurement of output, which is what the education system is based on, is not sufficient. Have to figure out a way by which you constantly create your own learning architecture, which the only way you can do that is through small experiments, through being entrepreneurial, through gathering feedback. And the answer, again is not as simplistic as, you know, hard skills are going away, soft skills are important. Specialists are not going to be important, generalists are going to be important. This is how we try to bucket solutions, but it's not quite as binary as that. The only thing that we can be entirely sure about is that if you have an iterative learning based approach towards your work, if you assume uncertainty as the constant, what that means is if you assume that you have to have an aim on where things are headed, place a bet on that, think in terms of placing that bet, and constantly update your model of where things are headed based on what you're learning on a daily basis, that's the only way you can build your own learning architecture, if you will. You have to constantly assume that where you play is uncertain. It's not going to be the same as it is today. And so you have to bet on that. And I would say that that's something that's true for everybody in jobs as well. Today your company is going through an uncertain, you know, is dealing with uncertainty. Everybody has a view on where AI is taking things. And again, it's not just AI, it's many forces. But in the midst of uncertainty, you can't look at your company as the unit that's going to guide you on the way forward. You have to bet, you have to learn, and you have to give that learning back to the company to signal that you've got it figured out even better than they've got it figured out. And I think that whole flip of I'm going to rely on somebody else to guide my career versus I'm going to be guiding, I'm going to be at the frontier of betting and learning what's happening so that everybody else who's confronted with uncertainty can, can see that, you know, I'm one step ahead. I think that flip is going to be really important. So I know I didn't answer the, you know, the junior, the youngster question really well. It's a very difficult question. But I believe there's some version of this that applies at every level where you have to unlearn the old game. Just assume that at some point it's going to go away from you. And then get comfortable with betting on uncertainty, running experiments and based on your learning, constantly change your roadmap of what you're betting on and how you're getting there.
- [72:08]
B
The thing that is ironic is that in order to become a great success in the new world, you have to select against the basic coding of human os. In other words, what's one of our biggest biases? Confirmation bias. We seek information that that proves in quotes that we are right where we should be seeking information that proves or suggests that we are wrong. And high agency versus low agency, expecting tutelage and a coach who's been through the game to be able to help you is kind of a norm. And yet what you are articulating, and I think correctly articulating, is that we're going to have to learn to try to unlearn these deeply embedded biases in ourselves. When you were talking about the person who thinks, you know, my game is a plus and everything else, I'm the exception. I used to have a gif that I would put up on Twitter to people who were saying something like that. And it was a. I said, this must be your screensaver. And it was a thing that says, you are the only exception. I know that I am not the only exception. I know that I am just as fallible and just as outdated and everything. But it took a lot of time for me to habituate this self interrogation like you have to. For you to be able to continue to iterate and succeed, you have to be able to exit an old mental model. And that is really easy to say and it is really, really difficult to do. And so I definitely agree with you that the younger people, honestly, they're going to be native to this environment and so they probably will excel. And we have some young, as you know, we do the fellowships every year. And oh my God, the brilliance that is out there among people who are still teenagers is. And you can literally see it in the way they construct their proposal. They are intuitively doing many of the things that you are advocating, whereas in people, well, certainly people of my age, a lot of people who are in their 60s are like, yeah, I have no incentive to be involved in that. Like your board member. Right, right. I'm only here another three years. And so their incentives are to either be reticent or even to actively push back. Whereas the younger people, they have no such incentives and they're doing fine. I think you nailed it with the people who really need the advice you just gave, which is people who are already successful in their careers, they got successful in a certain way, you would say, on focusing on the outputs as opposed to the outcomes inputs. Excuse me, but I mean, that itself would be an incredibly useful. Just that conversation would be incredibly useful because one of my worries that I've expressed repeatedly is there is going to be a cohort of people who through, and I underline this part, no fault of their own, are just not calibrated or designed for this new world. And I would love to come up with A way where we can do everything possible as a society to ameliorate to, in some way make that blow land less hard. Because, you know, you see all the back and forth right now and it's. I did not anticipate the anti AI response. Well, I did a little bit from, from the, from the old hierarchical guard, that's kind of a given. But from, from younger people as well, right. Because it's like, whoa, all the rules are changing and now you want me to change. And unlearning is hard. Right. The ability to unlearn, I think is much more important really than the ability to learn and then habituate. Right. You've got to constantly be on learning. You got to constantly, you know, all models are wrong, some are useful. If you take that attitude, you can switch models relatively easy. You can iterate, you can you, you can change. How in your career, how have you changed from, I mean like in, in, in, in AI time. Your book was published in 2025, that's decades ago in AI time, how own outlook and your own process changed from when you actually published the book to now?
- [77:45]
A
Well, so there, so there are a few different points over here. One is that when I wrote the book, I wanted it to be timeless in terms of concepts, if not in examples, because I knew it would not be timeless in examples if I wrote it based on 2025 AI. And so I rely a lot on historical analogies and so on. First is over the past year I've tried to constantly calibrate those concepts and see if with everything that's happening, those concepts play out to the liability. And it's helped me fine tune where it applies, where it does not apply. It's, you know, leading to an updated version of the updated edition this year which will talk about, you know, talk about things that, a lot more, with a lot more nuance. And so, you know, the first order effect of that was just that I would have to launch an updated version of the book much faster than I normally would. But then that would have been the same output fallacy. Right? Because that would mean that the faster AI moves, the faster I need to keep writing books. And that's not going to work. What I've tried to do with this new edition, which I'm still working on, is that before I write anything over there, and this is not why I build the knowledge graph, but now that I've built the knowledge graph, I've actually calibrated the book through the knowledge graph to see where it's working. It's consistent with what the knowledge graph would predict and where it's not. Because the fun of having this knowledge graph for me is that on a daily basis I'm seeing exactly what you talked about, things that I've talked about in the past, things that I've curated in the past, but I've not seen a certain connection. And I've seen those connections on a daily basis. Some of them get me excited, so I want to dig down further. So it's sort of a curiosity on steroids, if you will. Right. If you're naturally curious, then having this, you know, it's one thing to have an LLM that's constantly finding interesting stuff your way, but. But you don't always want to, you know. For me, the way the knowledge graph has transformed the way I work is I can just wake up in the morning and I can just ask it to throw me three surprising things or something that appears a lot in my work, but I've not connected it to something that appears very little in my work. Or it helps me see a lot of gaps in my own conceptual frame. And, and the reason I like it is that I want to keep thinking and I want to use AI to help me keep thinking even better. And what are the gaps in. When I keep, when I, when I think, the gaps are very often that I waste time trying to think about, you know, trying to figure out unknown unknowns. And what the knowledge graph helps me do is because it's structured around knowing what I already know, it has prescored concepts where I have done a lot of my work, it's able to identify unknown unknowns which are just at the level of the right delta. That stretches me a little bit to connect a well known concept to an unknown concept, to see it happen in a fundamentally different industry, to connect an unconnected historical precedent to it. So I've sort of created it as a way to help me think, which I don't think I get with an LLM because what happens with an LLM is I start with a question, it gives me responses, and in sifting through those responses, I learn how to ask better questions. But it's a very tedious way of learning how to think. At the end of a long session, I may come out of it learning something new, but because it doesn't know what I already know, it doesn't know where my mind is structured, it's not able to push me in the right way to increase my thinking. So it may sound a little theoretical as I say it, but only after having created that knowledge graph, I realized how important this idea of identifying the unknown unknowns becomes. The second thing that I think, take away AI and all of these things. One of the things that has really liberated me ever since I decided that my previous game is not going to be worth playing anymore and there's going to be a fundamentally new game, is that it sort of set me free to rethink that if I'm going to find a new game from scratch, I cannot optimize it around anything except longevity and fun, which is that it should be something that is organically fun for me. It should be something that I have the incentive and the interest to keep playing long enough. And hence it cannot be optimized around a well structured game, which was very much optimized around, you know, certain markers of success that the game led to. And so here, if I don't know those things beforehand, the only way to keep winning is to stay in the game and, and keep changing with the game. So that has sort of set me free because if I start with, is it fun? Is it something that I want to continue doing rather than keep getting burnt out? It helps me take a different approach to my work. One of the positive outcomes of having to rethink your game has just been that the only way you can play a game in a constantly changing playing field, which is what AI and other things are doing to us today, is to ensure that you're in the game long enough and that you're enjoying playing it. So I would, you know, there are two ways I can respond to what's happening. I can say that I want to just play the game to its. Play the old game to its maximum for as long as it works, or I want to reinvent myself towards the new game. But if I'm doing that, I might as well, you know, play it for the next 20, 30 years. And in order to do that, I should have fun while doing it. So what all of this has forced me to think about is, you know, if I'm rethinking what it means to be, you know, somebody who has traditionally used, say, books and certain very specific forms of narratives to address this overall point of how are things changing around us? How should industries think about this change? How should companies think about strategy, if broadly that overarching question that has always guided my work, which is, how does technological change force companies and individuals to rethink how they should compete? If that question continues to be of interest, I would rather create the surrounding infrastructure that helps me stay focused on answering that question, if that question is still relevant, and do it in a way that I continue to enjoy doing, doing this for the long term. So, long story short, the key thing that I'm. That this that I believe has been really refreshing about having to just stop and forget what I was doing in the past. And the thing, what I'm doing is that it gives me the opportunity to build it fun first, joy first, rather than speed first or, you know, accuracy first. And all of those things which actually impose a lot of overheads. And I think if I can keep focused on that and the underlying capabilities keep improving, then the notion of scale would also change. You should be in a position where if you are a fun first or joy first company or person or career, you should have the ability, you will ask the underlying capabilities improve. You will have the ability to keep scaling your impact with that. So that's, that's the way I think about it. I mean, the, the temptation always is to get faster at what I'm doing today. But I think it's, you know, it's, it's just, it just traps you in playing a game that's going to end soon.
- [86:04]
B
That's one of the things that I've been so excited. I've like, literally because of all these journals I, I have been waiting for AI since I was 22 years old and 1982, because the possibility, space, the learning new things, recombining things, to me that's like incredibly fun and invigorating and as you would put it, joyful. Right. And I think that the old idea that the 9 to 5 job, the whole structure of the way our economy works is going to change. And I personally think that it's not going to be easy. So I'm not Pollyannish or Panglossian about the whole thing. There's going to be a lot of disruption. And that's kind of a given in any kind of transformational technology. If you can look at it the way that you've just articulated, I think you're going to have a much easier go of it than if you're like, I would not want to be type that really believes because that's the way it's always been done here. Because you're not going to survive in the new environment.
- [87:32]
A
Right? Yeah, I know we're sort of, you know, there's a certain level of privilege associated with build a fun first career with, you know, constant learning and constant experiments because it's, we have to sort of think about that alongside this Whole point of how you know that there's growing wealth concentration, there's growing opportunity polarization and things of that sort of. So that is, you know that that is the context within which it would apply to many people. But the thing is that the one thing that is that that remains immutable to a large extent is that if you, if you just focus on the fact that the old game is going away and you're not and you either get mad about that or you just figure out how to play it better or play it faster, that's a clear path towards irrelevance, if you will. So given every set of constraints that you personally have, you have to figure out how to get to the new game. So everybody will have their own set of constraints. Yes, some will start with the, from a place of more privilege than others. And that's, you know, to some extent how things have always been. But more, the one thing that, that I think is going to be common to everyone is that if you keep simply focusing on playing the old game, you're not going to get to a win that's going to be significant because winning the wrong game is actually losing.
- [89:15]
B
Yeah. And so if I were to summarize it would be you've got to relearn, unlearn. Because so many of us have habituated ways of doing things that we're not even aware of them.
- [89:33]
A
Right.
- [89:34]
B
And, and, and I've undertaken a series of new projects, one of which is writing my first fictional book. And boy, like I was starting, I've written four books for non fiction books and the correlation between fiction and nonfiction I found is basically zero. So, so I've had to to learn it. But it's been really fun to learn how to do it and I couldn't have learned as quickly as I learned without AI, Right. It's like I did the traditional thing and read some books on how to write fiction and I even read a couple about how to write a screenplay. And then I watched some YouTube videos and I'm like, I don't think I've learned anything at all. And so I just went right to our AI platform and I, I just start it. And then I designed a very mean but very truthful editor that the AI simulated and it basically came back. This is horrible, this is awful. But it's interesting because it's a whole different conversation. But it's easier to take that advice from a machine than another person because from another person I actually experienced it. The first thing I ever wrote for publication, my wife Graduated summa cum laude with a degree in journalism. And I, I was the editor of my high school newspaper, and I thought, oh, I know how to write all this stuff. And so I, I wrote it and I said, hey, would you take a look at this? And this is back in the 90s. And so I actually handed her the, the paper that I wrote, and it came back a sea of red. And I got mad and it became, how can you say this to me? And all of that. And I was recounting it with her, and by the way, she was entirely right. All of her edits made it a vastly better piece of writing. But the point is, we were chatting about it and I said, so I modeled you into the AI and they're wicked mean to me, but I don't take it personally. And I just think that your underlying factors of curiosity, the joy of experimenting and the ability of understanding that everything's an iteration. You're not going to get it. You're not going to get the hole in one every time on the golf course. And, and it seems like selecting for those again is selecting against things that we've tried to beat out of children in the previous era, right? Like what was much of public education in the 19th and 20th century. But no, installing a correct answer machine in the brain and driving curiosity and all of that kind of stuff out because your society wanted you to follow orders. Society wanted you to be able to understand those orders and then execute them. So I, I definitely think this touches not only business, not only society, but the way we learn. And, and I think that it's, it's interesting to me because I think that we have to be open to every conceivable permutation here. Most of them won't work, right? But we'll learn from what doesn't work. Your knowledge graph, great example. It isn't the large language model that is really giving you all of these super new ideas. It's your knowledge graph. And people sometimes forget that part of it. It's not that that the. Who knows, maybe at some point in the future AI will, will get creative and will get all of those things. I have no idea and I lack the expertise to even comment on whether that is possible. But for now, that isn't the way it works. Right. It's a tool. It's a tool in the hands of a deeply curious person who is comfortable with uncertainty, is the best tool that's ever been invented and certainly in my lifetime. And I just, It's hard for me to articulate how excited I am by it, because it really leads to a whole new paradigm. How do you deal because you're dealing with external institutions, etc. I don't have that, that friction. How do you overcome a very deeply skeptical board? Back to the board, right, where you know, the CEO answers to the board and the board is likely made up of people close to my age whose incentives are very different, as you pointed out. You know, three years and out. How do you convince them? You really, really got to take this seriously.
- [95:01]
A
Yeah, I mean, so there are quite a few things over there. And I want to reiterate, you know, the summary that you brought in over there, which brings together a lot of the points that we've been talking about. It's a combination of uncertainty in the playing field with constantly changing and ever improving capabilities that are available at your disposal that allows you to innovate in the center and manage that uncertainty to your advantage. Right. That is what the new game is. The difference between a business and an individual is that an individual has emotional constraints and capacity constraints at a different level. And so that's why fund centric becomes very important. But, but for a business as well, you have to think about what your constraints are within which you can operate. But you have to know that what you're really going for is there's uncertainty outside. There's a new capability set internally that creates the opportunity for you to, within the unique constraints of your business, within whatever identity you have created for yourself, A, potentially question that identity, but B, within those constraints. And I won't say within those constraints, but in cognizance of those constraints, think about how you can leverage these capabilities to do something fundamentally new, create a fundamentally new way to compete in the, you know, in the playing field you define. Right. And in general, my point to any of these so called skeptical boards is so I think, you know, by default there are certain filters in terms of what comes my way. So I don't necessarily speak to somebody, to a company which is like at step zero, it's typically to companies who are at step four out of five where they've kind of done the adoption, they've tried to accelerate things internally, they've brought, you know, bought into the hype, but not seen the results. And then they realized that what I'm talking about is how to change that game, how to think about strategy in a fundamentally different way. So I don't have to necessarily go in and tell them that AI is important. They sort of know that everybody's talking about it. They've seen the value internally. They haven't yet figured out what that means for them in terms of whether they should just continue doing what they're doing or they should fundamentally flip. And so that is where I essentially make the case that you can place a bet on the fact that nothing is going to change externally. You can place a bet on the fact that things are not going to change externally. You're going to have better capability. You're already adopting it, so you're going to, you know, improve how you do what you do today. But you have to start placing bets on what if things change externally. And at the end of placing those bets, you know, you have to start by saying out, what are those scenarios? At the end of it, you could come back and say, it's not going to happen in my relevant timeline. And that's perfectly fine because, you know, the difference between having a view on what's going to happen and betting on it is the risk that you are willing to take against the timeline. So it's a perfectly valid statement to come back from it and say that I'm not willing to place that bet because I don't believe in that timeline. So you're accepting that risk. And that's fine. That's how you know, all allocation works in general. But you have to do the hard work of thinking through what all those scenarios are and what your firm looks like in those scenarios, and very cognizant, you know, be very cognizant of the fact that you have to place those bets. You cannot place those bets by omission, the fact that you did not place them because you did not run the scenarios. So you have to do that at the end of it if you still believe that it's not going to play out. Out. That's just the bets that you've. Those are just the best bets that you've placed. And there are going to be many scenario, many situations where companies are going to optimistically proactively place bets and they don't play out. And there are going to be situations where companies are going to, you know, not place bets even after seeing the scenarios, but not placing bets because you didn't do the hard work of seeing the scenarios, didn't do the hard work of projecting what's going to happen, didn't think through how these shifts are playing out. That is the failure case, if you will, because if you do the hard work of developing the scenarios, placing the bets, you already taken some steps to, you know, say this is going to happen. That's not going to happen. Which means that you already sort of perked your sensors to keep sensing on whether those bets are going to play out, which means you already started fine tuning a learning architecture and a feedback loop to see if that's going to happen. And with that, you'll keep tweaking your scenarios, you'll keep tweaking your bets, but if you never did that, you would just be, you know, an ostrich with your head in the ground and not thinking about what's going to happen. And so you. There is no architecture, no feedback loop, nothing created. So that's what's important. It's less about be the fast mover, do this, do that. It's okay to not be the fast mover if that's deliberate, but it's not okay to not be the fast mover if that's. If that happens out of omission.
- [100:27]
B
Yeah. And again, our alignment on how we look at this is strikingly similar. Spending a lot of time thinking about failure modes, it's not only, in my opinion, required, but it's incredibly instructive. Right. Because if you're a naturally optimistic type person, you might have a hard time thinking about all the way things could go wrong. And you know, the, the other thing that AI can do is simulate cognitive diversity. Like I'm, I'm constantly playing with, coming up with the most pessimistic model that I can possibly design, but making it very smart. And what's really interesting when you do that is your blind spots are revealed and we all have blind spots, every single one of us. And, and, and having kind of the red teaming of our own ideas, I, I just think that is such a valuable thing to have. And, and yet like, a lot of people look at me like I have three heads when I'm like, no, you got to think about all the, not only that you've got to think, you've got to give it to an intelligence that is, is like going to find those blind spots that you have. So. Well, listen, I always love talking to you and admire your work. You're working on the new version of the book that comes out when. Later this year. Perfect. I would love to have you back on again when that comes out. Because a topic we didn't even touch is like, what is going to happen to books, your books in particular? Are we going to move to a new way of packaging a book so that it can be constantly updated, so that it can be interrogated? I think that all of those things are going to Be like super useful. And I would love your view on them.
- [102:37]
A
Yeah, absolutely. I would love to. I think it's a topic I'm constantly thinking about. I would encourage whoever is listening to go to reshufflebook.com it's the companion side to the book where what I've tried to do is I've extracted the concepts from the book and I've shown that as a navigable graph so that you can see which concept connects to what you can work on applying that, I've tried to create different narratives, which all of it is again generated by the knowledge graph because once you map that all the concepts, it generates different narratives. So for different Personas, whether you are thinking about it in terms of jobs versus in terms of how you design the workforce and the organization, there are different tracks in which you can go through the book. So that's one attempt to think about how do you unbundle and rebundle the book, if you will, in new forms. But I think the larger thing that I'm really going for, and I'll just throw it out here and would love to discuss this again, is that the book was not an optimal bundle for a lot of things that it was used for. It was just forced on a lot of formats. So what I mean is that I still believe that a big idea with the persuasive narrative requires a book. It cannot be done with questions to an LLM where everybody's going to get a different version of the idea in a different narrative. But a lot of how to advice which has so far been packaged in a book is much better accessible in the context of your work in an LLM and so on. And so we'll have to really rethink the relationship between the jobs that we wanted to do and the formats that we were forcing those jobs on. And so, I mean, there's a lot of discussion over there and I look forward to that topic.
- [104:30]
B
Yeah, the user interface is going to change, there's no doubt about that. Sangeet, always a pleasure talking to you. As you might recall, our final question here is we make you emperor of the world. You can't kill anyone and you can't put anyone in a re education camp against their will. But what we can do is we hand you a magic microphone and you can say two things into it. And those two things are going to incept the entire population of the world. They're going to wake up whenever their morning is and they're going to think the two things that you say now were their own Ideas. And they're going to say, unlike all the other times where I had these ideas, shower ideas, or waking up ideas, I'm going to act on these two. What are you going to incept in the world's population?
- [105:24]
A
Assuming that these ideas can be personalized by the individual, I would want to create the drive for every individual to figure out what they are insanely curious about, which they would want to do, you know, just because. Just for the love of doing it, or which, you know, which they would want to think about just for the love of thinking about it. I think what's. What a lot of us are lacking is by outsourcing a lot of our thinking, we are not seeing the incentive for getting into training our thinking machine, if you will, our brain. And so creating an organic incentive mechanism for that, which is why I said it would be personalized. Everybody wakes up figuring out, here's what I'm insanely curious about. I'm going to start figuring out how to go after this, and here's how I'm going to. And at the same time, I would want to want everyone to think about it as if I'm so curious about this, I want to do it as long as I can. And so how can I do it in a way that I create the conditions for me to constantly think about it. Because if my conditions don't support this kind of curiosity, and if I'm constrained in other ways to do things which are more short term, that comes in the way of doing it. So I don't know if that answers the question directly, but I want to leave that with people.
- [106:49]
B
The way I would rephrase it is we're incepting everyone to wake up thinking, I am insanely curious about this. How can I go about satisfying that curiosity and sustaining it and building it into something that I can continue to do. Okay, so that's your first one. What's your second?
- [107:14]
A
I think the second piece is the sustaining part in terms of how do you sustain it? Right. So it's about being very clear that you don't simply. It's easy to be curious about something, it's very difficult to sustain it. And in order to sustain something, it's not just about the fun, but about having the discipline to sustain it. So yes, it's important to find what you're insanely curious about, but you should be disciplined enough, you should care about your curiosity so much that in order to protect it and in order to do it on an ongoing basis, you create the discipline and the conditions around it that can help sustain it. So the second piece is really about getting to that point where you know that this is not just something that I am, you know, it's not just a passion, it's a discipline. And how do you do I bring those two things together?
- [108:08]
B
I love them both. What's the old saying about curiosity being the cure for boredom? And there is no cure for curiosity. Right. Sangeet, thank you so much for your time and your ideas and can't wait for our next conversation.
- [108:24]
A
I look forward to it. It's been such a pleasure. Thank you. Once.