The AI Reshuffle of the Knowledge Economy - What Next?

Understanding AI's Impact on Industry Structure Good morning, good afternoon, good evening. Today we have an unusual episode of What Next? Because it is a crossover episode. Your host of What Next also has another YouTube show and podcast built around my recent book called Rethinking Work. And I speak to different people who are inventing the future of work, who are thinking about what will be the forces that will drive the future of work. One of my guests a few months ago was a gentleman by the name of Sangeet Paul Chaudhary. And after I recorded his episode, I said to myself, this should really have been also a What Next episode. Sangeet Paul Chaudhary is the best selling author of Platform Revolution and Reshuffle which is his most recent book and he explains how AI is re stacking the way knowledge is created, distributed and monetized and what that means for workers, companies and the future of competitive advantage. He has been one of the most sighted speakers from both Davos to Harvard Business Review. He's a sort after strategic consultant and his book has been very present and very, very forward-looking into what is happening today. So right now, listen to an episode of Rethinking Work, which also could very much have been called What Next, which is how the knowledge economy is being reshuffled by AI. Welcome back to Rethinking Work, a show about how work is changing and how we can change with it. I'm your host, Rashad Tobacco Allah. In this episode we're asking a big question. Who wins when AI restacks the knowledge economy? As artificial intelligence reshapes, our knowledge is created, distributed, and valued. The rules of competition are being rewritten not just for companies, but for entire industries and workforces. To help us explore this, I'm joined by Sangeet, Paul Chowdhury, Best Selling co-author of Platform Revolution and author of Reshuffle. Sangeet has advised CEOs at more than 40 Fortune 500 companies and pre IPO tech firms, and his work on platforms has been featured multiple times in Harvard Business Reviews Top ten must, reads a World Economic Forum Young global leader and senior fellow at UC Berkeley. He's one of the foremost thinkers on platforms, ecosystems and the future of the digital economy. Sangeet, welcome to rethinking work. Speaker 2 Thank you. This should thank you for having me here. Speaker 1 So as I was mentioning, I had the opportunity, thanks to you of getting an earlier copy of your book, which I have read with great interest. So before we start, can you tell me why you wrote the book or a little bit more about it's clearly a burning issue, burning platform, burning importance for you? Speaker 2 Right. Yeah. You know, the book is sort of the the most recent step in my journey of just trying to understand how shifts in technology shift competitive advantage. The question that I obsess over is what determines winners and losers whenever there's significant shift and whenever we're confronted with what I call structural uncertainty, where the structure of industries, the rules of competition are up for grabs. And that's something that was relatively there in the industrial economy where things were a lot more stable. But over the last 20 years in particular, and accelerating now more than ever before, structural uncertainty is is key. What very often happens is when you're faced with a shift in structure right now with AI, that's very much happening. If you keep on looking at things with the old frame, you're sort of assuming that the structure is not changing, that the industries will work in the same way. It's either going to, you know, substitute something or on the other hand, complement or improve something. Very often we we talk about will AI take my job or make me better at my job? Is AIA threat to our business or is going to help us in our business. All of that assumes that the structure stays the same. And as I had discussions with executives about this, I repeatedly saw that a lot of them were thinking about it under assumptions of the same structure. So the key motivation for writing this book were twofold. One, help them understand that it's we're really grappling a shift in structure over here. So you need to the frame first. And second, help them understand that, yeah, these these topics. So if you know what AI does to your job versus what AI does to competition and does it make you more competitive? They're deeply interlinked and you can't think of these as two separate topics. And very often when we look at AI, we we start by thinking of the jobs we can cut. On the other hand, my message is you need to think through how competition is changing and then think about what that means for your organization and, and the jobs within it. So I wanted to kind of get this message across. And that was the key idea of writing this book. AI's Impact on Tasks, Organizations, and Competition I only have got it across and I'm going to share 3 or 4 things. I've taken away a lot of things. I have pages and pages of notes. But for today's conversation, I'm going to basically focus on a few things. And if I've missed some key things, please add, you know those. But I'm going to first start with what seems to be one of the organizing structures of your book, which is very much what you mentioned, which is the nature of the task is changing. As a result, the organization design must change. And because the nature of the task is changing and organizational design is changing, your competitive set is changing. And this is to your particular point. It's that's one thought. Now the other one, which is what I took away after I read the entire book, was a what we're living today in a world is not just about making what we currently do more efficient and effective, but it is both about existential opportunities and existential threats. Right second, which is a chapter that you end with what you do not need is an AI strategy. Rather, you may want to rethink your overall strategy and sort of decide where to play and how to win, which is a couple of key things you've said. And the third one to a great extent is the technology itself, while very critical, may not be what will differentiate you. But it is both an understanding of how the technology is changing systems and the coordination of different systems and to understand where both the economic and what you say, contextual value are moving up or down the change chain and then figure out how to do that. Those are some of the big thoughts I've taken away, if that makes sense. Speaker 2 Yeah, absolutely. I'll, I'll, I'll respond to those in in turn. Sure, there's, there are a few layers to peel through on, on all of those. So on the first point, I, I think you know, the, the first point that I'm trying to make and that I focus a lot of the framing in the book on is don't stop your analysis of the impact of AI by stopping at the task. You need to think about the entire system within which that task sits. And this applies not just at the level of jobs, but even at the level of competition. The reason this is important is because a lot of our current framing or thinking about the impact of technology on jobs has been in terms of the impact of technology on the constituent tasks within the job. And that largely applies if you're talking about most technologies. For example, when access were replaced by chainsaws, you did not fundamentally change the structure of the lumber industry, but you did change the the, the rate at which, you know, trees could be cut and who could cut tree then what kind of skill was required. But with AI, it's not the technology is not just impacting the task, it's impacting the overall system. So you need to think of the system within which that task sits. So every task that we perform as part of our job sits inside a workflow. Workflows sit inside organizations. Organizations organize all of this work and compete with each other. And so there's a, there's a competitive ecosystem within which they sit. Now, in terms of how these interact with each other, when, when you think about, you know, the impact of AI, the impact of AI can play out both outside in and inside out. So I'll just complement what you shared outside and in the sense that AI could start as a competitive force that's acting on you because there's a fundamentally new kind of competitor that's coming into the industry or your competitors are playing in a different way. Instead of charging by the R, they're charging by outcomes. There could be many different ways in which the mechanism of competition could be changed. What was charged for in the past could be subsidized and value could be made, could be moved somewhere else and that could be the new value pool to be monetized. So the nature of competition, when it changes, organizations have to rethink what capabilities they want to invest in, in order to compete effectively. And that then changes what kind of tasks they will perform, how much they will allocate to which type of tasks and hence which types of jobs will hold value. So that's an outside in way outside in view of how competition changes with new technology and that changes jobs inside the firm. There's an inside out view as well because organizations don't just respond to competition, they also innovate in response to customer behavior. And in that scenario, the way organizations will think about it is to look at the capabilities that AI offers. Think about how you can organize those capabilities around your existing capabilities to create fundamentally new value. Which means then that you'll have to think about which tasks your workforce performs, which tasks machines perform, and how you combine them into new workflows to innovate in fundamental new ways. And that then has a rippling effect in how it changes competition externally, because now you are changing the basis of competition. So my key point is that if you think about tasks inside organizations, which then sit inside ecosystems where firms compete, these changes can happen both inside in and inside out and outside in. But you need to think about the whole system rather than just the, the task that you're you're looking at. So I'll just pause there so that, you know, it doesn't become too long, but I just wanted to clarify that difference. How Simple Tech Creates Systemic Industry Shifts So, so you know, one of the key things you've clearly mentioned is you have to basically think both how the rules of the competition changing or how competitive the changing of the system is changing. And you have a couple of really interesting examples in the book, which is 1 is about container ships and how the container was created. It basically gave countries like Singapore an edge because they actually created a system completely built around container ships or containers, which is what was become sort of the big intermodal transport. And the second is when the barcode came around, how Walmart reorganized around the barcode and as a result power moved from basically the supplier to the retailer. And those are some of the things you are showing are going to happen in the AIH. Speaker 2 Yeah. The key point that I'm that I make with both of those examples is that the technology, you know, the impact of technology on the system plays out not just because the technology is intelligence. Today we talk about artificial intelligence. We're focused on what kinds of benchmarks is it achieving. And you know, do you now have a PhD in your pocket or not? While all of those things are good to kind of help us move the frontier of technology, what really holds us back today is how our systems are absorbing and reshaping and restructuring around it. So the example of the container is something that's really interesting. I opened the book with it and I, you know, I had to look to a lot of examples before I settled on the container because whenever we talk about AI, the most common historical example is usually the shift towards electricity and how factories had to redesign. But it's sort of been overdone. And if you really look at the systemic effects, the, the impact of the container, even though it's invisible in the economy, was much larger in many ways. So if you look at the impact of the container before the container came in, we had brake bulk cargo. So essentially cargo did not have a standardized shape. It could be set up in different forms. So it was not stacked efficiency, but at ports it was manually loaded and unloaded from ships. So there was manual work involved by dockworkers and that slowed down how fast ships could turn at boats. Because of all of this, the key impact that happened was or the key impact of the slow work was essentially the long lead times and the unreliability of freight. So it was not just the slowness, but the unreliability. You did not know when something would reach a certain place. What the container did was first of all, you know, the 1st order effect, if you will, was that it speeded up this task of loading and unloading because now you had a standardized unit that had to be moved off the ship and you could create a crane to automate that. And so as a first order effect, the jobs of dock workers went to be. So yes, there was a question, question of, you know, will this new technology take my job or will it help me? It clearly took the job away. But that was not the end of what the container did. The container. You know, the real impact of the container happened when ships, trucks, and trains agreed on a common standard for the container. Because before that, everybody worked with different sizes of crates. Once they agreed on the same size, it meant that the container could move seamlessly across different forms of transport. And there was, and you know, along with this there was unification of contracting. You could move a container from source to destination with one contract. These two factors essentially made freight reliable and this had some really interesting effects because the unreliability of freight used to keep manufacturing local. You did not want to work with, you know, suppliers far out because you would, you just couldn't trust things reaching you on time. So manufacturing got unbundled and we started seeing a, you know, specialization of components happening and component level competition started happening because of the container effectively. In fact, the shift from mainframe computing where IBM owned everything to Microsoft and Intel opening up the industry and innovating at the component level happened alongside the shift towards container rotation. So there was a secular shift towards the unbundling of manufacturing and innovation at the component level. Second set of shifts that happened for instance, was that you did not need to have inventory stock you could work with just in time inventory. So a lot of the middle men started getting replaced even though technologies were not directly, you know, the technology of container was not meant to affect their specific role. But the second order effects played out that way in the order effects is really what we see as globalization, global supply chains, global trade, a new geopolitical landscape. All of that happened because of the container. So the key point here is that even if you look at a dump technology like the container, you see two or three things. The first thing is that the effects of technology don't play out just because of compounding of the performance of technology. All of that is great. Moore's law is great and you know, the benchmarks we are achieving with AI is great, but real world effects layout because there are frictions in the real world. And as those frictions get resolved, as you know, the friction of unreliability of freight in the case of the container, as those frictions get resolved, the entire system reorganizes itself because the previous system created buffers like, you know, stored inventory and local manufacturing in, in response to those constraints. But when those constraints and frictions go away, those buffers don't hold value the whole system, the audience itself. And that's that I think is the real, you know, the shift that we need to look at with AI as well. Speaker 1 That, you know, that example is very, it's the most powerful example I have I've come across. And to great extent, the first, second and 3rd order effects are what really makes a big difference, which is with the container, you know, clearly a, you begin to have the 1st order effect of more efficiency, less labor needed in certain places. But to a certain extent, you know, the second one is you began to have the ability to have intermodal transport. You basically began to have the same container moving from one place to another. That basically meant faster, less friction in the system. But then the third is it gave birth, as you say, to everything from globalization to modularization in other ways. And the world basically went into sort of unbundling of something into Lego pieces and then a rebundling of the Lego pieces into a new system I think, which is your unbundling, rebundling. And that is so seminally important. It's one of the things that an example I will provide and it's not anywhere close to the example that you have of. Speaker 2 The. Speaker 1 Sort of container ship or anything else. But in the previous revolution, the digital revolution, if the New York Times had decided that the digital revolution was going to make a way for its trucks to run more efficiently, it's printing presses to be more effective, to come up with algorithms on how to distribute newspapers better, it would not be the New York Times that it is today. Because in effect to your point, it was the 3rd effect, which is hey, do I actually need printing presses? Do I need trucks? Do I need page one meetings to almost the extreme, am I in the news business or online in the distribution of information and content business? And it made the change. So to a great extent, I remind people that it is that which is the existential opportunities and threats, which in your particular cases, you got to think about the impact on the task, the organization and the competitive set versus what you currently do, which most people are not doing. So what they're really doing is they're basically making it in if they try to make yesterday's systems more efficient for tomorrow versus rethinking about tomorrow. Speaker 2 Right. AI's Three-Level Impact on Business Strategy Yeah. And I think that's, you know, that's exactly what we've seen with the previous digital waves as well. If you want to bring it closer home. Initially we used to call what was happening as a shift from offline to online media. So we were just focused on, well, this is a cheaper way to get to distribute things. And, you know, those observers who who still think in terms of that frame keep shifting between, well, is offline winning this online winning? That's not really the point. The point is that the system reorganizes itself because take Amazon as an example. The the reason Amazon, you know, Walmart can't copy Amazon at what Amazon does is not because Amazon is online and Walmart is offline. It's because Amazon serves A fundamentally different demand set in its, you know, in its new system of online fulfillment. And that demand set essentially says that demand has to be fulfilled at home. Fulfillment at home has more variability. Somebody might be there, might not be there, you may leave the parcel or not, the dog might be there. So fulfillment in general is very different from fulfillment to the store. And hence it requires a different inventory system, a different warehousing system, a different map, and it requires a different way to manage uncertainty. So if we were just thinking of, well, this is just a new channel, we would not think about all that. But Amazon created a new system to make e-commerce possible, which is just impossible for a retailer with a different architectural set of investments in place to to copy with unless they were to RIP out everything they do well today. Speaker 1 This has now become very clear so our viewers and listeners will clearly understand why your book is so both compelling and, in you know, many ways, paradigm shifting. At least if it opens your mind and makes you think different. What advice would you give two groups of people And your book has advice, so I'm going to 1 is to people who are running companies. You know, you advise a lot of people who are running companies, organizations. What are the two or three pieces of advice you would give them Besides read your book, which is number 1 and #2 is for talent. Because one of the things you sort of talk about is that in this particular world, it's not necessarily that because there is going to be more wealth created, that it's going to be equally shared. And that talent has to position itself in the right place. You know, some of the things you talk about is above the algo versus below the algo, figuring out where the constraints are. And you use examples of sommeliers and other things. But a, what would you give advice to a board member, ACEOACMO, that's one. And then second, what advice would you give to talent? And maybe it's the same but. Speaker 2 Yeah. I think to, you know, to there is there are similarities because fundamentally both of them are grappling 2 things, the uncertainty in the structure in which they're operating and the fact that previously scarce capabilities are becoming easily accessible and hence the advantage associated with those capabilities may no longer hold. So there are some similarities between the two. If I were to think of, you know, what this means for a board member or CEO, somebody who's really thinking about ROI and really thinking about competition, the first thing to really think about is to understand that there's a difference between structural uncertainty and operational uncertainty. We've always had operational uncertainty. Demand is going down. How do you predict it? You can solve operational uncertainty with tools, and AI is actually very good at solving operational uncertainty. You can't solve structural uncertainty with tools. You need real ingenuity to think through how is the structure changing? And you need to think through what does this mean for my business? What previous advantages go away? What new advantages are coming in? And that's the key point that I make, You know, towards the end of the book, over the last 10 to 12 years, I've been repeatedly asked what should our platform strategy be? And now I'm asked, what should our AI strategy be? The point is, forget platform, forget AI, forget whatever is the newest buzzword. Think about where do you play and how do you win? What's the nature of the playing field? Traditionally, playing fields used to be very simple. Industries had stable boundaries. Consumers had relatively stable demand structures. Now all of those things are up for grabs because industry boundaries blur and consumer, you know, taste changes all the time. It's it's the playing field is much more complex and uncertain. And secondly, how you create advantage within it is not straightforward. So the first thing is, you know, do not look at AI as a way to do what you do faster, better, cheaper. Think of AI as a way to change your game based on the reality of how the playing field is changing. The second thing that I would just call out is that when you think about the impact of AIA, simple way to think about the impact of AI in terms of what it means for your business is that it'll impact your business at a business at at least three different levels. So there's one which is the operating level. Whatever showed up in your OpEx, you, you could use AI to, you know, bring down costs, improve speeds, all of that changes your OpEx. But at the end of the day, everybody has access to the same tools. So improvements in OpEx are not competitively beneficial. The second is when you look at your CapEx, especially if you're in an industry where you need to make significant R&D investments. Speaker 1 Before. Speaker 2 You can and in order to create value that you can then harvest and capture with a fairly significant period of time. So drug discovery, the classy example of that, mining, exploration and mining, the classic example of that. These are industries where the majority of your cap ex is inefficiently spent because of your inability to predict well, inability to model well, your inability to make, you know, bets with conviction. And these are all things that AI can help you do better within the, again, within the structure of your model. But instead of working on improving your OpEx, which everybody else had access to as well, it helps you really move the ROI because it helps remove uncertainty in your CapEx. But the real shift in AI, you know, in thinking about AI is to really think about how do we look at the fundamental assumptions of what's changing and change the way our entire industry is structured. And a very simple example of this is what happened in social networking, something that we are all familiar with, but we don't understand what happened below the surface. We see Instagram and then we see TikTok came up and it sounds like it was just the next social network. But what we often don't realize is that the at the time that TikTok came up, analysts widely believe that Instagram photograms network effects were unsurmountable. YouTube was unsurmountable. It was impossible for a new network to come because it was just difficult to build those network effects. And these previous other networks were built on the idea of a social graph. You had to connect with people before you could see value in the network, both as a producer and a consumer. What TikTok did was it did not require these connections. It used AI's predictive capabilities to look at what you were doing. It changed the form factor so it could capture more data in faster cycles, and it used that to predict what kind of content you were interested in. So it never needed a social graph. It bootstrapped around what is called a behavior graph. And so that's the idea when you think about really game changing shifts with AI and your business, you really have to think about how you change the fundamental assumptions of your industry around the capability of AI. So I'll post it on the, you know what we, what I'd say to leaders for people who are actually working, you and me and everybody else who is trying to create value for ourselves at an individual level. Reskilling for AI: Focus on Systemic Constraints I think the key point is that today we think of AI as an attack on a skill advantage. And we think that when AI takes over a certain skill and hence reduces our ability to charge the premium associated with acquiring it, with training for it, we need to just move to the next skill. We need to reskill. And I, I believe that reskilling is, is a cop out answer. I'm not saying the skills are not important, but saying that we need to reskill because the previous skilled premium has gone away is sort of a cop out answer because we, we're not really, we don't really have a, a clear vector or direction in terms of what do these skill to. And the answer to that is in order to know what to re skill to, you need to stop looking at skills and start looking at how the system around you is changing. What are the new frictions that are emerging over there? And how can you position yourself at the point of those frictions? And that's the key idea. You need to look for the new constraint in the system. I, I talk about three types of constraints that, you know, every system of work has there. There are constraints around skills, which are primarily what we see in our jobs. But then in our jobs we also solve constraints around risk. You know, we, we assume certain risks associated with certain outcomes and we get paid in, in proportion to that. And we also manage risks around coordination. In order to get something executed, you need to coordinate across multiple stakeholders. And with AI coming in, we need to start thinking about how the system is changing and how these constraints are shifting in new directions and then think about reskilling on that basis. So I'm not saying skills are not important, but you need to think about how the system changes and what the new constraints are. Speaker 1 Yeah. So to a great extent, how do you build new skills for a new world order? First you have to understand what the new world order is. And you know, the examples of your friction that you have is you have obviously a friction of basically the idea that today everybody, this is on skills, that today everybody can basically get information about wines from an app. But familiars are still very useful because in effect, now that there are so many wines and so much data that to the extent the familiar is part of the experience of going out and it's part of what you pay. It's what makes a 40 bottle, $40 bottle worth $250 as a similar. And then in fact, that's because it's from the skill friction to a great extent in the same way, you know, a anastologist has to do things, which is managing risk because the patient can die. So there is a risk friction. And similarly, while the computer and the AI can read radiology reports, the radiologist has to work with other doctors and other providers. And that basically is the coordination friction. So those are three examples of those frictions. See, I read your book in great detail, you know, which is which, which I think are some of the three examples. And I thought those are very, very powerful. McKinsey's AI Challenge and Future Organizational Structures And I think what's very, very powerful in both of these is how every industry is coming under significant risk. So recently, and you may have seen this, there was a article I think in that in the Wall Street Journal, I think, or maybe it was the New York Times on the managing partner of McKinsey saying AI is a existential challenge to us at the current time. And part of it is the way I do it. And this is not what he said, but this is what I look at it working, having worked with a lot of world class consultancy companies, they do a lot of things, but they basically have 4 things that they do. 1 is they have massive amounts of knowledge bases #2 is that they have very smart associates that work with those knowledge bases which they can mark up significantly to make margin. The third is they have the crystallized intelligence of their senior partners who have seen a lot can connect dots in new ways. And 4th is they have a cover your ass sort of reason, which is even if I want to do something as Aceoi, go hire one of these people to sort of give you a Good Housekeeping seal. Speaker 2 OK. Speaker 1 Now to a great extent, knowledge is far less important. Their knowledge bases are not that important as well as it's very hard to up up charge for your associates. And as a result, you basically have two of your planks of your business have gone and you've got the other two planks. And what they basically actually have become smaller. They've lost employees or become smaller, but they've now said that the average team that works at a McKinsey project used to be 14 people humans. Now the average team is 3 people and 12 agents or 18 agents, which is kind of interesting. And they now say, yes, we've gone from 45,000 to 40,000 people, but we also have 12,000 agents. This is to your point. So even world class companies like a McKinsey that is supposed to take you into the future is struggling to get to the future. Speaker 2 Yeah. I mean, we're, we're in the midst of so much flux that at this point we're still at a point where we don't yet know what the stable system will look like. Because what happens in times of change like this is you make bets and you, you try to REO the and then create a new system. And McKinley has done that with this, with this team structure, some bets work and some bets don't their reactions in response to that. And then over time things stabilize in terms of what the the new structure looks like. But I think there are certainly 3 or 4 issues over here that will converge to to create what these new structures look like. 1 is that a lot of work that needed to be done inside the organization with a large workforce can now be accomplished with smaller organizations and using external players. So I, I believe that there's a lot of entrepreneurial energy in every industry that will be unleashed because of this. So while on one hand we, we say that, you know, if you take out the ladder and if junior employees are not required, then how does your firm exist? We, we sort of fail to realize we're not asking the question of what, what does your firm do in the future? What does that look like? Does that even need a ladder? Does that look, does that have a fundamentally different structure? And in that industry structure, does, you know somebody without experience have fundamentally different way of plugging into how value is created. So all of that is still very much in flux, still up for grabs. SO1 is the entrepreneurial capacity that can be unlocked. The second is the amount of collaboration, what I call organizational coordination, that can happen beyond organizational boundaries because today we we have open boundary organizations, but a lot of the work that is sent outside to say a fail answer is a highly modular piece of work with very clearly defined goals and steps. But more work that involves more organizational context and tacit knowledge stays within the organization. So organizational boundaries will also change because increasingly a lot of that can be provided externally in the form of models. I, I could, you know, be running a company and I could train the company on my internal knowledge and provide that to a partner instead of requiring hundreds of meetings. And, you know, more complex collaboration could do it much more simply. There are many different ways in which these these changes will play out. AI Blurs Lines: Tech Providers Become Competitors Absolutely, absolutely. And in fact, you know, as we sort of conclude this, I think the whole idea of the organizational changing. So one of the reasons I really resonated with your book, which, you know, talks broadly about clearly the impact of AI on reshuffling everything, but it clearly reshuffles organizations. And in my book on the future of work, I talk about organizations in the future being not only smaller, but most of the value being created outside the organization, as well as in many ways these technologies, while clearly to make them today requires scale, which is all the, you know, 7 hundred $800 billion, the top four people are spending it. The technology itself allows, it's sort of like a lever that allows or a slingshot that allows David or Daniel to bring down Goliath. So in effect, it's it's it provides great advantages to small people too, such as large people, which is what your book sort of points out, which is one of those big things. Is there anything else that you'd like to share before I sort of summarize? Speaker 2 Yeah. I think, you know, one final point I'll, I'll just make is that as, as technology improves, there's going to be an increasingly blurting line between technology providers and what I call solution providers, the the players who actually solve end customer problems. And it's important to understand on what basis, if you are building on top of someone else's technology, as literally everybody's doing today, you need to understand to what extent your value proposition is driven by somebody else's technical performance and to what extent it's driven by the, the value that you're creating. So it's something that's it's something that's been bled out before AI as well. If you look at, you know, Uber versus Google, Uber uses Google Maps and then eventually way more gets into self driving as well. Those kinds of tensions will happen in other parts of the economy as well. So that's something that you just need to be thinking about in terms of how you resolve those tensions. Speaker 1 There is a chapter in your book about the book technology Solutions and what you call engines, right? You know, to a great extent and that in many ways sometimes your technology provider can basically become your competitor, which in many ways you know Google's biggest advertiser used to be booking.com and then Google vertically integrated into the business of travel. And in fact, I in the world of marketing, I often remind many of our clients that what prevents any of these large platforms, now that they have the data, they have the customer relationship, they have the measurement and they have the metrics, they can outsource manufacturing, which you do anyway. So the only thing they don't have is a brand name, but they can also create. And when you look at something like a Costco, you know, 30 to 40% of their revenue in certain segments come from Kirkland, which is theirs. Yeah. So this is, you know, to your point, when this shifts, a lot of people say, you know, we'll so sometimes your technology provider may be your competitor. Sometimes a solution provider may be a competitor. So you have to figure out that engine of technology solution and what you add, right. We've had the opportunity to hear from and learn from Sangeet Bo Choudhury. His book Reshuffle is a must read. It's available everywhere, including on Kindle platform and in the country that at least I grew up in. In India, it's available in both paperback as well as hardback. It is a book that I encourage everybody who is alive to actually read, and it's a very easy read and there's nothing else. If you just read the first two or three chapters, you get a very interesting grip. It raises a lot of interesting thoughts, and among those is the fact that the nature of the task is going to change. As a result, the nature of the organization is going to change and the nature of competition is going to change that. We are living potentially in a world where while the technology is important, this is not about the technology itself, but about what you bring to the technology. And understanding organizational shifts, understanding where value is created, understanding what your competitive sets might be. And finally, it's also potentially a place where before we reskill, we understand what exactly we are skilling for. And that is to understand where value is moving in the system. And value tends to basically move where there is friction. And people who can solve friction, whether that friction is one of too much information or one off coordination or one of risk management. Thank you very much. Speaker 2 Thank you, Richette, been a pleasure.