How Do You Strategize in the AI Era? - with Martin Reeves, Head of BCG’s Think Tank

28:14One of them is to play the current game very effectively, play it more than your competitors, you know, extract returns, drive efficiency. And you have to do that. Otherwise, you don't have the funds to pay for the future or the license to buy a different future. But you do have to have a different future because nothing lasts forever. So you also have to ask the second question, which is, what is the next game? And those games are not only somewhat contradictory, but they're actually very fundamentally different in nature. The mental procedures and the capabilities of optimization and exploitation of the current model are things like analysis, incremental learning, deduction, analysis.

28:59to relatively, it may be hard in its own way, it may be hard to find the next 5 % of efficiency, but you're dealing with data and well-established patterns. Innovation, on the other hand, and this is a topic of my latest book, like the button that changed the world, is very serendipitous. I mean, we never know. We may set out with the intention to find, as in the case of Sir Alexander Graham Bell, to find a multiplex telegraph, and we may accidentally discover the telephone. and we may have a certain vision of how the telephone would change the world. And usually it turns out to be completely wrong.

29:34What that useful thing does has far-reaching, serendipitous ramifications. So this sort of double game of what the strategists call strategic ambitxerity, exploring while exploiting involves very different mindsets and very different skills is what you're getting at here just to cut to the chase a little bit is what you're getting at that the people who are responsible for playing the current game effectively have difficulty thinking of what the next game is is that and so my hypothesis that achieving efficiency gains are necessary to unlocking actual durable competitive advantage the problem with my hypothesis is you're saying i think those same brains that are good at playing the current game may not be the brains that you can get it's not entirely true but it's it's it's probably mostly true um so in other words um so we have my first book uh your strategy needs a strategy i i looked at five species of strategy the big idea in the book was for different types of situations you actually need different fundamental approaches to strategy, you know, visionary, classical, adaptive, and so on.

30:47There are different, fundamentally different approaches to strategy. And we actually created a game using a sort of a form of artificial intelligence, if you will, it uses as some we call them a population of multi-armed banded algorithms to simulate any strategic situation. And then we had thousands of people playing this game over the years. So we're able to collect data on what type of strategy problems are people good at. And it turns out that there are about 3 % of people that are good at both the innovation game and the optimization game, but not a lot. And why? Because they're fundamentally different skills.

31:24So if you want to do both of these things, you have to do some subtle things. You have to, you've got to hire different types of people. You've got to have them get along with each other. You've got to balance the approaches. You've got to have teams that incorporate both components. You're going to make choices like, do I try really hard to hire ambidextrous leaders or do I hire people that are skilled in one or the other discipline and artfully combine them? Or do I sequence them in time saying, well, right now we need to focus on optimization. And then I, you know, change the people as the business progresses and match the skills to the to the situation.

31:58So that is the hard part of our dynamic strategy, which is this change from exploration to exploitation and then self-disruption or disruption from without going back to exploration needs to occur continuously. And that's a rather hard thing. And in competitive strategy, that's rather a good thing that is hard because therefore it raises for competitive advantage. So then how do you, okay, so I agree or I really appreciate that thought that training your existing workforce to derive efficiency gains is insufficient to discovering a new competitive advantage because so few of those people are capable of doing both.

32:41So let's take that as a given, even though perhaps we can discover it more. How does an organization structurally discover the next horizon of competitive advantage if its current employee-based is likely, you could say for lack of a better word, incapable of imagining it themselves? Well, you've got to, I think that question is equivalent to how do you harness serendipity? We know that serendipity is not the same thing as randomness, but we also know that it's extraordinarily difficult to predict. It's hard to predict innovation. So how do you enhance the power of serendipity in an organization?

33:20I think you can do it in a number of ways. I mean, I think you can expose the organization to heterogeneity. So an externally oriented organization is less likely to breathe in its own smoke and more likely to see something that doesn't fit, some anomaly that stimulates thought externally. So external orientation is one way. Hiring for cognitive diversity is another. Have different minds that are capable of looking at the same situation and coming up with different solutions. Having a culture that says, well, that's actually valuable. The fact that we're not completely aligned. You know, we agree to be aligned for the large part on the core business model.

33:56But in the new areas, we're deliberately unaligned where we're exploring. You know, not many cultures can do that. That's another thing you can do. You can train people. Um, so as part of the imagination machine book, I spent some time looking at educational systems and, and my big question was, you know, we're trained in all sorts of thought in our many different styles of thought in our educations, right? We're trained in deductive thought and analytical thought. But in terms of counterfactual thinking, the only training that I received was, uh, you know, freestyle drawing and role play in kindergarten.

34:29Uh, beyond that, I didn't receive any formal education in the arts of counterfactual thinking, But you can train people to do things like mine analogies. You know, what is this like? Multiple working hypotheses, which is we can look at it this way. We can look at it this way. To discriminate between the facts and choices of mental model. You know, often we'll say something like, I have a 2 % share of the pharmaceutical industry. This is a fact. Well, it's not a fact. It depends on how you frame the borders of the pharmaceutical industry and what you regard as, you know, within the share versus, you know, some other activity.

35:01It's a mental choice. And so, but you can train people to question assumptions and disclaim between facts and mental models. You can have a people that are skilled in deliberately manipulating the degree of divergence within a meeting, you know, making choices. This is a meeting where we're - Devil's advocacy, red teaming, things like that. But also like the choice of what is this meeting for? Is this meeting to optimize or is this meeting to actually to question and challenge? And how do we do that? And you can train people in that. I mean, I was fortunate to receive training in the Bono Six Thinking Hats approach, which essentially is choicefulness about different styles of thinking and deliberate agreement to most of a group of people on which ones to deploy.

35:45So there's a whole bunch of things that you can do to up your odds of being able to deploy collective imagination. You guys done a lot of research in the use of AI organizations. conversations you mentioned when we started the conversation that some things the person with ai was better at than other things that a person in ai was you know what are some of the other kind of like uh results of that kind of research that let people kind of like understand if they were are to allocate time and resources against some use of ai where are their good pots to be where as they're good hunting grounds. I think the initial view was this view.

36:31So Kai-Fu Lee, when he wrote his book, one of the early books by a true AI expert on what will AI be able to do and what won't it be able to do, essentially asserted this extremely plausible hypothesis that it was about ethics and empathy and imagination. Those were the things, the human bastions that would survive challenge from AI, substitutability by AI. I think we discovered that that's not the case with imagination because we kind of have these aids to imagination. And I think this actually mirrors a more general thought, which is in my book, Like, I look to the detailed evolution of the like button, this recognition tool that creates a currency of recognition that enabled the targeting of user feeds, that enabled a different type of advertising proposition and therefore permitted the takeoff of the social media industry and triggered the disruption of the marketing and advertising industry.

37:29And one of the really interesting things when I interviewed these pioneer companies of the invention of the light button is that none of the pioneers of the light button foresaw the eventual evolution of the light button. And so it's natural that in the early stages of the technology revolution, we we turn to the experts, the people that either purport to know something about the new technology or actually seem to because they, you know, they work for Google or they had, you know, esteemed jobs or professorships or whatever. But the track record of experts early in technology revolutions is pretty disastrous, actually.

38:02You'd be better off questioning than assuming. Isn't that to your other point of like somebody who was trying to come up with a better telegraph came up with a telephone. A lot of the time, the score kind of like take care of itself, but often not where you expect it to. So I think we're discovering, and I think part of innovation is looking for the unexpected. And competitive strategy, if you ask me what is generally true of the world, I could make some assertions. For instance, my wife yesterday was looking for some sort of portable Wi-Fi device for the car because one of my daughters has a long trip every week and she has to do her homework in the car, so she needs internet connection.

38:43and we speculated, wouldn't it be great if you could combine a hard disk drive with a battery pack with a MiFi with a router? Like, why couldn't you put that all in one package? Because right now, you have to buy the different pieces separately. And so I thought that was a really interesting discussion because we could say that, well, is that generally going to be an easy thing to do? No, that's pretty hard. It's going to be hard to get all of those devices into one box. But the thing about competitive strategies, it doesn't deal with what is generally true. it deals with the ability to create the exception you know strategy is all about exceptions if you do the things which are generally true of your you know what your competitors do you would be probably you wouldn't exist or at best you'd be you know one of one of a number of uh commodities in your sector i think devil's advocate thinking i think saying yes it's going to be generally hard for ai to do ethics but what can we do with ai and ethics and how could we support human ethics and looking for exceptions, looking for analogies in other spheres.

39:43I think that's one thing I could say. I think in terms of judging what is going to be generally hard and what isn't, though, I think, and this is my speculation, I think the training data is a good clue, isn't it? Which is, what is the AI being trained on? The AI is being trained on speech acts, what people say about something in large swathes of textual data. It's not trained on the physics of the world. The closest it gets to, you know, if I push this object, it's going to move or it isn't going to move. The closest you can get is what people say about the physics of the world. But what people say about the physics of the world and actually getting things to happen, the social physics and the actual physics, you know, it's probably going to be hard to do actual physics.

40:27I think, you know, there are certain areas where you can ask yourself the question, what is not in the training data? So that's not in the training data. there are going to be dominant themes, right? If you pick something that's very old, for instance, I was doing some research the other day on the very old discipline, which came out of the military of operations research. So a particular form of problem solving that was pioneered before powerful digital computers to assist with military problems, like finding downed pilots in vast swathes of the ocean or whatever. Because it's a very old subject and now a very obscure subject, the AI couldn't find it.

41:06The AI cannot tell you about your own intention, right? What is my purpose here? Your purpose is you get to decide. Some things are not immutable facts. They are choices made by human beings with their choicefulness and their agency. Now, the question is, what do you do with those signals of difficulty or ease? You're asking, where should I choose to deploy the AI? Well, you can look at it one of two ways, right? You could say, well, I don't want to be stuck funding something that's going to be very difficult because I might run out of money. So I'm going to go for a feasible application. Or you could go the other way and say, well, precisely because it's hard and it's never been done before, there might be a very big prize associated with that.

41:47And that's always a tension in competitive strategy. And that's a difficult, fuzzy managerial calculation because there's no data on what doesn't yet exist. so you learn your way to the right balance so that's where that's where a lot of art and fuzzy logic and and human the human judgment comes in because what you can't do on ai say look at the population of all things that don't exist and assign a probability to them you know just can't be done or you can say let me learn my way to the future with choices that combine the properties neither neither can humans right and what what innovators do is they create data through deploying prototype and experiments.

42:29And I do think actually I've been working with organizations that are creating, for example, synthetic audiences. And all of a sudden, if you can replicate a million users' decisions, you can actually almost Monte Carlo simulate what a population will do. And so all of a sudden, the cost of creating data is coming down multiple orders of magnitude. I think my final question is, how do you use AI kind of on an everyday basis? So what I've discovered, so I often do rapid exploration, rapid landscaping with AI. So if I'm beginning to think in a certain area, I ask the broadest questions I can. And the thing that really takes time in getting to know a new area is the really broad questions like, you know, historically, where did the current perspective come from?

43:18And, you know, what are the different schools of thought in this area? How does the discipline interact with this discipline? and what are 20 examples of the thing that you're studying. And so I do rapid exploration. What I found with, and this may change over time, what I found with the current technologies, you know, GPT-5 and the Grok and all of the latest models, is that when I really know an area quite deeply, the results I get are, one, alarmingly fallible. Like in detail, there's a lot of things that are not right and not true. And secondly, that it really does depend on what people are calling prompt engineering, as if there is a formula for asking the right questions.

44:02But that was always the most important question in strategy, which is, what's the question? And I used to have, when I had a PC in my office, now everybody has sort of laptops. I used to have a pinned on my PC, like the best questions, the questions I would use every day. And it was a reminder, the sticky note was to remind me to ask certain questions. and one of the questions was, what's the real question? Because things present themselves as a problem, right? The CEO says, please help me to do a 15 % cost reduction. But unless you ask, yeah, but what's the real problem there? Why would you want to do that?

44:38You don't get to the best framing of the problem. Another one of those questions was the diverging question of, What is that an example of? You know, okay, that question, that sort of subject is an example of some larger, you know, edifice of learning. What is that? And then the convergent question, which is, give me an example of that. What is an example of that? What is that an example of? Give me an example of that. You know, another one of these questions was, what are the best questions? The question, what are the best questions? Because sometimes, I'm normally in a consulting assignment or an innovation project, you don't know any answers.

45:15You may think you do, but generally things pan out that you don't. But what you can have is questions that force you to explore and bump into the surprises that trigger the imagination. And there's very firm research on that, that essentially the human mind is an anomaly detection machine. We detect, it's like the pictures the kids look at, spot five differences between these two versions of a drawing. We're extraordinarily good at that. And that's what triggers the... The question there is, do we dismiss the outlier or do we give it valence? And I think the tendency of the expert is probably to dismiss.

45:51The tendency of the novice or the beginner's mind is to give valence to the anomaly. Yeah, I'm not sure whether you, in your area, imagination plays an important role. I'm not sure whether you do this, but I often learn from people that know nothing about the subject. because by personally being forced to ask a question of somebody that knows nothing about a subject, forces me to frame it in common language terms. And if you describe market share in anti-cancer drugs without using the word market share or anti-cancer drugs, it really forces you to think a lot about the language. Often there's a lot of hidden assumptions in the words we use.

46:34And then also the reply from your kids that begins, well, I know nothing about that, But, you know, often it gives you a left field, an imaginative response that upon reflection, you know, often has an interesting imaginative seed to amplify into. I think today's conversation is really interesting. It feels to me like essentially we've explored the underlying capabilities of purposeful human thought in many different areas. The surface questions are, what can the AI do? How do organizations work? How can you be ambidextrous? How do you innovate? But actually, it all boils down to, in the first instance, how do you think effectively about those sorts of problems?

47:20And that's one of the common threads in strategy, like in the deep substratum, is how should we think about that? The question, how should we think about that? I wanted to end our discussion, Martin, because you are a think tank at a consulting firm whose job is to think about what's the next game, we would be remiss if we didn't ask you, if you think about where consulting is as an industry now, what's the next game for consulting given the emergence of generative AI and given that proficiency among consultants is insufficient for durable competitive advantage, what's the next game? Well, I think consulting is a business.

48:01is one thing I'd say. So that business has to renew itself. And so we're in the midst of a technology disruption. So can AI write PowerPoint slides? Yes. Can it perform difficult calculations? Yes. Can it find weak signals? Yes. So obviously something will change. And by the way, it has been changing. So I remember my first project in 1989 when I joined BCG was, it sounds absurd now because there would never be a project like this. It was to calculate the size of the European automotive spring market. So there are little springs in your doors, in your carburetors, in your... That's a classic case study interview question, isn't it?

48:47Right. And actually, the statistics didn't exist, which is precisely why somebody thinking of going into that space wanted us to have the answer to that question. So we had to interview in order to calibrate a model. We had to build a model of the industry. And you never do that now because data exists for most things and that data is traded and monetized. So and I could give you many other examples. So, you know, we have, for instance, you know, the advent of personal computers. Early in my career, I went to the Tokyo office and Japanese word processors. They existed, but they were incredibly cumbersome.

49:23um there was so the best was a i think a program called word star and it was incredibly complicated you had to do shortcuts of like ctrl c plus y to print or something you know and and would never do japanese so actually the accountant uh nakamura san he he hand drew the slides with calligraphy it's true and so you could never redo a slide because he would would refuse he's sleeping and And you could never have more than 20 slides in the deck because it was too much for the calligrapher. And so that changed, obviously. So AI will change things and we have to be open to change. What's different this time?

50:05What's different this time is it's faster moving and bigger units of CapEx involved, probably. So if I'm hiring different types of people, that's easy, right? I just hire a specialist with a particular type of skill. If I have to build a computer network or a large database or something, more money involved, higher stakes. And also we're at that stage where we don't really know. I mean, we expect great things. We're in the exuberance phase of technology. But there have been previous exuberance phases of artificial intelligence. And I remember when the DEC, the Digital Equipment Corporation, I think it was in the 80s, had machine learning tools that simplified procurement.

50:47procurement, or able to automate your procurement. And everybody thought this was going to take over the world. But I think all of the companies involved in that revolution, about a hundred or so startups around that theme, they all disappeared within 10 years. So we don't know the future and we're precisely at that stage now. But the second thing I'd say is the skills probably change and they're already changing. So you need probably to be less... Historically, we hired generalists. And then as clients became more demanding in different industries, we started to hire specialists in particular industries, and we trained specialists.

51:25Now we need technology specialists, but we also need a new type of generalist that is able to think deeply enough to question technologies. And a lot about the deployment of the technology is more about sociology and anthropology and the design of human systems. So it's almost like we need a barbell model. We need more emphasis on human skills and the specialties around that and more on technology skills. So already my company and I think most of the consulting companies are hiring for those new skills. I think we need to experiment. I mean, there was a phase when our business, like many other businesses, was like a comfortable optimization and growth game.

52:02You know, not necessarily easy, but it was, you know, mostly a case of more and better. Now we're bumping into different, right? Which is what is an assignment? How do you have, you know, deep technologists, you know, sort of like programmers and anthropologists in the same team, and they know how to talk to each other. And, you know, how do you deploy that? How does that work? That's a new, you know, that's a new organization model. So in short, I think consultants will be forced, like other industries, to adapt and experiment and learn with what seems like an inevitable disruption. And those times of disruption are when number twos become number ones, number ones become number threes, or they disappear entirely.

52:45So it's a high beta game. You know, the chances of something bad or good happening to you, the competitive volatility increases. So as a strategist, I, you know, strategists have this rather perverse behavior of like rubbing their hands in interesting times. So that sounds like difficult times, you know, if you'll see over a consulting company, but for me, that sounds like really interesting times. It's brilliant. That's a perfect place to end, actually. That's awesome. Thank you, Mark. Jeremy, that was so fascinating. A lot of stuff compacted into this, huh? It felt like a dog years conversation where it's going to take me a longer time to unpack it than I was able to do in real time.

53:29One thing that I'm just kind of reflecting on at this very moment is this idea that prompt engineering is fundamentally a strategy tactic. He said at the end, you know, the biggest thing in strategy is what's the question? And prompt engineering, obviously, it's what's the question. I think I never really thought about prompting as a strategy tactic. But I do believe that I've observed and I've experienced with everyone I've ever worked with and taught, the quality of your output is directly a function of the quality of your input with AI and with strategy as well. And so I'm dismissive oftentimes of conversations about prompt engineering because it sounds too technical or something like that.

54:12Like I don't like even the whole craft or the whole art of it. I think there's something distasteful about it. And yet when you frame it in terms of a tactic for better strategy, all of a sudden it kind of casts it in this totally different light, which I thought was pretty. And I think maybe even to bring that home, you know, the thing that he was saying also at the end where one of the questions that he normally had on his PC as like questions he would ask was, what's the real question? You would never imagine GPT-5 coming back to that, right? When you're saying like, hey, can you tell me like what this and this is?

54:47Like, could you imagine GPT-5 coming back and saying, yeah, what are you really asking about? Right. And I think, you know, like what you're saying there is like that basically is something you have to pre-bake into the question that you're posing, right? That is the strategy. You have to think about your thinking. It reminded me a lot of our conversation with Stephen Cosselin, the dean at Harvard, because it's really a metacognitive. I mean, his book is called Your Strategy Needs a Strategy. He talks about, so he's thinking about thinking. And I think there, what's interesting, Henrik, is you said GPT-5 would never do that.

55:20But you say dot, dot, dot. unless your custom instructions say, always ask me what's the real question. And then all of a sudden you have a cognitive prosthetic, a cognitive, you know, brain testosterone that always reminds you of the most important thing, which is fascinating. Which leads me to like, I think my, one of my observations was he talked about like basically innovation or originality on, you know, like imagination, ethics and empathy were the three things that some of his uh you know like some of the things that in the initial point of ai conversations we were kind of getting to that that would be difficult for humans to do and i think he made like obviously the compelling point that yeah but like these ai tools can be very kind of like great assistance in kind of those three areas probably like even ethics you know like it could probably help you think about what your ethics are uh for sure um but what is under on also underlying is that yes but it is like the human end as he always has pointed out like those are all things that at the end of the day nobody else will be able to answer really than you like what is your idea of a new idea what is your idea what's right and wrong what is your idea or what somebody else might think and and so yeah i think what he's articulating is that it's not like necessarily a binary like what can humans still be good at it is that the technology kind of will be used for everything but there are some things where the human end is the end yeah i mean one thing that i found fascinating just kind of riffing here on stuff that that stood out um one statement you know the track record of experts is very bad in regard to new technologies and the fact that someone's an expert, say an AI, you and I, experts in AI, right?

57:10Do we, do we have a humility as we approach this transformation? We're very bad at predicting where it's going to go. I think there's an intellectual humility there that I find refreshing and also challenging because I know you get asked questions a lot. I get asked questions a lot. And I think there's a real pressure when you, when you get asked a question to have an answer, you know, and the last thing you want to say is, I don't know. And yet, you know, some of these predictions of technologists in the past look, you know, patently absurd and ridiculous, specifically because they were unwilling to admit how little they knew.

57:48Yeah. The other thing that stood out to me was the simple question in, and I'm still kind of framing in the context of how do you get a competitive advantage or how do you stand out either as an individual or as a company? And I think the simple question of is that the simple question of what is not in the training data is an interesting kind of thought right and then the next one of course like what do you have as an organization or as an individual that you might be able to add either in your problems or in your question or in your decision making around this uh that will keep you competitive i think that is an interesting kind of thought process that i'd probably going to bring along for the next few weeks i mean one one kind of stat, which I found great, you know, from the game, the associated with your strategy needs a strategy.

58:38Only 3 % of players were good at the double game, the ambidexterity. Just recognizing that, because I do think in so many cases, the job of imagining what's next for an organization is given to people who are good at the current game. Yeah. The only thing I would say to that, it's obviously complicated, right? When you then actually sit in it. Because I think get a lot of companies realize that that hey this might become actually that's not true i think some companies definitely do not understand that they probably don't have the answer um some people do but i think the question that is obviously being posed to a ceo or head of innovation is like yeah that's great but you know we have to do something tomorrow we can't just be pondering and i think then maybe then the whole thing come back to experimentation and then trying to figure out Like how do we experiment as cheaply as possible so that you can afford quite a lot of shots and gold?

59:33So many of these conversations come back to this idea of culture of experimentation, don't they? They do a lot. But what I can't figure out of that is just kind of like an easy out. I'd be like, what are we going to do? We have no idea. Let's just try shit. And you kind of go like, yeah, but like it would be ideal that you can try shit that has some directionality of success, or at least like you had a reason for trying that specific thing. And so I'm a little bit kind of like torn between the, yes, we actually need to build a new operational model for a lot of these companies, because you're going to have generalists that have the ability to try a lot.

1:00:11And kind of, I think he was talking about taking imagination into something actionable. But how you actually do that is something that I'm not necessarily sure that we get why is there on when people say hey you should experiment i think uh to your point henryk in so far as experiment is um code for be willy-nilly um or you know and what i hate by the way is when people ex-post rationalize something as an experiment oh we're just experimenting right but that being said i do think if an experiment is deployed scientifically as you said purposefully with directionality, I think the danger is we don't want to do anything really nilly.

1:00:53So we don't do anything half baked or without full knowledge of how it's going to go. And I would say a reaction, a negative adverse reaction to kind of the wrong framing of experiment will lead many organizations to therefore not experiment. I think that's very fair. You know, and I really think this is, this is something that I took back from, you know, The Acorn Method, my last book about entrepreneurship in organizations, which had nothing to do with AI. But I think it was similar. And it is really, I think the best way to reduce the risk is to reduce the cost. And so a lot of time, the issue I think in an organization becomes that there's not a lot of room for origination.

1:01:34There's honestly often not a lot of new ideas. And once one materialize, it becomes the idea that then everybody's chasing. And then everybody's compounding stuff onto that idea because finally there's something new that is getting, seemingly getting approved. And then it has to work. It's like the reconciliation bill, not to go political, but it's like everything's got to be stuffed in this one thing rather than just being a spell, simple, straightforward experiment. And so, yeah, like the, and then, you know, it has to work and then people get nervous. And then when people get nervous, they get back to what they know, you know, like, and so therefore, like you end up with something that's actually not that much of a new idea anyways, because like.

1:02:13You can't have it fail. And so how you get into the habit of like the hundreds, thousands of experiments. And that is maybe something that we should figure out. How do you become better? Obviously, that's part of your world. Two questions that I find are very helpful for leaders. One is, what are we trying? And the emphasis there is on the word trying. You know, we don't know it's going to work, right? Try implies you don't know, a lack of certainty. And then the second question, you ready for it? What else are we trying? Because then that implies options, that implies volume, it implies parallelization, it implies potentially duplication of effort.

1:02:57I think if a leader would be comfortable asking, what are we trying? And what else are we trying? That would unleash a lot more experimentation. I like that a lot. The last thing I picked up on the conversation or these worked down was one of my grade at that AI can make me even better at? And I think it kind of goes back to fundamental questions that a lot of companies don't really ask themselves is obviously, who do I serve? What is the problem I'm solving for those people? A lot of people just define themselves for what they do, right? You know, like I'm a media company, but that's not really, you know, your customer don't care.

1:03:32But I think increasingly then it's like, am I actually looking at what's, and I look like not just in kind of like what I make my money on, but what do I think the organization is really good at? Like where do I have a culture of excellence? For example, at Bark, we're very good at supply chain. Like, you know, people see us being a creative kind of products for dogs brand, but we're very good at our supply chain. And so like when you then look at AI and saying, you know, like, hey, I need to have an advantage. Everybody else will have access to GPT-5. So like how do I not just take and make it my success metrics that everybody's using GPT-5?

1:04:07because everybody's going to use this gpt or whatever model they use then what can i do where i'll be able to kind of like magnify the impact they that ai can have that's thought sort of an interesting question in my mind or if you think about it as a multiplier like going back almost to our conversation with nicholas thompson yeah about if you what are your unwired capabilities what a different and for folks you have to listen to that episode the basic premise was your unwired human capabilities still matter because AI is a multiplier. And if, you know, and say the multiple is 10, if you bench 20 pounds, then now you can bench 200, right?

1:04:44But if you can bench 200, now you can bench 2000. I think another way to think about unwired capability by the multiplier of AI is to say, what am I great at? Or effectively ask yourself the question, which muscle set can lift more weight than others, right? Is it my supply chain? Like, do we have a deadlift of 900 in our supply chain? Great. Then the deadlift is actually worth amplifying with AI, right? That's an interesting kind of assessment criteria there. Maybe that's actually like one of the questions that, or one of the answers that we should give people when they ask us like, where should I go and try to implement AI?

1:05:21I think historically I've said like where you feel there's like basically bright spots where you have organizational kind of like pull. but another one we say like where are you already really good like excellence yeah yeah anyway yeah that's cool let's wrap it folks if you enjoyed this episode if you would like more big thinking from henrik wardlund and jeremy at least hit like subscribe share with your neighbor share with your kids share with your grandparent share with your dog jacksy and uh until next time bye