79 - The Cognitive Age

Speaker A: Foreign. Welcome to Tech Deciphered. We bring you the entrepreneur and investor views on big tech, VC and startup news, opinion pieces and research. We decipher their meaning and add inside knowledge and context. We also share our insights and experience with you with unique nuggets and lessons that we learned the hard way. No smoke and mirrors, no bs. Being nerds, we also discuss gadgets and pop culture news. Hi, I'm your co host Noon Gonsalfspieder, entrepreneur and venture capitalist, co founder and Managing Partner at Chameleon and Strive Capital.

Speaker B: And I am your co host Bertrand Schmidt, Entrepreneur in Residence at Red river west, co founder of App Annie. We have both been in tech for almost 25 years. Nuno is based in Silicon Valley while I am based in the Greater Seattle area, having previously worked and lived in Europe and Asia With Tech Deciphered, discover how the best entrepreneurs pitch, how investors think and what are the deep trends underlying the tech industry. You can check the latest on our website decipheredshow.com you can also connect with us on Twitter Schmidt and at Ngpedro.

Speaker A: If you enjoyed the show, do us a favor, subscribe, give us five stars and or leave a review on Apple Podcasts app or your favorite app. This will help other people discover Tech Deciphered. Welcome to episode 79 of uh, Tech Deciphered. Today we take a leap into the big unknown. This is a thesis episode, not your classic analysis in depth sharing episode. The big idea for this episode is that we may be approaching the cognitive age and how would one or how would a company compete in a world of infinite intelligence? The big idea again is that intelligence, which has been mostly scarce and expensive for all of human history, might become abundant and cheap. So if that happens, what happens to work? What happens to companies? What happens to society? This episode will be really framing a lot of these discussions, from knowledge workers to judgment workers addressing the AI native company and how does that change going into the human element and whether or not we're underestimating it and finally ending up going into scenarios, scenarios or feasible scenarios of a future where while intelligence is abundant, intelligence is quasi infinite or infinite itself.

Speaker B: Yes, big questions for this episode 79. We can start with from knowledge workers to, uh, judgment workers. Let's go back first to how came the knowledge worker. It's a 20th century invention from um, Peter Drucker in 1959. And the idea here is that that category might be splitting. The production of knowledge itself is on its way to being commoditized by AI. However, our perspective is that Judgment around production of knowledge is not disappearing and is staying for a bit controlled, managed by humans. What's your take on this, Nino? Do you agree with this split?

Speaker A: I think it's a little bit more profound than that. It's not just judgment. Definitely human judgment will be needed. We've seen agents perform all sorts of funny things in the wrong way when let alone to their own devices. Even some very well known AI researchers sort of coming forward and saying, hey, I tried to use this myself and actually I messed up some of my systems or I messed up some of my code, I messed up some of my flows for a period of time. And so I think just having human in the loop from a judging standpoint will be needed for a significant amount of time. And that is something you can't just delegate into machines, into algorithms, et cetera. The second part is ultimately there needs to be contextualization. And that contextualization I think comes from two forms, one from actual data, where the machine I think at some point will catch up or the machines will catch up.

Speaker B: Right.

Speaker A: The algorithms at some point on the data analysis will get better and better and have probably the closest to the truth death you can get, minus all the biases that are in the data. Just to be clear, because data has a ton of biases. We've looked at this in the past and discussed it at prior episodes. But maybe on that I think the machine has a chance to catch up or the machines have a chance to catch up. So there's less of distinctiveness from the human standpoint. But then on just attributes, uh, right. Like the ability, when you're judging some situation, you're in the middle of the situation, you're judging the person and how it's acting. In some ways a lot of the things that end up happening end up happening because they're human interaction.

Speaker B: Right.

Speaker A: There's someone on the other side. I see how they're delivering the message, how they're implicating. And we'll talk about it later in the context of the organization and what changes in companies. But I don't think it's just judgment. I think there's a little bit more than that. One of the reasons I went to the dark side of management early on in my career from being an engineer was Peter Drucker and sort of this notion of the knowledge worker which he later on sort of reemphasized with the publishing of his book, uh, which for me was seminal and defined a lot of my career in life. The post capitalist society. Right. Which Is this notion that information rich and information poor is going to be the key distinctiveness that will happen in the world. Right. The uh, two big camps, information rich, information poor, which links back to this invention of the term knowledge worker. That knowledge is going to be key in some ways. I think that's what we've seen for the last decades. So again, I think judgment's not going anywhere, but I think it's beyond judgment. There's elements of humanity and involvement that won't go away anytime soon where Human in the Loop are particularly critical. We'll discuss later some scenarios, but for me that's my stick in the ground. I think Human in the Loop is going to be critical for uh, many decades to come.

Speaker B: While we are talking about all of this and we share some possible scenarios, there is always that question. I mean this is moving so fast right now. If you think about a 10 years ago AI 5 years ago AI with the launch of ChatGPT3 and then AI the past 2 years, now we have agents that are running at scale. Things are moving very fast. I can tell you me in six months the change has been pretty dramatic in terms of what I can use AI for. So there is always that question that whatever we are thinking about cannot just be connected, uh, to what we were able to do six months ago or even today. We have to think and project ourselves at least in the next six to 12 months. I mean, of course we can go beyond that and we will do that with some future scenarios, but it's a very fast moving and it's not clear yet where are the limits.

Speaker A: I think it's, that's a very fair point. Let me sort of try to analyze things that I don't think will change anytime soon. Right. For the next few years. Agreed with you that many things will change and we'll have a lot better tools, platforms out there. So that will be difficult to predict what exactly won't change. I think the, there's elements of humanity and some of them do relate to judgment, like taste, having good or bad taste, having a view on it, on whether something looks good or bad. And obviously all of this sometimes is subjective, but some of it may not be as subjective as people think it is. The elements of contextualization, I think a little bit going back to what we did at Chameleon ourselves, where we built this platform, Mantis, and the objective of building Mantis was not really to replace us, was that it was a core augmentation layer in some ways that we would use investment or investor Judgment as humans in the loop to systematize pattern recognition and a variety of other things. But that mantis would really elevate all that judgment, not just in terms of timing us being more productive, but also in terms of the quality of the decisions we're making. So think of it as a little bit like having our human judgment in the context of operating chameleon at a higher altitude, Right. Where we are more aware of the things that are happening and how they actually happen. Right. The ability to really get to the data pieces and then make decisions on top of that that generate the needed alpha, in our case, for investors. So what I mean by this is I think there's always going to be core elements of humanity that I do think are going to be difficult for the machines to replace. For example, the taste piece, people are like, well, but I can figure out what's the taste in a market. Yeah, but that's mainstream. That doesn't identify what's the next big thing, which normally doesn't start from mainstream. It starts from something else. Could start from opinion leaders and influencers. It could start by someone having a different way of addressing a problem and having a solution that hasn't been thought through. So, for example, elements of creativity, I think, in human judgment and in human operations is something that I feel the machine will still have difficulty to replace.

Speaker B: And let's not forget how today current algorithms are working by feeding them enormous quantity of data, actually as much data as we can find. And finding more data, uh, is becoming a limitation these days. So what it means is that it's very hard for AI to think beyond its training data. Uh, I mean, there is some level of logic that's being added, but at the same time, take the launch of the iPhone. What was the opinion before launch? That no, it doesn't make sense. So not enough battery life, no keyboard, no this, no that. I mean, if you just base your analysis on what's written out there, what's being sold out there, you will just say, okay, it's going to fail. And AI might really follow that more generic advice and perspective, because that's what in the training data and that's what's there in volume. So it's, of course, raising a lot of questions of how do you improve the quality of the training data? Uh, how do you separate the wheat from the chaff? So there are a lot of questions there. And obviously it will get better over time, but it's still a critical part of how it's working today. So it won't be that easy to change. I really liked your point regarding Mantis. And I will say in general platforms that you build with AI or leveraging AI capacity, because when we say knowledge production is going to disappear, but we'll keep judgment, it will be a different type of judgment because the quantity and quality of knowledge we will have in front of us to build our judgment will be very different. If suddenly we have for free the work of 10 interns or five junior analysts or whatever, and you can run that on nearly anything you do in life or at work. It's completely dramatic. Your judgment was not used to be exercised so often because often you were missing quality data. To have a judgment before it was a lot of finger in the wind and trying to smell something, but you don't have enough to make a serious analysis. I mean, except if you are working as a strategy consultant as you used to do Nino. That part is actually quite interesting, that the judgment itself will be exercised much more often and hopefully on the base of much more in depth analysis for a lot of things. So we will work very differently.

Speaker A: We will go in depth, faster and more fact based, more database along the way. So the question some of you might have right now is is there some judgment that's going to go away? Right? Is there some judgment? We seem to be defining that there's this organization. We'll talk about it later, that goes from doers more into deciders. I think there's some nuances to that, so I'll just hit pause on that. But in terms of judgment, obviously there's judgment that has been hidden over the years under the pretense of being wisdom, but it's actually not wisdom. It's just repetitive tasking. Right. And it's rules based for the most. Right. So there's a lot of judgment done in particular in the white collared space that you could say it's just reps, right? People have been doing it all along like that. And so therefore to say I've done it before like this, so I'll do it the same way. And there's actually no best in class, no analysis, no nothing. It's just I've done it like that before, I think that type of judgment will disappear, right? Because again, algorithms will be as good, if not much better at that, right? They'll be better at figuring out actually this would be the better way to do this. Right? And that's how you play it forward. But then the question is if there are fundamental wise people in the organization, people that can really take that More complex elements of judgment. How do you go from the world we have today, which is a world of apprenticeship, where people come out of college, they go and work and they learn their way. And therefore, hopefully over time, some of them, not all of them, we know that, but some of them will develop that wisdom to be great decision makers 15, 20 years down the road. How do we do that in a world that now is saying, I don't need people out of college, right, Because I can do it myself and I can do individual contributor and I can have agents doing the work that would requires some manifestation of management in the middle. And so basically I don't need this stuff, right? So uh, I don't need you. It's a little bit the story we're in. So how do you create then this apprenticeship? How do we create then? Wisdom. My two cents on that is that wisdom because of what we were just discussing and what for example, myself and Bertrand were just saying, because of more often interactions with more data stressed information and insights. What will happen is people will get better through their own reps in whatever form they're doing right in day to day life, in internships, et cetera, et cetera. In some ways that will create the accelerated growth. It's a little bit the interactions with agents and the interactions with our beloved AI algorithms that will create that growth over time and maybe not as much with other people. So that still leaves the question around social interactions, but that's probably the way this gets sorted. Apprenticeship gets sorted through the machine and the human having more interactions.

Speaker B: In effect, I agree with you because when we talk about apprenticeship, I mean, in some ways a lot of time was wasted on stuff that were not that important. But in a way that was the price you had to pay in order to be there. When people make the big decision to try to get some wisdom from that one hour of interactions that's really useful and make a difference out of your full week. But the rest of your full week was just basic stuff that you had to do, like a machine in a way. So why not let a machine do that? That for me is a big question. And you could argue, uh, there is a transition period where it could be hard. For instance, if you can work hand in hand with AI smartly while you are doing your four or five years of universities, you could graduate with a very different knowledge, perspective, judgment, skill set than anyone who graduated, um, five years ago. So I think that part will require a question around, okay, how do you change education? You see what I mean? If you Keep education the same way, expecting that the output is someone that should go now into five years of apprenticeship. That's not going to work because companies will be no, no apprenticeship anymore. So on the contrary, you have to come much more knowledgeable and ready to use the tools as I mean the tools are so efficient that the bar pretty high. So you need to come already very well rounded and if the education is not doing their job that will be trouble. That part for me I think is often forgotten in some ways. The newfound importance of universities as a place to analysis, a trade, to really deliver uh, people who are ready for the workforce. And if, if on the business side the expectations change, I mean of course you have to change the education on the other side. My worry probably right now is that it doesn't look like universities are in touch with what businesses are looking for, businesses are working on. And of course that's very worrisome because the cost of universities has uh, increased very significantly. Not clear quality of education has improved at all. If anything it could be the opposite. So it's pretty scary. And of course it's going to raise a lot of questions how much is education worth in that type of situation. Maybe another point because we talk a lot about apprenticeship, how this stuff was useful and but at the same time, if we go back in time, not too long ago, in the 50s, if you wanted to be a developer, for instance, uh, 50s, 60s, I mean the job was very different. I mean there was barely any programmation language out there. You had to use punch cards. Your time truly spent doing the coding was very limited. And once you had your stuff working then debugging was a total nightmare. So my point is that no one is looking back to that time saying oh you know what, it was great, it was a great way to learn. And to do an apprenticeship for five years to do that crappy job of punching cards for the boss, that was little value in this shit. And guess what? Everyone is happy it's not being done anymore. By anyone. So I think we also have to see what AI is bringing in a similar way is that everyone's job is going to become quite different. There are a lot of big parts of the job who are m not going to look back with firmness. Just looking back as, wow, that was very machine like type of job. I'm glad I'm done with it and people will want to jump directly to the next step. You don't need to go to the punch card phase to be able to be a good developer. For the past 40 years. I guess it will be the same with AI.

Speaker A: I think so. And the difficulty we have as humans is to also visualize dramatically different scenarios and landscapes. Right. Professionally, it's difficult for us to say what are the jobs of the future. I mean, jobs have changed a lot in the last few decades. Not m. Even the last century. What people do, the migration initially from sort of the agricultural society to then the industrial society to then the services society, and in some ways the shifts within the services industry. And now we're seeing another shift. So we can't really anticipate what those jobs look like. Back to your point on education, because I think that's a very important point. If you're right now an undergraduate student, or a postgraduate student for that matter, and you're not figuring out your own mechanisms of learning outside of your syllabus, outside of what your professors are telling you, etcetera, you're going to face very difficult times. If you're not right now using all these AI tools proficiently, all, all these cycles of vibe coding, co working, et cetera, et cetera, with agents in the mix, you're going to have a really tough time if you're not, at this point in time as proficient as someone like myself or Bertrand. And given that we're nerds, we're relatively, uh, proficient with a lot of these tools that are out there on top of it. Some of us have our own platforms in house. Right. If you're not as proficient as we are with those tools, you're going to have a very difficult time because then people like us won't need you. And I think that's the sad truth. Right. Like at some point, if you're not needed, you're not needed. Then again, you may find something else that's more interesting for you to do. Start your own company, go join a new exciting job doing whatever it is that you need to do next, etc, etc. But again, I think the bar is very high. So if you're in college right now, again, undergrad, postgraduate, this is the time of transition. This is the worst time. It's not the best time. It's the worst time because education and all these institutions haven't adapted to it yet. So you need to adapt. You need to adapt, you need to adapt. If you don't, you're going to pay for it, not just in the loans you need to repay, but also in terms of actually having difficulty finding, you know, your career path in those first few critical years.

Speaker B: Yeah. You need to be especially proactive when you are facing this type of period where businesses are adapting as fast as they can because they all know it's going to be survival of the fetus very quickly. But universities typically are working on a very different pace and it's pretty guaranteed they are not going to have adapted as fast as businesses. And in time of big dramatic change, it will be trouble. It will be trouble. So yes, you will have not fun not saying. It's just part of the deal when you sign up for that loan and decided to go for university. But that's life. There has been issues before. It's not the first time, but yeah, you have to do something about it. And you talk about your perspective about, hey, why do we need you if you are not already fluent and very efficient with these tools and stuff. And the truth, in some ways it's even worse than that. Each time we spend with someone who is not efficient with all of this is less time we spend with the tools that are already providing magic for us. So it's a very, very big choice of, hey, do I spend more time doing this person or uh, do I just there is an opportunity cost or do I spend more time staying at light speed? Why do I slow down to do something else in the hope that maybe I will get return versus the light speed I'm already on? Um, it's a lot of tension again. It's certainly new. But if we want to look back, I think you talk about the switch from agricultural society, industrial society and now the service industry. I mean, the reality is that yes, we have made dramatic changes in the past. Before 140 years ago, we were 90% agricultural society in Europe, in the U.S. 90% of us. Today it's what, 2%? So my point is that that's a normal evolution. There is no progress without change. And sometimes the rate of change is soft and sometimes you have a step function and now is a step function. And it's also a pretty fast step function because before could take decades to get new stuff being put in place to have electricity coming up, this or that. And now we see that the rate of investment in AI is insane. Way beyond anything we have seen before. And two, in a way, a lot of the architecture behind the scene was already there to support an even faster transition. So what's new is might be the pace of the transition, how unnatural, uh, it might look. But at the same time, if you put yourself in the shoes of someone who lived 150 years ago, I mean, this was also a dramatic change for them from horses to cars, to planes, to rockets. Pretty big change, maybe uh, even bigger change.

Speaker A: So maybe the silver lining just to sort of bookend this section is one, there will be new roles. So there's a lot of things we can't anticipate. There will be new roles, there will be new jobs being created, new things that we can't really quite grasp yet. The second part is the rules are changing and they're changing, I would say in general for the better. Right? So if you are a decision making or an organization and you still have your job, you're probably making more important decisions with more data, with more tooling around you, with less red tape, hopefully over time. I know that will not hold true for all the big corporations out there that are listening to us, but it is starting to happen. Things are making uh, an impact in how decision making is made. There's less and less red tape along the way in certain organizations. There are more and more fact based discussions happening as we move along. So the silver lining is better jobs, more jobs, different kind of jobs in the future, hopefully as well. And secondly, the second part of the silver line is that the jobs that exist today hopefully will be more interesting certainly on the knowledge base and on the sort of disc, this judgment space that we're now introducing as part of this episode. Switching gears maybe to how does that shift? Right? So how does the company of the future look like? How does an AI native company look like? And I feel there's a lot of discussions on, oh, you only need one person to run everything. Uh, let's not go to that level. We've had a couple of episodes where we focused on AI as your co founder and a couple of other elements that you guys can go back to. Let's focus on more sort of evolutionary view of what's happening to organizations and maybe start with the org structure in general. We should see more flat organizations where mid level managers have to justify their pay in some ways because middle management are uh, routers, right? They are normally routing tasks, sometimes aggregating it, sort of synthesizing it and pulling it back up. And guess what, AI and agents in general are very good at that, right? So the synthesis piece, et cetera, et cetera is not as well needed. One could say there's several elements of middle management that are valuable, like the coaching of people, the creation of apprentices, the accountability that comes with some of middle management. But lo and behold, most of middle management is seen as a little bit of a thin line that doesn't need to necessarily exist. So I feel we're moving into a world of smaller teams, more senior teams, where there's more judgment at the top, where you'll have people that both do a mix of what we used to call management in its new form, but also a lot of individual contribution. And so if you're not used to that, if you're not used to anymore to be an individual contributor again and if you're a very senior organization, maybe this is the right time to either reinvent yourself, find some other job that doesn't require as much of that, which we'll have plenty of those jobs for the next few decades, or maybe retire. And I've actually shockingly enough seen people that have said, you know what, this thing is changing too fast, too dramatically. My industry is changing quite aggressively right now. I'm about to retire in a couple of years. I'm just going to retire now. I've literally met two people that have done that right. So, so again there's nothing wrong about it. I think we're again going through a step function and a huge shift, but figuring out where do you fit in sort of these new model of organizations, more senior at the top, small teams, more of a mix of individual contribution with management than it ever was done before.

Speaker B: I agree with you in some ways. I'm not surprised that some people may say, you know what, it's now time to retire. I feel a bit sad maybe because it means you, you don't like to keep reinventing yourself and changing your habits and thinking about new stuff. You were a creature of habits, I would say, if that's your conclusion. But everyone is entitled to their own opinion obviously and a uh, way of life. And I guess that's what happened again at regular times in the past. Uh, in terms of big change, what I can see is that rise of, you can call it the full stack in individual, someone that will have multiple roles inside the team. Before you had to really separate the role, I mean especially in the US there is such a clear separation between every role you can have in a company. Let's take a uh, tech company, you will have people who uh, are doing design, people doing different type of designs, people doing front end development, backend development operations. You see step by step a hyper specialization. I have seen that. And it's true that the level of complexity you had to deal with at some point requires some level of hyper specialization because it will take you 612 months in order to be really, really strong on a specific topic, a specific language, God forbid, trying to go Deep into something that you had no real experience into. But I feel with AI it's a big change actually. The opportunity to go beyond that. It's opportunity to do more, to touch more. You can combine designing and shipping code, product manag, shipping code, being an analyst and deploying. So of course we'll have to think how it works because put a marketer, uh, shipping code to production, maybe that will get you into trouble. But I think that there must be some change. We see AI changing dramatically, how fast we can get into something, something different from what we are used to. I think it would be crazy not to take that opportunity to dramatically change the scope of many positions and put an end to that hyper specialization. And I think for me in some ways hyperspecialization was kind of bad. There is only so much you want to be a specialist in because a lot of things, a lot of opportunities are actually coming from the mixing of many different ideas, many different perspectives and you lose that if you go to hyperspecialization.

Speaker A: I don't think the age that is coming is the age of the generalist. I think it's going to be the age of the multi specialist, right? We're going to go into an age of multi specialization which is a little bit mentioned it as well in the past, what Amazon defines as an athlete or T shaped or pie shaped, uh, uh, people, people that have on top amazing ability to do general management strategy, managing teams, et cetera, et cetera and then have spikes, right? Spikes into business development, corporate development, product management, whatever it is. And with AI and with agents, the development of those spikes as we've been discussing in this episode will actually be easier. It's almost like a given, right? So if you want to go deeper and deeper into a certain area, you can go much faster. So I think that level of multi specialization is going to be really cool to observe. I'm not sure we've had an age of multi specialization over the years. Maybe people would point out, well, you know the sort of the da Vinci example, right? People that are great across very different areas, maybe that's an example of multi specialization. But honestly from my perspective this is going to be an exciting time because of that. Because you'll have people that instead of being just focused on this area of sales, and I only do that, they can actually and should actually do a lot of other things. So work as we were talking before can be more interesting, more demanding as well because the uh, judgments you need to make are more complex, the context you need to actually Gain needs to be gained much faster and at a level of magnitude you haven't been able to do it before. Talk about information overload. But actually ultimately the rules can be a lot more interesting, a lot more exciting because I can jump around. And so if I'm an investor, in this case, we have two investors on this conversation. But if I'm an investor, one of the things that we start looking at is actually not just looking at a startup as is this startup doing something in AI or not? Is it AI enabled or not? Is it an AI platform or not? But actually more fundamental, is this an AI native startup, meaning organizationally, culturally, is this a company that's already in the age? How is the team working? How are they defining things here? It's not just they only have two or three people, it's like, what are those two or three people doing and how are they doing it? What cadence are they doing it on? What tools are they using? How are they making decisions? And I feel we're still actually relatively early on that track. It's very interesting because we've had all these companies raising mega rounds. First round out, we invested in one of them. But there have been many frontier labs out there raising a ton of money. But a lot of them don't have fundamentally a, uh, different way of doing business, of organizing themselves, of how they do the day to day. So while although they were working on cutting edge stuff, with very notable exceptions, they're actually not using it themselves. Right. They're not actually shifting how they do stuff themselves. Right?

Speaker B: Yeah. And for me that's very interesting because, you know, in the past I used to be quite conservative on um, how you manage and run a company in the sense that if you're already in tech, if you are already on the cutting edge of what technology can deliver and this and that, don't waste time trying to invent a new org structure, just focus on delivering something great, amazing, and be great at technology. That's already your huge differentiator. And at the time there was no real reason to innovate on the team organization. And I have seen so many teams who tried to innovate and it was just catastrophic because there was not much to innovate on because we had decades of optimization that we could leverage. There was no reason to invent. But here it's very different. There is a dramatic shift on how you can organize different company. I don't think there are any blueprint yet on, uh, what's the best way to do it, because it's Too new. But at the same time I will feel very bad to invest or support a company that first is not focused on AI or AI enabled, but at the same time is not trying to innovate on the team itself. Because if you don't do that, you are going to get killed by someone who is going to innovate better than you on not just the product, but on the org as well.

Speaker A: Indeed. And the shifts are pretty substantial. I mean if you look for example, just at hiring, what do you hire for? Uh, certainly there is this element of the multi specialized orchestrator which normally will be someone with quite a lot of wisdom and expertise, doesn't necessarily mean someone who's old, but someone who has the ability to work with all the AI tooling and platform out there and be an orchestrator of agents and why they do it, make judgments, make decisions, move stuff forward really, really, really quickly. Again, those jobs are going to be the best jobs. Right. The second part I think that is very interesting around hiring is you're going to sort of skew towards the elements that are potentially either very aligned with the use of AI tooling and platforms, sort of AI expertise or being AI native or someone who's used to using AI. So that's one side of the fence and on the other side you're gonna actually be optimizing to hire people that have the characteristics that will be difficult for AI to, to replace immediately, like taste and the notion of fundamental accountability and notion of implications, uh, the notion of how do you affect change in organizations, how do you affect change in individuals? Right. The elements of coaching and beyond coaching. So you'll be optimizing for those kinds of hires as well. And then last but not the least for me, I feel that there is a momentum already happening. I think it will happen even more, which is the tendency to underhire rather than over hire. So the moment of the good old days of blitzscaling, oh, let me go and hire 300 people to scale my, go to market and just land grab the market. Now that's not how it's going to work. People are going to try and first get the efficiencies in house with top talent and see if there's at the end the need to hire more people or not, rather than the other way around. So I think the issue here is a little bit what we alluded to before in this episode. There is a tax on individuals. If you hire more people, you'll have to manage people, you'll have to work with them, et cetera, et Cetera. And so if I don't need to, I might as well work with the agents that the tools and platforms that I use give me access to, because that's uh, a world that's much more efficient. Right.

Speaker B: I'm in total agreement with you on this. It's definitely raising way more questions than before because again, on one side you have the product, the technology used to build the products that are completely different. At the same time, all of this is also enabling new way to design organizations and to scale differently. Especially in a world where, as we have seen in 3, 612 months, stuff that you thought were impossible are suddenly becoming possible. So you, hey, I'm going to scale and burn a shitload, uh, of money for six months before I know if there is any return versus, you know what, maybe I just wait six months that AI has improved enough so that we don't need this new team, we don't need these people to do stuff. Because actually if you just wait six months, we will have stuff coming for free from either new AI models or new AI tools or this or that. If you remember, you know, we used to say that in mobile things were going three times as fast as on the web in term of pace of innovation and speed of development and stuff. I mean, we see it's 5x mobile.

Speaker A: I mean, maybe even more. Yeah, wow.

Speaker B: Yeah, maybe even more. Maybe 10x. So every assumption around bit scaling or scaling in general was based on past assumptions. It was. So it's not based on how is industry evolving today. Might make more sense for you to really grow your agents, spend more money on more tokens. I remember, I mean, of course Jensen is selling his business interest, but he was saying, hey, for uh, each one of my, uh, 450k engineers, he better spend 250k in tokens a year. Yes, and I'm not saying it's the right way to say it, but I think there is some truth in it and that would be something to think about. Have we maxed out the token usage per employee? I'm not talking in a stupid way, um, because token maxing and wasting money has no value and is as stupid as it gets. But if you are truly getting return on these tokens, can you use more, can you generate more, can you create more loops so that one engineer manage not just 10 engines but 50 agents, but uh, 200 agents? I think that the big questions versus trying to add more people, more people means more management, more issues, more this, more that. That would be a fair question. Another piece of the Puzzle is how do you build in a way your, I don't know if it's a digital twin, but more like your, the digital version of your companies represented by agents. How do you make sure that you, everything you do, uh, as a business is truly captured, is truly leveraged so that your agents are getting better and better, not just because the gets better but because you are putting more data ah. To it, because it has more opportunity to learn. Um, and as a result gets better at your specific business.

Speaker A: The next big thing is culture. Right. So how does culture change? And I think the biggest shift that I see is why would you do meetings all the time?

Speaker B: Yes.

Speaker A: At least at Chameleon we have a very small team. Just by the way, we have a very small team at Chameleon. We've reduced by way more than 50% the time we spend on meetings between each other across the board, one on ones partner meetings etc, etc. And I think we're really pushing to be more and more synchronous. Right. There's stuff you can process via message, like I was just asking one of my colleagues, like can you just send me that prompt for that so I can just do that on Cowork or can I uh, just go on Mantis and do this? Can you tell me the cycle or vice versa? Right, so, so basically it's a little bit like you're just going to do it. I don't need to meet, I don't need to meet all the time. There's some things where we still need to meet and interact and we need to brainstorm at times and we need to go to a different level of abstraction on the top end and then on the lower end there might be things that are a little bit more specific and governance related and operational related that we need to agree on that is sort of more sticky. But otherwise the culture is going to be biased towards build, go and do it rather than let's do a meeting.

Speaker B: Right? Yes.

Speaker A: And Async is the thing. I mean I more and more like we have a couple of interns this summer, like can we async this? And they're like, what does that mean? Like can we make this interaction asynchronous? Because synchronous interactions for me are very expensive. Right. So can you send me something that I can process and then I can send back to you? We don't waste time on you giving me context and whatever. And then I'm not ready quite yet because I need to process it and maybe I'm in between two meetings that I'm actually thinking about other things in my mind. So again, I feel that shifts how stuff is done. One, build rather than meeting. Two, Asynchronous versus synchronous. Some way Millennials had it right when they shifted a lot to messaging and stuff like that. Let's do asynchronous rather than synchronous. Those two elements from, uh, just an operating model of the company are significant. Maybe this is a good time for me just to put one parenthesis, because there's this thing that's bugging me as we're talking here. Everyone that is listening to us at this point in time might be saying, cool, but I work for this large organization and we're just. Now, uh, Everything we're saying here is contextualized by time. Right? We're giving you extreme situations. We're looking into the future. So some companies that we're talking about might be doing this already as we speak. Some of them might be in the process of doing this and might in the next couple of months be doing it like we are describing it here. Some of them might take years to get there. And then again, some of the companies that might take years might actually be destroyed in between or meanwhile and be disrupted. Some of them might not because they're in very legacy businesses and it's fine and it's okay. So again, don't take everything that Bertrand and I am saying today as this is gospel and it's going to happen tomorrow. And why the hell are we not doing it? We think aspirationally this is where you should be moving to as an organization, whatever size you're at.

Speaker B: Right.

Speaker A: And speed will matter, as we discussed before. But not everyone obviously is going to move as fast as we're describing it here.

Speaker B: Yes, me, for instance, I take inspiration often with what some of the AI Labs, Frontier AI Labs are doing, the way they are working, especially an OpenAI and anthropic, they are clearly at the top of the sphere in terms of what is it that you can do, because they have access to models we don't have access to, because they have unlimited tokens they can use for tasks. And they hire people who are, of course, 100% on AI. They are a best example of what is achievable if you have the top mines, if you have the latest models, if you have unlimited tokens. So from there you can take that for our needs and for our situation. And others in industries that are not as advanced definitely have, uh, some time. But as you say, I mean, things are moving fast, things are changing. Wall street is going to expect better returns. Because when we discuss all of this, the conclusion is that you should be able to do more with less. I mean, that's as real as it gets at some point. And by the way, that's what you see. Uh, you see better performance, better business performance right now. So even if you might not get disrupted, you better start there for some, it might take more time, and they might still be fine.

Speaker A: Maybe to sort of bookend this section. Clearly, what we're saying is organizations are going to change, Their mos are going to change, the structures are going to change. There are elements of what we discussed before in terms of judgment that are fundamental to this. The ability that in some ways, one would say a lot of the technique of getting solutions out there, even in brainstorming or problem solving, is going to get democratized. The algorithms are able to do that, right? On the other hand, having points of view and having wisdom is not necessarily democratized necessarily by the machines. It can be facilitated, can be more productive in achieving that level of wisdom, but wisdom still will matter at the end of the day. So we're not sort of saying that's out of the questions, actually. That's going to be the asset, right? People that have fundamental wisdom that can come to the table and frame things. And we see this even today on prompt engineering, right? On just creating prompts, uh, the better your prompt is, the better the outcome is going to be, right? The result that you get from the algorithms, right? And that's not going to change, in my opinion, anytime soon. That kind of UI interaction piece is not going to change anytime soon. So again, if you're an organization thinking through organizational structure, culture, if you're thinking through hiring, and these are some of the elements that we think will give you an opportunity. But I would go actually one step further, right? And on the positive side would say, actually they give you arbitrage. If you're able to move faster than your competitors and really adapt your org faster, you reap the benefits faster as well. And that's what many still say and relate to as the word innovation. That's how innovation gets accelerated. So I think there's a huge opportunity right now for arbitrage. If you move fast, experiment on new org structures, experiment with talent, you'll know that some of them will work well, some of them will fail miserably, right? So you can't experiment on literally everything. But on the other side, I think the doomsday scenario is if you don't. If you're on the other side and your competitors outpacing you on trying these different organizational models, structure, hiring models, operating models, they'll potentially just disrupt you. They'll do stuff that you thought you had the moton and lo and behold, you don't anymore, right? And sometimes it comes just from org, just from injection of people with a different mo, different operating model.

Speaker B: Maybe we can move to our next section about the human elements. Are we underestimating it or are we overestimating it? Three things that are a big part of the human elements. I mean emotion, creativity, synthesis. I mean, is it just soft skills, replaceable part? On the contrary, is it the durable part now that we have automated intelligence?

Speaker A: I mean, uh, I'll start with emotion first because I think it's probably the easiest of all the ones you've mentioned. Emotion is key, and many of you listening to us will know this. The way you deliver a certain message, the emotion that you have when you deliver it just in and of itself, this could be a sentence, right? It's something verbal, etc. Etc. Makes the difference between the person or the people on the other side actually adopting it or actually just resisting it. So emotion is critical. It's sort of what runs the world. Everyone talks about a bunch of things, but like emotion is a currency that is still naturally human. It will be, I feel, difficult for these AI tools and platforms to recreate it fully until there's some sort of really, really very high definition manifestation of them as avatars or some physical manifestation of them as robots and all that stuff. So it will take a while for that emotion to be manifested. So emotion, I think is still something that we as humans have as a moat and it's critical. As you mentioned before, I was a strategy management consultant at McKinsey and getting people to action is actually 80% about the delivery communication, the emotion that you surround around the project itself more than sometimes the truth, right? I mean, it's great to have the truth and to have something that is similar to the truth in terms of analysis, but in some ways that's not what really moves change. Change is moved by, I would argue, a significant amount of emotion and alignment on emotions, right?

Speaker B: And then you could argue that something that most politicians have perfectly understood. I mean, if you look at most campaigns these days, everything on emotions might be their tagline, might be one word. It's interesting when you see from that perspective that actually it's very little on facts, very little on all of this, but more about emotion. So you could argue, is it the same for businesses in the future? That's a fair question. I think creativity is another one that's quite important. At the same time, it's not so easy because I must say I'm quite amazed when I'm looking for creativity from AI, either to generate image, to generate video, to generate audio, to generate text, AI can be pretty creative. I still think you need to control its creativity. You need to understand what's good, what's bad, what's quality. But at, uh, the same time, I can see even in creative tasks, AI can be a very strong partner. And I'm talking about any creative task, like invent a name for a product, let's brainstorm the mission for the company. AI is actually, it can be doing a pretty impressive job, actually. You know, that's the type of job where you will hire experts, where you will use some of the best people in your team to help you for days. And we say you can do quite a lot. So it's an interesting one because I think there is some unique human creativity. And at the same time, AI can be pretty strong at creative tasks as well.

Speaker A: I agree. I mean, in particular, if it represents benchmarking, if it represents repetition, if it represents seeing the world and then coming up with something that presents itself as creative, to be honest, it can actually outpace humans, right? If it's like genuine light bulb moments of creativity, angles that haven't been tried before, certainly not in the same way. I think humans still have the advantage. But to your point, I agree. I mean, this is not a humans win kind of situation on the previous one, on emotion, still, part of it is because also on emotion there's exchanges, right? You and I might be looking at each other and from the facial expressions and the reactions, we judge that for AI to get there is going to take a long time. Right? There's going to be a lot of very complex algorithmic stuff put into that for AI to be able to create synthetic emotional behaviors, Right? But creativity, I agree with you, there's a lot more nuances to it today where it does have significant advantages. At the end of the day, synthesis depends. Synthesis. I feel if we're talking about holding a bunch of messy assumptions, contextualized inputs with different layers of data adjacent to them, and then trying to create and form one, uh, coherent, fully accountable point of view that you'd sort of stake something on, like a decision, a company, a business unit, whatever. I think humans have the advantage. And part of it is, I think the complexity of what we have today with generative pre trained transformers, today with GPTs, where the hallucination comes through, where it's really more statistical, the analysis over time. Maybe synthesis, uh, will be a uh, forte for AI, but right now I think we still have that ability to really be the ultimate decision makers and judge makers and have that wisdom put at the table to make those decisions. Honestly, models are very good on balancing both sides. So ended up sort of, as we say in Portuguese, neither fish nor meat. Right. So it's a sort of balance both sides. Answers like that's not helpful in most cases. When you're in a difficult position where for example the future of a company, company's almost dying, what do you do? I'm not sure your AI algorithms that are going to give you a great solution, right, because it will give you sort of a median or average solution which likely will lead you to a median or average outcome which in this case would be failure. So again, I think on synthesis there's some areas of advantage for human beings. If you are looking for clearly synthesized perspectives on certain elements that are maybe less edge focused, they're more sort of normal than the normal part of the normal distribution, then probably AI agents are brilliant at that. All the tools we have today are pretty good at that. And I think they'll just get better over time. So that's how I see synthesis.

Speaker B: I think a lot of improvements will come with a better fine tuning of agents to what's special about your company. Because if you just take a general agent, I mean there is only so much it can understand, your industry, your company, your way of working. So I think that part of making sure your agents are uh, finely trained, finely tuned on um, your own business so that they can give you a really well calibrated feedback will have a lot of important.

Speaker A: I think that's absolutely spot on, maybe to end it. What is definitely different about humanity? I mean definitely emotion. As we discussed some pieces of synthesis, creativity, maybe the light bulb creativity, not sort of the more repeatable creativity, the one that you can put it and encapsulate into processes. In some ways there is elements of us being physical which robots can still recreate. So that's definitely an advantage. The embodied were embodied. Uh, so that's obviously a huge advantage. And with that also comes advantages because we have to interpret each other and we have to see the complexities in physicality that lend to. It is human and the human element, categorical difference. If we're having a more philosophical discussion around this, I think it is, I think it will be for at least the foreseeable future and maybe decades to come. Even in whatever scenarios we'll discuss, which is our next section, scenarios.

Speaker B: I will say projecting beyond 10 years is always pretty hard on this because again, some of the improvements we are talking about, some we can imagine based on uh, how it has evolved. But at the same time there will be disruptions in AI. So stuff that we take for granted in terms of weakness especially, might not be there in a few years from now, either from because it has been solved through brute force or uh, because the field will have made significant change and improvements and discoveries, making some of our points moot. If we talk about embodiment, obviously robots are coming, so how fast, how cheap, that will be a big question. Right now they are not very smart, they are very usually specialized. But the more we move to more general form factor, humanoid form factor, the more I think will change. And also another piece of the puzzle is that we have the assumption of agents having trouble to convince humans and stuff. But at some point we keep assuming there are humans in the loop. But if we are talking about agents, convincing another agent not having embodiment might be even more efficient. So that will be another uh, perspective going forward. Um, we will have not just agents we control who are doing a job and scaling their job, but agents truly interacting with other agents. You have agents controlled by one person, one team in your company, working either together or uh, maybe not confrontationally, but trying to think and having different perspectives with another agent controlled by other teams. I don't think we have seen much of that for now. We have seen mostly agents that are controlled by one team doing one job in one direction, not multiple teams, agents working together or against or uh, in parallel with another team agent. So I think we will see some interesting things coming out of that.

Speaker A: Switching to scenarios. We love our two by twos. We haven't done one in a while. So this time it's a two by two. We have four scenarios. I think on one axis we would have potentially the capabilities of AI and so one side would be more incremental. The other side would be the extreme, full AGI. I'll define it in a bit so that we can at least have a little bit of a, uh, definitional view on what AGI is. And then the other axis would be how gains are distributed, concentrated versus broad. So obviously if they're very concentrated, it's more unequal. It only goes to a few companies, a few people, a few individuals. And if it's broad, it's much more dispersed through society, et cetera, et cetera. So AGI, just to try to define it, the formal definition of it is that it's an hypothetical AI that matches or exceeds human capabilities across virtually all cognitive and practical tasks. In some ways, AGI can learn reason and adapt to novel situations across any domain. And then there are several mutations on this, but there's one notion, or rather there's three notions that normally are across a lot of these definitions. One is generalization. Ability to seamlessly transfer knowledge from one domain to another without needing retraining, which is a very impressive skill that we humans still seemingly have autonomy and agency, the capacity to operate independently, set goals, plan and execute complex tasks. I think AI is sort of their ish with agents to a lot of that extent. And then last but not least, human parity. Right. Performing economically valuable work at or above the level of a typical human knowledge worker. If you listen to one of our last episodes, you'll realize that Bertrand and I have slightly different views on AGI and if it's already here or not. Uh, so I think definitionally maybe we have slightly different views on what the definition actually is. So for me, maybe AGI is a little bit more what some would call superintelligence and generalized superintelligence, strict to census. Bertrand is more connecting to AGI as in its prime definition, it's like behaves as well or better than a human kind of thing. So maybe that's what's leading us to differences on whether AGI has arrived or not.

Speaker B: Yeah, I think personally I will have a different scale where I will put AGI as, as you just say, in some ways, like, relatively similar in performance to your average human being, but on top of it, it's able to touch different domain that most humans are not able to do. I mean, usually there is some level of specializations where in AI it can be more generic. And I will put asi artificial superintelligence as clearly the step beyond something that on any dimension you pick, it's able to beat a human expert. And from my perspective, I think we already discussed that, but I think we are at AGI already. We have AIs that can do way better. Not just way better, but at least as well as human on many topics, sometimes better. Uh, and yes, there are some topics that are not for AI yet. I mean, embodiment, for instance. Yeah, the flock with your humanoid robot in 2026. So for me, we are partially there or fully there in AGI, but if we Take the stricter uh, definition, asi. We are definitely not there, but my guess is that it's moving quite fast. We might be there in a, uh, few years from now. I mean I don't think we are talking about multi decades, it's five years, maybe 10. And of course there are questions because people will say for instance, hey, how do you become truly super intelligent when all your training is based on human data? Ah, that's not an easy one because how do you train on that to be way better, not just a bit better, but way better. And some are, and maybe I'm going on a tangent, but some are looking at AI learning from AI, AI being taught from AI, AI fighting, we say AI challenging AI and the same way we saw this AlphaGo moment where AI was not trained anymore, like in chess with human moves, but has been trying to play against itself and that's when it reached superintelligence in Go. It reached super intelligence by playing against itself and basically letting go of that human baggage if you want and going to the next level. What I found interesting in that actually that first that's what happened. But two, there was some analysis that the average level of CO players and the top players went up after AlphaGo because AlphaGo in a way opened doors that humans didn't believe were open in front of them or ah, they didn't see them, they didn't see these doors, so they didn't bother to open them. And so AI opened new doors. But interestingly enough humans improved after that thanks to AI. You see what I mean? It was an interesting, okay, that self learning from AI was the way to go beyond the current level of human knowledge and human expertise. But at the same time humans were able to follow up. It was not like suddenly humans are ah, total useless crap. They improved. Did they still beat AI? Maybe not, but definitely it was also helpful.

Speaker A: So back to our scenarios. We're going to take the definitional extreme just for argument sakes, for scenarios. So we're going to talk maybe what you were saying, ASI rather than full AGI but like asi. So again, artificial superintelligence, right as the extreme on the one hand, let me talk about maybe the first scenario that would come to mind. Maybe we can call it the plateau kind of scenario. So all of this was great, but it was all smoke and mirrors. They were great at some cognition stuff. They're a great tool. At some point they're going to hit a wall. Hallucinations are never going to be a thing of the past. We can't fully trust them on really hardcore stuff. So we'll gain productivity enhancements, we'll keep gaining those productivity enhancements, but at some point in time we really won't reach asi. We really will sort of be stuck with what we have. It's a little bit like we get the next big thing, the next big spreadsheet, the next big Internet, but it's not like going to change the whole world beyond just productivity enhancements and amazing tools that we have available to us that makes us much better. In that sort of scenario. The winners will continue being fast adopters, probably small and medium businesses, because there won't be a push for maximum speed either. So they'll catch up at some point and then AI native companies will be better companies than other companies, but not necessarily overall disruptors across the board. It's not necessarily a new species of companies. It's sort of just companies that are a little bit better at doing stuff, which we also saw during the Internet phenomenon and that first big push forward and then bubble, where we had some companies that were fundamentally different how they operated. But it took us another couple of decades for companies to be more and more digitally native along the way. So basically interesting, but it's sort of boring, right? It's like, cool, we got tools, we got promised the world. What are the implications? Well, all these companies that are worth trillions and trillions of dollars are not worth trillions and trillions of dollars because at some point we'll face competition, commoditization. It will just be tools and platforms. They will not unlock that next stage. And therefore this will have been a bubble. And likely it would be a hard landing to that bubble. So that's the implication.

Speaker B: I will just say that, yes, I agree with you, but I will just say overall, even if it stopped today in terms of quality improvement, speed or stuff, or it barely improves, I still think we will have like 10 years of madness just to leverage, uh, everything that we have today.

Speaker A: Understood, Bertrand, but this is a scenario. This is a scenario. So I understand. Yeah, but maybe we're going to hit a wall and it's going to be we're going to hit that wall next year. Right. Or we're going to hit that wall in two years or whatever.

Speaker B: Yeah, possibly, possibly. I'm just saying we still have 10 years of goodness from that big push in AI we experienced over the past few years.

Speaker A: Absolutely. Agreed. But it's boring.

Speaker B: It's boring. It's a plateau scenario.

Speaker A: It's a plateau.

Speaker B: Yeah.

Speaker A: So the second one is more something that we have, AI, but humans in the loop are going to be critical along the way. The judgment work that we described early in the episode is going to be critical to everything that happens. So it's sort of, I would call it the augmentation scenario. Right. AI will be a great augmentation tool for humans, but humans will never really quite stop being in the loop. Some of the gains that AI has are broadly distributed in society and in the startup, big corporation and small medium business world. So everyone will have access to them. We humans are still very important. We have all these augmentation things and AI is mostly benign. There will be a couple of issues, but honestly, at the end of the day, we're just better. We're better faster, more data driven, more factually current. We're doing stuff faster. But humans are very much at the center of everything that gets done.

Speaker B: Yeah, I think that's uh, definitely a possible scenario. I would say more likely than the plateau scenario. I, uh, don't believe in the plateau scenario at this stage. I think we can move there. I certainly believe that everyone will benefit the same way that everyone benefited from the agricultural revolution, the industrial revolution, everyone ultimately benefited. I mean, you might have some individuals who really, really, really have trouble to adjust. But overall, the product they will still buy, the service they will still use, their kits. Things will get better because there will be new opportunity created. And again, let's not forget, it's always easier to see what's going to get lost than to imagine what's going to replace. It's always that paradox. And that's why some politicians try to take advantage of this, trying to just show the bad stuff that happened that's what might get them elected. That's not the good stuff that's coming down the road.

Speaker A: The third scenario, maybe at its extreme, would lead to the doomsday that some politicians do talk about, which is a bifurcation scenario, a scenario where the world gets to some types of asi, but it gets unevenly distributed. Right? Some players that own either platforms or infrastructure or energy or all of the above will control that. And because they control it, they will have massive productivity enhancements. Labor as a force and as a global force will lose a lot of its leverage. And in some ways the social contracts will just get strained.

Speaker B: Right?

Speaker A: It's like the big guys that have made a lot of money, they won't necessarily share that with the rest of the world. The people that are struggling in the middle class that might have been evaporated is having Difficulty making ends meet and there will be a huge social constraint, etc. In and of itself as a scenario doesn't need to necessarily mean that it is a doomsday scenario at its extreme could be a doomsday scenario where very few companies control the world, where the social constructs are destroyed in many parts of the world, where wars just start getting massified either through civil wars in many places or through actual wars between different countries. So that might lead to a doomsday scenario, but the scenario itself of bifurcation doesn't necessarily mean doomsday, it's just added it's extreme. Could be a doomsday scenario.

Speaker B: Yes, I think it's difficult to dismiss that scenario and just put a very low likelihood given again what we now have centuries of economic revolution, scientific revolution to look at in the past and it never happened. The only thing that happened is more wealth, more health for countries that pick democracy, that pick science, that pick capitalism. So I'm um, personally quite hopeful this will not happen. However, I could say that bad regulations could create that. I'm personally very worried about what's happening in Europe. Very bad decisions in terms of energy choice, very bad AI regulations that can create part of Europe as a permanent underclass. I mean if you don't have energy to run data center, uh, if you cannot use AI, if you cannot run AI, I mean, that would be trouble because US and China are not going to wait. So I think some of this could happen.

Speaker A: Yeah, honestly, it could happen. My biggest concern is not so much regulation. I think my biggest concern is unchecked companies, right? That there's a couple of unchecked companies that decide to just create monopolies around whatever they're doing and it will be difficult to take them out of place, in particular in political environments that are a little bit more appeasing to such companies. I actually, the whole Skynet scenario for me is not totally impossible, probably not quite in the way that Skynet happened, but it's not totally impossible. And we should be on our toes on this stuff because honestly, if we don't think through a lot of the implications of what we're doing, we might well end up there. Last, uh, scenario is a scenario of full abundance. We are at asi, Artificial Superintelligence and everything's broadly distributed, right? Everyone has access to it. All the gains are deliberately distributed, right? So either through policy or through the great companies of the world that decide to lead the way and share their profits not only with their employees, but with humans at large. And in some ways we either pivot to doing other signs of activities or we work less days, uh, a week, or there's other elements of this, like universal basic income or other things that come to fruition that might appease the masses, so to speak, and everyone's happy. And a genuine bunch of companies that, albeit our overlords, they are very good at, uh, basically distributing that wealth that gets created over time.

Speaker B: Yeah, I don't like this scenario personally. I mean, it might happen, but I don't think it's the right way to go. I think that again, I have that belief that capitalism and science would create opportunities for everyone. And I don't think there is a need for companies to share their profits beyond their employees and shareholders. I think that ultimately that if companies provide a good product, it will benefit the people who are using their product. And that's how you work as a business. But I can imagine definitely a world where even better AI, stronger AI can be broadly distributed and have a lot of benefit. But I think it's good if human beings are, uh, still work, do something meaningful. We don't create a class of people who are just entertaining themselves through the fruits of others. I don't think it ever ends well.

Speaker A: I mean, the Catholic in me, just to be clear, thinks of full abundance is heaven. And that will become after it's a different story.

Speaker B: Yes.

Speaker A: So maybe it doesn't need to happen here. Right here we need to toll and work. But it would be a good scenario, I guess. I agree with you. It's probably the most unlikely of all the four scenarios. If I had to put some probabilities to each of them, I would say second probably least, uh, probable scenario. And we're talking about the next 10 to 20 years, which is sort of what we can have some visibility on, not the next century.

Speaker B: Right.

Speaker A: Would probably be the scenario we just described around bifurcation that could end up in that doomsday. It is higher than the probability of full abundance. I think actually quite higher. But I don't think we'll be as high as other scenarios that we have at the table. I would put the first scenario I discussed, plateau, as the second most likely scenario. Uh, and it's a little bit like an inflection point. We hit an inflection point, we'll get productivity gains, we'll hit a wall. We'll get through to your point, the use of that productivity over the next decade or so. But then it's another inflection point to get to the next level and next layer that will take us finally to asi. And last but not the least, I think the most probable scenario, if I had to again look 10, 20 years down the road, is augmentation. Right? Where human loop will continue being critical and where these AI agents and platforms will be fundamentally shifting how we do things. So again, optimistic. Both those scenarios are relatively optimistic to be honest. And augmentation would be probably, I would allege, the most optimistic for human beings, probably the most positive for humanity in the broad sense, where we all have roles and we all have importance in whatever we're doing, obviously with new jobs and new job descriptions and new things that we're doing. But um, that I think would be my probability adjusted answer.

Speaker B: Yes, I agree with you. The full abundance is not so believable because there is always, uh, some limited resources. There is always a one for more. Uh, I'm not super sure it's ever possible. The plateau I think is really unlikely. There are still so many ways to improve. We might plateau in 10 years. That's a different story. But I can see that acceleration and slope we are on will take many years before slowing down. So I'm like too, I certainly believe, especially the optimist in me, the historian, uh, in me believes that strong AI, broadly distributed, uh, benefits is a likely scenario, um, with a possible risk through some more bifurcation between have and have not. But again, I think this situation will happen more if we have too much government control and mistakes in terms of how to let the best companies wins and let the market be efficient.

Speaker A: So key takeaways from today and before we move to conclusion, obviously intelligence is getting cheaper and cheaper, or perceived intelligence, uh, maybe we're just talking about analytical capabilities or other things, but as it gets cheaper, some elements of judgment, taste and even accountability are obviously getting more expensive. I think we have a very positive long perspective on the human element, that the human element will be distinctive, will be able to defend itself and cultivate its role in all sorts of processes of decision making, obviously including in the professional and work environment. It's not safe by default that we have to be in the loop, the humans. But we feel that there's a high chance that we'll be in the loop and we'll have something to give to these processes and to these adjustments and these decisions that we're making. We feel that it's a time for you to take some risks, qualified risks. It is important to see that there are a lot of productivity enhancements in the market right now. And so if you are not Acting more and more as an AI Native company and evolving your org structure, your culture, your hiring practices, et cetera, you may be left behind. And that might be one of the first angles of disintermediation or, or disruption by new entrants. Uh, we feel it's sort of a no regrets move in some ways. So AI Native matters. Being AI Native as an org or a company. And then last but not least, if you're not using these tools, if you're not playing around, if you're not using platforms and using these systems, get ready to be disrupted yourself. So unless you have some of these core skills that we just discussed in our episode that don't require any use of AI tools, you're going to be facing a difficult uphill battle along the way. So again, if you're in the midst of the AI, uh, transformation and you're not using it yourself, it's going to be difficult to justify a paycheck a couple of years down the road.

Speaker B: I think at the end of the day, AI is here. Whether you like it or not, AI is going to keep getting better and better. And what happened the past four years was nothing short of extraordinary in terms of AI improvements. Capex investment in AI. So I would be shocked if it's not growing even faster from here. So of course there will be jobs, little impacted with AI, but definitely if you are a knowledge worker, your job will be impacted by AI, and I would guess nearly anything you do in five to 10 years from now will be totally unrecognizable. If you were a knowledge worker, the job will have changed completely, dramatically. And if you are a business, you will have to adjust and adapt because there is simply no other choice. It's a capitalist system. People will find the best tools, we learn the best tools, we use them, and we'll try to beat you and we'll try to be there. So you have to be part of it, you have to make it work. And I think overall for society, this would be very, very incredible improvements to what we can build, what we can deliver, uh, in every industry. Personally, I'm very excited by that new cognitive edge in front of us, assisted by AI or expanded with AI. I don't think AI will supplant us, but definitely would be, uh, a big chunk of our future workforce. If you think about it, there is no limit to how much we can scale AI. It was good to talk basically from knowledge workers to judgment workers. Talk a bit more about what will look like the native AI company. Talk about the human element that humans can uniquely bring to the table. And finally, we went around four scenarios about where AI will bring us. Thank you, Nino.

Speaker A: Thank you, Bertrand. You can check the latest on our website decipheredshow.com you can connect with us on Twitter, Schmidt and GPEDRO. As a disclaimer, these are our own opinions. We're not representing the views of any company.

Speaker B: If you enjoyed the show, subscribe, give us 5 stars or leave a review on Apple Podcast, Apple App or your favorite app, which will help other people to discover tech decipher. Thank you for listening. See you next time.