Explore every episode of the podcast Humans + AI
| Title | Pub. Date | Duration | |
|---|---|---|---|
| Nisha Talagala on the four Cs of AI literacy, vibe coding, critical thinking about AI, and teaching AI fundamentals (HAI Ep2) | 07 May 2025 | 00:33:24 | |
“The floor is rising really fast. So if you're not ready to raise the ceiling, you're going to have a problem.” – Nisha Talagala About Nisha TalagalaNisha Talagala is the CEO and Co-Founder of AIClub, which drives AI literacy for people of all ages. Previously, she co-founded ParallelM where she shaped the field of MLOps, with other roles including Lead Architect at Fusio-io and CTO at Gear6. She is the co-author of Fundamentals of Artificial Intelligence - the first AI textbook for Middle School and High School students. Website: LinkedIn Profile: What you will learn
People Organizations & Companies Technical Terms Ross Dawson: Nisha, it's a delight to have you on the show. Nisha Talagala: Thank you. Happy to be here. Thanks for having me. Ross: So you've been delving deep, deep, deep into AI for a very long time now, and I would love to hear, just to start, your reflections on where AI is today, and particularly in relation to humans. Nisha: Okay, absolutely. So I think that AI has been around for a very long time. And there was a long time which was actually called AI winter, which is effectively that very few people working on AI—only the true believers, really. And then a few things kind of happened. One of them was that the power of computers became so much greater, which was really needed for AI. And then the data also, with the internet and our ability to store and track all of this stuff, the data also became really plentiful. So when the compute met the data, and then people started developing software and sharing it, that created kind of like a perfect storm, if you will. That enabled people to really see that AI could do things. Previously, AI experiments were very small, and now suddenly companies like Google could run really big AI experiments. And often what happened is that they saw that it worked before they truly knew why it worked. So this entire field of AI kind of evolved, which is, "Hey, it works. We don't actually know why. Let's try it again and see if it works some more," kind of thing. So that has been going on now for about a decade. And so, AI has been all around you for quite a long time. And then came ChatGPT. And not everyone knows, but ChatGPT is actually not the first version of GPT. GPT-1 and GPT-2 were pretty good. They were just very hard to use for someone who wasn't very technical. And so, for those who are technical—one thing is, you had to—actually, it was a little bit like Jeopardy. You had to ask your question in the form of an incomplete sentence, which is kind of fun in the Jeopardy sort of way. But normally, we don't talk to people with incomplete sentences hoping that they'll finish that sentence and give us something we want to know. So ChatGPT just made it so much easier to use, and then suddenly, I think it just kind of burst on the mainstream. And that, again, fed on itself: more data, more compute, more excitement—going to the point that the last few years have really seen a level of advancement that is truly unprecedented, even in the past history of AI, which is almost already pretty unprecedented. So where is it going? I mean, I think that the level—so it's kind of like—so people talk a lot about AGI and generalized intelligence and surpassing humans and stuff like that. I think that's a difficult question, and I'm not sure if we'll ever know whether it's been reached. Or I don't know that we would agree on what the definition is there, to therefore agree whether it's been reached or not reached. There are other milestones, though. For example, standardized testing has already been taken over by AI. AI's outperform on just about every level of standardized test, whether it's a college test or a professional test, like the US medical licensing exam. It's already outperforming most US doctors in those fields. And it's scoring well on tests of knowledge as well. And also making headway in areas that are traditionally considerably challenged—areas like mathematics and reasoning have become issues. So I think you're dealing with a place where, what I can tell you is that the AIs that I see right now in the public sphere rival the ability of PhD students I've worked with. So it's serious. And I think it's a really interesting question of—I think the future that I see is that we have to really be prepared for tools that are as capable, if not in some areas more capable than we are. And then figure out: What is the problem that we are trying to solve in that space? And how do we work collaboratively with the tools? I think picking a fight with the tools is unwise. Ross: Yeah, yeah. And I guess my broader view is that the intent of being able to create an AI of humans as a reference point was always misguided. I mean to say, all right, we want to create intelligence. Well, the only intelligence we know is human, so let's try to mimic that and to replicate what it does as much as possible. But this goes to the point, as you mentioned, of augmentation, where on one level, we can say, all right, we can compare humans versus AI on particular tests or so on. But there are, of course, a multitude of ways in which AIs can augment humans in their capabilities—cognitive and intellectual and otherwise. So where are you seeing the biggest potentials in augmenting intelligence or cognition or thinking or positive intent? Nisha: Absolutely. So I think, honestly, the examples sort of—I feel like if you look for them, they're kind of everywhere. So, for example, just yesterday—or the day before yesterday—I wrote an article about vibe coding. Vibe coding is a term coined by Andrej Karpathy, which is essentially the way he codes now. And he's a very famous person who, obviously, is a master coder. So he has alternatives—lots of ways that he could choose to write code. And his basic point is that now he talks to the machine, and he basically tells it what he wants. Then it presents him with something. And then he says, "I like it. Change this, change that, keep going," right? And I definitely use that model in my own programming, and it works really well. So really, it comes down to: you have something to offer. You know what to build. You know when you don't like something, right? You have ideas. This is the machine that helps you express them, and so on and so forth. So if you do that, that's a very good way of doing augmented. So you're creating something, and sometimes, when you see a lot of options presented to you, you're able to create something better just because you can see it. Like, "Oh, it didn't take me three weeks to create one. Suddenly I have fifteen, and now I know I have more cycles to think about which one I like and why." So that's one example—just of creation collaboratively. Examples in medicine just abound. The ability to explore molecules, explore fits, find new candidates for drugs—it's just unbelievable. I think in the next decade, we will see advancements in medicine that we cannot even imagine right now, just because of that ability to really formulate a problem, give a machine a task, have it come back, and then you iterate on it. And so I think if we can just tap humans into that cycle and make that transition—so that we can kind of see a bigger problem—then I think there's a lot of opportunity. Ross: So, which—that leads us to the next thing. So the core of your work is around AI literacy and learning. And so it goes to the question of: AI is extraordinarily competent in many domains. It can augment us. So what is—what are the foundational skills or knowledge that we require in this world? Do we need to understand the underlying architectures of AI? What do we need to understand—how to engage with generative AI tools? What are the layers of AI literacy that really are going to be important in coming years? Nisha: Very good question. So I can tell you that kind of early on in our work, we defined AI literacy as what we call the four C's. We call them concepts, context, capability, and creativity. Ross: Sorry, could you repeat this? Nisha: Yes—concepts, context, capability, and creativity. Ross: Awesome. Nisha: So, concept is—you really should know something about the way these tools are created. Because as delightful as they are, they are not perfect. And a good user who's going to use it for their own—who's going to have a good experience with it—is going to be able to pick where and how to interact with it in ways that are positive and productive, and also be able to pick out issues, and so forth. And so what I mean by concept is: the reliance of AI on data and being able to ask critical questions. "Okay, I'm dealing with an AI. Where did it get its data? Who built it? What was their motivation?" Like these days, AIs are so complex that what I tell my students is: you don’t know what it’s trying to do. What is its goal? It’s sitting there talking to you. You didn’t pay for it—so what is it trying to accomplish? And the easiest way to find out is: figure out who paid for it and figure out what it is they want. And that is what the AI is trying to accomplish. Sometimes it's to engage you. Sometimes it's to get information from you. Sometimes it's to provide you with a service so that you will pay, in which case the quality of its service to you will matter, and such like that. But it’s really important, when you’re dealing with a computer or any kind of service, that you understand the motivations for it. What is it being optimized for? What is it being measured on? And so forth. So there’s kind of concepts like that—about how these tools are created. That does not mean everyone has to understand the nuances of how a neural network gets trained, or what it means to have a loss function, or all these things. That's suitable for some people, but not necessarily for everyone. But everyone should have some conceptual understanding. Then context. Ross: Or just gonna say, those interesting patterns on dark patterns. A paper in dark patterns on AI, which came out last week, I think, in one of the domains was second fancy, where, essentially, as you suggest, AI can say, "You're wonderful" in all sorts of guises, which, amongst other things, makes you like it or more to use it more. Nisha: Oh yes, they definitely have. They definitely want you to keep coming back, right? You suddenly see that. And it's funny, because I was having some sort of an interaction with—I'm not gonna name which company wrote the model—and it said something like, "Yeah, we have to deal with this." And I'm like, there's no we here. It's just me. When did we become we? You're just trying just a little too hard to get on my good side here. So I just kind of noticed that. I'm like, not so good. But so concepts, to me, effectively means that—underlying the fundamental ways that these programs are built, how they rely on data, what it means for an AI to have a brain—and then the depth depends entirely on the domain. Context, for me, is really the fact that these things are all around us, and therefore you truly do want to know that they are behind some of the tooling that you use, and understand how your information is shared, and so forth. Because there's a lot of personal decisions to be made here, and there are no right answers. But you should feel like you have the knowledge and the agency to make your own choices about how to handle tools. So that's what I mean by context. It's particularly important for young people to appreciate—context. Ross: And I think for professionals as well, because their context is, you know, making decisions in complex situations. And if they don't really appreciate the context—and the context of the AI—then that's, that's not a good thing. Nisha: Absolutely. And then capability—really, it varies very much on domain. But capability is really about: are you going to be able to function, right? Are you going to be able to do a project using these tools? Or do you need to build a tool? Do you need to merge the tools? Do you need to create your own tools? So in our case, for young people, for example—because they don't have a domain yet—we actually teach them how to build AI from scratch. So one of the very common things that we do is: almost in every class, starting from third grade, they build an AI in their first class completely from scratch. And they train it with their own data, and they see for themselves how its opinions change with the information they give it. And that's a very powerful exercise because—so what I typically ask students after that exercise is, I ask them two questions. First question is: did it ever ask you if what you were teaching it was true? And the answer is always, no. You can teach it anything, and it will believe you. Because they keep teaching it information, and children being children, will find all sorts of hilarious things to teach a machine, right? And then—but then—they realize, oh, truth is not actually a part of this. And then the next question, which is really important, is: so what is your responsibility in this whole thing? Your responsibility is to guide the machine to do the right thing, because you already figured out it will do anything you ask. Ross: That’s really powerful. Can you tell me a little bit more about precisely how that works, and when you say, getting them to build their own AI? Nisha: So we have built a tool. It's called Navigator, and it's effectively a web-based front end to industry standard tools like TensorFlow and scikit-learn. And it runs on the cloud. Then we give each of our students accounts on it, and depending on how we do it, they can either—anonymized accounts, whatever we need to protect their privacy. At large-scale installations with schools, for example, it's always anonymous. Then what happens is they go in, and they're taken through the steps of building an AI. We give them a few datasets that are kid-friendly. So one other thing to remember when you're teaching young people is a lot of the data that's out there is not friendly to young people, so we maintain a massive repository of kid-friendly datasets. A very common case that they run is a crowdsourced dataset that we crowdsourced from children, which are sentences about happiness and sadness. So a child's view—like chocolate might be happy, broccoli might be sad, things like that. But nothing sad—children can relate to. So they start teaching about happy and sad. And one of the first things that they notice is—those of them that have written programs before—this is kind of hard to write a program for. What word would you be looking for? There's so many words. Like, I can't use just the word happy. I might say, "I feel great." I didn't use the word happy, but I'm clearly happy. So they're like, "Oh, so there's something here—more than just looking for words. You have to find a pattern somehow." And if you give it enough examples, a pattern kind of emerges. So then they train the AI—it takes about five minutes. They actually load up the data, they train an AI, they deploy it in the cloud, and it presents itself as a little chatbot, if you will, that they can type in some sentences and ask it whether it thinks they're happy or sad. And when it's wrong, they're like, "Oh, it's wrong now." Then there's a button they can press that says, "I don't think you're right." And then it basically says, "Oh, interesting. I will learn some more." They can even teach it new emotions. So they teach it things like, "I'm hungry," "I'm sleepy," "I'm angry," whatever it is. And it will basically pick up new categories and learn new stuff. So after the first five minutes, when they interact with it—within about 15 minutes—every child has their own entire, unique AI that reflects whatever emotions they chose to teach and whatever perspective. So if you want to teach the AI that your little brother is the source of all evil, then it will do that. And stuff like that. And then after a while, they're like, "Oh, I know how this was created. I can see its brain change." And now you can ask it questions about what does this even mean when we have these programs. Ross: That is so good. Nisha: So that's what I mean. And it has a wonderful reaction in that it takes away a lot of the—it makes it tangible. Takes away a lot of the fear that this is some strange thing. "I don't know how it was made." "I made it. I converted it into what it is. Now I understand my agency and my responsibility in this situation." So that's capability—and it's also creativity in an element—because every single one of our projects, even at third grade, we encourage a creative use of their own choosing. So when the children are very young, they might teach an AI to learn all about an animal that they care about, like a rabbit. In middle school, they might be looking more at weather and pricing and stuff like that. In high school, they're doing essentially state-of-the-art research. At this point, we have a massive number of high school students who are professionally published. They go into conferences and they speak next to PhDs and professors and others, and their work is every bit as good and was peer-reviewed and got in entirely on merit. And that, I think, tells me what is possible, right? Because part of it is that when the tools get more powerful, then the human brain can do more things. And the sooner you put— And the beautiful thing about teaching K–12 is they are almost fearless. They have a tremendous amount of imagination. They start getting a little scared around ninth grade—kicks in: "Oh, maybe I can't do this. Maybe this isn't cool. I'm going to be embarrassed in front of my friends." But before that, they're almost entirely fearless. They have fierce imagination, and they don't really think anything cannot be done. So you get a tool in front of them, and they do all sorts of nifty things. So then I assume these kids, I'm hoping, will grow up to be adults who really can be looking at larger problems, because they know the tools can handle the simpler things. Ross: That is, that is wonderful. So this is a good time just to pull back to the big picture of your initiatives and what you're doing, and how all of these programs are being put into the world? Nisha: Yeah, absolutely. So we do it in a number of different ways. Of course, we offer a lot of programs on our own. We engage directly with families and students. We also provide curriculums and content for schools and organizations, including nonprofits. We provide teacher training for people who want to launch their own programs. We have a professional training program, which is essentially—we work with both companies and individuals. In our companies, it's basically like they run a series of programs of their choosing through us. We work both individually with the people in the company—sometimes in a more consultative manner—as well as providing training for various employees, whether they're product managers, engineers, executives. We kind of do different things. And then individuals—there are many individuals who are trying to chart a path from where they are to where—first of all, where should they be, and then, how can they get there? So we have those as well. So we actually do it kind of in all forms, but we also have a massive content base that we provide to people who want to teach as well. Ross: And so what's your geographical scope, primarily? Nisha: So we're actually worldwide. The company—we started out in California. We went remote due to COVID, and we also then started up an office in Asia around that time. So now we're entirely remote—everywhere in the world. We have employees primarily in the US and India and in Sri Lanka, and we have a couple of scattered employees in Europe and elsewhere. And then most of our clients come from either the US or Asia. And then it's a very small amount in Europe. So that's kind of where our sweet spots are. Ross: Well, I do hope your geographical scope continues to increase. These are wonderful initiatives. Nisha: Thank you. Ross: So just taking that a step further—I mean, this is obviously just this wonderful platform for understanding AI and its role in having development capabilities. But now looking forward to the next five or ten years—what are the ways in which, for example, people who have not yet exposed themselves to that, what are the fundamental capability sets in relation to work? So, I mean, part of this is, of course, people may be applying their capabilities directly in the AI space or technology. But now, across the broader domain of life, work—across everything—what are the fundamental capabilities we need? I mean, building on this understanding of the layers of AI, as you've laid out? Nisha: Yeah, so I think that, you know, a general sort of—so if we follow this sort of the four C’s model, right—a general, high-level understanding of how AI works is helpful for everyone. And I mean, you know, and I mean things like, for example, the relationship between AI and data, right? How do AI models get created? One of the things I've learned in my career is that—so there's some sort of thing as an AI life cycle, like, you know, how does an AI get built? And even though there are literally thousands of different kinds of AI, the life cycle isn’t that different. There's like this relationship between data, the models, the testing, the iteration. It's really helpful to know that, because that way you understand—when new versions come out—what happened. Yeah, what can you expect, and how does information and learning filter through? You know, context is very critical—of just being aware. And these days, context is honestly not that complicated. Just assume everything that you're—everything that you interact with—has an AI in it. Doesn't matter how small it is, because it's mostly, unfortunately, true. The capability one is interesting. What I would suggest for the most broad-based audience is—really, it is a good idea to start learning how to use these foundation models. So I'm talking about the—you know—these models that are technically supposed to be good at everything. And one of the things—the one thing I've kind of noticed, dealing with particularly professionals, is—sometimes they don't realize the tool can do something because it never occurred to them to ask, right? It's one of those, like—if somebody showed you how to use the tool to, you know, improve your emails, right? You know the tool can do that. But then you come along and you're looking for, I don't know, a recipe to make cookies. Never occurs to you that maybe the tool has an opinion on recipes for cookies. Or it might be something more interesting like, "Well, I just burned a cookie. Now, what can I do? What are my options? I've got burnt cookies. Should I throw out the burnt cookies? Should I, you know, make a pie out of them?" Whatever it is, you know. But you can always drop the thing and say, "Hey, I burnt a cookie. Burned cookies." And then it will probably come back and say, "Okay, what kind of cookies did you burn? How bad did you burn them?" You know, and this and that. "And here are 10 things you can do with them." So I think the simplest thing is: just ask. The worst thing it'll do is, you know, it will come back with a bad answer. And you will know it's a bad answer because it will be dumb. So some of it is just kind of getting used to this idea that it really might actually take a shot at doing anything. And it may have kind of a B grade in almost anything—any task you give it. So that's a very mental shift that I think people need to get used to taking. And then after that, I think whatever they need to know will sort of naturally evolve itself. Then from a professional standpoint, I think—I kind of call it surfing the wave. So sometimes people would come to me and say, "Hey, you know, I'm so behind. I don't even know where to begin." And what I tell them is: the good news is, whatever it is that you forgot to look up is already obsolete. Don't worry about it. It's totally gone. You know, it doesn't matter. You know, whatever's there today is the only thing that matters. You know, whatever you missed in the last year—nobody remembers it anymore anyway. So just go out there. Like, one simple thing that I do is—if you use, like, social media and such—you can tailor your social media feed to give you AI inputs, like news alerts, right, or stuff that's relevant to you. And it's a good idea to have a feel for: what are the tools that are appropriate in your domain? What are other people thinking about the tools? Then just, you know, pick and choose your poison. If you're a professional working for a company—definitely understand the privacy concerns, the legal implications. Do not bring a tool into your domain without checking what your company's opinions are. If the company has no opinions—be extra careful, because they don't know, but they don't know. So just—there's a concern about that. But, you know, just be normal. Like, just think of the tool like a stranger. If you're going to bring them into the house, then, you know, use your common sense. Ross: Well, which goes to the point of attitude. And part of it's how—this—how do we inculcate that attitude of curiosity and exploration and trying things, as opposed to having to take a class, go in a classroom before you know what to do? And you have to find your own path by—learn by doing. But that takes us to that fourth step of creativity, where—I mean, obviously—you need to be creative in how you try to use the tools and see what you learn from that. But also, it goes back to this idea of augmenting creativity. And so, we need to be creative in how we use the tools, but also there are ways where we can hopefully create this feedback loop, where the AI can help us augment or expand our creativity without us outsourcing to it. Nisha: Absolutely. And I think part of this is also recognizing that—here’s the problem. If you're—particularly if you're a professional—this is less an issue for students because their world is not defined yet. But if you're a professional, there is a ceiling of some kind in your mind, like "this is what I'm supposed to do," right? And the floor is wherever you're standing right now. And your value is in the middle. The floor is rising really fast. So if you're not ready to raise the ceiling, you're going to have a problem. So it's kind of one of those things that is not just about the AI. You have to really have a mental shift—that I have to be looking for bigger things to do. Because if you're not looking for bigger things to do, unfortunately, AI will catch up to whatever you're doing. It’s only a matter of time. So if you don’t look for bigger things—that’s why the areas that feel like medicine are flourishing—is because there are so many bigger problems out there. And so, some of it is also looking at your job and saying, “Okay, is this an organization where I can grow? So if I learn how to use the AI, and I'm suddenly 10x more efficient at my job, and I have nothing left to do—will they give me more stuff to do?” If they don't, then I think you might have a problem. And so forth. So it's one of those—you have to find—there's always a gap. Because, look, we're a tiny little planet in the middle of a massive universe that we don't know the first thing about. And as far as we know, we haven't seen anyone else. There are bigger problems. There are way, way bigger problems. It’s a question of whether we’ve mapped them. Ross: Yeah, we always need perspective. So looking forward—I mean, you're already, of course, having a massive positive impact through what you are doing—but if you're thinking about, let's say, the next five years, since that's already pretty much beyond what we can predict, what are the things that we need to be doing to shape a better future for humans in a world where AI exists, has extraordinary capabilities, and is progressing fast? Nisha: I think really, this is why I focus so much on AI literacy. I think AI literacy is critical for every single human on the planet, regardless of their age or their focus area in life. Because it's the beginning. It's going away from the fear and really being able to just understand just enough. And also understanding that this is not a case where you are supposed to become—everyone in the world is going to become a PhD in mathematics. That's not what I mean at all. I mean being able to realize that the tool is here to stay. It's going to get better really fast. And you need to find a way to adapt your life into it, or adapt it into you, or whichever way you want to do it. And so if you don't do that, then it really is not a good situation. So I think that's where I put a lot of my focus—on creating AI literacy programs across as many different dimensions as I can, and providing— Ross: With an emphasis on school? Nisha: So we have a lot of emphasis on schools and professionals. And recently, we are now expanding also to essentially college students who are right in the middle tier. Because college students have a very interesting situation—that the job market is changing very, very rapidly because of AI. So they will be probably the first ones who see the bleeding edge. Because in some ways, professionals already have jobs—yes—whereas students, prior to graduating from college, have time to digest. It’s this year’s and next year’s college graduates who will really feel the onslaught of the change, because they will be going out in the job market for the first time with a set of skills that were planned for them before this happened. So we do focus very much on helping that group figure out how to become useful to the corporate world. Ross: So how can people find out more about your work and these programs and initiatives? Nisha: Yeah, so we have two websites. Our website for K–12 education is aiclub.world. Our website for professionals and college students—and very much all adults—is aiclubpro.world. So you can look there and you can see the different kinds of things we offer. Ross: Sorry, could you repeat the second URL? Nisha: It’s aiclubpro.world. Ross: aiclubpro.world. Got it? That's fantastic. So thank you so much for your time today, but also your—the wonderful initiative. This is so important, and you're doing a marvelous job at it. So thank you. Nisha: Really appreciate it. Thank you for having me. The post Nisha Talagala on the four Cs of AI literacy, vibe coding, critical thinking about AI, and teaching AI fundamentals (HAI Ep2) appeared first on Humans + AI. | |||
| HAI Launch episode | 30 Apr 2025 | 00:13:07 | |
In this launch episode of Humans Plus AI, Ross Dawson reflects on the podcast’s evolution—from Thriving on Overload to Amplifying Cognition, and now to its current form. Originally rooted in exploring how individuals navigate an age of overwhelming information, the podcast has shifted in response to rapid advancements in AI, especially following the release of ChatGPT. Dawson explains how this moment catalyzed a deeper integration of his long-standing work on human-AI collaboration, leading to a new focus on how we can not just survive, but thrive alongside intelligent machines.
The episode outlines the podcast’s new mission: to examine how AI can amplify human capabilities—from individual cognition and skill development to strategic thinking, work transformation, and collective intelligence. Dawson emphasizes a positive, forward-looking approach, aiming to spotlight the tools, mindsets, and societal structures needed to co-evolve with AI. Through interviews, insights, and community conversations, Humans Plus AI will serve as a guide for harnessing this unprecedented partnership between humans and machines to shape a better future. | |||
| Kunal Gupta on the impact of AI on everything and its potential for overcoming barriers, health, learning, and far more (AC Ep86) | 23 Apr 2025 | 00:33:55 | |
"Maybe the goal isn’t to eliminate the task or the human—but to reduce the frustration, the cognitive load, the overhead. That’s where AI shines." – Kunal Gupta About Kunal GuptaKunal Gupta is an entrepreneur, investor, and author. He founded and scaled global digital advertising AI company Nova as Chief Everything Officer for 15 years, with teams and clients across 30+ countries. He is author of four books, most recently 2034: How AI Changed the World Forever. Website: LinkedIn Profile:
Book: 2034: How AI Changed Humanity Forever What you will learn
Books Technical Terms & Concepts Ross Dawson: Kunal, it is awesome to have you on the show. Kunal Gupta: Thanks, Ross. Nice to see you. Ross: So you came out with a book called 2034: How AI Changed Humanity Forever. So love to hear the backstory. Yes, that's the book. So what's the backstory? How did this book come about? Kunal: Yeah, I've written a few books, but this is definitely the most fun to write and to read and reread, and at some points, to rewrite. So back in November 2022, ChatGPT launches. There's this view—okay, this is going to change our world, not sure how. So in the ensuing months, I had a number of conversations with friends and colleagues asking, "Hey, like, how does this change everything?" I asked people very open-ended questions, and the responses were all over the place. To me, what I realized was we actually just don't know, and that's the best place to be—when we don't know but are curious. So I started to host dinners, six to ten people at a time in my apartment. I was in Portugal at the time, and London as well. Over the course of 2023, I hosted over 250 people over a couple dozen dinners. The setup was really unique in that nobody knew who else was coming. Nobody was allowed to talk about work, nobody was allowed to share what they did, and no phones were allowed either. So that meant really everybody was present. They didn't need to be anybody, they didn't need to be anywhere, and they could really open up. All of the conversations were recorded. All the questions were very open-ended along the lines of—really the subtitle of the book—like, how does AI change humanity? And we got into all sorts of different places. So over the course of the dinners in the year, recorded everything, had to transcribe it, and working with an editor, we manually went through the transcripts and identified about 100 individual ideas that came out of a human. And it's usually some idea, inspiration, or some fear or insecurity. And we turned that into a book which has 100 different ideas, ten years into the future, of how AI might take how we live, how we work, how we date, how we eat, how we walk, how we learn, how we earn—and absolutely everything about humanity. Ross: So, I mean, there's obviously far more in the book than we can cover in a short podcast, but what are some of the high-level perspectives? It's been a bit of time since it's come out, and people have had a chance to read it and give feedback, and you've reflected further on it. So what are some of the emergent thinking from you since the book has come out? Kunal: Yeah, I probably hear from a reader or two daily now, sharing lots of feedback. But the most common feedback I hear is that the book has helped change the way they think about AI, and that it's helped them just think more openly about it and more openly about the possibilities. And that's where introducing over 100 ideas across different aspects of society and humanity and industries and age groups and demographics is really meant to help open up the mind. I think in the face of AI, a lot of parts of society were closed or resistant to its potential impacts, or even fearful. And the book is really designed to open up the mind and drop some of the fear and really to be curious about what might happen. Ross: So taking this—taking sort of my perennial "humans plus AI" frame—what are some of the things that come to mind for you in terms of the potential of humans plus AI? What springs to mind first? Kunal: Those that say yes and are open and curious about it—I really think it's an accelerant in so many different parts of life. I'll give an example of AI being used in government. I gave the fictitious example of Tokyo electing the first AI mayor, and how that went and what the implications of that were. I gave examples in Europe of AI being used to reduce bureaucracy and streamline all the processes. Government is an example of something that touches all of our lives in a very impactful way, and AI being used to help make better decisions—more objective decisions, decisions that aren't tied to ego or a four-year cycle—I think could lead to better outcomes for the aggregate of any given society or country or city. That's one example. Education is another clear example, in terms of how young people learn, but then also how old people learn. There are a couple of ideas around AI—this idea of AI literacy for not just young people, but also old people—and some interesting ways that comes to life. So those are a few examples covering a spectrum of how AI and humans can come together. Ross: So coming back to present and now and here. So what, in what ways are you using AI to amplify what you're doing? Or where is your curiosity taking you? Kunal: Absolutely everything. And my fiancée gets annoyed that I'm talking some days to ChatGPT more than I am to her. And we live together. We call ChatGPT my friend, because it gets embarrassing to just say ChatGPT so much within a single day. So, "as I was talking to my friend," "I was asking my friend," etc. There's a few areas of my life that I'm very focused on these days. I'd say health is a big one, and optimizing my health, understanding my health, testing. So making sense of kind of my health data beyond the basic blood tests. I've done lots of longevity-based testing and take lots of supplements. So going deeper and geeking out on that has been a lot of fun. Ross: So just digging into that. So do you collect data which you then analyze, or is this text-based, or is this using data to be able to feed into the systems? Kunal: So my interest on health started probably four years ago. Had some minor health issues that triggered me to start to do a bunch of testing. And then, being a tech guy, I got fascinated by the data that I was starting to collect in my body. So it happened, but four years of very consistent blood work, gut health, sleep data, with all the fitness and sleep trackers, smart scale, and lots, lots more. So I'd say that's one part—is I have a couple years' worth of data. I think the second part that I found interesting, because I've had a lot of data, is to use my own data as the baseline versus some population average, which is a different gene pool and a different geographic location. So seeing just the changes in my data over time, and then using reference ranges as one comparison point has been helpful. And then, I see lots of specialists for different health issues that I've dealt with over the years. And I have found AI, prompted the right way with the right data, as effective, if not more effective, than the human specialists. So I do walk into my specialist appointments now with a bunch of printouts, and I essentially fact what they tell me oftentimes in real time with ChatGPT and other AI tools. And that gives me just a lot more confidence in things I'm putting into my body, and things I'm doing to my body. Ross: How do the doctors respond to that? Kunal: I'm definitely unique in that sense—at least the specialists I see, they're not used to it. I would say probably like three to five doctors lean in and ask me how did I collect it, and want copies of the printouts. And two out of five are a little dismissive. And that's not surprising, I guess. Ross: There's just this recent data showing—comparing the patient-perceived outcomes from doctors—where basically they perceive the quality of the advice from the AI to be a little bit better than the doctors, and the empathy way, way better than doctors. Kunal: Yeah, yeah, I trust in my experience as well. Ross: So, but now you're uploading spreadsheets to the LLMs or other raw data? Kunal: Spreadsheets and PDF reports. And that's the annoying part, actually. I've done a couple dozen different tests on different parts of my body and get reports in all these different formats. It's all in PDFs from all these providers, and they give their own explanations using their own reference data. So it's hard to make sense of it. And I live between Australia and Portugal, so even a blood test in Europe versus blood tests in Australia—different metrics, different measurement systems, different reference ranges. So AI has helped me normalize the different formats of data. Ross: Yeah, but of course, you have to have that antenna into putting it in and asking it to normalize, and then be able to get your baseline out of that. Kunal: So I'd say it's just like this theme is—for the listeners or viewers—it's just feeling empowered. And health is a very sensitive topic, one that oftentimes, when we have issues, we feel helpless for them. And the support to help has helped me feel more empowered and more motivated, frankly, to improve my health. Ross: Yeah, well, I mean, just as a tiny, tiny example—my father went into some tests a little while ago, and we got back the report. It was going to be interpreted by the specialist when he went to visit them a week or two later. So I was actually able to get some kind of sense of what this cryptic report meant before waiting to find out the specialist's interpret for us. Kunal: Yeah, there's so much anxiety that could exist in waiting, and the unknown. So even if the known is good or bad, just the known is helpful versus the unknown. Ross: So in terms of cognition, or thinking, or creating, or ideation—or, I suppose, a lot of the essence of what you do as an entrepreneur and thinker and author—so what... So let's get tactical here. What are some of the lessons learned, and tools you use, or how you use them, or approaches which you’ve found particularly useful? Kunal: I'll give a very simple example that hopefully is relatable for many people. But it's figured a much deeper reflection for me—realizing I need to think differently. And as an adult, it's harder to change the way we think. So for my partner's father, who turned 70 earlier this year, we threw and hosted a big party on a boat in the Sydney Harbor. And three days before the party, I went to my partner. I was like, “We should have a photo booth on the boat.” And she dismissed it, saying, like, “This is three days. We don't have time. There's already too much work to do for the party.” She was feeling stressed. And the creative and entrepreneur in me—I heard it, but I didn't listen to it. So then I went to GPT and I said, “Is it actually allowed to have a photo booth on a boat?” And it's like, “Yes.” “Okay, can I get a photo booth vendor in three days, in Sydney?” And the answer was yes. I'm like, “Okay, who are 10 photo booth vendors in Sydney?” And it gave me 10 vendors. And then I was about to click into the first website, and then I just had this reaction. I was like, “This is too much work.” So then I said, “How can I contact all of these vendors?” And it gave me their phone numbers and email addresses. Then I was about to click the email address—and again, I was like, “Still too much work.” I was feeling quite impatient. So then I paused for a minute, and then I said, “Give me the email addresses, separated by commas.” And then I opened up Gmail, put the email addresses in BCC, and wrote up just a three-line email saying, “This is the date, this is the location, need a photo booth. Give me a proposal.” Within three hours, I had four proposals back, showed them to my partner, she picked one that she liked, and it was done. So the old way of doing that would have taken so many phone calls and missed calls and conversations and just a noise and headache. And this new way literally took probably less than seven minutes of my time, and we got to a solution. So that's an example. To abstract it out now—there's so many perceived barriers to the old way of doing things. And I think in simple daily life tasks, I'm still learning and challenging myself to just think differently of how to approach it. Ross: So, what you describe is obviously what many people say is the image for agentic AI. You should have an agent where you can just say to them exactly—give them the brief—and it will be able to go and do everything which you described. But at the same time, speaking in early April 2025, agents are still not quite there—as in, we don't have any agent right now which could do precisely what you've said. So where do you see that pathway in terms of agents being able to do these kinds of tasks? And how is it we use them? Where does that lead us? Kunal: This is such an interesting moment because we don't know that fun part. So we may end up with browser agents—agents that go, open up a browser, click in the browser, and use it on the user's behalf. And that might be with like 70% accuracy, and then 80%, and then 90%, and then it gets to “good enough” to schedule and manage things. We might end up with agents that make phone calls—and there's lots of demos flying around the internet—that make bookings and coordinate details and appointments on our behalf. Or it may be just a little simpler than that, which may be more realistic—kind of like the photo booth example I gave—which is an agent to just help us think through how to get the task done. And maybe it's not eliminating the task, but reducing the task. And I think we have a role to play there, as the human user, and the AI has a role to play. Understanding how to get the best of both versus the worst of both. The worst of both is impatience on the human and then incompetence on the AI—and then throwing the whole thing out. I do think there's a world where it's the best of both. And probably reframing the goal, which is not to eliminate the human, it's not to eliminate the task for the human, but to reduce the frustration, reduce the cognitive load, reduce the overhead—the time it takes to get something done. And software development—we can get into it, if you'd like—is, I think, an example where that's starting to show itself. It's not eliminating the human, but it's reducing the cognitive load and the time and the headache involved. Ross: So this goes—it's a very, very big question, very big and broad question—but this idea of reducing cognitive load, freeing up time so that, you know, the various ways we can put that is that it allows us to move to higher-order, more complex tasks and thinking and creativity, or to give us time to do other things. And I think there may be other frames around what that does, but if we are freeing up cognitive load, what do you see as the opportunities from that space? Kunal: Yeah, I see cognitive load as the critical path right now. I mean, there's so many ideas to explore and technologies to try, but there's a cognitive load to learn it. And I think we have a while to go where we won't find interesting, creative, or productive uses for our excess cognitive load—probably at least another... We won't—there won't be an excess because, even as AI frees us up, there's going to be more. There's still such a big backlog of things we're interested in, curious in, that we want to apply our cognitive load to—whether it's productive in an economic sense, or productive in a health sense, or productive in a friendship sense, or productive in a learning sense. So maybe that's the way to frame it—is that it'll become multidimensional. It won't be purely an economic motivation of work. And there may be other motivations that we have, but are often suppressed or not expressed, because the economic one takes place of this. Ross: Yeah, no. I mean, that goes, I think, to what is one of the greatest fallacies in this—people predicting future techno-unemployment—is that there's a fixed amount of work. And if we take away work by machines, then there's not gonna be much left to do with humans. Well, there's always more to do, and there's more to create and spend our time. So there's no fixed amount of work or ideation or thinking or whatever. But I think I like this idea that we are—humans are—curious. We are inventors, we are thinkers, and we are... I think this curiosity is a—if AI can help us or guide us or support us in being more curious because we are able to, amongst other things, learn things quickly, which would have previously required taking a degree, or whatever it may be—then that is a massive bonus for humanity. Kunal: Yeah, yeah, completely. I am curious—your take. Something I am worried about is if that curiosity becomes of a passive nature versus active. Passive meaning Netflix and Instagram and TikTok, with the consumption on these more passive platforms growing. And we saw that in the pandemic. We had a bunch of people who were not working, maybe getting some small paychecks from the government, and the response on aggregate was to consume versus create. And so I do worry—is what if the curiosity just turns into more scrolling and browsing, versus something that's that, you know. Ross: This goes to my last chapter of Thriving on Overlord, where I essentially talk about cognitive evolution—essentially saying we're getting this... it's evolution or devolution, in the sense of the most default path for our brain is to just continue to get easy stimulus. And so, essentially, there are plenty of people who start spending all their day scrolling on TikTok, or whatever equivalent they have. Whereas, obviously, there are some who say, “Well, all of this information abundance means that I can do whatever I want, and I will go and explore and learn and be more than I ever could be before.” And so you get this divergence. I think there's a very, very similar path here with AI, where there are people using AI as the... A lot of recent research is pointing to reduced cognitive functioning because we are offloading. And I often say, the greatest risk with AI is overreliance—where we just sort of say, “Oh, that's good enough. I don't need to do anything anymore.” And I think that's a very real thing. And of course, many other people are using these as tools to augment themselves, achieve far more, be more productive, learn faster. But I think one of the differences between the simple information space in which we've been living and the AI space we're now living in is that AI is interactive. We can ask the questions back. TikTok or TV screen and so on—you, well, you can create your TikTok. Sure, that's great if you do that. But the AI is inherently interactive. It doesn't mean that we use it in a useful way. I mean, the recent Anthropic economic index picked out "directive" as one of what it called "automation," where it says, “Do this,” and so it's just doing that—as opposed to a whole array of other ones, which are more around learning, or iterating, and having conversations, and so on, which are more the augmenting style. And there is still this balance, where quite a few are just getting AI to do things. But now we have far more opportunity than with the old tools to be participatory. Kunal: Yeah. I, yesterday, was using an AI web app, and I got stuck, and I had my first AI voice agent customer support call. So I just hit "Call," was immediately connected—no wait time. And then I described my problem, and it guided me through a few steps. And then I wasn't able to resolve it—which I assumed was going to be the case—but at the end, it gave me the email address for the startup behind the product, where I couldn't find the email address anywhere on the website. They probably do that on purpose. But it was probably like a two-minute interaction, and it was a very pleasant, friendly, instant conversation. And I didn't mind it. After that, I noticed—okay, this is the future. My customer service requests and support requests are going to be with AI and voice agents, and they'll be instant, and the barriers will come down. Some will be less shy to ask for help. Where today, the idea of calling for customer support feels so daunting, this actually felt quite effortless. Fine. It'll become more interactive. Ross: Yeah. Well, it is effort to type, and whatever the format people prefer—whether it's typing or speaking or having a video person to interact with—I mean, these are all ways where we can get through problems or get to resolution faster and faster. And I think this idea of the personalized tutor—I mean, I've always, since way before generative AI, always believed that potentially the single biggest opportunity from AI was personalized education. Because we are all different. We all learn differently, we all have different interests, and we all get stuck. In classrooms—those who go to school—it’s the same for everyone, with, if you're lucky, a fraction of a teacher’s time for personalized interaction. So that’s this—again, that takes the willingness and the desire to learn. But now we have access to what will be, very soon, some of the best, nicest, most interactive tutoring—well, not human. And I think that is critically different. But that requires simply, then, just the desire. Kunal: Yeah, I mean, on the desire—I’m curious for your take on this. I've noticed the capabilities of AI are growing at a very fast rate, and it feels like it's at a faster rate than the adoption of AI. So, like, the capabilities are growing at a faster rate than the adoption of the capabilities. And the gap is getting bigger. I was part of the smartphone revolution—2007, 2008—and built my business at that moment. And that was an example where the capabilities were higher than the adoption, but we quickly caught up. And then social media—same thing. Capabilities were ahead of the consumer, but the consumer caught up. Cloud computing—same again. Capabilities grew, and then enterprises caught up pretty fast. So in previous tech waves, in my lifetime at least, there's been an initial gap between capabilities and adoption, but it’s narrowed. And here, this feels like the opposite. It feels like the reverse—where the capabilities and the adoption, the gap is getting bigger. And I'm curious if you agree with that. And, I guess more importantly, what are the implications of that? And, I guess, opportunities. Ross: Well, what I think is there's always been this spectrum of uptake—from internet through to every other technology—and sort of how the early adopter through to the laggards. And now that is becoming far more accentuated, in that there are plenty of people who have never tried an AI tool at all, and there's plenty of people that spend their days, like you, interacting with the systems and learning how to use it better. And this is an amplifier, as in, those who are on the edge are more able to learn more and be able to keep closer to the edge. And those who are not involved are legally getting more behind. And this is one of the very concerning potentials for augmenting divides that we have in society—between wealth and income and access to opportunity. So I think it is real. I think that it's... it is the nature of it, as it starts to increase over time itself. Kunal: Yeah, yeah. In the book, I talk about AI—this moment when AI goes from being a noun to a verb. And, like, we've learned to speak, to walk, to write, to read, and then to AI—introducing this idea of AI literacy. And it boggles my mind that in a lot of parts of the world, schools are banning AI for kids. And that horrifies me, knowing that this is going to be as important as reading and writing. Ross: Yeah, no, I think that's absolutely true. So in our recent episode with Nisha Talaga, she runs basically AI literacy programs across schools around the world, and she's doing some extraordinary work there. And it's really inspiring—and doing obviously a very good job at bringing those principles. But yeah, I think that's really true, and I think that's a great sort of conclusion, and bringing that journey from the book and what we've looked at—and, I suppose, these next steps of how it is we use these tools, as you say, as a verb, not a noun. So where can people go to find out more about your work? Kunal: Yeah. So it's my book 2034, and my other books—find them all on Amazon, Audible, free on Spotify, like the AI-narrated version of my voice reading them to you. And then my website, kunalgupta.live, and I have an AI newsletter called pivot5.ai—the number five—and that's a daily newsletter that goes to a few hundred thousand people and kind of top-line summarized for a business leadership audience. Ross: Awesome. Thanks so much. Really appreciate your time, your insights. Kunal: Thank you. The post Kunal Gupta on the impact of AI on everything and its potential for overcoming barriers, health, learning, and far more (AC Ep86) appeared first on Humans + AI. | |||
| Lee Rainie on being human in 2035, expert predictions, the impact of AI on cognition and social skills, and insights from generalists (AC Ep85) | 16 Apr 2025 | 00:40:09 | |
“We could become obsolete by our own will—at least a portion of humanity just sort of giving up... But humans want to be valuable, want to be seen, want to be understood, want to be heard, want to think that their life matters. And this raises all sorts of questions about that.” – Lee Rainie About Lee RainieLee Rainie is Director of Imagining the Digital Future Center at Elon University. He joined in 2023 after 24 years of directing Pew Research Center’s Pew Internet Project, where his team produced more than 850 reports about the impact of major technology revolutions. Lee is co-author of five books about the future of the internet including “Networked: The New Social Operating System”. Website:
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Ross Dawson: Lee, it's a delight to have you on the show. Lee Rainie: Thanks so much, Ross. I'm looking forward to it. Ross: So you are director of the Imagining the Digital Future Center at Elon University. So that sounds like a wonderful initiative. Can you please tell us about it? Lee: It is a wonderful initiative, and I feel very fortunate to be here studying this subject at this moment. It's a center at Elon University of North Carolina that grew out of a partnership that I had with Elon in my previous job, when I worked for the Pew Research Center. There were some interesting, enthusiastic, ambitious professors here who were interested in the digital future, and they basically rolled out the red carpet to me and offered a lot of labor, a lot of brainpower, and a lot of assistance in interviewing experts about the future. One of the things that happened when I went to Pew in the first place, just at the turn of the millennium, was we were measuring adoption of technology—first the internet, then home broadband, and then a bunch of other things. But whenever I went out to speak about our findings, the first question from the audience was, "Well, that's all well and good. You're looking at the here and now, and fine, dandy, but what's the next big thing?" Because that's always the urgent question when you're thinking about digital technologies. So I began to work with the professors at Elon to see if experts really had a decent track record in looking at the future. The first project we did was looking at predictions about the rise of the internet and what it would do, both in social, political, and economic terms. We found 4,400 predictions that were made between 1990 and 1995 about the internet. And experts were largely on the mark, partly because it wasn't really so much future questions that they were looking at. They just knew what was coming out of the labs. They knew what they were working on. They knew what competitors were working on. And so it wasn't hard to really anticipate the future if you talk to the right people. So we built a database of experts, and it's a convenience database. There's no—this is not a representative sample of all expertise about digital technology. It's pioneers of the technology, it's builders of the technology, it's analysts. A lot of academics are in our database. And we just started asking in the year 2020, 2004, about things over the horizon. And it was a wonderful methodology, just to give us insight into the things that were around the corner. We're not pretending that it's quantitatively, scientifically accurate. We marry the methodologies of quantitative and qualitative work. And so it's basically smart people riffing on the future. Ross: So wanted to get to that. So I actually tend, whenever I use the word expert, I always use quotation marks, because who's an expert. I love what Marshall McLuhan said. Certainly the effect of the expert is the person who stays put, as the avatar is the one who continues to explore. But having said that, of course, yeah, some people know more about particular topics, and if we're looking into the future, we do that. So what the—in terms of—so have you looked back on the previous reports you've been doing during that period in terms of the degrees to which they were reflective of what did happen? Lee: We don't have a bad track record of predicting things. Often things happen sooner than the time frame we were suggesting to experts. Sometimes we were criticized for asking questions about—this is happening now, why are you thinking about this as a future issue? But they predicted the rise of the dominance of mobile connectivity about 15 years before it happened. They predicted the rise of violence-prone extremist groups enabled by digital technologies. They predicted the ways in which the boundary between work and leisure, work and home, work and studies would melt, and some of the consequences of that. They were also pretty good about looking at the downstream ill effects of social media before they became really evident to the world, starting in the mid-20 teens. So it wasn't bad. There have been some clunkers in there. And we—there were—we've, a couple of times, gone back and we've talked to the experts who saw things correctly and said, what were you thinking at the time? Or how did you know? And we've done one specific report on that, but often we just sort of amuse ourselves by doing that. And actually, to the point you were just making about experts, some of the best predictors here are foxes rather than hedgehogs in the Isaiah Berlin formulation. They are interesting generalists. They have a purchase on any number of angles into these questions, and they're not wedded to a single worldview or single ideology or a single even frame of mind about whether it's going to end up well or end up awfully. And so the foxes are looking good in these surveys. But again, I think there are interesting limits that we try to be careful about as we release these findings. It's a convenient sample of experts. So our database is built on people who make public pronouncements and people who increasingly are in public forums where technology is discussed, or conferences and things like that. But usually only between 10 and 15% of those we invite answer our questionnaires. It's totally self-selecting. It skews probably more heavily towards the academic analysts, who tend to be critics, than it is to the tech enthusiasts and the builders. And it's—the northern hemisphere is heavily represented here. The global south is not. English speakers, obviously, may find it easier to be dealing with us than others. So there are all sorts of ways this is not universal. This is not diverse in interesting respects. At the same time, we do have a diversity of folks who are builders and analysts and people who have long histories with this stuff, and people who are relatively new and critics almost from day one on this stuff. So we try to be clear about that. But it's not representative, and it's not scientific by any stretch of the imagination. Ross: Yeah, it's—well, we can't be. When you look at the future, the idea of foresight was we can't know. And so all methodologies have very increased validity. And obviously, it's valuable here. One of the points is, for each of your studies, I believe you always try to have one consolidating question, where you have to sort of find yourself on one side or the other. And so essentially, it becomes statistical. So it's never, of course, 100% of the experts believe one thing. There is some balance. And so I suppose you are looking for where there are substantial majorities of experts. And I suppose teasing into the detail of those—and in fact, I think one of the wonderful things about all the reports is you have the full, everything which is said by all of the experts in your report. So you can actually go to the detail, not just the statistical summaries. But this comes back to this sort of balance of what is meaningful. Is it when more than 60 or 70% of experts lean in a particular way? Is that an indicator that we should be taking into account? Where do we sort of see this as the balance of the statistical balance of experts starts to be a real guide to what we should be looking for? Lee: We don't have any firm rules of thumb about those things. It tends to be that if two-thirds or more of our experts say one thing rather than the other, we treat that as a notable finding. But the way that we have framed a lot of the findings in the past is as split verdicts. And particularly as we've gotten more heavily into analysis of qualitative answers—the essays, basically, or the open-ended answers that people are giving us—they themselves often can have smart things to say on both sides of the question. And so a lot of times where we find ourselves is trying to say this seems like it's the more prevalent view among the people that we're talking to, but there are a lot of nuances and caveats to sound, and just ways in which even the positive stuff can break bad or is moderated by worse kinds of findings. So there's a sort of intentional even-handedness to this. Although, as you're right, we ask a foundational question, which, in a way, is a wonderful independent piece of analysis for us. So people who give the more positive answer—we sort, we say, here's what they've said. And those who have given the more negative answer—we say, here's what they've said. But again, there's often sort of really interesting interplay between the negative things that positive people feel and the positive things that negative people feel. So we try to summarize all of that, as well as just give voice to a lot of their really smart answers. Ross: So the moment—I want to get to your fascinating new report, Being Human in 2035, which is a very, I think, relevant topic today. But first I just want to go back, because I have been for the last 10 years referencing a report which the Pew Internet Research ran in 2014. It was called AI, Robotics and the Future of Jobs. And I kept on quoting it, because essentially, the question was—I think the defining question was—will there be more jobs or less jobs? And 48% said that there were going to be more jobs, and 52% said there’ll be fewer jobs. I think consolidating that—I mean, I’ve framed that as like: positive view of the future of jobs, negative view of the future of jobs. And in fact, the negative ones were sometimes extraordinarily negative—as in, there’ll be complete devastation of employment. And the positive ones—there were a few sort of saying, “Oh, I’ll be dancing around with the flowers.” More of them would just say, “On balance, it will be good.” Now it’s now 2025, and we can pretty clearly say that the ones who were on the positive side—the 52% saying we would have more jobs—were right. And this goes to a time frame issue, of course. Well, maybe all the ones who were extremely negative were right, except that they were 10 years different in horizons. So we could ask exactly the same question now, with the very same intent. So just love to hear your reflections back now to 2025, since you were on that survey in 2014. Lee: It’s almost a perfect example of what we were talking about before. It’s one of those beautiful kind of split verdicts that gave voice to both sides of the dynamics that might have occurred. And in that report, those who were positive—thought more jobs would be created than negative jobs—said, “Look at history.” There have been any number of enormous disruptions in labor forces and basic economies over time, the grandest of which was the Industrial Revolution before the Information Revolution occurred. And yes, there’s disruption. Yes, there’s pain. A lot of people get hurt in the process, and a lot of jobs—specific jobs—are lost in the process. But history teaches us you get a wealthier society out of it. The prices of commodities come down, especially the essential stuff that people use, which makes it more affordable, which means more of it can be made to make a profit. And so history just constantly reminds us of the adaptability of human beings and resilience, and that change eventually gets absorbed in interesting ways. The negative folks—the folks who said history isn’t the good teacher here—basically said a number of things. First of all, this is different. And I think, arguably, the rise of intelligence of any kind—particularly heading towards artificial general intelligence or even superintelligence—is different from just having information and media change direction or new forms coming into being. And the other thing that they pointed out, which is still sort of really interesting, although we can’t see the interplay yet as clearly as they were arguing it: there’s never been this much change, this fast, on so many fronts in human history. So you add the informatics revolutions—and AI being part of that—to the cognitive revolution (we know so much more about the brain, so much faster than we ever used to), the nanotechnology revolution, the genomics revolution. And so it’s certainly at the level of absorption and being able to manage things well—one of the very cautionary notes they were sounding is, we don’t know how to do this stuff this fast, and create the guardrails and the cautions and the fixes that are going to be necessary as these things play through society. So, for the moment, yes, more jobs than not. And what I would do differently now, if I were going to field the same survey, is to talk about job functions rather than jobs themselves. One of the most striking things that’s happened is that technology has been baked into jobs. And so the thing that used to be called a clerk is different now from what a clerk does now. The thing that is called a nurse now is radically different from what a nurse used to be. And so, if you think about jobs as bundles of skills that earn pay, the bundles of skills inside jobs that have the same name now as they used to have are considerably different in many interesting ways. Ross: So let's step forward to Being Human in 2035 report—so fascinating and deeply, deeply relevant, very much of the moment in the sort of Zeitgeist and discussion. And essentially looking to what—not about jobs—but what it is to be a human being in 10 years from now. And I suppose the very short summary was that predictions—there's going to be lots of change—and most, or only 50%, believe that there'll be both positive and negative change. So we would like to dig into some of the specifics, but just like to get your reflections on the top-level findings from the report. Lee: I'm so glad you're asking this, particularly in the context of that 2014 report about the state of jobs. One of the things that we captured in that survey and then got amplified in future AI-related things was the beginning of arguments about, well, how are humans going to survive this onslaught if it turns out not to be good? How are we going to save ourselves, basically? And I think Erik Brynjolfsson, the great labor economist and now technology integrator, was one of the contributors to this. He, among others, was starting to make the case then that yes, AI will come aboard, and it will show higher levels of intelligence than at least some forms of human intelligence. And so the way to prepare for that—and the way to make sure people have some meaning out of life and have some work for pay in this life—is to think about what, in the good old days, used to be called soft skills. So as coding and math and sort of basic levels of logic and things like that got better and better at that, and potentially surpassed human capacity, the special secret sauce of human beings is things like social and emotional intelligence, and critical thinking, and empathy, and fluid thinking—that sort of adjusting on the fly—and sort of leadership large. You know, it's hard to think that machines will ever lead humans in any particular way. So there are things to start stressing now and inculcating—and particularly in institutional connections: K to 12 education, but especially in higher education—that's the kind of soft skill stuff you should be teaching. And in a way, we've come full circle in the new survey we did, because we took that to the test with our experts. We sort of said these seem to be—we listed 12 things that are critical human traits and skills and not necessarily replicable by machines, at least at the moment. And how do these experts think now that humans—those 12 traits—will survive and be influenced by AI as it continues to improve in the next decades? Ross: And yeah, I want to dig into some of those—those 12 specific cognitive and social traits—in a moment. But again, it comes back to, of course, these are look at the balance. On balance, nine are negative, or more clearly they'll be negatively impacting. Positively impacting—there's three, interestingly, or very interesting, where they believe there'll be positive impact rather than negative. And there's some quite large disparities towards believing that more negative is more impact. But this all still, of course, depends on what it is we do—individually, institutionally, and as a society. So perhaps these can be warning signals where we can respond so that we mitigate some of the negatives and accentuate the positives. Lee: Absolutely. I mean, in a way, that was the spirit of this inquiry—was to sort of sound the warnings that experts had, or give voice to the warnings that experts have. And there's a pretty strong sense that this isn't a settled issue yet, that things aren't inevitable, and humans have enormous capacity for change and plasticity and adaptability. Maybe highlighting the things that they were highlighting would encourage institutions of higher learning and anybody who's thinking about this to care about it. So it was interesting to see that there were nine areas where people said that the outcome would be more negative than positive. Let me focus for a moment on the three things where they were more positive than negative, which were creativity, curiosity, and decision making. Ross: And it was a better cognition. Lee: Metacognition was on the borderline as a negative. But it was the one at the bottom of the negative list, closest—where the delta between the mostly negative and mostly positive folks was the least pronounced. And so I think there’s interesting things to say about that in general. And even if you add metacognition to the list of positives, what seemed to be the organizing pattern of those positives was a thing that we didn’t ask in the survey. We didn’t ask about leadership, which is on a lot of lists of special human traits that can save our species or make our species still sort of unique and valuable in the world. And we partly didn’t ask it because it was a hard thing to ask in the context of versus machines—it just didn’t feel like the right thing on our list. But if you look at those now four things—I’ll take your point that metacognition is a maybe outlier case—those, that’s the secret sauce of leadership. If you’re curious and you are creative, and if you have the capacity to make decisions, especially in environments where you don’t have complete data and you have to sort of weigh a variety of factors and things... And now metacognition—if you can think about your thinking: Where are my blind spots here? Who else do I have to consult to fill in gaps in knowledge that I have? Crowdsourcing a decision is probably a good thing to do, and that's a sort of hack for metacognition. Just thinking about how well you think and where things are is kind of represented there. So in a way, what I think these experts told us, without our specifically asking it, is that great human leadership—in this sort of new sense of it, where it's inclusive, it's diverse, it's deeply crowdsourced, you're drawing on every capacity of human, social, and emotional intelligence, as well as just informational accuracy—might be this way that we pull ourselves out of whatever the problems are on those other dimensions. Ross: That is a fantastic and fascinating distillation, which I didn't—I've got to say—I haven't read every word of the report. It's pretty long. I didn’t see that point made. And I think that’s really important. Lee: Well, it’s only dawned on me as I've—in talking to you—just sort of, what are the patterns here between the nine negatives and the three positives? And the three positives are sort of very oriented towards action. You're doing something, you're creating something, you're exploring something. And the negatives are more—not withdrawn, in a way—it’s sort of internal calculations about social and emotional intelligence, and about empathy, and about critical thinking. Those seem a little bit more abstract and a little bit more—not necessarily of the moment. And you don't have any pressure to make a decision. So those are the longer-term human traits that serve them incredibly well. If you're empathic and have great social intelligence, you're going to just do yourself and your community a world of good. But in a way, that's a little bit—you don't necessarily go into a decision thinking, what is the empathic response here that in the long term is going to do me good? I've got to make a decision here—creativity, curiosity are going to serve me really well in the moment as I’m doing that. So it’s external, it’s action-oriented in an interesting way. Ross: So, I mean, there is—carefully. So one of the things, which I think is fairly intuitive, is that one of the things which is more positive is the curiosity and the capacity to learn. And of course, these are extraordinary learning tools—the large language models. And the curiosity is that, well, you can ask anything you want. You can get a half-decent answer. But the single most negative response is—some of you, or there's a lot of debate about at the moment—is capacity and willingness to think deeply about complex concepts. And this is something which goes to something I often say, which is the greatest risk is overreliance, where we start to say, Oh, well, it can do all of our complex thinking for us. We don’t need to do that. And so it's just to get your reflections on particularly those most negative aspects that you highlighted. Lee: I think you're right in the center of gravity of the expert respondents who gave us their answers. That is the sort of overarching concern that they express when you ask about particular dimensions of human traits. They just think that some portion of humanity is going to give up or default to the machines because they seem so smart. And over the time, as I've studied technology, there are just always people—people who don't feel on top of it, and feel daunted by it, or feel like satisficing is a good enough answer. You know, I don’t necessarily have to take this to the bank and build my life around it, but that seems okay enough. And so there's this broad sense, across these 12 dimensions of special human traits, that we could become obsolete by our own will—at least a portion of humanity just sort of giving up. If you remember the movie—the Pixar movie Wall-E—you know, the civilization up there was fat and happy and didn’t care about things, because all problems were solved, and everything seemed to be humming along just in a nice way. And no matter what you asked—about social-emotional intelligence, empathy, trust in broad human norms and things like that—there's this very strong sense that you well articulate: about people giving up or people feeling that they aren’t up to the job of being the sort of co-intelligence that can work with artificial intelligence. Ross: So one of the really nice things about the report, as well as the highlights, not just the statistical balance in the reports, but also highlight these are the very different and interesting opinions which come out from a number of people and interested in just any—anything which you sort of really struck you in the thinking and the ideas presented. Lee: We listed—one of the fun things to do when you get all these expert answers back is to find little gems, little nuggets. And my rule of thumb in highlighting them is, did it make me think, or did it change my sense of what's possible here? Or was it just brand spanking new, and I'd never heard of stuff like that. So we gathered about two dozen of these nuggets, and to sort of pick any number of them that are interesting: One really fabulous futurist, Paul Saffo, who used to run the Institute for the Future, talked about the first multi-trillion dollar corporation that employs no human workers except legally required executives and a board. It has no offices. It owns no property—physical property. It's basically run entirely through AI. It's a bit fanciful. Who knows whether it'll be in the multi-trillion dollar level. But you hear now about companies that are basically saying, stop hiring people, start using AI. And so this is sort of, you know, a way in which the future could play out in dramatic form. Another one of these respondents talked about AI religions and AI affinity formulations that are sort of brand new in the human condition. And so there are ways in which—this respondent talked about deity avatars that get followings and look a lot like cults, and actually speak to the same thing you were just asking about, where the AI dominates the relationship and so deeply understands humans that it can ethically override them and make moral decisions for them. And humans are, you know, outsourcing that kind of stuff. The final one that we had—well, there's one more—that from Vint Cerf, the creator, godfather of the Internet itself, who wrote the Internet protocols with some colleagues. His prediction was that soon enough, it might be necessary for us to prove in interactions that we're human. There are going to be so many bots and so many agents representing human beings—incredibly looking like human beings—that there's going to have to be some scheme for us to prove that we're the living, breathing, wetware that we are, rather than the avatars that are going to be so ubiquitous. I mean, a lot of people said there are going to be more digital agents operating in the world than there will be human agents. And Vint was speaking to that possibility—that, yeah, we're just—proof of humanity is going to be one of the things that is going to be part of our interactions in the world. Ross: Yeah? Well, the thing is, a lot of us will have not just digital twins, but digital triplets and quadruplets. Which one of us is the original, as opposed to all of the copies of us that are manifest? So the thinking about this, I suppose looking—and I think this is 2025—is a time when asking this question of what it is to be human. I think the reality is, we are—what it is to be human will be different in 10 years from now, and even more beyond that. And there was not so much the issues of the synthetic biology and so uncovered in this report, but still simply the impact of AI and the impact on our cognition. That’s the heart of what is the cognition. And that's so extraordinarily appropriate to be interviewing you on the Amplifying Cognition podcast, because that's exactly what this is about—understanding the impact of technology, and where possible, making them as tools to be able to amplify our capabilities. I think that for each of those nine negatives, we could—if we choose to and took it the right way—we could use those to enhance our cognition or social skills. And I think there's many people that do find that they are able to, in fact, use tools which they perceive to be enhancing their social relationships, for example. Lee: Yeah, sort of my favorite edge example of that is the great mystery of consciousness itself. And you can imagine just innumerable ways that brilliant AIs, combined with brilliant, creative explorers of that territory—I mean, maybe we're going to solve that great mystery about what it is and where it comes from, and its meaning, especially for us as a species. But throughout the universe, what does that maybe look like? Then there are sort of lower-order, glorious things to be thinking about. I mean, one of the strong predictions we've gotten over the years about the future of AI is the scientific breakthroughs that are going to come from it. And even at the level of popular consciousness—just general population—there's such great expectations about medical breakthroughs, and just general provision of medical care. The Global South, among others, might be the biggest beneficiary, potentially, of all of this. But up and down the healthcare stack—at the diagnostic level, at the treatment level, at the understanding of population dynamics and things like that—it’s interesting that people will separate that. That we’re just taking care of our wellness, potentially in a magnificent way. And yet, the other thing that they worry about, almost in the same breath, is how we're going to find purpose in a world where we're not paid for our work, or where the meaning of life has to come from other than the traditional sources that a lot of people have built their lives around. I mean, Americans in particular—their identity is their job, and their purpose in life and meaning in life is their job. So if the bad outcome eventually comes, that lots of jobs get so changed and so overtaken by AI skills and intelligences—humans are smart and creative—then there will be a lot of humans who can figure out how to live their lives wonderfully, with a lot more time to spend on the things that matter and create the things that have meaning. But a lot of people are going to potentially fall into that category of being complacent and eventually deciding, Well, I'm obsolete. We have a very dramatic set of examples in that—in the deaths of despair in America—where manufacturing jobs have left particular regions of the country, and the suicide rates have risen substantially, the addiction rates have risen substantially, the measures of well-being more generally have declined. And so we're now having examples, particularly for older white men, of the longevity data going down for the first time in history after just this amazing story of the past 120, 130, 140 years. That now, all of a sudden, the slope of the curve has turned on us, and it's just—it’s a testament to: wow. Humans want to be valuable, want to be seen, want to be understood, want to be heard, want to think that their life matters. And this raises all sorts of questions about that. Ross: Yeah, these deep, deep issues. So what is the approximate cadence of your report? These are big undertakings, of course, so you can't get them out all the time. Lee: We do one of these a year now, because it is, you know, it's a special effort. Plus, we don't want to wear our experts out. We're asking them to think metaphysically and existentially a lot, and they give us a lot of their time and effort, but asking them to do it multiple times a year would be overload. So our cadence is one a year on these big issues. But then right now, our immediate plan is to ask the same questions about the same traits and what's going to happen with AI of the general population. We're going to do a real scientific survey of American adults, just to see, in its own terms—that's going to be interesting—how regular folks think about this. But there's always interesting comparative analysis to do about how the elite community—the expert community—sees the world in the future differently from the way regular people do. And they're just sort of first-order questions that are relatively simple to do research on about what's going on in this world. Who's using this stuff? What are they getting out of it? How do they feel about it? What parts of their life do they feel like they're becoming dependent on it? Where do they think it's serving them negatively, or things like that? So this is the gift that keeps on giving. And there are a lot of very fresh research areas now to apply this to. And we're not going to do them all, but we do a bunch. Ross: So where can people find the research reports from this Imagining the Digital Future Center? Lee: If they look at Imagining the Digital Future Center—if they had to add to it, they can add Elon University—but they can find it there. And it's been interesting to try to make our material—we're a web publisher like everybody else—and so in this new age, we want to get attention for our work, and we want citations of our work, and we want to grow the footprint of the reputation of the center. And it’s way harder than it used to be now that AI systems are becoming essentially the go-to search functions for a lot of people, and there are hallucinations in the citations. And so sometimes we're cited well and accurately, and sometimes we're not. So it's an interesting world to be living in—at the promulgation of our information as well as the creation of our information. Ross: Well, I'm delighted to be able to share—to whatever—to my audience the findings, because I think they're very important. It's great—always great reports—everything, which both Pew Internet Research and Elon University—that has been wonderful, and I always make a point of looking at it. So next time you do a major report, I'd love to get you back on. Lee: Thank you, Ross. It’s a wonderful kind thing to say. The post Lee Rainie on being human in 2035, expert predictions, the impact of AI on cognition and social skills, and insights from generalists (AC Ep85) appeared first on Humans + AI. | |||
| Kieran Gilmurray on agentic AI, software labor, restructuring roles, and AI native intelligence businesses (AC Ep84) | 09 Apr 2025 | ||
"Let technology do the bits that technology is really good at. Offload to it. Then over-index and over-amplify the human skills we should have developed over the last 10, 15, or 20 years." – Kieran Gilmurray About Kieran GilmurrayKieran Gilmurray is CEO of Kieran Gilmurray and Company and Chief AI Innovator of Technology Transformation Group. He works as a keynote speaker, fractional CTO and delivering transformation programs for global businesses. He is author of three books, most recently Agentic AI. He has been named as a top thought leader on generative AI, agentic AI, and many other domains. Website: X Profile: LinkedIn Profile: BOOK: Free chapters from Agentic AI by Kieran Gilmurray Chapter 1 The Rise of Self-Driving AI Chapter 2: The Third Wave of AI Chapter 3 - Agentic AI Mapping the Road to Autonomy Chapter 4- Effective AI Agents What you will learn
People Technical & Industry Terms Ross Dawson: Hey, it's fantastic to have you on the show. Kieran Gilmurray: Absolutely delighted, Ross. Brilliant to be here. And thank you so much for the invitation, by the way. Ross: So agentic AI is hot, hot, hot, and it's now sort of these new levels of how it is we — these are autonomous or semi-autonomous aspects of AI. So I want to really dig into — you've got a new book out on agentic AI, and particularly looking at the future of work. And particularly want to look at work, so amplifying cognition. So I want to start off just by thinking about, first of all, what is different about agentic AI from generative AI, which we've had for the last two or three years, in terms of our ability to think better, to perform our work better, to make better decisions? So what is distinctive about this layer of agentic AI? Kieran: I was going to say, Ross, comically, nothing if we don't actually use it. Because it's like all the technologies that have come over the last 10–15 years. We've had every technology we have ever needed to make more work, more efficient work, more creative work, more innovative, to get teams working together a lot more effectively. But let's be honest, technology's dirty little secret is that we as humans very often resist. So I'm hoping that we don't resist this technology like the others we have slowly resisted in the past, but they've all come around to make us work with them. But this one is subtly different. So when you say, look, agentic AI is another artificial intelligence system. The difference in this one — if you take some of the recent, what I describe as digital workforce or digital labor, go back eight years to look at robotic process automation — which was very much about helping people perform what was meant to be end-to-end tasks. So in other words, the robots took the bulky work, the horrible work, the repetitive work, the mundane work and so on — all vital stuff to do, but not where you really want to put your teams, not where you really want to spend your time. And usually, all of that mundaneness sucked creativity out of the room. You ended up doing it most of the day, got bored, and then never did the innovative, interesting stuff. Agentic is still digital labor sitting on top of large language models. And the difference here is, as described, is that this is meant to be able to act autonomously. In other words, you give it a goal and off it goes with minimal or no human intervention. You can design it as such, or both. And the systems are meant to be more proactive than reactive. They plan, they adapt, they operate in more dynamic environments. They don't really need human input. You give them a goal, they try and make some of the decisions. And the interesting bit is, there is — or should be — human in the loop in this. A little bit of intervention. But the piece here, unlike RPA — that was RPA 1, I should say, not the later versions because it's changed — is its ability to adapt and to reshape itself and to relearn with every interaction. Or if you take it at the most basic level — you look at a robot under the sea trying to navigate, to build pipelines. In the past, it would get stuck. A human intervention would need to happen. It would fix itself. Now it's starting to work itself out and determine what to do. If you take that into business, for example, you can now get a group of agentic agents, for example, to go out and do an analysis of your competitors. You can go out and get it to do deep research — another agentic agent to do deep research, McKinsey, BCG or something else. You can get another agent to bring that information back, distill it, assemble it, get an agent to create it, turn that into an article. Get another agent to proofread it. Get another agent to pop it up onto your social media channels and distribute it. And get another agent to basically SEO-optimize it, check and reply to any comments that anyone's making. You're sort of going, "Here, but that feels quite human." Well, that's the idea of this. Now we've got generative AI, which creates. The problem with generative AI is that it didn't do. In other words, after you created something, the next step was, well, what am I going to do with my creation? Agentic AI is that layer on top where you're now starting to go, "Okay, not only can I create — I can decide, I can do and act." And I can now make up for some of the fragility that exists in existing processes where RPA would have broken. Now I can sort of go from A to B to D to F to C, and if suddenly G appears, I'll work out what G is. If I can't work it out, I'll come and ask a person. Now I understand G, and I'll keep going forever and a day. Why is this exciting — or interesting, I should say? Well-used, this can now make up for all the fragility of past automation systems where they always got stuck, and we needed lots of people and lots of teams to build them. Whereas now we can let them get on with things. Where it's scary is that now we're talking about potential human-level cognition. So therefore, what are teams going to look like in the future? Will I need as many people? Will I be managing — as a leader — managing agentic agents plus people? Agentic agents can work 24/7. So am I, as a manager, now going to be expected to do that? Its impact on what type of skills — in terms of not just leadership, but digital and data and technical and everything else — there's a whole host of questions. There is as much as there is new technology here Ross. Ross Dawson: Yeah, yeah, absolutely. And so, I mean, those are some of the questions, though, I want to, want to ask you the best possible answers we have today. And in your book, you do emphasize this is about augmenting humans. It is around how it is we can work with the machines and how they can support us, and human creativity and oversight being at the center. But the way you've just laid out, there's a lot of what is human work, which is overlap from what you've described. So just at a first step, thinking about individuals, right? Professionals, knowledge workers — and so they have had, there's a few layers. You've had your tools, your Excels. You've had your assistants which can go and do tasks when you ask them. And now you have agents which can go through sequences and flows of work in knowledge processes. So what does that mean today for a knowledge worker who is starting to have, where the enterprise starts to bring them in? Or they say, "Well, this is going to support it." So what are the sorts of things which are manifest now for an individual professional in bringing these agentic workforce play? What are the examples? What are ways to see how this is changing work? Kieran Gilmurray: Yeah, well, let's dig into that a little bit, because there's a couple of layers to this. If you look at what AI potentially can do through generative AI, all of a sudden, the question becomes: why would I actually hire new trainees, new labor? On the basis that, if you look at any of the studies that have been produced recently, then there's two roles, two setups. So let me do one, which is: actually, we don't need junior labor, because junior labor takes a long time to learn something. Whereas now we've got generative AI and other technologies, and I can ask it any question that I want, and it's going to give me a pretty darned good answer. And therefore, rather than having three and four and five years to train someone to get them to a level of competency, why don't I not just put in agentic labor instead? It can do all that low-ish level work, and I don't need to spend five years learning. I immediately have an answer. Now that's still under threat because the technology isn't good enough yet. It's like the first scientific calculator version — they didn't quite work. Now we don't even think about it. So there is a risk that all of a sudden, agentic AI can get me an answer, or generative AI can get me an answer, that previously would have taken six or eight weeks. Let me give you an example. So I was talking to a professor from Chicago Business School the other day, and he went to one of his global clients. And normally the global client will ask about a strategy item. He would go away — him and a team of his juniors and equals would research this topic over six or twelve weeks. And then they would come back with a detailed answer, where the juniors would have went round, done all the grunt work, done all the searching and everything else, and the seniors would have distilled it off. He went — he's actually written a version of a GPT — and he's fed it past strategy documents, and he fed in the client details. Now he did this in a private GPT, so it was clean and clear, and in two and a half hours, he had an answer. It literally — his words, not mine — he went back to the client and said, "There you go. What do you think? By the way, I did that with generative AI and agentics." And they went, "No, you didn’t. That work’s too good. You must have had a team on this." And he said, "Literally not." And he's being genuine, because I know the guy — he'd put his reputation on it. So all of a sudden, now all of those roles that might have existed could be impacted. But where do we get then the next generation of labor to come through in five and six and ten years' time? So there's going to be a lot of decisions need made. As to: look, we've got Gen AI, we've potentially got agentic AI. We normally bring in juniors over a period of time, they gain knowledge, and as a result of gaining knowledge, they gain expertise. And as a result of gaining expertise, we get better answers, and they get more and more money. But now all of Gen AI is resulting in knowledge costing nothing. So where you and I would have went to university — let's say we did a finance degree — that would have lasted us 30 years. Career done. Tick. Now, actually, Gen AI can pretty much understand, or will understand, everything that we can learn on a finance degree, plus a politics degree, plus an economics degree, plus, plus, plus — all out of the box for $20 a month. And that's kind of scary. So when it comes to who we hire, that opens up the question now: do we have Gen AI and agentic labor, and do we actually need as many juniors? Now, someone's going to have to press the buttons for the next couple of years, and any foresighted firm is going to go, "This is great, but people plus technology actually makes a better answer." I just might not need as many. So now, when it comes to the actual hiring and decision-making — as to how am I going to construct my labor force inside of an organization — that's quite a tricky question, if and when this technology, Gen AI and agentics, really ramps through the roof. Ross Dawson: I mean, these are — I mean, I think these are fundamentally strategic choices to be made. As in, you — I mean, it's, crudely, it's automate or augment. And you could say, well, all right, first of all, just say, "Okay, well, how do we automate as many of the current roles which we have?" Or you can say, "Oh, I want to augment all of the current roles we have, junior through to senior." And there's a lot more subtleties around those strategic decisions. In reality, some organizations will be somewhere between those two extremes — and a lot in between. Kieran Gilmurray: 100%. And that's the question. Or potentially, at the moment, it's actually, "Why don't we augment currently?" Because the technology isn't good enough to replace. And it isn't — it still isn't. And no, I'm a fan of people, by the way — don't get me wrong. So anyone listening to this should hear that. I believe great people plus great technology equals an even greater result. The technology, the way it exists at the moment, is actually — and you look at some research out from Harvard, Ethan Mollick, HBR, Microsoft, you name it, it's all coming out at the moment — says, if you give people Gen AI technology, of which agentic AI is one component: "I'm more creative. More productive. And, oddly enough, I'm actually happier." It's breaking down silos. It's allowing me to produce more output — between 10 to 40% — but more quality output, and, and, and. So at the moment, it's an augmentation tool. But we're training, to a degree, our own replacements. Every time we click a thumbs up, a thumbs down. Every time we redirect the agentics or the Gen AI to teach it to do better things — or the machine learning, or whatever else it is — then technically, we're making it smarter. And every time we make it smarter, we have to decide, "Oh my goodness, what are we now going to do?" Because previously, we did all of that work. Now, that for me has never been a problem. Because for all of the technologies over the decades, everybody's panicked that technology is going to replace us. We've grown the number of jobs. We've changed jobs. Now, this one — will it be any different? Actually — and why I say potentially — is you and I never worried, and our audience never worried too much, when an EA was potentially automated. When the taxi driver was augmented and automated out of a job. When the factory worker was augmented out of a job. Now we've got a decision, particularly when it comes to so-called knowledge work. Because remember, that's the expensive bit inside of a business — the $200,000 salaries, the $1 million salaries. Now, as an organization, I'm looking at my cost base, going, "Well, I might actually bring in juniors and make them really efficient, because I can get a junior to be as productive as a two-year qualified person within six months, and I don't need to pay them that amount of money." And/or, actually, "Why don’t I get rid of my seniors over a period of time? Because I just don't need any." Ross Dawson: Things that some leaders will do. But, I mean, it comes back to the theme of amplifying cognition. The sense of — the real nub of the question is, yes, you can sort of say, "All right, well, now we are training the machine, and the machine gets better because it's interacting. We're giving it more work." But it's really finding the ways in which the nature of the way we interact also increases the skills of the humans. And so John Hagel talks about scalable learning. In fact, Peter Senge used to talk about organizational learning — and that's no different today. We have to be learning. And so, saying, "Well, as we engage with the AI — and as you rightly point out — we are teaching and helping the AI to learn," we need to be able to build the process and systems and structures and workflows where the humans in it are not static and stagnant as they use AI more, but they’re more competent and more capable. Kieran Gilmurray: Well, that's the thing we need to do, Ross. Otherwise, what we end up with is something called cognitive offload — where now, all of a sudden, I'll get lazy, I'll let AI make all of the decisions, and over time, I will forget and not be valuable. For me, this is a question of great potential with technology. But the real question comes down to: okay, how do we employ that technology? And to your point a second ago — what do we do as human beings to learn the skills that we need to learn to be highly employable? To create, be more innovative, more creative using technology? Ross Dawson: I answered the question you just asked. Kieran Gilmurray: 100%, and this is — this is literally the piece here, so— Ross: That's the question. So do you have any answers to that? Kieran: No, of course. Of course. Well, mine is — it's that. So, for me, AI will be — absolutely — and AI is massive. And let me explain that, because everybody thinks it's been around. If we look at generative AI for the last couple of years — but AI has been around for 80-plus years. It's what I call an 80-year-old overnight success story. Everybody's getting excited about it. Remember, the excitement is down to the fact that I can now interact with — or you interact with — technology in a very natural sense and get answers that I previously couldn't. So now, all of a sudden, we're experts in everything across the world. And if you use it on a daily basis, all of a sudden, our writing is better, our output's better, our social media is better. So the first bit is: just learn how to use and how to interact with the technology. Now, we mentioned a moment ago — but hold on a second here — what happens if everybody uses it all the time, the AI has been trained, there's a whole host of new skills? Well, what will I do? Well, this for me has always been the case. Technology has always come. There's a lot less saddlers than there are software engineers. There might be a lot less software engineers in the future. So therefore, what do we do? Well, my one is this. All of this has been the same, regardless of the technology: let technology do the bits that technology is really good at. Offload to it. You still need to understand or develop your digital, your AI, your automation, your data literacy skills — without a doubt. You might do a little bit of offloading, because now we don't actually think about scientific calculators. We get on with it. We don't go into Amazon and automatically work out all of our product sets, because it's got a recommendation engine. So therefore, let it keep doing all its stuff. Whereas, as humans, I want to develop greater curiosity. I want to develop what I would describe as greater cognitive flexibility. I want to use the technology — now that I've got this — how can I produce even better, greater outputs, outcomes, better quality work, more innovative work? And part of that is now going, "Okay, let the technology do all of its stuff. Free up tons of hours," because what used to take me weeks takes me days. Now I can do other stuff, like wider reading. I can partner with more organizations. I can attempt to do more things in the day — whereas in the past, I was just too busy trying to get the day job done. The other bits I would be saying: companies need to develop emotional intelligence in people. Because now, if I can get the technology to do the stuff, now I need to engage with tech. But more importantly, I'm now freed up to work across silos, to work across businesses, to bring in different partner organizations. And statistically, only 36% of us are actually emotionally intelligent. Now, AI is an answer for that as well — but emotional intelligence should be something I would be developing inside of an organization. A continuous innovation mindset. And I'd be teaching people how to communicate even better. Notice I'm letting the tech do all the stuff that tech should do regardless. Now I'm just over-indexing and over-amplifying the human skills that we should have developed over the last 10, 15, or 20 years. Ross Dawson: Yeah. And so, your point — this comes about people working together. And so I think that was one of the — certainly one of the interesting parts of your book is around team dynamics. So there's a sense of, yes, we have agentic systems. This starts to change the nature of workflows. Workflows involve multiple people. They involve AI agents as well. So as we are thinking about teams — as in multiple humans assisted by technology — what are the things which we need to put in place for effective team dynamics and teamwork? Kieran Gilmurray: Yeah, so — so look, what you will see potentially moving forward is that mixture of agentic labor working with human labor. And therefore, from a leadership perspective, we need people — we need to teach people — to lead in new ways. Like, how do I apply agentic labor and human labor? And what proportion? What bits do I get agentic labor to do? What bits do I get human labor to do? Again, we can't hand everything over to technology. When is it that I step in? Where do I apply humans in the loop? When you look at agentic labor, it's going to be able to do things 24/7, but as people, we physically and humanly can't. So, how — when am I going to work? What is the task that I'm going to perform? As a leadership or as a business — well, what are the KPIs that I'm going to measure myself on, and my team on? Because now, all of a sudden, my outputs potentially could be greater, or I'm asking people to do different roles than they've done in the past, because we can get agentic labor to do it. So there's a whole host of what I would describe as current management consideration. Because, let's be honest — like when we introduced ERP, CRM, factory automation, or something else — it just changed the nature of the tasks that we perform. So this is thinking through: where is the technology going to be used? Where should we not use it? Where should we put people? How am I going to manage it? How am I going to lead it? How am I going to measure it? These are just the latest questions that we need to answer inside of work. And again, from a skillset perspective — from both a leadership and getting my human labor team to do particular work, or how I onboard them — how do I develop them? What are the skills that I'm now looking for when I'm doing recruitment? What are the career paths that I'm going to put in place, now that we've got human plus agentic labor working together? Those are all conversations that managers, leaders, and team leaders need to have — and strategists need to have — inside of businesses. But it shouldn't worry businesses, because again, we've had this same conversation for the last five decades. It's just been different technology at different times, where we had to suddenly reinvent what we do, how we do it, how we measure it, and how we manage it. Ross Dawson: So what are specifics of how teams, team dynamics might work in using agentic AI in a particular industry or in a particular situation? Or any examples? So let's ground this. Kieran Gilmurray: Yeah, so let's — let me ground it in physical robots before I come into software robots, because this is what this is: software labor, not anything else. When you look at how factories have evolved over the years — so take Cadbury’s factory in the UK. At one stage, Cadbury's had thousands and thousands of workers, and everybody ended up engaging on a very human level — managing people, conversations every day, orchestration, organization. All of the division of labor stuff happened. Now, when you go into Cadbury's factory, it's hugely automated — like other factories around the world. So now we're having to teach people almost to mind the robots. Now we have far less people inside of our organizations. And hopefully — to God — this won't happen in what I’d describe as a knowledge worker park, but we're going to teach people how to build logical, organized, sequential things. Because to break something down into a process to build a machine — it's the same thing when it comes to software labor. How am I going to break it and deconstruct a process down into something else? So the mindset needed to actually put software labor into place varies compared to anything else that we've done. Humans were messy. Robots can’t be. They have to be very logical pieces. In the past, we were used to dealing with each other. Now I'm going to have to communicate with a robot. That's a very different conversation. It's non-human. It's silicon — not carbon. So how do I engage with a robot? Am I going to be very polite? And I see a lot of people saying, "Please, would you mind doing the following?" No — it’s a damn robot. Just tell it what to do. My mindset needs to change. So if I take, in the past, when I'm asking someone to do something, I might say, "Give me three things" or "Can you give me three ideas?" Now, I've got an exponential technology where my expectations and requests of agentic labor are going to vary. But I need to remember — I’m asking a human one thing and a bot another. Let me give you an example. I might say to you, "Ross, give me three examples of..." Well, that’s not the mindset we need to adopt when it comes to generative AI. I should be going, "Give me 15, 50, 5,000," because it's a limitless vat of knowledge that we're asking for. And then I need to practice and build human judgment — to say, "Actually, I'm not going to cognitively offload and let it think for me and just accept all the answers." But I'm now going to have to work with this technology and other people to develop that curiosity, develop that challenging mindset, to suddenly teach people how to do deeper research, to fact-check everything that I'm being told. To understand when I should use a particular piece of information that’s been given to me — and hope to God it's not biased, not hallucinated, or anything else — but it’s actually a valuable knowledge item that I should be putting into workflow or a project or a particular document or something else. So again, it's just working through: what is technology? What's the technology in front of me? What's it really good at? Where can I apply it? And understanding that — where should I put my people, and how should I manage both? What are the skills that I need to teach my people — and myself — to allow me to deal with all of this potentially fantastic, infinite amount of knowledge and activity that will hopefully autonomously deliver all the outcomes that I've ever wanted? But not unfettered. And not left to its own devices — ever. Otherwise, we have handed over human agency and team agency — and that's not something or somewhere we should ever go. The day we hand everything to the robots, we might as well just go to the care home and give up. Ross Dawson: We'll be doing that soon. So around now, let's think about leadership. So, I mean, you've alluded to that in quite a few — I mean, a lot of it has been really talking about some of the questions or the issues or the challenges that leaders at all levels need to engage with. But this changes, in a way, the nature of leadership. As you say, you've got digital labor as well as human labor. The organization has a different structure. It impacts the boundaries of organizations and the flows of information and processes — cross-organizational boundaries. So what is the shift for leaders? And in particular, what are the things that leaders can do to develop their capabilities for a somewhat different world? Kieran Gilmurray: Yeah, it's interesting. So I think there'll be a couple of different worlds here. Number one is, we will do what we've always done, which is: we'll put in a bit of agentic labor, and we'll put in a bit of generative AI, and we'll basically tweak how we actually operate. We'll just make ourselves marginally more efficient. Because anything else could involve the redesign and the restructure of the organization, which could involve the restructure and the redesign of our roles. And as humans, we are very often very change-resistant. Therefore, I don't mind technology that I understand, and I don't mind technology that makes me more productive, more creative. But I do mind technology that could actually disrupt how I lead, where I actually fit inside of the organization, and something else. So for those leaders, there's going to be a minimal amount of change — and there's nothing wrong with that. That's what I call the "taker philosophy," because you go: taker, maker, shaper — and I'll walk through those in a second — which is, I'll just take another great technology and I'll be more productive, more creative, more innovative. And I recommend every business does that at this moment in time. Who wouldn't want to be happier with technology doing greater things for you? So go — box number one. And therefore, the skills I'm going to have to learn — not a lot of difference. Just new skills around AI. In other words, understanding bias, hallucinations, understanding cognitive offloading, understanding where to apply the technology and not. And by "not," I mean: very often people put technology at something that has no economic value. Waste time, waste money, waste energy, get staff frustrated — something else. So those are just skills people have to learn. It could be any technology, I've said. The other method of doing this is almost what I describe as the COVID method. I need to explain that statement. When COVID came about, we all worked seamlessly. It didn’t matter. There were no boundaries inside of organizations. Our mission was to keep our customers happy. And therefore, it didn’t matter about the usual politics, the usual silos, or something else. We made things work, and we made things work fast. What I would love to see organizations doing — and very few do it — is redesign and re-disrupt how they actually work. And I'm sitting there going, it's not that I'm doing what I'm doing and I've now got a technology — "Where do I add it on?" — as in two plus one is equal to three. What I'm sitting going and saying is: How can I fundamentally reshape how I deliver value as an organization? And working back from the customer — who will pay a premium for this — and therefore, if I work back from the customer, how do I reconstruct my entire business in terms of leadership, in terms of people, in terms of agentic and human labor, in terms of open ecosystems and partnerships and everything else — to deliver in a way that excites and delights? If we take the difference between bookstore and Amazon — I never, or rarely, go into a bookstore anymore. I now buy Amazon almost every time, not even thinking about it. If I look at AI-native labor — they’re what I describe as Uber’s children. Their experiences of the world and how they consume are very different than what you and I have constructed. Therefore, how do I create what you might call AI-native intelligent businesses that deliver in a way that is frictionless and intelligent? And that means: intelligent processes, intelligent people, using intelligent technology, intelligent leadership — forgetting about silos and breakdowns and everything else that exists politically inside of organizations — but applying the best technology. Be it agentics, be it automation, be it digital, be it CRM, ERP — it doesn’t really matter what it is. Having worked back from the customer, design an organization to deliver on its promise to customers — to gain a competitive advantage. And those competitive advantages will be less and less. I can copy all the technology quicker. Therefore, my business strategy won’t be 10 years. It possibly won’t be five. It might be three — or even less. But my winning as a business will be my ability to construct great teams. And those great teams will be great people plus great technology — to allow me to deliver something digitally and intelligently to consumers who want to pay a premium for as long as that advantage lasts. And it might be six months. It might be twelve months. It might be eighteen months. So now we're getting to a phase of almost fast technology — just like we have fast fashion. But the one thing we don’t want to do is play loose and fast with our teams. Because ultimately, I still come back to the core of the argument — that great people who are emotionally intelligent, who’ve been trained to question everything that they’ve got, who are curious, who enjoy working as part of a team in a culture — and that piece needs to be taken care of as well. Because if you just throw robots at everything and leave very few people, then what culture are you actually trying to deliver for your staff and for your customers? How do I get all of this work to deliver in a way that is effective, is affordable, is operationally efficient, profitable — but with great people at the core, who want to continue being curious, creating new and better ways of delivering in a better organization? Not just in the short term — because we’re very short-termist — but how do I create a great organization that endures over the next five or ten years? By creating flexible labor and flexible mindsets, with flexible leaders organizing and orchestrating all this — to allow me to be a successful business. Change is happening too quickly these days. Change is going to get quicker. Therefore, how do I develop an adaptive mindset, adaptive labor force, and adaptive organization that's going to survive six months, twelve months — and maybe, hopefully to God, sixteen months plus? Ross Dawson: Fantastic. That's a great way to round out. So where can people find out more about your work? Kieran Gilmurray: Yeah, look, I'm on LinkedIn all the time — probably too much. I should get an agentic labor force to sort that out for me, but I'd much prefer authentic relationships than anything else. Find me on LinkedIn — Kieran Gilmurray. I think there are only two of me: one’s in Scotland, who is related some way back, and the Irish one. Or www.kierangilmurray.com is where I publish far too much stuff and give far too much stuff — things — away for free. But I have a philosophy that says all boats rise in a floating tide. So the more we share, the more we give away, the more we benefit each other. So that's going to continue for quite some time. I have a book out on agentic AI. Again, it's being given away for free. Ross, if you want to share it, please go for it, sir, as well. As I said, let's continue this conversation — but let's continue this conversation in a way that isn't about replacing people. It's about great leadership, great people, and great businesses that have people at their core, with technology serving us — not us serving the technology. Ross: Fabulous. Thanks so much, Kieran. Kieran: My pleasure. Thanks for the invite. The post Kieran Gilmurray on agentic AI, software labor, restructuring roles, and AI native intelligence businesses (AC Ep84) appeared first on Humans + AI. | |||
| Jennifer Haase on human-AI co-creativity, uncommon ideas, creative synergy, and humans outperforming (AC Ep83) | 02 Apr 2025 | ||
"We humans often tend to be very restricted—even when we are world champions in a game. And I'm very optimistic that AI will surprise us, with very different ways of solving complex problems—and we can make use of that." – Jennifer Haase About Jennifer HaaseDr. Jennifer Haase is a researcher at the Weizenbaum Institute, and lecturer at Humboldt University and University of the Arts Berlin. Her work focuses on the intersection of creativity, Artificial Intelligence, and automation, including AI for enhancing creative processes. She was named as one the 100 most important minds in Berlin science. Website:
LinkedIn Profile: What you will learn
Websites & Platforms Concepts & Technical Terms Ross Dawson: Jennifer, it's a delight to have you on the show. Jennifer Haase: Thanks for inviting me. Ross: So you are diving deep, deep, deep into AI and human co-creativity. So just to hear—just back a little bit—sort of how you've embarked on this journey. I mean, love to—we can fill in more about what you're doing now. But how did you come to be on this journey? Jennifer: I would say overall, it was me stumbling into tech more and more and more. So I started with creativity. My background is in psychology, and I learned about the concept of creativity in my Bachelor studies, and I got so confused, because what I was taught was nothing like what I thought creativity was—or how it felt to me. It took me years to understand that there are a bunch of different theories, and it was just one that we were taught. But that was the spark of the curiosity for me to try to understand this concept of creativity. And I did it for years. Then, by pure luck, I started a PhD in Business Informatics, which is somewhat technical. The lens of how I looked at creativity shifted from the psychological perspective more into the technical realm, and I looked at business processes and how they are advanced by general technology—basic software, basically. Then I morphed—also, by sheer luck—I morphed into computer science from a research perspective. And that coincided with ChatGPT coming around, and this huge LLM boom happened two, three years ago. And since then, I'm deeply in there. I just fell, fell in this rabbit hole. Ross: Yeah, well, it's one of the most marvelous things. So the very first use case for most people, when they first use ChatGPT, is: write a poem in the style of whatever, or essentially creative tasks. And pretty decently does those to start off—until you sort of started to see the limitations at the time. Jennifer: Yeah, and I think it did so much. It's so many different perspectives. I think we—as I said, I studied creativity for quite a while—but it was never as big of a deal, let's say. It was just one concept of many. But since AI came around, I think it really threatened, to some part, what we understood about creativity, because it was always thought of as this pinnacle of humanness—right next to ethics. And I think intelligence had its bumps two or three decades ago, but for creativity, it was rather new. So the debate started of what it really means to be creative. I think a lot of people also try to make it even bigger than it is. But I think it is as simple as—a lot about creativity is, for example, in terms of poets—poetry is language understanding, right? And so LLMs are really good at it. And it's just the case. It's fine. I think we can still live happy lives as humans, although technology takes a lot over. Ross: Yes. So humans are creative in all sorts of dimensions. AI has complementary—let's say, also different—capabilities in creativity. And in some of your research, you have pointed to different levels of how AI is supporting us in various guises—through being a tool and assistant, through to what you described as the co-creation. So what does that look like? What are some of the manifestations of human-AI co-creativity, which implies peers with different, complementary capabilities? Jennifer: Yeah, I think the easiest way to look at it is if you imagine working creatively with another person who is really competent—but the person is a technical version of it, and usually we call that AI, right? Or generative AI these days. So the idea is that you can work with a technical tool from an eye-to-eye level. Really, the tool would have a—well, now we're getting into the realm of using psychological terms, right—but the tool would have a decent enough understanding so it would appear competent in the field that you want to create. I think the biggest difference we see to most common tools that we have right now—which I would argue are not on this level yet—tools like ChatGPT and others, they follow your lead, right? If you type in something, they will answer, sometimes more or less creatively. But you can take that as inspiration for your own creativity and your own creative process. And that really holds big potential. It's great. But what we are envisioning—and seeing in some parts already happening in research—I think this is the direction we're going to and really want to achieve more: that we have tools that can also come up with ideas, or important input for the creative problem. Not—when I say on their own—I don't mean that they are, I don't know, entities that just do. But they contribute a significant, or really a significant part of the creative process. Ross: So, I mean, we'll come back a little bit to the distinctions between how AI creativity contrasts to human creativity. But just thinking about this co-creative process—from your research or other research that you're aware of—what are the success factors? What are the things which mean that that co-creation process is more likely to be fruitful than not? Jennifer: I think it starts really with competence. And I think this is something, in general, we see that generative AI just became extremely good at, right? They know, so to speak, a lot and tailor a lot of knowledge, and that is very, very helpful—because we need broad associations, coming from mostly different fields, and connect that to come up with something we consider new enough to call it creative. That is a benefit that is beyond human capabilities, right? What we see right now those tools are doing—that is one part. But that is not all. What you also need is the spark of: why would something need to be connected? And I think that is especially where raising the creative questions, coming up with the goal that you want to achieve something too, is still the human part. But—it doesn’t need to be. That’s all I’m saying. But still, it is. Ross: So, I mean, there are some—very crude workflows, as in, you get AI to ideate, then humans select from those, and then they add other ideas, or you get humans and then AI sort of combines, recombines. Are there any particular sequences or flows that seem to be more effective? Jennifer: It's interesting. I think this is also an interesting question for human creative work alone, even without technology—like, how do you achieve the good stuff, right? And I think what you just described, for me, would be kind of like a traditional way of: oh, I have a need, or I have a want—like, I want to create something, or I want to solve something, or I need a solution for a certain problem. And I describe that, and I iterate a best solution, right? This is part of what we call the divergent thinking process. And then, at a certain point, you choose a specific solution—so you converge. But I think where we have mostly the more interesting creative output—for humans and now also especially with AI—is that you kind of reverse the process. So let's assume you have a solution and you need to find issues for it. For example, you have an invention. I think—yeah, I think it was that there's this story told about the Post-its, you know, the yellow Post-its. So they were kind of invented because someone came up with glue that does not stick at all—like, really bad glue. And they had this as the final product. Now it's like, "Okay, where can you make use of it?" And then they came up with, "Oh, maybe, if you put it on paper, you can come up with these sticky notes that just glue enough." So they hold on surfaces, but they don't stick forever, so you can easily erase them. They're very practical in our brainstorming work, for example. And this kind of reverse thinking process—it's much more random. And for many people, it's much more difficult to open up to all the possibilities that can be. What I've seen is that if you try to poke LLMs with such very diverse, open questions, it can be very interesting what kind of comes out there. Ross: Though, to your point, I mean, this is the way—the human frames, the AI can respond. But the human needs to frame—as in, “Here is a solution. What are ways to be able to apply?” Jennifer: And all the examples—like, what I'm thinking of right now—is what is working with the tools that we have with LLMs. And I think what you were asking me before about the fourth level that we described with this co-creation—these are tools that work a bit differently. These are tools that, for now, mostly exist in research because you still need a high level of computational knowledge. So, the work that I did—the colleagues that I work with—are from computer science or mathematicians who program tools that know some rules of the game, or some—let's call them—boundary conditions of our creative problem that we are dealing with. And then the magic—or the black box magic—of AI is happening. And something comes out. And sometimes we don’t really understand what was going on there. We just see the results. And then, with such results, we can iterate. Or maybe something goes in the direction as we assume could be part of the solution. So it becomes this iterative process between an LLM or AI tool doing something, we’re seeing the results, saying yes or no, nudging it into different directions, and so, overall, coming up with a potentially proper solution. This is—at least in the examples that we see. And if you have such a process and look over it, like what was happening, often what we see is that LLMs or AI tools in general—with their, let’s call it, broad knowledge, or the very intense, broad computational capacities that they have—they do stuff differently than we as humans tend to do stuff. And this is where it becomes interesting, right? Because now we are not bounded in this common way of thinking and finding associations, or iterating smaller solutions. Now we have this interesting artificial entity that finds very different ways of solving complex problems—and we can make use of that. Of course, we can learn from that. Ross: Absolutely. And I think you've pointed to some examples in your papers. I mean—other, sort of, I suppose we've been quite conceptual—so examples that you can give of either what people have done, or projects you've been involved with, or just types of challenges? Jennifer: I think—to explain the mechanism that I'm talking about—I think the first creative, artificial example, like the real, considered properly creative example, was when AlphaGo, the program developed to play Go—the game similar to, or somewhat similar to, chess but not chess—when this tool was able to come up with moves, like play moves, which were very uncommon. Still within the realm of possibilities, but very, very uncommon to how humans used to play. And so, I think what this new was back in 2016, right? When this happened—when DeepMind, from Google, built this tool and kind of revolutionized AI research. What it showed us is exactly this mechanism of these tools. Although they are still within the realm of possibilities—still within what we consider the rules, right, of the game—it showed some moves which were totally uncommon and surprising. And I think this shows us that we humans often tend to be very restricted. Even when we are world champions in a game, we are still restricted to what we commonly do—what is considered a good rule of thumb for success. And I'm very optimistic that AI will surprise us, like in this direction—with this mechanism—quite a lot in the future. Ross: Yeah, and certainly, related to what you're describing, some similar algorithms have been applied to drug discovery and so on. Part of it is the number-crunching, machine learning piece, but part of it is also being able to find novel ways of folding proteins or other combinations which humans might not have envisaged. Jennifer: Yeah, exactly. And exactly—it's in part because these machines are just so much more advanced in how much, or how many, information they can hold and combine. This is, in part, purely computational. It's a bit unfair to compare that to our limited brains. But it's not just that. It's not just pure information, right? It's also how this information is worked upon, or the processes—how information is combined, etc. So I think there are different levels of how these machines can advance our thinking. Ross: So one of the themes you've written about is designing for synergies—how we can design so that we are able to be complementary, as opposed to just delegating or substituting with AI. So what are those design factors, or design patterns, or mentalities we need? Jennifer: Well, I will propose, first up—I think it's extremely complicated. Not complicated, but it will become a huge issue. Because, let's say, if technology becomes so good—and we see that right now already with LLMs like ChatGPT—it’s so easy for us. And I mean that in a very neutral way. But lazy humans as we are—I think we are inherently lazy—it’s really tough for us to keep motivated to think on our own, to some degree at least, and not have all the processes overtaken by AI. So, saying that, I think the most essential, most important part whenever we are working with LLMs is: we have to keep our motivation in the loop—and our thinking to some degree in the loop—within the process. And so, we need a design which engages us as humans. I think it's easily seen right now with LLMs. When you need the first step in—like typing some kind of prompt, or even in a conversation—you have to initiate it, right? You have to come up with, maybe even, your creative task at first. And I think this will always be true, because we humans control technology by developing it, right? But even when you're more on the user end—forcing us to be in the loop, and thinking it through, and controlling the output, etc.—is one part. But I think what it also needs, especially for the synergy, is for the technology to adapt to us—to serve us, so to speak. And I think this is an aspect that is a little bit underdeveloped right now. What do I mean by that? I want a tool that serves me in my thinking. It should be competent enough that I perceive it as a buddy—eye to eye. That is the vision that I have. But I still always want the control. And I want it to adapt to me, and that I don't have to adapt too much to the tool. Right now, we’re mostly just provided with tools that we need to learn how to deal with. We need to understand how prompting works, etc., etc. And I want that reversed. I want tools which are competent enough to understand, “Okay, this is Jenny. She is socialized in this way. She usually speaks German,”—whatever kind of information would be important to get me involved and understand me better. I think this is the vision for synergy that I'm thinking of. Ross: No, I really like that. The idea of designing for engagement, because instead of saying, yeah, why is it going to make us want to be engaged and continue the process and want to want to be involved, as opposed to doing the hard work of telling the—keep on telling the AI to do stuff. Jennifer: Yes, and also sometimes—I mean, I work a lot with ChatGPT and other similar tools—and sometimes I'm like, I found myself, I hope I don't spoil too much, but sometimes I find myself copy-pasting too much because there's nothing left for me to do. And to some degree, it can happen that the tools are too good, right? Because they are meant to create the output as the output, but they are not meant to be part of this iterative thinking process. I think you can design it much better and easier to go hand in hand with what I'm thinking and what I want to advance. Maybe. Ross: Yeah, yes, otherwise the onus is on the human to do it all. So in one of your papers, you identify—you used a number of the different models, and I believe you found that GPT-4 was the best for a variety of ideation tasks. But you've also done some more recent research. I'd love to hear about strengths, weaknesses, or different domains in which the different models are good, or— Jennifer: Yeah, that's quite interesting, right? Because—okay, so going back to the start of the big—let's call it the big boom of LLMs, right? I think it was early '23, right, when ChatGPT came around. End of '22. Okay, so it took a while when it reached Germany—it was for us. No, just joking. But okay, so around this time, what we found was intense debates arguing that, although these tools are generative, they cannot be creative. And that was the stance held tightest—maybe especially from creativity researchers and mostly psychologists, right? As I mentioned before, it's a little bit of this fear that too much is taken over by technology. I think that is a strong contributor—even among researchers. So what we went out to do is—we basically wanted to ask LLMs the same creativity measures as we would do for humans. Like, when you want to know if a person holds potential for creative thinking, you ask them creative questions, and they have to perform—if they want to. And that's exactly what we did with LLMs. Back in the day, we did it with the LLMs that were easily reachable and free in the market—like ChatGPT. And now, we really redid it with the current LLMs, with the current versions. And—I don't know if you've seen that—but most LLMs are advertised, when the new versions come out, usually they are advertised with: they are more competent, and they are more creative. And so we questioned that. Is that really true? Is ChatGPT 4.5, for example—the current version—is it more creative than 3.5 back in the day? And what we find is—it's so messy, actually. Because for some tools, yes, they are a bit more creative than they used to be two years ago. But the picture is really not clear. You cannot really tell or say or argue that the current versions we are having are more creative than two years ago—or even more creative than humans. It's been interesting. We're not really sure why. But all we can say is that, on average, these tools are as good at coming up with everyday-like uses or everyday-like ideas for everyday problems. They are, on average, as good as humans—random humans picked from surveys. And I think that is good news, right? Because LLMs are easier to ask than random humans most of the time. But the promise that they become more and more creative with every new release, in our perspective, does not hold up. So that is the bigger, bigger picture. Let's start there. Ross: So that’s very interesting. So this is using some of the classic psychological creativity tests. And so you're applying what has for a long time been used for assessing creativity in humans, and simply applying exactly the same test to LLMs? Jennifer: And to be fair, within the creativity research community, we agree that those tests are not good. Okay, they're really pragmatic. We totally agree on that, so we do not have to fight for this point. But it's commonly what we use to assess human potential for creative thinking—or even more concise, for divergent thinking—which is only one important, but just one aspect, of the whole creative journey, let's say. And it basically just asks how good you are, on the spot, at coming up with alternative uses for everyday products like a shoe or toothbrush or newspaper. And of course, you can come up with obvious uses. But then there are the creative ones, which are not so easy to think of, right? And LLMs are good at that. They will deliver a lot of ideas, and quite a few of those are considered original compared to human answers. We also now used another test, which is a little bit more arbitrary even, but it proved to be somewhat of a good predictor for creative performance overall. And that is: you are asked to come up with 10 words which are as different from each other as possible. So very pragmatic again. And these LLMs—as they, you know, know one thing, and that is language—are, again, quite good at that on average. But it's not that you see that they are above average, or that a specific LLM would be above average. We see some variety, but the picture, I would say, is not too clear. And also, to mention—which was a little bit surprising to us, actually—is that those LLMs, we asked them several times, like, a lot of times, and the variance in terms of originality—the variance is quite huge. So if you ask an LLM like ChatGPT for creative ideas, sometimes you can have quite a creative output, and sometimes it's just average. Ross: So you did say that you're comparing them to random humans. So does that mean that generally perceived-to-be-creative humans are significantly outperforming the LLMs on these tasks? Jennifer: Yeah, yeah. So, but the thing is, there is usually no creative human per se. So there's nothing about a human that makes a human per se creative. We tend to differ a little bit on how well we perform on such tasks. Yes, we do differ in our mental flexibility, let's say. But a creative individual is usually an individual which found a very good fit between their thinking, their experience, and the kind of creative task they're doing. And just think about it, because this creativity can be found in all sorts of domains, right? And people can be good or less good in those domains, and that correlates highly with the creativity. So when we ask about the general, like, the ideas for everyday tasks, there is not really the creative individual, right? They are motivated individuals, which makes a huge difference for creativity measures. If you're motivated and engaged, that is something we take as granted. For LLMs, I guess if you compare them, the motivation is there. But what we see in terms of the best answers—the most original answers in our data sets—most of the time, not all, but most of the time, come from humans. Ross: Very interesting. So, this is the Amplifying Cognition podcast, so I want to sort of round up by asking: all right, so what's the state of the nation or state of the world, and where we are moving in terms of being able to amplify and augment human cognition, human creativity? So I suppose that could be either just, improving human creativity, or collaborating, or, you know, this co-creativity. Jennifer: I think the potential for significant improvements and amplifications has never been better. But I think at the same time as I'm saying that, I think the risks have never been higher. And that is because, as I said, we are lazy people. That's just what humanist means—and that is fine—but it also means that we have a great risk of using these technologies not for us, but being used by them, basically, right? So we can use ChatGPT and other tools to do the task for us, or we can use them to do the task more efficiently and better with them. I think this difference can be very gradual, very minor, but it makes the whole difference between success and big dependencies—and potentially failure. Ross: Yeah, and I think you make a point—which I often also do—which is over-reliance is the biggest risk of all, potentially. Where, if we start to just sort of say, “This is good, I'll let the AI do the task, or the creativity, or whatever,” it's dangerous on so many levels. Jennifer: Because it does good enough most of the time, right? Technology became so good for many tasks—not all, but many tasks—that it does it good enough. And I think that is exactly where we have the potential to become so much better, right? Because if you now take the time and effort that we usually would put into the task itself, we could just improve on all levels. And that is the potential I'm talking about. I think a lot is to be advanced, and a lot is to be gained—if we play it right. Ross: And so, what's on your personal research agenda now? Jennifer: Oh, I fell into this agentic LLM hole. Yeah, no, no—it's not just looking at individual LLMs, but to chain them and combine them into bigger, more complex systems to have—or work on—bigger and complex issues, mostly creative problems, and see where the thinking of me and the tool, yeah, excels, basically, right? And where do I, as a human, have to step in to fine-tune specific bits and pieces and really find the limits of this technology if you scale it up? That's my agenda right now. Ross: I'm very much looking forward to reading the research as you publish it. Jennifer: Thank you. Ross: Is there anywhere people can go to find out more about your work? Jennifer: Yeah, I collect everything on jenniferhaase.com. That's my web page. It's hugely up to date there, and you can find talks and papers. Ross: Fabulous. Love the work you're doing. Jennifer, thanks so much for being on the show and sharing. Jennifer: Thank you very much. It was—yeah, I love to talk about that, so thanks for inviting me. The post Jennifer Haase on human-AI co-creativity, uncommon ideas, creative synergy, and humans outperforming (AC Ep83) appeared first on Humans + AI. | |||
| Pat Pataranutaporn on human flourishing with AI, augmenting reasoning, enhancing motivation, and benchmarking human-AI interaction (AC Ep82) | 26 Mar 2025 | ||
“We should not make technology so that we can be stupid. We should make technology so we can be even smarter… not just make the machine more intelligent, but enhance the overall intelligence—especially human intelligence.” –Pat Pataranutaporn About Pat PataranutapornPat Pataranutaporn is Co-Director of MIT Media Lab’s new Advancing Humans with AI (AHA) research program, alongside Pattie Maes. In addition to extensive academic publications, his research has been featured in Scientific American, MIT Tech Review, Washington Post, Wall Street Journal, and other leading publications. His work has been named in TIME’s “Best Inventions” lists and Fast Company’s “World Changing Ideas.” Websites:
LinkedIn Profile: What you will learn
People Organizations & Institutions Technical Terms & Concepts Ross Dawson: Pat, it is wonderful to have you on the show. Pat Pataranutaporn: Thank you so much. It's awesome to be here. Thanks for having me. Ross: There’s so much to dive into, but as a starting point: you focus on human flourishing with AI, exactly. So what does that mean? Paint the big picture of AI and how it can help us to flourish as who we are and our humanity. Pat: Yeah, that's a great question. So I'm a researcher at MIT Media Lab. I've been working on human-AI interaction before it was cool—before ChatGPT took off, right? So we have been asking this question for a long time: when we focus on artificial intelligence, what does it mean for people? What does it mean for humanity? I think today, a lot of conversation is about how we can make models better, how we can make technology smarter and smarter. But does that mean that we can be stupid? Does it mean that we can just let the machine be the smart one and let it take over? That is not the vision that we have at MIT. We believe that technology should make humans better. So I think the idea of human flourishing is an umbrella term that we use to describe different areas where we think AI could enhance the human experience. For me in particular, I focus on three areas: how AI can enhance human wisdom, enhancing wonder, and well-being. So: 3 W’s—wisdom, wonder, and well-being. We work on many projects to look into these areas. For example, how AI could allow a person to talk to their future self, so that they can think in the longer term, to see that future more vividly. That’s about enhancing wonder and wisdom. We think a lot about how AI can help people think more critically and analyze information that they encounter on a daily basis in a more comprehensive way. And you know well-being, we have many projects that look at how AI can improve human mental health, positive thinking, and things like that. But at the end, we also focus on AI that doesn’t lead to human flourishing, to balance it out. We study in what contexts human-AI interaction leads to negative outcomes—like people becoming lonelier or experiencing negative outcomes such as false memories, misinformation, and things like that. As scientists, we’re not overly optimistic or pessimistic. We're trying to understand what's going on and how we can design a better future for everyone. That's what we're trying to focus on. Yeah? Ros: Fabulous. And as you say, there are many, many different projects and domains of research which you're delving into. So I'd like to start to dive into some of those. One that you mentioned was the Future You project. So I'd love to hear about what that is, how you created it, and what the impact was on people being able to interact with their future selves. Pat: Totally. So, I mean, as I said, right, the idea of human flourishing is really exciting for us. And in order to flourish, like, you cannot think short term. You need to think long term and be able to sort of imagine: how would you get there, right? So as a kid, I was interested in sort of a time machine. Like, I loved dinosaurs. I wanted to go back into the past and also go into the future, see what would happen in the future, like the exciting future we might have. So I really love this idea of, like, having a time machine. And of course, we cannot do a real time machine yet, but we can make a simulation of a time machine that uses a person’s personal data and can extrapolate that, and use other data to kind of see, okay, if the person has this current behavior, things that they care about, what would happen down the road—like what would happen in the future. So we built an AI simulation that is a digital twin of a person. And we first ask people to kind of provide us with some basic information: their aspiration, things that they want to achieve in the future. And then we use the current behavior that they have to kind of create what we call a synthetic memory, or a memory that that person might have in the future, right? So normally, memory is something that you already experienced. But in this case, because we want to simulate the future self, we need to build memory that you did not experience yet but might actually experience in the future. So we use language model combined with the information that the person gives us to create this sort of intermediary representation of person experience, and then feed that into a model that then allows us to create human-like conversation. And then we also age the image of the person. So when the person uploads the image, we also use a visual model that can kind of create an older representation of that person. And then combine these together, we are creating an AI-simulated future self that people can have a conversation with. So we have been working with psychologists—Professor Hal Herschfeld from UCLA—who looks at the concept of future self-continuity, which is a psychological concept that measures how well a person can vividly imagine their future self. And he has shown that if you can increase this future self-continuity, people tend to have better mental health, better financial saving, better decision, because they can kind of think for the long term, right? So we did this experiment where we created this future self system and then tested it with people and compared it with a regular chatbot and having no intervention at all. And we have shown that this future self intervention can increase future self-continuity and also reduce people’s anxiety as well. So they become much more of a future thinker—not only think about today’s situation, but can see the possibility of the future and have better mental health overall. So I think this is really exciting for us, because we built a new type of system, but also really showed that it had a positive impact in the real world. Ross: What were the ranges of ages of people who were involved in this research? Pat: Yeah, so right now, the prototype that we developed is for younger population—people that just finished college or people that just finished high school, people that still need to think about what their future might look like, people that still would benefit from having ability to kind of think in the longer term. And right now, we actually have a public demo that everyone can use. So people can go to our website and then actually start to use it. You can also volunteer the data for research as well. So this is sort of in the wild, or in the real world study. That’s what we are doing right now. So if people like to volunteer the data, then we can also use the data to kind of do future research on this topic. But right now, the system has been used by people in over 190 countries, and we are really excited for this research to be in the real world and have people using it. Ross: Fabulous. We'll have the link in the show notes. So, one of the other interesting aspects raised across your research is the potential positive impact of AI on motivation. I think that's a really interesting point. Because, classically, if you think about the future of education, AI can have custom learning pathways and so on. But the role of the human teachers, of course, is to inspire and to motivate and to engage and so on. So I'd love to hear about how you're using AI to develop people's positive motivation. Pat: Yeah, that's a really great question. And I totally agree with you that the role of the teacher is to inspire and create this sort of positive reinforcement or positive encouragement for the student, right? We are not trying to replace that. Our research is trying to see what kind of tools the teacher can use to improve student motivation, right? And I think today, a lot of people have been asking, like, well, we have AI that can do so many things—why do we need to learn, right? And we believe at MIT that learning is not just for the benefit of getting a job or for the benefit that you will have a good life, but it's good for personal growth, and it's also a fun process, right? Learning something allows you to feel excited about your life—like, oh, you can now do this, even though AI can do that. I mean, a car can also go from one place to another place, but that doesn't mean we should stop walking, right? Or you can go to a restaurant and a professional chef can cook for you, but it's also a very fun thing to cook at home, right? With your loved ones or with your family, right? So I think learning is a really important process of being human, and AI could make that process even more interesting and even more personal, right? We really emphasize a lot on the idea of personalized learning, which means that learning can be tailored to each individual. People are very different, right? We learn in different ways. We care about different things. And learning is also about connecting the dots—things that we already know and new things that we haven't learned before. How do we connect that dot better? So we have built many AI systems that try to address these. The first project we looked at was what happens if we can create virtual characters that can work with teachers to help students learn new materials. They can be a guest lecturer, they could be a virtual tutor that students can interact with in addition to their real teacher, right? And we showed that by creating characters based on the people that students like and admire—like, at that time, I think people liked Elon Musk a lot (I don’t know about now; I think we would have a different story)—but at that time, Elon Musk was a hero to many people. So we showed that if you learn from virtual Elon Musk, people have a higher level of learning motivation, and they want to learn more advanced material compared to a generic AI. So personalization, in this case, really helped with enhancing personalized feeling and also learning motivation and positive learning experience. We have shown this across different educational measures. Another project we did was looking at examples, right? When you learn things, you want examples to help you understand the concept, right? Sometimes concepts can be very abstract, but when you have examples, that’s when you can start to connect it with the real world. Here we showed that if we use AI to create examples that resonate with the student’s interests—like if they love Harry Potter, or, I don’t know, like Kim Kardashian, or whatever—Minecraft or whatever things that people like these days, right? Well, I feel like an old person now, but yeah, things that people care about. If you create an example using elements that people care about, we can also make the lesson more accessible and exciting for people as well, right? So this is a way that AI could make learning more positive and more fun and engaging for students. Yeah. Ross: So one of the domains you've looked at is augmented reasoning. And so I think it's a particularly interesting point now. In the last six months or so, we've all talked about reasoning models with large language models—or perhaps “reasoning” in quotation marks. And there are also studies that have shown in various guises that people do seem to be reducing their cognitive engagement sometimes, whether they're overusing LLMs or using them in the wrong ways. So I'd love to hear about your research in how we can use AI to augment reasoning as well as critical thinking capabilities. Pat: That's a great question. I mean, that’s going back to what I said, right? Like, what does it mean for humans to have smart models around us? Does it mean we can be stupid? I think that’s a degradation of humans, right? We should not make technology so that we can be stupid. We should make technology so we can be even smarter, right? So I think the end goal of having a machine or models that can do reasoning for us, rather than enhance our reasoning capability—I think that's the wrong goal, right? And again, if you have the wrong outcome or the wrong measurement, you're gonna get the wrong thing. So first of all, you need to align the goal in the right direction. That's why, in my PhD research, I really want to focus on things that ultimately have positive impact on people. AI models continue to advance, but sometimes humans don't advance with the AI models, right? So in this case, reasoning is something that's very, very critical. You can trace it back to ancient Greek. Socrates talked a lot about the importance of questioning and asking the right question, and always using this critical thinking process—not trusting things at face value, right? We have been working on systems—again, the outcome of human-AI interaction can be influenced by both human behavior and AI behavior, right? So we can design AI systems that engage people in critical thinking rather than doing the critical thinking for them. That could be very dangerous, right? These systems right now don't really have real reasoning capability. They're doing simulated reasoning. And sometimes they get it right because, on the internet, people have already expressed reasoning and thinking processes. If you repeat that, you can get to the right answer. I mean, the internet is bigger than we imagined. I think that's what the language models show us—that there's always something on the internet that allows you to get to the right answer. You have powerful models that can learn those patterns, right? So these models are doing simulated reasoning, which means they don't have real understanding. Many people have shown that right now—that even though these systems perform very well on benchmarks, in the real world they still fail, especially with things that are very unique and very critical, right? So in that case, the model, instead of doing the reasoning for us, could make us have better reasoning by teaching us the critical thinking process. And there are many processes for that. Many schools of thought. We have looked at two processes. One of them is in a project called Variable Reasoner. We made a wearable device—like wearable smart glasses—with an AI agent that runs the process of verifying statements that people listen to and identify and flag when the statement people listen to has no evidence to support, right? This is really, really important—especially if you love political speeches, or you love watching advertisements or TikTok. Because right now, social media is filled with statements that sound so convincing but have no evidence whatsoever. So this type of system can help flag that. Because, as humans, we tend to go—or we tend to follow along—if things sound reasonable, sound correct, sound persuasive, we tend to go with them. But things that sound persuasive or sound correct doesn't mean it's correct, right? It can use all sorts of heuristics and other fallacies to get you to fall into that trap. So our system—the AI—can be the system that follows things along and helps us flag that for us. We have shown that when people wear these glasses, when the AI helps them think through the statements they listen to, people tend to agree more with statements that are well-reasoned and have evidence to support, right? So we can show that we can nudge people to pay more attention to the evidence part of the information they encounter. That's one project. Another project—we borrowed the technique from Socrates, the ancient Greek philosopher. We showed that if the AI doesn’t give the answer to people right away but rather asks a question back—it’s kind of counterintuitive, like, well, but people need to arrive at that information for themselves— We showed that when the AI asked questions, it improved people’s ability to discern true information from false information better than AI giving the correct answer. Which some people might ask: why is that the case? And I think it's because people already have the ability. Many of us already have the ability to discern information. We are just being distracted by other things. So when the AI asks a question, it can help us focus on things that matter—especially if the AI frames the information in a way that makes us think, right? For example, if there is a statement like: "Video games lead to people becoming more violent," and the evidence is "a gamer slapped another last week." For example— If the AI starts to frame that into: "If one person stabs another person, does that mean that every gamer will become violent after playing video games?" And then you start to realize that, oh, now there's an overgeneralization. You're using the example of one to overgeneralize to everyone, right? If the AI frames the statement into a question like this, some people will be able to come up with the answer and discern for themselves. And this not only allows them to reach the right and correct answer but also strengthens their process as well, right? It's kind of like AI creating or scaffolding our critical thinking so that our critical thinking muscle can be strengthened, right? So I think this is a really important area of research. And there are many more research coming out that show how we can design AI systems that enhance critical thinking rather than doing the critical thinking for us. Ross: So in a number of other domains, there's been research which has showed that whilst in some contexts AI can produce superior cognition or better thinking abilities, when the AI is withdrawn, they revert back. So one of the things is not only using AI in the enhancement process, but post-AI—to actually enhance the norms. When you don't have the AI, that you're still able to enhance your critical thinking. So has that been demonstrated, or is that something you would look at? Pat: Yeah, that's a really important question. We haven't looked at a study in that sort of domain—what happens when people stop using the AI, or what happens when the AIs are being removed from people—but that's something that is part of the research roadmap that we are doing. At MIT right now, there's a new research effort called AHA. We want to create aha moments, but AHA also stands for Advancing Humans with AI. And the emphasis is on advancing humans, right? AI is the part that's supposed to help humans advance. So the focus is on the humans. We have looked at different research areas. We've already been doing a lot of work in this, but we are creating this roadmap for what future AI researchers need to focus on—and this is part of it. This is the point that you just mentioned: the idea of looking at what happens when the AI is removed from the equation, or when people no longer have access to the technology. What happens to their cognitive process and their skills? That is a really important part that is part of our roadmap. And so, for the audience out there—this April 10 is when we are launching this AHA research program at MIT. We have a symposium that everyone can watch. It's going to be streamed online on the MIT Media Lab website. You can go to aha.media.mit.edu, and see this symposium. The theme of this symposium is: Can we design AI for human flourishing? And we have great speakers from OpenAI, Microsoft. We have great thinkers like Geraldine, Tristan Harris, Sherry Turkle, Arianna Huffington, and many amazing people who are joining us to really ask this question. And hopefully, we hope that this kind of conversation will inspire the larger AI researchers and people in the industry to ask the important question of AI for human flourishing—not just AI for AI's sake, or AI for technological advancement's sake. Ross: Yeah, I've just looked at the agenda and the speakers—this is mind-boggling. Looks like an extraordinary conference, and I'm very much looking forward to seeing the impact that that has. So one of the other things I'm very interested in is this intersection of agents—AI agents, multi-agents—and collective intelligence. And as I often say, and you very much manifested in your work, this is not about multi-agent as a stack of different AI agents around. It's saying, well, there are human agents, there are AI agents—so how can you pull these together to get a collective intelligence that manifests the best of both? A group of people and AI working together. So I'd love to hear about your directions and research in that space. Pat: Yeah, there is a lot of work that we are doing. And in fact, my PhD advisor, Professor Pattie Maes, is credited as one of the pioneers of software agents. And she is actually receiving the Lifetime Achievement Award in ACM SIGCHI, which is the special interest group in human-computer interaction—this is in a couple of months, actually. So it's awesome and amazing that she's being recognized as the pioneer of this field. But the question of agents, I think, is really interesting, because right now, the terminology is very broad. AI is a broad term. AGI is an even broader term. And “agent”—I don't know what the definition is, right? I mean, some people argue that it's a type of system that can take action on behalf of the user, so the user doesn't need to supervise. This means doing things autonomously. But there are different degrees of autonomy—like things that may require human approval, or things that can just do things on their own. And it can be in the physical world, or the digital world, or in between, right? So the definition of agent is pretty broad. But I think, again, going back to the question of what is the human experience of interacting with this agent—are we losing our agency or the sense of ownership? We have many projects that look into and investigate that. For example, in one project, we design new form factors or new interaction paradigms for interacting with agents. This is a project we worked on with KBTG, which is one of the largest banks in Asia, where we're trying to help people with financial decisions. If you ask a chatbot, you need to pass back and forth a lot of information—like you need a bank statement, or your savings, or all these accounts. A chatbot is not the right modality. You could have an AI agent that interacts with people in the task—like if you're planning your financial spending, or investment, or whatever. The AI could be another hand or another pointer on screen. You have your pointer, right? But the AI can be another pointer, and then you can talk to that pointer, and you can feel like there are two agents interacting with one another. And we showed that—even just changing, using the same exact model—but changing the way that information is flowing and visualized to the user, and the way the user can interact with the agent, rather than going from one screen, then going to the chatbot, typing something, and then going back… Now, the agent has access to what the user is doing in real time. And because it's another pointer, it can point and highlight things that are important at the moment to help steer the user toward things that are critical, or things they should pay attention to, right? We showed that this type of interaction reduces cognitive load and makes people actually enjoy the process even more. So I think the idea of an agent is not a system by itself. It's also the interaction between human and agent—and how can we design it so that it feels like a collaborative, positive collaboration, rather than a delegation that feels like people are losing some agency and autonomy, right? So I think this is a really, really important question that we need to investigate. Yeah? Ross: Well, the thing is, it is a trust—a relationship of trust, essentially. So you and it. So there's the nature of the interface between the human, who is essentially trusting an agent—an agent to act on their behalf—and they're able to do things well, that they're able to represent them well, that they check nothing’s missed. And so this requires a rich—essentially, in a way—emotional interface between the two. I think that's a key part of that when we move into multi-agent systems, where you have multiple agents, each with their defined roles or capabilities, interacting. This comes, of course—MIT also has a Center for Collective Intelligence. I mean, I'd love to sort of wonder what the intersections between your work and the Center for Collective Intelligence might be. Pat: Well, one thing that I think both of our research groups focus on is the idea of intelligence not as things that already happen in technologies, but things that happen collectively—at the societal level, or at the collective level. I think that should be the ultimate goal of whatever we do, right? You should not just make the machine more intelligent, but how do we enhance the overall intelligence? And I think the question also is: how do we diversify human intelligence as well, right? Because you can be intelligent in a narrow area, but in the real world, problems are very complex. You don't want everyone to think in the same way. I mean, there are studies showing that on the individual level, AI can make people’s essays better. But if you look across different essays written by people assisted by AI, they start to look the same—which means that there is an individual gain, but a collective loss, right? And I think that’s a big problem, right? Because now everyone is thinking in the same way. Well, maybe everyone is a little bit better, but if they're all the same, then we have no diverse solution to the bigger problems. So in one project that we looked into is how do we use AI that has the opposite value as a person—to help make people think more diversely. If you like something, the AI could like the other thing, and then make the idea something in between. Or, if you are so deep into one thing, the AI could represent the broader type of intelligence that gets you out of your depth, basically. Or, if you are very broad, maybe the AI will go in deep in one direction—so complementing your intelligence in a way. And we have shown that this type of AI system can really drive collaboration in a direction that is very diverse—very different from the user. But at the same time, if you have an AI that is similar to the person—like has the same value, same type of intelligence—it can make them go even deeper. In the sense that if you have a bias toward a certain topic, and the AI also has a bias in the same topic as you, it can make that go even further. So again, it’s really about the interaction—and what type of intelligence do we want our people to interact with? And what are the outcomes that we care about, whether it's individual or collective? I think these are design choices that need to be studied and evaluated empirically. Yeah. Ross: That's fantastic. I mean, I have a very deep belief in human uniqueness. I think we're all far more unique than almost anybody realizes. And society basically makes us look and makes us more the same. So AI is perhaps a far stronger force in sort of pulling us together—society already is that, yeah. But I mean, to that point of saying, well, I may have a unique way of thinking, or just unique perspectives—and so, I mean, you're talking about things where we can actually draw out and amplify and augment what it is that is most unique and individual about each of us. Pat: Right, totally. And I mean, I think the former CEO of Google, right, he has said at one point that, why would an individual—why would a person—want to talk to another person when you can talk to an AI that is 100,000 million people at the same time, right? But I feel like that’s a boring thing. Because the AI could take on any direction. It doesn't have an opinion of its own, right? But because a human is limited to our own life experience until that point, it gives us a unique perspective, right? When things are everything, everywhere, all at once, it's like generic and has no perspective of its own. I think each individual person—whether it's the things they’re living through, things that influence their life, things they grew up with—has that sort of story that made them unique. I think that's more— to me, that is more interesting, and I think it's what we should preserve, not try to make everything average out. So for me, this is the thing we should amplify. And again, I talk a lot about human-AI interaction, because I feel like the interaction is the key—not just the model capability, but how it interacts with people. What features, what modality it actually uses to communicate with people. And I think this question of interaction is so interdisciplinary. You need to learn a lot about human behavior, psychology, AI engineering, system design, and all of that, right? So I think that’s the most exciting field to be. Ross: Yeah, It's fantastic. So in the years to come, what do you find most exciting about what the Augmenting Humans with AI group could do? Pat: Well, I mean, many big ideas or aha moments that we want to create—definitely. We have actually an exciting project announcing tomorrow with one of the largest AI organizations or companies in the world. So please watch out for that. There’s new, exciting research in that direction, happening at scale. So there's a big project that's launching tomorrow, which is March 21. So if this is after that, yeah. I think one thing that we are working on is—we're collaborating with many organizations, trying to focus and make them not just think about AGI, but think about HGI: Human General Intelligence. You know, what would happen to human general intelligence? We want everyone to flourish—not machines to flourish. We want people to flourish, right? To kind of steer many of the organizations, many of the AI companies, into thinking this way. And in order to do that, we first need a new type of benchmark, right? We have a lot of benchmarks on AI capabilities, but we don't have any benchmarks on what happens to people after using the AI, right? So we need new benchmarks that can really show if the AI makes people depressed, empowers, or enhances these human qualities—these human experiences. We need to design new ways to measure that, especially when they’re using the AI. Second, we need to create an observatory that allows us to observe how people are evolving—or co-evolving—with AI around the world. Because AI affects different groups of people differently, right? We had a study showing that—this is kind of funny—but people talk about AI bias, that it’s biased toward certain genders, ethnicities, and so on. We did a study showing that, if you remove all the factors, just by the name of people, the AI will have a bias based on the name—or just the last name, right? If you have a famous last name, like Trump or Musk, the AI tends to favor those people more than people who have a generic or regular last name. And this is kind of crazy to me, because you can get rid of all the demographic information that we say causes bias, and just the name of a person already can lead to that bias. So we know that AI affects people differently. We need to design this type of observatory that we will deploy around the world to measure the impact of AI on people over time—and whether that leads to human flourishing or makes things worse. We don't have empirical evidence for that right now. People are in two camps: the optimistic camp, saying AI is going to bring prosperity, we don’t need to care, we don’t need to regulate. And another group saying AI is going to be the worst thing—existential crisis, human extinction. We need to regulate and kill and stop. But we don't have real scientific empirical evidence on humans at scale. So that's another thing that MIT’s Advancing Human-AI Interaction is going to do. We're going to try to establish this observatory so that we can inform people with scientific evidence. And finally, what I think is the most exciting thing: right now, we have so many papers published on AI—more than any human can read, maybe more than any AI can be trained on. Because every minute there's a new paper being published, right? And people are not knowing what is going on. Maybe they know a little bit about their area, or maybe some papers become very famous, but we want to design an Atlas of Human-AI Interaction—a new type of AI for science that allows us to piece together different research papers that come out so that we have a comprehensive view of what is being researched. What are we over-researching right now? We had a preliminary version of this Atlas, and we showed that people right now do a lot of research on trust and explanation—but less so on other aspects, like loneliness. For example, that AI chatbots might make people lonely—very little research has gone into that. So we have this engine that's always running. When new papers are being published, the knowledge is put into this knowledge tree. So we see what areas are growing, what areas are not growing, every day. And we see this evolve as the research field evolves. Then I think we will be able to have a better comprehension of when AI leads to human flourishing—or when it doesn't—and see what is being researched, what is being developed, in real time. So these are the three moonshot ideas that we care about right now at MIT Media Lab. Yeah. Ross Dawson: Fantastic. I love your work—both you and all of your colleagues. This is so important. I'm very grateful for what you're doing, and thanks so much for sharing your work on The Amplifying Cognition Show. Pat Pataranutaporn: Thank you so much. And I'm glad that you are doing this show to help people think more about this idea of amplifying human cognition. I think that's an important question and an important challenge for this century and the future century as well. So thank you for having me. Bye. The post Pat Pataranutaporn on human flourishing with AI, augmenting reasoning, enhancing motivation, and benchmarking human-AI interaction (AC Ep82) appeared first on Humans + AI. | |||
| Amplifying Foresight Compilation (AC Ep81) | 19 Mar 2025 | ||
"We wanted to see what the effect of AI might be on forecasting accuracy… to our surprise, we find that even when the model gives biased or noisy advice, human forecasters still improve—something we didn’t expect." – Philipp Schoenegger "I kind of call these Gen AI systems a mirror. Pose it a question, play with scenarios, and see what comes out. It’s like an accelerant for thinking—pushing the boundaries of what’s possible." – Nikolas Badminton "Future thinking is an everyday practice. It’s about becoming more aware of what’s happening around us, sensing signals, and collectively imagining what’s next." – Sylvia Gallusser "The question of the future isn’t ‘How creative are you?’ but ‘How are you creative?’ Because what we can imagine, we can create—and we have a responsibility to build a better future." – Jack Uldrich About Philipp Schoenegger, Nikolas Badminton, Sylvia Gallusser, & Jack UldrichPhilipp Schoenegger is a researcher at London School of Economics working at the intersection of judgement, decision-making, and applied artificial intelligence. He is also a professional forecaster, working as a forecasting consultant for the Swift Centre as well as a 'Pro Forecaster' for Metaculus, providing probabilistic forecasts and detailed rationales for a variety of major organizations. Nikolas Badminton is the Chief Futurist of the Futurist Think Tank. He is a world-renowned futurist speaker, award-winning author, and executive advisor, with clients including Disney, Google, J.P. Morgan, Microsoft, NASA, and many other leading companies. He is author of Facing Our Futures and host of the Exponential Minds podcast. Sylvia Gallusser is Founder and CEO of Silicon Humanism, a futures thinking and strategic foresight consultancy. Previous roles include a variety of strategic roles at Accenture, Head of Technology at Business France North America, General Manager at French Tech Hub, and Co-founder at big bang factory. She is also a frequent keynote speaker and author of speculative fiction. Jack Uldrich is a leading futurist, author, and speaker who helps organizations gain the critical foresight they need to create a successful future. His work is based on the principles of unlearning as a strategy to survive and thrive in an era of unparalleled change. He is the author of 9 books including Business As Unusual. Websites: University Profile:
LinkedIn Profile: What you will learn
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Ross Dawson: Now, it's wonderful to see the work which you're doing. Speaking of which, recently, you were the lead author of a paper, AI-Augmented Predictions: LLM Assistants Improve Human Forecasting Accuracy. So first of all, perhaps just describe the paper at a high level, and then we can dig into some of the specifics. Philipp Schoenegger: Yeah. So the basic idea of this paper is: how can we improve human forecasting? Human judgmental forecasting is basically the idea that you can query a bunch of very interested and sometimes laypeople about future events and then aggregate their predictions to arrive at surprisingly accurate estimations of future outcomes. This goes back to work on Superforecasting by Philip Tetlock, and there are a lot of different approaches on how one might go about improving human prediction capabilities. There might be some training—it was called The Ten Commandments of Forecasting—on how you can be a better forecaster. Or there might be some conversations where different forecasters talk to each other and exchange their views. And we want to look at how we can—how we could—think about improving human forecasting with AI. I think one of the main strengths of the current generation of large language models is the interactive nature of the back and forth, having a highly competent model that people can interact with and query whenever they want really. They might ask the model, "Please help me on this question. What's the answer?" They might also just say, "Here's what I think. Please critique it. And so this opens up for human forecasters a whole host of different interactions, and we wanted to see what the effect of this might be on forecasting accuracy. Ross: So that's fascinating. I suppose one of the starting points is thinking about these forecasters. So I suppose, just so people can be clear, human forecasting in complex domains is superior to AI forecasting because they don't have those capabilities. So now you're saying humans are better than AI alone, but now the results of the paper suggest that humans augmented by AI are superior to either humans alone or AI alone. Philipp: At the current ammount of papers that I have published, yes, but depending on when this airs, there might be another paper coming out that adds another twist to this. But yes, in early work, we find that just a simple GPT-4 forecaster underperforms a human crowd, and on top of that, it underperforms just seeing 50% of every question. But in this paper, we find that if we give people the opportunity to interact with a large language model, which in this case was GPT-4 Turbo, and we prompted it specifically to provide super forecasting. So our main treatment had a prompt that explained The Ten Commandments of Superforecasting and instructed the model to provide estimates that take care of the base rate. So you look at how often things like this have typically happened, quantify uncertainty, and identify branch points in reasoning. But then we also looked at what happens if the large language model doesn't give good advice. What if it gives what we call biased advice? It might be more noisy advice. So what if the model is told to not think about the base rate—not think about how often things like this happen—to be overconfident, to basically give very high or very low estimates, and be very confident? And to our surprise, we find that actually, these two approaches similarly effectively improve forecasting accuracy, which is not what we expected. Ross: So I think that this is a really interesting point because, essentially, this is about human cognition. It is human cognition taking very complex domains and coming up with a forecast of a probability of an event or a specific outcome in a defined timeframe. So in this case, the interaction with the AI is a way of enhancing human cognition—they are basically making better sense of the world. And I guess one of the things that is more distinctive about your approach is, as you say, you could allow them to use anything, any ways of interacting, as opposed to a specific dynamic. So in this case, it was all human-directed. There was no AI direction. It is AI as a tool, with humans, I suppose, seeking to augment their own ways of thinking about this challenge. Philipp: Yes, that's right. And, of course, being human, the vast majority—at least a sizable amount—of participants simply asked the model a question, right? They just said, "Well, what's the question? What would be the closing value for the Dow Jones at the end of December?" and they just copied it in and saw what the model did. But then many others did not, and they had their own view. They typed in, "Well, I think that's the answer. What do you think?" or "Please critique this." And I think these kinds of interactions are especially promising going forward because there's also this whole literature on the different impact of AI augmentation on differently skilled participants, differently skilled workers. In my understanding, the literature is currently mixed, with studies finding different results. We didn't find a specific effect here, but other work finds that when the model just gives the answer, low performers typically tend to do better because, you know, they take a lot from the answer, and the model is probably better than them. But if the model is instructed to give guidance only, low performers tend to not be able to pick up on the guidance and follow it. But I think there is still a lot of interesting work to be done before we can pin this down because there's so much diversity in which models are being used. Nikolas Badminton: I do a lot of research on, with every key now, I into a ton of clients. You know, on the client side, I go into the industry. I call people in the industry. I read a ton of academic research behind the industry—stuff on the edge academically, as well as sort of what's in the mainstream and what's being done. And also, you know, those sort of edge players. When I start to move forward and start to create some new thoughts, then I can sort of start to play around with scenarios. And this is what's become really interesting to me. I know that you talk a lot about the augmentation of capability through the use of things like generative AI and the such like. This has been something that I've been playing with quite a lot—not only from the generation of textual content but also the exploration from a visual perspective as a helping mechanism to take us in whole new directions as well. I mean, in my work, it's like signals to trends, to scenarios, and to stories. I've really been trying to push the boundaries of what scenario exploration is with platforms like ChatGPT, Claude, and Gemini, and starting to see what we can do to look at positive and dystopian scenarios, which was obviously part of the work that I was doing, a part in Facing Our Futures. Over the last couple of years, since that book was completed, zero Gen AI sort of help, as it was in my book. And actually, very little Gen AI help is going to be in my next book because, contractually, you're not allowed to do this. So what we have—what we can do—is start to explore the mirror. I kind of call these Gen AI systems a mirror. Pose it a question. Pose it some scenarios. Try to work out and see what comes out of it. And generally, what I find is maybe I'm talking about energy and ecological ecosystems, and I'll pose a question, "What if renewable energy is pushed to the side, green initiatives are canceled, and we go full tilt into a maximalist fossil fuel society?" In preparation for this chat, I went into that to delve even deeper into the mechanisms behind that. And it's sort of interesting—you get this mirror of like, "Oh yeah, I kind of expect that, you know, the answers to come from that." Okay, let's push that out to 2050. Yeah, it's kind of an accelerant and whatever. It's kind of interesting when you start to think about the reference points of all these systems and where they're getting it from. Where something like Claude and ChatGPT actually feels like they've been drinking from the same fountain, and Gemini just seems to be a little bit freaky. So it's super interesting. As I went into it, it was like poetic and dystopic. For example, I asked this: "Describe a world in 2100 where environmentally friendly, non-carbon fuel solutions are discarded." And I went on and on in a prompt, very directional. The others would be like, "Here's a list of things that happen"—very cold. I didn't ask it to write in a particular style of a publication or anything like that. And then Gemini just came out with this. And this is fabulous: "The year is 2100. The gamble on renewables failed spectacularly. Big Oil, whispering sweet nothings of energy independence and economic growth, won the hearts and minds of a desperate world. The result? A planet drowning in its own fumes." And I kind of love that poetic nature. Gemini, I think, is sort of the unsung hero a little bit, right? In the scheme of things, suddenly, we're getting something interesting that starts to talk about the geopolitical chessboard, tech on steroids, violence, and exodus. And it's like—whoa. Ross: A lot of it, I think, is about sensitizing ourselves to signals so that we are more likely to notice the things that are relevant or important or point to things that might change in the future. And that's what futurists do. But how can we, I suppose, convey this as a capability or skill that others can learn and develop—that’ll been able to see and sense signals that, you know, point to change? Sylvia Gallusser: It's a very interesting thing with signals. It's like raw material. It's something that anybody can apprehend, and that's what makes future thinking something that really anybody can work with and develop as a personal skill. Because it's about becoming more aware of what is going on around us. And that's why I think it works really in tandem, in deal with the first step, which is about knowing always more, understanding always more about what is the long-term landscaping, and then being more aware of the variation. And this can go from analyzing behaviors of people around you—like, what changed during the pandemic? Were people more polite, more civilized? Did we see new behaviors, new words? Maybe also studying popular culture is a very interesting aspect because if you see what is going on in the media—TV series, movies, books—you also sense a lot of what people are attracted to. What new changes are starting when there's this kind of enthusiasm for a new book; sometimes, that means something. So how can you get more aware of this? It's really an everyday practice, and I like to say two things: it's a personal practice, and it's a collective practice. That's something you can really train yourself to do all the time—just reading the news, being aware of what is around you, just having your sensors open to the world around. And once again, it's all senses. It's about listening. It's about observing people around you. It's a different taste in the air. It's really multi-sensitive here. Why I say it's also collective is that, you know, the futurist community is very active. It's not that big; it's small. But it's very interconnected. And there are a lot of platforms to be able to exchange around signals. They call it sometimes signal swarming or signal scanning—you have different names for it—but the idea is that futurists love to exchange around that topic, to meet and say, "Hey, this week, what did you notice?" And once again, this STEEPLE aspect is interesting because when you're on your own, coming maybe from one industry or one profession, maybe you're a kind of a bias around one or the other. Like, I'm coming from technology, so at first, I would really focus on everything around new technology and so on. But I guess someone who's a psychologist might have a different opinion. An economist might see things differently. So coming together as a collective, as a community, is really interesting into enhancing and amplifying the way you connect with those signals around you. And finally, I would say, on top of it being collective, what's interesting when you want to bring a group, a population, a company, or a corporation to work around future thinking is to build the capability to do this. It's very simple. It can start with just an Excel file. It doesn't need something very fancy. But just bring people to come to see what signals are and get them to understand the texture of it—how does it look like? How does it sound like? And they start to log on their own signals. And then you already have a big bases of signals of change in a corporation. A great first way to enter the field of foresight. Ross: So one of the other things you were talking about was putting yourself in the scenario. And I suppose part of the practice is to create a useful scenario that thus helps you think about new things or envisage things that help shape your current actions. But as individuals, what are ways in which we can, I suppose, conceive of and bring ourselves—or enter into—I think you used the word meditation there. And, you know, I’d love to hear about that. What is that practice? How do we put ourselves, immerse ourselves in these useful future scenarios? Sylvia: Absolutely. Once again, you know, it can be very personal and intimate, or it can be something more collective. So I try to address both aspects because I think they can work really well together. You can develop your own future-thinking practice as an everyday discipline, let's say. I wrote a few years ago, an article about mental stretching exercises you can practice to work on that. It can go from dealing with different perspectives, trying to develop empathy, putting yourself in the shoes of someone else, and imagining a story. You know what? Actually, learning new languages and learning new cultures is also a great way to practice this perspective change and teasing things in different ways. Reading, listening, and learning about fiction, for me, has been an immense way to stretch myself to see futures that are possible and not necessarily dystopian. That's why I love to talk about science fiction, because we tend to think, to see science fiction as something very dystopian and very scary and not necessarily the good way to start for people who are scared about the future. But I would say there are more and more interesting science fiction now that create a future world that is not necessarily negative. They can be really engaging and develop a plot which has a narration where the problems are, but it doesn't mean that the negative aspect is the world-building. Like the story, to be interesting, needs to always have something of a dilemma or something of a complexity or a knot to it. But it can be interpersonal stories, not necessarily in the world-building around it. So I think science fiction and future fiction really offer us ways to think about the future. So, for example, the way we do it collectively with groups, and I was talking about those meditative exercises. A really great way we've been doing it in the past was around the future of the home. Because during the pandemic, the home evolved dramatically, and not just the structure but also the way we reorganized life within it. And I like to talk about the structures and the intangibles that happen in the home. So what we would do, for example, in terms of envisioning meditations with a few groups, was really you waking up in the future home you live in—maybe 10 years from now, 20 years from now. How do you wake up? What is the first trigger? What happens? Is it a wake-up call? Is it natural lighting? Do you still live in a bedroom? Like, we really start just—what do you smell? What do you think? What do you feel? How does it sound? So five senses meditation is really effective. Changing perspective, as I was saying, and so on. So these are different tools we would use to bring people to get into that state of the future and then go throughout a day in the life. Like, okay, what do you do from your bed? Then do you go to breakfast? Do you go to your bathroom? How does the bathroom look? Is it interactive? Do you live alone? Do you live with other people in a community? And just—it starts asking so many questions that people naturally get their minds to wander around the future home. And that was a really great tool to get a sense of that new type of space that could exist. And, oh, they would like that home to be. Because, once again, it is also about developing what would be our preferable future, our favorite futures, and building them. Jack Uldrich: And I've spent a lot of time as a futurist with the concept of unlearning. It's that people in organizations—it’s not that they can't understand the future is going to change. What we have a really difficult time doing is letting go of the way we've always done things. And so I think when we're talking about the future of work, to me, work does give most humans this intrinsic value, and they feel as though they're an integral part of a community. And so I think there will always be this innate need to be doing something—not just for yourself but on behalf of something bigger. And when I say bigger, typically I'm thinking of community. You just want to do something for, of course, yourself, your immediate family, but then your neighborhood and your community. And so as I think about the long-term future, one of the things I'm really excited about is—first, I'm going to go dark, but I think there's going to be a bright side to this. One of the things that I think is happening right now that's not getting enough attention, as a futurist, is that the internet is breaking. In the sense that there's so much misinformation and disinformation out there that we can no longer trust our eyes and our ears in this world of artificial intelligence. And I think that's going to become increasingly murkier, and it's going to be really destabilizing to a lot of people and organizations. So what's the one thing we still can trust? What's small groups that are right in front of us? And so I think one of the things we're going to see in a future of AI is an increased importance on small communities. There's some really compelling science that says the most cohesive units are about 150 people in size. And this is true in the military, educational units, and other things like that. And I think that we might start seeing that, but it's going to look different than in the past. Like, I'm not suggesting that we're all going to look like Amish communities here in the U.S., where we're saying no to technology and doing things the old-fashioned way. But the new communities of the future are—and now I'm just thinking out loud—something I want to spend more time thinking about. Like, what will that look like? What will the roles and the skills be needed in this new future? And again, I don't have any answers right now, just more questions and thinking. But it's one of these scenarios I could see playing out that might catch a lot of people by surprise. Ross: Yeah, very much so. I mean, we are a community-based species, and the nature of community has changed from what it was. And I think, you know, thinking about the future of humanity, I think a future of community and how that evolves is actually a very useful frame to round out. Jack, what advice can you share with our listeners on how to think about the future? I suppose you did a little at the beginning. But, I mean, do you have any concluding thoughts on how people can usefully think about the extraordinary change in the world today? Jack: Yeah, the first thing I would say is this—and I was just doing a short video on this. Ever since we've been in grade school, most of us have been asked the question or graded on the question of How creative are you? And if you ask most people, like on a scale of one to ten, to just answer that question, they'll do it. But you know what I always tell people? That's a bad question. The question of the future isn't How creative are you? It is How are you creative? Each and every one of us is creative in our own way. And as a futurist, I take that really seriously. We do have the ability to create our own future, but we first have to understand that we are creative, and most people don't think of themselves that way. So how do you nurture creativity? And this is where I'm trying to spend a lot of my time as a futurist. This is where the ideas of unlearning and humility come in. But I would say it starts with curiosity and questions, and that's why I like getting out under the night stars and just being reminded of how little I actually know. But then, it's in that space of curiosity that imagination begins to flow. And there's this wonderful quote from Einstein—most people would say he was one of the more brilliant minds of the 20th century. He said, Imagination is more important than knowledge. Like, why did Einstein, this great scientist, say that? And I think—and I don’t have proof of this—that everything around us today was first imagined into existence. It was imagined into existence by the human mind. The very first tool. The very first farm implement. And then farming as an industry, and then civilizations and cities and commerce and democracy and communism. They were all imagined first into existence. And so, what we can imagine, we can, in fact, create. And that's why I'm still optimistic as a futurist—this idea that we're not passive agents, that we can create a future. And I just like to remind people that our future can, in fact, be incredibly fucking bright. The idea that we can have cleaner water and sustainable energy and affordable housing and better education and preventive health care. We can address inequality. We can address these issues. People just have to be reminded of this. And so, at the end of the day, that's why I get fired up, and I don’t think I’ll ever sort of lose the title of futurist, because until my last breath, I’m going to be, hopefully, reminding people that we can create—and we have a responsibility to create—a better future. Let me just end on this. I think the best question we can ask ourselves right now comes from Jonas Salk, the inventor of the polio vaccine. And he said, Are we being good ancestors? And I think the answer right now is, we're not. But we still have the ability to be better ancestors. And maybe if I could just say one last thing—I also spend a lot of time helping people just embrace ambiguity and paradox. And here’s the truth: the world is getting worse. In terms of climate change, the rise of authoritarianism, inequality—you could say things are going bad. But at the same time, on the other hand, you could say the world is getting demonstrably better. It has never been a better time to be alive as a human. The likelihood that you’re going to die of starvation or war or not be able to read—never been lower. So the world is also getting better. But the operative question becomes: How can we make the world even better? And that’s where we have to spend our time. And that’s why we need creativity, curiosity, and imagination—to create that better future. The post Amplifying Foresight Compilation (AC Ep81) appeared first on Humans + AI. | |||
| AI for Strategy Compilation (AC Ep80) | 12 Mar 2025 | 00:32:02 | |
"AI can make the process of sensing for signals much faster and much more efficient. You can think of it as a supplement to our brain. It can sort through massive amounts of data, track the latest developments, and flash alerts when something important emerges." – Rita McGrath "What I found surprising in our exercises was how disruptive AI was. At first, I thought they would hate it, but they actually liked it. It made them stop and think because it forced them to break out of their usual patterns and consider ideas they wouldn’t have consciously introduced into the discussion." – Christian Stadler "AI can accelerate the foresight process. It can help generate diverse perspectives, identify second-degree impacts, and uncover biases we might not notice. Of course, human critical thinking is still essential—we shouldn’t accept AI outputs as absolute truth, but rather use them as a starting point." – Valentina Contini "One key area where AI excels is handling cognitive complexity. Humans struggle to hold thousands of variables in their heads, but AI can process vast amounts of interconnected data. The challenge is designing interfaces that allow humans to interact with this complexity in an intuitive way." – Anthea Roberts About Rita McGrath, Christian Stadler, Valentina Contini, & Anthea RobertsRita McGrath is one of the world’s top experts on strategy and innovation. She is consistently ranked among the top 10 management thinkers globally and has earned the #1 award for strategy by Thinkers 50. She is Professor of Strategy at Columbia Business School, and Founder of the Rita McGrath Group and Valize LLC. Her books include The End of Competitive Advantage and Seeing Around Corners. Christian Stadler is a professor of strategic management at Warwick Business School. He is author of Open Strategy, which was named as a Best Business Book by Financial Times and Strategy + Business and has been translated into 11 languages. His work has been featured in Harvard Business Review, New York Times, Wall Street Journal, CNN, BBC, and Al Jazeera, among others. Valentina Contini is an innovation strategist for a global IT services firm, a technofuturist, and speaker. She has a background in engineering, innovation design, AI-powered foresight, and biohacking. Her previous work includes founding the Innovation Lab at Porsche. Anthea Roberts is Professor at the School of Regulation and Global Governance at the Australian National University (ANU) and a Visiting Professor at Harvard Law School. She is also the Founder, Director and CEO of Dragonfly Thinking. Her latest book, Six Faces of Globalization, was selected as one of the Best Books of 2021 by The Financial Times and Fortune Magazine. She has won numerous presitigious awards and has been named “the world's leading international law scholar” by the League of Scholars. Websites:
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Ross Dawson: One of the key themes is strategy. How do we do strategy in a world that is accelerating, with all these overlay themes? There are, as you say, 10x shifts in many dimensions of work. This brings us to human capabilities. Humans have limited, finite cognition, even though we have extraordinary capabilities far transcending anything else. Now, we have AI to augment, support, or complement us. I’d like to dive in deep, but just to start—what is your framing around human capabilities in strategic thinking today, and how they are complemented by AI? Rita McGrath: Sure. Well, as I mentioned, human brains think in linear terms. We think immediately in terms of getting from here to there to avoid a predator. Back in the day when we were evolving, that worked pretty well. But we don’t do very well with exponential systems because they look small, and they look small, and they go small—until suddenly they don’t. It’s the whole “gradually, then suddenly” idea. What I argue is that you need to supplement what your brain can manage on its own. This is where I think AI comes in. What I’ve set up with companies is a series of what I call “time zero events,” which signal that a future inflection point has arrived. We don’t know exactly when, but we work backward and ask, “Before that happens, what would have to be the preceding situations?” AI can make that process of sensing for signals much faster and much more efficient. You can think of it as a supplement to our brain. It can sort through massive amounts of data, track the latest developments, and flash alerts when something important emerges. This allows us to blend human imagination—something AI is not very good at—with AI’s ability to crunch massive amounts of data. That’s where I think AI will have a lot of power in strategy. Ross: One of the core themes of my work, and I think yours as well, is sense-making. We have vast amounts of information out there. As strategists, we need to take in that information, make sense of it, and make effective decisions as a result. How can AI support our ability to comprehend how the world is working so that we can make better decisions? Rita: AI is really good at taking large amounts of information and breaking it into digestible chunks. Humanity has limits to how much information it can process. There’s actually a whole line of theory on this, which states that search, in the traditional sense, is not costless. Theoretically, a rational human being would entertain every possible combination of possibilities, create decision criteria, and then select the best option. But humans have cognitive limits, whereas machines have far fewer. Properly instructed, AI can present us with different pictures of the world. Another thing humans aren’t very good at is generating variety. Think of those old creativity exercises where someone asks you to come up with as many uses as possible for a paperclip. People start with obvious answers: “It can hold papers together,” “It can mark your place in a book,” “It can unlock things.” But after 50 or 60 uses, they run out of steam. Many ideas are anchored on the first few. Machines, on the other hand, don’t have those biases. They might generate 300 possible uses—sure, 200 of them might be terrible ideas, but they would be more divergent than what humans come up with. That’s where AI helps in sense-making. It shows us possibilities we wouldn’t have seen otherwise. Ross: Now, let’s dig into how AI can be used in the boardroom. One way that resonates with board directors is “red teaming,” where you have a decision and ask AI to generate counterarguments. AI can surface concerns that might not come up in human discussions. What other applications have you found valuable for AI in the boardroom? Christian Stadler: What I found surprising in our exercises was how disruptive AI was. Imagine a group of people who have worked together for a long time. Their discussions are smooth because they know how each other thinks. Then, I introduce ChatGPT into the meeting. I’d tell them, “Read these five pages,” and suddenly, they’re confronted with a long list of new insights. It disrupted their usual flow. At first, I thought they would hate it, but they actually liked it. It made them stop and think. The disruption forced them to break out of their usual patterns and consider ideas they wouldn’t have consciously introduced into the discussion. Ross: What are the ways in which you are seeing or applying tools to augment the foresight process? Valentina Contini: I started looking into this about two years ago, when GPT-3.5 was released. One of the things that frustrated me was that generating scenarios for companies took too long. You needed to involve multiple experts and stakeholders, which meant it only happened every three to five years. But in today’s rapidly changing world, that’s not enough. AI can accelerate the foresight process. It can help generate diverse perspectives, identify second-degree impacts, and uncover biases we might not notice. It’s especially useful in tools like a futures wheel, where many perspectives need to be mapped. AI can bring in unexpected viewpoints based on large-scale data analysis. Of course, human critical thinking is still essential—we shouldn’t accept AI outputs as absolute truth, but rather use them as a starting point. Ross: Human-AI collaboration involves complex problems where humans retain the highest-level context and decision-making ability, while AI complements our cognition. What does that interface look like? Anthea Roberts: This is one of the most fascinating questions of our time. Both humans and AI have different strengths, and the way we interact with AI is evolving. For example, when working with large language models, humans shift from being primary generators of content to being managers and editors. We direct how the AI works and refine its outputs. This requires metacognition—not just thinking about our own thinking, but also understanding how the AI thinks. One key area where AI excels is handling cognitive complexity. Humans struggle to hold thousands of variables in their heads, but AI can process vast amounts of interconnected data. The challenge is designing interfaces that allow humans to interact with this complexity in an intuitive way. A simple chat interface isn’t enough—we need tools that allow for narrowing focus, cognitive offloading, and iterative collaboration. Another challenge is balancing AI’s overwhelming amount of information with human discernment. Many people feel deluged by AI-generated content, making it crucial to develop skills for filtering and applying insights effectively. Ross: So AI not only provides information but also changes the way we think and interact with complexity? Anthea: Exactly. Over the last year and a half, I’ve realized that much of my work is metacognitive. I don’t tell people what to think, but I help them understand how they think. The same applies to AI—we need to recognize its biases, workflows, and limitations while leveraging its strengths. One of the biggest challenges will be developing interdisciplinary AI agents that can collaborate across different fields of expertise. AI will evolve into an indispensable partner in decision-making, but we need to ensure that humans remain in control of the broader context and ethical considerations. How we navigate this balance will define the future of AI-human collaboration. The post AI for Strategy Compilation (AC Ep80) appeared first on Humans + AI. | |||
| Collective Intelligence Compilation (AC Ep79) | 05 Mar 2025 | ||
“Collective intelligence is the ability of a group to solve a wide range of problems, and it’s something that also seems to be a stable collective ability.” – Anita Williams Woolley "When you get a response from a language model, it’s a bit like a response from a crowd of people. It’s shaped by the collective judgments of countless individuals." – Jason Burton "Rather than just artificial general intelligence (AGI), I prefer the term augmented collective intelligence (ACI), where we design processes that maximize the synergy between humans and AI." – Gianni Giacomelli "We developed Conversational Swarm Intelligence to scale deliberative processes while maintaining the benefits of small group discussions." – Louis Rosenberg About Anita Williams Woolley, Jason Burton, Gianni Giacomelli, & Louis RosenbergAnita Williams Woolley is the Associate Dean of Research and Professor of Organizational Behavior at Carnegie Mellon University’s Tepper School of Business. She received her doctorate from Harvard University, with subsequent research including seminal work on collective intelligence in teams, first published in Science. Her current work focuses on collective intelligence in human-computer collaboration, with projects funded by DARPA and the NSF, focusing on how AI enhances synchronous and asynchronous collaboration in distributed teams. Jason Burton is an assistant professor at Copenhagen Business School and an Alexander von Humboldt Research fellow at the Max Planck Institute for Human Development. His research applies computational methods to studying human behavior in a digital society, including reasoning in online information environments and collective intelligence. Gianni Giacomelli is the Founder of Supermind.Design and Head of Design Innovation at MIT’s Center for Collective Intelligence. He previously held a range of leadership roles in major organizations, most recently as Chief Innovation Officer at global professional services firm Genpact. He has written extensively for media and in scientific journals and is a frequent conference speaker. Louis Rosenberg is CEO and Chief Scientist of Unanimous A.I., which amplifies the intelligence of networked human groups. He earned his PhD from Stanford and has been awarded over 300 patents for virtual reality, augmented reality, and artificial intelligence technologies. He has founded a number of successful companies including Unanimous AI, Immersion Corporation, Microscribe, and Outland Research. His new book Our Next Reality on the AI-powered Metaverse is out in March 2024. Websites:
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Anita Williams Woolley: Individual intelligence is a concept most people are familiar with. When we're talking about general human intelligence, it refers to a general underlying ability for people to perform across many domains. Empirically, it has been shown that measures of individual intelligence predict a person's performance over time. It is a relatively stable attribute. For a long time, when we thought about intelligence in teams, we considered it in terms of the total intelligence of the individual members combined—the aggregate intelligence. However, in our work, we challenged that notion by conducting studies that showed some attributes of the collective—the way individuals coordinated their inputs, worked together, and amplified each other's contributions—were not directly predictable from simply knowing the intelligence of the individual members. Collective intelligence is the ability of a group to solve a wide range of problems. It also appears to be a stable collective ability. Of course, in teams and groups, you can change individual members, and other factors may alter collective intelligence more readily than individual intelligence. However, we have observed that it remains fairly stable over time, enabling greater capability. In some cases, collective intelligence can be high or low. When a group has high collective intelligence, it is more capable of solving complex problems. I believe you also asked about artificial intelligence, right? When computer scientists work on ways to endow a machine with intelligence, they essentially provide it with the ability to reason, take in information, perceive things, identify goals and priorities, adapt, and change based on the information it receives. Humans do this quite naturally, so we don't really think about it. Without artificial intelligence, a machine only does what it is programmed to do and nothing more. It can still perform many tasks that humans cannot, particularly computational ones. However, with artificial intelligence, a computer can make decisions and draw conclusions that even its own programmers may not fully understand the basis of. That is where things get really interesting. Ross Dawson: We'll probably come back to that. Here at Amplifying Cognition, we focus on understanding the nature of cognition. One fascinating area of your work examines memory, attention, and reasoning as fundamental elements of cognition—not just on an individual level, but as collective memory, collective attention, and collective reasoning. I’d love to understand: What does this look like? How do collective memory, collective attention, and collective reasoning play into aggregate cognition? Anita: That’s an important question. Just as we can intervene to improve collective intelligence, we can also intervene to improve collective cognition. Memory, attention, and reasoning are three essential functions that any intelligent system—whether human, computer, or a human-computer collaboration—needs to perform. When we talk about these in collectives, we are often considering a superset of humans and human-computer collaborations. Research on collective cognition has been running parallel to studies on collective intelligence for a couple of decades. The longest-standing area of research in this field is on collective memory. A specific construct within this area is transactive memory systems. Some of my colleagues at Carnegie Mellon, including Linda Argote, have conducted significant research in this space. The idea is that a strong collective memory—through a well-constructed transactive memory system—allows a group to manage and use far more information than they could individually. Over time, individuals within a group may specialize in remembering different information. The group then develops cues to determine who is responsible for retaining which information, reducing redundancy while maximizing collective recall. As the system forms, the total capacity of information the group can manage grows considerably. Similarly, with transactive attention, we consider the total attentional capacity of a group working on a problem. Coordination is crucial—knowing where each person's focus is, when focus should be synchronized, when attention should be divided across tasks, and how to avoid redundancies or gaps. Effective transactive attention allows groups to adapt as situations change. Collective reasoning is another fascinating area with a significant body of research. However, much of this research has been conducted in separate academic pockets. Our work aims to integrate these various threads to deepen our understanding of how collective reasoning functions. At its foundation, collective reasoning involves goal setting. A reasoning system must identify the gap between a desired state and the current state, then conceptualize what needs to be done to close that gap. A major challenge in collective reasoning is establishing a shared understanding of the group’s objectives and priorities. If members are not aligned on goals, they may decide that their time is better spent elsewhere. Thus, goal-setting and alignment are foundational to collective reasoning, ensuring that members remain engaged and motivated over time. Ross: One of the interesting insights from your paper is that large language models (LLMs) themselves are an expression of collective intelligence. I don’t think that’s something everyone fully realizes. How does that work? In what way are LLMs a form of collective intelligence? Jason Burton: Sure. The most obvious way to think about it is that LLMs are machine learning systems trained on massive amounts of text. Companies developing these language models source their text from the internet—scraping the open web, which contains natural language encapsulating the collective knowledge of countless individuals. Training a machine learning system to predict text based on this vast pool of collective knowledge is essentially a distilled form of crowdsourcing. When you query a language model, you aren’t getting a direct answer from a traditional relational database. Instead, you receive a response that reflects the most common patterns of answers given by people in the past. Beyond this, language models undergo further refinement through reinforcement learning from human feedback (RLHF). The model presents multiple response options, and humans select the best one. Over time, the system learns human preferences, meaning that every response is shaped by the collective judgments of numerous individuals. In this way, querying a language model is like consulting a crowd of people who have collectively shaped the model’s responses. Gianni Giacomelli: I view this through the lens of augmentation—augmenting collective intelligence by designing organizational structures that combine human and machine capabilities in synergy. Instead of thinking of AI as just a tool or humans as just sources of data, we need to look at how to structure processes that allow large groups of people and machines to collaborate effectively. In 2023, many became engrossed with AI itself, particularly generative AI, which in itself is an exercise in collective intelligence. These systems were trained on human-generated knowledge. But looking at AI in isolation limits our understanding. Rather than just artificial general intelligence (AGI), I prefer the term augmented collective intelligence (ACI), where we design processes that maximize the synergy between humans and AI. Louis Rosenberg: There are two well-known principles of human behavior: one is collective intelligence—the idea that groups can be smarter than individuals if their input is harnessed effectively. The other is conversational deliberation—where groups generate ideas, debate, surface insights, and solve problems through discussion. However, scaling these processes is difficult. If you put 500 people in a chat room, it becomes chaotic. Research shows that the ideal conversation size is five to seven people. To address this, we developed Conversational Swarm Intelligence, using AI agents in small human groups to facilitate discussions and relay key insights across overlapping subgroups. This allows us to scale deliberative processes while maintaining the benefits of small group discussions.
The post Collective Intelligence Compilation (AC Ep79) appeared first on Humans + AI. | |||
| Tim O’Reilly on AI native organizations, architectures of participation, creating value for users, and learning by exploring (HAI Ep11) | 23 Jul 2025 | 00:41:00 | |
https://youtu.be/aIVSz--wvEk “We’re in this process where we should be discovering what’s possible… That’s what I mean by AI-native — just go figure out what the AI can do that makes something so much easier or so much better.” – Tim O’Reilly About Tim O’ReillyTim O’Reilly is the founder, CEO, and Chairman of leading technical publisher O’Reilly Media, and a partner at early stage venture firm O’Reilly AlphaTech Ventures. He has played a central role in shaping the technology landscape, including in open source software, web 2.0, and the Maker movement. He is author of numerous books including WTF? What’s the Future and Why It’s Up to Us. Website: LinkedIn Profile: X Profile: Articles: An Architecture of Participation for AI? AI and Programming: The Beginning of a New Era What you will learn
Ross Dawson: Tim, it is fantastic to have you on the show. You were my very first guest on the show three years ago, and it's wonderful to have you back. Tim O’Reilly: Well, thanks for having me again. Ross: So you have seen technology waves over decades and been right in there forming some of those. And so I'd love to get your perspectives on AI today. Tim: Well, I think, first off, it's the real deal. It's a major transformation, but I like to put it in context. The history of computing is the history of making it easier and easier for people to communicate with machines. I mean literally in the beginning, they had to actually wire physical circuits into a particular calculation, and then they came up with the stored program computer. And then you could actually input a program one bit at a time, first with switches on the front of the computer. And then, wow, punch cards. And we got slightly higher level languages. First it was big, advanced assembly programming, and then big, advanced, higher level languages like Fortran, and that whole generation. Then we had GUIs. I mean, first we had command lines. Literally the CRT was this huge thing. You could literally type and have a screen. And I guess the point is, each time that we had an advance in the ease of communication, more people used computers. They did more things with them, and the market grew. And I think I have a lot of disdain for this idea that AI is just going to take away jobs. Yes, it will be disruptive. There's a lot of disruption in the past of computing. I mean, hey, if you were a programmer, you used to have to know how to use an oscilloscope to debug your program. And a lot of that old sort of analog hardware that was sort of looking at the waveforms and stuff — not needed anymore, right? I remember stepping through programs one instruction at a time. There's all kinds of skills that went away. And so maybe programming in a language like Python or Java goes away, although I don't think we're there yet, because of course it is simply the intermediate code that the AIs themselves are generating, and we have to look at it and inspect it. So we have a long way before we're at the point that some people are talking about — evanescent programs that just get generated and disappear, that are generated on demand because the AI is so good at it. It just — you ask it to do something, and yeah, it generates code, just like maybe a compiler generates code. But I think that's a bit of a wish list, because these machines are not deterministic in the way that previous computers were. And I love this framework that there's really — we now have two different kinds of computers. Wonderful post — trying to think who, name's escaping me at the moment — but it was called “LLMs Are Weird Computers.” And it made the point that you have, effectively, one machine that we're working with that can write a sonnet but really struggles to do math repeatedly. And you have another type of machine that can come up with the same answer every single time but couldn't write a sonnet to save its life. So we have to get the best of both of these things. And I really love that as a framework. It's a big expansion of capability. But returning back to this idea of more — the greater ease of use expanding the market — just think back to literacy. There was a time when there was a priesthood. They were the only people who could read and write. And they actually even read and wrote in a dead language — Latin — that nobody else even spoke. So it was this real secret, and it was a source of great power. And it was subversive when they first, for example, printed the Bible in English. And literally, when they printed the printed book — the printed book was the equivalent of our current, “Oh my God, social media turbocharged with AI, social disruption.” There was 100 years of war after the dissemination of movable type, because suddenly the Bible and other books were available in English. And it was all this mass communication, and people fought for 100 years. Now, hopefully we won't fight for 100 years. But disruption does happen, and it's not pretty. But it's not — there's a way that the millennialist kind of version of where this is somehow terminal is just wrong. I mean, we will evolve. We will figure out how to coexist with the machines. We'll figure out new things to do with them. And I think we need to get on with it. But I guess, back to this post I wrote called “AI First Puts Humans First,” there's a lot of pressure from various companies. They're saying you must use AI. And they've been talking about AI first as a way of, like, “If you try to do it with AI first because we want to get rid of the people.” And I think of AI first — or what I prefer, the term AI native — as a way of noticing: no, we want to figure out what the capabilities of this machine are. So try it first, and then build with it. And in particular, I think of the right way to think about it as a lot like the term “mobile first.” It didn't mean that you didn't have other applications anymore. It just meant, when companies started talking about mobile first, it meant we don't want it to be an afterthought. And I think we need to think that way about AI. How can we reinvent the things that we're doing using AI? And anybody who thinks it's just about replacing people is missing the point. Ross: Yeah, well, that's going back to the main point around the ease of communication. So the layers of which we are getting our intent to be able to flow through into what the computers do. So what struck me with the beginning of LLMs is that what is distinctive about humans is our intention and our intention to achieve something. So now, as you're saying, the gap between what we intend and what we can achieve is becoming smaller and smaller, or it's getting narrower and faster. Also, we can democratize it in the sense of — yeah, there is more available to more people in various guises, to different degrees, where you can then manifest in software and technology your intention. Yeah, so that democratizes — as you say, this is — there are ways in which this is akin to the printing press, because it democratizes that ability to not just understand, but also to achieve and to do and to connect. Tim: Yeah, there is an issue that I do think we need to confront as an industry and as a society, and that is what Cory Doctorow calls "enshittification." This idea — actually, I had a different version of it, but let's talk about Cory’s version first. The platforms first are really good to their users. They create these wonderful experiences. Then they use the mass of users that they've collected to attract businesses, such as advertisers, and they're really good to the advertisers but they're increasingly bad to the users. Then, as the market reaches a certain saturation point, they go, “Well, we have to be bad to everybody, because we need the money first. We need to keep growing.” I did a version of this. I wrote a paper called Rising Tide Rents and Robber Baron Rents, where I used the language of economic rents. We have this notion of Schumpeterian rents — or Schumpeterian profits — where a company has innovated, they get ahead of the competition, and they have outsized profits because they are ahead. But in the theory, those rents are supposed to be competed away as knowledge diffuses. What we've seen in practice is companies put up all kinds of moats and try to keep the knowledge from diffusing. They try to lock in their users and so on. Eventually, the market stagnates, and they start preying on their users. We're in that stage in many ways as an industry. So, coming to AI, this is what typically happens. Companies stagnate. They become less innovative. They become protective of their profits. They try to keep growing with, effectively, the robber baron rents as opposed to the innovation rents. New competition comes along, but here we have a problem — the amount of capital that's had to go into AI means that none of these companies are profitable. So they're actually enshittified from the beginning, or the enshittification cycle will go much, much more quickly, because the investors need their money. I worry about that. This has really been happening since the financial crisis made capital really cheap. We saw this with companies like Lyft and Uber and WeWork — that whole generation of technology companies — where the market didn't choose the winner. Capital chose the winner. The guy who actually invented all of that technology for on-demand cars was Sunil Paul with Sidecar. Believe it or not, he raised the same amount of money that Google raised — which was $35 million. Uber and Lyft copied his innovations. Their venture was doing something completely different. Uber was black cars summoned by SMS. Lyft was a web app for inner-city people trying to find other people to share rides between cities. They pivoted to do what Sunil Paul had invented, and they threw billions at it, and they bought the market. Sure enough, the companies go public, unprofitable. Eventually, after the investors have taken out their money — it's all great — then they have to start raising prices. They have to make the service worse. Suddenly, you're not getting a car in a minute. You're getting a car in 10 minutes. They're telling you it's coming in five, and it's actually coming in 15. So it's — and I think that we have some of that with AI. We're basically having these subsidized services that are really great. At some point, that's going to shake out. I think there's also a way that the current model of AI is fundamentally — it's kind of colonialism in a certain way. It's like, we're going to take all this value because we need it to make our business possible. So we're going to take all the content that we need. We're not going to compensate people. We're going to make these marvelous new services, and therefore we deserve it. I think they're not thinking holistically. Because this capital has bought so much market share, we're not having that kind of process of discovery that we had in previous generations. I mean, there's still a lot of competition and a lot of innovation, and it may work out. Ross: I'm just very interested in that point. There's been a massive amount of capital. There's this thesis that there is a winner-takes-most economy — so if you’re in, you have a chance of getting it all. But overlaid on that — and I think there’s almost nobody better to ask — is open source, where of course you've got commercial source, you've commercially got open source, and quite a bit in between. I'd love to hear your views on the degree to which open source will be competitive against the closed models in how it plays out coming up. Tim: I think that people have always misunderstood open source, because I don't think that it is necessarily the availability of source code or the license. It's what I call an architecture of participation. This is something where I kind of had a falling out with all of the license weenies back in the late ’90s and early 2000s, because — see, my first exposure to what we now call open source was with Berkeley Unix, which grew up in the shadow of the AT&T System V license. That was a proprietary license, and yet all this stuff was happening — this community, this worldwide community of people sharing code. It was because of the architecture of Unix, which allowed you to add. It was small. It was a small kernel. It was a set of utilities that all spoke the same protocol — i.e., you read and wrote ASCII into a stream, which could go into a file. There were all these really powerful concepts for network-based computing. Then, of course, the internet came along, and it also had an architecture of participation. I still remember the old battle — Netscape was the OpenAI of its day. They were going to wrest control from Microsoft, in just the same way that OpenAI now wants to wrest control from Google and be the big kahuna. The internet’s architecture of participation — it was really Apache that broke it open more than Linux, in some ways. Apache was just like, “Hey, you just download this thing, you build your own website.” But it wasn’t just that anybody could build a website. It was also that Apache itself didn’t try to Borg everything. I remember there was this point in time when everybody was saying Apache is not keeping up — Internet Information Server and Netscape Server are adding all these new features — and Apache was like, “Yeah, we’re a web server, but we have this extension layer, and all these people can add things on top of it.” It had an architecture of participation. The same thing happened with things like OpenOffice and the GIMP, which were like, “Okay, we’re going to do Microsoft Office, we’re going to do Photoshop.” They didn’t work, despite having the license, despite making the source code available — because they started with a big hairball of code. It didn’t have an architecture of participation. You couldn’t actually build a community around it. So I think — my question here with AI is: Where is the architecture of participation? Ross: I would argue that it's an arXiv, as in that now basically, the degree of sharing — where you get your Stability and your Googles and everyone else just putting it out on arXiv for your deep seek — really detailed. Tim: Yeah, I think that's absolutely right. There is totally an architecture of participation in arXiv. But I think there's also a question of models. I guess the thing I would say is yes — the fact that there are many, many models and we can build services — but we have to think about specialized models and how they cooperate. That's why I'm pretty excited about MCP and other protocols. Because the initial idea — the winner-takes-all model — is: here we are, we're OpenAI, you call our APIs, we're the platform. Just like Windows was. That was literally how Microsoft became so dominant. You called the Windows API. It abstracted — it hid all the complexity of the underlying hardware. They took on a bunch of hard problems, and developers went, “Oh, it's much easier to write my applications to the Windows API than to support 30 different devices, or 100 different devices.” It was perfect. Then Java tried to do a network version of that — remember, “Write once, run anywhere” was their slogan. And in some sense, we're replaying that with MCP. But I want to go back to this idea I've been playing with — it's an early Unix idea — and I've actually got a piece that I'm writing right now, and it's about groups. Because part of an architecture of participation is: what's the unit of participation? I've been thinking a lot about one of the key ideas of the Unix file system, which was that every file had, by default, a set of permissions. And I think we really need to come up with that for AI. I don’t know why people haven’t picked up on it. If you compare that to things like robots text and so on, there’s a pretty simple way. Let me explain for people who might not remember this. Most people who are developers or whatever will know something about this. You had a variable called umask, which you set, and it set the default permissions for every file you created. There was also a little command called chmod that would let you change the permissions. Basically, it was read, write, or execute — and it was for three levels of permission: the user, the group, and the world (everyone) right? So here we are with AI, saying, “We, OpenAI,” or “We, Grok,” or whoever, “are going to be world,” right? “We're going to Borg everything, and you're going to be in our world. Then you'll depend on us.” Then some people — like Apple maybe — are saying, or even other companies are saying, “Well, we'll give you permission to have your own little corner of the world.” That's user. “We'll let you own your data.” But people have forgotten the middle — which is group. If you look at the history of the last 20 years, it's people rediscovering — and then forgetting — group. Think about what was the original promise of Twitter, or the Facebook feed. It was: I can curate a group of people that I want to follow, that I want to be part of. Then they basically went, “No, no, actually that doesn’t really work for us. We’re going to actually override your group with our algorithmic suggestions.” The algorithmically generated group was a really fabulous idea. Google tried to do a manual version of that when they did — originally Buzz — and then, was it called Circles? Which was from Andy Hertzfeld, and was a great thing. But what happens? Facebook shuts it off. Twitter shuts it off. And guess what? Where is it all happening now? WhatsApp groups, Signal groups, Discord groups. People are reinventing group again and again and again. So my question for the AI community is: Where is group in your thinking? How do we define it? A group can be a company. It can be a set of people with similar beliefs. There’s a little bit of this, in the sense that — if you think Grok, the group is — even though it aspires to be the world-level — you could say Anthropic is the, let’s call it, the “woke group,” and Grok is the “right group.” But where’s the French group? The French have always been famously protective. So I guess Mistral is the French group. But how do people assert that groupness? A company is a group. So the question I have is, for example: how do we have an architecture of participation that says, “My company has valuable data that it can build services on, and your company has valuable data. How do we cooperate?” That’s again where I’m excited — at least the MCP is the beginning of that. Saying: you can make a set of MCP endpoints anywhere. It’s a lot like HTTP that way. “Oh, I call you to get the information that I want. Oh, I call you over here for this other information.” That’s a much more participatory, dynamic world than one where one big company licenses all the valuable data — or just takes all the valuable data and says, “We will have it all.” Ross: That's one of the advantages of the agentic world — that if you have the right foundations, the governance, the security, and all of the other layers like team, payments, etc., then you can get entirely an economy of participation of agents. But I want to look back from what you were saying around groups, coming back to the company’s point around the “AI first” or “AI native,” or whatever it may be. And I think we both believe in augmenting humans. So what do you see as possible now if we look at an organization that has some great humans in it, and we've got AI that changes the nature of the organization? It's not just tacking on AI to make each person more productive. I think we become creative humans-plus-AI organizations. So what does that look like at its best? What should we be aspiring to? Tim: Well, the first thing — and again, I'm just thinking out loud from my own process — the first thing is, there's all kinds of things that we always wished we could do at O'Reilly, but we just didn’t have the resources for, right? And so that’s the first layer. The example I always use is, there are people who would like to consume our products in many parts of the world where they don’t speak English. And we always translated a subset of our content into a subset of languages. Now, with AI, we can make versions that may not be as good, but they’re good enough for many, many more people. So — vast expansion of the market there, just by going, “Okay, here’s this thing we always wished we could do, but could not afford to do.” Second is: okay, is there a new, AI-native way to do things? O’Reilly is a learning platform, and I’m looking a lot at — yeah, we have a bunch of corporate customers who are saying, “How do you do assessments? We need to see verified skills assessment.” In other words, test people: do they actually know this thing? And I go — wow — in an AI-native world, testing is a pretty boneheaded idea, right? Because you could just have the AI watch people. I was getting a demo from one startup who was showing me something in this territory. They had this great example where the AI was just watching someone do a set of tasks. And it said, “I noticed that you spent a lot more time and you asked a lot more questions in the section that required use of regular expressions. You should spend some time improving your skills there.” The AI can see things like that. Then I did kind of a demo for my team. I said, “Okay, let me just show you what I think AI-native assessment looks like.” I basically found some person on GitHub with an open repository. I said, “Based on this repository, can you give me an assessment of this developer’s skills — not just the technical skills, but also how organized they are, how good they are at documentation, their communication skills?” It did a great write-up on this person just by observing the code. Then I pointed to a posted job description for an engineer working on Sora at OpenAI and said, “How good of a match is this person for that job?” And it kind of went through: “Here are all the skills that they have. Here are all the skills that they need.” And I go — this is AI-native. It’s something that we do, and we’re doing it in probably a 19th-century way — not even a 20th-century way — and you have completely new ways to do it. Now, obviously that needs to be worked on. It needs to be made reliable. But it’s what I mean by AI-native — just go figure out what the AI can do that makes something so much easier or so much better. That’s the point. And that’s why it drives me nuts when I hear people talk about the “efficiencies” to be gained from AI. The efficiencies are there. Like, yeah — it was a heck of a lot more efficient to use a steam engine to bring the coal out of the mine than to have a bunch of people do it. Or to drive a train. I mean, yeah, there’s efficiency there. But it’s more that the capability lets you do more. So we’re in this process where we should be discovering what’s possible. In this way, I’m very influenced by a book by a guy named James Bessen. It’s called Learning by Doing, and he studied the Industrial Revolution in Lowell, Massachusetts, when they were bringing cotton mills and textile mills to New England. He basically found that the narrative — AI had unskilled labor replaced skilled labor — wasn’t quite right. They had these skilled weavers, and then these unskilled factory workers. And he looked at pay records and said it took just as long for the new workers to become fully paid as the old workers. So they were just differently skilled. And I think “differently skilled” is a really powerful idea. And he said okay, why did it take so long for this to show up in productivity statistics — 20, 30 years? And he said, because you need a community. Again — this is an architectural part. You need people to fix the machines. You need people to figure out how to make them work better. So there’s this whole community of practice that’s discovering, thinking, sharing. And we’re in that ferment right now. That’s what we need to be doing — and what we are doing. There’s this huge ferment where people are in fact discovering and sharing. And back to your question about open source — it’s really less about source code than it is about the open sharing of knowledge. Where people do that. That goes back to O’Reilly. What we do — we describe our mission as being “changing the world by spreading the knowledge of innovators.” We used to do it almost entirely through books. Then we did it through books and conferences. Now we have this online learning platform, which still includes books but has a big live training component. We’re always looking for people who know something and want to teach it to other people. Then the question is, what do people need to know now that will give them leverage, advantage, and make them — and their company — better? Ross: So just to round out, I mean, you've already — well, more than touched on this idea of learning. So part of it is, as you say, there are some new skills which you need to learn. There's new capabilities. We want to go away from the old job description because we want people to evolve into how they can add value in various ways. And so, what are the ways? What are the architectures of learning? I suppose, as you say, that is a community. It's not just about delivering content or interacting. There's a community aspect. So what are the architectures of learning that will allow organizations to grow into what they can be as AI-native organizations? Tim: I think the architecture of learning that's probably most important is for companies to give people freedom to explore. There's so many ideas and so much opportunity to try things in a new way. And I worry too much that companies are looking for — they're trying to guide the innovation top-down. I have another story that sort of goes back to — it's kind of a fun story about open source. So, yeah, one of the top guys at Microsoft is a guy named Scott Guthrie. So Scott and one of his coworkers, Mark Anders, were engineers at Microsoft, and they had basically this idea back in the early — this is 20-plus years ago — and they basically were trying to figure out how to make Windows better fitted for the web. And they did a project by themselves over Christmas, just for the hell of it. And it spread within Microsoft. It was eventually what became ASP.NET, which was a very big Microsoft technology — I guess it was in the early 2000s. It kind of spread like an open source project, just within Microsoft — which, of course, had tens of thousands of employees. Eventually, Bill Gates heard about it and called them into his office. And they’re like, “Oh shit, we're gonna get fired.” And he's like, “This is great.” He elevated them, and they became a Microsoft product. But it literally grew like an open source project. And that's what you really want to have happen. You want to have people scratching their own itch. It reminds me of another really great developer story. I was once doing a little bit of — I'd been called into a group at SAP where they wanted to get my advice on things. And they had also reached out to the Head of Developer Relations at Google. And he asked — and we were kind of trying to — I forget what the name of their technology was. And this guy asked a really perfect question. He said, “Do any of your engineers play with this after hours?” And they said, “No.” And he said, “You're fucked. It's not going to work.” So that — that play, Ross: Yeah. Right? Tim: Encourage and allow that play. Let people be curious. Let them find out. Let them invent. And let them reinvent your business. Ross: That's fantastic. Tim: Because that’s — that will, that will — their learning will be your learning, and their reinvention of themselves will be your reinvention. Ross: So, any final messages to everyone out there who is thick in the AI revolution? Tim: I think it’s to try to forget the overheated financing environment. You know, we talked at the very beginning about these various revolutions that I’ve seen. And the most interesting ones have always been when money was off the table. It was like — everybody had kind of given up on search when Google came along, for example. It was just like, “This is a dead end.” And it wasn’t. And open source — it was sort of like Microsoft was ruling the world and there was nothing left for developers to do. So they just went and worked on their own fun projects. Right now, everybody’s going after the main chance. And — I mean, obviously not everybody — there are people who are going out and trying to really create value. But there are too many companies — too many investors in particular — who are really trying to create financial instruments. Their model is just, “Value go up.” Versus a company that’s saying, “Yeah, we want value for our users to go up. We’re not even worried about that [financial outcome] right now.” It’s so interesting — there was a story in The Information recently about Surge AI, which didn’t raise any money from investors, actually growing faster than Scale (scale.ai), which Meta just put all this money through — because they were just focused on getting the job done. So I guess my point is: try to create value for others, and it will come to you if you do that. Ross: Absolutely agree. That’s a wonderful message to end on. So thank you so much for all of your work over the years and your leadership in helping us frame this AI as a positive boon for all of us. Tim: Right. Well, thank you very much. And it's an amazing, fun time to be in the industry. We should all rejoice — challenging but fun. The post Tim O’Reilly on AI native organizations, architectures of participation, creating value for users, and learning by exploring (HAI Ep11) appeared first on Humans + AI. | |||
| Jacob Taylor on collective intelligence for SDGs, interspecies money, vibe-teaming, and AI ecosystems for people and planet (HAI Ep10) | 16 Jul 2025 | ||
"If we’re faced with problems that are moving fast and require collective solutions, then collective intelligence becomes the toolkit we need to tackle them." – Jacob Taylor About Jacob TaylorJacob Taylor is a fellow in the Center for Sustainable Development at Brookings Institution, and a leader of its 17 Rooms initiative, which catalyzes global action for the Sustainable Development Goals. He was previously research fellow at the Asian Bureau of Economic Research and consulting scientist on a DARPA research program on team performance. He was a Rhodes scholar and represented Australia in Rugby 7s for a number of years. Website: LinkedIn Profile: X Profile: What you will learn
Ross Dawson: Jacob, it is awesome to have you on the show. Jacob Taylor: Ross, thanks for having me. Ross: So we met at Human Tech Week in San Francisco, where you were sharing all sorts of interesting thoughts that we'll come back to. What are your top-of-mind reflections of the event? Jacob: Look, I had a great week, and largely because of all the great people I met, to be honest. And I think what I picked up there was people really driving towards the same set of shared outcomes. Really people genuinely building things, talking about ways of working together that were driving at outcomes for, ultimately, for human flourishing, for people and planet. And I think that's such an important conversation to have at the moment, as things are moving so fast in AI and technology, and sometimes it's hard to figure out where all of this is leading, basically.And so to have humans at the center is a great principle. Ross: Yeah, well, where it's leading is where we take it. So I think having the humans at the center is probably a pretty good starting point. So one of the central themes of this blog—for this podcast for ages—has been collective intelligence. And so you are diving deep into applying collective intelligence to achieve the Sustainable Development Goals, and I would love to hear more about what you're doing and how you're going about it. Jacob: Yeah, so I mean, very quickly, I'm an anthropologist by training. I have a background in elite team performance as a professional rugby player, and then studying professional team sport for a number of years. So my original collective is the team, and that's kind of my intuitive starting point for some of this. But teams are very well built to solve problems that no individual can achieve alone, and really a lot of the SDG problems that we have—issues that communities at every scale have trouble solving on their own—need a whole community to tackle a problem, rather than just one individual or set of individuals within a community. So the SDGs are these types of—whether it's climate action or ending extreme poverty or sustainability at the city level—all of these issues require collective solutions. And so if we're faced with problems that are moving fast and require collective solutions, then collective intelligence becomes the toolkit or the approach that we need to use to tackle those problems. I've been thinking a lot about this idea that in the second half of the 20th century, economics as a discipline went from pretty much on the margins of policymaking and influence to right at the center. By the end of the 20th century, economists were at the heart of informing how decisions were made at the country level, at firms, and so on. That was because an economic framework really helped make those decisions. I think my sense is that the problems we face now really need the toolkit of the science of collective intelligence. So that's kind of one of the ideas I've been exploring—is it time for collective intelligence as a science to really inform the way we make decisions at scale, particularly for our hardest problems like the SDG. Ross: One of your initiatives—so at Brookings Institution, one of the initiatives is 17 Rooms. I'm so intrigued by the name and what that is and how that works. Jacob: Yeah. So, 17 Rooms. We have 17 Sustainable Development Goals, and so on. Five or so years ago now—or more, I think it's been running for seven or eight years now—17 Rooms thought: what if we found a method to break down that complexity of the SDGs? A lot of people talk about the SDGs as everything connected to everything, which sometimes is true. There are a lot of interlinkages between these issues, of course. But what would it look like to actually break it down and say, let's get into a room and tackle a slice of one SDG? So Room 1: SDG 1 for ending extreme poverty. Let's take on a challenge that we can handle as a team. And so 17 Rooms gathers groups of experts into working groups—or short-term SWAT teams of cooperation, basically—and really gets them to think through big ideas and practical next steps for how to bend the curve on that specific SDG issue. Then there's an opportunity for these rooms or teams to interact across issues as well. So it provides a kind of “Team of Teams” platform for multi-stakeholder collaboration within SDG issues, but also connecting across the full surface of these problems as well. Ross: So what from the science of collective intelligence—or anything else—what specific mechanisms or structures have you found useful? Are you trying to enable the collective intelligence within and across these rooms or teams? Jacob: Yeah, so I think—I mean, they're all quite basic principles. We do a lot on trying to curate teams and also trying to run them through a process that really facilitates collaboration. But the principles are quite basic, really. I mean, one of the most fundamental principles is taking an action stance. One of the biggest principles of collective intelligence is that intelligence comes from action. This is a principle we get from biology. In biology, biology acts first and then learns on the run. So you don't kind of sit there and go, what kind of action could we take together as a multicellular organism—rather, it just unfolds, and then learning comes off the back of that action. So in that spirit, we really try to gear our teams and rooms into an action stance, and say, rather than just kind of pointing fingers at all the different aspects of the problem, let's say: what would it look like for us in this room to act together? And then, what could we learn from that? Trying to get into that stance is really foundational to the 17 Rooms initiative. And then I think the other part is really bonding or community—so knowing that action and community are two sides of the same coin. When you act together, you connect and you share ideas and information. But likewise, communities of teams that are connected are probably more motivated to act together and to be creative and think beyond just incentives. But like, what can we really achieve together? And so we try to pair those two principles together in everything that we do. Ross: So this comes back to this point—there's many classic frameworks and realities around acting and then learning from that. So your OODA Loop, your observe, orient, decide, act, or your Lean Startup loop, or Kolb’s learning cycle, or whatever it might be, where we act, but we only learn because we have data or insight. So that's a really interesting point—where we act, but then, particularly in a collective intelligence perspective, we have all sorts of data we need to filter and make sense of that not just individually, but collectively—in order to be able to understand how it is we change our actions to move more towards our outcomes. Do you have any structures for being able to facilitate that flow of feedback or data into those action loops? Jacob: Yeah, I think—and again, I'm very biased as an anthropologist here—so the third principle that we think about a lot, and that answers your question, is this idea of ritual. We're acting, we're connecting around that action, and that's a back-and-forth process. But then rituals actually are a space where we can elevate the best ideas that are coming out of that process and also challenge the ideas that aren't serving us. Famously across time for humans, ritual has been an opportunity both to proliferate the best behaviors of a society, but also to contest the behaviors that aren't serving performance. Ultimately—you don't always think about this in performance terms—but ultimately, when you look at it big picture, that's what's happening. So I think rituals of differentiation between the data that are serving us versus not, I think is really important for any team, organization, or community. Ross: That's really interesting. Could you give an example of a ritual? Jacob: Well, so there are rituals that can really—like walking on hot coals. Again, let's start anthropological, and then maybe we can get back to collective intelligence or AI. Walking on hot coals promotes behaviors of courageousness and devotion. Whereas in other settings, you have a lot of rituals that invert power structures—so men dressing up as women, women dressing up as men, or the less powerful in society being able to take on the behaviors of the powerful and vice versa. That actually calls out some of the unhelpful power asymmetries in a society and challenges those. So in that spirit, I think when we're thinking about high-performing teams or communities tackling the SDGs, I think there needs to be more than just... I'm trying to think—how could we form a ritual de novo here? But really, there needs to be, I guess, those behaviors of honesty and vulnerability as much as celebration of what's working. That maybe is easier to imagine in an organization, for example, and how a leader or leaders may try to really be frank about the full set of behaviors and activities that a team is doing, and how that's working for the group. Ross: So you've written a very interesting article referring to Team Human and the design principles that support—including the use of AI—and being able to build better team performance. So what are some of the design principles? Jacob: Well, I think this work came a little bit out of some DARPA work I did on a DARPA program before coming to Brookings around building mechanisms for collective intelligence. And when you boil it down to that fundamental level, it really comes down to having a way to communicate between agents or between individuals, which in psychology is referred to—the jargon in psychology is theory of mind. So, do I have a theory of Ross—what you want—and do you have a theory of what I want? That's basically social intelligence. It's the basic key here. But it really comes down to some way of communicating across differences. And then with that, the other key ingredient that we surfaced when we built a computational model of this, in a basic way, was an ability to align on shared goals. So it feels like there's some combination of social intelligence and shared goals that is foundational to any collective intelligence that emerges in teams or organizations or networks. And so trying to find ways to build those—whether that's at the community level... For example, if a city wants to develop its waste recycling program—but if you break that down, it really is a whole bunch of neighborhoods trying to develop recycling purposes. So the question for me is: do all those neighborhoods have a way of communicating to each other about what they're doing in service of a shared goal of, let's say, a completely circular recycling economy at the city level? And if not, then what kind of interaction and conversations need to happen at the city level so that you can share best practices, challenge practices that are hurting everyone, and then find a way to drive collective action towards a shared outcome. But I’d also think about that, like, at the team level, where there are ways to really encourage theory of mind and perspective sharing. Ross: So, in some of that work, you refer to digital twins—essentially being able to model how people might think or behave. If you are using digital twins, how is that put into practice in being able to build better team performance? Jacob: Yeah, great. Yeah, that's probably really where the AI piece comes in. Because that recycling-at-the-city-level example that I shared—this kind of collective intelligence happens without AI. But the promise of AI is to say, well, if you could actually store a lot of information in the form of digital twins that represented the interests and activities of, let's say, neighborhoods in a city trying to do recycling— Well, then beyond our human cognition, you could be trying to look for patterns and opportunities for collaboration by leveraging the power of AI to recognize patterns and opportunities across diverse data sets. The idea is you could kind of try to supercharge the potential collective intelligence about problem-solving by positioning AI as a team support—or a digital twin that could say, hey, actually, if we tweak our dials here and use this approach, that could align with our neighbor's approach, and maybe we should have a chat about it. So there's an opportunity to surface patterns, but then also potentially perform time-relevant interventions for human decision-makers to help encourage better outcomes. Ross: I think you probably should try a different phrase, because "digital twin" sounds like you've got a person, then you've got a copy of that person. Whereas you're describing it here as representing—could be a neighborhood, or it could be a stakeholder group. So it's essentially a representation, or some kind of representation, of the ways of thinking or values of a group, potentially, or community, as opposed to an individual. Jacob: Indeed, yeah. I think this is where it all gets a bit technical, but yeah, I agree that "twin"—"digital twin"—evokes this idea of an individual body. But if you extend that out, when you really take seriously some of the collective intelligence work, it's like intelligence collectives become intelligent when they become a full thing, like a body—when they really individuate as a collective. Teams really click and perform when they become one—so that it's no longer just these individual bodies. It's like the team is a body. So I think in that spirit, when I think about this, I actually think about neighborhoods having a collective identity. That could be reflected in their twin, or like, of the community. But I agree there's maybe some better way to imagine what that kind of community AI companion looks like at higher scales. Ross: So at Human Tech Week, you shared this wonderful story about how AI could represent not just human groups, but also animal species. Love to—I think that sort of really brings it to—it gives it a very real context, because you're understanding that from another frame. Jacob: Yeah. And I think it's true, Ross. I've been struck by how much this example of interspecies money—that I'll explain a little bit—is not only exciting because it has potential benefit for nature and the beautiful natural environment that we live in, but I think it actually helps humans understand what it could look like to do it for us too. And so, interspecies money, basically, is this idea developed by a colleague of ours at Brookings, Jonathan Ledger. We had a room devoted to this last year in 17 Rooms to try and understand how to scale it up. But what would it look like to give non-human species—like gorillas, or elephants, or trees—a digital ID and a bank account, and then use AI to reverse engineer or infer the preferences of those animals based on the way they behave? And then give them the agency to use the money in their bank account to pay for services. So if gorillas, for example, most rely on protection of their habitat, then they could pay local community actors to protect that habitat, to extend it, and to protect them from poachers, for example. That could all be inferred through behavioral trace data and AI, but then also mediated by a trustee of gorillas—a human trustee. It's quite a futuristic idea, but it's actually really hit the ground running. At the moment, there are pilots with gorillas in Rwanda, elephants in India, and ancient trees in Romania. So it's kind of—the future is now, a little bit, on this stuff. I think what it really does is help you understand: if we really tried to position AI in a way that helps support our preferences and gives agency to those from the bottom up, then what? What world would that look like? And I think we could imagine the same world for ourselves. A lot of our AI systems at the moment are kind of built top-down, and we're the users of those systems. What if we were able to build them bottom-up, so that at every step we were representing individual, collective, community interests—and kind of trading on those interests bottom-up? Ross: Yeah, well, there's a lot of talk about AI alignment, but this is, like, a pretty deep level of alignment that we're talking, right? Jacob: Right. And yeah, I think Sandy Pentland, who I shared the panel with—he has this idea of, okay, so there are large language models. What would it look like to have local language models—small language models that were bounded at the individual. So Ross, you had a local language model, which was the contents of your universe of interactions, and you could perform inferences using that. And then you and I could create a one-plus-one-plus-one-equals-three kind of local language model, which was for some use case around collective intelligence. This kind of bottom-up thinking, I think, is actually technically very feasible now. We have the algorithms, the understanding of how to train these models. And we also have the compute—in devices like our mobile phones—to perform the inference. It's really just a question of imagination, and also getting the right incentives to start building these things bottom-up. Ross: So one of the things you've written about is vibe teaming. We've got vibe coding, we've got viable sorts of things. You and your colleagues created vibe teaming. So what is it? What does it mean? And how do we do it? Jacob: Good question. Yeah, so this is some work that a colleague of mine, Kirsch and Krishna, and I at Brookings did this year. We got to a point where, with our teamwork—you know, Brookings is a knowledge work organization, and we do a lot of that work in teams. A lot of the work we do is to try and build better knowledge products and strategies for the SDGs and these types of big global challenges. The irony was, when we were thinking about how to build AI tools into our workflow, we were using a very old-school way of teaming to do that work. We were using this kind of old industrial model of sequential back-and-forth workflows to think about AI—when AI was probably one of the most, potentially the most, disruptive technologies of the 21st century. It just felt very ironic. To do a PowerPoint deck, Ross, you would give me the instructions. I would go away and draft it. I would take it back to you and say, “Is this right?” And you would say, “Yes, but not quite.” So instead, we said, “Wait a minute. The internet is blowing up around vibe coding,” which is basically breaking down that sequential cycle. Instead of individuals talking to a model with line-by-line syntax, they're giving the model the vibe of what they want. We're using AI as this partner in surfacing what it is we're actually trying to do in the first place. So Kirsch and I said, “Why don't we vibe team this?” Why don't we get together with some of these challenges and experts that we're working with and actually get them to tell us the vibe of what they've been learning? Homie Karas is a world expert—40-year expert—on ending extreme poverty. We sat down with him, and in 30 minutes, we really pushed him to give us, like: “Tell us what you really think about this issue. What’s the really hard stuff that not enough people know about? Why isn’t it working already?” These kinds of questions. We used that 30-minute transcript as a first draft input to the model. And in 90 minutes, through interaction with AI—and some human at the end to make sure it all looked right and was accurate—we created a global strategy to end extreme poverty. That was probably on par with anything that you see—and probably better, in fact, than many global actors whose main business is to end extreme poverty. So it's an interesting example of how AI can be a really powerful support to team-based knowledge work. Ross: Yeah, so just—I mean, obviously, this is you. You are—the whole nature of the vibe is that there’s no explicit, well, no specific, replicable structure. We're going with the vibes. But where can you see this going in terms of getting a group of complementary experts together, and what might that look like as the AI-augmented vibe teaming? Jacob: Well, I mean, you're right. There was a lot of vibe involved, and I think that's part of the excitement for a lot of people using these new tools. However, we did see a few steps that kept re-emerging. I've mentioned a few of them kind of implicitly here, but the big one—step one—was to really start with rich human-to-human input as a first step. So giving the model a 30-minute transcript of human conversation versus sparse prompts was a real game changer for us working with these models. It’s almost like, if you really set the bar high and rich, then the model will meet you there—if that makes sense. Step two was quickly turning around a first draft product with the model. Step three was then actually being patient and open to a conversation back and forth with the model. So not thinking that this is just a one-button-done thing, but instead, this is a kind of conversation—interaction with the model. “Okay, so that's good there, but we need to change this.” Or whatever you need to do to engage with the model there. And then, I think the final piece was really the need to go back and meet again together as a team to sense-check the outputs, and really run a rigorous human filter back over the outputs to make sure that this was not only accurate but analytically on point. This idea that sometimes AI looks good but smells bad—and with these outputs, sometimes we'd find that it's like, “Oh, that kind of looks good,” but then when you dig into it, it's like, “Wait a minute. This wasn’t quite right here and there.” So just making sure that it not only looks good but smells good too at the end. Yeah. And so I think these basic principles—we're seeing them work quite well in a knowledge work context. And I guess for us now, we're really interested in a two-barrel investigation with approaches like vibe teaming. On the one hand, it's really about the process and the how—like, how are we positioning these tools to support collaboration, creativity, flow in teamwork, and is that possible? So it's really a "how" question. And then the other question for us is a full "what." So what are we pointing these approaches at? For example, we're wondering—if it's ending extreme poverty, how could we use vibe teaming to actually... And Scott Page uses this term—how can we use it to expand the physics of collective intelligence? How can we run multiple vibe teaming sessions all at once to be much more inclusive of the types of people who participate in policy strategy formation? So that when you think about ending extreme poverty, it's ending it for whom? What do they want? What does it look like in local communities, for example? That idea of expanding the physics of collective intelligence through AI and approaches like vibe teaming is very much on our minds at the moment, as we think about next steps and scale-up. Ross: Obviously, the name of the podcast is Humans Plus AI, and I think what you're describing there is very much the best of humans—and using AI as a complement to draw out the best of that. Nice segue—you just sort of referred to “where next steps.” You've described a lot of the wonderful things you're doing—some fantastic approaches to very, very critically important issues. So where to from here? What's the potential? What are the things we need to be doing? What's the next phase of what you think could be possible and what we should be doing? Jacob: Yeah, I think I'm really excited about this idea of growing an alternate AI ecosystem that works for people and planet, rather than the other way around. Part of the work at Brookings is really setting up that agenda—that research agenda—for what that ecosystem could look like. We discussed it a little bit together at Human Tech Week. I think of that in three parts. There's the technical foundation—so down to the algorithms and the architectures of AI models—and thinking about how to design and build those in a way that works for people. That includes, for example, social intelligence built into the code. Another example there is around, in a world of AI agents—are agents working for humans, or are they working for companies? Sandy Pentland’s work on loyal agents, for example—which maybe we could link to afterward—I think is a great example of how to design agents that are fiduciaries for humans, and actors for humans first, and then others later. Then, approaches like vibe teaming—ways of bringing communities together using AI as an amplifier. And then I think the key piece, for me, is how to stitch the community of actors together around these efforts. So the tech builders, the entrepreneurs, the investors, the policymakers—how to bring them together around a common format. That's where I'm thinking about a few ideas. One way to try to get people excited about it might be this idea of not just talking about it in policy terms or going around to conferences. But what would it look like to actually bring together a lab or some kind of frontier research and experimentation effort—where people could come together and build the shared assets, protocols, and infrastructures that we need to scale up great things like interspecies money, or vibe teaming, or other approaches? Where, if we had collective intelligence as a kind of scientific backbone to these efforts, we could build an evidence base and let the evidence base inform new approaches—trying to get that flywheel going in a rigorous way. Trying to be as inclusive as possible—working on everything from mental health and human flourishing through to population-level collective intelligence and everything in between. Ross: So can you paint that vision just a little bit more precisely? What would that look like, or what might it look like? What's one possible manifestation of it? What's the— Jacob: Yeah, I mean, it's a good question. So this idea of a frontier experimental lab—I think maybe I'm a little bit informed by my work at DARPA. I worked on a DARPA program called ASSIST—AI, I mean, Artificial Social Intelligence for Successful Teams—and that really used this kind of team science approach, where you had 12 different scientific labs all coming together for a moonshot-type effort. There was that kind of idea of, we don't really know how to work together exactly, but we're going to figure it out. And in the process of shooting for the moon, we’re hopefully going to build all these shared assets and knowledge around how to do this type of work better. So I guess, in my mind, it's kind of like: could we create a moonshot for collective intelligence, where collective intelligence is really the engine—and the goal was trying to, for example, end extreme poverty, or reach some scale of ecosystem conservation globally through interspecies money? Or—pick your SDG issue. Could we do a collective intelligence moonshot for that issue? And in that process, what could we build together in terms of shared assets and infrastructure that would last beyond that one moonshot, and equip us with the ingredients we need to do other moonshots? Ross: Yeah, well, again, going back to the feedback loops—of what you learn from the action in order to be able to inform and improve your actions beyond that. Jacob: Exactly, yeah. And I think the key ingredients here are really taking seriously what we've built now in terms of collective intelligence. It is a really powerful, transdisciplinary scientific infrastructure. And I think taking that really seriously, and drawing on the collective intelligence of that community to inform, to create evidence and theories that can inform applications. And then running that around. I think what I discovered at Human Tech Week with you, Ross, is this idea that there's a lot of entrepreneurial energy—and also capital as well. I think a lot of investors really want to put their money where their mouths are on these issues. So it feels like it's not just kind of an academic project anymore. It's really something that could go beyond that. So that's kind of time for collective intelligence. We need to get these communities and constituencies working together and build a federation of folks who are all interested in a similar outcome. Ross: Yeah, yeah. The potential is extraordinary. And so, you know, there's a lot going on—not all of it good—these days, but there's a lot of potential for us to work together. And again, there's amplifying positive intent, which is part of what I was sharing at Human Tech Week. I was saying, what is our intention? How can we amplify that positive intention, which is obviously what you are doing in spades. So how can people find out more about your work and everything which you've been talking about? Jacob: Well, most of my work is on my expert page on Brookings. I'm here at the Center for Sustainable Development at Brookings, and I hope I'll be putting out more ideas on these topics in the coming months. I'll be mainly on LinkedIn, sharing those around too. Ross: Fantastic. Love what you're doing. Yeah—and yeah, it's fun. It's fantastic. So really, really glad you're doing that. Thank you for sharing, and hopefully there's some inspiration in there for some of our listeners to follow similar paths. Jacob: Thanks, Ross. I appreciate your time. This has been fun. The post Jacob Taylor on collective intelligence for SDGs, interspecies money, vibe-teaming, and AI ecosystems for people and planet (HAI Ep10) appeared first on Humans + AI. | |||
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Ross Dawson is globally recognized as a leading futurist, keynote speaker, entrepreneur, and authority on business strategy. He is Founding Chairman of the Advanced Human Technologies group of companies, and the bestselling author of five books, most recently Thriving on Overload: The 5 Powers for Success in a World of Exponential Information. Strong global demand has seen him deliver keynote speeches to business and government leaders in over 30 countries, while frequent media appearances include CNN, Bloomberg TV, SkyNews, ABC TV, Today and Sunrise shows, The New York Times, and many others. | |||
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| Helen Lee Kupp on redesigning work, enabling expression, creative constraints, and women defining AI (AC Ep78) | 19 Feb 2025 | ||
“I’m cautiously optimistic because never before has technology been as accessible as it is now—being able to interact with machines in a way that feels so natural to us, rather than in ones and zeros or more technical ways. AI shouldn’t replace what exists but augment and enhance our creativity, helping us tap into what makes us uniquely human.” – Helen Lee Kupp About Helen Lee KuppHelen Lee Kupp is co-founder and CEO of Women Defining AI, a community of female leaders applying and driving AI. She was previously leader of strategy and analytics at Slack and co-founder of its Future Forum. She is co-author of the best-selling book “How the Future Works: Leading Flexible Teams to do the Best Work of Their Lives". Website: Women Defining AILinkedIn Profile: What you will learn
People Companies & Organizations Technical Terms & Concepts
Ross Dawson: Helen, it is a delight to have you on the show. Helen Lee Kupp: It's good to be here. I love how we first started talking over an AI research paper. It was very random but awesome. Ross: Well, that's pushing the edges, trying to find what's out there and see what comes on the other side. AI is emerging, and we’re sitting alongside each other. How are you feeling about today and how humans and AI are coming together? Helen: I feel cautiously optimistic, and part of that is because I've been in tech for so long. Prior to getting much deeper into AI, I was working on flexible work and research around how to rethink and redesign how we, as humans, collaborate in a way that is more personalized, more customized, and helps more people bring their best selves to work and do their best work. It was serendipitous that around the same time, there was an increase in AI innovation. Now, we had technology to pair with the equation of redesigning work. COVID forced us to rethink work, not just from a people and process perspective but alongside rapid technological change. I'm cautiously optimistic because never before has technology been as accessible as it is now. We can interact with machines in a way that feels so natural rather than in ones and zeros or technical ways. Ross: I’m very aligned with that. One of the things you said was "bring your best self to work." I think of it as human potential. If we're creating a future of work, we have potential futures that are not so great and others that are very positive, where people express more of who they are and their capabilities. How can we create organizations like that? Helen: It starts with recognizing that everyone has different preferences and work styles. Organizations, teams, and leaders need to meet people where they are rather than force them into rigid structures that worked in the past. I often share this story—I’m deeply introverted. Despite jumping onto this podcast with you, I have always been an introvert. Navigating an extroverted world takes extra energy. In traditional office and meeting environments, I had to work harder to show up. However, when I had more diverse formats to interact with my team and leadership, it unlocked something for me. Instead of pretending to be the loudest in the room, I could find my own ways of expressing ideas—through text, written formats, or chat. It made work easier for me. When you think about how that manifests across a team, leaders and organizations must avoid putting rigid boxes around collaboration—whether it’s the hours we work or the place where we work. Increasing flexibility enables people to express themselves and bring forward ideas that might otherwise remain hidden. Ross: That’s a compelling vision. How do you bring that to reality? What do you do inside an organization to foster and enable that? Helen: One of the tools that helped in our research on the future of work and redesigning organizations is something simple—creating a team operating manual. The act of explicitly writing down the different ways we interact as a team opens up discussions. It allows for feedback: "Does this work for you? Should we try something different?" When these conversations don’t happen, implied assumptions remain—such as the norm of working in an office from nine to five. Explicitly stating and questioning these assumptions is step one. Then, organizations should give teams and managers the flexibility to define how they work within their sub-teams. Having operating manuals, sharing what works for your team, and bubbling up insights allow for a more bottom-up approach rather than a top-down one. It treats people like adults who understand their preferences and styles. Ross: That’s really nice. PepsiCo had an initiative where teams coordinated among themselves to determine their availability and collaboration methods. I wonder if we can push that further. People are often conditioned to fit into roles and adjust to their environments. Can we help people recognize their self-imposed constraints and flourish beyond them? Helen: This is where I’m cautiously optimistic about AI and how we integrate technology into work. When people start using AI, the initial question is often, "How can I do this more efficiently?" AI is a powerful tool that shortens tasks—like a calculator removing the need for mental math. However, once people move beyond efficiency, they begin asking, "What can I do differently?" AI allows us to do things we couldn’t before. It helps break conventional thinking. For example, if you use a large language model to generate 10 variations of an idea, it removes emotional bias. It shifts the conversation from defending one perspective to evaluating multiple ideas. This fosters creative discourse and integrates seamlessly into workflows without feeling like extra work. AI should not replace what exists but augment and enhance our creativity—helping us tap into what makes us uniquely human. Ross: So, AI helps individuals bring different perspectives and expand their thinking? Helen: Exactly. One of my favorite things to do with large language models is to open up the funnel. Whether it’s brainstorming writing styles, problem-solving, or scoping solutions, AI presents multiple potential paths. This reminds us that there is no single correct answer—only possibilities to explore. Ross: Gregory Bateson said wisdom comes from multiple perspectives. We now have multiple perspectives on demand. You work with leaders to redesign organizations. What guidance do you suggest? How can organizations evolve from existing structures? Helen: I don’t have the perfect answer for what the shape of organizations should be. However, we’ve been transitioning from hierarchical structures to teams-of-teams for a while, with varying success. The biggest challenge is breaking out of our mental paradigms of control. Flexible work means allowing managers and teams to design their workdays and collaboration methods rather than enforcing a company-wide approach. AI introduces another paradigm shift—it behaves unpredictably compared to traditional technology. Leaders must accept that they don’t have all the answers. Some of the best AI-driven innovations come from employees who work closely with the technology daily. For example, a data scientist evaluating AI’s role in data processing can quickly identify where it adds value and where it falls short. These innovations emerge at the edges, from individuals experimenting in real time. Leaders must create environments where experimentation, sharing, and collaboration thrive. Instead of dictating policies top-down, they should spotlight grassroots innovations and scale them across the organization. Ross: So, you’re describing emergence—where leaders set conditions for innovation rather than dictate precise rules? Helen: Exactly. Constraints breed creativity. If there are no guardrails or structures, people stick to the status quo and don’t innovate. Leaders must provide the right nudges—whether through hackathons, dedicated experimentation time, or open Slack channels to share discoveries. Some organizations set up "experiment hours"—weekly meetings where teams explore AI applications in a low-pressure, fun environment. This fosters creativity and keeps innovation moving. Ross: That’s a great example. Speaking of multiple perspectives, one of your recent ventures is Women Defining AI. What is it about? Helen: Women Defining AI started as an experiment about a year and a half ago. I had been working with generative AI models and noticed a significant gender gap in AI adoption. Data showed men adopting AI at higher rates than women, and anecdotally, I saw the same trend. Initially, it was just a study group where I shared what I was learning with other women. Within days, 50 people joined, and by month two, we had 150 members. It became clear that women wanted a space to ask questions, learn together, and experiment without judgment. Now, Women Defining AI is a virtual community that helps women at different stages of their AI journey. Whether it’s understanding AI’s role in their work, automating tasks, or building solutions, we guide them in gaining technical confidence and shaping the field. Some members have landed AI-related jobs or joined AI policy teams at their organizations. Having diverse perspectives in AI is crucial. Women in our community, particularly those from HR and other industries, quickly identify biases and blind spots that might otherwise go unnoticed. We need more voices questioning and shaping AI while we’re still in its early stages. Ross: That’s fantastic. Looking ahead to 2026, what excites you most? Helen: Personally, I’m excited about having our third baby! It’s a reminder of the new perspectives each generation brings. For Women Defining AI, 2025 will be the year we build in public. We’ve been experimenting and learning internally, but now we’re sharing real stories and projects to inspire more builders and technologists. Ross: That’s fantastic. Thank you for your time, insights, energy, and passion. Helen: Thanks for having me. The post Helen Lee Kupp on redesigning work, enabling expression, creative constraints, and women defining AI (AC Ep78) appeared first on Humans + AI. | |||
| Michel Bauwens on challenging presuppositions, meta-curation, changing paradigms, and creating narratives (Ep37) | 20 Oct 2022 | 00:35:06 | |
Michel Bauwens is the founder of the P2P Foundation working in collaboration with global researchers in exploring the potential of peer production. Michel travels extensively giving workshops, and lectures on P2P, commons, and the opportunities of a post-capitalist world. | |||
| Dr Kristy Goodwin on the four pillars to peak performance, digital guardrails, working with your biological blueprint, and improving micro-habits (Ep36) | 13 Oct 2022 | 00:34:33 | |
Robin Good is a writer, speaker, and change agent focused on content curation, learning, and collaboration. By emphasizing quality, credibility, and shared values, Robin has been helping entrepreneurs and small businesses share their content to develop long-lasting relationships and become reference points in their online market niches. | |||
| Robin Good on questioning authority, finding trusted advisors, focus sharing, and information design (Ep35) | 28 Sep 2022 | 00:42:09 | |
Robin Good is a writer, speaker, and change agent focused on content curation, learning, and collaboration. By emphasizing quality, credibility, and shared values, Robin has been helping entrepreneurs and small businesses share their content to develop long-lasting relationships and become reference points in their online market niches. | |||
| Cindy Otis on the disinformation landscape, analyzing content, identifying trustworthy sources, and information communities (Ep34) | 21 Sep 2022 | 00:30:32 | |
Cindy Otis is an author, disinformation expert, and former CIA officer. She is the author of books including True or False: A CIA Analyst’s Guide to Spotting Fake News. Cindy is a frequent media commentator on the Washington Post, New York Times, BBC, NPR, and CNN. | |||
| Pia Lauritzen on the possibilities of questions, collective curiosity, diverse question cultures, and making room for exploration (Ep32) | 17 Aug 2022 | 00:29:21 | |
Pia Lauritzen is the co-founder and chief scientific officer at Qvest, a technology company that unleashes the power of questions in companies and communities. She is the author of the book Questions and a regular contributor to strategy+business magazine. | |||
| Remi Kalir on social annotation, self curation, the connective tissue of ideas, annotation tools, and nuance for synthesis (Ep31) | 10 Aug 2022 | 00:34:02 | |
Remi is Associate Professor of Learning Design and Technology at the University of Colorado Denver, and a leading scholar of annotation. He is co-author of the book Annotation, published by MIT Press, and many journal articles on the subject. Remi is also Scholar in Residence at Hypothesis and the co-founder of Marginal Syllabus. | |||
| Rohit Krishnan on looking for surprise, passionate curiosity, dynamic loops, and creating your worldview (Ep30) | 27 Jul 2022 | 00:30:03 | |
Rohit is Investment Director at leading global venture capital firm Unbound, focusing on software and fintech. He was previously VP at Eight Roads Ventures and was leader of McKinsey’s growth tech practice. He is author of the Strange Loop Canon on Substack. | |||
| Sam McRoberts on connecting the dots, being humbly curious, introducing randomness, and thought experiments (Ep29) | 21 Jul 2022 | 00:28:36 | |
Sam is the CEO of global SEO agency VUDU Marketing, and the bestselling author of Screw the Zoo. He is the co-host of The Entrepreneur Cast podcast and frequently appears in media such as Forbes, Entrepreneur, Business Insider and many others. | |||
| Derek Laney on transcending emotional overload, openness for serendipity, balancing focus and discovery, and using threads well (Ep28) | 14 Jul 2022 | 00:38:41 | |
Derek Laney is Technology Evangelist for the Future of Work at collaboration platform Slack, having previously held a range of senior roles at Slack’s parent company Salesforce. | |||
| Berit Anderson on mapping influencers, noticing breaks in patterns, ignoring headlines, and information warfare (Ep27) | 05 Jul 2022 | 00:34:05 | |
Berit is COO of Strategic News Service and director of programs for the Future in Review conferences. She co-founded and was CEO of Scout.ai, a media company exploring the future of technology. Her work on information warfare has been widely featured in major media such as New Yorker and TechCrunch and she is a frequent international keynote speaker. | |||
| Human AI Symbiosis Compilation (AC Ep77) | 13 Feb 2025 | ||
"Generative AI is the first technology with an almost natural propensity to build a symbiotic relationship with us. But symbiosis isn’t always mutualistic—it can be parasitic, where AI benefits at the detriment of humans. How we deploy AI will determine which path we take." – Alexandra Diening "AI provides dual affordances—it can automate our work or augment our abilities. The key challenge is deciding where to draw the line. In low-stakes tasks, automation makes sense. But in high-stakes decision-making, human intuition is irreplaceable." – Mohammad Hossein Jarrahi "We talk a lot about lifelong learning, but we also need to embrace lifelong forgetting. If we keep piling new knowledge on top of outdated thinking, we won’t evolve. The future isn’t about ‘us vs. them’—it’s about humans and AI co-evolving together." – Erica Orange "AI isn’t just changing how we work—it’s changing what it means to be human. We are interlacing with technology more deeply than ever, and in the future, AI won’t just be something we use—it will be something we integrate into ourselves." – Pedro Uria Recio About Alexandra Diening, Mohammad Hossein Jarrahi, Erica Orange, & Pedro Uria RecioAlexandra Diening is Co-founder & Executive Chair of Human-AI Symbiosis Alliance. She has held a range of senior executive roles including as Global Head of Research & Insights at EPAM Systems. Through her career she has helped transform over 150 digital innovation ideas into products, brands, and business models that have attracted $120 million in funding . She holds a PhD in cyberpsychology, and is author of Decoding Empathy: An Executive's Blueprint for Building Human-Centric AI and A Strategy for Human-AI Symbiosis. Mohammad Hossein Jarrahi is Associate Professor at the School of Information and Library Science at University of North Carolina at Chapel Hill. He has won numerous awards for teaching and his papers, including for his article “Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making.” His wide-ranging research spans many aspects of the social and organizational implications of information and communication technologies. Erica Orange is a futurist, speaker, and author, and Executive Vice President and Chief Operating Officer of leading futurist consulting firm The Future Hunters. She has spoken at TEDx and keynoted over 250 conferences around the world, and been featured in news outlets including Wired, NPR, Time, Bloomberg, and CBS This Morning. Her book AI + The New Human Frontier: Reimagining the Future of Time, Trust + Truth is out in September 2024. Pedro Uria-Recio is a highly experienced analytics and AI executive. He was until recently the Chief Analytics and AI Officer at True Corporation, Thailand’s leading telecom company, and is about to announce his next position. He is also author of the recently launched book Machines of Tomorrow: From AI Origins to Superintelligence & Posthumanity. He was previously a consultant at McKinsey and is on the Forbes Tech Council. Websites:
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Ross Dawson: So, you've recently established the Human-AI Symbiosis Alliance, and that sounds very, very interesting. But before we dig into that, I'd like to hear a bit of the backstory. How did you come to be on this journey? Alexandra Diening: It's a long journey. I'll try to make it short and interesting. I entered the world of AI almost two decades ago through a very unconventional path—neuroscience. I'm a neuroscientist by training, and my focus was on understanding how the brain works. Naturally, if you want to process all the neuroscience data, you can't do it alone. You inevitably have to touch upon AI. That was my gateway into the field. As I started working with AI, I gained a basic understanding of how it operates from a technical perspective as a scientific discipline. At that time, there weren’t many people working in this kind of AI, so the industry naturally pulled me in. I started working in the business application of AI, progressively shifting from neuroscience to AI deployment within a business context. I worked with Fortune 500 companies across life sciences, retail, finance, and many more industries. That was my entry—my "chapter one"—into the world of AI. But as I began deploying AI within real businesses, I started noticing patterns. Sometimes AI projects succeeded, and sometimes they failed. I realized that success was most often achieved when we doubled down on human-centricity. That was an easy concept for me to grasp because cognitive science is my foundation. This human-centric approach became even more important with the emergence of generative AI. AI was no longer just in the background, crunching data and steering our decisions without us realizing it. AI has been around for quite some time, but suddenly, we could interact with it directly, almost like an agent. We could communicate with it using our language. It could capture emotions, build relationships with us, and augment our capabilities. It was no longer just a tool—it was becoming a social-technological actor. This realization led us to our hypothesis: generative AI is the first technology with an almost natural, almost default propensity to form a symbiotic relationship with humans. It’s not just a tool that does something or doesn’t—it’s about mutual interaction. The term "symbiosis" sounds very romantic, particularly because of the way pop culture has shaped our understanding of it. But in nature, symbiosis manifests across a spectrum of outcomes. It can be highly positive and mutualistic, where both parties benefit—humans improve, and AI gets better. However, it can also be parasitic, where one party benefits at the detriment of the other. This pattern became clear to me, especially as generative AI adoption increased. I saw the emergence of what I call "parasitic AI," and that realization started stealing my sleep. I was no longer proud of the AI world we were building. At the time, I was working for a multibillion-dollar tech company, and I doubled down on advocating for responsible AI and human-centric practices. But even with all the support in the world, I quickly realized that corporate agendas and business impediments limited the impact I could make. That’s why we established the Human-AI Symbiosis Alliance. Our goal is twofold: first, to educate people that AI can be parasitic. It’s not just a happy story, and it’s not simply about AI taking over—it’s about how we deploy it. Second, we want to teach and empower companies to steer AI development away from parasitism and toward mutualistic AI. Ross: We are deeply immersed in digital environments, and these systems are becoming increasingly human-like. You mentioned the idea of positive symbiosis. Achieving that requires well-designed systems and an understanding of how humans behave. What do you see as the foundational leverage points that can shift us toward a positive and constructive symbiosis between humans and AI? Alexandra: The most important realization is that AI is not a living entity. It’s just a large dataset. It doesn’t have consciousness, intent, or agency. Instead of seeing AI as something that will inherently harm us, we need to take responsibility for how we deploy it. Of course, we need to ensure AI is properly regulated, that it is trained on unbiased data, and that we establish appropriate guardrails. But there’s another chapter of the conversation that very few people talk about, and it keeps me up at night: the way we deploy AI. Deploying AI in a way that doesn’t harm individuals or companies is critical. No company wants to build parasitic AI within its environment. The main issue in deployment comes from literacy. Many software engineering companies are now venturing into AI without realizing that AI development is fundamentally different from traditional software development. You cannot deploy AI the same way you deploy web pages or apps. It has a completely different lifecycle, set of activities, and expertise requirements. Raising awareness about this difference is crucial. Beyond that, we need frameworks—structured processes that guide responsible AI deployment. We also need to recognize that AI is not just a technology we implement; it’s a symbiotic relationship we must architect. That means not only enhancing employee efficiency in the short term but also ensuring that AI doesn’t erode human skills over time. Otherwise, we risk creating a workforce that is highly efficient but, in the long run, less capable. Another crucial element is measurement. The traditional ways we measure technology success—primarily through productivity and efficiency—are outdated for AI. We need to consider additional factors, such as how AI impacts innovation, employee well-being, and a company's brand relationships. Instead of being shortsighted, we need a long-term focus on AI’s broader impact. Finally, AI brings entirely new risks, many of which are unprecedented. A very personal and tragic example is the case of a teenager who took his own life after interacting with an AI chatbot. When I used to warn clients about the importance of setting the right level of anthropomorphism and properly guarding AI to prevent harm, it often felt abstract. But now, unfortunately, we have a very tangible example of how things can go wrong. The key takeaway is that building a responsible, mutualistic AI requires expertise, proper architectural planning, accurate measurement frameworks, and a heightened awareness of risks. If we get those things right, we can steer AI away from parasitism and toward a future where it genuinely benefits society. Ross:In this section, we hear from Mohammad Hossein Jarrahi, Associate Professor at the University of North Carolina, Chapel Hill, from Episode 62. Ross: So, you have been focusing on human-AI symbiosis. I’d love to hear how you came to believe this is where you should be focusing your energy and attention. Mohammad Hossein Jarrahi: It was in 2017, and I was stuck in traffic. If I want to tell you the story, there was an IBM engineer being interviewed on NPR. They were asking him a bunch of questions about the future of AI. This was before the rise of ChatGPT and what I would call the consumerization of AI. As I was sitting in traffic with not much to do, something clicked. The engineer was providing examples that fit into three categories: uncertainty, complexity, and eco-locality. As soon as I got home, I immediately started sketching out an article and finished writing it within two weeks. The idea was that we, as humans, have very unique capabilities, but we tend to underestimate them. At the same time, the smart technologies we see today—at that time, primarily powered by deep learning—are inherently different from previous information technologies. This means we need a completely different paradigm to understand how humans and AI can work together. AI isn’t going to make us extinct, but we shouldn’t treat it as just another infrastructure technology, like Skype or other traditional communication tools. That’s when I realized that the term human-AI symbiosis—which comes from biology—was a perfect way to describe how two sources of intelligence can work together. Ross: That concept is very much aligned with my work and the people I engage with. The key question is, how do we make it happen? There are quite a few people exploring this path, but we don’t yet have all the answers. What are some of the pathways that could move us toward effective human-AI symbiosis? Mohammad: It really depends on the context. That’s the crux of the issue I’ve been exploring in my articles. The question of how much we can delegate to AI isn’t black and white. It exists on a spectrum between automation and augmentation. AI provides dual affordances—it can automate tasks or augment human capabilities. Automation means AI performs tasks autonomously with minimal supervision. Augmentation, on the other hand, keeps humans deeply involved, making them more efficient and effective. The balance between automation and augmentation depends on the context:
Machines are excellent at handling tasks that are repetitive, data-centric, and do not require intuition or emotional intelligence. However, humans excel at exception handling—making nuanced judgment calls. For example, consider loan applications:
That’s why, when it comes to organizational decision-making, AI shouldn’t be the sole authority. Stakeholder interests are often in conflict—what benefits shareholders may harm employees or customers. AI tends to optimize for one metric, but a human leader must strike a balance among competing priorities. Ross:I think a lot about the architecture of AI integration. Keeping humans in the loop is important, but where should humans be involved? That depends on the organization, decision type, and context. Are there structured ways we can design points of human involvement—whether in exceptions, approvals, or shaping judgment? Mohammad: The simplest answer is that humans should be involved whenever intuition is required. In my article on human-AI symbiosis, I described two decision-making styles:
For example, in algorithmic management, AI can assist managers, but the higher you go in an organization, the more important intuition becomes. Research in management and psychology has shown that holistic decision-making—which accounts for multiple stakeholders—relies heavily on intuition. If AI only optimizes decisions based on data, it risks missing broader considerations, such as company culture, long-term brand impact, or ethical concerns. That’s why judgment calls must remain in human hands. Ross: Next, we hear from Erica Orange, futurist and author of AI and the New Human Frontier, from Episode 59. Ross: What will allow us to master AI and ensure it benefits humanity? Erica Orange: That’s such a great question. I often talk about the difference between lifelong learning and lifelong forgetting. It’s common to hear that we should all be lifelong learners—constantly acquiring new knowledge to stay relevant. But if we keep layering new information on top of outdated thinking, we won’t truly evolve. We must also become lifelong forgetters—letting go of outdated assumptions, biases, and ways of working. I often tell my clients and audiences to identify one or two things they’re holding onto that no longer serve them. It could be a belief, a work habit, or an outdated mental model. The faster we embrace forgetting, the more space we free up for new ways of thinking. Another key point is to embrace the "AND" mindset instead of thinking in polarized extremes. We live in a world of hyper-polarization—social media echo chambers and tribalism reinforce "us vs. them" thinking. But the future isn’t either-or—it’s about "and." For example, when discussing humans and AI, there’s often fear of an "AI takeover." But AI isn’t replacing us—it’s collaborating with us. The reality is one of coexistence and co-evolution. The same applies to progress and stagnation, chaos and creativity, imagination and inertia—these forces always exist together. Ross: Finally, we hear from Pedro Uria Recio, author of Machines of Tomorrow, from Episode 50. Pedro Uria Recio: In Machines of Tomorrow, I explore AI through human history. From ancient aspirations of creating human-like machines to today’s generative AI revolution, AI has always been intertwined with our progress. One of the book’s key concepts is interlacing—the idea that humans and AI will become more intimately connected. Right now, we use smartphones for everything. The fact that they exist outside our bodies is merely an anecdote—in the future, they will be inside us. Brain-computer interfaces, robotics, and AI-driven medicine will interlace humans and AI, potentially transforming humanity into a new species. This shift won’t happen overnight, but AI will be central to our evolution. Ross: That wraps up this episode. Thank you to all our guests for their incredible insights on human-AI symbiosis. The post Human AI Symbiosis Compilation (AC Ep77) appeared first on Humans + AI. | |||
| Brenda Ramokopelwa on using external and internal lenses, developing young futurists, connecting rural Africa to global thinking, and validating ideas (Ep26) | 21 Jun 2022 | 00:34:10 | |
Brenda is a Futurist, Author, Keynote Speaker, and Award-Winning Risk and Governance professional. She is the CEO of the Transdisciplinary Agora for Future Discussion and Managing Director at D@leo Consulting Services in Johannesburg. | |||
| Julie Rasmussen on her 7 S’s system, using Slack for note-taking, identifying systemic issues, and finding white spaces (Ep25) | 15 Jun 2022 | 00:34:19 | |
Julie is a highly experienced corporate executive and entrepreneur, taking senior leadership and board roles in a range of industries and several countries for major organizations including Mary Kay, CVSL and EnXray. She is now the founder and CEO of She Banks, a fintech startup whose mission is to increase financial security for women. | |||
| Paul X. McCarthy on networks to find experts, identifying authorities, computational social science, and latent knowledge (Ep24) | 07 Jun 2022 | 00:26:45 | |
Paul is CEO of data science and research startup League of Scholars, which works with a wide range of organizations including Nature and News Corporation, and the cofounder of a number of other ventures, He is an Adjunct Professor at U of NSW and Honorary Research Fellow at Western Sydney University, and the author of Online Gravity, a successful book on how technology is rebooting economics. | |||
| Nick Abrahams on purpose and prioritisation, talking for mutual value, deliberate sharing and engagement, and telling stories for understanding (Ep23) | 31 May 2022 | 00:31:23 | |
Nick Abrahams is a leading lawyer, futurist, and keynote speaker. He is the Global Co-leader of the Digital Transformation Practice at Norton Rose Fulbright advising major organisations on technology M&A, blockchain and cryptocurrency, and digital transformation. | |||
| Joyce Gioia on identifying trends, scanning processes, stakeholder experience, and adopting personas for communicating (Ep22) | 25 May 2022 | 00:30:54 | |
Joyce is a strategic business futurist and President of The Herman Group appears regularly in the media, including in Entrepreneur Magazine, Business Week, The Wall Street Journal, and NPR. | |||
| Stephen Poor on discerning relevance, distilling facts, thriving for lawyers and legal students, and consciously seeing connections (Ep21) | 18 May 2022 | 00:32:30 | |
Stephen Poor is Chair Emeritus of leading employment law firm Seyfarth & Shaw, which has 900 lawyers across 17 offices and multiple continents. He led the firm as Chairman for 15 years, introducing a range of industry-leading innovations, and he now focuses on the firm’s client-facing technology strategy which includes robotics, AI, and cognitive computing. | |||