Humans + AI

Ross Dawson
  • 5.0 (1)
  • Management
  • Updated weekly

Exploring and unlocking the potential of AI for individuals, organizations, and humanity

  1. Natalie Buda Smith on AI in libraries, human at the center, deeper storytelling, and language recreation

    21 hr ago

    Natalie Buda Smith on AI in libraries, human at the center, deeper storytelling, and language recreation

    Join Natalie Buda Smith, Director of AI at the Library of Congress, as she explores how digital interfaces and AI are revolutionizing access to human knowledge and cultural memory. In this episode, you'll learn about the shift from primary source access to information intermediated by AI, the importance of preserving historical context through multiple digitization versions, and the challenges of navigating proprietary data and open APIs. Natalie Buda Smith shares firsthand insights into empowering staff with AI tools, fostering personalized information delivery, and how collaborative, AI-powered projects are surfacing new connections and creative storytelling across diverse collections.

    34 min
  2. Scott Wolfson on cognitive fitness, System 3 thinking, mastery skill games, and Strategic Imagination Machines

    10 Jun

    Scott Wolfson on cognitive fitness, System 3 thinking, mastery skill games, and Strategic Imagination Machines

    In this episode, CentaurianAI Co-Founder shares how becoming 'unbottable' is essential in the age of AI and explores the Centaur chess mindset for thriving alongside intelligent machines. Listeners will discover practical techniques—like brain dumping, ignorance mapping, and the 'think, prompt, check' approach—to boost cognitive fitness, foster independent thinking, and future-proof their unique value. The conversation delves into mastery skill games, motivational intelligence, and the power of collective, connected intelligence for building a truly wisdom-driven future with AI.

    36 min
  3. Hala Nelson on human machine coexistence, ontology first, AI driven digital twins, and bidirectional connections to reality

    3 Jun

    Hala Nelson on human machine coexistence, ontology first, AI driven digital twins, and bidirectional connections to reality

    Explore how AI is redefining the boundaries between uniquely human intelligence and machine capabilities, and discover which aspects of intelligence remain distinctly human. This episode delves into building smarter, more efficient organizations by leveraging the complementary strengths of people and AI—focusing on the critical role of an ontology-first approach, knowledge graphs, and live digital twins in digital transformation. Listeners will gain actionable insights into integrating dynamic processes for real-time decision-making, structuring enterprise knowledge, and eliminating organizational inefficiencies using practical, AI-powered solutions.

    32 min
  4. Ross Dawson on cognitive friction, beyond Human-in-the-loop, and AI-augmented strategy

    27 May

    Ross Dawson on cognitive friction, beyond Human-in-the-loop, and AI-augmented strategy

    “The value is created in the friction, in the engagement between humans and AI—the pushing back by the humans, the pushing back by the machines.” –Ross Dawson About Ross Dawson Ross Dawson is a futurist, keynote speaker, strategy advisor, author, and host of Amplifying Cognition podcast. He is Chairman of the Advanced Human Technologies group of companies and Founder of Humans + AI startup Informivity. He has delivered keynote speeches and strategy workshops in 33 countries and is the bestselling author of 5 books, most recently Thriving on Overload. Website: rossdawson.com LinkedIn Profile: Ross Dawson What you will learn The dangers of aiming for a frictionless experience between humans and AI Why meaningful engagement—rather than passive approval—between humans and AI is crucial for cognitive augmentation How human judgment and reasoning differ, and where AI excels versus where humans add irreplaceable value The four key pitfalls of the traditional ‘human in the loop’ approach to decision-making with AI Why too much delegation to AI can erode human vigilance, judgment, and accountability The importance of adversarial, not just assistive, collaboration with AI for complex, high-stakes tasks How ‘living strategy’—AI-augmented, continuously updated organizational strategy—addresses the limitations of static strategic planning The role of AI in surfacing diverse perspectives, supporting dialogue, and enabling truly adaptive decision-making Episode Resources Transcript Ross Dawson: I love speaking to the wonderful guests I have on my podcast. I always learn an enormous amount, but in this episode, I’ll share a little bit of an update for myself and delve into a few interesting things I’ve been seeing and doing lately, including some of the most interesting research papers I’ve seen on humans plus AI lately, looking at human in the loop and the ways in which we should be thinking about that, and AI and strategy. So, just a quick scan of what’s going on in humans plus AI. I’ve been traveling quite a bit, doing a lot of keynotes as much as possible on humans plus AI, and the resonance around the theme is really rising very rapidly. In fact, somebody recently mentioned that humans plus AI was a cliché, or just overworn at the moment. Since I first started using the phrase three and a half years ago, I think it’s wonderful that now it is gaining a lot of currency. People are talking about it, framing that. Yes, some phrases outlive their usefulness, but I think I’ll stick with humans plus AI for the foreseeable future. The research papers I’ve been looking at are focused on essentially cognitive augmentation and erosion, and that’s this critical domain where it’s not really clear around whether, or in which circumstances, our cognition erodes, and what it is we can do to make it augmenting. One of the excellent papers is titled Cognitive Agency Surrender: Defending Epistemic Sovereignty via Scaffolded AI Friction. It’s a bit dense, but it has some great research and analysis in it. The key finding, which it begins with, is that in human-computer interface research literature over the last while, we saw that last year, 2025, there was a big, big rise in this idea of driving human sovereignty in how it is we interact with computers. However, since last year to the first part of this year, we’ve in fact seen that fall dramatically, where the human sovereignty paradigm is reducing dramatically, and we are seeing this big rise in what is called the frictionless paradigm, saying: how do we get as little friction as possible between humans and AI? There are a number of really important points made in the paper, and really, the starting point is saying that we should stop treating frictionless AI as the goal. If we start to be frictionless, that is starting to essentially take the human out of the loop. The nature of humans is that we need to engage, we need to think, so we need to start building devil’s advocate agents into the systems and to aim for this thing where we start to have both this high degree of engagement with the AI, but also high friction. That friction is where we are trying to, essentially, the more complex one rising, having more and more friction, and in lower frictions, it’s just more so. Label tasks, but where we’re not just showing the reasoning, giving people the ability to think through tasks and how they think about that, but to be able to challenge, actively challenge people as they are thinking through things. More broadly, ensuring that the way in which we are designing systems is not emphasizing this frictionless, seamless flow between humans and AI, because that is where the value is created: in the friction, in the engagement between the humans and AI, the pushing back by the humans, the pushing back by the machines, to be able to drive us and move us forward. Some really interesting research here, which was very much echoed in another very interesting paper called A Task-Driven Human-AI Collaboration: When to Automate, When to Collaborate, When to Challenge. This idea is essentially saying that the default mode for complex, high-stakes work should be adversarial, not assistive. This is, again, obviously, looking at what types of tasks or what types of situations we’re in as to adjust how the machine works, but when we are working in the complex world, we need to be pushing back around the way people’s thinking. It becomes easier, and we’re not looking for the path of least resistance. We’re looking for ones where we’re adversarial. In fact, you can really see that there is no middle, what’s called this. There is no AI zone, which is in the middle, where essentially the intermediate tasks are ones where, in fact, involving AI can, or involving AI to human decision, involving human and AI decision, is not necessarily the best path. And so, what we need to focus on is the ends of the spectrum, where it becomes a truly collaborative task, or it is purely AI or purely human. This actually goes very neatly and smoothly into the work which I’ve been doing around human in the loop. People have been talking human in the loop all the time; it’s a very common framing. But what I’ve come to realize, and in fact, my research has borne this out, is that in the vast majority of cases when people say human in the loop, what they actually mean is that the human gives a stamp of approval at the end. An AI makes the decision, then the human says yes or no, or overrides it. That means that they are accountable, whoever the human is at the end. But there are a number of fundamental problems with this structure, four in particular. One is that people tend to defer to the AI. AI is usually right, and so, essentially, more and more, you are deferring to the machine. A number of studies have borne out this figure of a 93% approval rate in human approval on an AI or automated system, so very high levels of approval. This starts to become, “Well, by default, I’m going to accept this,” which tails to the second point, which is the decay of vigilance. Essentially, over time, you are paying less and less attention. It is easier and easier for the human to essentially pay attention and say, “It was probably right. It seems to be good.” My mind is wandering, and I’m not necessarily going to be taking the full attention, which my accountability should point to. This goes on to the next point, where this role of putting the human at the end of a decision actively erodes their judgment. In one of the frameworks which I shared a little while ago, there was the decision between reasoning and judgment. Reasoning, going through multiple steps, is something which actually AI can do. It’s looking at the different logic, looking at the steps, looking at the relationships, and being able to make a sequence of logic leaps to be able to get to a point. Judgment is the human part. That is the context, that is the thinking, that is the richness, that is the values, that is the ethics, that is what we bring to bear through the full extent of our human experience. So that is exactly what the human in the loop is: the human applying their judgment to something the AI has done. But if that is all the human does, provide a judgment at the endpoint, it actively erodes their judgment because they aren’t seeing all of the richness of the reasoning which went through to be able to create that decision. They are potentially being stuck in one single point and taken away from the richness of the context and the experience, which gives them that ability to be judgment. So, sticking a person in that human in the loop basically erodes their judgment and makes them less valuable over time, and essentially, obviously, is setting us up for a world where that human eventually gets taken out. The fourth problem is simply that this model cannot scale, where we are going to have more and more decisions. We need more and more accountability in systems, and just sticking people at the end of the human in the loop means that that’s going to limit how well we can build decisions that have an impact and have value. So these are some fundamental challenges. I guess this relates to some upcoming work, or some work which I have been spending a lot of time on, and which I’ll be releasing pretty soon now, whi

    18 min
  5. Kathleen deLaski on reimagining higher education, generational mobility, building AI skills, and human originality

    13 May

    Kathleen deLaski on reimagining higher education, generational mobility, building AI skills, and human originality

    “There’s a real ‘skillification’ movement where you just want to get the training you need when you need it.” –Kathleen deLaski About Kathleen deLaski Kathleen deLaski is the founder and board chair of Education Design Lab, which helps reimagine higher education. She is a senior advisor to Harvard’s Project on the Workforce and on the advisory board of the Taubman Center at the Harvard Kennedy School of Government. Kathleen is author of Who Needs College Anymore? Imagining a Future Where Degrees Won’t Matter. Website: whoneedscollegeanymore.org eddesignlab.org LinkedIn Profile: Kathleen deLaski What you will learn The evolving value of college degrees in a rapidly changing economy Who benefits most from higher education, including four key learner profiles The rise of ‘skillification’ and alternative pathways to career readiness How employers assess degrees and non-degree credentials in today’s job market The impact of AI on both education and workplace expectations Why AI literacy—and understanding its limits—matters for career success The growing divide between technical and non-technical learners regarding AI adoption Practical strategies for maximizing uniquely human skills—like originality and judgment—in an AI-powered world Episode Resources Transcript Ross Dawson: Kathleen, it’s a delight to have you on the show. Kathleen deLaski: Thanks for having me, Ross. Ross: So, amongst many other things to your name, you have a fairly recent book out called “Who Needs College Anymore?” So, does anyone need college anymore? Kathleen: Yes, the answer is yes. There are people who are looking to bash the notion of a three- or four-year university degree, but they need to look somewhere else. What I try to do in the book is serve two audiences. One is universities—what we call colleges in the US—who are actually in a state of panic right now about surveys showing that people are not valuing degrees anymore. It’s a perfect moment to reassess: what does a degree need to deliver as we approach the mid-21st century? That’s the hot topic, the debate that’s raging. To frame the question, “Who needs college anymore?” is to say, “Wow, you need to step up your value proposition in this age,” especially when, at least here, the number of 18-year-olds is dwindling and we have AI and technological solutions that allow people to get skills as needed. There’s a real ‘skillification’ movement where you just want to get the training you need when you need it. There’s also a questioning of hanging around to learn about the liberal arts, to do your philosophy, English, or history required classes—can’t we get right to the skills? That’s the debate that’s raging. So, colleges need to hear this message; that was one audience. Secondly, I know so many students—even in my own family—who are trying to parse the different messages they’re hearing. One message is, “You absolutely need a four-year degree if you want to get a ‘good job.'” The other message is, “College isn’t worth it anymore; you can just get the skills you need and get the job.” Meanwhile, families think the price tag is going up and up. Here, it’s staggering—although, in reality, universities in the US have actually begun to hold prices and even give a lot of discounts because they’re short on the number of folks coming through the door. So, all these confusing messages—I think families also need to understand who exactly, among different types of learners, does need a degree and who doesn’t. Which jobs, which age groups, which learning types? I actually walk through all those using a human-centered design approach. Ross: Human-centered is a good way to go. So I and others have talked about the unbundling of higher education, and there are a number of elements to that, including the educational processes, the social connections, sometimes the physical place, the links with employers and credentials. Of all the facets bundled together in a degree, the real focus, of course, is on the certification—you’ve got a degree—and the point to which that signals to employers. I suppose that’s usually the name of the game. It’s the differentiator. In the past, we’ve seen that in some fields—most notably software—where you can get some indicators of competence outside a degree, and employers have been more than happy to accept that. So, just focusing on the credential, what is the role of the credential today? Kathleen: Yeah, that’s an excellent question, because it’s particularly coming into question now. We have, like, 1.7 or 1.8 million different distinct credentials in the US alone. If you added the worldwide number, it would be bigger. So, what are learners to make of those? What are employers to make of those, when only a smaller percent are part of a degree? I say that we are absolutely at a time when the degree matters most, but there are many careers and moments in time when you can hack needing the whole degree. Those moments are in a very tight job market, where employers can’t find enough people, and in sectors that are either new—because people don’t know about them yet, they’re emerging—or they’re very old school, like insurance adjusters, where the workforce is retiring and nobody wants to do those jobs anymore. So, new and old sectors, as well as highly technical sectors that require constant upskilling to stay in the game—things like AI, quantum, and parts of cybersecurity fit into that category. The signal power of a non-degree credential rises in careers certain and certain moments of time, but the degree is always a nice booster. The point is, you can get away with not having the degree in the situations I just described. Ross: Yes, well, I was just about to leap to our current moment because it has a few specific characteristics. But let’s dig a little more into some of the book’s ideas. You describe four types of people for whom degrees are relevant, which suggests that people who don’t fit in those categories may have alternative paths. So, as you say, it’s related to the economy, the specific type of job or industry, but also to the individual and where they are in their life. Who are the people that do get the most value from a higher degree? Kathleen: This may be different in different parts of the world, but I think the basic principles probably carry over. The first category, and this is where the research is the best, is what I call a “class transporter.” That’s someone trying to move from a lower or off-the-grid economic class here in the US to the middle class. This is often an immigrant family, where the parents came to this country specifically so their kids could get ahead, knowing they would never be able to get a degree themselves. They’re working three minimum-wage jobs so their kids can live in a neighborhood with decent schools and then get into university. The entire family is lifted up into the next economic rung. Part of what the university degree does for that student is help with networking, code-switching, and, of course, the technical skills needed to land a role. That’s the number one category, because the research shows that in one generation, you can lift your family up. I actually start the book with the story of how my family did that in the 17th century. My relative came over, we think, in the belly of a ship as an indentured servant from England and was able to be one of the first students at this new college called Harvard, which was the first college in America. He got his son in—who’s my great-grandfather times seven—and then the family was off and running. He became a well-known minister, and his ten brothers and sisters didn’t get to go to college. That’s a very typical story even today. It’s that rags-to-riches story where college is so much a part of the American dream. It’s the launch pad, and that’s ingrained in all of us. So that’s the number one category. The others are probably more strange. Ross: On that, one of the things I’m very interested in globally is relative generational mobility. The countries with the greatest generational mobility are Scandinavia; Latin America has some of the least. Generational mobility—the ability for children to do better than their parents—America is actually not that high. For all the talk of the American dream, I’m not sure of any studies that show the role of education in generational mobility across countries. I’m not sure whether you do. Kathleen: That would be very interesting. Ross: Yeah, I guess a fair hypothesis would be that in America, that is particularly high. Kathleen:  Well, surprisingly to many of us—myself included when I started researching the book—only 38% of Americans get a four-year university degree, which always strikes people as really low. They think everybody has access, but the numbers are probably even lower in other places. It’s not like everybody gets to go to college here, either. So, The second category is what I call a “legitimacy labeler.” That’s someone who may not need to move an economic class, but they feel they need that piece of paper for their own self-confidence and self-realization. What’s interesting is this category is particularly populated by women and minorities. When you look at who goes in

    39 min
  6. David Vivancos on the end of knowledge, cognitive flourishing, resilient societies, and artificial democracy

    6 May

    David Vivancos on the end of knowledge, cognitive flourishing, resilient societies, and artificial democracy

    “Delegating knowledge is not the same as delegating wisdom. You learn by experience, and if you don’t have any experiences…you will get cognitive atrophy.” –David Vivancos About David Vivancos David Vivancos is an AI, data, and neuroscience serial entrepreneur, having cofounded five startups since 1995. He is a frequent keynote speaker and is the author of six books, including the Artificiology series. Website: vivancos.com LinkedIn Profile: David Vivancos What you will learn Why embracing advanced AI is crucial for human progress How shifting from digitization to automation and datification redefines value The evolving distinction between human-acquired and AI-generated knowledge How to avoid cognitive atrophy and actively exercise your mind alongside AI What cognitive flourishing means in a world of widespread AI augmentation Ways AI can transform and personalize education across all levels The importance of coexistence training as we prepare for AGI’s societal integration Why rethinking human identity, humility, and social structures is essential for a future with machine citizens Episode Resources Transcript Ross Dawson: David, it is wonderful to have you on the show. David Vivancos: Thank you very much, Ross. Glad to be here. Ross: So you have a more developed, or some would say, extreme view of the relative role of humans plus AI. I’d love to dig into where you think things are going, and how we can best respond. Perhaps the starting point is, you say that we should not be resisting or pushing back. We should fully embrace the shift towards very high levels of AI capability, or at some point, AGI. David: Yeah, that’s fully my point. I think we are in a moment in history where we are really building this technology that one day is not going to be a technology anymore. So, the sooner we start to embrace it, to teach it, and to be really in sync with what we are creating day by day, the better off we will be. So yes, my point of view is that we should embrace it. We should start building as soon as possible. We should fix most of the problems that humans have had over the last millennia, and some of these problems could be solved by using AI. So basically, our “fourth brain”—we have the three-part brain, but in reality, there’s only one brain—this fourth brain, AI, will help us solve all of these issues. So yes, it’s an opportunity. Ross: Yes. I mean, I think there’s always two sides—as in, every opportunity has a challenge, every challenge has an opportunity. So I always think we need to acknowledge challenges and focus on opportunities. I think we’ll get onto that in discussing some of the cognitive implications. You have a series of books which have really told the story over time around this. One of them was “Automate or Be Automated.” This idea of saying, well, there are things which machines, in the broader sense, can do in automating things. So, how would you frame that now, in terms of what it is that can be automated, and how do we position ourselves relative to that? Where do machines start to do what humans have done? David: Yep. I’ve been in this business of trying to build the impossible for the last 30-plus years. “Automate or Be Automated,” the book you mentioned, is from about six years ago. When I started creating and building technology, also about VR and many other things, about 30 years ago, the first companies were internet companies. Back then, what we did is what people now call digitization. But over the last 20–25 years, what we’ve mostly been doing is datification—gathering data and using that data for companies to grow and to understand what happens in the world. But over the last maybe 10 or 11 years, what I call the new golden age of AI, we are starting to build the capabilities to use that data to really build algorithms. Once we have that, we can start to automate, and with this automation, basically what we regain is time. I think time is our most precious asset, along with health and the people we love. Being able to stop doing these repetitive things over and over and put a machine to do that is a fundamental trait for humans. That book, six years ago, was about building a methodology of what can be automated in the digital world, but also in the physical world. That has changed over the last year and a half with the physicality of AI—humanoid robots. I was invited last year to attend the first humanoid Olympia in Greece, in Olympia, the place where 2,800 years ago, humans started to compete. We’ve just seen this week the explosion of the new race, for example, of the half marathon in China, where robots already beat the human mark. So yes, with automation, you need to see what you are doing, and if you are repeating anything, you can try to see if that can be automated by using an agent, by using the cloud, by using a robot—whatever. So yes, we should regain our time and automate, or be automated. It’s all about that. Ross: Yeah. I think people understand the automation thesis. It’s obviously not new—we’ve been automating things in various ways for centuries, at an increasing pace. Your following book was “The End of Knowledge.” This is an interesting framework, starting to get to cognition. The idea is that knowledge is built on experience of whatever kind, whether that’s just in data or otherwise. Obviously, humans use data just as much as machines. But where this starts to become a distinction, as well as a complementarity, is between AI-embedded knowledge and human knowledge. So why is it “the end of knowledge”? David: Yeah, that’s a really great question. It came as an epiphany for me. That book is from about three years ago. I’ve also been involved, of course, in building AI and AGI algorithms over the last 20 years. We started using GPT models before they became can across, but the GPT moment, a year before that, really marked the difference—when we started to be able to use AI in a very seamless way to regenerate and process knowledge. That book, “The End of Knowledge,” came from the realization that we are starting to delegate the production and understanding of knowledge to machines. That’s a critical shift in human history, because through history, humans have needed and used knowledge a lot. Knowledge is power. The more knowledge you have that others don’t, the more advantages you have to do whatever you want. That started to change back then. Now, what people call the “dead internet theory” is basically some of the things I expressed in that book earlier, because we are starting to generate more knowledge. In fact, we’ve already passed the point where most of the human-written knowledge since the printing press has been surpassed by the amount of knowledge we can create using AI. Myself, for example, I started learning to code when I was young. I’ve coded in more than 25 languages and written over a million lines of code in my life. That same number of lines of code, I might now write in the last couple of weeks. So as you can see, you have 40-plus years of your own life in a week. That’s why “the end of knowledge” means that the human capability to gather knowledge and to be knowledgeable about whatever you want can now be delegated to machines. That book marked the difference and started a new field that I now call artificiality. I didn’t know that when I started writing it, but I started this path of trying to see what happens when you delegate some of the main capabilities of your mind to a machine. Ross: Yeah, and I’d like to come back later to the themes of artificiality, machine citizenship, and the societal value we attribute to machines. But I want to start digging into the cognitive piece here. One of the points you make is that we do need to avoid cognitive atrophy. You say we need to have cognitive exercise in order to avoid cognitive atrophy—obviously, a strong analog to the physical world. We need to collaborate with others and with machines to do that. I’d love to get more specific around that. What is the nature of cognitive exercise that will avoid cognitive atrophy, which will enable us to keep our cognition refined and even improving? David: Yeah, that’s a fundamental piece. When we start to delegate all these things to machines, the easy thing to do—and probably the oldest human brain capability—is to not do it yourself. You just delegate everything, and you basically become like in the movie “Idiocracy,” which played out quite well what could happen if we do that. The thing is, with the current AIs—even with the latest releases, like DeepSeek and GPT-5.5—everything is changing quite fast. But even with those AIs, you still need to be in the loop. It’s good if you stay in the loop. I think it’s fundamental. Use the technologies—the AIs, I always call them in plural because there are many—and use as many as you can, but you should still be in the loop, at least for now. Maybe for a couple of years or months, I don’t know exactly, but for a while, you still need to have your hands on the wheel. If you use most of them and get all the information from all these AIs, as a human you need to understand the bias, because all AIs are going to be biased. We all know humans are biased; there are no unbiased humans. The same happens with AIs. But if you

    36 min
  7. Jon Husband on wirearchy, web weaving, the relational economy, and drift diving

    29 Apr

    Jon Husband on wirearchy, web weaving, the relational economy, and drift diving

    “What I’m really interested in and fascinated about is that, as AI penetrates and spreads throughout the workplace and gets placed into or integrated into workflows, the first thing that happens is that people in the mix are going to have to learn how to use AI and learn why to use AI when they do.” –Jon Husband About Jon Husband Jon Husband is the Founder and Principal of Wirearchy, a creative research and experimentation laboratory exploring the crossroads of AI and networked workplaces and society. He works as a coach, consultant, speaker and writer, and has co-authored three books, including Wirearchy. Website: wirearchy.com LinkedIn Profile: Jon Husband What you will learn The origins and evolution of wirearchy as a response to traditional organizational hierarchies How AI integration is reshaping knowledge work, workflows, and tacit knowledge within organizations The persistence of Taylorist job evaluation and why traditional work design remains resistant to change The rise of the relational economy and the increasing value of human judgment, trust, and relationships beyond financial exchange New approaches and tools for surfacing and mapping intangible or non-financial value exchanges in organizations The concept of emergence and the need to foster conditions for positive outcomes in complex adaptive systems Challenges and opportunities as organizations shift from rigid, control-based management to adaptive, networked, feedback-driven models Why coaching, facilitation, and skills like listening and allowing for emergence will be critical in navigating AI-augmented workplaces Episode Resources Transcript Ross Dawson: Jon, it is wonderful to have you on the show. Jon: Thank you very much, Ross, it’s good to see you again. Ross Dawson: We’ve known of each other and each other’s work for a very, very long time now from, I suppose, the roots of—yeah, I suppose you can crudely say—the intersection of knowledge and networks. So, as I think many of us who have come from that background, we now are thinking about humans and their relative role to AI. Some people will know of your wirearchy and a lot of your work of the past; others will not. So I’d love to just start off with: what is the concept of wirearchy? And then, how is that morphing or evolving, or are you building on that in how you’re thinking now? We’ll dig in and explore that. Jon: Okay, well, I started paying attention to knowledge work and work in organizations and so on as I changed careers in my early 30s, moving from banking, where I was in management, into management consulting. I ended up working for a large global HR consulting firm that, amongst several others—all the major consulting firms that address organizational issues—have services where they do what’s called job evaluation. What job evaluation does is put a size or a measure or a weight to a job, which then basically places it on the organization chart. I spent quite a few years writing thousands of job descriptions and helping streamline workflows and so on and so forth. So, when the internet came along, I had always been an avid reader, and I suppose a wannabe futurist—a wannabe Ross Dawson, if you will. I was reading all sorts of books back then. Instead of dating, because I was single in my mid-30s, I was spending Friday nights reading books about organizations, like “The Living Company” by Arie de Geus, the Tofflers’ work, “Powershift,” certainly Peter Drucker’s work. There was one day—well, I was reading all of these books, and all of the books were about the coming Information Age. The Information Age had not arrived yet; this was roughly late ’80s, early ’90s. All of a sudden, we hit 1994. I’m sitting in London, and I was just told by my team leader in my consulting firm that I was going to be proposed as one of the next global partners. Three weeks later, I quit my job in the consulting firm because I had begun to feel very uneasy about the work I was doing. If I was made a partner, your job becomes basically selling larger projects to keep the younger consultants employed. I realized that I would be selling methods that I had come to not believe in anymore, and the reason for that is that all of the job evaluation methods sold by all the major consulting companies are all versions of generic Taylorism. They have semantic statements that you pick to figure out a level of a job on a number of different factors. This is one of the things I’ve talked and written quite a bit about in wirearchy: this generic Taylorism is still deeply at the core of most of the work of most organizations. It’s how the work is designed. There has been now, what, 15 or 20 years—how far back does Enterprise 2.0 go?—about collaboration and cooperation and better knowledge management and sharing and transfer of knowledge, and so on and so forth. If you know these semantic statements, which are burned into my brain from this method—the Hay method—you realize that no amount of talking about doing things differently is going to make much difference. It’s not going to change much. And the remuneration—the way people get paid—every single person in every single company, is tied to all of that. It’s tied to your job size, it’s tied to the compensation practice, it’s tied to your performance management, it’s tied to your career plans, if an organization is still doing career planning. Frankly, it has not been touched in 75 years now. Ross Dawson: Used to describe it as a job as a box. Jon: Well, sure, and that’s where that term “think outside the box” comes from. I wrote an article about this at one point in time—oh, I can’t remember the title, so it doesn’t matter—but about the semantic statements essentially becoming semantic straightjackets, because they put limits around what you do. They’re a graded level of permissions, basically, or amounts of influence and authority, and that’s the codified, official organizational chart. So anyway, I was working with this all the time, and I realized if I was going to be made a big-time partner, I’d have to be selling these tools all the time. The internet had come along, so I quit, and I didn’t know what to do after that. I had to move from the UK because I was on a work permit, had to go back to Canada. When I went back to Canada, all the companies I tried to approach to work as an independent consultant didn’t want to engage me, because all of the work I’d been doing in the UK was with really large multinationals, and according to them, too sophisticated for what they were doing in Vancouver. But at the same time, I was still reading all the time—reading Charles Handy’s work, reading Gerard Fairtlough’s work on heterarchy, and so on. I came to believe very strongly that the ongoing sharing of information—which we were starting even 20 years ago to build into constant, incessant flows of information carried via hyperlinks—was going to inevitably begin to affect, I’m going to use the word affect, the traditional top-down power of hierarchy. That comes from the “knowledge is power” by Francis Bacon kind of perspective. Now, that was 25 years ago. What we’ve seen since is, of course, what you know—one umbrella term I could apply to much of what’s going on outside of organizations is the “enshittification” of the web. The same thing applies in a lot of ways, I think, to people doing work, sitting behind screens in organizations. Now, a whole host of things have happened in the past 10 or 15 years: there were armies of developers sitting in office spaces, all of them with their headphones on behind screens coding. There were all sorts of people beginning to understand how to use the internet. There were many failed attempts at effective knowledge management because of the idea that it’s still just good search, find documents, retrieval, without really paying any attention to the connections between people and how they work together, and so on. Ross Dawson: So, the frame there is, I mean, obviously, moving—the wirearchy being an arche of the organization being essentially a network. Obviously, there’s more richness to that as you describe the organization as a network, as opposed to the rigid structures, which are still very much rampant. But fast-forwarding to today, what we’ve overlaid is, whilst the old rigid structure is in place, organizations are effectively a lot more loosened up by Enterprise 2.0 and other types of frames, and essentially more peer communication. Now AI is changing a fundamental role, now being, in many ways, a participant in those workflows, in the creation of value. So where does that take us today, in this humans-plus—essentially wirearchy—pulled into where AI plays a role within those networks? Jon: Well, it’s a fascinating question for which I don’t have an answer. I have some responses, I suppose. The notion of wirearchy came, as you pointed out, out of everybody being wired, everybody being networked—the organization as a network. What I’m really interested in and fascinated about is that, as AI penetrates and spreads throughout the workplace and gets placed into or integrated into workflows, the first thing that happens is that people in the mix are going to have to learn how to use AI and learn why to use AI when they do.

    38 min
  8. Michael Gebert on designing freedom, human self-determination, cognitive sovereignty, and systems of agency

    22 Apr

    Michael Gebert on designing freedom, human self-determination, cognitive sovereignty, and systems of agency

    “Freedom no longer exists outside the systems, and it depends on the design. Coming back to the design, it’s about understanding that we need to distinguish between intelligent systems and agency.” –Dr Michael Gebert About Dr Michael Gebert Dr Michael Gebert is Chairman of the European Blockchain Association and co-founder of AI Expert Forum. He works at the intersection of artificial intelligence, digital sovereignty, and institutional responsibility. His book 2079 – Designing Freedom is just out. Website: 2079.life LinkedIn Profile: Dr Michael Gebert What you will learn How the concept of freedom extends beyond politics and economics to personal agency in an AI-driven world Why cognitive sovereignty is essential for maintaining individual responsibility and accountability as intelligent systems become more pervasive The shift from making decisions ourselves to designing the frameworks and conditions for decision-making with AI involvement How to distinguish optimization from true human empowerment when integrating AI tools into personal and organizational life Practical routines and metacognitive strategies for individuals to retain agency when collaborating with large language models and intelligent systems Why organizational leaders must prioritize cognitive sovereignty and human potential early in AI deployment, not just technical efficiency Insights into the challenges and importance of embedding frameworks for freedom and cognitive sovereignty within corporate, governmental, and policy structures The critical need for ambassadors of freedom within institutions to promote reflection, ongoing discussion, and the integration of responsible AI practices across all levels Episode Resources Transcript Ross Dawson: Michael. It is awesome to have you on the show. Michael Gebert: Hey, great to be on the show. Thanks for having me. Ross Dawson: So we connected first, probably around 15 years ago, and we were both involved in crowds, creating value from many people. And I think, you know, there’s one of the interesting points now is, I guess, you know, we still live in a world of many people. We’re trying to create collective value. AI is laid over that. So it’s interesting to see that journey from where we’ve come to where we are today. Michael Gebert: Absolutely, and I really remember visually when we first had contact about this very exciting topic of crowdsourcing and empowerment of the crowd, and really making people believe, not only in themselves, but really in communities. And therefore, not only strengths in terms of crowdfunding, crowd investing, their financial gains, but also being empowered in what they do. And this is a very fundamental, I would say, even a right for humanity to reflect on and do that. I think the methodology and technology back then helped a lot. And to be honest, I’m still partly involved in some of those efforts. Even the big crowdfunding platforms, also here in Europe and in Germany, are vital and really active. Of course, not in that dramatic media shift hype that we experienced, but they’re still there, and it proves that it’s a concept that should stay. Ross Dawson: Yep, absolutely. You know, there’s obviously collective intelligence, amongst other facets. But this goes to, I think, the frame of your new book, 2079, Designing Freedom. So freedom is an interesting word, and something which I hope we all aspire to. Michael Gebert: Yeah, you know, freedom, of course, is one of those very multifaceted words, right? It could be translated in a political context. It could be translated in an economic concept, meaning monetary-wise. It could be translated—and this is my translation—in a very personal, one-to-one reflection about how do I as a human being see myself in that surrounding, bombarded not only by information but by intelligent systems, basically AI as we describe them, and all that is behind those systems. Ross Dawson: So there’s a few things I want to dig into here. And I guess there’s another word there: designing. Obviously, at a societal infrastructure layer, we want to be able to design the systems whereby we can all individually have that freedom of choice in how we live our lives. Michael Gebert: Yeah, and not always, I would say, looking at the world geopolitically, of course, there is sometimes no choice. And if you are able to generate those choices, first of all by understanding how to design them, that’s a very good first step. So when I wrote the book, the prior part was basically a research paper I did, a small research paper also on ResearchGate. This is the foundation where I started thinking and reflecting. Basically, the core there is about a question that I think is becoming unavoidable now and for the future. The question is: if more and more cognition or judgment and action are delegated to intelligent systems, what has to be true for human beings in order to remain genuinely free? So the book is really about freedom, agency, responsibility, and at the end, about belonging in a world of increasingly disruptive intelligence. Ross Dawson: Yeah, yeah. So the word agency is obviously very much of the moment, in lots of ways. But I think human agency is absolutely critical. One of the central things you lay out in the paper, which I think is really, as you were saying a moment ago, is on everyone’s minds. You’re saying this idea of agency used to be about making decisions, whereas now, as you describe it, agency is shifting to authoring the conditions for decision making. So we’re not necessarily making the decisions ourselves, but we do control and guide the conditions, the context, or the structures for decisions so that we retain responsibility and accountability, and those decisions are the ones we would want. So how do we do that? Michael Gebert: Yeah, you know, the question before asking how is really to understand under what conditions do human beings remain authors of their lives when more and more of those decisions are shaped by, as you say, agency systems or whatever name they go by, whether fancy, new, or already existent. So the how—and it’s not about lifting a secret—is about going back to cognition and having that cognitive intelligence and cognitive roots, which are in us, but which, over the years—and you reflected on the last 15 years, especially the generation after 2008, meaning after the iPhone—have lost large parts of that ability, which is very human. So it’s not really a reshaping or something new. It’s also not a book advising how to; it is really a finger going up and saying, people, please remember that the deeper question is under what conditions do human beings remain genuinely free when more and more cognition, judgment, and action is to be owned back and not delegated to the systems. This is, of course, very formal in the need and in the demand, but especially, as you mentioned, when laying it out into organizations or government structures, it is hardcore policy and hardcore principle. You can write a lot of things in your genuine AI policies, but what I see right now is that in reality, first of all, nobody’s really reading them in depth. Secondly, there is really no reflection point on this cognition, judgment, and delegation. Therefore, this is really prior before any interest in how-to in terms of technology and what LLM to choose. This is really prior—it’s day zero—when you think about what’s going on, and when you think about how to position yourself, your company, and your team in there. Then this is the next step of thinking. Ross Dawson: So I want to come back to that, but I think one of the phrases you use is cognitive sovereignty, and this is in a context where one of the most shared papers recently is around cognitive surrender. Cognitive sovereignty is the opposite of cognitive surrender. But the reality is that in interacting with LLMs, it does change our cognition. Michael Gebert: As long as we, yeah, as long as we delegate cognition, basically. The auto effect is— Ross Dawson: Conversation with a human changes our cognition too, and I think we need to recognize that. So it’s not just conversing with LLMs. Conversing with a human changes the way we think, which is a good thing because we’re getting more diverse opinions. But obviously, LLMs are not humans, and while possibly that interaction could enhance our thinking, if we get some great ideas and different perspectives from an LLM, then we’re still retaining cognitive sovereignty. So let’s frame this: how do we as individuals get to cognitive sovereignty? What does that look like? Michael Gebert: Yeah. So first of all, I think we need to understand that when we delegate cognition to an AI, we redesign responsibility. This is undisputably non-negotiable. This is a fact. When you compare it to a human interaction, there is no default responsibility redesign necessary. It’s a reflection point, it’s a discussion. If it’s a good conversation, it’s uplifting for both ends. You go out of this conversation and you have, yeah, uplifted cognition. Surrendering cognition, as you said, is a very factual statement that brings a lot of views, but it’s basically raising the white flag and saying, I surrender. What I say is, no, it’s not time to surrender. It’s time to appreciate, and it is time to understand that freedom n

    38 min
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Exploring and unlocking the potential of AI for individuals, organizations, and humanity

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  • Creator
    Ross Dawson
  • Years Active
    2021 - 2026
  • Episodes
    200
  • Rating
    Clean
  • Show Website
    Humans + AI

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