Humans + AI – Détails, épisodes et analyse

In this thought-provoking episode, futurist Ross Dawson speaks with AI expert Dr. Nisha Talagala about the evolving role of artificial intelligence in education, work, and society. Nisha shares how AI has progressed from obscure experimentation to a mainstream force, catalyzed by powerful computing, vast data, and tools like ChatGPT. She highlights the shift from trying to replicate human intelligence to augmenting it—empowering people to work alongside AI in coding, medicine, and creative fields. At the heart of the conversation is AI literacy. Nisha introduces the "Four C’s"—Concepts, Context, Capability, and Creativity—as essential pillars to help individuals understand, engage with, and responsibly apply AI. Through her initiatives at AIClub, students as young as third grade are building their own AIs, gaining early insight into the ethics and mechanics behind the technology. She emphasizes that adapting to AI isn't about mastering complex algorithms—it's about cultivating curiosity, understanding motivation behind tools, and using AI to elevate human potential.

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.

“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 Gupta

Kunal 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:

Kunal Gupta

Kunal Gupta

LinkedIn Profile:

Kunal Gupta

Book:

2034: How AI Changed Humanity Forever

What you will learn

• Hosting secret AI dinners to spark human insight

• Using personal data to take control of health

• Why cognitive load is the real bottleneck

• When AI becomes a verb, not just a tool

• Reducing frustration through everyday AI

• The widening gap between AI capabilities and adoption

• Empowering curiosity in an AI-shaped world

Episode Resources

Books

• 2034: How AI Changed Humanity Forever

Technical Terms & Concepts

• AI

• AI literacy

• Agentic AI

• Cognitive load

• LLMs (Large Language Models)

• Reference ranges

• Automation

• Browser agents

• Voice agents

• Data normalization

• Longevity-based testing

• Health data

• Cloud computing

• Social media adoption

• Generative AI

Transcript

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.

“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 Rainie

Lee 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:

Lee Rainie

Lee Rainie

Being Human in 2035

 

University Profile:

Lee Rainie

LinkedIn Profile:

Lee Rainie

 

What you will learn

• Imagining the digital future through expert insights
• Reflecting on past predictions about technology and society
• Understanding the human traits most at risk from AI
• Exploring the impact of AI on jobs and identity
• Identifying creativity and curiosity as human advantages
• Confronting the danger of overreliance on machines
• Redefining leadership in a tech-driven world

Episode Resources

People

• Marshall McLuhan
• Isaiah Berlin
• Erik Brynjolfsson
• Paul Saffo
• Vint Cerf

Institutions & Organizations

• Imagining the Digital Future Center
• Elon University
• Pew Research Center

Reports & Projects

• Being Human in 2035
• AI, Robotics and the Future of Jobs

Concepts & Technical Terms

• Artificial General Intelligence
• Superintelligence
• Metacognition
• Cognitive revolution
• Genomics revolution
• Nanotechnology revolution
• Information revolutions
• Large language models
• Digital twins
• Critical thinking
• Soft skills

Transcript

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.

“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 Gilmurray

Kieran 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:

Kieran Gilmurray

X Profile:

Kieran Gilmurray

LinkedIn Profile:

Kieran Gilmurray

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

• Understanding the leap from generative to agentic AI

• Redefining work with autonomous digital labor

• The disappearing need for traditional junior roles

• Augmenting human cognition, not replacing it

• Building emotionally intelligent, tech-savvy teams

• Rethinking leadership in AI-powered organizations

• Designing adaptive, intelligent businesses for the future

Episode Resources

People

• John Hagel

• Peter Senge

• Ethan Mollick

Technical & Industry Terms

• Agentic AI

• Generative AI

• Artificial intelligence

• Digital labor

• Robotic process automation (RPA)

• Large language models (LLMs)

• Autonomous systems

• Cognitive offload

• Human-in-the-loop

• Cognitive augmentation

• Digital transformation

• Emotional intelligence

• Recommendation engine

• AI-native

• Exponential technology

• Intelligent workflows

Transcript

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.

“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 Haase

Dr. 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:

Jennifer Haase

Jennifer Haase

 

LinkedIn Profile:

Jennifer Haase

What you will learn

• Stumbling into creativity through psychology and tech
• Redefining creativity in the age of AI
• The rise of co-creation between humans and machines
• How divergent and reverse thinking fuel innovation
• Designing AI tools that adapt to human thought
• Balancing human motivation with machine efficiency
• Challenging assumptions with AI’s unconventional solutions

Episode Resources

Websites & Platforms

• jenniferhaase.com

• ChatGPT

Concepts & Technical Terms

• Artificial Intelligence (AI)

• Human-AI Co-Creativity

• Generative AI

• Large Language Models (LLMs)

• ChatGPT

• GPT-4

• GPT-3.5

• GPT-4.5

• Business Informatics

• Psychology

• Creativity

• Divergent Thinking

• Convergent Thinking

• Mental Flexibility

• Iterative Process

• Everyday Creativity

• Alternative Uses Test

• Creativity Measures

• Creative Performance

Transcript

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.

“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 Pataranutaporn

Pat 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:

MIT Media Lab

AI (AHA)

 

LinkedIn Profile:

Pat Pataranutaporn

What you will learn

• Reimagining ai as a tool for human flourishing

• Exploring the future you project and long-term thinking

• Boosting motivation through personalized ai learning

• Enhancing critical thinking with question-based ai prompts

• Designing agents that collaborate, not dominate

• Preventing collective intelligence from becoming uniform

• Launching aha to measure ai’s real impact on people

Episode Resources

People

• Hal Herschfeld

• Pattie Maes

• Elon Musk

Organizations & Institutions

• MIT Media Lab

• KBTG

• ACM SIGCHI

• Center for Collective Intelligence

Technical Terms & Concepts

• Human flourishing

• Human-AI interaction

• Digital twin

• Augmented reasoning

• Multi-agent systems

• Collective intelligence

• AI bias

• Socratic questioning

• Cognitive load

• Human general intelligence (HGI)

• Artificial general intelligence (AGI)

Transcript

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.

“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 Uldrich

Philipp 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:

Nikolas Badminton

Nikolas Badminton

Sylvia Gallusser

Jack Uldrich

University Profile:

Philipp Schoenegger

 

LinkedIn Profile:

Philipp Schoenegger

Nikolas Badminton

Sylvia Gallusser

Jack Uldrich

What you will learn

• How AI-augmented predictions enhance human forecasting
• The surprising impact of biased AI advice on accuracy
• Why generative AI acts as a mirror for future thinking
• The role of signal scanning in spotting emerging trends
• How creativity and imagination shape the future
• The evolving nature of community in an AI-driven world
• Why unlearning is key to adapting in a changing era

Episode Resources

People

• Philip Tetlock
• Jonas Salk

Books & Publications

• Superforecasting
• Facing Our Futures

Technical Terms & Concepts

• AI-augmented predictions
• Large language models (LLMs)
• The Ten Commandments of Forecasting
• The Ten Commandments of Superforecasting
• Forecasting accuracy
• Signal scanning
• Scenario planning
• Foresight strategy
• Generative AI
• Base rate
• Bias in AI
• Cognitive augmentation

Transcript

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 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 Roberts

Rita 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:

Rita McGrath

Rita McGrath

Christian Stadler

Valentina Contini

Anthea Roberts

Anthea Roberts

 

University Profile:

Rita McGrath

Christian Stadler

Anthea Roberts

 

LinkedIn Profile:

Rita McGrath

Christian Stadler

Valentina Contini

Anthea Robert

What you will learn

• Bridging human cognition and AI for better decision-making
• How AI disrupts traditional boardroom dynamics
• Enhancing foresight with AI-driven scenario planning
• The role of AI in sense-making and strategic insights
• Why AI-generated variety outperforms human creativity
• Managing cognitive complexity with AI augmentation
• The evolving partnership between humans and AI in strategy

Episode Resources

Companies & Organizations

• Wrigley
• ChatGPT
• OpenAI

Technical Terms & AI-Related

• Artificial Intelligence (AI)
• Large Language Models (LLMs)
• Generative AI
• Cognitive Complexity
• Metacognition
• Strategic Foresight
• Decision-Making Frameworks

Transcript

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 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 Rosenberg

Anita 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:

Gianni Giacomelli

Louis Rosenberg

University Profile:

Anita Williams Woolley

Jason Burton

LinkedIn Profile:

Anita Williams Woolley

Jason Burton

Gianni Giacomelli

Louis Rosenberg

What you will learn

• Understanding the power of collective intelligence
• How teams think smarter than individuals
• The role of ai in amplifying human collaboration
• Memory, attention, and reasoning in group decision-making
• Why large language models reflect collective intelligence
• Designing synergy between humans and ai
• Scaling conversations with conversational swarm intelligence

Episode Resources People

• Thomas Malone
• Steve Jobs

Concepts & Frameworks

• Transactive Memory Systems
• Reinforcement Learning from Human Feedback (RLHF)
• Conversational Swarm Intelligence
• Augmented Collective Intelligence (ACI)
• Artificial General Intelligence (AGI)

Technology & AI Terms

• Large Language Models (LLMs)
• Machine Learning
• Collective Intelligence
• Artificial Intelligence (AI)
• Cognitive Systems

Transcript

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.