The deep AI shifts that will reshape our lives

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AMY WEBB: I would characterize AI as a general-purpose technology with the ability to influence an economy and a society over time, such that it becomes a platform for other invention and innovation. Nobody in the Western world is like, “Oh, my God, electricity. That’s so cool.” It just is. And thankfully, we’re in a privileged place where we can even say that. We’re going to hit a point where the things we talk about when we talk about AI will just be like electricity, part of what powers other things. But it is not evolving in isolation, and it also doesn’t work in isolation. So, it’s a pretty significant force that impacts everything else that’s happening.

RANA EL KALIOUBY: Amy Webb is a futurist, author and CEO of Future Today Strategy Group. She says we’ve entered a convergence era, an era defined by the collision of technologies, capital flows, geopolitics, climate pressures and behavioral shifts at scale.

In this conversation, Amy and I unpack her 2026 convergence outlook and dig into AI-driven forces that are reshaping businesses and daily life. But most importantly, we discuss how leaders can take these insights and apply them to their organizations.

[THEME MUSIC]

I’m Rana el Kaliouby, and this is Pioneers of AI, a podcast taking you behind the scenes of the AI revolution.

Amy, so good to see you again. Thank you so much for being here.

WEBB: I know. I feel like we only see each other virtually these days. We’ve got to figure out how to connect in real life. Maybe the kids aren’t doing that anymore. They’re just extremely online and extremely AI.

EL KALIOUBY: My daughter’s actually really into in-real-life experiences. But I think we have a game plan. I’m coming to New York to visit your office and then get a curly-hair haircut.

WEBB: Yes. I will tell you who it is, but not publicly, because I don’t want this person to become overrun and I can no longer book her. So, we’ll talk about it. I’ll text it to you later.

EL KALIOUBY: I love it. That’s awesome. So, let’s dig in.

WEBB: I am ready.

Copy LinkWhy convergence matters more than tracking individual trends

EL KALIOUBY: So, you’ve been publishing a tech trends report since 2008, and it’s been read by millions of readers. I think the latest version had 1,000 pages.

WEBB: It did.

EL KALIOUBY: And then this year at South by Southwest, you killed the report. I wasn’t there in person, but I watched it. You actually did a whole funeral thing, and it was very cute and funny. So, instead of the trends report, you released the convergence outlook. I was kind of thinking, is a convergence outlook just a rebrand of a trends report? If not, how is it fundamentally different?

WEBB: That’s a great question. A trend report is really just, “This is our definition of a trend, and we track longitudinal trends.” A trend has to meet certain criteria. It’s the combination of signals that come together over time. There’s a whole data model behind it. So, these are individual, “Here is a thing that we have noticed and the trajectory we see it headed in.” At the moment, we’ve got 1,000-plus persistent trends that are evolving as they’re emerging, and we’re tracking them because we have to track them as building blocks for other inputs to our work.

A convergence is the intersection of multiple forces, trends and uncertainties that create something net new. The combined impact is greater than any one of those things alone. There are system-level changes. A convergence happens across multiple domains versus a singular domain. And convergences are always present. The key difference right now is that we appear to be in a convergence cycle. So, rather than one or two convergences popping up, there are a whole bunch of them happening at the same time. Convergences tend to redistribute power and value.

So, a trend might cause something to accelerate or decelerate, but a convergence influences who will win, where power will concentrate and what things might change. Crucially, they are hard to reverse. Once a convergence is really in motion, there’s no easy way to stop it because it’s a systems-level change, so all of the different systems reinforce each other. That’s the core difference. And what’s in the outlook is pure analysis. We spend a little bit of time explaining what it is, and the rest of the pages explain what that actually means and how it shapes things going forward.

Copy LinkHow to read the convergence map

EL KALIOUBY: I definitely want to get to how we action all of these convergences in our conversation, but let’s unpack some of them first. You have this map in your report-

WEBB: Topological map.

EL KALIOUBY: Yes, that we are going to pull up. So, Amy, what are we looking at here? It looks like a topological mountain-range-ish map. Help us read this.

WEBB: What we are looking at is something my team said, “Nobody’s going to understand what you’re trying to say here.” I spend a lot of time outdoors, and I spend a lot of time hiking and on my bike and looking at maps like these. A topological map is important not just to figure out how to get where you’re going, but also so you can have a better sense of terrain and how that terrain may be shifting and moving around. So, to me, that was a fitting metaphor for the convergence landscape.

What we’re trying to show are the forces that are in play, the convergences, and how they are pushing or impacting those different convergences that we’ve identified. That would include where the ground is stable, where we think there are some pressure zones and where we think there are fault lines. The point is to help organizations identify individual actions they can take so they’re not sitting on their hands, unprepared when a seismic shift, if you will, happens.

EL KALIOUBY: So, let’s pick AI, which is right at the center there. And actually, it is a key force in a lot of these convergences. Is that the right way to think about this?

WEBB: Yeah. Here’s the thing I think people are missing. Artificial intelligence itself is not a single technology, as you know, and as I’m sure listeners of the show know pretty well. It’s an umbrella term for a constellation of different technologies, some of which are now self-improving in different ways. So, I would characterize AI as a general-purpose technology with the ability to influence an economy and a society over time, such that it becomes a platform for other invention and innovation.

Nobody in the Western world is like, “Oh, my God, electricity. That’s so cool.” It just is. We’re going to hit a point where the things we talk about when we talk about AI will just be like electricity, part of what powers other things. But it is not evolving in isolation, and it also doesn’t work in isolation. So, AI is a driving force behind the frontiers of agriculture, where there are really interesting things happening now, and obviously longevity science, which I think people are probably somewhat aware of, but also continual monitoring and recording, making the physical world parsable, and even going as far as regulating our emotions or interacting with us in some way. It’s a pretty significant force that impacts everything else that’s happening.

Copy LinkThe infrastructure bottlenecks behind AI

EL KALIOUBY: Given that AI is so central to a lot of these convergences, let’s unpack some of them. I picked a few that I thought were particularly interesting. The first one is compute shock. And I love that you use the word shock. Your point of view is that there’s huge demand to use AI, but the physical infrastructure to support it — data centers and all of that — is really lagging, right?

WEBB: Right.

EL KALIOUBY: I would love for you to explain what that gap is and how the US is specifically addressing it.

WEBB: Sure. There are some components that make up this convergence, and some of the stuff we already know: exponential AI workload growth. And in some cases, if not the growth itself, then at least the hype. People are talking so much about the potential for growth that it kind of becomes its own thing.

Specialization of compute hardware. And this one is kind of big. We’ve got specialized chipsets and CPUs and TPUs and GPUs, and all of the potential opportunities and constraints in the supply chain and the geopolitical arena that come along with that. Memory constraints. Power is another. We know this. It’s a binding constraint. It’s so interesting to me. People talk so much about data centers and not at all about the power lines leading up to the data centers, which is kind of interesting. Thermal management. Water management.

EL KALIOUBY: Cooling?

WEBB: Cooling. This is a politically sensitive area. From my point of view, the breathless, apocalyptic discussions about water scarcity are somewhat overblown.

EL KALIOUBY: Really?

WEBB: Not immaterial, from my point of view. I’m one person.

EL KALIOUBY: Wait. Can you say more?

WEBB: Yeah. Look, to some degree, it depends. We have a server rack in our house because my husband and I don’t have hobbies. We just build more computer stuff, I guess. And so does our daughter. She’s 16. So, she’s actually extremely offline, except that she wants to be a designer of things and structures off-planet. Lunar architecture.

EL KALIOUBY: Cool. Is that a thing now? Wow. That’s amazing.

WEBB: It is becoming a thing. I didn’t know it was a thing. And this has been her only thing for six years now.

EL KALIOUBY: Wow.

WEBB: She’s got a whole plan. So, we tend to tinker and build and print stuff. Our basement is full of computers, and it gets warm down there.

EL KALIOUBY: Right?

WEBB: Even though it’s a basement. So, the point that I’m making is, even our rinky-dink, kludged-together system needs to be cooled. And that’s just the system we’ve got running the various things in our house. There’s a proposal to build a Manhattan-sized data center in Utah. You have to keep things cool. But there are other ways to do it besides just water. It’s not as simple as, data center means the lake next door is going to dry up, which is where the conversation devolved.

EL KALIOUBY: Oh, my God. Investment opportunity, right? Who is innovating on these cooling technologies? I want to talk to these people.

WEBB: 100%. I mean, the interesting irony of compute shock is we could wind up with a whole bunch of climate solutions, or at least attempts at climate solutions, to resolve it.

EL KALIOUBY: As a side effect.

WEBB: As a net-positive side effect, I think.

Copy LinkHow the global AI race will reshape power

EL KALIOUBY: Forbes recently reported that France’s president, Emmanuel Macron, said that unless Europe competes at a much higher level in AI, it will end up as a colony of either the US or China. And in this global race to build a sustainable AI infrastructure, that is a concern.

So, can you talk about how other countries are handling this compute shock, and where the US stands?

WEBB: Yeah. Well, I would love to add one little layer onto what Macron said. He’s not wrong. And as it relates to artificial intelligence, the United States has had this sort of totally laissez-faire, meaning economic-terms, approach. So, just let everybody throw spaghetti at the wall, and we’ll see what sticks, and then the capital follows without a plan.

And that’s fine because it does mean much more potential for innovation. But anybody who believes that 1,000 new AI companies will bloom and survive is kidding themselves. We will always, because of our economic structures, wind up with just a few people and companies who are at the nexus of all the power. And we won’t have any plan anyway. So, a lot of times when we’ve seen this type of technology supercycle in the past, what results are lawsuits on the other end, which is exactly what’s happening.

In China, that’s not the story. China has these five-year plans that come out every couple of years. And the current five-year plan is all about infrastructure. So, China’s going to let the United States pay to do all of the R&D. They’re going to fast-follow us.

EL KALIOUBY: And then they’ll just implement it.

WEBB: But by the time they’re ready to adopt, you know what will be the difference between Europe, the US and China? China is going to make it so that every single person can be online. Everybody’s going to have access to broadband in an affordable way. The entire country is orienting itself toward a future in which everybody can use technology. And the crazy thing is, if you talk to people in China, which I do, and you talk to people in the US and Europe, which I do, the Americans are like, “AI is going to come and take our jobs and then murder us in our sleep.” Or it’s the opposite: It’s going to usher in this magical unicorn era.

The Europeans are more skeptical. I spend time in the Middle East. I know you do too. So, there’s, I think, some amount of, “We’re here too. Don’t forget about us.”

EL KALIOUBY: Right. And a lot of investments, obviously, in AI infrastructure.

WEBB: That’s right. But in China, people are more concerned that they’re not learning fast enough and, therefore, can’t keep up with their colleagues. It’s a totally different attitude.

EL KALIOUBY: More of my conversation with Amy after the short break.

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There’s also a lot of discussion about whether the cost of compute is less expensive than the cost of human labor. And actually, one of my investment theses is that AI is eating into the labor market.
So I’m curious: How do you think about the cost of compute versus the cost of labor?

WEBB: I will say that, as a maxim, history is not necessarily a good predictor of what the future is going to look like, because you have to update your priors. Things change. With a lot of technologies that fall within the realm of automation, over time, the cost of running something automated versus the cost of human labor always inverts. The cost starts high and ends at zero, or negative, relative to what it would cost for a human. That was true with the printing press. That was true with certain factories at the beginning. I think that’s going to continue to be true, or at least it seems to be going forward.

Now, the big shift we’re seeing is not just with human coders, people who are writing code. It’s also because physical AI is a thing now. We’re starting to see interesting human jobs become less important.

Let me talk about sports for a moment, because that’s the thing nobody would think of. In a lot of endurance sports, you would have a coach, and the coach would look at your body and your data and think about how you’re eating and all of these different things and, with a calculator and a pencil, try to work it all out.

EL KALIOUBY: Customize a plan for you.

WEBB: That’s right. In more recent times, we have access to different types of computers. Runners have a heart rate monitor they can wear. I don’t know what I’m talking about with running. Cycling, I know. I’ve got a computer on my bike. I have a watch. I’ve got all these sensors. I’ve got sensors in my pedal. And after a ride, I can see when and where my pedal stroke was lagging. I can see what my breath was like when I was climbing. I can see all this data. And I have a tool that I kludged together that will analyze a lot of that data. There are similar apps now being deployed for football scouts. Previously, scouts for teams might’ve traveled constantly and gone to all these high schools and watched kids playing soccer. There are apps now where kids record themselves on the field playing and kicking. They get scored based on whatever the system is. And then those scores are sent to, instead of a fleet of scouts, one scout who’s using that data to make decisions.

Copy LinkThe rise of living intelligence

EL KALIOUBY: Absolutely. You were already alluding to this: the living intelligence convergence. I would love to hear your definition of living intelligence and some of your favorite examples.

WEBB: Sure. If you’ve not heard of living intelligence, it’s a term I made up a couple years ago, so don’t worry. Living intelligence is the convergence of artificial intelligence, advanced sensors and bioengineering. And they create this flywheel and a sort of omnidirectional system of data flowing. So, the data flows into the systems and back out of them, and that creates an adaptive system that can learn and can act.

Walmart had this. They never deployed it, but they built a grocery cart that, when you put your hands on it, started collecting your biometric information.

EL KALIOUBY: Really?

WEBB: There were sensors all over the place, plus sensors and cameras in the store. And the concept was, as you’re roaming around Walmart, you get to aisle six, and your kids are screaming because they want cheesy poofs, and you’re having a bad day. It’s hot. Everybody’s upset. And all you really want to do is find not cheesy poofs, but whatever the third thing was on your list, and you just want to get out of there because the store’s too crowded or whatever. The grocery cart would sense that you are super stressed, and it would ping a command center, and a store associate would come find you on aisle eight, recognize you, and be like, “How can I help? Let’s do this together. What is it that you’re looking for?”

EL KALIOUBY: That’s really cool.

WEBB: It depends on your perspective. That is either really cool or really invasive. But that is some of the reality of a world in which the technology systems become alive and use our data in real time.

EL KALIOUBY: I’m very passionate about the applications of this in health and wellness and what the different sorts of sensors could be on my body, in my body, around my home, et cetera, that can help paint a more comprehensive picture of my health and wellness and then be actionable. The key here is: How can it be actionable?

WEBB: Yes.

EL KALIOUBY: I don’t know where you sit on the spectrum. I wear a WHOOP quite religiously, and I will sometimes make decisions on what to do or not to do because I don’t want to mess with my green streak of recovery scores and whatnot.
So, what do you think of all of that?

WEBB: Look, my family had weird health issues growing up. My mom got sick very young, so I lost her at a very young age to cancer. It was a neuroendocrine cancer. My father had all kinds of issues and Parkinson’s. So it’s tough to see loved ones go through an illness.

That really prompted me to do everything I can. We get one chance. And I’m a robot, and I got to take care of the machine. So I have always been interested in having access to data. But the data alone aren’t useful. You need physicians in your life who are willing to work with that data and who know what to do. Systems learn a lot about us, and people are incentivized to get more clicks and therefore say crazier things. So, all the physical data that you can scrape, I’m totally into it, but I think you need a good guide to make sense of it.

EL KALIOUBY: Love it.

I’ll be right back. But first, a quick break.

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Copy LinkThe hidden cost of frictionless AI support

The last convergence I want to dig into is one that’s actually very close to a lot of the work I’ve done, which is —

WEBB: I figured you would want to talk about this.

EL KALIOUBY: Can you guess? Emotional outsourcing. As you know, I’ve spent my entire career building emotional intelligence into machines, not to replace humans, but to augment our abilities to understand and connect with one another in a very digital, technology-driven world.
I’m going to quote you here: “We’re outsourcing empathy. AI systems now provide validation, reassurance and companionship at scale, replacing relationships with people with platforms designed for sticky engagement.”
Can you unpack all of that?

WEBB: Sure.

EL KALIOUBY: That does not sound good.

WEBB: It doesn’t feel good either.
We’ve been tracking a lot in this space. And the reality is, for a variety of reasons, we are short of healthcare providers. People can’t afford healthcare. There are many valid reasons why mental health crises are on the rise. People are more isolated. So we know all of these things.

Technology has removed the friction from resolving those issues. Depending on whose study you look at, in this country, somewhere between 30% and 50% of Americans have used ChatGPT for mental health, which means that an LLM now is the largest provider of mental healthcare services in this country.

EL KALIOUBY: Wow. That’s such a crazy way to think about it, but it’s true.

WEBB: I know. It’s true. And the issue is that that is not what that system was designed to do. I don’t think anybody was intentionally being malicious, but there are some invasive ways that these systems everybody has access to are starting to impact how we feel.

Just as an example, I was thinking about getting different glasses. And my husband’s an eye doctor, so I got a guy. I can get glasses when I want. I took a photo of myself, and I sent it in. I think it was to Claude. I don’t remember.

EL KALIOUBY: That’s like having an affair, Amy. What?

WEBB: I know. But I was like, “What should I get? Style me.” And pretty quickly it devolved from, “Because your face is very oval, this is the shape you should look at, and here is why.” Instead, it was like, “These heavy glasses are making you come off as overly academic.”

EL KALIOUBY: It was making a judgment.

WEBB: Look, it’s taken a long time, but I’m now very self-assured and confident. And in a moment, that confidence eroded.

EL KALIOUBY: Wow.

WEBB: It eroded. I suddenly felt self-conscious about the glasses that I was wearing. And then finally I snapped out of it, and I was like, “This is ridiculous.” That’s analogous to the micro-moments people are having now every day that are, I think, slowly eroding their own sense of judgment and self.

I don’t think Sam Altman got into a secret underground bunker one day and said, “I’m going to fuck with everybody’s emotional well-being.” I don’t think that’s what happened. I think it’s a lack of planning and thinking through the knock-on effects.

EL KALIOUBY: Guardrails.

WEBB: I think guardrails evokes a sense of regulation. It’s not that. It is, if we do A, then what happens with B? And if B, then what happens with C? That line of thinking in advance didn’t happen. Or, if it did happen and people knowingly deployed anyway, then that’s not cool.

EL KALIOUBY: If you are going to your AI tool of choice to ask for advice on glasses, and I’m doing that for health or friendship or dating or even cooking instead of calling my mom or my sister, at scale, we are kind of eroding the moral fabric of society, right?

WEBB: Well, I think friction is a good thing. I think it is a tough thing to manage through, but ultimately, some friction is important. And when it’s easier to ask ChatGPT, “Does he like me? What do you think? How come they’re not calling back? Do you think I’m going to get this job? I’m feeling sad,” if it’s easier to do that because it’s free —

EL KALIOUBY: It’s 24/7.

WEBB: It’s 24/7. So now, if I’ve got a phone that’s already on me, in my pocket, and all I have to do is push a button and I get an answer, that answer’s so easy and so frictionless that I don’t think people think about getting a second opinion. You know what I mean? That’s too easy.

EL KALIOUBY: Too much work, right? It’s too much work to go get that human.

WEBB: And we haven’t even talked about how somebody outside of tech might use this and for what purpose. So, on the one hand, we could harness this in a way that’s very positive and help people get the attention that they need, personalize things more.

On the flip side, how would somebody look at what’s happening and not immediately try to figure out how to exploit it to monetize or to oppress? So where are these thoughts of yours literally going? And what happens afterward? And who has access? And how might that be used in the future to guide the decisions that you’re making? Or, in a world where we have agentic systems and self-improvement of systems—

EL KALIOUBY: And memory.

WEBB: And memory. Right. So, what’s on the horizon could be incredibly beneficial. And it could also, for some people, become a serious problem.

Copy LinkHow leaders can turn foresight into action

EL KALIOUBY: So, I want to transition into how we make it all applicable. Can you give us an example of how you’re working with maybe an organization that you can talk about?

WEBB: Yeah. We work in this field called strategic foresight, which is a way of modeling plausible futures using data and then bridging into strategy. So, where is the world going? Where will value or risk be created? And then how do we participate? What do we do about it? The intersection of those three questions, that’s what I do.

Leaders are not going to be the ones paying attention to trends. It’s too micro for them. They need sort of the bigger picture. And most leaders in most organizations do not have a North Star. It’s astonishing to me. A lot of corporate leaders, when we start with, “Why are we gathered here today? What would you like for us to do for you?” a lot of them have a KPI in mind. “We want to be a $1 billion company in,” pick a year, or, “We want our market to do whatever.” That’s the outcome of having made really good decisions. That’s not your strategy. That’s just a number. So, the convergences are useful because they sort of answer that question: “Where is the world going? And where will new value and new risk be created?” That’s what the convergences are used for by those leaders.

The hardest part is answering the third question, which is: How will we participate? So, you have to have a point of view that has to be data-backed, and you have to communicate it to everybody in the organization because, from that direction, we’ll figure out how to get there, and we’ll be willing to shift and change as new things happen. But that big point of view on the future shouldn’t change if you’ve done the pre-work.

EL KALIOUBY: Because things are accelerating and just moving so fast, my belief is that organizations need to also be experimenting. They need to be trying things out, failing, iterating, and that is so key. How do you reconcile some of this big thinking with, “We got to be on the ground doing the work”?

Are you nudging your companies to experiment?

WEBB: From my experience, the answer is very different depending on whether you are a publicly traded company and you have the Street to answer to, where within your earnings cycle you are, whether you’re a company that is going to IPO sometime in the next 12 to 24 months, and, if you’re a private company, however well-capitalized you might be. The private companies, for the most part, have more appetite to do some curiosity-based or strategy-based experimentation. We encourage this with everybody. We run what we call re-perception exercises. And this is a way to force you to perceive the world around you with a fresh set of eyes.

So, I lived in Japan for a long time. I also lived in China. A lot of the culture of our company comes from what I experienced and lived when I was in those two countries. And re-perception, drinking the same glass of water you’ve always had to drink but trying to feel something different, experience something different, is a way of, again, you have to learn how to do this, but it’s incredibly beneficial.

So, we have different ways that we take executives and teams through that for the purpose of getting to the strategy. Private companies are interested, and they are more willing to experiment. People in IPO mode or going after a new series, a new round of funding, are in a tough situation because they’re just trying to get whatever it is built. And they’ve got investors with their own opinions, and they probably have boards and advisers. So, honestly, we stay out of that space.

On the publicly traded company side, I am seeing zero appetite for experimentation. The biggest culprits are the big tech companies. These are the ones that will double down on iterating. They will spend all the money to iterate, and they are very much not interested in innovating. I will be happy to debate anybody, anytime. I have receipts.

Part of experimentation is the act of experimenting. It’s not necessarily to get a product on the other side. It’s to shift your thinking, because these people get stuck in ruts. And the changeover right now … CEO tenure is the shortest it’s ever been. They’re in their roles two years, four years, six years — not a lot of time. And lately, CFOs or COOs are the ones ascending into that CEO position. That’s because there’s so much uncertainty. The finance officer, the operations person — that gives everybody a sense of, “We’re not going to rock the boat.” Those people tend not to think expansively. I mean, they do in their roles, but not when it comes to, “Let’s meet the future where it’s headed.”

EL KALIOUBY: All right, Amy, parting thoughts. What’s one thing you want to leave us with today? I know the one thing I took from this conversation: a new word, re-perception. I love it.

WEBB: Here, how about this? It can be a re-perception exercise. I will give everybody homework.

EL KALIOUBY: Love it.

WEBB: I will give you the same homework that I give CEOs of blue-chip Fortune 50 companies, who sometimes do the homework and sometimes hem and haw, but then they do the homework because I make them.

Spend the next week doing one thing differently every day. It has to be some part of your routine. I do this a couple of times a year. The last time I did this, I parted my hair on the other side. And I cannot describe to you how weird that was for me. I could feel it. You have a lot of hair. I have a lot of hair. That one shift totally changed how I was thinking and feeling for that day. If you don’t have as much hair as we do, there are other things you could do. Have breakfast for dinner. Sleep on the other side of the bed from where you would normally sleep.

EL KALIOUBY: Oh, my God.

WEBB: It’s things that are deeply entrenched in your routine. Pick just one thing different every day, and just tweak it or change it or don’t do it, as long as you’re not impeding your health or hygiene. Do everything else you would normally do. Hopefully, you’re reading and ingesting new information, or you’re going to meetings, you’re looking at pitch decks, whatever it is that you’re doing.

At the end of that week, go back and see: Did you make any decisions differently? Did you wind up meeting with people and think, “Oh, you know what? I normally would not have taken that meeting, but I did”? Did you have any strange, small, or large aha moments? Almost uniformly, the answer to those questions is yes.

Now, if you’re resisting it and you’re just trying to go along, you’re going to miss the point.

EL KALIOUBY: Amy, thank you so much for joining us on the show. This was awesome.

WEBB: Thank you.

EL KALIOUBY: I don’t know if I’m ready to part my hair a different way, but I will definitely do Amy’s homework and try something different every week. If you do the same, please let me know what you’ve learned.

Thank you for listening. We will be back next week with a new episode.