Podcasts about exponential view

I've been seeing a recurring pattern with companies selling APIs, MCPs, data feeds, and other developer-focused AI products. While the technology is often sound if not impressive, sales momentum sometimes slows when prospects have to imagine how the product will create value in their own environment. My perspective on this is that the flexibility that makes these tools powerful can also make them harder to evaluate. Flexibility can adversely increase the Invisible Intelligence Gap, and I think certain types of AI-based solutions (LLM) may actually increase this because the boundaries of the product are often so much wider than ever before (if not invisible to the buyer). So, how to close this gap? Well, one way is to build a visual UI that showcases what's possible with your API/feed/data solution. You take the buyer out of the conceptual space and make things concrete. So today, that's what we dig into: when to consider adding a UI, how far you need to go with it, how you can use Copilot/AI agents to help customize these example implementations, and the benefits you might see.  Highlights / Skip to: The challenges of selling API-based analytics and AI products (0:56)  Why this topic matters right now (2:48) The Invisible Intelligence Gap that may be slowing your sales (3:34) Strategies for bridging the Invisible Intelligence Gap with a UI (user interface) layer (7:01) Client case study: the impact and results you may see adding a UI on top of your technical product (14:05) Signs that you should consider adding UI to your technical product (18:23) Leveraging humans' highly developed visual system to help potential customers see the full value of your product (26:24) Conclusion (27:32) Links Invisible Intelligence Gap Azeem Azhar's Exponential View (6/4/26 episode)  

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years. Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic. To keep up with the Exponential transition, subscribe to this channel or to my newsletter:  https://www.exponentialview.co/ — More than three years after ChatGPT's release, only 27% of executives say AI has met their ROI expectations. The history of factory electrification explains why — most companies are at the light-bulb stage, adding Copilot licenses rather than reconceptualizing their businesses around AI. In this episode I map the three stages of AI adoption, and show what it actually takes to move from chatbots to the autonomous company — the only stage where the moat becomes real. I covered: (01:40) Ford's electricity playbook: why AI adoption needs a complete rethink (03:51) The congestion problem: why AI gains stall (05:45) Chatbot to autonomous company: your three-stage roadmap (06:40) Why individual productivity gains won't build a moat — and what will (10:17) Which companies are getting AI transformation right (14:12) My 2029 AI adoption forecast — and how to stay ahead Read my essay "Why AI isn't showing up on your bottom line" on Substack: https://www.exponentialview.co/p/why-ai-isnt-showing-up-on-your-bottom-line — Where to find me: Exponential View newsletter: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azeem/ Twitter/X: https://x.com/azeem Production by EPIIPLUS1. Production and research: Baba Films, Chantal Smith, Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years. Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic. To keep up with the Exponential transition, subscribe to this channel or to my newsletter:  https://www.exponentialview.co/ ---- Greg Williams has joined EV as Executive Editor — two years in the search. He was editor-in-chief of WIRED UK, recognized as Editor of the Year (Technology) three times, and is a five-time novelist. Introducing him to our community in this week's episode became an opportunity to redefine what EV is: why we make maps instead of stories, and where I think AI is taking institutional media. We covered: (00:10) Why Greg joined EV (04:16) The four horsemen of the media apocalypse (05:42) Google Zero (06:47) AI: collaborator or adversary? (08:48) Tools, not information (11:09) We make maps, not stories (14:18) Building for AI to consume (17:52) AI can't summarize The New Yorker Read more about why we hired Greg here: https://www.exponentialview.co/p/exponential-view-greg-williams ---- Where to find me: Exponential View newsletter: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azeem/ Twitter/X: https://x.com/azeem Where to find Greg: https://www.uk.linkedin.com/in/greg-williams-0977a05 Production by EPIIPLUS1. Production and research: Baba Films, Chantal Smith, Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

“The technology we’re working with today really makes a lot of those best practices and mental models and the whole toolkit more accessible than ever to more people.” –Marshall Kirkpatrick About Marshall Kirkpatrick Marshall Kirkpatrick is founder of sustainabilty consultancy Earth Catalyst and AI thinking tool What's Up With That. His many previous roles include founder of influence network analysis tool Little Bird, which was acquired by Sprinklr, where he was last Vice President Market Research. Website: whatsupwiththat.app LinkedIn Profile: Marshall Kirkpatrick What you will learn How generative AI transforms cognitive tools and lowers barriers to advanced thinking Techniques to combine human and AI-powered sensemaking for richer insights Practical strategies for filtering and extracting value from infinite information The importance and application of diverse mental models in modern decision-making Methods to balance manual cognitive work with AI assistance for optimal outcomes The role of adaptive interfaces in enhancing individual cognitive capacity Metacognitive approaches to networks and how AI can foster organizational awareness Ethical and societal implications of democratizing access to AI-powered cognitive enhancements Episode Resources Transcript Ross Dawson: Marshall, it is awesome to have you back on the show. Marshall Kirkpatrick: Oh, thank you, Ross. It’s such a pleasure to be reconnecting with you here. Thanks for having me on. Ross Dawson: So back you were very, very early on in the podcast when it was Thriving on Overload, and it was interviews with the book, and you got incorporated—some of the wonderful things you were doing in Thriving on Overload. So I think today, in this world of generative AI, which has transformed everything, including the way in which we think, the Thriving on Overload themes are still super, super relevant, and in a way, we need to be talking about them more. That theme at the time was finite cognition, infinite information. How do we work well with it? I don’t know if our cognition has become more finite, but the information has become more infinite, and there’s just more and more. But also, it cuts two ways, as in, what is the source of all the information? AI is also a tool. So anyway, let’s segue from some of your cognitive thinking tools, technology-enabled cognitive thinking tools and so on, which we looked at. So how do you—where are we? 2026, what do you think about human cognition in our current universe? Marshall Kirkpatrick: Well, especially when you frame it up in Thriving on Overload terms. I mean, those were four, five long years ago that we last spoke, and the book that came out of it was just fantastic. I think it has some timeless qualities, and I think that the technology we’re working with today really makes a lot of those best practices and mental models and the whole toolkit more accessible than ever to more people. That’s what I hope. I think that, yeah, between individuals and organizations, there’s so much that, historically, someone like you or me or the people closest in our networks were willing and able to do and excited to do, that many other people said, “That sounds like a lot of work.” The bar is lower now, because a lot of just the raw cognitive processing can be outsourced into a technology that serves as a lever. Ross Dawson: Well, I mean, that idea of levers for these cognitive tools is interesting. I guess, the very crude way of saying it is, we’ve got inputs into our human brain, and then we are processing information. I’m just thinking out loud a bit here, but it’s like, okay, we have tools to be able to filter, to present, to find what is most relevant, to present it to us in the ways which are most useful—very obvious, like summarization, visualization. Then as we are processing it ourselves, we have dialog, or we can have interlocutors who we can engage with and be able to refine and help our thinking. Does that sort of make sense, or how would you flesh that out? Marshall Kirkpatrick: Yeah, I mean, when you put it that way, it makes me think about Harold Jarche and his Seek, Sense, Share model, right? I think that AI, especially when connected to things like search and syndication and other traditional technologies, can impact all three of those stages. It can hypercharge our search. I think the archetypal example of that, on some level, feels like the combinatorial drug research being done, where just an otherwise cognitively uncontainable quantity of combinatorial possibilities between molecules can be sought out and experimented with for a desirable reaction. And then that sensing, or the pattern recognition that AI is so good at, is something that we do as humans—some of us better than others—and it’s a lifelong muscle to build and what have you. But the AI is really, really good at it, and so it’s a ladder to climb up in some of that sensing. And then the sharing component becomes so much easier with the rewriting capabilities—turn A into B, reformat something into a summary or a set of bullet points, or ideas and words into code. AI is just so excellent for that translation that makes new levels of sharing possible. Ross Dawson: That’s fantastic. Yeah, I had Harold on the show again in the Thriving on Overload days. But you’re right, that’s extremely relevant. Let’s dig into that. I love that you brought up that combinatorial search, which is so important. As opposed to going into Perplexity to do a search, it’s far more interesting to find the uncovered connections between things, which are relevant to what you’re doing. And that’s— Marshall Kirkpatrick: Absolutely. I remember reading, years ago, Dan Pink’s book “A Whole New Mind,” which preceded the generative AI era. But he said, if your kind of work is something that’s easily reproducible by computers, good luck to you. You really are going to need uniquely human practices in the future, and what exactly those are, I’m not sure, because the one that he identified, I don’t think has proven to be uniquely human. But I really appreciated learning about it from him, and that was what he called symphonic thinking, or the ability to draw connections between seemingly unconnected phenomena. So for many years, I have been doing a personal exercise with pen and paper that I call triangle thinking, where I’ll take three different phenomena—maybe that’s the owl outside my window, one of the notes that I’ve taken on paper, and something I come upon on the internet, or maybe it’s three very deliberately related things. I label them A, B, and C, and I ask, what might A have to say about B? What might B offer to A, and vice versa? I write out the six unidirectional connections between those things. And without fail, one, two, or three of those end up being real keepers, where I say, “Aha, that’s a really interesting idea. I’m going to take action on that.” And now, by the time I’ve got the letter B written out, an AI has done that ten times over. I like to do it both ways—still both AI and with my naked brain—but that combinatorial ideation, the generative combinatorial ideation, is, yeah. I’m curious what your thoughts and experience and hope for that might be. Ross Dawson: Well, there’s a prompt I use called “Apply Diverse Thinking,” where it generates extremely diverse perspectives on a topic—who might those very unusual people to think about something be, and then what would they think about this particular situation? Of course, there are a whole array of different thinking tools. There’s Marshall McLuhan’s tetrad, which is a little bit similar to your thing where, again, you can and should do it—well, not manually. What’s the manual equivalent of brain? Marshall Kirkpatrick: Thoughtfully, perhaps. Yeah, good one—deliberately, manually. I mean, Azeem Azhar over at Exponential View uses a fountain pen and paper and will sometimes have his team come online and they’ll do two-hour thinking sessions with no AI allowed. They just get on, I believe, Zoom, and just think through things with pen and paper, individually and together. And then they’ll kick off OpenAI or what have you, and use all the tools afterwards. Ross Dawson: Yeah, well, a couple of things. Actually, research has shown that in brainstorming, it is better for everyone to ideate individually before doing it collectively. And of course, that’s unaided. I think there are analogs there where—actually, one of the frameworks I just released last week was basically to say, think it through for yourself before you ask the AI, because then you have a reference point. If not, you don’t have a reference point to say, “Well, what am I expecting it to do? Let me think it through for myself,” even if it’s just a little bit, as opposed to just going in blank—”All right, give me an answer.” Just that simple thing of thinking through for yourself first is enormous. What it does is, obviously, give you a reference point for that. And I’m going on a lot about appropriate trust at the moment—as in, trust the AI enough, but not too much, which I think is absolutely critical capability. And part of it is being able to say, “Well, this is what I think it should be giving me.” Now you have a reference point for what it gives you. Marshall Kirkpatrick: Yeah, that sounds great in many cases. I do think that’s the right tool for the job in a lot of places, but not necessarily all. I’m thinking of the Iron Triangle of product management—fast, cheap, good, pick two. On some level, just handing the AI the keys for certain decisions is uniquely fast and cheap, right? And maybe it’s good enough. Ross Dawson: Oh yeah. Well, you’ve got to choose your battles, because if you’re now doing ten times what you were doing last week, then maybe for a tenth of those you can do some thinking before you delegate it to the AI. Marshall Kirkpatrick: Yeah, a strategy for how to do that. I think, well, that sounds important—some checkpoints along the way, some random selection of testing things. Ross Dawson: Well, that’s interesting. One of the critical things people talk about with AI model oversight is sampling. As they say, “Okay, I’ve got 1,000 outputs—I’m going to take 20 of them and check how good they are.” You’re not checking every output, but you’re doing some kind of ongoing sampling. Marshall Kirkpatrick: Are you checking with your own deliberate brain, or are you checking with another AI? Ross Dawson: It could be either, depends on the case—how critical it is. This comes back, of course, to the fact that accountability is only human, and so the human who is accountable has to make that decision: “All right, I’m happy for another AI to check it,” or, “Actually, I want to go in myself to see.” And that’s a judgment call. Marshall Kirkpatrick: Totally. And it feels like a process design issue and a personal accountability matter. I mean, “The AI made me do it” is not a viable excuse. Ross Dawson: Let’s hope it remains that way. So, good for those Seek, Sense, Share stages. Sense is one of your superpowers, both in the way you think and also the way you use the tools. It’s probably worth introducing—now you’ve just released this wonderful product called What’s Up With That. So just tell us about the product, but also, I want to go to the bigger context of sense—sensemaking, how we use it generally, how AI can use that, and your role with the tool in that. Marshall Kirkpatrick: Yeah, you know, I think there are so many different ways that sense can be made of anything, so many different ways that anything you read or think about or do can be put into context. It’s just overwhelming. I think we all have our favorite—not all of us, but those of us who are into this have our favorite tools, our favorite ways to—you know, a lot of people will think about something in terms of its past, its present, and its future, or they will break it down in analysis into parts, or they’ll synthesize it together with other phenomena and see how to understand. I think sometimes of the famous Donella Meadows quote, the mother of systems thinking, who said, “Systems thinking isn’t any better than analytical linear thinking than a telescope is better than a microscope.” So there’s just a superabundance of fascinating, powerful tools that all provide different views on anything we’re trying to make sense of. One of the things that I’ve always found a lot of joy and usefulness and power in is learning about new lenses and processes and tools. Now that generative AI has put the ability to develop software into my hands—instead of having to go and hire someone else to build that software—I have built a system that takes as many of those different models and lenses and processes for making sense of something as I can. I mean, it would be trivial to pull up a list of 200 mental models. I might go visit Shane Parrish’s website and The Knowledge Project. I think of ones that would be particularly useful, like, “Tell me who the intellectual predecessors are of this thing I’m reading,” or one of the other capabilities inside of What’s Up With That—my favorite, probably, is a combinatorial one called Fertile Edges. That says, “Take what I’m reading right now, identify the topic that it is a constituent of, and then find other adjacent topics where innovative people have built bridges between those adjacent topics and what I’m reading about, and tell me who those people are.” And that’s really fun. So I have built this sensemaking system, and that’s a part of What’s Up With That. There are really three parts to it. The first is, it analyzes whatever you’re reading or watching, and it pulls out the net new, truly novel, most notable elements. Yesterday, I was telling you, it was a little bit inspired by the US military intelligence guideline that says, when you’re writing up a report about something, focus on what’s new in that situation—tell us what we don’t already know. That’s the first thing that What’s Up With That does. It says, “All right, here’s what’s new in this document relative to its field,” because we just drew a real-time map of the state of the art, and we say, “Okay, here’s what’s really novel there.” The second thing that it does is that toolbox full of all the different mental models and lenses, and it recommends a sequence. One of my favorite books I ever read was “On Grand Strategy,” about strategic thinkers throughout history, who talks about the significance of thinking in terms of sequences of actions. So now, What’s Up With That will say, “Here’s a sequence of analytical lenses we recommend that you subject this document to,” and with a click, it’ll go and do that for you—it’ll do that cognition for you and then just give you a report. The third thing that it does is probably—it, the shorthand for it is compound learning. You don’t have to remember all the things that you read anymore, because our system extracts the causal claims from everything you read, archives them, and then compares everything you read in the future that you analyze with our system to your library of causal connections in the past, to say, “Whoa, we just found a chain of claims that could surface a multi-step risk or opportunity that’s relevant to your work.” We do that both for your data exhaust—your history of things you’ve analyzed—and we do persistent monitoring of the web to detect anything that could be relevant to a project or chain by that same kind of symphonic synthesis and connection. So those are the categories that it has. Ross Dawson: Yeah, I think you’re only scratching the surface of what your tool actually does, and obviously, more generally, these are just pointing in wonderful ways to how you can go beyond saying, “Tell me about this, ChatGPT,” to some far more nuanced ways of getting AI to do it. Marshall Kirkpatrick: People have had the same challenge with Google, historically. Google has struggled with that, to figure out—”I’m feeling lucky” was probably the first intervention in a novice, beginner’s mind, coming to a hyper-complex opportunity space. Even still, now, 20 years since Google launched, I feel like you can tell people that they can search for “site:domain keyword” to find instances of that keyword not in the web at large, just inside that specific domain, and most people don’t know that. It’s a simple power, and there’s a bunch of things like that. So figuring out how to unlock—and I don’t know how much they’ve even worried about it, because they’ve got that cash cow of advertising—but people don’t even recognize, sometimes, whether they’re clicking on an ad or a search result. In polls, when people are asked, they say, “No,” even if they put the ads at the top or mark them as ads, or a bunch of stuff they do do, but nobody notices. So that interface of complexity and accessibility and scale—we’re in it again here now, in this generative AI era. There’s so much more that could be done than is immediately obvious. It’s a real challenge. So I’ve taken the approach that I have, which is to roll up a bunch of that and turn them into buttons and recommend them automatically and try to recommend them just in time, and stuff like that. But I’m sure lots of different people are going to try to respond to that gap of simplicity and complexity in different ways. Ross Dawson: Yeah, that’s—which comes back, I think, a little bit to, you know, I firmly believe that the heart of the future is interfaces. We have these extraordinary capabilities—against finite cognition and infinite capabilities, let’s call them. That’s very much to the individual. The adaptive interface, I think, is going to be absolutely critical. All right, well, it’s after lunch and I’m not feeling so—the interface adapts to you. Marshall Kirkpatrick: So I heard you say that. Ross Dawson: The interface adapts again. Marshall Kirkpatrick: Right? I heard you say that in a conversation with Ramez Naam some time ago. I was listening to that interview that the two of you did together while I was playing hacky sack out in front of my house. I grabbed my hacky sack and I said, “I’ve got to go inside and do something about this idea of Ross—yes, interface variability.” In that case, I did a little experiment that I didn’t implement because I decided not to, but the general idea I want to pursue further, and I’ll tell you what that experiment was. One of the capabilities inside of What’s Up With That is that you can get a reading review synthesized, so that instead of just a list of links, you can get a narrative document exploring the themes, weaving together the last ten articles that you’ve read, and it’s easier to remember and to think about. I decided to hit the Nanonets API and have an image put up at the top that illustrated the themes. Now, maybe it’s just because I read a lot of dystopian AI, authoritarian politics type of stuff, but the images were terrifying, and they’re kind of expensive and slow, and they also look kind of repetitive. I was like, “All right, Ross, I haven’t cracked that nut quite yet in the variable interface, but I think you’re really on to something there.” Ross Dawson: I’ll try to work on that too, a little bit. So coming back to this wonderful thing we laid out, alluding to some of the wonderful ways we can use for really rich investigation of ideas and how to think. It comes back to this frame of mental models. All of us get our mental models from the moment we’re born—we get this understanding of the world, which is hopefully useful. Sometimes, some people’s mental models are not very effective in guiding them in how they work. Our role is to continue evolving, getting better. I call it enriching mental models. Back in my first book, I talked about that, and of course, that’s in the context of the world changing, so mental models can’t be static anyway. In a way, what you’re pointing to is the many, many ways in which we can, at one point, improve our mental models. All right, I understand this linear lineage of thinking, and I can see the strands between that, and these neurons are connecting in my brain in some form. But how can we pull to that bigger picture of all of this lattice of things to be able to say, “All right, I am actually thinking better through these interactions”? Marshall Kirkpatrick: You know, I think that there is a visceral sense—a sense of safety that can come sometimes when a new mental model illuminates a risk that you hadn’t considered before, and you breathe a sigh of relief and say, “Oh, thank goodness, I can now account for that.” And there’s an excitement with opportunity. There is something about a collective greater-than-individual opportunity here, because it’s tempting to—I’m not sure what that looks like, but I feel like there’s some social and interpersonal and network-based. One of the other things I do is build systems for network self-awareness, to build metacognitive network monitoring kinds of systems. I feel like there are mental models on that level as well. Ross Dawson: So I’ve got to dig into that—metacognitive network monitoring. Explain Marshall Kirkpatrick: Yeah. So every one of us, and our organizations, exists in a network of customers, suppliers, competitors, regulators, thought leaders, with orbits that extend out. The signals are strongest in the closest ones, and perhaps they are weaker and harder to hear, but really significant coming from outer orbits—even from other industries or other topics. It is overwhelming. It is cognitively uncontainable for any of us to keep up with all the work being done, all the thoughts being shared, all the new developments and opportunities from all the different entities that we’re interconnected with. One of the other offerings that I build for organizations is a system where I go out and map as many of those as possible with people. Those might be your target accounts you’re wanting to sell to, or your peers in a community of practice. Then I set up systems, basically using RSS, email newsletters, web page change notification—the technical underpinnings—to say, especially when organizations are—there are some forms of communication that organizations do naturally by default, and those tend to be speaking to their own customers. If you can listen to what organizations are saying to their own customers at scale, you can pull in a large quantity of signal, and then the challenge is to winnow that down into just the filtered signals that are most relevant to your priorities. I’ve got a system that uses AI to do that. Then there are combinatorial possibilities as well. I’ve started merging that in with What’s Up With That now, for example, where when we’re watching your broader network and a signal gets picked up on the back end, we’re generating hundreds of possible scenarios for that signal to intersect with your work and projects and priorities, and then we’re filtering to say, “Yeah, but tell me just the subset of these that are most significant and imminent and actionable and interesting.” If there’s something, then we will alert you and tell you what’s going on. Otherwise, you never hear from us, and you just go about your business. But a couple times a day, I get alerts. Yesterday I got an alert that said, “Hey, one of the founders of Manus, the AI platform that Meta just acquired for $2 billion, just got detained in China trying to go back to Singapore. Given your interests in AI and anti-authoritarian politics and the infrastructure battles around AI, we thought you might want to know about this.” I said, “Thanks, What’s Up With That, I really appreciate it.” That’s an example of the sort of thing—so that’s how I do it. Other customers will take that and use it to populate a podcast or a newsletter, and do both an intake and an output as a conduit of that kind of network self-awareness. Ross Dawson: Yeah, well, as you know, my kind of—my metacognition is my mantra. I think one of the key points is this simple question: How can AI assist me in getting to a point of metacognition? I would argue, if we use AI even vaguely well, it’s already doing that, because you’re saying, “Okay, well, let me think about what I can do and what the AI can do,” and you’re starting to think of that system. The only thing that enables this humans plus AI is metacognition, because you can actually see above and see your role and the AI’s role. I think this broader question of saying, many of the things you’ve been talking about are how AI is helping us to get to a point in metacognition. Marshall Kirkpatrick: Ross, can I ask you a question adjacent to that? I think I am not the only one who wants to know, perhaps—and maybe this is a trade secret, I don’t know—but how you think about your analysis and sharing of scientific research papers online? You’re so good at that, and you do a lot of it, and it’s really valuable. It comes to my mind when you talk about metacognition—what role does that function, what are you doing there, what role do you see that playing in this bigger conversation? Ross Dawson: Well, I’ll just tell you the mechanics of it, which might partly answer your question. I go into, often, three or four of the AI engines, including Grok, actually, because it’s very good at search. I say, “Tell me the most interesting research papers in the last few weeks,” whatever—on, I might say, human-AI collaboration or AI and strategy, whatever it might be, just different frames. Then I go and look at them. To be frank, I probably should do some more filtering with AI and tell them, “Only from reputable authors,” etc., because I have to just look at a lot of stuff, but that’s useful in its own right. Then I start to see, okay, this is a paper which is not only interesting, but actually would be useful to summarize for other people. I do a lot of surfacing—a lot. I’m very quick at scanning, so that’s just a mental process. At that point, when I found the paper, I’ve got a Gemini gem and an OpenAI GPT, both of which I call Insight Distiller. Basically, I stick the paper in there, it comes out, and I always rewrite it. I will either prompt the AI to improve it in various ways, and then always just rewrite or choose which of the points I put in, and so on. So there’s actually a fairly manual process, but very, very AI-assisted. To your point, there’s so much extraordinary research going on, and people don’t look at it. The function, I think, is what you’re alluding to—it’s just like saying, “This is the essence of a paper, and you can read it in a few minutes and get some really good insights, and hopefully that will inspire you to go have a proper look at the paper, because there’s a lot more in there.” To myself, of course, going through all that is enormous and valuable to me, but it’s useful to others too. Marshall Kirkpatrick: Absolutely, wow. That is a high-touch. That’s great. I bet you really have a lot of compounding learning as a result of it. Ross Dawson: Yeah, it’s kind of this thing where, just the nature of how my brain works and my immersion in stuff, I think it somehow gets me to some decent understanding of what’s going on. So to round out, what’s the next phase? I think this is an extraordinary time, but in the frame of what we’re talking about—AI and cognition—from your perspective, or just the world’s perspective, where do we go from here? Marshall Kirkpatrick: Well, I think that it comes down, in part, to values. I can’t help but think about this K-shaped future that we risk moving towards, where some people are using all kinds of augmented capabilities and building on top of past experience and education and what have you, and income inequality just gets more and more intense. The gap between people who are excited about this stuff and can use it, and everyone else, just gets all the bigger. That’s not good for anybody. I really hope that isn’t the case. I’d love to get the J of exponential change without too much of the K of increasing inequality. I think that’s the direction we’re pointed in, but I do hope that we can democratize access to a lot of these capabilities and figure out how to use them in partnership with other ways of thinking—like Azeem and his team, writing on paper, like some of the indigenous traditional knowledge practices around the world that are very place-based and around ecosystem balance and recognizing humans as a part of nature, working with AI and technologies. I’d love to see this be an additive experience, more than a destructive experience for humanity and the rest of the planet. Ross Dawson: Yeah and that’s why you and I both working on is doing whatever we can to nudge things in those directions. So where can people go to find out more about your wonderful work? Marshall Kirkpatrick: Well, these days, I am pointing people mostly to whatsupwiththat.app. That’s kind of my home these days for all the different work. Ross Dawson: I’ll recommend it. Marshall Kirkpatrick: Oh, thank you so much, Ross. Ross Dawson: Very useful, and I’ve only just begun to use it so— Marshall Kirkpatrick: Awesome, well, let’s stick some of those papers in there and red team it and hit “Find Science” and get other scientific reviews of the claims in the paper, etc. Thanks—it’s so great to be back in touch with you here and not just watch from a distance, but to get to put our heads together like this is a real pleasure. Ross Dawson: Thanks so much, Marshall. The post Marshall Kirkpatrick on cognitive levers, combinatorial possibilities, symphonic thinking, and compound learning (AC Ep39) appeared first on Humans + AI.

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years. Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic. To keep up with the Exponential transition, subscribe to this channel or to my newsletter: https://www.exponentialview.co/ ---- Published in early March 2026, Andrej Karpathy's autoresearch AI tool makes autonomous scientific experimentation cheap and easy — but it was designed to solve machine learning problems. I wanted to see if I could apply its loop architecture to my own work: refining my worldview, testing arguments, solving business problems. In this video, I share how I adapted Karpathy's autoresearch loops for problems that aren't easy to quantify, how to avoid the local minima trap, and the broader impact of these kinds of methods. I covered: (02:11) The Karpathy Loop: what is it and how does it work (07:54) Extending the loop into business and thinking (09:46) The local minima trap (12:20) The escape harness: getting beyond “good enough” (16:05) What I've learned after 30 days (18:47) The loop economy: from doing to judging ---- Where to find me: Exponential View newsletter: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azeem/ Twitter/X: https://x.com/azeem Production by EPIIPLUS1. Production and research: Baba Films, Chantal Smith, Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years. Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic. To keep up with the Exponential transition, subscribe to this channel or to my newsletter:  https://www.exponentialview.co/ ---- Last week Jensen Huang shared the numbers from NVIDIA's order book: AI compute demand has grown a millionfold in two years. Much GTC coverage focused on chips, robots, data centers in space, but I think Jensen revealed something far more important in his keynote: “the inference inflection has arrived,” and this is about to transform how all companies should manage their budgets. The inference era is already the operating assumption of the world's most valuable company. In this week's podcast, I cover: (1:20) NVIDIA's $1 trillion order book (1:56) OpenClaw: our era's web browser (7:54) Training vs Inference: how AI is changing (12:50) Pre-fill vs. decode: the technical split (18:06) The Harness: why OpenClaw changes everything (18:59) The engine is useless without a car (22:21) From 100M to 870M tokens per day (24:29) Meet my agent R Mini Arnold's team (26:16) AI focus group simulations at $10–50 a run (29:36) Jensen's self-interest (and why he's still right) (33:07) AI governance: token budgets don't belong with IT (35:07) From training economy to inference economy Read my essay "Magnitudes of Intelligence" on Substack: https://www.exponentialview.co/p/the-hundred-million-token-day Access the solar supercyle model here: https://www.exponentialview.co/p/solar-supercycle ---- Where to find me: Exponential View newsletter: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azeem/ Twitter/X: https://x.com/azeem Production by EPIIPLUS1. Production and research: Baba Films, Chantal Smith, Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years. Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic. To keep up with the Exponential transition, subscribe to this channel or to my newsletter: https://www.exponentialview.co ---- Apple may have stumbled into one of the most defensible positions in AI. This was not on my radar – just two months ago, I was describing a credibility crisis at the company; they appeared wrong-footed on the most important technology of our times and an acquisition was their only plausible way out.  In this episode I work through what I and many other commentators missed – and what road lies ahead for Apple. I cover: (01:16) Why I was wrong about Apple (02:40) What's behind the Mac Mini shortage (04:07) China goes OpenClaw crazy (06:28) Perplexity builds on a Mac Mini (07:12) The edge case for Apple (09:05) Apple Moat 1: hardware (11:31) Apple Moat 2: privacy (15:47) The K problem: when good enough beats genius (18:08) Privacy, sovereignty & the diary problem Read my old position on Apple at Substack: https://www.exponentialview.co/p/ev-515 For a practical guide  my OpenClaw stack, click here: https://www.youtube.com/watch?v=aCG3dFRF3ek ---- Where to find me: Exponential View newsletter: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azeem/ Twitter/X: https://x.com/azeem Production by EPIIPLUS1.  Production and research: Baba Films, Chantal Smith, Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years. Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic. To keep up with the Exponential transition, subscribe to this channel or to my newsletter: https://www.exponentialview.co/ ----- AI has become so embedded in how I work that I can no longer cleanly separate it from my thinking. That raises a question I find genuinely unsettling: is intensive AI use making me a sharper thinker, or quietly doing the opposite? In this episode I pull back the curtain on my full research and writing process — the custom tools, the friction points, and the places where I'm still not sure I've got it right. For Ezra Klein, having AI summarize material is a disaster for original thought. But my AI systems are designed to protect the cognitive work that has to stay human, while they handle everything else. Knowing where to draw that line turns out to be the hardest and most important question. I covered: 00:00 - Is AI worsening our thinking? 02:35 - Ezra Klein on AI and the death of original thought 04:02 - Cognitive offloading vs cognitive surrender 09:20 - Signal detection at scale 11:06 - Why I use several AI personas to scan for different insights 13:37 - AI tells me what NOT to think about 16:25 - The value of quietness 19:07 - Small notebooks, small ideas 20:01 - Writing reveals what you don't yet know 23:24 - The golden thread 25:20 - Speaking drafts aloud 28:05 - How I stress-test my arguments before publishing 29:35 - Using AI to stress-test my own house views 31:44 - Stylometer: my AI style and grammar tool 33:10 - Did AI make the thinking better? For more on this week's topics, subscribe to my newsletter https://www.exponentialview.co/ ----- Where to find me: Exponential View newsletter: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azhar/ Twitter/X: https://x.com/azeem Production by EPIIPLUS1 Production and research: Baba Films, Chantal Smith, Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years. Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic. To keep up with the Exponential transition, subscribe to this channel or to my newsletter: https://www.exponentialview.co/ ----- Meet R Mini Arnold - my OpenClaw chief of staff, which manages the equivalent of a ten-person team from a Mac mini in my garden studio. While I slept, that AI team debugged its own code at 3am, researched a trending Substack essay using five parallel investigators, and wrote a 4,600-word script for this very episode in 40 minutes. The gap between people who've started building this way and those who haven't is widening every week.  I covered: 00:51 Introducing my OpenClaw agent “R Mini Arnold” 03:59 What my AI chief of staff actually does 07:58 The hardware and software stack 10:38 A morning brief before you wake up 12:05 Overnight agents: research and code 15:00 How I communicate with my agent 18:56 Example 1: the sovereign wealth fund 22:41 Example 2: how this video was written 26:34 What it costs 29:22 The soul.md personality spec 32:39 Am I losing the judgment muscle? 35:46 Individuals vs. Fortune 500s 38:25 What to try this week ----- Where to find me: Exponential View newsletter: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azhar/ Twitter/X: https://x.com/azeem Production by EPIIPLUS1 Production and research: Baba Films, Chantal Smith, Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years. Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic. To keep up with the Exponential transition, subscribe to this channel or to my newsletter: https://www.exponentialview.co/ ----- This is the first episode of AI Vistas, a new series where I bring together people I trust and respect to tackle a major question collectively.  Today's question: are we in charge of our AI tools, or are they in charge of us?  Joining me are Nita Farahany, distinguished professor of law and philosophy at Duke University and a leading thinker on cognitive liberty and mental privacy; Eric Topol, founder of the Scripps Research Translational Institute and one of the world's most cited medical researchers; and Rohit Krishnan, engineer, former hedge fund manager, and AI builder. Moderating the conversation is Nick Thompson, CEO of The Atlantic. We covered: (01:33) Introducing AI Vistas (03:51) The AI agent that made a financial decision mid-drive (05:48) What does it mean to act autonomously anymore? (08:42) Why AI harms are rarer than you'd expect (10:24) When AI outperforms doctors – and why that's complicated (15:20) Constituent competence: the skill you must never offload (18:50) De-skilling is already happening  (31:20) What can schools do better? (42:50) AI slop and "hollow-ware" (46:40) What is lost when AI does the creating? (49:18) When a tool gets good enough, we hand it off (50:11) Deliberate intent: keeping AI as a tool ----- Where to find me: Exponential View newsletter: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azhar/ Twitter/X: https://x.com/azeem Where to find Nick, Nita, Eric and Rohit: Thinking Freely with Nita Farahany: https://nitafarahany.substack.com/  Ground Truths with Eric Topol: https://erictopol.substack.com/  Strange Loop Canon with Rohit Krishnan: https://www.strangeloopcanon.com/  The Most Interesting Reads with Nick Thompson: https://nxthompson.substack.com/ Production by EPIIPLUS1 Production and research: Baba Films, Chantal Smith, Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years. Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic. To keep up with the Exponential transition, subscribe to this channel or to my newsletter: https://www.exponentialview.co/ ----- In this episode, I sit down with my friend Rohit Krishnan - writer of the Substack newsletter Strange Loop Canon - for a hands-on conversation about what it actually looks like to build with AI agents today. Between us we're burning through tens of billions of tokens a month - I hit nearly 100 million in a single day this week - and we share what we're each running on our own machines. We dig into the quirks and surprising power of tools like OpenClaw, Claude Code, and Cowork, debate why AI remains stubbornly bad at good writing, and zoom out to ask what a world of trillions of agents might actually look like — and what economic infrastructure it will need. We covered: (03:15) What's on your screen right now? (04:30) OpenClaw (06:27) Rohit's agent, Morpheus (11:06) Azeem's agent, R. Mini Arnold (19:25) The analyst is now a machine (22:36) 100 million tokens in a day: the new normal (24:44) Building tools to improve AI writing: Horace and Broca (32:19) Why writing is the hardest eval for LLMs (39:18) Towards a trillion agents (42:09) The agentic economy: coordination, identity, and exchange (46:33) How to get started with OpenClaw (51:18) The hardest leap for new users ----- Where to find me: Exponential View newsletter: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azhar/ Twitter/X: https://x.com/azeem Production by EPIIPLUS1 Production and research: Baba Films, Chantal Smith, Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years. Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic. To keep up with the Exponential transition, subscribe to this channel or to my newsletter: https://www.exponentialview.co/ ----In this episode, I'm joined by Jaime Sevilla, founder of Epoch AI; Hannah Petrovic from my team at Exponential View; and financial journalist Matt Robinson from AI Street. Together we investigate a fundamental question: do the economics of AI companies actually work? We analysed OpenAI's financials from public data to examine whether their revenues can sustain the staggering R&D costs of frontier models. The findings reveal a picture far more precarious than many assume; we also explore where the real infrastructure bottlenecks lie, why compute demand will dwarf energy constraints, and what the rise of long-running agentic workloads means for the entire industry. Read the study here: https://www.exponentialview.co/p/inside-openais-unit-economics-epoch-exponentialviewWe covered: (00:00) Do the economics of frontier AI actually work? (02:48) Piecing together OpenAI's finances from public data (05:24) GPT-5's "rapidly depreciating asset" problem (13:25) Why OpenAI is flirting with ads (17:31) If you were Sam Altman, what would you do differently? (22:54) Energy vs. GPUs; where the real infrastructure bottleneck lies (29:15) What surging compute demand actually looks like (33:12) The most surprising finding from the research (38:02) The race to avoid commoditization (43:35) Agents that outlive their models  Where to find me: Exponential View newsletter: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azhar/ Twitter/X: https://x.com/azeem  Where to find Jamie: https://epoch.ai or https://epochai.substack.com Where to find Matt: https://www.ai-street.co  Production by supermix.io and EPIIPLUS1 Production and research: Chantal Smith and Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Everyone's feeling jumpy about AI right now—and for good reason.The hype has been massive. The investment has been astronomical. But where's the actual return?In this episode, Azeem Azhar, founder of Exponential View and advisor to tech leaders and governments, breaks down why the next 18 months are make-or-break for AI. Companies need to prove there's real ROI, not just prototypes launched and tokens spent.We cover:What hard evidence would actually prove AI is working (hint: it's not usage metrics)Who can build a real moat with AI—and why the winners will likely come from unexpected places, as they have in previous tech transformationsThe physical constraints nobody wants to talk about: chips, data centers, power grids, and whether America's infrastructure is up to the taskWhy OpenAI's "ubiquity strategy" might be spreading too thin (and what Anthropic is doing differently)The "pragmatic addicts" problem: we're dependent on AI even though we don't trust itHow Azeem and his team use AI to be more productive, how they automate whatever they can, and why individual contributors are acting more like managers (of AI)Note: This interview was recorded months before the "SaaSpacolypse" (big market drop) of Feb 2026; the analysis is as relevant as ever. Chapters(01:51) - Why the next 18 months are the crucible for AI (04:09) - What hard evidence would actually prove AI ROI (not token counts!) (06:55) - Why it's so hard to measure AI's real impact (09:55) - Who can build a moat with AI? Winners will be in "odd places" (12:56) - Structural data advantages: why Waymo's edge is hard to replicate (14:34) - Coding agents and whether developers will become disillusioned with them (18:21) - Physical constraints: chips, data centers, power, and America's grid problem (21:25) - How the Gulf countries became an unexpected AI hub (28:02) - "Pragmatic addicts": why 75% of Americans distrust AI but use it anyway (31:45) - The narrative of AI can be very unappealing: heaven on Earth or dystopia (34:36) - How Azeem's team uses AI: augmentation vs. automation (40:06) - What should we be talking about besides AI? (43:46) - Sounds like science fiction: What Azeem can't believe is real and here today Links & Resources:Exponential View: https://www.exponentialview.co/Azeem's Boom or Bubble dashboard: https://boomorbubble.ai/Azeem's New York Times piece on America's electric grid challenge: https://www.nytimes.com/2024/12/28/opinion/ai-electricity-power-plants.htmlMore on the “MIT Study” claiming 95% of AI projects fail that Azeem and I both found to be really poorly done, but that is nonetheless is quoted by everyone: Here's Azeem tearing the study apart with data: https://www.exponentialview.co/p/how-95-escaped-into-the-worldAnd here's me riffing with Kwaku Aning on it. You know why Azeem liked my take? Because I actually read the thing, unlike ~95% of the writers out there who just quoted that 95% number: https://www.futurearound.com/p/did-anyone-actually-read-that-mit-ai-study-that-made-the-markets-swoon-i-didSupport Future Around & Find OutGet the newsletter: https://www.futurearound.comBecome a paid subscriber and help future proof this thing!: https://www.futurearound.comSponsor the show? Are you looking to reach an audience of senior technologists and decision-makers? Email me: dan@modernproductminds.com

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years.Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic.To keep up with the Exponential transition, subscribe to this channel or to my newsletter: https://www.exponentialview.co/-----A week before OpenClaw exploded, I recorded a prescient conversation with Mustafa Suleyman, CEO of Microsoft AI and co-founder of DeepMind. We talked about what happens when AI starts to seem conscious – even if it isn't. Today, you get to hear our conversation.Mustafa has been sounding the alarm about what he calls “seemingly conscious AI” and the risk of collective AI psychosis for a long time. We discussed this idea of the “fourth class of being” – neither human, tool, nor nature – that AI is becoming and all it brings with it.Skip to the best bits:(03:38) Why consciousness means the ability to suffer(06:52) "Your empathy circuits are being hacked"(07:23) Consciousness as the basis of rights(10:47) A fourth class of being(13:41) Why market forces push toward seemingly conscious AI(20:56) What AI should never be allowed to say(25:06) The proliferation problem with open-source chatbots(29:09) Why we need well-paid civil servants(30:17) Where should we draw the line with AI?(37:48) The counterintuitive case for going faster(42:00) The vibe coding dopamine hit(47:09) Social intelligence as the next AI frontier(48:50) The case for humanist super intelligence-----Where to find Mustafa:- X (Twitter): https://x.com/mustafasuleyman- LinkedIn: https://www.linkedin.com/in/mustafa-suleyman/- Personal Website: https://mustafa-suleyman.ai/Where to find me:- Substack: https://www.exponentialview.co/- Website: https://www.azeemazhar.com/- LinkedIn: https://www.linkedin.com/in/azhar- Twitter/X: https://x.com/azeemProduced by supermix.io and EPIIPLUS1 Ltd. Production and research: Chantal Smith and Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years.Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic.To keep up with the Exponential transition, subscribe to this channel or to my newsletter: https://www.exponentialview.co/-----At Davos 2026, the mood was unlike any previous World Economic Forum gathering. With Donald Trump arriving amid escalating geopolitical tensions and European leaders sounding alarms about sovereignty, I recorded live dispatches from the ground. In this special episode, I bring together observations from four days at the annual meeting, tracking the seismic shifts in global order alongside the practical realities of AI adoption in the enterprise.Skip to the best bits:(00:38) Day one at Davos(02:10) Three recurring themes through the week(03:55) Day three at Davos(05:12) Mark Carney's stirring speech(05:52) Why European leaders are sounding the alarm(06:51) Why technological sovereignty just became urgent(09:31) Day four at Davos(12:59) What leaders really have to say on AI adoption(14:07) The case for only using open source modelsWhere to find me:Exponential View newsletter: https://www.exponentialview.co/Website: https://www.azeemazhar.com/LinkedIn: https://www.linkedin.com/in/azhar/Twitter/X: https://x.com/azeemProduction by supermix.io and EPIIPLUS1. Production and research: Chantal Smith and Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years.Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic.To keep up with the Exponential transition, subscribe to this channel or to my newsletter: https://www.exponentialview.co/------In this episode, Peter McCrory, Head of Economics at Anthropic, unpacks the company's new Economic Index report. His team analysed millions of real Claude conversations to map exactly where AI is augmenting human work today and where it isn't. We explore the striking divergence between API and chat usage, why businesses need to extract tacit knowledge to unlock AI's potential, the "hollow ladder" risk for junior workers, and Anthropic's estimate that AI could add 1.0-1.8% to annual productivity growth over the next decade.Skip to the best parts:(00:00) Anthropic's Economic Index report(01:20) Claude's two distinct usage patterns(06:22) Examining AI's impact on the labor market(09:20) Where most businesses think too small(12:03) Why extracting tacit knowledge is so important(20:33) How do we create the next generation of experts?(23:22) Why people need to develop cognitive endurance(29:55) Long-term vs. short-term productivity(35:56) The future of human knowledge(37:46) Could AI's greatest impact go unmeasured?(41:55) How task bottlenecks have moved(46:09) Implementation resembles a staircase - not a curve(50:47) "Capability doesn't instantly deliver adoption"------Where to find me:Exponential View newsletter: https://www.exponentialview.co/Website: https://www.azeemazhar.com/LinkedIn: https://www.linkedin.com/in/azhar/Twitter/X: https://x.com/azeemProduction by supermix.io and EPIIPLUS1. Production and research: Chantal Smith and Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years. Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic. To keep up with the Exponential transition, subscribe to this channel or to my newsletter: https://www.exponentialview.co/ -------- In this episode, I share my outlook for 2026 and explain why AI tools now feel genuinely different. I explore how the act of making has been transformed, why authenticity and meaning will become the new scarcity, and whether the foundations of energy and capital can hold. I also address the question I was asked most in 2025: when will the AI bubble burst? Skip to the best bits: 00:00 Why AI feels different in 2026 01:59 The six shifts in AI 03:32 The "done list" era 06:43 From execution to orchestration 09:02 The agentic coding revolution 11:10 What's a Chief Question Officer? 13:58 Three ways value will be created 16:27 "Claude told me to use ChatGPT" 18:02 The AI usage gap 20:30 The new moat in 2026 26:10 How does solar growth affect AI? 28:53 Revisiting the bubble or boom question ------ Where to find me: Exponential View newsletter: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azhar/ Twitter/X: https://x.com/azeem Production by supermix.io and EPIIPLUS1 Production and research: Chantal Smith and Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years. Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic. To keep up with the Exponential transition, subscribe to this channel or to my newsletter: https://www.exponentialview.co/ ------ In this episode, Nobel Prize-winning economist Paul Krugman and I discuss how a strong US economy, high asset valuations, and rapid AI adoption are sitting in uneasy tension. We explore what past technology cycles can teach us, why safety nets struggle to address disruption, and where genuine optimism still makes sense. This is a January 2025 rerun, which remains strikingly relevant today. We covered: (01:09) State of the US economy (02:28) "That end of 1999 feeling" (05:08) Insights and lessons from the dotcom bubble (09:57) Why today's market is different (13:44) Understanding AI's role in labor displacement (16:05) Are LLMs "souped-up autocorrect"? (20:14) How job displacement erodes communities (23:40) 2025's looming threat of tariffs (26:16) AI's surprising impact on globalization (30:15) Can markets address inequality? (33:06) The maximum level of sustainable national debt (36:31) When should the Fed raise interest rates? (38:57) The need to revitalize local economies (44:53) Did Paul's 2025 predictions come true? ------ Where to find me: Exponential View newsletter: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azhar/ Twitter/X: https://x.com/azeem Production by supermix.io and EPIIPLUS1 Production and research: Chantal Smith and Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years. Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic. To keep up with the Exponential transition, subscribe to this channel or to my newsletter: https://www.exponentialview.co/ --- What made 2025 special? In this episode, I reflect on the past year and what it revealed: a K-shaped divide. On one track, AI models are now doing hours of high quality work, improving at exponential pace, and shifting how we work from doing to judging. On the other, organisations and the broader economy are struggling to keep up. Stay to the end for my seasonal film recommendation. I cover:(00:00) Intro (00:45) The state of tool usage in 2025 (6:10) The gap between AI progress and organizational adoption (9:53) AI's shockingly rapid revenue growth (11:17) The biggest mistake smart people make with AI (14:14)  The inescapable need for physical infrastructure (16:06) What everyone was asking in 2025 (18:08) The new winners of the AI economy (20:48) Why “K” is the letter of 2025 (24:08) Seasonal movie recommendation ---- Where to find me: Exponential View newsletter: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azhar/ Twitter/X: https://x.com/azeem  Production by supermix.io and EPIIPLUS1 Production and research: Chantal Smith and Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years. Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic. To keep up with the Exponential transition, subscribe to this channel or to my newsletter: https://www.exponentialview.co/ --- In this episode, I've distilled a year of extraordinary dialogue into one 20-minute briefing. I've spent 2025 in conversation with the architects of our future - the builders and thinkers redefining AI, energy, and the global economy.These are the "eureka" moments from my most exclusive interviews. From the future of "protopia" with Kevin Kelly to the hidden tech gaps with Dan Wang, this is your strategic roadmap for the exponential age.What you'll hear about:Part 1: AI as a general purpose techKevin Weil: The heuristic for startupsMatthew Prince: The “Socialist” pricing debateTyler Cowen: This will stifle the AI boomNick Thompson: The "NBA-ification" of JournalismKevin Kelly: From utopia to protopiaKevin Kelly: Technology as a "possibility factory”Part 2: How work is changingSteve Hsu: The future of educationThomas Dohmke: The inspectability turning pointBen Zweig: The new role for entry-level workersBen Zweig: Why are there so many hiring freezes?Ben Zweig: The eroding signal of higher educationPart 3: The physical world, compute, and energyGreg Jackson: The "crossing the road" metaphorGreg Jackson: Building a “show don't tell” companyDan Wang, The "physical reality" of AIPart 4: The changing US China landscapeDan Wang: The West's hidden tech gapJordan Schneider: The two types of accelerationismJordan Schneider: Why the US can learn from ChinaWhere to find me: Exponential View newsletter: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azhar/Twitter/X: https://x.com/azeem Production by supermix.io and EPIIPLUS1 Production and research: Chantal Smith, Marija Gavrilov and Hannah Petrovic Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years. Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic. To keep up with the Exponential transition, subscribe to this channel or to my newsletter: https://www.exponentialview.co/ --- In this episode, I look at the next 24 months of AI. The technology is improving rapidly – so what could hold back widespread transformation of how we work and live? I dig into the real constraints, from electricity shortages to institutional inertia, why mid-2026 matters for enterprise AI, and why so many people remain uneasy about a technology they use every day. I cover: (00:03) Predicting AI's next two years (01:50) How life changing are chatbots, really? (03:36) Our current biggest AI constraint (07:58) The remarkable increase in token efficiency (10:43) Why mid-2026 is a crucial turning point (13:01) Do we actually want AI in our lives? (15:28) Should organizations wait to jump in? (16:39) How is OpenAI reckoning with Gemini? (18:41) The market's reaction to OpenAI's code red (19:32) Where will value accrue in the supply chain? (20:51) What's the best strategy for middling powers?Where to find me: Exponential View newsletter: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azhar/Twitter/X: https://x.com/azeem Production by supermix.io and EPIIPLUS1 Production and research: Chantal Smith and Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years. Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic. To keep up with the Exponential transition, subscribe to this podcast or to my newsletter: https://www.exponentialview.co/ --- In this episode, I reflect on the third anniversary of ChatGPT's launch as a marker of where we are in the exponential age. As a product, ChatGPT captures the speed of technological progress, the new behaviours emerging around it and the widening gap between innovation and institutional change – all symptomatic of the era I called the exponential age in my 2021 book. I cover: (00:09) How ChatGPT became synonymous with AI (01:41) The rise of the reasoning model (03:53) Why NVIDIA's chip cycle is exponential (05:53) How general-purpose tech changes everything (07:59) The subtle power of building bespoke software (11:46) The iPhone calculation that breaks everything (14:53) Who profits from a general-purpose technology? (16:38) The software market example (20:07) Are we headed towards another .com crash?  Where to find me: Exponential View newsletter: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: /azhar Twitter/X: https://x.com/azeem Production by supermix.io and EPIIPLUS1 Production and research: Chantal Smith and Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The AI industry is sending mixed signals, with markets turning red while teams report real productivity gains. In this session I explore why we are living in a split reality, where individuals move faster with these tools but the wider economy is ambivalent. We once assumed juniors would get the biggest lift from AI, yet the newer agentic tools seem to reward senior workers who know how to structure problems and judge output. In this podcast, I look at the evidence behind that shift and explain how these gains collide with the slow grind of organisational processes. I cover: (00:00) AI productivity: A split reality (00:31) Decoding the stock market drop (02:53) Unpacking three years of AI productivity data (06:09) Does AI help junior or senior developers more? (09:54) The surprising group benefitting from AI (11:45) Why is there a productivity gap? (13:08) Most companies need a process overhaul (14:33) Anthropic's alarming discovery (16:45) So, are we moving quickly enough? (17:29) The counterintuitive truth about AI productivity  Where to find me:- Exponential View newsletter: https://www.exponentialview.co/ - Website: https://www.azeemazhar.com/ - - LinkedIn: https://www.linkedin.com/in/azhar - Twitter/X: https://x.com/azeem  Production by supermix.io and EPIIPLUS1 Production and research: Chantal Smith, Hannah Petrovic and Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Azeem Azhar is the founder of Exponential View, a newsletter and research platform on emerging technology read by over 130,000 executives and policymakers globally, and author of the bestselling book The Exponential Age.In this episode of World of DaaS, Azeem and Auren discuss:Diagnosing an AI bubbleData centers driving 33% of US GDP growthWhether energy will constrain AI before capital doesCircular financing in AI and funding quality risksLooking for more tech, data and venture capital intel? Head to worldofdaas.com for our podcast, newsletter and events, and follow us on X @worldofdaas.You can find Auren Hoffman on X at @auren and Azeem Azhar on X at @azeem.Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com)

Junior roles in AI-exposed fields are disappearing fast. The obvious culprit is AI rapidly automating entry-level jobs. And yet, this isn't quite right. What is driving the drop is managers' expectations about what AI will do, not the work that it's already replacing. I discussed this with Ben Zweig of Revelio Labs, which builds global workforce data from millions of individual profiles to track hiring, separations and job flows. Their data shows how expectation and uncertainty are reshaping the market.Together, we explored the future of work and shared practical advice for new grads. We covered: (01:15) What's happening in the labor market? (05:27) The inherent complexity of the labor market (06:24) How Revelio Labs captures labor market data (08:39) "The Canary in the Coal Mine" (11:52) Who does AI exposure harm the most? (13:01) How AI anticipation is harming the job market (15:15) Testing the expectation mismatch hypothesis (17:30) Could AI be creating more jobs? (20:44) Breaking down jobs into smaller tasks (27:33) Why large companies struggle to reorganize (30:35) Focus on creating adaptive, flexible roles (36:03) Managing AI's increasing capability (39:11) What entry-level workers need to do Where to find me: - Exponential View newsletter: https://www.exponentialview.co/ - Website: https://www.azeemazhar.com/ - LinkedIn: https://www.linkedin.com/in/azhar - Twitter/X: https://x.com/azeem Where to find Ben: - https://www.linkedin.com/in/ben-zweig/ - Twitter/X: https://x.com/BJZweig - Revelio Labs: https://www.reveliolabs.com/ Production by supermix.io and EPIIPLUS1 Production and research: Chantal Smith, Hannah Petrovic and Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The AI boom isn't just about chatbots.In this video, I explain why cloud companies and chipmakers are exploding in value: we're moving into an economy where computation becomes a fundamental input – like steel, electricity or oil.If that's true, our demand for compute could approach infinity.I also break down new data from Wharton's 2025 AI Adoption Report, which shows how AI agents and automated workflows are already spreading through major U.S. companies: https://knowledge.wharton.upenn.edu/special-report/2025-ai-adoption-report/Timestamps:(00:00) The economic shift to computation (00:40) The surprising Cloud business boom (02:52) Is the hardware industry growth a bubble? (03:18) What is computing, really? (04:31) Our insatiable appetite for computing (09:15) Our economic dependence on computation (10:54) The rise of agentic workforces (13:05) What does infinite demand actually mean? (15:23) The future of compute demandWhere to find me:Exponential View newsletter: https://www.exponentialview.co/Website: https://www.azeemazhar.com/LinkedIn: https://www.linkedin.com/in/azharTwitter/X: https://x.com/azeemProduction by supermix.io and EPIIPLUS1Production and research: Chantal Smith, Hannah Petrovic and Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Two weeks ago, in one of our most popular podcasts of the year, the investor and author Paul Kedrosky explained why he thinks AI is a bubble. In the last few days, practically everybody seems to agree.I hate this. I don't like feeling like my position is the same position as everybody else's. Conventional wisdoms are often more conventional than wise, and I've started to wonder: Is there a bubble of people calling AI a bubble?Today's guest says yes. Azeem Azhar is an investor and the author of the blog Exponential View. Like Paul, Azeem is a fantastic explainer and storyteller, and I'm satisfied that Plain English has now presented the strongest possible arguments for and against AI being a bubble. If you want to know where I land, you'll just have to listen to the end of the show. If you have questions, observations, or ideas for future episodes, email us at PlainEnglish@Spotify.com. Host: Derek ThompsonGuest: Azeem AzharProducers: Devon Baroldi and Kaya McMullen Learn more about your ad choices. Visit podcastchoices.com/adchoices

How big is the AI bubble? When it bursts, who will be hurt? What impact on jobs and wages in artificial intelligence having now? Robert discusses with Azeem Azhar, founder of Exponential View and tech investor. Find out more about how Google's AI is helping fuel the UK's growth and transformation and read the report at goo.gle/aiworks. Email: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠restismoney@gmail.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ X: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@TheRestIsMoney⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@TheRestIsMoney⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ TikTok: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@RestIsMoney⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://⁠⁠⁠goalhanger.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Assistant Producer: India Dunkley, Alice Horrell Producer: Ross Buchanan Head of Content: Tom Whiter Exec Producers: Tony Pastor + Jack Davenport Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this episode, I spoke with Dan Wang, author of “Breakneck: China's Quest to Engineer the Future”, shortlisted for the FT & Schroders Business Book of the Year.Dan is one of the most astute observers of China's technological and industrial development, and his annual letters from Beijing have long been required reading for those seeking to understand the country's evolving role in the world.We unpacked a bold thesis: China is not merely a competitor in AI and tech, but is re-imagining its entire state apparatus as an engineering state - in contrast to the more “lawyerly” institutions of the US and UK.If you're interested in AI, energy or geopolitics, this conversation is for you.We covered: (00:47) Why China is an engineering state(03:40) China's pro-engineering disposition(06:08) The role of market competition in China(08:07) Living through Zero COVID(11:35) What political science terms get wrong(12:58) Characteristics of a lawyerly society(15:23) What Americans misunderstand about China(21:54) Has China produced essential tech?(23:50) The AI divide: China vs. US(27:45) Differences in energy production(32:07) The inherent value of process knowledge(38:34) Is the US developing pro-engineering policies?(44:23) What does it take for countries to compete?Where to find me:Substack: https://www.exponentialview.co/Website: https://www.azeemazhar.com/LinkedIn: https://www.linkedin.com/in/azharTwitter/X: https://x.com/azeemWhere to find Dan:Website: https://danwang.co/LinkedIn: https://www.linkedin.com/in/danwang15/Twitter/X: https://x.com/danwwangProduction by supermix.io and EPIIPLUS1 Ltd, including Chantal Smith, Marija Gavrilov, Nathan Warren and Hannah Petrovic. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

I denne sidste AI Denmark inden sommerferien skal vi lidt op i perspektiv, og selvom vi stadig har blikket rettet mod den hjemlige andedam, så går turen over Atlanten og videre over hele USA til Silicon Valley.Silicon Valley er stadig – på godt og ondt – dér hvor en meget stor del af vores gennemdigitaliserede hverdag bliver skabt og styret, og målt på økonomi og indflydelse er techbranchen jo på størrelse med et mellemstort land.Det er også en af årsagerne til, at Danmark for cirka 8 år siden valgte at etablere en tech-ambassade i San Francisco, i håbet om at kunne interagere mere direkte med de gigantiske firmaer.I denne episode har jeg besøg af Claus Mackinney-Valentin fra den danske techambassade til en slags sommersamtale med fokus på AI i Silicon Valley set med danske briller, ikke mindst i lyset af de senere års geopolitiske omvæltninger.LINKSSIDEN SIDSTMark Zuckerberg annoncerer sin AI super-gruppe | The VergeAlle dem Mark Zuckerberg har ansat indtil videre | WiredZuckerberg tilbyder top-løn til topfolk | WiredSam Altman kritiserer Metas jagt på AI-folk | WiredAnthropic vinder i del af retssag — men er stadig i problemer for at stjæle bøger | The VergeAnthropic ødelagde millioner af trykte bøger for at skaffe træningsdata | Ars TechnicaCloudflare vil nu blokere AI-crawlere som standard | The VergeSenatet dropper plan om at forbyde AI-regulering | The VergeCaroline Stage vil bremse EU's AI-lovgivning | Version2SILICON VALLEYClaus Mackinney-Valentin på LinkedInClaus Mackinney-Valentin, Senior AI & Tech-rådgiver i Silicon ValleyTechambassadørens kontorAI 2027 - En forskningsbaseret AI-scenarieprognoseEt problem til 100 billioner dollars | Exponential View

At the start of the year, I made seven predictions about how 2025 would unfold. Six months in, it's time to mark my own work. From AI capability breakthroughs to autonomous vehicles, climate extremes to workforce transformation, I examine what I got right, what I missed, and why the 2027-2028 period will be when vertical AI hits the real economy in force.In this episode you'll hear:The AI wall that never came: Ten-million-token models exist, O3 scores 25% on Frontier Math vs GPT-4's 2%, but some models are inconsistent and overthink problemsWhen bots officially out-talk humans: My modeling shows LLMs crossed the threshold of producing more text than humans sometime this summerThe Waymo vs Uber SF battle: They've beaten Lyft and expanded to New York, but Tesla's Austin robo-taxi fleet changes the competitive landscapeClimate and energy predictions that were "too easy": Record climate extremes, 30% solar growth, and Indonesia's stunning EV jump from 20% to 80% in two yearsWhat I completely missed: The AI capex boom, humanoid robots at Figure/BMW/Amazon, and workforce impact with CEOs reporting 20-50% AI assistanceWhy getting too many predictions right is a problem: I reflect on whether scoring too well means I didn't push boundaries enough in my forecastingThe 2027-2028 turbulence ahead: Why four-year-old AI startups challenging incumbents while early adopters reap deep organizational benefits will create economic turbulenceOur new showThis was originally recorded for “Friday with Azeem Azhar”, a new show that takes place every Friday at 9am PT and 12pm ET. You can tune in through my Substack linked below.The format is experimental and we'd love your feedback, so feel free to comment or email your thoughts to our team at live@exponentialview.co.Azeem's links:Substack: https://www.exponentialview.co/Website: https://www.azeemazhar.com/LinkedIn: https://www.linkedin.com/in/azhar?originalSubdomain=ukTwitter/X: https://x.com/azeemTimestamps:(00:00) Grading my predictions from January 2025(01:23) #1: No AI Wall(03:59) #2: Warp-speed deployment(05:16) #3: Bots out-talk humans(06:24) #4: Waymo overtakes Uber in SF(08:31) #5: Climate extremes intensify(09:09) #6: Solar keeps breaking records(10:06) #7: EVs shift up a gear(11:12) The problem with predicting too accurately(12:01) What I missed(12:14) The CapEx boom around AI(13:56) The rise of humanoid robots(14:36) AI's impact on the workforce(18:40) Looking ahead(18:48) Infrastructure first, apps next(19:52) 2027/2028 will be a "period of fireworks"(21:39) When we'll find out if AI is a bubble(23:02) A question for the futureProduction:Production by supermix.io and EPIIPLUS1 Ltd

Broadcasting live from Paris, I tackle three massive technology stories that are reshaping our digital future. From Apple's stunning interface redesign to the collapse of traditional search advertising, and Sam Altman's vision of an AI singularity that's already begun - this episode captures the tectonic shifts happening in tech right now.I cover:(1:32) WWDC 2025:  Apple's AI challenges and new UI(6:06) The decline of Google's ad model(10:08) Sam Altman's Gentle Singularity essay(19:37) Live audience Q&A(19:45) Is the singularity really about Altman?(22:13) Is France carrying Europe's AI dreams?(24:58) Are you seeing promising AI hardware?(27:42) How will AI change software pricing?Our new showThis was originally recorded for “Friday with Azeem Azhar”, a new show that takes place every Friday at 9am PT and 12pm ET. You can tune in through my Substack linked below.The format is experimental and we'd love your feedback, so feel free to comment or email your thoughts to our team at live@exponentialview.co.Azeem's links:Substack: https://www.exponentialview.co/Website: https://www.azeemazhar.com/LinkedIn: https://www.linkedin.com/in/azhar?originalSubdomain=ukTwitter/X: https://x.com/azeemProduction by supermix.io and EPIIPLUS1 Ltd.

This week, I'm speaking with Kevin Weil, Chief Product Officer at OpenAI, who is steering product development at what might be the world's most important company right now.We talk about:(00:00) Episode trailer(01:37) OpenAI's latest launches(03:43) What it's like being CPO of OpenAI(04:34) How AI will reshape our lives(07:23) How young people use AI differently(09:29) Addressing fears about AI(11:47) Kevin's "Oh sh!t" moment(14:11) Why have so many models within ChatGPT?(18:19) The unpredictability of AI product progress(24:47) Understanding model “evals”(27:21) How important is prompt engineering?(29:18) Defining “AI agent”(37:00) Why OpenAI views coding as a prime target use-case(41:24) The "next model test” for any AI startup(46:06) Jony Ive's role at OpenAI(47:50) OpenAI's hardware vision(50:41) Quickfire questions(52:43) When will we get AGI?Kevin's links:LinkedIn: https://www.linkedin.com/in/kevinweil/Twitter/X: @kevinweilAzeem's links:Substack: https://www.exponentialview.co/Website: https://www.azeemazhar.com/LinkedIn: https://www.linkedin.com/in/azharTwitter/X: https://x.com/azeemOur new show:This was originally recorded for "Friday with Azeem Azhar", a new show that takes place every Friday at 9am PT and 12pm ET. You can tune in through Exponential View on Substack.Produced by supermix.io and EPIIPLUS1 Ltd.

Economist and polymath Tyler Cowen challenges Silicon Valley's optimistic projections about AI-driven economic growth. We explore what could slow AI's economic impact, despite its remarkable capabilities – and where humans find the new normal amidst major shifts.Timestamps: (00:00) Episode trailer (01:47)  The problem with Silicon Valley's AI-driven growth projections (06:02) The institutional bottleneck to AI progress (10:49) Markets aren't pricing in a radical AI future (12:53) Are we heading for a great job displacement? (17:02) Is GDP still worth talking about? (19:11) Who does AI benefit most? (21:11) Will AI cause a human identity crisis? (27:11) The education system's failure to adapt (35:34) How the Gulf could become a geopolitical powerhouse (39:10)  Could AI change religion? (46:46)  Closing thoughts Tyler's links: Marginal Revolution Blog: https://marginalrevolution.com/ Twitter/X: https://x.com/tylercowen Azeem's links: Substack: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azhar Twitter/X: https://x.com/azeemOur new showThis was originally recorded for "Friday with Azeem Azhar", a new show that takes place every Friday at 9am PT and 12pm ET. You can tune in through Exponential View on Substack.Produced by supermix.io and EPIIPLUS1 LTD

Thomas Dohmke, CEO of GitHub, joins Azeem to explore how AI is fundamentally transforming software development. In this episode you'll hear: (01:50) What's left for developers in the age of AI? (04:54) How GitHub Copilot unlocks flow state (07:09) Three big shifts in how engineers work today (10:47) Is software development art or assembly line? (15:26) Why developers are climbing the abstraction ladder (19:35) Have we already lost control of the code? (23:15) What it's actually like to work with AI coding agents (39:35) Welcome to the age of ultra-personalized software(45:37) Building the next-generation web Thomas's links:GitHub: https://github.com/LinkedIn: https://www.linkedin.com/in/ashtom/Twitter/X: https://x.com/ashtomAzeem's links:Substack: https://www.exponentialview.co/Website: https://www.azeemazhar.com/LinkedIn: https://www.linkedin.com/in/azharTwitter/X: https://x.com/azeemOur new show This was originally recorded for "Friday with Azeem Azhar", a new show that takes place every Friday at 9am PT and 12pm ET. You can tune in through Exponential View on Substack. Produced by supermix.io and EPIIPLUS1 Ltd

Aaron Levie, CEO & co-founder of Box, joins Azeem Azhar to explore how an “AI-first” mindset is reshaping every layer of Box – from product road-maps to pricing – and what that teaches the rest of us about building faster, smarter organisations.Timestamps:(00:00) Episode trailer(02:04) The "lump of labor fallacy" in sci-fi books(07:37) When individual productivity gains don't translate to teams(12:32) Box's Friday AI demos(21:23) How agents might redefine 100 years of management science(26:37) A lesson on AI innovation from the early days of Ford(29:52) Sundar Pichai, Satya Nadella, and Sergey Brin are coding again?(35:16) Pricing in a post-AI agent world(38:43) Cheaper tokens, heavier usage: AI's margin math(43:02) Solving AI's verifiability problem(48:24) How Aaron uses AI in his personal lifeAaron's links:Box: https://www.box.com/LinkedIn: https://www.linkedin.com/in/boxaaron/X/Twitter: https://x.com/levieAzeem's links:Substack: https://www.exponentialview.co/Website: https://www.azeemazhar.com/LinkedIn: https://www.linkedin.com/in/azharX/Twitter: https://x.com/azeemThis conversation was recorded for “Friday with Azeem Azhar”, live every Friday at 9 am PT / 12 pm ET. Catch it via Exponential View on Substack.Produced by supermix.io and EPIIPLUS1 Ltd

On April 28th, millions of people across Spain, Portugal and beyond were plunged into darkness in one of Europe's most severe blackouts in decades. Was it a cyberattack? A renewables failure? Or might things be a little more complex?This week, Tom Rivett-Carnac, Christiana Figueres, and Paul Dickinson dig into what we know, what we don't, and ask what this blackout really tells us about the transition to renewables. They speak with energy strategist Kingsmill Bond of Ember and hear an on-the-ground account from José Manuel Entrecanales, CEO of global renewables leader Acciona, to build a picture of how our grids function – and how they fail.Plus: what can we say when friends or colleagues claim that ‘renewables aren't reliable'? And, after our recent conversations reflecting on the legacy of Pope Francis, what might Pope Leo XIV mean for future climate leadership?Learn more

Lennart Heim, a researcher and information scientist at RAND Corporation, joins Azeem Azhar to unpack a provocative claim: China is catching up with US AI capabilities, but it doesn't matter. Timestamps: (00:00) Episode trailer (01:19) Lennart's core thesis (03:26)   Why compute matters so much (07:31)  The investment split between model R&D and model execution (11:18)  How test-time compute impacts costs (16:14) The geopolitics of compute (21:32) Why does the U.S have more compute capacity than China? (25:01)  The trade-off between economic needs and national-security needs (31:54)  How technology change might shift the battlegrounds (35:33)  Dealing with compute and power concentration (48:19)  Concluding quick-fire question  Lennart's links: Twitter/X: https://twitter.com/ohlennartPersonal blog: https://heim.xyz/Azeem's links:Substack: https://www.exponentialview.co/Website: https://www.azeemazhar.com/LinkedIn: https://www.linkedin.com/in/azharTwitter/X: https://x.com/azeemThis was originally recorded for "Friday with Azeem Azhar", a new show that takes place every Friday at 9am PT and 12pm ET. You can tune in through Exponential View on Substack. Produced by supermix.io and EPIIPLUS1 Ltd

Physicist and entrepreneur Steve Hsu, whose startup Superfocus tackles hallucination problems in large language models, joins Azeem to discuss AI agents, hallucination challenges and what happens when technology meets labor markets. They discuss: (01:31) The deeper shift that Superfocus represents (07:00)  Will models overcome hallucination? (10:15)  AI Agents can replace 80-90% of call center calls(12:27)  What it's like showing customer support AI to customer support people (22:36)  China's mayors are like mini CEOs (30:05)  What will matter most in the supposed "AI race"? (35:58) DeepSeek was not part of the Chinese Government (38:23)  How open source will change the future of deployment (40:59)  What the public doesn't understand about AI tail risk (48:01) How AI plush toys can teach French to 2-year-olds This was originally recorded for "Friday with Azeem Azhar", a new show that takes place every Friday at 9am PT and 12pm ET. You can tune in through Exponential View on Substack. Produced by supermix.io and EPIIPLUS1 Ltd

Sir Niall Ferguson, renowned historian and Milbank Family Senior Fellow at the Hoover Institution, joins Azeem Azhar to discuss the evolving relationship between the U.S. and China, Trump's foreign policy doctrine, and what the new global economic and security order might look like. (00:00)  What most analysts are missing about Trump (05:43)  The win-win outcome in Europe–U.S relations (11:17)  How the U.S. is reestablishing deterrence (15:50)  Can the U.S. economy weather the impact of tariffs? (23:33) Niall's read on China (29:29)  How is China performing in tech? (33:35)  What might happen with Taiwan (42:43) Predictions for the coming world order Sir Niall Ferguson's links:Substack: Time MachineBooks: War of the World, Doom: The Politics of CatastropheTwitter/X: https://x.com/nfergusAzeem's links:Substack: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azhar Twitter/X: https://x.com/azeem Our new show This was originally recorded for "Friday with Azeem Azhar" on 28 March. Produced by supermix.io and EPIIPLUS1 Ltd

In this episode, Azeem Azhar speaks with Ryan Petersen, CEO and founder of logistics platform Flexport, about the current state of global trade amidst escalating tariffs, geopolitical tensions, and technological disruption. Ryan offers unique insights from the frontlines of the US-China trade war and explores how businesses are adapting to a rapidly changing landscape. (00:00) Episode trailer (01:12) Ryan's overall thoughts and predictions (03:40) Why shipping is crucial to your everyday life (08:07) Why tariffs may actually increase global shipping (11:34) Who's pausing their China shipments? (14:29) The mindset of Flexport customers right now (16:02) Is this the end of globalization? (21:48) The fragility and resiliency of global trade (25:27) The most underrated story in the world (30:25) How tech has changed global trade (36:31) Who will win in the new trade settings? (41:20) What could a U.S-China trade deal look like? Ryan's links:Flexport https://www.flexport.com/ Twitter/X https://x.com/typesfast LinkedIn https://www.linkedin.com/in/rpetersen/Azeem's links: Substack: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azhar Twitter/X: https://x.com/azeem Our new showThis was originally recorded for "Friday with Azeem Azhar", a new show that takes place every Friday at 9am PT and 12pm ET. You can tune in through my Substack linked below. The format is experimental and we'd love your feedback, so feel free to comment or email your thoughts to our team at live@exponentialview.co.Produced by supermix.io and EPIIPLUS1 Ltd

Anthropic's co-founder and chief scientist Jared Kaplan discusses AI's rapid evolution, the shorter-than-expected timeline to human-level AI, and how Claude's "thinking time" feature represents a new frontier in AI reasoning capabilities.In this episode you'll hear:Why Jared believes human-level AI is now likely to arrive in 2-3 years instead of by 2030How AI models are developing the ability to handle increasingly complex tasks that would take humans hours or daysThe importance of constitutional AI and interpretability research as essential guardrails for increasingly powerful systemsOur new show This was originally recorded for "Friday with Azeem Azhar", a new show that takes place every Friday at 9am PT and 12pm ET on Exponential View. You can tune in through my Substack linked below. The format is experimental and we'd love your feedback, so feel free to comment or email your thoughts to our team at live@exponentialview.co.Timestamps:(00:00) Episode trailer(01:27) Jared's updated prediction for reaching human-level intelligence(08:12) What will limit scaling laws?(11:13) How long will we wait between model generations?(16:27) Why test-time scaling is a big deal(21:59) There's no reason why DeepSeek can't be competitive algorithmically(25:31) Has Anthropic changed their approach to safety vs speed?(30:08) Managing the paradoxes of AI progress(32:21) Can interpretability and monitoring really keep AI safe?(39:43) Are model incentives misaligned with public interests?(42:36) How should we prepare for electricity-level impact?(51:15) What Jared is most excited about in the next 12 monthsJared's links:Anthropic: https://www.anthropic.com/Azeem's links: Substack: https://www.exponentialview.co/Website: https://www.azeemazhar.com/LinkedIn: https://www.linkedin.com/in/azharTwitter/X: https://x.com/azeem

Kevin Kelly is a co-founder of Wired Magazine and a renowned author and futurist.  Decades ago, Kevin predicted much of today's technological and cultural landscape. In this discussion, he presents his new bold vision for what's coming next: The Handoff to Bots.In this episode, you'll hear:Why declining populations will radically reshape economiesWhat a bot-to-bot economy could look and feel likeWhy people of the future might be paid to read emailsHow AI could help humanity find deeper purposeWhy this future might be closer than you thinkKevin's links:Website/blog: https://kk.org/Twitter/X: https://x.com/kevin2kellyInstagram: / kevin2kelly  Azeem's links:Substack: https://www.exponentialview.co/Website: https://www.azeemazhar.com/LinkedIn: https://www.linkedin.com/in/azhar?ori...Twitter/X: https://x.com/azeemTimestamps:(00:00) Intro(02:17) The baby black hole behind Kevin's theory(10:49) Kevin's thesis: The handoff to bots(15:05) This world is closer than we think(19:32) The role of humans in this new world(21:23) Could monopoly influence pose a problem?(28:33) The nature of “struggle” in this new world(32:42) Could we see countries competing for population?(36:06) How a scarcity of humans might change what we value(42:30) What would 1994 Kevin think of 2025 Kevin's blog? Production:Production by supermix.io 

Could AI help you land an internship? This week in the News Roundup, Oz and producer Eliza Dennis explore the rise of vibecoding, what it means for the future of software development and how one college programmer hopes to reform the Big Tech hiring process. On TechSupport, Oz chats with the founder and researcher of the Exponential View newsletter, Azeem Azhar, about the latest AI innovation and its significance in the battle for technological supremacy.See omnystudio.com/listener for privacy information.

Azeem Azhar's mission is to help leaders stay ahead of the curve at a time when technology, especially AI, is evolving at an exponential rate. He founded the Exponential View newsletter, which offers insights and advice to an eager audience of 116,000+ people who want to get ahead in a rapidly changing world. He joined us to discuss the potential for AI to help business leaders navigate uncertainty and describe some of the ways that AI agents and deep research will fundamentally change the way we work.  WorkLab Subscribe to the WorkLab newsletter 

Patrick McKenzie (patio11) is joined by Azeem Azhar, writer of the Exponential View newsletter, to discuss the massive data center buildout powering AI and its implications for our energy infrastructure. The conversation covers the physical limitations of modern datacenters, the challenges of electricity generation, the societal ripples from historical largescale infrastructure investments like railways and telecommunications, and the future of energy including solar, nuclear and geothermal power. Through their discussion, Patrick and Azeem explain why our mental models for both computing and energy systems need to be updated.–Full transcript available here: www.complexsystemspodcast.com/ai-llm-data-center-power-economics/–Sponsors:  Safebase | CheckReady to save time and close deals faster? Inbound security reviews shouldn't slow down your team or your sales cycle. Leading companies use SafeBase to eliminate up to 98% of inbound security questionnaires, automate workflows, and accelerate pipeline. Go to safebase.io/podcast Check is the leading payroll infrastructure provider and pioneer of embedded payroll. Check makes it easy for any SaaS platform to build a payroll business, and already powers 60+ popular platforms. Head to checkhq.com/complex and tell them patio11 sent you.–Recommended in this episode:Azeem's newsletter: https://www.exponentialview.co/ Azeem Azhar's guest essay: The 19th-Century Technology That Threatens A.I. https://www.nytimes.com/2024/12/28/opinion/ai-electricity-power-plants.htmlElectric Twin: https://www.electrictwin.com/ Video of Elon Musk's Colossus https://www.youtube.com/watch?v=Tw696JVSxJQ Complex Systems with Travis Dauwalter on the electrical grid: https://open.spotify.com/episode/5JY8e84sEXmHFlc8IR2kRb?si=35ymIC0UQ5SKdV8rrBcgIw Complex Systems with Austin Vernon on fracking: https://open.spotify.com/episode/0YDV1XyjUCM2RtuTcBGYH9?si=YshjUXPEQBiScNxrNaI-Gw Complex Systems with Casey Handmer on direct capture of CO2 to turn into hydrocarbon: https://open.spotify.com/episode/0GHegWgLSubYxvATmbWhQu?si=xNYBjn0ZTX2IT_pAZ5Ozsg –Twitter:@azeem@patio11–Timestamps:(00:00) Intro (00:27) The power economics of data centers(01:12) Historical infrastructure rollouts(04:58) The telecoms bubble (06:22) Unprecedented enterprise spend on AI capabilities(11:12) Let's have your LLM talk to my LLM(16:44) Is there a saturation point?(19:25) Sponsors: Safebase | Check(21:55) What's in a data center?(24:52) The challenges of data centers(29:40) Geographical considerations for data centers(36:53) Energy consumption and future needs(40:48) Challenges in building transmission lines(41:35) The solar power learning curve(43:51) Small modular nuclear reactors(51:26) Geothermal energy and fracking(01:01:34) The future of AI and energy systems(01:12:57) Wrap

Kai-Fu Lee joins me to discuss AI in 2025. Kai-Fu is a storied AI researcher, investor, inventor and entrepreneur based in Taiwan. As one of the leading AI experts based in Asia, I wanted to get his take on this particular market.Key insights:Kai-Fu noted that unlike the singular “ChatGPT moment” that stunned Western audiences, the Chinese market encountered generative AI in a more “incremental and distributed” fashion.A particularly fascinating shift is how Chinese enterprises are adopting generative AI. Without the entrenched SaaS layers common in the US, Chinese companies are “rolling their own” solutions. This deep integration might be tougher and messier, but it encourages thorough, domain-specific implementations.We reflected on a structural shift in how we think about productivity software. With AI “conceptualizing” the document and the user providing strategic nudges, it's akin to reversing the traditional creative process.We're moving from a training-centric world to an inference-centric one. Models need to be cheaper, faster and less resource-intensive to run, not just to train. For instance, his team at ZeroOne.ai managed to train a top-tier model on “just” 2,000 H100 GPUs and bring inference costs down to 10 cents per million tokens—a fraction of GPT-4's early costs.In 2025, Kai-Fu predicts, we'll see fewer “demos” and more “AI-first” applications deploying text, image and video generation tools into real-world workflows.Connect with us:Exponential View

Dylan Patel, founder of SemiAnalysis and one of my go-to experts on semiconductors and data center infrastructure joins me to discuss AI in 2025. Several key themes emerged about where AI might be headed in 2025:1/ Big Tech's accelerating CapEx and market adjustmentsThe hyperscalers are racing ahead in capital expenditure, with Microsoft's annual outlay likely to surpass $80 billion (up from around $15 billion just five years ago). By mid-decade, total annual investments in AI-driven data centers could climb from around $150–200 billion today to $400–500 billion. While these expansions power more advanced models and services, such rapid spending raises questions for investors. Are shareholders ready for ongoing, multi-fold increases in data center build-outs?2/ The competitive landscape and new infrastructure playersThe expected explosion in AI workloads is drawing in a wave of new specialized GPU cloud providers—names like CoreWeave, Niveus, Crusoe—each gunning to become the next vital utility layer of AI compute. Unlike the hyperscalers, these players tap different pools of capital, including real-estate-like finance and private credit, enabling them to ramp up aggressively. This dynamic threatens the established order and could squeeze margins as competition heats up. The market is starting to understand that.3/ The semiconductor supply chain isn't the only bottleneckWe often talk about GPU shortages, but the real sticking point is broader infrastructural complexity. Yes, Nvidia and TSMC can ramp up chip supply. But even if you have enough high-end silicon, you still need power infrastructure and grid connectivity. Building multi-gigawatt data centers in the US—each the size of a utility-scale power plant—is now firmly on the agenda. In some states, data centers already consume 30% of the grid's electricity. By 2027, AI data centers alone could account for 10% or more of total US electricity consumption, straining America's aging infrastructure.4/ Commoditization of models and margin pressureA year ago, advanced language models were scarce and expensive. Today, open-source variants like Llama 3.1 are driving commoditization at speed, slicing away the profit margins of plain-vanilla model-serving. If your model doesn't outperform the best open source, you're forced to compete on price—and that's a race to the bottom. Currently, only a handful of players (OpenAI and Anthropic among them) enjoy meaningful margins. As models proliferate, value will increasingly flow to those offering distinctive tools, integrating closely into enterprise workflows and locking in switching costs.5/ Into 2025: exponential curves and new market normsDespite these challenges—soaring costs, stalled infrastructure build-outs, margin erosion—Dylan is confident that exponential scaling will continue. The sector's appetite for GPUs, specialized chips and next-gen data centers appears insatiable. We could easily see record-breaking fundraising rounds north of $10 billion for private AI ventures—funded by sovereign wealth funds and other capital pools that have barely scratched the surface of their capacity to invest in AI infrastructure. There's also a very tangible productivity angle. AI coding assistants continue to reduce the cost of software development. Some software companies could be looking at 20–30% staff reductions in these technical teams as high-level coding becomes automated. This shift, still in its early days, will have profound downstream effects on the entire software ecosystem.Find us:Exponential ViewSemiAnalysis

Technology changes have always meant business changes, but with technology changing this fast, how long can businesses keep up? How can businesses work with technology to increase their own yields exponentially?Azeem Azhar is the founder of Exponential View, a platform that features podcasts, newsletters, and video content. Azeem is also the author of the book The Exponential Age: How Accelerating Technology is Transforming Business, Politics and Society.Greg and Azeem discuss the rapid technological transformations reshaping business, politics, and society, transitioning from a linear era to an exponential age. Azeem explains the historical turning points of technological revolutions, the economic implications of these changes, and the role of general-purpose technologies like certain AI models and solar photovoltaics. They also go over the challenges and opportunities faced by corporations and government bodies in adapting to these rapid changes, and how to mitigate many problems with the practice of continuous learning within organizations.*unSILOed Podcast is produced by University FM.*Show Links:Recommended Resources:Ray KurzweilUnSILOed: Episode 360: Robert J. GordonCarnot's TheoremExperience Curve EffectsPhilosophy, Politics and EconomicsNvidiaSteve BallmerKenneth C. GriffinPerplexity AIClaude (language model)ChatGPTMichael PorterDaron AcemogluJames BoyleGuest Profile:Azeem Azhar's Exponential View PodcastAzeemAzhar.comLinkedIn ProfileWikipedia ProfileYoutube ChannelSocial Profile on InstagramSocial Profile on XHis Work:The Exponential Age: How Accelerating Technology is Transforming Business, Politics and SocietyExponential View PodcastExponential View NewsletterEpisode Quotes:Integrating technologies into organizations42:58: My observation with these technologies is that they're very, very powerful. There are  clearly some good directions to head in, but they're a bit complicated to bring into an organization. And then the question about learning is this: Are you willing to do the work to bring onboard a powerful technology that's a bit complicated? That may mean you got to read a document on a weekend rather than golf, or do you not want to do that work? And I love learning, as you do. This podcast is about learning. Of course, I'm going to tell people, "Just do the work, get learning." Because it's never going to stabilize, right? This technology is not going to stabilize. It'll get better in many different ways and, therefore, harder to use. I can drive a Tesla Model 3; I can't drive a V12 Ferrari. I'm not a good enough driver to drive a great car. And so we have to get better at them. And that ultimately is your choice.Are we all students in this exponential age?49:58: In this new world, into the exponential age, we all become students because the world is going to change so rapidly. On the other hand, the cost of being a student is much lower than it ever has been because I've got a professor in my pocket. I will continue to learn, and I can continue to actively learn about the world.AI's public good—who benefits and how?54:07: I think that, with AI, the potential public good and social good of being able to put humanity's knowledge into systems that can become freely and widely available should force a process—an open process of discussion about how those rewards should get split and who should get what.

As we race towards a future powered by AI and data centres, how will the insatiable demand for energy impact the environment? With the richest companies ploughing billions into energy generation, might there be some unexpected upsides for the climate transition? And can exponential technologies address the climate crisis on a finite planet? Cleaning Up host Michael Liebreich sits down with Azeem Azhar, founder of Exponential View, to explore the complex relationship between exponential growth, climate change, and the societal implications of transformative technologies. Michael and Azeem delve into the promises and pitfalls of a future shaped by the rapid advancements in renewable energy, battery storage, and artificial intelligence. This podcast was originally published on Cleaning Up.