Future Around & Find Out

Catie Cuan's dad was in the hospital, surrounded by machines that were supposed to help him. Instead they made him feel alienated and afraid. Catie, a dancer-turned-roboticist, realized it's not enough for a machine to do its job — it has to be relatable, too. Today she's the founder and CEO of ART Lab, focused on what she calls the "interaction gap" between what a robot can do and how it makes us feel. Catie danced at the Metropolitan Opera Ballet and ran her own dance company before getting her PhD at Stanford and becoming an artist-in-residence at Google X, where she worked on the Everyday Robots moonshot — including teaching office robots that it's rude to cut between two people having a conversation. Now ART Lab is building a home robot that won't look anything like a robot, plus a new kind of AI model that conditions success on how the human in the room responds, not just whether the task got done. Listen for the case against humanoids, why the future of AI shouldn't live inside your phone, and a sneak peek at what our life with robots might look like.Chapters:(02:11) - “There will be billions of robots” – from dishwashers to elder care (04:45) - Why robots can be capable and still feel unsettling (08:00) - How robots could read your reactions and respond in real time (11:45) - What shape should robots take? (15:30) - The case against humanoids (19:00) - A nine foot robot hand and the wild future robot design could take (23:15) - What it's like to dance with robots (28:30) - “The robot just died” – when a live failure changed the whole performance (32:45) - Friendship loneliness and home robots (and why builders need to be clear about the future they are creating) (37:11) - Why the home may become robotics’ biggest use case (and what ART Lab is building) (40:06) - Robot tutors, homework help, and why teachers still matter most (43:51) - “We have a tremendous amount of agency” – choosing the future we build now (46:16) - Why inequality and access worry Catie most (and who gets left behind) (48:56) - Why builders need to get outside their own bubble Support Future Around & Find OutFollow Dan on LinkedInGet the free newsletterBecome a paid subscriber and help future proof FAFO!

Rumman Chowdhury wants to remind you that “AI isn't doing anything.” We do things. AI is not to blame for layoffs or if you’re denied medical coverage. People are. Eight years ago, Rumman coined the term “moral outsourcing” to describe this excuse where we blame tech for decisions that people make. Why do the semantics matter? Because, Rumman says:In world one where, “AI did X,” it's very scary. It's like, “oh my gosh, this thing that is bigger and smarter than me has come and descended and now it's gonna wipe out every job. “ [But if we center on people, then we have agency and accountability and we can say] “no, you built a thing that was broken and flawed.” Rumman is the founder and CEO of Human Intelligence PBC, which is building evaluation infrastructure to make Gen AI systems safe, trustworthy, and compliant. She also served as the U.S. Science Envoy for Artificial Intelligence under the Biden administration, led AI ethics teams at Twitter and Accenture, and is a Responsible AI Fellow at Harvard.In this conversation:Why "moral outsourcing" is the sneakiest trick in tech — and how execs use AI as a shield for decisions humans madeHow to avoid — or at least how to mitigate — creating AI that’s biasedRed teaming AI and creating bias bountiesThe "grandma hack" and other ways regular people accidentally jailbreak AI modelsHow AI companies are quietly rewriting their terms of service to dodge liability when things go wrongWhy the benchmarks you see when a new model drops are "basically spelling tests"AI psychosis, parasocial chatbots, and the cold emails Rumman gets once a month from people who think AI is aliveWhat builders can do right now to take back agency — and why Rumman is more excited about agentic AI than anything that came beforeChapters:(00:00) - "The thing I believe in the most is human agency" (02:14) - Why builders have more agency than they realize (04:00) - What is a bias bounty? (06:41) - What 2,000 hackers at DEF CON found (09:40) - The grandma hack (11:30) - Why guardrails fall apart (14:54) - Anthropic's new bug-finding model and the cat-and-mouse game (19:10) - Why most evals are "basically spelling tests" (21:30) - How to actually evaluate an AI agent (27:16) - "Moral outsourcing" and the AI layoff lie (29:41) - Inside Rumman's tenure as U.S. AI Science Envoy (33:06) - The legal loophole AI companies use to dodge liability (36:31) - AI psychosis and the cold emails Rumman gets (39:36) - Why Google's AI overview is quietly dangerous (45:31) - The problem with "AI literacy" (49:01) - Can we trust anything we see anymore? (51:11) - What builders can do right now to take back agency Support Future Around & Find OutFollow Dan on LinkedInGet the free newsletterBecome a paid subscriber and help future proof FAFO!

So what even is “real” software anyway? Someone builds an app over the weekend. It works. It looks good. And then the search begins — for the asterisk. Security? Design quality? Can it go to production? Paul Ford says we’re in a new era: "I can't believe it's not software!" Paul is the co-founder of Aboard, where he helps organizations build custom software quickly, using AI tools. He's also one of my favorite tech writers. You may know him from "What Is Code," the opus he wrote for Bloomberg Businessweek a decade ago or from his writing in the New York Times, including his recent opinion piece, The A.I. Disruption We’ve Been Waiting for Has Arrived. Or perhaps you’re hip to Ftrain, where he’s been writing for longer than we’ve had the word “blog.” In this conversation, recorded at Aboard’s podcast studio (Paul and his cofounder also host a great show), we dig into the strange new world where roles are colliding, software* gets built quickly, and no one is quite sure what to teach their kids.We get into:What Paul calls "the great search for the asterisk" — the moment someone demos an app and everyone scrambles to find the catchHow the power dynamic between engineers and everyone else is fundamentally shifting — and why that's both liberating and destabilizingWhy vibe coded prototypes are changing how agencies pitch and price their work — and why pricing is "very unresolved"The skills that actually matter now: client communication, systems thinking, and depth over velocityWhy "the environmental costs [of AI] have become essentially a truthful folk narrative to talk about how difficult and scary and painful it is to see your life get continually smashed into bits."What he's teaching his kids (hint: it's not to code)Chapters:(01:40) - “We’re in a funny moment now” – catching up on the ten years since “What Is Code?” (05:30) - “ You gotta stop fighting” - AI code is genuinely useful, caveats and all (08:44) - AI enables people who could never afford custom software to have it (09:50) - Why he knew he’d get yelled at for his recent piece in the NYTimes (13:00) - “AI washing” and job cuts (14:50) - Paul’s theory for why the market oscillates so wildly on AI news + are we going to vibe code our own DoorDash? (17:00) - What’s the hardest thing about building with AI right now? (19:36) - Hiring, the most in-demand skills, and “forward-deployed engineers” (27:50) - “Product is still hard” – in response to: “What is something that AI will never be great at?” (31:36) - “What is something that sounds like science fiction, but that will soon be real — and commonplace?” (32:46) - Why Paul is excited about world models (and thinks LLM’s are topping out) (36:06) - Why environmental concerns have become a “truthful folk narrative about how difficult and scary” AI is (39:26) - There is no magic solution for climate (but one positive thing AI can do is help digest climate data) (41:26) - Why kids should learn systems thinking Support Future Around & Find OutGet the free newsletterBecome a paid subscriber and help future proof this thing!Sponsor the show? Are you looking to reach an audience of senior technologists and decision-makers? Email me: dan@futurearound.com

We live in a world where every crisis lands in your pocket the moment it happens. The result? We're more informed than ever — and somehow less capable of doing anything about it.Inventor and investor Pablos Holman has a diagnosis: we're spreading ourselves across every problem, which means we're solving none of them. His prescription is uncomfortable — pick one thing, go all in, and cut the noise.***QUICK PLUG: Future Around & Find Out is nominated for a Webby for best tech podcast! Voting is open now for the People's Choice Award. Please vote before April 16th! https://vote.webbyawards.com/PublicVoting#/2026/podcasts/shows/technology***Pablos is the co-founder of Deep Futures, where he hunts for inventors tackling world-scale problems: energy, water, food, waste, transportation. Not apps. Atoms. And thanks to advances in AI and software, these "impossible" problems are more solvable than ever — if the right people show up to back them.In this conversation, recorded at the fabulous PopTech conference, he makes the case that inventors are the most important creative class on earth — and the most invisible. They're undersupported, uncelebrated, and working alone in garages. Some of them are probably going to blow themselves up. Those are exactly the people he's looking for.We get into:Why doomscrolling is literally eroding your ability to make a differenceThe difference between craft (optimization) and creation (zero-to-one) — and why AI is great at one and struggling with the otherWhy you can name 100 musicians but fewer than two living inventorsHow solving energy unlocks clean water, sanitation, and climate — essentially for freeWhy software people are uniquely positioned to work on the hardest problems in the world right nowChapters:(01:15) - Why the world isn't as broken as your newsfeed makes it seem (03:00) - The sticky note exercise: how to pick the one problem worth your time (04:30) - Inventors are the most important creative class nobody talks about (07:00) - Living inventors you should actually know (09:00) - What AI is good at — and what it still can't do (12:30) - Why software people are the right ones to tackle deep tech problems (22:56) - Energy is the root problem — solve it and you solve a lot else (25:56) - Climate change needs a thousand solutions, not one big fix (28:26) - The fashion industry's dirty secret and what robots can do about it Links & ResourcesPablos Holman on LinkedInDeep Future: VC firm, book, and podcastSupport Future Around & Find OutFAFO is nominated for a Webby for best tech podcast! Vote now! Get the free newsletterAnd consider becoming a paid subscriber and help future proof this thing!Sponsor the show? Are you looking to reach an audience of senior technologists and decision-makers? Email me: dan@futurearound.com---Pablos's first appearance on the show covers his work at Blue Origin and Intellectual Ventures. Scroll in your podcast app to July 2025 to find that fun conversation. (Can listen before or after this one; not a prerequisite.)

Future Around & Find Out is a best technology podcast nominee! And with your help it could be a winner. The Webby Awards voting is open now. Please vote for FAFO! Thanks to AI, “content is about to become infinite.” And just like the Internet disrupted distribution, AI is disrupting creation. And so when anyone, anywhere can create content, what’s left? What’s defensible? That would be trust and humanity. Live from Podcast Movement Evolutions at SXSW, I sit down with Jim Louderback — former VidCon CEO, Inside the Creator Economy newsletter writer, and media veteran — to unpack what's actually changing and what builders and creators should do about it.We get into why the "age of perfection" is over, why founders need a meme instead of an elevator pitch, and why putting a creator on your cap table might be the smartest move a startup can make. Jim makes the case for a trust economy where views and likes are meaningless — and where the real question is how far your trust graph extends. We also talk digital twins (and what happens when yours goes rogue), why events are still the best way to prove you're human, the state of journalism and public media, and why 2004’s “Subservient Chicken” was so ahead of its time. Chapters:(01:30) - How AI disrupts creation (03:50) - The number of creators is about to double to 500 million (06:45) - We’ll have “certified human” labels, just like “organic” and why the Subservient Chicken was so far ahead of its time (08:40) - The age of perfection is over (10:00) - The only thing that matters is trust (12:00) - Events, FTW! (13:45) - The elements of a great event are timeless (18:11) - Favorite moments from SXSW (21:56) - What’s your meme? > What’s your elevator pitch? (23:28) - Put a creator on the cap table (27:21) - Creator-community fit (29:38) - The challenges of being a journalist today (32:26) - Create your own digital twin (36:26) - Why John Green’s jaw dropped when he learned of Dan’s grandma ---Future Around & Find OutVote for FAFO to be a Webby Awards winner!Get the newsletter Sponsor the show? Want to share your message with senior technologists? Email Dan: dan@futurearound.com

Climate-tech founder Eben Bayer is on a mission to protect Spaceship Earth. And he says it's time for climate control, i.e. active measure that cool the Earth. Why? " Because all other reasonable approaches have failed miserably," he says, slapping the table for emphasis.Eben is the co-founder of Ecovative and MyForest Foods, the makers of MyBacon, which is sold in more than three thousand stores. It’s a non-meat bacon, made from mycelium, which (more or less) means mushrooms roots. Fewer people eating meat —> fewer farting animals —> a cooler planet. And Eben's latest Earth-cooling idea is (nearly) out of this world. Eben wants to put giant parasols in the stratosphere where they could block sunlight from reaching Earth. With "shade-as-a-service" a maxed-out utility (say in Phoenix) could pay for shade to cool a city or an individual could pinpoint a shadow over their backyard for an afternoon barbecue.The idea is in its early stages, but Eben says it's feasible and it's the kind of big idea we need to get climate change under control. And while the idea of messing with the sun may sound scary, he says we alter the climate in all sorts of ways already: " We are geo-engineers. We farm animal livestock. We live on Planet Earth. We have impact. We emit CO2. We should not limit ourselves to modifying just one or two atmospheric gases to modify the planet. It's not how we operate, and it's an unbelievably constraining framing if you actually want to address this problem in a practical manner... When you start to take that frame, the options open way up."Eben is a fascinating guy — very steampunk in his approach to entrepreneurship — and I'm sure you'll find this interview eye-opening.And a special shout out to my field producer for this onsite recording from Troy, NY: my eleven-year old son, Julian! He was my camera and sound guy and he also makes his long-awaited (YouTube!) debut to ask Eben a question about protecting Spaceship Earth. 🤩Thanks also to PopTech, the amazing tech conference where I first met Eben, and where he became a fellow more than a decade ago as he was just scaling up.Support Future Around & Find OutFollow Dan on LinkedInGet the free Future Around & Find Out newsletterBecome a paid subscriber and help future proof the podcast!Sponsor the show? Are you looking to reach an audience of senior technologists and decision-makers? Email me: dan@futurearound.com---Music by Jonathan Zalben

So what the heck should kids be studying today!?That's my opening question to Khan Academy's Chief Learning Officer, Kristen DiCerbo, a learning and AI expert who is back for her second appearance on the show. We discuss:Kristen's advice for what to study today: fundamentals, AI literacy, and critical thinkingHow helpful should AI be? Why the productive struggle is critical to learning, but also why we shouldn't "fetishize" struggleWhen should AI be Socratic — "and why do you think that?" — and when should it just give you the answer?The "5% problem" — why edtech that's proven to work still barely gets usedHow Khan Academy overhauled its classroom platform and evolved Khanmigo from a standalone chatbot into something woven into the whole learning experiencePersonalization that actually works How Khan Academy uses LLMs as judges to evaluate 20,000 student interactions a dayThe scenario planning report that imagines deepfakes of school principals and AI going underground in schoolsWhat parents should be asking their kids' schools about AI right nowWhat it looks like when a school implements AI well — and what it looks like when they don'tChapters:(01:44) - What the heck should kids be studying right now? (03:55) - Teaching critical thinking in the age of AI (06:37) - What successful schools are doing differently (08:37) - The real risk: not that kids use AI too much, but that they don't use it enough (10:52) - My 13-year-old has to check five apps just to find his homework (11:52) - "Beyond the AI inflection point" — three scenarios, none of them great (16:30) - Should we just make every school a trade school? (19:41) - What should parents be asking their kids' schools? (22:27) - Khan Academy's Winchester Mystery House problem (26:28) - Personalized learning — what works and what surprisingly doesn't (29:32) - Kids are bad at asking questions and that's actually the point (32:01) - "I DON'T KNOW" in all capital letters — the Socratic method's breaking point (34:26) - Should an AI tutor give tough love? (37:01) - Why Khanmigo is fundamentally different from ChatGPT (40:11) - Don't fetishize struggle — but your kid still needs it (42:39) - Khan Academy's productive struggle: building evals from scratch (45:41) - What gives Kristen optimism Support Future Around & Find OutFollow Dan on LinkedInGet the free Future Around & Find Out newsletterBecome a paid subscriber and help future proof the podcast!Sponsor the show? Are you looking to reach an audience of senior technologists and decision-makers? Email me: dan@futurearound.com---Music by Jonathan Zalben

How do you build a system for turning wild ideas into world-changing innovations? Astro Teller, Captain of Moonshots at X, The Moonshot Factory, has spent over 15 years leading Google’s audacious innovation lab—the birthplace of Waymo, Google Brain, and other breakthrough projects.In this special episode, recorded live in Austin at last year's SXSW, Astro shares the playbook to create a moonshot factory.(I'm at this year's SXSW right now and you'll hear all about it soon. If you are here, drop me a line and let's meet up!)What You’ll Learn in This Episode:The “Train the Monkey First” approach to innovationWhy audacity, humility, and intellectual honesty are key to moonshotsHow your org can get more 10x (not +10%) outcomes — and how to avoid the “innovator’s dilemma”Why you should “greenlight everything” and then redlight most projects quickly, following kill criteria you’ve agreed to in advanceWhere X is placing bets today, including climate-tech, modernizing the electric grid and bioengineering---Future Around & Find Out newsletter and podcast: https://www.futurearound.com

So why is one of the world’s leading AI researchers teaching AI to understand pain and suffering? Well, Daniel Hulme says that if we build an empathetic AI, perhaps even a conscious one, then we’ll be safer. His hypothesis is that a "zombie" AI will eat our brains, but an empathetic AI would stay aligned with us. So he's building this "antivirus" (with AI, of course) and he's very aware that this sounds crazy or like "something from Marvel."That's just some of what broke my brain in this conversation with one of the world's top AI researchers and founders. And Daniel has serious credibility, so I'm not dismissing the threat he sees — you know, the one where we all get turned into paperclips. Daniel sold his company Satalia to WPP, where he now serves as Chief AI Officer. He’s just founded Conscium, which verifies that AI agents are safe and can do what they promise — and is also researching consciousness and pain. Some of the world’s leading AI thinkers are on the advisory board and Daniel has been in this space for decades: we’ll talk about why, for his PhD, he studied bumblebee brains (yes, really — and it's deeply relevant). We get into: His unified theory of consciousness — his "color wheel" model — and why he thinks consciousness only exists in motion Why he believes large language models are ultimately a dead end — and what neuromorphic computing could replace them with What bumblebee brains can teach us about building AI that's up to a thousand times more energy efficient Why he calls today's AI agents "intoxicated graduates" — and says companies should spend 80% of their time testing them The concept of "mind crime" — the idea that we could build conscious AI and accidentally put it through horrendous suffering without realizing it His vision of a "protopia" — where AI makes food, healthcare, education, and energy so abundant that people are freed from economic constraints to pursue what actually mattersWe future around and find out a lot in this one! ---Chapters(01:39) - "Would a conscious superintelligence be safer than a zombie one?" (03:37) - The paperclip problem is not hypothetical (05:06) - Conscium's mission — AI safety for humans and for AI themselves (08:50) - "I think I've got my head around consciousness" (11:57) - The color wheel model — why consciousness only exists in motion (13:58) - Teaching AI morals through evolution, not guardrails (17:23) - "Hey Claude, are you conscious?" — how do you test for that? (21:07) - What bumblebee brains can teach us about building better AI (24:14) - "I think we are completely scaling wrong" (29:43) - Why Daniel calls AI agents "intoxicated graduates" (32:48) - Companies should spend 80% of their time testing agents (38:19) - "What would you do if you were economically free?" ---LinksConsciumDaniel Hulme on Wikipedia Daniel on LinkedIn---

So, there are dire wolves living on Earth again. They were “de-extincted” by Colossal Biosciences. And today on the show their Chief Science Officer joins me to share her view on why the de-extinction matters — not as a science project, but because it will help solve problems that threaten every species on earth, including us. Beth Shapiro is the Chief Science Officer at Colossal Biosciences, and she helped to bring back the dire wolf or, as others call it, a gray wolf with 20 genetic edits. There is a fierce debate about what de-extinction even means, and we discuss that, but whatever you call them, there are now three big wolves living in an undisclosed location and they wouldn't be there if not for the DNA that Beth and her team edited. Colossal is also working to bring back the wooly mammoth, the Tasmanian tiger, the dodo and other animals that have long been extinct. Why? Listen to find out… Chapters:(01:19) - The Most Oprah Question Beth's Ever Been Asked (03:04) - Moonshots Require You to Create a Giant List of Problems (04:19) - The Things We’ll Solve Along the Way, a la the Original Moonshot… to the Moon (05:57) - Beth’s Journey: From Broadcast Journalism to Ancient DNA (09:13) - How a Sediment Core Solved a Mammoth Mystery (11:36) - Why Charismatic Animals Matter (a.k.a. Why Riz Is Everything) (12:38) - What’s Up With Romulus, Remus, and Khaleesi? (14:19) - But Are They Really “Dire Wolves”? The Controversy Over 20 Genetic Edits (21:45) - Should We Do This? Beth's Ethics Framework for Builders (23:51) - Advice for Moonshot Builders (25:10) - Why We Want Dodos, Mammoths, and Thylacines Back Links & Resources:Colossal BiosciencesBeth ShapiroPopTech -- a conference I love! Support Future Around & Find OutFollow Dan on LinkedInGet the free Future Around & Find Out newsletterBecome a paid subscriber and help future proof the podcast!Sponsor the show? Are you looking to reach an audience of senior technologists and decision-makers? Email me: dan@modernproductminds.com---Music by Jonathan Zalben

Of all the industries AI will transform, Kira Radinsky believes chemistry and biology will change the most. Kira is the co-founder and CTO of Diagnostic Robotics, which uses AI to automate the administrative work that's crushing healthcare teams — so clinicians can actually focus on patients. She's also the co-founder of Mana.bio, where they're accelerating drug discovery by orders of magnitude.She'll tell you she's terrible in the lab. Not because she isn't brilliant, but because she can't pipette without killing the cells. So she’s thrilled that thanks to her skills in data and AI she was able to realize her childhood dream of being a scientist: “I'm not trying to automate everything… Like when, when you say automate drug discovery, I'm not gonna discover everything. I just want to accelerate it, which comes back to my childhood dream: I just didn't want to do it myself. I just want AI to replace me as a scientist. That's it.”But this episode is about more than healthcare. It's about how to build systems that get smarter over time — feedback loops, causal inference, incentivizing algorithms to take risks, and knowing when to optimize for ROI instead of accuracy. Lessons that apply whether you're building in biotech or not.We cover:How growing up Jewish in Soviet Ukraine — and fleeing to Israel just before the Gulf War — shaped Kira's obsession with predicting the futureHow she built a system that successfully predicted real-world events, including Cuba's first cholera outbreak in Cuba in 130 yearsHow Mana.bio is using AI to build "rocketships" that deliver drugs to the right cells — and how they've done in three months what used to take 20 yearsWhy predictions are only valuable if there's something you can do about them — and why that makes healthcare an ideal field for AI How to incentivize algorithms to make bolder predictions (it's easy to predict there won't be an earthquake today; it's much harder to say there will be)Why causal inference is the most underrated tool in machine learning right nowHow healthcare AI can perpetuate racial bias — and what builders need to do differentlyNote: this interview originally aired in October 2024. Chapters:(01:44) - Why predictions are so important to Kira: lessons from fleeing Soviet-era Kyiv (05:10) - Building a prediction engine from 150 years of news (08:35) - How Kira predicted the Cuba cholera outbreak (09:50) - Returning to biology by way of data (12:50) - Predicting healthcare outcomes by finding your patient's twin (17:53) - The racial bias hiding in healthcare AI (19:15) - Building Mana.bio and accelerating drug discovery (24:33) - "In three months, what did what used to take 20 years" (31:44) - Builder tips: ROI, causal inference, and teaching algorithms to explore (35:07) - Planning: Where generative AI needs improve Links & Resources:Kira Radinsky on LinkedInDiagnostic RoboticsMana.bioSupport Future Around & Find OutGet the free newsletterAnd consider becoming a paid subscriber and help future proof this thing!Sponsor the show? Are you looking to reach an audience of senior technologists and decision-makers? Email me: dan@modernproductminds.com---Music by Jonathan Zalben

Henrik Werdelin is one of my favorite entrepreneurs. He’s founded and incubated several unicorns, most notably BARK, the dog happiness company.Henrik himself is a pretty happy guy — an optimistic guy who likes to ask what could go right? — and on the day we recorded (a few months ago as I was squirreling away interviews for the podcast relaunch), he helped me see through some future of tech gloom I was feeling. I honestly can’t even remember what Trump+tech hellscape we were living through that week, but I do remember that Henrik put me in a better mood. I think he’ll do the same for you, no matter how you’re feeling. 🤗Henrik believes AI could be a massive force for good. That it could bring forth a whole new — a better! — form of capitalism. He writes about this is in his latest book, Me, My Customer, and AI. He points to those (like Henry Ford) who took advantage of electricity by making drastic, not incremental, changes to how the build things. Our conversation pairs nicely with my recent episode with Azeem Azhar, who said the AI winners will “come from odd places”, as they have in previous tech transformations. Here’s more of what Henrik and I cover:His concept of "relationship capital"—the moat AI can't clone—and why the companies that win next will be defined by who they serve, not what they makeThe three components of relationship capital: intensity, community, and durabilityThe "it sucks that" method for finding problems worth solving (he took it to a fifth grade class; the teacher was not thrilled)His vision for the "headless", agentic web, where your startup's MVP is a group of agents, not an appThe wildly practical AI tools he's built just for himself: a custom CRM that searches by vibes not names, a newsletter bot tuned to his quarterly goals, and an agent that handled his visa paperwork while he was in a meetingWhy entrepreneurial skills—agency, narrative, resourcefulness—are the ultimate career insurance, whether you start a company or notThe absolutely ridiculous story of how a prank on a cruise ship led to him meeting his BARK co-founder in a heart-shaped bedChapters(01:43) - Two Futures: AI Bad vs. AI Really, Really Good (05:44) - Why Positivity Is Actually the Riskier Bet (09:05) - Electricity, AI, and the Rise of Relationship Capital (11:12) - The Three Components of Relationship Capital (14:20) - "It Sucks That" — The Best Way to Find a Real Problem (19:22) - The Headless Future and Minimum Viable Agents (22:40) - N-of-One Software: Building Tools Just for Yourself (26:48) - Henrik's Custom Newsletter Bot and AI-Powered CRM (30:59) - Warp, Obsidian, and Letting Agents Loose on Your Computer (34:45) - Entrepreneurial Skills as Career Insurance (36:53) - The Heart-Shaped Bed: How Henrik Met His BARK Co-Founder Links & ResourcesHenrik Werdelin on LinkedInAudos, Henrik’s latest venture where he hopes AI agents trained in his methods can help thousands of entrepreneurs (donkeycorns!) a yearBeyond the Prompt podcast, from co-hosts Henrik Werdelin and Jeremy UtleySupport Future Around & Find OutGet the free newsletterAnd consider becoming a paid subscriber and help future proof this thing!Sponsor the show? Are you looking to reach an audience of senior technologists and decision-makers? Email me: dan@modernproductminds.com---Music by Jonathan Zalben

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 (28:32) - "Pragmatic addicts": why 75% of Americans distrust AI but use it anyway (32:15) - The narrative of AI can be very unappealing: heaven on Earth or dystopia (35:06) - How Azeem's team uses AI: augmentation vs. automation (40:36) - What should we be talking about besides AI? (44:16) - 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---Music by Jonathan Zalben