BIG IDEAS BY NEW ECONOMIES

Ollie Forsyth
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Welcome to BIG IDEAS by NEW ECONOMIES - a show where we learn how the most iconic founders have turned crucible moments into global companies. www.neweconomies.co

  1. 1h ago

    Fireflies AI

    Subscribe to stay ahead of technology trends. Never miss future editions. Fireflies.ai founder Krish Ramineni built a profitable AI unicorn without spending a dollar on marketing Right now, AI startups are racing to raise bigger rounds at higher valuations, often before they have a real revenue stream. Our latest guest thinks a lot of them won’t survive it. As he puts it: “For every five winners, there’s going to be 5,000 losers.” Meet Krish Ramineni, co-founder and CEO of Fireflies.ai. He joined us to explain why Silicon Valley hype is broken, why “it’s never been easier to build a startup, but it’s never been harder to stand out,” and why he thinks a single-use-case AI product won’t last long. You might not know this, but Krish was one of the youngest product managers in Microsoft’s history, working on the first growth team for Microsoft Office. He left after a year to start Fireflies in 2016 with his co-founder and CTO, Sam Udotong. Back then, the AI notetaker category didn’t exist. Today, Fireflies serves more than 20 million people at over 500,000 organisations, including teams at 75% of the Fortune 500. Unlike many Silicon Valley startups, Fireflies hasn’t raised a funding round since 2021 and has been profitable since 2023. In June 2025, a tender offer for employee shares valued the company at $1 billion, despite it raising only $19M in its history. The founders model the company on Costco: skip the marketing, and put the money into value for members instead. In our episode, we learn: * Why distribution is AI’s hardest problem. Every growth channel gets saturated eventually, from product-led viral loops to Meta and TikTok ads to paid creator deals. Krish explains why viral moments keep getting shorter, why building in public can backfire, and why the next AI winners will be the ones with the best distribution * Why the AI bubble will hurt. AI is real, but the economics around it aren’t there yet. Krish explains why he doesn’t trust “annualised revenue” figures, why founders taking money off the table through secondaries worries him, and why he thinks there are “no moats in software” * How Fireflies grew from notetaker to AI teammate. Fred, Fireflies’ AI assistant, now handles email, Slack and the CRM, and Fireflies Talk, the company’s dictation tool, is free. Krish explains why he believes a dictation tool alone is “one Apple or Google update away from being irrelevant,” and how his team built Fireflies Talk in two months with only a few engineers * Why Fireflies is getting into hardware. The first device is Fireflies Fly, which snaps onto the back of your phone and captures phone and WhatsApp calls. Krish explains why iOS and Android left him no other option, and why he would rather sell the device at a steep discount than live off one-off hardware sales * Why the future belongs to small teams. Krish sees companies made up of two- and three-person pods, staff who can work across functions, and individual contributors who manage agents. He also explains why middle management is more exposed to AI than entry-level roles, why you don’t need to be in San Francisco to build, and why his ideal customer is a 60-year-old running a farm Watch or listen now across YouTube, Apple Podcasts, Spotify, and X Download the transcript 👇 Timestamps (0:00) Meet Krish Ramineni(2:15) The state of AI note-takers(4:47) Preparing for model disruption(8:26) Distribution is king, and hard(16:28) Will hyped startups disappear?(18:32) The next wave of AI companies(23:41) Should startups share their numbers?(26:52) Why Fireflies stopped raising(31:35) The Fireflies bundle(34:04) The hardware opportunity(40:37) Why dictation is hyped(43:40) Tools Fireflies shut down(46:48) Will digital twins attend meetings?(49:37) AI will improve middle management(51:57) How roles change in 12 months(57:13) Opportunities outside SF(1:02:08) Fireflies has news! Inside Fireflies, Krish Ramineni’s Profitable $1B Bet Against AI Hype Why the AI Bubble Will Hurt Krish is no AI sceptic. He is sceptical about the money around it. “AI is real. It works. But the valuations that are put on AI, the economics around AI is not as good as it needs to be.” He compares the moment to the dot-com bubble. Everyone thought web startups were a scam, and yet eBay, Amazon and Netflix came out of it. AI will produce real companies too, but “for every five winners, there’s going to be 5,000 losers.” Right now, he says, everyone is raising as if they are the winner. Two things worry him. Founders are taking tens of millions of dollars off the table through secondaries every round, even when the company doesn’t need the capital. And headline revenue numbers have changed meaning. Annual recurring revenue used to mean real subscriptions. Now companies quote “annualised revenue” from a single peak month of usage. When he hears one, his questions are simple: what is your churn, what is your retention, and what happens when the bigger players arrive? “I almost feel like it’s a vanity metric how much money these companies are raising.” Distribution Is the Hardest Part Building has never been cheaper. Getting noticed has never been harder. “It’s never been easier to build a startup, but it’s never been harder to stand out among the crowd.” Krish describes how every growth channel eventually gets saturated: The viral loop: seeing a Fireflies bot in a meeting was once novel enough to drive growth on its own. It still works, but less well over time. Paid ads: venture-backed companies pour money into Google, Meta and TikTok ads while the arbitrage lasts, until everyone else does too. Creators: startups now pay influencers to make ads that look organic, and the influencers have realised they can charge a lot more, whether a post performs or not. The windows of attention are shrinking too. Companies go viral for a few weeks and then disappear. Building in public can backfire, he warns: your business may not be growing, but now your competitors know exactly what you are doing. The Costco Playbook Krish’s answer to all of this comes from outside tech. “What Costco says is we’re not going to do marketing. We’re going to do whatever it takes to make our members happy.” Instead of spending on billboards, Fireflies puts the money back into the product. He tells the story of a rival in his market that burned millions on billboards and bus posters placed wherever VCs were in New York and San Francisco, purely to raise its next round. Fireflies went the other way: win on price, win on quality, and let customers do the talking. From Notetaker to AI Teammate Krish believes single-purpose AI products won’t last. “A single use case product will not have a long shelf life. You have to evolve into something that serves as a suite or serves problems end to end.” Most of the expansion came from customers. They asked for a desktop app so they could take notes without a bot, then a mobile app for in-person meetings, then an email assistant. Fred, Fireflies’ AI assistant, now drafts emails, sorts inboxes and summarises Slack channels. Because Fred already knows what happened in your meetings, its emails aren’t “random AI slop.” None of these come as extra subscriptions. They are part of the core plan, with AI credits only for automations. His long-term picture is Fred as a teammate you give more responsibility over time, in the way you would with a new hire: first your notes, then your emails, then your CRM. Why Dictation Is Just a Feature Fireflies recently launched Fireflies Talk, a dictation tool, and gives it away for free. Krish is unusually blunt about why. “For the longest time, I genuinely believed a dictation tool is one Apple, one Android update away from being irrelevant.” Strip away the marketing, he says, and most dictation products are speech recognition, an LLM and some cleanup. What interests him is voice as a new way to tell Fred to do work, not just to type. The team came up with the idea during a company offsite in Thailand in August and had a working version by… Get full access to NEW ECONOMIES at www.neweconomies.co/subscribe

  2. 3d ago

    Inflection AI

    Subscribe to stay ahead of technology trends. Never miss future editions. Inflection AI CEO Sean White builds AI that brings people together As we probably know by now, most of the AI race is about IQ: more GPUs, bigger models, and a sprint towards AGI. Our latest guest thinks that’s the wrong goal. As he puts it: “If I say I’m building AGI, it says nothing about whether your life is better.” Meet Sean White, CEO of Inflection AI, the $4 billion frontier lab behind the chatbot Pi. He joined us to explain why AI should be designed around emotional and relational intelligence, why we’ll all use several chatbots rather than one, and why “IQ gets you answers. EQ gets you understood.” You might not know this, but Sean has spent his career on how people and computers work together. His PhD research involved augmented reality glasses that cost millions of dollars. He ran wearables research at Nokia Labs, then served as Chief R&D Officer at Mozilla, where the company made privacy-first choices even when they cost revenue. He took over as CEO of Inflection in 2024, after co-founder Mustafa Suleyman left for Microsoft. He also sits on the board of the Earth Species Project, a nonprofit using AI to decode animal communication. Inflection is a public benefit corporation, co-founded by LinkedIn co-founder Reid Hoffman. Pi was trained on examples of high-EQ conversations, and Sean’s team is now building what he calls relational intelligence, meaning AI that helps groups of people, not just individuals. In our episode, we learn from Sean: * Why chasing AGI is a mistake. Sean says the problem is less about danger and more that AGI “focuses us in the wrong direction.” He explains why refocusing on human problems isn’t the same as slowing down, and why Inflection designs AI that won’t “steal more of your time and have you stay down in a dark corner.” * The future of chatbots. Why people already use three to six AI tools a week, and why AI will be even more fragmented than search engines have been since 1994. He also covers why the best AI should work in the background like the thousand processes running on your laptop, rather than leaving you to manage a thousand agents. * Privacy, trust and owning your data. Lessons from Mozilla, Inflection’s role in the Data Transfer Initiative, and his plain-English rule for AI memory: “no surprises.” He also explains why AI may be the best defence against AI-powered phishing and spam. * What AI hardware should look like. Why we only have “finite real estate” on our bodies for one watch, a ring or two and one pair of earbuds. He covers why most AR glasses are years away from being ready, and why companies should consider open-source hardware so devices like cochlear implants don’t become “a giant doorstop” when the company behind them shuts down. * Working with Reid Hoffman. What Sean learned from Reid’s habit of asking “what is the theory of the game,” why Reid responds faster than almost any entrepreneur he knows, and why Sean believes AI is pushing us to ask what it means to be a good human. Watch or listen now across YouTube, Apple Podcasts, Spotify, and X Download the transcript 👇 Timestamps (0:00) Meet Sean White(2:17) Inflection's AI Mission(3:09) Should AI Slow Down?(6:00) Where Inflection Is Today(7:41) The Role of the Chatbot(10:25) How We Should Use Chatbots(12:43) The Chatbot Experience(17:06) Should We Worry About Privacy?(21:10) Who Owns Your Data?(25:47) Building for the Next Few Years(29:56) Emotional Intelligence in AI(32:00) Will Inflection Make Hardware?(40:40) AI in the Next 12 Months(44:10) Will AI Models Collaborate?(45:38) Working with Reid Hoffman(50:20) What Makes a Good Human? Inside Inflection AI: Sean White on Why Chasing AGI Is the Wrong Goal Why Chasing AGI Is a Mistake When we asked whether AI should slow down, Sean’s answer was neither yes nor no. “The acceleration towards AGI is a mistake, less because of the dangers and more because it focuses us in the wrong direction.” To him, focusing on human problems instead isn’t a slowdown. It’s “a refocusing in the right direction.” The goal at Inflection is to design against the things people fear most about AI. “We’re not going to train the AI to somehow steal more of your time and grab more of you and have you stay down in a dark corner.” He also pushes back on the idea that EQ means a digital boyfriend or girlfriend. For Sean, emotional intelligence is about building the interface that best understands each person. From IQ to Four Kinds of Intelligence Inflection started out as one of the earliest frontier labs, in a race to “throw more GPU at this thing.” Bigger models did get smarter. But the team decided that being human-centered meant understanding people, “less about how good we are at thermodynamics and differential equations.” Pi was trained on examples of high-EQ conversations. The team then added relational intelligence, which means AI that works with groups rather than just individuals, and then agentic intelligence. Sean says that adds up to “almost four different kinds of intelligence.” You Won’t Use Just One Chatbot An Inflection researcher found that people use three, four, five or six different AI tools across a week. Sean keeps a graph of search engine usage from 1994 to 2026, showing a new leader every few years. He thinks AI will be even more varied than that. He also compares today’s AI to the early Mac, when people put 15 or 20 fonts on one flyer simply because they could. “Right now, we’re in this stage where we’re trying to shove AI everywhere.” He isn’t sold on the idea of everyone managing a thousand agents either. “As someone who has been in various leadership positions over the years, managing a thousand people directly is terrifying.” His model is the thousand processes already running on your laptop: they’re working all the time, and you never have to think about them. Privacy: No Surprises At Mozilla, any data stored in the cloud was encrypted on the user’s device, so the company couldn’t read it. Inflection is part of the Data Transfer Initiative, which lets users take their data with them. Sean’s rule for AI memory is simple. “You don’t want them to suddenly find that they’re hearing the phrases that they said… added to somebody else’s model, or that their memories have now been shipped off to a marketer somewhere.” He also sees personal AI as the best defence against AI-powered phishing and spam. Tools for tricking people have become far more sophisticated, and individuals need protection too, not just systems. The Laughter Metric The use case that surprised Sean most was people playing multi-user games with Pi around a table. Inflection has since built a prototype from it. “A reasonable metric is how much laughter there was in the system.” One version of Pi could join a group text, crack jokes and keep the conversation going. What AI Hardware Should Look Like Sean once ran a startup built on Google Glass. The team quickly learned the total addressable market was about 1,500 Glass wearers. His approach to hardware since then is pragmatic: * Finite real estate: “You get a watch, not 15 watches. You get one or two rings, you get one pair of earbuds.” * Glasses aren’t ready: most AR systems “won’t be ready for years” for everyday use. * Brain tech is too soon: adding EEG or focused ultrasound “in the next quarter or two” is premature. * Price matters: only a few people will buy a $2,000 device, while billions already have earbuds and phones. He also makes a case for open-source hardware. When the company behind a device shuts down, the product can become “a giant doorstop.” In the case of a cochlear implant, that means a device in someone’s head that no longer gets updates. Lessons From Reid Hoffman Sean started out on the engineering side. He says Reid showed him how many different things you can build. They talk often, sometimes several times a week. Their last call was about books. Their last work call was about go-to-market. Reid’s habit of game-playing shaped one of Sean’s favourit… Get full access to NEW ECONOMIES at www.neweconomies.co/subscribe

  3. Nov 8

    Recursive AI

    Subscribe to stay ahead of technology trends. Never miss future editions. You.com founder Richard Socher builds self-improving superintelligence For the past few years, the AI race has been about bigger models trained on more internet data. Our latest guest thinks that playbook is running out of road. As he puts it, a lot of people think the future is “kind of this prediction exercise, but it’s often a construction exercise.” Meet Richard Socher, co-founder and CEO of Recursive and founder of You.com. He joined us to explain why AI’s biggest opportunity is speeding up scientific discovery, why some rare genetic diseases could be curable within the next 12 to 24 months, and why aging will be medicine’s “ultimate final enemy.” You might not know this, but Richard was one of the researchers behind ImageNet and co-authored GloVe, two building blocks of modern AI. His first startup, MetaMind, was acquired by Salesforce, where he went on to serve as Chief Scientist. He then founded You.com, which reached a $1.5 billion valuation in 2025, and co-founded AIX Ventures. His new book, The Eureka Machine, argues that AI is the key to a new era of scientific breakthroughs. He wrote it over two and a half years of nights and weekends. Recursive came out of stealth in May with $650 million raised at a $4.65 billion valuation. The round was led by GV and Greycroft, and Nvidia. Richard leads a team of seven co-founders who previously worked at Meta FAIR, Google DeepMind and OpenAI. In our episode, we learn: * Why biology is AI’s next frontier. Why single-gene diseases are close to fixable, why biology still has to learn the “bitter lesson” that language AI already learned, and why the path runs through a virtual cell, then a virtual tissue, and eventually a full digital twin of a person * Why curing aging will take decades, not months. Every organ ages at a different speed, and clinical trials take years. The bar is far higher for drugs given to healthy people. Richard also explains why a longevity drug could create a multi-trillion-dollar company, and why scaling eventually makes every technology more equal * Why he isn’t buying calls to pause AI. He sees a “horseshoe theory” of AI optimism and doomerism, and explains why travel, farming and luxury handbags won’t change much with a thousand-times cheaper intelligence. He also covers why owning equity, and learning entrepreneurship, decides who wins from AI * Where startups can still win. Network effects, brand, data moats and what he calls “the moat of boring.” He explains why AI is turning the US into China’s no-SaaS market, and why China’s lead in open source works as soft power, much like Hollywood * Inside Recursive. How the team built Flint, a coding model that runs on a laptop, from scratch in three and a half months using its own “Eureka machine.” He also covers why eight co-founders still need one CEO, how he runs three organizations on 80-to-100-hour weeks, and why the West needs “better marketing for the future” Watch or listen now across YouTube, Apple Podcasts, Spotify, and X Download the transcript 👇 Timestamps (0:00) Meet Richard Socher(2:02) Richard’s new book(5:17) Who solves humanity’s biggest problems first?(8:12) How close are we to curing disease?(11:30) How Richard would solve aging(15:57) The opportunity in new drugs(19:04) Is AI moving too fast?(21:36) The superintelligence timeline(25:40) How we all get wealthier(28:45) Advice for hourly workers(33:00) The biggest superintelligence opportunities(35:32) Was Muse essential for Meta?(36:20) Will big tech wipe out startups?(40:05) US vs China: who wins?(43:16) Meet Flint, the new coding agent(46:10) Should founders just launch?(47:23) Raising $500M+ and managing multiple co-founders(49:29) How Richard manages his time(51:42) Should founders focus on one company?(54:40) Superintelligence in the next 12 months Inside Recursive, Richard Socher’s $650M Bet on AI That Improves Itself Some Rare Diseases Could Be Curable Within Two Years. Aging Will Take Decades. Recursive is building self-improving superintelligence: AI that can come up with, test and ship its own improvements. It was founded by Richard Socher, who worked on ImageNet, co-authored GloVe, sold his first startup MetaMind to Salesforce, and then became Salesforce’s Chief Scientist. He went on to found You.com, now valued at $1.5 billion, and co-founded AIX Ventures. His new book, The Eureka Machine, argues that AI is the key to a new era of scientific discovery. Recursive came out of stealth in May with $650 million raised at a $4.65 billion valuation. GV and Greycroft led the round, with Nvidia. Richard leads a team of eight co-founders who previously worked at Meta FAIR, Google DeepMind and OpenAI. Why He Wrote The Eureka Machine Richard wrote the book over two and a half years of nights and weekends. Part of the reason was frustration with where AI research had settled. “It felt to me like the field of AI was a little bit stuck in just saying, ‘Oh, we’re just going to take this and use more internet data and scale up these existing models,’ when I think the time was ripe to actually expand the use of AI into the sciences.” The field moved fast while he wrote. He had to rewrite chapters that said “someone should build a virtual cell” once major research groups started building one. He also pushes back on the idea that AI mostly destroys jobs. We can lament the people who sold candles, he says, but electricity created more jobs than the candle industry ever had. Rare Diseases First, Aging Last Richard thinks diseases caused by a single faulty gene will be curable within 12 to 24 months. Each one is rare, but together they affect a lot of people. “This isn’t just sci-fi anymore. This will happen.” Babies born with genetic diseases have already been cured. Aging, he says, will be medicine’s “ultimate final enemy.” The reason is data. Large language models learned language from text that billions of people wrote online. Nothing like that exists for biology yet. Richard’s path forward runs from a virtual cell to a virtual tissue, and eventually to a full digital twin of a person. “A lot of people think the future is kind of this prediction exercise, but it’s often a construction exercise. We need to actually build these things.” If he weren’t running companies, he says, he would work on automating biological data collection. Why Longevity Will Take Decades Even with a perfect molecule, aging isn’t one problem. Every organ ages at its own speed. The bar for approval is also much higher for drugs given to healthy people. A late-stage cancer patient will accept long-term risks, but a healthy 40-year-old taking a longevity drug won’t. Richard thinks whoever cracks longevity would build “a multi-trillion-dollar company.” He isn’t worried about the drug starting out expensive. Patents run out and generics follow, just as brick phones gave way to an iPhone that billionaires and teenagers both carry. “What are other goods and services that currently only billionaires have access to? It’s personalized healthcare advice, it’s a personal assistant, it’s a personal tutor for your kids… they’re bottlenecked on intelligence.” Subscribe to stay ahead of technology trends. Never miss future editions. Why He Isn’t Buying the Pause When we asked about recent calls from AI leaders to slow down, Richard was blunt. “I think if the agents at my company were committing a lot of felonies, I would probably say, ‘Hey guys, we, as in my company, are moving too quickly.’” To him, a global pause is unrealistic, and suspicious coming from labs that kept releasing new models the next week. It looks more like a way for those labs to “keep pole position.” He proposes a “horseshoe theory” of AI: extreme optimism and extreme doomerism end up closer to each other than they look. In his view, many industries won’t change much even with a thousand-times cheaper intelligence: Travel: people still want to see the pyramids in person. Farming: a cow won’t make a thousand times mor… Get full access to NEW ECONOMIES at www.neweconomies.co/subscribe

  4. Nov 4

    Dazzle AI

    Subscribe to stay ahead of technology trends. Never miss future editions. Former Yahoo CEO launches Dazzle Two to three years ago, the AI boom was almost entirely a story about the enterprise: coding agents, productivity gains and automated workflows. Today, as one of our recent podcast guests put it, the real question is how AI can “help humans become even more human.” Meet Marissa Mayer, founder and CEO of Dazzle. She joined us to explain why the AI boom is shifting from enterprise to consumer, why your camera roll may be the richest asset a personal assistant could ask for, and what it takes to build an AI agent people will trust with their most personal data. You might not know this, but Marissa joined Google when it had just 20 people and spent 13 years there, shaping some of its most iconic consumer products, including the famously simple search homepage. She then spent five years as CEO of Yahoo before returning to her product roots as a founder. Her previous startup worked on photo sharing, which led to the insight behind Dazzle: 40% to 50% of people’s camera rolls aren’t shareworthy at all. They’re screenshots of things to buy, photos of things to fix and appointment cards to remember. Dazzle raised an $8M seed round last October led by Kirsten Green at Forerunner, with Kleiner Perkins and Greycroft also participating. In our episode, we learn: * Why AI went to B2B first, why consumer AI has been “incredibly underinvested in,” and why Marissa thinks consumer AI could be three to five times bigger than enterprise by 2030 * Inside Dazzle’s two engines: a context layer built from your historical photos, and an “intent engine” that asks why you took each new photo. That turns your camera button into a “Get It Done” button that books restaurants, fixes garage doors and even hires a roofer * Why Dazzle keeps humans in the loop: dollar-value thresholds, the “time expectation” test, giving your assistant its own credit card and phone number, and a “when in doubt, leave it out” approach to privacy * How Marissa built her seed round: why Kirsten Green was top of her list, why she kept the investor group small, and why falling token costs and new models matter for compute-heavy consumer AI * What comes next: why going fully headless won’t work, “multiplayer” agents coordinating with friends’ and family’s assistants within the next year, and why product taste matters more than ever when “hard and long” are no longer the constraints Watch or listen now across YouTube, Apple Podcasts, Spotify, and X Download the transcript 👇 Timestamps (0:00) Meet Marissa Mayer(2:17) Why are personal assistants exploding?(3:19) Are assistants good for the ecosystem?(6:05) Why Marissa wanted to be a founder(9:08) From mega CEO to founder(12:17) What is Dazzle?(15:32) Why photos beat text(18:05) How does Dazzle work?(22:31) Dazzle already buys for users(28:05) Personalizing experiences(30:16) How Dazzle works with vendors(39:10) Raising $8M(41:51) Will compute costs come down?(45:08) When will agents buy for us?(46:57) Building trust with users(49:12) Dazzle 12 months out(51:02) The craziest concept for Dazzle(52:36) How far away is multiplayer?(54:37) Hardware: what would Dazzle build?(55:03) How Marissa’s boards think about AI(56:36) AI’s current inflection point Inside Dazzle, Marissa Mayer’s $8M Bet That Your Camera Roll Is a To-Do List 40% to 50% of Your Photos Aren’t Memories. They’re Instructions. Dazzle is a proactive personal AI assistant that learns who you are from your camera roll. It was founded by Marissa Mayer, who interviewed at Google when it had seven people, joined when it had 20, spent 13 years there and then five as CEO of Yahoo. Users can text Dazzle over iMessage, use the website or download the app, which reads the photos they choose to share. Dazzle raised an $8M seed round last October, led by Kirsten Green at Forerunner, with Kleiner Perkins and Greycroft also in the round. After months of testing with hundreds of users, the product launched publicly the week of our interview. It is already buying products, booking travel and restaurants, and hiring contractors for its users. Why Consumer AI Got Left Behind Marissa says AI went to the enterprise and B2B first, and the reason is partly linguistic. When people hear “AI,” they think of automating processes, which is a business frame. “People don’t think about how do I automate the processes in my house or in my morning routine.” She believes consumer AI needs a softer, more personal lens, one that helps people do more of what they already enjoy. “How can you take AI and help humans become even more human?” She thinks consumer AI has been badly underinvested in. By 2030, she expects it to be three to five times bigger than enterprise AI. From Shareworthy to “Get It Done” Dazzle grew out of Marissa’s previous startup, which was building an AI model to predict which photos people would want to share. When the team analyzed the results, 40% to 50% of people’s camera rolls weren’t shareworthy at all. They were photos of things to fix, screenshots of things to buy and appointment cards from the doctor. “There’s essentially instructions that could be good instructions for a personal assistant that are getting written down to your camera roll.” Dazzle’s motto is “pictures, not prompts.” Marissa borrows from the Transformers paper, “Attention Is All You Need”: your camera roll is a record of what you turned your attention to, and you cared enough to press the button. “Dazzle takes your camera button and turns it into a Get It Done button.” A Context Layer and an Intent Engine Dazzle splits the work in two. * The context layer: Dazzle builds this from photos a month or more old. It captures the people and places that matter to you, your aesthetics, the brands you surround yourself with, whether you have pets, and more. * The intent engine: this watches new photos and asks why you took each one today, and whether Dazzle can help. The context shows up in the answers. When Marissa asked what her daughter should do on a day off school, Dazzle suggested the Misdirection magic shop in San Francisco, where the owner teaches a trick with every purchase. It suggested bringing her best friend, whose name it had learned from photos. When Marissa’s mother pushed back on Dazzle’s recommendation of shorter skis, it explained that she is shorter and slighter in frame than her friends. “It often observes things about you that you might not even observe about yourself.” Every day, a “Daily Dazzle” email puts the photos you took that day at the top, with what Dazzle thinks you need to do about each one. Already Buying, Booking and Fixing One super-user re-roofed his house through Dazzle: it gathered quotes, he picked one and Dazzle hired the roofer. Dazzle’s CTO photographed a fraying garage door belt and had an on-site repair within 24 hours. Much of the value is administrative rather than transactional: * Dance studio closures: Marissa photographed the closure flyer and Dazzle added every date to her calendar. * School snacks: a snapshot of a snack sign-up sheet became an October 16th reminder, because Dazzle recognized her daughter’s name. * Food pickup: a handwritten “pick up at 5:30” on a catering order became a reminder when she was 10 minutes from the restaurant. * Gate codes: a screenshotted Airbnb gate code triggered a prompt to send it to her mom. “A personal assistant’s fundamental job is just to make sure that your life is smooth.” Travel is where the difference shows most. Marissa’s family now talks about “pre-Dazzle” and “post-Dazzle” trips. In Italy, Dazzle knew her kids loved escape rooms and booked a puzzle-based city tour. It also found an off-the-beaten-path mask-making shop. “Same dollars spent,” she says, but more personal. Why Headless Isn’t Enough Marissa thinks texting is a great low-commitment way to try an assistant, but not a complete interface. Her super-user had four or five projects running at once, which became confusing in a single iMessage thread. “It’s why S… Get full access to NEW ECONOMIES at www.neweconomies.co/subscribe

  5. Nov 1

    Media Execs: The New Playbook for Creators and Brands in 2026

    Subscribe to stay ahead of technology trends. Never miss future editions. During San Francisco’s Tech Week 2026 a few weeks ago, we co-hosted our first NEW MEDIA Summit with Yahoo, bringing together media’s biggest names for candid conversations on where the industry goes next.This panel, moderated by Jim Louderback brought together Joanna Stern, founder of New Things, Sona Iliffe-Moon, Chief Communications Officer at Yahoo, and Lillian Xu, VP of Podcast Business at Vox Media. They discussed why Joanna left The Wall Street Journal after 12 years, how her NBC News partnership works, what the NPR and How I Built This deals mean for creators, how to keep audience trust while running ads, and what the internet will look like in five years. Subscribe to stay ahead of technology trends. Never miss future editions. In our in-depth episode, we discuss: * Why Joanna Stern left The Wall Street Journal after 12 years, when 80% to 90% of her time was already going into YouTube, and how she de-risked going independent by negotiating her NBC partnership while still at the Journal * Why owning your IP matters most, and what the Kevin Roose and Casey Newton NPR deal and Guy Raz buying back How I Built This say about how institutions now partner with talent * How Vox Media is turning How I Built This into a franchise with video for the first time and the return of its two-day summit, and why Lillian Xu says every creator deal needs skin in the game and upside * How Joanna keeps trust while reading ads: a physical “money mic,” a published ethics policy, and turning down a possible million-dollar deal rather than take money from the frontier labs she covers * Why Sona Iliffe-Moon says brands can pay for a placement but never for an opinion, and why the panel thinks expert human voices, video and live events will win as AI summaries become part of our daily lives. Watch or listen now across YouTube, Apple Podcasts, Spotify, and X Download the transcript 👇 Timestamps (0:00) Welcome to the New Media Summit(3:05) Meet Joanna Stern: Why she left The Wall Street Journal(7:28) How media companies are partnering with creators(11:09) What changes when a journalist goes independent(12:33) Do the NPR deals make sense?(17:58) How to keep your audience's trust(19:21) How creators partner with brands(27:00) Does the audience care about paid ads?(30:50) Quick-fire round Lessons from these titans of media Joanna Stern: New Things Build a personal board. Joanna knows how to report and tell a story, but she’s the first to admit she doesn’t know how to sell, market or triple her growth. Her advice is to surround yourself with smart people from every side of the business, whether that’s a friend who understands sales or someone who knows marketing. They don’t need to work for you, but they need to be there when you need them. Lillian Xu: VP, Podcast Business at Vox Media Go in with your eyes open. Going independent is far more work than most people expect, so know what you’re taking on before you start. And when you strike a deal with a network or partner, make sure you have skin in the game and real upside, so everyone’s incentives are aligned. Sona Iliffe-Moon: Chief Communications Officer at Yahoo Lead with the relationship, not the sales deck. Brands can pay for a partnership or a placement, but they can never pay for an opinion, and the best paid work comes after a genuine relationship has been built. For comms teams, the rule with independent creators is the same as with any journalist: give them valuable information, access and respect. Jim Louderback: Inside The Creator Economy The rules of media are being rewritten in real time. Reporters are building their own companies, brands are finding their own voices and creators are hiring their own sales teams, so who pays whom is up for grabs. In that shift, trust is one of the few things that holds its value, and anyone building in this new world should guard it closely. Subscribe to Jim’s newsletter, Inside The Creator Economy. Where to connect with us Follow Ollie on LinkedIn: https://www.linkedin.com/in/ollieforsyth Follow Jim on LinkedIn: https://www.linkedin.com/in/jlouderbFollow Sona on LinkedIn: https://www.linkedin.com/in/sonamoonFollow Joanna on LinkedIn: https://www.linkedin.com/in/joannasternFollow Lillian on LinkedIn: https://www.linkedin.com/in/lillianlxu Previous episodes include Our partners: Harmonic: Your go-to startup database. Visit Harmonic to learn more. Prelint: Keeps every decision reviewed, recorded, and traceable. Head over to Prelint to learn more. VCX: The public ticker for private tech. Head over to GetVCX.com to learn more. Get full access to NEW ECONOMIES at www.neweconomies.co/subscribe

  6. Oct 22

    Klaviyo

    Subscribe to stay ahead of technology trends. Never miss future editions. Two to three years ago, software companies were still planning product roadmaps in years and quarters. Today, as one of our guests put it, the only question that matters is “what are we shipping this week?” That guest was Chano Fernández, co-CEO of Klaviyo. He joined us to explain how AI agents are reshaping marketing and customer engagement, why taste may be the biggest moat AI companies can build, and what it really takes for a public software company to stay relevant when the ground keeps moving. Chano held senior leadership roles at SAP before becoming co-CEO of Workday, where he helped expand its global and enterprise business. He was then co-CEO of the AI-native talent platform Eightfold, and spent two years on Klaviyo’s board, most recently as interim executive. In January, he became co-CEO alongside co-founder Andrew Bialecki. Andrew focuses fully on Klaviyo’s AI vision and products, while Chano runs go-to-market, operations, and G&A. Klaviyo now serves more than 200,000 customers, with around 9 billion profiles and 250 billion transactions a quarter on the platform. In our in-depth episode, we discuss: * Why email may become less relevant as AI agents start reading it for us, and why Klaviyo’s 14 years of data and infrastructure can’t be replicated with vibe coding * Inside Composer, Klaviyo’s agentic marketing tool: seven or eight sub-agents, and why getting a campaign to come out “80% or 90% right” for a CMO comes down to taste * Where software pricing is heading: from seats to consumption to outcome-based deals, and the 5% to 6% revenue share Chano is seeing emerge across the industry * How 70% to 80% of Klaviyo’s code is now written by AI, why its Friday product meetings show what shipped that week, and why a new product only needs to be 60% to 70% ready at launch * How the co-CEO model works in practice: a written operating agreement, two to three calls a day, and why only a handful of decisions each year really need both of them Watch or listen now across YouTube, Apple Podcasts, Spotify, and X Download the transcript 👇 Timestamps (0:00) Intro(1:10) How AI is changing marketing(2:24) Is email dead?(5:15) How Klaviyo has evolved(7:10) How Klaviyo stays relevant(10:35) Where business owners are focusing(14:10) What owning your audience means today(15:07) Building AI model partnerships(16:54) Would Klaviyo build its own AI model?(21:32) What great taste looks like(23:23) What great taste is missing(24:32) Are customers worried about pricing?(25:46) Shipping Klaviyo's latest products(28:17) Is the future of software headless?(30:43) An AI co-worker for Klaviyo users?(32:38) Is trust the new moat?(35:05) The role of the product roadmap(36:33) Klaviyo's favorite AI models(38:10) What's missing from the Klaviyo stack(39:22) Is SaaS pricing dead?(44:19) How much code AI writes(47:05) Keeping up with the speed of change(51:10) Will more companies have co-CEOs?(58:17) Running a public company(59:42) Is the market correcting?(1:01:45) Ollie becomes Chano's Chief of Staff Lessons from this episode with Chano 1. Agents will read your emails for you Chano thinks email’s role changes once AI agents start acting on our behalf. “I believe it will become less relevant, just because some of those agents will be the ones consuming many of those emails going forward.” Email won’t disappear. It will stay one of the cheapest and most efficient channels. Chano also expects agents to do more than read: they will use what they know about us to surface better products, offers and services. For brands, the challenge is personalizing at scale, and that is where automation and agents come in. 2. Marketing and service are becoming one Moving Klaviyo from a marketing tool to an AI agent platform wasn’t optional. “The boundaries between marketing and service have become more indistinct.” If a customer has just complained or asked for a refund, the next thing they want is not a promotion. Most brands can’t see that, because their data sits across six or seven disconnected systems with no real-time view. Chano says this is where Klaviyo’s infrastructure gives it an edge: around 9 billion profiles, 250 billion transactions a quarter, and the ability to send 100 million messages in 20 minutes. 3. Taste is the hardest thing to automate Klaviyo’s agent, Composer, builds campaign calendars and campaigns for brands that are short on people or ideas. Getting the output right comes down to taste. “If you’re a chief marketing officer, you want to see the campaign the agent is creating and say: wow, this is really my brand, my image, my language.” Code is fairly deterministic: good code is good code at any company. Brand voice is different for every customer. Klaviyo measures progress through thumbs-up ratings, how many campaigns go live without edits, and direct customer feedback. Each edit a brand makes feeds the next model. 4. Stay model-agnostic and route by task With AI labs shipping updates every few weeks, Chano says no company can afford to lock itself to one model. “It’s important to be as model-agnostic as possible.” Composer runs on at least seven or eight sub-agents, covering email templates, flow templates and campaign taste, and each can sit on a different underlying model. Klaviyo’s engineers test new releases quickly and change the routing when a better model appears. Chano expects model capabilities to become more standardized, so routing well will matter more than picking a winner. 5. Pricing is heading towards outcomes Klaviyo had a head start on the shift away from per-seat pricing. “We were never on seat-based pricing like many other SaaS companies.” Klaviyo’s pricing has always tracked sends and profiles, rising and falling with seasons like Black Friday. Its customer service agent already charges only when it resolves a conversation. Chano sees the industry settling on a base subscription with usage on top. Outcome-based deals come next, where the vendor takes a share of attributed revenue. The benchmark he is seeing is 5% to 6%. 6. Launch at 60% to 70% Around 70% to 80% of Klaviyo’s code is now written by AI, and the planning cadence has changed with it. “Two to three years ago, we were still talking about product roadmaps in years and in quarters, and the conversation today is: what are we shipping this week?” Every Friday, a group of engineers presents what shipped that week. The hard call is when to launch. Customers have less patience than in the old SaaS era, when a product had 12 to 24 months to mature. AI products can also improve dramatically within weeks. Chano’s threshold is a product that is 60% to 70% ready. 7. Co-CEOs only work with trust, complementary skills and low ego Chano was on Klaviyo’s board when the company decided Andrew Bialecki needed to spend all his time on product vision. “It’s not an easy model, but it’s a very effective one.” The two wrote down how they would run the company and speak two to three times a day. Andrew owns product; Chano owns go-to-market and the rest of the business. At first they held joint one-to-ones with leaders, which are no longer needed. Chano’s view is that only a handful of decisions each year, on strategy, culture and organization, need both of them. The rest can be made by either and reversed if needed. 8. Focus on inputs, not the share price With Klaviyo’s stock down 35% this year at the time of this recording, Chano says his answer is to focus on what the company controls. “If you take care of the right inputs, the outputs will take care of themselves.” For Chano, the inputs are innovation, happy and referenceable customers, the right culture, and getting the few big decisions right. He sees the market as trying to separate AI wrappers from companies with durable moats. In his view, that will get easier as more AI-native companies go public, and it could take anywhere from two to six quarters. Where to connect with us Follow Ollie on LinkedIn: https://www.linkedin.com… Get full access to NEW ECONOMIES at www.neweconomies.co/subscribe

  7. Oct 21

    TBPN

    Subscribe to stay ahead of technology trends. Never miss future editions. During San Francisco Tech Week a few weeks ago, we co-hosted our first New Media Summit with Yahoo, bringing together media’s biggest names for candid conversations on where the industry goes next. One of our speakers was Dylan Abruscato, President of TBPN, which has had a huge year. He joined us to share how a bootstrapped, 10-person live tech show became one of the most valuable ad slots in Silicon Valley, why live remains “a truly unskippable ad format,” and why reaching a niche audience can be worth more than reaching a big one. Dylan began his career in live TV at SNL and CNBC. He then helped run HQ Trivia through its rise and fall, and built his own live interactive game show before joining TBPN, a daily, three-hour tech variety show hosted by John Coogan and Jordi Hays. Under Dylan, it grew to roughly $25 million in annual revenue with 30 year-long sponsors, led by presenting sponsor Ramp. Six months ago, it was acquired by OpenAI. In our in-depth episode, we discuss: * Why the OpenAI deal had little to do with the show itself, and how the marketing and comms advisory work built into TBPN’s sponsorships became the real reason behind the acquisition * The Super Bowl stunts that preceded the deal: a hyper-local 15-second spot built from over a thousand founder logos, and claudewithads.com * How TBPN copied Formula 1’s sponsorship model: year-long deals, category exclusivity, 100% share of voice, and an ad read to Mark Zuckerberg as he took his seat * Why the show stayed intentionally niche, and why it only takes one or two founders switching platforms for a tech sponsor’s ad to pay for itself * Inside a daily live show: the 6 a.m. gym sessions, the three-hour run of show, last-minute guest booking, and the vibe-coded internal tools that power it all Watch or listen now across YouTube, Apple Podcasts, Spotify, and X Download the transcript 👇 Timestamps (0:00) Meet Dylan Abruscato(2:20) Why OpenAI acquired TBPN(6:48) What changed after the acquisition(7:43) Will frontier models come on the show?(8:17) What makes TBPN so vibrant(9:32) Prepping for a daily live show(10:46) Why live streaming is hot(12:04) Are niche audiences more valuable?(12:58) How the ad integrations work(15:44) Calling partners post-acquisition(17:10) Building alongside legacy media(18:17) The copycats(19:38) Will another TBPN-style acquisition happen?(20:08) OpenAI’s marketing services(21:02) Thinking about distribution channels(22:10) Dream guests(22:59) TBPN’s next 12 months(23:22) Audience Q&A Lessons from this episode with Dylan 1. The OpenAI deal was for the team, not the show Dylan says most people misread why OpenAI bought a live tech talk show. “I think what a lot of folks got wrong about the deal is that it had very little to do with the show itself and much more to do with the interest in us and the brains behind the operation.” Many of TBPN’s 30 sponsorship deals included marketing and comms advisory work, a tactic the team first used to win sponsors. OpenAI’s offer was simple: keep doing the show, wind down the advisory clients, and make OpenAI the only one. TBPN was profitable, had raised no money and had no plans to sell. Fidji Simo reached out the morning after the Super Bowl. 2. Build the show you’d want to watch The two stunts that came right before the deal both started as ideas the team found funny. “The truth is we’re just building the show that we always wanted to watch.” The first was a hyper-local 15-second ad that aired in San Jose at noon but still counted as a Super Bowl spot. It spelled out TBPN using over a thousand logos of past guests, and those founders shared it everywhere. The second was claudewithads.com, a reskinned Claude that gave one real answer and then rotated through sponsor ad reads. The team’s brainstorming rule is that if an idea makes them laugh, they run with it. 3. Live is the last unskippable ad format Dylan has worked in live TV at SNL, CNBC and HQ Trivia, and has argued for live for a decade. “It’s a truly unskippable ad format, and that’s kind of allowed us to command such high prices.” Almost nothing is watched live anymore. Sports is the exception, and even award shows are now consumed as recaps on X. TBPN’s audience is smaller than HQ’s was, but advertisers value it more because no one can fast-forward. 4. Go intentionally niche TBPN never explains the jargon. Where CNBC would spell out what AWS stands for, John and Jordi just say AWS or GPU, and the audience respects it. “It really only takes one or two founders watching the show to switch to this platform for the advertisement to pay for itself.” A small audience of founders, CFOs and VCs is worth more to a tech sponsor than a mass audience. As an example, John once read a Ramp ad straight to Mark Zuckerberg as he took his seat. 5. Copy Formula 1’s sponsorship model TBPN sold only year-long deals, split into a couple dozen categories with one sponsor per category. “Everyone got 100% share of voice in their category on everything that we did over the course of the year.” Sponsors got the same ad read in every episode, plus the ticker, merch, newsletter links and end cards on tweets. Dylan’s view is that repetition is what counts when a founder is finally ready to switch products. TBPN also never tried to upsell new placements partway through the year. The commercial mindset was there from day one. On the first episode, with one viewer, John did fake ad reads for sponsors the show didn’t have. When TBPN wound down its sponsors after the acquisition, viewers asked for the ad reads back. 6. Chemistry plus a fixed format The show depends on the friendship between its two hosts. “It’s like a true one-of-one friendship, and it’s just so relatable.” John and Jordi meet at the gym at 6 a.m. every day and work out the day’s lead story in the sauna. They go live at 11, and after the show ends they still call each other on the drive home. The format never changes: * Hour one: John and Jordi on the current thing, then live reactions to the timeline. * Hour two: one or two tier-one interviews. * Hour three: a lightning round of startup fundraising news. Most guests are booked last minute. Mag Seven–level names take six months or more of back-and-forth. 7. Be a companion to legacy media, not a competitor TBPN never tried to beat traditional outlets at breaking news. “We always viewed TBPN as a companion show.” When founders or publicists offered scoops, TBPN sent them to the Journal or Bloomberg. The founder would come on a few hours later to break the story down. Every show opens with a hard copy of the Journal. Dylan doesn’t want copycats making another three-hour daily show. He would rather see new formats in tech media, like a late-night tech show or an interactive Shark Tank. Where to connect with us Follow Ollie on X: https://x.com/ollieforsyth Follow Dylan on X: https://x.com/DylanAbruscato Watch and follow TBPN: https://www.tbpn.com Previous episodes include Our partners: Harmonic: Your go-to startup database. Visit Harmonic to learn more. Prelint: Keeps every decision reviewed, recorded, and traceable. Head over to Prelint to learn more. VCX: The public ticker for private tech. Head over to GetVCX.com to learn more. Get full access to NEW ECONOMIES at www.neweconomies.co/subscribe

  8. Oct 16

    Eric Ries

    Subscribe to stay ahead of technology trends. Never miss future editions. Eric Ries, creator of The Lean Startup and author of Incorruptible, joins us to explain why “financial gravity” pulls even the best companies away from their mission. He explains why the standard best practices founders are handed “are designed to make you lose control of your company.” Eric published The Lean Startup 15 years ago and went on to found the Long-Term Stock Exchange. He co-founded the AI research lab Answer.AI and helped start Virgil, an AI-enabled law firm for early-stage companies. In his latest book, Incorruptible, he studies the companies that have resisted this force, from Costco to Novo Nordisk to Anthropic. In our in-depth episode, we discuss: * How the default “any lawful act or activity” clause in a Delaware charter quietly commits founders to maximizing shareholder value, and why lawyers rarely tell them * The story of Sol Price, FedMart and the origins of Costco, and the “governance fortress” that has protected Costco’s $1.50 hot dog for 40 years * The three mission protective provisions every founder can set up in a matter of weeks: becoming a PBC, adopting a director’s oath, and appointing a mission guardian * The difference between a mission statement and a mission, and what Silicon Valley Bank’s charter revealed after its collapse * Why “it’s always too early until it’s too late,” how to win over the roughly 25 people who must agree to a change, and why Anthropic’s Long-Term Benefit Trust has become a commercial advantage Watch or listen now across YouTube, Apple Podcasts, Spotify, and X Download the transcript 👇 Timestamps (0:00) Meet Eric Ries(2:14) Eric’s New Book(3:30) How Startups Are Built Today(6:20) Why Companies Self-Destruct(16:37) Who’s to Blame When Companies Fail(21:25) The Blueprint for a High-Performing Company(26:52) How Companies Protect Themselves(33:13) Building Company Ethos and Structure(46:05) Why Companies Get Bullied(50:32) When Is It Too Late to Save a Company?(55:15) The Company Eric Would Have Saved(1:00:05) What Eric Is Excited About Next Lessons from this episode with Eric 1. Why great companies are destroyed by financial gravityEric explains why blaming a greedy investor or a reckless founder misses the real cause of most corporate collapses. * “There is a form of what I call financial gravity that operates on these companies. The ones that collapse, the ones that erupt in scandal, whether I’m talking about Boeing or FTX or Wells Fargo or Silicon Valley Bank, they’re all the same story.” * He compares it to a submarine with a weak hull. Asking whether the water is to blame is the wrong question. The right question is who designed the submarine. * Gravity acts on every company, yet some have lasted 50, 75 or even 500 years. Studying the ones that never collapse is how you learn to build differently. 2. How the firing of Sol Price led to the founding of CostcoSol Price capped his markups at 14% and treated customers as clients he owed a fiduciary duty to. His investors fought him for 20 years, then changed the locks on his office overnight. * “It is so much easier to destroy than to create. They managed to destroy in seven years what had taken Sol more than 20 to build.” * Within seven years of firing him, the investors had completely liquidated FedMart. Two weeks after his dismissal, Sol leased the office upstairs and started the Price Club, which later merged into Costco. * Costco has survived for 40 years because of what Eric calls a governance fortress. That structure lets its directors act as a bulwark against Wall Street pressure. 3. The six words in your charter that hand your company to shareholdersMost founders have never read the first sentence of their own corporate charter. Eric says that sentence decides who the company really serves. * “Since 1986 in the United States, whenever you see ‘any lawful act or activity,’ the courts have decided that actually means maximize shareholder value.” * The phrase sounds open-ended. Legally, it makes your company a financial instrument whose job is to enrich shareholders. * The same duty forced Twitter’s board to sue Elon to complete his deal. It also let Philip Morris take over an inhaled therapeutics company and destroy it. 4. Three mission protective provisions that take weeks, not yearsEric gives founders a short list of protections they can put in place early. Some take only days. * “Welcome to best practices. They are designed to make you lose control of your company by force or by corruption.” * First, become a public benefit corporation, a two-page filing in Delaware. Second, require every director to take an oath to protect the mission, much like the Hippocratic oath. Third, set up a mission guardian, such as a foundation or a purpose trust. * Companies owned by an industrial foundation, such as Novo Nordisk, are five times more likely to reach year 50. Eric’s explanation: “Checks and balances work.” 5. A mission statement is not a missionEric’s own deposits were at Silicon Valley Bank when it collapsed. Afterwards, he looked up what the bank had legally committed to. * “They had no mission at all. The whole thing was a scam.” * The bank’s mission statement promised to move the innovation economy forward. Its charter said “any lawful act or activity.” Even if its interest rate bets had paid off, they would have done nothing for customers. * His test: Google spends billions to make sure its quarterly reports are on time, while “Don’t be evil” was one sentence in the employee handbook. What a company invests in shows what it values. 6. It’s always too early until it’s too lateLawyers and investors often tell founders to deal with governance after product-market fit. Eric says that advice really means never. * “In most companies, there are at most 25 people that need to consent to anything like this.” * Veeva Systems became a public benefit corporation after it was already a $40 billion public company. It spent a year on investor relations and won 90% of the shareholder vote. * Eric’s line for pitching a change to investors: “It is my judgment as the founder of this company that it is essential to our strategy that we win the trust of [some group].” Framing it this way forces people to say exactly what they object to. 7. Why ethics is an accelerantEric uses Anthropic to show that integrity can be a source of commercial strength. * “People talk about ethics like it’s an impediment to business success, but here it is clearly an accelerant.” * Anthropic is governed by the Long-Term Benefit Trust, which appoints directors to its for-profit board. Eric argues this structure is a big part of why enterprises trust it more than its competitors. * Costco, Patagonia, Vanguard, Mondragon and Anthropic have different missions and cultures. The one thing they share is that each rejected the standard best practices for how a company should be structured. Where to connect with us Follow Ollie on X: https://x.com/ollieforsythFollow Eric on X: https://x.com/ericriesEric's new book - Incorruptible: https://www.incorruptible.co Previous episodes include See all previous episodes here 👉 Our partners: Harmonic: Your go-to startup database. Visit Harmonic to learn more. Prelint: Keeps every decision reviewed, recorded, and traceable. Head over to Prelint to learn more. VCX: The public ticker for private tech. Head over to GetVCX.com to learn more. Get full access to NEW ECONOMIES at www.neweconomies.co/subscribe

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About

Welcome to BIG IDEAS by NEW ECONOMIES - a show where we learn how the most iconic founders have turned crucible moments into global companies. www.neweconomies.co

Information

  • Creator
    Ollie Forsyth
  • Years Active
    2025 - 2026
  • Episodes
    67
  • Rating
    Explicit
  • Copyright
    © Ollie Forsyth
  • Show Website
    BIG IDEAS BY NEW ECONOMIES

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