GAEA Talks

GAEA Talks
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GAEA TALKS explores the transformative power of artificial intelligence. Featuring leading AI experts, industry leaders, professors, data scientists, policymakers, technologists, futurists, ethicists, and pioneers, the podcast dives into the latest AI trends, opportunities, and risks, examining AI’s evolving role in business and society. As AI continues to reshape industries and redefine possibilities, GAEA TALKS delivers deep insights into the challenges and breakthroughs shaping the future. Each episode features candid discussions with thought leaders at the forefront of AI innovation, cove

  1. 6월 24일

    #084 - The Real Meaning of Sovereign AI - Graeme Scott with Georgie Barrat

    This week on GAEA Talks, the format flips. Graeme Scott, the man who normally asks the questions, is in the hot seat. Guest host Georgie Barrat - one of the UK's leading technology journalists - interviews him on the topic he is most associated with: the real meaning of sovereign AI. Recorded live in the new London studio, this is a long-form, single-topic deep dive. Sovereign AI is one of the most-used and least-understood phrases in modern AI, and Graeme has been making the case for over a year that almost everyone in the public conversation is talking past each other. In this episode he sets out what he believes sovereign AI really means, why the token economy is a trap, why local and private AI is already shipping inside Apple and Google's stack, and why the UK is uniquely positioned to lead the next AI wave. Topics covered: What sovereign AI means to a person, a company, a multinational and a countryThe recent US restriction on a major foundation model and what it taught the industryWhy the token economy is costing the UK more than people realiseThe Apple, Google and AMD hardware play that nobody is talking aboutThe three stakeholders shaping the AI narrative and what each one is incentivised to obscureThe cognitive decline research starting to emerge from heavy LLM useThe three questions every leader should be able to answer about their AI architectureThe worst case and best case scenarios for the average person in the next three yearsWhy the UK's innovation DNA from Stephenson's Rocket to Alan Turing makes this the country's moment

    55분

  2. 6월 16일

    #083 - Why Drug Discovery Has To Go To Space with Mass Balance Founder Dr Toby Call

    Why does drug discovery need to go to space? In this week's episode of GAEA Talks, the second of season five and the second filmed in our new London studio, Graeme Scott sits down with Dr Toby Call, founder of Mass Balance, former co-founder of Chronomics, and alumnus of the International Space University. Toby explains why over forty percent of the proteins in the human body have no fixed structure, why this "dark proteome" includes some of the most important targets in cancer and Alzheimer's, and why AlphaFold and the rest of the current AI drug discovery stack cannot model them. He then makes the case for microgravity in orbit as the next great forcing function in biology, places that argument inside the wider story of the second space race, and brings it back down to earth with a clear-eyed view of sovereign AI, edge compute, and where the UK has to position itself over the next twelve to twenty four months. Essential listening for anyone building, funding or regulating the future of AI, life sciences or space. Topics covered: The dark proteome and why current AI hits a brick wall in drug discoveryMicrogravity as the next great forcing function in biologySpace data centres, cosmic ray bit flips and the radiative cooling debateSovereign AI through the lens of a country, a company and an individualThe UK's innovation track record from Stephenson's Rocket to DeepMindThe AGI fear moment and the AI autonomous kill chains already coming out of UkraineDr Toby Call on LinkedIn: https://www.linkedin.com/in/tobycallMass Balance: https://www.massbalance.bioGAEA AI: https://gaealgm.ai

    55분

  3. 6월 9일

    #082 - Own Your Means of Intelligence with Writer CEO May Habib

    This week on GAEA Talks, Graeme Scott sits down with May Habib - co-founder and CEO of Writer, one of the most consequential enterprise AI platforms in the world today, and one of the clearest voices in the industry on what it actually takes to make AI work inside the global two thousand.This is the season five opener, and there is nobody better to set the tone. May co-founded Writer in 2020 and has built it into the enterprise agentic platform that serves Mars, AstraZeneca, GlaxoSmithKline, UnitedHealthcare, Boots, Clorox, Metro Bank, Currys, Monzo, BNY Mellon, Edward Jones and many more of the most demanding enterprise buyers in the world. The company has raised four hundred million dollars to date, was valued at around two billion dollars at its Series C, employs close to five hundred people across six cities, and has built a reputation for being the rare AI company that actually understands what regulated, brand-critical, global enterprise looks like from the inside.In this episode, May lays out a complete framework for how enterprise AI is going to play out over the next six to twenty four months. She argues that individual productivity does not rewire an organisation for productivity, that frontier labs have left a huge gap by behaving like celebrities rather than partners, and that the next wave of enterprise AI will be defined by voice and mobile, by the complete rewrite of the sales and marketing tech stack, and by whether enterprises own their means of intelligence or rent it from the labs. She also lays out the most rigorous tokenomics conversation we have recorded on the show, and offers the single best three-step playbook for leaders that I have heard this year. Essential listening for any executive, founder, CIO or board member making decisions about enterprise AI in 2026.What you'll take away from this conversation:• Why individual productivity does not rewire an organisation for productivity• The "labs as celebrities" problem - why frontier providers leave the room before the work starts• The brand as code thesis that became Writer's product North Star• Why voice and mobile become the primary AI interface in the next six months• The coming complete rewrite of the sales and marketing tech stack• The operating agents concept - agentically constructed databases as the system of record• Why the iconoclasts inside the enterprise are not who you think they are• The C-suite power-law inside companies - and why the elite of AI power users matters more than headcount• Sovereign AI for enterprise - owning your means of intelligence, not renting it from the labs• Multi-jurisdiction compliance from day one - Mars, Cartier, Monzo, BNY Mellon, Edward Jones•Tokenomics as the new Econ 101 - and why shared context cuts token consumption twenty to thirty percent• Why frontier model pricing is going up the moment the IPOs close• The "you don't get fired for buying IBM" trap, now applied to a five million dollar monthly Anthropic bill• Why the squad-based approach beats incremental change every time• Why the future of work is not fewer people, but vastly more agents

    48분

  4. 6월 7일

    #081 - AI Through The Eyes Of A Quant With Tech Entrepreneur & Mathematician Dr Ewan Kirk

    This week on GAEA Talks, Graeme Scott sits down with Dr Ewan Kirk - founder of Cantab Capital Partners, former Goldman Sachs partner and Head of Quantitative Strategies, chair of the Isaac Newton Institute and non-executive director of BAE Systems.A mathematician by training, Ewan spent thirteen years at Goldman Sachs, rising to partner and leading a 120-strong European team of quants. In 2006 he founded Cantab Capital Partners, growing it from a team of two to roughly £4.5 billion under management before selling to Swiss asset manager GAM in 2016. Today he lives a "portfolio life" as philanthropist, board member, Royal Society Entrepreneur in Residence at Cambridge, and adviser to early-stage companies.In this episode, Ewan brings a quant's discipline to the AI conversation and refuses to let the hype slide by. He draws a hard line between deterministic systems and probabilistic LLMs ("complete the sentence - the cat sat on the - and almost all the time it'll say mat, but one day it'll say roof. Are you okay with that?"), explains why benchmark testing is fundamentally flawed once the test suites leak into the training data, and dismantles the boardroom reflex to "squeeze some AI in and hope magic happens." He's sharpest on the gap between commercial bets, where capitalism lets firms be wrong, and government bets on AI and quantum, where "there's no opting out." This is essential listening for any leader being told AI is coming and they'd better get on board or lose out.What you'll take away from this conversation:• Why an LLM is not "AI" - and the distinction between machine learning, data science and the chatbot layer everyone is actually buying• The deterministic vs probabilistic divide - why Cantab's backtesting and risk systems gave the same answer every time, and why LLMs fundamentally cannot• "The cat sat on the roof" - Ewan's one-sentence demonstration of why he'd never let an LLM run his bank account• Why benchmark testing is broken - public test suites end up in the training data, so beating the benchmark proves almost nothing• The boardroom trap - "it's not enough to say I'm going to squeeze some AI chatbots into my business and magic will happen. What magic are you looking for?"• Commercial risk vs government risk - why a firm being wrong is just capitalism, but a government going all-in on AI or quantum means "there's no opting out"• The marginal cost problem - why the LLM economy is not like the early internet, and why economics, not technology, may be what trips it up• The attention economy decoded - how emotional state drives behaviour, and how Facebook and Instagram monetised the thin layer on top of the open web• The three boundary conditions for AI's future - doom and mass unemployment, a productivity boom like the rise of computing, or trillions in capital vaporised• The decision-maker's toolkit - ask for the concrete not the abstract, demand a testable prediction, and run the randomised controlled trial before betting the business• "Is it a big number or a small number?" - the single heuristic Ewan reaches for every time he hears a statistic on the news• Why AI is ultimately a human problem - and why, stripped of the human element, "nearly everything would fall apart"

    1시간 34분

  5. 6월 2일

    #080 - Why Enterprise AI Needs a Knowledge Graph with Neo4j CEO Emil Eifrem

    This week on GAEA Talks Live from HumanX, Graeme Scott sits down with Emil Eifrem - co-founder and CEO of Neo4j, the creator of the world's leading graph database, and one of the architects of the knowledge graph movement that is now powering explainable AI inside governments, banks and Fortune 500 enterprises.Emil started Neo4j more than twenty years ago after his team hit the limits of tabular databases while building a content management system in Sweden. Their prototype graph engine was a thousand to a million times faster at traversing relationships. He has since built Neo4j into the category-defining graph database, used by organisations from one of the world's top five banks to every major research institution working on AI. He is a veteran of the Swedish open source community and one of the most cited voices in the industry on explainability and trust in AI.In this episode, recorded live at HumanX 2026 in San Francisco, Emil takes us inside the single biggest bet in enterprise AI right now - that knowledge graphs will become a default box in every serious AI architecture. He explains why vector search alone gives you similarity with no context, why mechanistic interpretability only solves half of the explainability problem, and why rag without a knowledge graph is a dead end for mission-critical decisions.What you'll take away from this conversation:- Why the last fifty years of tabular databases cannot model the real shape of enterprise data, and why graphs can- The "empirical versus subjective" framework for deciding what AI can and cannot be trusted to own- Why every AI decision still needs an accountable human - and why that makes explainability the critical constraint- How knowledge graphs complete the explainability story that mechanistic interpretability starts- Why vector similarity scores without context are dangerous for rag retrieval in the enterprise- The real lesson from the surgeon-and-hospital insurance rabbit hole - accountability has to be architected in from day one- Why platform shifts are the only moment database companies ever get built - and why the AI shift is the biggest yet- How one top five global bank went from three percent graph adoption pre-AI to twenty percent today- The Goldilocks rule for enterprise AI - don't start with self-driving or with trivia, pick the meaningful middle- Why "AI ready from a data perspective" is the single biggest five-year survival question for enterprises- Let the business problem drag the AI, and let the AI drag in the data - Emil's single best piece of adviceAbout Emil Eifrem:Emil Eifrem is the co-founder and CEO of Neo4j, the company behind the world's leading graph database. Born and raised in Sweden, he started programming as a child, became CTO of a Swedish startup at twenty, and co-invented the property graph model that is now the foundation of the entire graph database category. Neo4j is used by seventy five percent of the Fortune 100 and powers some of the most sensitive AI, compliance, fraud and intelligence workloads in the world.GAEA Talks is the enterprise AI podcast for leaders navigating the age of artificial intelligence. Subscribe for weekly conversations with the people shaping the future of business, technology and society.Emil Eifrem on LinkedIn: https://www.linkedin.com/in/emilefremNeo4j: https://neo4j.comHumanX: https://www.humanx.coGAEA AI: https://gaealgm.ai#AI #ArtificialIntelligence #GAEATalks #GAEATalksLive #HumanX #HumanX2026 #EnterpriseAI #Neo4j #KnowledgeGraph #GraphDatabase #ExplainableAI #RAG #LLMs #AIReliability #ResponsibleAI #AIGovernance #AIPodcast #GAEAAI

    52분

  6. 5월 27일

    #079 - When AI Goes To Work For You with Read AI CEO David Shim

    This week on GAEA Talks Live from HumanX, Graeme Scott sits down with David Shim - co-founder and CEO of Read AI, former CEO of Foursquare, founder of Placed, and one of the most original product thinkers in the AI ecosystem today.David's career is a study in spotting opportunities before anyone else. The son of Korean immigrants, he became one of the youngest registered stockbrokers in the United States at seventeen, after being emancipated from his parents so he could legally sign trading contracts. He went on to build and sell a coupon website out of his university fraternity, then founded Placed, the pioneering location analytics company that Snap Inc. acquired in 2017. He became CEO of Foursquare in 2021 and pivoted the business from check-ins into a full enterprise analytics platform. In 2021 he co-founded Read AI, the multimodal meeting intelligence platform that now serves close to five million users worldwide and signs up forty to fifty thousand new users every single day.In this episode, recorded live at HumanX 2026 in San Francisco, David lays out what he calls the narration layer of AI - the idea that meetings, decisions and digital interactions are not just about the words people say, but about how they react, where they engage, and where they tune out. He walks Graeme through Read AI's new Digital Twin product, codenamed Ada, which behaves not as a chatbot but as an email address, a Slack handle and a Teams alias that can answer for you, schedule for you and move work forward while you are unplugged. He shares his view that the AI magic has worn off in developed markets, that emerging markets are now driving the most practical adoption, and that the next breakthrough in consumer AI will look more like TikTok meets Tinder than another ChatGPT. This is one of the most useful conversations on the future of AI agents that GAEA Talks has recorded.What you'll take away from this conversation:• The narration layer thesis - why what people do in a meeting matters more than what they say• The Digital Twin and Ada - why your next AI assistant is an email address, not a chatbot• The sidebar pattern - how a good AI agent checks in with you before sending sensitive content• Why developed and emerging markets are now on completely different AI adoption curves• The TikTok-meets-Tinder analogy for the next breakthrough in consumer AI UX• Why coding and legal got AI first - and which professions are next• The healthcare, social work and Southeast Asian insurance use cases that have surprised the Read AI team• The early onset dementia user story that quietly changed how David thinks about product• Why you should never wait for perfection - "twelve other companies will have launched while you wait"The jigsaw puzzle approach to AI - you only need thirty to forty percent of the picture to know what it is• Why agents will eventually invent their own language to talk to each other• The Foursquare pivot lesson - turning check-in data into an analytics business• The Placed origin story - how location intelligence became a category and exited to Snap• David's advice on failure - the fear of failure is the biggest waste you can have• Why the youngest stockbroker in the nation grew up to build AI for the rest of usAbout David Shim:David Shim is the co-founder and CEO of Read AI, the multimodal meeting intelligence platform used by close to five million people across Zoom, Google Meet, Microsoft Teams and mobile field environments. Before Read AI, David was Chief Executive Officer of Foursquare, where he led the transformation of the company from a check-in app into an enterprise analytics and developer platform. Accelerate your company’s AI transformation, head to https://larridin.com/gaea and book a demo now. Thanks to Larridin for sponsoring today’s video!

    53분

  7. 5월 25일

    #078 - The End of Siloed Enterprise AI with Aily CEO Bianca Anghelina

    This week on GAEA Talks Live from HumanX, Graeme Scott sits down with Bianca Anghelina - founder and CEO of Aily Labs, 2025 CNBC Changemaker, and the former Head of Global Digital Finance at Novartis turned builder of the first enterprise decision-intelligence mobile app.Bianca's career sits at the intersection of business, finance and technology. She studied business and finance and added technology management at university, worked in consultancy and inside one of the world's largest pharmaceutical companies, and in 2018 implemented AI to disrupt her own multi-billion dollar PNL planning role at Novartis. That experience became the seed of Aily Labs, which she founded in 2020 to build a decision advisor for every employee in the enterprise. Aily Labs has been profitable since its first year, grew customer growth by five hundred percent last year, counts Sanofi, Johnson and Johnson and Teva among its clients, and now serves more than thirty eight thousand users worldwide.In this episode, recorded live at HumanX 2026 in San Francisco, Bianca explains why the era of siloed data has given way to an even bigger problem - siloed AI - and why connecting the dots across functions is now the most valuable thing an enterprise AI platform can do. She argues that AI has to move from collaboration to fusion, from processes to routines, and from vertical expertise to horizontal leadership.What you'll take away from this conversation:- Why decision intelligence is fundamentally different from workflow automation - it reinvents how companies operate- The difference between "optimising workflows with AI" and "running the company with AI"- Why siloed data has become siloed AI - and why breaking those silos is the real prize- The "fusion, not collaboration" model - and how AI agents collapse the last barriers inside the enterprise- The horizontal leadership shift - why AI turns every employee into a mini-CEO with access to the whole business- Why Steve Jobs' obsession with beauty applies to enterprise software and accelerates adoption at scale- The "routines, not processes" insight - and why daily AI use is more sustainable than quarterly projects- How one Aily customer scaled from three hundred to fifteen thousand users in a year by treating adoption as a KPI- Why shadow AI inside enterprises is a symptom - not a security problem - and how to channel it- Why the most optimised enterprise of the next decade will personalise AI for every user while optimising the global P&L- Why imperfect data should not stop enterprise AI projects - and what to do instead- The real risk for incumbents that fail to fuse AI into daily operationsAbout Bianca Anghelina:Bianca Anghelina is the founder and CEO of Aily Labs, the decision-intelligence platform that acts as an AI decision advisor for every employee in the enterprise. She was named a 2025 CNBC Changemaker and one of the top 50 CEOs to watch in 2024 by Pavilion. Before founding Aily Labs in 2020, Bianca was Head of Global Digital Finance at Novartis, where she ran one of the largest P&Ls in the pharmaceutical industry. She previously worked at BMW and Sandoz, and holds a background in business, finance and technology management.GAEA Talks is the enterprise AI podcast for leaders navigating the age of artificial intelligence. Subscribe for weekly conversations with the people shaping the future of business, technology and society.Bianca Anghelina on LinkedIn: https://www.linkedin.com/in/bianca-diana-anghelina-310b453Aily Labs: https://www.ailylabs.comHumanX: https://www.humanx.coGAEA Talks: https://gaealtalks.ai#AI #ArtificialIntelligence #GAEATalks #GAEATalksLive #HumanX #HumanX2026 #EnterpriseAI #DecisionIntelligence #AilyLabs #CNBCChangemaker #AIAdoption #EnterpriseSoftware #WomenInAI #WomenInTech #AIAgents #AIPodcast #GAEAAI

    29분

  8. 5월 21일

    #077 - The AI Platform Built for Sales Reps with Zig CEO Steve Ancheta

    This week on GAEA Talks Live from HumanX, Graeme Scott sits down with Steve Ancheta - serial founder, sixteen-year veteran of enterprise sales, and the founder and CEO of Zig, the first AI platform built for the sales rep rather than for sales management.Steve started in sales at eighteen, took the job to support his new son, and has spent his entire adult life inside the revenue function of technology companies. He has hired hundreds of sales reps, run multiple sales organisations, and watched the same eighty-twenty pattern play out inside every business he has been part of. That firsthand experience is the reason Zig exists - a product designed to give top sellers their own team of virtual employees, cut ramp time, prevent burnout and finally bring the same productivity revolution to sales that AI has brought to engineering.In this episode, recorded live at HumanX 2026 in San Francisco, Steve argues that the real moat in the AI era is domain expertise, that salespeople do not want organisation - they want controlled chaos, and that any tool built on assumed frameworks is building for the wrong user.What you'll take away from this conversation: The "jumping off a cliff and building a plane on the way down" mental model that separates real founders from pretendersThe strategic optimist vs pessimist framework - and why every great founder defaults to option DWhy eighty percent of revenue comes from twenty percent of sellers - and why no tool has ever actually been built for themWhy sales reps hate organisation and love controlled chaos - and why most CRMs are built for the wrong userThe Rick Rubin lesson - "I'm confident in my taste and my ability to know what I like" - applied to product buildingThe "does it feel like magic and is it stupidly simple" test that governs every Zig releaseWhy "how did that feel" is a better feedback question than "what do you think"The love it / don't hate it / not a fan / hate it internal feedback loop that keeps product velocity highWhy indifference - not hate - is the worst signal in product analyticsHow to prevent the six-to-nine-month sales ramp disaster by making performance predictable from week oneWhy AI will make the human side of sales more valuable, not lessWhat Steve Jobs and Chris Voss have in common, and why that combination is the future of AI product designAbout Steve Ancheta:Steve Ancheta is the founder and CEO of Zig, an AI platform that equips sales reps with a team of virtual employees so they can sell at top-performer level from day one. Before Zig, Steve spent more than fifteen years leading sales and revenue organisations in enterprise technology, where he hired and managed hundreds of sales professionals. He is a lifelong student of product design, entrepreneurship and the art of building technology around how humans actually behave - with influences drawn from Steve Jobs, Rick Rubin and Chris Voss.GAEA Talks is the enterprise AI podcast for leaders navigating the age of artificial intelligence. Subscribe for weekly conversations with the people shaping the future of business, technology and society.Accelerate your company’s AI transformation, head to https://larridin.com/gaea and book a demo now. Thanks to Larridin for sponsoring today’s video!Steve Ancheta on LinkedIn:   / steveancheta  Zig: https://www.getzig.comHumanX: https://www.humanx.coGAEA AI: https://gaealgm.ai #AI #ArtificialIntelligence #GAEATalks #GAEATalksLive #HumanX #HumanX2026 #EnterpriseAI #SalesTech #AIForSales #Zig #RevenueEnablement #SalesEnablement #Founders #AIPodcast #TechPodcast #GAEAAI

    46분
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GAEA TALKS explores the transformative power of artificial intelligence. Featuring leading AI experts, industry leaders, professors, data scientists, policymakers, technologists, futurists, ethicists, and pioneers, the podcast dives into the latest AI trends, opportunities, and risks, examining AI’s evolving role in business and society. As AI continues to reshape industries and redefine possibilities, GAEA TALKS delivers deep insights into the challenges and breakthroughs shaping the future. Each episode features candid discussions with thought leaders at the forefront of AI innovation, cove

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    GAEA Talks
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    2025년 - 2026년
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