GAEA Talks

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 discovery
Microgravity as the next great forcing function in biology
Space data centres, cosmic ray bit flips and the radiative cooling debate
Sovereign AI through the lens of a country, a company and an individual
The UK's innovation track record from Stephenson's Rocket to DeepMind
The AGI fear moment and the AI autonomous kill chains already coming out of Ukraine
Dr Toby Call on LinkedIn: https://www.linkedin.com/in/tobycallMass Balance: https://www.massbalance.bioGAEA AI: https://gaealgm.ai

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

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"

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

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.
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