GAEA Talks

This week on GAEA Talks, Graeme Scott sits down with Lesley Li - TEDx speaker, three-time Innovate Finance Women in FinTech Powerlist honouree, and CEO of the wealthtech company U Impact.
Filmed live in the new London studio, this is one of the most human conversations we have recorded on the show. Lesley grew up in the Drung ethnic minority of southern China, arrived in the UK with almost no English, took her first job as a cleaning girl and went on to earn two Master's degrees from the University of Cambridge and spend more than a decade at J.P. Morgan, Barclays and Mizuho. A sudden collapsed lung in 2024 became the turning point that led her to leave banking and dedicate her work to reigniting the human spark inside a rapidly automating world.
In this episode she lays out her Temple of Wisdom framework, explains why productivity will inevitably plateau while innovation is the true differentiator, and argues that friction is a feature not a bug in the age of AI.
Topics covered:
The Temple of Wisdom - experience, deep thinking, consciousness and the spark
Why most professionals are still on rubble rather than bricks
The Feel, Think, Act loop for humans in the AI age
Slow reading for faster thinking - Lesley's Intelligent Investor experiment
The neuroplasticity cost of outsourcing thought to LLMs
The smart contrarian framework and the mediocrity trap
The UK's engineering brain drain into financial services
Reciprocal AI - "make AI your bitch, not be the bitch of AI"

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 country
The recent US restriction on a major foundation model and what it taught the industry
Why the token economy is costing the UK more than people realise
The Apple, Google and AMD hardware play that nobody is talking about
The three stakeholders shaping the AI narrative and what each one is incentivised to obscure
The cognitive decline research starting to emerge from heavy LLM use
The three questions every leader should be able to answer about their AI architecture
The worst case and best case scenarios for the average person in the next three years
Why the UK's innovation DNA from Stephenson's Rocket to Alan Turing makes this the country's moment

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"