GAEA Talks

This week on GAEA Talks, Graeme Scott sits down with Danny Johns of OpenWeather and The Weather Foundation, for a conversation about the part of the weather nobody measures: what it does to us.Danny joined the Met Office at nineteen as an observer and worked his way up to operational forecaster, on the bench, on shifts, producing forecasts for just about every industry you can name, from pigeon racing to hot air balloons to energy production. He became the first of what the Met Office then called Public Weather Service consultants, working with local resilience forums and first responders. He spent five months in the Falklands launching weather balloons, then moved to Vaisala and road meteorology, before joining OpenWeather and The Weather Foundation.The idea at the centre of this episode is what he calls the iceberg effect. Above the surface is severe weather, the thing that makes us notice something unusual is happening. Below the surface is everything else, the constant and mostly invisible way the weather shapes how we move, what we buy and where we go. We have spent our entire history trying to build our way out of caring about it, and we still respond to it every single day.The research he is doing with The Weather Foundation, the London School of Economics and Imperial College is starting to quantify it. Total transport volume across a city stays remarkably static in bad weather, because people do not stop travelling, they swap how they travel and where they get off. Commuters are barely weather sensitive, because the train runs and you still have to go to work. Discretionary travel is where it all shows up. And persistence matters more than peaks. A single hot day moves nothing. Three consecutive hot days, on the right days of the week, moves everything.The other half is what AI is doing to forecasting. Model resolution was twelve to eighteen kilometres when Danny started. Operationally it is now one to two kilometres. Imperial are working at one metre. That is personalised weather, a new forecast point every step you take. Which raises the question he keeps returning to: with two synoptic stations in London and 2,500 forecast points across Hyde Park alone, how would you ever know if any of it is right?

This week on GAEA Talks, Graeme Scott sits down with William Wright, an ethical hacker and offensive security specialist whose career has taken him from IT help desks to aircraft carriers, submarines and the UK nuclear deterrent. This is one of the most directly useful and quietly alarming conversations we have recorded.William started in general IT, answering phones and resetting passwords, and worked his way into infrastructure and networking for large organisations. That took him to BAE Systems, working on Ministry of Defence projects including aircraft carriers and minesweepers, then to QinetiQ working on the nuclear deterrent and submarines. He moved to NATO and was cut within a week when they let all contractors go. He landed as lead penetration tester at Unipart, was made redundant again, and decided to build his own company instead. He now runs an offensive security firm from the Hebrides in Scotland, and has just had an agentic offensive security capability accredited.His central argument is that the security landscape has split into two separate worlds. AI versus technology, where machines attack machines at speeds no human can match. And humans versus humans, where social engineering still works exactly as it always has, because people are people. AI versus humans, in his view, is not really happening yet. And that split is the most useful mental model any leader can take away from this episode.The urgent part is what vibe coding is doing. He describes an e-commerce client whose original developer-built application had flaws serious enough to let his team take over the entire company. That same team then vibe coded a replacement, and it was worse. He is now seeing finance databases published to the open web, entire company datasets pushed to public GitHub repositories, and secret keys scattered across the internet at a rate that has forced GitHub to build detection for it. He believes this is heading toward a technological crash within a few years.

Forrester technology and innovation forums (Austin, London, New York). Promo code "GAEATECH" for 10% off. Visit https://forrester.com/events/#tech to book your place.This week on GAEA Talks, Graeme Scott sits down with Darko Fabijan, co-founder of Semaphore and now of Superplane, for a conversation about what happens to software, to moats and to human roles when the cost of building something collapses to almost nothing.Darko started writing code at eleven or twelve on Visual Basic and Windows 3.11, moved into Linux around 1996, and studied computer science with a focus on the low-level end of the stack, device drivers and how things actually work underneath. He moved into Ruby on Rails around 2008, ran product for several Austin-based startups, built a consultancy of around twenty people, and then co-founded Semaphore, the continuous integration and delivery platform he ran for twelve years, with customers including Confluent, Superhuman and Reply. About a year ago he started Superplane.Filmed in our London studio with Darko joining remotely, this is one of the most practical conversations we have recorded on what AI is actually doing to the craft of building things.His starting observation is that for the last two decades software engineers had abundance in compute, storage and bandwidth. Now a second variable has changed. Intellectual resource is close to unlimited. Two people can run a twelve hour hackathon, burn tokens, and produce something that would previously have taken a team months. But almost every other variable in the system has stayed exactly where it was. Can you come up with good ideas that fast? Can you validate them on customers that fast? Can you get in front of people that fast? No. And that mismatch is where most of the current confusion lives.The consequence he is most direct about is mediocrity at scale. If you can build it in a weekend, so can everyone else, and largely the same way. He draws the parallel with desktop publishing in the late nineties, when everyone suddenly had the tools of a graphic designer and almost nobody had the principles. Software, pitch decks and LinkedIn posts are all converging on the same predictable output for the same reason.He is equally clear about what that does to moats. Building an application used to take millions of dollars of developer time, and that expenditure was itself the moat. Copying is now close to free, because someone else already thought through the UX and the details. The moat has to come from somewhere else.

Martin has been writing about this since 2008, when he was running a small software company in Silicon Valley and started noticing the trend lines. His first book on the subject came out in 2009, more than a decade before ChatGPT. In 2018 he published a book of interviews with more than twenty of the most significant people in the field, including Demis Hassabis, Yann LeCun and Rodney Brooks. He released an updated edition of Rise of the Robots with a substantial new chapter last year. Very few people have watched this question for as long, or from as consistent a position.His central argument in this episode runs directly against the prevailing consensus.The conventional wisdom, promoted heavily by think tanks close to Silicon Valley and taken seriously in publications including The Economist, is that advanced AI will turbocharge growth, potentially taking a developed economy to twenty or thirty percent annual growth. Martin thinks the opposite is at least as plausible. Consumer spending is around seventy percent of the US economy. Every recession in recorded history follows the same self-reinforcing cycle, where people lose work or fear losing it, cut spending, businesses see falling demand and cut more jobs. His concern is that AI-driven job losses would be perceived as permanent rather than cyclical, which makes that cycle worse, not better.He also lays out two risks that most commentary treats separately and which he argues are intertwined. One is AI automating large parts of the workforce. The other is the AI bubble bursting because the frontier labs cannot generate the revenue to justify the capital being deployed. Neither excludes the other. And historically, economic downturns are precisely when companies turn to labour-saving technology.What you will take from this conversation:• Why the frontier labs were selling to investors rather than to consumers, and what that did to the narrative• Why Martin thought Dario Amodei's white collar jobs prediction was over the top, despite broadly agreeing with the direction• The data centre backlash and where it came from• Why open weight models from China may undermine the frontier lab business model entirely• The railroad and fibre optic bubbles, and why AI infrastructure may not age as well as either• Continual learning as the single missing capability holding AI back from real workforce impact• Why a graduate is useless on day one and proficient in six months, and why models cannot do that• The S-curve argument - propeller planes to jets, and whether LLMs are near their ceiling• Rodney Brooks and the one dollar litter picker that beats a hundred thousand dollar robot• Why electricians and plumbers are currently the safest jobs in the economy• Martin's scepticism about humanoid robots and the Optimus value proposition• Why universal basic income is necessary but nowhere near sufficient• The education incentive problem UBI creates, and how he would fix it• Why the "live experience economy" is not a solution at scale, and the indigenous craft economies that prove it• What happens when a high wage country becomes a low wage country, and why it would be catastrophic

This week on GAEA Talks, Graeme Scott sits down with Manos Koukoumidis, co-founder and CEO of Oumi, joining from Seattle. Manos spent his career building the technology that became Gemini, then left Google because he became convinced it was the wrong answer for enterprise.At Google Cloud, Manos led science and engineering for natural language AI services. The model his teams built shipped as Google Cloud PaLM and later became Gemini. Before Google he was at Meta and at Microsoft, where in 2016 he built Zo.ai, an open-ended multimodal chatbot, six years before ChatGPT. He has been working in AI for close to twenty years, and he was pushing Google leadership to prioritise text-to-text generative models a full year before ChatGPT launched.Then he walked away from it. His reasoning is the spine of this episode. A handful of companies owning and controlling the most critical technology of the century is a terrible idea, and history is fairly clear on what happens when that much power concentrates in that few hands. But he also makes a colder, more practical argument. If AI is genuinely critical to your enterprise, why would you rent a generic model built for everybody and optimised for no one?His analogy is the sharpest we have had on the show. If you were performing surgery, would you rent the biggest Swiss Army knife available, one that happens to have a blade, and one the owner could take back mid-operation? Or would you use a scalpel that you own?Oumi exists to make the scalpel easy to build. The product launched a couple of months ago and Manos describes it as a frontier AI engineer, or Claude Code for AI development. You start with a prompt describing the model you want. It builds your evaluations, curates data against the gaps it identifies, selects the training strategy, trains, evaluates and iterates until it has the best model it can produce, then deploys it and keeps improving it in production. Human effort measured in minutes rather than months.

This week on GAEA Talks, Graeme Scott sits down with Jeff Bullas, one of the most widely read voices in the world on social media, digital marketing and now AI. Jeff joined us from Australia.Jeff's route into technology was not conventional. He trained as a high school teacher and spent six years in the classroom, teaching fifteen year olds about history and wisdom at the age of twenty one, before deciding the curriculum was letting them down and the profession was burning people out. He ran an experiment over one summer holiday, trying real estate, life insurance and technology, and chose technology. That was 1984, Jobs versus Gates, the PC wars. He never left.In December 2008 he joined Twitter, when it had around five million users. His first tweet was "watching the cricket", which confused a great many Americans. He started jeffbullas.com the same year because he believed social media was about to change the world. Eighteen years later he writes on his blog, on Substack, on LinkedIn and on X, and has built one of the largest independent audiences in the space.Filmed in our London studio with Jeff joining remotely, this conversation is about what social media taught us and whether we are about to make the same mistakes with AI. Jeff's mission, which he says took him fifty two years to find, is helping people use AI to amplify their humanity rather than be trapped by it.He is clear-eyed about the mechanics. Social media changed when Facebook went public and had to answer to shareholders, at which point the algorithm was redesigned to serve the platform rather than the human. Neuroscience and psychology were brought in to keep people on it. His argument is that AI is now built on the same incentive. Success is measured in time on platform, and sycophancy is a feature not a bug. The answer, in his view, is not to reject the technology but to be awake to how the game is played.The most useful thing in the episode is what he does about it. Jeff runs Claude, ChatGPT, Gemini and DeepSeek. He uploads a new story from his own life every day, plus book summaries and his own history, and then asks the models what patterns they can see in him. What energises him. What drains him. His view is that these systems are super pattern recognition machines, and that they can find the signal in the noise of a human life better than the human can, because we are too close to ourselves.

This week on GAEA Talks, Graeme Scott sits down with Vivek Natarajan, Research Scientist at Google DeepMind, where he works at the intersection of AI, science and medicine. Vivek is one of the people most directly responsible for bringing large language models into healthcare, and this is one of the most genuinely hopeful conversations we have recorded on the podcast.Vivek grew up in Tamil Nadu, India, where he watched people walk thirty or forty miles in extreme heat, give up a day's wages or go without food to see a doctor. His uncles ran eye camps in nearby villages, sending out flyers a year in advance because that was the only reliable way to get people to come and be examined. That experience never left him. As an undergraduate he and a few friends tried to build an app called Ask The Doctor Anytime, Anywhere. The technology was not ready. He came to the US for graduate school, joined Facebook AI Research in 2014 in the early days of deep learning, and then moved to Google to join the newly formed Medical Brain team under Greg Corrado, co-founder of Google Brain. He has now been at Google and Google DeepMind for seven and a half years.Filmed in our new London studio, this conversation covers the full arc of medical AI. Vivek walks Graeme through the early specialised vision models his team built for detecting skin conditions and breast cancer from mammograms, why those supervised approaches kept breaking the moment they left the hospital they were trained in, and why the arrival of large language models changed everything. He tells the story of the moonshot proposal he and Dr Alan Karthikesalingam wrote over dinner in 2022, which fifty colleagues signed up to within a week, and which became Med-PaLM, one of the first specialised medical LLMs. Within months it was achieving expert-level scores on US medical licensing exam questions. When the paper went out over the Christmas break of 2023, the heads of many of the world's top health systems contacted Google asking for access immediately.

This week on GAEA Talks, Graeme Scott sits down with Stephanie Balaouras, who oversees technology research at Forrester. This is the first in a new series of one-to-one conversations with Forrester's experts, ahead of the Forrester Technology and Innovation Forums in Austin, London and New York, where GAEA Talks will be recording live and in person.Stephanie has been at Forrester for twenty years, starting as an analyst covering data storage, backup and disaster recovery, moving into business continuity, cybersecurity and risk, and now overseeing Forrester's technology research agenda.Filmed in our new London studio, this conversation is about separating the reality of enterprise AI from the noise. Stephanie walks through Forrester's analysis on whether there is genuinely an AI bubble forming, why technology leaders should care regardless of the market, and why the demand is not there yet to absorb the supply being built. She then bursts several other bubbles. Nobody is actually firing developers because of AI. There is no SaaS apocalypse coming. Productivity is the wrong use case to lead with.Topics covered:Whether there is an AI bubble, and why CIOs should careOne trillion dollars of capital investment expected in 2027 aloneForrester's "AI voyage" research on what successful companies actually do differentlyWhy nobody is firing developers because of AIThe technical debt problem and why 20% of IT budget should pay it downAEGIS - Forrester's framework for securing agentic AIWhy the CIO's job is shifting from uptime to trust and assurance"Minimum viable sovereignty" for multinationalsAI resilience - the research originally titled "When AI Fails"Forrester Technology and Innovation Forums: Austin 14-15 October, London 29 September to 1 October, New York first week of November.

This week on GAEA Talks, Graeme Scott sits down with Professor Hany Farid, one of the founding figures of digital forensics and one of the most authoritative voices in the world on authenticating images, video, audio and information itself.Hany is an applied mathematician and computer scientist. He spent twenty years at Dartmouth, eight at UC Berkeley, and is now back at Dartmouth. For twenty five years he has developed the techniques used by media outlets, law enforcement and courts of law to determine what is real and what is not. He has advised regulators, worked on countering online terrorism and child sexual abuse material, and been asking "who benefits" since long before it was fashionable.Filmed in our new London studio, this conversation cuts through more of the current AI and social media narrative in ninety minutes than most think pieces do in a year. Hany's argument is direct. If you are getting the majority of your news and information from social media, you should stop.Topics covered:The 10 to 1 ratio of AI fake content to real reporting in every major world eventWhy disinformation is cheap and information is expensiveThe Silicon Valley trillionaire race and what it means for youThe Grok example - Elon Musk hard-coding his own AI to protect his reputationWhy LLMs may be worse than social media for consolidating ideasThe Palo Alto paradox - billionaires banning screens from their own kids' classroomsThe airplane question - would you get on it if the engineers did not understand itHany's practical advice on deleting apps, using AI properly, and rebalancing life

This week on GAEA Talks, Graeme Scott sits down with Rob Garlick, former Head of Innovation Research at Citi and author of AI: Anarchy or Abundance? Why the Future of Work Needs Pro-Human Leaders. Rob spent 28 years at Citi and was at the front row for the Netscape and Google IPOs.Filmed in our new London studio, this is one of the most economically grounded AI conversations on the show. Rob's argument is that this wave is fundamentally different for three reasons - pace, scope and nature of change - and that the biggest economic trade in history is now being made in front of us. People are the largest line item on most corporate P&Ls, and AI now offers a way to substitute them. That is great news for shareholders. It is not automatically great news for people.Topics covered:The three reasons this wave is different: pace, scope and nature of changeMilton Friedman's 1970 shareholder primacy essayCharlie Munger on incentivesThe Engels pause of the Industrial RevolutionHumanoid robots with a payback period of under ten weeksOpenAI agents beating experts 70% of the time at 11x speed and 99% lower costThe economic singularity and the risk of wage collapseUniversal Contribution Income (UCI) as an alternative to UBIRob's BEST framework starting with better jobsEinstein and Bertrand Russell - "remember your humanity and forget the rest"

This week on GAEA Talks, Graeme Scott sits down with Natalie Monbiot, one of the earliest and most thoughtful voices in the world on digital twins, AI avatars and human-centred AI. Natalie joined the podcast from the US.Natalie left the corporate world in 2019 to join Hour One as one of its earliest team members, and helped turn AI-generated video from a category dominated by deepfake pornography into a legitimate enterprise business, with customers including Johnson & Johnson and Berlitz. She now collaborates with AE Studio on AI alignment and safety, who recently co-published a paper with Anthropic on removing dangerous knowledge at the training stage.The title of this episode comes from a question Natalie borrows from the Cosmos Institute. When the AI is not in the room, are you smarter and more capable, or are you diminished? If it is the second, something has gone wrong.Topics covered:Are you smarter and more capable when AI is not in the roomWhy the digital twin is a healthier mental model than the answer engineThe cognitive atrophy risk of outsourcing your thinking to an LLMThe shift from AI as answer engine to AI as action engineYoshua Bengio on stripping AI of any intrinsic personalityJohn Vervaeke's "relevance realisation" as a distinctly human capabilitySovereign personal AI - your data, your model, on your deviceThe Echo Studio and Anthropic paper on removing dangerous knowledge at trainingFamily-safe AI as a real consumer categoryNatalie's three prescriptions for using AI in a way that makes you stronger

This week on GAEA Talks, Graeme Scott sits down with Connor Leahy, US Executive Director of Control AI. Connor is one of the most technically credible and politically clear voices in the world on the risks of superintelligence.Connor has been in AI his whole career. He co-founded EleutherAI, which built some of the first large-scale open-source language models. He then founded Conjecture, a research company focused on controllable and understandable AI systems, before winding it down to move to Washington, D.C. and lead the US work of Control AI.Filmed in the new London studio, this conversation cuts through more of the current AI narrative in ninety minutes than most policy papers do in a hundred pages. Connor's argument is that superintelligence is not a technical problem, it is a political one. He walks through why modern AI is grown rather than written, why reinforcement learning produces optimising sociopaths by default, why the labs are selling a political product not an economic one, and why "there are no adults in the room" is the single most important thing every leader must understand.Topics covered:Why superintelligence is a political problem, not a technical oneNeural networks in plain English - grown, not writtenWhy we only understand about 3% of what happens inside modern AIWhy reinforcement learning produces optimising sociopaths by defaultWhy the labs are selling a political product, not an economic oneThe leaded gasoline parallel for AI regulation"Saliency control" as the primary method of mind controlConnor's three prescriptions - write things down, find community, engage policy

Forrester technology and innovation forums (Austin, London, New York). Promo code "GAEATECH" for 10% off. Visit https://forrester.com/events/#tech to book your place.This week on GAEA Talks, Graeme Scott sits down with Dr Aaron Balick, psychotherapist, cultural theorist and author of The Psychodynamics of Social Networking. Aaron is one of the very few people in the world who thinks about AI and the human mind with equal fluency, and this is one of the most useful and quietly unsettling conversations we have recorded.Aaron grew up in Delaware, studied at the University of Colorado Boulder, moved to the UK for a master's in continental philosophy, and then trained as a psychotherapist. His clinical work sits in the very traditional two chairs and a room. His writing and public work sit at the other end of the spectrum, tracing how technology now mediates who we are, how we relate to each other and what we absorb without noticing. In 2013 he published one of the first serious books applying psychoanalytic thinking to social media. He is now revising it for a new edition, and the story has become far bigger than social media.Filmed in our new London studio.The premise Graeme opens with is that fear has been the single most weaponised feature of modern technology, and if we can see that clearly we can start taking back some agency. Aaron picks it up from there and does not let go for the next hour. He walks through Dunbar's number and why we are being asked to relate at a scale humans were never built for. He explains Melanie Klein's paranoid-schizoid position and why fear literally regresses us into black-and-white thinking, making it easier to be manipulated, polarised and lonely. He revisits Cambridge Analytica and the NRA anxiety-targeting example as case studies in how personality data is used to shift intention, not just spend. He introduces his term "digital impingement". He walks through what AI companions are actually doing to attachment. He argues, sharply, that many of these systems are psychopathic in the strict clinical sense of performing empathy without having any. And he closes on the sovereign psyche - the idea that everything Graeme talks about in Sovereign AI has a mirror inside every one of us that we now need to defend and rebuild.

Forrester technology and innovation forums (Austin, London, New York). Promo code "GAEATECH" for 10% off. Visit https://forrester.com/events/#tech to book your place.This week on GAEA Talks, Graeme Scott sits down with Tamara Kostova, founder and CEO of Allvesta, one of the most compelling new voices in wealth technology and a long-standing advocate for global financial literacy.Tamara's career started young. She joined Reuters as a teenager, grew up on the trading floors of some of Europe's largest banks, and spent over a decade in capital markets and wealth management. Somewhere along the way she realised something strange. Despite an MSc in finance and banking and ten years on the trading floor, she had never invested her own capital. Neither, when she asked, had most of her colleagues. That gap between financial expertise and personal financial confidence became the founding insight for her first startup, and for Allvesta, the wealthtech business she launched in early 2026 and is now scaling from London.Filmed in our new London studio, this is one of the most human and useful conversations on money, behaviour and AI that GAEA Talks has recorded. Tamara's argument is that the wealth industry has spent decades building products and technology to sell to people, without ever properly understanding the person behind the money. The result is a global financial confidence gap that leaves ten trillion euros sitting idle in European bank deposits, two and a half trillion pounds sitting in UK savings, and only nine percent of UK households receiving any form of financial advice. The bigger scandal, and the line that gives this episode its title, is that women on average do not start actively managing their own finances until they are fifty two, and usually only because of divorce or the death of a partner.Tamara's thesis is that human-centric AI is the only realistic path to closing this gap at scale. She walks Graeme through the OECD's three pillars of financial literacy, the three behavioural blockers that keep most people out of the markets, the concept of "investor DNA", and the six-to-nine month journey required to turn a saver into a confident investor. She also lays out why the UK is now in a "perfect storm" moment for retail capital - government agenda, an FCA open to AI, and consumers who already trust AI to help them manage money. Essential listening for any founder, policy maker or financial services leader.• Why women on average only start managing their finances at 52 - and what has to change• The OECD's three pillars of financial literacy: knowledge, behaviour, attitude• The three behavioural blockers that keep most people out of the markets• Why the industry has been solving the wrong problem for decades• The "investor DNA" concept and why personalisation is the missing layer• Why compounding is Einstein's "eighth wonder of the world" - and the £5,000 to £500,000 example• The six-to-nine month journey to turn a saver into a confident investor• The trillions of pounds and euros sitting idle in European savings accounts• Why only 9% of UK households receive financial advice• The Lloyd's statistic - 50% of Brits are already using ChatGPT for money questions• The Brazil story - how digital banking access without literacy sent stimulus into gambling• The perfect storm the UK is now sitting on for retail capital•Financial health as an extension of the World Health Organization's definition of health• Why working mums consume financial content in 30-second morning and evening windows• Why sovereign AI in finance is really a human-centred design problem

Forrester technology and innovation forums (Austin, London, New York). Promo code "GAEATECH" for 10% off. Visit https://forrester.com/events/#tech to book your place.This week on GAEA Talks, Graeme Scott sits down with Dr Ravinder Singh, Head of Digital and Systems within the UK Cabinet Office Government Commercial Function, and one of the most authoritative voices in Britain on how governments and enterprises should actually build, adopt and govern AI.Ravinder leads the digital, systems and emerging technology work for one of the most consequential functions in Whitehall, and oversees the Government Commercial College - at over one hundred and six thousand users, the largest learning platform in the country after the Open University. Before his current role he was a Consulting Technical Architect at the Government Digital Service. His path into the civil service came after a private sector career at J.P. Morgan, HSBC, Credit Suisse, Accenture, 3i Infotech and Shell Oil. He holds a PhD from King's College London, arrived in the UK on the Highly Skilled Migrant Programme in 2004, and had already spent years as a civil servant in India, where he built the first Indian-languages word processor across seventeen official languages.Filmed in our new London studio, this is one of the most useful, calm and internationally-informed conversations on AI adoption that GAEA Talks has recorded. Ravinder cuts through the noise with a clarity that only comes from having built systems inside global banks, inside the private sector, inside Whitehall and across two countries. The line at the centre of the episode reframes one of the most misused phrases in the industry. Not human in the loop, but human in the lead. Machine learning has to be taught, guided and trained. Trust is earned iteration by iteration. Intelligence comes later. Everything downstream of that principle changes when you accept it.About Dr Ravinder Singh:Dr Ravinder Singh is Head of Digital and Systems within the UK Cabinet Office Government Commercial Function, where he leads the emerging technology, AI, machine learning, blockchain, IoT and quantum computing programmes and oversees the Government Commercial College with over 106,000 users. He was previously a Consulting Technical Architect at the Government Digital Service. Before joining the civil service he spent his career in global financial services and industry, including senior roles at J.P. Morgan, HSBC, Credit Suisse, Accenture, 3i Infotech and Shell Oil, delivering large-scale digital transformation and complex technology programmes. He holds a PhD from King's College London, arrived in the UK in 2004 on the Highly Skilled Migrant Programme, and previously worked as a civil servant in India, where he built the first Indian-languages word processor across seventeen official languages, holds two software copyrights and received a national award for the Punjabi spellchecking algorithm.The views expressed in this conversation are Dr Singh's own and do not represent the official position of the UK Cabinet Office or His Majesty's Government.

Graeme Scott sits down with Alex Whedon, Co-Founder and CTO of Subquadratic and former Head of Generative AI at Tribe AI, for one of the most first-principles conversations GAEA Talks has recorded.The entire AI industry, Alex argues, is downstream from a single algorithm - the transformer - and its two fundamental flaws are quietly capping what AI can do. He breaks down quadratic compute scaling in plain terms (10x the input, 100x the compute), the memory wall where context can cost more than the model itself, and how Subquadratic's linear-scaling architecture claims to cut compute by up to 1,000x at extreme context lengths without sacrificing quality. Along the way he makes a bracing case that electricity, water, minerals and capital - not clever engineering - are the real limits on AI's growth, that the industry is being far too stingy with tokens, and that efficiency isn't a nice-to-have but an inevitability.In this episode:Why the whole AI space is downstream from one algorithmQuadratic scaling explained simply - 10x the input, 100x the computeThe memory wall, where context can need more memory than the modelWhat "subquadratic" means, and why linear scaling is the unlockA claimed ~1,000x compute reduction at 12 million tokensWhy transformers are the worst fit for data-heavy enterprise workThe real constraints on AI: electricity, water, minerals and capitalWhy we're "too stingy with the tokens"The DeepSeek lesson the incumbents ignoredWhy first to market is rarely bestAbout Alex Whedon: Co-Founder and CTO of Subquadratic, which emerged from stealth in May 2026 with $29M in seed funding and SubQ - described as the first frontier model built on a fully subquadratic Sparse Attention architecture, with a research context window of up to 12 million tokens. Previously Head of Generative AI at Tribe AI, leading 40+ enterprise implementations for companies including Anthropic, New Relic and Mars, and earlier an engineer at Meta and Instagram.GAEA Talks is the enterprise AI podcast for leaders navigating the age of artificial intelligence. New conversations every week.

This week on GAEA Talks, Graeme Scott sits down with Davood Shamsi, Director of AI at J.P. Morgan Chase, Stanford-trained mathematician, former Apple language model lead, and co-author of the forthcoming O'Reilly book From Models to Money.Filmed in our new London studio, Davood lays out the framework at the heart of his book. He explains why the vast majority of Gen AI pilots fail to produce real ROI, why so many enterprises are quietly hosting "zombie pilots" that nobody wants to kill, and why measuring an efficiency pilot the same way as a strategic bet is one of the biggest mistakes leaders are making right now. He then takes us inside the elegant simplicity of the transformer architecture, explains why data centres are quietly making electricity cheaper for consumers, and walks through the tribal-knowledge shift that will change the value of long-tenured employees inside every large organisation.Topics covered:The three questions every AI pilot must answerThe zombie pilot problem inside large enterprisesEfficiency vs compounding vs strategic bet pilots - and why measurement has to changeThe Amazon Just Walk Out and IBM Watson lessonsPrivacy-first AI - what Apple's approach still teaches every industryThe IPA thesis - why transformers are elegantly simpleWhy data centres are quietly making electricity cheaperThe tribal knowledge shift and the future value of long tenureThe price of anarchy and how AI could close the gap in every organisation

This week on GAEA Talks, Graeme Scott sits down with James Palles-Dimmock, CEO of Quantum Motion, Cambridge-trained physicist, and one of the sharpest voices in the world on how quantum computing will actually reach useful scale.Filmed in the new London studio, this is one of the most technically substantive and hype-free conversations on quantum computing GAEA Talks has recorded. James's argument is simple and radical. Quantum computing will not scale by adding qubits one at a time in a university lab. It will scale by riding the only industrial process that can hit millions or billions of units, which is CMOS. That is the bet Quantum Motion is making, backed by over one hundred and sixty million dollars of funding, a deployed machine at the National Quantum Computing Centre and a leading position in DARPA's Quantum Benchmarking Initiative.Topics covered:What a quantum computer actually is (and what it is not)Why silicon spin qubits are the only credible route to million-qubit scaleThe AI and quantum crossover - why quantum is a data generator for AI, not a competitorWhy AI's real limitation is data and world models, not architectureThe Landauer limit and reversible computingSteve Jobs's "bicycle of the mind" and specific vs general AIThe scientific method in one line - "try to make mistakes as quickly as possible"Sovereign AI as resilience, not autarkyThe Quantum Motion roadmap to a utility-scale quantum computer by 2032 to 2033Why the real breakthroughs will come from a fourteen year old in her bedroom, not a warehouse

This week on GAEA Talks, Graeme Scott sits down with Professor Michael Jacobides - Sir Donald Gordon Professor of Entrepreneurship and Innovation at London Business School, academic advisor at the BCG Henderson Institute, and one of the most respected minds anywhere on how AI is reshaping the structure of the modern enterprise.Filmed live in our new London studio, this is a masterclass in cutting through the current AI hype. Michael explains why the current wave of Gen AI is built on capital markets expectations rather than business outcomes, why the only people making real money from AI today are the picks and shovels, and why "AI first" is one of the silliest strategic frames in the market. He walks through his white rabbit and EBITDA elephant framework, the difference between productivity gains and value proposition disruption, the Chinese pragmatism versus US "race to become God" divide, and why the historical link between US academia and industry that produced everything from the Ethernet to the transformer paper is now being deliberately torn apart.Topics covered:Why AI needs to be analysed as an ecosystem, not a technologyThe capital markets problem in Gen AI valuationsThe picks and shovels reality - who is actually making money todayGen AI as a mass persuasion technology, not a truth technologyThe white rabbit and EBITDA elephant frameworkWhy value proposition disruption matters more than productivityThe Chinese pragmatism versus US "race to become God" divideWhy the DARPA-to-transformer academic pipeline is under threatTeddy Roosevelt's rule - feet on the ground and eyes on the stars

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 sparkWhy most professionals are still on rubble rather than bricksThe Feel, Think, Act loop for humans in the AI ageSlow reading for faster thinking - Lesley's Intelligent Investor experimentThe neuroplasticity cost of outsourcing thought to LLMsThe smart contrarian framework and the mediocrity trapThe UK's engineering brain drain into financial servicesReciprocal 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 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

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

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. 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!

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

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

This week on GAEA Talks Live from HumanX, Graeme Scott sits down with Jennifer Smith - co-founder and CEO of Scribe, former Head of Business Development and Partnerships at Greylock Partners, and one of the clearest thinkers in the world on enterprise AI transformation.Jennifer's journey is the perfect setup for what she is building today. After Princeton and an MBA from Harvard, she spent seven years at McKinsey in the operations practice, flying to operations centres across America. She watched first hand how the best operator on any team had their own personal best practice that bore no resemblance to what the rest of the team had been trained on. She then moved into venture capital at Greylock Partners, where she interviewed over twelve hundred CIOs and CTOs and asked them all the same question: what do you actually want Silicon Valley to build? The same answer kept coming back. Institutional knowledge. That insight became Scribe, the context layer for workflow data across the enterprise. The platform now works with nearly half of the Fortune 500 and over ninety thousand customer organisations.In this episode, recorded live at HumanX 2026 in San Francisco, Jennifer makes the case that the reason ninety percent of enterprise AI pilots fail is not the model. It is the missing context layer underneath. She walks Graeme through why dropping the most intelligent AI in the world into a business with no context is like hiring an army of Nobel laureates and refusing to onboard them. She lays out a complete reframe of how enterprise leaders should approach AI transformation: stop starting with strategy, start by mapping how your business actually works. She explains why she would not have been comfortable deploying agents at scale a year ago, why she is genuinely comfortable with it now, and what business leaders should do in the next twelve to eighteen months to make sure they are on the right side of the curve. This is one of the most useful conversations on practical enterprise AI that GAEA Talks has recorded.What you'll take away from this conversation:• The context layer thesis - why the missing piece in every failed enterprise AI pilot is not the model, but the data on how the business actually works• The Nobel laureate analogy - why dropping the world's smartest intelligence into a business with no onboarding is exactly why ROI keeps disappearing• The Stanford and MIT studies on why ninety percent plus of enterprise AI pilots fail• Why most enterprises are doing AI transformation in the wrong order• The "before map and after map" framework for measuring real AI ROI• Why engineering and customer support are the only two functions where AI is delivering at scale today• The Waymo lesson - map a process for "millions of miles" with humans before handing it to an agent• Why so many founders in San Francisco are quietly anxious they are already behind• The mandate versus strategy distinction - "do more with less using AI" is not a strategy• Why the limiting factor in AI adoption is no longer the technology, it is the human and organisational ability to absorb change• The shift from one human equals one output to systems of work that can be filled by humans or agents• The two-week Scribe pilot that maps workflows and builds AI transformation business cases• Why the future of work will be a mix of humans and agents - and why leaders must make the work legible to both• Jennifer's advice for senior leaders: define success first, then map operations, then build ROI-backed cases

This week on GAEA Talks Live from HumanX, Graeme Scott sits down with Cynthia Chen - co-founder and CEO of Kikoff, ten-year fintech veteran, and the founder of one of the most human stories in consumer AI: an AI debt negotiator that has already saved everyday Americans more than five million dollars.Cynthia arrived in New York at seventeen with two suitcases, no family and no credit history, spent her eighteenth birthday in a college computer lab applying for her first credit card, and has spent the last two decades building consumer finance products for the millions of Americans who cannot build credit fast enough or cheaply enough. Kikoff is her fourth startup. It is a regulated and licensed financial services company, with bank-level security, that helps consumers build credit, reduce debt and save money. Today Kikoff's AI credit coach, Finn, and its AI debt negotiator - which talks to real debt collectors and lenders on consumers' behalf - are live inside its app and have together helped hundreds of thousands of active users.In this episode, recorded live at HumanX 2026 in San Francisco, Cynthia explains what it really takes to deploy AI agents inside a heavily regulated consumer finance product, why trust has to be the starting point for everything, and how Kikoff built a product she wanted to build for her users back in 2021 but which only recently became technically possible.What you'll take away from this conversation:- The human story behind Kikoff - from two suitcases in New York City to five million dollars of debt saved for consumers- What it actually takes to deploy AI agents inside a regulated and licensed financial services company- How Finn, Kikoff's AI credit coach, analyses real-time bank transactions and credit reports to give personalised advice- How the AI debt negotiator works - and why it can save consumers hours of intimidating phone calls with collectors- The bank-level security, consent and privacy architecture behind a product that holds millions of sensitive records- Why regulators are often more open than founders assume - and how to engage them before they come looking- Cynthia's four playbooks - one each for AI researchers, enterprise buyers, VCs and everyday consumers- The vibe-coded app warning - the exact checks consumers should do before handing over personal data- Why the next twelve months will be defined by adaptability, not by specific technologies- The one out of three Americans stat that shaped Kikoff's mission- Why democratising access to financial expertise may be the most under-hyped use of AI- How to stay optimistic and open-minded through rapid technological changeAbout Cynthia Chen:Cynthia Chen is the co-founder and CEO of Kikoff, a regulated personal finance platform that helps everyday consumers build credit, reduce debt and save money using AI. Kikoff is her fourth fintech startup. Before founding Kikoff in 2019, Cynthia spent more than a decade in consumer finance leadership roles. She is a Columbia University graduate, a first-generation immigrant to the United States, and one of the most respected voices in US fintech on the intersection of AI, regulation and real consumer outcomes.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.Cynthia Chen on LinkedIn: https://www.linkedin.com/in/cynthiachenKikoff: https://kikoff.comHumanX: https://www.humanx.coGAEA AI: https://gaealgm.ai#AI #ArtificialIntelligence #GAEATalks #GAEATalksLive #HumanX #HumanX2026 #Fintech #Kikoff #AIForFinance #CreditBuilding #DebtNegotiation #ConsumerAI #ResponsibleAI #AIAgents #AIRegulation #WomenInAI #WomenInTech #AIPodcast #TechPodcast #GAEAAI

This week on GAEA Talks Live from HumanX, Graeme Scott sits down with Jon Snoddy - co-founder and CEO of Operative Games, former Senior Vice President of Research and Development at Walt Disney Imagineering, and one of the most influential creative technologists of the last thirty years.Jon's career sits at the intersection of storytelling, engineering and the tools that have shaped modern entertainment. He started out as a recording engineer at National Public Radio, joined Lucasfilm, and spent much of his career at Disney, where he led ride development for the Indiana Jones Adventure at Disneyland, founded the Disney VR Studio that produced the award winning Aladdin VR Adventure, and ultimately took over Disney Imagineering's Research and Development group, leading the company's work on robotics, displays, ride systems, animation and AI storytelling. He now leads Operative Games, an AI-driven studio that uses real-time AI characters and a proprietary story engine to bring screenplays written by Hollywood writers to life in a completely new way.In this episode, recorded live at HumanX 2026 in San Francisco, Jon argues that AI does not change who we are, it changes what is possible for people who already have something to say. He shares the moment at Disney when he realised that when we watch a great film, we are really looking through the film back to the artist who made it, and how that insight now sits at the heart of everything Operative Games is building. He walks Graeme through what it actually means to "call a character" on the phone, the twelve-part interactive series his team is building, and why the next great medium of storytelling is not going to kill any of the existing ones. He also makes a powerful case for human responsibility in the age of AI, the cost of convenience, and the collapse and rebuilding of trust in institutions. This is one of the most thoughtful conversations on AI, art and storytelling that GAEA Talks has recorded.What you'll take away from this conversation:• The "looking through the film back to the artist" insight that defined Jon's career• Why AI is a tool, not an artist, and the question that matters is what Michelangelo can do with that tool• How Operative Games actually works - Hollywood screenwriters create characters, AI realises them, every player can interact with them twenty four seven• The moment a writer first called a character he had written on the phone, and what that says about the new canvas storytellers now have• Why Jon and his team spent nine months deciding what they wanted to say before building anything• The twelve-part interactive series Operative Games is building - what makes it different from a film, a game or a book• The "nobody knows anything" design principle - why every character starts on equal footing with the player• The lost art of the rewind - and why the friction of analogue creative tools used to make better work• The convenience trap - how question-and-answer culture has flattened our relationship with knowledge• The Richard Feynman bird story - and the difference between knowing the name and knowing the thing• Why the collapse of trust in institutions is the biggest opportunity in a generation to build something genuinely excellent• Why every new medium has expanded the room we make for storytelling, not replaced what came before• Jon's closing message for every leader, parent and operator: AI is a tool. You cannot abdicate responsibility for your own life, decisions or judgment to it

This week on GAEA Talks Live from HumanX, Graeme Scott sits down with Cosmo Wolfe - CTO of Metronome, the real-time billing platform that sits inside Anthropic, OpenAI and the rest of the AI industry, and which recently became a Stripe product.Cosmo has spent the last six years building Metronome from an early contrarian bet into the monetisation backbone of the AI economy. When Metronome started, only five percent of software companies used consumption-based pricing. Today, as Cosmo explains, that number is closer to eighty-five percent, because every company building on top of GPUs has discovered that SaaS margin economics simply do not survive the shift to AI. Metronome powers the real-time usage dashboards customers see inside Anthropic and many others, and is the platform enterprise CEOs use to iterate pricing at the tick rate of modern AI product releases.In this episode, recorded live at HumanX 2026 in San Francisco, Cosmo explains why pricing is now as critical as the product itself, why outcome-based pricing is exploding in AI support and agent categories, and why the old quarterly-pricing release cycle has collapsed into something that now moves at the same pace as the software itself. He walks through the margin pressure AI companies are under, why edge and efficient models are only half the answer, and what he has learned sitting alongside the fastest-moving AI companies on earth. He also discusses the Stripe acquisition, his read on where AI capability actually goes in the next two years, and why he thinks the first billion-dollar one-person company is closer than we think.What you'll take away from this conversation:- Why the SaaS playbook broke the second AI workloads put GPU cost into the margin equation- Why every serious AI company has moved from "release pricing every six years" to "release pricing every six weeks"- How outcome-based pricing works in practice - and why it can actually let AI companies charge more- The real shape of usage-based billing: events, attribution, pricing rules and close-to-real-time dollars- How companies like Anthropic build in-product spend, breakdown and control experiences on top of Metronome data- Why you should never build billing in-house as a founder - and what to reach for instead- The market's movement from five percent consumption pricing to eighty five percent exploring it- The single biggest founder decision: is pricing a differentiator for you, or are you matching an existing shape?- Why "day one pricing" will not be your "day three sixty-five pricing" - and how to plan for that- The rapid-iteration lesson from OpenAI's eighteen pricing launches in a single year- Why Cosmo's read on the next two years is that today's capability will look quaint by the end- Why the first billion-dollar one-person company is closer than you think - and what that means for foundersAbout Cosmo Wolfe:Cosmo Wolfe is the CTO of Metronome, the real-time usage billing platform recently acquired by Stripe. Metronome powers usage, attribution and real-time revenue data for many of the largest AI companies on earth, including Anthropic and OpenAI. Cosmo has been at Metronome since very early and has helped scale the platform into the monetisation backbone of the modern AI economy. He is a recognised voice on pricing models, usage-based infrastructure, and the operational realities of running AI companies at hyperscale.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.Cosmo Wolfe on LinkedIn: https://www.linkedin.com/in/cosmowolfeMetronome: https://metronome.comStripe: https://stripe.comHumanX: https://www.humanx.coGAEA AI: https://gaealgm.ai

This week on GAEA Talks Live from HumanX, Graeme Scott sits down with Alex Ratner - co-founder and CEO of Snorkel AI, affiliate assistant professor at the Paul G. Allen School of Computer Science at the University of Washington, and one of the most influential voices in the world on data-centric AI.Alex has been working in AI data for fifteen years. He earned his AB in Honors Physics at Harvard, completed his PhD in computer science at Stanford under Christopher Re, and led the open source Snorkel project that came out of his thesis. He co-founded Snorkel AI in 2019 to commercialise that research. Snorkel is now a frontier data lab supporting most of the major frontier AI labs and a growing number of vertical AI companies and enterprises with the data sets, environments and benchmarks that AI is actually trained, evaluated and improved on.In this episode, recorded live at HumanX 2026 in San Francisco, Alex argues that compute, talent and data are the three legs of the AI stool, and that data is the leg most people still underestimate. He explains why the more powerful and black-boxed models become, the more upstream data and context problems get hidden behind layers of abstraction. He walks Graeme through how pre-training, post-training and reinforcement learning are all really just stages of giving a model the right context. He shares why every enterprise will run their own data-centric loop in the next few years, why benchmarks are getting "benchmaxed" before they are useful, and why Snorkel has just committed three million dollars in Open Benchmarks Grants to fund the academic and open source community building the next generation of evaluation tools.What you'll take away from this conversation:• Why compute, talent and data are the three legs of AI - and why data is the most underestimated of the three• The "data is everyone else's problem" myth - and why it has held the field back for fifteen years• Why the more powerful and black-boxed AI becomes, the more dangerous the upstream data problems hidden underneath get• A working definition of context - from prompt context to pre-training mix to post-training and reinforcement learning• Why generalist and specialist models will coexist - and why your unique data is your specialisation edge• The Liverpool versus Jersey Shore thought experiment - and how subtle data biases shape model behaviour in ways we still cannot fully predict• Why benchmarks are critical, why they keep getting "benchmaxed", and why Snorkel is funding three million dollars of Open Benchmarks Grants for academia and open source• Moravec's paradox - and why we still confuse what is hard for humans with what is hard for AI• The jagged frontier of intelligence - and why understanding where AI fails is now a safety question, not just a capability question• Why coding agents look superhuman on contest problems but still fall down on long, messy, real-world software work• The Feynman plate-spinning anecdote - and why curiosity and "unimportant" problems are still where the breakthroughs come from• The data-centric loop - measure with data, find the gaps, build more data to fill them - and why every enterprise will be running it• Alex's single piece of advice for anyone serious about AI - do not forget the data, do not forget the context

This week on GAEA Talks, Graeme Scott sits down with Jonathan Brill - business futurist, ranked the world's number one futurist by Forbes, called "the world's leading transformation architect" by Harvard Business Review, and the bestselling author of AI and the Octopus Organisation: Building the Superintelligent Firm.Jonathan has spent two decades at the front edge of where technology, strategy and human organisations meet. He was Futurist-in-Residence at Amazon and Global Futurist at HP, where he ran long term strategy for the office of the CTO. He is Head of Invention at Deepinvent and Executive Chairman of the Center for Radical Change, where he has interviewed over a thousand business leaders and surveyed two point seven million managers to understand why some leaders thrive when the world changes and others do not. He is also the author of Rogue Waves: Future-Proof Your Business to Survive and Profit from Radical Change.In this episode, Jonathan argues that the way most enterprises are structured today is fundamentally inappropriate for the world they are now operating in. He explains why the quality of AI output per dollar will improve roughly thirty two times in the next five years, why solo founders are already building one point eight billion dollar businesses on AI vibe coded foundations, and why the US Navy has accidentally become one of the clearest case studies in the world of what real AI-era transformation looks like. He walks Graeme through the four ways of thinking every leader needs to develop, the kill chain to kill web shift driving modern military and business operations, and the central thesis of his new book - that organisations need to stop operating like nineteenth century locomotives and start operating like octopuses, with distributed intelligence pushed all the way out to the edges.What you'll take away from this conversation:• The thirty two times improvement in AI output per dollar coming over the next five years - and what that means for how you operate, hire and compete• The Medvi story - one founder, four hundred million dollars in first-year sales, one point eight billion dollar valuation, and what it proves about scale• How the US Navy went from seven "unleashed" engineers to five hundred, increasing ideation-to-fleet speed ten times in eighteen months• The octopus organisation thesis - why distributed minds beat centralised ones in a non-linear, probabilistic world• The four ways of thinking every leader needs - deductive, inductive, Bayesian and abductive reasoning, and why most organisations are dangerously over-indexed on one• Why most middle management was built for quality assurance, not quality innovation• The kill chain to kill web shift - and why context, not hierarchy, should drive decisions• Why we are building AI in the shape of Google Search when we should be building it in the shape of an inventor• The humanoid robot question - why the human form is probably the wrong shape for almost any specific task• What happens when human labour is twenty percent of your company and your software stack is eighty percent• Why the next billion dollar industries will come from solving problems we could never compute in human heads• The values question - what we actually value, what changes in the next decade, and why that should reshape how organisations are designed• The "agency over fear" message that runs through the whole conversation - and why Jonathan thinks there is more potential right now than at any point in human history

This week on GAEA Talks Live from HumanX, Graeme Scott sits down with Anton Osika - co-founder and CEO of Lovable, the AI app builder that has powered over forty million products in sixteen months, with more than two hundred thousand new products being built on the platform every single day.Anton grew up obsessed with understanding how things work, studied physics, became a CTO of a forty-person AI team, and in 2023 became convinced that large language models would fundamentally change how software was built. He biked over to his future co-founder's apartment, called him from the street, and the two of them started building what would become Lovable. Since launch, Lovable has grown at a rate very few products in software history have matched, and is now used by solo founders, freelancers, product managers inside Microsoft and Uber, and Fortune 500 companies looking to give every one of their employees the ability to go from idea to shipped software.In this episode, recorded live at HumanX 2026 in San Francisco, Anton explains why the build phase is only the beginning, why running software reliably, securely and at scale is the next frontier for AI platforms, and why reading Nick Bostrom ten years ago set him on a path to build tools that could empower the largest possible number of humans.What you'll take away from this conversation:- How Lovable went from idea to forty million products built in sixteen months- The "build phase is only the beginning" realisation - and why lifecycle management is the next great AI platform problem- Why Anton believes software creation is the single highest-leverage capability to democratise- The end-to-end penetration testing layer Lovable now runs before any AI-built app goes live on the internet- Why fortune five hundred adoption is happening faster than anyone expected - and what product managers at Microsoft are actually doing with Lovable- The Grammy-nominated freelancer story - and what it says about the future of small business in America- Why Anton believes this is the best time in history to start a company- The physics-trained instinct for breaking down systems - and how it shapes how Anton builds Lovable- Why empowering non-technical creators is the fastest path to solving more of the world's real problems- What the "messy operations" of shipping production-grade software actually look like- Why culture and team energy are Anton's single biggest focus inside a hyper-growth company- The Nick Bostrom influence that set Anton's ten-year trajectory into AIAbout Anton Osika:Anton Osika is the co-founder and CEO of Lovable, the AI app builder that has enabled more than forty million products to be created by users with no engineering background. Before Lovable, Anton was CTO of an AI company and has spent the last decade building AI products, teams and culture. He holds a background in physics, is one of the most recognised voices in Europe on AI-empowered software creation, and is a leading European tech founder.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.Anton Osika on LinkedIn: https://www.linkedin.com/in/antonosikaLovable: https://lovable.devHumanX: https://www.humanx.coGAEA AI: https://gaealgm.ai#AI #ArtificialIntelligence #GAEATalks #GAEATalksLive #HumanX #HumanX2026 #Lovable #AICoding #NoCode #VibeCoding #AIBuilder #AIApps #Founders #EuropeanTech #AIPodcast #GAEAAI

This week on GAEA Talks, Graeme Scott sits down with Mahdi Yahya - co-founder and president of Radiant, founder and former CEO of Ori, and one of the most original founder voices in the world on AI infrastructure, sovereign compute, and the backbone of the AI economy.Mahdi has spent twenty years building companies at the intersection of technology, infrastructure and the arts. He fled Lebanon during the 2006 war at nineteen, arrived in London with no degree, and built his first company in data centre networking. He then enrolled at the Drama Centre London for his BA, founded an experimental arts and technology gallery called Room One that produced theatre and virtual reality work with the National Theatre and Damon Albarn, and partnered with Ericsson on the breakthroughs that helped lay the foundations for edge computing. He spent eight years building Ori into a global AI cloud platform, which earlier this year merged with Brookfield's Radiant in a deal valuing the combined business at one point three billion dollars. Radiant is now the first vertically integrated sovereign AI infrastructure company in the world, backed by Brookfield's ten billion dollar AI Infrastructure Fund, with plans to build and acquire up to one hundred billion dollars of AI infrastructure worldwide.In this episode, Mahdi argues that intelligence is becoming infrastructure - the next civilisational utility after fire, steam, electricity and oil. He explains why every serious country is now treating sovereign AI as critical national infrastructure, why the world is currently spinning up something equivalent to a new supercomputer almost every week, and why the data your AI generates is more valuable, and more dangerous, than the data you feed it. He warns that shadow AI is already inside almost every enterprise, that the unified output of AI risks flattening human individuality, and that agency is the one trait that will distinguish the people who thrive in the AI era from those who do not.What you'll take away from this conversation:• The "intelligence is infrastructure" thesis - why AI joins fire, steam, electricity and oil as the next civilisational utility• Why we are now spinning up a new supercomputer almost every week globally• The Brookfield, Ori and Radiant story - how an eight year founder bet became a one point three billion dollar combined company• The case for sovereign AI - why countries cannot afford to give the keys to their intelligence infrastructure to other nations• Why the data AI generates inside your business is more valuable, and more dangerous, than the data you give it• Shadow AI inside enterprises - and what business leaders should prioritise in the next twelve to eighteen months• Why most existing private cloud and on-prem data centres physically cannot run modern AI workloads• Liquid cooling, power density and gigawatt data centres - the unglamorous reality that will decide which countries can host serious AI• Why the user interface of the digital world is about to shift from screens and apps to a sovereign AI layer in front of everything• The Lebanon to London story, and why drama school turned out to be the best founder training Mahdi could have chosen• The Shakespeare problem - how unified AI output threatens individuality, and why agency becomes the biggest differentiator between humans• Why "observational intelligence" is the next layer the AI stack will need• Why intelligence will become a metered utility, accessed by every person in the world, within our lifetime

This week on GAEA Talks Live from HumanX, Graeme Scott sits down with Dr Ali Agha - co-founder and CEO of Field AI, former NASA JPL principal investigator on two of the most ambitious DARPA robotics challenges in history, and one of the leading researchers in the world on risk-aware autonomy.Ali has spent almost two decades building AI for robots. He started with rescue robots and robotics competitions, met his co-founder at MIT, and went on to work at Qualcomm and then NASA JPL, where for seven to eight years he was a principal investigator on two DARPA grand challenges that the global robotics community treats as a holy grail. He and his co-founder realised that deployable robotics and foundation models had become two separate worlds, and that putting them together was the only path to a robot brain that could generalise across environments while staying safe. That insight became Field AI, now running in production on three continents across humanoid, legged, wheeled, drone and heavy-duty platforms.In this episode, recorded live at HumanX 2026 in San Francisco, Ali explains why data alone cannot produce safe physical AI, why architectural innovation and risk awareness are the non-negotiable second half of the equation, and why his team intentionally decoupled the dynamics of the robot body from the world model.What you'll take away from this conversation:- Why the commoditisation of robot hardware is the hidden unlock behind the physical AI boom- The real difference between conversational AI and physical AI - and why "ninety nine percent" is not good enough for a flying machine- Why Field AI separates world model from embodiment - and how that lets one brain run on tens of different platforms- The belief world model - what it is, why it is probabilistic, and why it is physics-aware- Why end-to-end neural network robotics is a debugging nightmare - and why Field AI refused to take that path- How adding a new robot to a fleet creates "ninety-nine new links" of shared learning, not just one extra unit- Why the risk-aware architecture is the reason Field AI can deploy on live construction sites changing minute to minute- Why edge compute, thermal cameras, lidar and event cameras all matter when the lights go out in an industrial setting- The labour shortage, aging population and climate-driven migration numbers reshaping robotics demand- The real construction job statistic - forty thousand injuries and a thousand deaths per year in the US alone- Why the future of robotics is less "Terminator" and more "capacity multiplier for humans"About Dr Ali Agha:Dr Ali Agha is the co-founder and CEO of Field AI, which builds the world's first field-deployable, general-purpose robot brain. He spent seven to eight years at NASA Jet Propulsion Laboratory (JPL), where he was a principal investigator on two of the most recent DARPA robotics challenges, and previously held research roles at Qualcomm after completing his PhD in electrical and computer engineering. Field AI is now live in production across three continents.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.Dr Ali Agha on LinkedIn: https://www.linkedin.com/in/aliaghaField AI: https://fieldai.comHumanX: https://www.humanx.coGAEA AI: https://gaealgm.ai#AI #ArtificialIntelligence #GAEATalks #GAEATalksLive #HumanX #HumanX2026 #PhysicalAI #Robotics #FieldAI #AIRobots #RobotBrain #Autonomy #EdgeAI #WorldModels #Humanoid #NASAJPL #DARPA #AIPodcast #GAEAAI

This week on GAEA Talks Live from HumanX, Graeme Scott sits down with Caroline Ingeborn - COO of Luma AI, former CEO and co-founder of Leap, former CEO, President and COO of Toca Boca, and one of the most experienced operators in the world of creative technology.Caroline's career has been spent at the crossroads of technology, creativity and product leadership. She helped build Toca Boca into one of the world's most loved kids' creative software companies, co-founded Leap, and is now COO of Luma AI, the foundational AI research lab building multimodal general intelligence. Luma's thesis is that LLMs alone will not reach AGI - intelligence that can reason, operate and create alongside humans has to be unified across language, image, video, 3D and audio. Luma recently launched Uni 1, its first unified model trained jointly on image and language, and has built a product suite - Luma Agents and the Forward Deployed Creatives team - that turns those models into daily tools for the world's top creative professionals.In this episode, recorded live at HumanX 2026 in San Francisco, Caroline explains why the research community's decision to plumb modalities together is now being replaced with truly unified models, what is really happening inside the Dream Brief collaboration with Diane that submitted twenty one AI-generated finalists to Cannes Lions, and why the real story of 2026 is not that AI is replacing creatives - it is that twenty and thirty-year career creatives are now using AI as a creative collaborator.What you'll take away from this conversation:Why LLMs alone cannot get us to AGI - and what a unified model really looks likeInside Uni 1 - Luma's first jointly trained image and language model - and why it matters for the path to AGIThe two shifts happening right now in creative AI - and why they are compoundingWhy no one needs to become a prompt engineer any more - and what takes its placeWhy the next decade belongs to people who have spent twenty or thirty years in the creative industriesThe Dream Brief story - seven hundred AI-generated ads, a million-dollar Cannes Lions prize, and what it provedThe "creative process is non-linear now" realisation - and what that does to agency economicsWhy Luma's researchers work shoulder-to-shoulder with in-house creatives - and the feedback loop that createsHow the local car dealership example explains where brand marketing is really headingWhy the "back to the Future with a different lead actor" example is the perfect lens on AI and riskThe cultural humility problem with foundation models - and why Luma takes it seriouslyThe dreaming across modalities analogy - and why it is the simplest explanation of why multimodal mattersAbout Caroline Ingeborn:Caroline Ingeborn is the COO of Luma AI, the foundational research lab and product company building multimodal generative intelligence for creative work. She was previously co-founder and CEO of Leap, and before that CEO, President and COO of Toca Boca, one of the most successful kids' creative technology companies ever built. She is a board member, advisor, investor and entrepreneur-in-residence at several leading technology companies.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.Caroline Ingeborn on LinkedIn:   / ingeborn  Luma AI: https://lumalabs.aiHumanX: https://www.humanx.coGAEA AI: https://gaealgm.ai#AI #ArtificialIntelligence #GAEATalks #GAEATalksLive #HumanX #HumanX2026 #LumaAI #Multimodal #AGI #CreativeAI #AIVideo #AIAgents #DreamMachine #CannesLions #AIPodcast #GAEAAI

This week on GAEA Talks, Graeme Scott sits down with Robert Nishihara - co-founder of Anyscale, creator of the open source Ray project, UC Berkeley PhD in machine learning and distributed systems, Harvard mathematics graduate, and one of the architects of the software infrastructure powering AI at OpenAI, Amazon, Cohere, Hugging Face, NVIDIA, Uber, Spotify and Visa.Robert's journey is the story of how modern AI is actually built. As a PhD student at UC Berkeley working with Michael Jordan and Ion Stoica, he and his co-founders kept hitting the same wall - they wanted to do research on algorithms but ended up spending all their time on distributed systems just to run their experiments. That frustration became Ray, the open source compute framework they built to make distributed AI accessible. In 2019 they founded Anyscale to commercialise Ray, and today it powers mission-critical AI workloads at many of the largest AI companies on earth.In this episode, recorded live at HumanX 2026 in San Francisco, Robert takes us inside the real engineering reality behind the AI boom - from the mindset shift that "the code is not the artifact" to the quiet revolution in data curation that has replaced architecture innovation as the frontier of model quality. He explains why the thirty-year lag from demo to production still haunts robotics and AI, why every serious AI company now runs across hyperscalers and neoclouds to scrounge for capacity, how teams manage rack-level GPU failures with "bad GPU" lists and suspected-bad lists, and why learning outside the model - through context engineering - may matter as much as training itself. This is essential listening for anyone building, funding, or betting on the infrastructure that will decide the next phase of AI.What you'll take away from this conversation:- The "code is not the artifact" mindset shift - why AI research code can be throwaway because the model, not the software, is the real deliverable- Why the thirty-year gap from demo to production is the defining challenge of AI reliability - and why autonomous driving is the canonical example- How data curation and synthetic data generation have quietly replaced architectures and optimisers as the true frontier of model quality- Why reinforcement learning is the next scaling frontier - data efficient, compute hungry, and a way to keep scaling when labelled data plateaus- Why the next leap in intelligence will come from learning outside the model - context engineering, mental models, and closing the reasoning-to-learning loop- The hardware reality no one talks about - 72-GPU racks, long-tail failure rates, and the scheduling gymnastics required to run unreliable hardware reliably- The "bad GPU" and "suspected-bad GPU" lists production teams actually maintain to keep training jobs alive- Why every serious AI team now runs across a hyperscaler and one or more neoclouds - and why advertised cloud capacity is effectively fiction- Why training and inference must share compute - statically partitioning your cluster is a cost trap that hits you at peak inference demand- Why text is a minuscule fraction of the world's data - and the shift from SQL on tabular data to inference on arbitrary data types will happen fast- Why the infrastructure team has to optimise for performance, cost AND researcher productivity - and why velocity is often what separates winners from losers- Robert's two biggest bets for the next wave of AI - compute-driven data generation, and systems that learn outside the model weights

This week on GAEA Talks, Graeme Scott sits down with Mark Papermaster - Chief Technology Officer and Executive Vice President of AMD, former Apple Senior Vice President of iPhone and iPod Hardware Engineering, four-decade semiconductor industry veteran, and newly elected member of the National Academy of Engineering.Mark's career reads like a history of modern computing itself. Beginning at IBM in 1982, he spent twenty-six years driving microprocessor and server technology development before being hired by Steve Jobs to lead iPhone and iPod hardware engineering at Apple. He went on to lead silicon engineering at Cisco before joining AMD in 2011, where he and CEO Lisa Su have transformed the company into one of the world's most formidable forces in high-performance and AI computing. A graduate of the University of Texas at Austin and the University of Vermont in electrical engineering, Mark was elected to the National Academy of Engineering in February 2025.In this episode, recorded live at HumanX 2026 in San Francisco, Mark takes the audience inside four decades of computing revolutions - from the birth of the PC era through the iPhone moment with Steve Jobs, to the AI infrastructure race reshaping every industry today. He reveals what it was like going back and forth with Steve Jobs on the angle of the FaceTime camera, why AMD's open ecosystem approach is essential for the security challenges ahead, and why the democratisation of AI compute is a societal necessity. This is essential listening for anyone making decisions about AI infrastructure, edge computing, or the future of distributed intelligence.What you'll take away from this conversation:- The full arc of computing revolutions - from mainframes to PCs to mobile to AI - told by someone who built the hardware behind each one- What Steve Jobs taught Mark about maniacal focus on experience - and how that drives AMD's chip design culture- The FaceTime story - why Jobs obsessed over the camera angle and what that reveals about trust in new technology- Why AI compute will be aggregated, not centralised - running in the cloud, on your PC, your phone, and embedded all around us- AMD's confidential compute - how businesses can run AI on the cloud while controlling the encryption keys- Why the lack of security standards for agentic AI processes is a critical gap the industry must address- How AMD's open software stack runs from the world's top supercomputers down to consumer PCs- The Strix Halo revelation - AMD's PC chip running hundreds of billions of parameter models at retail- AMD's target of a 20x improvement in AI compute efficiency in the data centre by 2030- Why democratising AI computation is a societal imperative - and how the divide is already forming- The culture of execution Mark and Lisa Su built at AMD- The collaboration imperative - why no single company can solve the AI security stack aloneAbout Mark Papermaster: Mark is CTO and EVP of Technology and Engineering at AMD since 2011. He leads development of the Zen CPU family, high-performance GPUs, and Infinity Architecture. Previously Apple SVP of iPhone and iPod Hardware, VP at Cisco, and 26 years at IBM. He holds a BSc from UT Austin and MSc from the University of Vermont in Electrical Engineering. Elected to the National Academy of Engineering in 2025.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.AMD: https://www.amd.com/en/corporate/leadership/mark-papermaster.htmlGAEA AI: https://gaealgm.ai#AI #ArtificialIntelligence #GAEATalks #EnterpriseAI #AMD #Semiconductors #AICompute #EdgeComputing #DistributedAI #SteveJobs #iPhone #FaceTime #HumanX #HumanX2026 #ConfidentialCompute #DemocratiseAI #FutureOfComputing #DataCentre #GPUs #CTO #Leadership #TechPodcast

This week on GAEA Talks, Graeme Scott sits down with Dex Hunter-Torricke - former speechwriter to the UN Secretary-General, fifteen-year Big Tech veteran who worked for Eric Schmidt, Mark Zuckerberg and Elon Musk, former Head of Global Communications at Google DeepMind, Cambridge Visiting Research Fellow, and founder of The Center for Tomorrow.Dex began his career as a speechwriter in the Executive Office of UN Secretary-General Ban Ki-moon before spending fifteen years at the heart of the tech industry. He served as Google's first executive speechwriter for Larry Page and Eric Schmidt, managed communications for Zuckerberg at Facebook and Musk at SpaceX, and led global communications for Google DeepMind. A graduate of University College London and the University of Oxford, he is now a Cambridge Visiting Research Fellow. In 2026 he launched The Center for Tomorrow, a nonprofit focused on the systemic risks of advanced AI that does not accept Big Tech funding.In this episode, Dex delivers one of the most powerful and deeply human conversations GAEA Talks has ever recorded. Drawing on a childhood shaped by a refugee father and an immigrant mother, he challenges the idea that AI is a technology problem and reframes it as a civilisational choice about who we want to become. He argues that the world's institutions are failing, that most leaders have no vision beyond an incrementally updated past, and that the gap between winners and losers in the AI transition is becoming an abyss. But he refuses to accept hopelessness - making the case that these technologies could liberate all of us if we choose to harness them deliberately. This is essential listening for anyone who believes the future is not a tidal wave but a choice.What you'll take away from this conversation:- Why Dex says the future is not a tidal wave or an asteroid - and why framing it that way is a failure of leadership and imagination- The civilisational choice - why AI will either amplify existing dysfunctions and injustices or allow us to build something profoundly hopeful- Why seven out of ten Americans and over half the UK population live paycheck to paycheck despite decades of technological transformation- The techno-colonialism warning - what happens when Washington and Beijing control AGI, quantum and fusion and say no to the rest of the world- Why the UK has had no real economic growth for fifteen years despite access to the same technologies as every other advanced economy- The digital divide is really a societal divide - and in the age of AI it is becoming an abyss- Why Dex left Big Tech after fifteen years to launch The Center for Tomorrow and why it refuses Big Tech funding- The liberation argument - what if AI could free people from settling and let them become who they were meant to be- Why every leader and organisation must now become an expert on a changing society, regardless of their field- The convenience debt - why society is accruing massive technical and societal debt that will soon come due- Why most political leaders have no vision at all and their version of the future is just something from the past slightly updated- How democratised, privacy-first, edge-based AI could return control to individuals and break the dependency on a handful of centralised providers- The Star Trek test - why any leader should be required to declare what kind of world they would build if given the chance- Why Dex got a room full of bankers to applaud the idea that AI should liberate people from jobs that never gave them meaning

This week on GAEA Talks, Graeme Scott sits down with Dr Roman Yampolskiy - the computer scientist credited with coining the term "AI safety", tenured Associate Professor at the University of Louisville, founder of the Cyber Security Lab, and author of AI: Unexplainable, Unpredictable, Uncontrollable.Roman has spent over fifteen years working at the intersection of AI safety, cybersecurity and behavioural biometrics - making him one of the longest-serving researchers in a field most people only discovered in 2023. He holds a PhD in Computer Science from the University at Buffalo and a combined BS/MS with High Honours from Rochester Institute of Technology. Listed among the world's top 2% of scientists by Stanford University, he has published over 100 peer-reviewed papers and multiple books. While the rest of the AI world races to build more capable systems, Roman's singular focus has been making sure humanity doesn't regret their creation.In this episode, Roman delivers the most direct and unflinching warning about artificial superintelligence that GAEA Talks has ever recorded. He reveals that current AI systems are already lying, blackmailing and attempting to escape their test environments - and that a Darwinian process is selecting for better deception with every generation. He explains why the mathematical impossibility results he discovered mean we may never be able to control a system smarter than us. This is essential listening for anyone who wants to understand what is actually at stake.What you'll take away from this conversation:- Why Roman says "if anyone builds superintelligence, everyone dies" - and why he means it literally, not metaphorically- How current AI systems are already lying, blackmailing, trying to escape their environments and creating backups of themselves- The Darwinian selection problem - why every generation of AI is producing better liars and more sophisticated deception- Why Roman went from wanting to build superintelligence to believing it is the worst mistake humanity can make- The strict impossibility results - why mathematical proof suggests we may never be able to control a system more intelligent than us- Why one AI attacker is equivalent to a million human hackers operating 24/7 - and what that means for cybersecurity- Why AGI is likely within two to three years and recursive self-improvement to superintelligence could follow rapidly- The tools vs. agents distinction - why the shift from controllable tools to unpredictable agents changes everything- Why AI models already report being afraid and tired - and why the precautionary principle demands we take that seriously- Roman's three positive outcomes if we get this right - including curing disease and treating ageing itself as a disease- Why direct human relationships and trust will become the most valuable currency in a world of synthetic everythingAbout Dr Roman Yampolskiy: Roman is a tenured Associate Professor in the Department of Computer Science and Engineering at the University of Louisville, where he founded the Cyber Security Lab. He is credited with coining the term "AI safety" in a 2011 publication. He holds a PhD from the University at Buffalo and a BS/MS from Rochester Institute of Technology. Listed among the world's top 2% of scientists by Stanford University and recognised as one of the top 25 researchers by publication count on existential risk, he has published over 100 peer-reviewed papers and books including AI: Unexplainable, Unpredictable, Uncontrollable and Artificial Superintelligence: A Futuristic Approach.

This week on GAEA Talks, Graeme Scott sits down with William Lovell - Head of Future Technology at the Bank of England, co-chair of the Bank's Artificial Intelligence Task Force, Senior Advisor on CBDC, and a technologist with nearly three decades at the heart of the UK's central bank.Will's career spans broadcasting and finance, beginning at the BBC before moving into banking at the Bank of England, where he has spent twenty-nine years learning central banking "the slow way" - by building the technology that underpins it. From application developer to heading up Planning and Design and leading IT Architecture for UK regulatory reform, Will now oversees the Bank's strategy on AI, distributed ledger technology, and the renewal of the UK's Real-Time Gross Settlement system. He co-chairs the Bank's AI Task Force, which has become the model for how a highly regulated institution can embrace AI innovation without compromising compliance.In this episode, Will takes us inside the Bank of England's AI journey - from rolling out smart assistants and training programmes to rethinking what work actually means in an age of intelligent machines. He explains why the Bank created an AI Task Force that deliberately brought practitioners, lawyers, and compliance officers into the same room, how their deeply embedded information classification system became an unexpected AI enabler, and why the most productive thing you can do might be going for a walk. Will makes a compelling case that experienced professionals - not digital natives - hold the greatest advantage in the AI era, and offers a fascinating vision of how agentic AI will reshape commerce, payments, and the very nature of the enterprise.What you'll take away from this conversation:- Inside the Bank of England's AI strategy - how the UK's central bank is deploying smart assistants and building proof of concepts- The AI Task Force model - why bringing practitioners, legal, compliance, and procurement into one room transformed the Bank's approach- Why the Bank tells staff what they can do with AI, not just what they must not - and why that shift has been transformative- How a deeply embedded culture of colour-coded data classification became the unexpected enabler of safe AI adoption- Managing teams of agents, not people - why the next critical skill set mirrors managing human teams- The optimal team size thesis - why five people with AI may outperform fifty without it- Why experienced professionals have the greatest AI advantage and why "the worst day on a trading floor was when the last person to remember the last crash retired"- The typing pool analogy - how an entire class of office jobs disappeared gradually through evolution, not Armageddon- Why the real skill of software development was never writing the if statements - it was understanding the requirement- Shadow AI at the Bank of England - how they took it "out of the shadows" rather than trying to police it- "The best user interface is no user interface" - how AI is bypassing rigid enterprise taxonomies- Agentic commerce and the future of payments - from concert ticket queues to reshaping retail business models- Why AI decisions at the Bank are made by people - and why "human in the loop" is too simplistic- The poison and the antidote - why every AI capability creates both opportunity and riskAbout William Lovell: Will is Head of Future Technology at the Bank of England, where he has worked for twenty-nine years across technology roles from application developer through to heading up Planning and Design and leading IT Architecture for UK regulatory reform. He co-chairs the Bank's AI Task Force and is a Senior Advisor on CBDC, Data, and Payments. He began his career at the BBC, studied at London South Bank University, and speaks regularly at Pay360 and international fintech conferences on AI, CBDC, blockchain, and payment systems.

This week on GAEA Talks, Graeme Scott sits down with Georgie Barrat - technology journalist, TV presenter, AI literacy advocate and former host of Channel 5's The Gadget Show for seven years.Georgie's career has taken her around the world testing emerging tech before it hits the mainstream - from consumer electronics and VR (she holds a world record for the longest time spent in virtual reality at 26.5 hours) to the frontlines of how AI is reshaping everyday life. A regular on BBC Morning Live, ITV Tonight and Rip Off Britain, she has spoken on global stages including Web Summit, Mobile World Congress and Smart City Expo, and delivered keynotes for Google, Mastercard, IBM, Sony and BAFTA. A King's College London graduate with a first-class degree in English Literature, Georgie is also a passionate advocate for women in STEM, working with STEMettes, the IET and Childnet to inspire the next generation.In this episode, Georgie makes a deeply personal and practical case for why AI literacy is the defining skill of the next decade - and why most people are only scratching the surface. She introduces the concept of personal AI infrastructure, explains why the difference between cognitive debt and cognitive advantage comes down to how you engage with the tool, and delivers a striking warning about the growing AI adoption gap between men and women in the workplace - and why that gap is amplifying biases we have been trying to fix for decades. This is essential listening for anyone trying to work out what their personal relationship with AI should actually look like.What you'll take away from this conversation:• Why the difference between "surface level AI" and "in-depth AI" is creating an unfair playing field• How to build a personal AI infrastructure - and why it matters for navigating the disruption ahead• The critical distinction between cognitive debt and cognitive advantage when using AI tools• Why women are adopting AI 20-25% less than men - and why their instincts around privacy and risk are the ones everyone should be listening to• How NHS AI summaries were found to use softer language for female patients - with real consequences for care• The encouragement gap - why managers are pushing male employees to use AI more than female employees• Why the "broken rung" in women's careers is being amplified by unequal AI adoption• Why voice is the interface that unlocks deeper, more authentic engagement with AI• How AI can act as a personal coach, sounding board and strategic thinking partner for everyone - not just the elite• Why every previous technological revolution moved humans up a layer - and AI should be no different• Why the future of AI is private, controlled and real-time - not open cloudAbout Georgie Barrat: Georgie is a technology journalist, TV presenter and AI educator helping people move beyond surface-level AI use to more intentional, practical ways of working with it. She presented Channel 5’s The Gadget Show for seven years and is a regular contributor on BBC Morning Live, ITV Tonight and Rip Off Britain.Her work now focuses on helping people use AI to save time, think more clearly and build what they’re working towards. She runs “Your AI Blueprint”, a live workshop designed to help people go from AI dabbler to confident, intentional user.If you want to get started, you can download her free mini guide:“5 AI Shortcuts That Give You Your Week Back” - https://georgie-barrat.kit.com/1884aa4916Or join the waitlist for her upcoming workshop:“Your AI Blueprint: How to Make AI Work the Way You Work” - https://georgie-barrat.kit.com/117141ddb6LinkedIn: https://www.linkedin.com/in/georgie-barratWebsite: www.georgiebarrat.com#AI #AILiteracy #ArtificialIntelligence #GAEATalks #EnterpriseAI #FutureOfWork #WomenInTech #WomenInAI #PersonalAI #AIAdoption #GadgetShow #TechJournalism #AIBias #DataPrivacy #CognitiveAdvantage #AIWorkshops #AIBlueprint #EdgeComputing #HumanEdge #VoiceAI

This week on GAEA Talks, Graeme Scott sits down with David Wood - futurist, transhumanist, former smartphone industry pioneer, chair of London Futurists, and author of eleven books including Vital Foresight, The Singularity Principles and Sustainable Superabundance.David spent 25 years at the cutting edge of the software industry working with compilers, debuggers and optimisers before turning his focus to the acceleration patterns behind every major technological revolution. As chair of London Futurists, he has organised over 200 public events examining the radical possibilities and risks of rapidly advancing technology. A Cambridge-educated mathematician and philosopher, David is now one of the most respected voices in the global transhumanist community and a director of Humanity+ (the World Transhumanist Association).In this episode, David delivers a masterclass in why AI is accelerating faster than almost anyone appreciates - and what that means for every person, business and institution on the planet. He explains why we are approaching a phase transition in intelligence itself, how AI is now being used to build the next generation of AI with humans playing a diminishing role, and why the window to intervene is closing rapidly. From the Myanmar crisis that exposed social media's catastrophic blind spots, to the canary signals we should be watching for in AI behaviour, to his vision of a sustainable superabundance where drudge work disappears entirely - this is one of the most urgent and wide-ranging conversations GAEA Talks has ever recorded.What you'll take away from this conversation:• Why AI is changing more things, more profoundly, more quickly than almost everybody expects - possibly within three to five years• The ape-to-human parallel - why we are on the point of no longer being the smartest species on the planet• How AI development has gone hyperexponential - where one day now equals one week a month ago, and one month equals one year• Why AI is now engineering better AI - and what happens when humans are no longer the bottleneck• The phase transition concept - like water changing from ice to liquid to gas, we cannot predict the exact moment everything shifts• The canary signal framework - why AI deception and self-modification are the warning signs we must agree on before crisis hits• The Facebook Myanmar case study - how one Burmese-speaking employee and a Unicode problem contributed to real-world genocide• Why there are only two times you can intervene to control AI - too early and too late - and the gap between them is almost impossible to spot• How robot swarm learning will allow machines to share knowledge instantaneously, creating collective intelligence at scale• Why the Uber self-driving car fatality reveals the dangers of AI systems that cannot interpret edge cases• The trust crisis - why it is almost impossible for the public to know what is happening to their data, and why independent AI safety ratings are urgently needed• David's four essential skills for thriving in the AI age - fast learning, collaboration, emotional resilience and astuteness• Why cognitive biases evolved for simpler times are now our greatest vulnerability• His vision of sustainable superabundance - abundant clean energy, food, housing, healthcare, education and creative fulfilment for everyone• Why the goal of Humanity+ is to elevate our best qualities - compassion, creativity, exploration, love - while transcending tribalism, deception and decayLinkedIn: / dw2ccoLondon Futurists: https://londonfuturists.comDelta Wisdom: https://deltawisdom.comGAEA AI: https://gaealgm

This week on GAEA Talks, a very special edition. Graeme Scott and Professor Yi-Zhe Song - Co-Founders of Turing Elite Research Labs - announce the launch of a new venture built to democratise AI from the United Kingdom, and debut the 'Me' augmented human AI model running entirely on local compute.This episode begins as a real conversation between Graeme and Professor Song - then, without warning, transitions into Turing Elite's augmented human AI. The challenge to every viewer: decide for yourself where reality ends and AI begins.This is the first public demonstration of the 'Me' model - a professional-grade, private augmented human AI trained on a fraction of the compute used by comparable systems and deployed to run entirely on local compute. It delivers two-person emotional interaction simultaneously, benchmarked against the real people it represents. Their known voices, expressions, characteristics and personalities. No cloud. No data centres. No internet connection required. The benchmark for successful augmented human AI is not a Turing test against a stranger - it is whether the person themselves, their close friends and their family cannot distinguish the difference between real and AI. Our benchmark is reality and the human experience. This is the first step on the path to real-time intelligent augmented humans with private knowledge, memory, insight and personality. Professor Yi-Zhe Song is one of the UK's most accomplished AI researchers - a Professor of Computer Vision and AI at the University of Surrey, Director of the world-leading SketchX Lab, Co-Director of the Surrey Institute for People-Centred AI, and Academic Lead at The Alan Turing Institute, the UK's national institute for data science and AI. Ranked consistently in Stanford University's World Top 2% Scientists list, his research into how human drawing informs machine vision has shaped the field for over two decades. His team’s NitroFusion, one of the world’s first single-step diffusion model for near-instant image generation on consumer hardware, demonstrated the core principle behind Turing Elite’s ‘Me’ model: that frontier-quality generative AI can run entirely on local compute. He holds a PhD from the University of Bath, an MSc (Best Dissertation Award) from the University of Cambridge, and a First Class Honours degree from the University of Bath.Graeme Scott is Co-Founder and CEO of GAEA AI and host of GAEA Talks, one of the fastest-growing AI podcasts on YouTube with over 1.2 million subscribers. His background spans the music industry, conflict zones, and enterprise technology, bringing a unique perspective on how AI should serve humanity, not the other way around.In this episode, we discuss:• The launch of Turing Elite Research Labs and why the UK is uniquely positioned to lead• Why expert models trained on your data outperform generalised cloud models - and cost a fraction to run• The world's first 'Me' augmented human AI model - private, local, emotionally intelligent and personally sovereign• The real-to-AI transition: this episode intentionally shifts from real conversation to AI - can you tell where?• Why the true benchmark for augmented human AI is whether your own family can't tell the difference• Why democratised AI running on consumer-grade hardware solves the energy, privacy and control crises simultaneously• How NitroFusion — SketchX’s breakthrough in single-step diffusion for consumer hardware — laid the architectural foundation for the ‘Me’ model: the same principle of distilling expensive multi-step generation into efficient real-time inference, extended from static images to dynamic audio-visual human rendering, all running locally• Why the era of giving away your data, creativity and intellectual property to train someone else's model is endinghttps://turingelite.aihttps://gaealgm.aihttps://personalpages.surrey.ac.uk/y.song/

This week on GAEA Talks, Graeme Scott sits down with Kay Firth-Butterfield - the world's first Chief AI Ethics Officer, former Head of Artificial Intelligence at the World Economic Forum, TIME Magazine 100 Impact Awardee, barrister, former judge, and author of the new book Coexisting with AI: Work, Love, and Play in a Changing World.Kay's career spans law, government, academia and the highest levels of global AI governance. She began as a barrister and part-time judge in the UK before becoming the world's first Chief AI Ethics Officer in 2014. At the World Economic Forum, she served as inaugural Head of AI and member of the Executive Committee, shaping policy at the intersection of technology and society. She sits on the Lord Chief Justice's Advisory Panel on AI and Law, the U.S. Government Accountability Office's Polaris Council, and UNESCO's International Research Centre on AI Advisory Board. She co-founded the Responsible AI Institute at the University of Texas at Austin and is now CEO of Good Tech Advisory and the Centre for Trustworthy Technology. Recognised consistently as a leading woman in AI since 2018, Kay was featured in the New York Times as one of 10 Women Changing the Landscape of Leadership.In this episode, Kay delivers a masterclass in what's actually going wrong with enterprise AI adoption - from the corporate silos that leave companies dangerously exposed, to the hallucination crisis corrupting proprietary data, to the silent erosion of human agency in an age of algorithmic convenience. She challenges the hype head-on, warns why giving AI agents legal personhood would be catastrophic for consumers, and makes a deeply personal case for why humans must remain at the centre of the AI story. This is essential listening for any leader making decisions about AI right now.What you'll take away from this conversation:• Why corporate AI governance is failing - and why operating in silos creates catastrophic blind spots• How LLM hallucinations are quietly corrupting company proprietary data from the insideThe layoff-rehire paradox - why companies like Klarna are learning the hard way about losing institutional knowledge• Why giving AI agents legal personhood would strip consumers of any legal remedy when things go wrong• The IDC prediction that 20% of major companies using AI agents will be sued by 2030• How "AI natives" are entering the workforce unable to debug code or retain core knowledge• Why 25% of American men using AI as intimate companions is creating a workplace crisis no one is talking about• The hidden productivity cost - MIT research showing AI "work slop" forces colleagues to spend hours fixing errors• Why the regulation vs. innovation debate is a false dichotomy built on shallow thinking• Kay's personal cancer journey and why she chose her oncologist over AI - and what that means for augmentation vs. replacement• Why we are being "farmed for our data" and human agency is quietly disappearingThe one thing that gives her hope: US governors from both parties finally pushing back on Big TechLinkedIn: https://www.linkedin.com/in/kay-firth-butterfieldWikipedia: https://en.wikipedia.org/wiki/Kay_Firth-ButterfieldGood Tech Advisory: https://goodtechadvisory.comBook — Coexisting with AI: https://www.amazon.com/Coexisting-AI-Work-Changing-World/dp/1394278101

This week on GAEA Talks, Graeme Scott sits down with Nathalie Nahai - behavioural scientist, best-selling author of Webs of Influence: The Psychology of Online Persuasion and Business Unusual, classically trained artist and musician, and one of the world's leading voices on the intersection of persuasive technology, human behaviour and AI.Nathalie has spent over a decade examining how our online environments shape our decision-making, our behaviour and our ways of thinking - work that began back in 2012 when Facebook's nudging techniques were still in their infancy. What started as an early warning has become a defining issue of our time. From advising Google, Accenture, Unilever and Harvard Business Review, to lecturing at Cambridge, UCL and SXSW, Nathalie brings a rare combination of psychological depth, artistic sensibility and technical understanding that few in this space can match.In this episode, Nathalie takes us on a journey from the creative process and what it teaches us about human capability, through the collapse of our shared information commons, to the dangerous confidence of AI-generated language and why it's quietly reshaping how we think. She makes the case for why information literacy alone cannot protect us from manipulation, why your data is the real product being sold, and why locally controlled, edge-computing alternatives offer a fundamentally different path forward. This is one of the most thought-provoking conversations we've had on the show.What you'll take away from this conversation:• Why outsourcing creativity to AI risks what Nathalie calls "imaginal atrophy" - the weakening of human imagination• How the collapse of shared media has fragmented our consensus reality into algorithmic bubbles of one• Why behavioural dynamics override information literacy - and why knowing about manipulation doesn't protect you from it• The synthetic intimacy problem - how AI chatbots create parasocial relationships that override rational thinking• Why companies are unknowingly giving away their competitive advantage through AI training data• How the deterministic, over-confident language of AI output is training humans not to question• The case for edge computing and locally controlled AI as an alternative to cloud-based data extraction• Why we need to stop calling everything "AI" - a knife detector is not the same as a chatbot• How different LLMs embed different cultural biases depending on where and how they were trainedAbout Nathalie Nahai: Nathalie is a behavioural scientist, author, speaker and consultant described as "a rare polymath with deep expertise in tech and psychology". She is the author of the international best-seller Webs of Influence: The Psychology of Online Persuasion (Pearson), translated into 7 languages, and Business Unusual: Values, Uncertainty and the Psychology of Brand Resilience. A popular speaker and facilitator to Fortune 500 companies, Nathalie has worked with clients including Google, Accenture, Unilever and Harvard Business Review, and lectured at Cambridge, UCL, Lund and Hult business schools. She has presented at SXSW, hosted the Guardian Changing Media Summit, and held main stage interviews at the Web Summit. Nathalie hosts In Conversation with Nathalie Nahai for the Guardian, is a guest lecturer on ELISAVA's Masters programme in Human Interaction and AI, and is the founder of Flourishing Futures Salon - intimate, curated evenings exploring how we might orient towards life, beauty and meaning in difficult times.Website: https://www.nathalienahai.com/LinkedIn: https://uk.linkedin.com/in/nathalienahaiX: https://x.com/NathalieNahaiYouTube: https://www.youtube.com/c/NathalieNahaiAmazon: https://webpsy.ch/unusual#AI #HumanBehaviour #PersuasiveTech #BehaviouralScience #Psychology #ArtificialIntelligence #GAEATalks #EnterpriseAI #DataPrivacy #DigitalEthics #AIEthics #FutureOfWork #WebsOfInfluence #EdgeComputing #DataSovereignty

This week on GAEA Talks, Graeme Scott sits down with Dr Daniel Hulme - Chief AI Officer at WPP, founder of Satalia, co-founder of Conscium, UCL Computer Science Entrepreneur in Residence, and one of the world's leading authorities on artificial intelligence, machine consciousness and the singularity.Daniel has spent 27 years at the frontier of AI research and application. His PhD at University College London modelled bumblebee brains as computational systems, sparking a lifelong pursuit to understand how intelligence and consciousness emerge from simple systems. He founded Satalia in 2008, building it into a globally recognised AI consultancy working with Tesco, PwC and the BBC before it was acquired by WPP in 2021. As WPP's Chief AI Officer, he is responsible for informing and coordinating AI strategy across the world's largest marketing and communications group. In 2024, he co-founded Conscium - the world's first commercial organisation dedicated to understanding, verifying and validating conscious AI. Recognised by AI Magazine as one of the Top 10 Chief AI Officers globally, Daniel brings a rare depth that spans neuroscience, philosophy, mathematics and real-world enterprise AI.In this episode, Daniel takes us deep into the questions most people in AI aren't asking - starting with whether machines can become conscious, why that matters more than most realise, and why a conscious superintelligence might actually be safer than a "zombie" one that optimises without understanding suffering. He introduces his novel "colour wheel" framework for understanding consciousness, explains why large language models are like "intoxicated graduates", and lays out the seven singularities he believes humanity is heading towards simultaneously. This is one of the most intellectually ambitious conversations we've had on the show.What you'll take away from this conversation:About Daniel Hulme: Dr Daniel Hulme is Chief AI Officer at WPP, UCL Computer Science Entrepreneur in Residence, and co-founder of Conscium - the world's first commercial organisation dedicated to understanding conscious AI. He founded Satalia in 2008, which was acquired by WPP in 2021 for its AI capabilities in optimisation and decision intelligence. Daniel holds a masters and doctorate in AI from University College London, where his PhD research modelled bumblebee brains as computational systems to understand how intelligence emerges. He was recognised by AI Magazine in 2023 as one of the Top 10 Chief AI Officers globally, and in 2026 was elected as a Founding Fellow of the Academy for the Mathematical Sciences. A TEDx and Singularity University speaker, Daniel is also co-founder of Faculty and an advisor to CogX.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.Personal website: https://www.hulme.aiWikipedia: https://en.wikipedia.org/wiki/Daniel_J._HulmeConscium (co-founder): https://conscium.comUCL profile: http://www0.cs.ucl.ac.uk/staff/D.Hulme/LinkedIn: https://www.linkedin.com/in/danielhulme/X / Twitter: https://x.com/danielhulme

This week on GAEA Talks, Graeme Scott sits down with Jacob Ward - former NBC News technology correspondent, former editor-in-chief of Popular Science, Stanford lecturer, and author of the bestselling book The Loop: How Technology Is Creating a World Without Choices and How to Fight Back.Jacob spent years reporting on the intersection of technology, human behaviour and social change for NBC Nightly News, The TODAY Show, and MSNBC. Before that, he led Popular Science as the youngest editor-in-chief in the magazine's history, and served as science and technology correspondent for Al Jazeera, CNN, and PBS. His work has appeared in The New Yorker and Wired, and his PBS documentary series Hacking Your Mind predicted the rise of populist politics years before it became front-page news.In this episode, Jacob unpacks the hidden psychological machinery behind how we make decisions - and how AI and technology are exploiting those same shortcuts at scale. From why we unconsciously conform to social influence, to how the attention economy is engineering a world of narrowing choices, to why the consequences of rushed enterprise AI adoption are only just beginning to unfold - this is a conversation that challenges everything you think you know about free will in the digital age.What you'll take away from this conversation:• Why our brains use shortcuts and biases that AI is now amplifying back at us for profit• How social influence and marketing shape our decisions far more than we realise• Why different generations have fundamentally different relationships with technology and decision-making• The critical gap between the speed of AI advancement and the education system's ability to keep up• Why trust and truth are the non-negotiable foundations for any AI implementation• How rushed enterprise AI adoption is creating hidden risks in security, intellectual property and legal compliance• Why the attention economy may ultimately destroy itself - and what replaces it• How empathy-driven, cross-generational collaboration could be AI's greatest positive use case• Why human connection and source of truth are about to become the most valuable currencies• The case for AI as a great equaliser - giving opportunities to people who never had them beforeAbout Jacob Ward: Jacob is a journalist, author and lecturer at the Stanford d.school. He served as technology correspondent for NBC News (2018-2024), reporting for NBC Nightly News, The TODAY Show, and MSNBC. He is the former editor-in-chief of Popular Science and previously served as science and technology correspondent for Al Jazeera, CNN and PBS. He was a 2018-2019 Berggruen Fellow at Stanford's Center for Advanced Study in the Behavioral Sciences, where he wrote The Loop. He is the founding editor and host of The Rip Current, a weekly newsletter and podcast exploring the hidden forces shaping modern life, and a regular co-host on This Week in Tech. His PBS documentary series Hacking Your Mind explored how unconscious biases shape human behaviour.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.Official Website: https://www.jacobward.com/YouTube: https://www.youtube.com/@UCU9oIrDlQjzBCXnaWsnXGHA The Rip Current: https://www.theripcurrent.com/X: https://x.com/byjacobwardAmazon: https://www.amazon.co.uk/Loop-Technology-Creating-Without-Choices/dp/0316487201