GAEA Talks

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 algorithm
Quadratic scaling explained simply - 10x the input, 100x the compute
The memory wall, where context can need more memory than the model
What "subquadratic" means, and why linear scaling is the unlock
A claimed ~1,000x compute reduction at 12 million tokens
Why transformers are the worst fit for data-heavy enterprise work
The real constraints on AI: electricity, water, minerals and capital
Why we're "too stingy with the tokens"
The DeepSeek lesson the incumbents ignored
Why first to market is rarely best

About 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 answer
The zombie pilot problem inside large enterprises
Efficiency vs compounding vs strategic bet pilots - and why measurement has to change
The Amazon Just Walk Out and IBM Watson lessons
Privacy-first AI - what Apple's approach still teaches every industry
The IPA thesis - why transformers are elegantly simple
Why data centres are quietly making electricity cheaper
The tribal knowledge shift and the future value of long tenure
The price of anarchy and how AI could close the gap in every organisation