GAEA Talks

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 care
One trillion dollars of capital investment expected in 2027 alone
Forrester's "AI voyage" research on what successful companies actually do differently
Why nobody is firing developers because of AI
The technical debt problem and why 20% of IT budget should pay it down
AEGIS - Forrester's framework for securing agentic AI
Why the CIO's job is shifting from uptime to trust and assurance
"Minimum viable sovereignty" for multinationals
AI 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 event
Why disinformation is cheap and information is expensive
The Silicon Valley trillionaire race and what it means for you
The Grok example - Elon Musk hard-coding his own AI to protect his reputation
Why LLMs may be worse than social media for consolidating ideas
The Palo Alto paradox - billionaires banning screens from their own kids' classrooms
The airplane question - would you get on it if the engineers did not understand it
Hany's practical advice on deleting apps, using AI properly, and rebalancing life