Episode two of our Forrester Technology and Innovation Forum series, recorded live in Austin, Texas.Graeme Scott sits down with Brian Hopkins, VP of the Emerging Tech Portfolio at Forrester, for the most direct conversation we have had on what AGI actually means, what the compute buildout has to earn back, and why the answer to both is less dramatic and far more useful than the headlines suggest.Brian came into analysis from industry, working in financial services and defence across IT and architecture. About fifteen years ago he became the first analyst at Forrester to use the term big data in research, at a point when nobody knew what it meant. He has spent the last four years applying that data and analytics grounding to AI.The question that opens the episode is the one his colleague Mike Gualtieri put at the centre of their research. If you had shown today's AI capabilities to an AI researcher ten years ago and asked whether this was AGI, what would they have said? Yes. Every time we get close to something we would once have called AGI, we move the goalpost further out. So Forrester stopped moving it. Their report, The Quiet Roar of Artificial General Intelligence, argues AGI arrives in stages rather than as a bolt of lightning, and that the first of those stages, competent AGI, is what we are already using. It needs a lot of supervision, it makes mistakes, it works in a limited domain over days rather than months, and it learns what it needs to know, asks when it is unsure, and writes its own tools to solve problems. The open question is not superintelligence. It is when the next stage arrives.The second half is about money, and it is the part enterprise leaders should watch twice. Forrester has been modelling the AI bubble question by comparing the compute buildout against non-farm worker productivity, the one measure that reliably shows economic value. Even on generous productivity assumptions, the numbers do not close. What is left over Brian calls dark matter, or latent value, and it has to come from engagement change and transformational change rather than efficiency. His conclusion is a challenge to his own industry. Stop selling enterprises frontier models, and start giving them a reason to trust the basics.

This is the first episode in our Forrester Technology and Innovation Forum series, recorded live in Austin, Texas. Over the coming months we will be sitting down with Forrester analysts and their clients at the Forums in Austin, London and New York.Graeme Scott opens the series with Alla Valente, Principal Analyst on Forrester's security and risk team, who covers governance, risk and compliance, third party risk, contract lifecycle management and, increasingly, all of the above as they collide with AI.Alla is asked the question every board is asking, which is whether AI is simply the next emerging technology. Her answer is no, and the reason is specific. With SaaS and cloud, organisations chose whether to adopt, when, where in the business and who got access. That choice no longer exists. AI is already inside your organisation whether you have a strategy for it or not. As she puts it, not having an AI strategy is a strategy. It is just not a very good one.From there she draws a distinction most organisations have not yet made. AI governance is a function. It is your policy, your charter, your process for deciding which use cases are acceptable. Governing AI is the execution of that, and it happens through compliance, risk management, security and responsible AI. Organisations reached for governance first because enterprise risk management was not mature enough to move at the speed AI demanded, and the gap between the document and the delivery is where the exposure sits.She is equally direct on third party risk. Almost nobody is building their own models. You are buying foundation models, buying data, using open source, which is also a third party. AI arrives through the ecosystem, and third party risk management in most organisations is deprioritised, federated and underfunded compared with enterprise risk.The section enterprise leaders should sit up for is contracts and concentration. Organisations are using AI to contract faster, but they have not contracted for AI. Most contracts still say nothing about model training, data access, who is responsible when there is an incident, who fixes it and what the recourse is. Her line on this is the sharpest in the episode: contracts are your AI guardrails that have teeth, and they are the only ones that do. Alongside it sits concentration risk. If your business runs on one provider's model and something makes that model unusable, how long is the disruption and how much can you absorb? Map it before the crisis, not after.

This week on GAEA Talks, Graeme Scott sits down with Aarti Samani, founder and CEO of Shreem Growth Partners and one of the leading voices on deepfake-enabled fraud and human manipulation resilience.Aarti spent over twelve years in investment banking, bringing one of the first automated algorithmic trading systems to the City of London. She moved into high growth technology and took two companies through exits, one to Microsoft and one to Medtronic, before joining a face biometric verification business where she scaled revenue twenty fold. That work put her opposite government and security customers, and it is where she started watching deepfake attacks being built in real time against systems she was helping defend. She holds an honours degree in Mathematics from Durham, teaches at MIT and Cambridge, and is a regular AI commentator for the BBC.Her argument is blunt. The cost of creating a convincing fake has collapsed, and so has the technical skill required to make one. Meanwhile the security perimeter has been built higher and higher. So attackers stopped going through the technology and started going through people, because the human line of defence is the one thing organisations never invested in.She walks through the cases in detail. The British engineering firm whose Hong Kong finance team lost twenty five million dollars after a video call on which every face was synthetic except the victim's. The UK retailers hit last summer through nothing more sophisticated than someone phoning the IT helpdesk pretending to be an employee. The Italian fashion houses who received a cloned voice note from their defence minister, timed against a real hostage story from days earlier.The idea that will stay with enterprise leaders is what she calls the trust tax. When a company has been hit, people stop responding. An email arrives from the chief executive and it takes five days, three checks and an IT ticket before anyone acts on it. That cost appears on no balance sheet and nobody is measuring it. The security KPIs all look excellent. The organisation has quietly ground to a halt.

This week on GAEA Talks, Graeme Scott sits down with Pip White, who leads Anthropic across the UK, Ireland, Israel and the Nordics.Pip did not set out for a career in technology. She started in advertising, then spent nearly fourteen years at HP across software, hardware and services, which she describes as her long-term PhD in everything technology. Leadership roles at Salesforce, Slack and Google Cloud followed, along with advisory work with scale-ups. She joined Anthropic almost exactly a year ago. She is also a mother of two teenage boys, one of whom has just finished his A levels and is working out what any of this means for the career choices ahead of him.Filmed in our London studio, this is a conversation about what AI adoption actually looks like inside large, complicated, hundred-year-old organisations, from someone who spends nearly all of her time in the room with the people making those decisions.The line at the centre of it is the one every board should hear. This is not a technology project. It does not sit in IT. It is not something a CIO can roll out the way they rolled out a SaaS platform or a cloud migration. It is a fundamental question about how an organisation works, and the companies making real progress are the ones where the chief executive has taken personal ownership of it.Pip is unusually candid about what she is hearing from customers. Chief executives are asking her whether their organisations will survive. Not whether they will save fifteen percent on a process, but whether a business that has been successful for a century remains relevant at all. That is a conversation she says she has never had before in her career.She is equally clear about what failure looks like. A million proofs of concept, all sitting inside IT, none of them going anywhere. Success looks like a single mandate from the top, in one meaningful area, that builds skills, literacy and confidence across the organisation.

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.