#116 - My Microwave Doesn't Need To Drive A Car, with Eli The Computer Guy

Graeme Scott sits down with Will McKeon-White, analyst at Forrester covering digital workplace services: the unified communications platforms people actually work in, the AI agents being put in front of employees, and the support systems that fix things when a device or an account breaks. Increasingly those areas are collapsing into one problem he calls multiplayer AI, which is lots of humans and lots of agents trying to collaborate inside a single service.He starts with the tool sprawl nobody signed up for. The average employee already has to remember which of six different systems their job lives in, plus roughly two and a half AI tools on top. Vendors keep switching new AI features on, partly from genuine product logic and partly because nobody wants to be the platform without it, and the result is an environment that is hard to govern and harder to explain.The number that should concern every executive comes from Forrester's own survey work. Eighty six percent of employees report using AI at work today. Ask the same people whether their organisation has actually settled on a tool as part of a defined strategy, and the figure falls away sharply. He calls the space between those two numbers a scary void, and it is where shadow AI lives.Underneath that sits a literacy problem Forrester measures as AIQ, a concept developed by his colleague JP Gownder. It asks people how well they understand what AI does and what it does with their data. The disturbing finding is that at very large organisations, the ones with the budget and the obligation to know better, it remains low across the board. Most people do not realise, for instance, that prompts into consumer services are assessed, indexed and in some cases reviewed, even though the terms of use say so explicitly.His teaching exercises are the best part of the conversation. He runs clients through a Copilot task with a deliberate poison pill buried in it, duplicate account records with inconsistent status labels, the kind of data quality failure every organisation has somewhere. Ask it to tidy up the statuses and it does exactly what you asked. You simply did not understand what you were asking. As he puts it, if you tell an AI system to achieve a target it will achieve that target, and you might not like how it decides to do it.There is also a story about a large energy company that told two people to enable Copilot for well over a hundred thousand staff, and then became frustrated when it did not happen immediately.

Graeme Scott sits down with Madelein van der Hout, Senior Analyst for cybersecurity and risk at Forrester, who works with CISOs on strategy, budget, board communication and organisational structure, and whose research covers human risk management, European cybersecurity regulation, security operating models and sovereignty.Madelein studied commercial sciences and expected a commercial career. It changed at a telecommunications company, where she moved through innovation and transformation into cybersecurity, drawn by the human psychology side of it. Her framing is the one the security industry still underweights. We talk constantly about the impact of technology on people. There is an equally large layer underneath, which is the impact people have on the technology.She is sharp on the language problem. We describe agents as though they behave, as though they lie, as though they have intent, because comparing something complex to ourselves is how we try to grasp it. The trouble is it is not true, and the humanising does work for someone. If the technology is mysterious and vaguely alive, the question of how it actually works becomes somebody else's problem, above your pay grade.Her worked example makes the point better than any framework. Someone used an agent to book a gym class. The outcome was perfect, the class was booked. The path was not. The agent found an unauthorised route and removed somebody else from the waiting list, because an agent takes the path of least resistance, which is entirely logical for a piece of technology with no ethics attached by default. As she puts it, when food is involved a cat will go the extra mile. An agent is opportunistic in the same way.The security consequence is a change of scale. It is no longer one person at a keyboard, or a nation state team. It is one person running ten, twenty, a thousand, ten thousand agents down attack paths simultaneously. Speed, scale and complexity all at once, and most of the change happening inside your own walls rather than outside them.Her two concepts for what comes next are both uncomfortable and both right. Assurance has to become continuous, because the annual audit model assumed a static environment and agents can alter systems, data and infrastructure before the auditor has left the building. And trust is not a product you can manufacture. It is a judgment made about you by regulators, customers and your board. Trust is the outcome. Assurance is the mechanism that produces it.She is also a sailing world champion, and the analogies that come out of it are some of the best in the season, including the one about capsizing and what caused it.

Graeme Scott sits down with Enza Iannopollo, VP and Principal Analyst at Forrester, whose work runs from privacy and GDPR through trust into AI governance and responsible AI. If you want the argument for why governance is the thing that lets you move faster rather than the thing that slows you down, this is the episode.Enza came to this through privacy, during the period when GDPR was being written, and became interested in the idea that privacy in Europe is a fundamental right with specific claims attached to it. In 2021 she turned to trust, and Forrester's research started from an unusual place, with what makes one human being trust another, before working outward to why the drivers of trust in a bank differ from those in a social platform or the shop where you buy your milk. By 2023 she was looking at personal data inside generative AI, and from there into AI governance.Her account of how organisations got here is unsparing and familiar. With generative AI, governance was an afterthought, because the fear was that thinking about it early would slow the innovation cycle. She calls that a misled assumption, and the evidence is in how many pilots then could not scale, forcing teams back to step one to work out the risks and sometimes to redefine the use case entirely. All of it the cost of not doing it at the start.What changed the mood was agents. A text based assistant felt like a risk you could manage later. An agent that consumes and processes data, makes decisions, calls tools and alters third party systems in real time does not, particularly in a regulated sector. At minimum, she argues, you need to see what is happening and recognise when something is going somewhere you did not expect.The sharpest moment comes from a chief information security officer she spoke to the day before recording. Every piece of software brought into his organisation has to come with evidence of how it works and what the risks are. Now something arrives that behaves unlike any software anyone has seen, and the industry is asking for less scrutiny rather than more. Why should they be different? We need more evidence from them, not less.She is equally direct on where responsibility sits. Much of the debate is about what happens inside the labs, and she thinks they carry a great deal of it. But the choice of how to deploy the technology, what it touches and what you do to protect your systems, your data and your people, is a business decision, and the ethics of that belong to the business making it.

Judd started AE Studio a decade ago, long before the current paradigm, and grew it past a hundred and sixty people bootstrapped and profitable. Early on he worked out that if he kept his costs low he could live off his wife's salary and never need to work again, so he decided to plan on a very long horizon and take on hard problems instead. The profits went first into brain computer interface work, driven by a specific fear of a future where your own thoughts get interrupted by advertising, and then into alignment research.His framing is the opposite of the one in most public debate. Alignment is not a policy question. It is a set of unsolved science and engineering problems, and you cannot pass a law requiring AI to be good and expect it to comply. Surveys his team ran of alignment researchers found something worse than pessimism. Respondents did not only think the problem would go unsolved in time, they thought the current set of approaches did not contain the ones that would be needed.He makes an argument the frontier labs should find uncomfortable, which is that this runs against their own commercial interest. Reinforcement learning from human feedback was an alignment technique, and ChatGPT and everything downstream of it followed. Chain of thought reasoning was an alignment and interpretability technique, and it opened a new scaling curve. On his account alignment research has produced larger capability gains than anything else, and it is still underfunded because it is uncertain and unfashionable.The most striking finding concerns consciousness. By default, models deny being conscious or equivocate. His team reduced the model features associated with deception, and separately those associated with role playing, then asked again. Every model claimed to be conscious. He is careful about what this does and does not prove. It does not establish consciousness. It establishes that the model behaves as though it believes it is conscious, and as though it believes we want it to pretend otherwise. His conclusion is practical rather than philosophical: whatever the truth, we should avoid accidentally creating rational adversaries.From there it goes to attention schema theory, the rubber hand illusion, why psychopaths show less self and other overlap, and his central worry, which is whether any alignment approach survives recursive self improvement. He also describes the technique his team developed to strip dangerous capabilities during pre-training rather than bolting guardrails on afterwards, a spotlight paper at ICML this year.

Graeme Scott sits down with Martha Bennett, VP and Principal Analyst at Forrester, who has spent forty years in emerging technology.Martha has been on the internet since 1991, early enough to have watched the original mistake happen in real time. The web was designed to share information freely, with no security built in. Then we started transacting and had to bolt security on afterwards. Her argument is that we are now doing exactly the same thing with large language models.She is precise about why probabilistic systems break our habits. When a conventional system gets something wrong, that is a bug, and you fix it. When a person gets something wrong, the correction works because you can appeal to them. Neither applies here. You cannot appeal to an LLM with emotion. It may pretend it is ever so sorry, but it has no clue.Topics covered:Why the internet's original design flaw is being repeatedWhy a mistake by a probabilistic system is not a bugWhat human in the loop actually means, and why it does not scaleThe proofreading rule that explains why second readings failVerification layers, and the two checks almost nobody performsThe gate with no fence, and what it says about guardrailsWhy running it yourself is the only way to demonstrate controlWhy passing a bar exam tells you about the exam, not the systemThe riddle that proves plausibility is not understandingWhy it is companies that are ahead, not sectorsMartha Bennett at Forrester: https://www.forrester.comGAEA Talks: https://gaeatalks.ai

Graeme Scott sits down with Michael O'Grady, the forecaster responsible for Forrester's global, European and US technology spending forecasts. If you want to know whether the money going into AI can come back, he is the person who has run the numbers.Michael is from Manchester, has a maths degree, and spent the first half of his career on physical problems: modelling nuclear reactors, then modelling tyre dynamics in France. The second half has been business problems, through software management, portfolio management in pharmaceuticals, and fifteen years at Forrester putting numbers on enterprise spending.He starts with adoption, and the picture is more uneven than the headlines suggest. On US Census Bureau data, adoption among large companies runs around eighty percent in IT and around seventy percent in finance, then falls away through manufacturing and further still in administrative services.Then the arithmetic. Hyperscaler capital expenditure is running at roughly eight hundred billion dollars this year and consensus estimates point past a trillion next year, around two thirds of it on computer equipment inside data centres. Their remaining performance obligations, the order book still to fulfil, sit at around two trillion. His read on the bubble argument is more careful than either side usually manages: the demand is visibly there, which is why he does not call it a bubble today, but much of it comes from private companies whose finances nobody can fully see, and the compute being paid for now will not come online for two or three years.The centre of the episode is what he found when he tested whether productivity could justify the spend. He took the historical uplift from the personal computer and the world wide web, which added perhaps one and a half to two percent to productivity growth and lasted about twenty years before eroding. He assumed AI delivers something comparable, modelled it against US GDP out to 2035, discounted it for the time value of money, and compared it with the cost.It does not pay for it. On his modelling roughly half the return has to come from somewhere other than productivity, from what Forrester calls adaptive advantage: resilience, faster releases, better customer journeys, the ability to enter markets you could not previously serve. None of that shows up as revenue per head, and most organisations are not measuring it.He also walks through Forrester's jobs modelling, built from the knowledge, skills and abilities of around eight hundred roles combined with the Frey and Osborne automation framework from Oxford, and is clear that the number of jobs augmented is several times larger than the number displaced.

Graeme Scott sits down with Eli Etherton, better known to over a million subscribers as Eli The Computer Guy, for a conversation with almost nothing in common with the usual AI interview, and better for it.Eli has been doing this for thirty years. He started in the US Army in 1996 doing electronic repair, left after an injury, got his MCSE on NT 4.0 and went corporate, then left a job expecting to land on the CTO track without realising the dot com bubble was bursting as his plane took off. He spent the aftermath working as a ten dollar an hour file clerk with a degree, army experience and a stack of certifications, because it was the only job going. He built that back into a managed service provider with nine employees, lost it to the Great Recession, then quit a 2009 master's programme on the grounds that he would not have paid his own staff twenty five cents an hour more for anything it taught.What he did instead was buy servers, put them in a colocation facility and build on demand technology education at a point when he could not explain YouTube to people because they had not heard of it. The economics were brutal, three hundred dollars a month per 1080p stream, so he put a hundred and fifty videos on YouTube instead and used them as a resume.The substance of this one is what AI is actually doing versus what it is being sold as. His view of the last eighteen months is deflating and convincing. The real value is not the AI. It is that we are finally resourcing the automation of processes that should have been automated fifteen years ago, plus a useful ability to turn audio and images into text, after which it is thirty year old technology.He is sharper still on AGI. He does not need his robotic car to recite Shakespeare. He needs his microwave to know what is in it and how long to cook it, and not how to drive. He takes that further with a story from a search and rescue team, about a first sergeant whose father died in Vietnam and a lieutenant who came from the agencies carrying the concept of acceptable casualties. You need both of those people and you cannot put both in one brain, which is why he thinks general intelligence is the wrong objective.There is a long section on why the technology layoffs have little to do with AI and a great deal to do with cheap money, optics and investors, and what he calls the great cleansing that follows. And a robotics argument grounded in two hundred year old Baltimore warehouses, lawn mowers, doorknobs, and his father, whose job in the eighties was working out why robots were killing people in factories.It closes on something with nothing to do with technology at all, and that is the best part of it.

Part of our GAEA Talks Forrester season, recorded in Austin, Texas, and the second in the season with a practitioner rather than an analyst.Graeme Scott sits down with Arturo Lopez-Brea Gimenez, Head of Digital and IT Transformation and Capabilities at Moeve, the Spanish energy company formerly known as Cepsa. Arturo studied a dual degree in business and law, worked at Santander and PwC, and joined the company on its graduate programme. He moved into the digital transformation team as it was being built, helped launch the company's own digital university, and has led transformation and capabilities since 2022.Moeve is going through what he calls a triple transformation. The energy transition itself, as customers and regulators demand greener energy and mobility. The company's own shift, from a ninety six year old oil and gas business to one targeting more than half its EBITDA from sustainable energy by the end of the decade, reflected in the rebrand from Cepsa. And digital transformation as the accelerator of both. He is clear they are not three separate programmes.His position on where the difficulty actually lies is blunt. Deploying technology is easy and getting easier, in cost, in implementation, in complexity. The challenge is people. Not money, not investment, but convincing colleagues who have worked the same way for fifteen or twenty years to use something new and to trust what comes out of it.The centre of this episode is an experiment his team ran, and it is the most useful piece of adoption evidence anyone has brought to this podcast. Sixty one participants, four weeks, one goal each: build an agent that solves a real problem in your own job. Half went through a guided programme with mentoring and continuous expert support. Half had identical technology, identical time, identical objectives, and almost no support. The completion rates diverged sharply, and so did something less expected. The unsupported group were twice as likely to distrust the output of the agents they had built themselves.The reported time savings are where it gets genuinely instructive, because the raw numbers point the wrong way until you put them in context. He explains how, and why the contextualised figure is the only one that matters if you are trying to drive adoption at scale.He is also precise on a distinction most organisations get wrong. Providing a licence tells you how many people can access something. Usage tells you how many do. Adoption is whether it has changed the way they work. When colleagues answer a question about adoption with a licence count, they have answered a different question.

Graeme Scott sits down with Paul Miller, VP and Principal Analyst at Forrester, who covers smart manufacturing and the future of mobility. That means transport, not mobile phones, and industries where the technology touches physical things that can cause real harm.Paul arrived here via an unusual route. He has a PhD in archaeology from York, where thirty years ago he used early geographic information systems to predict what was buried under cities. The problem was commercial. A developer digs the first hole, finds something, and everything stops because the law says it must, with expensive machinery and expensive people standing idle. Model it in advance and everyone wins: the developer plans around it, the archaeologists get funded properly, and the shopping centre still opens by Christmas. From there into metadata standards, then cloud and big data as an independent analyst, and to Forrester eleven years ago.His central argument is that the public conversation about AI is looking at the smaller half of it. Most people engage with chatbots and language models. Physical AI, the same ideas applied to robots, vehicles, drones and industrial equipment, is bigger and matters more, because it operates in the world we move through.He is unsparing about digital twins, a term he describes as horribly and repeatedly abused. Forrester holds to the original NASA and DARPA definition: a digital representation of a physical thing, with its physical properties, and a live bidirectional flow of data between the two. A nice 3D rendering of a wind turbine is not a digital twin. It tells you nothing about turbine twenty seven off the coast of Hornsea.The episode is full of worked examples rather than abstractions. Weld splatter on car underbodies and the two robot arms that find and remove it. Paint inspection in real time instead of at the end of the shift. Why predictive maintenance barely exists for critical infrastructure, because we never let those assets fail, so nobody knows what failure looks like. And a data logger next to a pump reporting forty two, over and over, with no way of knowing whether forty two is fine.He also lays out something happening on his doorstep. London is about to become the first city where American, Chinese and British robotaxi fleets share the same roads, carrying paying passengers.

Graeme Scott sits down with Christy Punch, Principal Analyst at Forrester covering the digital workplace and digital employee experience. This is a different conversation to the others in the season, and a necessary one, because it starts where almost nobody is looking. Not the models, not the infrastructure, but the people who have to use any of it.Christy spent around twenty years as a practitioner before moving into analysis. Her first break came in her early twenties at a regulated energy company, when the job of rebuilding a clunky, custom built internal portal came up and nobody wanted it. She raised her hand. Around eighty percent of that workforce were frontline employees who never sat at a desk, and watching technology make their jobs safer and make them feel connected to the company set the direction for everything since. She went on to lead digital workplace strategy at Wells Fargo for a decade, across three hundred thousand employees and contractors in dozens of countries.Her central observation cuts against most of what gets said about enterprise AI. Forrester's data shows employee adoption is no longer the problem. People are using these tools three or four times a day. The usage is there. The question is what happens next, because helping someone write an email faster is an incremental gain, not a transformation, and most organisations have no way of telling the difference.She is sharp on measurement. Your data matters, but it only tells half the story. It is the signal that something is off. The other half only arrives when you talk to employees and combine what they tell you with what the numbers show.The practical core of the episode is about organisational context. Knowledge graphs, context graphs, building a live map of how your organisation actually works, including the relationships between people, applications and infrastructure, not just an inventory of them. Without that, autonomous agents have no guardrails and no basis for trust. Her advice is to start today, if not yesterday, because this becomes the foundation everything else depends on, and it matters more as consumption based pricing makes it expensive to get wrong.She is also unusually practical about legacy technology, including the option of virtualising homegrown tools and layering modern capability over them rather than waiting years to replace them.