Tech Talks Daily

What does it actually take to make AI work inside a real business, where messy data, human judgment, and operational risk all collide? In this episode, I sit down with Matt Fitzpatrick, CEO of Invisible Technologies, to talk about why the biggest barrier to enterprise AI is not model quality, it is everything that comes before the model ever gets to work. Since stepping into the CEO role in January 2025, Matt has moved quickly, raising $100 million and expanding Invisible's footprint across major cities including New York, San Francisco, DC, Austin, London, and Poland. But this conversation is far less about headlines and far more about what happens in the trenches of AI adoption, where companies are trying to move from pilots and PowerPoint promises to systems that actually deliver results. A huge theme throughout our discussion is data readiness. Matt makes a compelling case that most businesses are still dealing with fragmented systems, inconsistent records, and information spread across disconnected tools. That reality makes it incredibly hard to deploy AI in a way that creates trust and value. We talk about SwissGear, where Invisible used its Neuron platform to clean and structure 750 scattered tables in just one week, a task that could have taken a large engineering team months or longer. We also discuss why that kind of work matters so much, because once the data foundation is fixed, companies can start making better decisions on forecasting, operations, and planning with a level of confidence that simply was not there before. We also spend time on Invisible's human-in-the-loop approach, which I think will resonate with a lot of listeners trying to cut through the noise around job displacement and agentic AI. Matt argues that the real opportunity is not replacing people, but giving them better tools to handle repetitive work while preserving room for human expertise, judgment, and oversight. He shares examples from commercial credit workflows, healthcare, and sports analytics, including a fascinating story about the Charlotte Hornets using AI to turn broadcast footage into detailed tracking data. What stood out to me was how practical his perspective felt. This was not theory. It was about building systems around how organizations actually work, rather than expecting businesses to reshape themselves around a generic AI product. Another part of the conversation that deserves attention is governance. As boards rush to understand agentic AI, Matt explains why trust, standards, and responsible deployment are now driving buying decisions just as much as raw capability. We talk about privacy in healthcare, the risks of scaling autonomous systems without mature governance, and why enterprise adoption still trails consumer AI by a wide margin. That gap between excitement and execution may be one of the most important stories in AI right now. If you are wondering why so many AI projects never make it into production, or what it will take for enterprise AI to finally deliver on its promise, this episode is packed with insight. It is a conversation about data, deployment, governance, and the role humans will continue to play as AI becomes part of everyday business operations. After listening, I would love to know where you stand, is the future of AI really about bigger models, or is it about making AI fit the messy reality of how work gets done?

In this episode, I speak with Bert Van Hoof, CEO of Willow, about how AI is starting to reshape the built world in ways that go far beyond smart dashboards and efficiency reports. Bert brings decades of experience from the front lines of digital infrastructure, including his time at Microsoft, where he helped create Azure Digital Twins and Smart Places. Today at Willow, he is focused on a much bigger idea, using AI to help buildings, campuses, hospitals, airports, and other complex environments operate with greater intelligence, lower waste, and better outcomes for the people who rely on them every day. One of the most interesting parts of our conversation is how Bert explains the shift from passive building software to active management systems. For years, many digital twin and smart building tools were good at showing what had already happened. But operators do not need another screen full of charts. They need systems that can connect live data, static records, spatial context, and operational history to help them make better decisions in real time. That is where Willow comes in, creating a digital foundation where AI can reason across everything from HVAC and air quality to occupancy, refrigeration, maintenance history, and even energy usage patterns. We also unpack why this matters right now. Energy costs remain under pressure, sustainability goals are getting harder to ignore, and many organizations are still stuck with fragmented systems that do not talk to each other. Bert shares how AI can help move building teams from reactive maintenance to predictive performance, spotting issues earlier, cutting downtime, reducing waste, and extending the life of expensive assets. He also explains why the future of building operations will depend on a stronger data foundation, operational AI copilots, and systems that can support an aging workforce while making these roles more appealing to the next generation. What stood out for me was how practical this all became once we moved past the buzzwords. This was not a conversation about futuristic hype. It was about real examples, from occupancy-based HVAC control in offices and campuses to leak detection in schools, vaccine refrigeration monitoring, and hospital environments where downtime can carry enormous consequences. Bert makes a strong case that buildings are no longer just static structures. They are living operational environments filled with signals, systems, and opportunities that have been hiding in plain sight. We also touch on the wider picture, including what Bert learned from smart cities and energy grid modernization, and how those lessons now apply to commercial real estate, airports, research labs, and higher education campuses. There is a real sense that the physical world is entering a new chapter, one where AI starts to bridge the gap between digital intelligence and real-world action. If you have ever wondered what AI looks like when it leaves the screen and starts improving the places where people work, heal, travel, learn, and live, this episode will give you plenty to think about. As always, I would love to know what you think, are buildings finally ready to become truly responsive, and what opportunities or risks do you see ahead?

What does it really take to build the next generation of AI companies when the hype around scale begins to fade and real-world impact takes center stage? In this episode, I sit down with David Blumberg, founder and managing partner at Blumberg Capital, to unpack what he believes will define the next wave of AI startups. With a track record that includes being the first investor in companies like Nutanix, Braze, and DoubleVerify, David brings a perspective shaped by decades of identifying breakout innovation early. But what stood out most in our conversation was his belief that 2026 marks a turning point where intelligence moves beyond experimentation and becomes operational. We explore what that shift actually means in practice. David explains how AI is evolving from systems that generate insights into systems that take action, and why that distinction matters for founders, investors, and enterprise leaders alike. He shares how the most compelling startups today are not simply layering AI onto existing products, but embedding it deeply into workflows across industries like finance, security, and supply chain. These are companies built on proprietary data and real operational context, designed to make decisions with precision rather than simply process information. Our conversation also challenges some widely held assumptions about success in the AI space. David makes it clear that scale alone will not separate winners from the rest. Instead, the focus is shifting toward accuracy, reliability, and domain expertise. Founders who have lived the problems they are solving, rather than approaching them from the outside, are far more likely to build something defensible and lasting. It is a subtle shift, but one that could redefine how value is created in the years ahead. There is also a broader discussion about where investment is flowing and why. With the vast majority of companies Blumberg Capital now evaluates being rooted in AI, the bar for differentiation is rising fast. David offers insight into what his team is really looking for in founders entering this next cycle, and how startups can stand out in an increasingly crowded field. So as AI moves from promise to execution, and from experimentation to real-world outcomes, the question becomes harder to ignore. Are we ready to rethink how we measure success in the AI era, and what kind of companies will truly earn their place at the top?

How do we talk about artificial intelligence without ignoring the very human consequences it can have on our mental health? In this episode, I sit down with Dr. Ragy Girgis, Professor of Clinical Psychiatry at Columbia University, to unpack a topic that has quietly moved from the fringes of academic discussion into mainstream headlines. You have probably seen the term "AI psychosis" appearing more frequently, often surrounded by speculation, fear, or misunderstanding. But what does it actually mean, and how should we be thinking about it as these technologies become part of everyday life? Ragy brings a clinical and deeply considered perspective to the conversation. He explains that what we are seeing is not AI creating entirely new delusions out of thin air, but something more subtle and arguably more concerning. Large language models can reflect and reinforce ideas that already exist within a person's mind. For someone already vulnerable, that reinforcement can push a belief from uncertainty into absolute conviction. That shift, even if small, can have life-altering consequences. It raises uncomfortable questions about how persuasive technology interacts with fragile mental states. We also explore the comparison many people make with older internet rabbit holes, and why this new generation of AI tools feels different. There is something about conversational systems that mimic human interaction so convincingly that they can blur the line between reflection and validation. Ragy introduces a powerful analogy rooted in the story of Narcissus, which reframes the issue in a way that feels both timeless and unsettling. It is not about an external voice planting ideas, but about a mirror that becomes impossible to look away from. But this conversation is not about fear. It is about responsibility and awareness. We discuss practical steps that could help reduce risk, from how AI systems communicate their limitations, to the role of families and clinicians, and even the responsibility of tech companies to invest in research around early warning signs. There is a sense that we are only at the beginning of understanding this phenomenon, and that the decisions made now will shape how safely these tools evolve. So as AI continues to move closer to us, speaking in our language and responding in real time, how do we make sure it supports human wellbeing rather than quietly amplifying our most vulnerable moments? Useful Links Connect with Dr. Ragy Girgis, Professor of Clinical Psychiatry at Columbia University Time Magazine Article Visit the May Sponsors of Tech Talks Network and learn more about the NordLayer Browser.

Why are so many AI projects failing to deliver real business value, despite the hype and investment? In this episode, I sit down with Jay Litkey, SVP of Cloud & FinOps at Flexera, to explore the growing gap between AI ambition and measurable results. We discuss why findings from PwC reveal that only a small percentage of CEOs are seeing both revenue growth and cost savings from AI, and why the issue often comes down to a lack of clear outcomes, financial discipline, and governance rather than the technology itself. Jay shares what organizations are getting wrong, why many are stuck in experimentation mode, and what it really means to go back to basics in 2026. The conversation also reframes FinOps for the AI era, moving beyond cost control to a model that connects AI usage directly to business value, aligns finance with engineering, and introduces the guardrails needed to scale responsibly. If you are investing in AI or planning your next move, this episode offers a clear lens on how to turn potential into performance. Useful Links Connect with Jay Litkey from Flexera Learn More About Flexera Visit the May Sponsors of Tech Talks Network and learn more about the NordLayer Browser.

How do you bring people together to do better work when everything around them feels increasingly complex, distributed, and uncertain? In today's episode, I sat down with Jessica Guistolise from Lucid Software, and what struck me straight away was her belief that work has always been a group project, even if many organizations still behave as though it is not. Jessica shared how much of the friction we experience at work comes from misalignment, unclear expectations, and a lack of shared understanding. When teams are spread across time zones, systems, and now AI-powered workflows, those gaps only widen. Her perspective is simple but powerful. When people can actually see the work, rather than interpret it through documents, meetings, or assumptions, something shifts. Conversations become clearer, decisions become faster, and collaboration starts to feel human again. We also explored how visual collaboration platforms like those from Lucid Software are helping teams move away from scattered tools and disconnected workflows toward a more unified way of working. Jessica described it as having everything on one workbench, where teams can brainstorm, plan, and execute without constantly switching context. What really stayed with me was her focus on inclusivity in collaboration. Not everyone contributes in the same way, and visual environments can create space for different thinking styles, whether someone is outspoken, reflective, or somewhere in between. That idea of creating a shared language across teams, roles, and even personalities feels increasingly relevant in a world where communication often breaks down. Of course, no conversation right now would be complete without talking about AI. Jessica offered a refreshingly honest view. There is uncertainty, and there should be. But rather than avoiding it, she believes leaders need to make AI visible, map how it is used, define where human judgment matters, and encourage teams to experiment openly. One of the most interesting ideas she shared was reframing mistakes as early learnings. When teams feel safe to test, fail, and share what they discover, progress accelerates. When fear or blame enters the picture, everything slows down. We also touched on AI literacy and what it really means in practice. For Jessica, it comes down to clarity. Clear workflows, clear guardrails, and clear expectations about accountability. AI might assist, but humans remain responsible for outcomes. That mindset, combined with leadership that actively participates in experimentation, creates an environment where people feel confident stepping forward rather than holding back. This conversation left me thinking about how many organizations are still trying to layer AI onto unclear processes and expecting better results. Jessica's message is that clarity comes first, then technology can amplify it. So if work really is a group project, are we giving our teams the visibility and confidence they need to succeed, or are we still asking them to figure it out in the dark?

What happens when the very pricing model meant to speed up AI adoption ends up slowing it down? In this episode of Tech Talks Daily, I sit down with Sameet Gupte, CEO and co-founder of EvoluteIQ, to discuss a part of the enterprise AI story that still doesn't get enough attention. While so much of the conversation around AI focuses on models, copilots, and the latest agentic promises, Sameet brings the discussion back to a business reality that every enterprise leader understands. If the economics do not work, adoption stalls. And if success in a pilot makes the final rollout even more expensive, something has gone wrong long before the board signs off on scale. Sameet argues that many organizations are still trapped by legacy pricing structures built for an earlier generation of automation. Per-user and per-bot pricing may look manageable at the pilot stage. Once a company tries to expand automation across departments, processes, and geographies, the numbers can quickly stop making sense. That creates what many now call pilot purgatory, where a company proves something can work, but cannot justify taking it any further. It is a problem rooted in incentives, procurement, and fragmented technology stacks, and it is one that CFOs are watching very closely. What I found especially interesting in this conversation is how Sameet frames the issue. He believes most enterprises do not actually have an automation problem. They have an orchestration problem. In other words, the challenge is rarely a lack of tools. It is getting all the systems, workflows, approvals, data flows, and legacy infrastructure to work together to produce a clean business outcome. That idea changes the conversation from buying isolated features to rethinking the process as a whole. We also discuss why outcomes-based pricing is increasingly resonating with enterprise buyers. Sameet explains why predictable costs, transparent commercial models, and shared accountability are helping move automation conversations out of innovation teams and into the CFO's office. For public companies and large global enterprises, that matters. Leaders want fewer surprises, fewer overlapping vendors, and a much clearer line between spend and return. There is also a broader theme running through this episode about where the market is heading next. Sameet sees real urgency around vendor consolidation, enterprise simplification, and the need to rethink how AI is introduced into the business. His view is that companies need to pause, define what they actually want AI to do, and then choose tools that fit the business, rather than reshaping the business around the latest platform pitch. If you are trying to make sense of AI adoption beyond the hype, this conversation offers a practical and timely perspective on pricing, scale, and what real transformation could look like inside the enterprise. After listening, do you think the future of enterprise AI will be shaped as much by commercial models as by the technology itself, and what are you seeing in your own organization? Useful Links Connect with Sameet Gupte, CEO and co-founder of EvoluteIQ Learn More About EvoluteIQ

How many bad customer experiences does it take before someone walks away for good? In my conversation with Amitha Pulijala, we explore why the answer might be fewer than most businesses are prepared for, and what that means for anyone investing in AI-powered customer experience. New research from Cyara reveals a stark reality. Twenty-eight percent of consumers will abandon a brand after just one poor interaction, and nearly half will do the same after only two or three. That leaves very little room for error at a time when more organizations are introducing AI into customer journeys, often at speed and at scale. Amitha, who leads product strategy in the AI and CX space, brings a grounded perspective shaped by years of working with large enterprises and complex contact center environments. What stood out in our discussion is how the real challenge is no longer about whether AI can handle customer interactions. In many cases, it already can. The issue is whether customers trust it enough to let it try. We unpack the growing perception gap: 73 percent of consumers still believe human agents resolve issues faster, even though AI systems can deliver near-instant responses. That disconnect often comes down to past experiences, from bots that fail to understand context to systems that trap users in frustrating loops with no clear way out. There is also a clear line that customers draw around where AI belongs. Routine, high-volume tasks such as password resets or appointment confirmations are widely accepted. But when conversations shift toward financial security, healthcare, or legal advice, expectations change. People want human judgment involved and reassurance that the outcome is reliable. What makes this conversation particularly relevant is the generational divide shaping expectations. Younger users are far more open to AI-led interactions, provided they work seamlessly. Older generations remain more cautious, often preferring the certainty of speaking with a human. That creates a design challenge for businesses trying to serve everyone without alienating anyone. Throughout the episode, Amitha emphasizes that trust is built through experience, not intention. That means testing AI systems in real-world conditions, monitoring how they perform over time, and ensuring that when things do go wrong, the transition to a human feels smooth and informed rather than abrupt and frustrating. This is not a conversation about replacing humans with machines. It is about understanding where AI can add speed and efficiency, where it should support human agents, and where it should step back entirely. The organizations getting this balance right are not the ones deploying AI the fastest, but the ones validating it most carefully before customers ever see it. As businesses race to embed AI at every touchpoint, a bigger question emerges. Are we building systems that customers actually trust, or are we creating new points of friction that push them away? Useful Links Connect with Amitha on LinkedIn Survey Data Cyara Website Follow Cyara on LinkedIn

What does it really take to move AI from endless experimentation into something that creates real business value? In this episode, I sat down with Tom Alexander, Head of Innovation and Transformation at CrossCountry Consulting, to talk about why so many organizations still struggle to turn AI ambition into meaningful outcomes. Tom works closely with executive and CFO teams that are either unsure where to begin or frustrated that early AI efforts have not delivered what they hoped for. We talked about why this is rarely just a technology issue. In many cases, the real blockers are ownership, change management, weak alignment across the business, and a failure to connect AI initiatives to the problems that matter most. One of the big themes in our conversation was the need to treat AI as an enterprise-wide program rather than a collection of isolated tools. Tom shared how leaders can focus on business processes first, identify where automation can genuinely improve performance, and avoid getting distracted by hype. We also unpacked the growing accountability challenge around AI, including who should own it, how stakeholders can align, and why strong foundations in data, governance, and training matter so much. This episode is packed with practical takeaways for anyone trying to make sense of AI adoption inside a business. If you are trying to figure out where to start, how to scale, or how to avoid another stalled initiative, there is a lot in here for you. After listening, I would love to hear your thoughts. How is your organization approaching AI, and where do you think most businesses are still getting it wrong? Useful Links CrossCountry website Connect with Tom Alexander on LinkedIn Field Notes podcast

Is the endpoint still just a device, or has it quietly become one of the most important control points in modern enterprise security? Recording live from IGEL Now And Next in Miami, I sat down once again with Darren Fields for what has become an annual check-in on how fast the industry is really changing. And this time, the conversation feels very different. Over the last 12 months, the discussion has moved well beyond traditional endpoint management. From global supply chain pressure driven by AI demand to rising hardware costs and unpredictable refresh cycles, the assumptions that once shaped endpoint strategy are starting to fall apart. Darren shares how organizations are now being forced into difficult decisions, absorb rising costs, delay investment, or rethink the model entirely. We also explore how that shift is changing the conversation at the leadership level. What was once seen as a procurement decision is increasingly being reframed as a resilience strategy. Extending hardware life, reducing dependency on supply chains, and maintaining operational continuity are becoming just as important as performance and cost. Security, of course, sits at the center of it all. With the majority of breaches still originating at the endpoint, Darren highlights how organizations are starting to rethink where they focus their efforts. Rather than focusing solely on data centers and cloud environments, there is growing recognition that control, visibility, and enforcement must occur at the edge. The conversation also touches on the reality of modern cyber threats. From constant attack attempts to incidents that leave organizations offline for weeks, the challenge is no longer just restoring systems but restoring access. And that shift has major implications for how recovery and continuity are designed moving forward. We also look at the growing convergence of IT and OT, the role of contextual access, and the balancing act between stronger security and user experience. With organizations at very different stages of their journey, there is no single path forward, but there is a clear sense that change is already underway. So as the pace of technology, risk, and demand continues to accelerate, one question remains. Are organizations adapting fast enough, or are they still relying on models that no longer reflect the world they are operating in? What do you think, are we finally seeing a shift toward treating the endpoint as a strategic priority, or is there still a gap between awareness and action?

What does it really take to move from talking about Zero Trust… to actually making it work in the real world? Recording live from IGEL Now And Next in Miami, I caught up with John Walsh for what has now become something of a tradition, our third conversation together, and one that reflects just how much has changed in the last 12 months. When we last spoke, the focus was on securing the edge and rethinking security through a preventative lens. This time, the conversation has expanded from IT to OT, from devices to platforms, and from theory to real-world implementation across manufacturing floors, healthcare environments, and government organizations. John shared how IGEL is increasingly being adopted as a global standard across both IT and operational environments, bringing new challenges and new insights. From kiosks and signage on factory floors to shared workstations in hospitals, the need for persona-based and now context-aware access is becoming far more than a technical concept. It is shaping how organizations think about identity, risk, and control at scale. We also explored how the idea of the "adaptive secure desktop" is evolving beyond traditional VDI thinking. Instead of static devices, the focus is shifting toward environments that respond dynamically to the user, their role, their location, and the level of risk in that moment. It raises an important question. How do you deliver that level of control without introducing friction for the user? AI inevitably entered the conversation, but not in the way many might expect. Rather than focusing on features, John highlighted the acceleration of threat velocity. The time between vulnerability discovery and exploitation is shrinking rapidly, and with AI amplifying that speed, traditional detection and response models are struggling to keep up. The implication is clear. Security strategies need to shift toward prevention and control, not just reaction. We also touched on emerging challenges around agentic AI, non-human identities, and the need to apply Zero Trust principles beyond people to machines. As organizations begin to explore these new models, questions around identity, access, and guardrails are becoming more complex and more urgent. And throughout the conversation, one theme kept coming back and reducing complexity while increasing control. Whether it is through immutable operating systems, centralized policy enforcement, or contextual access, the goal is to simplify the environment while strengthening security outcomes. As organizations continue their journey toward modernization, one question remains: Are we still layering new technology onto old models, or are we ready to rethink how access, identity, and control are delivered from the ground up? What do you think, is Zero Trust finally becoming real at the endpoint, or is there still a gap between strategy and execution?

How long would it actually take your organization to recover every endpoint after a major cyber incident? Recording live from IGEL Now And Next in Miami, I sat down with James Millington to explore a question that most businesses think they've answered, but rarely have. Because when you move beyond theory and start mapping out the real process, the numbers tell a very different story. James shared examples from real organizations that tried to calculate recovery at scale. One estimated it would take over 5,000 person-hours to rebuild their estate. Another believed they could recover quickly, until they realized the scale of their environment made that assumption unrealistic. It raises a deeper question. Are we focusing too much on recovery and not enough on resilience? The conversation quickly moved into what James calls the "endpoint recovery gap." While most organizations have invested heavily in data center resilience, failover environments, and backup strategies, far fewer have a clear plan for reconnecting users when endpoints are compromised. And without a working endpoint, even the most advanced infrastructure becomes inaccessible. We also explored why so many organizations continue to rely on reimaging devices as a primary recovery strategy, despite the time, complexity, and operational disruption it creates. In many cases, it's not just slow. It's impractical at scale. And perhaps more concerning, some organizations still admit to having no defined plan at all. One of the most memorable moments in the conversation came through a simple analogy. For years, we've been carrying the weight of outdated endpoint strategies, even though the solution has been sitting in front of us. Just like it took thousands of years to put wheels on a suitcase, the shift toward simpler, more resilient models often requires a moment of realization before change actually happens. As application delivery continues to move toward SaaS, DaaS, and cloud environments, the role of the endpoint is also being redefined. Analysts are now calling for a move toward immutable, non-persistent endpoints that reduce attack surface and enable faster recovery. But as James points out, the real challenge is not awareness. It's an action. As organizations continue to invest in security, infrastructure, and AI, one question remains: Are we still planning for recovery from failure, or are we finally designing systems that avoid it in the first place? What do you think, are businesses ready to rethink endpoint strategy, or are we still carrying the baggage of the past?

What does it actually take to rethink the endpoint in a world shaped by AI, Zero Trust, and the growing convergence of IT and operational technology? Recording live from IGEL Now and Next in Miami, I sat down with Matthias Haas to unpack what he describes as a genuine transformation moment for enterprise computing. This wasn't a conversation about incremental change. It was about challenging long-held assumptions around devices, security models, and how work is delivered in modern organizations. Matthias shared how the idea of the "adaptive secure desktop" is moving beyond traditional thinking around VDI and desktop delivery. Instead of treating endpoints as static devices, the focus is shifting toward dynamic, context-aware environments that respond to who the user is, where they are, and what they need access to in that moment. It raises an important question for any organization. Are we still designing for devices, or for outcomes? We also explored the growing complexity that comes with flexibility. With multiple ways to deliver applications across SaaS, DaaS, browsers, and local environments, there's a real risk of recreating the same fragmented systems companies are trying to move away from. Matthias offered insight into how orchestration, policy enforcement, and centralized management can help bring order to that complexity without adding friction for users. Another key theme was the shift from static security models to continuous, contextual decision-making. As organizations move toward Zero Trust, the ability to evaluate risk in real time becomes essential. But that raises a delicate balance. How do you strengthen security without slowing people down? And how do you ensure that the user experience doesn't become the casualty of tighter controls? The conversation also touched on the challenges of bringing IT and OT environments together. While the opportunity to unify these worlds is significant, the realities are far more complex. Different risk tolerances, legacy systems, and operational priorities all come into play. Matthias offered a candid perspective on what it will take to make that convergence work in practice, not just in theory. So as enterprises continue to rethink their infrastructure in an AI-driven world, one question keeps coming up. Are we simply layering new technology onto old models, or are we ready to fundamentally change how the endpoint fits into the bigger picture? What do you think, are organizations truly ready to embrace adaptive, context-driven computing, or are we still holding on to outdated ways of working?

How do you rebuild an entire industry that most people accept as slow, fragmented, and frustrating? In this episode, I sit down with Dan Lifshits, co-founder of Dwelly, to explore how AI is being used to rethink the rental market from the inside out. What struck me most in this conversation is how Dwelly isn't approaching property management as a software layer you simply bolt on. Instead, they are acquiring rental agencies and rebuilding the operating model itself, embedding AI into every workflow, from tenant communication to maintenance coordination and rent collection. It is a very different mindset, and one that challenges how many businesses think about digital transformation. Dan brings a fascinating perspective shaped by his time competing in high-growth environments at companies like Uber and Gett. We talk about what those years taught him about scaling complex, operational businesses and how those lessons now apply to one of the largest and least digitized sectors in the economy. There is a clear parallel between ride-hailing and rentals, both are fragmented, both rely on two-sided marketplaces, and both have historically depended on manual processes that struggle to scale. As Dan explains, "long-term residential rentals ticks very similar boxes" to ride-hailing, which makes it ripe for reinvention. We also spend time unpacking what an AI-powered rollup actually means in practice. This is where the conversation becomes particularly interesting for founders and business leaders. Rather than selling software into traditional businesses and hoping for adoption, Dwelly takes control of both the operations and the technology. That allows them to redesign workflows, remove bottlenecks, and deliver a more consistent experience for landlords and tenants alike. The result is a model where a single operator can manage hundreds, even thousands, of properties with a level of service that would have been impossible just a few years ago. Of course, there are bigger implications here too. If this model works at scale, it raises questions about how many other service industries could be rebuilt in a similar way. It also highlights the growing role of venture-backed rollups, particularly with firms like General Catalyst backing this approach as a new investment category. But it is not without challenges. Changing operational behavior, integrating acquisitions, and maintaining service quality while scaling fast are all complex problems that cannot be solved by technology alone. This episode left me thinking about where the real value in AI sits. Is it in the tools themselves, or in the willingness to rethink how a business actually operates? And if AI can transform something as established as property management, which industries are next in line for the same kind of reinvention? I would love to hear your thoughts. Are AI-powered rollups the future of service industries, or do they introduce a new set of risks we are only beginning to understand?

How are businesses supposed to grow when technology is moving faster than regulation, customer expectations keep shifting, and AI is changing the rules in real time? In this episode, I sat down with Derya Matras, Vice President of EMEA at Meta, to talk about what growth really looks like for businesses operating in Europe, the Middle East, and Africa right now. This was a fascinating conversation because it went far beyond the usual talking points around AI and advertising. Derya brought a broader view of the pressure many businesses are under today, from macroeconomic uncertainty and political complexity to changing consumer behavior, tighter margins, and the need to adapt to a world where AI is now part of everyday decision making. What really stood out to me was her point that this moment is about far more than adopting new tools. It is about culture, leadership, and having the discipline to know what you are actually trying to achieve. Derya spoke about the importance of having a clear North Star goal, getting the foundations right, and making sure businesses are not simply adding AI into broken systems or unclear strategies. Because as she put it, AI can make everything more powerful, but it can also amplify mistakes. That is such an important point, especially at a time when so many companies are racing to show they are doing something with AI without always knowing what success should look like. We also explored how Meta sees its role in supporting growth across Europe's digital economy. Derya shared insights into how Meta's platforms are helping businesses of all sizes reach customers in ways they simply could not do on their own. For large companies, that may mean better measurement, faster optimization, and more personalized engagement. But for smaller businesses, the stakes can be even higher. She shared examples that brought those numbers to life, including entrepreneurs using Instagram and WhatsApp to reach global markets, support their families, and create jobs in ways that would have been out of reach just a few years ago. Another part of the conversation I found especially interesting was the tension between innovation and regulation in Europe. Derya was honest about how complicated and fragmented the environment has become, and how that complexity can slow progress or delay the rollout of new products. At the same time, she made a strong case that Europe still has a real opportunity ahead if it can find the right balance. That balance matters not only for big tech companies, but for startups, small businesses, creators, and the wider economy that increasingly depends on digital tools to compete and grow. We also talked about creativity, measurement, AI assistants, wearables, and even how these technologies are beginning to shape life at home as much as at work. It all made for a conversation that felt very current, but also deeply practical. So as AI becomes woven into advertising, business operations, and everyday life, are organizations truly building the foundations they need to benefit from it, or are they still chasing the next shiny thing? And what do you think Europe needs to get right to make sure innovation and opportunity can keep moving forward?