Tech Talks Daily

• 3423: Johnson Controls Explains How to Cut Data Center Cooling Energy by 40%

  In this episode of Tech Talks Daily, I’m joined by Todd Grabowski from Johnson Controls to unpack the physics, products, and design choices shaping the next generation of data center cooling. It’s a practical conversation that moves from chips and compressors to water, power, and land constraints, and what it really takes to keep modern infrastructure reliable at scale. Todd brings three decades of experience to the table and a front-row view of how Johnson Controls and the York brand have kept their focus on energy efficiency, reliability, and sustainability for more than a century. That longevity matters when the market is moving fast. He explains why cooling now sits alongside power as the defining constraint for data centers, and why roughly forty percent of a facility’s energy can be spent on cooling rather than computation. If you lead technology, finance, or facilities, that single number should focus the mind. Todd walks through Johnson Controls’ YVAM platform and the York magnetic bearing centrifugal compressor at its core, with real numbers on what that means in practice. Consuming around forty percent less energy than typical cooling devices of the past five years and operating in ambient conditions up to fifty-five degrees Celsius, it is designed for the reality of hotter climates and denser loads. The naval pedigree of the driveline is a nice twist, since it was originally built for quiet and high-reliability conditions long before hyperscale data centers needed the same. Sustainability threads through the entire discussion. Todd lays out how the company holds itself to internal targets while engineering solutions that reduce customer resource use. We talk about closed-loop designs that do not consume water, careful refrigerant choices with ultra-low global warming potential, and product footprints that consider carbon impact from the start. It is a useful reminder that sustainability is a systems problem, not a single feature on a spec sheet. I was especially interested in the three resources Todd says every modern cooling strategy must balance. Land, because you need somewhere to reject heat. Power, because every watt pulled into cooling is a watt not used for compute. Water, because many regions are already under stress and consumption cannot be the answer. Good design weighs these factors against the climate, the workload profile, and the operational model, then standardizes wherever possible so the same unit can run efficiently in Scandinavia or Dubai without special tweaks. We also dig into what AI means internally for Johnson Controls. It is showing up in manufacturing lines, speeding up design cycles, and improving the fidelity of compressor and heat transfer models. That translates into quicker time to market and more confidence in performance envelopes. On the market side, Todd is clear that demand has not softened. If anything, efficiencies tend to unlock more use cases, and the net effect is more workloads and continued pressure on facilities to cool them well. If your team is wrestling with when to adopt liquid cooling, how to reduce PUE through smarter chiller choices, or how to plan for climate variability across a global footprint, this episode offers an honest, grounded view from someone who has shipped the hardware and lived with its trade-offs. It also doubles as a quiet celebration of engineering craft. The kind that rarely makes headlines, yet underpins everything we build in the AI age. ********* Visit the Sponsor of Tech Talks Network: Land your first job  in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA    

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• 3422: Meet Symphion and the Print Fleet Cybersecurity as a Service

  I’ve spent years talking about endpoint security, yet printers rarely enter the conversation. Today, that blind spot takes center stage. I’m joined by Jim LaRoe, CEO of Symphion, to unpack why printers now represent one of the most exposed corners of the enterprise and what can be done about it. Jim’s team protects fleets that range from a few hundred devices to tens of thousands, and the picture he paints is stark. In many organizations, printers make up 20 to 30 percent of endpoints, and almost all of them are left in a factory default state. That means open ports, default passwords, and little to no monitoring. Pair that with the sensitive data printers receive, process, and store, plus the privileged connections they hold to email and file servers, and you start to see why attackers love them. We trace Symphion’s path from a configuration management roots story in 1999 to a pivot in 2015 when a major printer manufacturer invited the company behind the curtain. What they found was a parallel universe to mainstream IT. Brand silos, disparate operating systems, and a culture that treated printers as cost items rather than connected computers. Add in the human factor, where technicians reset devices to factory defaults after service as second nature, and you have a recipe for recurring vulnerabilities that never make it into a SOC dashboard. Jim explains how Symphion’s Print Fleet Cybersecurity as a Service tackles this mess with cross-brand software, professional operations, and proven processes delivered for a simple per-device price. The model is designed to remove operational burden from IT teams. Automated daily monitoring detects drift, same-day remediation resets hardened controls, and comprehensive reporting supports regulatory needs in sectors like healthcare where compliance is non-negotiable. The goal is steady cyber hygiene for printers that mirrors what enterprises already expect for servers and PCs, without cobbling together multiple vendor tools, licenses, and extra headcount to operate them. We also talk about the hidden costs of DIY printer security. Licensing multiple management platforms for different brands, training staff who already have full plates, and outages caused by misconfigurations all add up. Jim shares real-world perspectives from organizations that tried to patch together a solution before calling in help. The pattern is familiar. Costs creep. Vulnerabilities reappear. Incidents push the topic onto the CISO’s agenda. Symphion’s pitch is straightforward. Treat print fleets like any other class of critical infrastructure in the enterprise, and measure outcomes in risk reduction, time saved, and fewer surprises. If you are commuting while listening and now hearing alarm bells, you are not alone. Think about the printers scattered across your offices and clinics. Consider the data that passes through them every day. Then picture an attacker who finds default credentials in minutes and uses a printer to move across your network.  Tune in for a fast, practical look at a risk hiding in plain sight, and learn how Symphion’s Print Fleet Cybersecurity as a Service can help you close a gap that attackers know too well. ********* Visit the Sponsor of Tech Talks Network: Land your first job  in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA  

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• Marketing Intelligence After Cookies: How Funnel Turns Data Into Decisions

  Marketing teams used to have a simple enough job: follow the click, count the conversions, and shift the budget accordingly. But that world is gone. GDPR, iOS restrictions, and browser-level changes have left most attribution models broken or unreliable. So what now? In this episode, I sat down with Fredrik Skansen, CEO of Funnel, to unpack how marketing intelligence actually works in a world where data is partial, journeys are fragmented, and the old models don’t hold. Since founding Funnel in 2014, Fredrik has grown the company into a platform that supports over 2,600 brands and handles reporting on more than 80 billion dollars in annual digital spend. That scale gives him a front-row seat to the questions every CMO and CFO are asking right now. Fredrik explains why last-click attribution didn’t just become inaccurate. It became misleading. With tracking capabilities stripped down and user signals disappearing, the industry has had to move toward modeled attribution and real-time optimisation. That only works if your data is clean, aligned, and ready for analysis. Funnel’s platform helps structure campaigns upfront, pull data into a unified model, apply intelligence, push learnings back into the platforms, and produce reporting that makes sense to the wider business. This isn’t about dashboards. It’s about decisions. We also talk about budget mix. Performance channels may feel safe, but Fredrik points out they are also getting more expensive. When teams bring brand and mid-funnel activity back into the measurement framework, the picture often changes. He shares how Swedish retailer Gina Tricot grew from 100 million to 300 million dollars in three years, in part by shifting spend to brand and driving demand earlier in the customer journey. That move only felt safe because the data supported it. AI adds another layer. With tools like Perplexity reshaping search behavior and the web shifting from links to answers, click-throughs are drying up. But it’s not the end of visibility. Content still matters. So does structure. The difference is that now your reader might be an AI model, not a human. That requires a rethink in how brands approach discoverability, authority, and engagement. What makes Funnel interesting is that it doesn’t stop at analytics. The platform feeds insight back into action, reducing waste and creating tighter loops between teams. It also works for agencies, which is why groups like Havas use it across 40 offices through a global agreement. If you're tired of attribution theatre and want to understand what marketing measurement looks like when it’s built for reality, this episode gives you a clear, usable view. Listen in, then tell me which decision you're still guessing on. Because marketing can be measured. Just not the way it used to be. ********* Visit the Sponsor of Tech Talks Network: Land your first job  in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA    

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• Why FeatureOps Might Be the Future of Software Delivery

  I invited Egil Østhus to unpack a simple idea that tends to get lost in release day pressure. DevOps gets code to production quickly, but users experience features, not pipelines. Egil is the founder of Unleash, an open source feature management platform with close to 30 million downloads, and he argues that the next step is FeatureOps. It is a mindset and a set of practices that separate deployment from release, so teams can place code in production, light it up for a small cohort, learn, and only then scale out with confidence. Here is the thing. Controlled rollouts, clear telemetry, and fast rollback reduce risk without slowing teams down. Egil explains how FeatureOps connects engineering effort to business outcomes through gradual exposure, full stack experimentation, and what he calls surgical rollback. Instead of ripping out an entire release when one part misbehaves, teams can disable the offending capability and keep the rest of the value in place. It sounds straightforward because it is, and that is the point. Less drama, more learning, better results. We also talk about culture. When releases repeatedly disappoint, trust between product and engineering frays. Egil shares examples where Unleash helped a hardware and software company move from blame to shared ownership by making rollout plans visible and collaborative. Another client, an ERP vendor, discovered that early feedback from a small group of users allowed them to ship a leaner version that met the need without months of extra scope. That is how FeatureOps saves money and tempers expectations while still delighting customers. AI enters the story too. Code is shipping faster, but reliability can wobble when autogenerated changes move through pipelines. Egil sees feature management as a practical control plane for this new reality. Feature flags provide a real time safety net and, if needed, a kill switch for AI powered functionality. Teams can keep experimenting while protecting users and brand equity. If you want to move beyond release day roulette, this episode offers a practical playbook. We cover privacy first design, open source flexibility, and why metadata from FeatureOps will help leaders study how their organizations truly build. To learn more, visit getunleash.io or search for Unleash in your favorite tool, then tell me how you plan to measure your next rollout’s impact. ********* Visit the Sponsor of Tech Talks Network: Land your first job  in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA      

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• From Bots To Agents: Building Trustworthy Autonomy With Hakkōda, an IBM Company

  I invited Atalia Horenshtien to unpack a topic many leaders are wrestling with right now. Everyone is talking about AI agents, yet most teams are still living with rule based bots, brittle scripts, and a fair bit of anxiety about handing decisions to software. Atalia has lived through the full arc, from early machine learning and automated pipelines to today’s agent frameworks inside large enterprises. She is an AI and data strategist, a former data scientist and software engineer, and has just joined Hakoda, an IBM company, to help global brands move from experiments to outcomes. The timing matters. She starts on the 18th, and this conversation captures how she thinks about responsible progress at exactly the moment she steps into that new role. Here’s the thing. Words like autonomy sound glamorous until an agent faces a messy real world task. Atalia draws a clear line between scripted bots and agents with goals, memory, and the ability to learn from feedback. Her advice is refreshingly grounded. Start internal where you can observe behavior. Put human in the loop review where it counts. Use role based access rather than feeding an LLM everything you own. Build an observability layer so you can see what the model did, why it did it, and what it cost. We also get into measurements that matter. Time saved, cycle time reduction, adoption, before and after comparisons, and a sober look at LLM costs against any reduction in FTE hours. She shares how custom cost tracking for agents prevents surprises, and why version one should ship even if it is imperfect. Culture shows up as a recurring theme. Leaders need to talk openly about reskilling, coach managers through change, and invite teams to be co creators. Her story about Hakoda’s internal AI Lab is a good example. What began as an engineer’s idea for ETL schema matching grew into agent powered tools that won a CIO 100 award and now help deliver faster, better outcomes for clients. There are lighter moments too. Atalia explains how she taught an ex NFL player the basics of time series forecasting using football tactics. Then she takes us behind the scenes with McLaren Racing, where data and strategy collide on the F1 circuit, and admits she has become a committed fan because of that work. If you want a practical playbook for moving from shiny demos to dependable agents, this episode will help you think clearly about scope, safeguards, and speed. Connect with Atalia on LinkedIn, explore Hakoda’s work at hakoda.io, and then tell me how you plan to measure your first agent’s value. ********* Visit the Sponsor of Tech Talks Network: Land your first job  in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA  

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