IT Infrastructure as a Conversation

Connecting Offshore AI With Resilient Infrastructure at Tampnet

How do you keep industrial systems useful and responsive when they operate far offshore, repairing a connection takes specialist transport, and a link to shore cannot be taken for granted? On Infra as a Service, I'm joined by Øyvind Skjervik, CTO of Tampnet, to discuss the infrastructure connecting offshore platforms, vessels, and wind farms. While much of the AI conversation focuses on chips and data centers, these environments bring a different set of questions. Where should data be processed, which applications need a fast response, and what must continue working if the preferred communication path becomes unavailable? Øyvind explains that reliability involves consistent performance, predictable latency, and resilience alongside uptime. Tampnet uses a combination of fiber, microwave, satellite, and private 4G and 5G connectivity. The mix depends on the location and the application. He is clear that the company cannot deliver the same capacity and latency everywhere, which makes understanding the operational requirement part of network design. We discuss why offshore redundancy needs to be considered before anything goes wrong. Geography, weather, corrosion, vibration, and power constraints all affect the choices available. A repair may depend on a vessel, a helicopter journey, or access controlled by the customer. Øyvind describes designing for continuity, with multiple transport paths and local processing, so an operation can continue while a fault is addressed. Application priorities matter as capacity changes. Robotics may depend on low latency, worker communication may place greater emphasis on coverage, and video analytics can demand both bandwidth and local processing. Øyvind describes connectivity modes that determine which services need support during degraded conditions. The discussion connects network performance with what people and equipment actually need to do, rather than treating every data flow alike. Industrial AI adds to that pressure. Video can consume substantial capacity, while large numbers of smaller devices can create problems if they all transmit at once. Øyvind argues that orchestration, prioritization, and security can become constraints alongside bandwidth. Connecting operational technology with IT also brings questions about access, segmentation, and organizational responsibility. Edge computing is an expanding part of Tampnet's work. Øyvind describes extending services beyond connectivity to the compute environment where industrial applications and AI-related processing can run. We discuss opportunities involving robotics, drones, video analytics, predictive maintenance, and safety. The interview does not present measured customer savings or a benchmark for those applications, but it explains why the location of computing resources affects the network and the operation around it. There is also an AI use case within connectivity itself. Øyvind describes using application requirements and network measurements to steer traffic across available communication paths. A latency-sensitive flow can receive different treatment from a backup or file transfer. It is a practical example of AI being used to help manage infrastructure, alongside the networks supporting other AI applications. The conversation returns repeatedly to boundaries. Tampnet provides an infrastructure foundation and works with customers on security and continuity, while it does not control every application or operating procedure. Testing degraded modes, rehearsing failover, and agreeing responsibilities remain part of making a connected service dependable. Please share your thoughts. As industrial AI reaches more remote environments, how should organizations decide which processing stays local and what level of connectivity each operation really needs?

Putting AI Compute to Work With ThisWay Global

Why can an organization invest in expensive AI infrastructure while researchers and employees still struggle to get the computing time they need? In this episode of IT Infrastructure as a Conversation, I speak with Stephan Fabel, CEO of ThisWay Global, about the decisions between buying hardware and producing useful results. Our conversation connects GPU utilization, workload placement, sovereign AI, university research, and the responsibilities that come with building a data center in a local community. Stephan describes two different problems that can disappear inside the same conversation about scale. One team may need thousands of GPUs to complete a demanding training job. Another institution may have many teams competing for a shared pool of computing resources. Software designed around a single large job does not automatically resolve the second problem. Research teams also build toward larger runs gradually, which can leave reserved capacity unused during development. For organizations managing mixed hardware across several locations, the question becomes where each job should run and why. A deadline, budget, data location requirement, or need for an auditable record can change the answer. Stephan argues that workload placement should make those tradeoffs understandable. Starting a job immediately may produce some progress without improving its completion time, while preventing other teams from using the same resources. We discuss how ThisWay Global approaches that problem through its Amalgamy software. The proposed benefit is matching jobs with suitable computing resources while respecting operational constraints. Stephan's account also raises a useful measurement question. Peak activity on a GPU tells us little about whether an institution is getting useful work from the capacity it pays for across the whole project. The conversation then turns to the planned research and AI compute center at Texas A&M's RELLIS campus. Stephan describes an effort to bring industry partners, academia, and research projects together around access to accelerated computing. He points to possible applications in crop yields, energy distribution, grid management, and weather prediction. These are examples of the research he hopes access can support, rather than results established by the planned facility. Sovereign AI introduces another set of responsibilities. For Stephan, control includes knowing where processing happens, retaining inspection rights, understanding who has access, and keeping a record of how data is handled. His description of being able to visit the infrastructure gives a physical meaning to a term often discussed through abstract policies. We also consider the operational relationship behind that control and the people needed to maintain it. Community participation runs throughout the discussion. A data center can create demand for roads, services, and qualified workers. Stephan argues that operators should plan training and routes into technical work alongside the facility itself. His examples include vocational education, equipment operation, and opportunities for students to work with research infrastructure. These are proposals and priorities he describes, rather than guarantees of local jobs. One story brings the access problem into focus. During an Uber journey in College Station, Stephan says a student began describing difficulties getting GPU time. The story illustrates his concern that access to computing resources can shape both research opportunities and preparation for employment. Hardware investment becomes a question about who can use the infrastructure, under what conditions, and to what end. I'd welcome your thoughts. How should your organization balance utilization, operational control, and access when deciding where its next AI infrastructure investment belongs?

Turning Operational Governance Into Business Value With IDS

What happens when an organization reports healthy infrastructure while weak data, hidden spreadsheets, and undocumented processes quietly increase its financial exposure? In this episode of IT Infrastructure as a Conversation, I speak with James Briers, Founder and CEO of Intelligent Delivery Solutions and creator of GovScape. James works where infrastructure, data, governance, and commercial reality meet. His argument is that governance has to move beyond policies, dashboards, and periodic audits if businesses want to understand their actual operating condition. James describes a recurring problem in professional services. Financial information passes through several systems, but inconsistent records leave finance teams invoicing too much or too little. Employees then spend additional time investigating discrepancies and manually reconciling the numbers. His proposed response begins with data integrity across those systems, followed by continuous monitoring that identifies issues before they affect billing. A person remains involved to validate the final result, but the repetitive reconciliation work becomes easier to manage. We discuss how continuous governance differs from a six-month or annual compliance exercise. James uses the idea of constant pruning. Rather than waiting for a large audit to expose a backlog of issues, teams can see smaller changes as they happen, assign responsibility, and correct them while the operational impact remains manageable. The goal is not another dashboard. It is an accurate view of the current state and a clear action for the person who owns the response. That information becomes far easier to discuss at board level when organizations translate governance gaps into commercial terms. James believes controls, risks, data quality, and unresolved issues should be connected with the potential cost to the business. This turns governance from a line on a transformation plan into a boardroom discussion about cash, risk exposure, and return on investment. Cloud migration is another area where good intentions can carry old problems forward. Moving unreliable data into a modern platform can produce a better-looking system with the same contradictions underneath. James recommends treating data quality as the starting point, repairing known problems before migration, and continuing to monitor the information once it reaches the new environment. We also discuss shadow systems and shadow AI. The spreadsheet on one employee's computer may sit outside the official service map while remaining central to a business process. AI agents can create similar dependencies at greater speed. James argues that organizations need visibility into which workflows use AI, what data those agents access, where that data moves, and whether the result creates a return or another source of cost. Can companies turn governance into a continuous business discipline before hidden data problems reach the customer or the board? Listen to the episode and share your thoughts with me.

Moving IT Service Management Beyond Tickets With Xurrent

What does it mean when an IT department meets every SLA, closes tickets quickly, and produces healthy reports, but employees across the business are still unhappy with the service they receive? In this episode of IT Infrastructure as a Conversation, I speak with Phil Christianson, Chief Product Officer at Xurrent, about why IT service management needs to move beyond ticket processing and focus more closely on the business outcomes technology teams support. Phil describes infrastructure support as a collaborative activity. When a website, application, or business system fails, restoring service often involves operations, networking, security, developers, application teams, and business owners. A blame-focused culture slows the response because teams begin defending their own areas instead of solving the problem together. He shares an example from a large ecommerce business where one team updated product prices while another sent outdated pricing information to Google for product listing ads. When the prices did not match the retailer’s website, Phil says Google removed the company’s ads for two days. The root cause was not a complex technical failure, but a lack of communication between internal teams. The story also raises questions about how companies measure IT performance. Ticket volumes, SLA breaches, resolution speed, and first-contact resolution provide useful operational data, but they do not fully explain whether employees trust IT or whether a service helps people do their jobs. Phil recalls a CIO joking that a service desk could report excellent KPIs while the whole company disliked working with IT. His point is that metrics need to be considered alongside customer satisfaction, employee experience, and business outcomes. He also questions whether many IT processes still reflect how organizations operate today. A workaround designed for one request a week can become a major burden when demand grows and the person holding the knowledge leaves. Xurrent encourages customers to review processes roughly every eighteen months and ask whether they would make the same decisions today. Reviews can expose outdated request templates, undocumented knowledge, repetitive manual work, and overloaded roles. AI adds another layer. Phil believes employees should receive a useful automated answer quickly, with a clear route to a person when needed. Large language models can locate knowledge, summarize requests, detect duplicates, improve routing, and identify missing information. The risk is that AI can confidently return outdated or incorrect information. IT teams therefore need to review responses and improve.

Building a Lean AI Native Banking Platform With myTU

Does a modern banking platform need a large collection of microservices and separate engineering teams, or can a compact monolith offer greater reliability and speed? In this episode of IT Infrastructure as a Conversation, I speak with Tomas Navickas, co-founder and CTO of myTU, about the architecture behind a cloud based digital banking platform and the lessons he has carried from two decades of building transactional systems. Tomas begins by challenging a familiar assumption. Infrastructure often receives the blame when an application performs badly, even though the application may be making poor use of the resources already available. His earlier work involved systems that managed large numbers of payment terminals, security changes and transactions that could not simply stop for maintenance. That experience also exposed the operational burden of running server rooms, backup facilities, generators and specialist infrastructure teams. When Tomas began building the current myTU platform, he chose mature cloud services and kept the architecture deliberately simple. The engineering team did not need to maintain physical infrastructure, and the application could be designed around services with known reliability. He also rejected the idea that every modern banking platform should default to microservices. His reasoning is organizational as much as technical. Tomas argues that software architecture and company structure tend to mirror one another. A distributed collection of services often produces distributed teams, additional handoffs and slower decisions. myTU chose one core engineering team and a compact monolith because the company wanted to remain lean, make decisions quickly and upgrade the platform as one consistent system. That choice comes with tradeoffs. Owning much of the technology stack gives myTU control over priorities, language support and product changes. A small team can react within a day when something important appears. The burden is finding engineers who can hold a wider understanding of the system, accept broader responsibility and work comfortably without the narrow boundaries found in a larger company. We also discuss what AI native banking means in practice. At myTU, AI is used across internal processes rather than being confined to customer support. Agents can parse poorly structured information, extract knowledge, summarize cases, gather evidence and suggest decisions. A person can then review the material and move the case forward with a clearer record of why the decision was made. Business onboarding shows where that approach can remove friction. Tomas says the largest problem is often delay. If a customer receives the next precise question immediately, they are much more likely to complete the process. A delay of 15 minutes, an hour or a day can cause them to abandon it. An agent can inspect a document, identify what is missing and ask for the correct item while the customer is still present. The conversation closes with a warning about speed. Tomas believes teams can move quickly, but shortcuts create hidden liabilities. Missing evidence, incomplete recovery paths and temporary design decisions can resurface months or years later. If a shortcut is used to test whether an idea has merit, the system must then be rebuilt properly. Is the industry too quick to equate modern architecture with distributed services, and could a smaller, more coherent system offer a better foundation for regulated AI? Listen to the episode and share your thoughts with me.

Solving the $2,000 Per Hour GPU Problem With Lumen

What happens when an enterprise invests heavily in AI compute but cannot move data quickly enough to keep those expensive processors working? In this episode of Infrastructure as a Conversation, I speak with Jim Fowler, Chief Technology and Product Officer at Lumen, about why network performance is becoming one of the defining factors in enterprise AI ROI. Jim brings experience from both sides of the relationship. He previously led technology programs inside large enterprises and served on Lumen’s board before joining its management team. His description of Lumen as a logistics company for information provides a useful way to understand the network’s role. Models can process information, but the network must deliver the right data to the right location at the required speed. Our conversation begins with the $2,000 per hour GPU problem. Jim recalls speaking with a Fortune 100 CIO whose company was training its own model using data stored across multiple clouds and regions. The company had GPU availability, but moving petabytes of data to those processors had become its largest bottleneck. Expensive compute was waiting for information. We discuss why an AI workload can perform well inside a controlled development environment before slowing down when users, data sources, and cloud services become distributed. Jim says many inference workloads require response times between 10 and 20 milliseconds. Factory automation can require latency below 10 milliseconds, while natural voice conversations may need responses within five to 10 milliseconds. Poor data movement also affects employees. Jim describes spending time with data scientists who began their mornings by moving information from three global regions. The transfers took between two and two and a half hours, leaving skilled employees waiting, attending meetings, or drinking coffee before they could begin modeling. In his words, these were “idle humans” waiting on the network. The problem becomes harder as AI workloads span public clouds, private data centers, colocation facilities, and edge environments. Jim says bandwidth growth between clouds and data centers is rising far faster than traffic between premises and cloud environments. Enterprises are also managing multiple carriers, tools, and operating systems, making performance and predictability harder to control. We examine data gravity and the decision to move compute closer to data rather than transporting large data sets over long distances. Security, resilience, sovereignty, compliance, and cost must be considered according to the requirements of each workload. Jim also explains why networking is beginning to behave like a cloud service. Traditional capacity could take weeks or months to provision. A software controlled network can increase or reduce capacity as demand changes. Lumen describes this model as Cloud 2.0, with automation, APIs, and real time control replacing static capacity planning. Our discussion closes with ownership. Network, cloud, application, data, and AI teams may each measure success differently. Jim believes a business owner should oversee the complete path from data source to AI outcome because a bottleneck can appear anywhere along the way. Could the biggest constraint on your AI investment be the network connecting your data and compute? Listen to the episode and share your thoughts with me.

How EdgeBeam Wireless Uses ATSC 3.0 for One to Many Data Delivery

Could the TV towers we drive past every day provide part of the answer to rising wireless traffic and last-mile network congestion? In this episode of IT Infrastructure as a Conversation, I speak with Conrad Clemson, CEO of EdgeBeam Wireless, about using existing ATSC 3.0 broadcast infrastructure as a one-to-many wireless data delivery layer. Most modern networks are designed around individual connections. That works well when every user requests different information, but it becomes inefficient when thousands or millions of devices need the same software update, live video stream, positioning correction, public safety message, or digital signage content. Conrad explains why broadcast infrastructure offers different economics. A cellular network may deliver an identical file separately to every endpoint. EdgeBeam Wireless can use TV broadcast capacity to transmit the shared payload once across a wide area. The distinction becomes clear in connected vehicles. Conrad says some automobile manufacturers have found that cellular communication charges across the life of a lower-cost vehicle could approach the cost of the vehicle itself. Software, maps, and AI models all need regular updates, creating demand for a delivery method that does not multiply the same payload across every car. We discuss how ATSC 3.0 added data capabilities to next-generation television infrastructure. Broadcast towers already have fiber connections, transmission equipment, spectrum, and regional coverage. EdgeBeam is adding the software, control, signaling, billing, and delivery components needed to use that infrastructure for internet data. Conrad also explains why EdgeBeam is beginning with business markets such as digital signage, public safety, and positioning services, where receivers can be deployed relatively quickly. Connected vehicles, fixed wireless access, consumer video, and autonomous systems represent larger opportunities that will require broader receiver availability. Digital signage provides another useful example. A one-gigabyte content update sent individually to 1,000 screens produces one terabyte of network traffic. Broadcast distribution can send the content once, allowing screens to store a local portfolio and select the most relevant material when needed. We also consider edge computing, live sporting events, AI infrastructure, and the pressure created when identical data crosses the wireless last mile through separate connections. EdgeBeam’s approach complements cellular networks by moving suitable shared traffic elsewhere and preserving cellular capacity for interactive and personalized services. Which workloads inside your network are still being delivered through thousands of duplicate connections, and could one-to-many distribution offer a better answer? Listen to the conversation and share your thoughts with me.

Why Uptime Does Not Mean Your IT Infrastructure Is Healthy

What if the IT systems your business depends on every day are working perfectly, right up until the moment they are not? In this episode of IT Infrastructure as a Conversation, I speak with Chris Bruce, founder of Idextrus, about the hidden technical debt inside mid-sized companies, why uptime should never be confused with infrastructure health, and what IT leaders should be looking for before aging systems become an operational or security crisis. Chris has spent more than 20 years working with companies across manufacturing, wholesale, retail, consumer packaged goods, software, and e-commerce. His approach begins by understanding how technology is actually used across the business, talking with employees and examining the systems, dependencies, security controls, and processes operating behind the scenes. One of the most dangerous assumptions Chris encounters is simple: "It just works." A server may have been running for years, but if it has not been patched or properly reviewed, that does not necessarily make it stable. Companies can also have business-critical systems that nobody fully understands, infrastructure maps that no longer exist, former employees whose knowledge was never documented, shared credentials, excessive permissions, and legacy applications that everyone is afraid to touch. We discuss the different forms of technical debt that infrastructure teams need to identify, including dependency debt, credential debt, documentation debt, and years of deferred upgrades. Chris explains why documentation can become one of the biggest infrastructure risks when the only person who understands a business-critical system leaves the company. The conversation also provides a practical framework for deciding what to do with legacy technology. Should a system be modernized, integrated with newer platforms, isolated while a migration plan is developed, or finally retired? Chris explains how business value, security exposure, architecture, dependencies, and maintenance costs should influence that decision. Cloud migration is another major theme. Simply moving an existing workload from an on-premises environment into AWS, Azure, or Google Cloud does not fix the problems already inside it and can make infrastructure more expensive. Chris explains why successful modernization begins with understanding what should move, why it should move, and whether the underlying architecture needs attention first. We also examine the AI readiness gap. As businesses introduce AI agents, automation, IoT, and increasingly connected applications, weaknesses in data quality, infrastructure, security, and system architecture can become more visible. Every new integration can also create another potential attack surface. For CIOs, infrastructure leaders, IT teams, and mid-sized businesses planning cloud modernization or AI adoption, Chris offers a practical infrastructure health check. Review your external attack surface, patching processes, software lifecycle, access permissions, backups, and system dependencies. Most importantly, test whether the business can explain how its critical systems connect and what would happen if one of them failed. This conversation is a reminder that infrastructure resilience is not measured by how long a system has managed to stay online. The better question is whether you understand what is running, who has access to it, what depends on it, how quickly you could recover it, and whether the infrastructure you have today is ready for what the business wants to do tomorrow.

How Gi21 Capital Sees The Next Wave Of AI Infrastructure Growth

What does it really take to build the infrastructure powering the AI economy? While much of the conversation around artificial intelligence focuses on models, applications, and breakthroughs, far less attention is given to the physical foundations making it all possible. Behind every AI workload sits an enormous amount of infrastructure, from power generation and cooling systems to land acquisition, grid connectivity, and data center design. In this episode of IT Infrastructure as a Conversation, I speak with Damir Špoljarič, a technology entrepreneur, investor, and infrastructure specialist whose journey began at just 17 years old when he founded VSHosting. Over the following two decades, he helped grow the company into one of Central Europe's leading cloud providers while building a data center that achieved something few facilities can claim, verified 100% uptime for more than a decade. Damir shares the lessons learned from designing for resilience at a level where failure simply isn't an option. He explains why many operators underestimate the importance of redundancy, how early decisions around infrastructure design can have consequences years later, and why reliability often comes down to planning for scenarios that may never happen. We also discuss how AI is changing the economics and engineering of modern data centers. As compute density continues to rise, traditional approaches are being pushed to their limits. Damir explains why liquid cooling is becoming increasingly important, how power requirements have changed dramatically, and what operators must consider when designing facilities capable of supporting next-generation AI workloads. The conversation also turns to Europe's growing demand for AI compute capacity and the challenges involved in bringing new facilities online. From securing grid connections and navigating lengthy permitting processes to finding suitable locations with access to affordable energy, Damir offers a behind-the-scenes look at obstacles that rarely make the headlines but shape the future of digital infrastructure. We also explore digital sovereignty, sustainability, renewable energy, and why waste heat from data centers may become an overlooked opportunity for local communities. Along the way, Damir shares his thoughts on robotics, long-term infrastructure investment, and why he believes demand for AI resources is still in its earliest stages. If you've ever wondered what sits beneath the AI services we use every day, this conversation offers a fascinating look at the engineering, investment, and strategic planning required to build the infrastructure supporting the next generation of technology. What role do you think Europe should play in building the infrastructure needed for the AI era, and are we moving quickly enough to meet future demand?

From SD-WAN to AI Traffic: How Enterprise Networks Are Evolving

What happens when AI workloads begin to overwhelm the network infrastructure originally designed for human browsing and SaaS consumption? In this episode of IT Infrastructure as a Conversation, I’m joined by Jamie Pugh from Globalgig to discuss why enterprise connectivity is rapidly becoming one of the biggest blind spots in the AI era. While much of the industry conversation focuses on GPUs, models, and data centers, Jamie explains why the network itself is now under growing pressure from entirely new traffic patterns driven by AI systems communicating with other AI systems. We explore how enterprise infrastructure was largely built around human behavior, employees accessing applications, downloading files, and consuming cloud services. AI changes that model completely. Today, agents are constantly interacting with tools, inference engines are querying massive data stores, and cloud environments are exchanging huge volumes of east-west traffic across regions in real time. Jamie explains why many SD-WAN architectures and broadband-heavy deployments were never designed for these sustained, burst-heavy workloads. The conversation also examines the growing importance of cloud on-ramps and why many organizations discover bottlenecks only after deploying AI-enabled services into production. Jamie shares how asymmetric broadband connections, fragmented carrier relationships, and static connectivity models can quietly introduce latency, resilience, and observability problems that directly impact AI performance and user experience. One of the most interesting parts of the discussion centers on how dependent modern workflows are becoming on AI tools. Jamie talks candidly about using platforms like Claude, Perplexity, and ChatGPT throughout his working day and why losing connectivity now feels less like a temporary inconvenience and more like losing access to an essential member of the team. That shift in expectation is forcing infrastructure leaders to rethink resilience, automation, and real-time observability across hybrid and multi-cloud environments. We also discuss programmable networks, predictive routing, network-as-a-service fabrics, and the growing move toward centralized control planes that can dynamically adapt to changing AI traffic patterns. Jamie explains why enterprises need to stop thinking purely about north-south traffic and start preparing for a future dominated by east-west communication between clouds, data centers, agents, and inference platforms. There is also a valuable conversation around governance, security, and data sovereignty as organizations increasingly bring AI inference closer to private infrastructure rather than relying entirely on public models. Jamie argues that networking, security, and AI strategy teams can no longer operate in silos if businesses want to scale AI safely and effectively. If your organization is building toward an AI-first future, this conversation offers a timely look at the infrastructure challenges many enterprises are only beginning to recognize.

Syndigo on Why AI Commerce Is Failing Without Better Product Data

What if the biggest problem in AI-powered commerce isn’t the AI at all, but the data feeding it? In this episode of IT Infrastructure as a Conversation, I spoke with Tarun Chandrasekhar, Chief Product Officer at Syndigo, about the hidden infrastructure powering modern commerce and why product data has suddenly become one of the most strategic assets inside every retail and consumer brand. As AI shopping assistants, conversational commerce, and agentic retail experiences rapidly move into the mainstream, many companies are discovering a hard truth. Their product information systems were never built for an AI-first world. Tarun explained why decades of fragmented product records, disconnected systems, inconsistent metadata, and siloed workflows are now becoming major blockers to reliable AI-driven discovery and personalization. This episode offers a fascinating look at why “single source of truth” projects continue to fail across enterprises decades after organizations first started chasing them. Tarun argued that this is less a technology problem and more a people-and-process problem, where organizational handoffs and disconnected ownership models continue to create friction across data pipelines. We also explored the rise of agentic commerce, AI readiness scoring for enterprise data, and how companies are now being forced to treat product data as infrastructure rather than simply marketing content. Tarun shared how smaller brands sometimes leapfrog larger enterprises by moving faster, adopting AI-native workflows more easily, and avoiding decades of technical debt. We also discussed Syndigo’s acquisition of OneWorldSync and how ratings, reviews, and product syndication data are increasingly interconnected within AI-powered commerce ecosystems. The long-term vision is a world where product data continuously improves through feedback loops between customers, retailers, AI systems, and manufacturers. If you work in retail technology, AI infrastructure, enterprise data management, supply chain systems, or digital commerce, this episode offers a valuable behind-the-scenes look at the systems quietly shaping the future of how products are discovered, trusted, and purchased online. Useful Links Syndigo Acquires 1WorldSync to Lead AI-First PXM Syndigo Connect with Syndigo’s Chief Product Officer, Tarun Chandrasekhar

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Why Infrastructure Needs A Survivability Layer: HyperBUNKER And The Shift To True Offline Recovery

In this episode, I’m joined by Imran Nino Eškić and Boštjan Kirm from HyperBUNKER, two leaders whose perspective has been shaped by more than 50,000 real-world data loss and ransomware cases. This is not a conversation about theoretical security models or another incremental backup feature. It’s a discussion about what actually survives when production systems, identity layers, and cloud replicas have all been compromised. For years, infrastructure has been designed around availability, scale, and performance. Recovery was treated as a process that would work when needed. But as attackers have grown more patient and methodical, they now target recovery paths first, quietly mapping environments and neutralising backup systems long before an incident becomes visible to the business. That shift forces a new architectural question for infrastructure leaders. Where is the layer that remains reachable when everything connected has been taken down? We explore why so many environments that claim to be air-gapped or immutable still rely on credentials, control planes, and automation, and how those dependencies create hidden single points of failure. Imran and Boštjan explain how HyperBUNKER introduces a physically isolated survivability layer into modern infrastructure, using a hardware-enforced, one-way ingestion process and a double air-gap design that removes the network from the vault entirely. No IP address, no inbound ports, and no authentication surface to attack. This leads to a wider conversation about infrastructure governance, cyber insurance, and regulatory pressure. Insurers are increasingly focused on whether a final, untouchable copy of critical data exists, because the largest financial losses now come from failed recovery rather than the initial breach. That reality is pushing offline recovery out of the basement and into board-level architecture discussions. We also tackle the practical challenge every organisation faces. If only a small percentage of data can be placed in a fully isolated vault, how do you decide what keeps the business alive? That decision, as we discuss, cannot sit with IT alone. It requires operational and executive alignment around what the company must have to restart after a catastrophic event. This episode reframes resilience as an infrastructure design principle rather than a security feature. It asks where a survivability layer should sit alongside cloud platforms, backup software, and existing controls, and why the future of Infrastructure as a Service may depend as much on guaranteed recovery as it does on uptime. If your architecture assumes that recovery will always be there when you need it, this conversation may change how you think about your entire stack.

How To Simplify Storage, Virtualization, And Recovery At Scale

What happens when the infrastructure your business depends on becomes too complex, too expensive, or too slow to recover when something goes wrong? In this episode of Infrastructure as a Service, I sit down with Tvrtko Fritz to talk about how EuroNAS has evolved from “NAS for the masses” into a platform helping organizations simplify storage, virtualization, backup, and scale-out infrastructure. What stood out to me in this conversation was how much of that journey was shaped by real customer pain, from complex deployments to the growing pressure of managing modern environments without a team of specialists. We also get into one of the biggest infrastructure talking points right now, the VMware shift. Tvrtko shares why many customers are not moving because they want to, but because they feel they have to, and how EuroNAS is helping reduce that friction with migration support, VM import tools, and a more predictable licensing model. It is a practical look at what organizations are really facing when they need a plan B but cannot afford disruption. Another part of the conversation I found especially interesting was how recovery speed is becoming just as important as backup itself. Tvrtko explains how instant backup and recovery can change the experience from hours of downtime to seconds, whether that means restoring a full virtual machine or pulling back a single file. We also talk about simplifying Ceph deployments, reducing setup times from days to minutes, and why infrastructure teams increasingly need solutions that let them focus on applications and outcomes rather than wrestling with storage architecture. If you are rethinking your virtualization strategy, looking for more predictable infrastructure costs, or trying to understand how to make enterprise storage less painful to manage, this episode is packed with useful insights. After listening, do you think the future of infrastructure belongs to platforms that hide complexity rather than expose it, and what would make you confident enough to make a switch? Share your thoughts.

PuppyGraph at IT Press Tour: Zero-ETL Graph Analytics on Your Existing Data

What does “infrastructure” mean when your data stays exactly where it is, yet suddenly behaves like a graph? I met Weimo Liu, CEO and co-founder of PuppyGraph, during an IT Press Tour presentation, and I wanted to bring his story to Infrastructure As A Conversation because this is a data infrastructure conversation at its core. Weimo’s pitch is simple to say and harder to pull off: keep a single copy of data in your lake or warehouse, skip the ETL pipelines, and still run graph queries with subsecond performance. Weimo’s background explains why this is more than a clever demo. He worked at TigerGraph, then on Google’s F1 team, and PuppyGraph sits right between those worlds. In our conversation, he walks me through how they treat graph queries as a set of node and edge operations that can be optimized, parallelized, and evaluated in a vectorized way, which is how they keep performance predictable when workloads get real. We also get into the practical details infrastructure teams care about. PuppyGraph is a read-only engine, which changes the trade-offs around concurrency, governance, and operational risk. Instead of copying data into a separate graph store and building a second set of controls, you can query relationships where the data already lives, then write results back into the lake for other engines to consume. The upside is simpler architecture and less duplication. The compromise is that you are not getting transactional graph updates, and Weimo is clear about why that is acceptable for the OLAP-style workloads his customers run. From there, the use cases start to make sense fast. Cybersecurity teams with logs sitting in object storage, fraud detection scenarios where latency matters, and internal AI chatbots that struggle with too many tables and brittle SQL generation. Weimo has a sharp analogy for that last part, text-to-graph queries behave more like a train on rails, which can help AI stay inside defined relationships and reduce messy answers. If you are building modern data platforms and you are tired of pipelines multiplying, this episode is a thought-provoking look at what happens when graph analytics becomes a query layer rather than a destination system. And it all started with a dog-themed name and a surprisingly cheap domain.

Why Infrastructure Complexity Is Still Being Underestimated

When does automating tasks stop being enough, and when does infrastructure itself need to become a shared conversation across teams? In this episode of IT Infrastructure as a Conversation, I’m joined by Peter Sprygada, Chief Architect at Itential, for a deep and refreshingly pragmatic discussion about how infrastructure operations have evolved from scripts and isolated automation into something far more complex and interconnected. Peter has spent more than a decade working across enterprise networks, cloud platforms, and automation tooling, and that experience shows in how he talks about what has genuinely changed, and what has not. We trace the journey from early network automation, born out of engineers trying to escape repetitive CLI work, to the point where automation alone starts to break down. Peter explains why automation excels in domains but struggles across end-to-end systems, and why orchestration becomes essential once infrastructure has to align with real business intent. Instead of teams pointing fingers when something fails, orchestration creates a common language that allows network, cloud, application, and platform teams to work toward the same outcome, even when they use different tools and terminology. We also tackle AI head-on, separating operational reality from conference stage promises. Peter shares his own initial skepticism, why treating AI as a tool rather than a silver bullet matters, and how the same lessons learned from automation apply again today. We talk about governance, guardrails, and what Peter calls the boring stuff, the logging, security, and controls that actually make innovation sustainable at scale. As infrastructure complexity continues to rise, he argues that many leaders still underestimate just how much engineering effort is required to keep modern platforms reliable. Looking ahead, Peter outlines how the traditional boundaries between orchestration, automation, and observability are already starting to blur, and why investing in platforms that can evolve matters more than chasing the latest shiny technology. This conversation stays grounded in real operational challenges, real tradeoffs, and real lessons learned in the field, not abstract futures. As infrastructure stacks continue to grow more complex and AI becomes part of daily operations, are IT leaders ready to treat infrastructure less like a collection of tools and more like an ongoing conversation that never really stops? Useful Links Connect with Peter Sprygada Learn more about Itential, Thanks to our sponsors, Alcor, for supporting the show.

Why the Endpoint Is Becoming the New Control Point for IT Leaders

What happens when someone who helped shape the early days of virtualization returns to the field to lead a new era of secure, flexible computing? At IGEL’s Now & Next event in Frankfurt, I sat down with Peter Goldbrunner, Regional Vice President for Central Europe, to explore how his decades of experience at Citrix and Nutanix are helping drive IGEL’s transformation into a software-focused security leader. Peter’s background provides him with a unique vantage point on the evolution of enterprise technology. From server-based computing to cloud and now prevention-first endpoint security, he has witnessed every wave of IT transformation firsthand. In this conversation, he explained how IGEL’s evolution is meeting the moment for customers facing economic pressure, complexity, and the demands of hybrid work. He also described why the endpoint is emerging as the new control point for business continuity and security, rather than the weakest link. We also discussed what it means to lead transformation within a company that has already reinvented itself. Peter shared how the German-speaking markets are balancing cost control with innovation, why local trust matters, and how IGEL’s role-based approach to security helps organizations prepare for what comes next. His perspective on collaboration, learning, and execution offered a clear reminder that transformation is not just about technology; it is about people, priorities, and timing. As the Now & Next tour continues, Peter’s insights demonstrate why resilience and prevention are now inseparable for any organization that wants to thrive in a world where security and productivity are inextricably linked. How can companies ensure their IT strategies are ready for what comes next? Useful Links Connect with Peter Goldbrunner, on LinkedIn Learn more about IGEL Follow on LinkedIn, Twitter and YouTube Tech Talks Daily is Sponsored by NordLayer: Get the exclusive Black Friday offer: 28% off NordLayer yearly plans with the coupon code: techdaily-28. Valid until December 10th, 2025. Try it risk-free with a 14-day money-back guarantee.

Observability without Overload: Lessons from Chronosphere’s Field CTO

In this episode of IT Infrastructure as a Conversation, I sit down with Bill Hineline, Field CTO at Chronosphere, to talk about something every IT leader has wrestled with at some point, observability. Bill has spent more than 25 years in the industry, including leading observability at United Airlines, so he knows firsthand what happens when data collection turns from helpful to overwhelming. We talked about why the old collect-everything mindset has created what Bill calls data landfills, how organizations can shift toward collecting data with intent, and why so many teams still struggle with alert fatigue and burnout. Bill also shared his take on how to build observability around business outcomes rather than dashboards, and why connecting metrics to real customer value is the only way to truly measure ROI. What really struck me was Bill’s perspective on culture. He believes that observability should be treated as a first-class citizen, something that empowers teams rather than burdens them. Moving from reactive firefighting to proactive, data-driven decision-making is not easy, but as Bill explains, it starts with asking one simple question: why are we collecting this data in the first place? If you have ever been woken up at 3 a.m. by an alert that turned out to be nothing, this conversation will feel painfully familiar. But it is also full of practical insights on how to change that story for good. Sponsored by NordLayer: Get the exclusive Black Friday offer: 28% off NordLayer yearly plans with the coupon code: techdaily-28. Valid until December 10th, 2025. Try it risk-free with a 14-day money-back guarantee.

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Phison Electronics and the Future of AI-Native Infrastructure

In this episode, I’m joined by Michael Wu of Phison Electronics, recorded shortly after our meeting on the IT Press Tour in Silicon Valley. Michael takes us inside Phison’s latest breakthrough: the aiDAPTIV+ platform and its integration with StorONE’s ONEai solution. Together, they’re reshaping how enterprises think about AI training, inference, and data sovereignty. Michael explains how aiDAPTIV+ acts as expansion memory for GPUs, reducing power consumption and cutting hardware costs by up to 10x. We also dig into the partnership with StorONE, which has produced a plug-and-play, storage-based AI solution that makes large language model training accessible to organizations of all sizes—including smaller businesses and universities that traditionally struggle with GPU access. From the launch of the E28 Gen5 AI-enabled SSD controller to the endurance-driven Pascari X200Z SSDs, Michael shares the technical innovations under the hood and what they mean for performance and reliability. He also looks ahead to future workloads, where models with trillions of parameters will demand smarter, more scalable storage architectures. If you’re an IT leader weighing the trade-offs between cloud-based AI and secure, on-premises solutions, or you’re simply curious about how storage is becoming central to AI acceleration, this conversation will give you a fresh perspective. ********* 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

Cohesity’s Infrastructure Playbook for Cyber Recovery and Insights

How can organizations protect their most valuable asset, data, while unlocking its full potential through AI-driven insights? That is the question I explored on the IT Press Tour in Silicon Valley during a face-to-face conversation with Sanjay Poonen, President and CEO of Cohesity. In this episode, Sanjay shares how Cohesity evolved from reinventing backup and recovery to leading the market in data security and cyber resilience. We discuss the game-changing acquisition of Veritas’s NetBackup business, the company’s growing footprint across healthcare, finance, government, and retail, and why uniting cultures and customers is key to their next chapter. We dive into how Cohesity is using AI to transform backup data into a source of real-time intelligence, including its patented Retrieval Augmented Generation (RAG) capabilities developed in partnership with NVIDIA. Sanjay also explains how this innovation is enabling businesses to meet strict data sovereignty requirements while benefiting from cloud agility. From cyberattack recovery and on-premises innovation to working with some of the biggest names in AI and cloud, Sanjay offers a candid look at leadership in a time of rapid growth. If you want to understand how AI and cybersecurity are converging to shape the future of enterprise data, this conversation delivers practical insight, strategic thinking, and a clear vision for what is ahead.

Can Estonia Compete with AWS? Storadera’s Tommi Kannisto Thinks So

In this episode of Infrastructure as a Conversation, I’m joined by Tommi Kannisto, founder of Storadera, a cloud storage company based in Estonia that’s quietly building a smarter, simpler alternative to hyperscalers like AWS. We dive into how Storadera has engineered its own storage software from scratch to deliver secure, S3-compatible cloud storage with a unique hyper-converged architecture. It’s all about cutting unnecessary hardware, avoiding bottlenecks, and delivering transparent pricing that makes sense to growing businesses. Tommi explains: How Storadera’s hyper-converged design replaces gateways and load balancers with lean software Why performance with small files became a key differentiator What makes their multi-tenant system attractive to retail partners The impact of data sovereignty concerns on customer growth across Europe Why Canada is now looking eastward, not southward, for storage partners The Estonian tech culture that helped birth 10 unicorns from a country of just 1.3 million We also talk about the cultural mindset that powers Estonia’s startup scene, from engineers cold-messaging CEOs for advice to a national infrastructure designed for digital innovation. Tommi shares Storadera’s future roadmap, including plans to use AI to optimize disk read and delete operations without raising prices. If you’re curious about what comes after hyperscale, why storage software still matters, or what makes Estonia such a hotbed for digital infrastructure innovation, this is an episode you’ll want to hear. 🔗 Learn more at storadera.com 📍 Want Storadera in your country? Join their regional waitlist on the website.