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.

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?

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.