The Right Fit

Cisco - More Than Networking

Most enterprises invest in high-end compute and connect it to network infrastructure that was designed for office traffic. Email, file shares, video calls. The assumption is that if the GPUs are powerful enough, the rest of the stack will keep up. It does not. A GPU can only process data as fast as the network delivers it. When the network runs at 200 gigabit and the workload demands 800, compute sits idle. You pay for the race car engine but starve it of fuel. For a basic chatbot, you can get away with it. Occasional prompts create short spikes. The system holds. But agentic AI runs at continuous peak load. The network speeds involved are moving from 200 gigabit to 400, to 800, and the next generation targets 1.6 terabit. Most enterprise IT teams have never worked at these levels. Meanwhile, the metric that will define AI economics is one most companies do not track yet: cost per token. In this discussion recorded at Cisco Studio Amsterdam, Sander ten Hoedt (Cisco) and Raymond Drielinger (MDCS.AI [http://MDCS.AI]) explain why AI infrastructure behaves like a production line, and why that production line fails when the data flow cannot keep up. Key topics include: * Why connecting expensive GPUs to an office network is like putting a Formula 1 engine in a car with narrow fuel lines. * How GPU utilization is often a network problem, not a compute problem, and why that directly drives up cost per token. * The difference between chatbot traffic and agentic AI workloads, and why the latter demands a fundamentally different infrastructure philosophy. * Why most enterprises do not yet measure cost per token, and why pharma and financial services are ahead of the curve. * Infrastructure checks every organization should run before scaling AI beyond pilot.

The Right Fit

Your first AI proof of concept succeeded. Your platform did not. The technology worked. The use case delivered results. Then someone asked how to scale it to more users, more use cases, and a platform that IT can actually manage. That question landed on desks that were never involved in the original project. Most organizations do not start from a blank canvas. They already run systems, enforce policies, and manage teams across departments. AI lands on top of all of it. The number of infrastructure parameters to choose from is overwhelming, and teams freeze because every decision feels high-stakes and irreversible. Meanwhile, security gets postponed. The first use case runs, a second agent follows, then a third. And then the question surfaces: how is any of this actually secured? Adding security after the fact means you already know you are too late. In this discussion recorded at Cisco Live Amsterdam, Jara Osterfeld (Cisco) and Remco van der Horst (Devoteam) explain how standardized infrastructure options give teams a shared vocabulary to decide faster, and why security belongs in the foundation from day one. Key topics include: * Why choice overload stalls more AI projects than bad technology, and how standardized "boxes" of infrastructure options break the deadlock. * How a shared vocabulary for AI infrastructure bridges the gap between AI teams, IT, and leadership. * Why security-by-design is a foundation choice, not an implementation phase, and what happens when organizations try to retrofit it. * The real risk of business teams building AI use cases while IT gets brought in after the platform is already expected to work. * Practical questions to test whether your AI foundation is ready for your fourth use case, not just your first.

Lorentz

No power, no compute. That is the reality in the Netherlands today. When Nvidia assessed Europe, the message was clear: there is no sovereign AI infrastructure here. Scandinavian countries have electricity. The Netherlands does not. If nothing changes, the next generation of AI talent will have to leave the country to do serious work. Lorentz is the response. A regional AI initiative built by entrepreneurs, for entrepreneurs, without government funding or European program delays. The model exists already. In Sweden, the Wallenberg family funded Berzelius, and within years an ecosystem of talent, startups, and commercial success emerged around it. Lorentz applies the same concept to the Netherlands, starting with a single cluster focused on Digital Health. The goal is not just compute power. It is bringing together investors, universities, consultancies, and startups around shared infrastructure. A place where AI use cases move from pilot to revenue. In this 45-minute discussion recorded at the Cisco Studio in Amsterdam, Viktor Mirovic (Lorentz) and Ken van Ierlant (Mr Data / AI Leadership program) explain why the Dutch need to stop waiting and start building. Key topics include: * Why a year in AI time equals a century, and why large national programs will arrive too late. * How 80 to 90 percent of enterprise IT budgets disappear into legacy systems, leaving no room for innovation. * The difference between AI as a "shiny object" and AI as a transformation of operating models. * Why sovereignty matters when your strategic advantage depends on proprietary data and models. * How Lorentz plans to replicate its first cluster across multiple regions and domains.

AI Infrastructure Costs

Most AI pilots never reach production. The technology works. The use case makes sense. Then the cloud bill arrives. Costs spiral before anyone sees it coming. It starts small. A few GPUs in the cloud. Reasonable invoices. Then the project scales. Storage costs appear. Data transfer fees stack up. That monthly cloud bill? It can multiply by thirty before finance even flags it. Meanwhile, GPUs sit idle. Storage and network cannot keep up with compute. Organizations invest in processing power, then watch it wait for data that arrives too slowly. Utilization rates below thirty percent are common. Pilots get cancelled, budgets freeze, and AI ambitions stall across the organization. In this 34-minute discussion recorded at the Cisco Studio in Amsterdam, Guy D'Hauwer (Automation Group) and Sander ten Hoedt (Cisco) break down what actually drives AI infrastructure costs and when it makes sense to move from cloud to owned infrastructure. Key topics include: * Why "cost per token" should be the metric every AI team tracks, and why most do not. * How cloud flexibility turns into cloud lock-in through services that stack fees on fees. * The break-even point where owned infrastructure starts delivering more capacity for the same budget. * Why GPU underutilization is rarely a GPU problem, and what bottlenecks actually cause it. * How prefab modular datacenters cut deployment time from months to days.

AI Center of Excellence

Giving people AI tools is not the same as AI adoption. Most employees are driven by their inbox. Add a strategic AI project on top, and enthusiasm alone will not create capacity. Without structure, AI becomes a side project for one eager person while leadership has no visibility into the risks underneath. At TU Eindhoven, the Supercomputing Center grew its AI team from one engineer to five in eighteen months. Demand keeps rising. Researchers, educators, and now industry partners all want access to compute, but raw compute power is only half the story. Every platform is a race track. You need the right car for it. And someone who knows how to drive. When specialists work alongside researchers, efficiency gains of six times are common. Without that support, teams burn time learning what others already know. The question for any organization is not whether to build AI capability, but how. Centralized through a Center of Excellence? Distributed through a hub and spoke model? The answer depends on risk appetite, maturity, and speed. In this 39-minute discussion recorded at the Cisco Studio in Amsterdam, Nick Brummans (TU Eindhoven) and Vera Schut (NXT Minds) share what they have learned about building AI competencies that actually stick. Key topics include: * Why giving employees AI tools without structure leads to invisible risk and wasted effort. * The difference between a Center of Excellence, a hub and spoke model, and letting the business figure it out. * How TU Eindhoven onboards researchers onto advanced AI platforms, and what trips them up. * Why knowledge is a muscle that requires consistent training, not a one-time workshop. * What smaller companies can do faster than enterprises stuck on legacy systems.