In this podcast, we examine AI infrastructure from an enterprise perspective. Guests with backgrounds in enterprise IT, cloud architecture, security, finance, and education join MDCS.ai in the Cisco podcast studio to share practical experience and informed viewpoints. Each episode addresses the questions that arise once AI initiatives move beyond experimentation and into production. How do you design infrastructure that truly scales? What happens to cost, performance, and control as AI workloads grow? How do organizations balance speed, security, data sovereignty, and long-term ownership? Rather than focusing on trends or product promotion, the discussions are grounded in real-world challenges—covering architectural choices, operating models, governance, accountability, and the trade-offs organizations must navigate when building or scaling AI environments. Your AI, Your Way is intended for AI leaders
Pitch Analysis
Required Pod Score for this show. PitchCentric checks your profile against host openness, topical fit, and audience signals before you generate a pitch.
Contact path
Verified email
Booking probability
28%
Guest openness
Selective
Verified email on file
80/100
Required Score
Sign up to generate a grounded pitch for Your AI, Your Way.
Your AI, Your Way is a technology podcast hosted by MDCS.AI & CISCO, with 8 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.
About the host
MDCS.AI & CISCO hosts Your AI, Your Way, a technology show with 8 episodes published.
Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.
Your AI, Your Way
The Right Fit
Jun 8, 202635 min
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.
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 ) 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.
<p>No power, no compute. That is the reality in the Netherlands today.</p><p>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.</p><p>Lorentz is the response. A regional AI initiative built by entrepreneurs, for entrepreneurs, without government funding or European program delays.</p><p>The model exists already. In Sweden, the Wallenberg family funded Berzelius, and within years an ecosystem of talent, startups...
<p>Most AI pilots never reach production.</p><p>The technology works. The use case makes sense. Then the cloud bill arrives. Costs spiral before anyone sees it coming.</p><p>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.</p><p>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...
<p>You cannot secure what you cannot see.</p><p>When cloud adoption started, employees picked their own tools without IT knowing. They called it Shadow IT. The same pattern is now repeating with AI.</p><p>Developers pull models from Hugging Face because it is convenient. Over one hundred thousand models live there. Most have never been security checked.</p><p>A well-known vendor recently published a PyTorch container with nearly one hundred documented vulnerabilities. Some can be patched. Some cannot.</p><p>For AI, there is no Patch Tuesday yet.</p><p>The risk goes beyond infrastructure. A...
Every question we get asked before someone starts their trial.
If you have a concern about deliverability, AI quality, data privacy, or whether this will actually work for your specific situation, it's probably answered below.
What is the difference between Founder Solo and Founder Pro?
Founder Solo gives you 50 AI pitches per month using the credit model (Standard pitches cost 1 credit, Enriched pitches cost 2). Founder Pro raises that to 200 credits per month and adds full Booking Probability access, unlimited Magic Match, Apollo enrichment credits, and data export capabilities. Both plans use the same credit system, so you can stretch your monthly budget further by using Standard-mode drafting.
How do agency tiers work?
Agency tiers have no base fee. You pay per managed client and per talent profile. Agency Standard is $199 per client per month; Agency Pro is $399 per client per month. Both add $39 per talent profile per month. Your own team's user seats are always free.
What is a talent profile?
A talent profile represents one person (founder, executive, or spokesperson) you are booking onto podcasts. It includes their bio, topics, headshots, and outreach history. Team plans include 5 profiles; agency plans are pay-as-you-go.
Can I switch plans later?
Yes, at any time. Upgrades take effect immediately; downgrades apply at the end of the current billing period. Contact support if you need help migrating between plan families.
Do you offer a free trial?
Every paid plan includes a 15-day free trial. Your card is saved at signup but you will not be charged until day 16. Cancel any time from your dashboard.
What happens if I cancel?
You keep access until the end of your current billing period. No charges after that. Your data is retained for 30 days in case you reactivate.
Is the 20% annual discount automatic?
Yes. Select Annual on the pricing toggle and the discounted price is applied automatically at checkout. The annual price shown is the full year cost.
What if I have more than 50 profiles or 20 clients?
That is our Enterprise tier. Contact our sales team and we will build a custom plan with volume pricing, a dedicated account manager, and SLA guarantees.
