From Signal Corps to Space: Building Networks That Can’t Fail with Troy MacDonald

Game On: What Retro Gaming Teaches Us About Modern Networks with Jeremy Bradberry

What can decades of hands-on operational experience teach us about the future of AI-driven networking? In this episode of Next-Gen Network Heroes, host Bob Slevin sits down with Jeremy Bradberry, Senior Network Engineer at Delaware North, for a conversation that spans everything from legacy manufacturing systems and mainframes to modern AI-assisted network operations. Jeremy shares how his early career working in industrial environments shaped the way he approaches networking today, giving him what he calls an “X-ray vision” into how technology connects directly to business operations. Jeremy explains why understanding operational workflows is just as important as understanding the technology itself. Drawing from real-world experiences modernizing aging infrastructure in manufacturing environments, he shares how practical problem solving, adaptability, and business alignment helped him create innovative solutions long before AI entered the picture. The conversation then shifts into how AI is changing the daily life of network engineers. Jeremy walks through practical examples of using tools like Copilot and Lucid to analyze large datasets, accelerate troubleshooting, automate documentation, and improve collaboration across teams. Rather than viewing AI as a replacement for expertise, Jeremy sees it as a force multiplier that gives engineers more time to focus on strategy, improvement, and innovation. Bob and Jeremy also discuss the importance of mentoring junior engineers, preserving strong operational standards, and ensuring organizations don’t lose sight of the fundamentals while adopting new technology. The episode closes with a fun look into Jeremy’s passion for retro gaming, console preservation, and how gaming culture unexpectedly parallels the world of networking and systems engineering. Takeaways: *  Understand the business behind the technology. The best network engineers don’t just know the infrastructure; they understand the operational impact when systems fail and how technology supports the business as a whole. * Modernization doesn’t always require a complete rebuild. Jeremy shares how creative solutions helped extend the life of legacy systems while still introducing modern networking capabilities. * AI works best as a force multiplier, not a replacement. Tools like Copilot can dramatically speed up troubleshooting, reporting, and documentation, but strong technical knowledge is still essential. * Better standards create better AI outcomes. Consistent naming conventions, interface descriptions, and operational discipline make AI tools significantly more effective. * Focus on asking the right questions. AI can surface insights quickly, but engineers still need the experience and operational understanding to guide investigations and validate results. * Use greenfield projects strategically. New deployments are opportunities to build future-ready environments that support automation, visibility, and scalability from the start. * Operational experience builds resilience. Jeremy’s early career in manufacturing environments taught him persistence, adaptability, and how to solve problems under pressure. * Collaboration improves outcomes. Sharing AI-driven insights across teams helps break down silos and allows organizations to solve problems faster and more effectively. Quote of the Show: * “If you know what questions to ask, it becomes all the more powerful of a vehicle for you to really innovate and move things forward.” Links: * LinkedIn: https://www.linkedin.com/in/jeremy-bradberry-7b0752142/ [https://www.linkedin.com/in/jeremy-bradberry-7b0752142/]  * Website: https://careers.delawarenorth.com/ [https://careers.delawarenorth.com/]

From Signal Corps to Space: Building Networks That Can’t Fail with Troy MacDonald

What does it take to succeed in networking when complexity is constantly increasing, and change never slows down? In this episode of Next-Gen Network Heroes, host Bob Slevin sits down with Troy (David) MacDonald, a network engineer at Blue Origin and former U.S. Army Chief Warrant Officer, to explore a career that spans from infantry beginnings to designing and managing large-scale, mission-critical networks. Troy shares how his unexpected entry into technology sparked a lifelong curiosity, from discovering dial tones in the desert to building modern fiber and satellite-based networks. Along the way, he developed a powerful “superpower”: the ability to translate complex technical needs into real-world solutions for stakeholders. The conversation dives into the realities of scaling networks in fast-growing environments, the challenges of monitoring and alert fatigue, and where AI can meaningfully improve operations without replacing human judgment. Troy also reflects on leadership, teamwork, and the importance of setting aside ego to build stronger, more collaborative network teams. From military lessons to modern enterprise challenges, this episode is a grounded and insightful look at what it really takes to operate and evolve networks in the AI era. Takeaways: * Start with conversation, not assumptions: Great network design begins with understanding real needs—not jumping straight to solutions. Taking the time to ask questions and clarify requirements prevents overengineering and builds trust with stakeholders. * Communication is a core technical skill: Networking isn’t just about infrastructure—it’s about translating complexity. Clear, consistent communication throughout a project is what keeps teams aligned and systems running smoothly. * AI’s biggest opportunity is reducing operational noise: One of the most valuable use cases for AI today is filtering alerts, identifying root causes, and reducing unnecessary escalations so engineers can focus on what actually matters. * Automation should be trusted—but verified: While AI and automation can handle repetitive tasks, human oversight is still critical. The balance between efficiency and validation is where real value is created. * Strong teams are built on trust, not ego: High-performing network teams leverage each other’s strengths and operate collaboratively. Letting go of an ownership mindset and embracing shared responsibility leads to better outcomes. * Growth amplifies complexity—and exposes gaps: Rapid organizational growth can quickly overwhelm monitoring and operations. Iteration, prioritization, and continuous refinement are essential to keeping systems manageable. * Curiosity is the foundation of a great career: From shortwave radios to satellite networks, staying curious and exploring new technologies drives long-term growth and opportunity in this field. Quote of the Show: * “A really good operating team leverages everybody’s strengths.” Links: * LinkedIn: https://www.linkedin.com/in/david-macdonald-1a4706334/ [https://www.linkedin.com/in/david-macdonald-1a4706334/]  * Website: https://www.blueorigin.com/ [https://www.blueorigin.com/]

True Visibility: How Liang Chen is Rethinking Network Monitoring

What happens when deep networking expertise meets low-level programming and a passion for invention? In this episode of Next-Gen Network Heroes, host Bob Slevin sits down with Liang Chen, Senior Network Architect at Texas Children's Hospital and a true innovator in network performance and visibility. With more than 25 years of experience in networking, plus advanced expertise in programming languages like C and Assembly, Liang has built his own next-generation traffic analysis platform from the ground up—designed to provide real-time, packet-level visibility at massive scale. Liang shares how his background in telecom helped shape his career, from building public IP networks in China in the early days of internet infrastructure to solving mission-critical performance challenges in healthcare today. He explains why traditional tools like SNMP and NetFlow can only go so far, and how his invention—EngineTA, a next-gen network traffic analyzer—captures and analyzes traffic at up to 200Gbps with zero packet loss. The conversation also explores AI in healthcare, troubleshooting large-scale imaging transfers to the cloud, and why research, curiosity, and persistence are essential for any network engineer looking to solve brand-new problems. Takeaways: * Traditional visibility tools have limitations: SNMP and NetFlow-based tools provide useful insights, but they often miss the full picture. Liang explains why sampled data isn’t enough when troubleshooting complex performance issues and why true packet-level visibility matters. * Packet-level analysis unlocks deeper troubleshooting: Liang’s tool analyzes every packet in real time, allowing teams to identify retransmissions, packet loss, latency, and asymmetric traffic paths faster and more accurately. * Zero packet loss is critical in network forensics: When troubleshooting sensitive environments like hospitals, missing even a small amount of data can hide the root cause. Complete packet capture ensures nothing gets missed. * Healthcare networks are mission-critical: At Texas Children’s Hospital, the network is a life-saving infrastructure. Fast root cause analysis and high availability are essential for patient care and operational continuity. * Research is a superpower: Liang emphasizes that one of the most valuable skills in tech is the ability to research effectively. Solving new problems often requires finding answers that don’t exist in documentation yet. * AI workloads are creating new network challenges: Large-scale AI initiatives, such as moving hundreds of terabytes of medical imaging data to the cloud, introduce new latency and performance bottlenecks. * Innovation often starts with frustration: Liang built EngineTA because existing tools couldn’t solve the problems he faced. Sometimes the best innovation comes from solving your own pain points. Quote of the Show: * “The most important thing for network performance is to get true visibility into the network.” Links: * LinkedIn: https://www.linkedin.com/in/liang-chen-a15536a5/ [https://www.linkedin.com/in/liang-chen-a15536a5/]  * Website: https://www.texaschildrenspeople.org/ [https://www.texaschildrenspeople.org/]

Bias Toward Action: Driving AI Innovation Across Global Networks with Greg Freeman

What does it take to lead innovation across one of the world’s largest telecommunications networks? In this episode of Next-Gen Network Heroes, host Bob Slevin sits down with Greg Freeman, Vice President of Network and Customer Transformation at Lumen Technologies, to explore how AI, automation, and curiosity are reshaping the future of network operations. Greg shares how his “superpower” lies at the intersection of deep network engineering expertise and AI fluency. From deploying over 375 deterministic workflows across millions of network interactions each year to building internal AI agents that can diagnose customer issues and answer network questions in natural language, Greg offers a firsthand look at what innovation at scale really looks like. The conversation dives into the evolution of AI methodologies—from machine learning and generative AI to agentic AI, MCP (Model Context Protocol), and A2A (agent-to-agent) protocols. Greg explains how his team balances continuous incremental improvement with breakthrough innovation, why “bias toward action” matters more than perfection, and how organizations can build cultures that empower experimentation. They also discuss customer expectations in the AI era, governance and security guardrails, and the changing role of network leaders as both operators and innovators. Plus, Greg shares practical career advice for the next generation of engineers and reveals the surprising inspiration behind one of Lumen’s newest AI agents: “Swaggert,” named after astronaut Jack Swigert of Apollo 13 fame. Takeaways: * Don’t Let Perfect Get in the Way of Progress: Innovation doesn’t have to start with a complete solution. Greg’s team often launches tools that cover 25–30% of the network and improves them over time. Small wins compound quickly and can lead to major transformation. * Balance Deterministic Workflows with Agentic AI: Traditional automation still has enormous value. The future lies in combining deterministic workflows with non-deterministic AI agents that can reason, adapt, and accelerate operations. * Create a Culture of Curiosity and Experimentation: Weekly demos, internal sandboxes, and proof-of-concept teams help Greg’s organization stay ahead of rapid AI shifts. Encourage teams to share ideas and showcase what’s possible. * Bias Toward Action Wins: Organizations can spend too much time admiring problems instead of solving them. Fast experimentation, rapid iteration, and learning quickly create momentum. * Governance Must Scale with Innovation: AI introduces speed and complexity, which makes governance even more important. Review data sources, permissions, authentication, and security implications before deployment. * Customers Now Care About Your AI Journey: The conversation has shifted from traditional network infrastructure discussions to how organizations are using AI to improve reliability, reduce toil, and create efficiency. * Follow Your Curiosity to Build Your Career: Whether it’s experimenting with deepfakes, AI avatars, or automation tools, curiosity-driven learning often leads to transferable skills and unexpected opportunities. Quote of the Show: * “We talk a lot about this around here: bias toward action. I’ve got a lot of problems I can admire, and we have no shortage of problems to admire. But we need to solve problems, so we need a bias toward action.” Links: * LinkedIn: https://www.linkedin.com/in/greg-freeman-9311521/ [https://www.linkedin.com/in/greg-freeman-9311521/] * Website: https://www.lumen.com/ [https://www.lumen.com/]

Securing the World's Biggest Machine: Critical Infrastructure, AI, and the Ethics of Innovation

What happens when decades of critical infrastructure experience meet today’s rapidly evolving AI landscape? In this episode, host Bob Slevin sits down with Ernie Hayden, award-winning author, former Navy nuclear officer, ethical hacker, and founder of 443 Consulting, for a deep dive into what it truly takes to secure modern, interconnected systems. Drawing on a career spanning electric utility SCADA networks to chemical manufacturing environments, Ernie explains why adopting a holistic lens is essential for both cybersecurity and operational excellence. By understanding inputs, outputs, dependencies, and system-wide relationships, organizations can better anticipate risk and improve performance. He also shares why regulated industries like power and nuclear have developed hard-earned practices that the broader business world would benefit from adopting. The conversation then turns to AI, where Ernie makes a critical distinction between AI as an analytical support tool and autonomous agents making real-time decisions. He outlines the risks of deploying agent-driven AI in high-stakes environments like power generation, where even a small mistake could have major consequences, while highlighting the immediate value of AI-assisted analysis. Throughout the discussion, he emphasizes the irreplaceable role of human judgment, especially in high-pressure situations where experience and intuition guide decisions no model can replicate. The episode closes with a look into Ernie’s work as a photojournalist and nature photographer in the Pacific Northwest, along with his perspective on why continuous learning and an innovate or die mindset have become essential in today’s fast-moving world. Takeaways: * Adopt a holistic, systems-level view of your infrastructure. Don't just look at individual components — understand how every system communicates with and depends on others, and ask what happens when any one link is severed. * Look to NERC CIP as a security framework, even if you're not a utility. These standards for cyber and physical security of the electric grid have proven themselves over time and can be adapted as a model for other regulated or high-risk industries. * Draw a "digital fence line" around your critical assets. Think of your security perimeter not just physically but digitally — monitor everything that comes in and goes out, and treat both with equal rigor. * Understand the difference between AI and AI agents before deploying either. AI that surfaces insights and recommendations for a human operator is a manageable first step; AI agents that take autonomous action in critical systems carry substantially higher risk and demand far greater scrutiny. * Document AI recommendations and require human approval before any action is taken. Especially in regulated or safety-critical environments, every AI-generated recommendation should be logged, explained, and acted on only with explicit human authorization. * Use AI as a powerful research accelerator, but verify outputs carefully. Tools like Perplexity are excellent for gathering information quickly, but hallucinations are a real risk — always validate AI-generated content before publishing or acting on it. * Don't wait for a crisis to start thinking about innovation. Whether you're running a refinery, a data center, or a telecom network, the organizations that are not actively incorporating AI and modern monitoring risk being overtaken by those that are. Quote of the Show: * "You just don't look at a bridge or a factory or a refinery. You look at it as a series of systems, and all these systems pile up and work together. And then what you're trying to do is understand how do they communicate, how do they support each other — and better than that, what if I cut those links?" Links: * LinkedIn: https://www.linkedin.com/in/enhayden/ [https://www.linkedin.com/in/enhayden/]  * Email: Enhayden1321@gmail.com [Enhayden1321@gmail.com]  * Photography Website: https://risingmoonnw.com/ [https://risingmoonnw.com/]  * Book Link: https://a.co/d/05Wa6Qk9 [https://a.co/d/05Wa6Qk9]