Enterprises are increasingly turning to open networking and they need guidance to ensure the right choices are being made. A great partner can ease the transition.

In this episode of the Tech Field Day podcast, brought to you by Cisco, Tom Hollingsworth is joined by Scott Robon, Ryan Booth, and Cisco’s Surbhi Paul explore how AI infrastructure demands are pushing organizations to rethink their network architectures.

The consensus is that while SONiC was once primarily a hyperscaler and experimental technology, it has matured significantly and is now appearing in real enterprise and U.S. government deployments. Cisco’s position is to meet customers where they are, supporting both their established NX-OS/ACI platforms and Sonic on Nexus 9K hardware, giving organizations the flexibility to shift software strategies without replacing physical infrastructure.

Open networking isn’t a one-size-fits-all solution. It requires honest assessment of a team’s internal skill set and operational maturity. Cisco frames its SONiC offering around three pillars: the Cisco 8000 series for hyperscale and web-scale use cases, the Nexus 9K for enterprise AI fabrics, and Nexus Hyperfabric for organizations that want the benefits of SONiC without the complexity of managing it themselves. Cisco differentiates itself by backing Sonic deployments with 24/7 TAC support and lifecycle management tools, bridging the gap between open-source flexibility and the reliability enterprises depend on.

Panelists:

Cisco Representative: Surbhi Paul, Product Leader at Cisco

Scott Robohn, CEO of Solutional

Ryan Booth, AI Consultant and Architect

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Learn more about AI Infrastructure Field Day 5 here.

Think you can just buy more GPUs to solve your AI infrastructure problems? Think again.

In this episode of the Tech Field Day Podcast, recorded live on-site at AI Infrastructure Field Day 5, host Alastair Cooke sits down with industry experts Ray Lucchesi, Frederic Van Haren, and Brandon Whichard to discuss why the endless race for new hardware is hitting a wall.

With supply chain shortages and massive power demands making upgrades difficult, companies must shift their focus toward optimizing the technology they already own. The panel explores how clever software tools, smaller foundation models, and efficient token management can squeeze maximum performance out of existing data centers.

The panel explores the real secret to scaling AI lies in smart software practices rather than just buying more chips.

Learn more about AI Field Day here.

The massive promises surrounding artificial intelligence are just history repeating itself.

In this episode of the Tech Field Day Podcast recorded on-site at AI Field Day 8, host Stephen Foskett is joined by Jim Czuprynski, Kate Scarcella, and Jeff Ello to debate the explosive world of AI hype.

They compare the current generative AI boom to past tech cycles like the dot-com era and early cloud computing. Together, they break down the brutal financial realities of a trillion-dollar AI infrastructure investment, dropping software precision, and the truth behind Large Language Models versus traditional machine learning.

Panelists:

Jim Czuprynski, Chief Storyteller at Zero Defect Computing

Jeff Ello, Head of AI at Purdue University

Kate Scarcella, Chair of CD Foundation

Hosts:

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tom Hollingsworth⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, Event Lead for Tech Field Day

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Alastair Cooke⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, Event Lead at Tech Field Day

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Stephen Foskett⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, President and Organizer of ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tech Field Day⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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Learn more about AI Infrastructure Field Day 5 here.

Think your expensive GPUs are the biggest factor in AI performance? Think again. Without a high-performance data storage strategy to keep them supplied with data, your AI infrastructure can become an expensive machine sitting idle. In this episode of the Tech Field Day Podcast, host Alastair Cooke is joined by industry experts Barton George and Ron Pagani Jr. to discuss the industry’s shift from data-intensive AI training to fast, transaction-heavy inference workloads and how data gravity is forcing organizations to rethink where they place data, caches, and compute resources. They also examine rising DRAM and SSD costs, the challenges of deploying Small Language Models (SLMs) at the edge, and the growing need for security and governance as autonomous AI agents become more common in enterprise environments.

Panelists:

Barton George, Founder of 808 Labs

Ron Pagani Jr., Founder of Open Technology Partners

Hosts:

⁠⁠⁠⁠⁠⁠⁠⁠⁠Tom Hollingsworth⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, Event Lead for Tech Field Day

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Alastair Cooke⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, Event Lead at Tech Field Day

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Stephen Foskett⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, President and Organizer of ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tech Field Day⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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Learn more about Tech Field Day at Cisco Live US here.

Is the AI tech bubble about to burst just like the dot-com boom did?

On this episode of the Tech Field Day Podcast ahead of Cisco Live 2026, host Tom Hollingsworth and experts Sam Clements, Ed Weadon, and Rob Coote look at the real state of enterprise AI.

They discuss why tech giants are facing layoffs despite record profits, and whether companies should stick to selling hardware infrastructure instead of forcing useless AI chatbots into everything. The panel compares today's AI hype to past tech trends, looking closely at rising token costs and heavy investor pressure. Watch to find out why betting your entire business strategy on unproven AI features might be a huge mistake for your organization.

Panelists:

Sam Clements, Technical Solutions Architect

Ed Weadon, Senior Network Engineer

Rob Coote, Director, Cybersecurity and Infrastructure

Hosts:

⁠⁠⁠⁠⁠⁠⁠⁠Tom Hollingsworth⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, Event Lead for Tech Field Day

⁠⁠⁠⁠⁠⁠⁠⁠⁠Alastair Cooke⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, Event Lead at Tech Field Day

⁠⁠⁠⁠⁠⁠⁠⁠⁠Stephen Foskett⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, President and Organizer of ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tech Field Day⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Follow the Tech Field Day Podcast ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠on X/Twitter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ or ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠on Bluesky⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and use the Hashtag #TFDPodcast to join the discussion. Listen to more episodes ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠on the podcast page of the website⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.

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Learn more about Mobility Field Day here.

AI is changing the way we maintain and troubleshoot networks, offering suggested fixes and knowledge about issues. But we aren’t ready to trust it in fully autonomous mode yet.

In this episode of the Tech Field Day Podcast, Dan Ryan, Chris Reed, and Alan Wang join Tom Hollingsworth on-site at Mobility Field Day to discuss why fully autonomous networking still feels out of reach despite rapid advances in AI. The group explores the tension between deterministic networking protocols like OSPF and BGP and the non-deterministic nature of LLMs, noting that while AI excels at parsing data and suggesting fixes, it often lacks the business context needed to distinguish real problems from expected anomalies.

They also raise concerns about a growing skills gap, arguing that overreliance on AI for Tier 1 and Tier 2 troubleshooting could prevent junior engineers from developing the deep expertise needed to validate AI-generated recommendations. Ultimately, the delegates agree that networking still requires a human-in-the-loop approach, with AI best suited for guided troubleshooting, scripting, and low-risk operational tasks rather than fully autonomous control.

Panelists:

Chris Reed, Principal Consultant

Alan Wang, Networking Analyst

Dan Ryan, Principal Solution Architect at Connection

Hosts:

⁠⁠⁠⁠⁠⁠⁠Tom Hollingsworth⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, Event Lead for Tech Field Day

⁠⁠⁠⁠⁠⁠⁠⁠Alastair Cooke⁠⁠⁠⁠⁠⁠⁠⁠⁠, Event Lead at Tech Field Day

⁠⁠⁠⁠⁠⁠⁠⁠Stephen Foskett⁠⁠⁠⁠⁠⁠⁠⁠⁠, President and Organizer of ⁠⁠⁠⁠⁠⁠⁠⁠⁠Tech Field Day⁠⁠⁠⁠⁠⁠⁠⁠⁠

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AI networking is quickly becoming a critical part of the enterprise. You need make the right choice about which hardware you want to use but you aren’t necessarily locked into a single choice. In this episode of the Tech Field Day podcast, brought to you by Cisco, Tom Hollingsworth is joined by Scott Robohn and Richard Licon of Cisco as they discuss rapidly evolving landscape of AI networking, emphasizing that organizations no longer have to settle for a “one-size-fits-all” approach. They explore the shift from purely software-driven innovation back to high-performance hardware, where custom silicon like Cisco Silicon One and merchant silicon are becoming central to infrastructure. The conversation highlights how the transition from AI model training to real-time inferencing in 2026 demands more flexible and programmable hardware pipelines. By leveraging tools like P4 programming, network engineers can now adjust their infrastructure mid-cycle to optimize traffic patterns and ensure consistent job completion times as workload needs shift.

The discussion also highlights Cisco’s strategic partnership with NVIDIA, offering customers a choice between distinct networking ecosystems based on their specific goals. For those requiring strict adherence to NVIDIA’s validated designs and the Secure AI Factory, the Cisco Nexus 9100 with Spectrum-X silicon provides a certified, high-performance path. Conversely, organizations seeking maximum flexibility and custom tuning for diverse workloads can opt for Cisco Silicon One. Ultimately, the experts agree that while the technical requirements of AI networking are increasingly complex, the availability of diverse silicon options and consistent operating systems like NX-OS or SONiC allows enterprises to build future-proof, composable infrastructure that can adapt to the unpredictable pace of AI development.

Panelists:

Richard Licon, Principal Technical Marketing Engineer at Cisco

⁠Scott Robohn⁠, Founder and CEO, Solutional

Hosts:

⁠⁠⁠⁠⁠⁠Tom Hollingsworth⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, Event Lead for Tech Field Day

⁠⁠⁠⁠⁠⁠⁠Alastair Cooke⁠⁠⁠⁠⁠⁠⁠⁠, Event Lead at Tech Field Day

⁠⁠⁠⁠⁠⁠⁠Stephen Foskett⁠⁠⁠⁠⁠⁠⁠⁠, President and Organizer of ⁠⁠⁠⁠⁠⁠⁠⁠Tech Field Day⁠⁠⁠⁠⁠⁠⁠⁠

Follow the Tech Field Day Podcast ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠on X/Twitter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ or ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠on Bluesky⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and use the Hashtag #TFDPodcast to join the discussion. Listen to more episodes ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠on the podcast page of the website⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.

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Learn More about AI Field Day 8 here.

Now that AI has enabled us to have an unlimited amount of content, generated on demand and instantly, we find ourselves questioning the quality of the output.

This episode of the Tech Field Day podcast, recorded prior to AI Field Day by delegates Barbara Roos, Guy Currier, Dave Graham, and Stephen Foskett, considers this common trade-off.

Large Language Models are fundamentally personal and responsive, delivering an answer that is statistically matched to your query. This can lead users astray, as a chatbot confidently answers every question regardless of insight.

These systems are best when paired with human domain expertise and fed quality data, but they are often starved of context.

Panelists:

Guy Currier, Research Director and Analyst at The Futurum Group

Barbara Roos, Founder and Principal Consultant at Trailhead Communications

Dave Graham, Head of Marketing at MLCommons

Hosts:

⁠⁠⁠⁠⁠⁠⁠⁠⁠Tom Hollingsworth⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, Event Lead for Tech Field Day

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Alastair Cooke⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, Event Lead at Tech Field Day

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Stephen Foskett⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, President and Organizer of ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tech Field Day⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Follow the Tech Field Day Podcast ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠on X/Twitter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ or ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠on Bluesky⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and use the Hashtag #TFDPodcast to join the discussion. Listen to more episodes ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠on the podcast page of the website⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.

Follow ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tech Field Day⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ for more information on upcoming and current event coverage ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠on X/Twitter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠on Bluesky⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠on LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, or ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠visit our website⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.