Jeetu Patel, a technology executive specializing in infrastructure and security, dives into the overlooked demand for AI infrastructure. He argues that power is the core constraint while GPUs are essential assets. Networking acts as a force multiplier, enabling massive scalability across data centers. Patel emphasizes the rising significance of edge AI with unified platforms for secure, plug-and-play solutions. He also highlights critical bottlenecks in AI adoption, including infrastructure needs, trust deficits, and data gaps, reshaping our understanding of AI's future.
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insights INSIGHT
Infrastructure Is The New Scarce Commodity
Infrastructure has become the most sought-after commodity in the AI era and is driving innovation in power, performance, and observability.
Jeetu Patel says the shortage of infrastructure limits how many users AI services can effectively serve.
insights INSIGHT
Power, GPUs, And The Network Triad
Power is the core constraint and GPUs are the core asset for AI systems.
Jeetu Patel calls network connectivity the force multiplier that lets many GPUs act as one coherent system.
insights INSIGHT
How “Scale Up, Out, Across” Evolved
Models scaled from single GPUs to servers, racks, clusters, and now multi-data-center ultra clusters.
Jeetu Patel explains "scale up, scale out, scale across" as the evolution needed for larger models.
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Big thanks to Cisco for sponsoring this video and sponsoring my trip to Cisco Partner Summit San Diego 2025.
This video is a deep dive with Jeetu Patel on why the real AI revolution is happening in infrastructure and networking, not just in GPUs or chatbots.
Jeetu explains that we are massively underestimating how much AI infrastructure the world will need. Power becomes the core constraint, GPUs are the core asset, and networking is the force multiplier that lets thousands of GPUs act as one system. He walks through how we went from models on a single GPU → 4–8 GPUs in a server → racks with hundreds of GPUs (like NVL-72 with 500+ GPUs) → clusters of racks, and now “scale across” between data centers when power and real estate are scattered across different regions.
The conversation then shifts to edge AI and Cisco Unified Edge: instead of doing all token generation in big data centers, some inference and token generation must move to the edge (branches, factories, hospitals, stadiums, stores) where data is created. Jeetu explains why edge devices need to be plug-and-play, remotely managed, and integrate compute, networking, security and observability in a single platform.
He also introduces the idea that AI is now constrained by three big bottlenecks:
• Infrastructure
• A trust deficit (people don’t trust AI yet)
• A data gap (models are mostly trained on human internet data, not on rich
machine data)
Jeetu explains how security becomes a prerequisite for productivity, not a trade-off, and describes Cisco’s work with Splunk, open-sourced time-series models, and machine data (logs, metrics, traces) to close the data gap by correlating machine data with human-generated data for better insights.
Globally, he talks about the “token generation race” – how every country now cares about having enough AI token generation capacity because it directly links to GDP and national security. He cites huge infrastructure build-outs with partners like G42 in the Middle East, at gigawatt and trillions-of-dollars scale.
Finally, Jeetu tackles the “AI will take my job” fear. He outlines three stages of
thinking:
1. “AI will take my job.”
2. “Someone who uses AI better will take my job.”
3. “Without AI, I won’t be able to do my job.”
His message to younger viewers: be excited, adopt AI as a companion, own your learning, and learn fast because AI compresses the time it takes to build skills.
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// Menu //
0:00 - Coming up
0:33 - "Networking is sexy"
02:24 - Scale up, scale out and scale across explained
04:47 - Cisco and Nvidia partnership
05:55 - Cisco and G42 partnership // Addressing the AI bubble
08:11 - New Cisco Unified Edge
11:08 - Agentic AI in the future
13:05 - Huge demand for networking
13:57 - The three constraints
16:38 - AI in the real world
19:26 - How AI will take jobs away
21:38 - Conclusion
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Disclaimer: This video is for educational purposes only.
David Bombal 