Artificial Intelligence is creating the kind of paradigm shifts not seen since the cloud revolution. Everyone is changing the way their IT infrastructure operates in order to make AI work better. In this episode of the Tech Field Day Podcast, Tom Hollingsworth is joined by John Freeman, Scott Robohn, and Ron Westfall as they discuss how AI is driving innovation in the networking market. They talk about how the toolsets are changing to incorporate AI features as well as how the need to push massive amounts of data into LLMs and generative AI constructs is creating opportunities for companies to show innovation. They also talk about how Ethernet is becoming ascendant in the AI market.
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AI is the Enabler of Network Innovation
Artificial Intelligence is creating the kind of paradigm shifts not seen since the cloud revolution. Everyone is changing the way their IT infrastructure operates in order to make AI work better. In this episode of the Tech Field Day Podcast, Tom Hollingsworth is joined by John Freeman, Scott Robohn, and Ron Westfall as they discuss how AI is driving innovation in the networking market. They talk about how the toolsets are changing to incorporate AI features as well as how the need to push massive amounts of data into LLMs and generative AI constructs is creating opportunities for companies to show innovation. They also talk about how Ethernet is becoming ascendant in the AI market.
Modern network operations and engineering teams have a bevy of tools they need to leverage like Python, GitHub, and cloud platforms. AI is just another one of those tools, such as using natural language conversational interfaces to glean information from a dashboard. This can also be seen in the way that AI is having a societal impact on the way that we live and work. The move toward incorporating AI into every aspect of software can’t help but sweep up networking as well.
Large amounts of data are being sent to large language models (LLMs) for storage and processing. Much like the big data crazy of years gone by we’re pushing more and more information into systems that will operate on it to discover context and meaning. Even more than before, however, is the need to deliver the data to the AI compute clusters that need to do the operations. The idea of data gravity is lost when the AI clusters have an even stronger pull. That means that the network must be optimized even more than ever before.
Ethernet is quickly becoming the more preferred alternative to traditional InfiniBand. While there are clear advantages in some use cases, InfiniBand’s dominance is waning as Ethernet fabrics gain ground in performance. When you add in the ease with which Ethernet can scale to hundreds of thousands of nodes you can see why providers, especially those that are offering AI-as-a-Service, would prefer to install Ethernet today instead of spending money on a technology that has an uncertain future.
Lastly, we discuss what happens if the AI bubble finally bursts and what may drive innovation in the market from there. This isn’t the first time that networking has faced a challenge from drivers of feature development. It wasn’t that long ago that OpenFlow and SDN were the hottest ticket around and everything was going to be running in software sooner or later. While that trend has definitely cooled we now see the benefits of the innovation it spurred and how we can continue to create value even if the primary driver for that innovation is now a footnote.
Podcast Information:
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