3. AI is Smarter Than Your Average Network Engineer – Tech Field Day Podcast

Welcome to the Tech Field Day podcast, where each time we meet, we bring together a group of independent IT experts in the community to discuss or debate a topic or a premise that is of importance to the enterprise IT community. This podcast is related to the Tech Field Day event series, which is an event series focused on practitioners that discusses important and key topics in enterprise IT technology. My name is Tom Hollingsworth.

I'm an event lead for Tech field, a part of the Futurum group. I'd like to take a moment for our guests to introduce themselves before we introduce today's premise, starting with Rita. Rita Younger on Twitter.

Find me at SDN girl Josh Wco. You can find me on X at or Twitter, whatever we're calling it these days at War cop. I'm Rob Coot.

You can also find me on X slash Twitter at at rob cot. Thank you very much for joining us today. Let's discuss the premise for this episode.

Unless you've missed some important news, you know that gen AI is a very hot topic in today's environment, whether we are using it to predict how, uh, autonomous vehicles will drive, whether we're using it to surface insights, or in the case of networking, allowing it to tell us more information about what's going on and suggest ways to make the network better. Wait, that kind of sounds like a job that a network engineer would do, and we want to know, is AI smarter than your average network engineer? So I'm gonna start off by opening this question up to the panel.

We all have a, a wealth of experience when it comes to network engineering, institutional knowledge, things that we've learned over the years, and quite honestly, we've had to relearn some things over the years. Do you think that your average AI or even your most advanced AI today is smarter than your average network engineer? Well, I believe that AI is going to help the network engineer.

Um, AI will never replace the network engineer today. Everyone's looking for simplicity and visibility in their network, and AI will give them that visibility first and foremost, as well as the simplicity. And it's really important when we're building these different AI models to help with networking, that we do keep the interfaces very simple to use.

Now, it's not gonna replace the network engineer. The network engineer is still gonna have to review what AI suggests as far as implementing changes and determine if they need to make that change or not. And having a continuous feedback loop so that we have the ability to inform the AI model if that suggestion was valid, is a very important part of that process.

Yeah, I I would have to think about also what we mean by average network engineer, I guess, right? If I, if I prompted an AI and says, give me your top three average network engineers, you know, what would they say? Right?

Um, it, it would be interesting to figure out like at what skill level we're looking at AI to like augment and or replace. Um, I think it's pretty common knowledge, right? We're expecting AI or, or those that are learning how to use AI to become more productive in their jobs, not necessarily replaced.

So I'm personally not afraid of like being, you know, the average network engineer being replaced by ai. It's maybe just a, you know, the, the, the individuals that are learning how to use it a little bit better are gonna maybe rise more to the top right in their teams. Yeah.

You mentioned the word average there, and I think if we look at the way, um, current AIS chat models and LLMs are being trained, uh, as they're trained more and more within these environments, I think average is a good word to use, you know, the regression to the mean, the, the LLM and the chat bot is going to learn from the questions it's asked and the people it's working with, and probably going to end up a fairly average tool based on the average people that it works with, right? So you're, it's only as good as the data that it's being fed and, and the models that it's learning from. So as more misinformation gets into these LLMs, the more you're gonna get misinformation out of them.

So, I mean, so let's, let's address the, the average elephant in the room, because that is a really good point, and I'm gonna fall back on everybody's favorite definition of average from a legendary comedian in George Carlin. So think about what you consider to be the average network engineer and realize that half of them are dumber than that by your own definition of average. But what is average?

If we have, if we have 200,000 people who have an associate level of knowledge and 50,000 people who have an expert level of knowledge, then the average tends closer to an associate level of knowledge. We see quite frequently that there is this gap between what people expect and reality. How many of you have ever seen the infamous, um, I want someone with A-C-C-I-A level of knowledge and this job pays $35,000 a year because it's really an entry level position when in fact, what you really want is someone who's slightly smarter than your average CCNA.

But I don't want to pay a very competitive rate because those people are expensive. I can see some kind of a gen AI solution offsetting that job role. It's like, this is entry level.

I'm not gonna pay somebody to basically, you know, program VLANs and, and do these kinds of things. I can get a script to do that, but that's how we all learn, right? Mm-hmm.

I mean, I, I didn't jump into a core switch in an ISP and start typing debug IP packet detail as my first job. I had to make my mistakes along the way. So is gen AI raising what we would consider to be an average level of network engineer to a point that's unsustainable?

I would say it's definitely a, another tool in the toolbox for any network engineer. Um, you know, and, and any tool that we use on a day-to-day basis is only as good as how we use it. And I think if you're, uh, whether you're an entry level engineer or an expert level en engineer, if you're using a chat bot or an AI to augment your day-to-day tasks, uh, we talk about trust, but verify, you know, take the output that the chat bot has given you and, you know, vet that against what you know and the change you're trying to make versus just having the chat bot even execute those changes for you, uh, makes it a powerful tool, but one you have to be careful with.

Yeah, I would, I would say from, from some of the AI things that I've seen for specific to network engineering, I would say my hot take is no, it's not smarter. Um, the, the data that's coming into the, the model, the questions that it's being asked and how that's getting fine tuned, uh, and how expensive it is. Some of these tools are not cheap.

And you, you brought up the pay thing, I would say I, I would rather have three to four entry level individuals, right? Learning and progressing in their skillset for the price that some of this AI stuff costs, right? Yeah, and you made a good point.

It's only as good as the data, uh, that is initially entered. Um, so we do need to make sure that that data is valid data and, you know, some of the solutions that we've looked at will take data from multiple vendors. Uh, so being able to analyze the network from end to end, even with multiple vendors, that is a really key thing that AI can do for us, because a lot of people who are trained in networking are trained in just one particular vendor, not multiple vendors.

So what happens when the paradigm shifts and we're no longer talking about on-premises traditional land networking, now we're fighting with, uh, you know, SD WAN or cloud-based networking, and the concepts may be similar, but we're still kind of on the, the cutting edge of things. And now I need to redeploy my assets. Well, in, in traditional networking, I grab three people who are not as tasked and be like, here's a book, learn how this VPC thing works.

But if it's an ai, do I have to get a new model? How long it's gonna take me to rewrite my code to adjust for these new ideas, how much it's gonna cost me? Yeah.

To retrain this model? I think we're seeing a lot of that today, right? As new products come along, as software gets progressed, right?

You, you're, you're in a constant motion of retraining things or adding new modules. It's like the module of module approach, right? And as we've seen through automation, right?

It's like, how many different automation modules am I gonna have to learn to actually make this thing work? Uh, I think that's just gonna be part of what we have to deal with. I'm gonna have to retrain, I'm gonna have to add in more modules, and you're gonna have that individual that becomes responsible for adding that.

I think we've seen a lot of people, especially on the OEM side, who's developing these products, right? A new vendor comes along, they now have to bolt that in, right? We're gonna have to take the same concept and put that into ai, right?

I've gotta add the data. I've got to teach it this new piece of software, I've gotta teach it this new vendor. So you're almost maybe even creating another role to keep the AI up to date, just like you would be training someone an individual.

Yeah. I keep picturing, uh, neo lying in the chair, waking up going, I know kung fu. Like you're just gonna be plugging these modules into your AI to constantly evolve it to keep up with the newer technologies.

But everybody has this fear, and we've heard about this over the years of, of different automation, um, you know, tasks or tools that have come along and cloud was gonna destroy networking. Automation was gonna destroy networking. Now AI is gonna destroy networking.

I don't think, uh, we've seen any of that come to realization. And I don't think AI is going to change that. It's again, gonna be just one more thing in our, our toolkit that we use on a regular basis.

You know, I'm almost over here smiling because I'm thinking back, um, hearing celebrities talk about how AI was gonna replace all of the writers in Hollywood. I'm like, are you kidding me? So I think the general public and the media, um, that they watch doesn't understand the value of AI and what AI can actually do.

Uh, AI is still gonna require human interaction. It will not replace jobs, but actually create more jobs within the tech sector. And I think utilizing the tools that are available through ai, there's just so much promise in the future.

Um, being able to cut down the meantime to resolution from days or hours to seconds or minutes, uh, is incredible. And we've seen outages with, uh, some of the large companies lately, and a human error can cause that outage. You know, if we had some way to verify, um, before the change was made, then that could have prevented an outage.

Money outages. Yeah. I like that point about, you know, being able to process things a lot faster.

That's definitely a value that AI brings, right? I can feed it a whole lot of data and as a human, I know what to ask it, right? The AI doesn't know what to ask itself.

I've got to provide it some context and some intent of like, I, I see this happening. Here's the human intent and here's the question I'm gonna ask it. It has the ability to process that huge amount of data that comes off the network a lot better than I can process it.

Yeah. Or even ingesting suggested changes to configurations and, and looking for possible, uh, outcomes that you didn't envision or you didn't predict. Like, if I make this BGP change or on my route, effector is gonna fall over.

Like good, good way to vet the, the changes you wanna make too. Mm-hmm. In, in a way it's kind of going back to your example, talking about the matrix, waking up saying, I know kung fu well, what was Morpheus next line?

Show me. Like we, we want to verify that the system is capable of doing the things and if coming up with creative solutions that we may not, but it kind of goes back to those issues that we run into all the time where, as Rita mentioned, like the hype around what AI is gonna give us is radically different than what it actually is doing to hear everyone talk about it. It's the unveiling of the first iPhone, right?

It's this magical communication device that's going to change the world when in fact, what we're actually doing with it today is making a slightly faster horse that eats a little less hay. To coin the phrase from Henry Ford, you can't sell a slightly faster horse to shift the industry. You have to over promise and then hope that the system will catch up.

I mean, it's only been a year effectively since we've really seen the hype start building around this idea of GPT algorithms, and already we've seen move and counter move. It's like, oh, it's gonna write all my homework for me. No, actually it's not.

And here's why it has limitations and here's things that we're finding out about it, and now people are like, well, I trust it to give me advice, but I'm never gonna let it go loose in my network to actually do any of these things. So do you feel that we are in strict, we are creating structure and restrictions around AI that prevent it from growing to a point where it could potentially eclipse our jobs? Yeah, I think, I think, um, to, to the horse analogy, I think we're at like a horse ride at the fair or the pony ride at the fair, right?

We want to try it out, but not necessarily let, let's just go for like the full horse riding experience, right? We just wanna try it out, make sure it goes around in encircle, get off of it and go, that was fun, and then go from there as we build some trust in how it works. Trust is a key word.

Yeah. Um, I don't know about you all, but I wouldn't trust a self-driving car. I know it's capable of it, nor would I trust a self-healing network today.

I need to be able to build up that trust. And I think we as an industry need to be able to build the trust in ai and that's only, that's gonna come from using it over not just a year as it's been over years. Yeah.

I mean, it's a popular phrase in networking and insecurity, trust, but verify. Right? Right.

We talk about that all the time. So, you know, a lot of these tools we see coming out have those caveats around them. They say, you know, these, these chat bots and LLMs are fallible.

They're only as good as the data you've put into them. And they can make mistakes. They're, I've heard them described as petulant teenagers.

You can't trust anything they say and they're often wrong. So you have to verify the information and the data you're getting out of them. Um, and that's gonna, that's what gonna require people to do.

Okay. But who's accountable? True.

But let me ask you this question, because we talk about AI as being petulant teenagers, but those are the same petulant teenagers that we eventually hire to be junior network administrators, and we watch them make failure mistakes and we train them not to do them anymore. And we encourage them to learn and to grow and to be better people. And eventually we do feel comfortable releasing them into the core of our network to, you know, do change windows and things like that without supervision.

Are we putting too much trust in people when we should be putting trust into things that we absolutely can control? Like algorithms? Those, those senior network engineers that have been doing the job for 20, 25 years, they still make mistakes.

Hmm. It doesn't matter how many lessons they've learned over the years, they might not make the same mistakes, but they still make mistakes. So I think chatbots and LLMs and all the AI tools, just like people are going to continuously learn, and in this industry, definitely, if you're not learning every day, you're not keeping up.

That's right. But, uh, to go back to that whole idea, yes, even the most senior network engineer is capable of forgetting the VLAN ad command or you using the wrong switch on a command that causes something to fall over and we just kind of shrug our shoulders and go, yeah, they're only human. But yeah, every time an AI makes a mistake, it is the, the sky is falling because computer programs are supposed to be perfect and they never make errors, and every error is a huge problem.

You know, you think about nasa, they, they've been interviewed multiple times now about the whole commercial space thing, and they're like, yeah, we don't blow up rockets because the first, the next rocket we blow up will be the last one we blow up. And it feels like we're holding certain things to a much higher standard than we would expect of anyone that is not generated. So kind of coming back to it, maybe are we being a little too lax with people?

Should we hold them to a higher standard? Yes. Spoken like somebody we should, who's a senior network engineer?

I mean, the, the, the reason that I said yes there is because you, you, you stop going down the criminality route of like, okay, we're humans. There's some, there's some things that you can have in a human conversation that you can't have in an AI conversation, right? And, and if we get over the criminality part of like, Hey, you made a mistake, that's fine.

We'll move on. How do we learn from this? What can we do better?

What processes can we put in to not make that same mistake again, knowing that you will make another? And I don't think we've, we've reached the level of trust, we've reached the level of ability to have that conversation with machines. Maybe one day we will, maybe we can start seeing how machines are talking to machines and see how that works out.

There's been some really fascinating things about that, about how right machines start developing their own language and start talking to one another and, and something completely misunderstood by a human. So I think there's some really cool things. We'll, we'll see.

But you know, yes, we should hold accountability and it's easier to have that accountability when we can have some, some emotions in a conversation with another person. So I'm gonna flip the script as we kind of go here to close this out. If you are the average network engineer today, what can you do to be smarter than an ai?

What's one tip you can give people listening to this podcast that will help them secure a future for their role? I would, I would, um, you know, parrot what I said earlier, which is trust but verify. You know, use AI as a tool, but don't use it to do your job.

Use it to enhance your job. Use it to get another set of eyes on a change you wanna make or a problem you're trying to face, but also still go to your seniors, your other network engineers, people in the community on Slack, on Discord, on on X or Twitter and ask questions. That's all you can do if, if you treat an AI tool as just another resource to ask questions.

But don't rely on that as a sole tool, the singular tool that you use to do your job. I think you'll be successful and you may end up an above average engineer. Yeah, use it.

Absolutely. I think we're gonna see more and more of it. Um, lots more field days are probably gonna be about ai.

So learning how to use it and how to prompt it and how to validate data coming from it. I think it's gonna be hugely important to augment your current job, make you better, better than average. And every network engineer is gonna make a mistake at some point.

Um, a tool that you can use, of course, is the AI tools to help prevent that mistake. Um, but any network engineer who has made a mistake does not take that lightly. They will never forget that experience.

I had a young network engineer that was beside herself about a mistake, and I said, let me tell you about the mistake I made. I can tell you exactly where it was. And then somebody who was kind of in between our ages popped in and said, let me tell you about my biggest mistake.

So mistakes happen. The more tools we have to prevent those mistakes, the better. So embrace ai.

Thank you very much for joining us today. Um, if people would like to connect with you and continue this conversation, where can they go to do that? Rob, Uh, as I mentioned earlier on, uh, x slash Twitter at Rob Coot, or I'm on LinkedIn as well, Same two places.

LinkedIn, Josh Wop or at Wop on X on Twitter And Rita. Younger on LinkedIn or on X Twitter, sdn girl. Alright.

Thank you very much for joining us for this episode of the Tech Field Day podcast. You can catch all of the episodes of this podcast on our YouTube channel, as well as in our podcast feed. Please make sure that you subscribe so that you don't miss an episode.

This TE podcast has been brought to you by Tech Field Day, the Home for Independent IT experts that bring you the conversations that you want to be having about enterprise IT technology. It's part of the Tech Field Day event series, which is a part of the Futurum group. com.

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