Artificial Intelligence (AI) is everywhere! In our phones, in our search results, and also more and more in our networks. But what does “AI” actually mean, especially for us as network engineers? If you’ve been hearing about GenAI, machine learning, or Cisco’s AI-powered tools and wondering how it all fits together, you’re not the only one. This post will give you a no-nonsense introduction to AI and why it’s becoming important in the world of network automation.
What Is AI, Really?
Let’s start simple: AI is about getting machines to do things that usually need human intelligence. That could mean understanding language, spotting a pattern, or making decisions.
But “AI” is more than just one technology. It’s a broad term that includes things like:
- Rule-based systems (think of
if X happens, then do Y) - Machine learning, where systems learn a pattern from data instead of being explicitly programmed
- Deep learning, which uses neural networks to find patterns in complex data (like images or speech)
- Generative AI, which creates new content like text, code, or images
So when someone says, “AI is changing everything”, they might be talking about any of these things, and one does something different.
A (Quick) History of AI
AI isn’t actually something new. It’s been around for years. Here’s a simplified timeline to give you some context:
The Rule-Based Era (1950s–1980s)
In the early days, AI meant writing hand-crafted logic. So for exampleif packet_loss > 10% → alert("High packet loss")
These systems were kind of rigid. They could only do what they were told. It’s useful, but it’s not really “intelligent”.
Statistical AI (1990s–2000s)
As computing power improved, AI systems started learning from data. Instead of writing logic by hand, engineers built models that learned patterns. So for example, spotting spam based on how often a word appeared in a text.
Deep Learning (2010s)
With the rise of big data and GPUs, neural networks became a real thing. These models are loosely based on the human brain and are really good at complex tasks like voice recognition and image classification.
Generative AI (2020s)
That’s when things got a bit weird (but also exciting). Instead of just recognizing data, generative models can create new content, like writing Python code or summarizing router configs. ChatGPT, GitHub Copilot and Cisco’s AI Assistant are all powered by this type of AI.
Why AI Matters
If you’ve ever asked ChatGPT to write a script, or used Copilot in VS Code, congrats, you’ve already used AI. Most of us have. But the big question now is: how do we move from casual ChatGPT scripts to solving real networking problems more efficiently?
Faster Automation
AI can be a great coding assistant. Need to generate a NetBox script? Can you refactor a Jinja2 template? Can you also check the API responses? GenAI tools can help you with that, and actually save hours.
Smarter Observability
Cisco and other companies are integrating AI into their observability platforms to spot patterns, predict outages, and correlate data across different layers. This helps you respond faster (or not at all, if it self-heals).
Security Threat Detection
AI doesn’t only detect known threats anymore. It can spot unusual network behaviour that traditional tools might miss, which is really useful in zero-trust or hybrid environments.
Okay, but… How can I use AI myself? Chances are very high that you actually used AI before…
- Auto-complete in VS Code? That’s AI.
- Asking ChatGPT a question? That’s definitely AI.
- Googling something? The answer you receive at the top is AI.
A Sign of the Times: Stack Overflow Becomes Quiet
If you’ve ever searched for a Python or API error and ended up on a Q&A website with code snippets and accepted answers, then that was probably Stack Overflow.
Stack Overflow has been the go-to site for developers for over 10 years. Programmers post questions, others reply with fixes, and the best answers get upvoted. Even if you’ve never created an account, chances are Stack Overflow has helped you debug something in the past.
But since late 2022, right around when ChatGPT became publicly available, the number of questions asked on Stack Overflow has dropped dramatically.
In early 2022, people were asking 100’000+ questions a month. By early 2025, that number fell to just under 3’000. To be fair, there’s been a gradual decline in Stack Overflow activity since around 2018. But since 2022, when ChatGPT became widely available, the drop has been dramatic.
Why should you post a question, wait for answers, and maybe deal with unfriendly replies when ChatGPT gives you a useful reply in seconds?
This shift is subtle, but very important. It shows us that:
- AI allows us to solve issues faster
- AI becomes the first stop when troubleshooting
- Knowing how to ask AI the right question to get the best answer is a real skill now
Conclusion
This post just scratched the surface of what AI means for us as network engineers. We looked at the basics, checked out real-life examples of how AI tools are already changing the way we troubleshoot, automate and learn. In the next few posts, we’ll look into:
- What’s difference between AI, machine learning and deep learning?
- What are Large Language Models (LLMs)?
- How does GenAI learn and what makes tools like ChatGPT work?
At first I didn’t want to jump on the hype train and talk about AI. But when you get past all the hype, it’s just another powerful tool, especially for those of us already automating parts of our networks. The more you understand how it works, the more you’ll get out of it. So if you’re ready to build faster and smarter workflows, stick around!
Follow DevNet Academy for more beginner-friendly posts on AI, automation, and how it all fits into the DevNet Expert or CCIE Automation world. And if this helped clarify things, share it with others who are also figuring out what this whole AI thing is about.
Happy automating!