How NotebookLM is revolutionizing learning with AI.

Transcript

So if you're a developer, you're probably pretty used to AI assistants. That suggests you know the next line of code. But the entire game is changing, and fast. We're moving way beyond simple coding companions to these powerful, autonomous AI agents that can actually take on entire projects. Today, we're going to break down exactly what this means for you and your team. Alright, so let's start with the basics. What in the world is an AI agent? Well, unlike a simple tool that just sits there waiting for your command, an agent is proactive. It can understand a goal, create a whole plan, and then act on that plan, all with very little human input. Think of it less like a calculator and more like an intern who can, you know, actually get things done. So how did we even get here? It feels like it happened overnight, but this has been a massive leap, moving from just helpful little sidekicks to what are essentially brand new members of the development team. This slide just nails the difference. Your traditional assistant, it's reactive, it helps you when you ask. But agentic AI, it's proactive. You can give it a goal like, hey, implement this new feature and it can plan it out, write the code, test it and even debug it on its own. The mission here isn't just to be a pair programmer anymore. It's to become a true peer programmer. And this isn't just some far off concept anymore. It's a whole new ecosystem of tools that's here right now. The market is absolutely. Exploding with options O let's try to map out who the key players are in the landscape is really diverse. You've got agents that live right inside your IDE like GitHub copilot. Then there are these super powerful command line tools for all the folks who live in the terminal. You even have entire AI first code editors like cursor and then weigh at the far end of the spectrum, you have these full blown AI teammates that aim to operate just like a human developer and Speaking of ambition, Devin AI launched with this incredibly bold claim. The goal isn't just to help you. Write code? No. The goal is to create an AI that can handle the entire software development lifecycle from start to finish. This is the ultimate vision of what agentic AI wants to be. OK, so with so many players in the game, how do you even begin to figure out which tool is right for you? Well, it really comes down to understanding how they're different on the few key things that actually matter to a development team. This is the question on every developers and every manager's mind right now, and it's not just about what features they have. It's about autonomy. It's about cost, and maybe most importantly, it's about privacy. Let's break it all down. First up, let's talk about autonomy. On one end of the spectrum, you've got Devin, which is shooting. Total end to end automation. Then you have tools like GitHub Copilot which are highly autonomous but are designed to keep the developer in the loop for safety, and others like Cordo AI. They focus their autonomy on very specific tasks like generating tests and reviewing PR's instead of trying to write new features from scratch. Next U Money. The pricing is well, it's all over the map. You've got the established players like GitHub Copilot with really simple, straightforward monthly fees. Amazon Q even has a surprisingly generous free tier. But then you have these usage based models like Claude code that could get really expensive really fast depending on how your team uses it. Now for many organizations, this slide is the deal breaker. Privacy. Can you host it yourself and keep your code inside your own walls? Tools like Tab 9 and Source Graph Cody say yes, which is a huge deal for companies with strict data policies. On the other hand, the big platforms like GitHub Copilot and Amazon Q their cloud only services. That means your code is being sent to their servers. You know, all of this incredible power introduces the central challenge of this whole agentic AI era. How do we unlock all of this amazing productivity without exposing our most valuable assets? And this is it right here. This is the core attention every single organization that's adopting these tools has to figure out how to navigate this. You want the speed, you want the innovation, but you absolutely cannot risk leaking your proprietary code, your customer data, or your intellectual property. And make no mistake, the risks are very real. Beyond just data leakage, these agents can and do generate insecure code. There's something called automation bias, where developers just start to blindly trust a flawed suggestion from the AI. And there are even scarier threats, like an AI hallucinating A malicious software package, which could create a massive supply chain vulnerability. O When you're faced with risks like that, what's the response? Well, major companies and even entire governments aren't just sitting around and waiting. They are actively building frameworks right now to adopt these tools safely. And we can see this happening in the real world. In Australia, big banks like Combank and and are going all in on AI, but with a huge emphasis on mandatory human review and tightly controlled boundaries. And then at a national level, you have regulators like Indonesia's OGK creating AI governance guidelines for their entire banking sector. That shows you just how seriously this is being taken. So how do you actually put this into practice? Well, a lot of organizations are turning to establish standards like the NIST AI Risk Management Framework. It's so popular because it gives you a clear four step playbook, govern, map, measure and manage. It gives you a structured way to think about and mitigate risk instead of justice reacting to problems after they happen. At the end of the day, there's no single silver bullet here. A really strong defense needs multiple layers. It all starts with a clear company policy. Then you add technical solutions like. Data masking and encryption. And above all else, you have to insist that a human is always in the loop to review and approve the AI's work. And so here we are, full circle. We've gone from simple code assistance to truly autonomous teammates. The technology is here. The real question for all of us now is how we're going to integrate these powerful new teammates into our workflows safely and effectively. Is your next hire going to be a person or an agent?