I recently spoke at CloudNexus, an AWS Cloud Club QAU event focused on cloud technologies and AI. It was a short talk, nothing fancy, mainly around Agentic AI and how students are currently using AI tools.
What surprised me wasn’t the questions. It was how confident students were that they were “using AI correctly”. Most of them weren’t.
“I asked them to generate code from ChatGPT”
During discussions, a lot of students openly said they use ChatGPT mainly for code generation. Not for understanding. Not for learning concepts. Just for getting code quickly.
And honestly, I would not blame them for this. It’s very fast. It feels somehow productive. But this is where problems start showing up later during debugging, real projects, or even basic system design conversations or in interviews.
The issue isn’t AI: it’s how we’re using it
One thing I tried to explain during the talk was simple:
Not every AI tool is meant to do the same job.ChatGPT is great. I use it too. But it shines more when you use it for:
- understanding concepts
- breaking down ideas
- writing or structuring content
- asking “why” questions
When you try to use it as your main coding engine, you miss context and context matters a lot in real-world development.
Code-focused tools exist for a reason
We talked a bit about tools like Cursor, Amazon Q etc. Thesetools work inside your codebase. They understand files, references, and structure.That’s why they feel more useful for development work.
Not because they are “smarter AI”, but because they are built for developers.
Where Amazon Q fits in
Since this was an AWS-focused event, I also talked about Amazon Q. What I personally find interesting about Amazon Q is that it doesn’t live outside your workflow. It helps inside AWS environments, where cloud engineers actually spend time. It’s less about asking random questions and more about:
- cloud-aware guidance
- best practices
- security-conscious suggestions
That difference matters when you move beyond tutorials.
Explaining Agentic AI in simple terms
To keep things simple, I explained AI in three phases:
- Chatbots: you ask, it replies
- Copilots: it understands your context
- Agents: you give a goal, it figures out the steps Agentic AI is less about prompts and more about delegation.That idea really clicked with students.
Why the cloud becomes unavoidable here
One thing I emphasized was that agent-based systems don’t work in isolation.They need infrastructure, scale and security.
This is where platforms like AWS Bedrock start making sense, not as buzzwords only, but as enablers for building AI systems that actually run in the real world.
What I really wanted students to take away
If I would summarize the talk in one line, it would be this: AI should help you grow, not replace your thinking.
- Use ChatGPT to learn.
- Use coding copilots to build.
- Use cloud AI to scale. And use agents when you’re ready to automate outcomes.
Final thought
Speaking at CloudNexus reminded me that students don’t need more tools.They need better guidance on how to use what already exists. There is a quote I read online saying:
AI isn’t going away.But engineers who understand how and why they use AI will always stand out.
here's the linkedin post for my recent talk:
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