From Geek to Star #8 - AI for Your Hard Skills (and Why It’s Not Optional Anymore)

If you missed the previous episodes, you can access them online here.

🗓️ This Week – Episode 8: AI for Your Hard Skills

Over the past months, I’ve had multiple discussions with startup founders and tech leaders across Europe and Asia in my current work and outside.

The pattern is striking: small teams, moving fast, and using AI as a core teammate.

One founder (with engineering skills) told me how he delivered himself a production-ready feature in 3 days (with a combo ChatGPT / Jira / Cline / Claude, producing also code and doing tests at night while he was sleeping…) instead of his own development team who had planned a whole dedicated sprint for it without a deep use of AI on their side. 

This is no longer “coming soon.” It’s happening.

For us engineers, architects, data experts, cybersecurity experts... it means this: your AI adoption curve will define your career trajectory.

Not just the fact that you use AI. But how.

🎯 Why This Shift Matters (Now)

If you’re a software engineer, SRE, platform architect… you’ve probably already seen the difference between:

• Those who use ChatGPT like a Stack Overflow substitute.
  

• And those who use AI as a co-designer, a reviewer, a planning partner.

Those in the second group move faster, solve harder problems, and free up mental space to focus on system-level decisions.

This brings us to a crucial point in the evolution of the T++ Engineer. For those who really want to leverage on AI, we should keep in mind that: 

1. GenAI is probabilistic, not deterministic: so for the same input (user story, task…), the code generated may not always be the same. It is a bit like asking two different human engineers to code the same user story: the code may sometimes differ. So you must use your own expertise and critical thinking to see if the code generated matches the overall needs you have in mind (functional requirements, technical requirements, maintainability…)

2. Engineers must keep system thinking at their level, decompose for GenAI: even if you can have very long prompts nowadays, it is likely not to be enough to describe the whole system you want to build. System design, breaking this into small logical items is something to be done by you and then you can leverage on the power of GenAI at a more granular level.

And that cognitive leap is what will separate average engineers from the next generation of impactful tech leaders.

🔍 The Rise of the Builder: A Preview of the Future

In an agentic world, the definition of “builder” is changing.

In a very near tomorrow, a builder may be someone who:

• Delivers value across layers (product to infra) without needing 10 tickets
  

• Understands how to leverage LLMs to orchestrate work (not just ask for code).
  

• Thinks ahead: if I automate this piece, what does it free me to explore next?
  

Even in large organisations, this shift may be coming. There is an opportunity for emerging profiles of “augmented engineers” who demonstrate more ownership, faster feedback loops, and tighter alignment with business impact.

🛠 3 Simple Habits to Start Practicing

Here are 3 practical habits I would suggest to strengthen your hard skills through AI, starting this week - I am not a technical expert per se, so if you are please do share also some habits and I will feature you in my Linkedin post:

1️⃣ Pair Daily With an AI Assistant (Like Cursor or Copilot)

Each morning, use AI to help write, improve, or test a function. Then review the reasoning: what would I do differently?

📚 Cursor: an editor made for AI-native devs.

2️⃣ Refactor With LLMs and Explain Your Thinking

Paste a recent snippet into Claude or GPT-4 and ask: “Refactor this like a senior engineer. Annotate and explain trade-offs.”

Use the result to sharpen your architectural judgment.

3️⃣ Start a Prompt Library for Systems Thinking

Build a personal prompt collection for your domain:

• “Suggest monitoring strategies for a microservice architecture.”
  

• “Compare design patterns for this kind of reliability need.”
  
  📚 Prompt Engineering Guide

💡 Think in systems. Use AI to model, to simulate, to challenge your assumptions, not just to generate code.

🧠 From Hard Skills to Thinking Skills

But let’s be clear: the future of hard skills isn’t just syntax mastery. It’s judgment. It’s systems-level understanding. It’s the ability to use AI tools intelligently, and to make sound decisions in uncertain environments.

This is where critical thinking becomes essential.

• What are the confidence levels in an AI-generated test suite?
  

• Where are the risks in blindly accepting LLM outputs?
  

• How do you balance speed vs resilience when automating a workflow?

A T++ engineer doesn’t just use AI—they guide it, just like a senior engineer guides a junior engineer (which can indeed in this case write code at the speed of light!).

🙏 I’d Love to Hear From You:

• What AI habits have helped you level up?
  

• Have you started seeing yourself more as a system builder than a task executor?
  

• Where does AI still fall short in your current work?

Just reply, I read every note.

Also, follow me on LinkedIn for behind-the-scenes reflections between newsletters.

✨ Stay curious, stay connected!

From Geek to Star by Khang | The Way Forward