In this video, you’ll see the real skills that matter now that AI is changing engineering work. These are the abilities that keep you valuable, relevant, and ahead of the curve. If you want to stay competitive, this is the checklist that actually counts.
As we know, AI is changing our industry faster than most people realise.
If your main skill is writing boilerplate code or implementing basic features, you’re in trouble. AI can do that stuff now, and it’s getting better every week.
Here’s what actually matters: the skills AI can’t replicate. Understanding what problem needs solving in the first place. Making architectural decisions with real trade-offs. Knowing when to ship simple versus when complexity is justified. These require human judgement, not pattern matching.
System thinking is absolutely crucial now. AI can write individual functions brilliantly. But can it design how an entire application should fit together? Can it understand the second and third-order effects of architectural choices? Can it balance competing priorities like performance, maintainability, and time to ship? Not really. That’s where you add value.
Product sense separates engineers who stay valuable from those who get replaced. AI doesn’t know what users actually need. It can’t tell you whether a feature solves a real problem or creates more confusion. It can’t prioritise what to build based on impact. You can. Develop that skill ruthlessly.
Communication becomes more important, not less. When AI can generate code, your ability to explain complex technical decisions to non-technical people matters more than ever. Your skill at writing clear documentation. Your talent for asking the right questions before building. These human skills compound your AI-amplified technical abilities.
Problem identification might now be the most valuable skill. AI is brilliant at solving well-defined problems. But spotting that a problem exists in the first place? Understanding the root cause versus symptoms? Knowing which problems are worth solving? That’s still very much a human game.
Knowing how to use AI effectively is also a skill. Not everyone uses it the same way.
Some engineers treat it like advanced autocomplete.
Others use it as a thinking partner that helps them explore approaches, spot edge cases, and prototype rapidly.
The gap between these two approaches is significantly massive.
Critical thinking about AI outputs is essential. AI confidently generates code that looks right but has subtle bugs. Can you spot when it’s wrong? Can you evaluate whether its suggested approach is actually the best one for your context? Or do you just accept whatever it generates and ship it without understanding?
In this video, I’ll break down each skill that matters in the AI era. System thinking and architecture (how to design robust systems that scale). Product sense (how to identify what’s worth building). Communication (how to influence decisions and explain technical choices). Problem identification (how to spot valuable problems before anyone asks). And AI fluency (how to use AI as a force multiplier without becoming dependent on it).
I’ve built many projects on my GitHub over the years. Take a look for inspiration or jump in to contribute! 🔥
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