East Meets North: Why the Engineering Talent Crisis Is Actually an AI Paradox

The smartest technology we've ever created is supposed to solve our biggest workforce problem. Instead, it's creating a paradox that could reshape entire economies.

Both Singapore and the UK are desperately short of engineers, reading World Bank data, IMF reports, digging into sector analyses between Northeast England and Singapore, and the numbers are kind of wild. But what's really got me thinking is this: AI was supposed to solve our talent shortage problem. Instead, it might be making it worse while simultaneously making it better. I know that sounds contradictory. Stay with me.

What I Found

Both Singapore and the UK are facing serious engineering talent shortages, but the patterns are different in ways that matter. The UK needs over 120,000 new engineers annually to meet demand, driven by infrastructure projects and net-zero goals. Singapore's facing something more specific: young engineers are abandoning traditional fields like aerospace and civil engineering for finance and tech, creating a brain drain within engineering itself.

Here's the salary reality check: A senior software engineer in Singapore earns around SGD 136,100 (£80,000), while London engineers can push £100,000+. But factor in Singapore's 5% tax rate versus the UK's 32%, and suddenly the net take-home gap shrinks fast. The economics of where to build an engineering career aren't straightforward.

The AI Bit, And Why It's Complicated

This is where it gets fascinating. AI is automating routine engineering tasks, CAD drafting, code writing, basic simulations, which should help with the shortage by making existing engineers more productive. Machine learning engineer hiring is up 340% since 2020. New roles are emerging: AI engineers, predictive maintenance specialists, digital twin analysts.

But here's the paradox I'm trying to wrap my head around: AI demand itself exceeds supply by more than 3:1. We've created a shortage within the shortage. And AI automation is reducing entry-level engineering positions, the traditional pathways for junior engineers to gain experience. Companies are hiring fewer graduates because AI tools handle simpler functions.