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One of our engineers graduated last December. AINNA is his first job, and he had been with us for roughly 3 months when this happened.

Working alongside AINNA智能体 AI, he shipped a complete financial 系统 — end to end — one that was later assessed at TRL9 through MOSTI, the highest level on the 科技 Readiness Level scale.

What interests me isn't the 系统 itself.

It's who can now build one, and how fast that capability shows up when a person works next to an AI 智能体.

At an event hosted by the Saudi Embassy recently, I said to someone I had just met:

For certain 系统 in manufacturing, a Chemical 工程 graduate may well be better placed to build the plant's IT 系统 than an IT graduate who doesn't understand the manufacturing process.

Not because a Chemical Engineer writes better code.

But because code is getting cheap, and AI keeps making it cheaper to produce.

A Chemical Engineer already understands process flow, pressure, temperature, chemical reactions, pumps, valves, safety, quality — and what actually happens on the production floor.
An AI 智能体, meanwhile, can help them stand up the database, the dashboard, the APIs, the automation, the monitoring layer and the application around it.

So the question going forward may no longer be:
"Who codes best?"

But:
"Who actually understands the problem that needs solving?"

AI doesn't remove the importance of IT.

But it does shift IT's position — from the team that builds everything to an enabler that lets domain experts build things themselves, far faster.

And that may be the real challenge for the next generation:
As the technical barrier drops, domain knowledge, problem solving and the ability to read how the 真实世界 actually behaves become the expensive parts.