Originally posted on trent.ai on November 11th 2025.

Originally inspired by a real bug and an even realer problem.

A Morning Routine Interrupted

The other morning, I was doing something ordinary. I walked past my mum’s allotment, checked the rain gauge, and as I do every day, tried to record the reading in Microsoft Excel on my iPhone.

Except this time, it didn’t work.

At first, I thought it was just me, maybe I’d done something wrong. But then someone sent me a link to The Register, confirming that the recent Excel deployment on iPhone and iPad had indeed broken for many users.

It was frustrating, yes — but also revealing. Because buried in that article was a claim that caught my attention: some major tech companies are now saying that up to 30% of their code is being written through AI-assisted or “vibe” coding.

That made me pause…and this is in addition to the recent claim discussed in our previous article, that a quarter of Y Combinator startups say 95%+ of their code is AI-generated.

The Hidden Cost of Superficial Automation

What if what we’re seeing with Excel isn’t just a bug, but an early sign of a much deeper issue?

I like to call it superficial automation.

Superficial automation happens when we assume the job our people are doing is simply to write code, file tickets, or push deployments. So, we replace that visible layer with an LLM or an AI assistant.

But the truth is, those people are doing something much deeper:

  • Understanding edge cases

  • Spotting subtle issues before they ship

  • Intuitively preventing cascading failures

  • Protecting customers from exactly this kind of problem

By automating the surface, the typing, the syntax, the visible effort, we risk losing the invisible expertise that keeps systems stable.

When the Surface Looks Smooth, but the Foundation Shifts

If Big Tech is already deep into AI-generated coding, we are seeing the first cracks, and AI agents will repeat Excel’s trajectory, except faster and at larger scale. Bugs slip through. Security checks get skipped. Context that once lived in engineers’ heads now gets abstracted away by an LLM that doesn’t truly understand consequences. And because these companies often limit themselves to their own in-house tools, their constraints can blind them to risks developing outside their ecosystem.

That’s why this is an opportunity for smaller, more agile teams; the ones who adopt AI thoughtfully, not superficially. They can design workflows that enhance judgment, not replace it.

Structural Automation: The Better Path Forward

This is why I’m excited at the work we’re doing at Trent AI, what I’d call structural automation, an automation that goes deep, not just wide.

Rather than using AI to replace human insight, we use it to amplify it, especially in cybersecurity and software reliability. The goal isn’t just faster code; it’s better, safer systems.

Because what’s the point of saving developer hours if the result is a broken Excel for millions of users?

A Raindrop Reminder

For now, my rainfall reading lives in my head. (24.4 mm, for the record) But that small inconvenience reminded me of something bigger, that progress in AI isn’t about replacing human intelligence; it’s about learning where it truly matters.

If we automate only the surface, we’ll keep running into hidden cracks. If we automate with understanding, we can build secure systems that stand the test of time.