AI Built It... But Can a Non-Developer Trust It?

AI Built It... But Can a Non-Developer Trust It?

While reading The Daily Telegraph over breakfast this morning, I came across an article called “Anyone’s Domain”, published on 21 October 2026. It discussed the risks of AI-generated software.

Most of the article focused on businesses using AI to build applications, but it got me thinking. How much of this applies to someone like me?

When people talk about the risks of AI writing code, they often mention large companies, customer data, security risks and compliance. These are all important concerns, especially when AI-generated code is used in systems that businesses rely on.

But I don't work for a software company.

My day job is in geotechnical instrumentation and data management. I work with telemetry, monitoring systems, sensors and data used in major civil engineering projects.

So, where does AI fit into my world?

Becoming a Developer Without Planning To

A few years ago, if I wanted to build a custom tool, dashboard or website feature, I had two choices. I could spend weeks learning how to code, or I could forget the idea and move on.

Things are very different now.

I regularly use tools like Claude, Gemini, GitHub Copilot and ChatGPT to build things I probably wouldn't have attempted a few years ago.

For example, I use AI to help me:

• Write Python scripts to process PDFs and Excel files.
• Work with SQL databases to query and check telemetry data.
• Build small web applications using HTML, CSS and JavaScript.
• Create automations using Power Automate and VBA.
• Find and fix coding problems without spending hours searching online.

AI doesn't magically do everything for me. I still need to explain what I want, test the results and work through problems when things go wrong.

The biggest difference is that I can now turn an idea into something that actually works, without having to learn everything from scratch first.

The Problem With Trusting AI

For someone like me, the biggest risk isn't necessarily a major security breach or a business-critical system failing.

It's believing that the AI knows what it's doing when it doesn't.

If you've used AI to write code, you'll probably recognise this experience.

1. You explain what you want.
2. The AI writes the code.
3. Everything looks professional.
4. The explanation sounds convincing.
5. You run it, and something breaks.

Sometimes the problem is easy to spot. Other times, you can spend hours trying to work out what's wrong.

Perhaps the AI has used a function that doesn't exist, suggested an outdated library or simply got the logic wrong.

The frustrating part is that the answer can look completely correct until you actually try it.

AI is very good at sounding confident. Unfortunately, confidence doesn't guarantee correctness.

That's something worth remembering whenever you're using it to build anything.

Sometimes, You End Up Arguing With It

There's another side to working with AI that I don't think gets enough attention.

Sometimes, it's genuinely frustrating.

You can spend several prompts explaining your requirements, discussing different approaches and finally agreeing on how something should work.

Then, halfway through, the AI decides to take a completely different approach.

I'll look at the result and think:

What are you doing? We literally just discussed this.

At that point, my reaction is somewhere between laughing and wanting to throw my laptop out of the nearest window.

There have definitely been moments when I've stared at the screen thinking:

Honestly, I'm going to kill you.

Of course, a few seconds later, I remember it's not a person. It's a machine generating an answer based on the information it has.

So I take a breath and tell it:

No, not that way. Do it this way instead.

Usually, we eventually get back on track.

That's one of the strange things about using AI. The conversation feels natural enough that you start treating it like a colleague. But it doesn't understand the discussion in quite the same way a human does.

Sometimes it follows your instructions perfectly. Other times, it confidently changes things you never asked it to change.

Working with AI has taught me a little more patience.

And occasionally, a few new swear words.

Trust It, But Check Its Work

Over time, I've developed a simple rule:

AI writes the first draft. I'm responsible for the final result.

Whenever I build a script, automation or website feature, I try to follow a few basic steps:

• Read through the code rather than blindly accepting it.
• Test everything locally before putting it on a live server.
• Ask the AI to review its work and look for possible problems.
• Check what happens when something goes wrong, not just when everything works.
• Make sure I understand what the important parts of the code actually do.

I don't always understand every line straight away, and that's part of the learning process. I can ask questions, work through the explanation and test things until they make sense.

But there's one rule I try to stick to.

If I can't understand what a piece of code is doing, I shouldn't be rushing to put it on my server.

That doesn't mean I need to become an expert programmer. It means I need to understand enough to recognise problems, ask sensible questions and know when something needs a closer look.

Why This Matters

One of the best things about AI is that it allows people to build tools around the work they already understand.

A geotechnical engineer can automate data processing.

A photographer can build a portfolio website.

A project manager can create a reporting dashboard.

Someone running a home server can automate backups, monitoring and other routine tasks.

None of these people needs to become a professional software developer overnight.

They already understand the problems they want to solve. AI can help them bridge the gap between that knowledge and the technical skills needed to build a solution.

Of course, the more important the application, the more careful the testing needs to be. A small personal script is very different from software controlling critical infrastructure.

But for everyday projects, the possibilities are enormous.

Things I've Actually Built With AI

This isn't just theoretical for me. I've used AI to work on real projects, including things I probably wouldn't have attempted a few years ago.

B26 Sheldon: Building a Local Website

One example is my B26 Sheldon website, which focuses on local news, updates and information for the Sheldon community.

Building and managing a website involves more than writing articles. There's the technical side, the structure of the site and all the little things that need fixing along the way.

AI has helped me tackle parts of that work without having to become a professional web developer first.

It's also given me the opportunity to work on something useful for the local community. Instead of simply having an idea for a local website, I can develop it, make changes and keep improving it as I go.

Home Servers and Self-Hosting

I've also been working on home server and self-hosting projects.

These involve setting things up, troubleshooting problems and figuring out how different parts of a system work together.

Sometimes things go smoothly. Other times, I end up going back and forth with AI trying to work out why something that should work simply doesn't.

It's a good example of how AI can help you learn while doing something practical. Rather than spending hours searching through documentation before getting started, I can ask questions, try a solution and learn from the result.

Of course, I still need to check what the AI suggests, especially when changes could affect a running service or the security of my server.

Camini: From an Idea to a Published App

Then there's Camini, an app I developed and published on the Google Play Store.

That was a different kind of experience. It wasn't just about getting code to work; it was about taking an idea and turning it into an actual product that people could download.

I no longer actively support Camini. It was a niche product in a market where much larger companies offered similar solutions, making it difficult to justify continuing to develop it.

But I don't see that experience as a waste of time.

Building and publishing an app taught me something important: getting a product built is only one part of the challenge. Finding your place in the market, standing out from established competitors and deciding whether a project is worth continuing are just as important.

AI can help you build a product, but it can't guarantee that people will use it or that it will succeed commercially.

Sometimes, you have to recognise when it's time to move on. That's part of building things, too.

What These Projects Have Taught Me

Each of these projects has given me a different experience.

B26 Sheldon is an ongoing website project. My home server gives me plenty of opportunities to learn through trial and error. Camini gave me experience of taking an idea all the way to an app published on a major platform.

None of this means AI did everything for me. I still had to make decisions, work through problems and learn along the way.

But without AI, some of these projects might never have moved beyond the idea stage.

And that's what makes it so useful. It gives you the opportunity to try things you might otherwise have considered beyond your reach.

Turning Ideas Into Real Projects

I think this is where AI really makes a difference.

Over the past year, I've worked on things that probably wouldn't exist without it.

Not because they were impossible to build before, but because I wouldn't have had the time to learn everything needed to get started.

Some of the tools and projects I've worked on began with a simple question:

I wonder if I can build something that does this?

In the past, that question might have led to a few Google searches before the idea ended up on a list of things to try someday.

Now, I can often have a working prototype in an evening.

It might not be perfect, and it will probably need a few rounds of testing and fixing. But at least I have something to work with.

That is a big change.

AI gives me the freedom to experiment, learn as I go and try things that would previously have been outside my skill set.

For me, that's one of its biggest advantages. It doesn't remove the need to learn. It makes learning and building much more accessible.

So, Can a Non-Developer Trust AI?

My answer is yes, but not blindly.

AI is a useful assistant, a patient tutor and, at times, a very capable junior developer. It can save hours of work and help turn ideas into real projects.

But it can also make mistakes, overlook important details and confidently suggest solutions that don't work.

And it won't take responsibility if something goes wrong.

That's why I think the real skill isn't simply getting AI to write code. It's learning how to check the results, question its decisions and understand enough about what you're building to make sensible choices.

You don't have to know everything before you start. But you do need to be willing to learn, test and take responsibility for the finished product.

For me, that's the real value of AI. It's not about replacing knowledge or pretending that everyone is now a professional developer.

It's about giving curious people the opportunity to build things they might never have attempted otherwise.

And when the AI ignores everything we've discussed and decides to do its own thing?

Well, I suppose that's just another debugging session.

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Inspired by “Anyone's Domain”, published in The Daily Telegraph on 21 October 2026.