Stop Watching. Start Building.
For years, Cain Lazenby had watched software ideas come and go.
Some were business ideas. Some were personal projects. Some were simply observations about problems that seemed unnecessarily difficult to solve. Like many people who have spent decades working in digital, he’d become accustomed to the reality that building software was expensive, technically challenging and, for most small businesses and individuals, largely out of reach.
Then AI arrived.
Not overnight, of course. Like many people working in digital, Cain had been experimenting with artificial intelligence tools since ChatGPT first appeared, mostly at the free and entry-level end of the market. He used it occasionally, tested new tools when they emerged and watched the endless stream of headlines promising that AI would fundamentally change how businesses operated.
But he wasn’t entirely convinced.
“I think I was like most people. I was interested, but mostly standing on the sidelines watching everyone else tell me what AI could do.”
At the same time, he was hearing much of the same excitement from another source — his own children.
“My kids are 14 and 19, and every second thing you see on social media is someone saying they’ve built an app, created a business, made money online or discovered some secret opportunity using AI,” he said.
After a while, he became less interested in what everyone else was saying and more interested in figuring out what was actually possible.
So, around three months ago, he decided to stop watching and start building.
Finding a Problem Worth Solving
Cain had always maintained an interest in investing. He liked the idea of spreading risk across different industries and asset classes that didn’t necessarily move together. He’d owned shares for years and already had a CMC account, although by his own admission he had mostly adopted what he describes as a “set and forget” approach.
“I’ve always had an interest in how markets work. The problem was I never really had the time to properly learn everything that seemed to be required.”
As he spent more time trying to understand investing, he quickly discovered what many new investors discover: there wasn’t a shortage of information.
There was too much of it.
There were endless indicators, endless strategies, endless YouTube videos, endless opinions and countless platforms, all claiming to offer a better way to analyse the market.
He found himself jumping between CMC, TradingView, Yahoo Finance, brokers, AI tools and browser tabs that seemed to multiply faster than he could close them.
Eventually, he asked himself a simple question.
“Could I build something that simply grades stocks for me?”
Not to replace research. Not to tell him what to buy. Simply to help identify which stocks might deserve a closer look.
What he expected to become a small experiment quickly became something else entirely.
When the Rabbit Hole Opened Up
For nearly three decades working in digital, building websites, brands, online systems and user experiences for businesses and local government organisations, primarily throughout Tasmania but with clients extending much further afield.
He had built digital platforms before using website builders, low-code systems and various development frameworks, but he had never attempted to build a software application from scratch using AI-assisted development.
What surprised him wasn’t that AI could generate code.
It was that ideas he’d carried around for years suddenly felt achievable.
“I’ve always had ideas. The problem was they always ran into one of three barriers — cost, time or technical capability.”
For the first time, those barriers didn’t feel quite so immovable.
Instead of looking at an idea and immediately thinking, “That’s too hard,” he found himself asking a different question.
“I wonder if I can actually build that.”
And then he would try.
Sometimes it worked.
Sometimes it broke.
Sometimes it broke spectacularly.
But unlike previous software ideas, he wasn’t blocked from continuing.
Building Everywhere Except Sleeping
The project quickly consumed his spare time.
He worked on it late at night after work. He worked on it while travelling. He worked on it in airport lounges. He worked on it sitting in taxis and Ubers. He worked on it while waiting for his wife to finish shopping in Melbourne.
“Looking back, I probably spent an unhealthy amount of time thinking about what feature I could build next.”
He estimates he spent more than 200 hours building the platform over three months.
His family, he says, weren’t particularly surprised.
“They’re pretty used to me disappearing down rabbit holes. The difference this time was that I actually kept coming back with something tangible.”
The first major breakthrough came when the platform successfully generated its first stock grading score.
“It sounds ridiculous now, but that was probably the moment where I thought, ‘Okay, this might actually work.'”
The Article That Changed My Thinking
Around this time Cain came across a New York Times article profiling entrepreneur Matthew Gallagher, who had used AI tools to rapidly build and scale his business.
The story resonated at that time.
Not because of the revenue figures.
Not because of the billion-dollar headlines.
But because it challenged an assumption.
“The thing that stuck with me wasn’t the money. It was the realisation that the assumptions I’d been carrying around about software development for the last twenty years might no longer be true.”
Fifteen years earlier, Cain Lazenby had been involved in an app development venture & was a director of the company where pursuing software ideas often required significant investment capital and substantial financial risk.
“Back then, if you wanted to build software, you could genuinely end up risking everything.”
“This felt different now, ideas now seemed more achievable on my own steam and time” Lazenby said.
It Was Never Really About Stocks
At some point during the third week, he realised that Stock Muster had stopped being about investing.
The investing problem had simply become the vehicle for learning something much bigger.
“Stock Muster was really just the excuse I gave myself to properly understand what AI-assisted software development could actually do.”
The platform evolved organically.
He rarely changed direction. Instead, each completed feature simply led to another question.
What would make this more useful?
What would make this more intuitive?
What would I want to see if I was using this every day?
Occasionally, he asked AI for suggestions.
More often, he trusted his own instincts.
“I’ve spent a long time building digital experiences for people. At some point, you develop a gut feel for what users are actually going to find useful.”
The Biggest Lesson
There were still challenges.
The biggest came when he decided the platform had to be hosted in Australia.
“That wasn’t negotiable.”
The migration created technical challenges that were significantly more difficult than anticipated.
But perhaps the biggest lesson wasn’t that AI could build software.
It was that AI had removed barriers he’d spent years assuming were permanent.
“I don’t think AI replaces people. I think it gives people permission to attempt things they previously thought were impossible.”
Today, Stock Muster is live and continues to evolve. He’s already built another small application, TickUps, currently in beta testing with family members, and admits he now has a growing list of software ideas he’d eventually like to pursue.
He’s not sharing those ideas just yet.
“I’d like to build them first.”
He paused for a moment.
Then laughed.
“Although if you’d told me three months ago that I’d be sitting in Melbourne Airport building a stock analysis platform on my phone, or refining features while my wife was shopping, I probably wouldn’t have believed you. Looking back, it’s been less about building a stock app and more about discovering what’s now possible. And honestly, I’m excited to see what comes next.”.
