I’ve always loved computers: games, building things, anything.
But I didn’t start my journey with a formal degree — I started by teaching myself.
When I was 21, I moved into a co-working space in Cape Town, a novel concept at the time.
That's where I met Jason, a former Googler building his own business — Indie Shuffle, a platform like SoundCloud for indie artists.
I was instantly captivated by what he was doing.
One day, I simply asked: “Can you show me how to do what you’re doing?”
Jason was generous. He shared the self-learning path that had helped him pivot into coding — resources, guidance, and the mindset to figure things out on your own.
That conversation lit the fuse.
From that point on, I was building things online, writing code, and solving problems with technology.
The Master's Decision
For years, I toyed with the idea of a Master’s degree.
Eventually, at 30, I was ready.
In 2023, I was accepted into the University of York’s Computer Science program, specialising in Artificial Intelligence.
On paper, it was perfect — a structured way to deepen my technical skills.
But reality didn’t match the brochure.
When Structure Becomes a Cage
The program required heavy self-learning, which I was fine with — I’d been doing it for years.
The difference was the rigidity.
The joy of exploration was replaced with outdated material, inflexible requirements, and little room to adapt to modern realities.
It was fully remote, which made networking almost impossible.
The instructors felt inaccessible.
And on top of my full-time job, the 20+ hours a week of study became exhausting.
I wasn’t just tired. I was starting to wonder if this path was even relevant anymore.
The “Penny Drop” Moment
The turning point came during a semester in Advanced Programming.
By day, I was writing Python applications for my job. By night, I was grinding through a Java module using the provided course notes — and getting nowhere.
In frustration, I pasted a question into ChatGPT. Instead of hints, it gave me a fully functional solution. And it hit me:
Why am I forcing myself through this outdated, rigid process when the world has changed?
Yes, AI won’t magically make you a great developer. You still need to understand documentation, think critically, and debug. But AI can accelerate learning, shorten feedback loops, and turn ideas into working prototypes faster than ever.
Choosing Momentum Over a Diploma
I started a Master’s in Computer Science (AI) with genuine excitement.
But in parallel, I was building products and solving problems that felt more aligned with where I wanted to go.
The trade-off became obvious: spend my time optimising for grades, or optimise for outcomes. I chose the latter. I left the program to double down on building, shipping, and learning through execution.
It wasn’t a decision against academia — it was a decision for momentum. Since then, I’ve learned faster, shipped more, and created more value than I could have in a classroom.
If there’s a theme here, it’s simple: follow the work that compounds.
The Lesson
Six months after I quit, US Federal Reserve data reported that computer science graduates faced one of the highest unemployment rates of any degree.
For me, that was validation.
Sometimes quitting isn’t failure — it’s strategic focus.
If your intuition tells you the future is moving in a different direction, listen to it.
The bravest move you can make is to step off the prescribed path and start building the one that actually gets you where you want to go.