Most people assume investors learn new technology by reading about it. Adrian Vanzyl took a different path. Well after his early career was already established, he sat down and earned formal certifications. These covered machine learning, deep learning, and neural networks. It wasn’t for a resume line. It reflects a broader view: secondhand summaries aren’t enough for a technology reshaping every industry he invests in.
That decision says something important about how he approaches expertise. It’s a pattern worth unpacking for founders and investors alike.
Why Secondhand Understanding Isn’t Good Enough
Plenty of investors talk fluently about AI without ever having built anything with it. They pick up vocabulary from pitch decks and conference panels. That vocabulary often sits just far enough from the real mechanics to create false confidence.
False confidence is dangerous in venture investing. An investor might not tell a genuine technical moat from a thin API wrapper. That investor will misprice risk. They’ll back the wrong companies. Or worse, they’ll give bad advice to the right ones. Understanding the real mechanics changes the quality of every conversation that follows. That means knowing what a model can and can’t do, and where the real engineering challenges sit.
How Adrian Vanzyl Approaches Learning at Every Career Stage
Adrian Vanzyl has always treated learning as something with no expiration date. He earned his medical degree Adrian Vanzyl in the late 1980s. He moved into internet technology in the 1990s, well before it was an obvious career pivot. He built and scaled companies across Asia in the 2000s and 2010s. Each of these moves meant starting over as a beginner in some way, even after building real expertise elsewhere.
That comfort with beginner status is rare. Most professionals build deep expertise in one domain. Then they default to pattern-matching every new development back to what they already know. Real understanding sometimes requires the harder path. That means sitting through the actual coursework and doing the actual exercises. A decades-long track record doesn’t exempt you from confusion. Not in your first week of a new subject.
What the Certifications Actually Cover
The specific coursework matters here. Deep learning specialization. Neural network fundamentals. Sequence models. Hyperparameter tuning and optimization. This isn’t survey-level content designed for executives who want talking points. In fact, it’s technical material aimed at people who intend to actually build things.
That distinction matters for how the resulting judgment gets applied. An investor who understands hyperparameter tuning has a very different conversation with a technical founder. That part looks good “As a result” is a clear transition word, both sentences stay under 20 words, and the meaning is intact. For instance, they can spot when a founder is glossing over a real technical weakness. They can also recognize genuine innovation faster, because they know what the baseline difficulty actually looks like.
Why This Matters for Founders Raising Capital Today
Founders pitching AI-enabled products right now face a strange paradox. Every investor claims to understand AI. Very few actually do. That gap creates real risk for founders. An investor who doesn’t understand the technology may overvalue a thin feature. Or they might undervalue a genuinely hard technical achievement, simply because they can’t tell the difference.
A founder pitching a technically serious AI product benefits enormously from an investor who can evaluate it on the merits. Investors with real technical grounding tend to ask sharper questions during diligence. They tend to set more realistic expectations for what’s achievable on the current roadmap. They’re also less likely to chase hype cycles, since real technical understanding tends to look past the current news cycle.
The Broader Lesson for Anyone Building a Career
There’s a version of career development that treats expertise as something you accumulate once and then coast on. That version doesn’t hold up well against a technology landscape that keeps changing underneath everyone’s feet.
The alternative is treating your own knowledge as something to keep testing. You keep rebuilding it, no matter how much credibility you’ve already earned elsewhere. That’s uncomfortable. It means occasionally being the least experienced person in the room again, on purpose. This happens well into a career most people would consider already established.
What This Looks Like in Practice
For founders and operators wondering whether relearning is worth the time, the honest answer is: it depends. It depends on how central the technology is to your business. If AI is a peripheral feature, a working vocabulary is probably enough. If AI is core to your product or your investment thesis, secondhand understanding eventually becomes a liability.
Adrian Vanzyl’s own approach has been to prioritize formal coursework over conference panels and pitch decks. It’s a small decision on paper. In practice, it shapes every technical conversation that comes after – with founders, with co-investors, and with the technology itself.