According to Business Insider, Michael Lane—Rev’s vice president for development with over 20 years of tech experience—is going back to school for a master’s degree in AI starting spring 2026. His journey started back in 1995 when he had to drop out of University of Toronto’s computer engineering program because he couldn’t afford tuition, a decision that left him with “a lot of regret.” He eventually completed his bachelor’s through distance learning in 2006 while working in tech roles at companies like Logitech and Kobo. Now leading AI product engineering at transcription company Rev, Lane says ChatGPT’s release felt like “an explosion” that convinced him superficial online courses aren’t enough for actually building AI tools.
The AI Learning Gap
Here’s the thing about AI that Lane nailed: there’s a massive difference between using AI tools and understanding how to build them. He’s taken courses on Udemy and Coursera, and while they’re great for learning how to use AI in daily work, they don’t provide the foundational knowledge needed for actual development. You can’t just watch a few tutorials and expect to understand machine learning algorithms or neural networks at the level required to guide engineering teams.
And that’s exactly why he’s pursuing a formal degree. As a VP, he doesn’t need to write code every day—but he does need to understand the code base deeply enough to help his team solve complex problems. When your engineers are discussing transformer architectures or reinforcement learning, you can’t just nod along. You need to actually get it.
Why Managers Need Deep Tech Knowledge
This is where Lane’s perspective gets really interesting. Most tech managers eventually stop coding entirely—they become pure people managers who focus on budgets and timelines. But Lane still codes daily, and he recognizes that his real value comes from bridging the gap between technical implementation and strategic direction.
Basically, if you’re managing AI teams without understanding the technology, you’re essentially flying blind. How can you set realistic deadlines? How can you evaluate whether your team’s approach makes sense? You become dependent on your engineers to translate everything, which creates bottlenecks and misalignment.
The Broader Implications
Lane’s move highlights a bigger trend in tech right now. We’re seeing experienced professionals across industries realizing that AI isn’t just another tool they can pick up through weekend workshops. The technology is moving so fast that even veterans with decades of experience feel like they’re starting over.
And his point about AI being transformative like the iPhone? That’s spot on. But here’s the catch: while everyone’s rushing to add AI features, very few companies have the deep technical expertise to build truly innovative AI products rather than just slapping ChatGPT APIs onto existing software. That expertise gap is becoming the new competitive moat in tech.
So what does this mean for the industry? We’re probably going to see more mid-career professionals heading back to school or taking extended sabbaticals for deep technical training. The days of learning everything on the job might be ending for cutting-edge technologies. And honestly, that’s probably a good thing—building AI responsibly requires understanding it thoroughly, not just knowing which buttons to push.
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