Most conversations about AI today are only a few years deep. Dr. George Tzanetakis has been building machine learning systems since before "AI" was a term anyone wanted attached to their work, through an AI winter, the rise of deep learning, and now the era of large language models.

George, a University of Victoria computer science professor, researcher, and RingStone's AI subject matter expert, sits down to talk through three decades inside the field: early genre-classification work in the '90s, a demo he once gave Jeff Bezos, and the years nobody wanted to call their work AI.

Where AI goes from here isn't just a technology question. It's about which companies can survive the shift once today's pricing stops being subsidized, and whether the smaller, specialized models chasing frontier AI can close the gap.

Discovery is the part of the deal that most teams try to move through quickly.

Management wants to keep momentum, avoid slowing things down. The problem is that discovery is the real work.

When it gets rushed, context is incomplete, answers lack depth, and inconsistencies start to show up across the business. None of it looks critical on its own. But together, it changes how investors interpret what they are seeing.

That shift in confidence is what hits valuation.

In this episode, we break down how this shows up in real deals and where it starts to impact outcomes.

What makes or breaks a distributed team?

In this episode of Diligence Unplugged, we go behind the scenes with Hazem Abolrous and Iain Bishop to explore how high-performing remote teams are really built. From global hiring and team autonomy to AI’s role in development, they share real-world strategies that work - and the red flags to avoid.

Whether you're scaling a product, investing in tech, or leading cross-border teams, this is your insider’s guide to distributed development done right.