AI hardware is having a moment. Across industries, teams are racing to build intelligent devices that can see, hear, move, and interact — all at the edge. The demand is real and growing fast.

But so is the failure rate. Hardware is unforgiving. Hidden costs pile up, timelines slip, and quality issues surface only after significant capital has been burned. Most teams hit the same traps: designing from scratch when the building blocks already exist, over-engineering before validating a single market assumption, and underestimating how different shipping hardware is from shipping software.

There is a smarter way. This talk breaks down what that looks like in practice — and how the right starting point can save you months, money, and a lot of pain.