The industrial revolution now reshaping AI

In Q3 of 2025, Bot Auto achieved its first “driver-out” run on public roads: a trip in which the truck drove itself with no human behind the wheel, and in our case, no humans in the cab at all. This is a milestone reached by only a tiny handful of AV trucking programs. From the founding of the company to that milestone, we spent just $212,552 on one category of work that is usually very expensive in AI: paying people to manually label training data—for example, drawing boxes around cars and pedestrians—so a neural network can learn from them. To many people that number does not sound like a breakthrough. It sounds like something is missing: a cost not counted, a line item not disclosed, or some clever maneuver hidden just outside the frame. Such skepticism makes sense, because in AI, annotation is usually not a rounding error; it is a major line item. To see why this matters, consider nuScenes, a well-known dataset in autonomous driving. In total, it contains only about 5.5