While everyone's debating when AI leaves the screen, a hundred or so driverless trucks have already logged 45 million kilometers on highways. That's KargoBot, running L4 across 20+ trunk routes, 1.4 billion ton-kilometers of freight hauled so far. The data never stops coming, and at PB scale raw compute alone doesn't get a model to its next version. Our bit: — compute clusters for training and inference, built to scale with the fleet — object storage with a caching layer in front, so training isn't waiting on IO — TI-ONE, our ML platform, holding the iteration pipeline together Software agents retry for free, a 40-ton one doesn't. That's what makes infra fun to work on. What's the part that breaks first when your agent leaves the screen?