Operations | Monitoring | ITSM | DevOps | Cloud

In 1846, Britain poured roughly 7% of its national income into railways, proportionally about three times what the U.S. spends on AI infrastructure today. The technology delivered everything it promised, and a generation of investors still lost their shirts. What sorted the winners from the wreckage wasn't conviction about the technology; it was whether ROI was measured or asserted. The man who fixed that problem gave his name to the S&P 500.

When engineering teams evaluate AI agents, the first questions are usually about capability. Which model performs best? How much faster can it write code? What's the return on investment? Security, if it enters the conversation at all, tends to come later. Patrick Dawkins, Principal Software Engineer at Upsun, thinks that's backward. Over the past year, he's been building the infrastructure that enables AI agents to operate safely within engineering teams.

AI is changing artifact management in two ways at once. Every AI-generated pull request, dependency update, and automated build creates more container images, packages, and Helm charts than ever before. Registries are growing faster than engineering teams can manage them, driving up storage costs and leaving thousands of stale artifacts behind. At the same time, the cost of deleting the wrong artifact has never been higher.

Summary Physical AI—machines and industrial systems that sense conditions, reason, and act in the real world—needs two things from operational data: detailed history for training, and real-time telemetry for inference. Traditional data historians weren’t built for either at the speed Physical AI requires. Four gaps result: limited real-time access, compression that strips model-relevant signal, IT/OT fragmentation, and site-by-site architectures.