Since I previously shared how we are scaling AI usage so quickly without costs rising at the same rate, there’s been a lot of interest in the engineering details. So I asked the team to open up the hood and share what we’ve learned. One thing we’ve always been very good at @Uber is understanding the economics of a system at a deep level. And I’ve always believed that constraints drive innovation. We took the opportunity to turn the cost problem into an engineering problem. We broke AI spend down into its underlying levers and started systematically attacking each one: • Scaling vendor-neutral managed agents • Building Uber SWE benchmarks to measure what actually works • Optimizing prompt caching • Making tool and MCP usage more efficient • Grounding trajectories in our context graph • Building efficient, reusable agent skills • Giving engineers real-time cost visibility and optimization tips There’s a lot more work to do in this space: scaling more autonomous agents, dynamic model routing, a self-evolving context graph, and continuously updating efficient agent skills. There's still a tremendous amount to invent here, and we’ll keep sharing what we learn as we build and deploy it at Uber. This is an incredible time to be an engineer. The economics of software are changing, the way we build is changing, and engineers have an opportunity to help define what comes next. Read the deep dive here: uber.com/us/en/blog/efficien… and let us know what you think.