In "Confession" episode 2, Dara Khosrowshahi reveals a mistake from years ago that still bugs him. He opens up about hitting rock bottom as Uber CEO. Dara and I are cousins and share many stories from growing up together. I love his candor and humility. Enjoy!

¡Bienvenidos a la familia madridista! 🤍 @UberEats_es

From food delivery to grocery, retail and now catering - we’re expanding what you can get through Uber Excited to welcome @ezCater to Uber, helping restaurants grow and @Uber_Business customers better serve their employees and clients

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.

Uber, Zipline Partner With Goal of Hitting 1 Million Drone Deliveries a Day on.wsj.com/45wF37a

We are bringing robotaxis to the streets of Japan! 🇯🇵 By the end of 2026, riders in Tokyo will be able to experience the next generation of mobility right from the Uber app. We’re teaming up with @TAXI_HINOMARU, @NissanMotor, and @wayve_ai to launch our autonomous vehicle pilot. Together, we’re ready to transform how people move around one of the world's most dynamic cities!

Today, we’re launching Alpamayo 2 Super, our frontier open reasoning model for autonomous vehicles. Beyond seeing, Alpamayo understands and reasons through the complex world - thinks before it acts. It’s a powerful backbone for robotaxis, trucks, shuttles, delivery vans, tractors and the long tail of mobile robots—billions of autonomous machines someday. We’re releasing it for commercial use under OpenMDW-1.1 so teams can inspect it, fine-tune it and deploy it—open models advance safety and security. The next wave of AI is robotics—and it starts with autonomous vehicles. Great work, Alpamayo team! blogs.nvidia.com/blog/alpama…

Zoox clears final federal hurdle to launch paid robotaxi service techcrunch.com/2026/07/30/zo…