Enterprise AI Summit
A deeper recalibration is unfolding inside large organizations right now, one most dashboards cannot measure. You feel it in engineering standups, in boardrooms where strategy once moved at spreadsheet speed, and in late-night Slack threads where someone quietly asks whether the company is still structured for the world that is arriving. Artificial intelligence is no longer a feature conversation. It is an operating model conversation. Teams are discovering that dropping a model into an existing stack does not create an AI organization. It creates friction. The companies moving fastest are redesigning the way work moves. Different rhythms. Different roles. Different expectations for how humans and machines collaborate. That tension is becoming a defining pressure point across the startup ecosystem.
That pressure is exactly why the Enterprise AI Summit exists. On April 9–10, 2026, IT Revolution brings its 1st Enterprise AI Summit to San Jose, California. Gene Kim scheduled the gathering because the velocity of AI inside modern software organizations has made the traditional conference cycle feel outdated. When development itself is evolving in real time, the operators responsible for production systems cannot wait a year to compare notes. They need rooms where builders, operators, and executives exchange real operational stories instead of theoretical roadmaps.
Step into the room and the pattern becomes obvious quickly. This is not a parade of product demos. It is a practitioner gathering. The kind of environment where the people responsible for running large scale systems decide to speak openly about what is actually working. Leaders connected to OpenAI, Amazon, The Walt Disney Company, Vanguard, and national defense operations will be present alongside founders and technical architects building the infrastructure layer of the AI era. Engineers who have deployed systems under pressure. Operators who understand what happens after the prototype collides with production traffic.
Joe Beutler carries the OpenAI perspective, where the discussion has already moved beyond tools toward the structure of AI native teams. Jason Cox brings the operational reality of running global site reliability engineering at The Walt Disney Company, where uptime is not theoretical. Tim Cochran connects the conversation to Amazon’s builder experience. Devlin McConnell is exploring generative AI implementation inside Vanguard. Commander Collin Fox brings the strategic lens of the U.S. Navy Naval Surface Forces and Task Force Hopper. Matt Jones of Sigma Defense Systems, Tisson Mathew of Skypoint.ai, Ryan Martens of Manifest AI and Rally Software, Tom Kilcommons of Newton, Ben Grinnell FBCS of Newton Impact, Dustin Warner, and Kent Beck round out a group that represents something rare in technology gatherings. People responsible for systems where failure has consequences.
That mix is intentional. Gene Kim and IT Revolution have spent years studying how high performing technology organizations operate. DevOps reshaped how software teams ship and maintain systems. Artificial intelligence is now applying pressure to the same organizational mechanics. What responsibilities shift when machines generate code. How reliability changes when models influence production behavior. What leadership looks like when development cycles compress even further.
These are not academic questions. They are operational questions that will define how companies compete over the next decade. And across the startup ecosystem, the organizations that learn how to structure AI native teams first will not simply move faster. They will operate differently.
Because once that shift takes hold, the rest of the startup ecosystem tends to follow the organizations that already figured out how the new machinery of work actually runs.