trivago

The AI conversation this year has been a lot! New models every week, full fledged agents, OpenClaw and Claude Code dropping back to back, real uncertainty about jobs, industry level layoffs and honestly just a lot of loud opinions from every direction. But here is what I actually want to talk about: how trivago has responded to all of it. No vague reassurances. No panic. Just a clear point of view and actual resources to back it up. 1. Up-skilling the entire company, not just tech. Ops, customer support, everyone 2. An internal copilot system where anyone can build and share agents across the org 3. Access to all the latest models plus additional API keys for whoever needs them 4. One dedicated hour every day to solve real problems using AI And the framing from our CEO Johannes Thomas has genuinely stuck with me. He has been open about how his thinking has evolved, talked honestly about the headwinds and where he sees our edge. The image below captures it better than I can. This kind of directness is rare when most of the conversation out there is just just "AI this, AI that." On my end I have been putting in the work too. -> Built a POC for GEO query evaluation and understanding fan out queries -> Tracked down bugs quietly hurting our SEO pages and bot infrastructure -> Now building a research agent to keep me on top of how the Google SERP landscape is shifting globally. Honest reflection: building has gotten easy. The real challenge is scaling, thinking through infra, security and ownership after the POC stage. The question is no longer how fast we move. It is how often we are building the right things. But at least good ideas will not die in a backlog because of bandwidth anymore. That feels like real progress!

At trivago, we had exactly the same problem. We didn't rebuild the Kotlin services into Python, but apart from that this article could have been written by one of our ranking engineers!