I joined @nvidia nine years ago today, against the advice of a colleague who said their comp was below market. But I really believed in the mission and the people, so I went. I met @__simt__, Michael Garland, and Jared Hoberock on the C++ committee. I thought they were the smartest people there. I worked at @BerkeleyLab on concurrent and parallel programming paradigms, mostly for SIMD. @__simt__ convinced me that SIMT was a superior model for parallelism. He sold me on a revolutionary new GPU architecture where threads would independently scheduled: Volta, the basis of all modern GPUs. I asked him three times to find me a place at @nvidia. The first two times, he wasn't sure where to place me or if I'd like it. The third time, he had me come in to talk to Chris Lamb (now VP of CUDA). We chatted for two hours and he outlined the role he believed modern C++ would play in CUDA. I came back for an 8 round interview panel. All of the questions were answered with std::atomic. No one was sure what I would be working on. I, a modern C++ person, was placed in the CUDA driver org, which was written almost entirely in C. On my first day, my manager told me I'd be responsible for Thrust and CUB, @nvidia's first two open source libraries. No one had worked on them for a while and there was no product manager. I'd have to do it all. I put together a roadmap and got the libraries back on GitHub. I started sending status updates from CCCL, the CUDA Core Compute Libraries team, which did not exist. Eventually, they let me hire three people to fill out the team. We built libcu++ and abstractions for all the quirky new features being added to our chips to support these AI workloads that seemed to be growing in importance rapidly. After a few years I was asked switch orgs and guide the roadmap for our HPC C++ compiler. Programming languages and models quickly became my role, from stdpar to CUDA-Q to CUDA Python to CUDA Tile to autoresearch. What a ride it's been!