We're building a public cloud on our own hardware, and one of our bets was to GPU-enable our customers. We created Fly GPU Machines, which are Docker/OCI containers running inside virtual machines with Nvidia GPUs. However, we underestimated the importance of AI/ML and overestimated the demand for GPU-enabled workloads. The main problem is that developers don't want GPUs; they prefer using LLMs like OpenAI's GPT. Our hardware product/market fit is rough, and we're scaling back our GPU ambitions without sacrificing our isolation story or developer experience. We learned that our customers didn't want a new Javascript runtime but just wanted their native code to work, and similarly, we were wrong about GPUs. This experience has taught us the importance of being open to learning from our mistakes and adjusting our bets accordingly.