My increasingly strong opinion is that compute is the biggest moat in AI. Scientific breakthroughs don’t stay secret for long. Papers get published, researchers move, ideas leak, open source catches up. Give it enough time and almost everyone has access to roughly the same algorithms. What doesn’t diffuse nearly as fast is the ability to run 10x bigger training runs, collect 10x more RL experience, serve billions of inference tokens, and iterate faster because you can afford thousands of experiments in parallel. Scaling compute is brutally hard. It’s GPUs, power, substations, networking, cooling, land, permits, supply chains… and hundreds of billions of dollars. You can’t just decide to build that overnight. The more I look at the industry, the more it feels like the frontier is less about having one genius idea and more about who can compound compute the fastest. Scaling laws reward whoever can keep scaling. That’s a very hard moat to replicate imo!