AI Has Two Bottlenecks 🧠 Following $TAO and $RENDER makes one distinction in AI infrastructure increasingly obvious. Building more capable AI models is one challenge. Having enough computing power to run them reliably is another. Most projects focus on one side of that problem. $B3 is approaching both through two different products. B3OS focuses on reliable execution for LLM agents, integrating with existing systems to help agents complete tasks consistently. B3IQ addresses the physical side, allowing customers to finance NVIDIA GPU systems while B3 handles hosting and operations. The audiences are different, but their needs are connected. An AI developer needs agents that can execute reliably. A company deploying those agents also needs dependable access to the hardware running them. And as agents move beyond answering questions toward performing continuous tasks, demand for both could grow considerably. The challenge is that software reliability and GPU infrastructure are two very different businesses to scale. But I think that's also what makes B3 worth watching. Reliable agents and owned compute could become two of the most important pieces of the next AI cycle. 👀 #AI #DePIN