Larridin, Inc. buying intent

Wrote this 'US vs China in AI' note late last night, but was too tired to trust my grammar, so posting it now aided by fresh dark roast. The latest AI Index from Stanford Institute for Human-Centered Artificial Intelligence (HAI) confirms what we've all been feeling for a year+ now: China has essentially closed the model performance gap with the U.S. The number that actually stopped me, though, was further down. The US now has 5,427 AI data centers to China's 449, and global AI data center load hit 29.6 gigawatts by end of 2025, roughly what New York state consumes (yowza). Here's another shocking reflection for me: very little of this maps onto the actual conversation happening inside most enterprises right now. Sure, model quality has become abundant and is getting cheaper by the quarter. And yes, compute is concentrated and expensive, but that is a hyperscaler relationship problem, not an adoption problem. But neither of those is what is keeping the 85% of organizations still trying to get a first AI agent past internal legal review. The scoreboard I think we should all be watching, is "deployed verified value per dollar of compute". Nobody is winning that one yet, because the verification layer barely exists. A cheaper, more accessible model gets more organizations to the starting line, but the starting line was never really the problem. What stops them is everything that comes after: who authorized this, how do we know it did what we think it did, and what happens when it is wrong. That infrastructure barely exists yet, and the organizations that figure it out will compound an advantage that has nothing to do with which lab won the latest benchmark. What gets measured at the infrastructure layer and what gets measured at the organizational layer are not the same problem, and solving one has never automatically solved the other.