"Sam was early, but perhaps right - the combination of the model with compute is the moat". When I first met Sam and spent some time with him, he would talk about building GWs of capacity and at every next meeting his ambition for compute got higher - to a point where I started half listening, since I was thinking of all the other repercussions. The raging debate is where is the value being created perhaps where will it be captured. We are quick to write off models and are debating open weight and open source, worthy debates but the action is elsewhere. The infrastructure layer is the current beneficiary of spend, and appropriately the innovation engine of silicon valley is targeting chips, inference, memory and anything that can satisfy the voracious demand for infrastructure spend. But it's the people building compute who are doing the spending here. The neo clouds have raced ahead and and credit to them for seeing the trend early - they were able to grab early permits, land and power to be able to deploy compute. It continues to become harder to deploy compute as the demand curve goes vertical. The hyperscaler earnings have shown that they don't intend to relinquish their infrastructure advantage and are continuing to increase capex to both satisfy and anticipate future demand. Anyone not there yet, is probably concerned about raising funds and not the viability of the spend. The Frontier LLM players are into the act and are working hard to aggregate compute since they know they will need more not just to train but to deploy. The race is on, for the Frontier models to win, they will need to look more like hyperscalers with a twist, how to embed large application layer profit pools into their business model. You can see it happening. coding, science, legal, and soon physical AI. At this scale timing will become an important vector, including the financial markets, hope much patient capital is out there. The market likes to surprise.