AI's concentration risk: "Top 10% of customers account for 99.5% of model-serving spend and 99% of neocloud spend, leaving the bottom 90% of firms with 0.5% and 1%...The bottom line is that adoption is broadening while the spending base is not, and AI infrastructure will keep depending on a small set of heavy spenders until the tail scales up." This is certainly evidence of the technology's immaturity—over time the spending base will expand as more companies figure out how to effectively integrate AI to unlock operational value. However, it's also evidence that adoption challenges are far more persistent than the model builders anticipated. I quoted Sam Altman on this in my recent report on "The AI Trade" ( "The economy just has so much inertia. People just keep doing the same things. They keep buying from the same company. They keep using their tools in the same way. I think that’s actually a positive in many ways. It’s going to make this big transition in front of us go smoother and slower. But I think it means we’ve all been too ambitious on timelines." It's not just about inertia. AI is still plagued by its weaknesses, from hallucination to agentic workflows breaking down midstream. But to the inertia point, AI puts unprecedented transformational demands on enterprises. As I warned in my December report on "GenAI & Productivity" ( "As much attention was paid to the headline 95% failure estimate by MIT researchers, their explanation for that failure rate was likely a more important long-term consideration in understanding when and how companies will realize productivity gains from genAI. To quote the researchers: “The dominant barrier to crossing the GenAI Divide is not integration or budget, it is organizational design.” McKinsey is delivering a similar message: “Building a business for the agentic age will require a fundamental rewiring of how the business operates, innovates, and protects sources of value creation.” Deloitte is saying much the same: “This is not about adding another tool; it’s about fundamentally rethinking how work gets done from the top down.” It's difficult to look at modern history and identify an enabling technology that demanded the depth and speed of organizational transformation being suggested for genAI today." For all of AI's capabilities, there is no path to ~$2.5t in annual AI revenue (the estimatdd amount required to offset CAPEX) unless the vast majority of enterprises become relative "heavy spenders" on a manageable timeline. Instead, the tail is elongating slowly while evidence mounts that today's "heavy spenders" are pulling back their spending. According to Ramp data, the top 1% of spenders, the cohort that drives ~80% of OpenAI and Anthropic’s enterprise revenue, cut per-employee spend by nearly 10% in August. Chart link: