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:

Stunning stat: "Anthropic’s investors are expecting the company to reach a valuation of $2tn when it goes public in the coming weeks. Add in SpaceX, which began trading at $2tn after its IPO in June, and OpenAI, which is considering raising money privately at $1.2tn ahead of a public listing next year, and these companies alone could be worth well north of $5tn. Now compare that with the entire history of IPOs from 1980 to 2025. The 3,365 tech companies that went public in that period were worth a combined $4.1tn when they started trading." While I've written much about AI's transformative potential across sectors, I've always been dubious about how much value hyperscalers can seize enabling that transformation. As I dissect in my recent report on "The AI Trade" ( it's not about user acquisition. OpenAI claims to have over one billion active users across its services and two million businesses using its AI models. Anthropic has claimed to have more than 300,000 business customers. The question is not whether they can bring users to their services, but rather how much average revenue they can generate per customer relative to the price of building and maintaining their models. The cost of compute is inflating at the same time competition is depressing token pricing power. That's a precarious dynamic when so much hinges on the success of two companies. To again quote the report: "It’s hard to overstate how much hinges on these IPOs. As mentioned in the Executive Summary, OpenAI and Anthropic will account for 13% of AWS revenue and 27% of Google Cloud revenue this year. As for Microsoft, OpenAI alone accounts for roughly 70% of its AI-specific revenue ($24.1 billion out of an estimated $34 billion total for the fiscal year ending in June 2026). OpenAI has committed to tens of billions of spending on chips from the likes of Nvidia and Broadcom. Deepening circularity concerns, tech giant earnings growth has been increasingly driven by paper gains in the private-market valuations of Anthropic and OpenAI. In Q2, Amazon, Alphabet, Nvidia, Meta, and Microsoft reported $160 billion in cumulative “other income”, trouncing the $69 billion in “other income” reported in Q1. To quote the FT: “These one-off valuation boosts, derived in large part from enthusiasm around AI, risk distorting the financial picture at a time when investors are closely scrutinizing tech earnings.” If either Anthropic or OpenAI stumble in their IPOs, it’ll hit tech giants on multiple balance-sheet fronts and likely ripple through the US and global economy. A pin-prick popping of the AI bubble may not be our base case, but if a pin is out there, it’s likely the revenue versus spending trajectories of Anthropic and OpenAI." FT link:

FT: "Leading US AI labs such as OpenAI and Anthropic are releasing cheaper models as they fight to retain cost-conscious customers who are switching to cut-price alternatives from Chinese rivals. The price war comes as rising AI bills push companies to curb usage and seek cheaper models, helping Chinese developers including Moonshot and DeepSeek make inroads with users from Silicon Valley to Europe. OpenAI recently said that it was slashing prices for GPT-5.6 Luna, its “fastest and most affordable model”, by 80 per cent. Anthropic has launched Claude Opus 5, touting the system’s “frontier intelligence . . . at half the price” of Fable 5, the company’s most capable model. The moves have helped decrease prices that customers are paying for models from leading US labs by almost a quarter since mid-July." In my December report on "GenAI & Productivity" ( I warned about the pricing power challenges faced by US hyperscalers: "While there’s a lot of speculative fear about how a single LLM could rise to dominance and what that could mean for economic, societal, and political stability, we believe the bigger concern for investors today is how relative model parity could compromise pricing power. Tech giants have thrived on monopolies and duopolies for a decade or more. Now, they’re in an LLM arms race where it’s unclear when or even if ever leadership will be sustainable." Since, my concern about the commoditization of AI has only intensified as Chinese models have risen to power. According to OpenRouter data, Chinese models accounted 4.4% of token usage by US companies in January. Today, that share is over 60%. Meanwhile, enterprise model router adoption has skyrocketed and frontier labs have been increasingly shifting from subscriptions to usage-based, metered billing, business models more akin to utilities than the per-seat models SaaS companies thrived on over the past decade. As RBC warned in July: “Oil, natural gas, and electricity are all important. Entire economies depend on them. But importance alone does not guarantee strong economics or durable profitability. When supply expands aggressively and is increasingly interchangeable, competitive forces tend to compress returns over time.” Far more to come in my next report! Learn about Sage Road Research here: Interested in subscribing? Message me. FT link:

FT: "Prices for credit default swaps, popular tools to bet against corporate debt, tied to Oracle, SpaceX, Alphabet, Amazon, Meta, Broadcom and Nvidia have risen to record highs in recent days...'Credit markets don’t deal well with uncertainty, and the sheer unpredictability of the pace and cost of AI financing is triggering a serious crisis of confidence right now.'"