The problem with these earnings projections is that the business model of the hyperscalers driving them has materially changed in the last few years.
In the past, these huge U.S. tech firms were capital-light businesses with large “moats” driven by high switching costs. This, in turn, helped them generate supernormal earnings for more than a decade without the dreaded mean reversion one would expect from increased competition.
But now the hyperscalers have become highly capital-intensive businesses – due to the massive AI infrastructure spending spree – and sections of those moats have gotten a lot slimmer.
This reflects the fierce competition across the AI value chain.
Hyperscalers typically have long-term agreements with specific developers of large language models (LLMs), most notably Anthropic and OpenAI.
Currently, LLM users appear willing to switch between AI platforms depending on the quality and cost. For example, when OpenAI's ChatGPT was overtaken in performance by Anthropic's Claude in March, users could switch with little friction, based on data from the Ramp AI Index about changes in AI spending share. Such consumer moves are expected to continue as AI rapidly develops, which will result in a shift in data centre load from one provider to another.
This means that hyperscalers are either at the mercy of users’ preference for one specific model provider, or they will have to compete with other data centre providers for the business of each new market leader. In the former case, their revenue growth will slow if their partner model falls behind. In the latter, their margins will shrink from increased competition.
Alphabet may be in a somewhat advantaged position here, since it has its own competitive model that it can run on its own stack, though it currently appears to be getting more closely linked to Anthropic, based on reports from the Information in June.
The result of all this may not just be negative free cash flow, which is already on the horizon (or already here, in certain instances), but negative net profits, unless the hyperscalers cut back their capex.
And if they cut back on capex, we can expect a large drop in revenue for the semiconductor firms that have benefited from all this spending – and thus a decline in earnings in that part of the market.
This is just one example of the co-dependencies among companies in the AI ecosystem. In its Annual Economic Report 2026, the Bank for International Settlements (BIS) analysed the revenue of hyperscalers and semiconductor companies, separating circular financing arrangements – where companies from different parts of the AI value chain are financing each other – from true arm’s-length contracts. They found that in 2025, over half of hyperscalers’ revenue and almost all of the chipmakers’ revenue could be traced to circular financing arrangements.
If that merry-go-round stops because hyperscalers cut back on capex or their margins shrink, earnings for the S&P 500 .SPX overall could slip, potentially dropping back toward trend.
QQQ•
