A look at the economics of Microsoft and Google inserting LLMs into search, crushing profitability and requiring massive capex, and the impact on the companies
Context & Ripple Effects
Google had signaled that LaMDA would become a companion to search, putting the company and Microsoft on a path to make model inference part of a high-volume consumer product. SemiAnalysis frames the immediate constraint as the cost of doing so, rather than model availability alone.
Later coverage of nearly $80B in quarterly AI-infrastructure spending by Google, Meta, and Microsoft shows how the search-era compute burden became part of a broader capital-investment cycle.
First-order effects
- Google and Microsoft face lower search profitability when LLM responses add recurring inference expense to each query, forcing both companies to absorb higher operating costs or limit how broadly they deploy the feature.
- The two companies must commit substantial capital to the infrastructure supporting LLM-enabled search, shifting resources toward AI capacity.
Second-order effects
- Google and Microsoft’s search competition becomes partly a contest over inference efficiency and available compute, because richer model features carry a larger cost burden at scale.
- The need to fund search AI reinforces the infrastructure race already visible in record combined capex among major platforms, concentrating spending power among companies able to finance it.
Third-order effects
- If LLM answers become a standard search interface, inference becomes a durable cost of goods sold for search rather than a temporary product-development expense.
- Search economics may increasingly favor firms that can pair consumer distribution with large-scale infrastructure, making capital intensity a product constraint as well as a finance issue.
The trend: Search is evolving into an answer-engine market in which model-inference costs and infrastructure capacity shape product strategy as much as relevance does.