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AI inference spending will overtake databases as the largest software market by 2027, fundamentally breaking the economics that made traditional SaaS profitable. This shift forces application companies to manage massive infrastructure costs that will compress margins and demand new operational approaches.
- Inference market grows from $25b (2025) to $350b (2027), passing databases at roughly 2x the size, making it the dominant software category.
- Gross margins collapse below SaaS norms (72%) because inference costs become a major COGS line item, forcing vendors to either build proprietary models to reduce token overhead or accept margin compression.
- Customer-owned infrastructure (BYOK) trades top-line revenue for gross margin—vendors get higher percentage margins but on drastically smaller contracts.
- Token price wars won't solve the problem; capability improvements keep resetting frontier model prices upward, so companies must optimize through smaller models, better routing, and efficient harnesses.
OpenAI and Anthropic are approaching record IPOs but face enormous costs for AI model training. OpenAI expects a staggering $121 billion in computing expenses by 2028, leading to significant projected losses, while Anthropic anticipates similar challenges but on a smaller scale. Both companies are rapidly releasing new AI models, intensifying the competition and cost pressures.
- OpenAI projects $121 billion in cumulative computing expenses by 2028, driving major projected losses despite revenue growth.
- OpenAI's revenue is set to hit $1 billion in 2024 (up from $540 million in 2023), with a potential valuation around $100 billion.
- Anthropic trails with projected 2024 revenue of $300 million (up from $100 million in 2023), growing more slowly but leaning on its safety-focused reputation to attract investors.
- Both companies are racing to release new models, intensifying competitive and cost pressures ahead of their IPOs.