When AI Costs More Than the Engineer

Anthropic spends 2.3x its payroll on compute. ^{With ~5,000 employees & roughly $10b in inference & training spend in 2026, that works out to about $2m of compute per employee per year against a likely all-in comp of $500k+.}

The rest of the software market trails. The top 1% of companies spend $89k per engineer per year on AI, 40% of a fully-loaded $224k senior engineer salary^{. The median spends $137. That is the gap : 2.3x at the frontier, 0.4x at the top of the market, near zero at the median.}

How close does the rest of the market get? Three scenarios bracket the answer.

Bear (token deflation wins), Base (top-1% trajectory tapers), Bull (rest of market reaches Anthropic’s ratio by 2029). Each scenario maps to an annual AI bill per engineer.

In the Bull case, the AI bill alone per engineer matches an entire median-SaaS employee’s revenue contribution. ^{Anthropic & OpenAI already generate $14m & $6.5m in revenue per employee, the highest in the Forbes Global 2000.}

The cost structure follows the revenue structure.

Bull drivers : frontier model prices hold as training costs plateau & demand outruns supply. Agentic workflows consume tokens at orders-of-magnitude higher rates than chat, with Goldman Sachs projecting a 24-fold rise in token consumption by 2030. ^{If a rival ships features faster, the AI bill stops being optional.}

Bear counterweights : token prices have fallen 10x per year for three years. ^{Open-weight models close the quality gap at a fraction of the cost. Companies that ration usage by role or workload bend the curve.}

One of these scenarios will land closer to truth in 2029. Which one are you modeling for 2027?