By 2029, a typical company's AI bill per engineer could cost more than the engineer's salary. That is the conclusion from venture analyst Tomasz Tunguz using three scenario models. As compute costs approach or surpass labor costs, AI spending is shifting from an optional tool budget to a structural line item alongside wages on the P&L.
Anthropic's Ledger: Compute Spending Already 2.3x Salary
According to SaaStr, Anthropic has roughly 5,000 employees but spent about $10 billion on inference and training in 2026. That translates to roughly $2 million in compute per employee, versus an estimated total compensation of over $500,000 per Levels.fyi. That means compute spending is 2.3 times salary. This ratio is unprecedented in the software industry. Data from Ramp AI Index (June 2026) shows that the top 1% of firms spend about $89,000 per engineer annually on AI, or 40% of a senior engineer's $224,000 salary; the median firm spends just $137, nearly zero. The gap between the top and the median is nearly 680 times.
Three Scenarios for 2029 AI Bills
Tunguz frames three paths. The bear case assumes token prices keep falling, offsetting demand growth. The base case sees the top 1% growth curve slow. The bull case assumes the overall market reaches Anthropic's current ratio by 2029. Each scenario converts the AI bill to a percentage of the $224,000 senior engineer base salary, assuming 5% annual wage growth.
Key figures (vs. $224k baseline):
- 2026: All three at $90k (40%)
- 2027: Bear $106k (45%), Base $164k (70%), Bull $258k (110%)
- 2028: Bear $118k (48%), Base $259k (105%), Bull $444k (180%)
- 2029: Bear $106k (41%), Base $363k (140%), Bull $596k (230%)
The bear figure declines after 2028 as the percentage drop outpaces wage inflation.
Driver: Agentic Workflows Surge Token Use
The bull scenario hinges on increasingly complex AI agentic workflows. Autonomous, multi-step task execution consumes tokens at orders of magnitude greater than chat mode. Goldman Sachs predicts token consumption will grow 24x by 2030. Anthropic and OpenAI generate $14 million and $6.5 million in revenue per employee respectively, the highest among the Fortune Global 2000. Cost structures track revenue — companies that can afford it are usually those that earn it back.
Counterforce: Plummeting Model Pricing and Open Source
The pull to the bear scenario is equally real. OpenAI's GPT-4 class model input pricing has dropped from $30 per million tokens at launch in March 2023 to under $3 by 2026, falling 10x per year for three years. Open-source models are approaching frontier performance. DeepSeek-V3 and successors deliver results comparable to top closed models at one-tenth to one-thirtieth the API cost. The open vs closed source debate is a defining political question of the AI era — cheap open models determine whether the bear case becomes reality. Companies can also proactively cap usage to flatten the curve without waiting for price drops.
Structural Shift: From Tool Budget to Second Salary
The real insight is not that AI is expensive, but that AI spend is becoming a structural line item comparable to labor. In the bull case, a single engineer's AI bill could match the median revenue per employee at a public SaaS company (roughly $250,000). This is no longer a tool's cost magnitude — it is a second salary. When compute costs compete with wages on the same P&L, enterprises must decide now which future they are budgeting for.