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1021: We got addicted to an AI model we can't talk about
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Cost Implications of AI Model Usage
At 29:53 · chapter starts 28:27
Wes asks the inevitable: are AI inference costs going to spiral into thousands per employee per month? Dax has actual data to offer. At their 5x surge level, OpenCode's inference spend amounts to roughly 15% of their payroll — notable, but manageable for a tech company. [1] — Dax Raad "AI model cost ~15% of payroll: At 5x usage, OpenCode's AI inference costs represent roughly 15% of their total payroll — a manageable overh…" 28:44 And the structural trajectory is downward. His estimate: Anthropic and OpenAI are currently running approximately 90% margin on inference, excluding R&D. Breakeven is 10x cheaper than current prices. [2] — Dax Raad "Dax estimates Anthropic and OpenAI are running ~90% margins on inference, not counting R&D. That means breakeven is 10x cheaper than curren…" 29:10 Training losses are separate — they don't factor into inference economics. For open-source models, OpenCode can already host at a 70% discount to cost even using GPU middlemen. Direct GPU ownership would approach those same 90% margins. The narrative that OpenAI and Anthropic are perpetually unprofitable confuses training investment with inference margins — these are very different line items on the P&L.
At 5x usage, OpenCode's AI inference costs represent roughly 15% of their total payroll — a manageable overhead for a tech company.
Dax estimates Anthropic and OpenAI are running ~90% margins on inference, not counting R&D. That means breakeven is 10x cheaper than current prices. For open-source models with middlemen, OpenCode already hosts at 70% discount to cost.
Dax estimates Anthropic and OpenAI are making approximately 90% margin on inference, meaning breakeven cost could be 10x cheaper than current prices.
