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> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# The Training Tax

**[Security Boulevard](https://daily.dev/sources/securityboulevard)** · 7 min read · 0 upvotes · 0 comments

## Summary

Training a frontier AI model costs nine figures once, but serving it at scale costs nine figures every month. At 100 million daily active users, cumulative inference spend overtakes a $100M training budget in just 9 days, and after one year, inference accounts for 97.7% of total spend. The analysis models crossover points using July 2026 API pricing from OpenAI, Anthropic, and Google, showing that price cuts cannot outpace user growth — doubling users halves the crossover window. The real competitive moat is inference efficiency (quantization, speculative decoding, distillation), not training scale. Labs that optimize only for training are solving half the problem; the lab that serves cheapest captures the deployments that matter.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://securityboulevard.com/2026/07/the-training-tax>

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---

Tags: [#llm](https://daily.dev/tags/llm), [#openai](https://daily.dev/tags/openai), [#ai-inference](https://daily.dev/tags/ai-inference)

[View this post on daily.dev](https://daily.dev/posts/the-training-tax-ptgs6efyh)

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