Multiverse Computing cut an AI model to half its parameter count, stored it in a 4-bit format, and reported a coding score slightly above the full-precision original. Augur explains why that result could lower the cost floor for serving capable AI, why company-run benchmarks are not enough, and how teams can test whether the savings survive contact with their own work.
The episode also examines why low token prices can produce higher total task costs, what ChatGPT Work's new admin analytics suggest about renewal scrutiny, and why sovereign AI buyers need a written definition of control.
Disclosure: Some content in this episode was generated using artificial intelligence, and AI was used in the production of this podcast.
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Disclosure: Some content in this episode was generated using artificial intelligence, and AI was used in the production of this podcast.
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