arXiv (All)
open access
From Token-Max to Outcome-Max: How You Use AI Determines Its Productivity
Generative artificial intelligence (AI) models can perform increasingly complex tasks, yet greater AI usage does not necessarily translate into proportional productivity gains. We identify token-max as one source of this inefficiency: when token consumption is treated as productive effort, agents ar
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Regulating ChatGPT and other Large Generative AI Models#199603
- Advancements and Applications of Generative Artificial Intelligence and show the Experimental Evidence on the Productivity Effects using Generative Artificial Intelligence#96200
- Evaluating Inference Compute for Generative AI: A Framework for Enterprise Workloads#1216469
- Evaluating Inference Compute for Generative AI: A Framework for Enterprise Workloads#1210273
- From Tool to Partner — Intelligence Externalization and Co-Creative Relationships in LMT — Conceptual Foundations#167177
Record · ID 1201710
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive