Measuring the Openness of AI Foundation Models: Competition and Policy Implications
Sciences Po Digital, Governance and Sovereignty Chair, Working Paper
35 Pages Posted: 14 Jun 2024
Date Written: May 14, 2024
Abstract
This paper presents the first comprehensive evaluation of AI foundation model licenses as drivers of innovation commons. It introduces a novel methodology for assessing the openness of AI foundation models and applies this approach across prominent models such as OpenAI’s GPT-4, Meta’s Llama 3, Google’s Gemini, Mistral’s 8x7B, and MidJourney’s V6. The results yield practical policy recommendations and focal points for competition agencies.
Keywords: Foundation models, large language models, LLMs, generative AI, competition, antitrust, open source, innovation commons, public policy, ChatGPT
JEL Classification: K21, K20, L17, L86, E14, B52, K24, 030, 031, 032, 033, 034, 038
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