According to @ylecun, he did not work on Llama; Llama 1 was built by a small FAIR-Paris team, while Llama 2–4 were produced by Meta’s GenAI product organization, and his contribution was pushing for Llama 2 to be open sourced, source: Yann LeCun on X https://twitter.com/ylecun/status/1993840625142436160. He added that he stopped leading FAIR in 2018 and has since focused on self-supervised learning for video, world models, and planning, and his post did not include any new product releases or licensing changes, source: Yann LeCun on X https://twitter.com/ylecun/status/1993840625142436160. For traders, this is an authorship and organizational clarification without new catalysts; Llama 2’s commercial-use license from Meta remains available and supports broad deployment, while decentralized GPU networks oriented to AI workloads—such as Akash Network’s marketplace—continue to provide infrastructure for running open-source models, sources: Meta AI Llama 2 announcement https://ai.meta.com/blog/llama-2/; Akash Network documentation https://docs.akash.network/; Yann LeCun on X https://twitter.com/ylecun/status/1993840625142436160.

According to Cas Abbé, IO Intelligence has launched a new platform featuring over 25 powerful AI models, including Llama 2, DeepSeek, and Mistral, which is causing concern among major companies like Meta and Mistral. This expansion in AI accessibility may lead to increased competition and innovations in AI applications, potentially influencing market dynamics. (Source: Cas Abbé on Twitter)

According to @rosstaylor90, reinforcement learning (RL) techniques like PPO have been applied successfully to Llama 2 base models, achieving over 90% accuracy on GSM8k with verifiable rewards. This highlights the effective use of RL in improving model performance, a critical insight for traders considering AI-backed trading strategies.