Jeff and Seymour briefly discuss Google I/O which happened last week including the release of PaLM 2.  Most of the episode is spent on the strategic and ecosystem implications of an internal Google strategy memo and the impact of open source. They also discuss AI Supply Chains in the context of a series of MIT blog posts from April. Finally, they draw analogies from earlier eras of tech history including the rise of the PC industry and IBM/Oracle's relationship with Linux in the late 20th century. Links:

• Jordan Burgess pushes back on Google strategy memo
• Andrej Karpathy's perspective
• Andrej's overall view of current LLM landscape which is similar to Jeff and Seymour's four part framework of understanding gen AI: (1) pre-trained foundation model; (2) fine-tuning; (3) whole product / end user UX; and (4) distribution
• Examples of fine-tuning methods: (1) RHLF; (2) LoRA; (3) LLaMA Adaptor
• Explanation of quantization to make foundation models lighter weight 

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