AI in 2024 - efficiency over model size (Nick Jakobi)
This video is sponsored by Cohere Nick Jakobi, Director of Product for the modeling team at Cohere, discusses the practical realities of deploying large language models in enterprise environments. The conversation covers how Cohere differentiates in a crowded LLM market through enterprise-focused capabilities like retrieval-augmented generation (RAG), tool use, and multilingual support, rather than competing purely on model scale. Jakobi offers a candid perspective on the economics of model selection: most enterprise customers gravitate toward the smallest model that solves their use case, making cost and latency as important as raw capability. He describes the emerging "token game" where model providers compete on price-performance ratios, and argues that making medium-sized models cheaper and faster may be more commercially valuable than pushing the frontier of model intelligence. The discussion extends into broader questions about how generative AI is reshaping education, workforce dynamics, and information ecosystems. Jakobi shares personal reflections on whether traditional education remains relevant when AI tools are becoming ubiquitous, and offers a measured take on AI safety that centers on the practical challenges of disinformation and autonomous agency rather than speculative existential risks. --- REFERENCES: Company: [00:00:00] Cohere https://cohere.com/ Paper: [00:00:00] Episode Shownotes https://www.dropbox.com/scl/fi/sjwqllqh1ughuu8ismonk/NickJakobi2.pdf?rlkey=d20wydhhala2hmh4yaoo55wyy&st=unso2w6i&dl=0 [00:00:30] RAG Paper https://arxiv.org/abs/2005.11401 Person: [00:00:20] Nick Jakobi https://www.linkedin.com/in/nickjakobi/ Product: [00:14:50] Cohere Command R https://cohere.com/command Tool: [00:20:50] LMSYS Chatbot Arena https://chat.lmsys.org/ --- LINKS: Full Transcript: https://app.rescript.info/share/d694e87868aa6d0272fe26e0a7b4de87 Download PDF transcript: https://app.rescript.info/api/public/sessions/72c99a6b30e9b8d0/pdf
