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Sara Hooker

Co-Founder, Adaption

  • 1 episode on Chain of Thought
  • 6 topics covered
  • 1 full transcript
  • Nov 2024 first on the show
Sara Hooker

At the time of her Chain of Thought appearance, Sara Hooker was VP of Research at Cohere and head of Cohere for AI. In the 2025 predictions episode she made the case for a shift toward smaller, more optimized models and talked through what that means for AI safety and research.

Cover art for GenAI Predictions for 2025 | Databricks & Cohere

Latest episode with Sara Hooker · EP 3

GenAI Predictions for 2025 | Databricks & Cohere

Sara Hooker, Craig Wiley & Vikram Chatterji ·

Show notes & transcript → All listening options →

Episodes

What the conversations cover

These notes summarize the episodes featuring Sara Hooker, including other speakers in panel conversations. Follow a source link to read the discussion in context.

EP 3 GenAI Predictions for 2025 | Databricks & Cohere

  • Vikram Chatterji predicted that by the end of 2025 open-source LLMs would catch up to the best closed-source models — naming Anthropic’s Claude models which are “super expensive” and which he almost thinks of as “concept models” showing “the art of the possible” — for large context windows on a majority of tasks, citing Galileo’s Hallucination Index as evidence open source was closing the gap fast. Episode takeaways
  • Chatterji predicted 5-to-10 of what were dismissed in 2023 as “wrapper apps” would each hit $100M in ARR by the end of 2025, arguing AI breaks the old mobile-app adoption curve where even DoorDash or WhatsApp took a while to reach $100M in revenue. Episode takeaways
  • Yash Sheth called 2025 “an era of toolification,” and Wiley agreed: rather than “boil the ocean” with one single my-company-GPT.com, teams deploy narrow, purpose-built tools and smaller agents and orchestrate them together — a model tuned for a narrow capability beats one giant bot, and function-calling usage now signals a team’s AI maturity. Episode takeaways

In their words

Yeah, and this is also very crucial what you're describing, which is, let's say you align on a set of preferences and you're tailoring them. You want to know, can you then track progress against your new set of objectives? So it's super important. It's actually critical. It's part of the same problem space.

Sara Hooker GenAI Predictions for 2025 | Databricks & Cohere · 35:00

Transcript excerpts; ellipses mark omitted words. Some automatically selected quotes have filler words removed.