AI Models. Weighing the chances of commodity vs differentiation.

This week is a discussion of the points of differentiation vs commodification in various AI models. Including how these points might change over time, and might change between language, image, and video models. Then Fraser pivots to orienting around jobs to be done in AI, and how the various models have a huge gap between being capable of doing something, and doing it well. 

We then talk about Claude 3, the nature of benchmarking, and the rapid dropping LLM prices. Lastly, we cover a startup subject, debating the merits of SAFE (Simple Agreement for Future Equity) vs. priced equity rounds in early-stage funding. 

  1. Claude 3 from Anthropic
  2. LMSys Chatbot Leaderboard for which models are sticking out right now
  3. For areas where models don't differentiate, we are back to the 7 powers framework. Hamilton Helmer's website with overview of the 7 Powers framework:, Sachin Rekhi has a good detailed primer on the 7 Powers as well.
  4. Carta guide on priced rounds vs SAFEs, which we don't really agree with all the pros and cons, but is a decent overview
  • (00:00) - Opening
  • (00:31) - Are models an inevitable commodity?
  • (05:58) - How image, video, and other models may pan out differently than language
  • (08:20) - It's not the model, it's the customer
  • (10:20) - Jobs to be done in AI - 1. Can it do it... 2. very well.. 3. exactly how I want it to.
  • (22:13) - Claude 3 by Anthropic
  • (25:25) - ELO Leaderboard Results
  • (26:25) - Claude 3 Haiku and Falling LLM Prices
  • (30:33) - SAFES vs priced equity rounds
AI Models. Weighing the chances of commodity vs differentiation.
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