Evolution and Challenges of AI Models Beyond Language

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Exploration of the evolving frontiers of AI models beyond language, including image models, text to speech, and speech to text technology. The chapter highlights the challenges in creating scalable and useful models, emphasizing the importance of intention and taste in solving real product problems. It also discusses the concept of model differentiation in a competitive landscape and explores advancements in image generation and text-to-speech technology.

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.

Links:

Claude 3 from Anthropic
LMSys Chatbot Leaderboard for which models are sticking out right now
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: https://7powers.com/, Sachin Rekhi has a good detailed primer on the 7 Powers as well.
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