#208 The three paths AI could take from here - Shawn Wang SWYX interview

Today Quincy Larson interviews Shawn Wang. He's a software engineer, founder of the AI Engineer conference, and host of the Latent Space podcast focused on applying the latest models toward getting work done.

We talk about:

- How even if LLMs plateau, there will be still paths to better output through surrounding harness code

- And three big areas researchers are exploring to further improve model performance: World Models, Multi-modality, and Embodied AI

- Which skills Shawn thinks are most important for developers going forward

- And why Shawn thinks you should switch your own self teaching from "just-in-time learning" to "just-in-case learning"

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Links from our discussion:

- Shawn's Tiny Teams Playbook: https://www.latent.space/p/tiny

- Shawn's interview with FeiFei Li: https://www.latent.space/p/after-llms-spatial-intelligence-and?utm_source=publication-search

- Boots Theory: https://en.wikipedia.org/wiki/Boots_theory