Séb Krier

Huge fan of multi agent systems, agent based modelling, and social intelligence – these frames still seem really absent from mainstream AI discourse except in a few odd places. Some half-baked thoughts:

1. Expecting a model to do all the work, solve everything, come up with new innovations etc is probably not right. This was kinda the implicit assumption behind *some* interpretations of capabilities progress. The ‘single genius model’ overlooks the fact that inference costs and context windows are finite.

2. People overrate individual intelligence: most innovations are the product of social organisations (cooperation) and market dynamics (competition), not a single genius savant. Though the latter matters too of course: the smarter the agents the better.

3. There’s still a lot of juice to be squeezed from models, but I would think it has more to do with how they’re organised. AI Village is a nice vignette, and also highlights the many ways in which models fail and what needs to be fixed.

4. Once you enter multi-agent world, then institutions and culture start to matter too: what are the rules of the game? What is encouraged vs what is punished? What can agents do and say to each other? How are conflicts resolved? It’s been interesting seeing how some protocols recently emerged. We’re still very early!

5. Most of the *value* and transformative changes we will get from AI will come from products, not models. The models are the cognitive raw power, the products are what makes them useful and adapted to what some user class actually needs. A product is basically the bridge between raw potential and specific utility; in fact many IDEs today are essentially crystallized multi agent systems.

Here is the link.