Tokenless

Self improving systems

Building And Structuring An AI Native Company

Building And Structuring An AI Native Company

Tom Blomfield of Y Combinator discusses the paradigm shift towards AI-native companies, moving beyond traditional human-centric hierarchies. He introduces the concept of self-improving AI loops—where systems continuously learn and evolve without human intervention—and illustrates this with examples like YC's self-healing data agents and living user manuals. Blomfield explores the vision of 'AI employees with VMs' leading to a 'company brain,' where humans transition to the 'edge' for intuition and real-world interaction. He concludes with practical advice for founders: prioritize token burn over headcount, ensure all data is AI-legible, and leverage AI for strategic simulations like investor calls.

How to Build a Self-Improving Company with AI

How to Build a Self-Improving Company with AI

YC General Partner Tom Blomfield explains how to move beyond the traditional hierarchical company structure and build a self-improving organization using AI. He introduces the concept of recursive, self-improving AI loops that can optimize a company's operations, products, and knowledge base while the founders sleep.

How to Build a Self-Improving Company with AI

How to Build a Self-Improving Company with AI

YC General Partner Tom Blomfield explains how to move beyond the 'copilot' mindset and restructure companies as series of recursive, self-improving AI loops. He details how to make company knowledge legible to AI, creating systems that improve overnight with minimal human intervention, ultimately rendering traditional middle management obsolete.

The Unbearable Lightness of Agent Optimization — Alberto Romero, Jointly

The Unbearable Lightness of Agent Optimization — Alberto Romero, Jointly

This talk introduces Meta-ACE, a learned meta-optimization framework that dynamically orchestrates multiple strategies (context evolution, adaptive compute, hierarchical verification, and more) to maximize AI agent performance. The framework profiles each task to select an optimal strategy bundle, overcoming the single-dimension limitations of previous methods.