# Building the Automated AGI Lab: Core Automation's Jerry Tworek and Rohan Anil

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Text version: https://stenobird.com/podcast/training-data/building-the-automated-agi-lab-core-automation-s-jerry-tworek-and-rohan-anil.md
Podcast: [Training Data](https://stenobird.com/podcast/training-data)
Published: 2026-07-29T09:00:00+00:00
Episode link: https://pscrb.fm/rss/p/traffic.megaphone.fm/CPUAI4789823358.mp3
Audio file: https://pscrb.fm/rss/p/traffic.megaphone.fm/CPUAI4789823358.mp3
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JSON: https://stenobird.com/v1/public/podcasts/training-data/episodes/building-the-automated-agi-lab-core-automation-s-jerry-tworek-and-rohan-anil
Duration seconds: 2951

## Resource

The transformer architecture has reached a scaling plateau where the next leap in intelligence requires architectural innovation rather than just more data. Core Automation founders Jerry Tworek and Rohan Anil discuss moving beyond pre-training and RL toward systems capable of true continual learning and test-time adaptation.

## Highlights
- Main idea: The current era of AI has mastered large-scale pre-training and RL, but the transformer architecture lacks the fundamental capability for continual learning
- Failure mode: Transformers suffer from a lack of test-time adaptability and are increasingly reliant on distilling older models via internet-scale data
- Practical takeaway: To find the next breakthrough, researchers must move away from making transformers more efficient and instead focus on making architectures more expressive
- Main idea: The bottleneck for frontier labs is the release cycle; they are too locked into the current coding-agent race to experiment with radical architectural shifts
- Practical takeaway: The path to superior architecture lies in building an automated lab that can execute high-frequency experiments, starting with automating kernel generation

## Topics

Transformer Architecture, Artificial General Intelligence, Reinforcement Learning, Continual Learning, Machine Learning Research, Core Automation, Model Scaling, Test-time Adaptation

## Chapters
- 1:00 — The Vision for Core Automation: Introduction to the founders and their transition from OpenAI and Google to building a new type of AI research lab.
- 5:00 — The Limits of the Transformer: An analysis of why the transformer architecture is reaching its functional limits and why scaling alone is insufficient.
- 12:00 — The Timing of Architectural Research: Discussing why the market is currently optimized for scaling and why the window for new architectural breakthroughs is opening.
- 19:00 — Beyond Scaling: The Need for Expressivity: The argument that current technology only scales to a subset of human intelligence and needs more powerful, adaptive structures.
- 23:00 — The Age of Experience and RL: Exploring the nuances of reinforcement learning versus behavioral cloning and the potential for more efficient learning algorithms.
- 34:00 — The Automated Lab Strategy: How Core Automation intends to use automation to iterate on kernels and architectures faster than traditional research cycles.
- 41:00 — Finding the Breakthrough: The search for the 'perfect plot' and the goal of creating systems that improve through their own daily operations.

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