#324 Sharon Zhou: Inside AMD's Plan to Build Self-Improving AI
AI is not just getting smarter. It is getting faster by learning how to optimize the hardware it runs on. In this episode, Sharon Zhou, VP of AI at AMD and former Stanford AI researcher, explains how language models are beginning to write and optimize their own GPU kernel code. We explore what self improving AI actually means, how reinforcement learning is used in post training, and why kernel optimization could be one of the most overlooked scaling levers in modern AI. Sharon breaks down how GPU efficiency impacts the cost of training and inference, why catastrophic forgetting remains a challenge in continual learning, and how verifiable rewards from hardware profiling can help models improve themselves. The conversation also dives into compute economics, synthetic data, RLHF, and why infrastructure may define the next phase of AI progress. If you want to understand where AI scaling is really happening beyond bigger models and more data, this episode goes under the hood. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (00:00) Preview and Intro
(00:25) Sharon Zhou's Background and Transition to AMD
(02:00) What Is Self-Improving AI?
(04:16) What Is a GPU Kernel and Why It Matters
(07:01) Using AI Agents and Evolutionary Strategies to Write Kernels
(11:31) Just-In-Time Optimization and Continual Learning
(13:59) Self-Improving AI at the Infrastructure Layer
(16:15) Synthetic Data and Models Generating Their Own Training Data
(20:48) AMD's AI Strategy: Research Meets Product
(23:22) Inside the NeurIPS Tutorial on AI-Generated Kernels
(30:59) Reinforcement Learning Beyond RLHF
(39:09) 10x Faster Kernels vs 10x More Compute
(41:50) Will Efficiency Reduce Chip Demand?
(42:18) Beyond Language Models: Diffusion, JEPA, and Robotics
(45:34) Educating the Next Generation of AI Builders
SaaStr 707: The Where, When, and How of AI with Theory Ventures, Open AI, MotherDuck and Lamini
SaaStr 707: The Where, When, and How of AI with Theory Ventures, Open AI, MotherDuck and Lamini
Hear the latest insights on AI from leading VC Tomasz Tunguz of Theory Ventures Theory Ventures, joined by Jordan Tigani, Founder and CEO of Mother Duck Maggie Hott, GTM at OpenAI, and Sharon Zhou, Co-Founder and CEO of Lamini. Together they discuss the new architecture for building Software as a Service (SaaS) applications with data & machine learning at their core, moderated by Tomasz.
The focus of the episode centers around the transformative role of data, which has become an integral part of the production stack. The speakers also delve into the implications of this shift and explore how leveraging data within the architecture empowers developers to create more robust, intelligent, and scalable SaaS solutions.
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