#329 Izhar Medalsy: How AI Solves Quantum Computing’s Biggest Problem
Eye On A.I.
Quantum computing has been “5 years away” for decades. So what’s actually holding it back? In this episode of Eye on AI, Craig Smith sits down with Izhar Medalsy, Co-founder & CEO of Quantum Elements, to break down the real bottleneck in quantum computing today and why the future of the industry may depend more on classical systems and AI than quantum hardware itself. Izhar explains how digital twins of quantum systems are being used to simulate real hardware, generate massive amounts of training data, and solve one of the biggest challenges in the field: noise and error...
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#328 Kevin Tian: Exploring Doppel's AI-Native Social Engineering Defense Platform
Eye On A.I.
AI is changing more than just productivity. It’s changing what we can trust. In this episode, Kevin Tian, Co-founder and CEO of Doppel, breaks down how AI is enabling a new wave of social engineering attacks—from deepfake phone calls to impersonation across LinkedIn, YouTube, and search engines. The reality is this:Deepfakes are just one part of a much bigger problem. Attackers are now operating across multiple channels at once, using AI to manipulate people, not just systems. And as these attacks scale, the real risk isn’t just fraud or data loss—it’s the erosion of trust in...
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#327 Baris Gultekin: The Next Phase of AI - Agents That Understand Your Company’s Data
Eye On A.I.
This episode is sponsored by Modulate. Meet Velma, voice AI that detects tone, intent, and stress: Baris Gultekin, Head of AI at Snowflake, breaks down how enterprise AI is actually being built, deployed, and scaled today. From running AI directly inside governed data environments to enabling natural language access across entire organizations, this conversation explores the shift from experimentation to real-world impact. You’ll learn why Snowflake’s core philosophy centers around bringing AI to the data, how data agents are transforming decision-making across teams, and...
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#326 Zuzanna Stamirowska: Inside Pathway's Post-Transformer Architecture Designed for Memory and On-the-Fly Learning
Eye On A.I.
This episode is sponsored by tastytrade. Trade stocks, options, futures, and crypto in one platform with low commissions and zero commission on stocks and crypto. Built for traders who think in probabilities, tastytrade offers advanced analytics, risk tools, and an AI-powered Search feature. Learn more at This episode dives into why Pathway’s Baby Dragon Hatchling (BDH) might mark the beginning of the post-transformer era in AI. Zuzanna Stamirowska, Pathway’s CEO and co‑author of BDH, explains why today’s transformer-based LLMs hit a wall on long-horizon reasoning, how memory...
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#325 Phelim Brady: Why AI's Future Depends on Human Judgement
Eye On A.I.
AI often looks fully automated. But behind the scenes, a huge amount of human judgment is shaping how these systems actually work. In this episode, Craig Smith speaks with Phelim Bradley, co-founder and CEO of Prolific, a platform that connects millions of real people with researchers and AI labs to evaluate and improve AI systems. They explore the hidden human layer behind modern AI, why traditional benchmarks are becoming less reliable, and why AI companies increasingly rely on real human feedback to measure model performance in the real world. Phelim also explains how...
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#324 Sharon Zhou: Inside AMD’s Plan to Build Self-Improving AI
Eye On A.I.
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...
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#323 David Ha: Why Model Merging Could Be the Next AI Breakthrough
Eye On A.I.
This episode is sponsored by tastytrade. Trade stocks, options, futures, and crypto in one platform with low commissions and zero commission on stocks and crypto. Built for traders who think in probabilities, tastytrade offers advanced analytics, risk tools, and an AI-powered Search feature. Learn more at Artificial intelligence is reaching a turning point. Instead of building bigger and bigger models, what if the real breakthrough comes from letting AI evolve? In this episode of Eye on AI, David Ha, Co-Founder and CEO of Sakana AI, explains why evolutionary strategies and collective...
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#322 Amanda Luther: The Widening AI Value Gap (Inside BCG's AI Research)
Eye On A.I.
In this episode of Eye on AI, Craig Smith speaks with Amanda Luther, Senior Partner at Boston Consulting Group and global lead of BCG’s AI Transformation practice, about what their latest 1,500-company AI study reveals about the widening gap between AI leaders and laggards. Only 5% of companies are truly “future-built” with AI embedded across their core business functions. These firms are seeing measurable gains in revenue growth, EBIT margins, and shareholder returns. Meanwhile, 60% of organizations are either experimenting or struggling to extract real value. Amanda breaks down how BCG...
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#321 Nick Frosst: Why Cohere Is Betting on Enterprise AI, Not AGI
Eye On A.I.
This episode is sponsored by tastytrade. Trade stocks, options, futures, and crypto in one platform with low commissions and zero commission on stocks and crypto. Built for traders who think in probabilities, tastytrade offers advanced analytics, risk tools, and an AI-powered Search feature. Learn more at In this episode of Eye on AI, Nick Frosst, Co-Founder of Cohere and former Google Brain researcher, explains why Cohere is betting on enterprise AI instead of chasing AGI. While much of the AI industry is focused on artificial general intelligence, Cohere is building...
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#320 Carter Huffman: Exploring The Architecture Behind Modulate's Next-Gen Voice AI
Eye On A.I.
This episode is sponsored by tastytrade. Trade stocks, options, futures, and crypto in one platform with low commissions and zero commission on stocks and crypto. Built for traders who think in probabilities, tastytrade offers advanced analytics, risk tools, and an AI-powered Search feature. Learn more at Voice AI is moving far beyond transcription. In this episode, Carter Huffman, CTO and co-founder of Modulate, explains how real-time voice intelligence is unlocking something much bigger than speech-to-text. His team built AI that understands emotion, intent,...
info_outlineAI 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.
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(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