Hidden Layers: AI and the People Behind It Podcast Por KUNGFU.AI arte de portada

Hidden Layers: AI and the People Behind It

Hidden Layers: AI and the People Behind It

De: KUNGFU.AI
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Hidden Layers: AI and the People Behind It, is a series focused on all things artificial intelligence. Hosted by our Co-Founder and CTO, Ron Green, who uses his 20+ years of AI experience to break down complex topics into digestible, engaging conversations. ‍

If you’re a tech professional, or just looking to better understand the world of AI, you’re in the right place. Each episode will explore cutting-edge technical advances, discuss the art of the possible, and review some of the incredible work being done in the field.

Kung Fu Solutions Inc 2024
Episodios
  • The "AI Bubble" Bubble | EP.51

    The "AI Bubble" Bubble | EP.51
    Mar 12 2026
    Is the AI bubble narrative itself a bubble? Billions of dollars are flowing into chips, data centers, and frontier models. From the outside, it can look speculative. But from inside the industry, the signal looks very different. In this episode of Hidden Layers, Ron Green is joined by Michael Wharton and Dr. ZZ Si to discuss what it actually feels like to build with AI today. They explore rapid advances in model capabilities, the growing power of coding agents, and why many organizations are still struggling to absorb the productivity gains AI already enables. They also examine the massive capital investment in AI infrastructure and debate what signals would actually indicate the industry has hit a plateau. 00:00 – Is the AI Bubble Narrative Itself a Bubble? 03:00 – Rapid Advances in AI Model Capabilities 05:35 – Coding Agents and the Changing Development Workflow 09:30 – Benchmarks Showing AI Capability Acceleration 16:20 – Verifying AI Outputs and the Limits of Evaluation 18:20 – CAPEX, Chips, and the Dot-Com Bubble Comparison 21:50 – What Would Actually Signal an AI Bubble 26:30 – Why AI May Become a Utility
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    32 m
  • Did AI Kill Programming? | EP. 50

    Did AI Kill Programming? | EP. 50
    Feb 19 2026
    Are AI coding tools actually replacing programmers, or just changing how software gets built? In this episode of Hidden Layers, Ron Green sits down with Dr. ZZ Si and Michael Wharton to unpack what has shifted with modern coding agents, what has not, and where the hype breaks down. They share concrete examples from their own workflows, including how coding tools have moved from autocomplete to handling larger chunks of work, and why the real bottleneck is no longer writing syntax, but defining intent, architecture, and product direction. The conversation also explores how these tools are reshaping team velocity, why senior engineers tend to get more leverage from AI than junior developers, and the risks of weakening the talent pipeline if companies stop investing in early-career engineers. The episode closes with a candid look at what skills will matter most in an AI-assisted world, how abstraction layers are changing the role of programmers, and whether we may already be near peak computer science graduates. 00:00 – The rise of AI coding tools 03:07 – How workflows are changing 06:27 – Team velocity and delivery speed 08:19 – Product thinking vs. engineering execution 09:46 – Is programming actually dying? 11:41 – What “programming” means now 15:23 – Senior vs. junior developer leverage 16:33 – The developer talent pipeline 18:21 – Ego, identity, and automation 19:08 – Before vs. after: building with AI 22:30 – Debugging and fixing issues with AI 24:42 – Spec-writing and product shaping with AI 26:49 – The future of computer science grads 29:20 – Closing reflections
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    30 m
  • Your AI Is Too Big, Too Expensive, and Probably Wrong | EP. 49

    Your AI Is Too Big, Too Expensive, and Probably Wrong | EP. 49
    Jan 22 2026
    What if the most powerful AI in your organization isn’t the biggest model you can buy, but the one trained on data only you own? In this episode of Hidden Layers, Ron Green is joined by Dr. ZZ Si and Michael Wharton to break down why domain-specific AI models consistently outperform general-purpose systems in real enterprise environments. They explore how narrowly scoped models deliver higher accuracy, lower costs, better reliability, and stronger governance, especially when built on proprietary data. Through real-world examples spanning finance, industrial systems, healthcare, and document understanding, the conversation tackles when to build custom models, when to rely on APIs, and how to identify AI initiatives that actually make it into production. The takeaway is clear: focus beats scale, and specificity is often the fastest path to durable competitive advantage. Chapters 00:00:00 What Is Domain-Specific AI 00:01:15 General Models vs. Focused Systems 00:02:48 Performance, Cost, and Model Size 00:04:13 Proprietary Data as Advantage 00:07:58 Why AI Fails in Production 00:08:42 Real-World Domain-Specific Examples 00:10:54 How to Decide What to Build 00:14:53 Scale, Accuracy, and Uncertainty 00:18:49 The Spectrum of Domain-Specific AI 00:27:01 What We’d Build Differently Today
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    30 m
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