Hidden Layers: AI and the People Behind It

2025 was another defining year for artificial intelligence. In this special AI Year in Review episode of Hidden Layers, Ron Green is joined by Emma Pirchalski, Michael Wharton, and Dr. ZZ Si to break down what actually mattered in AI this year.The team recaps the biggest developments from 2025, revisits their predictions from 2024 to see what held up (and what didn’t), and shares honest, experience-driven predictions for 2026. Topics include multimodal models, agents, enterprise adoption, governance gaps, workforce impact, ROI pressure, and where AI is truly headed next.This episode cuts past hype to focus on what leaders, builders, and decision-makers should actually be watching as AI moves from experimentation to execution.Chapters00:00:00 Welcome and Introduction to 2025 AI Year in Review00:00:56 Emma's Working Models Podcast Announcement00:01:48 Top AI Developments of 202500:16:29 Reviewing 2025 Predictions00:25:08 2026 Predictions00:36:49 Closing Thoughts

Artificial intelligence is shifting from prediction to autonomy—and “agentic AI” is leading the charge. In this episode of Hidden Layers, KUNGFU.AI’s Ron Green, Dr. ZZ Si, and Michael Wharton unpack what it really means for machines to act on their own, what’s hype versus real progress, and how far we are from true artificial general intelligence (AGI).They discuss how coding agents are transforming development workflows, why agentic AI is both overhyped and underutilized, the challenges of scaling reliable autonomy, the connection between AGI, biology, and lifelong learning, and whether new architectures or cognitive inspiration will take us the rest of the way.00:00 – Intro: From prediction to autonomy01:30 – What is agentic AI?05:00 – Coding agents and creative workflows08:00 – Reliability, risk, and real-world use12:30 – The agentic hype cycle16:00 – Why businesses underuse (and overuse) AI19:00 – Narrow AI and domain-specific intelligence22:00 – The AGI timeline debate26:00 – Learning from biology and cognition33:00 – Lifelong learning and what’s missing today

In this episode of Hidden Layers, Ron is joined by Michael Wharton and Dr. ZZ Si to explore one of the most pressing and puzzling issues in AI: hallucinations. Large language models can tackle advanced topics like medicine, coding, and physics, yet still generate false information with complete confidence. The discussion unpacks why hallucinations happen, whether they’re truly inevitable, and what cutting-edge research says about detecting and reducing them. From OpenAI’s latest paper on the mathematical inevitability of hallucinations to new techniques for real-time detection, the team explores what this means for AI’s reliability in real-world applications.

In this episode of Hidden Layers, we dive into the most important AI developments of the month. We cover OpenAI’s highly anticipated and controversial GPT-5 release, debate where we really are on the AGI timeline, explore groundbreaking new world models like Google’s Genie 3 and Tencent’s Huanyuan Gamecraft, and unpack Meta’s DINO V3 image encoder breakthrough.

In this episode of Hidden Layers, host Ron talks with Dr. Hannah Lu, assistant professor at the University of Texas at Austin and core faculty at the Odin Institute for Computational Engineering and Sciences. Dr. Lu is pioneering the use of AI-powered surrogate models to make complex scientific simulations—like CO₂ absorption in geological formations—faster, more accurate, and more useful for real-world decision-making.They discuss:How surrogate models work and why they’re so powerfulThe challenges of applying AI to physics-based systemsHow digital twins and uncertainty quantification are shaping the future of environmental modelingThe intersection of generative AI, physics constraints, and climate science