What happens when AI writes code faster than anyone can test it?

In this episode of Eye on AI, Craig Smith sits down with Dan Faulkner, CEO of SmartBear, to explore one of the most underappreciated risks of the AI coding boom. As tools like Claude Code and Codex push software development to unprecedented speed, the systems built to validate that software are being left behind. Dan makes a distinction that every engineering leader needs to hear: clean code passing unit tests is not the same as an application that actually works.

Dan introduces the concept of application integrity, continuous and measurable assurance that your software does everything it was intended to do and nothing it was not. He explains why the gap between what AI builds and what teams actually validate is already creating hidden risk in production, and why that risk compounds the faster you ship.

We also get into the new failure modes that agentic AI is introducing. Slop squatting, instruction inversion, cascading errors. These are not theoretical. They are happening now, at scale, in codebases that no human has fully read.

Dan also walks through SmartBear's autonomy ladder framework and their newest product BearQ, a team of AI agents that explores your application, builds a knowledge graph, authors tests, runs them, and updates everything as your app evolves. The key distinction: it is built to augment human teams, not replace them.

Finally, Dan shares his honest take on the future of software engineering. The fallacy was always that coding was the hard part. The hard part is knowing what to build. That skill is not going anywhere.

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(00:00) Introduction and Dan Faulkner's Background

(01:05) What SmartBear Does: Testing and API Lifecycle Management

(03:27) AI Is Outpacing Application Testing

(07:51) Slop Squatting, Instruction Inversion and New AI Failure Modes

(17:31) Black Boxes, Technical Debt and the Expertise Crisis

(22:00) How to Avoid Self-Validating AI Systems

(24:11) The Autonomy Ladder and BearQ

(31:30) Why Testing Must Be Continuous and Everywhere

(36:31) Infrastructure Risk and Automation Bias

(44:11) The Future of QA and New Specialist Roles

(50:44) How Teams Use SmartBear Tools Today

(58:57) The Future of Software Engineering and Human Roles

This episode is sponsored by Modulate. Most voice AI focuses on transcription. Velma takes it further by actually understanding conversations, analyzing tone, timing, stress, and intent using its Ensemble Listening Model architecture. Explore the live preview: https://preview.modulate.ai/ What does it actually mean to build a foundation model for robots? In this episode of Eye on AI, Craig Smith sits down with Sergey Levine, co-founder of Physical Intelligence and professor at UC Berkeley, to explore a fundamentally different approach to building robots, one inspired not by programming a single perfect machine, but by training AI on the broadest and most diverse data possible so robots can learn, adapt, and operate in the unpredictable real world.

Sergey explains why the secret to general-purpose robots isn't perfecting one single machine, but training on massive, diverse data from all kinds of robots and even humans. The more variety the model sees, the better it gets. Just like ChatGPT learned from all the text on the internet, robotic foundation models learn from every robot that has ever moved, grabbed, or interacted with the real world.

We also get into the big humanoid robot debate. Are they the future, or is it mostly hype? Sergey gives an honest and technical take on why the form factor conversation is changing now that foundation models exist, and why that actually opens the door for more creativity, not less.

Finally, Sergey shares what he's most excited about next, building a true data flywheel where robots get smarter the more they are deployed, creating a continuous learning cycle that could change everything.

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(00:00) Introduction: What Are Foundation Models for Robots?

(01:44) Meet Sergey Levine: Physical Intelligence and UC Berkeley

(02:51) Breaking Down Foundation Models for Non-Technical People

(06:46) Why Real World Data Beats Simulation

(15:00) Building a Broad Robotics Foundation From Scratch

(24:00) The Open World Problem in Robotics

(40:00) Generalist vs Specialist Robots: Which Wins?

(47:00) Humanoid Robots: Real Innovation or Just Hype?

(55:10) The Future: Continuous Learning and the Data Flywheel

(56:23) Guilty Pleasure: Sci Fi and Thinking Beyond the Limits

AI has been trained like software.

But what if it should be grown like life?

In this episode of Eye on AI, Craig Smith sits down with Sebastian Risi, professor and leading researcher in neuroevolution and artificial life, to explore a fundamentally different approach to building intelligence, one inspired by how nature evolves, grows, and adapts.

Sebastian explains why traditional AI systems are limited by fixed architectures and one-time training, and how evolutionary algorithms can create systems that continuously learn, self-organize, and even grow their own neural structures over time.

They dive into concepts like plastic neural networks that keep updating during their lifetime, AI systems that can recover from damage, and models that develop from a single "cell" into complex structures, similar to biological organisms.

The conversation also explores how combining large language models with evolutionary search could unlock more creative and open-ended problem solving, from merging specialized models to building AI systems capable of generating and testing scientific ideas.

If you want to understand where AI is headed beyond today's transformer models, and why the future may look more like living systems than software, this episode offers a clear and thought-provoking perspective.

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(00:00) Why copy nature's evolution for AI

(01:20) What neuroevolution actually means

(05:52) How evolutionary search replaces gradients

(08:03) Plastic neural networks and continuous learning

(11:53) Growing neural networks like living systems

(18:08) Scaling challenges and limits of growth

(23:16) Can evolving systems replace LLM training

(27:28) Continual learning and model merging

(30:27) Artificial life, self-repair, and resilience

(35:10) AI scientists and evolution with LLMs

AI has been trained like software.

But what if it should be grown like life?

In this episode of Eye on AI, Craig Smith sits down with Sebastian Risi, professor and leading researcher in neuroevolution and artificial life, to explore a fundamentally different approach to building intelligence, one inspired by how nature evolves, grows, and adapts.

Sebastian explains why traditional AI systems are limited by fixed architectures and one-time training, and how evolutionary algorithms can create systems that continuously learn, self-organize, and even grow their own neural structures over time.

They dive into concepts like plastic neural networks that keep updating during their lifetime, AI systems that can recover from damage, and models that develop from a single "cell" into complex structures, similar to biological organisms.

The conversation also explores how combining large language models with evolutionary search could unlock more creative and open-ended problem solving, from merging specialized models to building AI systems capable of generating and testing scientific ideas.

If you want to understand where AI is headed beyond today's transformer models, and why the future may look more like living systems than software, this episode offers a clear and thought-provoking perspective.

Subscribe for more conversations with the people building the future of AI and emerging technology.

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(00:00) Why copy nature's evolution for AI

(01:20) What neuroevolution actually means

(05:52) How evolutionary search replaces gradients

(08:03) Plastic neural networks and continuous learning

(11:53) Growing neural networks like living systems

(18:08) Scaling challenges and limits of growth

(23:16) Can evolving systems replace LLM training

(27:28) Continual learning and model merging

(30:27) Artificial life, self-repair, and resilience

(35:10) AI scientists and evolution with LLMs

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 correction. They dive into how his team improved Shor's Algorithm from 80% to 99% accuracy on IBM hardware, without changing the hardware itself, and what that means for the future of quantum performance. The conversation also explores how AI is being used to optimise quantum systems, why classical computing will continue to play a central role in quantum development, and what milestones to watch as the industry moves closer to real-world applications. If you want to understand where quantum computing actually stands today and what will unlock its next phase, this episode gives you a clear, grounded perspective. Subscribe for more conversations with the people building the future of AI and emerging technology.

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(00:00) The 99% Accuracy Breakthrough (Quantum's Turning Point) (01:03) Why Quantum Hardware Alone Isn't Enough (03:50) Digital Twins Explained (The Missing Layer) (08:09) The Real Problem: Noise, Instability & Environment (15:43) From 80% to 99% on Shor's Algorithm (26:36) How AI Is Fixing Quantum's Biggest Bottleneck (33:53) Inside the Platform: From Circuit to Optimization (40:51) Logical Qubits & Scaling Quantum Systems (43:34) The Limits of Simulation vs Real Quantum Hardware (54:29) When Quantum Becomes Useful (Real Timeline)

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 everything we see online. Kevin explains how Doppel is building an AI-native defense platform to detect, map, and shut down these attacks in real time, and why the future of cybersecurity will be defined by AI vs AI. If you're thinking about AI, security, or the future of trust online—this conversation is essential.

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(00:00) AI Deepfakes & The Collapse of Trust (01:56) Why "Social Engineering" Is Bigger Than Phishing(05:20) Deepfakes, Misinformation & Multi-Channel Attacks(09:16) The Rise of Deepfake Phone Calls(12:43) How Attackers Manipulate AI & Search Results(14:39) The Origin Story Behind Doppel(18:55) How Doppel Detects & Stops Attacks in Real Time(22:55) Can Attackers Misuse AI Defense Tools?(24:26) How to Tell What's Real vs Fake Online(28:20) What Is Human Risk Management?(30:36) AI vs AI: The Future of Cyber Defense(34:04) What CEOs Must Do About AI Threats(37:18) Working with Platforms Like YouTube & LinkedIn(39:52) Can We Ever Fully Stop Deepfakes?(44:40) How Doppel Works for Enterprises

This episode is sponsored by Modulate. Most voice AI focuses on transcription. Velma takes it further by actually understanding conversations, analyzing tone, timing, stress, and intent using its Ensemble Listening Model architecture. Explore the live preview: https://preview.modulate.ai/

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 what it takes to build trustworthy AI systems with governance, guardrails, and high-quality retrieval at the core.

Baris also shares how leading companies are already saving thousands of hours through AI-driven automation, why culture and leadership determine AI success, and what the future looks like as agents move from pilots to full-scale production.

If you want to understand where enterprise AI is actually headed and what separates hype from real execution, this episode breaks it down.

(00:00) The Evolution of Snowflake AI

(01:40) Baris Gultekin: Background & AI Mission

(02:59) Why AI Must Run Next to Data

(04:29) Inside Snowflake's AI Infrastructure

(09:08) Model Choice vs Product Layer Strategy

(12:16) Building Trust: Governance, Guardrails & Quality

(16:01) How Enterprise Agents Are Built & Orchestrated

(20:10) AI Adoption Across the Entire Organization

(24:39) Reasoning vs Retrieval: What Matters More

(27:43) Real Use Case: Faster Decision-Making with AI

(31:44) AI as a Co-Pilot for Leaders

(36:52) Preparing Data for AI at Scale

(38:46) What the AI Data Cloud Really Means

This episode is sponsored by tastytrade.

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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 and synaptic plasticity are built directly into BDH's architecture, and what that means for continual learning, hallucinations, and "generalization over time." The conversation ranges from complexity science and brain-inspired computation to practical implications for real-world, small-data, and safety‑critical applications.

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(00:00) The Core Problem: Why Today's AI Lacks Memory

(03:16) Pathway's Mission to Bring Memory Into AI

(04:53) Zuzanna's Background in Complexity Science

(10:30) Why Transformers Reset Like "Groundhog Day"

(14:34) The Brain-Inspired Dragon Hatchling Architecture

(23:59) How the Network Learns and Builds Connections

(37:38) Performance vs Transformers on Language Tasks

(49:37) Productizing the Technology With NVIDIA and AWS

(54:23) Can Memory Solve AI Hallucinations?

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 demographic differences influence how models are evaluated, why human judgment remains critical even as AI improves, and how the collaboration between humans and AI will shape the next phase of development.

This conversation reveals the human backbone behind today's AI systems.

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(00:00) Preview and Intro

(02:45) Founding Prolific And Early Pain Points

(06:30) From Mechanical Turk To Representativeness

(09:55) Academic Research And AI Use Cases Split

(13:40) Vetting Real Participants And Fighting Fraud

(17:45) Scale, Community Growth, And Talent Mix

(22:00) High-Complexity Projects Over Commoditised Labeling

(26:40) Measuring Model Persuasion With Live Conversations

(30:20) Demographic-Aware Model Preference Benchmarks

(34:10) The Rise Of Human Evaluation Over Benchmarks

(38:00) Enterprise Model Choice And Continuous Evaluation

(42:00) Why Humans Won't Disappear From The Loop

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