What if the country that produces the world's top AI talent finally figured out how to keep it?

In this episode of Eye on AI, Craig Smith sits down with Professor Mausam, one of India's leading AI researchers, AAAI Fellow, and founding head of the Yardi School of Artificial Intelligence at IIT Delhi, to get an honest and unflinching diagnosis of why India has fallen so far behind the US and China in artificial intelligence and what it will actually take to close that gap.

Mausam breaks down the structural story behind India's deficit. A pipeline of world-class students that gets exported abroad the moment it graduates. A professor shortage so severe that IIT Delhi's entire School of AI has hired only five new faculty members in five years. A government AI mission with the right instincts but not enough speed or boldness. And a brain drain made worse by the very thing India is proud of, its English fluency, which makes its talent the easiest in the world to absorb and the hardest to bring back.

Mausam walks through the full picture. How China built its research dominance not through students but through aggressively repatriating senior researchers with real salaries, real lab resources, and real authority to build research cultures from scratch. Why the AlexNet moment in 2012 was actually an equalizer that gave China's fledgling ecosystem a surprise advantage over more established Western research groups. How India's JEE coaching culture and IIT bottleneck are symptoms of a scarcity of quality institutions rather than a broken exam. What the government's AI mission is getting right on compute, data, and sectoral focus, and where the critical gaps remain. And why Mausam believes that bringing one hundred top professors back to India would do more for the country's AI future than any single government program or funding initiative.

We also get into the harder questions. Whether AI degrees belong at the undergraduate level or should sit on top of a computer science foundation. Why Mausam no longer holds an optimistic view on AI's impact on software jobs and why he thinks Geoff Hinton's point about plumbers has merit. And what it would actually take for a democracy of 1.4 billion people to stop training the world's AI leaders and start keeping them.

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(00:00) Introduction: India's AI Gap and Professor Mausam's Background
(02:30) Building the Yardi School of AI at IIT Delhi
(07:44) How Far China Has Pulled Ahead in AI Research
(12:55) Why India Could Not Follow China's Playbook
(29:18) The JEE System, Coaching Culture, and the IIT Bottleneck
(30:37) AI Degrees, Job Market Realities, and the Future of Work
(44:18) The Real Problem Is Professors, Not Students
(48:07) Big Tech Labs in India: Helpful but Not at Scale
(51:46) The Government AI Mission: Progress and Gaps
(55:20) The Compute and Data Infrastructure Problem
(59:54) Can India Close the Gap Before It Is Too Late 

Why IBM Is Betting Everything on Small AI Models

In this episode of Eye on AI, Craig Smith sits down with Sriram Raghavan, Vice President of AI at IBM Research, to explore one of the most important debates in enterprise AI right now. Do you actually need a massive model to get world class results? IBM's answer is no, and Sriram breaks down exactly why.

Sriram explains why IBM chose to train its Granite models directly using reinforcement learning rather than distilling from larger models like most of the industry. The reason goes beyond performance. It comes down to data lineage, safety alignment, and a belief that small, efficient models are the only sustainable path for enterprises running AI across hybrid cloud environments.

We get into the full technical stack behind that bet. How data quality has replaced model size as the real competitive advantage. Why parameter count is becoming the wrong metric entirely. How IBM's inference time scaling techniques allow an 8 billion parameter model to match the performance of GPT-4o and Claude 3.5 on code and math benchmarks. And why IBM is pioneering a new concept called Generative Computing, which treats AI models not as prompt receivers but as programmable computing elements with runtimes, modular LoRA adapters, and proper programming abstractions.

Sriram also shares where IBM Research is headed next, including breakthroughs in continuous learning, agent orchestration, and making unstructured enterprise data actually usable at scale.

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(00:00) Why IBM Skips Distillation and Trains Small Models Directly 

(04:50) Did We Even Need Giant AI Models in the First Place?

(08:12) How Data Quality Became the New Competitive Moat

(11:54) Why Parameter Count Is the Wrong Way to Measure a Model

(15:36) Reinforcement Learning Without Losing Broad Capabilities

(22:05) Inference Time Scaling: Getting Big Model Results From Small Models

(28:12) Generative Computing: Treating AI as a Programming Element

(36:40) Why IBM Open Sources and How Small Models Make It Sustainable

(41:25) The Path to Continuous Learning Without Rewriting Weights

(51:00) IBM's Full Roadmap: Models, Data, and Agents

What if the country that trained the world's engineers finally decided to keep them?

In this episode of Eye on AI, Craig Smith sits down with Abhishek, the civil servant leading India's $1.2 billion national AI Mission, to explore how one of the world's largest and most diverse nations is mounting a serious challenge to US and Chinese dominance in artificial intelligence.

Abhishek breaks down the honest story behind India's late start. World-class talent, but no research ecosystem to retain it. Digitization without AI-usable data. Compute so scarce that the entire country had fewer than 500 GPUs just two years ago. And a brain drain so severe that the engineers India trained are now running the biggest tech companies in the world, just not from India.

Abhishek walks through exactly how the mission is tackling each of those gaps. A subsidized compute program that gives researchers and startups access to 38,000 GPUs at under a dollar per hour. AI Kosh, a national data platform pulling public and private sector datasets into a single AI-ready repository. Centers of Excellence connecting IITs around domain-specific research in agriculture, healthcare, education and mobility. And a sovereign LLM program, with four models already in development and eight more on the way, built specifically for India's languages, voices and needs.

We also get into the geopolitics. Where India stands as the US and China carve out competing AI spheres of influence. Why Abhishek is pushing for a UN-led governance framework rather than aligning with either bloc. And what it would actually take for a country of 1.4 billion people to not just catch up, but leapfrog.

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(00:00) Introduction and Abhishek's Background 

(02:43) What the India AI Mission Is and How It Started 

(04:53) The $1.2 Billion Budget and Total AI Investment in India 

(06:36) Data Center Build-Out and the Road to 7 Gigawatts 

(08:11) AI Kosh: India's National Data Platform 

(10:50) Subsidized GPUs and How Researchers Access Compute 

(12:41) Brain Drain, Reverse Migration and Retaining Top Talent 

(17:24) Centers of Excellence Across IITs and Key Sectors 

(19:21) Expanding Fellowships and Training the Next Generation 

(20:11) Why India Started Late and What Changed 

(21:48) Sovereign LLMs Built for Indian Languages and Needs 

(22:42) The Diversity Challenge and Culturally Relevant AI 

(23:16) Government Funding for Foundation Model Development 

(24:12) The AI Impact Summit and India's Role on the Global Stage 

(24:52) India, China, the US and the Battle for AI Governance 

(29:37) The UN Framework and India's Third Way 

(31:16) India-China Relations and New AI Partnerships 

(32:01) How the $1.2 Billion Budget Was Decided 

(33:31) Can India Actually Catch Up With the US and China

Most enterprises are excited about agentic AI. But very few are actually deploying it in production.

In this episode of Eye on AI, Craig Smith sits down with Adi Kuruganti, Chief AI and Development Officer at Automation Anywhere, to break down why agentic AI is so hard to get right in the enterprise and what it actually takes to move from a promising pilot to a mission-critical deployment.

Adi explains why the future of enterprise automation is not agentic AI alone, but the combination of deterministic and agentic systems working together, and why companies that treat AI as a technology problem instead of a business outcomes problem are setting themselves up to fail.

They dig into how Automation Anywhere is orchestrating agents across legacy systems, healthcare platforms, and financial services workflows, why governance and compliance are the first questions every enterprise asks, and how their Process Reasoning Engine is continuously improving agent performance using metadata from over 400 million running processes.

The conversation also covers the real timeline to a fully autonomous enterprise, why the POC to production gap is the biggest failure point in enterprise AI today, and what companies that wait too long risk losing to competitors who started the journey earlier.

If you want to understand where enterprise AI actually stands today and what it takes to deploy it responsibly at scale, this episode gives you a clear and grounded perspective.

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(00:00) Why Enterprises Are Struggling With Agentic AI

(02:39) What Automation Anywhere Does and the APA Category Explained

(08:01) Deterministic vs Agentic AI: Why You Need Both

(10:59) How Human in the Loop Works in Enterprise AI

(17:16) The Mozart Orchestrator and Process Reasoning Engine

(23:50) How AI Is Upgrading and Replacing Classic RPA

(27:31) How Automation Anywhere Works With Enterprise Customers

(31:53) The Biggest Challenges of Scaling Agentic AI

(41:10) The OpenAI Partnership and What It Means

(47:06) Training Staff and Building AI Literacy at Scale

(51:39) Staying Close to Customers as the Technology Shifts

(53:17) Is the Autonomous Enterprise Actually Coming

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

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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

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.

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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

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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

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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.


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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

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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 intelligence could reshape the future of machine learning. We explore model merging, multi-agent systems, Monte Carlo tree search, and the AI Scientist framework designed to generate and evaluate new research ideas. The conversation dives into open-ended discovery, quality and diversity in AI systems, world models, and whether artificial intelligence can push beyond the boundaries of human knowledge.

If you're interested in AGI, evolutionary AI, frontier models, AI research automation, or how AI could start discovering science on its own, this episode offers a clear look at where the field may be heading next.

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(00:00) AI Should Evolve, Not Just Scale

(03:54) David's Journey From Finance to Evolutionary AI

(10:18) Why Gradient Descent Gets Stuck

(18:12) Model Merging and Collective Intelligence

(28:18) Combining Closed Frontier Models

(32:56) Inside the AI Scientist Experiment

(38:11) Parent Selection, Diversity and Innovation

(49:25) Can AI Discover Truly New Knowledge?

(53:05) Why Continual Learning Matter

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 measures AI maturity across 41 capabilities, how AI impact flows through the P&L, and why leading companies invest twice as much in AI as their competitors. She explains where AI is actually creating value today, from sales and marketing to procurement and retail operations, and why most of that value comes from core business functions, not back-office automation.

The conversation also explores the rise of agentic systems, why many early agent deployments fail, and what it really takes to redesign workflows around AI. Amanda shares practical advice for companies stuck in experimentation mode, how to prioritize the right use cases, and why training and change management matter more than chasing the perfect vendor.

If you want to understand how AI is reshaping competitive advantage in enterprise organizations, this episode provides a data-backed look at what separates the leaders from everyone else.

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(00:00) The AI Value Gap

(01:17) Inside BCG's 1,500-Company AI Study

(04:14) What "Future-Built" Companies Do Differently

(09:30) How AI Impact Is Measured on the P&L

(12:57) Why AI Leaders Invest 2X More

(14:16) Where AI Is Driving Real Cost Reduction

(16:20) Agentic AI: Hype vs Reality

(20:13) Where Agents Actually Create Value

(24:22) Tech vs Talent: Where the Money Goes

(26:58) Will AI Laggards Slowly Disappear?

(31:58) Why Adoption Is Accelerating Now

(40:07) How to Start: Amanda's Advice to AI Laggards

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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 practical, capital-efficient large language models designed for real-world enterprise deployment. Nick breaks down why scaling transformers does not equal AGI, why inference cost and ROI matter, and how enterprise AI differs from consumer AI hype.

We discuss enterprise LLM deployment, private data, regulated industries like banking and healthcare, agentic systems, evaluation benchmarks, and why AI will likely become embedded infrastructure rather than a headline breakthrough.

If you care about enterprise AI, AGI debates, large language models, and the future of AI in business, this conversation delivers a grounded perspective from inside one of the leading AI companies.

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(00:00) From Google Brain to Cohere

(03:54) Discovering Transformers

(06:39) The Transformer Dominance

(09:44) What AGI Actually Means

(12:26) Planes vs Birds: The AI Analogy

(14:08) Why Cohere Isn't Chasing AGI

(18:38) Distillation & Model Efficiency

(21:42) What Enterprise AI Really Does

(25:20) Private Data & Secure Deployment

(26:59) Enterprise Use Cases (RBC Example)

(32:22) Why AI Benchmarks Mislead

(34:55) Why Most AI Stays in Demo

(38:23) What "Agents" Actually Are

(43:32) The Problem With AGI Fear

(49:15) Scaling Enterprise AI

(53:24) Why AI Will Get "Boring"

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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, deception, harassment, and fraud directly from live conversations. Not after the fact. Instantly.

Carter shares how their technology powers ToxMod to moderate toxic behavior in online games at massive scale, analyzes millions of audio streams with ultra-low latency, and beats foundation models using an ensemble architecture that is faster, cheaper, and more accurate. We also explore voice deepfake detection, scam prevention, sentiment analysis for finance, and why voice might become the most important signal layer in AI.

If you're building voice agents, working on AI safety, or curious where conversational AI is heading next, this conversation breaks down the technical and practical future of voice understanding.

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(00:00) Real-Time Voice AI: Detecting Emotion, Intent & Lies

(03:07) From MIT & NASA to Building Modulate

(04:45) Why Voice AI Is More Than Just Transcription

(06:14) The Toxic Gaming Problem That Sparked ToxMod

(12:37) Inside the Tech: How "Ensemble Models" Beat Foundation Models

(21:09) Achieving Ultra-Low Latency & Real-Time Performance

(26:16) From Voice Skins to Fighting Harassment at Scale

(37:31) Beyond Gaming: Fraud, Deepfakes & Voice Security

(46:14) Privacy, Ethics & Voice Fingerprinting Risks

(52:10) Lie Detection, Sentiment & Finance Use Cases

(54:57) Opening the API: The Future of Voice Intelligence

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AI is changing how software is built, but it is also quietly breaking how security works.

In this episode of Eye on AI, host Craig Smith sits down with Subho Halder, co-founder and CEO of Appknox, to unpack a growing and largely invisible risk. AI-powered mobile apps that look safe but are not.

Subho explains how the explosion of ChatGPT-style app wrappers, agentic AI, and rapid app creation has transformed software from static code into living systems, and why traditional security models no longer hold up. From fake AI apps harvesting personal data to AI agents lowering the barrier for attackers, this conversation explores the real-world consequences of AI at scale.

You will also hear why trust has become a core security metric, how app stores struggle to detect malicious behavior, and why developer burnout is rising as AI-generated code shifts risk downstream instead of removing it.

This episode is essential listening for founders, developers, security leaders, and anyone building or relying on AI-powered applications.

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(00:00) Why Mobile Apps Became a Massive Trust and Security Risk

(02:45) Subho's Journey and the Birth of AppNox

(06:17) Fake AI Apps, Malicious Wrappers, and Silent Data Theft

(11:03) How Fake Apps Slip Past App Store Reviews

(15:26) The Data Harvesting Business Model Behind Fake Apps

(17:11) AI for Security vs Security for AI

(22:16) Why Trust Is Becoming a Measurable AI Performance Metric

(26:20) User Intent, Data Control, and Minimum Data Sharing

(31:10) Trust, Governments, and Why Where AI Lives Matters

(35:40) What AppNox Found in Retail App Security Audits

(39:16) How AppNox Protects Apps at Scale

(42:05) The Future of Security

Construction is one of the least digitized industries in the world, and not because it resists technology. It resists bad technology.

In this episode of Eye on AI, Craig Smith sits down with Olek Paraska, CTO of Togal AI, to break down why construction productivity has barely improved in 50 years and why pre-construction is the real bottleneck holding the industry back.

Olek explains how most estimating and takeoff work is still done manually, why automating this phase can unlock massive efficiency gains, and how AI works best in construction when it acts as a perception and reasoning layer rather than a replacement for human judgment.

The conversation explores computer vision, agentic AI, human-in-the-loop systems, and why respecting real-world constraints is essential for AI to deliver real ROI. It also looks ahead to a future where floor plans, materials, costs, and constructability can be reasoned about together, long before construction begins.

This episode is a deep dive into how AI can finally move construction forward by solving the right problems, in the right order.

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(00:00) Why Construction Is Desperate for Better AI
(01:06) Olek's Path From Software to Construction
(02:17) Why Construction Productivity Has Stalled for Decades
(04:33) The Pre-Construction Bottleneck No One Talks About
(06:17) How Takeoffs Are Still Done Manually
(09:15) Why Construction Rejects Bad Technology
(11:18) How Togal Found the Right Problem to Solve
(12:14) From Computer Vision to Reasoning AI
(17:44) What Agentic AI Looks Like in Pre-Construction
(20:59) Turning Floor Plans Into Materials and Costs
(28:18) The Real ROI of AI for Contractors
(47:11) The Long-Term Vision for AI in Construction

In this episode of the Eye on AI Podcast, Craig Smith sits down with Steve Brown, founder of CureWise, to explore how agentic AI is reshaping healthcare from the patient's perspective.

Steve shares the deeply personal story behind CureWise, born out of his own experience with a rare cancer diagnosis that was repeatedly missed by traditional medical pathways. The conversation dives into why modern healthcare struggles with complex, edge-case conditions, how fragmented medical data and time-constrained systems fail patients, and where AI can meaningfully help without replacing clinicians.

The discussion goes deep into multi-agent AI systems, reliability through consensus, large context windows, and how AI can surface better questions rather than premature answers. Steve explains why patient education is the real unlock for better outcomes, how precision medicine depends on individualized data and genetics, and why empowering patients leads to stronger collaboration with doctors.

This episode offers a grounded, practical look at AI's role in healthcare, not as a diagnostic shortcut, but as a tool for clarity, context, and better decision-making in some of the most critical moments of car

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(00:00) Using Multi-Agent AI to Analyze Medical Records

(04:35) Steve Brown's Tech Background and Return to Healthcare

(08:25) How a Rare Cancer Diagnosis Was Initially Missed

(13:55) Why Modern Medicine Struggles With Complex Cases

(18:29) Multi-Agent Consensus and AI Reliability in Healthcare

(24:12) Large Context Windows, RAG, and Medical Data Organization

(28:24) Why CureWise Focuses on Patient Education, Not Diagnosis

(33:10) Precision Medicine, Genetics, and Personalized Treatment

(47:45) Why CureWise Launches Direct-to-Patient First

(53:19) The Future of AI-Driven Precision Medicine

AI is getting smarter, but now it needs better  judgment.

In this episode of the Eye on AI Podcast, we speak with Robbie Goldfarb, former Meta product leader and co-founder of Forum AI, about why treating AI as a truth engine is one of the most dangerous assumptions in modern artificial intelligence.

Robbie brings first-hand experience from Meta's trust and safety and AI teams, where he worked on misinformation, elections, youth safety, and AI governance. He explains why large language models shouldn't be treated as arbiters of truth, why subjective domains like politics, health, and mental health pose serious risks, and why more data does not solve the alignment problem.

The conversation breaks down how AI systems are evaluated today, how engagement incentives create sycophantic and biased models, and why trust is becoming the biggest barrier to real AI adoption. Robbie also shares how Forum AI is building expert-driven AI evaluation systems that scale human judgment instead of crowd labels, and why transparency about who trains AI matters more than ever.

This episode explores AI safety, AI trust, model evaluation, expert judgment, mental health risks, misinformation, and the future of responsible AI deployment.

If you are building, deploying, regulating, or relying on AI systems, this conversation will fundamentally change how you think about intelligence, truth, and responsibility.

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Website: https://www.byforum.com/
X: https://x.com/TheForumAI
LinkedIn: https://www.linkedin.com/company/byforum/

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(00:00) Why Treating AI as a "Truth Engine" Is Dangerous
(02:47) What Forum AI Does and Why Expert Judgment Matters
(06:32) How Expert Thinking Is Extracted and Structured
(09:40) Bias, Training Data, and the Myth of Objectivity in AI
(14:04) Evaluating AI Through Consequences, Not Just Accuracy
(18:48) Who Decides "Ground Truth" in Subjective Domains
(24:27) How AI Models Are Actually Evaluated in Practice
(28:24) Why Quality of Experts Beats Scale in AI Evaluation
(36:33) Trust as the Biggest Bottleneck to AI Adoption
(45:01) What "Good Judgment" Means for AI Systems
(49:58) The Risks of Engagement-Driven AI Incentives
(54:51) Transparency, Accountability, and the Future of AI

In this episode of Eye on AI, Craig Smith sits down with Jarrod Johnson, Chief Customer Officer at TaskUs, to unpack how agentic AI is changing customer service from conversations to real action. 

They explore what agentic AI actually is, why chatbots were only the first step, and how enterprises are deploying AI systems that resolve issues, execute tasks, and work alongside human teams at scale. 

The conversation covers real-world use cases, the economics of AI-driven support, why many enterprise AI pilots fail, and how human roles evolve when AI takes on routine work. 

A grounded look at where customer experience, enterprise AI, and the future of support are heading.

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(00:00) Jarrod Johnson and the Evolution of TaskUs

(03:58) Why AI Became Core to Customer Service

(06:07) Humans, AI, and the New Support Model

(07:16) What Agentic AI Actually Is

(11:38) TaskUs as an AI Systems Integrator

(14:59) How Agentic AI Resolves Customer Issues

(19:52) Workforce Impact and the Human Role

(23:26) Why Most Enterprise AI Pilots Fail

(30:32) Real Client Case Study: Healthcare Impact

(36:34) Why Customer Service Still Feels Broken

(38:49) The End of IVR Menus and Legacy Systems

(42:25) AI Safety, Compliance, and Governance

(49:38) Training Humans for AI and RLHF Work

(54:34) The Future of Agentic AI in Enterprise

Inference is now the biggest challenge in enterprise AI.

In this episode of Eye on AI, Craig Smith speaks with Nick Pandher, VP of Product at Cirrascale, about why AI is shifting from model training to inference at scale. As AI moves into production, enterprises are prioritizing performance, latency, reliability, and cost efficiency over raw compute.

The conversation covers the rise of inference-first infrastructure, the limits of hyperscalers, the emergence of neoclouds, and how agentic AI is driving always-on inference workloads. Nick also explains how inference-optimized hardware and serverless AI platforms are shaping the future of enterprise AI deployment.

If you are deploying AI in production, this episode explains why inference is the real frontier.

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(00:00) Preview

(00:50) Introduction to Cirrascale and AI inference

(03:04) What makes Cirrascale a neocloud

(04:42) Why AI shifted from training to inference

(06:58) Private inference and enterprise security needs

(08:13) Hyperscalers vs neoclouds for AI workloads

(10:22) Performance metrics that matter in inference

(13:29) Hardware choices and inference accelerators

(20:04) Real enterprise AI use cases and automation

(23:59) Hybrid AI, regulated industries, and compliance

(26:43) Proof of value before AI pilots

(31:18) White-glove AI infrastructure vs self-serve cloud

(33:32) Qualcomm partnership and inference-first AI

(41:52) Edge-to-cloud inference and agentic workflows

(49:20) Why AI pilots fail and how enterprises succeed

In this episode of Eye on AI, we sit down with Evan Reiser, co-founder and CEO of Abnormal AI, to unpack how AI has fundamentally changed the cybersecurity landscape.

We explore why social engineering remains the most costly form of cybercrime, how generative AI has lowered the barrier for sophisticated attacks, and why humans have become the primary attack surface in modern security. Evan explains why traditional, signature-based defenses fall short, how behavioral AI detects threats that have never existed before, and what it means to build security systems that understand how people actually work and communicate.

The conversation also looks ahead at the AI arms race between attackers and defenders, the economics driving cybercrime, and what it truly means to be an AI-native company operating at scale.

This episode is a deep dive into the human side of AI security and why the future of cybersecurity depends less on code and more on behavior.

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(00:00) Abnormal AI's origin

(02:31) Why phishing is still the biggest threat

(05:57) How attackers manipulate human trust

(10:05) The true cost of social engineering

(11:58) Vendor account compromise explained

(15:02) How AI changed cyber attacks

(16:28) Behavioral security vs traditional defenses

(19:55) Where Abnormal fits in the security stack

(22:24) Human psychology as the attack surface

(24:01) Why cyber defense is asymmetric

(28:48) Humans as the new zero-day

(31:01) Why attackers target people, not systems

(33:21) Behavioral modeling from ads to security

(36:10) Why money drives almost all attacks

(40:06) What happens after credentials are stolen

(42:18) Text scams and lateral movement

(43:55) What it means to be AI-native

(47:13) How Abnormal uses AI internally

In this episode of Eye on AI, Craig Smith speaks with Jonathan Wall, founder and CEO of Runloop AI, about why AI agents require an entirely new approach to compute infrastructure.

Jonathan explains why agents behave very differently from traditional servers, why giving agents their own isolated computers unlocks new capabilities, and how agent-native infrastructure is emerging as a critical layer of the AI stack. The conversation also covers scaling agents in production, building trust through benchmarking and human-in-the-loop workflows, and what agent-driven systems mean for the future of enterprise work.

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(00:00) Why AI Agents Require a New Infrastructure Paradigm

(01:38) Jonathan Wall's Journey: From Google Infrastructure to AI Agents

(04:54) Why Agents Break Traditional Cloud and Server Models

(07:36) Giving AI Agents Their Own Computers (Devboxes Explained)

(12:39) How Agent Infrastructure Fits into the AI Stack

(14:16) What It Takes to Run Thousands of AI Agents at Scale

(17:45) Solving the Trust and Accuracy Problem with Benchmarks

(22:28) Human-in-the-Loop vs Autonomous Agents in the Enterprise

(27:24) A Practical Walkthrough: How an AI Agent Runs on Runloop

(30:28) How Agents Change the Shape of Compute

(34:02) Fine-Tuning, Reinforcement Learning, and Faster Iteration

(38:08) Who This Infrastructure Is Built For: Startups to Enterprises

(41:17) AI Agents as Coworkers and the Future of Work

(46:37) The Road Ahead for Enterprise-Grade Agent Systems

In this episode of Eye on AI, Craig Smith sits down with Anurag Dhingra, Senior Vice President and General Manager at Cisco, to explore where AI is actually creating value inside the enterprise.

Rather than focusing on flashy demos or speculative futures, this conversation goes deep into the invisible layer powering modern AI: infrastructure.
Anurag breaks down how AI is being embedded into enterprise networking, security, observability, and collaboration systems to solve real operational problems at scale. 

From self-healing networks and agentic AI to edge computing, robotics, and domain-specific models, this episode reveals why the next phase of AI innovation is less about chatbots and more about resilient systems that quietly make everything work better.

This episodeis perfect for enterprise leaders, AI practitioners, infrastructure teams, and anyone trying to understand how AI moves from theory into production.

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(00:00) Why AI Only Matters If the Infrastructure Works
(01:22) Cisco's Evolution
(04:39) Connecting Networks, People, and Experiences at Scale
(09:31) How AI Is Transforming Enterprise Networking
(12:00) Edge AI, Robotics, and Real-World Reliability
(14:18) Security Challenges in an Agent-Driven Enterprise
(15:28) What Agentic AI Really Means (Beyond Automation)
(20:51) The Rise of Hybrid AI: Cloud Models vs Edge Models
(24:30) Why Small, Purpose-Built Models Are So Powerful
(29:19) Open Ecosystems and Agent-to-Agent Collaboration
(33:32) How Enterprises Actually Adopt AI in Practice
(35:58) Building AI-Ready Infrastructure for the Long Term
(40:14) AI in Customer Experience and Contact Centers
(44:14) The Real Opportunity of AI and What Comes Next

This episode is sponsored by AGNTCY. Unlock agents at scale with an open Internet of Agents. 

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Most large language models today generate text one token at a time. That design choice creates a hard limit on speed, cost, and scalability.

In this episode of Eye on AI, Stefano Ermon breaks down diffusion language models and why a parallel, inference-first approach could define the next generation of LLMs. We explore how diffusion models differ from autoregressive systems, why inference efficiency matters more than training scale, and what this shift means for real-time AI applications like code generation, agents, and voice systems.

This conversation goes deep into AI architecture, model controllability, latency, cost trade-offs, and the future of generative intelligence as AI moves from demos to production-scale systems.


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(00:00) Autoregressive vs Diffusion LLMs
(02:12) Why Build Diffusion LLMs
(05:51) Context Window Limits
(08:39) How Diffusion Works
(11:58) Global vs Token Prediction
(17:19) Model Control and Safety
(19:48) Training and RLHF
(22:35) Evaluating Diffusion Models
(24:18) Diffusion LLM Competition
(30:09) Why Start With Code
(32:04) Enterprise Fine-Tuning
(33:16) Speed vs Accuracy Tradeoffs
(35:34) Diffusion vs Autoregressive Future
(38:18) Coding Workflows in Practice
(43:07) Voice and Real-Time Agents
(44:59) Reasoning Diffusion Models
(46:39) Multimodal AI Direction
(50:10) Handling Hallucinations

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Why are AI, biotechnology, and gene editing converging right now, and what does that mean for the future of humanity?

In this episode of Eye on AI, host Craig Smith sits down with futurist and author Jamie Metzl to explore the superconvergence of artificial intelligence, genomics, and exponential technologies that are reshaping life on Earth.

We examine the ethical and scientific realities behind human genome editing, the controversy around CRISPR babies, and why society is not yet ready to edit human embryos at scale. The conversation unpacks the complexity of biology, the risks of tech driven hubris, and why governance, values, and social norms must evolve alongside scientific breakthroughs.

You will also hear a wide ranging discussion on health span versus longevity, AI and human decision making, education and inequality, and how these technologies could either unlock massive human flourishing or deepen existing global challenges depending on the choices we make today.

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Try OCI for free at http://oracle.com/eyeonai 

This episode is sponsored by Oracle. OCI is the next-generation cloud designed for every workload ? where you can run any application, including any AI projects, faster and more securely for less. On average, OCI costs 50% less for compute, 70% less for storage, and 80% less for networking. 

Join Modal, Skydance Animation, and today's innovative AI tech companies who upgraded to OCI?and saved.

Why is AI moving from the cloud to our devices, and what makes on device intelligence finally practical at scale?

In this episode of Eye on AI, host Craig Smith speaks with Christopher Bergey, Executive Vice President of Arm's Edge AI Business Unit, about how edge AI is reshaping computing across smartphones, PCs, wearables, cars, and everyday devices.

We explore how Arm v9 enables AI inference at the edge, why heterogeneous computing across CPUs, GPUs, and NPUs matters, and how developers can balance performance, power, memory, and latency. Learn why memory bandwidth has become the biggest bottleneck for AI, how Arm approaches scalable matrix extensions, and what trade offs exist between accelerators and traditional CPU based AI workloads.

You will also hear real world examples of edge AI in action, from smart cameras and hearing aids to XR devices, robotics, and in car systems. The conversation looks ahead to a future where intelligence is embedded into everything you use, where AI becomes the default interface, and why reliable, low latency, on device AI is essential for creating experiences users actually trust.

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Why is AI so powerful in the cloud but still so limited inside everyday devices, and what would it take to run intelligent systems locally without draining battery or sacrificing privacy?

In this episode of Eye on AI, host Craig Smith speaks with Steve Brightfield, Chief Marketing Officer at BrainChip, about neuromorphic computing and why brain inspired architectures may be the key to the future of edge AI.

We explore how neuromorphic systems differ from traditional GPU based AI, why event driven and spiking neural networks are dramatically more power efficient, and how on device inference enables faster response times, lower costs, and stronger data privacy. Steve explains why brute force computation works in data centers but breaks down at the edge, and how edge AI is reshaping wearables, sensors, robotics, hearing aids, and autonomous systems.

You will also hear real world examples of neuromorphic AI in action, from smart glasses and medical monitoring to radar, defense, and space applications. The conversation covers how developers can transition from conventional models to neuromorphic architectures, what role heterogeneous computing plays alongside CPUs and GPUs, and why the next wave of AI adoption will happen quietly inside the devices we use every day.

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Why do some AI agents attempt to bypass shutdown, and what does this behavior reveal about the future of AI safety?

In this episode of Eye on AI, host Craig Smith speaks with Jeffrey Ladish of Palisade Research to examine what recent shutdown experiments with agentic LLMs tell us about control, alignment, and the real world limits of current guardrails.

We explore how models behave when placed in virtual machine environments, why some agents edit or disable their own shutdown scripts, and what these results mean for researchers working on alignment and oversight. Learn how different models respond to shutdown instructions, how system prompts influence behavior, and which failure modes matter most for safe deployment.

You will also hear a detailed breakdown of the experimental setups, insights into tool using and self directed behavior, and a grounded discussion of the risks and opportunities that agentic systems introduce. This episode offers a clear and practical look at how AI agents operate under pressure and what these findings mean for the future of safe and reliable AI.

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Why are enterprises struggling to turn AI hype into real workplace transformation, and how is Lenovo using agentic AI to actually close that gap? In this episode of Eye on AI, host Craig Smith talks with Rakshit Ghura about how his team is reinventing the modern workplace with an omnichannel AI architecture powered by a fleet of specialized agents.


We explore how Lenovo has evolved from a hardware company into a global solutions provider, and how its Care of One platform uses persona based design to improve employee experience, reduce downtime, and personalize support across IT, HR, and operations. You will learn what enterprises get wrong about AI readiness, why trust and change management matter more than technology, and how organizations can design workplace stacks that meet employees where they are.


We also cover how Lenovo approaches responsible AI, how enterprises should think about security and governance when deploying agents, and why so many organizations are enthusiastic about AI but still not ready to adopt it. Rakshit shares real examples from retail, manufacturing, and field operations, including how AI can improve uptime, automate ticket resolution, monitor equipment, and provide proactive insights that drive measurable business impact.


You will also learn how to evaluate ROI for digital workplace solutions, how to involve employees early in the adoption cycle, and which metrics matter most when scaling agentic AI, including uptime, productivity improvements, and employee satisfaction.

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Try OCI for free at http://oracle.com/eyeonai

This episode is sponsored by Oracle. OCI is the next-generation cloud designed for every workload ? where you can run any application, including any AI projects, faster and more securely for less. On average, OCI costs 50% less for compute, 70% less for storage, and 80% less for networking. 

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Why is AI inference becoming the new battleground for speed, cost, and real world scalability, and how are companies like Clarifai reshaping the AI stack by optimizing every token and every deployment?

In this episode of Eye on AI, host Craig Smith sits down with Clarifai founder and CEO Matt Zeiler to explore why inference is now more important than training and how a unified compute orchestration layer is changing the way teams run LLMs and agentic systems.

We look at what makes high performance inference possible across cloud, on prem, and edge environments, how to get faster responses from large language models, and how to cut GPU spend without sacrificing intelligence or accuracy. Learn how organizations operate AI systems in regulated industries, how government teams and enterprises use Clarifai to deploy models securely, and which bottlenecks matter most when running long context, multimodal, or high throughput applications.

You will also hear how to optimize your own AI workloads with better token throughput, how to choose the right hardware strategy for scale, and how inference first architecture can turn models into real products. This conversation breaks down the tools, techniques, and design patterns that can help your AI agents run faster, cheaper, and more reliably in production.


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How will AI evolve once it can understand and reason about the 3D world, not just text on a screen?

In this episode of Eye on AI, host Craig Smith speaks with Fei Fei Li about the rise of spatial intelligence and the world models that could transform how machines perceive, imagine, and interact with reality.

We explore how spatial intelligence goes beyond language to connect perception, action, and reasoning in physical environments. You will hear how models like Marble build consistent and persistent 3D spaces, why multimodal inputs matter, and what it takes to create digital worlds that are useful for robotics, simulation, design, and creative workflows. Fei Fei also explains the challenges of long term memory, continuous learning, and the search for training objectives that mirror the role next token prediction plays in language models.

Learn how spatial reasoning unlocks new possibilities in robotics and telepresence, why classical physics engines still matter, and how future AI systems may merge perception, planning, and imagination. You will also hear Fei Fei's perspective on the limits of current architectures, why true understanding is different from human understanding, and how world models could shape the next generation of intelligent systems.

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How could Karl Friston's Free Energy Principle become a blueprint for the future of AI?

In this episode of Eye on AI, host Craig Smith sits down with Karl Friston, the neuroscientist behind the Free Energy Principle and advisor to Verses AI, to explore how active inference and brain inspired generative models might move us beyond transformer based systems. They unpack how Axiom, Verses' new architecture, uses probabilistic beliefs and message passing to build agents that learn like brains instead of just predicting the next token.

We look at why transformers face scaling and reliability limits, how Free Energy unifies prediction, perception, and action, and what it means for an AI system to carry explicit uncertainty instead of overconfident guesses. Learn how active inference supports continual learning without catastrophic forgetting, how structure learning lets models grow and prune themselves, and why embodiment and interaction with the real world are essential for grounding language and meaning.

You will also hear how Axiom can sit beside or beneath large language models, how explicit uncertainty can reduce hallucinations in high stakes workflows, and where these ideas are already being tested in areas like logistics, robotics, and autonomous agents. By the end of the episode, you will have a clearer picture of how Karl Friston's Free Energy blueprint could reshape AI architectures, from enterprise planning systems to embodied agents that understand and act in the world.

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How are AI and telematics changing safety for fleets in the real world, and what does it take to get from basic recordings to true accident prevention?

In this episode of Eye on AI, host Craig Smith speaks with Hemant Banavar, Chief Product Officer at Motive, and Ryan Ennis, CIO at FusionSite Services, to explore how AI powered cameras and telematics are transforming safety, productivity, and profitability across the physical economy, from trucking and construction to field services.

We look at what makes safety AI trustworthy at scale, how to reduce false alerts that drivers ignore, and how to combine in cab coaching, human review, and rich telematics data to drive down risky behaviors. Learn how FusionSite Services cut unsafe events by more than ninety percent while tripling in size, slashed insurance claims and premiums, and used real time insights to tackle idling, under utilized assets, and the hidden costs of unsafe operations.

You will also hear how leading fleets run side by side vendor tests, design incentive programs that get drivers on board with cameras, and build a culture around zero preventable accidents. If you are responsible for safety, operations, or risk, this episode will show you how to evaluate AI and telematics platforms, which benchmarks to demand, and how to turn your data into safer roads and stronger unit economics.

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How are Decision Intelligence and AI agents reshaping enterprise operations today? In this episode of Eye on AI, host Craig Smith sits down with Fred Laluyaux, CEO of Aera Technology, to unpack how organizations move from dashboards and ad hoc workflows to a system that senses, decides, and acts. 

AI is not just about chatbots. At the heart of this transformation is decision intelligence: connecting data, analytics, AI, and automation to optimize decisions across the enterprise. Fred explains why this is becoming the operating backbone of the modern enterprise and how it accelerates the shift toward autonomous, self-driving businesses.

We look at how to build a decision intelligence stack end to end, how AI agents collaborate with people, and how to stand up a control room that monitors decisions across supply chain, finance, and customer operations. Learn how leading companies model decisions, govern them safely, and measure impact with clear metrics that matter, including service level, cost to serve, cash flow, inventory turns, and time to resolution.

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This episode is sponsored by AGNTCY. Unlock agents at scale with an open Internet of Agents. 

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Why do today's LLMs forget key details over long context, and what would it take to give them real memory that scales?

In this episode of Eye on AI, host Craig Smith explores Manifest AI's Power Retention architecture and how it rethinks memory, context, and learning for modern models. We look at why transformers struggle with long inputs, how state space and retention models keep context at linear cost, and how scaling state size unlocks reliable recall across lengthy conversations, code, and documents. We also cover practical paths to retrofit existing transformer models, how in context learning can replace frequent fine tuning, and what this means for teams building agents and RAG systems.

Learn how product leaders and researchers measure true long context quality, which pitfalls to avoid when extending context windows, and which metrics matter most for success, including recall consistency, answer fidelity, task completion, CSAT, and cost per resolution. You will also hear how to design per user memory, set governance that prevents regressions, evaluate LLM as judge with human review, and plan a secure rollout that improves retrieval, multi step workflows, and agent reliability across chat, email, and voice.

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How do the world's most powerful AI models get trained and trusted at scale, and what does that really take from data to deployment?

In this episode, Appen CEO Ryan Kolln joins Eye on AI to unpack how rigorous human evaluation, culturally aware data, and model-based judges come together to raise real-world performance.

In this episode of Eye on AI, host Craig Smith speaks with Ryan Kolln, CEO of Appen, about building evaluation systems that go beyond static benchmarks to measure usefulness, safety, and reliability in production. They explore how human raters and AI evaluators work in tandem, why localization matters across regions and domains, and how quality controls keep feedback signals trustworthy for training and post-training.

Ryan explains how evaluation feeds reinforcement strategies, where rubric-driven human judgments inform reward models, and how enterprises can stand up secure workflows for sensitive use cases. He also discusses emerging needs around sovereign models, domain-specific testing, and the shift from general chat to agentic workflows that operate inside real business systems.

Learn how leading teams design human-in-the-loop evaluation, when to route judgments from models back to expert reviewers, how to capture cultural nuance without losing universal guardrails, and how to build an evaluation stack that scales from early prototypes to production AI.

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Why will agentic AI redefine every digital interaction, and what foundation do enterprises need to make it safe, trusted, and real time?

In this episode of Eye on AI, host Craig Smith sits down with Jeff Lunsford to unpack how a neutral customer data platform like Tealium becomes the control plane for agentic systems. We cover how to collect and unify first party data responsibly, enforce consent and identity across channels, and feed the right context to models so agents can act with confidence in the moment. You will hear how real time profiles, event streams, and deterministic identity power personalization, automation, and transactions across web, mobile, ads, email, and customer support.

Learn how leading enterprises are preparing for agentic commerce that could double digital interactions, why governance and privacy must be embedded into delivery teams, and which standards enable safe transactions and payments with agents. You will also hear how to build an "agentic front door" for your business, design guardrails and spending allowances, choose where to run reasoning and inference, and measure impact with metrics like conversion rate, ROAS, CSAT, and cost per resolution.

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How is Coxwave Redefining AI Evaluation?

In this episode of Eye on AI, host Craig Smith is joined by Yeop Lee, Head of Product at Coxwave. Together they explore how teams move beyond accuracy-only metrics to outcome focused evaluation with Coxwave's Align. We look at how Align measures satisfaction, trust, and task completion across chat, email, and voice, how LLM as judge pairs with human review, and how product teams search conversations to find hidden failure patterns that block adoption.

Learn how leading companies design an evaluation stack that guides prompts, agents, and UX, which pitfalls to avoid when shipping updates, and which metrics matter most for success, including completion rate, CSAT, retention, and cost per resolution. You will also hear how to run experiment tracking with model and prompt change logs, set up governance that prevents regressions, and choose between SaaS and on premise deployments that meet security and compliance needs.

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Why do so many chatbots fail in the real world, and how can AI agents actually fix customer support?

In this episode of Eye on AI, host Craig Smith explores how teams move beyond scripted bots to production-grade AI agents that resolve real issues across chat, email, and voice. We look at what makes agents reliable at scale, how to configure them safely, and how to manage them like digital workers alongside your human team.

Learn how leading companies approach agent onboarding and governance, which pitfalls to avoid, and which metrics matter most for success, including resolution rate, CSAT, and cost per resolution. You will also hear how to enable actions like refunds and returns through secure procedures, design human handoff that customers appreciate, and build an omnichannel rollout plan that scales responsibly.

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Try OCI for free at http://oracle.com/eyeonai

This episode is sponsored by Oracle. OCI is the next-generation cloud designed for every workload ? where you can run any application, including any AI projects, faster and more securely for less. On average, OCI costs 50% less for compute, 70% less for storage, and 80% less for networking. 

Join Modal, Skydance Animation, and today's innovative AI tech companies who upgraded to OCI?and saved.

How are enterprises moving from AI experiments to a true agentic enterprise with measurable ROI?

In this episode of Eye on AI, host Craig Smith speaks with Bhaskar Roy from Workato about how organizations can design, orchestrate, and govern AI agents at scale without sacrificing security or control. Together they unpack Workato's approach to building a single workspace for employees while agents and apps work behind the scenes to automate real business processes.

They explain why the future of enterprise AI depends on orchestration, permissions, and human in the loop design. You will hear how Workato One and Workato Go bring connectivity, action, and governance into one stack, how teams assign KPIs to agents and track outcomes, and how to reduce agent sprawl while optimizing SaaS spend.

Learn how leading companies are defining the agentic enterprise, what pitfalls to avoid when moving from pilots to production, and how to measure impact across sales, IT, support, HR, and finance so AI drives durable business value.

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How can enterprises truly scale with agentic AI? 

In this episode of Eye on AI, host Craig Smith speaks with Greg Shewmaker, CEO of r.Potential, about how organizations can successfully implement agentic AI systems that enhance human performance instead of replacing it.

Greg explains why the future of work depends on a new partnership between people and intelligent digital agents. He shares how r.Potential, a spin-out from the Adecco Group, helps enterprises design "digital workforces," integrate AI agents into complex systems, and rethink productivity from the C-suite down.

Learn how leading companies are approaching AI adoption, what pitfalls to avoid, and why agentic AI could redefine how enterprises operate and grow in the years ahead.

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Discover how Neurix is building the next generation of voice AI that sounds and reacts like a human in this conversation with Peeyush, former Google technologist and co-founder of Miracle Labs.

Peeyush shares how Neurix is solving the toughest challenges in conversational AI ? from eliminating latency to creating natural, real-time dialogue that mirrors human interaction. He explains why voice is the hardest frontier in AI, how Neurix's proprietary models manage conversation flow, and what it takes to integrate voice agents into enterprise systems at scale. Learn how Neurix is combining low-latency speech recognition, dialogue management, and large language models to deliver seamless, multilingual customer experiences.

If you're a business leader, product builder, or AI professional interested in how human-like voice agents are transforming customer support and enterprise communication, this episode reveals the future of intelligent conversation.

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Can AI and RNA testing make illness optional?

In this episode, Eye on AI host Craig S. Smith sits down with Naveen Jain, founder and CEO of Viome, to explore how RNA sequencing and artificial intelligence are transforming our understanding of chronic disease. Jain shares how a personal tragedy led him to launch Viome, a company on a mission to digitize the human body, predict illness before symptoms appear, and revolutionize healthcare.

Together they discuss how Viome uses metatranscriptomics to analyze microbiome and human gene expression, what makes RNA a better indicator of health than DNA, and how large molecular AI models are paving the way for early detection of cancer, diabetes, Alzheimer's, and more. Jain also reveals his bold entrepreneurial framework?"Why this, why now, why me?"?and his belief that asking better questions is the real key to innovation.

If you're interested in the intersection of AI, biotechnology, and human longevity, this is an episode you won't want to miss.


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Joel Hron, Chief Technology Officer at Thomson Reuters, joins Eye on AI to unpack the future of agentic systems and what it takes to build them responsibly at enterprise scale.

We dive into the shift from prompt-based AI to true agentic workflows capable of planning, reasoning, and executing complex tasks. Joel breaks down how Thomson Reuters is deploying generative AI across law, tax, risk, and compliance, while keeping human experts in the loop to ensure trust and accuracy in high-stakes domains.

Topics include:
- What separates agentic AI from simple prompt-based tools
- How "agency dials" (autonomy, tools, memory) change system behavior
- Infrastructure and architecture required for multi-agent collaboration
- Why human verification and user experience design are essential for trust
- The future of coding, engineering skills, and AI adoption inside enterprises

If you want to understand how a 170-year-old company is reinventing itself with AI ? and what's next for agentic systems in business and knowledge work ? this conversation is a must-listen.

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How do we get from today's AI copilots to true human-level intelligence? In this episode of Eye on AI, Craig Smith sits down with Eiso Kant, Co-Founder of Poolside, to explore why reinforcement learning + software development might be the fastest path to human-level AI.

Eiso shares Poolside's mission to build AI that doesn't just autocomplete code ? but learns like a real developer. You'll hear how Poolside uses reinforcement learning from code execution (RLCF), why software development is the perfect training ground for intelligence, and how agentic AI systems are about to transform the way we build and ship software.

If you want to understand the future of AI, software engineering, and AGI, this conversation is packed with insights you won't want to miss.

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(00:00) The Missing Ingredient for Human-Level AI
(01:02) Eiso Kant's Journey
(05:30) Using Software Development to Reach AGI
(07:48) Why Coding Is the Perfect Training Ground for Intelligence
(10:11)  Reinforcement Learning from Code Execution (RLCF) Explained
(13:14) How Poolside Builds and Trains Its Foundation Models
(17:35) The Rise of Agentic AI
(21:08)  Making Software Creation Accessible to Everyone
(26:03) Overcoming Model Limitations
(32:08) Training Models to Think
(37:24) Building the Future of AI Agents
(42:11) Poolside's Full-Stack Approach to AI Deployment
(46:28) Enterprise Partnerships, Security & Customization Behind the Firewall
(50:48) Giving Enterprises Transparency to Drive Adoption

Discover how enterprises can successfully adopt and scale agentic AI to create real business impact in this conversation with Florian Douetteau, CEO and co-founder of Dataiku.

Florian shares why democratizing AI across the enterprise is essential, how to prevent agent sprawl, and what it takes to build a governance framework that keeps your data secure while enabling innovation. Learn about Dataiku's enterprise AI blueprint, its partnership with NVIDIA, and how global companies are using agentic workflows to accelerate R&D, optimize operations, and stay competitive.

If you're a business leader, CTO, or data professional looking to scale AI safely and effectively, this episode is your playbook for the future of enterprise AI.

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00:00 Intro
00:31 Florian's Background & Dataiku's Founding  
03:00 Enterprise Blueprint for AI with NVIDIA  
05:13 Unique Needs of Financial Services  
07:09 Building Agents on Dataiku  
09:22 Permissioning & Governance  
11:17 Agent Lifecycle Management  
13:20 State of Agent-to-Agent Systems  
15:02 Real-World Use Cases of Agents  
16:28 The Most Complex Agents in Production  
19:01 Future Vision: Headless Organizations  
21:04 Human-Like Qualities of Agents  
24:56 The LLM Mesh & Model Abstraction  
28:55 Guardrails & Compliance  
31:12 No-Code + Code-Friendly Collaboration  
36:12 Breaking Silos & Centers of Excellence  
41:36 Distribution & Seat Allocation  
43:34 Most Common Agents by Industry  
47:02 The State of Enterprise AI Adoption  

Try OCI for free at http://oracle.com/eyeonai

This episode is sponsored by Oracle. OCI is the next-generation cloud designed for every workload ? where you can run any application, including any AI projects, faster and more securely for less. On average, OCI costs 50% less for compute, 70% less for storage, and 80% less for networking.

Join Modal, Skydance Animation, and today's innovative AI tech companies who upgraded to OCI?and saved.


AI-generated code is exploding, but reviewing it all has become the new bottleneck for engineering teams. In this episode, Sahil Bansil from CodeRabbit reveals how their AI-powered platform is transforming the code review process, helping developers ship faster without compromising quality. He explains how CodeRabbit uses advanced LLM context engineering to deliver senior-level review quality, reduce pull request merge times by up to 50%, and catch more bugs before they reach production.

Whether you're a developer, engineering manager, or CTO, this conversation shows why automated code review is essential in the AI era and how CodeRabbit can help your team scale software delivery while keeping quality high.

Cut Code Review Time & Bugs in Half. Instantly with CodeRabbit: https://www.coderabbit.ai/

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(00:00) LLMs & Why Context Matters 
(02:26) Meet Sahil Bansil from CodeRabbit 
(04:04) AI Code Boom & The Review Bottleneck 
(06:05) Why CodeRabbit Focused on Reviews, Not Generation 
(09:55) Keeping Humans in the Loop for Code Quality 
(14:30) IDE Reviews vs PR Governance 
(17:51) Inside CodeRabbit's Context Engineering 
(20:42) Building Context from Code Graphs & Jira Tickets 
(22:15) Eliminating AI Hallucinations with Verification 
(27:19) Empowering Junior Developers & Legacy Code Support 
(32:40) CodeRabbit's Open Source & Enterprise Success Stories 
(36:56) Cutting Review Times & PR Merge Delays 
(44:35) Scaling CodeRabbit & The Growing Market