Eye On A.I.

Quantum computing has been "5 years away" for decades. So what's actually holding it back? In this episode of Eye on AI, Craig Smith sits down with Izhar Medalsy, Co-founder & CEO of Quantum Elements, to break down the real bottleneck in quantum computing today and why the future of the industry may depend more on classical systems and AI than quantum hardware itself. Izhar explains how digital twins of quantum systems are being used to simulate real hardware, generate massive amounts of training data, and solve one of the biggest challenges in the field: noise and error 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. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (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. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (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. Meet Velma, voice AI that detects tone, intent, and stress:http://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. Trade stocks, options, futures, and crypto in one platform with low commissions and zero commission on stocks and crypto. Built for traders who think in probabilities, tastytrade offers advanced analytics, risk tools, and an AI-powered Search feature. Learn more at https://tastytrade.com/ 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. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (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. 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 (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

This episode is sponsored by tastytrade. Trade stocks, options, futures, and crypto in one platform with low commissions and zero commission on stocks and crypto. Built for traders who think in probabilities, tastytrade offers advanced analytics, risk tools, and an AI-powered Search feature. Learn more at https://tastytrade.com/ 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. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (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. Stay Updated: Craig Smith on X: https://x.com/craigssEye on A.I. on X: https://x.com/EyeOn_AI (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

This episode is sponsored by tastytrade. Trade stocks, options, futures, and crypto in one platform with low commissions and zero commission on stocks and crypto. Built for traders who think in probabilities, tastytrade offers advanced analytics, risk tools, and an AI-powered Search feature. Learn more at https://tastytrade.com/ 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. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (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"

This episode is sponsored by tastytrade. Trade stocks, options, futures, and crypto in one platform with low commissions and zero commission on stocks and crypto. Built for traders who think in probabilities, tastytrade offers advanced analytics, risk tools, and an AI-powered Search feature. Learn more at https://tastytrade.com/ 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. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (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

This episode is sponsored by tastytrade. Trade stocks, options, futures, and crypto in one platform with low commissions and zero commission on stocks and crypto. Built for traders who think in probabilities, tastytrade offers advanced analytics, risk tools, and an AI-powered Search feature. Learn more at https://tastytrade.com/ 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. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (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. Stay Updated: Craig Smith on X: https://x.com/craigssEye on A.I. on X: https://x.com/EyeOn_AI (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 Stay Updated: Craig Smith on X: https://x.com/craigssEye on A.I. on X: https://x.com/EyeOn_AI (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. Want to know more about Forum AI? Website: https://www.byforum.com/ X: https://x.com/TheForumAI LinkedIn: https://www.linkedin.com/company/byforum/ Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (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. Stay Updated: Craig Smith on X: https://x.com/craigssEye on A.I. on X: https://x.com/EyeOn_AI (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. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (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. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (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. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (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. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (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. Visit https://agntcy.org/ and add your support. 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. Stay Updated: Craig Smith on X: https://x.com/craigssEye on A.I. on X: https://x.com/EyeOn_AI (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

This episode is sponsored by AGNTCY. Unlock agents at scale with an open Internet of Agents. Visit https://agntcy.org/ and add your support. 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. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI

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. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI

This episode is sponsored by AGNTCY. Unlock agents at scale with an open Internet of Agents. Visit https://agntcy.org/ and add your support. 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. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI

This episode is sponsored by AGNTCY. Unlock agents at scale with an open Internet of Agents. Visit https://agntcy.org/ and add your support. 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. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI