Eye On A.I.

Eye on A.I. is a biweekly podcast, hosted by longtime New York Times correspondent Craig S. Smith. In each episode, Craig will talk to people making a difference in artificial intelligence. The podcast aims to put incremental advances into a broader context and consider the global implications of the developing technology. AI is about to change your world, so pay attention.

#322 Amanda Luther: The Widening AI Value Gap (Inside BCG's AI Research) 02/19/2026

#322 Amanda Luther: The Widening AI Value Gap (Inside BCG's AI Research) 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: Eye on A.I. on X: (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 /episode/index/show/aneyeonai/id/40149570

#321 Nick Frosst: Why Cohere Is Betting on Enterprise AI, Not AGI 02/17/2026

#321 Nick Frosst: Why Cohere Is Betting on Enterprise AI, Not AGI 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 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: Eye on A.I. on X: (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” /episode/index/show/aneyeonai/id/40133525

#320 Carter Huffman: Exploring The Architecture Behind Modulate's Next-Gen Voice AI 02/11/2026

#320 Carter Huffman: Exploring The Architecture Behind Modulate's Next-Gen Voice AI 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 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: Eye on A.I. on X: (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 /episode/index/show/aneyeonai/id/40069330

#319 Subho Halder: Why Traditional App Security Fails in the Age of AI 02/01/2026

#319 Subho Halder: Why Traditional App Security Fails in the Age of AI 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 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: Eye on A.I. on X: (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 /episode/index/show/aneyeonai/id/39949285

#318 Olek Paraska: How AI Is Fixing the Biggest Bottleneck in Construction 01/29/2026

#317 Steven Brown: Why Modern Medicine Needs AI-Assisted Decision Making 01/25/2026

#317 Steven Brown: Why Modern Medicine Needs AI-Assisted Decision Making 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: Eye on A.I. on X: (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 /episode/index/show/aneyeonai/id/39866025

#316 Robbie Goldfarb: Why the Future of AI Depends on Better Judgment 01/23/2026

#316 Robbie Goldfarb: Why the Future of AI Depends on Better Judgment 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: X: LinkedIn: Stay Updated: Craig Smith on X: Eye on A.I. on X: (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 /episode/index/show/aneyeonai/id/39861900