Eye On A.I.

Craig S. Smith

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.

  1. 2h ago ·  Video

    Video Is About to Stop Being One-Way (and That Changes Everything) | Victor Riparbelli, Synthesia

    Every company is creating content that nobody reads, nobody watches, and nobody remembers, and the CEO of the AI platform that 90% of Fortune 100 companies use to fix that just explained what comes next. In this episode, Craig Smith sits down with Victor Riparbelli, co-founder and CEO of Synthesia, to discuss the $4 billion company that is now redefining what video communication means for the enterprise. The conversation opens with the founding insight that still drives the company: AI is going to drive the marginal cost of creating video to zero, which changes not just how content is produced but who can produce it and for whom. Victor describes how Synthesia found its first real market not in Hollywood - which rejected the technology as too low quality - but in corporate trainers and educators who were comparing it not to a film but to a 10-page PDF no one was reading. The most forward-looking section of the conversation covers Synthesia's next product: moving video from a one-way broadcast into a two-way interactive conversation, where an AI avatar can conduct a real-time sales demo, simulate a customer for sales training, draw graphs on screen to explain pricing, and score whether the person on the other side actually understood the content. Victor also makes a sharp prediction about where AI entertainment will actually emerge, not in cinemas or on Netflix, but from film students with laptops posting 17-minute short films on Instagram, the same way synthesizers didn't replace pianos but created entirely new genres of music. Key Topics Covered: ●     How Synthesia found its first real market: corporate trainers creating content nobody was reading, who compared AI video not to Hollywood but to a PDF, and found it vastly superior ●     The transition from one-way video broadcast to two-way interactive avatar conversations, and what that means for sales demos, corporate training, and education ●     Why Hollywood will be the last industry to adopt AI video, and why the first AI-generated entertainment will come from broke film students on Instagram, not studios ●     Why AI content won't replace real video, it will become its own genre, the same way synthesizers didn't replace guitars but created electronic music ●     How the CEO uses Claude daily for strategic thinking, playing devil's advocate, and replacing the long memo with a voice note As AI video tools proliferate, this conversation offers one of the clearest frameworks for understanding where the technology is actually headed, not toward Hollywood, but toward transforming the way every company communicates internally and externally, with interactive AI avatars replacing the static website as the primary interface between a business and its customers. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI. Craig Smith on X: https://x.com/craigss EYE On A.I. on X: https://x.com/EyeOn_AI   Connect with Victor Riparbelli LinkedIn: https://uk.linkedin.com/in/victorriparbelli

  2. Jul 15

    6 in 10 Enterprises Can't Find the Root Cause When Their AI Workloads Fail | Paul Appleby, Virtana

    Companies are spending billions building AI factories, but most of them can't tell you why their AI workloads are failing, whether their GPUs are actually being used, or what their infrastructure is going to cost them when agents start running at scale. Paul Appleby, CEO of Virtana, joins Craig Smith to discuss the findings of their AI Factory Reality Check study, a research report that reveals a striking and underappreciated gap between the pace of AI infrastructure investment and the governance needed to run it safely and efficiently. Six in ten enterprises, the study found, cannot automatically identify root cause when an AI workload fails, a problem that compounds fast once you're running critical services on AI infrastructure at scale. The conversation covers the mechanics of Virtana's observability platform, capturing 20,000 metrics per second across the entire AI stack, correlating them in real time, and increasingly using agentic capabilities to remediate failures automatically, but its most important insights are structural. Appleby makes a sharp observation that cuts through a lot of AI optimism: token costs are falling, but token consumption is exploding, meaning the total cost of running agentic AI systems is still going up even as the per-unit price drops. He also tracks a cultural shift inside enterprises - IT resilience reporting that used to happen annually now happens weekly - as evidence that technology risk has become a board-level conversation in a way it simply wasn't before. The result is a conversation that's less about the promise of AI and more about what it actually takes to make it work at production scale. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.

    6 in 10 Enterprises Can't Find the Root Cause When Their AI Workloads Fail | Paul Appleby, Virtana

  3. Jul 13

    Inside the Enterprise Browser Rebuilding Security for the AI Era | Bradon Rogers, Island

    AI is moving faster than enterprise security systems were designed to handle. In this episode of Eye on A.I., Craig Smith speaks with Bradon Rogers, Chief Customer Officer at Island, Island about how companies are struggling to govern the rise of AI agents, browser-based workflows, and unsanctioned AI tools inside the workplace. The conversation explores why traditional "block-and-control" security models are breaking down and how a new approach, embedding policy directly into the browser and user workflows, may offer a path forward. It also dives into emerging risks like prompt injection and autonomous agent behavior, and why enterprises are increasingly becoming multi-AI environments by default. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.

    Inside the Enterprise Browser Rebuilding Security for the AI Era | Bradon Rogers, Island

  4. Jul 10

    What Industrial AI Actually Looks Like | Kriti Sharma, Nexus Black

    Most AI is built for people sitting at desks. Kriti Sharma builds it for the people who work in refineries, aircraft hangars, and utility networks responding to wildfires at 4 a.m. and she spends weekends on-site with them to make sure what she builds actually holds up. In this episode, Kriti joins Craig Smith to discuss what industrial AI really looks like when failure genuinely isn't an option, and why the gap between an impressive AI pilot and a production-grade AI system is so much wider in the physical world than most technology companies appreciate. The conversation is grounded in three specific products from Nexus Black, the elite AI unit Kriti leads inside IFS. The first is Resolve, a predictive maintenance platform built in close collaboration with William Grant's - the distillery behind Glenfiddich and Hendricks Gin - that is projected to save £8.4 million per year at a single factory by reading complex engineering schematics, identifying failure patterns before they occur, and giving frontline technicians step-by-step guidance on their phones without requiring them to remove a safety glove to type. The second is an airworthiness compliance tool for commercial airlines that automates a process currently consuming weeks of human engineering time, where a single mistake carries regulatory fines of up to $20 million and grounding a fleet costs $140 million per day. The third is a disaster response coordination system for utilities, built in partnership with Anthropic, designed to help field crews coordinate during wildfires, hurricanes, and grid outages in ways that, as a California disaster responder told Kriti directly after the most recent wildfire season, will get communities back online and hospitals lit up faster than ever before. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.

    What Industrial AI Actually Looks Like | Kriti Sharma, Nexus Black

  5. Jul 7

    The Biggest AI Security Problem Isn't the Model. It's This. | Devvret Rishi

    What is an AI agent, really? Strip away the hype, and it's a model with access - to tools, APIs, databases, email, anything that lets it take real action instead of just generating text. That access is exactly where the risk lives, and Devvret Rishi, GM of AI at Rubrik, and former co-founder & CEO of Predibase, joins Craig Smith with a string of real-world incidents that make the case concrete: AWS reporting four major outages in 90 days after deploying coding agents, a Meta-related agent that deleted someone's emails while they were actively asking it to stop, and Rubrik's own internal pilot catching incidents that, without governance in place, would have gone unnoticed. The conversation lays out the impossible choice most enterprises are facing right now - block AI agents and forfeit the ROI boards are demanding, or grant access and hope nothing breaks - and walks through how Rubrik's approach uses small, fine-tuned AI models to enforce plain-English security policies on every single agent action in real time. It closes on one of the most underexamined risks ahead: as agents increasingly talk to other agents to get work done, a layer of activity is forming that no human is watching, and the question of who's accountable when something goes wrong in that layer is only getting more urgent. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.

    The Biggest AI Security Problem Isn't the Model. It's This. | Devvret Rishi

  6. Jul 5

    Big Pharma Fails 50% of the Time in Phase Three. AI Can Fix That | Vin Singh, BullFrog AI

    It costs up to $2 billion and fifteen years to develop a drug, and big pharma still fails half the time at the final stage. BullFrog AI founder, Chairman, and CEO Vin Singh joins Craig Smith with a clear diagnosis of why: the industry keeps picking the wrong drug target from the beginning, and no amount of downstream optimization fixes a fundamentally wrong starting point. Built on AI technology originally developed at Johns Hopkins' Applied Physics Lab, BullFrog has assembled a three-stage platform that cleans messy clinical data, runs causal analysis to map disease pathways, and then ranks competing drug targets using a competitive framework that removes the subjectivity most pharmaceutical decision-making still relies on. The most striking results in this conversation come from two case studies: work with the Lieber Institute for Brain Development - analyzing thousands of post-mortem brains - that led to the identification of potential driver genes for depression, bipolar disorder, and schizophrenia in months from data that researchers had spent fifteen years studying, and a pancreatic cancer trial where BullFrog's platform identified a patient subgroup with survival rates three times higher than the study average. Vin also delivers a candid assessment of the broader AI-pharma landscape: more than 90% of AI deals in the space are missing their milestones, most companies are wrapping open-source tools rather than building genuine technology, and the shakeout between players and pretenders is already well underway. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.

    Big Pharma Fails 50% of the Time in Phase Three. AI Can Fix That | Vin Singh, BullFrog AI

  7. Jul 2

    AI Agents Are Failing and It's Almost Never the Model's Fault | Alberto Pan, Denodo

    After two years of AI pilots, enterprises are finally diagnosing what went wrong, and the answer keeps coming back to data. Alberto Pan, CTO of Denodo, joins Craig Smith to walk through the findings of the company's AI Trust Gap Report: a survey of 850 enterprise data leaders that reveals the dominant failure modes of enterprise AI agents are almost never the model's fault. They're caused by stale data, missing context, and inconsistent semantics across the hundreds of data sources agents need to access to do real work. Pan explains why traditional data warehouse and lake house architectures - built for analytics, not real-time decision-making - are creating an invisible ceiling on AI performance, and how Denodo's logical data management approach lets agents query data where it lives without centralizing it first, while enforcing consistent governance across every source in one place. The conversation also identifies two specific traps most organizations fall into as they try to scale AI - over-centralizing data into a single system, or building custom ad hoc data layers for every agent - and why both approaches collapse in a multi-agent world where agents need to cooperate, share context, and work from a common semantic foundation. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.

    AI Agents Are Failing and It's Almost Never the Model's Fault | Alberto Pan, Denodo

  8. Jun 29

    How Modern Science Got Consciousness Wrong From the Start | Philip Goff

    What if consciousness isn't a byproduct of complex brains, but a fundamental feature of reality itself, present, in some rudimentary form, all the way down to electrons and quarks? Philip Goff, a philosopher at Durham University and one of panpsychism's leading contemporary advocates, joins Craig Smith to make that case, arguing that modern science's founding move - separating the mathematical world physics studies from the subjective experience we know only from the inside - solved one problem while quietly creating another we've never resolved. The conversation inevitably turns to AI: could a large language model ever be conscious? Goff's answer is a careful, well-reasoned no, not because he thinks consciousness is magical, but because his framework treats it as something closer to the physical substance of reality than an abstract computation, making him skeptical that anything resembling current AI architecture could cross that threshold. Along the way, he tackles one of the genuine open mysteries in his field: if natural selection only cares about behavior, why did evolution bother making us conscious at all, and what would it even mean to find experimental evidence for an answer. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.v

    How Modern Science Got Consciousness Wrong From the Start | Philip Goff
See All (366)

4.8
out of 5
64 Ratings

About

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.

Information

  • Creator
    Craig S. Smith
  • Years Active
    2018 - 2026
  • Episodes
    366
  • Rating
    Clean
  • Copyright
    © Eye On A.I.
  • Show Website
    Eye On A.I.

You Might Also Like