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

    −12 h

    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

    1 tim 1 min
  2. AI Is Reading 15 Million X-Rays a Year With No Human in the Loop | Prashant Warier, Qure.ai

    20 juni

    AI Is Reading 15 Million X-Rays a Year With No Human in the Loop | Prashant Warier, Qure.ai

    Eighty percent of lung cancer cases are diagnosed too late, not because the signals aren't there, but because nobody was looking at the right moment. Prashant Warier, co-founder and CEO of Qure.ai, joins Craig Smith to explain how his company is changing that using a tool most people already encounter: the routine chest X-ray. Cure's Lung Nodule Malignancy Risk Score - validated in the CREATE study - analyzes X-rays people get for unrelated reasons, identifies high-risk nodules, and flags which patients need follow-up CT scans. The result is a detection rate of 54 positive patients out of 100 flagged as high-risk, compared to the 2 out of 100 found by standard CT screening programs. That's not a marginal improvement. That's a different category of outcome. The conversation covers the full landscape of where AI diagnostics actually stands today: the 15 million TB screening X-rays that Cure reads autonomously every year across 70 countries with no radiologist in the loop, because in many of those countries there are only two radiologists for the entire nation; the 26 FDA clearances and 200-plus published studies that underpin the company's clinical credibility; and the regulatory barriers that currently prevent patients from uploading their own scans and getting an AI read directly. Warier also makes his sharpest prediction: within 5 to 10 years, primary care will be AI-first, the first conversation you have when something feels wrong won't be with a doctor, it will be with an AI. Based on what Cure is already doing at scale today, that timeline is harder to dismiss than it might sound. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.

    42 min
  3. Only 12% of Companies Generate Value From AI. Here's What They're Doing | Sanjeev Vohra, Genpact

    18 juni

    Only 12% of Companies Generate Value From AI. Here's What They're Doing | Sanjeev Vohra, Genpact

    Genpact surveyed 500 senior executives to understand why companies are investing in AI but not seeing the value, and what they found was both clarifying and uncomfortable. Sanjeev Vohra, Genpact's Chief Technology and Innovation Officer, joins Craig Smith to share the results: only 12% of companies qualify as genuine AI leaders, meaning they're deploying AI in production environments, generating measurable business outcomes, and have the governance systems in place to actually assess that value. The other 88% are somewhere between experimenting and stalled, and the most common culprit isn't the technology or the C-suite. It's what Vohra and his clients call the "frozen middle", the operationally stretched middle managers who are too busy to lead the transformation and too central to the business to be bypassed. The conversation covers the full landscape of what separates leaders from the rest: why co-pilots are a stepping stone that most companies are mistaking for the destination; why 99% of enterprises have no real AI governance program even as agents begin to proliferate; how Genpact's own CEO writing code on a Friday afternoon became the most powerful AI adoption signal in the company; and why Vohra's sharpest piece of advice is also the simplest, progress over perfection, because the companies still waiting for a complete roadmap before they start have already fallen behind. His formula for what's coming: engineers who are 10 times more productive, business professionals who are 3 times more capable, and organizations that treat that as a baseline expectation, not a stretch goal. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.

    59 min
  4. India Is Becoming an Architect of the Global AI Order | Ivana Bartoletti of Wipro

    16 juni

    India Is Becoming an Architect of the Global AI Order | Ivana Bartoletti of Wipro

    The Global AI Summit just happened in New Delhi, and the message from India was clear: this country is no longer just writing code for the rest of the world. It's becoming an architect of the global AI order. Ivana Bartoletti, Chief Privacy and AI Governance Officer at Wipro and Council of Europe advisor, joins Craig Smith to unpack what that shift actually means. Her frame is the sharpest line of the episode: Europe writes the rules, the US writes the checks, and India is writing the code, in 22 languages. But she's careful to add that the AI race isn't just a technical one. It's about institutional capacity, the ability to absorb AI capability and drive it into real applications that serve real people at scale. The conversation ranges across the full landscape of AI's global moment: why companies that announced 100% AI replacement in customer service quietly had to rehire the humans they let go; why the popular narrative of "Europe regulates, America innovates" is a myth that doesn't survive contact with California's actual AI rules; and why India's strategic choice may prove to be the most durable positioning in a field where trust is becoming the scarcest resource. Bartoletti speaks from a genuinely rare vantage point: a European executive, sitting in Germany, working for an Indian company, advising the Council of Europe, watching the geopolitical AI order reorganize itself in real time. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.

    56 min
  5. The New BRAIN Of the Enterprise | Ryan Gavin

    13 juni

    The New BRAIN Of the Enterprise | Ryan Gavin

    One company now has more AI agents deployed in its organization than it has human employees. Slack's CMO Ryan Gavin dropped that stat into a conversation with Craig Smith, and then immediately identified the secondary problem it creates: when your digital workforce outnumbers your human one, how do employees know which agent to call for which task? That orchestration problem, and the conversational interface that solves it, is what this episode is really about. Gavin describes Slack bot's transformation from a notification tool into what he calls the ChatGPT moment for the enterprise, an AI that doesn't just understand the internet, but understands your business, your team, your customers, and your company's entire conversational history, all the way back to day one. The conversation covers the full arc of what this shift means in practice: a Salesforce executive walking into an unfamiliar meeting and being praised for their questions, because Slack bot had prepared them in minutes using the team's full history; a marketer who built his own data scientist agent over a weekend and is now completely unshackled from the bottleneck that was slowing him down; and Gavin's most honest admission, that he's been saying for years that AI won't replace jobs, but this is the first time he actually believes it, because the soul-crushing "work of work" is finally shrinking, and what's left is the kind of creative, high-energy output that people actually want to do. The inbox, he says, is a deathtrap in the AI era. The companies that figure out how to move beyond it will outperform their competitors by multiples. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.

    53 min
  6. AI Is Already Resolving 90% of Customer Service Tickets - and It's Getting Smarter | Shashi Upadhyay, Zendesk

    12 juni

    AI Is Already Resolving 90% of Customer Service Tickets - and It's Getting Smarter | Shashi Upadhyay, Zendesk

    Zendesk went private two weeks before ChatGPT launched, and the moment it came out, it was obvious that customer service would never be the same again. Shashi Upadhyay, head of product, engineering, and AI at Zendesk, joins Craig Smith to explain what the company has built since: a self-improving AI system that doesn't just resolve tickets but learns from every failure, studies what the human did to fix it, and gets measurably better over time. He calls it the resolution learning loop, and for Zendesk's best customers, it's already resolving 70 to 90% of incoming tickets autonomously, up from the 10 to 20% that chatbots managed just a few years ago. The conversation goes deep on the engineering decisions that actually matter: why hallucination is a feature, not a bug, and why the real challenge is knowing exactly when to switch from creative AI to deterministic code; why Zendesk acquired Forethought and what made their approach to going live in days rather than months so valuable; and why, despite all the momentum, Upadhyay estimates we are only about 5% through the adoption of AI in customer service. The bottleneck isn't the technology, it's the change management required to restructure how human and AI workforces operate together. His vision of the end state is striking: personal AI agents talking directly to enterprise AI agents, resolving 90% of issues instantly, while humans focus exclusively on the complex, high-value interactions that genuinely require them. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.

    57 min
  7. Every Enterprise Is About to Have a 100,000 Agent Problem | Oren Michaels of Barndoor AI

    6 juni

    Every Enterprise Is About to Have a 100,000 Agent Problem | Oren Michaels of Barndoor AI

    AI agents can now connect to every tool your employees use. The problem is that connecting them and trusting them are two completely different things, and most enterprises have figured out the first without solving the second. Oren Michaels, co-founder and CEO of Barndoor AI, joins Craig Smith to explain why that gap is the defining challenge of the agentic enterprise era. His framework is simple and sharp: agents are like enthusiastic interns. They will absolutely do something when you ask them to. Whether it's what you intended is another matter, and when an agent can act across Salesforce, Slack, email, and calendar simultaneously, the blast radius of a misunderstood instruction is far larger than anything a human intern could cause. The conversation covers the 100,000 agent problem - the reality that each agent handling a discrete task needs its own set of rules about what it's allowed to do, and that number scales to a size no human team can govern manually - and why traditional identity management systems were never built for the failure modes AI agents create. The new threat isn't bad actors getting in; it's authorized people using allowed tools with agents that still do the wrong thing. Barn Door's governance layer sits between the agent and the tools it can access, specifying exactly what each agent is permitted to do in each context, and Venn brings that same capability to individuals who want to understand what's possible before their organizations catch up. This is one of the most practically useful conversations available about what enterprise AI governance actually looks like. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.

    1 tim
  8. More Customers Chose the AI Agent Than Anyone Expected | Tom Chen, Aircall

    4 juni

    More Customers Chose the AI Agent Than Anyone Expected | Tom Chen, Aircall

    Every time you hit a phone tree or a chatbot with canned answers, you're experiencing the gap between what AI can already do and what most companies are still delivering. Craig Smith sits down with Tom Chen, Chief Product Officer at Aircall, to explore why that gap is closing fast, and what it means for any business that relies on voice as a customer communication channel. Tom makes a case that is both practical and counterintuitive: AI voice agents aren't better than your best human rep, but they are better than your average one. They never get frustrated. Their patience is infinite. Their tone never changes. And they can handle 100 concurrent calls at a fraction of the cost of a human operation, without lunch breaks, without bad days, and without going off script. The conversation covers a finding that should change how any business thinks about AI adoption: when one of Aircall's customers gave callers the explicit choice between a human agent and a faster AI agent, far more people chose the AI than anyone expected, and satisfaction scores went up. Tom also identifies the real bottleneck that most businesses don't see coming: it's not the AI technology, which is increasingly commoditized. It's the tribal knowledge, the undocumented expertise that lives in the heads of long-tenured employees and never gets captured anywhere, that determines whether an AI agent performs well or not. Until that knowledge is surfaced, even the best voice agent will underperform. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.

    57 min
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Om

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

  • Skapad av
    Craig S. Smith
  • Aktiva år
    2018 - 2026
  • Avsnitt
    359
  • Åldersgräns
    Barnvänligt
  • Copyright
    © Eye On A.I.
  • Programmets webbplats
    Eye On A.I.

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