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.
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American Companies Have 36 Months to Go AI-Native or Get Left Behind | Drew Cukor, TWG AI
08/13/2026
American Companies Have 36 Months to Go AI-Native or Get Left Behind | Drew Cukor, TWG AI
The same tools that slowed the U.S. military down in Afghanistan (PowerPoint, Excel, email, and Word) are now slowing American businesses down in the AI race. Drew Cukor spent 30 years as a Marine intelligence officer, helped build Project Maven into a battlefield command and control system, served as Chief Data Officer at JP Morgan, and is now leading AI transformation at TWG AI. In this episode, he joins Craig Smith to make a case that most enterprise AI strategies are fundamentally broken, not because the technology isn't there, but because companies are storing their data in Microsoft file folders where it becomes inaccessible to AI, appointing AI officers who block progress rather than enable it, and mistaking chatbot deployments for transformation. Cukor's prescription is specific: take a company's core workflows apart, how it acquires customers, delivers services, handles back office operations, and rebuild them from scratch with AI embedded throughout, protected inside Palantir Foundry, delivered within 36 months, with the CEO owning the outcome rather than delegating it to a CTO or a made-up AI officer role. The stakes, he argues, are not abstract: China is going AI-native from the start without the legacy infrastructure that's slowing American enterprise, token spend is approaching the cost of a human salary making poorly designed AI workflows as expensive as bad hiring decisions, and the window for acting is closing. The most important video he recommends any business leader watch isn't one where the AI wins, it's the footage of Lee Sedol losing to AlphaGo and realizing mid-game that he no longer understands how the game works. That moment, Cukor says, is coming for every legacy business that doesn't move now. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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In 5 Years, 90% of What You Use AI For Will Run on Your Smartphone | Paolo Ardoino, Tether
08/10/2026
In 5 Years, 90% of What You Use AI For Will Run on Your Smartphone | Paolo Ardoino, Tether
Hundreds of billions of dollars are flowing into AI data centers right now, and Paolo Ardoino, CEO of Tether - the company behind the world's most widely used stablecoin with 573 million users - thinks that investment is going to age very badly. In this episode, he joins Craig Smith to explain QVAC, Tether's open-source platform for running AI on smartphones, laptops, and edge devices, and to make a case that within five years, 90% of what ordinary people use AI for will run entirely on consumer hardware, without touching a data center. The evidence is already there: Tether's team built a 4-billion-parameter medical AI model that outperforms Google's 27-billion-parameter MedGemma, running on a good smartphone, and a 1.7-billion-parameter version that runs on the average $80 smartphone available in Africa. The deeper argument in this conversation is philosophical as much as technical. Ardoino applies the same disintermediation logic that made sending dollars to the world's unbanked free - zero transaction fees, revenue from treasury bill interest - to AI: "not your AI, not your intelligence." If you don't control how your AI runs and your data never leaves your device, the AI is genuinely yours. If it does, someone else is getting smarter with your information. He also makes a pointed economic argument: the AI companies currently charging $200 for subscriptions that cost $1,000 to $5,000 to deliver are subsidizing growth while private, and when they go public, retail investors will absorb the gap. His prescription isn't to stop building, it's to build differently, toward millions of small interacting models rather than trillion-parameter monoliths, toward devices that think locally rather than systems that route everything through Ireland and back. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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Why People Are Paying 10x More for AI | Sid Sheth, d-Matrix
08/06/2026
Why People Are Paying 10x More for AI | Sid Sheth, d-Matrix
The AI chip market looks monolithic from the outside - NVIDIA dominates, and everyone else is fighting for scraps. But d-Matrix's CEO Sid Sheth argues that the market is quietly splitting into two distinct tiers, and the one that's exploding right now is the one NVIDIA's architecture isn't built for. In this episode, Sid joins Craig Smith to explain the "premium token economy": a new class of AI inference where interactivity is the product, users pay ten times more per million tokens for instant responses, and the memory bandwidth limits of GPU-based systems create a structural ceiling that purpose-built architectures don't have. The conversation is unusually candid about what AI actually looks like at the executive level: Sid describes using Claude as a sounding board for M&A strategy, producing full integration plans in 15 minutes that used to require entire banking advisory teams, and watching AI shift from a tool that echoed his ideas back at him to one that genuinely disagrees, flags what he missed, and pushes back with enough confidence to be useful. He also makes the case that we're at the beginning of a shift from individual agents to what he calls "organizational AI" - teams of agents running entire company functions at a high level of abstraction - and that the infrastructure bet d-Matrix is making positions them directly in the path of that wave. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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AI Agents Fixing Your IT Before You Even Know Something Broke | Erhan Giral & Ryan Manning, BMC Helix
08/03/2026
AI Agents Fixing Your IT Before You Even Know Something Broke | Erhan Giral & Ryan Manning, BMC Helix
Most enterprise IT teams spend the majority of their time fighting the same fires repeatedly. BMC Helix is building the AI system that handles those fires automatically, detecting anomalies, tracing root cause through millions of asset relationships, generating remediation plans, and learning from every incident it resolves. Craig Smith sits down with Erhan Giral, VP of AI Strategy and Innovation at BMC Helix, and Ryan Manning, Chief Product Officer at BMC Helix, to explain how agentic AI is transforming IT service management from a reactive, human-driven process into something closer to a self-healing system, and why doing that at enterprise scale requires a fundamentally different architecture than most AI deployments attempt. The most technically interesting part of this conversation is where BMC Helix is headed: building "gyms", synthetic data center environments where AI agents deliberately break things and learn to fix them overnight, 24 hours a day, generating the bespoke operational training data that text-based foundation models can no longer provide. Erhan describes an architecture of specialized sub-agents, anomaly detection, log analysis, root cause analysis, remediation planning, that work in a hierarchy, passing hypotheses between each other until they converge on an answer, fine-tuned to reason the way a specific enterprise's best IT engineer would rather than the way a generic documentation page reads. For customers, the results are measurable: 25 to 50% cost reduction, fewer recurring outages, and IT staff who can finally go home at a predictable time rather than spending their nights firefighting problems that could have been prevented. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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Real AI Transformation Costs HALF of Everyone's Salary for 2 Years | Chris Blackburn, Liatrio
07/30/2026
Real AI Transformation Costs HALF of Everyone's Salary for 2 Years | Chris Blackburn, Liatrio
Most companies think they're transforming with AI. They're not, and the gap between what they believe and what's actually happening on the ground is costing them far more than they realize. In this episode, Craig Smith sits down with Chris Blackburn, founder and CEO of Liatrio, a consultancy that has spent a decade embedding directly inside large enterprises to help them actually change how they work, not just what tools they use. The conversation opens with a striking data point: the average enterprise Blackburn works with operates at just 5 to 6% efficiency, meaning employees spend only three to three-and-a-half hours per week on work that genuinely creates value, compared to Toyota's benchmark of 70%. The core argument is that AI is being applied to the wrong part of the problem: individual productivity gains don't flow through to the bottom line if the organizational system around the individual - the approvals, handoffs, bureaucracy, and middle management layers - stays exactly the same. Blackburn introduces a concept he calls "strangling the enterprise": rather than trying to transform a 5,500-person organization all at once, build a small, low-bureaucracy unit inside it that operates with radical autonomy, proves the model works, and expands outward. The episode closes with a frank conversation about what real transformation actually costs: roughly half of total compensation spend across the organization, sustained for two years, a number Blackburn describes as "absolutely insane" and one he believes most CFOs aren't yet prepared to confront. Key Topics Covered: ● Why the average enterprise operates at 5-6% efficiency, and what Toyota's 70% benchmark reveals about the scale of the opportunity AI could unlock ● The critical distinction between individual productivity gains and system-level improvement, and why saving an hour doesn't automatically improve the bottom line ● "Strangle the enterprise": how to build a small, autonomous AI-native unit inside a large organization rather than trying to transform the whole thing at once ● Why most CEOs are dangerously disconnected from the actual work being done, and what McKinsey says about how much time they should be spending on transformation ● What AI transformation actually costs: roughly half of total compensation spend, sustained over two years, and why most CFOs aren't ready for that number ● Why AI isn't just changing jobs but changing life - from shorter work weeks to longer health spans - and what the farming analogy reveals about how slowly societies absorb new productivity As enterprises pour money into AI tools while reporting little bottom-line impact, this conversation offers the most operationally honest account available of why that gap exists, and what organizations that actually want to close it need to be willing to do differently. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI. Craig Smith on X: EYE On A.I. on X: Connect with Chris Blackburn LinkedIn:
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"According to NASA's Definition of Life, I'm Not Alive" - Why Nobody Can Define Life | Dr. Kate Adamala
07/29/2026
"According to NASA's Definition of Life, I'm Not Alive" - Why Nobody Can Define Life | Dr. Kate Adamala
Nobody has ever built a cell from scratch - assembled entirely from purified molecules on a shelf - that can feed itself, grow, and split into daughter cells through its own genetic activity. Until now. Dr. Kate Adamala, a synthetic biologist and a professor of genetics at the University of Minnesota, whose lab just published a landmark paper on what she calls "spud cells," joins Craig Smith to explain what her team built, why it matters, and what it will take to go from proof of concept to a platform that could eventually replace every molecule civilization currently extracts from petrochemicals. The conversation is as philosophically rich as it is technically specific: Adamala argues that life has no magic ingredient, and that the universe itself is predisposed to give rise to it. She describes the spud cell not as a mic drop but as biology's Sputnik moment: proof that you can escape the gravity well of evolution and build lifelike systems from the ground up. The episode also covers the most alarming biosecurity question in synthetic biology right now: mirror life - cells built from mirror-image molecules that would be invisible to every immune system on earth and potentially capable of replicating uncontrollably in the environment. Adamala led the global call to pause all mirror life research in 2024, and she explains both why that research was so dangerous and why the spud cell doesn't move the field any closer to that red line. Craig also asks the question nobody else thought to ask: could AI now simulate the billions of years of molecular evolution that a primordial sea would need millions of years to complete - running a few trillion iterations computationally to find what emerges? Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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Video Is About to Stop Being One-Way (and That Changes Everything) | Victor Riparbelli, Synthesia
07/28/2026
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: EYE On A.I. on X: Connect with Victor Riparbelli LinkedIn:
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6 in 10 Enterprises Can't Find the Root Cause When Their AI Workloads Fail | Paul Appleby, Virtana
07/15/2026
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.
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Inside the Enterprise Browser Rebuilding Security for the AI Era | Bradon Rogers, Island
07/13/2026
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.
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What Industrial AI Actually Looks Like | Kriti Sharma, Nexus Black
07/10/2026
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.
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The Biggest AI Security Problem Isn't the Model. It's This. | Devvret Rishi
07/07/2026
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.
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Big Pharma Fails 50% of the Time in Phase Three. AI Can Fix That | Vin Singh, BullFrog AI
07/05/2026
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.
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AI Agents Are Failing and It's Almost Never the Model's Fault | Alberto Pan, Denodo
07/02/2026
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.
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How Modern Science Got Consciousness Wrong From the Start | Philip Goff
06/29/2026
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
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AI Is Reading 15 Million X-Rays a Year With No Human in the Loop | Prashant Warier, Qure.ai
06/20/2026
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.
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Only 12% of Companies Generate Value From AI. Here's What They're Doing | Sanjeev Vohra, Genpact
06/18/2026
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.
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India Is Becoming an Architect of the Global AI Order | Ivana Bartoletti of Wipro
06/16/2026
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.
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The New BRAIN Of the Enterprise | Ryan Gavin
06/13/2026
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.
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AI Is Already Resolving 90% of Customer Service Tickets - and It's Getting Smarter | Shashi Upadhyay, Zendesk
06/12/2026
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.
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Every Enterprise Is About to Have a 100,000 Agent Problem | Oren Michaels of Barndoor AI
06/06/2026
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.
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More Customers Chose the AI Agent Than Anyone Expected | Tom Chen, Aircall
06/04/2026
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.
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Why the Future of AI Isn't Just Bigger Models. It's Models That Evolve | Risto Miikkulainen of Cognizant
06/02/2026
Why the Future of AI Isn't Just Bigger Models. It's Models That Evolve | Risto Miikkulainen of Cognizant
Most AI systems follow a gradient, a mathematical slope that tells them exactly how to improve, step by step, toward a known goal. Neuroevolution doesn't follow any gradient. Instead, it runs hundreds or thousands of competing solutions simultaneously, spreads them across the space of possibilities as broadly as possible, and lets the best ones recombine, the same logic that drives biological evolution. The result, as Risto Miikkulainen explains to Craig Smith, is creativity: solutions that no human designer would have anticipated, that emerge routinely from the evolutionary process. Miikkulainen is a professor at UT Austin and VP of AI Research at Cognizant AI Labs, and he has been working on this field since the 1980s, which makes him both a historian of it and one of its most active frontiersmen. The conversation covers a remarkable range: a mystery model that outperformed every competitor in a recent stock trading competition with forensic footprints pointing to neuroevolutionary AI; Sakana AI's system that autonomously designed experiments, wrote a paper, and had it accepted at a major machine learning conference; and a pandemic decision system that trained overnight and made country-specific recommendations by morning, with Iceland actually following some of them, all the way to the prime minister. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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How AI Is Reinventing Elder Care | Chia-Lin Simmons of LogicMark
06/01/2026
How AI Is Reinventing Elder Care | Chia-Lin Simmons of LogicMark
One in four people over 65 will experience a fall, and for most of them, the technology designed to help is a device that hasn't meaningfully changed since the 1980s. Chia-Lin Simmons, CEO of LogicMark, joined Craig Smith to make the case that this gap is both unnecessary and solvable, and that AI is finally making it possible to shift personal safety from reactive to predictive. Her company's Freedom Alert Max doesn't just detect falls after they happen, it builds a personalized digital twin of each user, tracking steps, sleep patterns, and medication adherence over time to identify the subtle signs of health decline that even daily caregivers often miss. The conversation is one of the most grounded and human discussions of applied AI you'll hear, covering why Apple Watch fall detection was engineered for crash detection, not elderly falls; why AI can flag a problem but a human needs to hear the breathing on the other end of the line; and why the 700,000 caregiver shortage in America makes technology like this not a luxury but a scaling mechanism. For anyone navigating aging parents, their own future, or the sandwich generation pressures in between, this episode is both practically useful and genuinely moving. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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The App of the Future Is Voice — Not a Screen. Mitel's CTO Luiz Domingos Explains Why.
05/28/2026
The App of the Future Is Voice — Not a Screen. Mitel's CTO Luiz Domingos Explains Why.
Luiz Domingos has spent 25 years watching enterprise communications evolve, from IP telephony to cloud to AI, and his assessment of where things stand now is unusually concrete. Companies have moved past the strategy deck phase. AI is being embedded directly into contact centers, compliance workflows, and communication pipelines, and the question executives are asking has shifted from "which model is smartest" to "which deployment reduces friction and stays compliant." Domingos is direct about what gets in the way: you cannot pour AI into a legacy architecture and expect transformation, and cloud-only AI doesn't solve the latency or data sovereignty problems that regulated industries face every day. In this conversation with Craig Smith, Domingos covers the practical mechanics of how Mitel is applying AI across its portfolio, from real-time transcription and sentiment analytics in contact centers, to agentic workflows that turn conversations into automated tickets and follow-ups. He draws a clear line between AI agents (which give recommendations) and agentic AI (which takes actions), a distinction the market consistently confuses. He also makes a prediction worth noting: within five years, voice will replace the traditional app interface as the primary way people interact with enterprise AI systems. For any CIO or CTO trying to move from experimentation to real ROI, his framework - start with workflow friction, not pilots - is the most actionable takeaway in the episode.
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Is ChatGPT Conscious? A Pioneer of AI Explains | Dr. Terry Sejnowski
05/28/2026
Is ChatGPT Conscious? A Pioneer of AI Explains | Dr. Terry Sejnowski
A fly with 100,000 neurons can fly, find food, and reproduce. A $100 million supercomputer cannot. Dr. Terry Sejnowski used that observation to silence a room full of MIT AI researchers in the 1980s, and it remains just as sharp today. Sejnowski is one of the foundational figures in the history of deep learning, co-inventor of the Boltzmann machine, and a professor at the Salk Institute who has spent his career studying both the brain and the machines we build to imitate it. In this conversation with Craig Smith, he turns that dual perspective on ChatGPT, and what he finds is something genuinely clarifying: not a human mind, not a threat to humanity, but an alien intelligence that has absorbed more knowledge than any brain ever could while remaining fundamentally empty when nobody is talking to it. The conversation covers the full landscape of what current AI is missing - from goals and reinforcement learning to the constant self-generated flow of thought that defines consciousness - and why the word "understanding" is so ambiguous that even the world's top cognitive scientists can't agree on whether ChatGPT has it. Sejnowski also makes the case that hallucinations aren't a flaw to be engineered away but the flip side of creativity itself, that we are in a pre-Copernican era when it comes to understanding intelligence, and that the real future of AI lies not in scaling language models further but in looking at what nature has already solved, from field mice to fruit flies. His new book is written for the general public and available now. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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Your Child's Data Profile Starts Before They're Born | Eamonn Maguire of Proton
05/28/2026
Your Child's Data Profile Starts Before They're Born | Eamonn Maguire of Proton
Your child's data profile doesn't start when they get their first phone. It starts before they're born, the moment a parent emails a gynecologist or visits a fertility clinic website. That's the core argument behind Born Private, Proton's new initiative that lets parents reserve an email address for their child at birth, anchoring their digital identity in a privacy-preserving ecosystem before the profiling machine gets started. Craig Smith sits down with Eamonn Maguire, Engineering Director, Machine Learning & AI at Proton, who has spent his career at the intersection of data, security, and visualization to explore what's really happening to our data and what, if anything, we can do about it. The conversation covers the mechanics of how just three email sign-ups can allow Google to infer your age, politics, and religion; why OpenAI and Anthropic have shown "not much regard for the law" when it comes to training data and copyright; and why social media platforms are operating like unregulated gambling companies - engineering addiction with no structural incentive to stop. It's one of the most grounded, specific, and genuinely alarming conversations about digital privacy you'll hear, and it ends with a simple, actionable proposition: privacy should be a decision you make at birth, not a problem you try to solve after the damage is done. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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Training AI Models Without a Billion-Dollar Data Center | Steffen Cruz of Macrocosmos
05/25/2026
Training AI Models Without a Billion-Dollar Data Center | Steffen Cruz of Macrocosmos
Training a frontier AI model today requires hundreds of thousands of GPUs, months of compute time, and a budget that only a handful of companies on earth can afford. Steffen Cruz, co-founder and CTO of Macrocosmos, thinks that model is about to break, and he's spending his time building what comes next. His project IOTA, operating within the BitTensor blockchain ecosystem, uses distributed training to split large language models across thousands of devices located around the world, coordinated by blockchain, and powered by surplus cheap energy wherever it exists. After nine months of research, the system can reproduce baseline benchmark performance using what Cruz calls "wonky vegetables" - unreliable, churning, globally distributed compute - and turn it into something indistinguishable from centralized training if you use the right approach. The conversation with Craig Smith covers the mechanics of how this actually works, why the blockchain's role is far narrower and more practical than most people assume, and why the Mac mini stockpiling trend creates an unexpected supply of distributed compute that can earn passive income when idle. Cruz's target: a 70 billion parameter model by mid-2025, trained at 10-20% of what it would cost through a hyperscaler, and aimed squarely at the legal firms, hospitals, and cash-strapped startups that have been waiting to train their own sovereign models but couldn't afford the price tag. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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The Single Biggest Barrier to AI Adoption Isn't the Technology — It's This | Errol Gardner of EY
05/22/2026
The Single Biggest Barrier to AI Adoption Isn't the Technology — It's This | Errol Gardner of EY
Errol Gardner has spent 35 years advising the world's largest organizations through major technology transitions, and his assessment of where enterprise agentic AI actually stands is one of the most grounded you'll hear anywhere. His number: less than 1 out of 10 on a maturity scale. Not because the technology isn't ready, but because deploying agentic AI across an organization doesn't tweak how it works, it requires rebuilding how it works. And that is a fundamentally different kind of challenge than anything the AI hype cycle is currently acknowledging. In this conversation with Craig Smith, Gardner walks through why cloud adoption still hasn't reached 7 out of 10, what that means for agentic AI timelines, why the single biggest barrier to adoption is human resistance rather than technical limitation, and why governments will ultimately have to step in to manage workforce displacement at scale. He also raises a question that almost nobody is asking: is the value exchange between the technology sector and traditional industries sustainable in the long run? It's a conversation that doesn't just describe where AI is, it explains why the gap between the narrative and the reality has never been wider. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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Oliver Dial of IBM: Quantum Advantage Is Happening This Year
05/19/2026
Oliver Dial of IBM: Quantum Advantage Is Happening This Year
IBM's VP of Quantum Systems, Oliver Dial, has spent his career building quantum computers from the ground up, and he's unusually direct about what they can and can't do. In this conversation with Craig Smith, Oliver Dial walks through where the field actually stands in 2026: quantum utility was achieved in 2023, quantum advantage is the target for this year, and a fully error-corrected machine capable of tackling the hard problems is on IBM's roadmap for 2029. That last milestone, Dial says, now feels both achievable and terrifying. The episode is worth your time because Dial doesn't hype. He explains why IBM built a 1,000-qubit computer and then took it apart almost immediately, why Google's quantum advantage claims remain scientifically contested, and how a new error-correcting code IBM developed just reduced the qubit overhead required for fault-tolerant quantum computing by an order of magnitude. For anyone trying to understand what quantum computing will actually mean for their industry, and when, this is the clearest map of the road ahead available right now. If this conversation changed how you think about the future of computing, subscribe to Eye on A.I. for weekly conversations with the researchers and builders shaping what comes next.
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Why Agentic-First Startups Won't Disrupt Enterprises as Fast as Everyone Thinks | Kris Lovejoy
05/15/2026
Why Agentic-First Startups Won't Disrupt Enterprises as Fast as Everyone Thinks | Kris Lovejoy
Kris Lovejoy, Global Strategy Leader at Kyndryl, has spent her career at the intersection of IT infrastructure and security. Right now, she's one of the people enterprises call when they want to move from AI experimentation to real deployment. Her diagnosis is clear: agentic AI is a bullet train sitting on tracks built for 30 miles per hour. The technology is ready. Most organizations aren't, and the gap between a successful pilot and a production system running at scale is far wider than the hype suggests. In this conversation with Craig Smith, Lovejoy walks through why IT service management is the smartest entry point for agentic adoption, how cost savings of up to 90% in that area can fund broader modernization, and why the security risks in agentic systems are less about sophisticated hackers and more about misconfiguration, bad context, and human error. She closes with a specific prediction: half of traditional IT administration tasks will be handled by AI agents by 2031, and a surprising take on who will actually thrive in the agentic era: not coders, but people trained to ask the right questions. For anyone making decisions about AI adoption, this is the most practical conversation available right now. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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