
Eye On A.I.
by Craig S. Smith
Is this your podcast?Craig S. Smith is a seasoned journalist and former correspondent for The New York Times, recognized for his in-depth reporting on technology and its societal impacts. With a rich background in journalism, he brings a seasoned perspective to…
Insights from recent episode analysis
Audience Interest
- artificial intelligence advancements
- global technology implications
Podcast Focus
- discussing AI developments
- contextualizing AI progress
Publishing Consistency
- weekly or more episodes
- active for seven years
Platform Reach
- available on major podcast platforms
- growing audience base
Insights are generated by CastFox AI using publicly available data, episode content, and proprietary models.
Most discussed topics
Brands & references
Total monthly reach
Estimated from 9 chart positions in 9 markets.
By chart position
- 🇺🇸US · Technology#10300K to 1M
- 🇦🇺AU · Technology#1965K to 30K
- 🇳🇱NL · Technology#8210K to 30K
- 🇮🇳IN · Technology#1081K to 10K
- 🇩🇰DK · Technology#653K to 10K
- Per-Episode Audience
Est. listeners per new episode within ~30 days
96K to 328K🎙 Daily cadence·330 episodes·Last published today - Monthly Reach
Unique listeners across all episodes (30 days)
321K to 1.1M🇺🇸92%🇦🇺3%🇳🇱3%+6 more - Active Followers
Loyal subscribers who consistently listen
128K to 437K35K real followers tracked across platforms
Market Insights
Platform Distribution
Reach across major podcast platforms, updated hourly
Total Followers
—
Total Plays
—
Total Reviews
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* Data sourced directly from platform APIs and aggregated hourly across all major podcast directories.
On the show
From 10 epsHosts
Recent guests
Recent episodes
AI Is Reading 15 Million X-Rays a Year With No Human in the Loop | Prashant Warier, Qure.ai
Jun 20, 2026
Unknown duration
Only 12% of Companies Generate Value From AI. Here's What They're Doing | Sanjeev Vohra, Genpact
Jun 18, 2026
Unknown duration
India Is Becoming an Architect of the Global AI Order | Ivana Bartoletti of Wipro
Jun 16, 2026
Unknown duration
The New BRAIN Of the Enterprise | Ryan Gavin
Jun 13, 2026
Unknown duration
AI Is Already Resolving 90% of Customer Service Tickets - and It's Getting Smarter | Shashi Upadhyay, Zendesk
Jun 12, 2026
Unknown duration
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| Date | Episode | Topics | Guests | Brands | Places | Keywords | Sponsor | Length | |
|---|---|---|---|---|---|---|---|---|---|
| 6/20/26 | ![]() 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. | — | ||||||
| 6/18/26 | ![]() 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. | — | ||||||
| 6/16/26 | ![]() 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. | — | ||||||
| 6/13/26 | ![]() 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. | — | ||||||
| 6/12/26 | ![]() 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. | — | ||||||
| 6/6/26 | ![]() 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. | — | ||||||
| 6/4/26 | ![]() 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. | — | ||||||
| 6/2/26 | ![]() 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. | — | ||||||
| 6/1/26 | ![]() 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. | — | ||||||
| 5/28/26 | ![]() 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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| 5/28/26 | ![]() 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. | — | ||||||
| 5/28/26 | ![]() 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. | — | ||||||
| 5/25/26 | ![]() 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. | — | ||||||
| 5/22/26 | ![]() 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. | — | ||||||
| 5/19/26 | ![]() 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. | — | ||||||
| 5/15/26 | ![]() 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. | — | ||||||
| 5/6/26 | ![]() Loris Degioanni: Why AI Is Breaking Cybersecurity, and What Comes Next | AI has fundamentally changed the cybersecurity threat landscape, not by inventing new attack types, but by collapsing the timeline. The same tools that make software developers more productive are now being used by attackers to move from vulnerability disclosure to active exploit in a matter of hours. That shift, argues Loris Degioanni, CTO and founder of Sysdig, changes everything about how defense needs to work. In this episode, Craig Smith talks with Loris Degioanni about why human-centered security is becoming a structural liability, what "headless cloud security" means in practice, and why the coding agent (tools like Claude Code or Codex) may become the new operating system through which all enterprise security workflows run. It's a conversation about architecture, urgency, and what it actually means to fight a tank when you've been trained to use a baseball bat. If this conversation made you think differently about AI and security, subscribe to Eye on A.I. for weekly conversations with the people building and defending the future. | — | ||||||
| 5/1/26 | ![]() #342 Andrew Thangaraj: The $5,000 IIT Degree: Can India Fix Its Broken Education System? | What if the most competitive exam in the world is also the most destructive? In this episode of Eye on AI, Craig Smith sits down with Professor Andrew Thangaraj, faculty at the Department of Electrical Engineering at IIT Madras, to explore how one of India's most prestigious institutions is quietly dismantling the system it helped build. Andrew lays out the honest reality of higher education in India. Two and a half crore kids reach college age every year. Only 90 lakh make it to college. And the IITs, the most coveted institutions in the country, take just 17,000. The competition to reach those seats has become so extreme that students are losing their childhoods, their development is stunted, and even those who make it through are often unemployable because the system rewards knowledge over skills. Andrew walks through exactly how IIT Madras is responding. A full, IIT-branded undergraduate degree in data science delivered entirely online for under five lakhs, roughly $5,000. No JEE required. No elite school background needed. Just a 10th standard foundation and the willingness to do the work. The program flips the traditional model, putting hands-on skills and real projects before theory, building in multiple exit points for students who need to start earning before they finish, and scaling to over 40,000 active students through a hybrid of faculty-recorded lectures, full-time instructors, and a remarkably active student community. We also get into the bigger picture. Why India's AI talent gap is as much a culture problem as a numbers problem. Whether India can leapfrog into AI leadership the way China did after rebuilding its research ecosystem. Where AI tools are already being tested inside the program and where they still fall short. And how AI deployed in Indian languages, in agriculture, and in the courts could drive the kind of societal change that no corporate productivity tool ever will. Subscribe for more conversations with the people shaping the future of AI and emerging technology. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (00:00) Introduction and Andrew Thangaraj's Background (01:29) India's Higher Education Bottleneck (03:45) Designing a $5,000 IIT Degree (09:27) Why Graduates Still Lack Skills (12:31) When the Program Started and How It Got Approved (13:56) Program Structure, Diplomas and Multiple Exit Points (17:52) Who the Program Reaches and Surprising Student Stories (24:57) Older Students, Working Professionals and International Enrollment (29:55) Can India Leapfrog in AI (34:03) Data Centers, Power and Infrastructure Gaps (40:57) How Involved Are the IITs in India's AI Mission (46:00) AI for Languages, Farms and Courts | — | ||||||
| 4/30/26 | ![]() #341 Celia Merzbacher: Beyond the Buzzword: The Real State of Quantum Computing, Sensing, and AI in 2025✨ | quantum computingAI+4 | Celia Merzbacher | Quantum Economic Development Consortium | 2025 | quantum technologyAI+8 | — | 44m 55s | |
| 4/29/26 | ![]() #340 Steffen Cruz: Training AI Without Data Centres✨ | AI trainingdistributed computing+4 | Steffen Cruz | StargateColossus+3 | — | AI modelsdistributed training+5 | — | 46m 25s | |
| 4/28/26 | ![]() #339 Eamonn Maguire: Your Child Has a Data Profile Before They're Born✨ | data privacyAI ethics+4 | Eamonn Maguire | LumoChatGPT+3 | Oxford | data profileAI+5 | — | 45m 30s | |
| 4/24/26 | ![]() #338 Amith Singhee: Can India Catch Up in AI? IBM's Amith Singhee on What It Will Take✨ | AI developmentIndia's AI infrastructure+4 | Amith Singhee | IBM | IndiaSouth Asia | AIIndia+6 | — | 46m 54s | |
| 4/23/26 | ![]() #337 Debdas Sen: Why AI Without ROI Will Die (Again)✨ | AI valueenterprise AI+4 | Debdas Sen | mcube™TCG Digital+1 | — | AIROI+8 | — | 51m 17s | |
| 4/20/26 | ![]() #336 Professor Mausam: Why India Is Losing the AI Race and What It Will Take to Catch Up✨ | AI talent retentionIndia's AI challenges+4 | Professor Mausam | IIT DelhiAAAI | IndiaUS+1 | artificial intelligenceIndia+7 | — | 1h 00m 05s | |
| 4/19/26 | ![]() #335 Sriram Raghavan: Why IBM Is Betting Everything on Small AI Models✨ | enterprise AIsmall AI models+4 | Sriram Raghavan | Granite modelsGPT-4o+3 | — | AI modelsdata lineage+5 | — | 1h 00m 21s | |
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