Eye On A.I.

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.
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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.
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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. 
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(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

What does the quantum industry actually look like right now, beneath all the hype?
In this episode of Eye on AI, Craig Smith sits down with Celia Merzbacher, Executive Director of the Quantum Economic Development Consortium (QED-C), to break down the real state of quantum technology in 2025. From market growth and enterprise readiness to the growing intersection with AI, Celia brings a grounded insider perspective on where the industry stands and what comes next.
Celia explains why the quantum market is growing faster than even the companies inside it predicted, with revenues rising roughly 27% year over year and actual numbers consistently beating forecasts. She also makes clear that the future is not quantum replacing classical computers. It is hybrid systems combining both to solve problems that simply cannot be solved today, with early use cases already emerging in pharmaceuticals, energy, finance, and defense. 
We also get into quantum sensing, the most underrated corner of the quantum world. From biomedical imaging already in clinical trials to quantum clocks powering GPS and financial transaction timestamping, sensing is already partially commercialized and quietly reshaping industries most people have never connected to quantum at all.
Finally, Celia addresses the AI question directly. Will AI replace quantum? No. The two are complementary. AI is already accelerating quantum hardware design and algorithm discovery, and quantum may eventually improve how AI systems are trained. She closes with a clear message for enterprise leaders: the transition to quantum will not be a migration. It will be a paradigm shift, and the time to start preparing is now. 
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Timestamp:
(00:00) Introduction: What Is QED-C and Why Does It Exist?
(01:57) Celia Merzbacher on Her Background and Role
(04:32) Annual Market Survey: How Fast Is Quantum Actually Growing?
(09:10) Where Quantum Revenue Is Coming From Today
(11:11) Timeline and the Race to Utility-Scale Quantum Computing
(13:23) Early Use Cases: Pharma, Energy, Finance and Hybrid Computing
(16:14) What Is Quantum Sensing and Why It Matters
(20:39) The Three Pillars: Hardware, Error Correction and Algorithms
(27:40) How Enterprises Should Start Preparing for Quantum Now (38:39) AI and Quantum: Allies Not Competitors

What if you could train a frontier AI model without building a single data centre?
In this episode of Eye on AI, Craig Smith sits down with Steffen Cruz, co-founder and CTO of Macrocosmos, to explore a radical alternative to the way AI models are built today. Instead of billion-dollar GPU warehouses, Steffen is training large language models using idle compute from devices distributed around the world, coordinated through the Bittensor blockchain.
Steffen breaks down why the centralised data centre model is heading toward a wall. Projects like Stargate and Colossus cost tens of billions of dollars, and as appetite for larger models grows, the economics simply stop making sense. He explains how distributed training flips this on its head, tapping into surplus energy, underutilised GPUs, and even consumer devices like Mac Minis to train models at a fraction of the cost.
We also get into IOTA, Macrocosmos's flagship technology, an orchestration layer that takes compute nodes scattered across the globe and makes them act like a single supercomputer. No single device runs the full model. Instead, each one carries a small slice, a technique called model parallelism, and together they can train frontier-scale models that would otherwise be out of reach for startups, researchers, and enterprises.
Finally, Steffen shares what he's building toward: 70 billion parameter models trained at 10 to 20 percent of centralised costs, a two-sided marketplace for compute, and a future where anyone with a spare GPU or Mac Mini can earn passive income while contributing to the democratisation of AI.
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Timestamp:
(00:00) Introduction: The Problem With Blockchain AI Projects
(06:39) Meet Steffen Cruz: From Subatomic Physics to Decentralised AI
(09:16) What Is a Bittensor? The Blockchain Built for AI
(11:53) How the Blockchain Actually Works: Registry, Clock, and Rewards
(15:08) Why Data Centres Are Hitting a Wall
(22:01) Distributed Training vs Federated Learning: What's the Difference?
(27:47) Train at Home: Turning Your Mac Mini Into a Passive Income Machine
(32:49) IOTA Explained: Building a Global Supercomputer From Spare Parts
(39:43) How the Network Scales: From 256 Nodes to Limitless Compute
(44:39) The Road Ahead: 70B Parameter Models and the Future of Affordable A