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.

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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.

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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.

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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.

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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.

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.

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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.

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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.

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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.

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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.

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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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(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

What if your child already has a data profile, and they haven't even been born yet?

In this episode of Eye on AI, Craig Smith sits down with Eamonn Maguire, Director of Engineering for AI and ML at Proton, to explore one of the most urgent and underappreciated questions in the age of AI: who owns your data, who is building a profile on you, and what can actually be done about it?

Eamonn brings a rare combination of depth and range to this conversation. With a PhD from Oxford, a postdoc at CERN, and years at Facebook engineering ML systems to detect internal and external threats, he now leads Proton's AI efforts, including Lumo, their end-to-end encrypted alternative to ChatGPT. He makes a compelling case that the surveillance economy is not just a privacy problem but a behavioral one, where the systems profiling you are not only observing who you are but actively shaping who you become.

We get into how just three data points are enough for advertisers to infer your age, political leanings, religion, and spending habits. We discuss why trusting mainstream AI platforms with sensitive data is a structural problem, not just a policy one, and why the AI labs with the best models got there by acquiring the most data, often with little regard for copyright law. Eamonn also breaks down the difference between truly open models and open washing, and explains how Proton builds AI that is genuinely private by design, with local indexing, encrypted memory, and user-controlled data sharing.

Then there is Born Private, Proton's initiative to give children a private digital identity from birth. It sounds simple on the surface, but the conversation it opens up is anything but. Data collection on your child begins before they are born, the moment a parent emails a gynecologist or a fertility clinic. Eamonn argues that until we start thinking about privacy the way we think about other rights, from the very beginning, the surveillance machine will always have a head start.

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(00:00) Introduction and Meet Eamonn Maguire

(00:38) From Bioinformatics to CERN to Facebook: Eamonn's Career Arc

(05:23) How Proton Started in the CERN Cafeteria

(09:23) What Mainstream AI Platforms Actually Do With Your Data

(13:00) Copyright, Training Data, and Why Big Labs Can't Be Trusted

(15:10) Open Models vs Open Washing: What Truly Open AI Looks Like

(24:22) How Lumo Works: Encrypted Memory and No Data Leakage

(31:18) Born Private: Reserving a Private Email Address at Birth

(33:00) How Data Profiling Starts Before Your Child Is Born

(34:26) How Three Data Points Become a Complete Profile

(39:07) Molly Russell and the Consequences of Algorithmic Profiling

(53:55) The Full Proton Ecosystem: Mail, VPN, Drive, Lumo, and Workspace

What if the country that trains the world's engineers finally built the infrastructure to match its talent?

In this episode of Eye on AI, Craig Smith sits down with Amith Singhee, Director of IBM Research India and CTO of IBM India and South Asia, to explore where India actually stands in the global AI race and what it will take to close the gap.

Amith gives an honest, ground-level assessment of why India has been slow to compete. The talent has always been there. But until recently, the investment, the compute infrastructure, and the institutional intent hadn't come together in a sustained, coordinated way. That's changing, and Amith explains exactly what's different now.

He walks through IBM Research India's 27-year presence in the country, the research it's doing on foundation models, hybrid cloud AI deployment, agentic systems, and quantum computing. He also explains why building AI from India doesn't just help India. Working with less data, less compute, and more linguistic diversity forces better engineering and makes IBM's models more generalizable for the entire world.

We also get deep into the technical frontier. Why catastrophic forgetting is one of the key unsolved problems standing between current AI and anything more capable. How IBM is already shipping continual learning in practice through its COBOL modernization tools, helping enterprises decode decades of legacy code before the engineers who wrote it are gone. And why agentic AI, for all the hype, still has a mountain of unglamorous enterprise engineering left to climb before it becomes truly reliable.

Plus, what Amith would tell an 18-year-old engineer in India today about what skills will actually matter in an AI-driven world.

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(00:00) Introduction and Amith Singhee's Background 

(06:26) Why IBM Set Up Research in India 

(11:45) Can India Compete in AI 

(15:18) How IBM Collaborates With Indian Universities 

(19:25) Why India Has Been Slow in AI 

(24:50) IBM's Hybrid Cloud AI Research Focus 

(27:34) How Data Scarcity in India Makes Better AI 

(31:18) Fine-Tuning Models Without Losing General Knowledge 

(35:03) Continual Learning and Catastrophic Forgetting 

(38:25) COBOL and Legacy Code Modernization 

(42:11) Agentic AI Hype vs Enterprise Reality 

(48:09) What Young Engineers Should Study Today 

What does it actually take to prove that AI delivers real value in the industries that keep the world running?

In this episode of Eye on AI, Craig Smith sits down with Debdas Sen, CEO of TCG Digital and Joint Managing Director of Lummus Digital, to explore what serious enterprise AI looks like when it is applied to some of the most complex, high-stakes problems on the planet. Problems like compressing years of catalyst research into weeks, predicting refinery failures before they happen, and accelerating drug development timelines that could determine how long a life-saving medicine takes to reach patients.

Debdas has spent nearly 30 years in data and AI, living through every hype cycle from the data warehousing era of 1997 to today's agentic revolution. He makes a compelling case that the AI community has one defining job right now: prove the ROI, or risk another AI winter.

We also get into what makes TCG Digital's platform mcube? different. It is not a horizontal tool. It is a domain-first, agentic AI ecosystem built for the kinds of massive, multi-variable problems that horizontal platforms cannot touch. Debdas breaks down how mcube? bridges legacy enterprise infrastructure with cutting-edge agentic systems, why hybrid modeling beats pure AI in energy and life sciences, and how the platform keeps private enterprise data protected while still drawing on the best of what public LLMs have to offer.

Finally, Debdas shares where he sees the industry heading next, a future where agents from different providers can reason together in a neutral space, where inference and reasoning keep improving, and where the companies that go deepest into domain will pull furthest ahead.

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TCG Digital on LinkedIn: https://www.linkedin.com/company/tcgdigital/ 

(00:00) Introduction and Meet Debdas Sen

(01:30) 30 Years in Data and AI: From Data Warehousing to Agentic Systems

(03:02) What TCG Digital Actually Does (04:32) Inside mcube?: How the Platform Works

(10:06) Domain vs Horizontal: Why Specificity Wins in Enterprise AI

(18:29) Catalyst R&D: Collapsing 12 Months of Research Into One

(30:38) Predicting Plant Failures Before They Happen

(36:51) Solving the Trust and Hallucination Problem in Enterprise AI

(44:51) The Six-Layer Architecture of mcube?

(47:05) What Is Genuinely New About Agentic AI

(49:22) What Young People Should Study to Work in Serious AI

(53:14) Velocity to Value: Why ROI Must Be Tracked From Day One

What if the country that produces the world's top AI talent finally figured out how to keep it?

In this episode of Eye on AI, Craig Smith sits down with Professor Mausam, one of India's leading AI researchers, AAAI Fellow, and founding head of the Yardi School of Artificial Intelligence at IIT Delhi, to get an honest and unflinching diagnosis of why India has fallen so far behind the US and China in artificial intelligence and what it will actually take to close that gap.

Mausam breaks down the structural story behind India's deficit. A pipeline of world-class students that gets exported abroad the moment it graduates. A professor shortage so severe that IIT Delhi's entire School of AI has hired only five new faculty members in five years. A government AI mission with the right instincts but not enough speed or boldness. And a brain drain made worse by the very thing India is proud of, its English fluency, which makes its talent the easiest in the world to absorb and the hardest to bring back.

Mausam walks through the full picture. How China built its research dominance not through students but through aggressively repatriating senior researchers with real salaries, real lab resources, and real authority to build research cultures from scratch. Why the AlexNet moment in 2012 was actually an equalizer that gave China's fledgling ecosystem a surprise advantage over more established Western research groups. How India's JEE coaching culture and IIT bottleneck are symptoms of a scarcity of quality institutions rather than a broken exam. What the government's AI mission is getting right on compute, data, and sectoral focus, and where the critical gaps remain. And why Mausam believes that bringing one hundred top professors back to India would do more for the country's AI future than any single government program or funding initiative.

We also get into the harder questions. Whether AI degrees belong at the undergraduate level or should sit on top of a computer science foundation. Why Mausam no longer holds an optimistic view on AI's impact on software jobs and why he thinks Geoff Hinton's point about plumbers has merit. And what it would actually take for a democracy of 1.4 billion people to stop training the world's AI leaders and start keeping them.

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(00:00) Introduction: India's AI Gap and Professor Mausam's Background
(02:30) Building the Yardi School of AI at IIT Delhi
(07:44) How Far China Has Pulled Ahead in AI Research
(12:55) Why India Could Not Follow China's Playbook
(29:18) The JEE System, Coaching Culture, and the IIT Bottleneck
(30:37) AI Degrees, Job Market Realities, and the Future of Work
(44:18) The Real Problem Is Professors, Not Students
(48:07) Big Tech Labs in India: Helpful but Not at Scale
(51:46) The Government AI Mission: Progress and Gaps
(55:20) The Compute and Data Infrastructure Problem
(59:54) Can India Close the Gap Before It Is Too Late 

Why IBM Is Betting Everything on Small AI Models

In this episode of Eye on AI, Craig Smith sits down with Sriram Raghavan, Vice President of AI at IBM Research, to explore one of the most important debates in enterprise AI right now. Do you actually need a massive model to get world class results? IBM's answer is no, and Sriram breaks down exactly why.

Sriram explains why IBM chose to train its Granite models directly using reinforcement learning rather than distilling from larger models like most of the industry. The reason goes beyond performance. It comes down to data lineage, safety alignment, and a belief that small, efficient models are the only sustainable path for enterprises running AI across hybrid cloud environments.

We get into the full technical stack behind that bet. How data quality has replaced model size as the real competitive advantage. Why parameter count is becoming the wrong metric entirely. How IBM's inference time scaling techniques allow an 8 billion parameter model to match the performance of GPT-4o and Claude 3.5 on code and math benchmarks. And why IBM is pioneering a new concept called Generative Computing, which treats AI models not as prompt receivers but as programmable computing elements with runtimes, modular LoRA adapters, and proper programming abstractions.

Sriram also shares where IBM Research is headed next, including breakthroughs in continuous learning, agent orchestration, and making unstructured enterprise data actually usable at scale.

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(00:00) Why IBM Skips Distillation and Trains Small Models Directly 

(04:50) Did We Even Need Giant AI Models in the First Place?

(08:12) How Data Quality Became the New Competitive Moat

(11:54) Why Parameter Count Is the Wrong Way to Measure a Model

(15:36) Reinforcement Learning Without Losing Broad Capabilities

(22:05) Inference Time Scaling: Getting Big Model Results From Small Models

(28:12) Generative Computing: Treating AI as a Programming Element

(36:40) Why IBM Open Sources and How Small Models Make It Sustainable

(41:25) The Path to Continuous Learning Without Rewriting Weights

(51:00) IBM's Full Roadmap: Models, Data, and Agents

What if the country that trained the world's engineers finally decided to keep them?

In this episode of Eye on AI, Craig Smith sits down with Abhishek, the civil servant leading India's $1.2 billion national AI Mission, to explore how one of the world's largest and most diverse nations is mounting a serious challenge to US and Chinese dominance in artificial intelligence.

Abhishek breaks down the honest story behind India's late start. World-class talent, but no research ecosystem to retain it. Digitization without AI-usable data. Compute so scarce that the entire country had fewer than 500 GPUs just two years ago. And a brain drain so severe that the engineers India trained are now running the biggest tech companies in the world, just not from India.

Abhishek walks through exactly how the mission is tackling each of those gaps. A subsidized compute program that gives researchers and startups access to 38,000 GPUs at under a dollar per hour. AI Kosh, a national data platform pulling public and private sector datasets into a single AI-ready repository. Centers of Excellence connecting IITs around domain-specific research in agriculture, healthcare, education and mobility. And a sovereign LLM program, with four models already in development and eight more on the way, built specifically for India's languages, voices and needs.

We also get into the geopolitics. Where India stands as the US and China carve out competing AI spheres of influence. Why Abhishek is pushing for a UN-led governance framework rather than aligning with either bloc. And what it would actually take for a country of 1.4 billion people to not just catch up, but leapfrog.

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(00:00) Introduction and Abhishek's Background 

(02:43) What the India AI Mission Is and How It Started 

(04:53) The $1.2 Billion Budget and Total AI Investment in India 

(06:36) Data Center Build-Out and the Road to 7 Gigawatts 

(08:11) AI Kosh: India's National Data Platform 

(10:50) Subsidized GPUs and How Researchers Access Compute 

(12:41) Brain Drain, Reverse Migration and Retaining Top Talent 

(17:24) Centers of Excellence Across IITs and Key Sectors 

(19:21) Expanding Fellowships and Training the Next Generation 

(20:11) Why India Started Late and What Changed 

(21:48) Sovereign LLMs Built for Indian Languages and Needs 

(22:42) The Diversity Challenge and Culturally Relevant AI 

(23:16) Government Funding for Foundation Model Development 

(24:12) The AI Impact Summit and India's Role on the Global Stage 

(24:52) India, China, the US and the Battle for AI Governance 

(29:37) The UN Framework and India's Third Way 

(31:16) India-China Relations and New AI Partnerships 

(32:01) How the $1.2 Billion Budget Was Decided 

(33:31) Can India Actually Catch Up With the US and China

Most enterprises are excited about agentic AI. But very few are actually deploying it in production.

In this episode of Eye on AI, Craig Smith sits down with Adi Kuruganti, Chief AI and Development Officer at Automation Anywhere, to break down why agentic AI is so hard to get right in the enterprise and what it actually takes to move from a promising pilot to a mission-critical deployment.

Adi explains why the future of enterprise automation is not agentic AI alone, but the combination of deterministic and agentic systems working together, and why companies that treat AI as a technology problem instead of a business outcomes problem are setting themselves up to fail.

They dig into how Automation Anywhere is orchestrating agents across legacy systems, healthcare platforms, and financial services workflows, why governance and compliance are the first questions every enterprise asks, and how their Process Reasoning Engine is continuously improving agent performance using metadata from over 400 million running processes.

The conversation also covers the real timeline to a fully autonomous enterprise, why the POC to production gap is the biggest failure point in enterprise AI today, and what companies that wait too long risk losing to competitors who started the journey earlier.

If you want to understand where enterprise AI actually stands today and what it takes to deploy it responsibly at scale, this episode gives you a clear and grounded perspective.

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(00:00) Why Enterprises Are Struggling With Agentic AI

(02:39) What Automation Anywhere Does and the APA Category Explained

(08:01) Deterministic vs Agentic AI: Why You Need Both

(10:59) How Human in the Loop Works in Enterprise AI

(17:16) The Mozart Orchestrator and Process Reasoning Engine

(23:50) How AI Is Upgrading and Replacing Classic RPA

(27:31) How Automation Anywhere Works With Enterprise Customers

(31:53) The Biggest Challenges of Scaling Agentic AI

(41:10) The OpenAI Partnership and What It Means

(47:06) Training Staff and Building AI Literacy at Scale

(51:39) Staying Close to Customers as the Technology Shifts

(53:17) Is the Autonomous Enterprise Actually Coming

What happens when AI writes code faster than anyone can test it?

In this episode of Eye on AI, Craig Smith sits down with Dan Faulkner, CEO of SmartBear, to explore one of the most underappreciated risks of the AI coding boom. As tools like Claude Code and Codex push software development to unprecedented speed, the systems built to validate that software are being left behind. Dan makes a distinction that every engineering leader needs to hear: clean code passing unit tests is not the same as an application that actually works.

Dan introduces the concept of application integrity, continuous and measurable assurance that your software does everything it was intended to do and nothing it was not. He explains why the gap between what AI builds and what teams actually validate is already creating hidden risk in production, and why that risk compounds the faster you ship.

We also get into the new failure modes that agentic AI is introducing. Slop squatting, instruction inversion, cascading errors. These are not theoretical. They are happening now, at scale, in codebases that no human has fully read.

Dan also walks through SmartBear's autonomy ladder framework and their newest product BearQ, a team of AI agents that explores your application, builds a knowledge graph, authors tests, runs them, and updates everything as your app evolves. The key distinction: it is built to augment human teams, not replace them.

Finally, Dan shares his honest take on the future of software engineering. The fallacy was always that coding was the hard part. The hard part is knowing what to build. That skill is not going anywhere.

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(00:00) Introduction and Dan Faulkner's Background 

(01:05) What SmartBear Does: Testing and API Lifecycle Management 

(03:27) AI Is Outpacing Application Testing 

(07:51) Slop Squatting, Instruction Inversion and New AI Failure Modes 

(17:31) Black Boxes, Technical Debt and the Expertise Crisis 

(22:00) How to Avoid Self-Validating AI Systems 

(24:11) The Autonomy Ladder and BearQ 

(31:30) Why Testing Must Be Continuous and Everywhere 

(36:31) Infrastructure Risk and Automation Bias 

(44:11) The Future of QA and New Specialist Roles 

(50:44) How Teams Use SmartBear Tools Today 

(58:57) The Future of Software Engineering and Human Roles

This episode is sponsored by Modulate.
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What does it actually mean to build a foundation model for robots?

In this episode of Eye on AI, Craig Smith sits down with Sergey Levine, co-founder of Physical Intelligence and professor at UC Berkeley, to explore a fundamentally different approach to building robots, one inspired not by programming a single perfect machine, but by training AI on the broadest and most diverse data possible so robots can learn, adapt, and operate in the unpredictable real world.

Sergey explains why the secret to general-purpose robots isn't perfecting one single machine, but training on massive, diverse data from all kinds of robots and even humans. The more variety the model sees, the better it gets. Just like ChatGPT learned from all the text on the internet, robotic foundation models learn from every robot that has ever moved, grabbed, or interacted with the real world.

We also get into the big humanoid robot debate. Are they the future, or is it mostly hype? Sergey gives an honest and technical take on why the form factor conversation is changing now that foundation models exist, and why that actually opens the door for more creativity, not less.

Finally, Sergey shares what he's most excited about next, building a true data flywheel where robots get smarter the more they are deployed, creating a continuous learning cycle that could change everything.

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(00:00) Introduction: What Are Foundation Models for Robots?

(01:44) Meet Sergey Levine: Physical Intelligence and UC Berkeley

(02:51) Breaking Down Foundation Models for Non-Technical People

(06:46) Why Real World Data Beats Simulation

(15:00) Building a Broad Robotics Foundation From Scratch

(24:00) The Open World Problem in Robotics

(40:00) Generalist vs Specialist Robots: Which Wins?

(47:00) Humanoid Robots: Real Innovation or Just Hype?

(55:10) The Future: Continuous Learning and the Data Flywheel

(56:23) Guilty Pleasure: Sci Fi and Thinking Beyond the Limits

AI has been trained like software.

But what if it should be grown like life?

In this episode of Eye on AI, Craig Smith sits down with Sebastian Risi, professor and leading researcher in neuroevolution and artificial life, to explore a fundamentally different approach to building intelligence, one inspired by how nature evolves, grows, and adapts.

Sebastian explains why traditional AI systems are limited by fixed architectures and one-time training, and how evolutionary algorithms can create systems that continuously learn, self-organize, and even grow their own neural structures over time.

They dive into concepts like plastic neural networks that keep updating during their lifetime, AI systems that can recover from damage, and models that develop from a single "cell" into complex structures, similar to biological organisms.

The conversation also explores how combining large language models with evolutionary search could unlock more creative and open-ended problem solving, from merging specialized models to building AI systems capable of generating and testing scientific ideas.

If you want to understand where AI is headed beyond today's transformer models, and why the future may look more like living systems than software, this episode offers a clear and thought-provoking perspective.

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(00:00) Why copy nature's evolution for AI

(01:20) What neuroevolution actually means

(05:52) How evolutionary search replaces gradients

(08:03) Plastic neural networks and continuous learning

(11:53) Growing neural networks like living systems

(18:08) Scaling challenges and limits of growth

(23:16) Can evolving systems replace LLM training

(27:28) Continual learning and model merging

(30:27) Artificial life, self-repair, and resilience

(35:10) AI scientists and evolution with LLMs

Quantum computing has been "5 years away" for decades.

So what's actually holding it back?

In this episode of Eye on AI, Craig Smith sits down with Izhar Medalsy, Co-founder & CEO of Quantum Elements, to break down the real bottleneck in quantum computing today and why the future of the industry may depend more on classical systems and AI than quantum hardware itself.

Izhar explains how digital twins of quantum systems are being used to simulate real hardware, generate massive amounts of training data, and solve one of the biggest challenges in the field: noise and error correction.

They dive into how his team improved Shor's Algorithm from 80% to 99% accuracy on IBM hardware, without changing the hardware itself, and what that means for the future of quantum performance.

The conversation also explores how AI is being used to optimise quantum systems, why classical computing will continue to play a central role in quantum development, and what milestones to watch as the industry moves closer to real-world applications.

If you want to understand where quantum computing actually stands today and what will unlock its next phase, this episode gives you a clear, grounded perspective.

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(00:00) The 99% Accuracy Breakthrough (Quantum's Turning Point) 
(01:03) Why Quantum Hardware Alone Isn't Enough 
(03:50) Digital Twins Explained (The Missing Layer) 
(08:09) The Real Problem: Noise, Instability & Environment 
(15:43) From 80% to 99% on Shor's Algorithm 
(26:36) How AI Is Fixing Quantum's Biggest Bottleneck 
(33:53) Inside the Platform: From Circuit to Optimization 
(40:51) Logical Qubits & Scaling Quantum Systems 
(43:34) The Limits of Simulation vs Real Quantum Hardware 
(54:29) When Quantum Becomes Useful (Real Timeline)

AI is changing more than just productivity.

It's changing what we can trust.

In this episode, Kevin Tian, Co-founder and CEO of Doppel, breaks down how AI is enabling a new wave of social engineering attacks?from deepfake phone calls to impersonation across LinkedIn, YouTube, and search engines.

The reality is this:
Deepfakes are just one part of a much bigger problem.

Attackers are now operating across multiple channels at once, using AI to manipulate people, not just systems. And as these attacks scale, the real risk isn't just fraud or data loss?it's the erosion of trust in everything we see online.

Kevin explains how Doppel is building an AI-native defense platform to detect, map, and shut down these attacks in real time, and why the future of cybersecurity will be defined by AI vs AI.

If you're thinking about AI, security, or the future of trust online?this conversation is essential.

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(00:00) AI Deepfakes & The Collapse of Trust
(01:56) Why "Social Engineering" Is Bigger Than Phishing
(05:20) Deepfakes, Misinformation & Multi-Channel Attacks
(09:16) The Rise of Deepfake Phone Calls
(12:43) How Attackers Manipulate AI & Search Results
(14:39) The Origin Story Behind Doppel
(18:55) How Doppel Detects & Stops Attacks in Real Time
(22:55) Can Attackers Misuse AI Defense Tools?
(24:26) How to Tell What's Real vs Fake Online
(28:20) What Is Human Risk Management?
(30:36) AI vs AI: The Future of Cyber Defense
(34:04) What CEOs Must Do About AI Threats
(37:18) Working with Platforms Like YouTube & LinkedIn
(39:52) Can We Ever Fully Stop Deepfakes?
(44:40) How Doppel Works for Enterprises

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Baris Gultekin, Head of AI at Snowflake, breaks down how enterprise AI is actually being built, deployed, and scaled today. From running AI directly inside governed data environments to enabling natural language access across entire organizations, this conversation explores the shift from experimentation to real-world impact.

You'll learn why Snowflake's core philosophy centers around bringing AI to the data, how data agents are transforming decision-making across teams, and what it takes to build trustworthy AI systems with governance, guardrails, and high-quality retrieval at the core.

Baris also shares how leading companies are already saving thousands of hours through AI-driven automation, why culture and leadership determine AI success, and what the future looks like as agents move from pilots to full-scale production.

If you want to understand where enterprise AI is actually headed and what separates hype from real execution, this episode breaks it down.

(00:00) The Evolution of Snowflake AI

(01:40) Baris Gultekin: Background & AI Mission

(02:59) Why AI Must Run Next to Data

(04:29) Inside Snowflake's AI Infrastructure

(09:08) Model Choice vs Product Layer Strategy

(12:16) Building Trust: Governance, Guardrails & Quality

(16:01) How Enterprise Agents Are Built & Orchestrated

(20:10) AI Adoption Across the Entire Organization

(24:39) Reasoning vs Retrieval: What Matters More

(27:43) Real Use Case: Faster Decision-Making with AI

(31:44) AI as a Co-Pilot for Leaders

(36:52) Preparing Data for AI at Scale

(38:46) What the AI Data Cloud Really Means

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This episode dives into why Pathway's Baby Dragon Hatchling (BDH) might mark the beginning of the post-transformer era in AI.

Zuzanna Stamirowska, Pathway's CEO and co?author of BDH, explains why today's transformer-based LLMs hit a wall on long-horizon reasoning, how memory and synaptic plasticity are built directly into BDH's architecture, and what that means for continual learning, hallucinations, and "generalization over time."

The conversation ranges from complexity science and brain-inspired computation to practical implications for real-world, small-data, and safety?critical applications.

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(00:00) The Core Problem: Why Today's AI Lacks Memory

(03:16) Pathway's Mission to Bring Memory Into AI

(04:53) Zuzanna's Background in Complexity Science

(10:30) Why Transformers Reset Like "Groundhog Day"

(14:34) The Brain-Inspired Dragon Hatchling Architecture

(23:59) How the Network Learns and Builds Connections

(37:38) Performance vs Transformers on Language Tasks

(49:37) Productizing the Technology With NVIDIA and AWS

(54:23) Can Memory Solve AI Hallucinations?

AI often looks fully automated. But behind the scenes, a huge amount of human judgment is shaping how these systems actually work.

In this episode, Craig Smith speaks with Phelim Bradley, co-founder and CEO of Prolific, a platform that connects millions of real people with researchers and AI labs to evaluate and improve AI systems.

They explore the hidden human layer behind modern AI, why traditional benchmarks are becoming less reliable, and why AI companies increasingly rely on real human feedback to measure model performance in the real world.

Phelim also explains how demographic differences influence how models are evaluated, why human judgment remains critical even as AI improves, and how the collaboration between humans and AI will shape the next phase of development.

This conversation reveals the human backbone behind today's AI systems.

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(00:00) Preview and Intro

(02:45) Founding Prolific And Early Pain Points

(06:30) From Mechanical Turk To Representativeness

(09:55) Academic Research And AI Use Cases Split

(13:40) Vetting Real Participants And Fighting Fraud

(17:45) Scale, Community Growth, And Talent Mix

(22:00) High-Complexity Projects Over Commoditised Labeling

(26:40) Measuring Model Persuasion With Live Conversations

(30:20) Demographic-Aware Model Preference Benchmarks

(34:10) The Rise Of Human Evaluation Over Benchmarks

(38:00) Enterprise Model Choice And Continuous Evaluation

(42:00) Why Humans Won't Disappear From The Loop

AI is not just getting smarter. It is getting faster by learning how to optimize the hardware it runs on.

In this episode, Sharon Zhou, VP of AI at AMD and former Stanford AI researcher, explains how language models are beginning to write and optimize their own GPU kernel code. We explore what self improving AI actually means, how reinforcement learning is used in post training, and why kernel optimization could be one of the most overlooked scaling levers in modern AI.

Sharon breaks down how GPU efficiency impacts the cost of training and inference, why catastrophic forgetting remains a challenge in continual learning, and how verifiable rewards from hardware profiling can help models improve themselves. The conversation also dives into compute economics, synthetic data, RLHF, and why infrastructure may define the next phase of AI progress.

If you want to understand where AI scaling is really happening beyond bigger models and more data, this episode goes under the hood.


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(00:00) Preview and Intro

(00:25) Sharon Zhou's Background and Transition to AMD

(02:00) What Is Self-Improving AI?

(04:16) What Is a GPU Kernel and Why It Matters

(07:01) Using AI Agents and Evolutionary Strategies to Write Kernels

(11:31) Just-In-Time Optimization and Continual Learning

(13:59) Self-Improving AI at the Infrastructure Layer

(16:15) Synthetic Data and Models Generating Their Own Training Data

(20:48) AMD's AI Strategy: Research Meets Product

(23:22) Inside the NeurIPS Tutorial on AI-Generated Kernels

(30:59) Reinforcement Learning Beyond RLHF

(39:09) 10x Faster Kernels vs 10x More Compute

(41:50) Will Efficiency Reduce Chip Demand?

(42:18) Beyond Language Models: Diffusion, JEPA, and Robotics

(45:34) Educating the Next Generation of AI Builders

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Artificial intelligence is reaching a turning point. Instead of building bigger and bigger models, what if the real breakthrough comes from letting AI evolve?

In this episode of Eye on AI, David Ha, Co-Founder and CEO of Sakana AI, explains why evolutionary strategies and collective intelligence could reshape the future of machine learning. We explore model merging, multi-agent systems, Monte Carlo tree search, and the AI Scientist framework designed to generate and evaluate new research ideas. The conversation dives into open-ended discovery, quality and diversity in AI systems, world models, and whether artificial intelligence can push beyond the boundaries of human knowledge.

If you're interested in AGI, evolutionary AI, frontier models, AI research automation, or how AI could start discovering science on its own, this episode offers a clear look at where the field may be heading next.

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(00:00) AI Should Evolve, Not Just Scale

(03:54) David's Journey From Finance to Evolutionary AI

(10:18) Why Gradient Descent Gets Stuck

(18:12) Model Merging and Collective Intelligence

(28:18) Combining Closed Frontier Models

(32:56) Inside the AI Scientist Experiment

(38:11) Parent Selection, Diversity and Innovation

(49:25) Can AI Discover Truly New Knowledge?

(53:05) Why Continual Learning Matter

In this episode of Eye on AI, Craig Smith speaks with Amanda Luther, Senior Partner at Boston Consulting Group and global lead of BCG's AI Transformation practice, about what their latest 1,500-company AI study reveals about the widening gap between AI leaders and laggards.

Only 5% of companies are truly "future-built" with AI embedded across their core business functions. These firms are seeing measurable gains in revenue growth, EBIT margins, and shareholder returns. Meanwhile, 60% of organizations are either experimenting or struggling to extract real value.

Amanda breaks down how BCG measures AI maturity across 41 capabilities, how AI impact flows through the P&L, and why leading companies invest twice as much in AI as their competitors. She explains where AI is actually creating value today, from sales and marketing to procurement and retail operations, and why most of that value comes from core business functions, not back-office automation.

The conversation also explores the rise of agentic systems, why many early agent deployments fail, and what it really takes to redesign workflows around AI. Amanda shares practical advice for companies stuck in experimentation mode, how to prioritize the right use cases, and why training and change management matter more than chasing the perfect vendor.

If you want to understand how AI is reshaping competitive advantage in enterprise organizations, this episode provides a data-backed look at what separates the leaders from everyone else.

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(00:00) The AI Value Gap

(01:17) Inside BCG's 1,500-Company AI Study

(04:14) What "Future-Built" Companies Do Differently

(09:30) How AI Impact Is Measured on the P&L

(12:57) Why AI Leaders Invest 2X More

(14:16) Where AI Is Driving Real Cost Reduction

(16:20) Agentic AI: Hype vs Reality

(20:13) Where Agents Actually Create Value

(24:22) Tech vs Talent: Where the Money Goes

(26:58) Will AI Laggards Slowly Disappear?

(31:58) Why Adoption Is Accelerating Now

(40:07) How to Start: Amanda's Advice to AI Laggards

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In this episode of Eye on AI, Nick Frosst, Co-Founder of Cohere and former Google Brain researcher, explains why Cohere is betting on enterprise AI instead of chasing AGI.

While much of the AI industry is focused on artificial general intelligence, Cohere is building practical, capital-efficient large language models designed for real-world enterprise deployment. Nick breaks down why scaling transformers does not equal AGI, why inference cost and ROI matter, and how enterprise AI differs from consumer AI hype.

We discuss enterprise LLM deployment, private data, regulated industries like banking and healthcare, agentic systems, evaluation benchmarks, and why AI will likely become embedded infrastructure rather than a headline breakthrough.

If you care about enterprise AI, AGI debates, large language models, and the future of AI in business, this conversation delivers a grounded perspective from inside one of the leading AI companies.

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(00:00) From Google Brain to Cohere

(03:54) Discovering Transformers

(06:39) The Transformer Dominance

(09:44) What AGI Actually Means

(12:26) Planes vs Birds: The AI Analogy

(14:08) Why Cohere Isn't Chasing AGI

(18:38) Distillation & Model Efficiency

(21:42) What Enterprise AI Really Does

(25:20) Private Data & Secure Deployment

(26:59) Enterprise Use Cases (RBC Example)

(32:22) Why AI Benchmarks Mislead

(34:55) Why Most AI Stays in Demo

(38:23) What "Agents" Actually Are

(43:32) The Problem With AGI Fear

(49:15) Scaling Enterprise AI

(53:24) Why AI Will Get "Boring"

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Voice AI is moving far beyond transcription.

In this episode, Carter Huffman, CTO and co-founder of Modulate, explains how real-time voice intelligence is unlocking something much bigger than speech-to-text. His team built AI that understands emotion, intent, deception, harassment, and fraud directly from live conversations. Not after the fact. Instantly.

Carter shares how their technology powers ToxMod to moderate toxic behavior in online games at massive scale, analyzes millions of audio streams with ultra-low latency, and beats foundation models using an ensemble architecture that is faster, cheaper, and more accurate. We also explore voice deepfake detection, scam prevention, sentiment analysis for finance, and why voice might become the most important signal layer in AI.

If you're building voice agents, working on AI safety, or curious where conversational AI is heading next, this conversation breaks down the technical and practical future of voice understanding.

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(00:00) Real-Time Voice AI: Detecting Emotion, Intent & Lies

(03:07) From MIT & NASA to Building Modulate

(04:45) Why Voice AI Is More Than Just Transcription

(06:14) The Toxic Gaming Problem That Sparked ToxMod

(12:37) Inside the Tech: How "Ensemble Models" Beat Foundation Models

(21:09) Achieving Ultra-Low Latency & Real-Time Performance

(26:16) From Voice Skins to Fighting Harassment at Scale

(37:31) Beyond Gaming: Fraud, Deepfakes & Voice Security

(46:14) Privacy, Ethics & Voice Fingerprinting Risks

(52:10) Lie Detection, Sentiment & Finance Use Cases

(54:57) Opening the API: The Future of Voice Intelligence

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AI is changing how software is built, but it is also quietly breaking how security works.

In this episode of Eye on AI, host Craig Smith sits down with Subho Halder, co-founder and CEO of Appknox, to unpack a growing and largely invisible risk. AI-powered mobile apps that look safe but are not.

Subho explains how the explosion of ChatGPT-style app wrappers, agentic AI, and rapid app creation has transformed software from static code into living systems, and why traditional security models no longer hold up. From fake AI apps harvesting personal data to AI agents lowering the barrier for attackers, this conversation explores the real-world consequences of AI at scale.

You will also hear why trust has become a core security metric, how app stores struggle to detect malicious behavior, and why developer burnout is rising as AI-generated code shifts risk downstream instead of removing it.

This episode is essential listening for founders, developers, security leaders, and anyone building or relying on AI-powered applications.

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(00:00) Why Mobile Apps Became a Massive Trust and Security Risk

(02:45) Subho's Journey and the Birth of AppNox

(06:17) Fake AI Apps, Malicious Wrappers, and Silent Data Theft

(11:03) How Fake Apps Slip Past App Store Reviews

(15:26) The Data Harvesting Business Model Behind Fake Apps

(17:11) AI for Security vs Security for AI

(22:16) Why Trust Is Becoming a Measurable AI Performance Metric

(26:20) User Intent, Data Control, and Minimum Data Sharing

(31:10) Trust, Governments, and Why Where AI Lives Matters

(35:40) What AppNox Found in Retail App Security Audits

(39:16) How AppNox Protects Apps at Scale

(42:05) The Future of Security

Construction is one of the least digitized industries in the world, and not because it resists technology. It resists bad technology.

In this episode of Eye on AI, Craig Smith sits down with Olek Paraska, CTO of Togal AI, to break down why construction productivity has barely improved in 50 years and why pre-construction is the real bottleneck holding the industry back.

Olek explains how most estimating and takeoff work is still done manually, why automating this phase can unlock massive efficiency gains, and how AI works best in construction when it acts as a perception and reasoning layer rather than a replacement for human judgment.

The conversation explores computer vision, agentic AI, human-in-the-loop systems, and why respecting real-world constraints is essential for AI to deliver real ROI. It also looks ahead to a future where floor plans, materials, costs, and constructability can be reasoned about together, long before construction begins.

This episode is a deep dive into how AI can finally move construction forward by solving the right problems, in the right order.

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(00:00) Why Construction Is Desperate for Better AI
(01:06) Olek's Path From Software to Construction
(02:17) Why Construction Productivity Has Stalled for Decades
(04:33) The Pre-Construction Bottleneck No One Talks About
(06:17) How Takeoffs Are Still Done Manually
(09:15) Why Construction Rejects Bad Technology
(11:18) How Togal Found the Right Problem to Solve
(12:14) From Computer Vision to Reasoning AI
(17:44) What Agentic AI Looks Like in Pre-Construction
(20:59) Turning Floor Plans Into Materials and Costs
(28:18) The Real ROI of AI for Contractors
(47:11) The Long-Term Vision for AI in Construction

In this episode of the Eye on AI Podcast, Craig Smith sits down with Steve Brown, founder of CureWise, to explore how agentic AI is reshaping healthcare from the patient's perspective.

Steve shares the deeply personal story behind CureWise, born out of his own experience with a rare cancer diagnosis that was repeatedly missed by traditional medical pathways. The conversation dives into why modern healthcare struggles with complex, edge-case conditions, how fragmented medical data and time-constrained systems fail patients, and where AI can meaningfully help without replacing clinicians.

The discussion goes deep into multi-agent AI systems, reliability through consensus, large context windows, and how AI can surface better questions rather than premature answers. Steve explains why patient education is the real unlock for better outcomes, how precision medicine depends on individualized data and genetics, and why empowering patients leads to stronger collaboration with doctors.

This episode offers a grounded, practical look at AI's role in healthcare, not as a diagnostic shortcut, but as a tool for clarity, context, and better decision-making in some of the most critical moments of car

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(00:00) Using Multi-Agent AI to Analyze Medical Records

(04:35) Steve Brown's Tech Background and Return to Healthcare

(08:25) How a Rare Cancer Diagnosis Was Initially Missed

(13:55) Why Modern Medicine Struggles With Complex Cases

(18:29) Multi-Agent Consensus and AI Reliability in Healthcare

(24:12) Large Context Windows, RAG, and Medical Data Organization

(28:24) Why CureWise Focuses on Patient Education, Not Diagnosis

(33:10) Precision Medicine, Genetics, and Personalized Treatment

(47:45) Why CureWise Launches Direct-to-Patient First

(53:19) The Future of AI-Driven Precision Medicine

AI is getting smarter, but now it needs better  judgment.

In this episode of the Eye on AI Podcast, we speak with Robbie Goldfarb, former Meta product leader and co-founder of Forum AI, about why treating AI as a truth engine is one of the most dangerous assumptions in modern artificial intelligence.

Robbie brings first-hand experience from Meta's trust and safety and AI teams, where he worked on misinformation, elections, youth safety, and AI governance. He explains why large language models shouldn't be treated as arbiters of truth, why subjective domains like politics, health, and mental health pose serious risks, and why more data does not solve the alignment problem.

The conversation breaks down how AI systems are evaluated today, how engagement incentives create sycophantic and biased models, and why trust is becoming the biggest barrier to real AI adoption. Robbie also shares how Forum AI is building expert-driven AI evaluation systems that scale human judgment instead of crowd labels, and why transparency about who trains AI matters more than ever.

This episode explores AI safety, AI trust, model evaluation, expert judgment, mental health risks, misinformation, and the future of responsible AI deployment.

If you are building, deploying, regulating, or relying on AI systems, this conversation will fundamentally change how you think about intelligence, truth, and responsibility.

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Website: https://www.byforum.com/
X: https://x.com/TheForumAI
LinkedIn: https://www.linkedin.com/company/byforum/

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(00:00) Why Treating AI as a "Truth Engine" Is Dangerous
(02:47) What Forum AI Does and Why Expert Judgment Matters
(06:32) How Expert Thinking Is Extracted and Structured
(09:40) Bias, Training Data, and the Myth of Objectivity in AI
(14:04) Evaluating AI Through Consequences, Not Just Accuracy
(18:48) Who Decides "Ground Truth" in Subjective Domains
(24:27) How AI Models Are Actually Evaluated in Practice
(28:24) Why Quality of Experts Beats Scale in AI Evaluation
(36:33) Trust as the Biggest Bottleneck to AI Adoption
(45:01) What "Good Judgment" Means for AI Systems
(49:58) The Risks of Engagement-Driven AI Incentives
(54:51) Transparency, Accountability, and the Future of AI

In this episode of Eye on AI, Craig Smith sits down with Jarrod Johnson, Chief Customer Officer at TaskUs, to unpack how agentic AI is changing customer service from conversations to real action. 

They explore what agentic AI actually is, why chatbots were only the first step, and how enterprises are deploying AI systems that resolve issues, execute tasks, and work alongside human teams at scale. 

The conversation covers real-world use cases, the economics of AI-driven support, why many enterprise AI pilots fail, and how human roles evolve when AI takes on routine work. 

A grounded look at where customer experience, enterprise AI, and the future of support are heading.

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(00:00) Jarrod Johnson and the Evolution of TaskUs

(03:58) Why AI Became Core to Customer Service

(06:07) Humans, AI, and the New Support Model

(07:16) What Agentic AI Actually Is

(11:38) TaskUs as an AI Systems Integrator

(14:59) How Agentic AI Resolves Customer Issues

(19:52) Workforce Impact and the Human Role

(23:26) Why Most Enterprise AI Pilots Fail

(30:32) Real Client Case Study: Healthcare Impact

(36:34) Why Customer Service Still Feels Broken

(38:49) The End of IVR Menus and Legacy Systems

(42:25) AI Safety, Compliance, and Governance

(49:38) Training Humans for AI and RLHF Work

(54:34) The Future of Agentic AI in Enterprise

Inference is now the biggest challenge in enterprise AI.

In this episode of Eye on AI, Craig Smith speaks with Nick Pandher, VP of Product at Cirrascale, about why AI is shifting from model training to inference at scale. As AI moves into production, enterprises are prioritizing performance, latency, reliability, and cost efficiency over raw compute.

The conversation covers the rise of inference-first infrastructure, the limits of hyperscalers, the emergence of neoclouds, and how agentic AI is driving always-on inference workloads. Nick also explains how inference-optimized hardware and serverless AI platforms are shaping the future of enterprise AI deployment.

If you are deploying AI in production, this episode explains why inference is the real frontier.

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(00:00) Preview

(00:50) Introduction to Cirrascale and AI inference

(03:04) What makes Cirrascale a neocloud

(04:42) Why AI shifted from training to inference

(06:58) Private inference and enterprise security needs

(08:13) Hyperscalers vs neoclouds for AI workloads

(10:22) Performance metrics that matter in inference

(13:29) Hardware choices and inference accelerators

(20:04) Real enterprise AI use cases and automation

(23:59) Hybrid AI, regulated industries, and compliance

(26:43) Proof of value before AI pilots

(31:18) White-glove AI infrastructure vs self-serve cloud

(33:32) Qualcomm partnership and inference-first AI

(41:52) Edge-to-cloud inference and agentic workflows

(49:20) Why AI pilots fail and how enterprises succeed

In this episode of Eye on AI, we sit down with Evan Reiser, co-founder and CEO of Abnormal AI, to unpack how AI has fundamentally changed the cybersecurity landscape.

We explore why social engineering remains the most costly form of cybercrime, how generative AI has lowered the barrier for sophisticated attacks, and why humans have become the primary attack surface in modern security. Evan explains why traditional, signature-based defenses fall short, how behavioral AI detects threats that have never existed before, and what it means to build security systems that understand how people actually work and communicate.

The conversation also looks ahead at the AI arms race between attackers and defenders, the economics driving cybercrime, and what it truly means to be an AI-native company operating at scale.

This episode is a deep dive into the human side of AI security and why the future of cybersecurity depends less on code and more on behavior.

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(00:00) Abnormal AI's origin

(02:31) Why phishing is still the biggest threat

(05:57) How attackers manipulate human trust

(10:05) The true cost of social engineering

(11:58) Vendor account compromise explained

(15:02) How AI changed cyber attacks

(16:28) Behavioral security vs traditional defenses

(19:55) Where Abnormal fits in the security stack

(22:24) Human psychology as the attack surface

(24:01) Why cyber defense is asymmetric

(28:48) Humans as the new zero-day

(31:01) Why attackers target people, not systems

(33:21) Behavioral modeling from ads to security

(36:10) Why money drives almost all attacks

(40:06) What happens after credentials are stolen

(42:18) Text scams and lateral movement

(43:55) What it means to be AI-native

(47:13) How Abnormal uses AI internally

In this episode of Eye on AI, Craig Smith speaks with Jonathan Wall, founder and CEO of Runloop AI, about why AI agents require an entirely new approach to compute infrastructure.

Jonathan explains why agents behave very differently from traditional servers, why giving agents their own isolated computers unlocks new capabilities, and how agent-native infrastructure is emerging as a critical layer of the AI stack. The conversation also covers scaling agents in production, building trust through benchmarking and human-in-the-loop workflows, and what agent-driven systems mean for the future of enterprise work.

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(00:00) Why AI Agents Require a New Infrastructure Paradigm

(01:38) Jonathan Wall's Journey: From Google Infrastructure to AI Agents

(04:54) Why Agents Break Traditional Cloud and Server Models

(07:36) Giving AI Agents Their Own Computers (Devboxes Explained)

(12:39) How Agent Infrastructure Fits into the AI Stack

(14:16) What It Takes to Run Thousands of AI Agents at Scale

(17:45) Solving the Trust and Accuracy Problem with Benchmarks

(22:28) Human-in-the-Loop vs Autonomous Agents in the Enterprise

(27:24) A Practical Walkthrough: How an AI Agent Runs on Runloop

(30:28) How Agents Change the Shape of Compute

(34:02) Fine-Tuning, Reinforcement Learning, and Faster Iteration

(38:08) Who This Infrastructure Is Built For: Startups to Enterprises

(41:17) AI Agents as Coworkers and the Future of Work

(46:37) The Road Ahead for Enterprise-Grade Agent Systems

In this episode of Eye on AI, Craig Smith sits down with Anurag Dhingra, Senior Vice President and General Manager at Cisco, to explore where AI is actually creating value inside the enterprise.

Rather than focusing on flashy demos or speculative futures, this conversation goes deep into the invisible layer powering modern AI: infrastructure.
Anurag breaks down how AI is being embedded into enterprise networking, security, observability, and collaboration systems to solve real operational problems at scale. 

From self-healing networks and agentic AI to edge computing, robotics, and domain-specific models, this episode reveals why the next phase of AI innovation is less about chatbots and more about resilient systems that quietly make everything work better.

This episodeis perfect for enterprise leaders, AI practitioners, infrastructure teams, and anyone trying to understand how AI moves from theory into production.

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(00:00) Why AI Only Matters If the Infrastructure Works
(01:22) Cisco's Evolution
(04:39) Connecting Networks, People, and Experiences at Scale
(09:31) How AI Is Transforming Enterprise Networking
(12:00) Edge AI, Robotics, and Real-World Reliability
(14:18) Security Challenges in an Agent-Driven Enterprise
(15:28) What Agentic AI Really Means (Beyond Automation)
(20:51) The Rise of Hybrid AI: Cloud Models vs Edge Models
(24:30) Why Small, Purpose-Built Models Are So Powerful
(29:19) Open Ecosystems and Agent-to-Agent Collaboration
(33:32) How Enterprises Actually Adopt AI in Practice
(35:58) Building AI-Ready Infrastructure for the Long Term
(40:14) AI in Customer Experience and Contact Centers
(44:14) The Real Opportunity of AI and What Comes Next

This episode is sponsored by AGNTCY. Unlock agents at scale with an open Internet of Agents. 

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Most large language models today generate text one token at a time. That design choice creates a hard limit on speed, cost, and scalability.

In this episode of Eye on AI, Stefano Ermon breaks down diffusion language models and why a parallel, inference-first approach could define the next generation of LLMs. We explore how diffusion models differ from autoregressive systems, why inference efficiency matters more than training scale, and what this shift means for real-time AI applications like code generation, agents, and voice systems.

This conversation goes deep into AI architecture, model controllability, latency, cost trade-offs, and the future of generative intelligence as AI moves from demos to production-scale systems.


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(00:00) Autoregressive vs Diffusion LLMs
(02:12) Why Build Diffusion LLMs
(05:51) Context Window Limits
(08:39) How Diffusion Works
(11:58) Global vs Token Prediction
(17:19) Model Control and Safety
(19:48) Training and RLHF
(22:35) Evaluating Diffusion Models
(24:18) Diffusion LLM Competition
(30:09) Why Start With Code
(32:04) Enterprise Fine-Tuning
(33:16) Speed vs Accuracy Tradeoffs
(35:34) Diffusion vs Autoregressive Future
(38:18) Coding Workflows in Practice
(43:07) Voice and Real-Time Agents
(44:59) Reasoning Diffusion Models
(46:39) Multimodal AI Direction
(50:10) Handling Hallucinations

This episode is sponsored by AGNTCY. Unlock agents at scale with an open Internet of Agents. 

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Why are AI, biotechnology, and gene editing converging right now, and what does that mean for the future of humanity?

In this episode of Eye on AI, host Craig Smith sits down with futurist and author Jamie Metzl to explore the superconvergence of artificial intelligence, genomics, and exponential technologies that are reshaping life on Earth.

We examine the ethical and scientific realities behind human genome editing, the controversy around CRISPR babies, and why society is not yet ready to edit human embryos at scale. The conversation unpacks the complexity of biology, the risks of tech driven hubris, and why governance, values, and social norms must evolve alongside scientific breakthroughs.

You will also hear a wide ranging discussion on health span versus longevity, AI and human decision making, education and inequality, and how these technologies could either unlock massive human flourishing or deepen existing global challenges depending on the choices we make today.

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Try OCI for free at http://oracle.com/eyeonai 

This episode is sponsored by Oracle. OCI is the next-generation cloud designed for every workload ? where you can run any application, including any AI projects, faster and more securely for less. On average, OCI costs 50% less for compute, 70% less for storage, and 80% less for networking. 

Join Modal, Skydance Animation, and today's innovative AI tech companies who upgraded to OCI?and saved.

Why is AI moving from the cloud to our devices, and what makes on device intelligence finally practical at scale?

In this episode of Eye on AI, host Craig Smith speaks with Christopher Bergey, Executive Vice President of Arm's Edge AI Business Unit, about how edge AI is reshaping computing across smartphones, PCs, wearables, cars, and everyday devices.

We explore how Arm v9 enables AI inference at the edge, why heterogeneous computing across CPUs, GPUs, and NPUs matters, and how developers can balance performance, power, memory, and latency. Learn why memory bandwidth has become the biggest bottleneck for AI, how Arm approaches scalable matrix extensions, and what trade offs exist between accelerators and traditional CPU based AI workloads.

You will also hear real world examples of edge AI in action, from smart cameras and hearing aids to XR devices, robotics, and in car systems. The conversation looks ahead to a future where intelligence is embedded into everything you use, where AI becomes the default interface, and why reliable, low latency, on device AI is essential for creating experiences users actually trust.

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This episode is sponsored by AGNTCY. Unlock agents at scale with an open Internet of Agents. 

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Why is AI so powerful in the cloud but still so limited inside everyday devices, and what would it take to run intelligent systems locally without draining battery or sacrificing privacy?

In this episode of Eye on AI, host Craig Smith speaks with Steve Brightfield, Chief Marketing Officer at BrainChip, about neuromorphic computing and why brain inspired architectures may be the key to the future of edge AI.

We explore how neuromorphic systems differ from traditional GPU based AI, why event driven and spiking neural networks are dramatically more power efficient, and how on device inference enables faster response times, lower costs, and stronger data privacy. Steve explains why brute force computation works in data centers but breaks down at the edge, and how edge AI is reshaping wearables, sensors, robotics, hearing aids, and autonomous systems.

You will also hear real world examples of neuromorphic AI in action, from smart glasses and medical monitoring to radar, defense, and space applications. The conversation covers how developers can transition from conventional models to neuromorphic architectures, what role heterogeneous computing plays alongside CPUs and GPUs, and why the next wave of AI adoption will happen quietly inside the devices we use every day.

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This episode is sponsored by AGNTCY. Unlock agents at scale with an open Internet of Agents. 

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Why do some AI agents attempt to bypass shutdown, and what does this behavior reveal about the future of AI safety?

In this episode of Eye on AI, host Craig Smith speaks with Jeffrey Ladish of Palisade Research to examine what recent shutdown experiments with agentic LLMs tell us about control, alignment, and the real world limits of current guardrails.

We explore how models behave when placed in virtual machine environments, why some agents edit or disable their own shutdown scripts, and what these results mean for researchers working on alignment and oversight. Learn how different models respond to shutdown instructions, how system prompts influence behavior, and which failure modes matter most for safe deployment.

You will also hear a detailed breakdown of the experimental setups, insights into tool using and self directed behavior, and a grounded discussion of the risks and opportunities that agentic systems introduce. This episode offers a clear and practical look at how AI agents operate under pressure and what these findings mean for the future of safe and reliable AI.

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Why are enterprises struggling to turn AI hype into real workplace transformation, and how is Lenovo using agentic AI to actually close that gap? In this episode of Eye on AI, host Craig Smith talks with Rakshit Ghura about how his team is reinventing the modern workplace with an omnichannel AI architecture powered by a fleet of specialized agents.


We explore how Lenovo has evolved from a hardware company into a global solutions provider, and how its Care of One platform uses persona based design to improve employee experience, reduce downtime, and personalize support across IT, HR, and operations. You will learn what enterprises get wrong about AI readiness, why trust and change management matter more than technology, and how organizations can design workplace stacks that meet employees where they are.


We also cover how Lenovo approaches responsible AI, how enterprises should think about security and governance when deploying agents, and why so many organizations are enthusiastic about AI but still not ready to adopt it. Rakshit shares real examples from retail, manufacturing, and field operations, including how AI can improve uptime, automate ticket resolution, monitor equipment, and provide proactive insights that drive measurable business impact.


You will also learn how to evaluate ROI for digital workplace solutions, how to involve employees early in the adoption cycle, and which metrics matter most when scaling agentic AI, including uptime, productivity improvements, and employee satisfaction.

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