Eye On A.I.

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

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