Eye On A.I.

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