Can Generative AI Be Secured? Amazon's Chief Security Officer Weighs In

In this episode of Eye on AI, Amazon's Chief Security Officer Stephen Schmidt pulls back the curtain on how Amazon is using AI-powered cybersecurity to defend against real-world threats. From global honeypots to intelligent alarm systems and secure AI agent networks, Steve shares never-before-heard details on how Amazon is protecting both its infrastructure and your data in the age of generative AI.

We dive deep into:

• Amazon's MadPot honeypot network and how it tracks adversaries in 90 seconds

• The role of AI in threat detection, alarm triage, and code validation

• Why open-source vs. closed-source models are a real security debate

• The critical need for data privacy, secure LLM usage, and agent oversight

• Amazon's $5M+ Nova Trusted AI Challenge to battle adversarial code generation
  
  

Whether you're building AI tools, deploying models at scale, or just want to understand how the future of cybersecurity is evolving—this episode is a must-listen.

Don’t forget to like, subscribe, and turn on notifications to stay updated on the latest in AI, security, and innovation.

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(00:00) Preview

(00:52) Stephen Schmidt’s Role and Background at Amazon

(02:11) Inside Amazon's Global Honeypot Network (MadPot)

(05:26) How Amazon Shares Threat Intel Through GuardDuty

(08:06) Are Cybercriminals Using AI?

(10:28) Open Source vs Closed Source AI Security Debate

(13:09) What Is Amazon GuardDuty

(17:44) How Amazon Protects Customer Data at Scale

(20:18) Can Autonomous AI Agents Handle Security?

(25:14) How Amazon Empowers SMBs with Agent-Driven Security

(26:18) What Tools Power Amazon’s Security Agents?

(29:25) AI Security Basics

(35:34) Securing AI-Generated Code

(37:26) Are Models Learning from Our Queries?

(39:44) Risks of Agent-to-Agent Data Sharing

(42:08) Inside the $5M Nova Trusted AI Security Challenge

(47:01) Supply Chain Attacks and State Actor Tactics

(51:32) How Many True Adversaries Are Out There?

(53:04) What Everyone Needs to Know About AI Security

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What if AI could predict exactly when you'd get sick—and help you prevent it?

In this episode of Eye on AI, Dr. Eric Topol, world-renowned cardiologist, author, and AI health pioneer, joins us to unveil the future of preventive medicine. We dive deep into the themes of his new book Super Agers, which lays out a groundbreaking blueprint for extending healthspan—not just lifespan—through the power of multimodal AI and deep biological data.

Dr. Topol explains how AI models can now analyze a full-stack of human data—genomics, proteomics, metabolomics, microbiome, and more—to forecast age-related diseases like cancer, Alzheimer’s, and heart disease decades before symptoms appear. This isn’t science fiction. It’s here now.

If you're interested in the intersection of AI, longevity, and the future of medicine, this is a must-listen.

Where AI Works tackles the big questions shaping AI’s role in business today, cutting through the hype to deliver actionable insights for leaders. Brought to you by the Wharton School, in collaboration with Accenture, this podcast combines cutting-edge research with real-world case studies to uncover how top companies are using AI to upskill workforces, enhance customer experiences, boost productivity, and streamline operations.

Check it out: https://link.cohostpodcasting.com/f5e223b4-da0c-4fc8-bbf3-5f24c15f8fd2?d=sxo9xhJN2

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(00:00) The Power of Precision Medical Forecasting

(01:53) Eric Topol’s Journey into Digital & AI Medicine

(03:27) Using AI to Prevent Aging-Related Diseases

(05:25) The Challenge of Health Data Privacy & Ownership

(09:05) Genetic Risk to Deep Data Insights

(11:20) Personalized Prevention Through Lifestyle & Biomarkers

(13:59) Why Anti-Aging Drugs Are Still Years Away

(16:18) What are Organ Clocks

(19:34) The Longevity Industry’s Flawed Use of AI

(21:59) Top AI Pioneers Endorse “Super Agers”

(24:21) Which Longevity Startups Are Getting It Right?

(26:27) Why Topol Refuses to Join Longevity Startups

(28:57) Topol’s Own Health Data & Lessons Learned

(30:25) How Accurate Is AI at Predicting Disease Timing?

(31:47) The Truth About Genetic Risk and Cancer Detection

(33:33) AI-Driven Cancer Detection: A Smarter Approach

(38:51) How Precision Medicine Has Evolved

(41:02) The Risky Reality of Anti-Aging Interventions

(44:39) Why Healthspan Matters More Than Lifespan

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.

Offer only for new US customers with a minimum financial commitment. See if you qualify for half off at http://oracle.com/eyeonai

In this episode of Eye on AI, host Craig Smith speaks with Prashanth Chandrasekar, CEO of Stack Overflow, about how one of the internet’s most trusted platforms for developers is adapting to the era of generative AI. With over 60 million human-curated Q&A pairs, Stack Overflow is now at the center of AI development — not as a competitor to large language models like ChatGPT, but as a foundational knowledge base that powers them.

Prashanth breaks down how Stack Overflow is partnering with OpenAI, Google, and other LLM providers to license its data and improve AI accuracy, while also protecting the integrity of its community. He explains the rise of OverflowAI, how Stack Overflow for Teams is fueling enterprise-grade co-pilots, and why developers still rely on expert human input when AI hits its “complexity cliff.” The conversation covers everything from hallucination problems and trust issues in AI-generated code to the monetization of developer data and the evolving interface of the web.

If you want to understand the future of developer tools, AI coding assistants, and how human knowledge will coexist with autonomous agents, this episode is a must-listen.

Subscribe for more deep dives into how AI is reshaping the world of software, enterprise, and innovation.

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(00:00) Intro

(02:31) Prashanth’s Journey from Developer to CEO

(05:18) Why Stack Overflow is Different from GitHub

(08:51) The Power of Community and Human-Curated Knowledge

(12:53) Stack Overflow’s Data Strategy for AI Training

(17:26) Why Stack Overflow Isn’t Competing with OpenAI

(20:36) How Stack Overflow Powers Enterprise AI Agents

(26:13) OverflowAI, Gemini, and the Future of Dev Workflows

(30:09) Inside Stack Overflow for Teams

(33:29) Safeguarding Quality: The Fight Against AI Slop

(38:32) Licensing, Attribution, and Protecting the Knowledge Base

(43:19) Business Strategy in the Age of Generative AI

This episode is brought to you by Extreme Networks, the company radically improving customer experiences with AI-powered automation for networking.Extreme is driving the convergence of AI, networking, and security to transform the way businesses connect and protect their networks, delivering faster performance, stronger security, and a seamless user experience.

Visit https://www.extremenetworks.com/ to learn more.

In this episode of Eye on AI, we sit down with Ivan Shkvarun, CEO of Social Links and founder of the Dark Side AI Initiative, to uncover how cybercriminals are leveraging generative AI to orchestrate fraud, deepfakes, and large-scale digital attacks—often with just a few lines of code.

Ivan shares how his team is building real-time OSINT (Open Source Intelligence) tools to help governments, enterprises, and law enforcement fight back. From dark web monitoring to ethical AI frameworks, we explore what it takes to protect the digital world from the next wave of AI-powered crime.

Whether you're in tech, cybersecurity, or policy—this conversation is a wake-up call.

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From cybersecurity to law enforcement — discover how Social Links brings the full potential of OSINT to your team at http://bit.ly/44sytzk

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(00:00) Preview

(02:11) Meet Ivan Shkvarun & Social Links

(03:41) Launching the Dark Side AI Initiative

(05:16) What OSINT Actually Means Today

(08:39) How Law Enforcement Trace Digital Footprints

(12:50) Connecting Surface Web to Darknet

(16:12) OSINT Methodology in Action

(20:23) Why Most Companies Waste Their Own Data

(21:09) Cybersecurity Threats Beyond the IT Department

(26:25) BrightSide AI vs. DarkSide AI

(30:10) Should AI-Generated Content Be Labeled?

(31:26) Why We Can’t “Stop” AI

(35:37) Why AI-Driven Fraud Is Exploding

(41:39) The Reality of Criminal Syndicates

This episode is sponsored by Netsuite by Oracle, the number one cloud financial system, streamlining accounting, financial management, inventory, HR, and more.NetSuite is offering a one-of-a-kind flexible financing program.

Head to https://netsuite.com/EYEONAI to know more.

AWS partnered with Forrester Research to understand how software providers (ISVs), in particular, plan to drive profitable growth with generative AI, how they are uniquely approaching generative AI development, and the key challenges they’re facing.

In this conversation with Jeffrey Hammond, Global ISV Product Strategist at AWS, he dives into the findings of the research and discusses how — particularly with AWS’s help — ISVs can drive profitable growth and succeed in the gen AI gold rush.

Jeffrey helps software product management leaders leverage AWS cloud services to accelerate product delivery, create new revenue streams, reduce technical debt, and optimize operational costs.

You’ll learn:

• Why “toil reduction” is the fastest path to GenAI ROI

• How AWS’s GenAI Innovation Center helps companies cut costs and ship faster

• What most ISVs get wrong about trust, security, and customer communication

• The secret to scalable AI product pricing—and what Canva got right

• Why agentic workflows and federated models are the next frontier in software

Whether you're building on AWS or just exploring GenAI adoption, this conversation is packed with frameworks, examples, and strategy.

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(00:00) The Future of Work with Generative AI

(03:20) Inside AWS: How Jeffrey Supports AI Innovation

(06:00) What the Forrester Survey Reveals About AI Adoption

(09:15) From Hype to Value: Building Real GenAI Use Cases

(13:45) How ISVs Are Reducing Toil and Driving Efficiency

(17:10) Balancing Innovation with Trust and Security

(22:00) AWS Programs That Help ISVs Win with AI

(28:00) GenAI Product Strategy: Accuracy, Cost & Pricing Models

(34:30) Overcoming Infrastructure Challenges in GenAI

(39:45) The Rise of Agentic Workflows and Interoperability

(46:00) The Biggest Tech Disruption in Decades?

This episode is sponsored by the DFINITY Foundation.

DFINITY Foundation's mission is to develop and contribute technology that enables the Internet Computer (ICP) blockchain and its ecosystem, aiming to shift cloud computing into a fully decentralized state.

Find out more at https://internetcomputer.org/

In this episode of Eye on AI, we sit down with Sid Sheth, CEO and Co-Founder of d-Matrix, to explore how his company is revolutionizing AI inference hardware and taking on industry giants like NVIDIA.

Sid shares his journey from building multi-billion-dollar businesses in semiconductors to founding d-Matrix—a startup focused on generative AI inference, chiplet-based architecture, and ultra-low latency AI acceleration.

We break down:

• Why the future of AI lies in inference, not training

• How d-Matrix’s Corsair PCIe accelerator outperforms NVIDIA's H200

• The role of in-memory compute and high bandwidth memory in next-gen AI chips

• How d-Matrix integrates seamlessly into hyperscaler and enterprise cloud environments

• Why AI infrastructure is becoming heterogeneous and what that means for developers

• The global outlook on inference chips—from the US to APAC and beyond

• How Sid plans to build the next NVIDIA-level company from the ground up.

Whether you're building in AI infrastructure, investing in semiconductors, or just curious about the future of generative AI at scale, this episode is packed with value.

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(00:00) Intro

(02:46) Introducing Sid Sheth

(05:27) Why He Started d-Matrix

(07:28) Lessons from Building a $2.5B Chip Business

(11:52) How d-Matrix Prototypes New Chips

(15:06) Working with Hyperscalers Like Google & Amazon

(17:27) What’s Inside the Corsair AI Accelerator

(21:12) How d-Matrix Beats NVIDIA on Chip Efficiency

(24:10) The Memory Bandwidth Advantage Explained

(26:27) Running Massive AI Models at High Speed

(30:20) Why Inference Isn’t One-Size-Fits-All

(32:40) The Future of AI Hardware

(36:28) Supporting Llama 3 and Other Open Models

(40:16) Is the Inference Market Big Enough?

(43:21) Why the US Is Still the Key Market

(46:39) Can India Compete in the AI Chip Race?

(49:09) Will China Catch Up on AI Hardware?

This episode is sponsored by Thuma. Thuma is a modern design company that specializes in timeless home essentials that are mindfully made with premium materials and intentional details.

To get $100 towards your first bed purchase, go to http://thuma.co/eyeonai

Can AI Ever Reach AGI? Pedro Domingos Explains the Missing Link

In this episode of Eye on AI, renowned computer scientist and author of The Master Algorithm, Pedro Domingos, breaks down what’s still missing in our race toward Artificial General Intelligence (AGI) — and why the path forward requires a radical unification of AI's five foundational paradigms: Symbolists, Connectionists, Bayesians, Evolutionaries, and Analogizers.

Topics covered:

• Why deep learning alone won’t achieve AGI

• How reasoning by analogy could unlock true machine creativity

• The role of evolutionary algorithms in building intelligent systems

• Why transformers like GPT-4 are impressive—but incomplete

• The danger of hype from tech leaders vs. the real science behind AGI

• What the Master Algorithm truly means — and why we haven’t found it yet
  
  

Pedro argues that creativity is easy, reliability is hard, and that reasoning by analogy — not just scaling LLMs — may be the key to Einstein-level breakthroughs in AI.

Whether you're an AI researcher, machine learning engineer, or just curious about the future of artificial intelligence, this is one of the most important conversations on how to actually reach AGI.

📚 About Pedro Domingos: Pedro is a professor at the University of Washington and author of the bestselling book The Master Algorithm, which explores how the unification of AI's "five tribes" could produce the ultimate learning algorithm.

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(00:00) The Five Tribes of AI Explained

(02:23) The Origins of The Master Algorithm

(08:22) Designing with Bit Strings: Radios, Robots & More

(10:46) Fitness Functions vs Reward Functions in AI

(15:51) What Is Reasoning by Analogy in AI?

(18:38) Kernel Machines and Support Vector Machines Explained

(22:23) Case-Based Reasoning and Real-World Use Cases

(27:38) Are AI Tribes Still Siloed or Finally Collaborating?

(32:42) Why AI Needs a Deeply Unified Master Algorithm

(36:40) Creativity vs Reliability in AI

(39:14) Can AI Achieve Scientific Breakthroughs?

(41:26) Why Reasoning by Analogy Is AI’s Missing Link

(45:10) Evolutionaries: The Most Distant Tribe in AI

(48:41) Will Quantum Computing Help AI Reach AGI?

(53:15) Are We Close to the Master Algorithm?

(57:44) Tech Leaders, Hype & the Reality of AGI

(01:04:06) The AGI Spectrum: Where We Are & What’s Missing

(01:06:18) Pedro’s Research Focus

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.

Offer only for new US customers with a minimum financial commitment. See if you qualify for half off at http://oracle.com/eyeonai

In this episode of Eye on AI, Craig Smith sits down with Brice Challamel, Head of AI Products and Innovation at Moderna, to explore how one of the world’s leading biotech companies is embedding artificial intelligence across every layer of its business—from drug discovery to regulatory approval.

Brice breaks down how Moderna treats AI not just as a tool, but as a utility—much like electricity or the internet—designed to empower every employee and drive innovation at scale. With over 1,800 GPTs in production and thousands of AI solutions running on internal platforms like Compute and MChat, Moderna is redefining what it means to be an AI-native company.

Key topics covered in this episode:

• How Moderna operationalizes AI at scale

• GenAI as the new interface for machine learning

• AI’s role in speeding up drug approvals and clinical trials

• The future of personalized cancer treatment (INT)

• Moderna’s platform mindset: AI + mRNA = next-gen medicine

• Collaborating with the FDA using AI-powered systems

Don’t forget to like, comment, and subscribe for more interviews at the intersection of AI and innovation.

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(00:00) Preview

(02:49) Brice Challamel’s Background and Role at Moderna

(05:51) Why AI Is Treated as a Utility at Moderna

(09:01) Moderna's AI Infrastructure

(11:53) GenAI vs Traditional ML

(14:59) Combining mRNA and AI as Dual Platforms

(18:15) AI’s Impact on Regulatory & Clinical Acceleration

(23:46) The Five Core Applications of AI at Moderna

(26:33) How Teams Identify AI Use Cases Across the Business

(29:01) Collaborating with the FDA Using AI Tools

(33:55) How Moderna Is Personalizing Cancer Treatments

(36:59) The Role of GenAI in Medical Care

(40:10) Producing Personalized mRNA Medicines

(42:33) Why Moderna Doesn’t Sell AI Tools

(45:30) The Future: AI and Democratized Biotech