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.

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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.

AGNTCY - Unlock agents at scale with an open Internet of Agents. Visit https://agntcy.org/ and add your support.

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. 

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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

This episode is sponsored by Indeed. 

Stop struggling to get your job post seen on other job sites. Indeed's Sponsored Jobs help you stand out and hire fast. With Sponsored Jobs your post jumps to the top of the page for your relevant candidates, so you can reach the people you want faster.

Get a $75 Sponsored Job Credit to boost your job’s visibility! Claim your offer now: https://www.indeed.com/EYEONAI

In this episode, renowned AI researcher Pedro Domingos, author of The Master Algorithm, takes us deep into the world of Connectionism—the AI tribe behind neural networks and the deep learning revolution.

From the birth of neural networks in the 1940s to the explosive rise of transformers and ChatGPT, Pedro unpacks the history, breakthroughs, and limitations of connectionist AI. Along the way, he explores how supervised learning continues to quietly power today’s most impressive AI systems—and why reinforcement learning and unsupervised learning are still lagging behind.

We also dive into:

• The tribal war between Connectionists and Symbolists

• The surprising origins of Backpropagation

• How transformers redefined machine translation

• Why GANs and generative models exploded (and then faded)

• The myth of modern reinforcement learning (DeepSeek, RLHF, etc.)

• The danger of AI research narrowing too soon around one dominant approach

Whether you're an AI enthusiast, a machine learning practitioner, or just curious about where intelligence is headed, this episode offers a rare deep dive into the ideological foundations of AI—and what’s coming next.

Don’t forget to subscribe for more episodes on AI, data, and the future of tech.

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(00:00) What Are Generative Models?

(03:02) AI Progress and the Local Optimum Trap

(06:30) The Five Tribes of AI and Why They Matter

(09:07) The Rise of Connectionism

(11:14) Rosenblatt’s Perceptron and the First AI Hype Cycle

(13:35) Backpropagation: The Algorithm That Changed Everything

(19:39) How Backpropagation Actually Works

(21:22) AlexNet and the Deep Learning Boom

(23:22) Why the Vision Community Resisted Neural Nets

(25:39) The Expansion of Deep Learning

(28:48) NetTalk and the Baby Steps of Neural Speech

(31:24) How Transformers (and Attention) Transformed AI

(34:36) Why Attention Solved the Bottleneck in Translation

(35:24) The Untold Story of Transformer Invention

(38:35) LSTMs vs. Attention: Solving the Vanishing Gradient Problem

(42:29) GANs: The Evolutionary Arms Race in AI

(48:53) Reinforcement Learning Explained

(52:46) Why RL Is Mostly Just Supervised Learning in Disguise

(54:35) Where AI Research Should Go Next

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.

In this episode of Eye on AI, Craig Smith sits down with Barr Moses, Co-Founder & CEO of Monte Carlo, the pioneer of data and AI observability. Together, they explore the hidden force behind every great AI system: reliable, trustworthy data.

With AI adoption soaring across industries, companies now face a critical question: Can we trust the data feeding our models? Barr unpacks why data quality is more important than ever, how observability helps detect and resolve data issues, and why clean data—not access to GPT or Claude—is the real competitive moat in AI today.

What You’ll Learn in This Episode:

• Why access to AI models is no longer a competitive advantage

• How Monte Carlo helps teams monitor complex data estates in real-time

• The dangers of “data hallucinations” and how to prevent them

• Real-world examples of data failures and their impact on AI outputs

• The difference between data observability and explainability

• Why legacy methods of data review no longer work in an AI-first world

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(00:00) Intro

(01:08) How Monte Carlo Fixed Broken Data  

(03:08) What Is Data & AI Observability?  

(05:00) Structured vs Unstructured Data Monitoring  

(08:48) How Monte Carlo Integrates Across Data Stacks

(13:35) Why Clean Data Is the New Competitive Advantage  

(16:57) How Monte Carlo Uses AI Internally  

(19:20) 4 Failure Points: Data, Systems, Code, Models  

(23:08) Can Observability Detect Bias in Data?  

(26:15) Why Data Quality Needs a Modern Definition  

(29:22) Explosion of Data Tools & Monte Carlo’s 50+ Integrations  

(33:18) Data Observability vs Explainability  

(36:18) Human Evaluation vs Automated Monitoring  

(39:23) What Monte Carlo Looks Like for Users  

(46:03) How Fast Can You Deploy Monte Carlo?  

(51:56) Why Manual Data Checks No Longer Work  

(53:26) The Future of AI Depends on Trustworthy Data 

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

What happens when AI agents start negotiating, automating workflows, and rewriting how the enterprise world operates?

In this episode of the Eye on AI podcast, Will Grannis, CTO of Google Cloud, reveals how Google is leading the charge into the next frontier of artificial intelligence: agentic AI. From multi-agent systems that can file your expenses to futuristic R2-D2-style assistants in real-time race strategy, this episode dives deep into how AI is no longer just about models—it's about autonomous action.

In this episode, we explore:

• How AgentSpace is transforming how enterprises build AI agents

• The evolution from rule-based workflows to intelligent orchestration

• Real-world use cases: expense automation, content creation, code generation

• Trust, sovereignty, and securing agentic systems at scale

• The future of multi-agent ecosystems and AI-driven scientific discovery

• How large enterprises can match startup agility using their data advantage

Whether you're a founder, engineer, or enterprise leader—this episode will shift how you think about deploying AI in the real world.

Subscribe for more deep dives with tech leaders and AI visionaries.

Drop a comment with your thoughts on where agentic AI is headed!

(00:00) Preview and Intro

(02:34) Will Grannis’ Role at Google Cloud

(05:14) Origins of Agentic Workflows at Google

(09:10) How Generative AI Changed the Agent Game

(12:29) Agents, Tool Access & Trust Infrastructure

(14:01) What is Agent Space?

(16:30) Creative & Marketing Agents in Action

(23:29) Core Components of Building Agents

(25:29) Introducing the Agent Garden

(28:06) The “Cloud of Connected Agents” Concept

(33:53) Solving Agent Quality & Self-Evaluation

(37:19) The Future of Autonomous Finance Agents

(40:55) How Enterprises Choose Cloud Partners for Agents

(43:50) Google Cloud’s Principles in Practice

(46:27) Gemini’s Context Power in Cybersecurity

(49:50) Robotics and R2D2-Inspired AI Projects

(52:39) How to Try Agent Space Yourself

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

Visa’s President of Technology, Rajat Taneja, pulls back the curtain on the $3.3 billion AI transformation powering one of the world’s most trusted financial networks.

In this episode, Taneja shares how Visa—a company processing over $16 trillion annually across 300 billion real-time transactions—is leveraging AI not just to stop fraud, but to redefine the future of commerce.

From deep neural networks trained on decades of transaction data to generative AI tools powering next-gen agentic systems, Visa has quietly been an AI-first company since the 1990s. Now, with 500+ petabytes of data and 2,900 open APIs, it’s preparing for a future where agents, biometrics, and behavioral signals shape every interaction.

Taneja also reveals how Visa’s models can mimic bank decisions in milliseconds, stop enumeration attacks, and even detect fraud based on how you type. This is AI at global scale—with zero room for error.

What You’ll Learn in This Episode:

• How Visa’s $3.3B data platform powers 24/7 AI-driven decisioning

• The fraud models behind stopping $40 billion in criminal transactions

• What “agentic commerce” means—and why Visa is betting big on it

• How Visa uses behavioral biometrics to detect account takeovers

• Why Visa rebuilt its infrastructure for the AI era—10 years ahead of the curve

• The role of generative AI, biometric identity, and APIs in the next wave of payments

The future of commerce isn’t just cashless—it’s intelligent, autonomous, and trust-driven.

If you’re curious about how AI is redefining payments, security, and digital identity at massive scale, this episode is essential viewing.

Subscribe for more deep dives into the future of AI, commerce, and innovation.

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(00:00) Introduction

(02:57) Meet Rajat Taneja, Visa’s President of Technology

(04:02) Scaling AI for 300 Billion Transactions Annually

(05:27) The Models Behind Visa’s Fraud Detection

(08:02) Visa’s In-House AI Models vs Open-Source Tools

(10:54) Inside Visa’s $3.3B AI Data Platform

(12:29) Visa’s Role in E-Commerce Innovation

(16:24) Biometrics, Identity & Tokenization at Visa

(21:14) Visa’s Vision for AI-Driven Commerce

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, Yoav Shoham, co-founder of AI21 Labs, shares his insights on the evolution of AI, touching on key advancements such as Jamba and Maestro. From the early days of his career to the latest developments in AI systems, Yoav offers a comprehensive look into the future of artificial intelligence.

Yoav opens up about his journey in AI, beginning with his academic roots in game theory and logic, followed by his entrepreneurial ventures that led to the creation of AI21 Labs. He explains the founding of AI21 Labs and the company's mission to combine traditional AI approaches with modern deep learning methods, leading to innovations like Jamba—a highly efficient hybrid AI model that’s disrupting the traditional transformer architecture.

He also introduces Maestro, AI21’s orchestrator that works with multiple large language models (LLMs) and AI tools to create more reliable, predictable, and efficient systems for enterprises. Yoav discusses how Maestro is tackling real-world challenges in enterprise AI, moving beyond flashy demos to practical, scalable solutions.

Throughout the conversation, Yoav emphasizes the limitations of current large language models (LLMs), even those with reasoning capabilities, and explains how AI systems, rather than just pure language models, are becoming the future of AI. He also delves into the philosophical side of AI, discussing whether models truly "understand" and what that means for the future of artificial intelligence.

Whether you’re deeply invested in AI research or curious about its applications in business, this episode is filled with valuable insights into the current and future landscape of artificial intelligence.

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(00:00) Introduction: The Future of AI Systems

(02:33) Yoav’s Journey: From Academia to AI21 Labs

(05:57) The Evolution of AI: Symbolic AI and Deep Learning

(07:38) Jurassic One: AI21 Labs’ First Language Model

(10:39) Jamba: Revolutionizing AI Model Architecture

(16:11) Benchmarking AI Models: Challenges and Criticisms

(22:18) Reinforcement Learning in AI Models

(24:33) The Future of AI: Is Jamba the End of Larger Models?

(27:31) Applications of Jamba: Real-World Use Cases in Enterprise

(29:56) The Transition to Mass AI Deployment in Enterprises

(33:47) Maestro: The Orchestrator of AI Tools and Language Models

(36:03) GPT-4.5 and Reasoning Models: Are They the Future of AI?

(38:09) Yoav’s Pet Project: The Philosophical Side of AI Understanding

(41:27) The Philosophy of AI Understanding

(45:32) Explanations and Competence in AI

(48:59) Where to Access Jamba and Maestro