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