This episode features a fascinating conversation with Sid Sheth, CEO and Co-Founder of d-Matrix. With a deep background in building advanced systems for high-performance workloads, Sid and his team are at the forefront of AI compute innovation—specifically focused on making AI inference more efficient, cost-effective, and scalable for enterprise use. Host Seth Earley dives into Sid’s journey, the architectural shifts in AI infrastructure, and what it means for organizations seeking to maximize their AI investments.

Key Takeaways:

• The Evolution of AI Infrastructure: Sid breaks down how the traditional tech stack is being rebuilt to support the unique demands of AI, particularly shifting from general-purpose CPUs to specialized accelerators for inference.
• Training vs. Inference: Using a human analogy, Sid explains the fundamental difference between model training (learning) and inference (applying knowledge), emphasizing why most enterprise value comes from efficient inference.
• Purpose-built Accelerators: d-Matrix’s approach to creating inference-only accelerators means dramatically reducing overhead, latency, energy consumption, and cost compared to traditional GPU solutions.
• Scalability & Efficiency: Learn how in-memory compute, chiplets, and innovative memory architectures enable d-Matrix to deliver up to 10x lower latency, and significant gains in energy and dollar efficiency for AI applications.
• Market Trends: Sid reveals how, although today’s focus is largely on training compute, the next five to ten years will see inference dominate as organizations seek ROI from deployed AI.
• Enterprise Strategy Advice: Sid urges tech leaders not to be conservative, but to embrace a heterogeneous and flexible infrastructure strategy to future-proof their AI investments.
• Real-World Use Cases: Hear about d-Matrix’s work enabling low-latency agentic/reasoning models, which are critical for real-time and interactive AI workloads.

Insightful Quote from Sid Sheth:

“Now is not the time to be conservative and get comfortable with choice. In the world of inference there isn’t going to be one size fits all... The world of the future is heterogeneous, where you’re going to have a compute fleet that is augmented with different types of compute to serve different needs.”

Tune in to discover how to rethink your AI infrastructure strategy and stay ahead in the rapidly evolving world of enterprise AI!

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book

In this episode of the Earley AI Podcast, host Seth Earley sits down with Charles Migos, a veteran toolmaker whose career has spanned animation, post-production, visual effects, and large-scale media systems. Charles is recognized for his innovative approach to making emerging technologies—particularly generative AI—intuitive and accessible for creatives at every level, not just technical experts. Drawing on decades of experience, Charles shares what it means to design tools that empower storytellers and production teams, accelerate creativity, and address industry-specific needs.

Key Takeaways:

• The media and entertainment industry has been under stress due to macroeconomic changes and the disruptive impact of generative AI.
• Creative jobs and processes are being transformed as AI tools dramatically speed up and democratize production workflows, bridging the gap between concept and execution.
• Traditional creative roles are shifting: Directors, producers, and even clients can now participate directly in visual storytelling without years of technical training.
• Instead of relying solely on prompt-based AI systems, innovation lies in giving creatives precise, fine-grained control for rapid exploration and content iteration.
• Asset repurposability, modularity, and the ability to build on prior creative work are becoming crucial for agencies and studios to scale, reduce costs, and increase creative output.
• Protecting intellectual property and ensuring ethical AI use—particularly around deepfakes and content control—is fundamental as generative tools become more powerful.
• The foundation of successful creative AI lies in deeply understanding and honoring existing workflows, enabling faster collaboration, clearer communication, and greater creative trust.

Insightful Quote from Charles Migos:

"Your ability to create faster and better than you ever have before, your ability to collaborate with your team and your stakeholders in ways you never have before, and the ability to communicate around what you create as that team effort is what matters most."

Tune in to explore how AI is reshaping the creative landscape and to gain actionable insight on building truly human-centered design into emerging tech.

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book

In this episode of the Earley AI Podcast, host Seth Earley sits down with Abhi Yadav, founder of iCustomer AI—a cutting-edge composable decision intelligence company. With a deep background in enterprise technology, Abhi pioneered the Customer Data Platform (CDP) category and is now leading innovation in advanced multigraph frameworks and customer intelligence. He is also an advisor to leading data management organizations and is passionate about helping brands reshape how they connect with customers by making data truly "AI-ready."

Together, Seth and Abhi explore the evolving landscape at the intersection of AI, data, and enterprise maturity. They dive deep into the challenges and opportunities organizations face as they strive for higher levels of decision intelligence, highlighting both the technical and strategic shifts driving the industry forward.

Key Takeaways:

• The evolution from traditional MDM (Master Data Management) and CDP platforms toward a “knowledge engineering era” powered by AI and multi-graph architectures.
• Why a single source of truth is more about a unified, reconcilable semantic layer than a single data repository.
• The importance of developing robust taxonomies, ontologies, and knowledge graphs to contextualize customer data and orchestrate intelligent decisioning at scale.
• Clear definitions and distinctions between zero-party, first-party, second-party, and third-party data—and why they matter for privacy, compliance, and effective marketing.
• Challenges enterprise leaders face in scaling decision intelligence, especially in determining the right balance of human and machine-led decisions.
• The emerging need for "decision lineage": tracking not only what data was used, but how and why decisions were made, for compliance and transparency.
• Advice for founders, technologists, and enterprise leaders: avoid the hype and focus on solving real, specific problems before building platforms or dreaming of category creation.

Insightful Quote from Abhi Yadav:

"Don't follow the hype, follow the real problem. There are still so many real problems—and the more specific that problem is, the more impact you can make. Platforms and categories come later; start with solving measurable problems today."

Tune in for a thoughtful, actionable conversation on unlocking the true potential of enterprise AI and creating data architectures that drive meaningful impact.

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book