Technically Speaking with Chris Wright

What does digital sovereignty actually mean when most AI runs on someone else's black box? In this episode of Technically Speaking, Red Hat CTO Chris Wright talks with Jered Floyd from the Red Hat Field CTO team about what sovereignty really requires in the age of AI infrastructure.

They examine the four pillars of modern sovereign operations: data sovereignty, operational sovereignty, technology sovereignty, and assurance sovereignty. Along the way, Chris and Jered also take on a common misconception: that digital sovereignty is just a compliance checkbox, solved by hosting data in the right region. Real sovereignty is about control, autonomy, and resiliency across your entire stack, not just where your data sits.

The discussion also gets into why this has become urgent in just the last year or so, and why open source is turning out to be one of the few real paths to keeping that control. Tune in to hear why smaller, open-weight models are challenging massive black-box AI infrastructure, and how organizations can prepare for upcoming regulatory compliance boundaries, like the Digital Operational Resilience Act (DORA), within next-generation agentic workflows.

If you're thinking about how much of your AI stack you actually own versus rent, this one's worth your time.

Go behind the scenes of Red Hat's internal innovation engine to discover how we evaluate emerging open source tech and choose exactly where to place our next major technology bets. In this episode of Technically Speaking, Red Hat Chief Technology Officer Chris Wright sits down with Steve Watt, Vice President of the Office of the CTO at Red Hat, for a clear, "200-level" look at what's coming down the road. Together, they map out how Red Hat’s pathfinding group tracks open source innovations and shapes raw clay into enterprise-ready layouts. Steve shares how his teams follow proven open source innovators and build strategic university partnerships to stay ahead of an AI cycle that changes every single week.

Dive into the mechanics of composable intelligence, where the conversation explores the vLLM Semantic Router. This open source project, incubated within Red Hat, allows businesses to selectively route inference requests across specialized small language models or fall back to larger cloud models based on real-time cost, data privacy, and semantic analysis. Chris and Steve also break down how confidential computing protects data, showing how secure enclaves can keep proprietary model weights safe even when running in third-party environments.

Finally, discover how the hardware landscape is moving past GPU scarcity. Learn how new matrix multiplication instructions are opening up inference workloads on modern x86 CPUs, how specialized accelerators like Cerebras, Grok, and IBM Spyre are meeting strict data center power budgets, and how the OpenReg project makes it easier for silicon startups to integrate with PyTorch. Tune in to see why the real enterprise AI secret sauce is no longer just the model itself, but the open source infrastructure backing it.

While generative AI demos are flashy, moving those capabilities into a reliable, secure enterprise production environment is a different ballgame. In this episode of Technically Speaking, Red Hat CTO Chris Wright sits down with longtime Red Hatter and engineering leader Cat Weeks to dismantle the myth that you need a massive ""Frontier Model"" for every task.

Together, they explore the practical realities of enterprise AI, advocating for a ""right tool for the job"" approach. The conversation dives deep into the necessity of moving away from monolithic models toward a diverse fleet of specialized Small Language Models (SLMs) and agentic workflows. They also tackle the critical ""illities"", reliability, security, and portability, that often derail projects after the proof-of-concept phase.

Beyond the code, Chris and Cat discuss the human and process challenges of AI adoption, from navigating employee fear to ensuring data stewardship. Tune in to discover the differences between RAG and fine-tuning, how to manage agent identities, and why the future of enterprise AI belongs to a toolbox of specialized solutions, not just one giant hammer.

A decade ago, the shift from monolithic applications to microservices created an explosion of complexity that only Kubernetes could tame. Today, the enterprise is facing a similar inflection point. We are moving beyond simple chatbots to "Agentic AI"—autonomous systems capable of planning, tool calling, and executing complex workflows. But with this innovation comes fragmentation: how do you manage security, memory, observability, and scale across thousands of agents and models?

In this episode of Technically Speaking, Red Hat CTO Chris Wright sits down with fellow Red Hatter Tushar Katarki to explore the critical infrastructure needed for the next wave of AI. They discuss why the agentic era needs its own "Kubernetes," the rise of the Model Context Protocol (MCP) for connecting data, and the emerging concept of an "Agent Mesh" to handle identity and security.

Tune in to discover how platform engineering can bridge the gap between AI innovation and production stability, enabling enterprises to build scalable, open platforms for the future of automation.

We often hear about AI's potential to generate text and code, but its application in healthcare and life sciences promises a more profound impact: solving our most fundamental challenges in disease and medicine. This isn't just about applying existing LLMs; it's about building entirely new foundation models that understand the complex language of biology itself. To explore this new frontier, Red Hat CTO Chris Wright speaks with Dr. Jianying Hu, an IBM Fellow, Global Science Leader of AI for Health, and Director of Healthcare and Life Sciences Research at IBM.

Dr. Hu shares her extensive insights and expertise on:
•  The fundamental shift from general-purpose LLMs to domain-specific AI foundation models that learn the complex, multimodal language of biology.
•  How these new models power ""in silico"" simulations to accelerate drug discovery, validate new experiments, and test therapeutic efficacy.
•  The critical role of collaboration in applying AI, as seen in the IBM & Cleveland Clinic Discovery Accelerator, to create models that lead to personalized medicine.
•  Why open science and shared community benchmarks are essential for driving the next wave of medical breakthroughs.

Join in as they explore how this foundational, collaborative work is creating a new paradigm for scientific discovery. And discover why the future of medicine isn't just about a single AI model, but about building an open, iterative ecosystem that combines deep domain expertise with the power of AI to understand biology from the ground up.