Technically Speaking with Chris Wright

Red Hat

Struggling to keep pace with the ever-changing world of technology? For experienced tech professionals, making sense of this complexity to find real strategic advantages is key. This series offers a clear path, featuring insightful, casual conversations with leading global experts, innovators, and key voices from Red Hat, all cutting through the hype.

Drawing from Red Hat's deep expertise in open source and enterprise innovation, each discussion delves into new and emerging technologies-- from artificial intelligence and the future of cloud computing to cybersecurity, data management, and beyond. The focus is on understanding not just the 'what,' but the important 'why' and 'how': exploring how these advancements can shape long-term strategic developments for your organization and your career. Gain an insider’s perspective that humanizes complex topics, helping you anticipate what’s next and make informed decisions. Equip yourself with the knowledge to turn today's emerging tech into valuable, practical strategies and apply innovative thinking in your work.

Tune in for forward-looking discussions that connect the dots between cutting-edge technology and real-world application, leveraging a rich understanding of the enterprise landscape. Learn to navigate the future of tech with confidence.

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13 episodes
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Avg 24 min
English

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S2 · E4
September 2 · 22 min
How Red Hat cleared IT debt for scalable AI ft. Marco Bill
While conversations around enterprise AI tend to focus on high-level possibilities, less attention is given to the foundational plumbing required to make systems actually run. What happens when an enterprise's financial data is copied across 73 different spreadsheets or bogged down by thousands of legacy virtual machines? In this episode of Technically Speaking, host and Red Hat CTO Chris Wright sits down with Red Hat CIO Marco Bill to peek behind the curtain of enterprise IT transformation and explore why the secret to AI readiness is radical simplification. Building on 20 years of productive friction between engineering and IT, Marco shares an insider's look at what it really takes to clean up legacy technical debt. From standardizing workloads on Red Hat OpenShift to taking the bold step of decommissioning 2,400 unused dashboards, Marco explains how reducing infrastructure and data complexity creates the clean, single source of truth necessary for enterprise AI. Chris and Marco dive into the real-world trajectory of enterprise AI adoption—moving from associate productivity boosts to high-ROI business process transformation. The conversation also tackles the modern pitfalls of technology governance. They discuss how overly dense legal policies accidentally fuel "shadow AI," and why organizations must replace legalese with automated security guardrails and developer-friendly workflows. Plus, they examine why the explosive demand for AI compute and accelerators is bringing bare metal infrastructure full circle within hybrid cloud strategies. Tune in to discover why true innovation often looks like subtraction, and learn how enterprise leaders can clean up their data foundations today to build scalable, secure, and AI-native organizations for tomorrow.
S2 · E3
August 5 · 23 min
Standardizing the AI stack with PyTorch ft. Steven Pousty
You might view PyTorch simply as a tool for data scientists building models, but it has become a critical infrastructure bridge for modern AI-powered workloads. Acting as the functional "kernel" of the modern AI stack, PyTorch handles the complex mathematical abstractions and hardware offloading necessary to turn raw computing power into scalable enterprise systems. In this episode of Technically Speaking with Chris Wright, Red Hat Chief Technology Officer Chris Wright sits down with Steve Pousty, Principal AI Community Architect in the Red Hat Open Source AI Program Office. Together, they map out how open source is driving the evolution of data architecture and why Red Hat is focusing heavily on this ecosystem. They draw clear historical parallels to the early days of Linux and Kubernetes, outlining how the enterprise landscape is rapidly transitioning from raw upstream projects to fully supported, standardized AI distributions.The conversation explores the key architectural pillars within the PyTorch Foundation ecosystem. Chris and Steve break down the importance of distributed computing using cluster systems like Ray, hardware-agnostic model serving powered by vLLM, and optimization frameworks like Helion DSL that slash execution runtime constraints at scale. They also detail the introduction of Kubernetes-native distributed inference engines like llm-d and explain how open source governance establishes a neutral playing field that provides true hardware choice for the data center. Tune in to discover how to transition your AI strategies from lab experiments to robust enterprise deployments.
S2 · E2
July 1 · 25 min
Defining sovereign AI with open source ft. Jered Floyd
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.
S2 · E1
June 3 · 29 min
Inside open source AI strategy ft. Steve Watt
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.
S1 · E8
January 7 · 26 min
Build a production-ready AI toolbox ft. Cat Weeks
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.
S1 · E7
Dec 3, 2025 · 28 min
Platform engineering for AI agents ft. Tushar Katarki
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.
S1 · E6
Nov 5, 2025 · 27 min
Driving healthcare discoveries with AI ft. Jianying Hu
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.
S1 · E5
Oct 1, 2025 · 21 min
Security for the AI supply chain ft. Aeva Black
The software supply chain has always been a critical battleground, but AI introduces an exponential increase in scale and complexity. We are no longer just securing lines of code; we are now responsible for securing the models that generate it. This a new reality that shifts the entire attack surface for distributed IT systems. To explore this new frontier, Red Hat CTO Chris Wright speaks with Æva Black, an open source security and policy expert. Æva Black shares their extensive insights and expertise on: • The fundamental shift from securing code to securing the data, training processes, and models that make up the AI supply chain. • New, emerging attack vectors, such as exploiting model quantization, and how they are analogous to hardware threats like Spectre and Meltdown. • The growing burden of low-quality, AI-generated contributions on open source communities and the risk this poses to project sustainability and security. • Why the concept of “model provenance” is essential for building trust in AI systems. • The rising importance of public policy and government funding to protect and sustain open source as the critical digital infrastructure it has become. Listen in as they explore how the foundational principles of open source including transparency, collaboration, and community-driven governance, offer our most promising path forward. And discover why the health and sustainability of open source communities are directly tied to the security of our AI-powered future and what enterprise leaders can do to move from passive consumers to active contributors in this critical ecosystem.
S1 · E4
Sep 3, 2025 · 30 min
Taming AI agents with observability ft. Bernd Greifeneder
As modern IT systems grow too complex for humans to effectively manage, there's growing interest in turning to autonomous AI agents for operations. While powerful, these agents introduce new challenges around trust, reliability, and control. To explore how to solve for this, Red Hat CTO Chris Wright speaks with Bernd Greifeneder, Founder and CTO of Dynatrace [LINK: https://www.dynatrace.com], a company that has long focused on managing complexity with AI. Bernd Greifeneder shares his industry insights and expertise on: • The evolution from simple application architectures to massive, ephemeral microservices environments that are beyond human-scale to manage. • Balancing creative, generative AI with fact-based, causal AI to create a reliable and deterministic foundation for autonomous operations. • The critical need for high-quality, real-time data to create a "digital twin" of a production environment, enabling true root cause analysis instead of just correlation. • Why building trust is the biggest challenge for agentic AI and how a "human-in-the-loop" approach is essential before handing over the keys. Tune in for an in-depth discussion on the future of autonomous IT. This conversation is critical for any SRE, developer, or technology leader preparing to manage not just their systems, but the AI agents that will run them.
S1 · E3
Aug 6, 2025 · 26 min
Inside distributed inference with llm-d ft. Carlos Costa
Scaling LLM inference for production isn't just about adding more machines, it demands new intelligence in the infrastructure itself. In this episode, we're joined by Carlos Costa, Distinguished Engineer at IBM Research, a leader in large-scale compute and a key figure in the llm-d project. We discuss how to move beyond single-server deployments and build the intelligent, AI-aware infrastructure needed to manage complex workloads efficiently. Carlos Costa shares insights from his deep background in HPC and distributed systems, including: • The evolution from traditional HPC and large-scale training to the unique challenges of distributed inference for massive models. • The origin story of the llm-d project, a collaborative, open-source effort to create a much-needed ""common AI stack"" and control plane for the entire community. • How llm-d extends Kubernetes with the specialization required for AI, enabling state-aware scheduling that standard Kubernetes wasn't designed for. • Key architectural innovations like the disaggregation of prefill and decode stages and support for wide parallelism to efficiently run complex Mixture of Experts (MOE) models. Tune in to discover how this collaborative, open-source approach is building the standardized, AI-aware infrastructure necessary to make massive AI models practical, efficient, and accessible for everyone.
S1 · E2
Jul 2, 2025 · 20 min
Building more efficient AI with vLLM ft. Nick Hill
Explore what it takes to run massive language models efficiently with Red Hat's Senior Principal Software Engineer in AI Engineering, Nick Hill. In this episode, we go behind the headlines to uncover the systems-level engineering making AI practical, focusing on the pivotal challenge of inference optimization and the transformative power of the vLLM open-source project. Nick Hill shares his experiences working in AI including: • The evolution of AI optimization, from early handcrafted systems like IBM Watson to the complex demands of today's generative AI. • The critical role of open-source projects like vLLM in creating a common, efficient inference stack for diverse hardware platforms. • Key innovations like PagedAttention that solve GPU memory fragmentation and manage the KV cache for scalable, high-throughput performance. • How the open-source community is rapidly translating academic research into real-world, production-ready solutions for AI. Join us to explore the infrastructure and optimization strategies making large-scale AI a reality. This conversation is essential for any technologist, engineer, or leader who wants to understand the how and why of AI performance. You’ll come away with a new appreciation for the clever, systems-level work required to build a truly scalable and open AI future.
S1 · E1
Jun 4, 2025 · 29 min
Scaling AI inference with open source ft. Brian Stevens
Explore the future of enterprise AI with Red Hat's SVP and AI CTO, Brian Stevens. In this episode, we delve into how AI is being practically reimagined for real-world business environments, focusing on the pivotal shift to production-quality inference at scale and the transformative power of open source. Brian Stevens shares his expertise and unique perspective on: • The evolution of AI from experimental stages to essential, production-ready enterprise solutions. • Key lessons from the early days of enterprise Linux and their application to today’s AI inference challenges. • The critical role of projects like vLLM in optimizing AI models and creating a common, efficient inference stack for diverse hardware. • Innovations in GPU-based inference and distributed systems (like KV cache) that enable AI scalability. Tune in for a deep dive into the infrastructure and strategies making enterprise AI a reality. Whether you're a seasoned technologist, an AI practitioner, or a leader charting your company's AI journey, this discussion will provide valuable insights into building an accessible, efficient, and powerful AI future with open source.
May 30, 2025 · 43 sec
Technically Speaking w/ Chris Wright: Deeper tech, more insights
Ready to go deeper into the ever-evolving landscape of technology? Technically Speaking, hosted by Red Hat CTO and SVP of Global Engineering Chris Wright, is back and reimagined to guide you with more depth and candor. This series cuts through the noise, offering insightful, casual, and now even more in-depth conversations with leading experts from across the globe. Each discussion delves further into new and emerging technologies, helping you understand not just the 'what,' but the 'why' and 'how' these advancements will impact long-term strategic developments for your company and your career. From AI and open source innovation to cloud computing and beyond, Chris Wright and his guests humanize technology, providing an unparalleled insider’s look at what’s next with enhanced detail and open discussion. The revamped ""Technically Speaking with Chris Wright"" champions innovation and thought leadership, blending even deeper-dive discussions with updates on the latest tech news. Tune in for richer insights on complex topics, explore varied perspectives with greater nuance, and equip yourself to shape the future of technology. Discover how to turn today's emerging tech into tomorrow's strategic advantage.

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