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.
Can agentic AI patch the internet? ft. Mike McGrath
Red Hat CTO Chris Wright interviews Mike McGrath, Vice President of Lightwell Engineering, about how Project Lightwell uses AI to revolutionize open source vulnerability patching and secure the software supply chain.
2 Sept 2026TRANSCRIPT
How Red Hat cleared IT debt for scalable AI ft. Marco Bill
Red Hat CTO Chris Wright interviews Red Hat CIO Marco Bill about simplifying IT infrastructure to enable scalable AI and transform business processes.
5 Aug 2026TRANSCRIPT
Standardizing the AI stack with PyTorch ft. Steven Pousty
Red Hat CTO Chris Wright interviews Principal AI Community Architect Steven Pousty about standardizing the AI stack with PyTorch, exploring open-source evolution and enterprise AI deployment.
1 Jul 2026TRANSCRIPT
Defining sovereign AI with open source ft. Jered Floyd
Red Hat CTO Chris Wright interviews Jered Floyd about how open source enables true AI sovereignty through control, autonomy, and resiliency across the technology stack.
3 Jun 2026TRANSCRIPT
Inside open source AI strategy ft. Steve Watt
Red Hat CTO Chris Wright interviews Steve Watt, VP of the Office of the CTO, exploring Red Hat's open source AI strategy, composable intelligence, and the evolving hardware landscape.
7 Jan 2026TRANSCRIPT
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.
3 Dec 2025TRANSCRIPT
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.
5 Nov 2025TRANSCRIPT
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.
1 Oct 2025
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.
3 Sept 2025
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.
6 Aug 2025
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.
2 Jul 2025
Building more efficient AI with vLLM ft. Nick Hill
Red Hat's Nick Hill discusses the evolution and optimization of AI, highlighting the role of open-source projects like vLLM in creating efficient inference stacks, in an interview on AI engineering.
4 Jun 2025
Scaling AI inference with open source ft. Brian Stevens
Brian Stevens, Red Hat's SVP and AI CTO, discusses scaling AI inference using open source, highlighting the shift to production-quality solutions and the impact of projects like vLLM in this interview with Chris Wright.
30 May 2025
Technically Speaking w/ Chris Wright: Deeper tech, more insights
Red Hat CTO Chris Wright explores emerging technologies like AI and cloud computing with industry experts, offering insights into their strategic impact in this interview.
Inside a recent episode
Can agentic AI patch the internet? ft. Mike McGrath
Published 7 Oct 2026 · Transcript excerpt
[…] Internally, we've kind of referred to it as patching the internet. That's all. No big deal. And yet we need automation. We need tools. We need AI, LLMs to help us through this process of not just discovery, but remediation. I think a really interesting aspect here is We, you know, take an average enterprise, which is built of thousands of applications, each of which has thousands of dependencies. So you get this sort of order of magnitude thing with that sandwich that you're describing being on the order of hundreds of thousands of different small language specific modules. If you're trying to manage all of that as an enterprise and think about the change associated with all of that, I think that's by itself overwhelming, just the sheer volume. […]
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