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SUMMARY: How real-time power flow optimization at the edge is helping data centers and the electrical grid handle surging AI energy demands more efficiently. By unlocking hidden capacity and dynamically managing power systems, we explain how existing infrastructure can support significantly more compute without massive new buildouts. GUEST: Marissa Hummon, CTO Utilidata SHOW: 1020 SHOW TRANSCRIPT: The Reasoning Show #1020 Transcript SHOW VIDEO: https://youtu.be/ItcpU8UjOFE SHOW SPONSORS: Nasuni - Activate your data for AI and request a demoShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!SHOW NOTES: Utilidata (homepage)AI Data Center to Receive 50% Capacity Boost with AI Power OrchestrationKEY TOPICS: Differences between grid power dynamics vs. AI workloadsEdge AI for real-time power flow optimizationUnlocking stranded capacity in existing infrastructure“4-to-make-3” vs. “4-to-make-4” data center designAI training vs. inference power consumption patternsRole of NVIDIA-powered edge compute modulesGrid modernization and coordination with utilitiesSecurity and resilience in critical infrastructureKEY MOMENTS: From centralized AI models to edge-based decision-makingDefining efficiency: utilization vs. thermal performanceWhy AI workloads aren’t as constant as they seemNVIDIA partnership and edge compute in power systemsUsing redundancy to increase usable capacityIncreasing density of AI compute and hidden capacityData center vs. utility responsibilitiesAddressing data center bottlenecks and scaling challengesCustomer landscape: hyperscalers to enterpriseSecurity, resilience, and critical infrastructureKEY INSIGHTS: AI workloads are dynamic, not constant: Training and inference create fluctuating power demands that can be optimized.Edge intelligence is critical: Real-time sensing and decision-making at the edge unlock efficiency gains not possible with centralized models.Hidden capacity exists: Many data centers have up to 2x unused power capacity due to lack of visibility and control.Software-defined power is the future: Faster control loops allow systems to safely exceed traditional design limits.Efficiency = utilization: The biggest gains come from better use of existing infrastructure, not just improving hardware efficiency.TAKEAWAYS: AI infrastructure growth is as much an energy challenge as a compute challengeReal-time, edge-based control systems are key to scaling sustainablyExisting grid and data center investments can go further with smarter orchestrationThe future of AI scaling depends on aligning compute innovation with energy intelligenceFEEDBACK? Email: show @ reasoning dot showBluesky: @reasoningshow.bsky.socialTwitter/X: @ReasoningShowInstagram: @reasoningshowTikTok: @reasoningshow

SUMMARY: Shadow AI is growing much faster than known AI adoption across businesses. How can IT teams get Shadow AI under control? GUEST: Uri Haramati, CEO at Torii SHOW: 1020 SHOW TRANSCRIPT: The Reasoning Show #1020 Transcript SHOW VIDEO: https://youtu.be/AUrh_xICPzM SHOW SPONSORS: ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoSHOW NOTES: Torii (homepage) Topic 1 - Welcome to the show. Tell us about your background and your focus at Torii.  Topic 2 - Is Shadow AI really a security problem—or is it a product-market fit problem inside the enterprise? Topic 3 - Why does Shadow AI spread faster—and become more dangerous—than traditional Shadow IT? Topic 4 - What’s the first signal a company should look for to know Shadow AI is already happening? Topic 5 - How do you balance visibility vs. control without killing the productivity gains that drove Shadow AI in the first place? Topic 6 - How should organizations rethink ‘data loss prevention’ in a world where the leak is a prompt, not a file? Topic 7 - What does a ‘well-governed’ AI environment actually look like in practice—day-to-day for an employee? Topic 8 - “Do you think Shadow AI ever fully goes away—or does it become a permanent operating model that companies need to design around?” FEEDBACK? Email: show @ reasoning dot showBluesky: @reasoningshow.bsky.socialTwitter/X: @ReasoningShowInstagram: @reasoningshowTikTok: @reasoningshow

SUMMARY: Have we reached a point where coding is a solved problem? And if so, what are the downstream effects on companies that need software to differentiate their business? GUEST: Brandon Whichard, Co-Host of Software Defined Talk SHOW: 1019 SHOW TRANSCRIPT: The Reasoning Show #1019 Transcript SHOW VIDEO: https://youtu.be/q0mksIKcBzk SHOW SPONSORS: ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoSHOW NOTES: The New Kingmakers (Stephen O’Grady - 2014)Developer Growth Rates[Via ChatGPT]  A useful way to think about it: Typing code → mostly commoditizedDesigning systems → partially assistedOwning outcomes → still very humanTopic 1 - How many years into Public Cloud did we assume that Cloud had solved the IT problem?  Topic 2 - Developers - what are we solving for? 10% of time coding, mostly on the last 10-15% Lots of time in planning meetings (decoding requirements, resource planning, updates, etc.)Decent amount of time fixing, troubleshooting, technical debt reductionTopic 2a - Business people have unlimited ideas, and most ideas are money + tech What would be their interface to problem solving without developers? (is this just a shift to consultants)Is this a massive opportunity for a great PaaS 3.0 company (e.g. is Vercel an example?)Topic 3 - [Hypothetical] Let’s assume a fairly normal company fired all their software developers tomorrow. How long before they could get a moderately complex new application of integration into production?  Topic 4 - Nobody likes to work on legacy code - missing source, missing engineers, etc. What do we call any code written by AI that was abandoned within the last 6-12 months?  FEEDBACK? Email: show @ reasoning dot showBluesky: @reasoningshow.bsky.socialTwitter/X: @ReasoningShowInstagram: @reasoningshowTikTok: @reasoningshow

SUMMARY: The RAG (Retrieval Augmented Generation) pattern is one of the most frequently used to augment LLMs with context-specific information. Let’s explore RAG.  GUEST: Roie Schwaber-Cohen, Head of Developer Relations at Pinecone SHOW: 1018 SHOW TRANSCRIPT: The Reasoning Show #1018 Transcript SHOW VIDEO: https://youtu.be/-kZZEMR341Q SHOW SPONSORS: Nasuni - Activate your data for AI and request a demoShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!SHOW NOTES: Topic 1 - Welcome to the show. Tell us a little bit about your background, and what you focus on these days at Pinecone  Topic 2 - Let’s begin by talking about RAG systems. What are they? Why do companies choose to use them? What benefits do they provide in AI systems? Topic 3 - At a high level, RAG sounds straightforward—retrieve relevant context, generate an answer. But in practice, where does it break first as systems scale? Topic 4 - I’ve heard that RAG systems can return answers that are technically correct but fundamentally wrong. What’s a concrete example of that happening in production—and why does it slip past most teams? Topic 5 - In traditional systems, we assume there’s a single source of truth. But in enterprise environments, ‘truth’ is often versioned, contextual, and conflicting. How should teams rethink ‘truth’ when building AI systems? Topic 6 - A lot of teams assume their knowledge base is ‘good enough’ for RAG. What do they usually underestimate about the messiness of real enterprise data? Topic 7 - There’s a growing narrative that better reasoning models can compensate for weaker retrieval. From what you’ve seen, where does that idea fall apart? Topic 8 - If correctness depends on things like timing, policy scope, or configuration, how should teams design systems that understand context—not just content? Topic 9 - Looking ahead, what replaces today’s RAG architectures? What patterns are emerging among teams that are actually getting this right?” FEEDBACK? Email: show @ reasoning dot showBluesky: @reasoningshow.bsky.socialTwitter/X: @ReasoningShowInstagram: @reasoningshowTikTok: @reasoningshow

SUMMARY:  Discover how AI is transforming software development and what it means for engineering leaders.  GUEST: Jeff Keyes, Field CTO at AllStacks  SHOW: 1017 SHOW TRANSCRIPT: The Reasoning Show #1017 Transcript SHOW VIDEO: https://youtu.be/cXPu8iWeB0k SHOW SPONSORS: ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoSHOW NOTES: Topic 1 - Welcome to the show. Tell us a little bit about your background, and what you focus on these days at AllStacks.  Topic 2 - You’ve been talking to a lot of engineering leaders using AI coding tools—what’s the most surprising gap you’re seeing between increased code generation and actual delivery outcomes? Topic 3 - Why does increasing developer output with AI often lead to more debugging, duplication, or cleanup instead of faster delivery? Topic 4 - You’ve described an ‘invisible rework loop’—can you walk us through what that looks like inside a modern engineering team? Topic 5 - As code generation gets easier, where does the real bottleneck shift in the software delivery lifecycle? Topic 6 - How do unclear product or engineering specifications get amplified in an AI-assisted development environment? Topic 7 - If traditional metrics like lines of code or velocity are becoming misleading, what should engineering leaders actually measure to know if AI is improving delivery? Topic 8 - What does a ‘healthy’ AI-assisted development workflow look like 12–18 months from now? FEEDBACK? Email: show @ reasoning dot showBluesky: @reasoningshow.bsky.socialTwitter/X: @ReasoningShowInstagram: @reasoningshowTikTok: @reasoningshow

SUMMARY: With the explosion of AI-generated code and applications, the modern SRE requires an AI-native approach to managing complex systems.  GUEST: Anish Agarwal - CEO/Cofounder of Traversal SHOW: 1016 SHOW TRANSCRIPT: The Reasoning Show #1016 Transcript SHOW VIDEO: https://youtu.be/hF3MCRDhMno SHOW SPONSORS: Nasuni - Activate your data for AI and request a demoShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!SHOW NOTES: Traversal (homepage)Topic 1 - Welcome to the show. Tell us a little bit about your background, and what you focus on these days at Traversal.  Topic 2 - AI is dramatically accelerating code generation, but not improving production outcomes. What’s fundamentally breaking in the traditional SRE model—and where do you see the biggest friction between speed and reliability? Topic 3 - What are the most common failure patterns or mistakes you’re seeing in production from AI-generated code—and what’s driving them? Topic 4 - AI can generate functional code, but it often lacks context about how systems behave in production. How is this changing what ‘good observability’ needs to look like? Topic 5 - How do you see SRE evolving in an AI-first world? Does it become more automated, more policy-driven, or even partially autonomous? Topic 6 - For organizations that want to embrace AI-assisted development but avoid production chaos, what are the most important guardrails they should put in place? Topic 7 - If we fast-forward 2–3 years, what does a ‘modern’ production stack look like in a world where most code is AI-generated? What capabilities become absolutely essential? In one sentence—what’s the #1 thing a CTO should do right now? FEEDBACK? Email: show @ reasoning dot showBluesky: @reasoningshow.bsky.socialTwitter/X: @ReasoningShowInstagram: @reasoningshowTikTok: @reasoningshow

SUMMARY:  Today’s episode is all about a transformation happening in customer service—one that’s moving us from static systems and scripted workflows into something far more dynamic: AI systems that can actually learn and improve over time. GUEST: Shashi Upadhyay (President of Product, Engineering, and AI at Zendesk) SHOW: 1015 SHOW TRANSCRIPT: The Reasoning Show #1015 Transcript SHOW VIDEO: https://youtu.be/IQaxE-DjIpo SHOW SPONSORS: ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoSHOW NOTES: The future of service belongs to self-improving AITopic 1 - Welcome to the show. Tell us a bit about your background and your focus today.  Topic 2 - You describe this moment as a shift from systems of record to intelligent systems of action. What’s fundamentally broken in today’s customer service model that’s forcing this transition now? What changed in the last 2–3 years to make this possible? Topic 3 - There’s been a lot of AI in customer service that overpromised and underdelivered. What are the biggest gaps between what customers actually need—like resolution—and what legacy automation has been delivering? Topic 4 - The concept of a “self-improving” system is really powerful. What’s actually new here—what enables AI to improve with every interaction without constant human tuning? Topic 5 - You’ve moved from assistive copilots to what you call “agentic AI” that can resolve issues end-to-end. Where are we today on that journey—and what still requires human involvement? Topic 6 - Voice has historically been one of the hardest channels to automate. What changes with this new generation of AI that makes even complex, multi-step voice interactions solvable? Topic 7 - If we fast-forward 2–3 years, what does a “best-in-class” customer service experience look like in an AI-first world? FEEDBACK? Email: show @ reasoning dot showBluesky: @reasoningshow.bsky.socialTwitter/X: @ReasoningShowInstagram: @reasoningshowTikTok: @reasoningshow

SUMMARY:  Brian (@bgracely) and Brandon Whichard (@bwhichard, Software Defined Talk and Failover Media) discuss the biggest AI news stories from the month of March, 2026.  SHOW: 1014 SHOW TRANSCRIPT: The Reasoning Show #1014 Transcript SHOW VIDEO: https://youtu.be/XwyAC-hxOQY SHOW SPONSORS: VENTION - Ready for expert developers who actually deliver? Visit ventionteams.comSHOW NOTES: Links to all the AI News covered in this months showFEEDBACK? Email: show @ reasoning dot showBluesky: @reasoningshow.bsky.socialTwitter/X: @ReasoningShowInstagram: @reasoningshowTikTok: @reasoningshow

SUMMARY: With @bwhichard, we dig into how daily work-life changes when you make @AnthropicAI @claudeai the center of all workflow activities.  SHOW: 1013 SHOW TRANSCRIPT: The Reasoning Show #1013 Transcript SHOW VIDEO: https://youtu.be/zEmEH0t67js SHOW SPONSORS: VENTION - Ready for expert developers who actually deliver? Visit ventionteams.comSHOW NOTES: Topic 1 - How long have you been living the Claude-life, and when did it dawn on you to make this central to your day-to-day activities?  Topic 2 - What were the biggest hurdles you had to overcome before you trusted the system and started letting it have ownership over tasks and workflows? Topic 3 - What are some of your best practices in terms of machine setup, how or where you store data, how you decide what to give it access to? Walk me through your thoughts around things like keeping things simple, where to be complex, how you think about security, etc. Topic 4 - How are you learning to give it more responsibilities, or just figure out new ways to be productive with it?  Good resources you’re pulling from? Any tips to make it use less tokens?Skills marketplaces?Topic 5 - What have been some of the biggest barriers to successful adoption, or just areas where you’re still struggling to get it to do the things you want? Or are you still in the learning curve stage and things just keep growing on one another? Topic 6 - If you took the knowledge and skills you have now in Claude-life into your day-job, how do you see yourself working, as well as working with the rest of your team/teams? Would it bother you if you didn’t think they were using AI tools as much?  FEEDBACK? Email: show @ reasoning dot showBluesky: @reasoningshow.bsky.socialTwitter/X: @ReasoningShowInstagram: @reasoningshowTikTok: @reasoningshow

SUMMARY: We dig into the NVIDIA GTC keynote and highlight three things - accelerated computing for everything, the complexity of the new inference stack, and NVIDIA’s “open” software stack including NemoClaw. SHOW: 1012 SHOW TRANSCRIPT: The Reasoning Show #1012 Transcript SHOW VIDEO: https://youtu.be/aXOr91q76yM SHOW SPONSORS: VENTION - Ready for expert developers who actually deliver? Visit ventionteams.comSHOW NOTES: NVIDIA GTC 2026 (Keynote)NVIDIA NemoClaw - OpenClaw + OpenShell + NVIDIA Agent ToolkitNVIDIA adds Groq LPU to their rack systemsNVIDIA to invest $26B in Open Weight ModelsInterview with Jensen about Accelerated Computing (Stratechery) Topic 1 - Jensen’s trying to paint the bigger picture of accelerated computing everywhere (robotics, autonomous driving, gen-ai, physical ai - but also just everyday enterprise apps). Everything is about keeping the stock price up, and margins high. The stock price provides the warchest to fight off all foes.  Topic 2 - The inference architecture is a complex mix of GPUs, CPUs, ASICs/LPUs, high-speed networking and seems very different from the training architecture. How big is the burden on data center providers? What are the inference alternatives emerging?  Topic 3 - Jensen talked a lot about OpenClaw and eventually about NVIDIA’s NemoClaw. How does his interest in Agentic AI tie into his interest in building NVIDIA’s own frontier model FEEDBACK? Email: show @ reasoning dot showBluesky: @reasoningshow.bsky.socialTwitter/X: @ReasoningShowInstagram: @reasoningshowTikTok: @reasoningshow

SUMMARY: Morgan Foster talks about the Kagenti project, which enables an AI Agent agnostic framework for security, authentication, identity and zero-trust. SHOW: 1011 SHOW TRANSCRIPT: The Reasoning Show #1011 Transcript SHOW VIDEO: https://youtu.be/djFZruLEDiw SHOW NOTES: Kagenti (homepage)Kagenti (use-cases)“Old Things that look like Agents”“What makes Agents different?”CNV - What Makes Agents Different?“Handing your phone to a stranger, why Agents need their own identity” Topic 1 - Welcome to the show. Tell us a little bit about your background and areas you focus on today.  Topic 2 - Tell us a bit about the Kagenti project and the types of challenges it’s trying to solve for Agentic AI deployments.  Topic 3 - How much commonality exists between different Agentic frameworks that a common, agnostic agentic orchestration approach can work? And how much difference still exists and would drive companies to silo’d deployments?  Topic 4 - How far should an Agentic Orchestration framework go, and what types of things do you expect will still be Agentic framework dependent?  Is Kagenti more of a control-plane element, or more of a data-plane element? Topic 5 - As Kagenti evolves, what are some of the adjacent things that people should be keeping an eye on that might be a dependency, or could shift the direction of the project? FEEDBACK? Email: show @ reasoning dot showBluesky: @reasoningshow.bsky.socialTwitter/X: @ReasoningShowInstagram: @reasoningshowTikTok: @reasoningshow

SUMMARY: Brian talks about the rapidly expanding gap between people and companies that augment their work with AI and those who are making AI the center of their work world.  SHOW: 1010 SHOW TRANSCRIPT: The Reasoning Show #1010 Transcript SHOW VIDEO: https://youtu.be/tFyLlCnkbsM SHOW SPONSORS: VENTION - Ready for expert developers who actually deliver? Visit ventionteams.comSHOW NOTES: WHY THE NEED FOR A CODE RED?  Velocity of Code (new companies)Velocity of Productivity (employees)Velocity of Analysis (strategy)AgentOpsToken FactoriesDevs for Business, Re-Wiring the Concept of Business AnalystFEEDBACK? Email: show @ reasoning dot showBluesky: @reasoningshow.bsky.socialTwitter/X: @ReasoningShowInstagram: @reasoningshowTikTok: @reasoningshow

SUMMARY: Sally O’Malley (Principle Software Engineer @RedHat, Maintainter @OpenClaw) talks about her early experiences of immersing herself into OpenClaw and evolution of the OpenClaw community. SHOW: 1009 SHOW TRANSCRIPT: The Reasoning Show #1009 Transcript SHOW VIDEO: https://youtu.be/7xARBtgiMQg SPONSORS: VENTION - Ready for expert developers who actually deliver? Visit ventionteams.comSHOW NOTES: OpenClaw - Personal AI AssistantOpenClaw - RedditOpenClaw, OpenAI and the Future (Peter Steinberger - OpenClaw creator)OpenClaw Foundation (coming soon) Topic 1 - Welcome to the show. Tell us a little bit about your background in software engineering.  Topic 2 - You recently jumped into the deep end of the pool with OpenClaw. Tell us about the week of immersion with this new technology.  What did you go into it thinking about?What did you learn, what did you create?What new sorts of things did you have to try?Topic 2a - For anyone that’s new to OpenClaw, can you give us the basics of what OpenClaw does? Topic 3 - You mentioned that this is a very different (or completely different) paradigm of how software is created. Can you walk us through the differences, your observations, how you had to really rethink things that you did before and after? Topic 4 - In your day job, you’re focused on software that’s used by large enterprises that have to be concerned with security and stability, as much as they do innovation. How do you see the existing OpenClaw fitting into that world?  How do you expect that OpenClaw might need to change?How do you expect that enterprises might need to change to adapt to this new capability that might be unleashed with their employees?Topic 5 - You (very) recently were accepted as a committer to the OpenClaw project. I know it’s only been a few days, but what is opening your eyes about how this community operates, especially in comparison to other open projects you’ve worked on?  We could probably have an entire podcast on AI development in open communities.FEEDBACK? Email: show @ reasoning dot showBluesky: @reasoningshow.bsky.socialTwitter/X: @ReasoningShowInstagram: @reasoningshowTikTok: @reasoningshow

Erwan Menard - SVP Product Management @CrusoeAI talks about the evolution of NeoClouds, the challenges of matching the speed of data centers, GPUs and software, and how everything is evolving to megawatts and tokens.  SHOW: 1008 SHOW TRANSCRIPT: The Reasoning Show #1008 Transcript SHOW VIDEO: https://youtu.be/eA8vpPmSW0Y SPONSORS: VENTION - Ready for expert developers who actually deliver? Visit ventionteams.comSHOW NOTES: Topic 1 - Welcome to the show. Tell us a bit about your background, and what you focus on now at Crusoe.  Topic 2 - There has obviously been a lot of coverage of AI data center buildouts all over the world for the last few years. Tell us about Crusoe, and your approach to providing “neocloud” services.  Topic 3 - What are the biggest challenges facing Crusoe today and in the immediate future - is it technology, energy, financing for expansions, etc.? Topic 4 - Crusoe started as a bitcoin-focused company and has evolved to more of a GenAI-focus. What types of architectural changes did you have to make for this new type of workload? And how do those impact the quality of the services your customers expect from Crusoe? Topic 5 - Is your focus more on environments to enable model training and customization, or more focus on inference for customer-facing applications?  Topic 6 - A lot has changed in AI in the last couple years. What has changed the most in the last couple years, and what are you expecting to change the most over the next couple years?  Topic 7 - Sovereign AI and Private AI have become much bigger topics over the last 12-18 months, and we’d expect that to grow. What unique things is Crusoe doing to adapt to these changing requirements from customers? FEEDBACK? Email: show @ reasoning dot showBluesky: @reasoningshow.bsky.socialTwitter/X: @ReasoningShowInstagram: @reasoningshowTikTok: @reasoningshow

OVERVIEW: Welcome to The Reasoning Show! We dig into one of the foundational building blocks of modern Generative AI, the AI Agent. So what is an AI Agent, and what do we need to think about for the next couple years?  SHOW: 1007 SHOW TRANSCRIPT: The Reasoning Show #1007 Transcript SHOW VIDEO: https://youtu.be/4tdj0S0AyhM SHOW SPONSOR: VENTION - Ready for expert developers who actually deliver? Visit ventionteams.comSHOW NOTES: Topic 1 - We’re 3+ years into the Generative AI era. Why do you think AI Agents, or Agentic AI, is now getting so much attention?  Topic 2 - If someone asked you to explain what an AI Agent is, how would you do that?  Topic 3 - What are some of the core elements of AI Agents that you’re seeing impact how people think about and use agents? How to define tasksLanguages and frameworksAbility to orchestrate multiple agentsHuman-in-the-Loop vs. AutonomousOther?Topic 4 - AI Agents are going to spark the great “how much should I pay for this?” discussion. Have you given this any thought yet?   Topic 5 - How do you expect to use AI agents in your day-to-day work, and how do you expect this to impact Enterprise businesses? ESSENTIAL READING Building Effective Agents" by Anthropic: designing agents with tools, memory, reasoning loopsA Comprehensive Review of AI Agents: how agents perceive, reason, decide, actTop 20 AI Agent Concepts You Should Know: covering ReAct, Chain of Thought, memory typesAI Agents in Action: Foundations for Evaluation and Governance: structured foundation for safety aspects of agent deploymentEssential Viewing AI Agentic Design Patterns w/ AutoGen: build autonomous agents that use toolsAI Agent Systems w/ crewAI: orchestrating teams of agentsLangGraph Course: how to build stateful, reliable agentsFrameworks & Tools  LangGraph: standard for complex, stateful agents (nodes, edges, loops)CrewAI: Best for structured task delegation and multi-agent collaborationAutoGen: Microsoft's framework for multi-agent conversational systemsModel Context Protocol (MCP): standard for connecting agents, tools, datan8n: no-code/low-code visualFEEDBACK? Email: show @ reasoning dot showBluesky: @reasoningshow.bsky.socialTwitter/X: @ReasoningShowInstagram: @reasoningshowTikTok: @reasoningshow