SUMMARY: Brian digs into Jensen’s NVIDIA GTC 2026 keynote and highlights three things - accelerated computing for everything, the complexity of the new inference stack, and the broader discussion of NVIDIA’s “open” software stack including NemoClaw. 

SHOW: 1012

SHOW TRANSCRIPT: The Reasoning Show #1012 Transcript

SHOW VIDEO: https://youtu.be/aXOr91q76yM

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SHOW NOTES:

• NVIDIA GTC 2026 (Keynote)
• NVIDIA NemoClaw - OpenClaw + OpenShell + NVIDIA Agent Toolkit
• NVIDIA adds Groq LPU to their rack systems
• NVIDIA to invest $26B in Open Weight Models
• Interview 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

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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?

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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

SPONSORS:

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SHOW NOTES:

WHY THE NEED FOR A CODE RED? 

• Velocity of Code (new companies)
• Velocity of Productivity (employees)
• Velocity of Analysis (strategy)
• AgentOps
• Token Factories
• Devs for Business, Re-Wiring the Concept of Business Analyst

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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:

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SHOW NOTES:

• OpenClaw - Personal AI Assistant
• OpenClaw - Reddit
• OpenClaw, 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.

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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:

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SHOW 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?

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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:

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SHOW 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 tasks
• Languages and frameworks
• Ability to orchestrate multiple agents
• Human-in-the-Loop vs. Autonomous
• Other?

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 loops
• A Comprehensive Review of AI Agents: how agents perceive, reason, decide, act
• Top 20 AI Agent Concepts You Should Know: covering ReAct, Chain of Thought, memory types
• AI Agents in Action: Foundations for Evaluation and Governance: structured foundation for safety aspects of agent deployment

Essential Viewing

• AI Agentic Design Patterns w/ AutoGen: build autonomous agents that use tools
• AI Agent Systems w/ crewAI: orchestrating teams of agents
• LangGraph Course: how to build stateful, reliable agents

Frameworks & Tools 

• LangGraph: standard for complex, stateful agents (nodes, edges, loops)
• CrewAI: Best for structured task delegation and multi-agent collaboration
• AutoGen: Microsoft's framework for multi-agent conversational systems
• Model Context Protocol (MCP): standard for connecting agents, tools, data
• n8n: no-code/low-code visual

FEEDBACK?

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This episode marks the transition from The Cloudcast to The Reasoning Show, focusing more on AI and cloud topics. Brian Gracely (@bgracely) and Brandon Whichard (@bwhichard, @SoftwareDefTalk) discuss recent trends in AI, the evolution of tech teams, and the shifting landscape of enterprise AI tools.

SHOW: 1006

SHOW TRANSCRIPT: The Cloudcast #1006 Transcript

SHOW VIDEO: https://youtube.com/@TheCloudcastNET 

NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST - "CLOUDCAST BASICS" 

SHOW NOTES:

• Link to February 2026 News and Articles

FEEDBACK?

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Aaron and Brian discuss how The Cloudcast will be changing going forward, signaling an industry shift from Cloud Computing to AI.  

SHOW: 1005

SHOW TRANSCRIPT: The Cloudcast #1005 Transcript

SHOW VIDEO: https://youtube.com/@TheCloudcastNET 

NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST: "CLOUDCAST BASICS"

SHOW NOTES:

Topic 1 - What are we announcing? (“The Reasoning Show”, a.k.a. “Reasoning”)

• New name, same RSS feeds (same podcast players)
• New logo
• Some existing/renamed and some new social media channels (will be listed)
• Same co-hosts (and maybe some new friends)

Topic 2 - Why are we changing The Cloudcast?

• The majority of our content is AI focused now
• Cloud has become mostly stable, AI is at the beginning and so much is changing
• We’re not abandoning Cloud, just giving it the amount of coverage needs (monthly Cloudcast, monthly CNOTM)
• Social media algorithms have changed audience acquisition

Topic 3 - Hasn’t The Cloudcast already been covering AI for a while? 

• Yes, it’s been about 50/50 since 2024
• We’ve been discussing this change for almost a year. 
• We actually discussed having an OpenAI-specific podcast at one point, but so much as changed in the market (which is a good reason for a diverse podcast)

Topic 4 - What can we expect from the new podcast?

• Still on Wednesday and Sunday
• Both audio and video formats (Apple, Spotify, YouTube, TikTok/Insta (clips))
• Trying to make it easier for someone new to follow along - more concentration around core topics, but not exclusively
• AI Technology, AI Economics, AI Trends, AI (Business) Use-Cases, AI Things to Watch, AI Productivity, AI Regulation
• We’ll still do a “Cloudcast” once a month, as the Cloud underpins almost everything AI
• We’ll likely do a “Reasoning Basics” spin-off, like Cloudcast Basics. 
• We’ll get a newsletter pulled together (hopefully weekly). 

Topic 5 - Anything else?

• Listeners don’t have to do anything to keep getting their week podcast
• Subscribe to the social media channels (show notes)
• Leave a 5-star review on the podcast players
• Tell a friend to check out the show

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This episode explores the evolving economics of AI development, the rising costs associated with AI agents, and the implications for businesses and developers. It highlights the shift from centralized to decentralized computing, the importance of understanding token budgets, and the future of AI project management.

SHOW: 1004

SHOW TRANSCRIPT: The Cloudcast #1004 Transcript

SHOW VIDEO: https://youtube.com/@TheCloudcastNET 

CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotw

CHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"

CHAPTERS

00:00 - Celebrating Milestones and AI Insights

02:48 - The Cost of AI Agents and Realizations

06:05 - Centralization vs. Decentralization in AI

08:51 - The Evolution of AI Economics

12:03 - Future Trends in AI and Project Management

14:52 - Connecting AI to Real-World Economics

KEY TOPICS:

• AI agent costs and pricing models
• The shift from centralized to decentralized computing
• Token budgets and project economics in AI
• Historical transitions in computing infrastructure
• Future trends in AI project management

SHOW NOTES

• When AI Tokens cost more than your employees 
• Should you own (or generate) your own tokens? 
• On running a startup of Claude Code agents: 1 Billion tokens a month
• Can AI grow corn? 

WE'VE REACHED A POINT WITH AI WHERE PEOPLE ARE STARTING TO THINK ABOUT THE BUSINESS IMPACTS

• How much should an AI project cost?
• How do we translate an AI token into some unit that a business can understand?
• How companies be their own AI token factories?

FEEDBACK?

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Aaron and Brian review some of the latest AI model releases and discuss how they would evaluate them through the lens of an Enterprise AI Architect. 

SHOW: 1003

SHOW TRANSCRIPT: The Cloudcast #1003 Transcript

SHOW VIDEO: https://youtube.com/@TheCloudcastNET 

NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST: "CLOUDCAST BASICS" 

SHOW NOTES:

• Last Week in AI Podcast #234
• Artificial Analysis.AI
• Opus 4.6 Release
• GPT Codex 5.3 Release
• GLM-5 Release
• OpenAI Preparedness Framework
• Sam’s Tweet that 5.3 Codex hit “high” ranking for cybersecurity
• Fortune Article on 5.3 high ranking

TAKEAWAYS

• The frequency of AI model releases can lead to numbness among users.
• Evaluating AI models requires understanding their specific use cases and benchmarks.
• Enterprises must consider the compatibility and integration of new models with existing systems.
• Benchmarks are becoming more accessible but still require careful interpretation.
• The rapid pace of AI development creates challenges for enterprise adoption and integration.
• Companies need to be proactive in managing the versioning of AI models.
• The industry may need to establish clearer standards for evaluating AI performance.
• Efficiency and cost-effectiveness are becoming critical metrics for AI adoption.
• The timing of model releases can impact their market reception and user adoption.
• Businesses must adapt to the fast-paced changes in AI technology to remain competitive.

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