The Reasoning Show

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 demo
SHOW 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 show
Bluesky: @reasoningshow.bsky.social
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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 demo
SHOW NOTES:
The New Kingmakers (Stephen O’Grady - 2014)
Developer Growth Rates
[Via ChatGPT]  A useful way to think about it:
Typing code → mostly commoditized
Designing systems → partially assisted
Owning outcomes → still very human
Topic 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 reduction
Topic 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 show
Bluesky: @reasoningshow.bsky.social
Twitter/X: @ReasoningShow
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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 demo
ShareGate - 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 show
Bluesky: @reasoningshow.bsky.social
Twitter/X: @ReasoningShow
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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 demo
SHOW 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 show
Bluesky: @reasoningshow.bsky.social
Twitter/X: @ReasoningShow
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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 demo
ShareGate - 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 show
Bluesky: @reasoningshow.bsky.social
Twitter/X: @ReasoningShow
Instagram: @reasoningshow
TikTok: @reasoningshow