• 31 minutes 43 seconds
    Enabling AI Governance for M365

    SUMMARY: As AI agents become embedded in everyday work, Microsoft 365 governance is no longer a back-office compliance exercise. it’s the “traction control” that lets enterprises innovate faster without losing control of their data, identities, and workflows.

    GUEST: Richard Harbridge, Principal Industry Advisor, Microsoft 365 at ShareGate

    SHOW: 1028

    SHOW TRANSCRIPT: The Enterprise AI Show #1028 Transcript

    SHOW VIDEO: https://youtu.be/sgqg7uqErA0

    SHOW SPONSORS:

    SHOW NOTES:

    Topic 1 - Welcome to the show. Tell us about your background, and what you focus on today. Tell us about Sharegate. 

    Topic 2 - How has generative AI changed the definition of “governance” inside Microsoft 365 environments?

    Topic 3 - What are organizations underestimating about AI readiness in M365?

    Topic 4 - What do you think about “oversharing risk” in the era of AI assistants?

    Topic 5 - What patterns are you seeing around shadow AI and unsanctioned SaaS usage?

    Topic 6 - How should organizations rethink identity and access management for AI-driven workflows?

    Topic 7 - What does good AI governance look like operationally—not just as a policy document?

    FEEDBACK?

    17 May 2026, 5:00 am
  • 40 minutes 53 seconds
    An AI Market Analysis, May 2026

    SUMMARY: RIP Reasoning, hello The Enterprise AI Show. We do a point-in-time analysis of the AI market for May 2026, across 11 major categories. 

    SHOW: 1027

    SHOW TRANSCRIPT: The Enterprise AI Show #1027 Transcript

    SHOW SPONSORS:

    SHOW NOTES:

    FEEDBACK?

    13 May 2026, 5:00 am
  • 33 minutes 39 seconds
    AI, Data Centers, and the Power Crunch

    SUMMARY: We  explore one of the most overlooked bottlenecks in the AI boom: energy and infrastructure and  why power availability is becoming the limiting factor.

    GUEST: Wannie Park, Founder/CEO of PADO AI

    SHOW: 1026

    SHOW TRANSCRIPT: The Reasoning Show #1026 Transcript

    SHOW VIDEO: https://youtu.be/satMQRxKQC8

    SHOW SPONSORS:

    SHOW NOTES:

    1. AI’s Hidden Constraint: Power

    • AI growth is no longer limited only by GPUs and compute
    • Power generation, cooling, and grid interconnects are emerging as major bottlenecks
    • Data centers could account for 10–12% of North American power demand in coming years

    2. Why Data Centers Are Being Reimagined

    • Traditional data centers were built for enterprise IT, not AI-scale workloads
    • AI infrastructure introduces:
      • Massive power density needs
      • Advanced cooling challenges

    3. The Grid Wasn’t Built for AI

    • Utilities are designed around peak demand scenarios
    • Most grids run well below peak capacity most of the time
    • AI workloads create volatile and unpredictable consumption patterns
    • Long interconnection timelines are pushing companies toward alternative infrastructure models


    4. GPU Utilization Is Surprisingly Low

    • GPU clusters are often underutilized because of:
      • Scheduling inefficiencies, Cooling limitations, SLA constraints
      • Effective GPU utilization may be as low as 12–13% in some environments

    5. Cooling as a Major Optimization Layer

    • Legacy data centers often cool entire zones inefficiently
    • Pado AI aligns
    • AI workloads, Cooling systems, Power allocation
    • Workload-aware orchestration helps optimize cooling and compute efficiency


    6. The Rise of “Compute Forecasting”

    • Pado forecasts compute demand instead of energy demand
    • The platform models:
      • GPU workloads, Power consumption, Cooling requirements, SLA priorities
      • Goal: maximize “compute per megawatt”

    7. AI Workloads Become Time-Aware

    • AI providers may increasingly:
      • Shift workloads to off-peak periods
      • Incentivize delayed non-urgent jobs
      • Dynamically balance compute demand
      • Users are already seeing variable inference latency in real-world AI systems

    8. Sustainability vs Reliability vs Profitability

    • Operators must balance:
      • Uptime expectations, Infrastructure costs, Sustainability goals
      • Renewable adoption is growing, but reliability still drives investment in natural gas and battery-backed systems

    9. Brownfield vs Greenfield Opportunities

    • Pado AI is focused primarily on existing (“brownfield”) data centers
    • Existing enterprise infrastructure can often be extended and optimized instead of rebuilt
    • Enterprises may gain significant AI capability without hyperscale GPU deployments

    FEEDBACK?

    10 May 2026, 5:00 am
  • 37 minutes
    AI News of the Month for April 2026

    SUMMARY:  Brian Gracely (@bgracely) and Brandon Whichard (@bwhichard, Software Defined Talk and Failover Media) discuss the biggest AI news stories from the month of April, 2026. 

    SHOW: 1025

    SHOW TRANSCRIPT: The Reasoning Show #1025 Transcript

    SHOW VIDEO: https://youtu.be/Gl-49dmAgBs

    SHOW SPONSORS:

    SHOW NOTES:

    FEEDBACK?

    6 May 2026, 5:00 am
  • 43 minutes 59 seconds
    The 2026 AI Draft

    SUMMARY: Draft guru Brandon Whichard (Software Defined Talk) joins us for the inaugural AI Draft, where we predict the next year of AI winners, losers, trends, and headlines. 

    GUEST: Brandon Whichard, Software Defined Talk

    SHOW: 1024

    SHOW TRANSCRIPT: The Reasoning Show #1024 Transcript

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

    SHOW SPONSORS:

    SHOW NOTES:

    Brian’s Picks

    1. Google
    2. Major AI-centric IPO in 2026 ($1T valuation)
    3. Amazon (cloud)
    4. Company has more Agents that Employees
    5. TSMC (hardware)
    6. AMD (hardware)
    7. Family asks about AI at the holidays
    8. Data center issue causes a significant change to human existence

    Brandon’s Picks

    1. Anthropic
    2. NVIDIA
    3. Broadcom
    4. OpenAI (frontier model)
    5. AI Consumption-based pricing (end of subsidies)
    6. AI Energy Demand
    7. The end of “vibe-coding”
    8. Sam Altman out at CEO of OpenAI

    FEEDBACK?

    3 May 2026, 5:00 am
  • 34 minutes 58 seconds
    Halt & Retool: Rewriting Software Development in the Age of AI Agents

    SUMMARY: Exploring how to fully embrace AI-driven, agent-based software development, resulting in dramatically increased productivity and faster feature delivery. It highlights a broader shift in engineering—from writing code to orchestrating AI agents.

    GUEST: Sam Ramji, CEO/Co-founder at Sailplane

    SHOW: 1023

    SHOW TRANSCRIPT: The Reasoning Show #1023 Transcript

    SHOW VIDEO: https://youtu.be/q50s0oL37pQ

    SHOW SPONSORS:

    SHOW NOTES:


    1. The “Halt and Retool” Moment

    • A single-day build and deployment of a production feature triggered a company-wide realization
    • Paused all development to reassess how AI fundamentally changes engineering workflows
    • Creating “shock moments” (like stopping work) is key to driving mindset shifts

    2. From Coding to Agent Orchestration

    • Developers are shifting from writing code → managing AI agents
    • Work resembles “multi-boxing” or conducting an orchestra of parallel agents
    • Success depends on coordinating tasks, not executing them directly

    3. The Rise of Harness Engineering

    • Defined as everything between raw AI prompts and production-ready output
    • Focus: eliminating friction across the software development lifecycle 
    • Key practices:
      • Logging agent errors and friction points
      • Continuously refining workflows and tooling
      • Letting AI reflect on and improve its own mistakes

    4. Spec-Driven Development Becomes Critical

    • Poor specifications lead to exponential inefficiencies
    • Teams now spend significantly more time on design and specs than coding

    5. Measuring the Impact

    • ~3x increase in code velocity
    • Near-zero “bit rot” 
    • Faster feature delivery—sometimes within 24 hours


    6. Token Maxing & Developer Fitness

    • Higher token usage often signals better workflows and deeper integration with AI
    • Performance becomes about system design, not efficiency constraints


    7. New Tools & Interfaces

    • Increased use of voice interfaces over typing
    • Terminal-first workflows replacing traditional IDE-centric approaches
    • AI-accessible knowledge bases becoming standard


    8. The Future of Software Engineering

    • Within ~6 months: developers may stop writing code
    • Within ~12 months: developers may stop reading code
    • Focus shifts to:
      • Intent, design, and orchestration. Domain expertise and problem modeling

    FEEDBACK?

    29 April 2026, 5:00 am
  • 35 minutes 13 seconds
    The Zero-CVE Mirage: Hardening Software in the Age of AI Attacks

    SUMMARY: How software development is rapidly evolving in the age of AI and automation. Matt Moore shares how his team is rethinking secure software supply chains, scaling infrastructure, and safely integrating AI agents into development workflows.

    GUEST: Matt Moore, CTO at Chainguard 

    SHOW: 1022

    SHOW TRANSCRIPT: The Reasoning Show #1022 Transcript

    SHOW VIDEO: https://youtu.be/9Q0kWkTYRs8

    SHOW SPONSORS:

    SHOW NOTES:


    Scaling Challenges & “Factory” Evolution

    • Early automation relied on tools like GitHub Actions
    • At scale, simple systems broke due to:
      • Massive event volumes
      • API rate limits (e.g., GitHub quotas)
      • Exponential fan-out effects
    • Key innovation: custom work queue + reconciliation model
      • ~90% event deduplication
      • Controlled throughput and backpressure
      • Improved reliability and system stability
    • Introduced Driftless 
    • Built on reconciliation principles (inspired by Kubernetes):
      • Compare desired vs. actual state
      • Continuously reconcile differences
    • Benefits:
      • Resilience to missed events
      • Automatic retries and recovery
      • Scales better than purely event-driven systems

    AI Agents in Software Development

    • AI is dramatically accelerating development workflows
    • Chainguard uses agents to:
      • Remediate vulnerabilities (CVEs)
      • Update dependencies
      • Fix failing tests and adapt to upstream changes

    Key Design Philosophy

    • Least privilege → “least tool call”
      • Avoid giving agents full system access
      • Provide narrowly scoped tools for specific tasks
    • Delegate execution to sandboxed systems (e.g., CI pipelines)
    • Focus on safe, controlled automation

    Industry Shift: Velocity vs. Security

    • Explosion of AI-driven tools (e.g., autonomous PR generation)
    • Massive increase in development velocity
    • New risks:
      • Poorly secured agent frameworks
      • Malicious or unsafe automation patterns

    Key Takeaways

    1. Scale changes everything
      • Simple systems break under massive workloads
      • Purpose-built infrastructure becomes necessary
    2. Reconciliation > pure event-driven systems at scale
      • More resilient, predictable, and controllable
    3. AI is a force multiplier—but requires guardrails
      • Unrestricted agents introduce serious risk
      • Constrained, purpose-built agents are safer and more effective
    4. Continuous learning is mandatory
      • AI tooling is evolving too fast for static skillsets
      • Teams must actively experiment and adapt

    FEEDBACK?

    26 April 2026, 5:00 am
  • 25 minutes 23 seconds
    The Grid’s Breaking Point: Can AI Save the Infrastructure It’s About to Crash?

    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: 1021

    SHOW TRANSCRIPT: The Reasoning Show #1021 Transcript

    SHOW VIDEO: https://youtu.be/ItcpU8UjOFE

    SHOW SPONSORS:

    SHOW NOTES:

    KEY TOPICS:

    • Differences between grid power dynamics vs. AI workloads
    • Edge AI for real-time power flow optimization
    • Unlocking stranded capacity in existing infrastructure
    • “4-to-make-3” vs. “4-to-make-4” data center design
    • AI training vs. inference power consumption patterns
    • Role of NVIDIA-powered edge compute modules
    • Grid modernization and coordination with utilities
    • Security and resilience in critical infrastructure

    KEY MOMENTS:

    • From centralized AI models to edge-based decision-making
    • Defining efficiency: utilization vs. thermal performance
    • Why AI workloads aren’t as constant as they seem
    • NVIDIA partnership and edge compute in power systems
    • Using redundancy to increase usable capacity
    • Increasing density of AI compute and hidden capacity
    • Data center vs. utility responsibilities
    • Addressing data center bottlenecks and scaling challenges
    • Customer landscape: hyperscalers to enterprise
    • Security, resilience, and critical infrastructure

    KEY 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 challenge
    • Real-time, edge-based control systems are key to scaling sustainably
    • Existing grid and data center investments can go further with smarter orchestration
    • The future of AI scaling depends on aligning compute innovation with energy intelligence

    FEEDBACK?

    22 April 2026, 5:00 am
  • 29 minutes 23 seconds
    Shadow AI is Faster Than Your Governance: Why Guardrails are Failing

    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:

    SHOW NOTES:


    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?

    19 April 2026, 5:00 am
  • 33 minutes 19 seconds
    The Junior Dev Crisis: Who Inherits the Code When AI Does the Work?

    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:

    SHOW NOTES:

    [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?

    15 April 2026, 5:00 am
  • 28 minutes 42 seconds
    RAG Won’t Save Your Messy Data: The Brutal Truth About AI Reliability

    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:

    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?

    12 April 2026, 5:00 am
  • More Episodes? Get the App