Podcast
The Enterprise AI Show
Hosted by Massive Studios · EN
The Enterprise AI Show explores the AI journey for Enterprise companies around the world. [formerly The Cloudcast]
As the AI revolution moves from experimentation to execution, The Enterprise AI Show provides the clarity needed to lead. Join Aaron Delp and Brian Gracely as they explore the intersection of generative AI, enterprise systems, and global business strategy. Each episode features clear-headed conversations with the people making actual decisions—founders, investors, and practitioners—focusing on the technical architectures and business models that drive real-world ROI.
New shows every Wednesday and Sunday.
Topics: Enterprise AI strategy · The AI Economy · LLMs in production · AI leadership · Agentic AI · Digital Sovereignty · Machine Learning · AI startups · Cloud Computing
44episodes
Episodes
Newest firstAll episodes
AI News of the Month - May 2026
1w ago00:37:54Tap to summarizeSUMMARY: Brian Gracely (@bgracely) and Brandon Whichard (@bwhichard, Software Defined Talk and Failover Media) discuss the biggest AI news stories from the month of May, 2026. SHOW: 1031SHOW TRANSCRIPT: The Reasoning Show #1031 TranscriptSHOW VIDEO: https://youtu.be/MNihDdBSteISHOW SPONSORS:Nasuni - Activate your data for AI and request a demoShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!SHOW NOTES:Links to all the AI News covered in this month’s showFEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow
Transcribe →Why Enterprise AI Economics Are Changing
1w ago00:32:28Tap to summarizeSUMMARY: The biggest enterprise AI question may no longer beWhich model is smartest? Instead, which organization can most effectively operationalize, govern, and economically scale AI agents across the business?’SHOW: 1030SHOW TRANSCRIPT: The Enterprise AI Show #1030 TranscriptSHOW VIDEO: https://youtu.be/acOBfRI0P3USHOW SPONSORS:ShareGate - ShareGate Protect. Microsoft 365 Governance. We got this.Nasuni - Activate your data for AI and request a demoSHOW NOTES:Opening Thesis - Was the first wave of AI adoption artificially cheap? - The industry may be transitioning from subsidized growth to usage-based economics. Key Topics 1. Evidence AI Was Subsidized Massive CAPEX vs low end-user pricing Generous enterprise bundles Frontier model access for $20/month 2. The Hidden Economics of AI Agents - Agents consume exponentially more inference Tool orchestration, retries, memory, verification 3. Why Frontier Labs Are Shifting Focus From benchmark supremacy to orchestration Governance, memory, connectors, MCP, workflows 4. Forecasting AI Pricing 12 Months: Commodity inference gets cheaper - Frontier reasoning remains premium 24 Months: AI billing resembles AWS-style infrastructure billing Runtime, memory, latency and orchestration become billable 36 Months: Outcome-based pricing emerges AI spending shifts from IT budgets to labor budgets Final Takeaways Commodity AI becomes utility-priced Frontier reasoning becomes premium Agents reshape enterprise economicsKey Conclusions1. AI probably was subsidizedThe economics strongly suggest adoption-first pricing.2. The subsidy era may be endingPremium tiers and metered pricing are emerging.3. AI agents fundamentally alter economicsUsage scales exponentially with autonomy.4. Commodity AI and frontier reasoning are separatingOne becomes cheap.One becomes premium.5. The real battle is moving upward in the stackThe future moat may be:orchestrationgovernanceworkflowsenterprise contextoperational toolingFinal Closing Thought“The biggest enterprise AI question may no longer be:‘Which model is smartest?’Instead:‘Which organization can most effectively operationalize, govern, and economically scale AI agents across the business?’”FEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow
Transcribe →Can AI Agents be held Accountable?
2w ago00:30:06Tap to summarizeSUMMARY: As AI Agents are being brought into complex, regulated workflows, we explore the importance of accountability and accuracy, and how platforms and harnesses accomplish that goal. Can the CFO really fall in love with AI? GUEST: Ram Venkatesh, Co-Founder/CTO of Sema4.aiSHOW: 1029SHOW TRANSCRIPT: The Enterprise AI Show #1029 TranscriptSHOW VIDEO: https://youtu.be/Lc3XS44Ixg4SHOW 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 about your background, and what led you to create Sema4.ai?. Topic 2 - AI Agents vs. Automation 2.0. What Actually Changed. Tell us about the Sema4.ai platform and capabilities. What challenges does it solve today?Topic 3 - You’re initially focused on solving challenges for the CFO, which means there is a ROI-focus all the time. Why did you target that segment of the business first?Topic 3a - What are the biggest hidden costs in enterprise AI deployments today?Topic 4 - Sema4.ai emphasizes “your LLM, your VPC, your data.” What are the biggest considerations for companies looking to create these private/sovereign AI solutions? What typically gets overlooked?Topic 5 - How do you tend to frame the conversation about AI trustworthiness, and the role of humans vs. agents for enterprise work? Topic 6 - It feels like so much has changed or evolved with AI in the last 2-3 years. How does an Enterprise think about this much change for something that will be core to many critical applications? What will the Enterprise Architecture look like in 2 years?Topic 7 - Sema4.ai emerged partly from the acquisition of Robocorp and has roots in open-source automation. Do you have a perspective on the role open-source will play in AI going forward? FEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow
Transcribe →Enabling AI Governance for M365
2w ago00:31:43Tap to summarizeSUMMARY: 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 ShareGateSHOW: 1028SHOW TRANSCRIPT: The Enterprise AI Show #1028 TranscriptSHOW VIDEO: https://youtu.be/sgqg7uqErA0SHOW SPONSORS:ShareGate - ShareGate Protect. Microsoft 365 Governance. We got this.Nasuni - Activate your data for AI and request a demoSHOW NOTES:Nearly 1 in 3 Organizations Report AI-Driven Data Exposure IncidentsOther Resources:A complete checklist for Microsoft 365 governance https://sharegate.com/guides/checklist-for-microsoft-365-governance Request a demo of ShareGate: Get a 1:1 ShareGate demo tailored to your Microsoft 365 use case Article around that divide of confidence vs reality of data exposure sharegate.com/blog/93-of-it-leaders-are-confident-in-their-ai-governance-but-nearly-1-in-3-report-data-exposure-incidents The State of Microsoft 365 industry report with more stats and insights - State of Microsoft 365 2025 | Free survey report – ShareGate | Sharegate (new one coming SOON)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?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow
Transcribe →An AI Market Analysis, May 2026
3w ago00:40:53Tap to summarizeSUMMARY: 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: 1027SHOW TRANSCRIPT: The Enterprise AI Show #1027 TranscriptSHOW SPONSORS:Nasuni - Activate your data for AI and request a demoShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!SHOW NOTES:Reviewing the Major AI Vendors FEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow
Transcribe →AI, Data Centers, and the Power Crunch
3w ago00:33:39Tap to summarizeSUMMARY: 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 AISHOW: 1026SHOW TRANSCRIPT: The Reasoning Show #1026 TranscriptSHOW VIDEO: https://youtu.be/satMQRxKQC8SHOW SPONSORS:ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoSHOW NOTES:1. AI’s Hidden Constraint: PowerAI growth is no longer limited only by GPUs and computePower generation, cooling, and grid interconnects are emerging as major bottlenecksData centers could account for 10–12% of North American power demand in coming years2. Why Data Centers Are Being ReimaginedTraditional data centers were built for enterprise IT, not AI-scale workloadsAI infrastructure introduces:Massive power density needsAdvanced cooling challenges3. The Grid Wasn’t Built for AIUtilities are designed around peak demand scenariosMost grids run well below peak capacity most of the timeAI workloads create volatile and unpredictable consumption patternsLong interconnection timelines are pushing companies toward alternative infrastructure models4. GPU Utilization Is Surprisingly LowGPU clusters are often underutilized because of:Scheduling inefficiencies, Cooling limitations, SLA constraintsEffective GPU utilization may be as low as 12–13% in some environments5. Cooling as a Major Optimization LayerLegacy data centers often cool entire zones inefficientlyPado AI alignsAI workloads, Cooling systems, Power allocationWorkload-aware orchestration helps optimize cooling and compute efficiency6. The Rise of “Compute Forecasting”Pado forecasts compute demand instead of energy demandThe platform models:GPU workloads, Power consumption, Cooling requirements, SLA prioritiesGoal: maximize “compute per megawatt”7. AI Workloads Become Time-AwareAI providers may increasingly:Shift workloads to off-peak periodsIncentivize delayed non-urgent jobsDynamically balance compute demandUsers are already seeing variable inference latency in real-world AI systems8. Sustainability vs Reliability vs ProfitabilityOperators must balance:Uptime expectations, Infrastructure costs, Sustainability goalsRenewable adoption is growing, but reliability still drives investment in natural gas and battery-backed systems9. Brownfield vs Greenfield OpportunitiesPado AI is focused primarily on existing (“brownfield”) data centersExisting enterprise infrastructure can often be extended and optimized instead of rebuiltEnterprises may gain significant AI capability without hyperscale GPU deploymentsFEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow
Transcribe →AI News of the Month for April 2026
4w ago00:37:00Tap to summarizeSUMMARY: 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: 1025SHOW TRANSCRIPT: The Reasoning Show #1025 TranscriptSHOW VIDEO: https://youtu.be/Gl-49dmAgBsSHOW SPONSORS:Nasuni - Activate your data for AI and request a demoShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!SHOW NOTES:Links to all the AI News covered in this months showFEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow
Transcribe →The 2026 AI Draft
May 300:43:59Tap to summarizeSUMMARY: 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 TalkSHOW: 1024SHOW TRANSCRIPT: The Reasoning Show #1024 TranscriptSHOW VIDEO: https://youtu.be/BjT_HKhOcRESHOW SPONSORS:ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoSHOW NOTES:Brian’s PicksGoogleMajor AI-centric IPO in 2026 ($1T valuation)Amazon (cloud)Company has more Agents that EmployeesTSMC (hardware)AMD (hardware)Family asks about AI at the holidaysData center issue causes a significant change to human existenceBrandon’s PicksAnthropicNVIDIABroadcomOpenAI (frontier model)AI Consumption-based pricing (end of subsidies)AI Energy DemandThe end of “vibe-coding”Sam Altman out at CEO of OpenAIFEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow
Transcribe →Halt & Retool: Rewriting Software Development in the Age of AI Agents
Apr 2900:34:58Tap to summarizeSUMMARY: 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 SailplaneSHOW: 1023SHOW TRANSCRIPT: The Reasoning Show #1023 TranscriptSHOW VIDEO: https://youtu.be/q50s0oL37pQSHOW SPONSORS:Nasuni - Activate your data for AI and request a demoShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!SHOW NOTES:Halt and Retool (presentation) OpenAI Harness EngineeringAnthropic Harness Engineering1. The “Halt and Retool” MomentA single-day build and deployment of a production feature triggered a company-wide realizationPaused all development to reassess how AI fundamentally changes engineering workflowsCreating “shock moments” (like stopping work) is key to driving mindset shifts2. From Coding to Agent OrchestrationDevelopers are shifting from writing code → managing AI agentsWork resembles “multi-boxing” or conducting an orchestra of parallel agentsSuccess depends on coordinating tasks, not executing them directly3. The Rise of Harness EngineeringDefined as everything between raw AI prompts and production-ready outputFocus: eliminating friction across the software development lifecycle Key practices:Logging agent errors and friction pointsContinuously refining workflows and toolingLetting AI reflect on and improve its own mistakes4. Spec-Driven Development Becomes CriticalPoor specifications lead to exponential inefficienciesTeams now spend significantly more time on design and specs than coding5. Measuring the Impact~3x increase in code velocityNear-zero “bit rot” Faster feature delivery—sometimes within 24 hours6. Token Maxing & Developer FitnessHigher token usage often signals better workflows and deeper integration with AIPerformance becomes about system design, not efficiency constraints7. New Tools & InterfacesIncreased use of voice interfaces over typingTerminal-first workflows replacing traditional IDE-centric approachesAI-accessible knowledge bases becoming standard8. The Future of Software EngineeringWithin ~6 months: developers may stop writing codeWithin ~12 months: developers may stop reading codeFocus shifts to:Intent, design, and orchestration. Domain expertise and problem modelingFEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow
Transcribe →The Zero-CVE Mirage: Hardening Software in the Age of AI Attacks
Apr 2600:35:13Tap to summarizeSUMMARY: 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: 1022SHOW TRANSCRIPT: The Reasoning Show #1022 TranscriptSHOW VIDEO: https://youtu.be/9Q0kWkTYRs8SHOW SPONSORS:ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoSHOW NOTES:Chainguard Factory 2.0DriftlessAFScaling Challenges & “Factory” EvolutionEarly automation relied on tools like GitHub ActionsAt scale, simple systems broke due to:Massive event volumesAPI rate limits (e.g., GitHub quotas)Exponential fan-out effectsKey innovation: custom work queue + reconciliation model~90% event deduplicationControlled throughput and backpressureImproved reliability and system stabilityIntroduced Driftless Built on reconciliation principles (inspired by Kubernetes):Compare desired vs. actual stateContinuously reconcile differencesBenefits:Resilience to missed eventsAutomatic retries and recoveryScales better than purely event-driven systemsAI Agents in Software DevelopmentAI is dramatically accelerating development workflowsChainguard uses agents to:Remediate vulnerabilities (CVEs)Update dependenciesFix failing tests and adapt to upstream changesKey Design PhilosophyLeast privilege → “least tool call”Avoid giving agents full system accessProvide narrowly scoped tools for specific tasksDelegate execution to sandboxed systems (e.g., CI pipelines)Focus on safe, controlled automationIndustry Shift: Velocity vs. SecurityExplosion of AI-driven tools (e.g., autonomous PR generation)Massive increase in development velocityNew risks:Poorly secured agent frameworksMalicious or unsafe automation patternsKey TakeawaysScale changes everythingSimple systems break under massive workloadsPurpose-built infrastructure becomes necessaryReconciliation > pure event-driven systems at scaleMore resilient, predictable, and controllableAI is a force multiplier—but requires guardrailsUnrestricted agents introduce serious riskConstrained, purpose-built agents are safer and more effectiveContinuous learning is mandatoryAI tooling is evolving too fast for static skillsetsTeams must actively experiment and adaptFEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow
Transcribe →