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
51episodes
Episodes
Newest firstAll episodes
Chaotic AI Markets: Focus on what you can control
6d ago00:23:07Tap to summarizeSUMMARY: On Father's Day, how would you explain some of the volatility of the AI market to your father? What advice might he give you to navigate the ups and downs and uncertainties?SHOW: 1038SHOW TRANSCRIPT: The Enterprise AI Show #1038 TranscriptSHOW VIDEO: https://youtu.be/T2ZIYLpl_cESHOW SPONSORS:ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoOutShift by Cisco - “Scaling Out Superintelligence” The Internet of Cognition architectureSHOW NOTES:Leaked documents show OpenAI is losing billionsAnthropic’s Fable and Mythos models banned from non-US foreign nationalsThe AI layoff wave is becoming a powder kegProfessors says AI-related job losses are inevitableTHESIS: On this Father’s Day, with an AI market that often times doesn’t make any sense, I thought about the type of advice that my father gave me over the years and how it would apply to this time of significant change. Show up, keep up and shut upMake yourself invaluableFocus on what you can controlBe an expert in somethingWhen in doubt, get closer to people and how money is madeWhen things don’t make sense, focus on fundamentalsMarkets can be irrational way longer than you can be solventTry and think a couple steps aheadFEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow
Transcribe →AI Cyber is expanding a Vulnerability Gap
1w ago00:26:03Tap to summarizeSUMMARY: As tools like Mythos create new AI-cybersecurity concerns, CIOs and CISOs need to be prepared for two challenges: Security Remediation and Patch to Production acceleration. SHOW: 1037SHOW TRANSCRIPT: The Enterprise AI Show #1037 TranscriptSHOW VIDEO: https://youtu.be/H5KxoiEIfUoSHOW SPONSORS:Nasuni - Activate your data for AI and request a demoOutShift by Cisco - “Scaling Out Superintelligence” The Internet of Cognition architectureShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!SHOW NOTES:Project Lightwell (Red Hat and IBM)Athena (Chainguard)Anthropic Project GlasswingOpenAI GPT 5.5-CyberTHESIS: Major initiatives are forming to help enterprise organizations combat security vulnerability threats found or created using new AI-cyber tools such as Anthropic Mythos. What are the key considerations, and what additional steps do organizations need to take to be advantaged by these capabilities? Part 1The Breaking Point and the Mythos MomentThe scope of open source security and supportPatches, disclosures and upstream open sourceClearinghouses, EOs, Laws and CommunitiesRemediation - Build vs. BuyPart 2How fast can you get from Patch to Production?Mitigation before patchingFast path and stable patch pipelines?Automation in patching vs. automation in deploymentFEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow
Transcribe →Do CIOs need to create an Enterprise AI Harness?
2w ago00:22:52Tap to summarizeSUMMARY: How can CIOs balance innovation and control as they roll our AI capabilities across their organization. How can they balance onboarding, experience, security and flexibility? SHOW: 1036SHOW TRANSCRIPT: The Enterprise AI Show #1036 TranscriptSHOW VIDEO: https://youtu.be/ZgkMF7G3YfoSHOW SPONSORS:OutShift by Cisco - “Scaling Out Superintelligence” The Internet of Cognition architectureShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoSHOW NOTES:Andy Weir (The Martian) on Eps. 193Systems of Record Won the SaaS Era - Clearinghouses Will Win the Agents EraHarness Engineering is where Enterprise AI becomes realTHESIS: It comes up as different control points, but CIOs are ultimately trying to figure out how to get the value from Enterprise AI while delivering a set of consistency across different teams and use-cases. Let’s explore what this “Enterprise Harness” is starting to look like. Enterprise Clearinghouse Enterprise Intelligence (a.k.a. Middleware)Enterprise Catalog - Models as a Service, Agents as a ServiceEnterprise Skills or Shareable Prompt HarnessesSymantec Routing to ModelsAI Gateway ControlsFEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow
Transcribe →Should CIOs have a backup plan for AI?
2w ago00:48:31Tap to summarizeSUMMARY: If the cost of public AI continues to rise, because of various market shortages, should CIOs start looking at backup plans to better own their AI journeys and futures?SHOW: 1035SHOW TRANSCRIPT: The Enterprise AI Show #1035 TranscriptSHOW VIDEO: https://youtu.be/ngBBpP2LgdoSHOW SPONSORS:ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoOutShift by Cisco - “Scaling Out Superintelligence” The Internet of Cognition architectureSHOW NOTES:THESIS: Between pending IPOs (Wall St. demands), high user-demand, GPU/TPU shortages, Data Center shortages, Model prices increasing (open models fading away), the cost of using AI is going to get more expensive over time. Should CIOs start thinking about a Backup plan to their current AI adoption that has lower cost alternatives?Topic 1 - Assuming you could get access to GPUs/TPUs/Accelerators, and suitable data center space to host them, what would be your thinking as a CIO if you felt like you needed to own some aspect of your AI roadmap/journey? Topic 2 - Assuming the normal “Shadow AI” backlash that you’d receive for offering something that wasn’t “frontier” level, how would you go about trying to communicate that within your organization?Topic 3 - What metrics or KPIs would you initially target to try and get buy-in that your approach was acceptable and moving towards the company goals?FEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow
Transcribe →What are the incentives to share AI learning curves with teammates?
3w ago00:21:20Tap to summarizeSUMMARY: When we get to the end of 2026, how will enterprise companies be measuring the success of their AI projects? And how well will their teams be sharing their AI learning curves?SHOW: 1034SHOW TRANSCRIPT: The Enterprise AI Show #1034 TranscriptSHOW VIDEO: https://youtu.be/TvIFwNN-6ckSHOW SPONSORS:Nasuni - Activate your data for AI and request a demoOutShift - “Scaling Out Superintelligence” The Internet of Cognition architectureShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!SHOW NOTES:Why AI Economics are changingHow will team collaboration evolve with Enterprise AI?Topic 1 - How do we measure AI-adoption success? Number of workloads?Financial metrics (Spend, ROI, Costs-Saved, etc.)?Speed improvements?People-level?Topic 2 Right now the AI tools are very individual-centric The machinery to share, even at the basic enterprise-level, is very difficultThe experience to share is non-deterministic, just as everyone’s working style is different.Topic 3 - The motivation to share is still unknown. How do you encourage collaboration when so many companies are laying off people, or the specter of that happening is growing?What was the motivation before (team goals?) and how does that change now? People don’t want to be monitored, so how does a manager have visibility?What happens when companies remove the managers (“the counters”)? FEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow
Transcribe →Cerebras is disrupting the market with Fast Inference
3w ago00:35:21Tap to summarizeSUMMARY: After the first successful AI IPO of 2026, we dig into what makes the Cerebras WSE architecture unique in the market for fast inference. GUEST: Andy Hock, Chief Strategy Officer at Cerebras AISHOW: 1033SHOW TRANSCRIPT: The Enterprise AI Show #1033 TranscriptSHOW VIDEO: https://youtu.be/ed2nVbOtZiASHOW SPONSORS:OutShift - “Scaling Out Superintelligence” The Internet of Cognition architectureShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoSHOW NOTES:OpenAI announces 750MW partnership with CerebrasCerebras and AWS partnershipCerebras announces IPOTopic 1 - Welcome to the show. Tell us about your background, and what you focus on today. Topic 2 - For anyone that’s not familiar with Cerebras, give us an overview of the company, and especially an overview on the Cerebras technologies (e.g. Wafer-Scale Engine).Topic 3 - Cerebras’ WSE architecture is different from many of the GPU or GPU-like architectures in the market today. Centralized vs. distributed architectures always have their tradeoffs. Walk us through the technical and economic value of the Cerebras architecture.Topic 4 - Congratulations on the recent IPO (raised $5.55B). Let’s use that as a point in time vs the previous planned IPO. How has the market changed in that timeframe, and how has the Cerebras position changed? Topic 5 - Cerebras (today) offer both WSE hardware, and Cerebras Cloud (API) - very different GTM paths. Can we expect both of those to stay top priorities, or have the market dynamics shifted such that the priorities shift more towards the WSE business - as we’re seeing OpenAI, AWS and other engagements announced?Topic 6 - Is Cerebras a training and inference company, or are the economics of inference significantly different enough that it needs to be the sole focus of the company (for now)? Topic 7 - How much effort is it for any company to add support for the Cerebras chips if they have previously been using other architectures?Topic 8 - An IPO is a major milestone for any company, but the markets will now look for your future story. How do you see the AI market evolving over the next 2-5 years, and what are some things that people aren’t understanding yet about how it will evolve?FEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow
Transcribe →How will team collaboration evolve within Enterprise AI?
4w ago00:30:56Tap to summarizeSUMMARY: The biggest enterprise AI question is which organization can most effectively operationalize, govern, and economically scale AI agents across the business.SHOW: 1032SHOW TRANSCRIPT: The Enterprise AI Show #1032 TranscriptSHOW VIDEO: https://youtu.be/GsK_RUnYroISHOW SPONSORS:ShareGate - ShareGate Protect. Microsoft 365 Governance. We got this.Nasuni - Activate your data for AI and request a demoSHOW NOTES:Opening Thesis - How will team collaboration evolve within Enterprise AI?Question: Any suggestions on how to introduce enterprise-level governance and standardisation for agentic coding? Like skills, rules, plugins, context etcKey Topics 1. This isn’t a Coding-specific problem. Every team has this issue. If your processes weren’t well defined and enforced before, they will be worse nowNot it’s not just process standardization, but “buy-in” standardization2. Everything moves so fast, so managers don’t have the answers (yet) AI value is being created bottom-up, but paid for (and mandated) top-downThe current measurements aren’t useful (tokenmaxxing, all-or-nothing, etc.)3. The governance tools don’t exist yet.And it’s not clear that anyone wants them. They didn’t want them before. How do you even define governance? What’s the baby step before that, reuse and basic sharing? 4. Are we ready to invest in “Centers of Excellence” again? 5. We under-estimate the “creativity” element in human buy-in. Is success measured in improvement or replacement?How much of that did “you” do? We don’t know how to measure that.We haven’t lived through an AI-centric promotion cycle yet6. Bottom-up and Top-down need to find some common language and middle ground. Have they walked a mile in each other’s shoes yet (or lately)?How to bring a reality to the hype vs. demands vs. learning curve?How long is an AI-centric cycle vs. a pre-AI-centric cycle? FEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow
Transcribe →AI News of the Month - May 2026
May 2700:37:54Tap 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
May 2400:32:28Tap 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?
May 2000:30:06Tap 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
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