• 16 minutes 33 seconds
    The Zitron Bear Case: What's Right, What's Wrong?

    SUMMARY: In this episode, Aaron and Brandon tackle the provocative critiques of AI by Ed Zitron, a vocal opponent in the tech industry. They delve into the bear case against AI, exploring both the merits and flaws of Zitron's views.

    SHOW: 1052

    SHOW TRANSCRIPT: The Enterprise AI Show #1052 Transcript

    SHOW VIDEO: https://youtu.be/Fm3T5bT_8CQ

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     Topic: The Zitron Bear Case — What's Right, What's Wrong?

    • Why now? Ed Zitron went on CNBC's Squawk on the Street to lay out his bear case against OpenAI and Anthropic, covering questionable finances, AI's lack of ROI, and framing the whole thing as a symptom of the tech industry running out of hypergrowth ideas. CNBCX
    • Past: Every hype cycle gets its designated skeptic — dot-com had its shorts, cloud had its "just a fad" crowd, crypto had its own chorus. Zitron's been running this playbook since the early ZIRP-era "subprime AI crisis" pieces.
    • Present: Zitron's specific claims — OpenAI's burn rate math, the "nobody's making money on inference" argument, the case that Anthropic and OpenAI shouldn't be allowed to IPO with the numbers they'd have to report — stack up against actual usage/revenue data Brian and Aaron are seeing in the market. YouTube
    • Future: If Zitron's right about the economics, what's the unwind look like? If he's wrong, what is he missing about where value actually accrues (infra, tooling, harnesses vs. raw model access)?

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    9 August 2026, 5:00 am
  • 15 minutes 22 seconds
    Do You Even Need That Trillion-Parameter Model?

    SUMMARY: We continue our Models and Money series. In this episode, Brian and Aaron explore the current state and future of AI models, focusing on model size, model harnessing, and intelligent model routing. They discuss whether bigger models are always better, the economics of AI, and how enterprise applications can benefit from tailored AI solutions.

    SHOW: 1051

    SHOW TRANSCRIPT: The Enterprise AI Show #1051 Transcript

    SHOW VIDEO: https://youtu.be/tkJmeazn8Bs

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    Topic: When will the models/harnesses be good enough?

    • Why now? Benchmark Maxxing - cost to build/host/maintain 1+ trillion parameter model
    • Past: The “wow” moments in versions really stopped around GPT4… (maybe?)
    • Present: Race to the top/bottom, millions spent to gain SOTA for a few days
    • Future: Will the pendulum swing back? Will bigger/faster always rule?

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    5 August 2026, 5:00 am
  • 46 minutes 8 seconds
    AI News of the Month - July 2026

    SUMMARY:  Brian Gracely (@bgracely) and Brandon Whichard (@bwhichard) discuss the biggest AI news stories from the month of July 2026. 

    SHOW: 1050

    SHOW TRANSCRIPT: The Enterprise AI Show #1050 Transcript

    SHOW VIDEO: https://youtu.be/9u-uAaQVjXk

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    2 August 2026, 5:00 am
  • 40 minutes 25 seconds
    How AI Stacks are rewriting the Rules of Business

    SUMMARY: Brian speaks with Dave Vellante, Co-Founder/CEO theCUBE, about how AI is changing the entire tech stack, the evolution of systems of intelligence, and how the competitive landscape is forcing companies to make difficult decisions about their AI future.

    SHOW: 1049

    SHOW TRANSCRIPT: The Enterprise AI Show #1049 Transcript

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

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    SHOW NOTES:

    Topic 1 - The new technology stacks being driven by AI. Where is intelligence being built and distributed? 

    Topic 1a - Where is value in the stack being created and commoditized?

    Topic 1b - Where do you see powerful software ecosystems defending themselves and where are they most vulnerable because of AI?

    Topic 2 - Alex Karp’s thesis that the harness will generate more value than the models, and owning and managing the harness is a path to enable companies to better control their AI future. 

    Topic 3 - Is AMD potentially cracking NVIDIA’s monopoly on AI accelerators? 


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    29 July 2026, 5:00 am
  • 19 minutes 24 seconds
    When Will the Models be Good Enough?

    SUMMARY: Brian and Aaron explore the current state and future of AI models, focusing on model size, model harnessing, and intelligent model routing. They discuss whether bigger models are always better, the economics of AI, and how enterprise applications can benefit from tailored AI solutions.

    SHOW: 1048

    SHOW TRANSCRIPT: The Enterprise AI Show #1048 Transcript

    SHOW VIDEO: https://youtu.be/1IHXNrYlYCA

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    Topic: When will the models/harnesses be good enough?

    • Why now? Benchmark Maxxing - cost to build/host/maintain 1+ trillion parameter model
    • Past: The “wow” moments in versions really stopped around GPT4… (maybe?)
    • Present: Race to the top/bottom, millions spent to gain SOTA for a few days
    • Future: Will the pendulum swing back? Will bigger/faster always rule?

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    26 July 2026, 8:00 pm
  • 25 minutes 3 seconds
    AI's Impact on Trust and Brand

    SUMMARY: Brian talks Melissa Rosenthal from Outlever about the intersection of AI, brand, and marketing. They explore how AI impacts trust, brand consistency, and operational efficiency, offering insights for organizations navigating AI adoption.

    SHOW: 1047

    SHOW TRANSCRIPT: The Enterprise AI Show #1047 Transcript

    SHOW VIDEO: https://youtu.be/PEqyOxE9PIw

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    RESOURCES:

    Outlever

    State of Brand Article

    KEY TOPICS:

    • AI's impact on trust and brand perception
    • Operational efficiencies and AI workflows
    • Challenges of AI implementation and guardrails
    • Measuring ROI and costs of AI
    • The importance of brand consistency in AI interactions

    TAKEAWAYS


    • AI can speed up engineering and operational tasks by 100X.
    • Many companies are still in pilot stages with AI, lacking governance.
    • Brand consistency is at risk when AI interactions don't align with brand values.
    • Experimentation with AI is costly and often lacks clear ROI.
    • Organizations need to orchestrate AI workflows across teams for better outcomes.

    CHAPTERS/TOPICS:

    00:00 Introduction to AI's impact on enterprise and brand

    00:30 Melissa Rosenthal's background and Outlever's focus

    01:05 The trust issue: AI replacing human interactions

    01:55 How AI speeds up workflows and system building

    03:00 Risks of AI in customer-facing interactions

    04:11 Brand touchpoints and AI's influence on brand perception

    05:05 Training AI to reflect brand values

    05:59 Responsibility and handling AI mistakes

    06:53 Current state of AI governance in companies

    08:12 ROI and costs of AI experimentation

    08:57 The early stage of AI adoption and lessons learned

    10:11 Future outlook: AI in marketing and brand orchestration

    10:58 The importance of workflow orchestration across teams

    12:03 Bridging the gap between technology and marketing

    12:54 The chaos and chaos in AI adoption today

    14:03 Risks of homogenized brand messaging with AI

    15:02 Predictions for AI in marketing in the next year

    16:04 Reorganizing teams around AI outcomes

    17:04 The missing link: connecting technology and brand strategy

    18:03 Final thoughts and key takeaways


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    22 July 2026, 5:00 am
  • 34 minutes 47 seconds
    What is a Behavioral Agent Automation Platform?

    SUMMARY: Steven Walchek, CEO at Liminal, discusses secure AI enablement for regulated industries and why most enterprises are stuck in perpetual AI pilots. We explore the "agentic cliff" and how Behavioral Agent Automation Platforms (BAAPs) discover and deploy agents by observing how work actually happens.

    SHOW: 1046

    SHOW TRANSCRIPT: The Enterprise AI Show #1046 Transcript

    SHOW VIDEO: https://youtu.be/nVte_ZKDID4

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    RESOURCES:

    KEY TOPICS:

    • Horizontal security for generative AI
    • Data privacy and compliance in AI
    • Security layers in AI deployment
    • Agentic AI and behavioral automation
    • Future industry trends in AI security

    TAKEAWAYS

    • Security for AI must be integrated at the application layer, not just network or perimeter.
    • Data privacy concerns are central to AI adoption in regulated industries.
    • Organizations need a security layer that supports multi-model, multi-provider AI engagement.
    • Behavioral automation and agentic AI require observability and policy enforcement.
    • The AI industry is still in early adoption, with significant growth expected in the next five years.

    CHAPTERS/TOPICS:

    00:00 Introduction and guest introduction

    02:09 Steven's background and journey in tech

    03:54 The core problem of AI security in regulated industries

    07:45 Data privacy concerns and industry challenges

    12:01 Liminal's approach to AI security and compliance

    15:52 Security at the application layer and network layer

    20:02 Agentic AI, behavioral automation, and observability

    30:04 Future trends and industry outlook

    31:46 How to connect with Liminal and closing remarks


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    19 July 2026, 5:00 am
  • 14 minutes 49 seconds
    Does FinOps need an update for the AI world?

    SUMMARY: On today’s "Models and Markets" - we explore about the FinOps experience from Cloud is having to adapt to the changing demands of Enterprise AI. 

    SHOW: 1045

    SHOW TRANSCRIPT: The Enterprise AI Show #1045 Transcript

    SHOW VIDEO: https://youtu.be/Plb88y-IkZY

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    SHOW NOTES:

    Topic: Finops for AI?

    • Why now? Cost of tokens goes up as model performance increases, but still needs subsidies…
    • Past: FinOps for Cloud - prices grew out of control, needed centralization for expense management and capital allocation
    • Present: TokenMaxxing, the move from per-seat to per-token pricing
    • Future: What happens when you can’t afford the Ferrari anymore? Will there be a glut of FinOps for AI startups? What happens when usage is regulated and centralized?

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    15 July 2026, 5:00 am
  • 18 minutes 23 seconds
    Buy, Build or Rent your AI?

    SUMMARY: Something new - "Models and Markets" - Aaron and Brian explore how recent news and macro trends are causing more companies to explore whether they should Buy, Build or Rent their AI future. 

    SHOW: 1044

    SHOW TRANSCRIPT: The Enterprise AI Show #1044 Transcript

    SHOW VIDEO: https://youtu.be/vcroGCXd3N4

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    SHOW NOTES:

    Topic: Own AI or Rent AI?

    • Why now? Fable 5 and GPT 5.6 get restricted in the US
    • Past: Private Cloud (on-prem/server huggers) vs. Public cloud vs. *gasp* hybrid cloud
    • Present: OSS Models vs. Big API models
    • Future: What happens when the subsidies go away, and rational business practices hit the industry??

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    12 July 2026, 5:00 am
  • 24 minutes 23 seconds
    Unstructured Data in an AI World

    SUMMARY: While we spend a lot of time discussing AI models, we don’t always spend enough time on the challenges of managing the unstructured data used to train, tune, and enable those models. 

    SHOW: 1043

    SHOW TRANSCRIPT: The Enterprise AI Show #1043 Transcript

    SHOW VIDEO: https://youtu.be/OAqnuhorMJ4

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    SHOW NOTES:

    Topic 1 - Welcome to the show. Tell us a bit about your background and where you focus today at Nasuni

    Topic 2 - We’ve spent two years talking about models. Are we finally entering the era where the biggest differentiator is data quality rather than model quality?

    Topic 3 - When customers inventory their AI-ready data, what surprises them most?

    Topic 4 - Where is the intersection of file data, metadata, and RAG systems that augment a company’s AI experience with their own data?

    Topic 5 - People talk about AI governance, but isn’t most AI governance actually data governance?

    Topic 6 - Are today’s enterprise file systems designed for machine consumers (AI Agents) instead of human consumers?

    Topic 7 - What are the economics of data, in your world, as it relates to AI?

    Topic 8 - What’s next for enterprise file platforms?


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    8 July 2026, 5:00 am
  • 28 minutes 40 seconds
    Are companies giving away their secrets to AI?

    SUMMARY: Are CEO's frustrated with the lack of control, costs and sovereignty of their AI environments? 

    SHOW: 1042

    SHOW TRANSCRIPT: The Enterprise AI Show #1042 Transcript

    SHOW VIDEO: https://youtu.be/xgQv8WP-DNI

    SHOW SPONSORS:

    SHOW NOTES:


    1. There’s a level of unhappiness and distrust of the frontier labs from CEOs
    2. There needs to be an application layer on top of LLMs (e.g. “harness”, Palantir Ontology)
    3. This application layer prevents the LLMs from learning your business from your data
    4. “Alpha” is business differentiation (ability to outperform the market)
    5. He questions why the frontier model labs are charging by tokens and not outcomes (questions the entire AI business model)
    6. He questions “the true cost” of AI outputs 
    7. He claims that CEOs are now concerned about frontier labs entering the business of the customers - brings up an interesting misunderstanding of how interacting with LLMs works (“we’re safe, it’s deployed in our VPC”)

    FEEDBACK?

    5 July 2026, 5:00 am
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