• 21 minutes 20 seconds
    What are the incentives to share AI learning curves with teammates?

    SUMMARY: 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: 1034

    SHOW TRANSCRIPT: The Enterprise AI Show #1034 Transcript

    SHOW VIDEO: https://youtu.be/TvIFwNN-6ck

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    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 difficult
    • The 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”)? 

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    5 June 2026, 5:00 am
  • 35 minutes 21 seconds
    Cerebras is disrupting the market with Fast Inference

    SUMMARY: 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 AI

    SHOW: 1033

    SHOW TRANSCRIPT: The Enterprise AI Show #1033 Transcript

    SHOW VIDEO: https://youtu.be/ed2nVbOtZiA

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    Topic 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?


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    3 June 2026, 5:00 am
  • 30 minutes 56 seconds
    How will team collaboration evolve within Enterprise AI?

    SUMMARY: The biggest enterprise AI question is which organization can most effectively operationalize, govern, and economically scale AI agents across the business.

    SHOW: 1032

    SHOW TRANSCRIPT: The Enterprise AI Show #1032 Transcript

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

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    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 etc

    Key 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 now
    • Not it’s not just process standardization, but “buy-in” standardization

    2. 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-down
    • The 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 yet

    6. 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? 

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    31 May 2026, 5:00 am
  • 37 minutes 54 seconds
    AI News of the Month - May 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 May, 2026. 

    SHOW: 1031

    SHOW TRANSCRIPT: The Reasoning Show #1031 Transcript

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

    SHOW SPONSORS:

    SHOW NOTES:

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    27 May 2026, 5:00 am
  • 32 minutes 28 seconds
    Why Enterprise AI Economics Are Changing

    SUMMARY: 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?’

    SHOW: 1030

    SHOW TRANSCRIPT: The Enterprise AI Show #1030 Transcript

    SHOW VIDEO: https://youtu.be/acOBfRI0P3U

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    SHOW 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 economics

    Key Conclusions

    1. AI probably was subsidized
    The economics strongly suggest adoption-first pricing.

    2. The subsidy era may be ending
    Premium tiers and metered pricing are emerging.

    3. AI agents fundamentally alter economics
    Usage scales exponentially with autonomy.

    4. Commodity AI and frontier reasoning are separating
    One becomes cheap.
    One becomes premium.

    5. The real battle is moving upward in the stack
    The future moat may be:

    • orchestration
    • governance
    • workflows
    • enterprise context
    • operational tooling


    Final 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?’”

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    24 May 2026, 5:00 am
  • 30 minutes 6 seconds
    Can AI Agents be held Accountable?

    SUMMARY: 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.ai

    SHOW: 1029

    SHOW TRANSCRIPT: The Enterprise AI Show #1029 Transcript

    SHOW VIDEO: https://youtu.be/Lc3XS44Ixg4

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    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? 


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    20 May 2026, 5:00 am
  • 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

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    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?

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    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

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    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

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    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

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    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

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    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

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    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
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