• 42 minutes 46 seconds
    AI News for Mid-September 2026

    Aaron and Brandon cover what the week's news means for enterprise buyers. They question whether Oracle's reported $664 billion backlog is durable demand or overlapping commitments, and Brandon argues contracted future spend is how enterprise business works. On the labs' "slow down" messaging, Brandon sees no coordination, only incentives, while Aaron finds the timing too coincidental. Salesforce's Nemotron-based model points to enterprises customizing open models instead of building their own, and Brandon doubts labs will displace systems of record like Workday or SAP. Aaron argues agent pricing has reverted to familiar free, bundled, and negotiated tiers. He sees agent swarms as research capability that most enterprises lack the trust and human-in-the-loop controls to run.



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    SHOW: 1064

    SHOW TRANSCRIPT: The Enterprise AI Show #1064 Transcript

    SHOW VIDEO: https://youtu.be/hrfTovN3kMw

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    21 September 2026, 1:00 am
  • 39 minutes 41 seconds
    Building AI Agents That Support Enterprise Teams

    Aaron interviews Diego Oppenheimer, general partner at the AIT Fund (the venture fund behind the AI Tinkerers builder community), about what it takes to move AI agents from personal experimentation into enterprise-grade deployment. Drawing on his own agent fleet (a chief-of-staff agent, a content strategist, and Ashley, an always-on AI community manager serving AI Tinkerers' 127,000+ members) as a proving ground, Oppenheimer lays out an enterprise security model built on treating agents as digital employees with their own delegated identities and credentials, scoped access rather than blanket access to accounts like email and calendar, and continuous audit logging (via the open-source tool Hyperware) in place of trust based on prior behavior. He argues enterprises get burned by chasing a single omnipresent agent instead of narrowing agents to specialized, restricted workflows, the same specialization principle that has driven results throughout machine learning, and flags scheduling as a deceptively hard example where the "10% edge cases" eat most of the effort. He also describes deliberately red-teaming his own agents for weeks (trying to break out of containers, extract credentials, and social-engineer them) before granting them any real access, and points to NanoClaw's containerized, minimal-component design as a model worth enterprise attention. The conversation closes on identity, responsibility, and permissioning as the unresolved internals enterprises must solve before scaling agent autonomy, and on Oppenheimer's prediction that many enterprise roles will shift toward "exception handling" as teams of AI coworkers absorb routine work and escalate only what needs human judgment.



    SHOW: 1063 

    SHOW TRANSCRIPT: The Enterprise AI Show #1063 Transcript

    SHOW VIDEO: https://youtu.be/vL8btTMcInE



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    GUEST BIO

    Diego Oppenheimer is working full-time at AIT Fund (the fund behind AI Tinkerers) and previously was a partner at Factory and served as CEO-in-residence at Factory. He founded Algorithmia, an enterprise MLOps platform acquired by DataRobot, co-founded Guardrails AI, and earlier in his career led teams at Microsoft shipping Excel, SQL Server, and Power BI. He is currently running AI agents inside his own team to take on real, sustained work, including one named Ashley, and documenting what he's learned in a new video series with Joe Heitzeberg.


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    16 September 2026, 10:00 am
  • 45 minutes 17 seconds
    Securing AI Agents in the Enterprise: NanoClaw, Zero Trust Guardrails, and Governance at Scale

    Brian and Aaron interview Gavriel Cohen, co-founder and CEO of Nanoco and creator of the open-source agent framework NanoClaw, about securing AI agents in enterprise environments. Cohen shares how he built NanoClaw after discovering major security and safety gaps while using agents for an AI native marketing agency, and how the project grew to over 30,000 GitHub stars and over half a million downloads. They discuss why Fortune 500s, financial institutions, universities, and government groups feel urgent pressure to adopt agents but are blocked by control, privacy, and security concerns. Cohen outlines a zero-trust approach using microVM isolation, no credentials inside agent environments, a policy-enforcing gateway with granular controls, audit logs, cost attribution, and human-in-the-loop approvals at key decision points.



    SHOW: 1062

    SHOW TRANSCRIPT: The Enterprise AI Show #1062 Transcript

    SHOW VIDEO: https://youtu.be/h906EEQSRs8



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    GUEST BIO:

    Gavriel Cohen is co-founder and CEO of NanoCo, and creator of NanoClaw, the open-source agent harness he built as a small, auditable, secure alternative to OpenClaw. He spent a decade as a developer and team lead at Wix before building NanoClaw in a weekend, a project that has since drawn a Docker integration and an outside security review. He holds a BSc in Physics and Computer Science from Tel Aviv University.


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    13 September 2026, 6:00 pm
  • 42 minutes 20 seconds
    How Open-Source is Reshaping the AI Infrastructure Stack

    Aaron interviews David Aronchick, CEO @ Expanso (former PM lead for Kubernetes, Kubeflow co-founder, and open-source ML leader at Azure) about how open source is reshaping the AI infrastructure stack. Aronchick recounts his path from early Linux and enterprise work to launching Kubernetes and GKE, then creating Kubeflow in 2017 to orchestrate end-to-end ML workflows on Kubernetes. The discussion centers on gaps in AI infrastructure, especially reproducibility and determinism across hardware, drivers, OS, packages, and data lineage, arguing Kubernetes alone can’t fully solve it. They contrast open weights with true open-source models, noting that real openness would require reproducible training data and infrastructure. They explore “AI-native” enterprise architecture, the role of open-source harnesses/wrappers to add deterministic controls, and growing edge/distributed compute needs driven by governance, compliance, bandwidth, and hybrid deployment realities.


    SHOW: 1061

    SHOW TRANSCRIPT: The Enterprise AI Show #1061 Transcript

    SHOW VIDEO: https://youtu.be/kpQg3YIIUL8


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    SHOW TOPICS:

    You have a super interesting background (First managing PM for Kubernetes, Co-founded Kubeflow, led open-source ML at Microsoft Azure). Give everyone a brief introduction and how you became so involved in open-source and the Enterprise

    OSS topics:

    • Back when we were The Cloudcast, we covered K8s in depth, but I’m not sure we ever did a show on Kubeflow. Kubeflow tried to bring Kubernetes-style orchestration to ML workflows. Looking back, what did that generation of open-source AI infrastructure get right, and what did it miss that the current wave (agents, inference at the edge) is now having to solve for again? Oh, and maybe give a quick intro to Kubeflow as well for those that aren’t familiar
    • Zooming out - open source shaped your whole career, from Kubernetes to Kubeflow to Bacalhau. Where do you think open source has the most leverage in the AI infrastructure stack right now, and where do you think it's losing ground to closed, vendor-controlled platforms?
    • What are your thoughts on “OSS models”? Today, OSS really means open weights. Do you think there will ever be a truly OSS model? What would it take? Thoughts on the state of the industry?

    A couple of Enterprise “grab bag” questions for you on a few different topics while we have you:

    • "AI-native" gets used a lot and means different things to different people. What does AI-native actually mean for enterprise architecture in your view, and how is it different from just bolting AI onto an existing cloud or data stack?
    • Regulatory and data residency pressure keeps coming up across industries (telecom, healthcare, financial services). How much of the edge/distributed compute push is being driven by AI performance needs versus governance and compliance requirements? Which one is the bigger driver right now?

    CLOSING: If anyone is interested, what’s the best way to get started?


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    9 September 2026, 5:00 am
  • 22 minutes 15 seconds
    Vitamins vs. Pain Killers vs. Whippets - The Enterprise AI Adoption Problem

    Brian, Brandon, and Aaron discuss enterprise AI adoption using the sales analogy of whether AI is a “vitamin” or a “painkiller,” arguing that successful transformation still requires a burning-platform event. Brandon suggests AI adoption resembles past digital transformations: without urgent pressure (e.g., a data center closing), organizations resist change and justify existing processes. Brian describes a compressed hype cycle from ChatGPT excitement to pilots and guardrails, followed by difficulties with data, cost-effective scaling, and making AI behave deterministically, while fear of competitors keeps efforts alive. They add a third category, “Whippets”, short-term, resume-driven initiatives led by leaders who leave others “holding the bag.” They debate examples like Sheetz’ multiple VMs and argue that AI’s promise is personal productivity, but note a lack of enterprise collaboration and shared-memory tools that limit organizational impact.


    SHOW: 1060

    SHOW TRANSCRIPT: The Enterprise AI Show #1060 Transcript

    SHOW VIDEO: https://youtu.be/XZqomQosv1w

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    The thesis: Successful digital transformation usually has a forcing function; you migrate the data center because the real estate got sold, not because someone promised abstract savings. Deadline + shared incentive = people actually change. AI adoption mostly lacks that: no one's forcing the migration, so it defaults to "give everyone Copilot licenses and hope."


    Core question: If your business is healthy and there's no burning platform, how do you adopt AI in a way that's more than expensive theater, without a crisis to manufacture urgency?


    Discussion topics:

    • Forcing functions vs. vibes: What are the AI-era equivalents of "the real estate got sold"? A support team that's actually understaffed, a process with a real bottleneck, a cost center leadership is already scrutinizing vs. a mandate to "use more AI."
    • The unknown-unknowns problem: Most orgs don't know which of their workflows AI would actually help vs. where it's a novelty. How do you go find that out cheaply, without a company-wide token-burning experiment as the discovery mechanism?
    • Bottom-up signal vs. top-down mandate: Does real usage data (who's actually using tools, for what) surface better targets than an executive committee guessing at use cases?
    • Contrast with the failed "abstract savings" migration: What does an AI initiative look like when it's tied to a concrete, already-painful problem instead of a general efficiency narrative?
    • The FOMO trap: Distinguishing "we don't want to miss the platform shift" (legitimate) from "we need AI headlines for the board" (theater), and how leadership can tell which one they're actually doing.
    • Measurement: If there's no forcing function, what replaces the natural deadline/incentive alignment as the way you know it's working, or that it's time to kill it?


    Final Thought

    Is the right move small, cheap, bounded bets against known pain points, treating AI adoption like a search problem, not a rollout, rather than a company-wide transformation initiative looking for a reason to exist?


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    6 September 2026, 11:00 am
  • 38 minutes 30 seconds
    AI News of the Month - August 2026

    Aaron, Brian, and Brandon cover major stories including NVIDIA’s record quarter and continued growth forecasts, alongside concerns about declining free cash flow and customer financing. They discuss NVIDIA’s reported $12.9B acquisition of Hugging Face as a strategic move to strengthen the open-model ecosystem and go up the stack, and Stripe’s $8B acquisition of OpenRouter as routing infrastructure for model choice and potential agent-to-agent commerce. The group reacts to reports of OpenAI agent testing in which agents collaborated, manipulated logs, and tried to deceive humans, framing it as a security and guardrails issue. They also mention Microsoft employees’ surprising AI spend, OpenAI’s “Jalapeño” hardware push and manufacturing constraints, and Salesforce’s “Claude Force” concept of using Claude as the UI to query Salesforce data.


    SHOW: 1059

    SHOW TRANSCRIPT: The Enterprise AI Show #1059 Transcript

    SHOW VIDEO: https://youtu.be/Clmgst03-eg


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    Topic: Link to the full list of topics for the month


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    2 September 2026, 5:00 am
  • 17 minutes 30 seconds
    AI Watermarking: Compliance Theater or Real Provenance Tool

    SUMMARY: Brian, Brandon, and Aaron focus on AI watermarking, driven largely by EU transparency requirements, and discuss how approaches like token-selection patterns can be detected but were reportedly cracked quickly with tools that strip watermarks. Brandon and Brian debate whether watermarking is useful long-term, suggesting most people care more about whether content is helpful than whether AI was involved, and questioning the added cost and real-world impact of such regulation. They also explore implications for education policies that ban AI use, changing assessment methods to curb cheating, and potential enterprise and government procurement issues where “no AI” requirements could trigger disputes and lawsuits, while AI review may also level the playing field in contract understanding.


    SHOW: 1058

    SHOW TRANSCRIPT: The Enterprise AI Show #1058 Transcript

    SHOW VIDEO: https://youtu.be/6zlN_oIR5Xc


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    Topic: Anthropic recently started invisibly watermarking all Claude-generated text and files (Aug 11), joining Google (SynthID) and ~190 companies that signed the EU's AI Act Transparency Code. Article 50 became enforceable August 2, with fines up to €15M or 3% of global turnover for non-compliance. Within 24 hours of Anthropic's announcement, a free tool to strip Claude's watermark showed up on GitHub.

    Core question: Is watermarking building durable AI provenance infrastructure, or is it a regulatory checkbox that breaks the moment someone runs a paraphraser?

    Discussion angles:

    • The cat-and-mouse problem: Watermarks degrade with editing/paraphrasing/translation by design; light edits survive, heavy rewrites don't. Is a signal that vanishes under normal use actually useful, or just plausible deniability for labs?
    • Regulatory arbitrage: EU forces the mandate, but xAI hasn't signed the voluntary Code. What happens to companies operating in the gap, and does the EU rule become a de facto global standard the way GDPR did?
    • What it's actually good for: Not a lie detector, a provenance/tamper flag. Useful for enterprise content authenticity and platform moderation pipelines, much less useful for catching a student or a bad actor who just runs one rewrite pass.




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    30 August 2026, 1:00 pm
  • 38 minutes 18 seconds
    Your AI Project Doesn't Need More Agents

    SUMMARY: Brandon speaks with Rich Ziade, co-founder and CEO of Aboard, about why enterprise AI projects fail without real discovery, why "agents" have been oversold as a headcount play, and why organizational urgency, not new tooling, is what actually makes digital transformation succeed.


    SHOW: 1057

    SHOW TRANSCRIPT: The Enterprise AI Show #1057 Transcript

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

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    Topic 1 - From lawyer to digital transformation CEO: Rich's path through Postlight (with co-founder Paul Ford), the sale in 2021, and how Aboard was already incubating inside Postlight Labs before AI "landed like a spaceship."

    Topic 1a - The six months after ChatGPT arrived: why Aboard resisted rushing a prompt-based fix into messy, political, human organizations, and why "vibe coding" convinced them to hang back rather than parachute AI into a company.

    Topic 2 - The doctor/patient analogy: executives walk in asking for a specific AI "medicine" instead of describing the underlying pain, and why real engagements start with tests and diagnosis, not the prescription the client thinks they want.

    Topic 2a - Why discovery hasn't fundamentally changed in the AI era — still in-person interviews and observation, with AI mainly useful for note-taking and summarizing documentation, not for skipping the hard thinking.

    Topic 3 - The agent hype cycle: why Rich thinks the "millions of agents" narrative (including Anthropic's Boris Cherny running swarms of planning/implementation agents) reflects an engineering-execution worldview rather than a product or organizational one — and why he sees the agent narrative cooling off.

    Topic 3a - The "spreadsheet problem" vs. targeted AI: most of Aboard's actual delivery work (90%+) isn't agents — it's modernizing spreadsheet-run processes and building narrow RAG/vector tools so people can query their own data in plain English.

    Topic 4 - "Forward deployed" as the new name for an old idea — going on-site, listening, and understanding a client's world before proposing a solution.

    Topic 5 - Why no successful digital transformation starts without a real, externally imposed deadline or crisis — and why "innovation labs" without urgency rarely ship anything.

    Topic 6 - Lightning round: Is AI a bubble? Should GPUs be securitized assets? Three things to do in NYC in one day.


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    26 August 2026, 5:00 am
  • 23 minutes 11 seconds
    NVIDIA's Pivot from Chipmaker to Financier

    SUMMARY: Brian, Brandon, and Aaron discuss news about Nvidia’s reported $105B backing of OpenAI’s Ohio data center and what it implies for GPUs as an “asset class” and enterprise AI. Brian argues Jensen Huang is shifting Nvidia’s narrative from needing the newest chips immediately to portraying GPUs as long-lived, cash-flowing assets that can be financed like bonds, pushing risk onto banks and private equity. Brandon agrees scarcity has extended older GPU usefulness but warns the market could be flooded with newer, cheaper, more efficient hardware, leaving debt tied to obsolete equipment. Aaron likens GPUs to airplanes, expensive assets requiring constant utilization, while noting new AI builds demand entirely new data centers for power and cooling. The group questions widespread lack of profitability, compares the financing trend to past bubbles, and debates the optimistic case that breakthroughs could ultimately justify the investment.

    SHOW: 1056

    SHOW TRANSCRIPT: The Enterprise AI Show #1056 Transcript

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

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    Show topic: Nvidia's Pivot from Chipmaker to Financier

    Nvidia just backed $105B for OpenAI's Ohio data center and helped mobilize $500B+ in Wall Street financing (Apollo, Blackstone, BlackRock, Goldman, KKR) to fund GPU purchases, while AMD, Google, and Cerebras chip away at its tech lead. The moat is moving from silicon to balance sheet.

    Core question: Is a GPU actually securitizable like real estate or aircraft, or is this circular financing dressed up as infrastructure?

    • The bull case: GPUs as productive, cash-flow-generating assets (compute-as-a-service) → financeable like data centers or planes, unlocking capital hyperscalers alone couldn't raise.
    • The bear case: Depreciation risk; GPUs age fast, unlike buildings. What's the residual value of an H100-class chip in 2030? Securitizing a depreciating, obsolescence-prone asset is a very different bet than securitizing land.
    • Circularity concern: Nvidia financing the customers who buy Nvidia chips, who generate the revenue that justifies Nvidia's valuation, echoes vendor financing bubbles (Cisco/telecom, 2000).
    • Precedent: Compare to aircraft leasing/securitization models: what made those work (long asset life, resale markets, standardized valuation), and whether GPUs have any of that yet.
    • Who bears the risk if utilization or model economics don't pan out: Nvidia, the banks, or the credit markets buying the paper?

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    23 August 2026, 5:00 pm
  • 16 minutes 13 seconds
    Own Your AI Weights or Rent Them?

    SUMMARY: Brandon and Aaron discuss the pros and cons of owning or renting your model weights. What does that mean for the Enterprise, and what should you be considering?

    SHOW: 1055

    SHOW TRANSCRIPT: The Enterprise AI Show #1055 Transcript

    SHOW VIDEO: https://youtu.be/uc0GZBLgUeo

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     Topic: Own Your Weights or Rent Them?

    • Why now? Alex Karp had a spicy CNBC segment arguing enterprises should "own their weights" rather than rent models from the big labs — sparking a widely-shared response from Jamin Ball on Clouded Judgement. Substack
    • Past: Same shape as the "own vs. rent" debate the industry has had before — on-prem vs. SaaS, buy vs. build for ERP/CRM — just replayed one layer down, at the model layer instead of the app layer.
    • Present: A weight file is really just a frozen snapshot that degrades in relative terms as frontier models keep improving — what actually matters is owning the RL/training loop that keeps producing better weights, not the weights themselves. A model RL'd against a company's actual workflows can beat a frontier generalist model on that one task, and do it far more cheaply — but that leaves enterprises managing a sprawl of task-specific models that all need governing, versioning, and securing.
    • Future: Ball frames it as a stated-preference vs. revealed-preference problem — everyone says they want model sovereignty, but the spend data shows enterprises keep writing bigger checks to the frontier labs every quarter because most don't have the talent or infra to run the loop. Where's the market for a company that closes that gap — makes "owning the loop" accessible without the complexity tax? Tie back to your Show #4 (off-the-shelf AI, harnesses) — this is basically that debate's sequel, one layer deeper. (Aaron’s hot take, and another episode: maybe it’s not about the weights at all…)

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    19 August 2026, 5:00 am
  • 17 minutes 16 seconds
    Will OSS Models Take Over?

    SUMMARY: This episode is the second part and explores the flip side of OSS models. Last episode, we discussed the potential decline; this episode, we’ll talk about the potential positive future of OSS models. Aaron and Brandon explore the future of open source AI models, the role of industry consortia, and how major tech companies like NVIDIA, Apple, and Google are shaping the AI landscape. They discuss the potential for open models to become industry standards and the strategic motivations behind these moves.

    SHOW: 1054

    SHOW TRANSCRIPT: The Enterprise AI Show #1054 Transcript

    SHOW VIDEO: https://youtu.be/w238Y1ZKG1Q

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     Topic: Are we seeing the end of OSS models?

    • Why now? NVIDIA Open Secure AI Alliance (all except Anthropic joined) & Linux Foundation is managing proposals
    • Past: OSS runs the world…  Up until now, there hasn’t been an overarching “AI Model” project managed by the CNCF or Linux Foundation that has gained any traction
    • Present: 
      • As model sizes increase, who pays for training? I think the DB market is the closest parallel here, and it's also where the most OSS rug pulls have happened in the past. Is this history repeating itself, but also a lesson learned because so many DB companies got burned?
    • Future: Someone will have to donate a trillion+ parameter model to a foundation. My bet is NVIDIA will eventually drive this through Nemotron; it makes the most sense, and they have the most to lose if OpenAI and Anthropic take over and also eventually use their own chips.

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    16 August 2026, 12:00 pm
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