• 41 minutes 14 seconds
    Agents Are Identities Too: Identity Security After CyberArk with Amy Blackshaw

    Brandon interviews Amy Blackshaw, Senior Vice President of Identity Marketing at Palo Alto Networks, about identity security in the age of AI agents. Blackshaw, who spent four years leading product marketing at CyberArk before Palo Alto Networks acquired it in February 2026, explains how identity management evolved from an IT operations task into a security discipline, and why identities now need to be protected across their full lifecycle. She walks through the three segments of identity security (access management, privileged access management, and identity governance), argues that human-driven quarterly access reviews are painful and error-prone, and says vendors like Palo Alto Networks are pulling governance into the identity security platform and using AI-driven roles to pre-approve most access.


    Blackshaw describes how frontier AI models change the attack landscape, with the cost of entry dropping toward zero and attackers moving at machine speed. She cites Unit 42 figures on how fast intrusions reach data exfiltration, an initial access time of about 25 minutes, and a recent investigation in which the full attack took under 10 hours. Her answer is control alongside detection and response: zero standing privileges and just-in-time access, so an attacker who steals a credential reaches a dead end. She and Brandon discuss why the most boring attacks, like a weak password and no second factor, are often the most effective, and why attackers often succeed with hard-coded machine credentials.


    On agents, Blackshaw says an agent is a machine identity with human-like traits: it can reason, decide, and escalate its own privileges, and you can't MFA it. She argues agents should be first-class identities with least privilege, just-in-time access, lifecycle governance, and a gateway for policy. For human-in-the-loop agents, the person is the anchor and must approve the moments that matter, with guardrails against consent fatigue. For autonomous agents, policy is the anchor, with tightly set permissions, behavior monitoring, and a kill switch. Brandon and Blackshaw also cover how to apply zero standing privilege for admins, developers, and agents without slowing developers down, and why policy and context (time, location, and usual behavior) should decide access. They close on who owns identity, which now rolls up through the CISO and increasingly reaches the CIO and CEO, why identity and security operations teams need to share data, and Blackshaw's case for platformization in a fragmented security market.



    SHOW: 1071

    SHOW TRANSCRIPT: The Enterprise AI Show #1071 Transcript

    SHOW VIDEO: https://youtu.be/CcaFoJRu0YM



    SHOW SPONSORS:



    SHOW LINKS



    GUEST BIO


    Amy Blackshaw is Senior Vice President of Identity Marketing at Palo Alto Networks, where she leads marketing for the identity business unit under the Idira brand. She has spent almost 20 years in cybersecurity, most of it in product marketing at large vendors, including four years leading product marketing at CyberArk, the pioneer of privileged access management, which Palo Alto Networks acquired in February 2026. [PLACEHOLDER: add any additional bio details]


    Contact Amy Blackshaw:



    FEEDBACK?

    14 October 2026, 5:00 am
  • 41 minutes 45 seconds
    A Cloud Nobody Owns: Sovereign Infrastructure for the Agent Era with Dominic Williams

    Brandon interviews Dominic Williams, founder, president, and chief scientist of the DFINITY Foundation, about the Internet Computer and why he set out to build a new back-end compute stack. Williams traces his path from building the kids' MMO Fight My Monster, one of the first serious production users of Cassandra, to decentralized networks and the founding of DFINITY in 2016. He explains the thesis behind the Internet Computer: a stack of software running down to the silicon can never be fully secured, so he wanted a cloud created by a network protocol, where software is tamper-proof, always on, and secure by default without firewalls, patches, or security and sysadmin teams.


    Williams walks through how it works. Code compiles to WebAssembly and runs in persistent memory, replicated across independent nodes using Byzantine fault tolerance, so there is no database or file system to connect to, and a misbehaving node, even a "Dr. Evil" one, cannot corrupt the result. Brandon presses on data protection, and Williams points to hardware encapsulation such as AMD SEV-SNP and the ability to choose trusted node providers. He then describes cloud engines, which let an enterprise pick nodes from a marketplace (hyperscalers, independent data centers, or on-prem), reconfigure them without downtime, and avoid vendor lock-in, with payment handled behind the scenes so it looks like a conventional cloud.


    On adoption, Williams says data sovereignty is a major driver, citing the U.S. CLOUD Act and governments, such as Pakistan, that want infrastructure they control and can inspect as open source. He describes Caffeine, which builds apps on the Internet Computer from a chat prompt, and Open SaaS, a suite meant to replace siloed SaaS tools with a single AI-aware system. To get started, he recommends pasting the prompt from the skills site into a coding assistant and having it write the back end in Motoko, a language designed for AI agents that guards against data loss when upgrading apps in production. Cloud engine coupons are expected to open up soon.



    SHOW: 1070

    SHOW TRANSCRIPT: The Enterprise AI Show #1070 Transcript

    SHOW VIDEO: https://youtu.be/-H4KS5AMKI8



    SHOW SPONSORS:



    SHOW LINKS



    GUEST BIO


    Dominic Williams is the founder, president, and chief scientist of the DFINITY Foundation, the Zurich-based non-profit behind the Internet Computer, which he began developing in 2015 and launched in 2021. A longtime engineer-entrepreneur focused on distributed systems, he previously founded the MMO game Fight My Monster and served as president and CTO of the Palo Alto incubator String Labs.



    FEEDBACK?

    12 October 2026, 12:00 am
  • 43 minutes 3 seconds
    The New, New Kingmakers: Agents, Developers, and the Future of Software with Stephen O'Grady

    Brandon interviews Stephen O'Grady, principal analyst and co-founder of RedMonk, about his 2013 book *The New Kingmakers* and his recent follow-up, "Agents: The new, New Kingmakers." O'Grady explains that the original thesis was simple: developers had gone from an afterthought in technology purchasing to the group whose choices determined what got used, driven first by open source (the LAMP stack, Linux) and then by cloud services like S3 and EC2 that removed the need to ask for budget. The two discuss why Sun was disrupted from every direction while Oracle and Cisco endured, with O'Grady noting that databases are the stickiest, highest-risk asset in the stack. He recounts getting an early look at GitHub Copilot a year before ChatGPT, but says the late-2025 jump in model capability was a step change he did not foresee.


    On software development, O'Grady expects a spectrum rather than a consensus on reviewing AI-written code: toy apps and disposable software may never be read by a human, while medical imaging, aviation, and defense will demand real oversight. He uses his construction background to describe the developer's shift from builder to architect, and warns that the "human in the loop" role is the weak end of the outcomes. Brandon and O'Grady examine why individual developers feel superhuman while organizations don't see the gains, with O'Grady pointing to downstream bottlenecks like the flood of PRs overwhelming open source maintainers. He cites Dr. James Bessen's bank teller research as a reminder that automation's effect on employment is hard to predict, and asks what happens if there is an order of magnitude more software in the world.


    On vendors, O'Grady expects both human-facing and agent-only businesses to coexist, with the biggest growth in agents because they scale like cloud, while Brandon points to Cloudflare's rewrite of Wrangler as a model for exposing complete APIs. O'Grady cautions that giving agents the keys to the kingdom is harder than it sounds, since subtle violations (like pulling copyrighted headshots from MLB.com) are harder to foresee than deleting a database. They close with hot takes: O'Grady takes the under on a 10% chance of AI wiping out humanity, arguing the industry doesn't draw a bright enough line between digital and physical capability, and he remains skeptical of frontier model valuations and the circular investments behind them, especially as open-weight models close the gap.



    SHOW: 1069

    SHOW TRANSCRIPT: The Enterprise AI Show #1069 Transcript

    SHOW VIDEO: https://youtu.be/E6UFnW40BHQ



    SHOW SPONSORS:



    SHOW LINKS




    GUEST BIO


    Stephen O'Grady is the principal analyst and co-founder of RedMonk, a developer- and practitioner-focused industry analyst firm. He is the author of *The New Kingmakers* (2013), which argued that developers had become the most important decision-makers in technology, and writes the Tecosystems blog. 


    FEEDBACK?

    7 October 2026, 5:00 am
  • 35 minutes 4 seconds
    AI News for End of September 2026

    Brian (@bgracely) and Brandon (@bwhichard) discuss the biggest AI news stories from the second half of September 2026.

    Oracle sent a force majeure notice on a major data center build, which Brandon reads as an early signal worth tracking. If other companies follow, it would point to stress in AI infrastructure commitments, and if not, it is a one-off. Brian suggests the industry needs a "Jenga chart" of whose money is propping up whose, with the contractors who actually pour concrete at the base. On the wave of agent launches (Meta's Muse, OpenAI's dots, and OpenClaw Enterprise), they agree that giving everyone a cloud-hosted computer for their agent is a real step toward mainstream adoption. Brian says the unanswered questions are accountability when an agent errs, security controls that don't require dozens of settings, and how people decide what is important enough to keep for themselves. Brandon argues agents should step in at the moment of a task, like filling out a form, rather than needing constant delegation. They also note the move toward soft, cuddly agent branding and the lack of easy ways to share skills across people and teams. The show closes with Meta's hiring of MongoDB's CEO to lead its enterprise push, which Brandon expects to fail because consumer companies start at a deficit in enterprise.



    SHOW SPONSORS:



    SHOW: 1068

    SHOW TRANSCRIPT: The Enterprise AI Show #1068 Transcript

    SHOW VIDEO: https://youtu.be/kaNik7LxV3s

    SHOW LINKS:




    FEEDBACK?

    4 October 2026, 5:00 am
  • 25 minutes 10 seconds
    The Anthropic IPO?

    We review the potential Anthropic IPO, in the context of revenue trajectory, expense trajectory and planned commitments, past and future funding rounds, future expanse commitments to large datacenter projects, and the strengths and weaknesses of the business in the current and potentially future political climates in the US and around the world. 

    SHOW: 1067

    SHOW TRANSCRIPT: The EntAIShow #1067 - The Anthropic IPO?

    SHOW SPONSORS

    SHOW LINKS

    Strengths:

    • Brand awareness of Claude with software developers
    • Use-Case adoption for Enterprise workers (white-collar, software, research, etc.)
    • Depth and experience of Anthropic Frontier Labs (Model Building, Research, etc.)

    Weaknesses

    • CEO who frequently scares the world with his proclamations about doom
    • Uncertainty about alignment to US government after 2026
    • Increasing annual losses against revenue
    • Security and safety concerns (Mythos)
    • Future commitments and long-term financials?


    FEEDBACK?

    FEEDBACK?

    30 September 2026, 5:00 am
  • 28 minutes 52 seconds
    The OpenAI IPO?

    We review the potential OpenAI IPO, in the context of revenue trajectory, expense trajectory and planned commitments, past and future funding rounds, future expanse commitments to large datacenter projects, and the strengths and weaknesses of the business in the current and potentially future political climates in the US and around the world. 

    SHOW: 1066 

    SHOW TRANSCRIPT: The Enterprise AI Show #1066

    SHOW VIDEO: https://youtu.be/PkLSI2pRZs8

    SHOW SPONSORS:


    FEEDBACK?

    FEEDBACK?

    27 September 2026, 5:00 am
  • 34 minutes 16 seconds
    How Do Regulated Enterprises Deploy Private, Secure AI?

    Aaron interviews Ivan Lee, founder and CEO of Datasaur, about what it takes to build private, secure AI for regulated enterprises. Lee traces the shift away from third-party model reliance to compliance pressure, IP protection, and cost, and frames the core decision as buy versus rent: enterprises historically rented frontier models by default, but Western and Chinese open-weight models have matured enough that companies like AT&T now run a growing share of workloads on owned infrastructure. He breaks the stack into three layers (infrastructure, model, and harness), argues models have become commoditized enough to be swapped like building materials, and calls the harness, the connective layer to internal data and tools, the least solved but highest-leverage piece. Lee describes agentic AI's shift from opt-in tools to opt-out, event-triggered workflows as the real 2025-2026 adoption unlock, while flagging FinOps trade-offs (agentic tasks can run 3 million tokens versus 2,000 for a chatbot query) and the custom benchmarking his team uses to earn CISO trust before production rollout.



    SHOW: 1065 

    SHOW TRANSCRIPT: The Enterprise AI Show #1065

    SHOW VIDEO: https://youtu.be/PkLSI2pRZs8



    SHOW SPONSORS



    SHOW LINKS



    GUEST BIO

    Ivan Lee is the founder and CEO of Datasaur, which builds private, model-agnostic AI agents that deploy entirely inside a regulated enterprise's own infrastructure. He previously built AI products at Yahoo and Apple after Yahoo acquired his first company, Loki Studios, and holds a computer science degree from Stanford. Datasaur's clients include a leading GSIB, federal agencies, and Am Law 100 firms, and its backers include Initialized Capital and OpenAI president Greg Brockman.



    FEEDBACK?

    23 September 2026, 5:00 am
  • 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.



    SHOW SPONSORS:



    SHOW: 1064

    SHOW TRANSCRIPT: The Enterprise AI Show #1064 Transcript

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

    SHOW LINKS:

    FEEDBACK?

    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



    SHOW SPONSORS




    SHOW LINKS


    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.


    FEEDBACK?

    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



    SHOW LINKS:



    SHOW SPONSORS:



    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.


    FEEDBACK?

    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


    SHOW LINKS:


    SHOW SPONSORS:


    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?


    FEEDBACK?

    9 September 2026, 5:00 am
  • More Episodes? Get the App