Aller au contenu
The Enterprise AI Show
PodcastActualitésGestionAnglais

The Enterprise AI Show

par Massive Studios

NouveauAI’s Netflix Moment: What Early Cloud Teaches Us About the Next Phase11 novembre 2026 · 22 min
Épisodes
1 114
Parution
plusieurs fois par semaine
Dernier épisode
11 Nov 2026
Site webFlux RSS

À propos du podcast

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 

Participants

Épisodes 1 114

  1. AI’s Netflix Moment: What Early Cloud Teaches Us About the Next Phase

    22 min

    Brian and Brandon discuss what happens to enterprise AI if the market corrects and the era of "move fast and break things" gives way to hard questions about ROI. Brandon argues the biggest divide is between …

    Notes de l’épisode

    Brian and Brandon discuss what happens to enterprise AI if the market corrects and the era of "move fast and break things" gives way to hard questions about ROI. Brandon argues the biggest divide is between people who have only used a chatbot and those who have “touched the superpower” by using AI to finish a task that once took too long to attempt, and he says a CFO who only sees the token bills can’t see the value those tokens created. Brian traces enterprise adoption through several phases, from AI policies that mostly meant buying Microsoft Copilot, to employees pushing for Claude Code and Codex, to a token-maxing push with no clear goal. He compares the moment to early cloud, when Adrian Cockcroft’s Netflix talks split the industry into believers and companies that couldn’t imagine getting there, and predicts a bumpy year and a half in which some companies pull back, many search for balance, and a few race ahead. Brandon pushes back on the term vibe coding, arguing that nobody really writes code anymore and that AI is the best developer most people have ever worked with. He describes running a beta program where he pastes emailed feedback into an agent and ships a fix within half an hour, then has a second agent from another provider run a security audit. They agree AI can be a single-player game that rewards people willing to experiment, and Brandon closes with advice to replace “yeah, but” with “what if.”

    SHOW: 1079

    SHOW VIDEO: https://youtu.be/oTRgH0nRnvM

    SHOW SPONSORS:

    SHOW LINKS


    FEEDBACK?

    Chapitres (16)

  2. Claude, Make Me Rich: Finding Anthropic’s Next Giant Market

    29 min

    Brian and Brandon debate how a newly hired product leader at Anthropic would find growth big enough to match the expectations of an IPO, which Brian expects around the end of 2026. Brandon says he would start …

    Notes de l’épisode

    Brian and Brandon debate how a newly hired product leader at Anthropic would find growth big enough to match the expectations of an IPO, which Brian expects around the end of 2026. Brandon says he would start in the research group, hunting for breakthroughs that could serve markets like finance and healthcare, and raises contextual ads even though he jokes the idea could get him fired. Flipping Alex Karp’s warning about AI systems learning a company’s differentiation, Brian proposes a product that sits between employees and their data, then shows executives where to cut costs in the 70 to 80 percent of work he estimates doesn’t set them apart. Pointing to Sierra’s outcome-based pricing, Brandon suggests Anthropic take over a system, absorb the risk, and get paid only when it delivers. His AI-assisted car purchase leads to a discussion of opt-in, intent-based commerce, and Brian argues Anthropic still lacks the kind of aggregation point he sees as the biggest way to make money. They close on Brandon’s half-joking plan: wait until the model is smart enough, then ask it how to make money.

    SHOW: 1078

    SHOW VIDEO: https://youtu.be/X5jtuUbcvbY

    SHOW SPONSORS:

    SHOW LINKS

    GUEST BIO

    N/A this week

    FEEDBACK?

    Chapitres (13)

  3. From $2 Trillion to $20 Trillion: What Anthropic Has to Become Beyond Claude

    26 min

    Brian and Brandon debate what Anthropic has to become if its IPO succeeds and, in Brian’s hypothetical, it must grow from a $2 trillion company into a $20 trillion one. Brandon argues the natural next step …

    Notes de l’épisode

    Brian and Brandon debate what Anthropic has to become if its IPO succeeds and, in Brian’s hypothetical, it must grow from a $2 trillion company into a $20 trillion one. Brandon argues the natural next step after Claude Code is rules-based back office work like tax, accounting, and finance, and he floats a free Claude tool that prepares a basic 1040 tax return as a national proof of concept, which raises the question of whether Anthropic wants to take on Intuit directly. Brian sees a Claude version of the ERP as a logical target, given how much customers dislike the consultants and constant data integration that come with those systems, though it would mean entering SAP and Oracle territory. They also weigh whether Anthropic would announce these moves or let them emerge quietly, with Brian pointing to its claim that it could rewrite mainframe code, which he says sent IBM’s stock down, and Brandon predicting most of it will be emergent. The boldest ideas build on Bending Spoons: Brandon proposes “Claude Equity,” where Anthropic buys companies and runs them with a few people and AI, while Brian imagines using IPO money to build cheap replacements that drive down incumbents’ value before buying or swapping them. Brandon cites Anthropic’s new source control work as a possible shot at GitHub, but Brian warns that at the growth investors may expect, replacing GitHub is like a fly on a horse’s back.

    SHOW: 1077

    SHOW VIDEO: https://youtu.be/C8MY3IhJtXM

    SHOW SPONSORS:

    SHOW LINKS

    GUEST BIO

    N/A this week



    FEEDBACK?

    Chapitres (13)

  4. AI & Cloud News - End of October 2026

    42 min

    Brian and Brandon follow the money through a big month in AI, starting with Cisco president Jeetu Patel handing a 27-year-old ex-founder from acquired startup EzDubs unlimited AI tokens to rewrite 98% of …

    Notes de l’épisode

    Brian and Brandon follow the money through a big month in AI, starting with Cisco president Jeetu Patel handing a 27-year-old ex-founder from acquired startup EzDubs unlimited AI tokens to rewrite 98% of Cisco’s legacy code in 24 months. Brandon likes the audacity but argues the odds improve with more believers who know the systems being rebuilt, while Brian, a 14-year Cisco veteran, recalls an earlier Cisco team that won a blank check to work only on cool projects and warns the friction lands on people, not ideas. On cloud earnings, Brian notes Microsoft now breaks out Azure at about $29 billion a quarter, with Google Cloud near $25 billion and AWS at almost $46 billion, while both point to advertising misses at Amazon and Google as fuel for investor worry about who pays for the AI buildout. Brandon urges Microsoft employees offered buyouts to take them and become the AI experts other enterprises need. The episode closes on Anthropic’s S-1: Brandon argues demand is the only number that matters and that people underestimate exponential growth, while Brian counters that Anthropic is far from profitable, carries compute commitments it can’t exit, and is rushing to market as growth slows.

    SHOW: 1076

    SHOW VIDEO: https://youtu.be/Ryn5wwb5afY

    SHOW SPONSORS:

    SHOW LINKS

    GUEST BIO

    N/A this week

    FEEDBACK?

    Chapitres (21)

  5. OpenClaw Enterprise: Making AI Agents Safe for the Enterprise

    42 min

    Brian interviews Derek Carr and Mrunal Patel, distinguished engineers at Red Hat, about OpenClaw Enterprise, the new open-source project that brings Red Hat, NVIDIA, and the OpenClaw community together to make …

    Notes de l’épisode

    Brian interviews Derek Carr and Mrunal Patel, distinguished engineers at Red Hat, about OpenClaw Enterprise, the new open-source project that brings Red Hat, NVIDIA, and the OpenClaw community together to make persistent AI agents safe and manageable at enterprise scale. The work started with OpenShell, a deny-by-default sandbox the pair helped build with NVIDIA that treats every agent as potential malware. Agents never see credentials (a supervisor egress proxy injects them from outside), kernel features like Landlock restrict what they can touch, and when a blocked agent hits a wall, OpenShell can ask it why it tried, so a human can decide whether to allow the action now or never. Running real agents in that sandbox showed them that not everything should be untrusted. Memory, for example, is an enterprise record. That insight led them to OpenClaw’s own push to decompose agents into trusted and untrusted parts. OpenClaw Enterprise borrows the Hypershift hosted control plane pattern: gateways, memory, and session state live on a control plane cluster, while agent harnesses run in disposable OpenShell sandboxes on a data plane cluster, so a failed sandbox is simply replaced with full context. Enterprises set a permission ceiling; every new agent starts least privileged and grows only up to that limit, and blessed templates for roles like team Q&A, software co-development, and chief of staff make good agent setups repeatable instead of tribal knowledge. The guests also highlight multi-user agents, where colleagues steer the same agent together rather than flooding each other with AI-generated pull requests, and Skipper, the project’s own support agent that preserves the reasoning behind past decisions. The stack is model-agnostic and runs anywhere, including on self-hosted inference such as Dynamo or llm-d. With more than 3,000 pull requests merged since its September launch, the project is targeting a 1.0 release around year-end, with enterprise pilots and production rollouts expected in 2027.



    SHOW: 1075

    SHOW VIDEO: https://youtu.be/yS2HEiGbxqo



    SHOW SPONSORS:



    SHOW LINKS



    GUEST BIO(s)

    Derek Carr is a Senior Distinguished Engineer at Red Hat, where he has worked on OpenShift and in the Kubernetes community since 2014. He is a former member of the Kubernetes Steering Committee and has held long-running leadership roles in Kubernetes SIG Node and SIG Architecture. His recent work focuses on how enterprises can run AI agents safely on Kubernetes, including Red Hat’s work with NVIDIA’s OpenShell on kernel-enforced agent sandboxing.

    Mrunal Patel is a Distinguished Engineer at Red Hat, where he has worked on Linux containers, Kubernetes, and OpenShift since 2011. He started the CRI-O container runtime, is a maintainer of runc and the OCI runtime specification, and serves as a chair and technical lead of Kubernetes SIG Node. With Derek, he is now applying deep container isolation expertise to secure execution for AI agents on Red Hat AI.



    FEEDBACK?

    Chapitres

    Chargement des chapitres…

  6. Confidence or Con? The Psychology of Tech’s Boldest Leaders

    28 min

    Brian and Brandon take a detour from the usual tech news to ask a question every enterprise buyer of AI should be asking: why do some of tech’s most celebrated leaders project absolute confidence in claims that …

    Notes de l’épisode

    Brian and Brandon take a detour from the usual tech news to ask a question every enterprise buyer of AI should be asking: why do some of tech’s most celebrated leaders project absolute confidence in claims that later prove false? Starting from the new Elizabeth Holmes documentary, they debate whether that kind of conviction is innate or learned. Brian argues it develops in stages, as each funding round and glowing press profile ties a founder’s identity more tightly to a story they must defend at all costs. Brandon describes a “contrarian effect,” in which a venture model with low hit rates rewards bold founders and tells them they are special. He recommends The Halo Effect as an antidote to success stories that confuse correlation with causation. They also question the VC preference for founders with hardship backstories. The conversation closes on Brandon’s framework of operators, believers, and pragmatists, and why every organization needs pragmatists willing to question big promises before they become fiction.



    SHOW: 1074

    SHOW TRANSCRIPT: The Enterprise AI Show #1074 Transcript

    SHOW VIDEO: https://youtu.be/DXmB8_YwNRQ



    SHOW SPONSORS:



    SHOW LINKS


    GUEST BIO

    N/A this week



    FEEDBACK?

    Chapitres

    Chargement des chapitres…

  7. Never Do the Same Work Twice: Rethinking CI for the Agent Era with Dan Manges

    41 min

    Brandon interviews Dan Manges, co-founder and CEO of RWX, about why continuous integration is the bottleneck for engineering teams working with coding agents. Manges, previously CTO at Braintree and Root …

    Notes de l’épisode

    Brandon interviews Dan Manges, co-founder and CEO of RWX, about why continuous integration is the bottleneck for engineering teams working with coding agents. Manges, previously CTO at Braintree and Root, explains that he started RWX after watching CI tooling fail to scale with fast-growing codebases in regulated industries. He argues that feedback loops under five to ten minutes keep engineers in a flow state, and that slower loops push people (and agents) into costly context switching.


    Manges describes how RWX differs from traditional CI platforms. Instead of splitting pipelines into jobs that each spin up a virtual machine and repeat the same setup, RWX models work as a directed graph of tasks with declared dependencies, so everything runs with maximum parallelization and only once. He cites Datadog's recent blog post about the strain that cloning code across so many parallel jobs put on its Git servers. Failures can be retried from the point of failure, and compute can be sized per step. He explains that RWX uses content-based caching rather than Docker's lineage-based caching, so a cache miss can be followed by a cache hit and only the work affected by a change runs. Under the hood, he says, RWX built its own distributed container runtime on the same low-level primitives as Docker, runs on AWS, and is hosted by default, with an option to run inside a customer's own VPC.


    On agents, Manges says every RWX workflow can run from a CLI without a commit and push, which gives agents a fast, synchronous, more accurate and token-efficient loop. He recommends telling agents to run the CI workflow before they open a pull request, so the status check goes green instantly through caching, and argues there is no longer a reason for a human to be in the loop for build failures. He notes that review practices will vary widely, from thorough human review to software factory automation, and mentions a customer that has agents write throwaway Playwright tests for QA. He describes Honeycomb's move from CircleCI, which he says doubled their PR velocity, and the "AI legible CI" they describe. Looking ahead, Manges says the bottleneck in software engineering is shifting from writing code to validating it, and that RWX is expanding into sandboxes, preview apps, and custom webhooks. He says customers have asked about code hosting and review, but he promises nothing.



    SHOW: 1073

    SHOW TRANSCRIPT: The Enterprise AI Show #1073 Transcript

    SHOW VIDEO: https://youtu.be/2QnbYmRltgM



    SHOW SPONSORS:



    SHOW LINKS



    GUEST BIO

    Dan Manges is the co-founder and CEO of RWX, a cloud platform for running CI/CD and other automation for engineering teams and their coding agents. He was previously the founding CTO of Braintree, the payments company that acquired Venmo and was acquired by PayPal, and co-founded car insurance carrier Root, which went public in 2020.

    Contact Dan Manges:



    FEEDBACK?

    Chapitres

    Chargement des chapitres…

  8. AI News - Mid-October 2026

    36 min

    Brian and Brandon open mid-October’s AI news of the month by asking whether a yes-or-no answer engine like Jev could have saved the Commanders from a clock-management meltdown, and why a binary classifier is …

    Notes de l’épisode

    Brian and Brandon open mid-October’s AI news of the month by asking whether a yes-or-no answer engine like Jev could have saved the Commanders from a clock-management meltdown, and why a binary classifier is worth a reported $7.5 billion. A six-month check-in on their May AI draft leaves the scorecard roughly even, with Anthropic the clear winner over Google. Then come the financial cracks. CoreWeave’s borrowing costs have reportedly jumped from around 7% to about 15%, adding to Oracle’s contract pullback as yellow flags for the neocloud and data center layer. Meanwhile, Anthropic and OpenAI keep pulling in revenue even as enterprise CIOs struggle to find ROI. Brandon weighs Andrej Karpathy’s claim that only a few thousand people have seen what AI can really do against the labs’ opposite storytelling styles. The pair also question why Meta keeps promoting its frontier models when its Muse personal agent looks like the real monetization story. They close on whether software fundamentally changed in the last two weeks. Brandon argues AI has put professional-grade app building within reach of millions, pointing to his upcoming PodEditor app, but says finding demand and building a business are as hard as ever. They see Bending Spoons’ model of running acquired apps with AI and a skeleton crew as a possible playbook for established vendors.



    SHOW: 1072

    SHOW TRANSCRIPT: The Enterprise AI Show #1072 Transcript

    SHOW VIDEO: https://youtu.be/Kp7AH_IdwIg



    SHOW SPONSORS:


    FEEDBACK?

    Chapitres

    Chargement des chapitres…

  9. A Cloud Nobody Owns: Sovereign Infrastructure for the Agent Era with Dominic Williams

    42 min

    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 …

    Notes de l’épisode

    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?

    Chapitres

    Chargement des chapitres…

  10. The New, New Kingmakers: Agents, Developers, and the Future of Software with Stephen O'Grady

    43 min

    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 …

    Notes de l’épisode

    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?

    Chapitres

    Chargement des chapitres…

  11. AI News for End of September 2026

    35 min

    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 …

    Notes de l’épisode

    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?

    Chapitres

    Chargement des chapitres…

  12. The Anthropic IPO?

    25 min

    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 …

    Notes de l’épisode

    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?

  13. The OpenAI IPO?

    29 min

    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 …

    Notes de l’épisode

    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?

  14. How Do Regulated Enterprises Deploy Private, Secure AI?

    34 min

    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 …

    Notes de l’épisode

    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?

    Chapitres

    Chargement des chapitres…

  15. AI News for Mid-September 2026

    43 min

    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 …

    Notes de l’épisode

    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?

    Chapitres

    Chargement des chapitres…

  16. Building AI Agents That Support Enterprise Teams

    40 min

    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 …

    Notes de l’épisode

    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?

    Chapitres

    Chargement des chapitres…

  17. Securing AI Agents in the Enterprise: NanoClaw, Zero Trust Guardrails, and Governance at Scale

    45 min

    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 …

    Notes de l’épisode

    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?

    Chapitres

    Chargement des chapitres…

  18. How Open-Source is Reshaping the AI Infrastructure Stack

    42 min

    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 …

    Notes de l’épisode

    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?

    Chapitres

    Chargement des chapitres…

  19. Vitamins vs. Pain Killers vs. Whippets - The Enterprise AI Adoption Problem

    22 min

    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 …

    Notes de l’épisode

    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

    SHOW LINKS:


    SHOW SPONSORS:


    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?


    FEEDBACK?

    Chapitres

    Chargement des chapitres…

  20. AI News of the Month - August 2026

    39 min

    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 …

    Notes de l’épisode

    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


    SHOW LINKS:


    SHOW SPONSORS:


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


    FEEDBACK?

    Chapitres

    Chargement des chapitres…

  21. AI Watermarking: Compliance Theater or Real Provenance Tool

    18 min

    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 …

    Notes de l’épisode

    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


    SHOW LINKS:


    SHOW SPONSORS:

    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.




    FEEDBACK?

    Chapitres

    Chargement des chapitres…

  22. Your AI Project Doesn't Need More Agents

    38 min

    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 …

    Notes de l’épisode

    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

    SHOW SPONSORS:


    SHOW LINKS:

    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.


    FEEDBACK?

  23. NVIDIA's Pivot from Chipmaker to Financier

    23 min

    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 …

    Notes de l’épisode

    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

    SHOW SPONSORS:

    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?

    FEEDBACK?

  24. Own Your AI Weights or Rent Them?

    16 min

    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: 1055SHOW TRANSCRIPT: The Enterprise …

    Notes de l’épisode

    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

    SHOW SPONSORS:

    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…)

    FEEDBACK?

  25. Will OSS Models Take Over?

    17 min

    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 …

    Notes de l’épisode

    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

    SHOW SPONSORS:

    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.

    FEEDBACK?

  26. Are We Seeing The End Of OSS Models?

    17 min

    SUMMARY: In this episode, Aaron and Brandon explore the potential decline of open-source models in AI, discussing market trends, financial challenges, and the future of OSS in the AI landscape.SHOW: 1053SHOW …

    Notes de l’épisode

    SUMMARY: In this episode, Aaron and Brandon explore the potential decline of open-source models in AI, discussing market trends, financial challenges, and the future of OSS in the AI landscape.

    SHOW: 1053

    SHOW TRANSCRIPT: The Enterprise AI Show #1053 Transcript

    SHOW VIDEO: https://youtu.be/-9iwoC5-muE

    SHOW SPONSORS:

    Topic: Are we seeing the end of OSS models?

    • Why now? The trend towards fewer and fewer Apache models on the high end.
    • Past: The OSS “rug pull” joke comes to mind: HashiCorp, MongoDB, Redis, Elastic
    • Present:
      • Governments might jump in: The US Government with Mythos and GPT. Rumors are that China might start to restrict their high-end. Companies:
      • Kimi K3 as an example, 100M users or 20M in revenue. Others that used to be Apache are now closed
    • Future: Maybe something like a Modified MIT license will likely be the future, as the models now cost millions to produce and the shelf life is measured in weeks to months

    FEEDBACK?

  27. The Zitron Bear Case: What's Right, What's Wrong?

    17 min

    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 …

    Notes de l’épisode

    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

    SHOW SPONSORS:

    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)?

    FEEDBACK?

  28. Do You Even Need That Trillion-Parameter Model?

    15 min

    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 …

    Notes de l’épisode

    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

    SHOW SPONSORS:

    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?

    FEEDBACK?

  29. AI News of the Month - July 2026

    46 min

    SUMMARY: Brian Gracely (@bgracely) and Brandon Whichard (@bwhichard) discuss the biggest AI news stories from the month of July 2026. SHOW: 1050SHOW TRANSCRIPT: The Enterprise AI Show #1050 TranscriptSHOW …

    Notes de l’épisode

    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

    SHOW SPONSORS:

    SHOW NOTES:

    FEEDBACK?

  30. How AI Stacks are rewriting the Rules of Business

    40 min

    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 …

    Notes de l’épisode

    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

    SHOW SPONSORS:


    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?


    FEEDBACK?

Voici les 40 derniers épisodes sur 1 114. Les épisodes plus anciens se trouvent dans le flux du podcast.

Recommandé par The Enterprise AI Show

Podcasts similaires

Plus de podcasts : Actualités
C’est votre podcast ?

Confirmez-le avec une entrée dans votre flux : vous recevez ici un code, vous l’ajoutez comme podcast:txt dans le canal de votre flux, et nous le vérifions. Votre podcast apparaît ensuite comme « vérifié » sur SoundNode.

Genres populaires

PopOldiesHip-hopRockAnnées 80SchlagerActualitésAlternatifDanceMetalTechnoLes années 90ÉlectroHouse

Paramètres

Affichage

Langue

DeutschEnglishEspañolFrançaisPortuguês

Détermine les stations affichées en premier sur la page d’accueil.

Historique

Les stations et épisodes écoutés récemment et tes dernières recherches sont seulement enregistrés dans ce navigateur.

Notifications

Un court message quand un podcast que tu suis publie un nouvel épisode. Désactive un podcast précis avec la cloche sur sa page.

Compte

Enregistrez vos favoris – dans l’app et sur le site.

App StoreGoogle PlayAmazon Fire TVSonos

Raccourcis clavier

/
Ouvrir la recherche
Espace
Lecture ou pause
← →
Dans le lecteur : 15 s en arrière, 30 s en avant (épisodes) · dans une rangée : tuile suivante
↑ ↓
Dans une rangée : cible dans la tuile
Début / Fin
Dans une rangée : première ou dernière tuile
Esc
Fermer la fenêtre ou le menu
?
Cet aperçu
En cours de lecture

Voir la station

À suivre

    Ajoutez des épisodes avec « À suivre » – ils seront lus après l'épisode en cours.