The Enterprise AI Show

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 SHOW LINKS: Anthropic Watermarking Claude Support on Watermarking SHOW SPONSORS: Nasuni - Activate your data for AI and request a demo 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? Email: show @ the enterprise ai show dot com Bluesky: @TheEntAIShow.bsky.social Twitter/X: @TheEntAIShow Instagram: @TheEntAIShow

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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 SHOW SPONSORS: Nasuni - Activate your data for AI and request a demo SHOW LINKS: https://www.aboard.com/ https://aboard.com/podcast/ 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? Email: show @ the enterprise ai show dot com Bluesky: @TheEntAIShow.bsky.social Twitter/X: @TheEntAIShow Instagram: @TheEntAIShow

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 SHOW SPONSORS: Nasuni - Activate your data for AI and request a demo 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? Email: show @ the enterprise ai show dot com Bluesky: @TheEntAIShow.bsky.social Twitter/X: @TheEntAIShow Instagram: @TheEntAIShow

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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 SHOW SPONSORS: Nasuni - Activate your data for AI and request a demo 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? Email: show @ the enterprise ai show dot com Bluesky: @TheEntAIShow.bsky.social Twitter/X: @TheEntAIShow Instagram: @TheEntAIShow

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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 SHOW SPONSORS: Nasuni - Activate your data for AI and request a demo 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? Email: show @ the enterprise ai show dot com Bluesky: @TheEntAIShow.bsky.social Twitter/X: @TheEntAIShow Instagram: @TheEntAIShow

Are We Seeing The End Of OSS Models?

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: Nasuni - Activate your data for AI and request a demo 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? Email: show @ the enterprise ai show dot com Bluesky: @TheEntAIShow.bsky.social Twitter/X: @TheEntAIShow Instagram: @TheEntAIShow

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

SUMMARY: In this episode, Aaron and Brandon tackle the provocative critiques of AI by Ed Zitron, a vocal opponent in the tech industry. They delve into the bear case against AI, exploring both the merits and flaws of Zitron's views. SHOW: 1052 SHOW TRANSCRIPT: The Enterprise AI Show #1052 Transcript SHOW VIDEO: https://youtu.be/Fm3T5bT_8CQ SHOW SPONSORS: Nasuni - Activate your data for AI and request a demo 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? Email: show @ the enterprise ai show dot com Bluesky: @TheEntAIShow.bsky.social Twitter/X: @TheEntAIShow Instagram: @TheEntAIShow

Do You Even Need That Trillion-Parameter Model?

SUMMARY: We continue our Models and Money series. In this episode, Brian and Aaron explore the current state and future of AI models, focusing on model size, model harnessing, and intelligent model routing. They discuss whether bigger models are always better, the economics of AI, and how enterprise applications can benefit from tailored AI solutions. SHOW: 1051 SHOW TRANSCRIPT: The Enterprise AI Show #1051 Transcript SHOW VIDEO: https://youtu.be/tkJmeazn8Bs SHOW SPONSORS: Nasuni - Activate your data for AI and request a demo 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? Email: show @ the enterprise ai show dot com Bluesky: @TheEntAIShow.bsky.social Twitter/X: @TheEntAIShow Instagram: @TheEntAIShow

AI News of the Month - July 2026

SUMMARY: Brian Gracely (@bgracely) and Brandon Whichard (@bwhichard) discuss the biggest AI news stories from the month of July 2026. SHOW: 1050 SHOW TRANSCRIPT: The Enterprise AI Show #1050 Transcript SHOW VIDEO: https://youtu.be/9u-uAaQVjXk SHOW SPONSORS: Nasuni - Activate your data for AI and request a demo SHOW NOTES: Links to all the AI News covered in this month’s show FEEDBACK? Email: show @ the enterprise ai show dot com Bluesky: @TheEntAIShow.bsky.social Twitter/X: @TheEntAIShow Instagram: @TheEntAIShow

How AI Stacks are rewriting the Rules of Business

SUMMARY: Brian speaks with Dave Vellante, Co-Founder/CEO theCUBE, about how AI is changing the entire tech stack, the evolution of systems of intelligence, and how the competitive landscape is forcing companies to make difficult decisions about their AI future. SHOW: 1049 SHOW TRANSCRIPT: The Enterprise AI Show #1049 Transcript SHOW VIDEO: https://youtu.be/EgIvsBnZnmg SHOW SPONSORS: Nasuni - Activate your data for AI and request a demo ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this! SHOW NOTES: How AI Stacks are rewriting the Rules of Business 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? Email: show @ the enterprise ai show dot com Bluesky: @TheEntAIShow.bsky.social Twitter/X: @TheEntAIShow Instagram: @TheEntAIShow

When Will the Models be Good Enough?

SUMMARY: Brian and Aaron explore the current state and future of AI models, focusing on model size, model harnessing, and intelligent model routing. They discuss whether bigger models are always better, the economics of AI, and how enterprise applications can benefit from tailored AI solutions. SHOW: 1048 SHOW TRANSCRIPT: The Enterprise AI Show #1048 Transcript SHOW VIDEO: https://youtu.be/1IHXNrYlYCA SHOW SPONSORS: ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this! Nasuni - Activate your data for AI and request a demo 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? Email: show @ the enterprise ai show dot com Bluesky: @TheEntAIShow.bsky.social Twitter/X: @TheEntAIShow Instagram: @TheEntAIShow

AI's Impact on Trust and Brand

SUMMARY: Brian talks Melissa Rosenthal from Outlever about the intersection of AI, brand, and marketing. They explore how AI impacts trust, brand consistency, and operational efficiency, offering insights for organizations navigating AI adoption. SHOW: 1047 SHOW TRANSCRIPT: The Enterprise AI Show #1047 Transcript SHOW VIDEO: https://youtu.be/PEqyOxE9PIw SHOW SPONSORS: Nasuni - Activate your data for AI and request a demo ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this! RESOURCES: Outlever State of Brand Article KEY TOPICS: AI's impact on trust and brand perception Operational efficiencies and AI workflows Challenges of AI implementation and guardrails Measuring ROI and costs of AI The importance of brand consistency in AI interactions TAKEAWAYS AI can speed up engineering and operational tasks by 100X. Many companies are still in pilot stages with AI, lacking governance. Brand consistency is at risk when AI interactions don't align with brand values. Experimentation with AI is costly and often lacks clear ROI. Organizations need to orchestrate AI workflows across teams for better outcomes. CHAPTERS/TOPICS: 00:00 Introduction to AI's impact on enterprise and brand 00:30 Melissa Rosenthal's background and Outlever's focus 01:05 The trust issue: AI replacing human interactions 01:55 How AI speeds up workflows and system building 03:00 Risks of AI in customer-facing interactions 04:11 Brand touchpoints and AI's influence on brand perception 05:05 Training AI to reflect brand values 05:59 Responsibility and handling AI mistakes 06:53 Current state of AI governance in companies 08:12 ROI and costs of AI experimentation 08:57 The early stage of AI adoption and lessons learned 10:11 Future outlook: AI in marketing and brand orchestration 10:58 The importance of workflow orchestration across teams 12:03 Bridging the gap between technology and marketing 12:54 The chaos and chaos in AI adoption today 14:03 Risks of homogenized brand messaging with AI 15:02 Predictions for AI in marketing in the next year 16:04 Reorganizing teams around AI outcomes 17:04 The missing link: connecting technology and brand strategy 18:03 Final thoughts and key takeaways FEEDBACK? Email: show @ the enterprise ai show dot com Bluesky: @TheEntAIShow.bsky.social Twitter/X: @TheEntAIShow Instagram: @TheEntAIShow

What is a Behavioral Agent Automation Platform?

SUMMARY: Steven Walchek, CEO at Liminal, discusses secure AI enablement for regulated industries and why most enterprises are stuck in perpetual AI pilots. We explore the "agentic cliff" and how Behavioral Agent Automation Platforms (BAAPs) discover and deploy agents by observing how work actually happens. SHOW: 1046 SHOW TRANSCRIPT: The Enterprise AI Show #1046 Transcript SHOW VIDEO: https://youtu.be/nVte_ZKDID4 SHOW SPONSORS: ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this! Nasuni - Activate your data for AI and request a demo RESOURCES: Liminal AI Steven Walchek - LinkedIn KEY TOPICS: Horizontal security for generative AI Data privacy and compliance in AI Security layers in AI deployment Agentic AI and behavioral automation Future industry trends in AI security TAKEAWAYS Security for AI must be integrated at the application layer, not just network or perimeter. Data privacy concerns are central to AI adoption in regulated industries. Organizations need a security layer that supports multi-model, multi-provider AI engagement. Behavioral automation and agentic AI require observability and policy enforcement. The AI industry is still in early adoption, with significant growth expected in the next five years. CHAPTERS/TOPICS: 00:00 Introduction and guest introduction 02:09 Steven's background and journey in tech 03:54 The core problem of AI security in regulated industries 07:45 Data privacy concerns and industry challenges 12:01 Liminal's approach to AI security and compliance 15:52 Security at the application layer and network layer 20:02 Agentic AI, behavioral automation, and observability 30:04 Future trends and industry outlook 31:46 How to connect with Liminal and closing remarks FEEDBACK? Email: show @ the enterprise ai show dot com Bluesky: @TheEntAIShow.bsky.social Twitter/X: @TheEntAIShow Instagram: @TheEntAIShow

Does FinOps need an update for the AI world?

SUMMARY: On today’s "Models and Markets" - we explore about the FinOps experience from Cloud is having to adapt to the changing demands of Enterprise AI. SHOW: 1045 SHOW TRANSCRIPT: The Enterprise AI Show #1045 Transcript SHOW VIDEO: https://youtu.be/Plb88y-IkZY SHOW SPONSORS: Nasuni - Activate your data for AI and request a demo ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this! SHOW NOTES: Topic: Finops for AI? Why now? Cost of tokens goes up as model performance increases, but still needs subsidies… Past: FinOps for Cloud - prices grew out of control, needed centralization for expense management and capital allocation Present: TokenMaxxing, the move from per-seat to per-token pricing Future: What happens when you can’t afford the Ferrari anymore? Will there be a glut of FinOps for AI startups? What happens when usage is regulated and centralized? FEEDBACK? Email: show @ the enterprise ai show dot com Bluesky: @TheEntAIShow.bsky.social Twitter/X: @TheEntAIShow Instagram: @TheEntAIShow

Buy, Build or Rent your AI?

SUMMARY: Something new - "Models and Markets" - Aaron and Brian explore how recent news and macro trends are causing more companies to explore whether they should Buy, Build or Rent their AI future. SHOW: 1044 SHOW TRANSCRIPT: The Enterprise AI Show #1044 Transcript SHOW VIDEO: https://youtu.be/vcroGCXd3N4 SHOW SPONSORS: ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this! Nasuni - Activate your data for AI and request a demo SHOW NOTES: Topic: Own AI or Rent AI? Why now? Fable 5 and GPT 5.6 get restricted in the US Past: Private Cloud (on-prem/server huggers) vs. Public cloud vs. *gasp* hybrid cloud Present: OSS Models vs. Big API models Future: What happens when the subsidies go away, and rational business practices hit the industry?? FEEDBACK? Email: show @ the enterprise ai show dot com Bluesky: @TheEntAIShow.bsky.social Twitter/X: @TheEntAIShow Instagram: @TheEntAIShow

Unstructured Data in an AI World

SUMMARY: While we spend a lot of time discussing AI models, we don’t always spend enough time on the challenges of managing the unstructured data used to train, tune, and enable those models. SHOW: 1043 SHOW TRANSCRIPT: The Enterprise AI Show #1043 Transcript SHOW VIDEO: https://youtu.be/OAqnuhorMJ4 SHOW SPONSORS: Nasuni - Activate your data for AI and request a demo ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this! SHOW NOTES: Topic 1 - Welcome to the show. Tell us a bit about your background and where you focus today at Nasuni Topic 2 - We’ve spent two years talking about models. Are we finally entering the era where the biggest differentiator is data quality rather than model quality? Topic 3 - When customers inventory their AI-ready data, what surprises them most? Topic 4 - Where is the intersection of file data, metadata, and RAG systems that augment a company’s AI experience with their own data? Topic 5 - People talk about AI governance, but isn’t most AI governance actually data governance? Topic 6 - Are today’s enterprise file systems designed for machine consumers (AI Agents) instead of human consumers? Topic 7 - What are the economics of data, in your world, as it relates to AI? Topic 8 - What’s next for enterprise file platforms? FEEDBACK? Email: show @ the enterprise ai show dot com Bluesky: @TheEntAIShow.bsky.social Twitter/X: @TheEntAIShow Instagram: @TheEntAIShow

Are companies giving away their secrets to AI?

SUMMARY: Are CEO's frustrated with the lack of control, costs and sovereignty of their AI environments? SHOW: 1042 SHOW TRANSCRIPT: The Enterprise AI Show #1042 Transcript SHOW VIDEO: https://youtu.be/xgQv8WP-DNI SHOW SPONSORS: ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this! Nasuni - Activate your data for AI and request a demo SHOW NOTES: Palantir and NVIDIA partnership (June 2026) - first 11 minutes Palantir CEO (Alex Karp) on CNBC “The VPC Privacy Illusion - Why Private LLMs still expose your data” The biggest mistake organizations make isn’t choosing the right model, it’s focusing on models at all (via LinkedIn) There’s a level of unhappiness and distrust of the frontier labs from CEOs There needs to be an application layer on top of LLMs (e.g. “harness”, Palantir Ontology) This application layer prevents the LLMs from learning your business from your data “Alpha” is business differentiation (ability to outperform the market) He questions why the frontier model labs are charging by tokens and not outcomes (questions the entire AI business model) He questions “the true cost” of AI outputs He claims that CEOs are now concerned about frontier labs entering the business of the customers - brings up an interesting misunderstanding of how interacting with LLMs works (“we’re safe, it’s deployed in our VPC”) FEEDBACK? Email: show @ the enterprise ai show dot com Bluesky: @TheEntAIShow.bsky.social Twitter/X: @TheEntAIShow Instagram: @TheEntAIShow

AI News of the Month - June 2026

SUMMARY: Brian Gracely (@bgracely) and Aron Delp (@aarondelp) discuss the biggest AI news stories from the month of June, 2026. SHOW: 1041 SHOW TRANSCRIPT: The Enterprise AI Show #1041 Transcript SHOW VIDEO: https://youtu.be/SXmPOgE5jGk SHOW SPONSORS: Nasuni - Activate your data for AI and request a demo ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this! SHOW NOTES: Links to all the AI News covered in this month’s show FEEDBACK? Email: show @ the enterprise ai show dot com Bluesky: @TheEntAIShow.bsky.social Twitter/X: @TheEntAIShow Instagram: @TheEntAIShow

Enterprises are concerned about AI Costs, Governance and Trust

SUMMARY: As AI within the Enterprise matures, we look at 10 concerns and challenges that are still causing Chief AI Officers to worry about success in the future. SHOW: 1040 SHOW TRANSCRIPT: The Enterprise AI Show #1040 Transcript SHOW VIDEO: https://youtu.be/RyB4m17YK_4 SHOW SPONSORS: Nasuni - Activate your data for AI and request a demo OutShift by Cisco - “Scaling Out Superintelligence” The Internet of Cognition architecture ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this! SHOW NOTES: THESIS: After spending time with a number of Enterprise companies, what are a list of challenges and concerns they still have in implementing GenAI across a broad set of use-cases within the Financial Services industry? Everybody started with what was available (e.g. CoPilot) Enterprise implementations (now) aren’t autonomous Rising costs are the looming concern Governance is a rising concern Measurements of improvement are available, but varied Explaining measurements is complicated Explaining trust is more complicated Use-cases are fragmented, but there if you apply the technology, but not always obvious De-centralized (shadow AI) to Centralized to De-centralized (semi-controlled) The learning curves are very asymmetrical across teams Not everyone has access to Mythos or GPT-5.5-Cyber (yet) FEEDBACK? Email: show @ the enterprise ai show dot com Bluesky: @TheEntAIShow.bsky.social Twitter/X: @TheEntAIShow Instagram: @TheEntAIShow

A Day in the Life of a Forward-Deployed Engineer

SUMMARY: What does a Forward-Deployed Engineer actually do? And what about deploying AI Harness? Let’s dig into the real-world with these evolving AI concepts and technologies. SHOW: 1039 SHOW TRANSCRIPT: The Enterprise AI Show #1039 Transcript SHOW VIDEO: https://youtu.be/QY0fqu2O84M SHOW SPONSORS: OutShift by Cisco - “Scaling Out Superintelligence” The Internet of Cognition architecture ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this! Nasuni - Activate your data for AI and request a demo SHOW NOTES: Mozilla Thunderbolt launched Mozilla Thunderbolt (homepage) Topic 1 - Welcome to the show, tell us a bit about your background and what you focus on these days. Topic 2 - Let’s talk about the role of Forward Deployed Engineer, it’s being talked about a lot, but you’re living in that world now. What problems are FDEs usually tasked with trying to solve, or new things to implement? Topic 3 - We’ve seen other roles (DevOps, PlatformEng, etc.) that evolved from other roles or skills. What type of background lends itself to success in FDE? What skills are needed going forward? Topic 4 - You’re also working on some AI harness implementations. What can you tell us about those challenges and the technologies behind the harness? Topic 5 - At what point does an AI harness make sense for a company? What types of AI challenges typically require those next steps? Topic 6 - Working in the middle of this evolving AI space, what are some perspectives you’ve gained over the last 6-12 months? What do you wish you knew ahead of time? FEEDBACK? Email: show @ the enterprise ai show dot com Bluesky: @TheEntAIShow.bsky.social Twitter/X: @TheEntAIShow Instagram: @TheEntAIShow