The Enterprise AI Show

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 come
Bluesky: @TheEntAIShow.bsky.social
Twitter/X: @TheEntAIShow
Instagram: @TheEntAIShow

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 come
Bluesky: @TheEntAIShow.bsky.social
Twitter/X: @TheEntAIShow
Instagram: @TheEntAIShow

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 come
Bluesky: @TheEntAIShow.bsky.social
Twitter/X: @TheEntAIShow
Instagram: @TheEntAIShow

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 come
Bluesky: @TheEntAIShow.bsky.social
Twitter/X: @TheEntAIShow
Instagram: @TheEntAIShow

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 come
Bluesky: @TheEntAIShow.bsky.social
Twitter/X: @TheEntAIShow
Instagram: @TheEntAIShow