Joel Christner, (@joelchristner, Founder/CEO at @viewyourdata) discusses the complexities of data management in AI, structured and unstructured data, the importance of RAG pipelines and vector databases.

SHOW SUMMARY: Aaron and Joel discusses the complexities of data management in AI, focusing on the concept of universal data representation. They explore the challenges organizations face with structured and unstructured data, the importance of RAG pipelines and vector databases, and the implications of data privacy in regulated industries. The conversation also touches on managing model versions and the emerging patterns in AI tooling that can help enterprises effectively utilize AI technologies.

SHOW: 925

SHOW TRANSCRIPT: The Cloudcast #925 Transcript

SHOW VIDEO: https://youtube.com/@TheCloudcastNET

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SHOW NOTES:

• View.io website

Topic 1 - Welcome to the show, Joel. Give everyone a quick introduction.

Topic 2 - Our topic today is everything data and how to represent it and embed it into AI systems. First, what is the challenge with data, structured or unstructured, in organizations today and what is behind the concept of Universal Data Representation

Topic 3 - Industry or customer specific data today is big challenge for organziations, especially in highly regulated industries such as healthcare, financial services, etc. The most prevalent solution I am seeing is taking an existing foundational model and then adding a RAG pipeline vs. the cost and time to fine tuning. What are you seeing?

Topic 4 - Even when companies have good data, that doesn’t mean that data makes it into the AI pipeline correctly, this is where the embedding problem and your concept of Universal Data Representation comes into play, correct?

Topic 5 - But, once you get the first model out, then what? How should the data and models be handled over time? How do you create a platform and a continuous feedback loop to improve the results over time?

Topic 6 - What are the most successful use cases you are seeing today with your customers?

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Where does the next phase of AI-assistants for software development go next? Is it an evolution of developer productivity, or a complete rethinking of the barriers and limitations for broader software development?

SHOW: 924

SHOW TRANSCRIPT: The Cloudcast #924 Transcript

SHOW VIDEO: https://youtube.com/@TheCloudcastNET

CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotw

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SHOW SPONSORS:

• Cut Enterprise IT Support Costs by 30-50% with US Cloud

SHOW NOTES:

WHERE DO AI DEVELOPER-ASSISTANTS GO NEXT?

• A year ago it felt like co-pilots were one of the entry point use-cases for AI.
• Since then we’ve seen numbers say the uplift is 10-20% productivity.
• Microsoft claims that 20-30% of their code is now written by AIs.
• We’ve seen many senior developers speakout that it’s not replacement level technology and they don’t trust it.
• Companies like Cursor have a $9-10B valuation. Windsurf just got purchased for $3B by OpenAI.
• Microsoft has Co-Pilot (based on OpenAI models). Google and Amazon are rumored to be launching their own.
• Lots of companies are launching agents (IBM, Salesforce, Oracle, etc.), and lots of agent frameworks now exist.
• So where does it go next? Is it just wide-spead adoption of developer productivity?
  • Is it specialized functions within developer workflows? (e.g. CI/CD, documentation, security evaluations, bug fixes, long-term maintenance, etc.)
  • How far are we from teams being just a few architects, leads, Sr. Devs, and then teams of AI’s (agents, etc.)?
    • Is that a good thing for Sr. Dev personalities that didn’t want to focus on soft-skills?
    • Does that allow for greater experimentation against feature-requests or stories, since they can create more, test more, etc.?
    • Do we start to see companies create skunkworks teams/groups that try to adopt this approach?
    • Are product managers ready to have zero-backlogs and the demand for new ideas to increase?

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Siqi Chen, CEO & CFO @ Runway, talks about his journey from JPL developer to Founder, CEO/CFO of a financial planning and analysis (FP&A) startup. We focus on how to build products that customers crave and how a customer-centric view differs from traditional product management.

SHOW: 923

SHOW TRANSCRIPT: The Cloudcast #923 Transcript

SHOW VIDEO: https://youtube.com/@TheCloudcastNET

CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotw

NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST: "CLOUDCAST BASICS"

SPONSORS:

• Cut Enterprise IT Support Costs by 30-50% with US Cloud

SHOW NOTES:

• Runway website
• Behind What Seems Like an Overnight Success (video)

Topic 1 - Welcome to the show, Siqi. First, your combination of technical and business/financial background is fascinating. How did you go from coding at NASA to Head of Product at Zynga to CEO/CFO for a finance platform startup? Give everyone a quick introduction.

Topic 2 - One thing I’ve noticed as a trend in your background is the core concept of building. What has been your philosophy in building products? How do you build products that customers demand?

Topic 3 - Let’s talk about AI and AGI for a moment. We hear all the time how disruptive this will be. What are your thoughts here, and how do we develop both adaptability and resiliency to new technologies?

Topic 4 - Let’s talk FP&A (financial planning & analysis). Our core listeners out there tend to skew more towards the tech and infrastructure side, but a core theme of this show is always to be learning as much of the business as possible to apply those concepts. As someone with a background in both worlds, plus now running an FP&A startup, what do you wish folks on the technical side of the house knew more about to make their jobs easier?

Topic 5 - We posted a link in the show notes for a video you did on the “overnight success” of Runway. It was a good representation and origin story of how something can go viral with the right mindset and product-market fit. Tell everyone about that as Runway approaches 5 years now.

Topic 6 - What is your biggest challenge in the FP&A space today? Is it AI? We’ve seen a lot of AI disruption in coding, legal, and other areas requiring deep data pool insights. Is this any different?

Topic 7 - If anyone is interested, what do you have coming up? How can folks follow you, say hey, or check out what you’ve got going on at Runway?

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Tech CEOs are making bold proclamations, from WhyQ to It is what it is. How will companies navigate this spectrum as they seek innovation, accountability and profitability throughout 2025?

SHOW: 922

SHOW TRANSCRIPT: The Cloudcast #922 Transcript

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CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotw

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SHOW SPONSORS:

• Cut Enterprise IT Support Costs by 30-50% with US Cloud

SHOW NOTES:

• Amazon 2024 CEO Letter to Shareholders - A Why Culture
• Uber CEO says “it is what it is” about changing benefits

WHEN DOES WHY BECOME IT IS WHAT IT IS?

• Communicating is difficult, especially as the company grows in size (or is remote)
• Communicating change is difficult, even when communication channels are strong
• Outside of finance, tech tends to pay at the high end of salaries and perks
• We’re in an interesting time of challenging economics and pressure from AI
• Perks are difficult to pull back, because business success isn’t evenly rewarded
• Why is positioned as open culture, or strategy, but it’s also about day-to-day behavior
• It is what it is a decision, but it’s also accountability and continued viability
• Most leaders are going to have to manage between Why and It is what it is in 2025
• Most workers are going to have to work between Why and It is what it is in 2025

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Aaron Delp, Brian Gracely, and Brandon Whichard discuss the top stories in Cloud and AI from April 2025, including OpenAI, MCP, VibeCoding and Hyperscaler earnings.

SHOW: 921

SHOW TRANSCRIPT: The Cloudcast #921 Transcript

SHOW VIDEO: https://youtube.com/@TheCloudcastNET

CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotw

NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST: "CLOUDCAST BASICS"

SPONSORS:

• Cut Enterprise IT Support Costs by 30-50% with US Cloud

SHOW NOTES:

• Link to April 2025 News and Articles

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As more companies begin to adopt AI into their workforce and day-to-day processes, it will be interesting to watch how their learning curve is spread across knowledge workers.

SHOW: 920

SHOW TRANSCRIPT: The Cloudcast #920 Transcript

SHOW VIDEO: https://youtube.com/@TheCloudcastNET

CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotw

CHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"

SHOW SPONSORS:

• Cut Enterprise IT Support Costs by 30-50% with US Cloud

SHOW NOTES:

• AI Horseless Carriages (AI user-experiences)

HOW WILL WE VIEW AN AI AGENT IN THE CONTEXT OF HUMANS OR “USERS”

• The low-hanging fruit, simple on-ramp is the key to early AI adoption
• Google and Microsoft are already showing revenue increases, likely through the productivity apps bundling
• Expect prices to increase slowly, but frequently as adoption happens and companies get used to the knowledge worker productivity increases (or expectations)
• Curious how knowledge workers are adopting, sharing, increasing their learning curve
• Sharing still seems to be lacking within the AI tools. Not just sharing of an individual task, but sharing of learning curves, best practices, datasets
• Is there a dataset collection opportunity? This feels like Big Data or Data Lake 5.0.

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Mark Freydl (CEO/Founder @codiacio) talks about the challenges of blending IaaC, DevOps and Platform Engineering to drive efficient software development lifecycles.

SHOW: 919

SHOW TRANSCRIPT: The Cloudcast #919 Transcript

SHOW VIDEO: https://youtube.com/@TheCloudcastNET

CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotw

NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST: "CLOUDCAST BASICS"

SHOW NOTES:

• Codiac website
• Codiac at Tech Field Day

Topic 1 - Welcome to the show, Mark. Give everyone a quick introduction.

Topic 2 - Before we dig into the tech, let’s start with the problem. What problem were you seeing in software development that wasn’t being solved with IaaC, Platform Engineering, DevOps, etc.? Where does SDLC fit into this?

Topic 3 - Have microservices helped or hurt? We hear all the time about the loose coupling of microservices and benefits towards production, but doesn’t that also make it harder to develop? How do you recreate an environment where you are dependent on a bunch of microservices in a development pipeline?

Topic 4 - I get the feeling this is all about removing friction. But where and how? I see Kubernetes as a blessing and curse many times. It’s an awesome application platform, as long as you aren’t the one that has to do the care and feeding on it. Thoughts?

Topic 5 - The goal here I believe is a closed loop system that is beneficial for developers and SRE’s, but how do you balance closed loops vs. extensibility and abstraction of different platforms to the systems that are truly write once.

Topic 6 - How does the culture and relationships in the org have to change to meet the changes in the tech?

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There is a lot of excitement around AI Agents. Here are five questions we're asking ourselves about how AI Agents will be used and managed.

SHOW: 918

SHOW TRANSCRIPT: The Cloudcast #918 Transcript

SHOW VIDEO: https://youtube.com/@TheCloudcastNET

CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotw

CHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"

SHOW SPONSORS:

• Cut Enterprise IT Support Costs by 30-50% with US Cloud

SHOW NOTES:

• Anthropic warns that fully AI employees are a year away
• AuthZ Agents Chaos
• Why we need to get AuthZ right
• AI Agent Gateway and AI Agent Mesh
• Auth in the age of AI Agent (Cloudcast Eps. 885)
• Trust will make or break AI Agents
• Introducing the Agent2Agent Protocol (A2A - Google)
• Introducing the Model Context Protocol (MCP - Anthroopic)
• Top 9 AI Agent Frameworks

HOW WILL WE VIEW AN AI AGENT IN THE CONTEXT OF HUMANS OR “USERS”

• [Who] How is an AI agent represented in the context of a person, a group, an organization?
• [What] How will we audit Agent created data or content? Will we need a way to label Agent-created information?
• [Where] Will we have agents that are focused on non-human-interactions (e.g. optimizations) vs. human-interactions (e.g. empathy)?
• [When] Are today’s auth systems able to manage AI agents, or will they need to be re-engineered to deal with different scale, granularity of actions?
• [Why] Why will we choose an Agent (as the implementation) over another form or working on a problem?
• [How] Are today’s auth systems able to manage AI agents, or will they need to be re-engineered to deal with different scale, granularity of actions?

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