Are the dynamics of the hyperscale cloud providers changing? Will the leaders of the last decade continue for the next 3-5 years, or the next decade? Let’s explore how the market is now rapidly changing.
SHOW: 934
SHOW TRANSCRIPT: The Cloudcast #934 Transcript
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TECHNOLOGY WAVES CHANGE MARKET DYNAMICS; INCUMBENTS DON’T ALWAYS LEAD
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Simon Yu (Co-Founder @SpeakeasyDev) discusses the rise of MCP servers, API integration into AI systems, including SDK generation, and the emergence of the Agentic web.
SHOW: 933
SHOW TRANSCRIPT: The Cloudcast #933 Transcript
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Topic 1 - Welcome to the show, Simon. Give everyone a quick introduction.
Topic 2 - API tooling and platforms for AI has become the hot topic this year. Of course, Agentic AI has made significant contributions to this. What trends do you see that we need to pay attention to? What problems are organizations trying to solve?
Topic 3 - Let’s dig into MCP Servers. The interest in MCP has taken off. At the highest level, why do they exist?
Topic 4 - MCP servers and all the recent announcements around an Agentic Web have me thinking… Do we need to prepare for an Internet where AI agents talk to each other? We had humans and GUIs (web frontends), then we saw the rise of APIs. Is this a third wave or an evolution of the API wave?
Topic 5 - How do SDKs and things like AI API tooling play into all of this? API tools to generate SDKs or AI documentation aren’t new. How does the abstraction of AI change this process?
Topic 6 - In our experience, API-level integrations are challenging to productize. Sometimes it comes down to something as simple as who is going to pay for it, and developers often have a voice and a seat at the table, but don’t have the budget. What has been your experience?
Topic 7 - How does the business side of the house see an advantage to this? What metrics tend to matter and are measurable?
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As Father's Day rolls around, we explore some tales from people taking on their first professional jobs, and how those lessons can relate to managers of newer hires.
SHOW: 932
SHOW TRANSCRIPT: The Cloudcast #932 Transcript
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SOMETIMES WE FORGET HOW CHALLENGING THAT FIRST JOB WAS
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Tobi Knaup (@superguenter, VP/GM of Cloud Native @Nutanix) talks about the evolution of the cloud-native ecosystem, the intersection of AI and Kubernetes, and expectations of the next few years.
SHOW: 931
SHOW TRANSCRIPT: The Cloudcast #931 Transcript
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Topic 1 - Welcome to the show! Full disclosure for everyone out there, I worked for Tobi at Nutanix. Give everyone a brief introduction and a little about your background.
Topic 2 - This is a throwback for our long-time listeners. We had Ben Hindman on episode #211 almost 10 years ago, when D2IQ was Mesosphere, and we also spoke to Dave Lester, who was at Twitter back in 2014. I’m not going to ask you to catch everyone up on 10 years of your company and the history… but I will encourage everyone to go back and listen to that podcast. It is an excellent snapshot of the early days of cloud native and containers. Today, we will talk a bit about the state of cloud native. The most recent KubeCon EU was a few months ago. What were your thoughts around the event and the current state of the industry?
Topic 3 - What are the most prominent challenges organizations face today with Cloud Native adoption? You hear about the complexity, you hear about Kubernetes is a platform to build platforms… still true?
Topic 4 - Where do you think Cloud Native goes in the next 2-3 years? What technologies or design patterns (besides AI, we’ll talk about that later) evolve, and where does the next round of adoption come from?
Topic 5 - Let’s talk about storage and data services quickly. Data services for K8s is messy, really messy at times. Give everyone an overview of the problem at scale and the challenges, especially in multi-cloud environments, which I’m finding more and more.
Topic 6 - I’d be remiss if I didn’t mention AI and its impact in the space. AI came along and sucked all the air out of the room for a time. How do you think about AI today, now that the dust has settled a bit? Is it just an “app” to run on top? How will AI impact cloud native longer term?
FEEDBACK?
As we try and predict the future, we explore the possibilities of technology augmenting or replacing the middle of the bell curve. What has history taught us, and where do we expect it to repeat itself or at least rhyme?
SHOW: 930
SHOW TRANSCRIPT: The Cloudcast #930 Transcript
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MOST OF US ARE THE MIDDLE TODAY….BUT WHAT ABOUT TOMORROW?
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Brian Gracely (@bgracely) and Brandon Whichard (@bwhichard, @SoftwareDefTalk) discuss the top stories in Cloud and AI from May 2025, including Google I/O, Microsoft Build, OpenAI, and Jony Ive.
SHOW: 929
SHOW TRANSCRIPT: The Cloudcast #929 Transcript
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Is it ever a good time for a company to announce vaporware? Let’s discuss the pros and cons of pre-announcements, vaporware, and the strategy of products that may or may not ever exist.
SHOW: 928
SHOW TRANSCRIPT: The Cloudcast #928 Transcript
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IS IT EVER A GOOD TIME TO PRE-ANNOUNCE SOMETHING? OR VAPORWARE ON PURPOSE?
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Anand Kannappan (@anandnk24, CEO @PatronusAI) talks about evaluating AI models for hallucinations, managing data quality, automating the process, and optimizing models.
SHOW: 927
SHOW TRANSCRIPT: The Cloudcast #927 Transcript
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Topic 1 - Welcome to the show, Anand. Give everyone a quick introduction.
Topic 2 - Our topic today is Preventing AI Model Hallucinations. Before we dig into that, I wanted to ask about your time as Lead Data Scientist at Meta. What was it like to be early into that organization, and what did you take away from your time there?
Topic 3 - Ok, let’s dig into model evaluations and hallucinations. Let’s start at the beginning. How do model hallucinations come about?
Topic 4 - When evaluating models for hallucinations, how does a developer or a data scientist know fact from fiction? Due to its size, complexity, and number of parameters, it’s not feasible to simply fact-check and manually verify inputs to outputs. How is this process evaluated and automated with some level of confidence? Additionally, numerous benchmarks are available. What are your thoughts on the usefulness of the benchmarks?
Topic 5 - How does the concept of data quality play into this? How would we know when a model was given insufficient or improper data vs. a hallucination
Topic 6 - We often hear about how frontier models are running out of training data, and increasingly, synthetic data is being used. Does this impact hallucinations in any way?
Topic 7 - The last item I wanted to ask you about, Partonus AI, also pertains to model optimization. Can you explain that process?
FEEDBACK?
The industry is debating whether AI should be viewed as augmenting or replacing human-centric tasks. But we don’t yet have a framework to discuss the technical and business impacts of that spectrum of decisions.
SHOW: 926
SHOW TRANSCRIPT: The Cloudcast #926 Transcript
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HOW DO WE THINK ABOUT THE INVOLVEMENT OF HUMANS WITH AI SYSTEMS?
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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
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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
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WHERE DO AI DEVELOPER-ASSISTANTS GO NEXT?
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Siqi Chen (@blader, CEO/CFO @Runwayco), talks about his journey from JPL developer to Founder 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
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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?
FEEDBACK?
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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WHEN DOES WHY BECOME IT IS WHAT IT IS?
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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
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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
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HOW WILL WE VIEW AN AI AGENT IN THE CONTEXT OF HUMANS OR “USERS”
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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
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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
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HOW WILL WE VIEW AN AI AGENT IN THE CONTEXT OF HUMANS OR “USERS”
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Taylor Pechacek (Head of Product, API Collaboration Core @getpostman) talks about APIs, AI, and MCP (Model Context Protocol). We cover what is top of mind for developers and what has shifted in the industry recently.
SHOW: 917
SHOW TRANSCRIPT: The Cloudcast #917 Transcript
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Topic 1 - Welcome to the show. Tell us a bit about your background, and what you focus on these days at Postman? Tell us a little bit about Postman, for our audience that might not be familiar.
Topic 2 - Let’s begin with some broader concepts - in today’s world, where does a developer interact with software vs. an API - what are the most important things they have to think about?
Topic 3 - What are the standards that are most important for developers these days? Where does OpenAPI Spec fit into that space?
Topic 4 - APIs are now so pervasive. What are the best practices for API usage patterns and governance? What are some of the things that Postman is doing to improve this for developers and companies?
Topic 5 - Let’s talk a little bit about how you’re seeing AI change how developers work, and if/how AI is impacting APIs.
Topic 6 - Help us understand how you expect to see APIs interacting with AI Agents. What will the bigger picture look like for these interactions, and how will they be different than current API interactions?
Topic 7 - Can we finish by talking about MCP (Model Context Protocol) a little bit? This is a new standard that is being adopted by many companies.
FEEDBACK?
Instead of focusing on augmenting existing teams and processes with AI, are we beginning to see more companies looking to replace teams with AI? Will economic pressure increase this likelihood?
SHOW: 916
SHOW TRANSCRIPT: The Cloudcast #916 Transcript
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HUMAN-AUGMENTED vs HUMAN-REPLACEMENT IS A COMPLEX AI STRATEGY
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Evan Powell (@epowell101, CEO at DeepTempo) talks about the intersection of CyberSecurity, Deep Learning and new AI techniques to improve operational security.
SHOW: 915
SHOW TRANSCRIPT: The Cloudcast #915 Transcript
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Topic 1 - Welcome back to the show, Evan. It’s hard to believe it’s been over ten years since we last spoke in your StackStorm days. Give everyone a quick introduction.
Topic 2 - Today's topic is the intersection of Deep Learning and CyberSecurity. Let’s start at a high level. As someone who has founded several companies, what’s interesting enough in this space for you to jump in?
Topic 3 - I talk to many customers about AI and use cases. If they don’t have a leading use case, I often ask where they have a lot of data, and they want insights data. The needle in the haystack scenario. It could be text, unstructured data, log files, data lakes, just about anything. Is this scenario correct when it comes to CyberSecurity? What are the leading use cases?
Topic 4 - I’ve been reading and hearing how AI has been a boon for hackers; what are your thoughts on the ever-lasting cat-and-mouse game in the security space and how it has evolved with AI?
Topic 5 - Let’s dig into the tech; at DeepTempo’s core, you use Deep Learning and specific foundational models. You wrote a great blog recently on Cybersecurity models. When folks hear the term foundational or frontier model, they think of LLMs and GenAI. How is this different?
Topic 6 - Is this a SaaS model? On-prem? Where does the tech sit and why?
FEEDBACK?
Are companies starting to get concerned that AI isn’t meeting expectations? Concern is a part of any new technology adoption curve, but let’s explore some areas where expectations might not be meeting results.
SHOW: 914
SHOW TRANSCRIPT: The Cloudcast #914 Transcript
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IS THREE YEARS ENOUGH TIME FOR ANY TECHNOLOGY TO TAKE OVER THE WORLD?
Miscellaneous stuff:
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Dan Lee, (@_dan_lee_, CEO Nooks.ai, @JoinNooks) talks about agentic AI vs. AI assistants, their differences, and we explore ROI in this space vs. GenAI and common paths to adoption.
SHOW: 913
SHOW TRANSCRIPT: The Cloudcast #913 Transcript
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Topic 1 - Welcome to the show, Dan. Give everyone a quick introduction.
Topic 2 - Our topic today is AI Agents vs. Assistants. Let’s start there. Many out there are hearing the terms and will often think an AI Agent is equal to an AI Assistant. What’s the difference?
Topic 3 - One of the big problems I’ve seen with AI adoption vs. hype is the ROI. GenAI hit the scene and brought AI into the mainstream, but the world is quickly moving on to Agentic AI. I’ve mainly seen this play out because the ROI on “chatbots” just isn’t there most of the time and also everyone is still scared for data leaks/privacy and hallucinations.
Topic 4 - We’ll get into Nooks.ai in a bit but before we do, I wanted to point out that your company started prior to the big breakout of GenAI. How did you see the market back then and has LLM’s and transformer models in general changed your approach to AI Assistants?
Topic 5 - When taking customers through the adoption process for AI Assistants, what are the typical steps? How do you go about getting consensus and buy-in from the various orgs in the company? Are there common hurdles folks need to consider?
Topic 6 - With Nooks, you’ve decided to conquer the domain of sales and sales prospecting. What was your thoughts here in deciding where and how to remove friction from sales? Is this decision and thought process transferable to other areas? Any universal considerations?
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How is it possible for the 900lb gorilla of an industry to stumble or fall from it's heights? Let's explore how emerging trends can knock industry leaders off the top of the mountain.
SHOW: 912
SHOW TRANSCRIPT: The Cloudcast #912 Transcript
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IT’S DIFFICULT TO STAY THE BIGGEST COMPANY FOREVER
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Aaron Delp, Brian Gracely and Brandon Whichard discuss the top stories in Cloud and AI from March 2025 including recent M&A activity.
SHOW: 911
SHOW TRANSCRIPT: The Cloudcast #911 Transcript
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SEGMENTS COVERED IN THE SHOW:
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What happens when you consider moving from a technical role to a lesser technical role? What are the trade-offs? What new skills do you need to evolve?
SHOW: 910
SHOW TRANSCRIPT: The Cloudcast #910 Transcript
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WHAT HAPPENS DURING AND AFTER AN ACQUISITION?
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