We check in on data gravity to see how generative AI and conversations about metadata and thinking on data lakes impacts data gravity thinking in general.
Data gravity is a concept that has been propagated by David McCrory, a friend of mine, who defined this idea that data itself, the aggregation of data, the use and transit of data has a gravitational effect. That it pulls more data to it as well as workloads towards it.
We jumped right into impacts of data gravity in this conversation.
Is hardware going to be innovative and change? Brian Cantrell brings up oxide computing and some of their design motivation.
Today we discuss our skepticism about some of his points, as well as the impacts for cloud distributed Compute hardware design mainframes, cloud, repatriation, and a whole bunch of topics about next generation thinking in Compute infrastructure management and applications.
We are officially starting our cloud2030 book group and I hope you will join us – we are going to be reading Data Cartels by Sara Landon, followed by Investments Unlimited by John Willis and crew.
How does AI chat and generative AI have the potential to disrupt everything we know about social media? Today we talk Twitter versus mastodon.
We spend most of our time talking about the power, influence and simple use cases for generative AI.
Is this going to break Mastodon, Twitter and other forms of social media? We have a pretty compelling conversation about that, too.
If you’re a fan of Mastodon and Twitter, jump forward to about 30 minutes in when we really start getting down to that topic. Stay tuned for our agenda as a bonus extra in the back half of the podcast.
What is generative AI and what are people now just generically calling ChatGPT?
We put these things in a technical frame, meaning can we use generative AI to improve our programming, testing or automation? What does it take to use these concepts in ways that iteratively improve IT infrastructures.
We review the state of chat, ChatGPT, AI infrastructure and things like that.
Is platform engineering effective at hiding complexity from developers? Today we tear apart what platform engineering is doing, how it came about and what it’s trying to be.
We discuss what companies are trying to accomplish with platform engineering – how can successful efforts improve outcomes for development teams and operations teams by improving collaboration in contracts? Why and how is that important, and what do those efforts entail?
How can the intersection of generative AI machine learning and artificial intelligence be applied to environments using digital twins? Today we discuss digital twins and artificial intelligence.
How can we improve the simulations, the systems, the interactions that we build? How can we correctly model complex components of everything from cars to pumps in ways that allow us to then build on top and build more intelligent systems.
Platform engineering is quite buzzy and has a lot of hype at the moment.
Today we dug behind the hype to acknowledge how the term is being used and misused. We cover why this is a topic of interest, how it’s driving customer thinking around operations and development teams, how it’s working to establish standard operating procedure around infrastructure and operations to support a business, and how those needs drive the evolution of our technology, infrastructure and design thinking.
We break down the edge compute cluster by the Chick-fil-A team, and we talk about how they use Kubernetes, specifically K3s in 2500 of their restaurants to build an IoT and restaurant management system. This system uses Intel Knucks, a commodity commercial residential grade hardware.
It’s an update on a four year old Kubernetes story with a lot of buzz, and they show how they have been successful building this system.
If you’re interested in Kubernetes, Edge DevOps and distributed systems, this episode has a lot to enjoy.
How do we improve the time to decision for CIOs? Today we talk about general business practice and how we can help.
Technical innovators and architects create value for the teams that they support. This can either be from an automation perspective, which is what RackN does, or from a data perspective, which Tyler describes in the podcast today. These are real challenges.
When we flip the script and talk about the miscommunication between how CIOs see business challenges and translate business challenges into technical delivery, however, we get into a fascinating set of discussions where Joanne brings up some key challenges for 2023. We also discuss mapping those from CIOs into implementation, followed by ultimately trying to find ways that people can make fast, valuable decisions that feel right.
This conversation is rooted in important conceptual executive level thinking. We’ll include the list of points that Joanne talks about below as well as in the show notes, and I highly recommend you check those out.
Joanne Friedman’s Key Challenges for 2023 1. Tackle inflation and margin pressure 2. Migrate supply chain disruption 3. Make sustainability a pillar 4. Calibrate talent management strategy 5, Streamline procurement and sourcing 6. Strengthen digital thread 7. Prioritize innovation initiatives
We try to name these key challenges, but in a lot of cases we are talking about the list or the graphic. You’ll need to refer to the show notes in order to see the complete one.
This episode is a one person retrospective on Cloud 2030. As well as what we want to be talking about and doing over the next year. I hope you’ll take a second and listen to my reflection on what we’ve been talking about and where things are going.
Think about what you want the show to be. This podcast is structured around the discussions we come up with together, as well as current events. We try to think deeply and in an unusual way here, and I hope you’re getting a lot of that.