Transcript

Announcer: Hello, and welcome to Screaming in the Cloud with your host, Chief Cloud Economist at The Duckbill Group, Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.

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Corey: Welcome to Screaming in the Cloud, I’m Corey Quinn. When I get bored and the power goes out, I find myself staring at the ceiling, figuring out how best to pick fights with people on the internet about Kubernetes. Because, well, I’m basically sad and have a growing collection of personality issues. My guest today is probably one of the best people to have those arguments with. Chen Goldberg is the General Manager of Cloud Runtimes and VP of Engineering at Google Cloud. Chen, Thank you for joining me today.

Chen: Thank you so much, Corey, for having me.

Corey: So, Google has been doing a lot of very interesting things in the cloud, and the more astute listener will realize that interesting is not always necessarily a compliment. But from where I sit, I am deeply vested in the idea of a future where we do not have a cloud monoculture. As I’ve often said, I want, “What cloud should I build something on in five to ten years?” To be a hard question to answer, and not just because everything is terrible. I think that Google Cloud is absolutely a bright light in the cloud ecosystem and has been for a while, particularly with this emphasis around developer experience. All of that said, Google Cloud is sort of a big, unknowable place, at least from the outside. What is your area of responsibility? Where do you start? Where do you stop? In other words, what can I blame you for?

Chen: Oh, you can blame me for a lot of things if you want to. I [laugh] might not agree with that, but that’s—

Corey: We strive for accuracy in these things, though.

Chen: But that’s fine. Well, first of all, I’ve joined Google about seven years ago to lead the Kubernetes and GKE team, and ever since, continued at the same area. So evolved, of course, Kubernetes, and Google Kubernetes Engine, and leading our hybrid and multi-cloud strategy as well with technologies like Anthos. And now I’m responsible for the entire container runtime, which includes Kubernetes and the serverless solutions.

Corey: A while back, I, in fairly typical sarcastic form, wound up doing a whole inadvertent start of a meme where I joked about there being 17 ways to run containers on AWS. And then as that caught on, I wound up listing out 17 services you could use to do that. A few months went past and then I published a sequel of 17 more services you can use to run Kubernetes. And while that was admittedly tongue-in-cheek, it does lead to an interesting question that’s ecosystem-wide. If I look at Google Cloud, I have Cloud Run, I have GKE, I have GCE if I want to do some work myself.

It feels like more and more services are supporting Docker in a variety of different ways. How should customers and/or people like me—though, I am sort of a customer as well since I do pay you folks every month—how should we think about containers and services in which to run them?

Chen: First of all, I think there’s a lot of credit that needs to go to Docker that made containers approachable. And so, Google has been running containers forever. Everything within Google is running on containers, even our VMs, even our cloud is running on containers, but what Docker did was creating a packaging mechanism to improve developer velocity. So, that’s on its own, it’s great. And one of the things, by the way, that I love about Google Cloud approach to containers and Docker that yes, you can take your Docker container and run it anywhere.

And it’s actually really important to ensure what we call interoperability, or low barrier to entry to a new technology. So, I can take my Docker container, I can move it from one platform to another, and so on. So, that’s just to start with on a containers. Between the different solutions, so first of all, I’m all about managed services. You are right, there are many ways to run a Kubernetes. I’m taking a lot of pride—

Corey: The best way is always to have someone else run it for you. Problem solved. Great, the best kind of problems are always someone else’s.

Chen: Yes. And I’m taking a lot of pride of what our team is doing with Kubernetes. I mean, we’ve been working on that for so long. And it’s something that you know, we’ve coined that term, I think back in 2016, so there is a success disaster, but there’s also what we call sustainable success. So, thinking about how to set ourselves up for success and scale. Very proud of that service.

Saying that, not everybody and not all your workloads you need the flexibility that Kubernetes gives you in all the ecosystem. So, if you start with containers your first time, you should start with Cloud Run. It’s the easiest way to run your containers. That’s one. If you are already in love with Kubernetes, we won’t take it away from you. Start with GKE. Okay [laugh]? Go all-in. Okay, we are all in loving Kubernetes as well. But what my team and I are working on is to make sure that those will work really well together. And we actually see a lot of customers do that.

Corey: I’d like to go back a little bit in history to the rise of Docker. I agree with you it was transformative, but containers had been around in various forms—depending upon how you want to define it—dating back to the ’70s with logical partitions on mainframes. Well, is that a container? Is it not? Well, sort of. We’ll assume yes for the sake of argument.

The revelation that I found from Docker was the developer experience, start to finish. Suddenly, it was a couple commands and you were just working, where previously it had taken tremendous amounts of time and energy to get containers working in that same context. And I don’t even know today whether or not the right way to contextualize containers is as sort of a lite version of a VM, as a packaging format, as a number of other things that you could reasonably call it. How do you think about containers?

Chen: So, I’m going to do, first of all, a small [unintelligible 00:06:31]. I actually started my career as a system mainframe engineer—

Corey: Hmm.

Chen: And I will share that when you know, I’ve learned Kubernetes, I’m like, “Huh, we already have done all of that, in orchestration, in workload management on mainframe,” just to the side. The way I think about containers is as a—two things: one, it is a packaging of an application, but the other thing which is also critical is the decoupling between your application and the OS. So, having that kind of abstraction and allowing you to portable and move it between environments. So, those are the two things that are when I think about containers. And what technologies like Kubernetes and serverless gives on top of that is that manageability and making sure that we take care of everything else that is needed for you to run your application.

Corey: I’ve been, how do I put this, getting some grief over the past few years, in the best ways possible, around a almost off-the-cuff prediction that I made, which was that in five years, which is now a lot closer to two, basically, nobody is going to care about Kubernetes. And I could have phrased that slightly more directly because people think I was trying to say, “Oh, Kubernetes is just hype. It’s going to go away. Nobody’s going to worry about it anymore.” And I think that is a wildly inaccurate prediction.

My argument is that people are not going to have to think about it in the same way that they are today. Today, if I go out and want to go back to my days of running production services in anger—and by ‘anger,’ I of course mean in production—then it would be difficult for me to find a role that did not at least touch upon Kubernetes. But people who can work with that technology effectively are in high demand and they tend to be expensive, not to mention then thinking about all of the intricacies and complexities that Kubernetes brings to the foreground, that is what doesn’t feel sustainable to me. The idea that it’s going to have to collapse down into something else is, by necessity, going to have to emerge. How are you seeing that play out? And also, feel free to disagree with the prediction. I am thrilled to wind up being told that I’m wrong it’s how I learn the most.

Chen: I don’t know if I agree with the time horizon of when that will happen, but I will actually think it’s a failure on us if that won’t be the truth, that the majority of people will not need to know about Kubernetes and its internals. And you know, we keep saying that, like, hey, we need to make it more, like, boring, and easy, and I’ve just said like, “Hey, you should use managed.” And we have lots of customers that says that they’re just using GKE and it scales on their behalf and they don’t need to do anything for that and it’s just like magic. But from a technology perspective, there is still a way to go until we can make that disappear.

And there will be two things that will push us into that direction. One is—you mentioned that is as well—the talent shortage is real. All the customers that I speak with, even if they can find those great people that are experts, they’re actually more interesting things for them to work on, okay? You don’t need to take, like, all the people in your organization and put them on building the infrastructure. You don’t care about that. You want to build innovation and promote your business.

So, that’s one. The second thing is that I do expect that the technology will continue to evolve and are managed solutions will be better and better. So hopefully, with these two things happening together, people will not care that what’s under the hood is Kubernetes. Or maybe not even, right? I don’t know exactly how things will evolve.

Corey: From where I sit, what are the early criticisms I had about Docker, which I guess translates pretty well to Kubernetes, are that they solve a few extraordinarily painful problems. In the case of Docker, it was, “Well, it works on my machine,” as a grumpy sysadmin, the way I used to be, the only real response we had to that was, “Well. Time to backup your email, Skippy, because your laptop is going into production, then.” Now, you can effectively have a high-fidelity copy of production, basically anywhere, and we’ve solved the problem of making your Mac laptop look like a Linux server. Great, okay, awesome.

With Kubernetes, it also feels, on some level, like it solves for very large-scale Google-type of problems where you want to run things across at least a certain point of scale. It feels like even today, it suffers from having an easy Hello World-style application to deploy on top of it. Using it for WordPress, or some other form of blogging software, for example, is stupendous overkill as far as the Hello World story tends to go. Increasingly as a result, it feels like it’s great for the large-scale enterprise-y applications, but the getting started story of how do I have a service I could reasonably run in production? How do I contextualize that, in the world of Kubernetes? How do you respond to that type of perspective?

Chen: We’ll start with maybe a short story. I started my career in the Israeli army. I was head of the department and one of the lead technology units and I was responsible for building a PAS. In essence, it was 20-plus years ago, so we didn’t really call it a PAS but that’s what it was. And then at some point, it was amazing, developers were very productive, we got innovation again, again. And then there was some new innovation just at the beginning of web [laugh] at some point.

And it was actually—so two things I’ve noticed back then. One, it was really hard to evolve the platform to allow new technologies and innovation, and second thing, from a developer perspective, it was like a black box. So, the developers team that people were—the other development teams couldn’t really troubleshoot environment; they were not empowered to make decisions or [unintelligible 00:12:29] in the platform. And you know, when it was just started with Kubernetes—by the way, beginning, it only supported 100 nodes, and then 1000 nodes. Okay, it was actually not for scale; it actually solved those two problems, which I’m—this is where I spend most of my time.

So, the first one, we don’t want magic, okay? To be clear on, like, what’s happening, I want to make sure that things are consistent and I can get the right observability. So, that’s one. The second thing is that we invested so much in the extensibility an environment that it’s, I wouldn’t say it’s easy, but it’s doable to evolve Kubernetes. You can change the models, you can extend it you can—there is an ecosystem.

And you know, when we were building it, I remember I used to tell my team, there won’t be a Kubernetes 2.0. Which is for a developer, it’s [laugh] frightening. But if you think about it and you prepare for that, you’re like, “Huh. Okay, what does that mean with how I build my APIs? What does that mean of how we build a system?” So, that was one. The second thing I keep telling my team, “Please don’t get too attached to your code because if it will still be there in 5, 10 years, we did something wrong.”

And you can see areas within Kubernetes, again, all the extensions. I'm very proud of all the interfaces that we’ve built, but let’s take networking. This keeps to evolve all the time on the API and the surface area that allows us to introduce new technologies. I love it. So, those are the two things that have nothing to do with scale, are unique to Kubernetes, and I think are very empowering, and are critical for the success.

Corey: One thing that you said that resonates most deeply with me is the idea that you don’t want there to be magic, where I just hand it to this thing and it runs it as if by magic. Because, again, we’ve all run things in anger in production, and what happens when the magic breaks? When you’re sitting around scratching your head with no idea how it starts or how it stops, that is scary. I mean, I recently wound up re-implementing Google Cloud Distinguished Engineer Kelsey Hightower’s “Kubernetes the Hard Way” because he gave a terrific tutorial that I ran through in about 45 minutes on top of Google Cloud. It’s like, “All right, how do I make this harder?”

And the answer is to do it on AWS, re-implement it there. And my experiment there can be found at kubernetesthemuchharderway.com because I have a vanity domain problem. And it taught me he an awful lot, but one of the challenges I had as I went through that process was, at one point, the nodes were not registering with the controller.

And I ran out of time that day and turned everything off—because surprise bills are kind of what I spend my time worrying about—turn it on the next morning to continue and then it just worked. And that was sort of the spidey sense tingling moment of, “Okay, something wasn’t working and now it is, and I don’t understand why. But I just rebooted it and it started working.” Which is terrifying in the context of a production service. It was understandable—kind of—and I think that’s the sort of thing that you understand a lot better, the more you work with it in production, but a counterargument to that is—and I’ve talked about it on this show before—for this podcast, I wind up having sponsors from time to time, who want to give me fairly complicated links to go check them out, so I have the snark.cloud URL redirector.

That’s running as a production service on top of Google Cloud Run. It took me half an hour to get that thing up and running; I haven’t had to think about it since, aside from a three-second latency that was driving me nuts and turned out to be a sleep hidden in the code, which I can’t really fault Google Cloud Run for so much as my crappy nonsense. But it just works. It’s clearly running atop Kubernetes, but I don’t have to think about it. That feels like the future. It feels like it’s a glimpse of a world to come, we’re just starting to dip our toes into. That, at least to me, feels like a lot more of the abstractions being collapsed into something easily understandable.

Chen: [unintelligible 00:16:30], I’m happy you say that. When talking with customers and we’re showing, like, you know, yes, they’re all in Kubernetes and talking about Cloud Run and serverless, I feel there is that confidence level that they need to overcome. And that’s why it’s really important for us in Google Cloud is to make sure that you can mix and match. Because sometimes, you know, a big retail customer of ours, some of their teams, it’s really important for them to use a Kubernetes-based platform because they have their workloads also running on-prem and they want to serve the same playbooks, for example, right? How do I address issues, how do I troubleshoot, and so on?

So, that’s one set of things. But some cloud only as simple as possible. So, can I use both of them and still have a similar developer experience, and so on? So, I do think that we’ll see more of that in the coming years. And as the technology evolves, then we’ll have more and more, of course, serverless solutions.

By the way, it doesn’t end there. Like, we see also, you know, databases and machine learning, and like, there are so many more managed services that are making things easy. And that’s what excites me. I mean, that’s what’s awesome about what we’re doing in cloud. We are building platforms that enable innovation.

Corey: I think that there’s an awful lot of power behind unlocking innovation from a customer perspective. The idea that I can use a cloud provider to wind up doing an experiment to build something in the course of an evening, and if it works, great, I can continue to scale up without having to replace, you know, the crappy Raspberry Pi-level hardware in my spare room with serious enterprise servers in a data center somewhere. The on-ramp and the capability and the lack of long-term commitments is absolutely magical. What I’m also seeing that is contributing to that is the de facto standard that’s emerged of most things these days support Docker, for better or worse. There are many open-source tools that I see where, “Oh, how do I get this up and running?”

“Well, you can go over the river and through the woods and way past grandmother’s house to build this from source or run this Docker file.” I feel like that is the direction the rest of the world is going. And as much fun as it is to sit on the sidelines and snark, I’m finding a lot more capability stories emerging across the board. Does that resonate with what you’re seeing, given that you are inherently working at very large scale, given the [laugh] nature of where you work?

Chen: I do see that. And I actually want to double down on the open standards, which I think this is also something that is happening. At the beginning, we talked about I want it to be very hard when I choose the cloud provider. But innovation doesn’t only come from cloud providers; there’s a lot of companies and a lot of innovation happening that are building new technologies on top of those cloud providers, and I don’t think this is going to stop. Innovation is going to come from many places, and it’s going to be very exciting.

And by the way, things are moving super fast in our space. So, the investment in open standard is critical for our industry. So, Docker is one example. Google is in [unintelligible 00:19:46] speaking, it’s investing a lot in building those open standards. So, we have Docker, we have things like of course Kubernetes, but we are also investing in open standards of security, so we are working with other partners around [unintelligible 00:19:58], defining how you can secure the software supply chain, which is also critical for innovation. So, all of those things that reduce the barrier to entry is something that I’m personally passionate about.

Corey: Scaling containers and scaling Kubernetes is hard, but a whole ‘nother level of difficulty is scaling humans. You’ve been at Google for, as you said, seven years and you did not start as a VP there. Getting promoted from Senior Director to VP at Google is a, shall we say, heavy lift. You also mentioned that you previously started with, I believe, it was a seven-person team at one point. How have you been able to do that? Because I can see a world in which, “Oh, we just write some code and we can scale the computers pretty easily,” I’ve never found a way to do that for people.

Chen: So yes, I started actually—well not 7, but the team was 30 people [laugh]. And you can imagine how surprised I was when I joining Google Cloud with Kubernetes and GKE and it was a pretty small team, to the beginning of those days. But the team was already actually on the edge of burning out. You know, pings on Slack, the GitHub issues, there was so many things happening 24/7.

And the thing was just doing everything. Everybody were doing everything. And one of the things I’ve done on my second month on the team—I did an off-site, right, all managers; that’s what we do; we do off-sites—and I brought the team in to talk about—the leadership team—to talk about our team values. And in the beginning, they were a little bit pissed, I would say, “Okay, Chen. What’s going on? You’re wasting two days of our lives to talk about those things. Why we are not doing other things?”

And I was like, “You know guys, this is really important. Let’s talk about what’s important for us.” It was an amazing it worked. By the way, that work is still the foundation of the culture in the team. We talked about the three values that we care about and how that will look like.

And the reason it’s important is that when you scale teams, the key thing is actually to scale decision-making. So, how do you scale decision-making? I think there are two things there. One is what you’re trying to achieve. So, people should know and understand the vision and know where we want to get to.

But the second thing is, how do we work? What’s important for us? How do we prioritize? How do we make trade-offs? And when you have both the what we’re trying to do and the how, you build that team culture. And when you have that, I find that you’re set up more for success for scaling the team.

Because then the storyteller is not just the leader or the manager. The entire team is a storyteller of how things are working in this team, how do we work, what you’re trying to achieve, and so on. So, that’s something that had been a critical. So, that’s just, you know, from methodology of how I think it’s the right thing to scale teams. Specifically, with a Kubernetes, there were more issues that we needed to work on.

For example, building or [recoding 00:23:05] different functions. It cannot be just engineering doing everything. So, hiring the first product managers and information engineers and marketing people, oh my God. Yes, you have to have marketing people because there are so many events. And so, that was one thing, just you know, from people and skills.

And the second thing is that it was an open-source project and a product, but what I was personally doing, I was—with the team—is bringing some product engineering practices into the open-source. So, can we say, for example, that we are going to focus on user experience this next release? And we’re not going to do all the rest. And I remember, my team was like worried about, like, “Hey, what about that, and what about this, and we have—” you know, they were juggling everything together. And I remember telling them, “Imagine that everything is on the floor. All the balls are on the floor. I know they’re on the floor, you know they’re on the floor. It’s okay. Let’s just make sure that every time we pick something up, it never falls again.” And that idea is a principle that then evolved to ‘No Heroics,’ and it evolved to ‘Sustainable Success.’ But building things towards sustainable success is a principle which has been very helpful for us.

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Corey: When I take a look back, it’s very odd to me to see the current reality that is Google, where you’re talking about empathy, and the No Heroics, and the rest of that is not the reputation that Google enjoyed back when a lot of this stuff got started. It was always oh, engineers should be extraordinarily bright and gifted, and therefore it felt at the time like our customers should be as well. There was almost an arrogance built into, well, if you wrote your code more like Google will, then maybe your code wouldn’t be so terrible in the cloud. And somewhat cynically I thought for a while that oh Kubernetes is Google’s attempt to wind up making the rest of the world write software in a way that’s more Google-y. I don’t think that observation has aged very well. I think it’s solved a tremendous number of problems for folks.

But the complexity has absolutely been high throughout most of Kubernetes life. I would argue, on some level, that it feels like it’s become successful almost in spite of that, rather than because of it. But I’m curious to get your take. Why do you believe that Kubernetes has been as successful as it clearly has?

Chen: [unintelligible 00:25:34] two things. One about empathy. So yes, Google engineers are brilliant and are amazing and all great. And our customers are amazing, and brilliant, as well. And going back to the point before is, everyone has their job and where they need to be successful and we, as you say, we need to make things simpler and enable innovation. And our customers are driving innovation on top of our platform.

So, that’s the way I think about it. And yes, it’s not as simple as it can be—probably—yet, but in studying the early days of Kubernetes, we have been investing a lot in what we call empathy, and the customer empathy workshop, for example. So, I partnered with Kelsey Hightower—and you mentioned yourself trying to start a cluster. The first time we did a workshop with my entire team, so then it was like 50 people [laugh], their task was to spin off a cluster without using any scripts that we had internally.

And unfortunately, not many folks succeeded in this task. And out of that came the—what you you call it—a OKR, which was our goal for that quarter, is that you are able to spin off a cluster in three commands and troubleshoot if something goes wrong. Okay, that came out of that workshop. So, I do think that there is a lot of foundation on that empathetic engineering and the open-source of the community helped our Google teams to be more empathetic and understand what are the different use cases that they are trying to solve.

And that actually bring me to why I think Kubernetes is so successful. People might be surprised, but the amount of investment we’re making on orchestration or placement of containers within Kubernetes is actually pretty small. And it’s been very small for the last seven years. Where do we invest time? One is, as I mentioned before, is on the what we call the API machinery.

So, Kubernetes has introduced a way that is really suitable for a cloud-native technologies, the idea of reconciliation loop, meaning that the way Kubernetes is—Kubernetes is, like, a powerful automation machine, which can automate, of course, workload placement, but can automate other things. Think about it as a way of the Kubernetes API machinery is observing what is the current state, comparing it to the desired state, and working towards it. Think about, like, a thermostat, which is a different automation versus the ‘if this, then that,’ where you need to anticipate different events. So, this idea about the API machinery and the way that you can extend it made it possible for different teams to use that mechanism to automate other things in that space.

So, that has been one very powerful mechanism of Kubernetes. And that enabled all of innovation, even if you think about things like Istio, as an example, that’s how it started, by leveraging that kind of mechanism to separate storage and so on. So, there are a lot of operators, the way people are managing their databases, or stateful workloads on top of Kubernetes, they’re extending this mechanism. So, that’s one thing that I think is key and built that ecosystem. The second thing, I am very proud of the community of Kubernetes.

Corey: Oh, it’s a phenomenal community success story.

Chen: It’s not easy to build a community, definitely not in open-source. I feel that the idea of values, you know, that I was talking about within my team was actually a big deal for us as we were building the community: how we treat each other, how do we help people start? You know, and we were talking before, like, am I going to talk about DEI and inclusivity, and so on. One of the things that I love about Kubernetes is that it’s a new technology. There is actually—[unintelligible 00:29:39] no, even today, there is no one with ten years experience in Kubernetes. And if anyone says they have that, then they are lying.

Corey: Time machine. Yes.

Chen: That creates an opportunity for a lot of people to become experts in this technology. And by having it in open-source and making everything available, you can actually do it from your living room sofa. That excites me, you know, the idea that you can become an expert in this new technology and you can get involved, and you’ll get people that will mentor you and help you through your first PR. And there are some roles within the community that you can start, you know, dipping your toes in the water. It’s exciting. So, that makes me really happy, and I know that this community has changed the trajectory of many people’s careers, which I love.

Corey: I think that’s probably one of the most impressive things that it’s done. One last question I have for you is that we’ve talked a fair bit about the history and how we see it progressing through the view toward the somewhat recent past. What do you see coming in the future? What does the future of Kubernetes look like to you?

Chen: Continue to be more and more boring. There is the promise of hybrid and multi-cloud, for example, is only possible by technologies like Kubernetes. So, I do think that, as a technology, it will continue to be important by ensuring portability and interoperability of workloads. I see a lot of edge use cases. If you think about it, it’s like just lagging a bit around, like, innovation that we’ve seen in the cloud, can we bring that innovation to the edge, this will require more development within Kubernetes community as well.

And that’s really actually excites me. I think there’s a lot of things that we’re going to see there. And by the way, you’ve seen it also in KubeCon. I mean, there were some announcements in that space. In Google Cloud, we just announced before, like, with customers like Wendy’s and Rite Aid as well. So, taking advantage of this technology to allow innovation everywhere.

But beyond that, my hope is that we’ll continue and hide the complexity. And our challenge will be to not make it a black box. Because that will be, in my opinion, a failure pattern, doesn’t help those kinds of platforms. So, that will be the challenge. Can we scope the project, ensure that we have the right observability, and from a use case perspective, I do think edge is super interesting.

Corey: I would agree. There are a lot of workloads out there that are simply never going to be hosted in the cloud provider region, for a variety of reasons of varying validity, but it is the truth. I think that the focus on addressing customers where they are has been an emerging best practice for cloud providers and I’m thrilled to see Google leading the charge on that.

Chen: Yeah. And you just reminded me, the other thing that we see also more and more is definitely AI and ML workloads running on Kubernetes, which is part of that, right? So, Google Cloud is investing a lot in making an AI/ML easy. And I don’t know if many people know, but, like, even Vertex AI, our own platform, is running on GKE. So, that’s part of seeing how do we make sure that platform is suitable for these kinds of workloads and really help customers do the heavy lifting.

So, that’s another set of workloads that are very relevant at the edge. And one of our customers—MLB, for example—two things are interesting there. The first one, I think a lot of people sometimes say, “Okay, I’m going to move to the cloud and I want to know everything right now, how that will evolve.” And one of the things that’s been really exciting with working with MLB for the last four years is the journey and the iterations. So, they started somewhat, like, at one phase and then they saw what’s possible, and then moved to the next one, and so on. So, that’s one. The other thing is that, really, they have so much ML running at the stadium with Google Cloud technology, which is very exciting.

Corey: I’m looking forward to seeing how this continues to evolve and progress, particularly in light of the recent correction we’re seeing in the market where a lot of hype-driven ideas are being stress test, maybe not in the way we might have hoped that they would, but it’ll be really interesting to see what shakes out as far as things that deliver business value and are clear wins for customers versus a lot of the speculative stories that we’ve been hearing for a while now. Maybe I’m totally wrong on this. And this is going to be a temporary bump in the road, and we’ll see no abatement in the ongoing excitement around so many of these emerging technologies, but I’m curious to see how it plays out. But that’s the beautiful part about getting to be a pundit—or whatever it is people call me these days that’s at least polite enough to say on a podcast—is that when I’m right, people think I’m a visionary, and when I’m wrong, people don’t generally hold that against you. It seems like futurist is the easiest job in the world because if you predict and get it wrong, no one remembers. Predict and get it right, you look like a genius.

Chen: So, first of all, I’m optimistic. So usually, my predictions are positive. I will say that, you know, what we are seeing, also what I’m hearing from our customers, technology is not for the sake of technology. Actually, nobody cares [laugh]. Even today.

Okay, so nothing needs to change for, like, nobody would c—even today, nobody cares about Kubernetes. They need to care, unfortunately, but what I’m hearing from our customers is, “How do we create new experiences? How we make things easy?” Talent shortage is not just with tech people. It’s also with people working in the warehouse or working in the store.

Can we use technology to help inventory management? There’s so many amazing things. So, when there is a real business opportunity, things are so much simpler. People have the right incentives to make it work. Because one thing we didn’t talk about—right, we talked about all these new technologies and we talked about scaling team and so on—a lot of time, the challenge is not the technology.

A lot of time, the challenge is the process. A lot of time, the challenge is the skills, is the culture, there’s so many things. But when you have something—going back to what I said before—how you unite teams, when there’s something a clear goal, a clear vision that everybody’s excited about, they will make it work. So, I think this is where having a purpose for the innovation is critical for any successful project.

Corey: I think and I hope that you’re right. I really want to thank you for spending as much time with me as you have. If people want to learn more, where’s the best place for them to find you?

Chen: So, first of all, on Twitter. I’m there or on LinkedIn. I will say that I’m happy to connect with folks. Generally speaking, at some point in my career, I recognized that I have a voice that can help people, and I’ve experienced that can also help people build their careers. I’m happy to share that and [unintelligible 00:36:54] folks both in the company and outside of it.

Corey: I think that’s one of the obligations on a lot of us, once we wanted to get into a certain position or careers to send the ladder back down, for lack of a better term. It’s I’ve never appreciated the perspective, “Well, screw everyone else. I got mine.” The whole point the next generation should have it easier than we did.

Chen: Yeah, definitely.

Corey: Chen Goldberg, General Manager of Cloud Runtimes and VP of Engineering at Google. I’m Cloud Economist Corey Quinn, and this is Screaming in the Cloud. If you’ve enjoyed this podcast, please leave a five-star review on your podcast platform of choice, whereas if you’ve hated this podcast, please leave a five-star review on your podcast platform of choice, along with an angry rant of a comment talking about how LPARs on mainframes are absolutely not containers, making sure it’s at least far too big to fit in a reasonably-sized Docker container.

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