Tech Won't Save Us - How the Cloud Reshaped the Internet w/ Dwayne Monroe

Episode Date: July 21, 2022

Paris Marx is joined by Dwayne Monroe to discuss what it’s like to work in a data center, how the cloud came to hold a dominant position, and the consequences of its control by companies like Amazon, Microsoft, and Google.Dwayne Monroe is a cloud technologist and aspiring Marxist theorist of technology, with twenty years of experience architecting large-scale computational systems. Follow Dwayne on Twitter at @cloudquistador.Tech Won’t Save Us offers a critical perspective on tech, its worldview, and wider society with the goal of inspiring people to demand better tech and a better world. Follow the podcast (@techwontsaveus) and host Paris Marx (@parismarx) on Twitter, and support the show on Patreon.Find out more about Harbinger Media Network at harbingermedianetwork.com.Also mentioned in this episode:Dwayne wrote about cloud computing for Logic Magazine. He’s also written about a public cloud and the metaverse on his blog.Amazon’s cloud infrastructure in the eastern United States experienced a major outage in December 2021.Residents in various parts of the world have been questioning the logic of building data centers, including in the United States and New Zealand.In July, the network of Canadian telecom giant Rogers went down, leaving millions without service.Support the show

My position is that the future has been halted by the tech industry. It's presenting itself as giving us the future, but what it's really doing is preventing the future. And so in order for us to actually craft a future for computation, we have to circumvent or somehow deal with them. Hello and welcome to Tech Won't Save Us. I'm your host, Paris Marks, and this week my guest is Duane Munro. Duane is a cloud technologist and aspiring Marxist theorist of technology with 20 years of experience architecting large-scale computational systems. In this week's conversation, we talk about the cloud, this set of data centers that covers the world, but that because of the name we use to refer to it makes it sound as though, you know, when we access all these things online, we're just pulling

this data and transmitting this data that just kind of floats out there in this ethereal way, when actually there's a huge material footprint to that with a ton of workers, with a ton of server farms, with a ton of energy use, and all of these things that come with it. I really enjoyed this conversation because Dwayne has personal experience working in these data centers, but also has been working with cloud solutions for a long time. And so he has a lot of insights to bring to this conversation from those periods and from those experiences. And so we talk a lot about, you know, how the cloud, how this infrastructure came to be, what its impacts have been in encouraging different types of computation, in encouraging companies that are supposedly building these AI solutions, these thinking computers, and

whether that is an accurate representation of what is actually going on here, as well as where this might be going in the future and how we should think about responding to it. Just because this infrastructure exists, is it something that we should seek to dismantle in favor of a more decentralized computational process? Or should we instead be thinking about the types of regulations that should be taken in order to rein these things in, in order to make them serve the public, and whether we should actually have a public cloud instead of allowing

private companies like Amazon, Microsoft, and Google to control these data centers and everything that goes on there. So I really enjoyed this conversation. I found it incredibly insightful, and I hope that you'll like it too. As always, if you like this conversation, make sure to leave a five-star review on Apple Podcasts or Spotify. You can also share the show on social media or with any friends or colleagues

who you think would learn from it. If you enjoy listening to the podcast every single week, all of these critical conversations about the tech industry, consider joining supporters like Michael from London and Caitlin from Los Angeles by going to patreon.com slash tech won't save us and becoming a supporter. Thanks for listening and enjoy this week's conversation. Dwayne, welcome to tech won't save us. Thank you very much. I'm really looking forward to chatting with you. I've been wanting to do an episode on the cloud because I think it is like this really important infrastructure that we maybe sometimes underestimate in its importance.

And, you know, I've been paying attention to your work for a while. And you had this fantastic article in Logic Magazine recently where you looked broadly at the cloud and kind of the impacts that it's had, but then also look specifically and refer back to your personal experience in working in data centers and working with these technologies, implementing cloud solutions, all these sorts of things. And so I think that that also gives us an important perspective when we are considering the impact that this technology and these systems have had. And so, you know, to start our conversation, I was hoping that we could talk a bit about that kind of more physical,

that kind of personal experience that you have in dealing with these things since you've worked in these data centers. So can you start by telling us a little bit about what it's actually like in those spaces? Yeah, yeah, of course. So first of all, let's define what we mean by data center. A data center is a warehouse, essentially, where computers known as servers, which are like people's home computers, their laptops and what have you, just more powerful, more memory, more processing ability and so forth, are concentrated together to provide a

service. And other equipment is collected with those servers, such as storage equipment and cooling equipment, which is absolutely necessary because computers generate a tremendous amount of heat because they're quite wasteful, actually. There was a recent paper, I think from Intel, that talked about how wasteful computation actually is. Like we're not really getting all of the computation for our buck, so to speak. But anyway, when I started my career in the 1990s, one of the things I had to do was, as we said, rack and stack these servers.

What does that mean? Well, that means that in the case of a large company, such as a pharmaceutical firm that had a massive data center, like the size of some people's suburban cul-de-sacs or bigger, servers would come in from Dell or Compaq before Dell and IBM and even Sun up for a network connection to the corporate network and to access to the internet eventually and make sure that everything was properly balanced and work with the cooling engineers to make sure that the cooling balance was good. And so it was a very physical activity. And often you would hurt yourself, as I mentioned in the Logic Magazine article. You would cut your hands. You, your knees would hurt because you were like on your knees having to rack these, these things. And

the rooms are quite cold. And because as I said, they have to be. And so there was nothing ethereal at all about working with these systems. And today, when we talk about cloud, we're still talking about data centers like Microsoft and Amazon and Google. This is what they've got. They've got data centers and are just renting out access to the computing and the database capacity. But behind the scenes, there are people doing today what I was doing earlier in my career. Different equipment, but it's the exact same principle. So, yeah, it was a very industrial,

I would say, in a way, like kind of an industrial task, even though the way that it's presented is as if, you know, it's science fiction or magic or something. Yeah, no, I thought it was really fascinating to get that description of the data centers and of like what that work was like. There are a few like points that stood out to me. I think the first, like when you described how, you know, there were injuries that came of this work, how it was kind of like a factory in a sense, like a factory of computing

and how the space was really cold. Like when we think about Amazon as one of the main cloud providers with the Amazon Web Services, AWS, like it almost seemed similar but different in a way to its warehouses, like this industrial scale, really large. People are getting injuries. Obviously, it's a very different type of work. But then in the case of the data centers, it has to be kept really cold for this computing. But then it seemed like in the warehouses,

Amazon keeps it particularly warm for the robots that are in the warehouses there. Yeah. Yeah. Or, or I'm not quite sure what they're about there. Like what the actual technical explanation for that is. I've always suspected that it's because they, they don't want to have the expense of actually properly cooling such large warehouse spaces for the people who work there. And that some of the robots that they use have probably a higher tolerance for the heat, or perhaps they even have cooling rooms for them. It wouldn't shock me at all if they did something like that, like the very limited capacity robots, because they simply move things from A to B. They can't actually do the warehouse work. Try as they might to make that happen. It's not actually possible. But yes,

in the concentration of computing power in a data center, cooling is absolutely necessary, because otherwise the machines would burn themselves out. And fire was, of course, a concern, because that could occur. And earlier, there was a method for suppressing fire called halon gas, which I do not believe is in use anymore. It's been replaced by another gas. That's not quite as injurious to human life because with halon was dispensed, it would take all the oxygen. I believe it would convert it to nitrogen. I could be wrong about that. It's been a number of years, but I think it would actually remove the oxygen from the air, therefore making fire impossible. And one particular evening, and I think, and of course probably it was a Friday because everything seemed to happen on Friday when you

had to go out. There was a false fire alarm and the gas started descending and my manager just grabbed my shirt and just dragged me out of the room as these doors. I mean, it was like a movie with the lights, the klaxons, and these giant doors closing. And the gas just descending. And yeah, yeah, yeah. So, as I said, nothing ethereal about that at all because we quite literally could have died. Yeah, no way. Like in a data center, just asphyxiated you know and and like you know for most people using the internet you know even

unknowingly interacting with these data centers and now like you know the cloud computing which are just bigger data centers really um you know you would never know that something like that actually happens in those spaces. That's precisely right. That's precisely right. And that there are people, you know, looking at screens and so forth, monitoring. There's an area of work called site reliability engineering. And one of its tasks is correctly architecting a solution or a system so that it's resilient against a failure, but also monitoring.

So there are people just burning, burning hours, burning hours of their lives, just making sure these systems continue to function. And although, you know, I, I no longer do that. I moved my career into the more abstract area of it, but I know that's happening. Like when I connect to say an Azure interface and I create solutions, I know that behind the scenes, when I connect to say an Azure interface and I create solutions, I know that behind the scenes, if I spin something up that, you know, it's happening in a data center and there are people actually behind that curtain, like it's, it's not, even the term cloud is quite hilarious because it's obviously designed as a

marketing hook to make you think that there's just this glowing thing that's providing this computational service. Right? So yes, it was boring work, but also in some senses quite interesting. And I also had a chance to see the scale at which computation can occur because the consulting firm that I worked for in those days, their focus was on Fortune 500 and 1,000 firms. So the data centers that I worked in were like really just quite big. I mean, one of them actually helped to build it from the ground up. Well, not the building, but, you know, like turning it from a giant empty space into a data center.

And we would ride our bikes around before all the racks were in place and all the servers were delivered on pallets, giant trucks. We just ride our bikes around. Yeah, because there was so much space. And before the Liebert, which is the name of a company that produces these massive cooling units, before they were in place and it was just normal air conditioning, we just ride our bikes around. But within a few months, it was just filled with like hundreds, if not thousands of like, you know, empty and then slowly being like filled in with the various, I don't know, objects that fill it up.

Stuff, exactly. All sorts of things. And yeah. So, yeah, that is the background. And then that is what informs my view. And I think that also is what prevents me from being, well, as I've said in other conversations, from being fooled. This applies even to things that seem esoteric, like machine learning or what have you, or

GPT-3 or DALI or what have you. I'm seeing data centers. Others see, oh, you know, artificial general intelligence is on the way. But what I'm seeing are computers in racks generating heat. And that's why my question is always, okay, fine, the mathematics is interesting. You're not producing intelligence, but the mathematics is interesting, as even Chomsky would say. But tell me what's going on in that data center. Because that tells me what the actual cost is, what the power consumption is,

the industrial process that informs what you are presenting as this ethereal, again, science fiction-y kind of thing that you've built. I want to get to some of those questions. But first, I do want to just talk a little bit more about your experience in seeing this kind of transition play out. And in particular, you talked in your article, you talked about how these companies, some of them at least, had no cloud policies, you know, at least in the early days as cloud was emerging. Maybe this is an obvious question, but why would that be? Why would some companies not want to use these cloud solutions?

So imagine that you are a vice president or a CIO at a major corporation in like 1995 or better yet, because it wasn't an option then, better yet like 2005, 2006 when Amazon Web Services first became available. Now you have just overseen the spending of millions of dollars for maybe a refresh cycle. And by refresh cycle, I mean, that's when, say, a collection of servers had reached the end of their life and you've received a budget from the board and they'd said, okay, here's 8 million, buy new servers. And you've done your dog and pony show to the board explaining the value, which was all funny money back then.

Like there was no way to really, you know, to really in any accurate sense, like determine what it would be, but this is what CIOs would do. And, and so you've done all that, and this is your bread and butter, and this is the basis of your power within the organization. When someone comes along and says, well, you know what? Goodbye to all that. We can actually relocate that whole function to somewhere else. Now, companies had co-location, and I'd worked with companies that did co-location. And what that means is rather than staging these servers in a data center that you owned, you would rent space from another company, such as Rackspace, which is a major player in that industry, but you still had the ability to visit, to see it. And so there was a physical connection to the investment. So at the executive level, that was the concern, the loss of power.

But at the level of the technologists, there also was a concern about a loss of relevance and therefore a loss of jobs, right? So at the executive level, as I said, you know, the nature of the business or the way that your business had been run for my God, by then, probably since, I don't know, long before I started in the sixties or seventies, there's a way that information technology was managed. And this was a challenge to that. And so that's why companies would have a no cloud policy. In addition, security teams often would be very concerned, often very legitimate concerns is, well, how do we know that this system has been properly secured? Their own systems probably weren't properly secured, but at least there was the potential to properly secure it. And in those early days before the major cloud vendors had their ISO certifications and so forth,

their very security certifications, there was no way to know. So there were some legitimate concerns, some power concerns, some political concerns, and some job concerns was a mixture of things there at that time. Yeah, I appreciate you outlining that, and particularly like going through the different layers of it, right? Because I think that makes a lot of sense. Like, you know, you can understand why people would be reluctant to embrace something new that's going to change the dynamics of power, change the way that things work, especially when you're used to a certain way of doing it. Yeah, I mean, I'll give you like a really brief, a very brief anecdote to tell you how, when I first became a consultant focused entirely

on what we call cloud transformation. In a meeting with CIO, with members of the IT team, various people in a conference room all together in those days long before COVID, people said to me quite openly, but what happens to my job? And the CIOs in particular client says, yeah, but I don't have any control. I mean, they were just quite upfront about it, what their concerns were. And my job as a consultant,

it was to assuage their fears and massage them, which as the technology stack grew more sophisticated and more capable became easier. And also it was less of a sales pitch because, you know, they are capable and you can do things with them. But yeah, I mean, I have many memories of these conversations and some of these conversations still occur today, but the political winds have changed in the industry. And now CIOs are getting fired for being in the way, whereas before they might have had problems with their jobs if they said, oh, yes, of course, let's let's do that. No, I think it makes a lot of sense. Now, in the article, you also describe like how cloud does come into these organizations. And as you're talking about, you were dealing with a lot of like larger organizations. So I guess my question probably has two parts, right? Like, first of all,

how does then cloud kind of enter into these larger organizations like the ones that you're talking about? And is that kind of generalized across the board? Or would that work differently for smaller companies? Like, you know, we see all the startups that form that are kind of taking advantage of the cloud, you know, in the, in say the early 2010s and things like that. And so I guess they probably have a different relationship to it. Would that be right? That is correct. So for larger enterprises, as we call them, and I'll just use an example, let's say you're a massive paperclip company and you had a massive, maybe I'll use the word massive one more time for good measure, and you had a large investment in your own data center,

or perhaps a co-location, cloud came in, as I said in the article, kind of through the back door, because people needed to solve problems. And the process of procuring equipment and getting it approved and all of that was often quite slow. So different departments would say, we need, you know, you know how it is, we need this now. And so people would say, well, I can't really get it to you now because I haven't got approval. But I could deploy it on AWS or I could deploy it on Azure or Google Cloud Platform. In the beginning, it was only really Amazon Web Services in the early days because they were the only company actually offering anything serious in this area. So for enterprises, they were coming through the back door, typically, through development teams or

engineering teams that were trying to solve problems. That was how I was introduced to it, as I said in the article. For a few years later down the road, smaller firms that needed a lot of computing power but didn't have the capital could begin to spin up solutions, create solutions on the cloud and punch above their weight from a computational perspective. And also mid-range firms, there was a, I helped a friend with a medical billing company once and like a $500 million company. So large enough, but not global, you know, enterprise scale. They had a data center that probably was the size of somebody's apartment building, maybe a little less than that. For them, cloud was a way of saying, well, you know, this is not our core function.

We're spending millions that we could be spending someplace else. We're spending millions on computation and high salaries for people. And what if we could simply offload that function to these cloud providers? Now, there's a misunderstanding because it turns out that you still needed people who were decently paid. And that probably came as a shock to many organizations who thought, oh, I'll just give this away. But at least they no longer had to worry about ordering servers and having data centers of any type or doing a co-location contract or any of that. Today, however, I would say the mind space has changed dramatically, as I said. And so CIOs of larger organizations are expected to have the cloud strategy.

Startups, of course, the use case is still exactly what it was in the past. Many of the so-called machine learning or AI startups that are spinning up all kinds of nonsense, 10 years ago, they would not have been able to do that because they would have had to have made major investments in computing equipment they could not have afforded unless they got like a billion dollars in round A funding or something. So that's an impact. And the mid-range firms, the value proposition, as they say, for them is absolutely being able to have the service that they need without the overhead of having to manage the actual hardware. Yeah, no, I think you can see why that happens. And I guess I wonder,

are there many large companies today that would still have their own data centers? And by extension, does the, I guess, economy of scale offered by, you know, something like AWS or Google Cloud kind of reduce the cost of, you know, I guess, managing your data or whatnot if you move it onto one of these cloud platforms rather than having your own data center? You see, the answer to that is no. In the beginning, the sales pitch was, if you do this, by this I mean, you know, get rid of your data center and move everything to a public cloud. And there's a distinction made between what's called a private cloud, which is something that you would build within your own data center using a technology such as OpenShift, for example, which is a platform that allows you to create an abstraction layer to the computing power that you have in your own data center. Yeah, the sales pitch originally was, if you move everything to a public cloud, it'll be instantly cheaper. But this is not true. So I'll give you an example.

One of my clients in the past had 3,000 servers in their own data center hosting databases. They were a rather large fashion house. And so they had a pretty massive investment in databases, both for tracking what's happening in various stores around the globe and various other things that they did. They were convinced by one of the major cloud vendors to move everything to their infrastructure. It was Amazon, as a matter of fact. And actually, their bills went up. And the reason is because with cloud infrastructure, you are billed for everything. Everything is a trackable metric that generates a cost. Now, on the positive side, you can track those costs. You can see them in real time, which is new. That's something that

wasn't possible, isn't possible with on-premises, as we call it, data center resources. You have to, what we used to call, sweat the assets. You'd buy it and then you'd have to just use that equipment for five to 10 years because the company made the investment. With cloud, you can see that, oh, I'm spending 3 million a month, for example, and I see exactly how that's being generated. So not instantly cheaper, but it is possible to be cheaper through a proper architecture, but it is also possible to move at greater speed. And this gets me back to what I was mentioning a little while ago about these AI startups, right? So they can just spin up using the services. I'll now use Azure Cognitive Services because I know that quite well, which offers language models and text-to-speech and all these various services

as application programming interfaces or APIs. That is to say, services that you can plug into and just consume as a utility. So this gives them the ability to operate in a way that they could not have done earlier. And so for them, for companies in that space, it's possible to not necessarily be cheaper, but because they don't have the overhead, they can offer a service more rapidly and then reach, the idea is reach profitability at a faster pace. Yeah. So a number of benefits, move faster. You this, this part of your business that is not core to your business away from like, have someone else manage it, who, you know, theoretically is better at doing that. And you can also have access to these tools and these kinds of applications that you

wouldn't have just on your own kind of programs. That's right. I mean, even in earlier in my career, only the very largest of organizations, like I, I consulted over a decade ago at a power utility in Philadelphia. And I mean, that company, of course, had access to an Everest of capital. And so they wanted to build on-premises a system that allowed them to determine what the power consumption forecast would be for the Eastern seaboard. Because Philadelphia is part of the, of course, the grid and generates quite a bit of power, actually. And I think it's called North America. I think it's called PJM.

And so they had that money. But if we started a little company, we wouldn't have had that cash, right? But today, we could start up a little company and said, well, we'll do this analysis for you. And then rather than having to invest in all this extremely expensive equipment and having to house it somewhere in a data center, we can simply have maybe hire some developers. And then those developers can plug into the API, as I mentioned, of the analytical services made possible by the cloud provider. So I guess I'm starting to plan the tech won't save us AI startup right now.

But, you know, I think that gives us really good insight into, you know, how these services kind of rolled out on the end of these companies and kind of experience from, you know, the perspective of people who are in these companies, I would like to zoom out. And I think it'll allow us to talk a bit more about the AI startups and things like that as well. But I want to kind of go back to the moment that cloud services emerge. Like, what are the companies, you know, we've already talked about some of them, obviously, but I guess just to kind of lay it out for people, what are the companies that are that are leading this? And what is the moment when these technologies, these solutions really start to be made? Like, why does this happen? So the story is told that Amazon was the first,

and that Amazon had developed a method for abstracting computer services internally, so that they would be able to fulfill their own needs for their growing need for computational power and that they had developed a method internally for, for offering that as a service so that if your line of business needed computational power, whatever purpose that rather than you having to, um, order, you know, your own servers and, and then have it, then have the requisition for it to be added to Amazon's data centers and so forth, that you would simply have your developers build against this computational infrastructure that was commonly available. And at some point, it said that Bezos, who knows, but someone within Amazon said this

would actually be something that would be interesting to customers. The first service that they offered is called, because it's still available, Someone within Amazon said this would actually be something that would be interesting to customers. The first service that they offered is called, because it's still available, S3, which, as I recall, stands for Simple Storage Service. So storage located somewhere else. Now, it may seem strange, like, well, why is that important? Well, the reason that's important and would be compelling to people is that one of the chief problems for applications is where do I store my data? Obviously, you have databases, but there are other types of things or objects that you may need to store images and other types of things. Files, like let's say, for example, if you're pulling up a record, that record could be

stored within a database, but it also could be stored in a separate storage container, electronic storage container, for those who aren't familiar. So Amazon, by offering this service, created something that became instantly attractive to developers because they could say, I can develop my application. Now at the time, it might still be on-premises mostly at the computational layer, but I can point it towards this storage that was elastic, that is to say, it could expand with my needs. So rather than having to go over and over and over again to the till or hat in hand to management and say, oh, well, I thought we only needed $2 million worth of storage, but it turns out we need $5 million worth of storage. You could simply continue to use this S3. So it was a clever move on Amazon's part because that did become the hook

so that when you added other services, such as Elastic Compute, or as it's known now, EC2, which are virtual machines, which just means that it's just a virtualized version of the very servers that I was talking about at the operating system level, which is what controls the computer. When you offered higher level services, now you had the foundation, which was storage. And so now people were hooked because they saw the value.

And as we mentioned earlier, the developers got excited and engineering people also got excited because they could see all the solutions they could build, again, without having to once again go to management and say, oh, well, I miscalculated, which Paris happened all the time. I mean, you would always get it wrong. You would always do like a forecast of what you needed and you were always wrong because, you know, either you overdid it. And so everyone's mad at you because you said, oh, we need 10 million. And like, well, most of the CPUs on these servers are not even, I'm not even like they're like a 1% utilization. Why, why the hell we spend $10 million? Or you didn't do enough. And this comes up in my article in Logic, of course, right?

Where this particular company, the book ordering system fell down like every year because there just wasn't enough like hardware capacity to meet the need and people yelling and working late and all sorts of nonsense going on. So the solution to that problem was to have the storage and then to have those higher level services. So Amazon was first. Microsoft had what they called Windows Azure service, which Steve Ballmer, who was the CEO at the time, man who some geeks might know as a dude who liked to scream a lot, one of the early managers and executives at Microsoft during the Gates days. Windows Azure, which it was just Microsoft Windows Server.

And they weren't quite serious about it. But then under the leadership of Satya Nadella, they became extremely serious about it. And then it became a contender. Google came in third with its Google Cloud Compute, and they decided to turn their attention and their resources to building a competing infrastructure to what Amazon and Microsoft had done. And those are the three major players. Now, there's also Alibaba in China. There is Oracle, which has, it pains me to say, because Larry Ellison is a sinister character, but Oracle's database service is actually useful to people. IBM has a cloud service, which everyone makes fun of. So there are various companies in the space, but the three major players from North America would

be in order of dominance, Amazon, Microsoft, and Google. And would it mainly be that, you know, Amazon kind of pioneered this because they saw it was something that they needed internally and then decided to spin it off as more of a public facing business as well, or, you know, rather it's still within Amazon, but, you know, to offer it as a different service beyond their kind of e-commerce platform. I think so. But then I guess when it comes to Google and Microsoft, were they basically just seeing that and saying, oh, wow, this, they did actually, you know, offer a number of services, as we said, that were quite compelling. Like the service primitives, as we geeks say, you know, that were quite compelling.

Microsoft fumbled for a bit in the space, particularly when Balmer's vision was just to have, like, access to versions of a window servers, you know, in the cloud. But then when they realized under Nadella, when they realized that, well, actually we have a pretty powerful story as they say to tell, which is Microsoft still dominates in the, in the corporate data center. They still dominate on the corporate desktop. And companies like here in Europe, I think it's 70%, companies have a very deep investment in

Microsoft. And so what Microsoft strategy became was, what can we do to our story? Microsoft story would be their marketing hook would be, you already know us from your on-premises, your data center. Now we can, we can take that into public cloud. And that's why their products such as, you know, more recent versions of windows server are quote unquote cloud aware, you know, so that they can immediately take advantage of in the case of Azure, it's a blob storage, which is the equivalent to Amazon's S3. And so they totally retooled their offerings to be, quote unquote, cloud aware. So that became their selling point for corporations. And Google focused on what they considered to be their sweet spot, which is analytics.

So although they offer some of the other services that their competitors do, it's services such as BigQuery and other like database and analytics focused offerings that have become, I think, the most compelling use case for many organizations when it comes to Google. And for some of the largest customers I've had, there's been a multi-cloud strategy, but not one in which a single application spans multiple clouds, but one in which it's the right tool for the right job. So Microsoft Azure is used for core services. Amazon is used for customer facing stuff. And GCP is used for analytics. And those bills are insane. But yes, I've seen that with some of the largest enterprises I've seen and actually designed solutions that use that strategy. Yeah, I guess then you have like some degree of

security is not the right word, but I guess redundancy and stuff, you know, facing using different clouds. To some degree. Yeah, to some degree, although there's a fallacy that it's a method of preventing one of the things that people talk about in this industry a lot is lock-in, right? But it's not because you're using different platforms for different things. containers, which is a way of like a lightweight version of a server. And, and tech, other technologies such as Kubernetes, which allow you to, you know, to orchestrate containers and move them from place to place. These things exist,

but nothing is ever as simple or as easy as it's advertised to be. And I've witnessed a number of projects that in which someone swoops in and says, Oh, you just go from cloud to cloud. And it never, it never, it never works out actually the way that people, you know, presented as go from cloud to cloud. And it never works out, actually, the way that people, you know, present it as working out. That makes perfect sense. I do want to build on this line of questioning. But I remembered one other kind of question that I had about what we were talking about earlier. So I want to briefly return to that. And that question is, you know, you were talking about, and you were writing in the article about what it was like to work at, I guess, these data centers kind of pre-cloud and in the transition to cloud. As these data centers become larger, as there's more and more focus on these cloud data centers,

these massive data centers controlled by Amazon and Microsoft and Google, how does that change the work in these data centers when you go from, say, a data center that's controlling the work for one Fortune 500 or Fortune 1000 company to working in one of these data centers that are part of this cloud infrastructure that are these, you know, massive, massive sites for computing? Like, what is the difference there? Yeah, obviously, the scale is much greater. I mean, the largest data center I worked in was for a pharmaceutical firm. And they had more than one. They had multiples and actually spread across the United States and one in Madrid, which I had the pleasure of visiting. But even so, not as large in scale as

what these three companies have done. They have spent many billions of dollars year on year to expand their data center footprint. If anyone can just Google, for example, AWS data centers, and you'll see this big map or Amazon, or I'm sorry, or Azure or GCP. And you'll see maps across the globe showing where these data centers approximately are. And so for a site reliability

engineer and for a server engineer in these cloud data centers, of course, things are much more tightly controlled. And as I said, they're much larger. And they don't use computing elements the way that we did in the data centers in the 90s and early 2000s, whereas we would get like an entire server with fans and memory and so forth from Dell or from another company and then rack and wire it up. They're using computing elements distributed amongst, they still use racks, of course, but they use computing elements in a proprietary configuration to save space and to be able to cram as much computing power into the spaces as possible. All the principles remain

exactly the same. However, it's just a change in scale and the fact that, of course, they are serving many, many, many customers around the globe. Whereas, for example, the pharma that I worked for, well, it was them. They owned it all and all the computing power was for their purposes, no one else's purposes. Whereas these cloud providers, they become kind of the all roads lead to Rome. There's multiple rooms, but all roads are leading to them, right? And so they have a scale of complexity, a scale of concern about security, both physical and digital security, concerns about power. It is not an exaggeration to say there'll be orders of magnitude greater than, even though it's the same principles, but orders of magnitude greater than what I was doing in the 90s and early 2000s. Yeah, which is why they are known, the category of computation that they fill is called a hyperscale. And how does this hyperscale, how does this movement to cloud

computing to these major data centers controlled by Amazon, Microsoft, and Google, like benefit these corporations and, you know, I guess, enhance their power over computing, over the internet, over what we do online. Well, anyone who is old enough to remember what was called the Microsoft tax has the ability to already suss this out, right? And what was the Microsoft tax? Well, it was the idea that Microsoft through their monopoly or near monopoly over desktop computing in corporate environments and other aspects of corporate compute in the 90s and into

the early 2000s, that their power and their wealth was derived by essentially exacting a tax on almost every business. Whether you were a mom and pop with a laptop in your store or a mid-sized firm like the one that the medical billing company I mentioned, or you were a massive pharmaceutical firm. In each case, you had to find a way of playing ball with Microsoft, right? Because they had you in a critical way, because you needed this computational power in order to run your business. So take that reality and just turn it to 11,000, because now these companies not only have, say, control at the operating system level or the desktop level, the entire computational infrastructure is taken into their hands. So they become a part, a critical part of global infrastructure in a way that is actually quite new.

And one of the things I say to people is, how long do you think this can really go on? How long do you think, say, an Amazon can run these systems? Do you really think it'll be 100 years and they'll still have these computers running? Get out of here. So, putting this in corporate hands obviously is a problem, you know, because more and more companies and therefore individuals are subject to the profit incentives and concerns and indeed, in some extent, the whims. Now, of course, they do provide a service. And so it's not as if they're just going to turn it off because, you know, I don't know, Nadella had a bad morning or something. That's not going to happen. But it's more a matter of such level of power should not be in private hands. I have

nothing against the idea of abstracting computational power and offering as a service. That's fantastic. And that's also not new. I mean, mainframe's offering this, Jesus, when I was a baby. So that's not new. What's new is the concentration of this in private hands. Yeah. So I think that that is something that each of us should be mindful of. Here in Europe, there are individuals who are calling for the creation of truly public cloud. That is to say, abstracted computation, database and storage offered as a public good. But it's difficult. It's difficult because there's lobbying by, of But it's difficult. It's difficult because there's lobbying by, of course, these majors. It's difficult because it's expensive. And there

are people who believe that somehow these companies have magical powers and that no one can do what they did, which of course is nonsense. They didn't come from the future. They're just people like you and me. But I think that's really what's needed. I think that in order to get control over power usage, they get control over the politics of this, because it's not tenable to have like three North American companies have so much sway over the direction of computation. Yeah, I think you're completely right. And like what you're describing really brings to mind a number of articles that I've been reading recently, like concerns in the United States about the location of these data centers and

the kind of power that these companies can exert when they choose where to locate them, the subsidies that they get for them, but also like, you know, the power that they require in the water and what they extract from these communities where they're located. I was reading a story recently in New Zealand where there are proposals for like a bunch of new data centers, I believe in the north of the country and like the communities they were grappling with, like, is this really what we want? Like we're already struggling to hit our renewable energy goals. And if these companies come in, they're going to use up a ton of power and it's going to make it more difficult for those to achieve. I remember reading in Africa as well, like concerns

about do we want these major companies to come in and build their data centers and then kind of control the African internet rather than, you know, controlling it ourselves, right? So there are all these concerns with having, with allowing these major companies that have this degree of power over really the internet. And people have criticized the PRC for being quite strict on this. And there are many reasons to criticize Beijing, but as there's many reasons to criticize any government anywhere. However, strategically, it makes perfect sense to say no to Facebook. It makes perfect sense to say no to these companies dominating your computational infrastructure. Another reason why this concerns me is fragility, as I mentioned. So for example, there was an outage of a portion of AWS's infrastructure. I think it was the US East One region. And for those who don't know, because of the scale,

the infrastructure of cloud providers is divided into regions. So US East One, which I believe is based out of Virginia in the United States, there was an outage of a portion of that infrastructure, which brought down the services provided by a number of private entities, schools, I believe maybe the hospital was impacted. Now in the past, all of these organizations would have had their own computational infrastructure, which that's wasteful in its own way, the way we did it in the 90s and early 2000s. However, you could see that cracks are starting to show, I think, in the site reliability, as we mentioned earlier, of these hyperscalers. Because operating at a hyperscale begins to expose the infrastructure to the effects of complexity theory.

Because you're getting more and more complex, there's more and more compute, there's more and more need to maintain, there's more surface area for things to go wrong. And it's inevitable that things will go wrong in various places. I believe there may even have been some 911 outages. I mean, just all sorts of things that happen when even a portion of the infrastructure of these providers goes down. And there's no recourse other than the private recourse of, well, I signed a, you know, a, what was it, a service agreement with you or, you know, I mean, these are very capitalist sanctions, but not like a public sanction. Like how do we make sure that this computational infrastructure is serving our needs and is redundant? And if it costs more, we as a, you know, as a populace might say, okay, that's fine because I don't want my hospital to go down. When I'm getting my x-ray, they should be able to scan that x-ray and put it someplace, put the data someplace, for example. So yeah, that I think is the primary concern that I certainly have, which is the

concentration of power, of political power and socio-technical power. Because I mean, computation, as I've said many times, is a command control technology. And if you are in command of it, then you have command and control over a large portion of a society. Yeah, as I was rereading your article in preparation for the interview, I was thinking of that as well, because, you know, I don't know if you saw the news recently, but one of Canada's major telecom providers went down the other day. It was down for 15 hours, and phone service, cable service, internet service was lost to hospitals to 911, like everything, right? It's, you know, basically one of the dominant players.

And one of the responses to that was that, you know, we need more competition in telecom. But then I was like, but, you know, there's there's kind of a fundamental infrastructure here and infrastructure that kind of links the whole country. And I was like, is competition on infrastructure really going to make a difference? Or do you just need to build more redundancy into the system? Like, because I don't think you're going to get much, much competition on a national telecom infrastructure like that, right? And so I guess, you know, when you're thinking of cloud, like, is the response that we need to like decenter it and have less centralization of these infrastructures or to have more redundancy built into it so that, you know, these sorts of outages don't have the same degree

of impacts if it, if it all goes down in this way. That's a technical matter. And certainly those companies do whatever they can, you know, to, I mean, cause obviously they don't want it to go down. It's just happening because failure is an emergent property of complexity. Right. And it's just inevitable that it would happen. So I don't fault them for the failure, but it's the failure of government to control, right. To oversee what this is. I'm not aware of any oversight of any sort, really, of cloud services.

I'm not, I can't think, I mean, I'm sure there's some federal statutes in the United States and certainly here in Europe, there's GDPR and in California, there's the California data protection law. So there's some measures of control and there are, of course, you know, security certifications and so forth they have to go through. But what I mean is active oversight of this critical infrastructure, the way that there was decades ago, many decades ago, oversight of critical infrastructure, such as you mentioned telecom. The very reason the AT&T was broken up, now that didn't turn out the way that people hoped, but the very reason that happened was because there was concern, and I think it might have been under Sherman antitrust, but there was concern about this private entity, Bell, having so much control over something that was so important to the nation. And in those days,

of course, there still was the vestige of what I would call the World War II era, federal command and control bureaucracy before the neoliberal era, right? And there was still people who remembered when the government would just say to Chrysler, I need 10,000 tanks. Well, I don't want to build tanks. I don't care that you don't want to build tanks, you're building tanks. And so there was still a vestige of that kind of person in government. Those people are all gone now, of course, or they've retired or they're dead. And now we have, you know, the total neoliberalization of government's approach, which means that, well, you know, these companies must be fantastic. They're all geniuses. Isn't Elon Musk a genius? You know, the lack of questioning, the lack of doubt,

and the assumption that there's some special sauce going on in Silicon Valley, which those of us who monitored a space carefully know that that there is not, of course. Yeah. And, you know, it's a bit beyond our conversation, but that's basically how I feel about the telecom issue in Canada as well. It's like, I think focusing on competition is the wrong like way to look at it. And what we really need is just greater role for the government and actually ensuring that these technologies and these systems serve, you know, the public instead of just, you know, these major telecom companies. Yeah, so I totally agree. You know, you were touching on the AI piece of this and how cloud computing allowed kind of the explosion of all these kind of AI companies promising that, you know, these thinking computers were going to change so many aspects of how we live for the

better and what have you. And we've seen little of that, I think, actually play out. What do you make of, you know, because you've written a bit about this on your blog, like, I guess, what do you make of the way that these AI companies, these artificial companies have been framing themselves in this technology over the past decade or so, and how the cloud has kind of allowed that to be possible? And I guess how it has misled the public on what these technologies can actually do. What you have is a convergence of several factors. One being the false narrative of the creation of thinking machines.

The other being the false narrative that the systems that are being deployed are flawless, as happens with, say, facial recognition or companies coming out and saying, we can detect who's cheating on a test if they're taking a test through Zoom or Teams or what have you. So there's the hype, but then there's the ability to deploy systems, as we mentioned earlier, at scale that's made possible by a cloud provider. So the cloud providers are either purposely or not an enabler or a force multiplier of the proliferation of these systems, which are built on hype. Then there's the gullibility of,

well, not just government, but corporations, because the atmosphere that we're in, in which people believe that these systems are flawless and that we are further along than we actually are sort of acts as a kind of cultural sales pitch for these companies. And also the fact that governments and companies are looking to trim their headcounts, right? So you can say, oh, well, I don't need to, you know, to have so many people

examining documents or working at the DMV. I can just use facial recognition or whatever cobbled together system they come up with. Like, for example, you've written quite a bit about these so-called automated supermarkets and let's self-checkout and what a bit of nonsense that actually is, right? But it's presented as if somehow it's futuristic, somehow it's better. No one can explain other than, oh, I'm in and out in a minute. Well, the extra five minutes it took you to stand in line with a person is that really so terrible what are you doing are you protecting the earth from an incoming asteroid i mean what are you doing that's so important

um you know that you're like oh my god i had to stand in line behind someone. Like, oh, okay. It's fine. So yeah, so it's a combination of these factors moving together to enable these companies to get funding from VCs who see money to be made, whether it's long-term or not, they don't really care. They'll get a return on their investment. And as I said, the gullibility of the customer base and the hype cycle that is acting, as I said, a cultural sales pitch for this. But were it not for the cloud providers, there would be far, far fewer because the VCs would have to make the investment in the hardware and the engineering capacity and the data center real estate. It's a different type of investment, a different type of commitment. It's not as lightweight or as lightweight seeming as, hey, Paris and Duane have this great idea. And then we got this guy from Caltech and he's a genius somehow. And our facial recognition software will be able to predict, you know, whether this person was going to commit a crime in five months. There are people who believe that, right? I also, and this is a little bit off our topic, but I also blame what I will call the effects of science fiction, which I'm a science

fiction fan and have been since I was a kid. But I think that some people's brains have been rotted, not because science fiction exists, but because there's no counterbalancing narrative to, yeah, it's nice that you saw that nice story about a supercomputer took over the world, but here's why that can't happen. And the very companies that we're talking about take advantage of this, as I say, culturally presented sales pitch for what they're selling, and perhaps even bolster it to some extent. So that's why we have, what's one of the reasons why we have this massive problem, this big problem with the proliferation of these AI systems that are having negative impacts on people's lives is because it's far too easy, far too easy to just spin up things

and then just sell them into the world. Yeah, I completely agree with you on the sci-fi piece as well, right? The tech people in Silicon Valley who are making use of that, who are trying to realize it, but then also the broader public who has consumed it for so long and just kind of believes like, okay, this is the direction that things should go, right? And it really, I think, allows people to kind of take their guard down when thinking about these ideas or approaching these narratives that we're receiving from the tech industry. Or also not just take their guard down, but sometimes be fatalistic. And I'll give you an example. Every time you see a video of a Boston Dynamics big dog, right? Or not the big dog, the spot. And the way that they film it is as if it's autonomously moving around the streets of New York patrolling. Everyone thinks about that same Black Mirror episode. Metalhead, I believe

it was, right? Everyone thinks about that episode. And so the fiction of that episode then causes people who should know better in many cases to think, my God, it's here. This autonomous death machine is here. And then you're at pains to say, no, if the camera were to pull back a bit, you would see the person with the remote control actually directing the spot unit. I think there was Eric Adams in New York. There was a picture of, which I think Boston Dynamics probably wasn't happy about, because you could see him walking up the stairs with the spot directing it. It's a drone. It's a remote control device, right? The engineering piece, of course,

is the legs. I mean, that is an engineering accomplishment. But it's not possible to build a truly autonomous system that can perceive and say, oh, I'm going to kill that guy and not that guy. No, that cannot be done. But the science fiction milieu has convinced people that this is happening. So not just the dreams, but also the nightmares are assumed to be 100% possible. And this puts those of us who challenge this in an interesting and difficult spot, because not only do we have to challenge the narrative of Silicon Valley, we also have to help our friends kind of snap out of it and say, no, no, this isn't, you're not dealing with the thing that you saw in that movie or that show. It's still a threat, but for a

different reason than the reason that you're thinking. Yeah. This is, this is one of the reasons why I really liked Alex Rivera's Sleep Dealer as well, because, you know, it kind of showed like the humans, the exploited humans behind the supposedly like acting machines, right? In a way that I think a lot don't. But like on that question about futures, I feel like one of the elements of the futures that some of these tech companies are selling us now are really dependent on this massive scale up in computing power that they assume would want to happen and that they, well, they want to realize, like they want more and more of this computing for more computing to

govern more of our lives effectively, right? And you can see this, I think, with streaming video, streaming media, how that has required a lot more computing power in recent years and how there's now a push for streaming video games, which are expected to account for a lot more. And Facebook's vision for this metaverse that would require this massive expansion in computing power. I guess, what do you make of these visions by these tech companies for these kind of futures? Or to have us even more subjected to all of this computing power and the increased requirements for it? They are absolutely trying. However, I think that this infrastructure will collapse upon itself of its own weight. Meta, for example, is certainly realizing that the task that they set for

themselves is extraordinarily complex. And you may recall, and I did mention this in, I think, a piece that I wrote for my blog, that Intel executive did actually a quite good technical breakdown of why the metaverse as described by meta is not achievable with current technology. And the amount of new computer equipment and computing equipment of a different type that would be required and the scale that would be required. And all that someone has to do is to ask themselves a couple of basic questions, like the Oculus Rift, what was the maximum number of people who were using that? Probably a couple of 100,000 people. And then you think about all that was required in terms of bandwidth and hardware and manufacturing to make that happen. And now you're talking about expanding that to a billion people. I mean, come on, right? Obviously, you're talking about something that has never been achieved and certainly would not be achieved by a completely wacky company, not just wacky, but sinister,

like Meta. But then there's another part of this, which is that computers are built or are created based upon industrial process. There are minerals, there's silicon, there's metal, there's plastic, there are people mining things. There is a supply chain for computation that is completely obscured. And so what you're talking about is ramping up the extractive industries that support it. You're talking about racking up or increasing the power consumption. They're real estate consumed. It is not sustainable and is not the least bit achievable, right? And I think that 50 years from now, whatever shape the world is in, one thing that we will not be talking about is some of the technologies that these companies are deploying, because they will have totally fallen apart.

And then we'll be the digital versions of, you know, of ancient Roman cities. Except the servers will be shut down. So we won't be able to even go find them. The servers will be shut down. Right, exactly. You speak ghost towns. I mean, and we know this is coming. This day is definitely coming.

Yeah, no, absolutely. And I really appreciate you drawing attention to the material piece of this quest, right, of the company, because I think it's so important and doesn't get nearly the attention that it deserves. I appreciate you giving me your time to have this conversation. I want to end with a final question. You've talked about, you know, the need for government to actually step in and have some regulation on cloud computing and actually, you know, look at what kind of impacts this is having and ensuring that it actually serves the public. And you've also talked about the need for potentially of a public cloud. So I wonder, what do you think our response to this growth of cloud computing and its control by, you know, largely these three major companies, you know, what is our response to that? And how do we ensure that whatever form cloud computing takes, that we ensure it's a form that actually benefits the public rather than just the kind of goals of these major corporations? You know, I wrote a piece about this a little while ago called Public Cloud as a Public Good, in which I lay out at least the beginning, I think, of an idea.

I think the first step would be, well, I mean, not to put too fine a point on it, nationalization of at least some of this infrastructure, right? I think that would be the first step. And then the skilling up of ordinary citizens who were interested. There are people who are neglected and in neglected places who would be excited and very engaged to participate in a community computational infrastructure. I know that I, as a kid growing up in West Philly, which wasn't nearly as wild as Will Smith would have you believe, but there were ups and downs. And it was exciting when I got interested in computing. It expanded my world, right? And I got to meet all sorts of interesting people through computation.

And I think back to that time in which my young self, what did I want to do? Like solve problems for people using this machinery. That's what it's really for. And so I think that's what's required is the nationalization and the creation of parallel systems from the ground up are built with people's needs in mind. One idea that I lay out in the piece that I wrote on my blog was that, you know, the effects of climate change are global, but they also manifest differently in different places. And so we need a method for ingesting data, analyzing data, determining what these local effects are in order to determine what mitigation strategies should be. And computation should play a role in that, but computation that is a public good. One of my inspirations for this is the Cybersyn project that Stafford Beer oversaw in Allende's Chile. I think that that provides also a great model for what computation could be, particularly now that we have orders of magnitude more computing power,

this future has been intercepted by the dictates, the demands of Silicon Valley. My position is that the future has been halted by the tech industry. It's presenting itself as giving us the future, but what it's really doing is preventing the future. And so in order for us to actually craft a future for computation, we have to circumvent or somehow deal with them. That's pretty much my feeling on that. No, I completely agree with you. And I think that is a great place to leave it as well. Duane, thank you so much for taking the time. I've really enjoyed this conversation. Thank you. Yeah, thank you so much. Duane Munro is a cloud technologist and aspiring Marxist theorist of technology.

You can follow him on Twitter at at CloudKistador. You can follow me at at Paris Marx, and you can follow the show at at Tech Won't Save Us. Tech Won't Save Us is produced by Eric Wickham and is part of the Harbinger Media Network. If you want to support the work that goes into making the show every week, you can go to patreon.com slash tech won't save us and become a supporter. Thanks for listening. Thank you.