Snowflake, Confluent and 4 Types of Coordination Platforms (Tech Strategy – Podcast 112)


This week’s podcast is about Snowflake and Confluent. But the main topic is how to think about coordination, collaboration and standardization (CCS) platform business models. And the 4 types of these platforms.

You can listen to this podcast here or at iTunes and Google Podcasts.

Here is my new book:

I’ve now started breaking this into 4 sub-types of CCS platforms. They are:

  1. Communication. Zoom, Slack, etc.
  2. Data Intelligence. Snowflake and Confluent.
  3. Team Projects. Manual and complicated projects like architecture, media creation, software development.
  4. Operational Automation.

My explanation for Snowflake’s core platform.


Related articles:

From the Concept Library, concepts for this article are:

  • Hierarchies of Control
  • Coordination, Collaboration and Standardization (CCS) Platforms
  • Enterprise B2B

From the Company Library, companies for this article are:

  • Snowflake
  • Confluent

Photo by Zdeněk Macháček on Unsplash


I write, speak and consult about how to win (and not lose) in digital strategy and transformation.

I am the founder of TechMoat Consulting, a boutique consulting firm that helps retailers, brands, and technology companies exploit digital change to grow faster, innovate better and build digital moats. Get in touch here.

My book series Moats and Marathons is one-of-a-kind framework for building and measuring competitive advantages in digital businesses.

Note: This content (articles, podcasts, website info) is not investment advice. The information and opinions from me and any guests may be incorrect. The numbers and information may be wrong. The views expressed may no longer be relevant or accurate. Investing is risky. Do your own research.

——Transcription below

Welcome, welcome everybody. My name is Jeff Towson and this is Tech Strategy. And the topic for today, Snowflake, Confluent, and Four Types of Coordination Platforms. Yet another wordy title. But really just want to talk about coordination and collaboration platforms, which is one of my five types. Hopefully, I think today is going to be helpful. I think I’ve kind of finally teased this question apart, so I’m going to lay out that thinking. And I think it’s really helpful for looking at companies like Snowflake or another one like Confluent. But a lot of the ones I’ve talked about recently, Tuya and Medallia and all these, I think it’s the same basic approach. So I’m going to lay out that today. Hopefully that will be pretty helpful. Let’s see some current events. I’ve been reading a ton about SenseTime, which is… the largest computer vision company in China and Asia. It was supposed to go public. It has since been put on the US entity blacklist, in theory, last week. Although I’ve heard reports maybe it’s not true, but I think it is. They put, this is basically the US government’s entity list under the Department of Commerce, which basically forbids access to a lot of the tech supply chain coming out of the US. Huawei got hit. Couple years ago, a subsidiary of SenseTime, SenseTime Beijing, got hit last year. And as of last time I heard Friday-ish that the whole company was going to put on it, they’ve halted their IPO in Hong Kong, which was set. We’ll see what happens next, but a fascinating company. It’s one of the best IPO filing documents I’ve read. in China, which is really arguably the leader in this. So anyways, that’s great. I will write a lot about that over the next week or two, and I’ll probably do a podcast on it. But yeah, there’s a ton there. Very, very helpful. So that’s sort of coming up. Other issue, a couple of little housekeeping items. Turns out I’ve just gotten word back that the book, my new book that came out, some of the graphics are a little bit fuzzy. which is weird because they look great in the draft but I’ve checked it as well and they look a little funny. So that’s going to be automatically updated and fixed. So if you have an eBook and you’ve noticed that, don’t worry about it. Those will be sort of uploaded at a higher resolution. It’ll show you get downloaded automatically. That problem will go away. If you’ve bought a paperback book, take a look at it because it looked fine in my version but I haven’t checked one that’s been shipped out. So take a peek and see if there’s an issue with any sort of… Usually the problem has been a little bit of the text it gets fuzzy when it’s smaller. These are the graphics. If you have one of those, take a look. If you see a problem, just send me a note on LinkedIn and we’ll take care of it for you. Okay, but hopefully that should all be fixed. I don’t know, in the next week or two, it should all get cleaned up. One other housekeeping item on the webpage. Some people have had some trouble logging in. There’s a… issue where it keeps asking you to log in. That should have been fixed by now. It was a cache issue that there were caches of the web page around the world so it would load faster, but then it also would not recognize an update. Anyways, that should have been fixed by now. So hopefully that’s all good. One problem done, the other problem should be resolved quite quickly. Okay. And let’s see, other stuff coming up. I’m going to talk about SenseTime Red Hat. I’ll talk about that a little bit today. And you’ll be getting some emails on Confluent in the next day. So a lot of stuff coming up. Hopefully it’s going to be pretty good. I think it’s pretty good. If you’re not a subscriber, feel free to go over to You can sign up there, free 30 day trial, see what you think. Oh, and my standard disclaimer, nothing in this podcaster and my writing on the website is investment advice. The numbers and information for me and any guess may be incorrect. Abuse and opinions expressed may no longer be relevant or accurate. Overall, investing is risky. This is not investment advice. Do your own research. And with that, let’s get into the topic. Okay, now there are two concepts for today. One you’ve heard about me talk about before, which is hierarchies of control. And the other is a new one, which is what I’m calling the four types of coordination platforms. Actually, I’m starting to say CCS platforms, which is yet again, not terribly clear, but. It’s short for coordination, collaboration, and standardization platforms. Now this is going to kind of be the so what for today. You know, I’ve given you five types of platform business models. I think these are particularly helpful in understanding network-based businesses where the business is not, hey, it’s a linear business where the factory stuff goes in one side, it comes out the other. You add value. That’s your business. You sell it into the market. You know, these are businesses where the core assets are networks of nodes and linkages, whether they’re logistics hubs and trucks and trains or protocols of computers or telephone lines or social networks, but businesses that are based on networks and that take advantage of that. And that’s really where the world is going. I mean, every year things get more and more connected. And really the connections between things are becoming more and more important as opposed to this idea that you know a bunch of standalone businesses that just ship things. So you know I kind of put this on three levels. I put traditional linear business models that to me is checkers. It’s relatively straightforward. We know how a consulting firm works. We know how a factory works, a retailer. Next level of chess. That’s… Platform business models, network based, more user groups, more complicated, whether you’re the orchestrator or you’re participating in one, okay. And then next level up is 3D chess and that’s when we start talking about ecosystem strategy, which is, you know, it’s really complicated. This is Apple versus Google versus Epic Games versus Tencent. where the connections are so complicated and there’s so many businesses going on, that it’s really difficult to sort of figure out the strategy for those businesses. And people are working on this. There’s some authors that are taking this part, not authors, but professors and other people. I generally don’t talk about that too much because I think it’s such a small number of companies and it’s so difficult. I prefer to stay at the platform level, the chess level. You know five types of platform business models marketplace platform payment platform innovation and audience builder platform learning platform and coordination platform And by looking at it at that way I can start to take apart the components and even when it starts to get bigger Like when you can start to add one platform to another platform to another platform At least you can see the component parts in a company like Alibaba tencent You can kind of see the moving pieces at that level. So it’s still sort of my favorite approach. But within that approach, I’ve laid out five platform business models, and I’ve then broken them down into subtypes. We talked a lot about marketplace platforms for services, Airbnb, Ctrip, DD, Uber, Airbnb for products, Taobao, Shopee, Mercado Libre. And then within that, we can talk about is it local? Is it national? Is it international? Airbnb is a global marketplace for services, very powerful business model. DD is a commoditized local services marketplace. Turns out not that powerful. Are the services commodities? Are they differentiated services? Are they products? Are they digital goods? We’ve been doing this for several years. We can break. that type of platform business model, a marketplace, into lots of pieces. Take it apart, subtypes. It’s like saying, look, these are all big cats. A marketplace business model is like big cats. But there’s tigers and there’s lions and there’s pumas and cheetahs and it’s different types. Now, in the last couple of months, I’ve been sort of digging into the enterprise side of things, talking a lot about tuya, medallia, snowflake. the cloud companies, Kingsoft Cloud. You know, there’s interesting stuff going on on the B2B side, especially in Asia, which is pretty far behind the West in this regard. And trying to sort of, you know, take that apart. And I’ve been kind of realizing, look, you know, most all of these businesses would be digital platforms under what I’ve been saying is the coordination and standardization platform. So one of the five types, payment, marketplace, learning, innovation, and coordination. But I’ve never really teased out different types of coordination platforms the way I have from marketplaces. So that was kind of the goal for today, is to tease those out and sort of talk about, one, which are more powerful than others, which have different dynamics than others, and getting to the hierarchies of control, which ones are really in charge. So the four types that I’ve been thinking a lot about are basically communications, Slack, Zoom. Yes, these are enterprise coordination platforms, but they’re overwhelmingly in their interaction type. It’s about communication. Number two, data intelligence. This is kind of what Snowflake is doing, what Confluent is doing. Whenever you hear companies talk about data ecosystems, which is kind of a big deal right now. Usually what they’re talking about is consolidating data into one place from all the sources, giving one sort of transparent view of information, and then doing a lot of real-time analysis, whether that’s AI or more traditional data analytics. But that’s the heart of what they do. And that’s, I think, what Snowflake is doing. And then that feeds into other things. Number three would be team projects. this would be more hands-on complicated workflows. So if a bunch of people are getting together on GitHub to write some new software, you mean you’ve got a fairly advanced team, or let’s say something simple like you’re building a building and you have a platform you all meet on to discuss the latest design, the artistic aspects, the structural engineer. the architect. That’s how you would do a complicated project. This type of workflow, you’d put it there. But on a team project, it usually has a beginning and an end. It could be tools for creating animated movies. It could be creating the designs for buildings, engineering, things like that. Generally, more manual, hands-on, more complicated. Number four is what I’m calling sort of operational automation. This is when parts of your business start to communicate. Once you get data intelligence, once you get data consolidated, everything becomes smarter. You’re tracking everything. What that tends to lead into almost immediately is let’s start to automate certain decisions. So the data feeds in. The algorithms check the data. All the pricing for all the items in our store is decided on, and it is immediately changed without humans. So when you get the data, what I’ve been calling the digital core, once you get that in place, you can begin to automate certain workflows. Now, you could call that an interaction between various user groups. The difference is these user groups are not human. I’ve been bringing this up over the last couple months when we talk about platform business models as enablers of interactions. I never said the users had to be human. We always assume they do. They are. They don’t have to be. We can see digital agents engaging with each other, and this is what facilitates. We could see, I think we will see this, we will see marketplaces between companies and their supply chain where it’s not a person in the purchasing office calling someone or going through a website and say, we want to buy a bunch of desks. It could be one digital agent, an AI, an algorithm at the buyer communicating with a digital agent at the seller. And that’s how it happens. So as we get to more operational automation, I think we’re gonna see this. So those are sort of my four. Communication, data intelligence, team projects, operational automation. And just like marketplaces, then you sort of start asking questions and tease them apart. So if we go back to Snowflake, and they have some pretty interesting language on how they sort of describe themselves, but here’s the terms they’re gonna say quite a lot. If you read their 10Ks and stuff, you’ll see these… verbs over and over. We are in the business of storing data and they’re often called a data warehousing as a service company, but their real core use case or at least their first initial use case is always about storage consolidation of your data. So storing, then you hear the term standardizing. We’re storing and we’re standardizing the data. This in practice actually means kind of a lot of data engineering stuff like that. You have to remove errors. You have to do it cheaply. You can’t just dump data in a big bucket. It’s not, I mean, you have to sort of segment it and label it and standardize it so it’s searchable and things like that. It can be quite a lot of work. Consolidating of data. This is when you hear terms like data lakes. We’re putting it all into one location. So not just storing it, but we’re consolidating. The phrase that the CEO of Snowflake uses a lot is. a single source of the truth where everyone in the company can now have access to one standardized set of information. That’s kind of your single source of the truth. Other verbs sharing and discovering. So everyone can see this. There’s a lot of discovery looking on and you can share information from one part of the organization to another part of the organization and often outside of the organization to say a supplier, things like that. There’s a lot of governance in that about what can be shared and when. And the last word you kind of hear verb wise is analyzing. You know, we got the data in one place. Let’s immediately start running data analytics. Let’s immediately start doing AI. And this can all happen slowly. Like something like, let’s say a Starbucks, you gather all the data, you store it, you standardize it, you consolidate it. There’s some sharing between you and the people that produce your cups and whatever. And there’s analysis, but it’s not minute by minute. You know, it happens over days and weeks. I mean, your average Starbucks outlet doesn’t change minute by minute. And they don’t make dramatically different purchasing orders or marketing or pricing. Then, you know, it’s not at that speed. But if we were to look at something like a large retailer, like a Walmart or an Amazon, I mean, they’re gonna be very dynamic. They’re gonna be looking at, you know, weather patterns, traffic, how people are behaving in city by city, they’re gonna adjust pricing all the time. They’re gonna update their inventory all the time because if you’re gonna promise 12 hour delivery, you’ve gotta have pretty good projections on what people are gonna buy 12 hours from now, you gotta have the inventory close to them that matches that projected demand, much more dynamic. But when we kinda look at how they describe themselves, all of that One, it should jump up on your radar of, look, this is clearly a coordination, collaboration, and standardization platform business model. That is clearly what they’re doing. It’s not a marketplace, it’s not payment, it’s not innovation, although they have a separate platform for that. This is all about coordination and collaboration. That should jump out at you. And then you say, okay, if you buy my four types of these, and I’ll put the list of those four in the show notes. Data intelligence number two should jump out. I mean, isn’t that really what Snowflake is very specifically doing? We are in the coordination and collaboration and standardization business between parties, between users, and our focus is data and the intelligence that we can derive from it. We’re not really doing like operations, although this can feed into operations, but they don’t really talk about that very much. It’s not about communication and calls and video chats and emails. No, it’s about the data and the intelligence that comes from. So, I mean, Snowflake really jumps out at me as this is a pure breed data intelligence CCS platform, you know, coordination, collaboration and standardization. OK, so that’s kind of, you know, how I start to take that one apart. And that’s really where they’re beginning. I think the other thing that makes Snowflake a little confusing is, okay, if you buy my argument, that look, it’s not a data ecosystem. It’s not, you know, I think my model is much cleaner view of how to view this. This is an interactions based business and the interactions are happening between users. Okay, who are the users? And this is why I think Snowflake gets a little confusing because when they mentioned their key partners, They’ll talk about their corporate clients. These are companies that use their product and this can be individuals within a company. It can be teams within a company and it can be the company itself. Okay. They’ll talk about corporate partners. This could be users outside of a company such as the supply chain, the suppliers, customers. So, if we look at, you know, It’s almost like we can draw a line down and say, look, what platform business models really do is they’re a network-based business model that is in the business of lowering the Coasean transaction coordination costs. That’s sort of Ronald Coase, you know, when you go out into the market, I’ve talked about this before, when you go out into the market. you can do things directly yourself as a company or you can use the market and just buy it as you need it. You can hire an accountant full-time within your company or you can just contract accounting in the market when you need it. Why do companies make those decisions? Well, they decide whether to do something themselves versus do it through marketplace based on the transaction costs, which includes searching, negotiating, quality assessment, information asymmetry and other things. If the transaction costs also called… coordination costs by the way, are high, then the company tends to do it in-house. We hire an accountant full-time. If they’re low, we just do it as we need it. And that could be the same for producing an item. Do we buy the hubcaps for the cars we’re making in the marketplace or is it very difficult and we do it in-house? Elon Musk does most everything in-house at Tesla. But traditional auto companies do everything through their supply chain partners. Okay Those, what they call, CoSien transaction costs, those CoSien coordination costs, we talk about those in terms of outside a business. But you can also have those inside a business. How well does your marketing department deal with your R&D department? How does your product department that makes iPads deal with your product department that makes iPhones? And one of the reasons economies of scale don’t go on forever is as you get bigger, you get certain advantages of scale. I’ve talked about these a lot, but you also get disadvantages of scale. One of the disadvantages of scale is you get more complexity, you get bureaucracy, you get a lot of self-interest, and you get an increase in the internal coordination costs. It is very hard for General Electric to coordinate all the activities happening in that company. in a way that a small company, everyone pretty knows what everyone else is doing. Well, that’s kind of the same idea of these CoSien coordination costs. So a company like Snowflake, its primary use case is focused on the same transaction coordination costs within a company, not external to a company like going out into a marketplace. A marketplace platform business model like Taobao and Shopee, they are focused on the external. coordination costs. Buyer dealing with the shopper halfway across the country. So the primary use case is we’re going to address internal coordination costs, bring them down in the area of data, storing, sharing, intelligence, all of that. Now second to that, they can also extend between Huda Maki, you know, they make packages and things like that. It’s a Swedish company, I think you know, they supply a lot of the cups and things like that to Starbucks Okay, you can start to over you can start to have those two companies work together in terms of data intelligence So this this data intelligence coordination Business, which is what snowflake does yes. It’s mostly focused on internal but it can also be between companies and It can increasingly be at the ecosystem level If everyone in the entire industry is using the same standards for how to assess and manage data, that starts to have additional benefits. So there’s almost three levels here that we can hit with this business model. Anyways, I think part of that does, I think most of that, which is why I like this business model so much, I don’t think it comes across in the way they talk about their business or a data ecosystem. And who the users are, that this is the interaction that we’re facilitating. And the users that I describe, and I’ll put my slide for this in the notes, is internal providers and users of data and external providers and users of data. These are the user groups where you are facilitating interactions for in the area of coordination, specifically data and intelligence from that. Okay. That’s a lot of me talking about theory. It’s easier when I have graphics, but I’ll put the main graphic in the notes. And that brings us to the sort of second concept for today. The main concept for today is the four types of coordination platforms, CCS platforms. The other one is hierarchies of control. When we talk about this on the B2C side, which I’ve talked about before, it’s pretty clear. Like, okay, we have… the iPhone. The iPhone is a platform business model. It is a network-based business model. What does the iPhone really do? It connects me to you and you to everybody else. You know, it puts these devices in people’s pockets and we all become nodes in a network. And the foundation of that is Apple in this case, or you could say Android. Okay. But then on top of that, we have other networks that have been built, like let’s say Facebook, which is a social network. Again, it’s about connections between people. Those are your nodes and interactions are social and transactional in many cases. But that network-based business model, Facebook sits on top of the iPhone, which is another network-based business model. So who’s kind of in charge? And… For the B2C side, the mobile side, the PC side, you see networks built on top of networks built on top of networks. But it’s pretty clear who the hierarchy of control favors. Facebook is actually an exception. In almost all cases, Android and Apple can pretty much ban any apps they want on their system. And the apps can’t do a single thing about it. So that’s Parler, the, you know, counter to Twitter that Google and Apple both banned. It’s just gone. Well, it’s back now, but it was gone for a while. You know, that is an exercise in the power of the hierarchy. And you can kind of tell who’s in charge. There’s a couple gray areas where it’s on, there’s sort of an ongoing negotiation like Facebook versus Apple. Like there is a bit of an ongoing thing there. If Apple were to ban Facebook, as an app, it would actually annoy consumers greatly. And Apple probably would never do that. Facebook is, they’re stronger than Facebook, but Facebook is strong as well. But you see an ongoing tug of war between literally Tim Cook and Mark Zuckerberg, because I don’t think they like each other very much. And Apple will reset what you can do in terms of data tracking on the iPhone and Zuckerberg will go online and blast him. You can see that sort of. hierarchy of control sort of playing out in real time between them. And you see that with Apple and Google and Facebook. Payment platforms tend to be quite powerful. You see an ongoing dynamic between Stripe and PayPal and MasterCard and Visa. And every now and then, you know, these banking networks, most of the digital mobile payment platforms use the banking network. And every now and then those companies, the masters at Card and Visa will tell these companies, you need to ban someone and Stripe and PayPal and Patreon will have to do it. So there’s this interesting hierarchy, but we kind of know how it works in B2C, more or less. I think people underestimate how powerful like MasterCard and Visa are, generally speaking. Okay, but when we move to the enterprise side, B2B. it’s really like more complicated when you think about the hierarchy of control. Who really controls, you know, let’s say a major company like Starbucks. Let’s say they put in a Microsoft, you know, they’ve got a big ERP system. They’ve got a big database. They’ve probably got Microsoft. They’ve probably got Oracle. They probably have in there. Maybe some of their employees are using Slack, but Zoom is also in there. I mean, there’s a lot of players sort of in their enterprise system. And it’s kind of a question of who’s really at the top of the pyramid and who’s not. And who can force, you know, who ends up kind of working for who or obeying who. Traditionally Oracle,, Microsoft, you know, those companies have been very powerful historically. But there’s a lot of interesting stuff happening right now. I think Snowflake is interesting. I mean, I would have said three years ago, the companies I’m really paying attention to and the hierarchies of control of B2B enterprise software are AWS, Azure, and Google Cloud, because most of these companies are moving from on-premise to cloud, or at least a hybrid to run their operations. And those companies are in a very good position to start adding features and services and become in many ways the operating system for these companies. You know, they are a major threat to them, which is why it was so interesting when Microsoft sort of, you know, the fact that Microsoft has Teams, which is great, it has Office, it has Enterprise, and it has Azure, that is, I think I say that wrong, Azure. I always say Azure. That’s a really powerful play in terms of who’s in control within the major companies. Okay, but then you see a company like Snowflake come along and they could really be very powerful within that because in the hierarchy of control, whoever controls the data, you know, that is like the nervous system of a business now. They could be very powerful within that. So like the two… companies I’m kind of looking as like maybe major players that are going to show up at the top of the hierarchy. On the B2B side it’s like Snowflake and there’s a couple others and on the B2C side it’s Epic Games where the gaming companies are becoming so powerful and as everything sort of becomes gaming like and the metaverse starts to take shape and then you look at where Epic Games is positioned and you’re like wow I mean and you can see them basically picking a fight with with Apple. about their payment percentage. So anyways, that’s kind of the other idea for today and just to sort of keep in mind that like when you look at the B2B enterprise side, hierarchy of control, it’s a lot less clear to me who’s really running the show. Is it about what the more critical function is? Maybe. Is it about who has the deepest switching costs? Yeah, you may have a more important function, but we are locked into this company, which is what ERP has traditionally been. And that’s why I’ve never been totally optimistic about companies like Zoom. Because Zoom, I’m always like, yeah, I like Zoom. I think it’s a great product. They seem to be very low in the hierarchy of control. And it looks to me like they could be commoditized very quickly. So I was always looking for them to sort of build switching costs, but we’ll see. Okay, last topic for today, which is Confluent. Really kind of an interesting company. You know, it’s an enterprise, I mean, it’s basically an enterprise operating system. So, I mean, we’re in the same space. And if you read their description of their business, what they’ll talk about is like, we provide data tools. Data tools. that, and the phrase they use is data in motion. We put your data in motion. Instead of it being stored in one location that’s difficult to access, we’ve sort of built a system where the data’s always moving and you get a consolidation of your data. You get greater transparency. This becomes the central nervous system of your business. And from then you get a couple benefits. Number one, you get more data-driven improvements and innovation for your customers. So you feed that data into the customer experience and you continually improve and improve. The other one is you get this data-driven improvements and innovation in the back office, which is operational efficiency and things like that. So basically the story sounds a lot like snowflake. I just think that the language is not as good, the present, you know, it’s not as… compelling of a model, it’s much more limited. So why are they interesting? Well, they’re interesting because they have one thing that Snowflake does not, and that they’re based on open source. I mean, they are based on Apache, which was actually created by the founders of Confluent. It’s an open source enterprise system for basically data. And that kind of makes, I mean, open source is a really powerful tool. Because when we go back to say like 1992, and we look at operating systems for businesses back then, we saw two models. We saw the Microsoft model, which was all about proprietary software. And then we saw Red Hat and some others, which was open source based on Linux. So you- For 30 years we’ve seen two different sort of models for enterprise software, proprietary software versus open soft software. People don’t talk about it, but most companies, well not most, lots and lots of companies use both. Open source is a really powerful mechanism. It’s complicated, it’s difficult, it’s very hard to manage. But I mean, don’t kid yourself, it is a powerful, powerful mechanism if you can get it to work. And so, when Confluent starts talking about, we’re doing data and all this stuff, and it sounds very similar to Snowflake, but then they immediately say, but we’re all about open source in Apache, Kafka. And you’re like, it’s the same scenario we saw. If Snowflake is proprietary software that they control, this is the open source analog that we saw the same situation kind of 30 years ago. and Red Hat and these companies, they’ve been around forever. So Red Hat was built on the Linux kernel and it looks like Confluent is built on the Apache kernel. So that’s a really interesting sort of complication to all of this. And I don’t know how it’s gonna play out but I’m keeping a close eye on it. For those of you who aren’t familiar, I’ve never really talked about open source on this. I mean, it’s a whole world. I’ll give you some quotes from This is from Confluent, but basically, it’s a community-powered approach for developing reliable, high-performing operating systems. So the kernel that everyone builds upon, it’s developed openly. The code is well-known, you can see it. It’s totally transparent. Microsoft kind of let you see all their code. And that has some really interesting benefits. You get the collective input of developers all over the world. You get their resources. You get their effort. You get their knowledge. And you get this really global community that collectively sort of develops, maintains, and continually enhances the software. Now, that’s really hard to get going. But if you can get something like that going, I mean, compare that to hiring five or 10,000 software engineers in Redmond or something. I mean, it is just a much more powerful tool to do anything. And anyone can see the source code, you can inspect it, you can suggest change it, and then you can take it, you can customize it, copy it, and distribute it yourself. So everybody can play with it. So it’s really… two to three things happening. One, it’s the fact that you’re using open source. Two, it’s the fact that you’re using generally a general public license that lets people adapt and copy and republish the source code, but then you add, you know, you make some money by doing services or adding proprietary features on Dopadat that you charge license for. But the main software is open source. And you know, you can see that there’s just a lot of value to a business for that. You know, your typical big business doesn’t have to sort of call Microsoft or whoever every time they want to make a change or customize it. You know, they can do it themselves because they have their own code. And every time there’s a bug problem or a virus or something new, you know, because the source code is open, everyone can look at it and find the problem very, very quickly as opposed to waiting for some, you know, proprietary company to issue a patch. And you don’t really know what’s going on. So the openness, the transparency, the ability to customize it to yourself, all of that is really, really powerful. So there’s this idea of like, well, what if we take the open source engine and we combine that with data intelligence, which is really what Snowflake does. And that kind of gets you confluent. It’s potentially a big deal. The trick with open source, of course, is it gives and it takes away. You know, it’s for every amazing thing that it gives you, like flexibility and all these developers who, you know, check everything. You got to keep the developers happy because, you know, they’re not getting paid. So you can’t tell them what to do. You don’t own it. If you start trying to charge money as a company, you can make them annoyed. You know, there’s a lot of problems with trying to keep this community going and they limit you in tremendous ways. but it’s also powerful. So it’s kind of this interesting thing to watch. Anyway, that’s kind of my, I’m not gonna go too much into that, but that’s kind of how I look at this. Like, you know, the takeaways for this would be think in terms of collaboration platforms and think about the type of interaction between what types of user groups. And the more you can be specific about the users, which I’ve tried to do here, I think it becomes clear. Okay, then within collaboration, what type of collaboration? Is it communication? Is it data intelligence? Is it operations? Is it team project? Well, okay, now within, I think these two companies, we have a clear case of data intelligence. There’s a lot going on, but then you have these sort of two contrasting approaches. One is the open source and one is the proprietary. And we kind of said, you know, we could, everything I just told you about Snowflake versus Confluent, we could have said the same thing about Microsoft versus Red Hat in 1995. And both companies did well. I mean, Microsoft became Microsoft, of course, but Red Hat ended up getting acquired by IBM two years ago for 32 or $34 billion. So both companies survived 30 years, so they can both work. Anyways, that’s kind of my takeaway from this. I’ll go a bit more into Red Hat and some others in the next part, either in the articles or in the podcast, but yeah. It’s a really kind of fascinating question. And I think that’s enough theory for today. The two takeaways, hierarchies of control and coordination platforms or CCS platforms. As for me, I literally just got back to Bangkok about an hour ago. I was actually recording this in Hua Hin and my microphone broke in the middle. So if you’ve noticed a change in the audio quality halfway, that was the moment my microphone broke last night. Anyways, just got back was looking around rented a scooter went up in the mountains a bit was you know playing with the idea of looking at locations around there maybe for Some sort of farm or something. I don’t know. I really do like this idea We’ll see if it works out, but it was kind of a fun way to spend a weekend if nothing else Yeah, but that’s pretty much it. I’m looking forward to spider-man which comes out here in Thailand this weekend so a couple I think a week behind the West so I’m kind of looking for that because I like the Marvel movies and They really haven’t been very good. The sort of MCU’s phase four has really been disappointing. Like I loved the, like the Avengers end game and all that was great. And then it just, in my opinion, really cratered. So hopefully this is, you know, the resurgence. So I’ve been looking forward to that. So that’s on my week. But other than that, I hope everyone is doing well. Hope everyone’s staying safe and I will talk to you next week. Bye bye.


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