Huawei’s New AI Tech Stack and “All Intelligence” Strategy (Tech Strategy – Podcast 180)

This week’s podcast is about Huawei’s new AI tech stack, which is impressive in its ability to provide end-to-end solutions. Also their new “All Intelligence” Strategy.

You can listen to this podcast here, which has the slides and graphics mentioned. Also available at iTunes and Google Podcasts.

If you’re interested in talking digital strategy and transformation for your business, contact us at TechMoat Consulting.


Related articles:

From the Concept Library, concepts for this article are:

  • Artificial Intelligence
  • Cloud
  • Digital and AI Transformation

From the Company Library, companies for this article are:

  • Huawei

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Welcome welcome everybody. My name is Jeff Towson and this is the tech strategy podcast from techmo consulting Where we analyze the best digital businesses of the US China and Asia and the topic for today Huawei’s AI tech stack and new all intelligence strategy Now I spent last week in Shanghai I went to the really my favorite sort of one of my favorite events definitely my favorite Huawei event which is their Huawei Connect Conference, which is put on by their, as far as I can tell, it’s put on by their enterprise business group. So that’s kind of where I live is, digital tools resulting in transformation of businesses, right, that sort of intersection. So that’s kind of what this was about, huge event, absolutely huge event. I’ll give you a little bit of the details. But out of that has come their new AI strategy. in fairly good detail. So those of you who are subscribers, I sent you an article today, I’m gonna send you another one tomorrow. These are pretty dense. I mean, there’s no way around it. These, I mean, basically it’s slide after slide showing the AI tech stack. I think it’s incredibly important, but it is dense. I mean, I would get a cup of coffee, take some time. It’s really worth your time, because this is what… they are providing to businesses in China and internationally to help them not just become digital businesses, not just digital transformation, but AI transformation. So this is what I think a lot of the internal tech of businesses are gonna look like. Anyways, very important, that’s going out. One article went out, the next one’s going out tomorrow morning. And that’s kind of what I wanna talk about here as well. So that will be the topic for today. Let’s see, any housekeeping stuff? Nope, standard stuff. We have the China retail tech tour in a couple months, going out to go to some of the sort of leading tech and retail. So it’s kind of e-commerce retail, CPG, that sort of stuff in China. That’s gonna be awesome. If you’re interested in that, you can reach out to me or go to You’ll see all the details there. And I think that’s it. Standard disclaimer, nothing in this podcast or in my writing is investment advice. The numbers and information from any guests may be incorrect. The views 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. Now, before I get to the standard sort of key concepts for today, I wanted to try something a little different, which I’m sort of calling… 30 seconds in tech news. And there’s some important stuff happened in the last week and I want to at least put that on the radar and maybe give my opinion on it quickly, quickly. Number one, Daniel Jang, who is going from group CEO of Alibaba to head of cloud, has stepped out of that role, which was a surprise. And in theory, Eddie Wu is gonna take over. Eddie Wu is now the group CEO. Joe Tsai is the chairman and Daniel Jang was going from group CEO to cloud CEO. And apparently not. He’s not doing that. And Eddie Wood is going in to take that role as well, but it’s pretty clear this is temporary for him. So not clear what happened. Something clearly happened. This is obviously not the plan. Um, who knows what could be politics, maybe health issue. Who knows what it is. But that’s definitely a bit of a, I mean, this is their biggest growth engine going forward is cloud. Cloud and domestic e-commerce are the biggest guns. And now they don’t have a clear CEO for that for the long term, which is really what we want. So anyways, generally not good, but we don’t know what’s going on totally yet. Big news, second one, Tsai Niao, this is also Alibaba. Tsai Niao is going public in Hong Kong. Okay, interesting. gonna read that for sure but there was a quote in the news release which was surprising which was they said only thirty percent this was impressed reports about the idea only thirty percent of their revenues coming from alibaba now that’s not what i have been told and i thought it was like ninety percent if it turns out they’re going public in thirty percent of the revenues from Keep in mind JD Logistics is down to about 50% of their revenue coming from JD Proper. But that’s really not what I was told, so maybe I was wrong. It makes more sense if you’re gonna go public, it’s weird to go public when 90% of your revenue comes from your parent company. But we’ll see what the truth is. That’s supposed to be any time. And last issue, and then I’ll move on. Panda, which is Food Panda, sorry. which is delivery here out of Germany. There’s rumors that they’re talking about selling a lot of their food panda in Southeast Asia to grab. That would be very good news for grab. You know, this is the game of food deliveries I’ve talked about many times is economies of scale, but not purchasing power. It’s usually economies of scale in fixed costs, which are IT, which tend to be centralized. And then it’s economies of scale based on geographic density. Well, this would dramatically increase their geographic density in some markets. Well, maybe significantly. Um, you know, e-commerce based on products like Lazada, you get economies of scale and logistics and IT. Well, we don’t get that fixed cost in logistics in food delivery. Really. We get it in IT. The other one we get is economies of scale and geographic density, uh, which is more writers, more orders, um, per, you know, city area. Anyways, this would play into that. MNA is a good way to get there. If it’s true, that’s really smart, whoever did that. Okay, with that, let me get into the topic. I think that was about 30 seconds. Nope, more than 30 seconds. Okay, sorry, I’ll be faster next time. All right, key concepts for today. So we’re talking about, really about the AI tech stack that is emerging, and the key concepts here are obviously generative AI, which is not really a concept, it’s a topic. And then the larger topic, which is where I spend a huge amount of my time, which is digital and AI transformation of businesses. We used to say digital transformation of businesses. Now we talk about digital and AI because it turns out it’s pretty different at almost every level. So this would go under digital and AI transformation. Those are not really concepts or tools to use, but big categories of thinking and you can find those in the concept library listed. With that, let me get into the content. Now. Okay, so I flew out to Shanghai, I love going to Shanghai. Stayed in Lujiazui, I love Lujiazui. My office used to be right there. I used to have this great view from my office to the river. I would watch the barges go by all day long. They light them up at night, which is great. The area’s really developed, this was a good seven years ago. Fantastic park, and that’s where we’re kinda staying. And then the Shanghai Expo, which is where the Huawei Connect Conference was. I mean, this tells you kinda how old I am. I remember when the Shanghai exposition happened in 2010. And it was a major, it was a huge event. The numbers were unbelievable. Everybody talked about it forever. You know, the millions of visitors they were getting. It was actually mentioned in the business case for Shanghai Disneyland. Because the question was, will people fly in from around China to go to Disneyland in Shanghai? And then everyone pointed to the Expo numbers. And you know, there was basically a huge piece of land along the river just south of Lujiazui. On the east side, lots of pavilions. They eventually ripped all those down, except for a handful of them, like the China one. I think the Japan one stayed. Saudi Arabia had a good pavilion. And then, you know, they’ve turned it into a major expo center, and then the real estate developers went crazy. My old jokes, my assistant, it used to be if you wanted to go to the pavilions. Each country had a pavilion like Saudi had one, Japan had one, and they had these huge lines. I mean, you would have to wait like for the popular ones like China and Saudi Arabia, you had to wait for hours to get in. And my assistant, she went down one day to take the day off to go to all the pavilions and she came back at the end of the day and I said, well, how many did you see? And she goes, oh, well, all of them. I was like, how in the world did you see all of them? And I finally got it out of her that she put on a fake pregnancy belly. and just walked to the front of the line for each of the pavilions which is, I don’t know, it’s kind of a China thing. It’s so clever, a little dodgy, but you gotta admit it’s clever. Anyways, that’s how she did. Anyway, so they’ve reclaimed all the land and that’s where the Huawei Connect conference is which basically means it is a eight-story building just full of conference rooms and grand ballrooms and they took the whole building as far as I can tell. And there were seminars on everything, education, banking, financial service, industrial, AI. I mean, you could learn any of the problems, you can’t go to all of them, but I mean, there was unbelievable amount of content and people flew in from everywhere. I mean, there were tons of dudes from Africa showing up, Latin America, Brazil, the Sao Paulo government. I mean, it was crazy. Anyway, so it’s a huge event, a lot of fun. The opening thing was Sabrina Meng, the CFO and now rotating chairwoman, gave her talk. And, I mean, basically it was the all-intelligent strategy and laying out what they’re doing. And the whole thing is a pretty much a pitch to businesses. Here are your problems as they relate to AI. Here are the opportunities this major event creates for you. Here are the risks it creates for you. Here are the problems you’re going to have to become an intelligent enterprise. And here’s our solutions and services that solve those problems, right? So pretty good business pitch really, coming from the enterprise group. And then the cloud business is usually underneath that or ancillary to it. Okay, so Sabrina Monk gives a talk. It was good. She didn’t say anything about their new Mate 60 phone, which everyone was hoping that she would. She didn’t, they had their big product launch day four days later, also didn’t say much. about that so who knows what’s going on with that. But here’s the basic my takeaways. Alright, so Huawei’s has these strategies they announce every four to five years like 2000, I’m going to say 1413, they announced their all IP strategy, which was basically businesses all need to become digital. They need to digitize and they need to connect. They called that the all IP. strategy that was followed by the all cloud strategy which was everything’s moving from enterprise to cloud or hybrid well this is the all intelligence strategy which is obviously the next evolution of cloud like not only moving all your operations and your digitized operations to the cloud and but we’re also making it intelligent which is a major change and within that story their pitch and their pretty good differentiated value proposition is basically end to end. We’re just not a cloud company like AWS or Azure. We’re not just a connectivity service like Ericsson. No, we do everything. We do everything from devices in your hand, edge computing, IoT sensors, to connectivity to cloud. The whole thing is integrated. And that pitch is, I guess, pretty compelling in certain industries such as, say, transportation, financial services, government, and then there’s security and trust and compatibility that cuts across the whole integrated solution. That’s been their pitch for quite a long time. Well, this is basically the intelligence strategy is almost a new version of this, where instead of talking about edge computing, devices in your hand, IoT sensors, they talk about intelligent perception. That was their new terminology, which is basically, look, this is gonna be a lot of sensing. It’s not just like you have a device in your hand that connects with your office or whatever. We have sensors everywhere, cameras, radar, that are all gonna be gathering data and doing things with that data and integrating it. So, One of the issues you come to when you want to go intelligent as opposed to digitized is the data gets more complicated. This is all, not all, mostly unstructured data. It’s not things in spreadsheets from the ERP system. It is radar and sensors and, you know, little devices all over the factory and cameras on the street. All of that data is coming in a very unstructured way. So you need different systems which they’re calling intelligent perception. to sort of slice it, standardize it, share it in a way. And AI requires an unbelievable amount of data. So it’s a huge amount of data that’s unstructured that really has to be handled a different way in many, most cases. That gets you to intelligent connectivity. It’s not just edge device to connectivity, it’s intelligent connectivity, their phrase, because, you know. you’ve got to share all this data and collaborate around this data to work on it. Well, again, that’s more complicated when the data is messy, which is the nature of AI. How do you collaborate and how do you move these sort of the data upstream to the cloud where it’s going to be understood and then down from the cloud back to devices automation where you can show intention and automation? So that connectivity piece is also kind of more complicated. And then we get up to the cloud and we’re basically talking about intelligent computing, again, their phrase, because it turns out AI needs a tremendous amount of computing power. And David Wang and actually Sabrina said the same thing. They basically said this is the biggest bottleneck is the pure amount of computing power you need to do AI is pretty stunning. And. So obviously their cloud services become intelligent computing, which is talking about data centers, cloud, how much is gonna be in-house, and you do this as a service flexible as you need, that’s the benefit of cloud. But yeah, that’s a big deal. So they kind of position it all this way that, hey, you’ve got all these issues, challenges, and going intelligent as a business is very complicated. It’s a whole new thing, right? And then they’ve got solutions for that. So that’s the basic pitch of what we would call, you know, all their all intelligence strategy. But it’s really what we’re talking about is digital versus AI transformation of businesses. And the AI transformation is significantly different than what we were talking about two years ago when we were talking about digital transformation. It wasn’t this messy. It didn’t have this huge amount of crazy data. it wasn’t running these foundation AI models. It didn’t require all of this compute power all the time. You know, you gotta have that data flowing in huge amounts to train the models. So you have to develop your own models, but then you have to use the models and then you have to retrain them often frequently. So it’s a bit of a different process and therefore it has different architecture and hence the AI tech stack, which is. kind of what I’ll talk about next. All right, now this is where things are gonna get a bit quite a lot more dense, and this is the stuff I’m sending out to subscribers tomorrow. I’m gonna basically send you 15 slides that are just tech architecture mapped out. It’s, and they’re complicated, no doubt. It’s really worth your time going through them. Get a big cup of coffee and like, you know. spend time looking at them because we’re really looking at in many ways is going to be the new tech architecture going forward. And I think these are great. I think they’re mapped out really, really well. All right. Now, what is Huawei’s strategy? Huawei always has a, I think I’ve told this story before. Like I asked the chairman years ago, Guo Ping, I thought I had a really good question because I wrote up a bunch of questions and my big question was, you know, of all the use cases you can see with digital transformation for enterprise, which one do you think is the most compelling? Which industry, what business, what use case? And I thought I was gonna get a good answer and he basically said, I don’t know. Like we don’t do that. We provide the infrastructure, the digital infrastructure that others build on. But we don’t do that level. And that’s pretty much their same position for what they’re doing with intelligence. They’re gonna try and be the key provider for foundational technology for intelligence. So they’re gonna bring that foundational technology that others bring into their companies to make themselves more intelligent. and then use that in lots of ways, but that’s not really something they’re gonna do directly. And that’s why so much of their stuff is open source. I mean, it’s really very different than say open AI. I mean, they really sort of do this open source of what they’re doing. They try and build in sort of as much security and trust, so everyone, and they want developers and businesses to sort of take their models and build on them. It’s a pretty. I think it’s a very good approach. Anyways, okay. So as mentioned, you know, if you have a company, factory, bank, hospital, retailer, and you want to do AI transformation as opposed to digital transformation, although they’re kind of mixed together, you know, the limits you’re gonna have, the challenges you’re gonna have are, I just mentioned computing power. You’re gonna need a ton more of… power at your fingertips in a flexible way, but also it has to be kind of specialized. You know, the data centers you would build to train and run AI models is different. And they have their whole ascend computing architecture. And they, at the same event, they announced their new super cluster ascend chip and data center. They’re building data centers like in Thailand and across Southeast Asia for companies. And also if you want to. run it from the cloud. So one is the computing power. Number two would be sort of the data, which I mentioned that, you know, AI requires a ton of it, has to be, it’s messy, it’s unstructured, it’s images and videos, not spreadsheets. The other thing about that is the data has to be industry specific. So if you’re gonna do factories for cars in Thailand or Malaysia, you need industry-specific data for that, for it to work. So that’s gonna be a problem. It’s gonna be a challenge. And then you need to be able to slice it, standardize it, tag it, share it, so others can collaborate, at the same time maintaining security. And the data is gonna move in both directions. It’s gonna come in from the bottom, the sensors, the cameras, all of that. It’s gonna go up through the connectivity layer. run through the models based on that you’re going to get prediction those are going to run mostly in the cloud and then that is going to send down orders and intention and insights the other direction so the data is flowing up and the data is flowing down The algorithms obviously are a big challenge. Everybody talks about the big generative AI models, the large language models. So that’s GBT, that’s Lama. But you have image generation models. You have scientific models now that people are building. So these large language models, then you have multimodal models, which combine text and, let’s say, image or video. So there’s at least four or five major generative AI models everybody’s talking about. Okay, here’s the other problem with that. Those are gonna have to become industry specific as well. And you really, what you end up doing, and I’ll show this in the show notes, I’ll put one or two of the slides for the tech stack, but basically industry specific models get built on top of foundation models. That’s why it’s important that they’re open-sourced. You can adapt them customize them It can be done for an industry and everybody uses let’s say an industry specific Generative AI model or it can be done within companies that have a little more scale They can customize their own internal models but generally speaking the industry specific models sit on top of the foundation models a company like Huawei is going to provide the foundation models and the computing power and then a bunch of tools that help you build industry models and your data suite on top of that and things like that. And then it actually gets even worse because you have to go beyond industry models. You have to become more specific and build scenario-specific models where this could be everything from a specific Q&A function that you’re gonna put on your website as an e-commerce company. That would be a scenario-specific model with an industry, retail. based on a larger foundation model, which could be Chachi BT, it could be Llama. Huawei’s model is called Pangu, which is gonna be a big one. Huawei is already probably one of the top five cloud companies in the world, depending how you measure it. Baidu is big in this space. They have Paddle Paddle, which is an important one. There’s about seven or eight. What’s another one? iFlytech actually has one. Alibaba has one. Obviously Microsoft has GBT, Google, Facebook open source. There’s probably eight or nine major foundation models out there and some of them are proprietary and closed. Some of them are open source and you can do what you want, but there’ll be more. So you’ve got the algorithm problem that you have to build as a company. And then of course you have deployment. You have to actually integrate this into your workflows and start getting your staff to use this stuff and start to, well. That application deployment thing, I mean, that’s 10 years of work. It is gonna take a long time to turn the companies we know today into companies that have intelligence embedded across all of their operations. I mean, it’s exciting, but everyone talks about the data, the computing power, and the algorithms. But the application deployment, that’s what I do, right? That’s working with companies, deciding, and this is kind of what we spend most of our time doing is you work with companies and you first tell them the strategy that’s gonna help them win which basically means this is what winning looks like for you and based on that yes you could spend a lot of time doing twenty different intelligence capabilities but the truth is winning and losing is going to come down to three so when you focus strategy down that tells you what you actually have to build in the company in terms of intelligence and digital capabilities it gives you the path the critical path from where you are to winning. But in addition, there’s 10 or 20 other initiatives you gotta do, but there’s three or four that really matter. Okay, so based on sort of how I’ve teed that up, let me sort of talk about the tech stack, and I’ll put the slide in the show notes. It doesn’t show up in the iTunes, you have to click over to the website and you’ll see it there. But okay, Pengu is their foundational AI model. They’re calling that L0, which is basically in the tech stack, that is the foundation model level. And they talk about really five different models that they’re building. One is the Pangu NLP model. So that’s natural language processing. That’s your text-based model. That’s your large language model. The Pangu multimodal model, that’s combining different types of media. text, video, audio, things like that. They have something called the Pengu CV model. I’m not totally sure what that is, to tell you the truth. The prediction model, the scientific computing model, and it was at the foundation model. You can use, if you’re a business and you wanna start working with this stuff, well, you can basically access that in one of two ways. You can take those and just sort of buy it as a service. you know, on your cloud provider, which in this case would be Huawei, you can basically do model as a service, plug into their pay a fee and use the model. Or you can take that model and then use one of the basically model development tools and start customizing that to yourself, your own business needs. Okay, interesting. And it doesn’t have to be just Huawei with Pangu, you can use, you know, any other model within there, this is sort of open platform, fine. Below that level, which would be below L0, you have the data level. That’s where you’re getting all your general data, your industry data, your multilingual data, your data engineering. I mean, there’s this whole data capability. And they offer basically a suite of services for doing data engineering in a company. And several, lots of companies do this, like Snowflake does this. right, they do sort of data architecture. And, you know, Google Cloud does this, AWS does this, Azure does this. So a lot of people sort of do the data level. And then the level at the one below that would be the cloud service. So it’s sort of base level is the computing architecture. Then comes the data level, then comes the foundation level. Okay. Interesting. And you can see companies like AWS offering that. They already offered the cloud. A company like Snowflake came in and sort of specialized in the data and got there a bit ahead of companies like Azure. And now the next level they’re adding is L0, which is the foundation models. So we’re seeing most cloud companies are doing that playbook. Alibaba Cloud is doing similar. But then you move up one level to the industry models. And. That’s really interesting because that’s when you’ll get like, let’s say the Pangu government model or the Pangu finance model or the Pangu mining model or what else do we have? The Pangu automotive model, the Pangu healthcare model, the Pangu R&D model. So suddenly you’re getting L1 models that are specialized for various industries. That’s interesting. a lot of cloud companies aren’t gonna go that far down the stack. As you start to move more specialized, it helps if you have the whole end-to-end solution offered where you’re not just doing the cloud, but you’re also putting the sensors which you make and the IoT devices which you make into the factories in the automotive sector. That’s why this sort of end-to-end solution that Huawei does is actually compelling because they’re not really a software company. historically, they’re an equipment and hardware company that has moved upstream into software and data and now into AI. So you can see in certain sectors that’s gonna make more sense. Industry model and then the top level would be the scenario specific model. And that could be like, you know, for the government specific L1 model, you would have the L2 scenario specific models like city event handle. which could be like fires and calling the police and calling the ambulances. Under the finance model, it could be like financial anomaly analysis, which is something like a bank would deal with. Here’s something that might be fraud. Under the mining model, the Pengu mining model, which would be L1, they’ve already got automated mining devices that are in Shanxi. I was gonna go to this a couple months ago and see the automated. Well, It helps if you make the mining equipment and the IoT devices and the 5G connectivity that controls those things all the way up into the cloud. Suddenly, the scenario specific L2 models you can do are things like controlling drilling rigs deep in the ground because you’re putting in the control devices, the 5G connectivity, the models at this scenario level and at the mining level and so on. So you can kind of see how that makes sense. And if you’re a company, you can develop these on your own. You can customize, or you can take most of these just off the shelf and plug and play. So it’s fairly compelling. I’ll put the slide in the show notes, but it’s really worth looking at. And out of that sort of playbook, which is what I think most enterprises are gonna have to do. There’s various services and kits you can use from a company like Huawei or from others. Like, you know, you can use cloud services, you can use their model development kits that help you develop your own model. They call that model arts. You can do model hosting, because generally if you’re gonna host these models, they take a lot of computing. You can do ecosystem development, where you start working with partners and other companies and industry alliances, because sharing data. is important. There’s sort of network effects to be claimed. So there’s various services and kits for companies and developers at every level of that sort of tech stack I just sort of went through. You know, if you’re a CEO or CTO listening to this, it’s kind of like, all right, what do I do? You really need to do three things. You need to move, you start probably with data engineering. you’ve got to start getting the data architecture and ecosystem running. Because if you don’t have data, you can’t do anything. And again, you can do that in-house. You can hire consultants. You can tap into Plug and Play, which companies like Huawei and Google offer and Snowflake. OK, so you’ve got to do the data engineering piece. And this is usually where most companies I talk to start. They often work with a cloud provider, and their first projects are in data engineering and getting that going. From then, you’re probably talking about application development. Okay, what apps can we put into our company that we can start plugging those into the workflows? And again, you can do those plug and play, take standardized ones, like I just kind of announced or mentioned, or you can develop your own. So you can do application development, application sort of plug and play. And then the third one is model development. Are we gonna take sort of standard models and just use model as a service, or are we gonna start to customize them for our company? How much are we going to customize everything? Are we going to develop our own models ourselves? And the basic answer to that question is within your critical core operations, the things that make you a company, maybe not your HR system, maybe not your finance and billing system, but let’s say if you’re an e-commerce company and you’re talking about the user experience on the e-commerce site, you’ve got to do that yourself. You can’t be a competitive and innovative e-commerce company without running your own models and doing your own apps. The same way you couldn’t be a good retailer in this world and say, hey, we’re a good retailer, but we don’t run our own stores. So now you kind of gotta run the stores, that’s kind of the business. So within model development, how much you’re gonna bring in house, how much you’re gonna customize. You basically chart out the strategic capabilities and accept that you’re gonna end up doing them yourself because that is just core operations now. That’s how I would think about it. Data engineering, application development, and then model development. And there’s a sliding scale for how much you bring in-house versus how much you use an API versus how much you just plug and play. I think that’s most of what I wanted to go through. It’s, you know, as I said, it’s really worth your time to go through these tech architectures in detail and sort of get a sense for, I think this is what companies are going to look like 10 years from now. I think this is core operational structure for most companies. Everyone’s going digital and everyone’s going intelligent. And if you’re a business that hasn’t built in sort of intelligence capabilities into your business, it’s just going to be. I mean, your competitors are gonna do stuff that just looks magical to you. It’s like you have no, how can they possibly doing this? Well, they have intelligence built into the key areas of their operations, basically. So yeah, I would take a long look at the tech architecture. And I think that’s it for content. The two concepts to think about, yeah, generative AI. It’s a huge subject, not a concept, topic to. We really all have to understand this stuff. And then digital and AI transformation, super important. It’s pretty much most of what I do for a living these days. And it’s getting more complicated. And the whole idea of transformation, like you’re gonna do a transformation and you’re over, it’s not really how it works. It’s like a constant evolutionary race. Every business has to constantly evolve and evolve and evolve. And it… doesn’t really end as far as I can tell. Nah, not always, sometimes it does, but for a lot of companies it just keeps going. Anyways, those are sort of the two concepts or two topics for today. And I think that’s about it. Not much going on, I’m just working like crazy, flying around like always, having a pretty good time. Anything fun this week? Oh, yeah, here’s one recommendation. I’ve been, I started watching this TV show BattleBots. which I think everyone knows about, but me, because it’s the robots in the cage and they fight and it’s like UFC for robots and it’s just super fun. Like the fights are kind of okay, the robots smash each other fine. The part I find like endlessly entertaining is one, there’s a strategy component. Like, do you have a robot that spins a blade or has a hammer or flips you over? or flamethrower. I watched one last night where the robot had basically a cannon and it shot a projectile at the other robot which didn’t really work. So one there’s a strategy component which I kind of like. But part the I think the part that’s most fun is the contestants. Because like as far as I can tell most of the everyone’s an engineer right because they all build their bots. And I think there’s limits on what you can spend. So it’s not big companies. It all looks like two or three people came out of their garage to build their robot. And it looks like nobody has ever been to the gym. Like it’s either like 45 or 50 year old white guys with a belly. That’s your most common like picture of like an engineer. Or it’s like some 19 year old crazy person. who design something and they’re doing it. And there’s women too, but it’s definitely weighted over towards the middle-age male engineer demographic. And it’s really fun watching them all build this stuff in their garage. And yeah, that’s the part I kind of, it’s like a whole demographic you don’t really think about much. All these engineers behind the scenes. Yeah, I find it really entertaining. So I’ve been watching that like crazy. It’s pretty fun. I’m gonna watch Tombstone for those of you who follow. I’m just catching up everyone. I got seasons to catch up. So I’ve been watching that robot Tombstone with the big spinning blades. It just cuts apart the other robots. Anyways, that’s my plan for the Friday night is the match with Tombstone. Anyways, that’s it for me. Hope this is helpful and I will talk to you next week. Bye bye.


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.

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