Getting Ready to Adapt and Ride The AI Wave of Opportunity with Maarten Ectors

Welcome back to another episode of Mastering Tech Growth. I'm Mike, your host, and today we're discussing a topic that's profoundly reshaping our planet: artificial intelligence, technology influence, transforming impacts on society, and future of work. Our guest today is Martin Ectors, a multi-award-winning exponential growth innovator and strategist with a distinguished career leading groundbreaking projects at major corporations and pioneering startups. Martin is at the front end of AI, Web3, and other next-generation technologies. His experience spans from launching the world's first blockchain-based reinsurance to setting up award-winning AOT businesses and advising on major coin listings. And climate initiatives. In this episode, we'll explore how AI and other technologies is not just a tool but a catalyst for change across industries. We'll discuss its impact on the workforce, its integration into our daily lives, and how can we prepare for the future where AI enhances human capabilities. But before we jump in, into the conversation, here's a quick reminder to our listeners. If you enjoyed the show, please subscribe, leave us a comment on Apple Podcasts, Spotify, YouTube, or wherever you get your podcasts from. Your support helps us to reach more listeners and bring value and insights to our community. All right, let's get started. Martin, welcome to the show.

Thank you for having me.

Of course, it's going to be fun, Martin. So we did set the stage a little bit. So the very first question. Can you elaborate on how AI is currently reshaping traditional job roles and what does that mean to the workforce?

It depends what you do. You can see it a little bit, you can see it a lot. So if you are an Uber driver and you go to California or you go to Austin or whatever, you'll see Waymo driving around. They have just surpassed 100,000, uh, trips per week. Soon it will be millions per day, per hour. And of course, if you are a driver and then robotaxis come along, you are going to be made irrelevant. So for certain job categories, AI will just stop the, the, the whole industry. And it's very similar to like if you were a stamp vendor, or if you were like a photo, film, uh, seller or whatever. Um, a new technology comes along, people jump onto the new technology, and whoever is in the old business, uh, only very few tend to survive, uh, if any. Uh, so if you're unfortunately in that category, you will be directly impacted. Then there's the other category where like, um, you have exponential workers. So basically, if you're a lawyer and you do 5 contracts a day, um, and now with ChatGPT you could do 100 contracts an hour, then all of a sudden you're going to be able to charge less and do more than a, than a lawyer that still does manually with over template contracts. But there's no elastic market for these contracts. So, so not because you're 100 times more productive will there be 100 times more work. So what we will see in those markets is that the AI-enhanced worker will very quickly eat the market of the rest. And then there's going to be markets where AI will not that much impact. Unfortunately, the list is very short. So in a lot of other technologies, most markets weren't impacted. But in AI, yeah, if you're, if you're a hairdresser or, or, um, a dentist— although I've seen dentist robots, although I've seen hair cutting robots— the chances that, like, that's the complete thing that we're going to see that's going to be less. If you're a psychologist that like deals with like people that have anxiety about AI, chances are that you still have— are going to have work and perhaps a lot of it. So there is always like a group that is neither impacted or on the opposite, like if you are an AI scientist, you can't put down the phone. Everybody will be calling and saying, can you come and help us? So the impact can be extremely devastating to extremely lucrative, and it depends what you do.

So the majority of the, um, or not the majority, but the most affected markets is quite easy predictable. It's at least from my point of view, it's anything you do with content, right? Anything what's, what's written content. You mentioned lawyers. Yeah. Journalists. Copywriters, probably marketing, again, copywriting, anything what is text related and likely images and audio for generative AI, we're basically saying that they will be able to do more for a lot less than they're doing right now.

Of course, you have your view as a podcaster that like your industry will be most impacted, but like other people that are in other industries, if you are in transport and you see the robo taxis coming, if you are, for instance, trading, imagine AI trading. So anybody will see their industry as sort of a tsunami, an AI tsunami coming towards them. But in a lot of industries, that's exactly it. An AI tsunami is coming and it's almost reaching the shoreline.

Okay, so it's then, it's the data then. So fundamentally speaking, any job which can accumulate data and draw conclusions out of that data, that's where AI will come in and effectively change a lot.

Data is a very good starting point. Uh, if the work can be done remotely and doesn't need in-person, then, uh, you're also, uh, uh, in the center. Um, if it's very repetitive, that is a key thing. And AI is just one of the things. So, so AI by itself is, is perhaps not going to disrupt your industry, but What if we add robotics to it? So, for instance, if you think about, like, you work in a supermarket, yes, you've seen the Ocados of this world and Amazon Freshes, but you could go and be in one of their delivery centers. But then the future could be totally different. So the future could be that like we don't buy food in the same way. What if like food gets printed and 3D printed, right? Yes. And, and there's a lot of companies looking into 3D printing meat and fish that, uh, and, and some of them are working with Michelin star chefs and so on. So that could be an example of where like, yes, AI is involved in that, but it's like the 3D printing, um, that really impacts your market. Okay, then there's this completely other markets, like if you think about, um, diamond trade and jewelries, um, at the moment factory-made diamonds are made in China where like a 2-carat diamond used to cost £20,000, they now cost £20 on AliExpress. So it has nothing to do with AI, but technology will make that whole market implode because there's not a single market that can divide its prices by 1,000 and still be lucrative.

I get you. So we're effectively talking about new economic paradigms which we don't— maybe we can guess and presume just based on how things are moving, but there will be new things coming out of advancements, technology, AI, which is probably hard to digest right now because it needs a little bit of time. But it will just change how we do things, you know, consuming food, buying food as a good example, which will put some people out of work and probably create new job roles, right? So that data scientist which you mentioned, as in it's probably going to be one of the most sought-after expertise. So if I'm— if let's say I'm in the beginning of my career and I, you know, maybe just finished uni or whatnot, would it be your advice to just start looking at like big data, learning to train AI modules, or, you know, looking at the cutting-edge technologies, you know, maybe blockchain, maybe robotics?

My advice would be do something that will require human contact.

Elaborate.

Because like anything that has some human contact to add value is the harder part to substitute. So, so if you're a therapist, if you are like even cutting people's hair or painting their nails, that's a lot harder because people don't go to paint their nails only for painting their nails. Like, yes, you could have a 3D printer that like paint nails, but it's also often the process that's more important— the getting out of the house, chatting to others, and so on. Uh, so, so in general, should you go and rush and study the latest AI? If you're about to graduate, that might be good, but if you're 12 years old, then like, um, you still have a lot of years to learn. So, so there will not be a general rule then. And, and yeah, things will change. It's, it's very hard to say that's going to be the future role that, that like everybody finds work. And I can explore that later on in the conversation.

So I do love that human contact piece which you just mentioned, Martin, because I go to the hairdresser and I do love the process of it. You know, like if I could put my head into some magic box and get my hair cut, I would probably still go to the hairdresser and get hair done like that. So I absolutely see that. Okay, so that's a good shout. So what signs are you actually seeing right now, coming back to the workforce, that we could say that AI is already making an impact to the workforce, or we're seeing that, you know, accelerating of that impact happening because of AI? It's just, I'll give you context a little bit, Martin. Sorry. So from my perspective, like I work for a, like a big enterprise, right? And there's pretty much zero AI right now. Like I use AI in personal life, a lot of automation driven around AI processes, not around AI, but with AI, right? So I want to bring that into work. And it's because it's a big enterprise. They simply don't trust the tools. They don't want to give their data. They don't want to give like any documents, any information. To, you know, like a public AI like ChatGPT or Gemini of Google. So there's literally no utilization of AI to boost any productivity whatsoever. And that raises for me that question is like, if we see AI having an impact, uh, to, to businesses and workforces, like where do you see that happening and how that impact looks like, like currently?

Yeah, so I will answer your question, but let me explain a more basic thing first. So technologies have very predictable curves normally, and there's two things I personally look for. Um, I once worked in Ubuntu, and they stole away the market from like companies like Microsoft and IBM before either of them knew there was a market. And the person that did that did strategy before me, and he was called Simon Wortley, and And he is now famous for the Gartner Maps, and they're about predicting where technology is going to go and how to make strategic decisions on like knowing where the future's going to go. And what he did in 2008 was not looking at AI, but looking at cloud computing. Back then he discovered there was an online bookstore that was starting with something called cloud, but nobody had heard about Amazon Web Services yet. And, and even in 2010, when I started using it, it was just compute and storage. So what did he do? He made a map that said, like, okay, every type of new technology starts in this fuzzy area where we don't know what it's good for. Like, somebody in a lab discovered something, we don't know what we can use it for. And a good example was like the whole blockchain thing started. Somebody invented it, we don't know yet what to do with it, so it went to that phase. The second phase is that like, hey, throw expensive consultants at it and they can do something with it that adds value. AI is a good example. Like some years ago, you needed very, very expensive data scientists to actually come up with something that you could use if you wanted to to high-frequency trading, then you had to have the, the most expensive quantum engineers come up with things like that. Then the next stage is it now becomes a product. You can buy it, and then it becomes exponentially cheaper. Think about like that, uh, own model that you were doing versus like buying a license for ChatGPT, which cost you about $240,000 a year. So instead of paying $240,000 to like some top-end consultants, you now pay $240,000. So it's a thousand times cheaper. So that's happening a lot now, and this is where like enterprise adoption starts, because it moves away from a very complex project to something everybody can now start using in the enterprise. And that's where like The big enterprises are trying to stop this, but medium and small enterprises, why are we still calling a lawyer? Let's write some contract. Why are we like making or having a writer for this or that? Let's have ChatGPT do it. So you see that like more agile, smaller organizations start using these new technologies very quickly. And then there's something really that like Simon Wortley found out, which was that it then gets pushed to commodity. And product vendors are trying to stop that from happening because commodity then, like, it's the same if you have product A or B or C. So they don't want that to happen. So a lot of times there's barriers, but if somebody gets over the other line and gets to commodity, then new things can happen. And the easiest example I can give you is in cloud computing. It used to be expensive to buy compute. You went to IBM, HP, and so on. You bought them in boxes. It cost hundreds of thousands to get these boxes in a data center. Then Amazon came along and all of a sudden it was like seconds to get thousands of virtual servers. And a whole new industry came out of that. It was no coincidence that all of a sudden Netflix came out streaming. You had like Snapchat, Uber, and all these things because it coincided with unlimited compute. And this is what happens when a new capability becomes a commodity— then new industries get born. And this is what people have a, a problem with. They don't see those new industries, but they see the disruption of the old. So that's why it's so difficult to predict what's going to happen in society, because you don't make a link between your job as an IBM server salesperson, uh, coming to an end and Uber starting. So that's a foundational thing that you need to understand if you want to predict where things are going. And then the other thing which is very important to understand where the future is going is exponential growth. Most of us can only grasp linear growth. We expect something to improve at 10% every year, but exponential growth is like an S-curve. Nothing happens, nothing happens, nothing happens, nothing happens. Boom, everything happens. And that's what AI is on. If you look at like ChatGPT, it is only a year and some months old that we have access to like ChatGPT messaging. And it's not that long that we couldn't combine like images and sound with text. So it takes a little bit of time, but then now everybody is investing in it. So the growth is starting to come to that S-curve where it immediately has a big impact. And this is where you can see that same effect as with cloud computing. Once it becomes a commodity, then lots and lots of other industries can build on top. And it's still a little bit too expensive for energy consumption, the, the chipset and so on. But now we are seeing that mobiles are getting AI chips. So if you start thinking what could be possible if like all these big models that in the cloud are now in our pockets. So that shift is happening and that will be very transformative. So if you understand that, then you can start looking, okay, what is going to happen in the future? Now, I told you before, AI is one of the things. The one thing that is more important than anything else is energy. Without energy, everything stops in the world. And we're seeing the biggest change ever happening around energy, a more foundational thing than AI. So where in the '80s, '90s, we could only run a calculator on solar, in the '90s, we could run a laptop, uh, oh, sorry, on batteries. And then it became a mobile phone. Now it's a car. Next is a city. So the prices of batteries have like gone down exponentially. Their power has gone up exponentially. The same for solar energy and wind energy. And at this moment in China, it's cheaper to buy an electric vehicle than an internal combustion. That will have such a profound effect on society because all of a sudden, if you're in oil, if you're in car manufacturing, even if you're in lots of other industries, you'll be impacted. But even if you're in energy distribution, you, you now find that your customers put panels on their roof and start producing. And on a sunny day, say like, I'm selling you energy, I don't want your energy, your energy is useless to me. So, so basically we will soon, and we're talking 5 years from now, have an oversupply of battery power and, and, and solar power, and as such have almost unlimited energy. And that is one of the core things that is needed for like expansion of AI, because AI is very energy efficient. But if we can go and just put in all deserts, all type of solar farms, we could have unlimited, uh, supply. And at the moment, we're getting to the point that we can easily print, um, solar panels. So the cost of, like, incorporating them in windows and then making them transparent, which is still in R&D phase, is a couple of years ago and away. And then any building can start producing its own energy by just having all these skyscrapers with like energy-producing windows. So that— those are sort of the foundational things that will change things, and they will change everything from like, hey, if we have unlimited energy, we can cool down places that are too hot, heat up places that are too hot. We can convert salty water into drinking water and spray it where it wasn't economically viable. We can have hydrogen and so on. So that means all these basic things will drive a lot more the changes of AI than just a better ChatGPT. So why is your company not using it? Because it's not affected in its core. But if all of a sudden you have the robotaxis picking up packages everywhere, the humanoids coming out delivering them to your door, robot factories in containers producing 3D printing things personalized, then there's going to be a lot of industries that like can't follow that speed of change. Okay, then, and that's going to be the, the biggest change in the next years.

I, I love that, which leads to my next question is, if you're a big enterprise and you know you probably have teams looking into this and you know potentially doing some market research or R&D around AI. So, so you understand the impact it will bring with that exponential curve. So you want to protect yourself as a business because you don't want to be that, you know, dinosaur jumping the train last and then it's being too late effectively to catch up with that exponential growth businesses will have by utilizing, you know, the technologies, AI in this, for the sake of argument, in this, in this example. So what do I do? You know, let's say I'm a CEO like What's my game plan?

So let me tease out something else first to answer your question. You said, I'm a big enterprise. Have you ever thought why enterprises become big and what is the definition of a big enterprise?

In my mind and in my experience through the career, they all get big through the mergers. So it's always someone, you know, who leads, reach certain sizes, they start buying other companies and they usually grow through acquisitions. Position. And they, you know, enterprise in my mind, again, it's a world company, has headquarters in multiple big cities. It has either product or services serving either the whole globe or majority of the globe, thousands of employees at the market cap of billion plus.

Almost the last word you said was thousands of employees. And that, let's analyze that one for a second. Big enterprise meant thousands and thousands of workers, sometimes even hundreds of thousands of workers. Why is that? Well, if you think about it, the traditional way of being the leader in a market meant you had branches everywhere, you had large sales force everywhere, you had like call centers everywhere, you had like more programmers, more this, more that. More people was more important to win. And that was because economies of scale on your call center, your sales team, and so on made you win. Now, if we look at like a company like Telegram, which I think 900 million users are using every month, they have roughly 100 users, 100 employees. So there's a lot of like companies now that scale through technology, not through users, not through workers. So what is the problem with our current way of structuring companies? Well, they're— the only thing they are very, very bad at is change, rapid change. They're like oil tankers. What's the only thing that's going to happen in the next years? We're going to get an avalanche of change. So that's the biggest problem for large companies. Their size isn't helpful. So if you really want to have a big impact, don't try to change the core business from one to another one. Start spinning off like crazy small teams that like can launch exponential businesses. Because the only thing that like, uh, saves large enterprises is they have the resources to do that. Because their core value from yesteryear's competition is their weakness tomorrow. The science is the worst thing they can focus on. They need speed, they need adaptability, they need to think outside of the box. They need teams that can do 500 different things in a small unit, not 500 people that are all the same. So our way of organizing companies is built on a model that's no longer what we need tomorrow.

Would you have any examples, Martin, where you think enterprises are doing this well? Or do you think all of them are not able, not like not able, but in a disadvantageous position to adapt to coming change?

At a certain scale, you'll always have inefficiencies and a lot of middle management and so on. But if you look at how Amazon grew its web services, they were called pizza teams. And the name came because if a team couldn't be fed at night of 2 large pizzas, then they were too big. So 10, 12 people were the maximum. Then more people, you had to divide it in 2 of 6. So, so basically, um, that model, uh, Amazon used it to start launching lots and lots and lots and lots of different web services in parallel. So, so basically they've scaled in this sort of like very much like the human body type of thing. You divide a cell and you divide it and you divide it and you keep on growing the organism. That's a lot better way of organizing than like organizing by role. So all the companies that are very much like, I'm the chief financial officer, I have 5,000 accounting accountants reporting into me. They have a problem because every new product decision that goes far away from the beaten track, they can't take a decision. They need the product chief, product officer, the chief marketing officer, the chief technology officer, the chief financial officer, the chief operating officer, and the CEO all to sit together to make a decision. There's no unit that could go and say, like, I feel empowered, I have all the resources to go for this path. So the companies that are very much structured have never— and that's the other thing— have never had to go from zero to one. That's the biggest problem, uh, because, um, companies that have been doing for 30, 40 years the same model and just scaled it, they have no people that know how to, like, grow a business from zero into $1 million, and it's a totally different, uh, skill set that you need. So those companies are especially vulnerable.

Okay, I got you. So we talked a little bit about the business. So let's maybe go to professionals. Let's say, you know, I am part of that big enterprise, and because of this size, as you mentioned, they are tankers and I have my own thing, so I know that things could be done better. Maybe I don't like what's going on, maybe I should change the businesses. But in general, how should especially technology professionals think about coming change? And what would be some tactical advice from your perspective of how they should position themselves so they can, you know, not like adopt the change, but embrace it when it comes and embrace it in such a way that there's a, there's a, there's a real value to them. Like they're not getting side pushed or they don't lack skills or they're not finding any roles. Yeah.

So, um, it's, it's a very broad question and it depends heavily on your skill set. So if you are an AI scientist that works in a department for diesel engines, in Volkswagen, you can do a lot, but it's not your AI that will be disrupted, it's the diesel engine that will be disrupted. So, so you need to look at like where you are. If, if you're in an industry that like has no future, then like you need to get out of that industry.

Okay, so maybe to make it easier, let's just take, um, let's just take a very common UK role, um, in technology space. UK has a lot of startups, there's a lot of entrepreneurs, you know, building things on AI and, you know, and other stuff. But very common, uh, sought-after role is something to do with the cloud computing, like a DevOps, like a sysadmin type of situation. It's like it never runs out of type of roles. So let's say I'm like a you know, like a team leader, DevOps level, something like that. Like, what am I doing right now? Like, I want to plan my career for next 3 years.

Cool. So if you're DevOps, you have to think what's going to change, not what's going to stay the same. And the compute might go from a data center to like more local, because a lot of AI. You don't want a robot taxi to phone home to call center and say like, at this light, when this light changes from green to red, should I stop or not? Because imagine there's like an interruption, it would drive through the traffic light. So you'll see that a lot more things will have to be run closer to, to where the users are.

So that's definitely a thing, isn't it, Martin? Sorry. Sorry, that's edge computing, isn't it? It's just having data centers all around the big cities.

Okay, so, so if you know this, then like being able to do DevOps in cloud and edge could like amplify your runway. And then the other part is, if you are a data scientist, normally you use some tools like Jupyter and you write in Python and you come up with like some code And then you take that to a team that now needs to use it. And if they're engineers, what they actually have to do is start all over again. Because if this is interpreting videos or whatever, whatever that data scientist did is a good, like, blueprint, but they can't run this at scale. So DevOps for AI is the other one that you need to definitely understand how can you make models that get deployed on the edge, in the cloud, and so on, that can scale out. And there's two parts. There's the training part. So, so if a lot of people do things on, on your website, can you take that and retrain and even go to personalized AI recommendations? So if I can train for this customer, uh, and their financial situations and now predict and recommend what they should invest in, then that is going to make it very hard for them to leave my bank versus another bank. So the DevOps person that goes either towards Edge or AI DevOps is going to have a lot longer runway than the person that just stays doing what they always have been doing.

Okay, I love that. Let me give you two more, and we can be quicker, yeah, if you want. So when you were talking, one which came to mind is, is coders. It's just like programming, you know, C++, Sharp, maybe web development, maybe the same Python people. So they can get a lot of like code assistance, right, from generative AI. How that would change? And as well, where do you see like technical architects coming in play? Like when I think about technical architects or architecture in general, you know, like someone has to see the broader picture. So I feel like for them, the vastness of everything is just so complex and big. Like, I feel like they have to specialize somehow. So those two roles very quickly, Martin.

Yeah. So you said coders and the other one was?

Technical architects.

Technical architects. Okay. So the coders have a problem that generative AI will be on a path to make them irrelevant. So, so pure taking requirements, or even like taking a, a Figma design and turning that into HTML and Node.js and whatever technology they want to use, that's a problem because very soon you ask, uh, ChatGPT, hey, make me a design for this, hey, generate the code for that. So, so there is an issue there that like run-of-the-mill coding is in danger of being generative AI'd. Um, that doesn't mean there will not be programmers, but I think this is where the architects are going to become more important because the architect can tell the AI instead of the coders what system needs to be built. So, so where one might have less work, the other one might just see who they talk to change. And one thing is true with AI, um, is that like the more expert you are in a field, the better you can evaluate how good the AI is and fix problems. At this moment, yes, it can generate code, but if you ask it to generate something that no other programmer can do, it will generate code, it will not work. And that's the thing, like, like being an expert still helps. So I think very soon we'll see that like AI-enhanced experts is the model for the next 5 years. Afterwards It will depend.

So we're basically saying the maturity of generative AI to fully replace coding is just not there. So there will be a need and expertise at the very high level to really look at what's being generated and put it together. So instead of like coding, you're gonna just build stuff, like you're just gonna become a builder, right? You just take snippets and you combine the solution, which is probably more complex for generative AI to fully comprehend, but the but the bricks of that building is, is definitely generated for you, which means you're not going to be writing that from scratch. Like, that's in the past, that's done, right? Okay.

Yes. And, and, and, and yes, there are programmers that design the systems on which this AI runs, but they are very specialized, uh, and, and, and, and so on. And not the majority of of people doing.NET today.

Yeah, I get you. I mean, I, I come with a big background in coding from like last 20 years or so. I was, you know, a web developer mainly, but now if I need to do something, uh, I, I just ask for a solution basically, and then I just tweaked a little bit and it just plugs in place. So it's very easy for me to, you know, what you mentioned, in danger. I think it's Like, it's past that. I think it's very, very clear. Like, it's gone. Like, the raw skills of going from like 0 to 1 in coding is just, just obsolete in my mind. But that's just my personal opinion. You know, I'm sure, you know, listeners will have others. So if you do, please leave some comments and let's have some discussion. So now, how does AI and technology affect my mom and dad who have no idea about coding? They're not using ChatGPT. How is this now already in their lives, which they don't even realize, or how this will be later in their lives? Let's take like robotaxis away from that, you know, let's take maybe robots away, but like maybe some use cases which are a little bit more to the heart, a little bit more to the current year or maybe next year.

Yeah, so basically, I don't know if you tried ChatGPT's speech recognition and it talking back. So that will very much in the next 24 months be to a level that it opens up towards people that are less technical. So where you had like Alexa, switch on or off the light, you can now go like, Alexa, get me an appointment with the hairdresser. So basically, it will be a lot more complex tasks, but with a way as if you would have like a personal assistant sitting next to you taking notes and doing whatever you ask them.

I'm doing that already, Martin, by the way. Just sorry to jump in because you gave this use case and I was like, yeah, I already have that. So I have an app which recognizes voice. I mean, it connects up with a third-party API. I don't want to mention the names just to not advertise, but it recognizes the voice, right? So, and then it basically takes my command and I have an API connection through the automation tool like Zapier or Make, and that just goes to my Google. So if I need meetings, I just go like, book a meeting with Steve, and it's in my Google.

So you asked your parents and senior citizens. So basically technology needs to like become boring and boringly easy so that it, it is used by everybody. So, so not everybody that's 80 years old has a smartphone. So you, you have this adoption problem. So you will have like people that never move to smartphones and still have an old Nokia lying around. So, so, so you— all the technologies that become so simple that you don't have to think, they are the ones that earn mass adoption. So simplicity is still the key to— so if you see something that like super simple, and then another thing is if people sleep in the street for it. So, so the, the way I do stock investment is often looking at like, okay, has this company market fit? Uh, because if people sleep in the street to get their product As was the case with the early Teslas, then like, um, yeah, the first iPhones or whatever, they normally tend to grow. So simplicity and, and like people love it. And this is ChatGPT is the best example. First version of ChatGPT was not launched as a finished product. It's like, hey, here's our prototype thing, what do you think? Month later it had a million users and, and they had like problems scaling it.

So I like that mentioning of Tesla, Martin, is because you mentioned exponential growth. And if, if anyone listening is investing, just open up Tesla's stock price and just zoom out to like 10 or 12 years. It literally goes nothing, nothing, nothing, and that, that exponential curve just shoots to the moon. It's, uh, it's pretty much how the technology will evolve, isn't it? Sorry to interrupt, by the way.

Yeah, you know, that's exactly it. And now if you then think further, what happens if you're affected? What happens if you get made redundant? If that company you worked for like collapses overnight, what should happen next? So on one side, I think we should have a societal discussion about universal income if like the robots start doing the work. Put taxes on them and use that income to make it that nobody's hungry. There is going to be certain things that will become exponentially cheaper. So like, hey, if electricity is cheap, heating your home is going to be cheap. So basically the universal income will be combined with like certain things will become cheaper, so it will be easier to pay for them. But as a society, We are not used to that either. We're used to the prices going up every year, not the prices going down with 100% every year. Um, so, so that's sort of like going to be an adjustment. And then the thing I'm currently involved in is like the future of work. Can you make platforms that are like Netflix, but sort of the Netflix of work? You don't have to apply to be able to see a movie on Netflix, and neither should you have to apply to work. You should go there and have suggestions made based on your skill, Martin, and based on your interest. Martin, here are things you can be doing the next hour. And there might be things that are boring but extremely well-paying. So if I'm, uh, medically trained, looking at like the, the images where the, the, the AI can't say if it's cancer or not and making my professional opinion and explaining why, uh, can be extremely well paid for like doing that for an hour, but boring as hell. So, so basically I can do that for an hour now, earn 3 times more than I would get on a normal day. So my day, like, my— the part of work that I do for financial stability might get shorter. So now that I have more hours in the day, what can I do with those other hours? Well, as humans, we don't only work for financial gain. We also work to, to do something that is valuable. So perhaps I could help charities a couple of hours. Perhaps I can learn new skills because in 3 months' time AI predicts that they will be important and what I'm currently doing will run out of steam. And, and perhaps I can have conversations with humans that like are in edge cases that AI can't solve. So my working day should be a combination of all of these things and could be not 8 hours, could be shorter, could be on different times in the day and so on. And I might think universal income is good enough for me. So, but that's sort of like a future where like if we don't get these platforms built and we're going to see millions and millions of people being made redundant because of AI, The other part is war between the haves and have-nots, the trillionaires of AI and the zero-naires that are there in the millions. So, so it is in the industry's advantage to look at, can we make those Netflixes work? And that's why I'm working on those type of things.

Okay, so you mentioned that, uh, you know, project and sort of have a little bit of background on it. So can we use this as a case where, as a real-world application, where effectively, you know, you try to combine all of the, all of the technologies or all the edge or future technologies and, and bring something, you know, like a signal which has significant impact, and that is happening. It's like, what's the lessons you're getting from it? It's like, are people ready? Maybe it's too soon. Is the tech ready? Maybe it's too expensive to scale. It's like, like your general insight of what you just discussed is like, what are the challenges for you to actually make that real?

Yeah, so, so technology-wise, the good thing is with, with like Facebook open sourcing their AI and then it be able to be fine-tuned for very specific use cases, it will soon be possible to, to like, if you have enough data of a worker, to then learn and, and do better and better and better at these micro jobs. So, so, so that part I'm not that, uh, worried about. The other part, uh, that, that like is relatively new, and this is, this is where like society has two ways of innovation. It has a center-driven innovation that like is sort of a magnet, and the more money you throw it, the more power you get. And then you have the opposite that is sort of like anti-center innovation. So AI is a very good example of this thing that like is sort of a in the center, because the more money you have, the more NVIDIA cards you can buy, the more NVIDIA cards, the more advanced AI you can train, and so on and so on. So it's all about like, if you have the best AI, it's also because you have the most money. And as such, the big companies will be all flocking towards it. Whereas I'm also very much involved for, for years already in Web3 technologies, where it's completely the opposite. Can I make— can I take the business rules of a market put them in code, and now let the market be completely automated and take out the intermediary that used to take like 20% margin and now do this for 0.2% margin. So, so that combined with AI can be trans— transformative for, for lots and lots of use cases because it can be this part that brings you to work and then the AI that helps you do the work and can be also the part that pays you and can execute things. And then the last part is the IoT part, if you physically can interact. So whatever decision you and the AI and a smart contract take can also be executed and something can be produced or whatever. Yeah, that can be— generate completely different industries and transform things. So I don't see the future of how we organize work as being these large companies, but fluid companies where people find a common problem that a small group has, very quickly generate a solution, start selling that for some weeks, disassemble, and go on to do the next business. So companies that like have no employees, they come together with experts in different fields do something for some weeks, then it runs out of steam and they go to the next thing.

It's a very sci-fi, sci-fi view, Martin. But because you're talking about— I'm thinking about like cyberpunk game or like a very sci-fi-ish type of, uh, future, which is cool. Don't maybe, you know, don't get me wrong, that sounds very, very cool.

No, but think about it. If you're a tax advisor And the micro jobs can be, hey, can I deduct this? Hey, how should I do this? Hey, what is— what would be a good plan for my pension savings? And so on. So they can be very small jobs where you just swipe left to right and say like, yes, you can deduct it, or no, you cannot, to like working with AI on making a personalized plan for your pension savings, uh, for the next 30 years based on your skills and so on. So, so, but why would you have to work for one of the big, uh, accountancy firms if like you have to apply, you have to sit in management meetings and this and that, if you could be just switching on your computer and saying, I wanna do a little bit of this work. Hmm.

So it's like a distributed workforce effectively, or that gig economy just taken to the next level where it has that scale to actually give enough job to, well, pretty much the whole globe and give meaningful work and can like, um, high-skilled work.

Because we see gig economy as like the bottom-feeding part of society.

The cheaper one. Yep.

Yeah. But with ChatGPT now, like the most complex rocket design could be made by a group of engineers that never met, that were put together because AI assembled them based on their complementary skills.

Okay. I get you. I, I can see that. So, so this is, uh, so if people want to learn more, where would they go to check that?

So, so we're building that. So, so one of the projects I'm, I'm involved in is canDAO.io. Uh, so can, C-A-N, and DAO is from distributed autonomous organizations,.io. And, and we're trying to mix social networks with business networks and crypto in a new way that like Everybody can earn and we're trying very simple things like, can I swipe left or right to give you a signal and get paid for it? But yeah, that signal could be, do I have cancer or not? Mm-hmm. Can I deduct this from my taxes or not? So, so the, the, the, the method of earning might be very simple, but the skill required might not be.

Okay. I love that. We'll put that by the way in the show notes, uh, any domains you'll give Martin. So. If any listener is interested in the concepts and just want to learn more, it's going to be in the show notes. So, uh, we have this, uh, one question, Martin, which is in the spirit of the show, and we ask every guest— or some guests I forgot to ask, but majority of them have a say about this. So if you would need to choose one item, let's describe it like that, which is the most influential for any tech venture growth What that would be, but you can only choose one.

The most influential for any tech, um, product market fit. So whatever you do, you can build it. If you don't build something that solves for a lot of people a problem and they can easily acquire your services and find out about it, you can have the best technology, any technology, it will fail. So if you don't have user adopting and an easy go-to-market, hence product-market fit, technology is irrelevant.

Oh my God, this is like you're preaching to my ear. And I say that to every single tech founder because they love technology, they love coding, and they believe that if they put all the passion into that backend and to that API, into the cloud, you know, it means something. Realistically speaking, it means nothing. Like, people use the end user element, and whatever is the backend, as long as it works and deliver product promises, they will pay for it. So the tech, while it's very influential in like scaling and the cost of business running and things like that, it's not what actually makes or breaks your startup.

And this is why the way I like to organize, and this is what I've been doing very recently, is I don't like teams together with like tech or business or marketing or whatever. I like to mix them. These micro teams should have somebody knows about business, somebody knows of like marketing and go-to-market, somebody knows about product and how to get that done, and some technical people that can do the front and the backend and so on. And like once it starts scaling, put some more people and then you divide into 2 cells and 4 cells and so on. But it's sort of like you should put those two together because if you build something that nobody wants, that's bad. But if you sell somebody something that you can't deliver, that's also bad. But putting them together creates this magic.

Yeah. I mean, I would choose the second one because that seems like a good problem to have because you already won and you just now like, you know, I have the revenue coming in. I have this problem which I call a good problem, which is like, how do we scale that? Where the first problem is you just wasted months of your time and people's times and probably money and got nothing. So I would choose the second one.

If, if you can do it, yes, because you can afterwards like find somebody that does it. But if somebody just, uh, um, uh, gives you money— let's, let's say there's a lot— it has been a lot of crowdfunding campaigns where a lot of money was raised they didn't deliver, and at the end somebody else made the product. So that's the, the risk of that model.

I get you. I lost a lot with the ICOs of crypto.

Exactly, exactly, exactly.

So much and stuff, and it just never happened. Yeah, exactly.

You need both, uh, you need great delivery, fast delivery, scalable delivery, combined with great customer insight, uh, and, and the solutioning attitude.

Okay, market product fit, one thing, start with that. Good. I mean, honestly, market product fit was mentioned by many of our guests. It does seem to be likely the, the winning item. And what we're doing with this, you know, question being asked, it's like we have a series about this and eventually it's going to be, you know, a book about that. So to finish, let's, um Let's, let's be practical and tactical. Like, I am listening right now, you know, maybe I have an AI startup, maybe I'm building something on top of ChatGPT, maybe I'm training my own modules. You know, I'm a small startup, there's plenty of them in here in UK, maybe I have 4 to 12 people. What would be your practical advice, Martin, to them, to the business owners? Like, how can they make sure they win in the next couple of years? Years?

Yeah, focus on understanding. So, so I, I have been advising lots of, uh, startups and early ventures, and some, uh, tell me, hey, we built this great technology, it didn't work, can you help us explain it to others? But I also am working with like solopreneurs that like I'm helping really to find that like market fit, and people just go too fast over that phase. Like, with generative AI, social networks have been swamped with posts. So the biggest problem nowadays is to actually talk to humans and find out what's wrong and what you can solve for them. At like building something and they will come because the problem is the cost of building has gone down exponentially. You just ask ChatGPT to generate this and that and, and, and so on. But like it also is generating 100,000 tweets and nobody now can like see your important tweet between the 100,000 other ones. So getting out there and talking to people and building something with them and inviting them every week to to see the progress is probably the most important thing that a 4-person team can do.

Build in public approach.

Yes. And very much putting it out there and allowing people to vote and participate and feel engaged. And even better if you could have people build on top, like personalizing things. If you have spent the last 3 years personalizing a product, even if a competitor has the same features or better, You are not switching because you have a personalized product, a platform that could personalize more can't substitute for that. Mm-hmm. And then there's a couple of other things that like as a startup, you should always look for network effect. So if, if like I basically have it and then invite you and now there's more value to the both of us and we then invite our friends and now there's even more value. What happens very soon is that like, even if we're not the best platform, but we have most people, any competitor can just not, uh, eat our lunch. Like LinkedIn is a good example. Technology-wise, they're no longer as good as they used to be. There's too much, um, uh, bad quality things happening there, but they have the network. It's very hard to, to basically, um, compete with them because the next biggest social network is infinitely smaller. So, so that's a focus that you should always try to have, this, this like network effect where like you have a positive spinning wheel, a flywheel that once in motion it starts accelerating and you get this very big avalanche. That just takes everything on the spot.

I love that. I would add integrations from my perspective as well, Martin. It's like I use so many tools in personal life and business life. Anytime I have issues integrating tools one with each other, for me it's like a deal breaker because you have like these API middlemen like Zapier or, you know, or Made. Omake.com, which allows you to connect up the nodes and, and just have like workflows going through, you know, 5 or different tools. And anytime I get any tool which I cannot integrate with, for me it's just like, it's no-go. So advice I give as well to startup, which is maybe a little bit of maturity phase, maybe it's later, but just make sure you can integrate with other tools. It's just, you're not going to solve all the problem. There's going to be something you don't have and you don't want to build So have ability for people to find that and solve that through the integrations.

And, and integrations, and, and even more important, plugins or apps or whatever you want to call them, because you can never build the 100% solution that is 100% for all your users. But if you have a plugin or an app mechanism where that user now can get from 500 other partners the perfect solution you can have the perfect solution for everybody.

Amen to that. Amen to that. All right, so that wraps up our conversation today. A huge thank you to Martin Ectors for sharing such profound insight of AI transformative impact on society and in general technology. Was, uh, was the likely most sci-fi episode we have, you know, and hopefully everyone's enjoyed it. Martin, if people want to know more about you, about things you do, how do they get in reach? How they find you?

Yes, so, so at the moment you can find me on LinkedIn and, uh, you can also find me on Kandao. Um, and, and yes, uh, I, I'm really building this solution where like people can come and build ventures together.

Excellent.

That's where we're working on.

We'll have the details in the show notes as well, so just look it down and you'll have the links for Martin. If you enjoyed the episode, Please do not forget to subscribe, leave us a comment in the form of a question or suggestions on Apple Podcasts, Spotify, YouTube, or anywhere you get your podcasts from. Your feedback helps us to grow. Until the next time, stay safe and see you online.