Programming Has Changed - Here's What I Focus On Now

Okay, so let's talk about the current state of the tech industry and where things are heading. And pretty much the question, will your tech job, will the things that you are learning right now, will they still be relevant in the future? This is a question that comes up a lot nowadays in my communities, where every day people are learning AI engineering, starting as a freelancer, and the question pretty much becomes, "Dave, with all of these AI agent harnesses, agentic engineering, developments, like what are we going to do in a year from now, in 2 years from now? " Things are definitely going to change, and I don't foresee a future where we don't need software engineers, AI engineers, data scientist at all. That's not what I'm seeing, but there are going to be differences. And in this video, I want to give you some tips. Pretty much the same, I want to give you the same advice that I share in my communities as well, how I kind of like preparing myself, kind of like upskilling in a different direction, and pretty much how I see things. So, I want to get to the whiteboard. I created this little image over here that I want to go over, and hopefully by then, it will make a lot more sense. So, there are three concepts that you need to be aware of, and that are the following three over here. So, we have AI-enabled, AI-first, and AI-native. And these are pretty much terms to describe a company's ability or effectiveness in how they are using AI. Where currently, what we're seeing right now, most companies are becoming AI-enabled. So, if we look at the anatomy, at the top, you have the shareholders, the board, the CEO, and the founder, whatever. And underneath, like inside the business, you have the operational layer. Now, nowadays, most companies will, especially more digital companies, right? They will use employees, will use some type of AI tools. For a lot of companies, still not every employee, but most companies, most knowledge workers will use some type of AI tool, whether that's just using ChatGPT or way more advanced tooling. But these companies are AI-enabled. And what that pretty much means from a kind of like work workflow perspective of how the business runs, not much has changed just yet. Employees have just become a little bit more effective because they can now use some AI tools. The next kind of like stage, this is a an kind of like an evolutionary phase, is what you would call AI-first. So, this is a business where the business is is trying to be optimized around the technology AI as a way to like fuel every process and fuel every system. So, this means either re-engineering the current processes that were in place before AI was kind of like at the level where it is right now. Or, for example, with newer companies, where it's a lot easier to engineer workflows and systems from the ground up, right? So, you can see I've kind of like illustrated it over here. I kind of like I have flipped the order. So, now, not the employees come first, but the AI agents come first. Also notice how here I just say AI tools, here they really become AI agents, and AI agents can take on a variety of different forms, right? This can be someone using Claude codes, this can be also some type of tool, but really with a more kind of like a deeper level of automation and integration potential. Or it can be an entirely custom custom-developed AI platform or even operating system for the company. And now that becomes the primary layer through which the employees essentially manage their day-to-day activities and their tasks. But it's flipped around. AI-first, that is the lens through how things are optimized. So, you'll see right now that especially software companies, companies more that operate in the digital world, you'll see companies adopting this style and more transitioning towards this. Because from a shareholder perspective, this is of course very interesting, because now, with essentially less people, we can get the same or in also in cases even more output than before. So, that means more revenue and more business growth for the company itself, for the shareholders. Now, if we go one step further, that is what you would call AI-native. So, this is a company that is entirely [clears throat] built and engineered from the ground up with AI agents in mind. So, every process is pretty much set up, "How can an agent do this? What data do we need? What systems input outputs? " And I've kind of like visualized this in a way where it's still this AI agent layer that's on top of here, but now with even less

headcount, we can manage the company. And in the future, we'll probably we'll most likely see a future where we even get the employee out of it overall. So, you just have someone literally deciding to start some type of business that is entirely run by AI agents. Now, currently in today's reality, that is still really hard to do. There probably already some businesses out there that do this, but especially if you look at companies at like any reasonable substantial size, you're still going to need humans. Why am I telling you all of this? Well, this is very important to keep in mind, because in my opinion, in my view, this is what almost all businesses, especially that operate in the digital world, are going to optimize for. Because all of these shareholders from all of these companies, both small and large, are going to look around, they look at the trends, and they see these AI-first companies, they see these AI-native companies, and they just simply have to keep up. Because otherwise, they'll literally be competed away. You now have new companies that have the advantage of starting fresh, using the latest technologies, that can move so much quicker than a huge enterprise that is essentially stuck in old processes, lots of headcount, where now smaller companies can just come ahead and like just crush them and take their market share. So, in my opinion, this is kind of like inevitable that more and more companies are going to move to the right of the spectrum. First AI-first, and then newer companies AI-native. So, if you kind of like take this as a given, then how can you use this to your advantage as someone who is in tech right now? Well, you should simply play along this trend. So, one of the best things, in my opinion, that you can focus on is learn how you can help companies to become AI-first, and then later AI-native. But the the steps are pretty much the same. Like from a development perspective, the way you build AI agents that help AI-first companies is pretty much the same compared to AI-native, because it's like the difference is the process and how the business is set up, which is mostly the responsibility of the shareholders, the founders, et cetera. So, that skill, knowing how to build AI agents that can automate pretty much entire roles, entire workflows, is going to be, in my opinion, one of the kind of like the skills, the qualities of a developer of an engineer that's going to be highly in demand. Now, a little bit of a nuance to that, because then you can think, "Okay, but what does that mean, right? What do I need to learn? " Well, it doesn't necessarily just mean one kind of like skill in terms of you need to be really good at AI engineering, or software engineering, or really good at data engineering. It is going to be a combination of that. In my last video, I also talked about how I think it's really important for everyone in tech to kind of like move more towards becoming full stack. So, in the age of AI, where AI agents can just do so much things, in my opinion, if you can only do one vertical, there is a high chance that at some point, you're simply just going to be automated away. Because you then become the bottleneck in the system. So, being able how to solve problems end-to-end, which is what this is all about, becoming AI-first, focusing on a particular problem or workflow within a business, and then knowing, "Okay, this is the starting point. These are the data sources that we need. This is the process around it, and here is the automation or the tool or the software that I can build around it. " Again, literally to automate it end-to-end. And the thing with end-to-end automations is that it usually touches multiple parts of the stack, right? You need a database, you need some type of deployment, you need a back end. Maybe you need a front end as well. Maybe you need to create a dashboard, so the actual shareholders or the employees managing the system can still have some control over it, have some like human-in-the-loop interaction going on with the AI agents. So, coming full stack becomes more important. But also, your ability, and this is something new, your ability to essentially come in, so this is going to be you as the developer. Let's give this a blue color. So, you come in into kind of like any business, whether that's as a full full-time employee or as an external service provider, like a freelancer or consultant, which is what I've been doing for the past years, you come in, and you not only know how to build these things, but you can also help to map out the process and to map out and identify the workflows that can be automated. Because in a world where I kind of like

where still the reality where we live right now, even if a company is AI enabled, shareholders usually think in terms of roles and head count. So, oh, there's some type of problem, we need to grow. Okay, let's figure out who we need to hire. But that's really an old way to think about it. Because in a world where AI agents can do a lot of the heavy lifting, you shouldn't think in head count, but you should think in workflows. So, you should pretty much look at, okay, we have some type of problem or system or process within our company, and that is what we need to automate. So, instead of just thinking, okay, here's an employee and we give it this task, this task, we decompose it to the actual workflow itself and then figure out, okay, how can we how we can we give that just that particular workflow to an AI agent. The problem is, if you kind of like go from this world over here to this world over here, what often happens is that a single employee is often responsible for multiple kind of like workflows or processes that they need to adhere to. So, they know when, for example, every, I don't know, beginning of the month, they know to they need to do XYZ. They need to create the report and they need to pull data from some type of system. And if the data is not there, they know that it can also be in another place. So, these workflows are often not as well defined, lots of edge cases, and humans can naturally handle that really well. And that is mostly usually just the result of kind of like bad management and not having like just everything really dialed in. And like as the company grows, this always happens. Like you cannot afford this, but things get messy. So, for example, John gets this needs to do this and then he also eventually he takes over and he needs to do something else and then later someone else comes in and So, things get messy. That is really the reality of how most businesses operate as they grow. So, what you then need to do, and this is the skill that I think is really important beyond just the engineering skills, is being able to come in and to look at the entire business and through some type of audit, which is which could literally be asking questions, interviewing employees, figuring out where the waste is going. So, especially kind of like the type two waste, right? So, we have type one type two waste. Type one is the is absolutely necessary for the process, we can't get around it. Type two is literally just waste that we can get rid of. What are steps within the process that either are duplicate that we can get rid of data that's moving from one place to another. People are manually creating and moving stuff around. Those are all very good triggers for you to start like diving in and to say, "Hey, I think we can optimize this using AI. We can build a process around it. " But before you are able to like do that effectively, because you as the engineer, you know the capabilities of AI automation and what you can automate and what not. For generally, for people who not in tech, they still find this whole world of AI like one big mystery. They think AI is this one big umbrella term and like, yeah, you have ChatGPT and maybe they are aware of club, but AI is just this big this big black box. You know exactly pretty much how it works. You know what you can automate with it. You know where the challenges are. So, you can step in and you can look at a process and then say, "Are you really doing it like that? Like I can build a simple script in like literally 30 minutes to automate that for you. I can literally turn it into a skill and you can automate that. " And that's the value that you bring to the table. But that is a position that you have to kind of like get That's work towards to be able to get the chance to look at the business like that. Because if you're just working as a developer, it's often very hard to get enough information from a company to actually start looking at the processes at that level. Because it's usually very separated, right? The developers kind of like don't bother with the systems and the processes of the business overall. So, in larger organizations, this can be really tricky. But another trend that I kind of like foresee in this is that it used to be that the the developers worked for the big enterprises, right? So, big development teams, large teams. And what you'll now find is that almost every company will become some somewhat of a software company. There will be an a software component to it because you simply need it to survive. Because you need AI in order to manage things. And that sets you up with the opportunity to step in as a developer also in a small company, literally a local

business down the corner. And again, this can be maybe as a regular job, but also as a freelancer, also as an outside consultant. Because most of the people still most of the business owners have no idea what they need to do with AI. They are still catching up. They are still figuring out. So, then you coming in, showing you how to build these cool AI agents, that's really going to be the play. And then essentially, if we move more towards AI native, this is going to be more and more of a it's just going to play a bigger role in this overall. So, to drive this point home pretty much is what is the future of your tech job going to look like? In my opinion, the developers that will come out on top are the ones that can help businesses to move from AI enabled to AI first to AI native. That's the process that you need to focus on. And it's the actual hard skills of being a or of knowing how to build end-to-end AI automations, which generally covers the full stack. But it's also being able to identify the right processes and get essentially to an ordering and a prioritization of how to implement those. All right, so that's what I recommend. If you are in tech right now, whether at the beginning or at the end of your career, this is a recurring theme that at least I am focusing on and I also I'm also recommending everyone in my community to pay special attention to. Now, if you want to continue learning or watching more of my videos, I've kind of like two things. First, if you are interested in starting as a freelancer, maybe already wanting to learn how you can sell solutions like this to businesses, but don't really know where to get started or how to find that first client, you can check out first link in the description. I run a community of hundreds of freelance data and AI professionals and we're all pretty much here to make more money, work on fun projects, and create freedom. So, if that is interesting to you, make sure to check out that link. And then next, I will link another video up here, which I uploaded recently. This is how I think about creating an AI operating system. So, this is what I'm building to automate my own entire company and this is also what we're experimenting with our clients to again set them up to really become AI first.