Transcription
The job isn't fun for me anymore. A software developer wrote that to me last week. A friend of mine: 10 years of experience. Not because of one project, but because of a question. His daily routine looks like this. Ticket open. Copilot generates a suggestion. He reads the code. He understands it roughly. He has 40 minutes of budget per ticket on average, because his project manager assumes that the AI will produce solutions faster. So he commits the next ticket. At the end of the day, he doesn't know what he did. He says he feels like quality control for a machine that he doesn't really trust. He babysits the AI, but he doesn't have enough time to really check it. And I read that and thought to myself, that's exactly what I do every day too. I start Cloud Code. I give it a task for Morpheder or one of my other projects, the AI writes. I check. I correct. Next task. My subtitles are done by AI. My brainstorming is done with AI. Research is supported by AI. The code for my projects is largely written by AI, and I'm the one who looks over it. And if I'm honest, for a few months now it has felt different than it did a year ago. Back then it felt like a tool. Now it feels like a kind of assembly line work. But the most exciting thing is that this suddenly no longer only affects IT professionals. Yes, they are hit the hardest, and we are feeling it intensely right now. But many of you have also written to me who have nothing to do with IT and still notice that something is just strange, as if one is working towards one's own end. There is a reason why this is the case, a reason that honestly scared me, but we'll get to that later. The feeling that has become everyday life for many, that the job is simply no longer the same, and also an explanation for why it is so incredibly mentally exhausting. And because all of this wouldn't leave my mind, I naturally started researching again. Not what experts say about AI and jobs, you've all heard that, there's little new there, but are there data that explain why all of this feels so strange? Can it be measured? And what I found initially confused me, to be honest, because the official answer is: "The OECD finds hardly any measurable effect of AI on the labor market in its Employment Outlook." At least in our case. The unemployment rate in Germany is 6.4%, Switzerland 3.2%, Austria 7.5%. All quite stable for years, and yet people write to me every week in the comments that their job feels broken, that they can't even get started. On Reddit, there's a whole thread that has gained quite a bit of traction in r/xerienceDeaths with the title "Reviewing someone else's AI Slop," and one of the top comments asks: "What does saving an hour or two writing code which I actually like give me if I have to spend that same hour or two deshitifying it?" A university study analyzed over 1000 such posts. The result: Developers no longer feel like programmers, but like unpaid prompt engineers for their own colleagues. And open-source software is struggling with this in particular. Quite a few projects have completely stopped accepting pull requests from other developers. That is, code that is voluntarily contributed by others, because it usually doesn't meet the standards. And I myself had very similar problems figuring out if a contribution was helpful at all, and then having to fix most of them in the end. That's what costs the most work in the end. So the statistics say: Everything is fine. But people say something has died. And I wanted to know who is right. The quick answer is relatively simple. Both are right, of course, but that's exactly the perverse part of it. Traditional labor market statistics measure three things very well. Who is employed? Who is unemployed? And how many were laid off? So if a company fires 1000 people tomorrow, you see that immediately. If the same company simply doesn't refill 1000 positions over 5 years, you see nothing at all. No one becomes directly unemployed because no one was fired. So the positions have simply disappeared quietly. And this is not a new problem, by the way. In the 70s, when electronic typesetting systems replaced typesetters in the printing industry, one of the toughest labor disputes in post-war West German history, with a strike in 1978, was really bitter, it was virtually invisible in employment figures. Why? Well, the unions had negotiated collective agreements that kept the typesetters employed at screen terminals. Same pay, even the same title for the most part, but the actual profession was still dead. The typesetters sat at data terminals and typed out texts, and when they retired early, the profession simply disappeared without any mass layoffs. And the same with ATMs, actually. Germany had just 200 of them in the early 80s. 10 years later, there were over 45,000, and yet there were no mass layoffs at bank counters. The banks simply retrained their staff for sales. This means that if you used to give out money, you would then advise and sell insurance. So the job itself changed, but the statistics only showed employment. So, what do we conclude? Professions are collapsing quietly. This was already the case in the 70s and 80s. And statistics sometimes take decades to catch up at all. This time, however, that offers me little comfort, to be honest, because for the first time there is a study that tracks this whole thing in real-time for the DACH region, and I'll show it to you in a moment. But of course, we all use technology to work more efficiently. That's not what I want to debate here at all. Technology has always fundamentally moved us forward, but it should also be used to make things truly better for all of us. And the reality for many is currently simply different. Fortunately, however, not necessarily in the private sphere. And for that, I received the HP Omen 16 from Notebooks Billiger and I'm even allowed to do a small Linux vs. Windows test for you, a video within a video, so to speak. The notebook is still on offer for Easter, so feel free to check it out. It comes with an Intel Core U75H processor, a 2K IPS display with up to 165 Hz, 24 GB RAM, and 1 TB SSD. And the core component for gaming is, of course, an Nvidia RTX 5070. And now the twist. Windows 11 is of course pre-installed. We'll put Linux on it. Cash, to be precise. And even the setup of Linux was as easy as rarely before. You can simply go into the BIOS, boot from USB, and you have everything you need for an easy Linux setup. You can, of course, simply install Steam, and if you bought Cyberpunk, for example, via GOG, like I did, then you can play the games via Heroic Games. Almost all Epic games, GoG, and even Amazon are integrated as stores. Oh, and of course, ray tracing also works under Linux. And no joke, if you look at the benchmarks, I probably wouldn't even notice that one is Windows and the other is Linux. Linux had one frame per second less in Cyberpunk with the same settings. By the way, you can also activate frame generation under Linux. I had it off for the benchmark, but that's an Nvidia feature where the next frame is generated in between using AI. And nowadays it looks incredibly good and makes the whole game smoother. And it also looked incredibly good for Linux in Black MU Kong, because DLSS 4.5 now works fully on Linux. This uses the special AI chip in the notebook to upscale the resolution. The rest remains the same, because the AI chip is normally not used at all during gaming. With the recommended settings, we get 53 FPS under Linux here, but unfortunately the game is quite clearly optimized for Windows. There's about 20 FPS more there. Something still needs to be done there. I played several other games, of course. The new Life is Strange Reunion runs smoothly, of course, and not even Resident Evil 9 is an issue. Just start it and it runs, and there are no official benchmarks here, but here too, start, play, and I had 120 FPS consistently with relatively good settings, and the nice thing is that the Omen stays permanently cool thanks to the special Omen Tempest cooling. So if you've ever wanted a notebook with real power, I think this is a very fair price in the Easter sale, and of course a great offer, especially, but not only, for the Linux faction. Thanks to Notebooks Billiger for supporting this video, and you'll find the link next to me and, as always, in the description below. But what about the world of work? Well, the economic research institute of ETH Zurich, the KU, published a study last year that I want to recommend to you and of course link, like all other sources, and by the way, it's even in German, which honestly felt incredibly weird to read. Regardless, what they did was take the Swiss labor market register and job advertisement data from 2010 to 2025. They classified professions according to their AI exposure or adopted the classification from another paper, also linked below, and then looked at how unemployment in these professions developed before and after the introduction of ChatGPT in 2022. The study design is pretty cool, it's called difference-in-differences for the non-statisticians among you. You compare two groups, namely professions that are heavily affected by AI and professions that are less or hardly affected, and then see if a gap opens up, i.e., if the values differ afterward. This is quite practical because you can rule out one thing, namely that from 2010 to the end of 2022, the groups ran practically in parallel; when the economy was good, when the economy was bad, the professions rose or fell together. But then in 2022, at the end of 2022, the ChatGPT release, the lines diverged. For programmers, database developers, analysts, proofreaders, i.e., precisely the professions that are likely most affected by language models, unemployment rose by up to 27% more than in the control group, i.e., those who were less affected. Michael Siegenthaler, one of the two authors, put it quite directly in an interview: "The results strongly suggest that generative AI is behind the increase in unemployment in some highly exposed professions." But the exciting part, which I had to read three times, it wasn't just about unemployment figures, it was actually about job advertisements. In highly exposed professions, they fell to 60 to 70% of their pre-ChatGPT level. So, in 3 years, about a third fewer jobs for programmers in Switzerland. Not because companies are going bankrupt, but because they simply don't need the positions anymore, and the effect doesn't hit everyone equally. For employees under 50, unemployment in these professions has almost doubled. Those over 50 are much less affected, and there's a very simple reason for that. The older ones are in existing contracts, meaning protection against dismissal, works council agreements, collective bargaining rights. So those who are in it stay in it more easily. But those who are younger and are applying now, the jobs are simply no longer there. In addition, the older ones, i.e., the seniors, are often the ones who can get everything out of AI and quickly see when the AI makes a mistake again. The younger ones are only just building up this expertise. Stanford confirms exactly this with US data. Bring Jolfs Son and his team have shown that for career starters between 22 and 25 in AI-exposed professions, aka programmers, for example, employment has decreased by almost 20% compared to the peak. In Big Tech in the US, the share of university graduates in new hires has fallen from 15% before the pandemic to only 7% today. The younger ones can't get in anymore, and the ones at the top only notice it when no one is coming up from below. So back to my friend from the beginning. With 10 years of experience, he's still in. He already has a job. But he told me that his company hasn't advertised a junior position in a year. And that's not uncommon for German companies either. I've heard exactly this from so many of you. The question is therefore not who is to blame. The question is who benefits and who pays the bill, because right now the younger ones, who aren't even at the table yet, are paying, because they have no lobby, no collective bargaining agreements, no union, or anyone to negotiate for them. They just notice that the door is closed. So the door is closing for the younger ones. Official statistics don't really see this, and those who are already in think for the moment that everything is fine. But it does concern them, because what's happening inside is almost stranger than what's happening outside. And for that, labor sociology also has a term: Job Hollowing. I learned the term recently and I really like it now because it describes it incredibly well. Job Hollowing doesn't describe a job disappearing, but rather becoming hollow in content. The title remains, and the salary remains. The desk remains, in the best case. But the tasks that made up the profession, why one chose it, the independent thinking and problem-solving, and the creative work, are being taken over piece by piece by the machine. And what remains for the human is simply to check whether the machine was right in the end. Under time pressure and without enough context. Here too, babysitting of AI. The Boston Consulting Group published a study on this in the Harvard Business Review in March with almost 1500 people in AI-intensive roles. 14% report what the study calls AI Brainfry: extreme mental exhaustion from monitoring AI output. 33% more decision fatigue, and as a logical consequence, 39% more serious errors. What do you think is the source of all the bugs on YouTube that we all feel all the time? People who check AI output all day make more mistakes than those who do their work by hand, and at the same time, 39% more of them want to quit than in the control group. AI Brainfry was described by participants as a kind of brain fog, and holy shit, is that accurate. Every week I have some days, usually Friday to Sunday, when I work on my projects and tests to stay up to date on AI and software development, security, and so on. And the mental hangover or fog is exactly what describes it best. In practice, you have to do so many things at once. The pressure you put on yourself doesn't get any smaller. So I have not one, but I counted, nine projects simultaneously, of which you only know about three. Mor Reeder and the premium RSS feed, which you get via Patreon by the way, and the project we started together in the stream. So I'm constantly switching back and forth and letting it run overnight because it's running, and then I'll jump up from dinner because there was an error and I need to fix it quickly, because then I need another hour where it doesn't need me, and then I have to wait again. But the corporate pressure is unfortunately not only growing for me. Of course, if a bug comes, I can't afford to fix it. Unlike YouTube, which hasn't fixed the same errors for months. But why actually, why does it break us so much to watch a machine work? It's actually less work, less typing, and less thinking. It should actually be much more relaxing than before. But it's not. And the explanation for that, I researched that too, is almost 80 years old. Norman Mcworth, a British psychologist, studied Royal Air Force radar operators in 1948. This will become relevant again soon. They stared at a screen for hours, waiting for blips. His finding: after 15 to 30 minutes of passive monitoring, the detection rate drops measurably. Not because the people were unmotivated, but simply because our brains can't do that. Passive monitoring is not what we can normally do. We are built to do things. If we only watch, the brain switches to an energy-saving mode, whether we want it to or not. And when I think about it, and yes, I did exactly that for 8 hours yesterday, and watching a series doesn't count, of course. You have to stay attentive, just like with this YouTube video here. At least something is happening there. But Parasuraman and Mansei added to that in 2010 with a detail important for us: Automation Complacency, i.e., blind trust in automated systems, which occurs in both beginners and experts, and no, you can't improve it through training. We often trust technical systems more than ourselves because we know our own mistakes, and the effect is not necessarily alien with AI. There's an interesting fact where you can see exactly that in practice. With self-driving cars, they had to do exactly that. Google gave their employees test cars with the instruction to please stay attentive and be able to take over in emergencies. What happened? Well, the drivers were putting on makeup, playing on their phones, one even actually fell asleep on the highway. The then-VP John Krafcik said afterwards: "What we found was pretty scary. It's hard to take over because they have lost contextual awareness." So we are really, truly shit at staying, well, really attentive, even if you don't have ADHD, like I do. Quite simply, because attentive is so vague. What am I supposed to pay attention to? And Google drew the consequence from that, which is also of interest to us here. They completely removed the steering wheel because it's simply safer to take people out of the equation entirely than to just let them sit in the middle and hope they pay attention. So Level 4 instead of Level 3, fully automatic instead of semi-automatic. The dangerous middle ground of humans monitoring machines is exactly what we are doing in all knowledge work right now. And the automotive industry has already proven that such a thing cannot work. And now imagine the daily life of a software developer or graphic designer or whatever in 2026 for a moment. 8 hours, ticket after ticket, Copilot or Cloud or whoever generates it, and they check it. Air traffic controllers, by the way, are allowed to work for a maximum of 2 hours at a time. Then a half-hour break is mandatory because the error rate increases so drastically. We sit for 8 hours in front of machine output, often even on multiple tickets simultaneously from multiple systems, and the company then assumes that we will become even more productive with it. Do you know what my friend said about his 40 minutes per ticket? The first two hours are fine. After that, I merge things that I can no longer properly read. And that doesn't just affect software developers. My partner used to be an MTLA, a medical-technical laboratory assistant. In the lab, she analyzed blood counts, i.e., counted hundreds of blood cells under a microscope, then categorized them, recognized patterns, signs of infections, anemias, blood cancer, and so on. The German Cancer Research Center has developed an AI system called Hemorasis that doesn't count 100 cells per sample like humans, but 10,000. This allows for a statistically more accurate result than if a human were to do it. But one also has to rely on the system being able to do that in the end. The system was introduced at the time so that the device and the human did the analysis. Until it was then safe enough that the device could provide quite good answers, or the device was good enough because it was also further improved. I'll come back to that in a moment, and it's no different on social media. According to an analysis by originality.ai, over half of all longer posts on LinkedIn are now AI-generated and perform 45% worse on average in terms of engagement than human ones. The economic logic behind it is wonderfully cynical. Whether it's good or not, the main thing is that it costs nothing, or at least less. We remember Ultralativ. "For us, it's enough." Instagram is no better in this regard, of course. On the contrary, personally, I even feel that we have reached about 80% here when it comes to text posts. Text posts, by the way, with which people who would compete with us, for example, are made. For us, a full workday goes into a short, i.e., as a team. We hardly do text posts, but we actually wanted to, but not when I have to hear that an AI did it, even though I spent hours on it myself. That's demotivating to death. Freelance orders, which I also regularly accepted in the past, have fallen by 21% within 8 months after ChatGPT for automatable work. Writing jobs by about 30%. This affects self-employed people in particular. I hardly get any inquiries anymore, except perhaps for advice on how to use AI more efficiently in a company. The illustrators' organization has written an open letter to 50 German publishers: Please refrain from using AI-generated images. Such things don't happen if they haven't happened before. And also the voice actors, who are boycotting Netflix, by the way, because they are supposed to provide their own voices as training data without additional compensation, are perhaps known to most of you. So lab, content, code, design, language, it's the same mechanism everywhere. The demanding work goes to the machine, and the tedious ass-end that you don't want to do remains with the human. My partner and I often talk about it at home, and she says that counting wasn't necessarily the most exciting part. The exciting part were the special cases, the really unusual ones, and precisely those are now increasingly falling into the AI's area of responsibility, because it's getting better and better. But there's even a word for this strange feeling: Heteromation. In automation, the machine becomes the tool of the human. In heteromation, you are the tool of the machine. So you validate the output, take responsibility if something goes wrong, and stick your neck out, as lawyers have to do now. But you still do something. You only see this when you understand how the models are trained. Every time you correct Copilot or any other AI, you make Copilot better. Not so that your correction lands directly in the next update tomorrow, but your acceptance rates, your rejections, your bug reports are signals that are aggregated and flow into the training of the next generation. RLHF Reinforcement Learning from Human Feedback, and you are the Human Feedback. With my partner, it's the same. The diagnostic patterns she learned over years are now in the system's training data. So her expertise has become the dataset. Software testing, which used to be its own department with its own budget, is now outsourced to the customers in most companies. For cost reasons, of course. The bug reports you submit are increasingly processed automatically by AI and then simply flow back into improving the product. We provide the data, the AI learns from it. The next version will need you a little less, and that in itself is perverse. But the most perverse thing about it is that we can no longer get out of it. You can't stop participating. The tools make you more productive at work today. Me too. I can't give up Cloud Code or Open Code or whatever they're called. It's already too good. Without the tools, the projects and thus the products wouldn't exist. Economically speaking, I literally can't afford to read every study for my videos completely before I know if I even need it, or if it contains what I need. Of course, I read them afterward. But 99% of all studies are discarded for the videos, and I know I don't need to look at them anymore because I know there's nothing relevant for the video in them. So I have to use AI to produce even one video a week, or I would have to significantly increase my team, which I can't afford because the economic pressure is increasing. And it's exactly the same for software developers, for designers. The clients pay less because you can do it with AI, so it becomes cheaper for others. And that's exactly the trap. Not just mine, but yours too. None of us ever chose this. But whoever stops now falls behind. You are training data, and you know that, or at least suspect it, just like I do. It feels like shit, but hey, at least we're still needed, right? Well, synthetic training data is currently getting good enough that models increasingly train each other, and if that's enough, then you're no longer training data. With Nemotron 4, an open-source AI from Nvidia, 98% of the alignment training data comes from an AI model. The external review, i.e., the human who looks over it and says, "Yep, that's okay" or "No, not really." That is now built into the system. So we take the outputs and improve the next generation with them. And that's exactly where we take the human out of the pipeline. A little history lesson. In 1844, there was an uprising of handloom weavers in Silesia. Mechanical looms had made their work redundant. Heinrich Heine even wrote a poem about it, which was then banned. We had that in German class at some point. But the weavers didn't become unemployed. Many continued to work in factories on mechanical looms for significantly less money. The statistics of the time would have said that textile production is growing, employment is growing, everything is fine. But the reality was pauperization, i.e., impoverishment with full employment. And so you sit at a machine you didn't design, at a pace you don't control. You recognize the quality that you are no longer allowed to deliver yourself, and you go home with less, while the factories produce more than ever before. I don't want to compare your living conditions. So in 1844 there was also famine. In 2026, at least there's a salary that comes more or less on time for most people, while the job below you breaks away. But what's the same is that in both cases, the work was dismantled from the inside out. What brings in money is done by the machine. What is annoying remains with humans. And in both cases, statistics only saw stable employment for years. That sounds somewhat familiar to us now, only that today we are no longer sitting at the loom, but with a coding assistant. That's a lot of heavy stuff at once, and yes, I know, I'm sitting here at my desk with five monitors, everything is running more or less, and yet I still notice how strangely quiet it all is and how strange it feels. SAP, as part of a major restructuring in 2024, changed nearly 10,000 positions, and what is even stated in their own annual report is spicy. Employee satisfaction has fallen from 80% in 2023 to 74%. A historic low. In the first half of 2024, it was even 72%. That sounds like a small amount, but you feel something like that. You probably know at least one person who simply no longer knows why they get up in the morning. For what? That kills motivation completely, and that in turn kills actual work capacity. So, on paper, little changes. The job is still there, the money lands in the account, but something is missing for us, and it's becoming the industry standard that it's missing. Deutsche Bank announced at the end of January that it would close around 100 branches and introduce an AI assistant in customer service. Claudio Desantes, Head of Private Customers, said in the original tone: "Employees who retire or leave the bank for other reasons will not be replaced." A few weeks later, Ergo, with 17,000 employees in Germany, announced it would cut 1000 jobs by 2030, i.e., 200 per year. Lena Lindemann, Head of HR, says: "We want to leverage the efficiency potential of AI, but at the same time empower employees to work in other jobs for us." Affected are call centers and claims processing. All through natural fluctuation, i.e., no dismissals. That means 500 retraining positions. This is all socially compatible, and no one is demonstrating against it. And the official explanation for this is, well, demographics. The Boomers are retiring, hundreds of thousands per year. By 2035, about a quarter of the population will be over 67. Let's see if the retirement age will still be the same, but you understand what I mean. But yes, that's true. Demographics is the relatively convenient explanation, which is honest, but AI makes it profitable not to have to replace jobs. Without AI, you would have to replace the positions, otherwise work would pile up. With AI, you can just let it go for now, and it doesn't really show at first. According to the DIHK, 48% of German companies are currently not looking for new personnel at all. In the statistics, employment is stable, but 48% is a lot. Imagine you're 22, you've done everything right, even got a degree, and almost half of all companies are simply not looking for anyone anymore. And almost simultaneously, OpenAI recently published a 13-page policy paper explaining to the government how to manage the consequences of their own products. Public funding, four-day week, sustainable social benefits, all ideas the government should implement. What OpenAI itself says: $100,000 for scholarships and 1 million API credits for a company that generates billions. In the paper, they even admit, and I quote at this point, translated, of course: "There is a risk that economic gains will be concentrated in a small number of companies like OpenAI," and then they suggest that governments should please do something about it, not them. And yes, I know, that's how it's intended. Lucia Velasco from the Inter-American Development Bank summarized it quite aptly: "OpenAI is the party with the most vested interest in this conversation, and the proposals shape an environment in which OpenAI operates with considerable freedom." That makes me a little angry, to be honest. Not because the proposals themselves are bad, but because they know exactly that there is currently no government that can or even wants to implement something like that. Energy crisis, pension crisis, defense, migration. AI social policy is really not on any priority list, at least I haven't seen one. And OpenAI can then comfortably stand up in 5 years and say, "We told you so, why are you complaining?" But then it will be too late for all of us. Or is it? Our German Digital Minister Carsten Wildberger of the CDU, by the way, said things in an interview with the Neue Osnabrücker Zeitung this March that I have never heard from a CDU minister, let alone expected from a CDU minister. I quote: "The era in which industry was a job machine is coming to an end. And if AI takes away the jobs of computer scientists, mathematicians, and many others, then these people need a different, meaningful occupation." And then, "I am convinced that a universal basic income can be part of the solution." Feel free to read that again. Rewind too. A CDU minister spoke positively about basic income. The CDU has rejected UBI for decades. So this is not to be read as a proposal. They don't actually want to admit that. It's an admission. The CDU is basically telling us: "Hey, our previous economic model is now a sunset model and might not work as well as we thought." And if they say that, then it's really serious. But no one on this planet can beat Stockfish. No grandmaster comes close to this chess AI. And yet chess has exploded in popularity since then. Not despite it, but probably precisely because of it. The goal was never to beat the computer. That's utopian. It was always the craft itself. In pilot training, the FAA recommends regular flying without autopilot so that pilots don't lose their manual skills if the thing fails. And then I have a question. Why do you actually do things, i.e., why did you start programming, designing, writing texts? For my part, I didn't start doing it because I wanted to make economic profits. I started both with IT and with YouTube, i.e., writing scripts and narrating, out of passion, because I personally enjoy doing it. Out of passion, and for me personally, and don't get me wrong, I'm incredibly happy that apparently many people also want to watch what I enjoy doing, and sometimes it's really, really hard. Sometimes I have to push through. Last week's script, for example, I wrote it three times, no joke, three times, and then just threw it away, which is why you didn't have a video at Easter. That was very hard for me, and I'm incredibly sorry for you, but as a person, I want to write something that I stand behind and that I also feel. And I believe that's exactly what makes the difference we see in the results. I'd like to remind you again briefly. AI-generated LinkedIn posts have 43% less engagement. Why that is, I think you can't really pinpoint it interpersonally, but I personally notice when a post just wants my like, or when it's really about a message. I've started scheduling days when I try to skip Cloud, i.e., for coding, and I know that doesn't really help you. That's my personal plaster on a structural problem. But I also told my friend that he should build something without Copilot again. He laughed and said he doesn't have time for that, at least not anymore, and at least not at work. But after a few days, he described to me that he wants to start a private project, and that he will also use AI for it, at least partially, but with it, not from it. He wants to solve a problem that has been bothering him for several months. And then it suddenly doesn't matter at all if it takes a little longer, and you can create good code with AI or take your time and write the code yourself. Take your time for good results and, for example, good graphics, regardless of whether they are handmade or AI-generated, because without the ever-worsening time pressure, you can also decide for yourself what you really want to do yourself and what not. And personally, since I've been doing it this way, it's fun again. But I also know that doesn't necessarily help professionally. We are currently seeing, and I've said this many times before, the industrial revolution for cognitive work. We, who specifically chose this job, probably have very little desire for assembly line work, at least most of us. But we don't necessarily have a choice either. And yet I believe that if you give people with a passion the opportunity, something truly special can come out of it. Because exactly this creativity is what is needed for it, and that is precisely what AI lacks to this day. Perhaps that's why so many self-employed businesses have been registered as never before. And I think that's exactly what should be supported. But hey, I'm just a sample from a single person. So I'd be particularly interested in how it is for you. Are you still a developer, copywriter, designer, or have you become a full-time inspector by now? Until next time in cyberspace. Bye.