Ever since ChatGPT, AI has been on everyone’s lips. What was still science fiction just a few years ago has now become reality. AI is increasingly permeating nearly every area of life and shaping many aspects of our daily routines. As an IT company, we’ve been addressing this topic since 2015 and have been incorporating machine learning and artificial neural networks (ANNs) into our projects. In addition, as part of our research activities in collaboration with universities in the region, we’ve successfully developed AI models for various application areas.
To mark Artificial Intelligence Appreciation Day, we spoke with three of our employees. In this interview, they share their experiences and predictions with us and offer interesting insights into their work. Among other things, they discuss open-source models, economic aspects, and the impact on various areas within the IT industry.
First up is Danny Hucke, who serves as our Digital Product Design Team Lead and Research Specialist. Danny has been working closely with AI for quite some time, and we asked him for his take on the current situation.
I definitely see one of the main reasons for the rapid success in recent years as the ability to scale very quickly and very broadly today—more data, more parameters, more computing power, and more storage. Even today, this still comes with very high costs. The current AI revolution did not come about through fundamentally new theories or algorithms, as these have essentially existed for decades. Computing power and scalability are clearly the decisive factors here.
Nevertheless, in my experience, AI hasn’t yet fully made its way into technical work. Sure, even here at our company, more and more developers are using tools like ChatGPT for their research or when, for example, an SQL query is needed. A handful of employees also use GitHub Copilot regularly—but it hasn’t caught on with the vast majority yet. However, anyone who wants to remain competitive in software development over the next few years won’t be able to do without AI systems—and that applies not only to software development.
When you say “not just for software development,” which industries specifically do you have in mind?
The first thing that actually comes to mind is the creative industry. That really surprised me—and probably many others as well—that the first jobs to be significantly affected aren't assembly-line jobs, but rather those that actually require an enormous amount of human creativity.
In general, however, I definitely see AI as a time-saver and an enabler. The barrier to entry for topics that used to be rather complicated has been significantly lowered. The machine understands not only my language but also complex languages such as programming languages. The system is just as smart as it was before, but now I can tell it what to do.
These tools provide quick access to a variety of resources, combined with a wide range of options for taking action. For example, anyone who wants to explore a new framework today can simply ask ChatGPT. The fear of the so-called “blank page” has become obsolete, and overcoming that first hurdle is easier than ever. Especially in the medical field, we’re facing possibilities that could certainly be described as revolutionary—Google is currently at the forefront here. But a lot will change in everyday office life as well; Microsoft, for example, is currently equipping its entire Office suite with AI features.
As far as software development is concerned, given the current state of the technology, I don’t see AI completely replacing us humans. Above all, it can take over tasks that don’t require much mental effort, such as writing boilerplate code. Even more complex tasks will most likely not be able to be outsourced to AI systems in the next 1–2 years. However, simple tasks—such as building basic data APIs or creating less complex websites—are already being handled exceptionally well by AI models today.
These tools will also be used much more extensively in our personal lives, handling everything from travel bookings to ordering food. The Plus version of ChatGPT already offers a wide range of plug-ins and integrations, a trend that will only grow significantly in the future.
Those are actually pretty good predictions. Do you see any risks associated with AI that we might face in the future?
Sure, despite the advantages, there are of course risks as well—and you don’t even need dystopias in which machines take over the world to see that. The disinformation campaigns of recent years are a good example. With AI, it’s now possible to scale these campaigns to unprecedented levels—using machines that write at a human level. What used to require entire armies of trolls can now be accomplished by small groups using AI programs. With relatively little effort, it’s also possible to generate voices that are virtually indistinguishable from those of real people.
Technology never stands still, and improvements are being made every day. AI is already being used today for various forms of scams, phishing, and disinformation, and I believe we’re currently on the cusp of things getting really dicey. However, it’s also important to note that these tools can be used for other purposes as well—for example, to protect oneself against such attacks.
To what extent this has to do with the intelligence of these systems—or whether these systems are intelligent at all—is a matter of debate. What they can definitely do is make plans. Anyone who has ever asked ChatGPT to generate step-by-step instructions for a specific problem knows this—regardless of how good the answer turns out to be. If these systems are given the opportunity to carry out their plans in the real world—for example, through access to other programs, the internet, APIs, etc.—a great deal of damage can be caused here if their intentions are malicious.
However, in my view, we are still a long way from achieving Artificial General Intelligence (AGI). That is, the form of AI that, as an autonomous system, combines human cognitive abilities with the computational power of computers. It’s important to note that no one can explain how consciousness arises or which „algorithms“ in our brains actually give rise to our cognitive abilities. Perhaps the current models aren’t all that far off. But I believe—though I can’t prove it—that there are still key elements missing before machines can truly surpass human cognitive abilities. I come from a background in mathematics, and in my conversations with ChatGPT, for example, I don’t see these models coming close to finding solutions and proofs for complex mathematical problems the way we humans can.
As I said, we’re not yet at a point where AI behaves like a human brain, but the mere possibility of this development poses risks in itself. No one can predict the future; the only thing that’s clear is that major changes are coming our way in the next few years, and I definitely see AI as an opportunity to enrich and improve our lives—both personally and professionally. The key here is simply not to trust the systems blindly and to double-check every solution. Whether it’s code or financial advice: never accept anything you don’t understand.
Next, we started with Thomas Schäfer who works for us as a software engineer.
I first came into contact with AI while I was still in college. Back then, I wrote my master’s thesis on artificial intelligence. I really enjoyed the topic because there are so many areas of application where AI can support us humans—and I deliberately say “support.” At the moment, I don’t think AI could ever replace humans on a large scale to the same degree. However, in the area of pre-filtering, I see a very successful use case for AI support. That was exactly what my master’s thesis was about. An AI model was trained to detect hate speech on the internet and pre-sort comments. This saves time and allows moderators to work more efficiently.
How do you assess the current situation?
I have to admit, I’m quite amazed at how quickly progress has moved over the past five years—especially that open-source models are increasingly able to match the success of major commercial models. Generally speaking, I’m not a fan of sharing internal company data with an AI that you don’t host yourself. The case of Samsung and ChatGPT comes to mind. These tools can, of course, be a huge help in the software development process, but at what cost? Often, the data entered is used to train that very code interpreter. We need to raise awareness about this.
Fundamentally, there’s also the question of whether to use open-source models, which, while constantly improving, still lag behind applications such as ChatGPT or GitHub Copilot in terms of competitiveness. Do you want to use state-of-the-art technologies with all their associated advantages and disadvantages, or would you rather wait a little longer until open-source models have caught up?
That's an interesting comparison. What has been your experience with tools like that?
As a software developer, you naturally always want to stay up to date. I, too, use tools like ChatGPT to explore how it all works. Currently, however, I’m only using it in my personal life—and even then, only with fictional data. Abstracting the data so that it can no longer be traced back to its source would take me more time in my work context than coming up with a solution on my own.
Generally speaking, I find all these applications incredibly exciting because there’s so much you can do with them. However, I see a problem with attempts to simply outsmart an AI, sacrificing valuable (computing) time in the process, rather than using the AI productively.
AI tools are becoming increasingly important and will eventually take over large parts of our work in software development—this transition must be carefully considered. Especially when dealing with massive amounts of data that could no longer be processed using human resources, the use of AI offers significant advantages in aggregating data and thus drawing conclusions.
Lately, there have been a number of articles circulating in various media outlets that address the dangers of AI—even going so far as to describe doomsday scenarios. As someone who works in this field, what is your take on this?
Basically, AI does exactly what it is taught to do—a neural network does not create new knowledge. Personally, I also find it difficult to use the word „learned“ in this context. By doing so, we’re anthropomorphizing something that isn’t human and that fundamentally differs in how it works from what we understand as human learning. As far as our jobs as software developers are concerned, I don’t see any risk from the use of AI, nor do I foresee any dystopias in which AI takes over the world. Nevertheless, the dangers are real.
Much of what we do is already determined by AI. For example, the order in which posts appear on social media is determined by AI—and that’s actually problematic. People present themselves as successful on social media, and these posts get a lot of clicks. This ensures that you’re shown more and more of these kinds of posts. Every day on these platforms, we’re bombarded with a flood of successful and happy people, and our brains can’t seem to switch off—we start comparing ourselves to these utopian ideals. We should avoid learning from social media and instead view it as a cautionary tale for AI ethics. I myself now use only Mastodon and rarely LinkedIn—I’m always on the lookout for ethically sound solutions.
I see another problem in the flood of so-called “fake” sites created by entire content farms using AI tools. These sites deliver 100% AI-generated content and often cater to niche audiences—they’re loaded with affiliate links that are more about making a quick buck than offering actual help. These are real issues that we need to be aware of. Overall, however, these are all fairly manageable risks that we can comprehend with our human brains. Of course, it always depends on how these things are used, but on the whole, the opportunities outweigh the risks here, and we should take advantage of them.
As far as our work in software development is concerned, AI tools will take over large parts of it sooner rather than later. Even today, we already use code scanners to improve code quality. This convenience also allows us to focus on more complex and important issues, which is why software development will continue to be in human hands. These tools support our work rather than replacing us—they will remain, or will become, an integral part of the company’s future toolkit.
Let's now move on to Marcel Haelke, who leads our Data Engineering team. We wanted to ask him what opportunities—and what risks—he sees for data science as AI continues to advance.
In general, I view the achievements in the field of AI as a promising opportunity for data science. After all, the development of the underlying algorithms is, in and of itself, pure data science. The advancement of AI in general—especially when it comes to machine learning algorithms—is certainly no exception. However, I don’t currently see this leading to a rapid replacement of data scientists or to their eventual disappearance. Rather, we’re gaining a powerful new tool in this field. For example, there is still a considerable amount of manual work involved in preparing and cleaning test data. But as AI systems become increasingly intelligent, these tasks could, of course, be outsourced in the future.
Advances in the field of AI fundamentally open up a wide range of new possibilities. Machines have the advantage of possessing a neutral perspective, which allows them to recognize more patterns and identify deviations from them. Their perspective is not limited to what is obvious to humans. In this respect, one could certainly venture the hypothesis that machines expand the scope of data science rather than replace it. Whether it involves business decisions or troubleshooting various systems, AI support can drive significant progress in these areas. Debugging will be simplified, and it is highly likely that systems will be developed that can independently search for sources of error. In the long run, nothing in the field of data science will remain as it is today. Many of the algorithms we work with today are designed to power precisely such intelligent systems.
Still, I don’t think developers will be phased out overnight. But AI can, of course, help speed up processes. These tools can already handle writing simple code or, for example, automatically generate and verify classes. Of course, this doesn’t completely replace humans in software development yet, but we also need to pay attention to how the competition is using these tools. As a result, products can be offered with less effort and possibly at a lower cost—this is something companies need to stay on top of and also train their own developers on how to use these tools properly. Of course, many issues remain unresolved here: What licenses govern copyright, and what happens to the generated code? Above all, it’s important to be transparent with your own customers.
As far as jobs are concerned, I don’t see AI causing any positions to disappear anytime soon. We’re still grappling with a shortage of skilled workers, and I think it’s very unlikely that this situation will reverse so quickly that we’ll have to let skilled workers go. That doesn’t mean I rule out such a development in the long term. Overall, the whole issue is a double-edged sword. On the one hand, it naturally has the potential that, at some point, fewer developers will be needed because so much can simply be outsourced or generated by AI systems. Of course, fewer people will also be needed for the remaining tasks—operating these tools and reviewing their results. At the same time, however, it also opens up entirely new project opportunities, and this reduced workload makes it possible to take on and implement more projects on the side. So we’ll be able to run many more projects in parallel, and that, of course, will require more people.
That's an interesting perspective. Aside from economic considerations, where do you see challenges in dealing with AI that affect us as a society?
I see some risks in relying too heavily on these tools to provide a ready-made solution. Especially with code you didn’t write yourself, it’s all the more important to take a closer look to ensure that the functionality and security standards are implemented exactly as intended.
Of course, this enormous volume of data on the Internet—which is used to train these algorithms—can also pose a risk. Among other things, there is an extremely large amount of false data and misinformation circulating, which could be disseminated even more strategically in the future. We need to keep our eyes open here. What does the algorithm learn from? Where does it get its information for queries? Can it be negatively influenced?
As the company’s sustainability officer, I naturally also think about the enormous amounts of computing power these applications consume. Of course, this also has an impact on our environment. Sure, AI tools can make a lot of things easier, but at what cost? How much carbon emissions are generated, for example, when you have ChatGPT plan your vacation? I think there needs to be much greater awareness of this issue, not only in the IT industry but also among individual users. The more people use these tools, the more computing power is naturally required, and that generates new emissions. Depending on where these systems are located, security standards naturally play a major role as well, and these systems can become both a weapon and a target.
I see another risk in distinguishing AI-generated content from human-generated content. What’s real and what isn’t? It’s easy to be fooled here. Fake news has been a topic of discussion almost everywhere in recent years, and I already see problematic trends here that could be exacerbated by AI. But perhaps these systems will also help us identify such content.
Last but not least, what do you think about robots taking over the world in the future?
I think this is just media hype right now. That doesn’t mean we shouldn’t always keep in mind, as we develop these systems, whether what we’re currently working on can still be controlled—or whether we understand how it works. I see this as a clear responsibility for developers. It’s important to take advantage of these opportunities and use them for the benefit of humanity.
We would like to thank our employees for these insightful perspectives, and we wholeheartedly agree that AI represents a tremendous opportunity to bring about positive change in our world. We are currently seeking new solutions that will benefit both our customers and our employees. Our expertise in AI and machine learning is already giving us a significant edge. Together with our customers, we’re shaping a smart future—through software development.
