In Part 1 of this interview, we explore what generative AI actually is, dispel common myths (like fears of AI taking your job or thinking like a human), and discuss why soft skills and higher-order thinking will become essential in an increasingly AI-augmented world. 

Supertrends: Can you give a brief overview of what generative AI is and why it’s important to understand it? 

Jonas Bjerg: The generative part of generative AI refers to how the artificial intelligence generates new content—an output of sorts—whether it’s writing text, composing music, producing images, or even generating software code. 

Unlike traditional AI, which might classify data or make predictions, generative models learn patterns from vast amounts of training data and then generate new “original” output when given an input (often a simple text prompt). For example, models like GPT-4 can write an essay from scratch, and tools like DALL·E 2 can produce an image file. 

In practical terms, generative AI has huge implications for productivity and creativity as it is a new way for humans to interact with computers. It is the first friction-free interface, as you can now talk to a computer like you would to a human, and the computer will understand you. Gone are the days of navigating through keyboard shortcuts or complex menu systems. It can draft emails or write software, as long as you explain exactly what you want it to do.  

Tasks that once exclusively required human effort can now be automated. Many organizations have noticed these benefits. In fact, about one-third of companies were already regularly using generative AI in at least one business function as of late 2023, and projections show over 80% of firms could deploy generative AI by 2026. This rapid adoption means even early-career professionals will likely encounter generative AI tools in their jobs. 

Understanding generative AI is crucial to harnessing its potential responsibly—and to avoid being caught off-guard by the changes it’s bringing to workplaces across industries. As with any powerful technology, the more we understand it, the better we can leverage it for positive outcomes while managing its risks. 

Supertrends: What sparked your interest in generative AI, and how has your perspective evolved over time? 

Jonas Bjerg: I was fortunate enough to study my master's in San Francisco and Silicon Valley, so I got a front-row seat to everything AI before ChatGPT became mainstream. My interest began with sheer amazement. 

I vividly remember attending a class at Stanford years ago and seeing a demo of a generative adversarial network (GAN) that created Kanye West songs in seconds that sounded like they were made by the original artist. It was mind-blowing. The concept of a machine producing something so convincingly human was fascinating and a bit eerie. That early exposure got me curious about how these models worked under the hood.  

At the time (mid-2018), generative AI was mostly an experimental topic in labs—think of those fuzzy AI-generated images or basic computer-generated music. It felt like a niche frontier of AI. My perspective then was mostly excitement at the technical feat: “Wow, neural networks can imagine things!” 

Over time, my perspective has evolved tremendously. The field progressed from those early GAN experiments to the advent of powerful large language models. A true turning point was the release of ChatGPT around 2022, which thrust AI into headlines worldwide. 

Seeing an AI system generate fluent, coherent answers to almost any question made me realize generative AI was no longer just a novelty—it was becoming a useful tool. By then, I had my second master's degree  in data science and was training and building my own neural networks. So, naturally, my focus shifted from just how cool the tech is to what it means for people.  

Over the years, I’ve grown more thoughtful about the implications: How can this tech augment human creativity? What are the ethical challenges? So, it’s been a journey from fascination with what generative AI can do to a deeper consideration of why it matters for everyone and how we can shape its impact positively. 

Supertrends: What are some common misconceptions about generative AI that you've encountered? 

Jonas Bjerg: There’s a lot of hype around generative AI, which of course also brings misconceptions with it. One I encounter often is the idea that generative AI is truly intelligent or sentient. People see AI writing an article or coding and assume it “understands” the task like a human would. 

In reality, these models don’t possess genuine understanding or intent—they’re powerful pattern mimickers. They predict likely words or pixels based on training data. As a result, they can appear smart, but they lack the full breadth of human cognition. Their “thought process” is fundamentally statistical, not conscious reasoning. 

So, while a generative AI might sound authoritative, it doesn’t actually know things in the way we do—it has no awareness or comprehension of truth versus fiction unless such distinctions were encoded in its training. An example of this is if you ask the model which is bigger, 9.11 or 9.9, then models released prior to 2025 would say that nine point eleven is bigger than nine point nine, which of course is not how math works, showing it doesn’t truly understand what’s happening, but just mimicking what it thinks we want to hear. 

Another common misconception is that generative AI will inevitably replace humans entirely. I’ve had colleagues worry that AI will make artists, writers, or designers obsolete. It’s true that these tools can produce astonishing artworks or articles, but saying they replace human creativity misses the point. 

Generative AI lacks the personal experiences, emotions, and cultural context that human creators draw upon. The content an AI generates is based on patterns in prior data—it can remix and mimic styles, but it doesn’t create with intent or meaning. Human creativity often stems from imagination, empathy, or lived experience, which AIs simply don’t have. 

In practice, many creators use AI as a partner or tool—for example, to brainstorm ideas or generate rough drafts which the human then refines. Rather than replacing creatives, generative AI often augments them, handling repetitive aspects so humans can focus on inspiration and quality. 

In short, it’s very unlikely that you will lose your creative job to AI anytime soon. Instead, you are more likely to lose your job to someone using AI more effectively. I also hear the myth that generative AI is only about text or images—essentially, that it’s limited to writing chat responses or making digital art. 

In reality, the technology spans many domains. It can generate audio (like cloning a musical style or a human voice), video, and even come up with designs or blueprints. It’s been used to help invent new pharmaceutical molecules and materials, which is far beyond just “words and pictures”. 

This misconception likely persists because the most publicized examples are chatbots and image generators. But one of my goals when writing my book was to broaden the reader's view: generative AI is a general concept of AI-driven creativity, and its applications are diverse—from composing symphonies to proposing engineering designs or driving cars. Understanding these nuances helps people approach the technology more realistically, without the inflated fears or expectations. 

Supertrends: You wrote a book called “The Early-Career Professional’s Guide to Generative AI.” What inspired you to write this guide, specifically for early-career professionals? 

Jonas Bjerg: When I decided to write “The Early-Career Professional’s Guide to Generative AI,” it was largely motivated by what I was seeing (and hearing) in the news and in the industry. The emergence of tools like ChatGPT caused a mixture of awe and anxiety among many professionals—especially those just starting their careers. 

On one hand, everyone was amazed at what the technology could do; on the other, there was this undercurrent of worry: “Is AI going to take my job? How do I keep up?” I was inspired to write the guide as a way to navigate these feelings and questions. A kind of counterweight to the news articles’ doomsday narrative. 

For instance, as a student about to embark on 10+ years of medical training, it can feel overwhelming to find the motivation to study for all that time when the news is constantly telling you that your job will be performed by AI shortly. The fact of the matter is those narratives are many years from being the reality. Even if we had an AI doctor tomorrow, we would still need human doctors. Both to handle the human touchy-feely parts of the job, but also for years to come during the transition. It is true that you might not get to be a surgeon that does the actual surgery/cutting (as robots already do that incredibly well), but you will still be needed in some fashion in the room for years to come. So if your dream is to become a doctor, then you should absolutely still choose that path! 

AI is coming, but for decades, it’s likely to remain an assistant or tool—not a replacement. Generative AI is a transformative development—possibly as pivotal as the internet or mobile phone revolutions—and early-career folks stand at the frontlines of this change. I wanted to provide them with a roadmap to understand the tech, and a bit of reassurance that history shows new technology—while disruptive—also creates new opportunities for those prepared to adapt. 

In the book, I start by contextualizing generative AI in a broader historical perspective. We’ve seen disruptive innovations before (like the web), and while they do change the nature of work, they also often lead to growth and new roles over time. I felt it was important to convey that perspective—to replace fear with curiosity. 

Writing specifically for early-career professionals made sense because this cohort has the most to gain (and potentially lose) depending on how they respond to AI’s rise. They’re building foundational skills and habits now. I wanted to speak directly to them: to demystify the jargon (what is a transformer model? How does ChatGPT actually work under the hood?), to highlight the emerging opportunities (new career paths, new efficiencies in work), and to address the risks and ethics so they can become responsible AI adopters. 

In essence, the inspiration was to empower the next generation entering the workforce. Instead of feeling swept up by a “tech tsunami,” with a guide in hand they can feel more like navigators who understand the waves. 

Supertrends: How is generative AI currently transforming industries, and which sectors are leading the way? 

Jonas Bjerg: Generative AI is shaking up industries across the board, though some sectors are certainly leading the pack. One of the most visible impacts has been in media and marketing. Companies now use generative AI to create content at scale—from drafting personalized marketing emails to generating social media graphics. It’s like having a tireless junior copywriter or artist on call. 

In fact, surveys show the highest adoption of generative AI is often in functions like marketing/sales and product development, where there’s a constant need for creative content and new ideas. For example, advertising agencies use AI to brainstorm campaign concepts or even produce entire ads sometimes. This has accelerated the creative process in those fields, allowing human professionals to iterate faster and test more concepts than before. 

Interestingly enough, if we go back ten years, all AI experts agreed that creative jobs (like those marketing tasks) would be the last to be automated by AI—long after simpler repetitive tasks or blue-collar jobs. Paradoxically, those statements probably contributed to the creative fields attracting more capital, because they were seen as a “harder” problem to solve in the eyes of venture capitalists. That, in turn, meant creative AI companies received more resources and were developed faster and more effectively than experts originally predicted. 

Unsurprisingly, the technology sector is also a major leader. Software companies are integrating generative AI to assist in coding and design. Developers have tools like GitHub Copilot that can auto-generate code snippets or suggest fixes, which speeds up programming tasks. This is transforming how software is built, with AI handling routine boilerplate code so engineers can focus on higher-level architecture or solving tricky problems. Personally, I would estimate that I have gotten 50-80% faster at producing code after using AI tools when I program. 

Beyond these, professional services and knowledge work industries are embracing generative AI. Law practices and financial services are experimenting with AI to generate first drafts of reports, summarize documents, or even sift through legal contracts for relevant clauses. These tasks, which used to take many billable hours, can be accelerated with AI assistance. Interestingly, recent data showed particularly large jumps in generative AI use in sectors like professional services (which includes fields like law, HR) and even in areas like energy and manufacturing. In manufacturing, for instance, generative AI can optimize designs of components or generate synthetic data for simulation tests. 

Perhaps one of the most life-changing impacts is in healthcare and science. Here, generative AI is used for drug discovery—proposing molecular structures for new medications and for generating synthetic patient data to train other AI models without risking privacy. Some hospitals have trialed AI that can draft patient reports or assist in diagnostic suggestions based on medical images (with human doctors supervising, of course). 

These examples show a pattern: sectors that have a lot of data and creative/problem-solving tasks are leaping ahead with generative AI. They are leading the way in integrating these tools into daily workflows and, as a result, are transforming their operations—often seeing improvements in speed and efficiency. It’s an exciting time because we’re essentially watching every industry ask “how can generative AI make us better?” and then boldly experimenting with the answers. 

Supertrends: In what ways do you see generative AI changing the nature of work and the skills required in the workforce? 

Jonas Bjerg: Generative AI is increasingly becoming a collaborator in the workplace, and that’s changing what work looks like as well as the skills people need. First, we’re seeing a shift in the nature of tasks. Routine drafting or initial ideation can now be offloaded to AI. For example, an analyst might use AI to generate a first draft of a report or some code, then spend their time refining and checking it. This means that many jobs will evolve to focus more on review, guidance, and complex problem-solving, with AI handling the grunt work. 

Workers will need to be good at working with AI: knowing how to craft a prompt or question to get useful results, and then applying their own expertise to polish or verify the AI’s output. In essence, every professional might become a bit of an “AI manager,” directing intelligent tools to achieve the outcome they want. A lot of people talk about prompt engineering as a job, but I find it as absurd as the idea of someone being employed solely to write Google search queries. I do believe prompting will be as basic a skill as Word or PowerPoint is nowadays, but it won’t be its own job. 

Crucially, soft skills and higher-order thinking become even more important. Why? Because when AI can handle formulaic tasks, human workers differentiate themselves through critical thinking, creativity, and interpersonal skills. The ability to evaluate AI’s suggestions critically—to spot errors, biases, or impractical ideas—is paramount. It’s no longer enough to have hard knowledge; one must apply judgment. As one analysis succinctly put it, the question is no longer whether AI will affect your job, but how you will use it to augment your productivity or creativity. 

That captures the idea that professionals should focus on what uniquely human value they bring (such as contextual understanding, ethical reasoning, empathy in decision-making) and let the AI do what it does best (speed and pattern generation). We’ll also likely see an increased emphasis on skills like data literacy (understanding how AI models use data, since interacting with AI often involves interpreting data-driven outputs) and on continuous learning, since AI tools and best practices will keep evolving. 

If memory serves, statistics from the World Economic Forum predict that within five years, around 44% of workers’ core skills will need updating because of AI and automation. That doesn’t mean people need to throw out their expertise, but they’ll certainly have to adapt—often by learning to work alongside AI. For instance, a marketer will still need creativity and brand knowledge, but now also needs to know how to get the best results from an AI content generator and then inject human flair into the final output. A project manager will still manage timelines and people, but also might use AI tools for scheduling or risk analysis, meaning they should understand those tools’ outputs. 

Generative AI is shifting the balance of tasks in many professions: more automation of routine work, and a premium on human skills of oversight, strategy, emotional intelligence, and creative innovation. Those who cultivate these human skills and learn to effectively leverage AI will find themselves in a very strong position in the workforce.