Boden’s framework emphasizes the multifaceted character of creativity across diverse fields. Creativity of the first kind navigates within established rules and constraints, while the second thrives on serendipity and exploration, and the third leans on intuition and insight. The first and second forms of creativity work well with AI tools because they have the computational strength to follow specific algorithms to create something and can also use random exploration to produce unexpected results. By using these computational capacities, artists can journey into hitherto unknown territories, unearthing new possibilities. AI algorithms, for instance, can analyze extensive datasets, discern connections, and fabricate novel combinations that might have otherwise escaped human consideration. AI’s data processing capabilities enrich the exploratory dimension of creativity, enabling artists to probe new aesthetic possibilities. But when creativity calls for intuition, insight, and personal expressions of the human condition—the third type of creativity—AI remains an onlooker.

Artificial intelligence algorithms, for example, are able to examine large datasets, identify patterns, and create new combinations that would not have occurred to humans. The ability of AI to process data enhances creativity's exploratory aspect and empowers artists to explore novel aesthetic possibilities. However, AI stays a spectator when creativity demands the third kind of creativity—intuition, insight, and unique expressions of the human condition.

Although AI is incredibly useful in the formative and recursive stages of creativity, it is important to understand that AI cannot fully replicate the deeply imaginative and human aspects of artistic expression. While maintaining the distinct vision and emotional depth that only human creativity can evoke, artists must use AI as a collaborative tool.

Furthermore, the generation of novel concepts, items, and solutions depends heavily on creativity. The capacity for ideation, iteration, and unconventional thinking becomes a catalyst for advancement in an increasingly artificial intelligence-shaped world. Entrepreneurship is also fueled by creativity because it allows people to see trends, spot opportunities, and produce value that is superior to that of AI algorithms.

As more routine tasks become automated, education that encourages creativity prepares people for jobs that call for special human abilities. It cultivates a mindset of continuous learning and adaptation, empowering people to navigate a changing labor market impacted by advancements driven by artificial intelligence. In general, the combination of AI and human creativity has the potential to address difficult global issues and build a more inventive and durable future.

Furthermore, the generation of novel concepts, items, and solutions depends heavily on creativity. The capacity for ideation, iteration, and unconventional thinking becomes a catalyst for advancement in an increasingly artificial intelligence-shaped world. Entrepreneurship is also fueled by creativity because it allows people to see trends, spot opportunities, and produce value that is superior to that of AI algorithms.

As more routine tasks become automated, education that encourages creativity prepares people for jobs that call for special human abilities. It cultivates a mindset of continuous learning and adaptation, empowering people to navigate a changing labor market impacted by advancements driven by artificial intelligence. In general, the combination of AI and human creativity has the potential to address difficult global issues and build a more inventive and durable future.

But these days, generative AI apps like ChatGPT and Midjourney pose a serious threat to this unique status and fundamentally change autonomous and salaried creative work. Using vast datasets and user feedback as their sources of learning, these new generative AI models are able to generate original text, images, audio, or a mix of these media. As a result, it appears that generative AI will specifically impact jobs that are centered on delivering content, such as writing, designing graphics, coding, and other tasks that normally call for a high level of expertise.

From such an angle, the determination of the final proportion of creative labor to be performed by AI and the remainder by humans will fall to businesses and society. As we proceed with incorporating generative AI into our day-to-day work lives, striking this balance will be a crucial challenge.