How Generative AI Could Disrupt Creative Work

Authors: David De Cremer, Nicola Morini Bianzino, and Ben Falk

The “creator economy” is currently valued at around $14 billion per year. Enabled by new digital channels, independent writers, podcasters, artists, and musicians can connect with audiences directly to make their own incomes. Internet platforms such as Substack, Flipboard, and Steemit enable individuals not only to create content but also to become independent producers and brand managers of their work. While many kinds of work were being disrupted by new technologies, these platforms offered people new ways to make a living through human creativity.

In the face of technological change, creativity is often held up as a uniquely human quality, less vulnerable to the forces of technological disruption and critical for the future. Indeed, behavioral researchers even call the skill of creativity a human masterpiece.

Today, however, generative AI applications such as ChatGPT and Midjourney are threatening to upend this special status and significantly alter creative work, both independent and salaried. Jobs focused on delivering content—writing, creating images, coding, and other jobs that typically require an intensity of knowledge and information—now seem likely to be uniquely affected by generative AI.

What isn’t clear yet is what shape this kind of impact will take. We propose three possible—but, importantly, not mutually exclusive—scenarios for how this development might unfold. In doing so, we highlight risks and opportunities and conclude by offering recommendations for what companies should do today to prepare for this brave new world.

Three Possible Futures

An explosion of AI-assisted innovation

Today, most businesses recognize the importance of adopting AI to promote the efficiency and performance of their human workforce. For example, AI is being used to augment health-care professionals’ job performance in high-stakes work, advising physicians during surgery and used as a tool in cancer screenings. It’s also being used in customer service, a lower-stakes context. And robotics is used to make warehouses run with greater speed and reliability, as well as reducing costs.

With the arrival of generative AI, we’re seeing experiments with augmentation in more creative work. Just back in 2021, GitHub introduced GitHub Copilot, an AI “pair programmer” that aids human coders. More recently, designers, filmmakers, and advertising execs have started using image generators such as DALL-E 2. These tools don’t require users to be very tech savvy. In fact, most of these applications are so easy to use that even children with elementary-level verbal skills can use them to create content right now. Pretty much everyone can make use of them.

This scenario isn’t (necessarily) a threat to people who do creative work. Rather than putting many creators out of work, AI will support humans to do the work they already perform, simply allowing them to do it with greater speed and efficiency. In this scenario, productivity would rise as reliance on generative AI tools that use natural language reduces the time and effort required to come up with new ideas or pieces of text. Of course, humans will still have to devote time to possibly correct and edit the newly generated information, but overall, creative projects should be able to move forward more quickly (see the article “How Generative AI Can Augment Human Creativity”).

We can already glimpse what such future holds: With reduced barriers to entry, we can expect many more people to engage in creative work. GitHub Copilot doesn’t replace the human coder, but it does make coding easier for novices, as they can rely on the knowledge and vast reams of data embedded within the model rather than having to learn everything from scratch. If more people learn “prompt engineering”—the skill of asking the machine the right questions—AI will be able to produce very relevant and meaningful content that humans will need to edit only somewhat before they can put it to use. This higher level of efficiency can be facilitated by having people speak instructions to a computer via advanced voice-to-text algorithms, which will then be interpreted and executed by an AI like ChatGPT.

The ability to quickly and easily retrieve, contextualize, and interpret knowledge may be the most powerful business application of large language models. A natural language interface combined with a powerful AI algorithm will help humans in coming up more quickly with a larger number of ideas and solutions that they subsequently can experiment with to reveal more and better creative output. Overall, this scenario paints a world of faster innovation where machine-augmented human creativity will enable mainly rapid iteration.

Machines monopolize creativity

A second possible scenario is that unfair algorithmic competition and inadequate governance leads to the crowding out of authentic human creativity. Here, human writers, producers, and creators are drowned out by a tsunami of algorithmically generated content, with some talented creators even opting out of the market. If that were to happen, then an important question that we need to address is: How will we generate new ideas?

A nascent version of this scenario might already exist. For example, recent lawsuits against prominent generative AI platforms allege copyright infringement on a massive scale. What makes this issue even more fraught is that intellectual property laws have not caught up with the technological progress made in the field of AI research. It’s quite possible that governments will spend decades fighting over how to balance incentives for technical innovation while retaining incentives for authentic human creation—a route that would be a terrific loss for human creativity.

In this scenario, generative AI significantly changes the incentive structure for creators and raises risks for businesses and society. If cheaply made generative AI undercuts authentic human content, there’s a real risk that innovation will slow down over time as humans make less and less new art and content. Creators are already in intense competition for human attention spans, and this kind of competition—and pressure—will only rise further if there is unlimited content on demand. Extreme content abundance, far beyond what we’ve seen with any digital disruption to date, will inundate us with noise, and we’ll need to find new techniques and strategies to manage the deluge.

This scenario could also mean fundamental changes to what content creation looks like. If production costs fall close to nothing, that opens up the possibility of reaching specific—and often less included—audiences through extreme personalization and versioning. In fact, we expect the pressure to personalize to go up fast because generative AI carries such great potential to create content that is increasingly representative of the specific consumer. As a case in point, BuzzFeed announced it will personalize its content such as quizzes and tailor-made rom-com pitches with OpenAI’s tools.

If the practice of enhanced personalized experiences is applied broadly, then we run the risk of losing the shared experience of watching the same film, reading the same book, and consuming the same news. In that case, it will be easier to create politically divisive viral content and significant volumes of mis/disinformation as the average quality of content declines alongside the share of authentic human content. Both would likely worsen filter bubble effects, where algorithmic bias skews or limits what an individual sees online.

Yet even in this relative dystopia, there remains a significant role for humans to make recommendations of existing content in this ecosystem. As in other very large content markets, like music streaming services, curation will become more valuable relative to creation as search costs rise. At the same time, however, high search costs will lock in existing artists at the expense of new ones, concentrating and bifurcating the market. This will result in a small handful of established artists dominating the market with a long tail of creators retaining minimal market share.

“Human-made” commands a premium

The third potential scenario that we could see develop is one where the “techlash” against giant tech companies regains speed, this time with a focus against algorithmically generated content. One plausible effect of being inundated with synthetic creative outputs is that people will begin to value authentic creativity over generated content and may be willing to pay a premium for it. While generative models demonstrate remarkable and sometimes emergent capabilities, they suffer from problems with accuracy, frequently producing text that sounds legitimate but is riddled with factual errors and erroneous logic. For obvious reasons, humans might demand greater accuracy from their content providers and may therefore rely more on trusted human sources than on machine-generated information.

In this scenario, humans maintain a competitive advantage against algorithmic competition. The uniqueness of human creativity, including awareness of social and cultural context both across borders and through time, will become important leverage. Culture changes much more quickly than generative algorithms can be trained, so humans maintain a dynamism that algorithms cannot compete against. In fact, it is likely that humans will retain the ability to make significant leaps of creativity, even if algorithmic capabilities improve incrementally.

In the development of this scenario, it follows that political leadership will have to strengthen governance to deal with the potential downside risks. For instance, content moderation needs are likely to explode as information platforms are overwhelmed with false or misleading content, and therefore must be countered with human intervention and carefully designed governance frameworks.

How to Prepare for Generative AI

Creativity has always been a critical prerequisite for any company’s innovation process and hence competitiveness. Not too long ago, the business of creativity was a uniquely human endeavor. However, as we’ve illustrated, the arrival of generative AI is about to change all this. To be prepared, we need to understand the accompanying threats and challenges. Once we understand what is to change and how, we can prepare for a future where the creativity business will be a function of human–machine collaborations. Below, we provide three recommendations that workers should consider as they adopt generative AI to create business value and profit in today’s creative industries.

Prepare for disruption, and not only to your job

Generative AI could be the biggest change in the cost structure of information production since the creation of the printing press in 1439. The centuries that followed featured rapid innovation, sociopolitical volatility, and economic disruption across a swath of industries as the cost of acquiring knowledge and information fell precipitously. We are in the very early stages of the generative AI revolution. We expect the near future therefore to be more volatile than the recent past.

Invest in your ontology

Codifying, digitizing, and structuring the knowledge you create will be a critical value driver in the decades to come. Generative AI and large language models enable knowledge and skills to transmit more easily across teams and business units, accelerating learning and innovation.

Get comfortable talking to AI

As AI becomes a partner in intellectual endeavors, it will increasingly augment the effectiveness and creativity of our human intelligence. Knowledge workers therefore will need to learn how to best prompt the machine to perform their work. Get started today, experimenting with generative AI tools to develop skills in prompt engineering, a prerequisite skill for creative workers in the decade to come.

• • •

With generative AI, a major disruptor of our creative work has emerged. Businesses and the world at large will be impatient to apply the new emerging technologies to boost our level of productivity and content generation. Be prepared to invest significant time and effort to master the art of creativity in a world dominated by generative AI.

At the same time, we also need to seriously consider what these new technologies mean for being a creative human today and how much importance we wish to assign to the role of human authenticity in art and content. In other words, with generative AI at the forefront of our work existence, what will our relationship with creativity be? It was Einstein who said that creativity is intelligence having fun. Creative work is thus also something that brings meaning and emotion to the lives of humans.

From that perspective, businesses and society will be responsible to decide how much of the creative work will ultimately be done by AI and how much by humans. Finding the balance here will be an important challenge when we move ahead with integrating generative AI in our daily work existence.

TAKEAWAYS

Through the automation and customization of content creation, generative AI has the potential to transform the creative process. Applications that use generative AI, including ChatGPT and Midjourney, are proliferating and pose a threat to all types of creative work.

✓ There are three scenarios that could occur because of generative AI’s impact on creativity: an explosion of AI-assisted innovation, the monopolization of creativity by machines, or a premium placed on human-produced content.

✓ Individuals and businesses should be ready for disruption, invest in knowledge ontologies, and become comfortable speaking with AI.

✓ When incorporating generative AI into creative work, we must consider what we want our continuing relationship with human creativity to be.