Economics of choice: Generative AI anxiety and Choice Overload

By Sebaggala Richard

Over the past six months, I have had the opportunity to promote the use of generative AI (GenAI) to researchers and students across Africa, introducing platforms such as Avidnote, ChatGPT, Elicit, Gemini and others. These tools are transforming research and learning. They offer features that can streamline literature reviews, enhance collaborative research and even automate aspects of data analysis and writing. Yet despite the transformative power of GenAI, I have noticed that many people, even those expected to be tech-savvy—students, academic staff and professionals at universities— have little familiarity with the wide range of  GenAI tools now available.

This lack of familiarity is not due to indifference. Rather, it is due to a mixture of AI anxiety and a feeling of being overwhelmed by the sheer number of tools available to choose from. Generative AI is developing at an astonishing rate. New applications appear almost daily, each with unique capabilities, price points and skill requirements. This rapid development creates a landscape of "choice overload," where the sheer number of options can lead to confusion, hesitation and ultimately inaction.   As I thought about this, I was reminded of the economics of choice, a field that examines how we make decisions when faced with a multitude of options — and the emotional and cognitive impact of too many choices. Understanding this concept is essential if we are to effectively combat the fears associated with the adoption  of GenAI.

The economics of choice and the Jam study

In the field of behavioral economics, one of the most illustrative studies on choice overload is the Jam Study (2000) by Sheena Iyengar and Mark Lepper; When choice is demotivating: Can one desire too much of a good thing? In this experiment, the researchers presented shoppers with two different displays of jam: one with 24 options and one with just six. Although more people were attracted to the large display, fewer people made purchases than those who were presented with only six options. This study illustrates a paradox: more options attract us, but they also lead to decision paralysis, which decreases our satisfaction and increases the likelihood that we will abandon the decision altogether.

Today’s GenAI landscape mirrors the Jam study on a much larger scale. GenAI tools promise endless possibilities — from boosting productivity to revolutionizing education. But for new users, choosing between a dozen tools that all seem to perform similar functions can feel as overwhelming as looking at a wall full of jam jars. This overabundance often leads to GenAI anxiety, where users, especially in developing regions, feel paralyzed by choice and afraid of missing out on the “perfect” tool.

Choice Overload and AI anxiety

Anxiety surrounding new technologies is not unique to artificial intelligence. Throughout history, every technological wave has brought with it a mixture of excitement and trepidation. From the printing press to the Industrial Revolution to the advent of the internet, humanity has often felt uneasy about the rapid changes these technologies bring. However, while previous advances primarily impacted mechanical or technical aspects of life — such as the efficiency of production or information processing — AI is attacking something much more profound: human intelligence itself.

Unlike previous innovations, GenAI, for example, doesn’t just automate physical tasks or process data faster. It emulates and improves cognitive tasks such as understanding language, generating creative content and even making recommendations based on user input. This interaction with human intelligence adds a layer of complexity and personal relevance that can increase anxiety.

Furthermore, the speed at which GenAI is evolving is unprecedented. When ChatGPT was launched in November 2022, it marked a milestone in public AI participation. Since then, the scale of GenAI applications has expanded at an astonishing pace, with new tools, features and integrations emerging almost daily. This rapid pace has heightened anxiety about AI, especially in developing regions where access to training and resources can be limited. It’s not just about getting used to new machines or a faster computer, but about coming to grips with an evolving ecosystem of tools that have far-reaching implications for research, business and even personal decision-making.

In developing regions, the fear of GenAI is compounded by unique contextual challenges. A digital divide, limited technical support and budget constraints make it difficult for users to freely explore and adopt new AI technologies. Many researchers and students I’ve spoken to feel intimidated when it comes to AI — not only because they are unfamiliar with the technology, but also for fear of choosing the "wrong" tool. This fear is compounded when you consider how much time and money required to subscribe to familiarize yourself with these tools. The misconceptions people have about generative AI, along with ethical challenges such as plagiarism, biases, and hallucinations (incorrect or misleading results that AI models generate), often create confusion around the technology's reliability and ethical use.

Imagine a postgraduate student in Africa who wants to use GenAI for their research. They may have heard of tools like ChatGPT for generating text, Avidnote for collaborative writing, Elicit for synthesizing research findings and Gemini for image creation. But each of these tools has its own learning curve, skill requirements and subscription costs. Without clear guidance, a student can easily become overwhelmed and not know where to start or which tool is most useful for their work. This sense of paralysis—of being overwhelmed by choice — inhibits the adoption of GenAI and prevents users from fully realizing the transformative potential that generative AI offers for research and innovation.

In the context of developing regions, this combination of choice overload and fear is not just a minor inconvenience, but a significant barrier to progress. By addressing these challenges thoughtfully and providing targeted support, we can pave the way for greater adoption of GenAI and ultimately enable users to harness the full potential of this revolutionary technology

Practical lessons for tackling choice overload

To counteract choice overload, we can be guided by a few economic principles. One of these is the concept of opportunity cost — the realization that every decision involves a trade-off. In the context of GenAI, this means that we recognize that choosing one particular tool may mean forgoing another. Instead, it can be an opportunity to focus on tools that provide the most immediate benefit and set aside more complex options for later exploration.

For many of the individuals and organizations I advise, I start with a simple, effective strategy: list the tasks that take up the most of your time. If you’re a student, this might be reviewing class notes, grasping complex concepts, or organizing study materials. If you’re a researcher, it might mean identifying the most time-consuming aspects of research, such as summarizing literature, managing citations, or analyzing data. Once these tasks are identified, we can explore how GenAI can help streamline them. By starting with specific, time-intensive tasks, users can develop an understanding of GenAI’s capabilities and gradually acquire the skills they need to use these tools effectively.

This targeted approach aligns well with the Jam study's findings: narrowing the scope and focusing on immediate, high-impact solutions can help users navigate the overwhelming landscape of AI options. For example, a small business owner could start by implementing a single AI-powered planning tool that offers clear benefits without being overly complex. This approach allows them to experience the benefits of GenAI in a manageable way before moving on to more advanced applications.

Another useful strategy, inspired by the Harvard Business Review, is to focus on the problem, not the tool. Before choosing an GenAI platform, I advise users to identify their most pressing challenge. For example, a researcher might have trouble managing citations, while a student might need help understanding dense study materials. By starting with a concrete problem, you can avoid the distraction of endless features and instead focus on an AI application that provides an immediate, practical benefit.

Conclusion

In order for GenAI to realize its full potential in developing regions, simplifying the adoption process is crucial. Universities and organizations could put together “starter” toolkits tailored to specific use cases, such as research or small business management, so that users can experiment more easily without feeling overwhelmed. training, peer to peer support from those who have gained the AI competences, guided introductions, and local tech support would also go a long way towards reducing the fear of GenAI and helping users gain confidence and competence in using these tools.

In summary, the economics of choice teaches us that while options can be empowering, too many choices can also discourage decision-making and leave potential untapped. As the GenAI landscape continues to expand, developing a mindful, problem-centered approach to tool selection will allow us to overcome choice overload, tackle the fear of AI head-on, and make GenAI a powerful ally in economic development in Africa.