You Do Not Need Every AI Tool: A Lesson from Econometrics

By Sebaggala Richard (PhD)

 

 

Every few weeks a new artificial intelligence tool is introduced with the promise of transforming research, teaching, writing, coding, and analysis in academia. The pace of innovation is impressive, but it has also created a certain level of anxiety within universities. Students feel compelled to experiment with every new platform they encounter. Lecturers worry about keeping pace with rapidly changing technologies. Researchers sometimes feel that failure to adopt the latest tool may leave them behind.

 

The real problem, however, is not the abundance of AI tools. It is that in trying to use all of them, researchers risk fragmenting their attention and weakening the depth of their thinking.

 

In many cases, the response to this environment has been predictable. Instead of building deep competence with a small number of tools, people begin to accumulate platforms. They open accounts on multiple systems, experiment briefly with each one, and then move to the next new tool when it appears. The result is often the opposite of what they intended. Productivity declines rather than improves.

 

Whenever I observe this pattern today, I remember a lesson from my econometrics training many years ago. At the time, we were being introduced to statistical software packages such as Stata, EViews, and SPSS. These programs were widely used in universities and research institutions around the world, and for students beginning to learn applied econometrics the choice of software seemed overwhelming. Many of us were unsure which package we should invest time in learning.

 

Our lecturer offered a simple but memorable analogy. He told us that one does not need to drive every car in order to become a good driver. What matters is learning one vehicle thoroughly and understanding how it works. He then advised us that if we learned Stata properly, we would not miss much from the other packages, and that the skills we acquired would make it easier to understand any other software we might encounter later.

 

At the time the comment seemed like practical advice about software. With experience, however, it becomes clear that the point was much deeper. The lesson was about mastery and focus. In economics we often think about the efficient allocation of scarce resources. Attention is one of those scarce resources. When attention is spread across too many tools, the quality of learning and productivity declines.

 

The current environment of artificial intelligence tools presents a similar challenge. A growing number of platforms now offer support for academic tasks such as summarizing literature, drafting text, generating code, analysing documents, and organizing research materials. Systems such as ChatGPT, Gemini, Claude, Perplexity, Elicit, Avidnote, ResearchRabbit, Scite, and NotebookLM have become increasingly visible in academic discussions. Each claims to provide significant advantages for research and knowledge work.

 

Students therefore frequently ask which of these tools they should learn. The question resembles the one we asked about econometrics software years ago. The answer is also similar. Researchers do not need to learn every available platform. What matters is developing a deep understanding of a small number of tools and learning how to use them effectively in an intellectual workflow.

 

When researchers attempt to use every tool available, several difficulties tend to emerge. The first is fragmentation of workflow. Instead of concentrating on the research problem itself, the researcher spends time switching between multiple systems. The second is superficial knowledge. Individuals may become familiar with the basic interface of several platforms without developing the skill required to use any of them effectively. The third is cognitive overload. Mental effort is directed toward managing tools rather than analysing data, developing arguments, or interpreting results.

 

There is, however, a deeper and less visible cost. When researchers constantly switch between AI systems, they do not only fragment their workflow; they also fragment their thinking. Each system structures responses differently, suggests particular framings, and nudges users toward specific ways of expressing ideas. Over time, this can weaken intellectual coherence. Instead of developing a consistent analytical voice, the researcher begins to adapt to the logic of whichever tool is being used at the moment.

 

Part of this confusion is reinforced by the strong competition currently taking place among major artificial intelligence developers. Large technology firms are investing heavily in AI systems and competing to build the most capable digital assistants. This has produced intense comparisons between leading platforms such as ChatGPT, Claude, and Gemini. Each system has particular strengths. Some are particularly effective in analysing long documents, others integrate well with search engines or cloud services, and others perform strongly in coding and structured analysis.

 

For most academic researchers, however, the differences between these systems are less important than the discussion surrounding them might suggest. Modern AI models already possess capabilities that would have been considered remarkable only a few years ago. They can summarize academic papers, assist in structuring literature reviews, explain theoretical frameworks, generate programming scripts, and help refine academic writing. The critical issue is therefore not access to artificial intelligence tools but the ability to use them thoughtfully.

 

From an economic perspective, this behaviour reflects classic problems of bounded rationality and switching costs. Each new tool requires time to learn, cognitive effort to integrate, and attention to evaluate. When these costs are ignored, researchers over-invest in exploration and under-invest in mastery. The result is diminishing returns to additional tools and, in many cases, a decline in overall productivity.

 

In practice, a focused combination of tools can already provide substantial support for academic work. Systems such as ChatGPT are particularly useful as intellectual companions during the research process. They can assist in refining research questions, clarifying conceptual frameworks, designing surveys, interpreting statistical output, and improving the structure of academic writing. When used carefully, such systems function less as automated generators of text and more as conversational partners that help researchers examine their reasoning.

 

Other platforms offer strengths in areas such as document analysis and information synthesis. Systems like Gemini are often helpful when researchers are working with large reports or multiple documents that need to be summarized and compared. Tools such as Claude have become known for their ability to handle very long texts and produce structured explanations. When used selectively, these capabilities can significantly reduce the time required to extract insights from extensive material.

 

The broader principle underlying this discussion is familiar in economics. Productivity does not necessarily increase with the number of technologies employed. It increases when individuals develop comparative advantage in the use of particular tools. A researcher who understands three systems deeply will usually work more efficiently than someone who attempts to use ten different platforms at once. Mastery compounds over time. Once the logic of AI interaction is understood, adapting to new tools becomes relatively straightforward.

 

This observation also has implications for universities. Institutions sometimes respond to technological change by attempting to introduce students to a large number of platforms. A more effective approach would focus on teaching core competencies. Students should learn the principles of AI literacy, critical engagement with algorithmic outputs, responsible and ethical use of AI, and disciplined integration of a few tools into their research workflow. The objective is not simply to familiarize students with technology but to help them think more effectively in an environment where intelligent systems are widely available.

 

Looking back, the lesson from my econometrics lecturer was not primarily about statistical software. It was about maintaining focus in a world that constantly presents new options. That insight remains highly relevant today. Artificial intelligence tools will continue to appear at a rapid pace, and debates about which system is superior will likely persist.

 

In the age of artificial intelligence, the constraint is no longer access to tools. It is the ability to think clearly while using them. The danger is not missing out on AI tools; it is becoming cognitively shallow while using them. Researchers who benefit most from these technologies will not be those who pursue every new platform, but those who develop disciplined, focused, and reflective ways of working with a few powerful tools.

 

In the same way that learning to drive one vehicle well provides the foundation for driving many others, mastering a few tools can provide the foundation for productive research in the age of artificial intelligence.