At a Loss for Words: Evaluating the Influence of ChatGPT on the Global Economy

This article appeared in Technology Dispatch.

In this article, we delve into the impact of LLMs on the global economy by:

LLMs are advanced AI algorithms that leverage deep learning and vast data sets to generate, understand, summarize, and predict content. For example, ChatGPT is a prominent LLM that can produce human-like text instantly based on prompts (TechTarget).

ChatGPT's rapid growth, gaining 1 million users in just 5 days, has stirred concerns about job security. Previously untouched by technology, creative arts jobs seemed immune due to their emotional nature. However, LLMs have introduced unexpected shifts.

Globally, ChatGPT automates tasks in logistics, recruitment, and customer service. While concerns exist, the World Economic Forum predicts growth in roles like Business Intelligence and Information Security Analysts. Though routine roles may decline, net job growth is projected. ChatGPT's all-encompassing potential as a General Purpose Technology and its ability to perform cognitive tasks raise concerns about job displacement. Yet, it also offers augmentation rather than replacement. New jobs, like prompt generators and AI trainers, emerge as automation advances, demanding innovation for idea generation. With the initial disruption settling, new opportunities arise. The impact of LLMs leans toward displacing labour, creating roles that balance automation and creativity. The future involves leveraging AI ethically, and job roles like AI trainers and data scientists are pivotal in shaping this transition. Thus, LLMs reshape job landscapes, emphasizing both displacement and potential for novel roles.

To glean a greater understanding of the same, we look at the correlation between interest in ChatGPT (used as a proxy for adoption) and the real GDP growth figures for 2022-23 and 2024-25.

We see that the correlation coefficient between interest in adoption and current GDP (2022-23) growth rates is -0.119. On the other hand, we see that the correlation coefficient between interest in adoption and forecasted future GDP (2024-25) growth rates is -0.138. Given these figures, on the level of the world, there is no correlation or a very weak negative correlation.

Due to the newness of the technology and limited data, our current reporting lacks a clear definition. Yet, we believe valuable insights can still be extracted from the existing but incomplete data. It's crucial to recognize that the adoption of technology takes time; for instance, Fogel's Nobel-winning study on Railways revealed that their influence on American prosperity wasn't immediate, but rather emerged nearly a century later.

In a vein similar to the analysis undertaken above, we once again look at the correlation between interest in adoption and unemployment rates. While the data processing for the former remains the same, we extract data for the latter using the World Bank's open-access data sources. Again, the dataset reveals no correlation (0.047).

Recognizing the limitations of global correlation coefficients, we delve into the nuances of different country groupings. This approach unveils disparities stemming from varying development experiences. Major advanced economies undergo scrutiny to determine if their economic advancement yields distinct results. We also assess their performance relative to each other and the global average. Our analysis extends to emerging market economies and densely populated regions. This enables us to gauge how the technology affects areas abundant in labour and still developing.

The table below summarises the findings (for detailed calculations refer to the Excel attached in the appendix):

While the service sector predominantly experiences the highest impact among industries (as shown by chart 2), a closer examination of the combined histograms highlights that the influence on industries is overshadowed by the effect on occupations (chart 3), once again emphasizing the greater impact on unemployment than on GDP.

For example (Depiction of two examples on either side of the spectrum using chart 5):

For example (Depiction of two examples on either side of the spectrum using chart 5):

These charts and tables have an interesting interpretation. A mode of 0.7 tells us that in terms of sheer numbers, a significant number of jobs out of the sample can be automated. However, a mean of 0.58 takes into account the degree of automation. Say, for example, we look at the jobs of three workers in the creative sector whose jobs will be impacted by ChatGPT – writers, editors, and craft artists. However, we could easily illustrate the degree of job exposure for each professional as follows: writers > editors > craft artists. Now, writers and editors are more easily replaceable, and, thus, on an individual level, there is a high job replaceability for writers and editors. Craft artists (craft, not visual), on the other hand, barely bear the brunt of this technology. So, precisely because of these reasons, the industry impact remains low in terms of the net values (as shown by the mean) even as the impact of ChatGPT on individual jobs that are more susceptible to technological advancements remains high (as shown by a mode of 0.7).

We extrapolate the results of this industry to the entire service sector, now which helps us come back to Chart 4. The U-shape of the bars tells us that when taken as a sector, there's relatively little impact of automation on the global front (hence the even spread without skew). However, on a more granular, individual job level, there are greater repercussions.

As data evolves, deeper insights into LLMs' global economic impact will emerge.