Last week, I performed an analysis of the heterogeneous treatment effects of AI on unemployment. Despite analyzing multiple variables, like my primary analysis, the results were all null. This week, I focused on organizing my research, compiling regression results, and holistically reflecting my results in the context of existing literature and public sentiment.

How DiD we get here?

It started with public sentiment. The release of ChatGPT rocked the world and changed many dynamics. From emails to code, many tasks could be automated. Numerous companies cited AI as the reason for layoffs, and navigating the job market has become a nightmare. The rhetoric from both tech leaders and the general public seem to point towards one very simple chain of logic: AI is replacing workers.

But basic economic intuition seems to point towards the opposite direction. If the average employee can use AI to do so much more, companies will hire more workers to produce a greater profit. In the long run, even if AI does replace current jobs, new jobs will be created, and productivity will increase.

This contradiction is what makes research into how AI affects the economy so intriguing. It’s what inspired many economists to tackle the question.

Not All that Glitters is Gold

However, when many economists tried to answer the question, they all arrived at the same, confusing conclusion: it’s not doing much. Nobel prize winners like Daron Acemoglu found no detectable changes in employment and wages. Professors from top universities have contradicting claims of whether AI will increase or decrease inequality. Even total factor productivity was predicted to increase by only 0.71%. Nothing added up to the drastic claims of tech CEOs, anecdotal reports by average individuals, or reasoning provided by companies that laid off workers.

This is when I decided that I wanted to take a shot at the answer myself. I decided to use new, cutting-edge econometric methods that increase statistical power to detect the previously undetectable. I wanted to look at what conventional wisdom had to say about the heterogeneous effects of AI across different subgroups, especially across education levels and age. Despite having high hopes, my analysis corroborated the literature: no significant effects.

A Series of Unfortunate Events

Why is the job market so tough? Why does it seem that AI is affecting the economy so much? What is really happening? There are a few potential explanations, but the general consensus is relatively simple: there was simply a series of unfortunate events. ChatGPT was released during a volatile time, just a few years after COVID-19 strained supply chains and sent the world into a recession. Since then, interest rates have remained high to curb inflation at the expense of employment. Even as COVID-19 died down late 2024, new threats to the labor market began to arise. President Donald Trump’s unpredictable tariff policies have put many on edge, making hiring risky for companies, quitting risky for employees, and investing risky for investors. 

So What about AI?

While AI certainly has significant potential, its lack of impact has a few explanations. First, AI may be suffering the same productivity paradox as IT in the 1970s. A mix of adjusting to technology, IT mismanagement, and other factors may be stifling growth. Second, AI’s long-term productivity effects may be offset by short-term substitution effects. As Hampole et al. (2025) found, there is causal evidence to suggest that augmentation and automation are effectively canceling each other out. Whatever the case, AI has not had a significant effect on the economy.