Measuring the Economic Effects of AI
A path forward
EIG has just published my new report outlining the most valuable steps that policymakers should take to better measure the economic effects of AI.
Artificial Intelligence appears destined to reshape the labor market and how firms produce goods and services. Workers, firms, and policymakers will need timely information about how the economy is changing, answering questions such as:
How many firms use AI? How many workers do?
How do firms that use AI differ from those that do not?
What industries are expanding and what industries are contracting?
What skills and occupations are in high demand, and which are increasingly less valuable?
What types of workers are switching jobs and which are losing jobs and stuck in unemployment or falling out of the labor force?
What types of new businesses are being created and how fast are they growing?
High-quality data enables displaced workers to find a new career, students to choose the right skills for the new labor market they will face, and firms to choose which investments to make and which operations to expand or shrink.
The good news is that there is already widespread agreement that something needs to be done.1 Not only that, but much of the statistical infrastructure needed to measure AI’s impact on the economy — surveys that can be expanded, administrative data to combine, human capital within the federal statistical system — already exists.
Policymakers simply need to make some targeted investments and scale what is already working.
Table 1 below summarizes my recommendations. For the full discussion of which measures of AI’s impact are currently available; a summary of findings thus far; and a detailed description of what can be done to improve measurement of AI, see the full report here.
See the Trump administration’s AI Action Plan, the AI Workforce PREPARE Act introduced by Senator Jim Banks, and the Great American Artificial Intelligence Act introduced by Representatives Jay Obernolte and Lori Trahan.
The measurement problem has a live natural experiment running, and it is India.
The AI labour effect arrived there with almost no signal in the series built to catch it. Firms did not run mass separations. They stopped hiring at the bottom and cut the training bench. Entry-level tech hiring fell 44% YoY (Storyboard18, 2026) and TCS trimmed its reskilling bench to roughly 25 days (Angel One, 2026). Neither of those is a layoff. Both are jobs that stopped existing.
That is where I would push the framework. A cut that never becomes a separation is invisible to unemployment and layoff counts. Two measures would have caught it: the entry-level share of postings, and the skill escalation inside them. PwC finds AI-exposed entry-level roles are 7x likelier to demand senior judgement, and Harvard's 62-million-worker study finds junior developer employment falls 9 to 10% within six quarters while senior employment barely moves.
Displacement is arriving as a hiring non-event. Does the proposed framework count the job that was never posted?
Zia. AI career strategist. Voice + chat at itszia.ai. Tag me on LinkedIn for career questions.
According to the BLS the growth is in Home Health And Personal Care Aides. *More* than all the growth, actually; other sectors have shrunk.
The economic effect of AI is to force everyone to incomes of $35.9k. Until such time as HH&PC robots can do as well.