OpenAI’s Drive to Automate Entry-Level Banking Tasks and Its Impact on Junior Roles

The Fortune article reveals that OpenAI’s “Mercury” initiative is collaborating with over 100 former Wall Street bankers to script and train models to perform entry-level investment banking tasks like building financial models and preparing pitch decks, with projected automation potentially covering 60–70% of such grunt work within a year. While this suggests a dramatic shift in task structure, it stops short of forecasting massive layoffs, instead anticipating role transformations.

Corroborating these signals, the Stanford study reports that workers aged 22-25 in AI-exposed roles—customer service, accounting, junior software development—have experienced a 13% drop in employment since 2022, much steeper than for older or less-exposed cohorts. Additionally, Revelio Labs data show a ~35% decline in entry-level job postings since 2023 among public tech and startup firms. These patterns indicate AI is disproportionately affecting early-career opportunities.

Broader industrial surveys support this: two-thirds of global organizations expect to slow entry-level hiring due to AI, with 91% reporting that responsibilities have shifted or disappeared and 69-71% anticipating fewer learning paths or leadership development opportunities for junior staff. Gartner forecasts by 2029 one-third of finance roles will be “shared jobs,” where AI and human workers collaborate in either managerial or competitive models.

Strategically, finance firms may face three major shifts. First, the pipeline from junior analyst to senior roles could narrow dramatically if fewer entry points exist. Second, hiring criteria will likely pivot: firms are increasingly valuing AI fluency, adaptability, and skills in monitoring model output over domain knowledge or credentials. Third, unaddressed, these changes may exacerbate demographic imbalances and cause long-term capability gaps—if young workers can’t acquire experience, who fills future senior analyst, VP, or partner roles?

Open questions include: how will compensation adjust for transformed roles? Will regulators demand transparency/auditing in AI-trained financial work? What is the timeframe before automation begins to replace outright rather than augment roles? And how will education/training systems evolve to meet the changing demand?