Stories
Is AI Replacing Entry-Level Jobs in Financial Markets? QuantInsti Survey Reveals Mixed Views
- •A QuantInsti survey conducted during its 16th Anniversary Annual Algo Trading Conference reveals that financial professionals are divided on whether AI is eliminating or merely reshaping entry-level roles.
- •Nearly 49% of respondents highlighted leveraging AI tools effectively as the most crucial skill for getting hired over the next two years, surpassing traditional coding and statistics.
- •Over 72% of participants reported using AI for trading tasks in the past three months, with strategy ideation, research, and code debugging being the most common applications.
- •Industry experts like Mridul Arora and Ajay Sharma note that firms are building for leverage rather than headcounts, emphasizing that human judgment and soft skills remain irreplaceable by AI.
- •The survey highlights regional differences, showing that 35.7% of respondents based in India have not yet integrated AI into their trading workflow compared to lower percentages in the Americas and Europe.
Key Questions Answered
Respondents to a QuantInsti survey conducted in September 2026 are divided on whether entry-level roles are disappearing, with 25.8% expecting fewer seats, 30.3% predicting the shape will change with similar seats, 32.6% saying it is too early to say, and 11.2% anticipating growth. Similarly, a Swiss job platform study found that junior job postings across white-collar sectors like finance fell following increased AI adoption.
When asked to choose the single most important skill for getting hired over the next two years, 49.2% of respondents cited getting real work out of AI tools, followed by statistics and validation methods at 14.9%, market and product knowledge at 14.9%, stakeholder communication at 13.3%, and writing and reading code at 7.7%.
The primary concern among respondents regarding AI in trading is that a strategy will look strong in a backtest but ultimately fail in live use, which was cited by 38.9% overall and 47.0% of self-directed traders. Other concerns include everyone ending up with identical generated ideas, existing skills counting for less, and an inability to explain what the model did.
