Survey Overview

QuantInsti conducted a voluntary, self‑selected survey of 185 respondents during and after its 16th Anniversary Annual Algo Trading Conference (held 24 September 2026). The conference attracted participants from over 110 countries. The questionnaire asked whether entry‑level roles in financial markets are disappearing, changing shape, or growing, and collected views on required skills, AI and coding usage, and concerns.

Expectations for Entry‑Level Seats

Overall, 25.8 % of respondents said entry‑level seats will be fewer, 30.3 % expect the role to change shape while the number of seats remains roughly constant, 32.6 % said it is too early to tell, and 11.2 % anticipate growth. Responses varied by experience: 24.4 % of those with under one year in markets, 39.5 % of those with 1‑3 years, 17.8 % of those with 3‑7 years, 15.8 % of those with 7‑15 years, and 25.0 % of those with 15 + years expect fewer seats.

Skills Deemed Most Important

When asked to select the single skill most important for hiring over the next two years, 49.2 % chose “getting real work out of AI tools”, 14.9 % each selected statistics and validation methods and market/product knowledge, 13.3 % chose communication with stakeholders, and 7.7 % chose writing and reading code.

AI and Coding Adoption

72.1 % of respondents reported using AI for some trading task in the past three months. Their main uses were strategy ideation or research (27.9 %) and writing or debugging code (27.3 %); 9.8 % used AI for data pipeline work, 7.1 % for reading research faster, while 27.9 % had not used AI yet. Regarding coding, 35.2 % said none of their trading is coded (entirely discretionary), 24.7 % are not currently trading, 23.6 % use code for research and place orders manually, 11.0 % run automated execution under supervision, and 5.5 % are fully automated, meaning 40.1 % employ code in some capacity. Among those who already run code, 9.7 % have used AI in the past three months, compared with 39.6 % of non‑coders (including non‑traders) who have not used AI, indicating an association between coding and AI usage.

Concerns About AI

The most cited concern (38.9 %) was that strategies that look strong in backtests may fail in live trading. Other concerns were homogenisation of ideas (22.2 %), erosion of existing skill relevance (16.7 %), inability to explain model behaviour (16.1 %), and regulatory/compliance uncertainty (6.1 %). Among self‑directed traders, 47.0 % named the backtest‑live gap as their top worry.

Illustrative Example

During the conference’s opening workshop, Stefan Jansen demonstrated an AI‑built strategy that initially yielded a naïve t‑statistic of 6.1; after correcting for overlapping consecutive predictions, the statistic fell to 2.0, highlighting the risk of over‑optimistic backtest results.

Regional Insights – India

Just over half of respondents were based in India. Compared with the Americas and Europe/UK, 35.7 % of Indian respondents had not used AI in their workflow (versus 24.2 % and 21.4 % respectively), 16.3 % cited writing or debugging code as their primary AI use (versus 45.5 % and 39.3 %), 27.4 % listed backtest failure as their top concern (versus 57.6 % and 39.3 %), and 21.1 % expected fewer entry‑level seats (versus 42.4 % and 17.9 %).

Commentary from Industry Leaders

Mridul Arora, Partner at Elevation Capital, cautioned that “the worst‑case view is too strong” but acknowledged that “some of the old doors are closed” and that firms are building for leverage rather than headcount. Ajay Sharma, President of Edelweiss Public Alternatives, distinguished screen‑based tasks (which AI can outperform) from soft‑skill‑dependent activities that cannot be replaced. Tony Lee, CFA, Chief Brokerage Officer at Alpaca, emphasized that engineers must not only code but also be adept at prompting AI agents. Sameer Kumar, CTO and Partner at iRage, advised that domain knowledge and jargon are prerequisites before leveraging AI, and suggested building AI projects as a résumé boost.

Survey Availability

The full survey report, containing detailed results by role, experience, and region, is available from QuantInsti.