This thesis investigates the impact of artificial intelligence (AI) tools on the stock market, focusing on its effects on risk, performance, and concerns. Through an analysis of existing literature and an experiment, this study aims to provide insights into the potential benefits and drawbacks of using AI in stock market trading. The research explores how AI can contribute to increased efficiency, accuracy, and profitability in stock trading, as well as the potential risks and concerns associated with its use, such as biased models and transparency. By examining the implications of AI in stock trading, this thesis aims to provide a comprehensive assessment of its overall impact on the financial industry. The literature study maps out and categorizes existing risks, concerns and the performance of AI in the stock market mentioned in studies and articles on the subject,while the experiment focuses on a LSTM (Long Short Term Memory) model implementation and the evaluation of its performance and risks. The findings in the study shows that a deep learning model of LSTM, does outperform the NASDAQ 100 index on all occurrences that it was tested on in a simulated stock market using the Backtrader framework. Results from the experiment also point towards the fact that the risks of implementing the model are mitigatable to a great extent, if the implementer are aware of them. The literature study also discusses and complements potential concerns with the model implementation and how to mitigate the identified risks as well as the AI performance.