Can an algorithm outperform the average angel investor? And if it can, does that also mean it will make less biased investments? Researchers put these questions to the test: They built an investing algorithm and put it head to head with 255 angel investors in a simulation, asking it to select the most promising investment opportunities among 623 deals from one of the largest European angel networks. The results? The algorithm significantly outperformed the average novice investor and even experienced investors who fell prey to cognitive biases, but was bested by the top tier of experienced investors, who could control for their own biases. While the algorithm may have made less biased choices when it came to the race and gender of the founders it picked, it also reflected systemic inequalities, and illustrated the limits of how algorithmic investing can be used to address deep social inequalities. Even so, the experiment offers a vision for how — and when — investors might deploy similar algorithmic aids in their investing, and how it might lead to better and fairer decisions.