In this study we examine how ChatGPT makes asset allocation decisions based on simulated data. We feed 100,000 hypothetical investor profiles into ChatGPT and ask it to recommend an asset allocation between a diversified equity fund and a diversified bond fund for each investor (client). We include a rich set of variables for each client such as their age, income, race, risk aversion, financial knowledge, and health. We instruct ChatGPT to act as a financial advisor. However, we do not require it to prioritize any set of variables over others. Our evidence indicates that ChatGPT recommends a higher equity allocation for those with less risk aversion and for those who are more optimistic, confident, and financially knowledgeable. Our evidence does not indicate that ChatGPT uses client demographic data such as age, race, gender, and marital status in forming its recommendations. Our results suggest that ChatGPT may allow investor biases (such as overconfidence) to influence its asset allocation recommendations. ChatGPT may also inadvertently hurt the financial success of clients with less financial knowledge by steering them away from equities. Since it ignores client age, ChatGPT may also recommend allocations that are too conservative for some of the young investors and too aggressive for some of the old investors. This study contributes to the literature on the applications of Artificial Intelligence (AI) to portfolio allocation and household financial management decisions.