The popularity of machine learning (ML), deep learning (DL), and artificial intelligence (AI) has grown rapidly in recent years. It is often assumed that the advances offered by ML and DL mainly apply to predictive tasks. However, ML and DL algorithms can, at least in principle, also be used for inference, including tasks such as effect size estimation or partitioning of explained variation. In my talk, I will discuss the promise and pitfalls of using AI for statistical inference in macroecological research, using examples from different applications such as species distribution models, community ecology, or the analysis of bipartite networks. I will start by reviewing Explainable AI (xAI) algorithms that can extract effect sizes and variable importance from fitted models, and show how these map to traditional statistical indicators. As a next step, I will discuss how we can provide statistical guarantees such as p-values and confidence intervals for these xAI metrics. A key problem that arises in this context is understanding what inductive biases are introduced by the various ML algorithms and their hyperparameters, and how these affect the bias in xAI effect estimates. These biases can make interpretation of fitted models problematic. On the other hand, well-tuned ML and DL algorithms perform automatic model and complexity selection, and thus may offer better alternatives for dealing with structural uncertainty than statistical model selection tools. I will conclude that there is reason to be cautiously hopeful that ML algorithms may merge with other methods of statistical inference, opening up an exciting new option for macroecological analyses.