To handle even wider tables with hundreds of columns, GAIT's approach can be extended by implementing techniques such as graph sparsification and node sampling. Graph sparsification involves reducing the number of edges in the graph while preserving its essential structure. This can help in managing the computational complexity of processing large graphs with numerous columns. Node sampling techniques can be used to select a subset of nodes in the graph for processing, allowing for more efficient computation while still capturing the essential dependencies between columns. By incorporating these methods, GAIT can effectively scale to handle tables with hundreds of columns without compromising performance.