Scholarship has highlighted the rise of political influencer networks on YouTube, raising concerns about the platform’s propensity to spread and even incentivize politically extreme content. While many studies have focused on YouTube’s algorithmic infrastructure, limited research exists on the actual content in these networks. Building on Lewis’s (2018) classification of an “alternative influencer” network, we apply structural topic modeling across all text-based autocaptions from her study’s sample to identify common topics featured on these channels. This allows us to gauge which topics appear together and to trace politicization over time. Through network analysis, we determine channel similarities and evaluate whether deplatformed channels influenced topic shifts. We find that political topics increasingly dominate the focus of all analyzed channels. The convergence of culture and politics occurs mostly about identity-driven issues. Furthermore, more extreme channels do not form distinct clusters but blend into the larger content-based network. Our findings illustrate how political topics may function as connective ties across an initially more diverse network of YouTube influencer channels.