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It has since become crucial for monitoring HIV infection progression and assessing the efficacy of antiretroviral therapy. However, during the coronavirus disease 2019 pandemic, the term \u201cviral load\u201d became widely popularized, not only for the scientific community but for the general population. Viral load plays a critical role in both clinical patient management and research, providing valuable insights for antiviral treatment strategies, vaccination efforts, and epidemiological control measures. As measuring viral load is so important, why don't researchers discuss the best way to do it? Is it simply acceptable to use raw <jats:italic>C<\/jats:italic><jats:sub>t<\/jats:sub> values? Relying solely on <jats:italic>C<\/jats:italic><jats:sub>t<\/jats:sub> values for viral load estimation can be problematic due to several reasons. First, <jats:italic>C<\/jats:italic><jats:sub>t<\/jats:sub> values can vary between different quantitative polymerase chain reaction assays, platforms, and laboratories, making it difficult to compare data across studies. Second, <jats:italic>C<\/jats:italic><jats:sub>t<\/jats:sub> values do not directly measure the quantity of viral particles in a sample and they can be influenced by various factors such as initial viral load, sample quality, and assay sensitivity. Moreover, variations in viral RNA extraction and reverse\u2010transcription steps can further impact the accuracy of viral load estimation, emphasizing the need for careful interpretation of <jats:italic>C<\/jats:italic><jats:sub>t<\/jats:sub> values in viral load assessment. Interestingly, we did not observe scientific articles addressing different strategies to quantify viral load. The absence of standardized and validated methods impedes the implementation of viral load monitoring in clinical management. The variability in cell quantities within samples and the variation in viral particle numbers within infected cells further challenge accurate viral load measurement and interpretation. 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