In Argentina, HIV diagnosis is reached by voluntary testing or symptom-based case findings. However, because of the high proportion of infected individuals unaware of their serologic status new strategies are required. In this article we show how a mathematic model predicts the impact of expanding HIV testing in Argentina. The model is based on time-dependent Markov matrixes and applies parameters-dependent transition-probabilities obtained from both national and international cohort studies. Outputs include time on clinical stages and therapy regime, CD4-count, viral-load, infection-state and age; mortality rates and proportion of unidentified infection at a population-level. Simulations were performed for current testing strategy and for a theoretical scenario with earlier diagnosis. We show how our prediction suggests that diagnosis before onset of symptoms would increase life expectancy by 10.7 years. Also, we show how a reduction of time to diagnosis to 5 or less years from infection would reduce mortality rates in the first year of HAART from 7.6% to 2.1%, the proportion of unrecognized infection from 43.2% to 23.8% and the proportion of individuals with unaware infection needing treatment from 12% to 0.2%. Based on this prediction we stress the importance of implementing health policies aimed at detecting HIV infection in early stages in Argentina.