This study introduces a novel method to experimentally quantify molecular complexity using assembly theory and spectroscopy. Assembly theory defines molecular complexity as the number of steps required to construct a molecule from its building blocks, known as the molecular assembly index (MA). The researchers demonstrate that MA can be inferred from spectroscopic data, including infrared (IR), nuclear magnetic resonance (NMR), and tandem mass spectrometry (MS/MS) measurements. By analyzing the number of peaks in IR spectra, carbon resonances in NMR, and molecular fragments in MS/MS, the MA of unknown molecules can be reliably estimated. This approach provides the first experimentally quantifiable method for determining molecular assembly, enabling the study of complex molecule evolution and serving as a unique marker of evolutionary processes. The study shows that the number of IR peaks in the fingerprint region (400-1500 cm⁻¹) correlates with MA, with a Pearson correlation coefficient of 0.86. Similarly, the number of distinct carbon types in NMR spectra correlates with MA, with a correlation coefficient of 0.87. These findings support the hypothesis that molecular complexity can be inferred from spectroscopic data without prior knowledge of the molecule's structure. The researchers also developed a recursive algorithm to estimate MA from tandem mass spectrometry data, achieving a correlation coefficient of 0.73 between predicted and expected MA values. The study further demonstrates that combining multiple spectroscopic techniques improves the accuracy of MA inference. For example, a combined model using 0.55 times NMR and 0.45 times IR inferred MA values showed a correlation coefficient of 0.91. Additionally, the study shows that 13C DOSY spectroscopy can be used to deconvolute complex mixtures, enabling the accurate determination of MA for individual components. The results highlight the potential of using spectroscopic measurements to quantify molecular complexity, which has applications in drug discovery, the origin of life, and artificial life. The study provides a robust framework for inferring molecular assembly from experimental data, offering a new approach to understanding the complexity of molecules in various contexts.This study introduces a novel method to experimentally quantify molecular complexity using assembly theory and spectroscopy. Assembly theory defines molecular complexity as the number of steps required to construct a molecule from its building blocks, known as the molecular assembly index (MA). The researchers demonstrate that MA can be inferred from spectroscopic data, including infrared (IR), nuclear magnetic resonance (NMR), and tandem mass spectrometry (MS/MS) measurements. By analyzing the number of peaks in IR spectra, carbon resonances in NMR, and molecular fragments in MS/MS, the MA of unknown molecules can be reliably estimated. This approach provides the first experimentally quantifiable method for determining molecular assembly, enabling the study of complex molecule evolution and serving as a unique marker of evolutionary processes. The study shows that the number of IR peaks in the fingerprint region (400-1500 cm⁻¹) correlates with MA, with a Pearson correlation coefficient of 0.86. Similarly, the number of distinct carbon types in NMR spectra correlates with MA, with a correlation coefficient of 0.87. These findings support the hypothesis that molecular complexity can be inferred from spectroscopic data without prior knowledge of the molecule's structure. The researchers also developed a recursive algorithm to estimate MA from tandem mass spectrometry data, achieving a correlation coefficient of 0.73 between predicted and expected MA values. The study further demonstrates that combining multiple spectroscopic techniques improves the accuracy of MA inference. For example, a combined model using 0.55 times NMR and 0.45 times IR inferred MA values showed a correlation coefficient of 0.91. Additionally, the study shows that 13C DOSY spectroscopy can be used to deconvolute complex mixtures, enabling the accurate determination of MA for individual components. The results highlight the potential of using spectroscopic measurements to quantify molecular complexity, which has applications in drug discovery, the origin of life, and artificial life. The study provides a robust framework for inferring molecular assembly from experimental data, offering a new approach to understanding the complexity of molecules in various contexts.