This paper investigates the phenomenon of "sloppy" parameter sensitivities in systems biology models. It shows that many models in the field exhibit a "sloppy" sensitivity spectrum, where the eigenvalues of the sensitivity matrix are evenly distributed over many decades. This implies that the behavior of the model is primarily sensitive to a small number of parameter combinations, rather than individual parameters. The authors tested this phenomenon across 17 models from the literature and found that all of them exhibited sloppy sensitivity spectra. The paper also discusses the implications of this sloppiness for model building and parameter estimation. It argues that collective fits to large amounts of data often leave many parameters poorly constrained, and that direct parameter measurements must be both very precise and complete to usefully constrain model predictions. The authors demonstrate this with a detailed analysis of a growth-factor-signaling model, showing that even with precise measurements, many parameters remain poorly constrained. The paper concludes that sloppy sensitivity spectra are a universal feature of systems biology models. This has important implications for model building, suggesting that modelers should focus on predictions rather than on parameter values. The results highlight the power of collective fits and suggest that modelers should focus on predictions rather than on parameters. The paper also emphasizes the importance of uncertainty analysis in model building, as the behavior of sloppy models is highly sensitive to parameter combinations.This paper investigates the phenomenon of "sloppy" parameter sensitivities in systems biology models. It shows that many models in the field exhibit a "sloppy" sensitivity spectrum, where the eigenvalues of the sensitivity matrix are evenly distributed over many decades. This implies that the behavior of the model is primarily sensitive to a small number of parameter combinations, rather than individual parameters. The authors tested this phenomenon across 17 models from the literature and found that all of them exhibited sloppy sensitivity spectra. The paper also discusses the implications of this sloppiness for model building and parameter estimation. It argues that collective fits to large amounts of data often leave many parameters poorly constrained, and that direct parameter measurements must be both very precise and complete to usefully constrain model predictions. The authors demonstrate this with a detailed analysis of a growth-factor-signaling model, showing that even with precise measurements, many parameters remain poorly constrained. The paper concludes that sloppy sensitivity spectra are a universal feature of systems biology models. This has important implications for model building, suggesting that modelers should focus on predictions rather than on parameter values. The results highlight the power of collective fits and suggest that modelers should focus on predictions rather than on parameters. The paper also emphasizes the importance of uncertainty analysis in model building, as the behavior of sloppy models is highly sensitive to parameter combinations.