Photosynthesis phenotyping is increasingly important for crop improvement, but its main limitation is not measurement throughput alone. It is the physiological interpretation of scalable signals. Direct methods, including gas exchange, CO2 response analysis, isotope-based approaches, and canopy flux measurements, define photosynthetic carbon assimilation and capacity, but remain difficult to deploy at scale. Optical and imaging approaches provide high-throughput observations of plant function, but they mainly capture indirect, context-dependent signals. We argue that a core challenge in the field is the frequent conflation of proxy measurements with photosynthetic carbon assimilation. Scalable traits often reflect integrated physiological states rather than carbon flux itself, and their relationship to photosynthesis depends on environmental context, biological scale, and the processes limiting carbon gain. Progress will therefore require treating scalable measurements as inferential signals rather than direct physiological quantities. It will also require stronger physiological benchmarks and validation frameworks across genotypes, environments, developmental stages, and spatial scales.




