<p>Advances in artificial intelligence over the last decade have presented a wide range of opportunities to improve the economic, ecological, and social performance of forest management systems. From new sensors to algorithms that can manage big data, artificial intelligence can transform the forestry sector and perhaps provide a pathway forward for effective and efficient management of forest systems challenged by labor, financial, and climate-related concerns. However, in the rush to implement these systems, the outcomes from artificial intelligence need to be evaluated with caution rather than accepted without examination. Here, we illustrate a simple case of the responses large language models might provide regarding the relative amount of near- and mid-infrared energy reflected by broadleaf deciduous and evergreen coniferous trees during the summer in temperate and boreal biomes. The responses may be consistent or inconsistent with general scientific knowledge and the responses may be consistent or inconsistent from one model to another.</p>