In knowledge work, understanding the level of abstraction you’re operating on can make all the difference. A famous example helps illustrate the concept: “This is a picture of a painting of a painting of a pipe.” At the simplest level, we have the concrete—the pipe itself. Beyond that, we move to representations: the painting of the pipe, which is one level removed, and then the painting of the painting, which adds yet another layer of abstraction.

The concrete level is the easiest to grasp—it’s direct and tangible. However, the higher levels of abstraction, those that deal with representations of representations or broader conceptual thinking, are harder to understand and often tricky to apply appropriately. Knowing when and how to move beyond the concrete level is a skill, one that isn’t always intuitive.

Humans and language models alike face challenges in handling abstraction. We can easily mix up the layers, treating abstract representations as if they were concrete objects. Some individuals and systems struggle to work beyond the concrete level at all, sticking only to the simplest, most tangible concepts. This can lead to oversimplified results when the task or concept at hand requires more nuanced thinking across abstract layers.

These difficulties also create challenges for building tools that support knowledge work. Tools must be designed to navigate and present information at multiple levels of abstraction, making them both accessible and capable of handling complexity. This is especially vital when creating systems or agents intended to work alongside humans, as their ability to handle abstraction impacts their usefulness and relevance in knowledge-intensive tasks.

Understanding levels of abstraction isn’t just a theoretical exercise—it’s an essential skill for working smarter. By recognizing these layers and the challenges they bring, we can design better tools, make better decisions, and approach problems with greater clarity. Mastering abstraction enables us to connect the concrete with the conceptual, leading to more effective knowledge work overall.