Dwarf Fortress, Emacs, & AI: The allure of generative complexity

There is a shared soul shard between Dwarf Fortress, Emacs, and AI that lured me to them and has kept me engaged for over a decade. For a long time, I struggled to articulate the connection, managing only to describe Dwarf Fortress as the Emacs of games. But this analogy, while compelling, doesn’t fully capture the deeper resonance these systems share. They are not merely complicated; they are complex—tools for creativity that reward immersion and exploration.

Complicated, Complex, Dev.

To understand the allure, let’s revise the distinction between complicated and complex. Complicated systems, say a spinning-disk microscope, consist of interlocking parts (each with internal complications) that interact in predictable ways. They require technical expertise to master, but their behavior remains largely deterministic and I tire of them soon.

Complex systems, see Cynefin framework, exhibit emergent behavior. Their value/fun lies in the generative possibilities they unlock rather than the sum of their parts.

Dwarf Fortress, Emacs, and AI live on the froth of this complexity. None of these systems exist as ends in themselves. You don’t play Dwarf Fortress to achieve a high score (there isn’t one, you eventually lose). You don’t use Emacs simply to edit text, and you don’t build AI to arrange perceptrons in aesthetically pleasing patterns. These are platforms, altars for creation. Dev environments.

In Emergence We Trust

Like language with the rules of poetry, these environments are generative places enabling exploration of emergent spaces. Emergence, which manifests both in the software but also in you. There is always a point where you find yourself thinking, I didn’t expect I could do that. In Dwarf Fortress first you fight against tantrum spirals and then through mastery, against FPS death. Similarly, Emacs enables workflows that evolve over time, as users build custom functions and plugins to fit their unique needs. In AI, emergence arrives rather late but it’s there. Putting together datasets, training them, optimizing, starting over, are complicated but not complex per se. The complexity (and emergence) is in the capabilities of the trained network. Things infinitely tedious or difficult are a few matrix multiplications away.

This desire for emergence is spelunking. It rewards curiosity and experimentation but demands patience and resilience. Mastery begins with small victories: making beer in Dwarf Fortress, accessing help in Emacs, or implementing a 3-layer neural network. Each success expands your imagination. The desire to do more, to push the boundaries of what’s possible, becomes an endless rabbit hole—one that is as exhilarating as it is daunting.

Complexity as a Gateway to Creativity

The high complexity of these systems—their vast degrees of freedom—opens the door to infinite creativity. This very openness, however, can be intimidating. Confronted with the sprawling interface of Emacs, the arcane scripts of Dwarf Fortress, or the mathematical abstractions of AI, it’s tempting to retreat to the familiar. Yet this initial opacity is precisely what makes these systems so rewarding. Engaging with something that might blow up in your face—whether it’s drunk cats, a lisp error, or an exploding gradient—forces you to give up.

But just then you have an idea, what you tried this…

Awaken, H. ludens.