Life is arguably the primordial world-optimizer, with biotic factors being known to have caused major shifts in the abiotic world to suit their needs: oxygen-rich atmosphere, fertile soils, etc. We find ourselves in a "blind watchmaker" position where we can bring into existence systems beyond our comprehension by merely defining a computational niche and applying optimization pressure. One way of modeling niche-bound ecologies is in term of their inputs and outputs: what kinds of energy and matter they consume in relation to what they offer for others to consume. If framing ML models as ecologies adapting to an (often unnaturally fixed) computational niche, all sorts of neat parallels arise: transfer learning as exaptation, instrumental convergence as convergent evolution, regularizers as stressors enforcing resilience, gradualism, meta-learning as internal selection, etc. From here, we could build on ideas from ecosystem engineering and remnants of cybernetics (which, temptingly, means steersman) to explore the vocabulary of stable "arrangements" of ecologies, similar to the clever setups involved in backtranslation, diffusion, or adversarial training.