The reading room · Digna Legi
What living systems teach us about true agency
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Why read this
Alan Love separates agency, directionality, and function to argue that organisms may remain agents, not mere stimulus-response machines.
AI brief · Checked against source text
The main idea
Alan Love distinguishes agency, directionality, and function to argue that living systems are not well captured as mere stimulus-response machines. Organisms can maintain behavioral options, pursue goals such as foraging or reproduction, and alter environments in ways that outlast them. AI sharpens the comparison because it can appear agent-like while operating through different physical organization and mechanisms.
Go a little deeper
Embodiment matters
Love does not deny similarities between brains and AI architectures, but he marks a decisive difference: organisms are physically distributed systems with bodies, muscles, nervous systems, and environments. The point is not that AI cannot solve tasks, but that task performance alone may hide very different causal organization beneath apparently similar behavior.
Agency is not just motion
Directionality separates living pursuit from mere physical trajectory. A squirrel moving to collect food and a ball rolling downhill both have a path, but only the first is treated as organized around aims. This distinction keeps agency from collapsing into any patterned movement while still leaving room to ask what evidence warrants goal attribution.
Function is historically variable
Function is not presented as a fixed inventory of biological jobs. Eating and mating are basic needs, but the traits that serve them differ across life, and one trait may acquire, change, or carry multiple functions. That makes agency relevant to evolution because small organismal actions may help shape later functional possibilities.
AI is a test and a trap
AI can help biology by making artificial systems that can be altered with unusual precision, exposing possible limits or variants of agency-like capacities. But the same comparison invites anthropomorphism: people may infer thinking or feeling from behavior without enough evidence. Love’s useful stance is mutual learning paired with interpretive restraint.
A case from the article
Earthworms as environmental agents
Love uses earthworms to show agency below charismatic or cognitively rich examples. As they move through soil, consume it, defecate it, and churn it, they create conditions more favorable to their own continued existence. Because that modified environment can persist beyond an individual worm, ordinary behavior can become evolutionarily consequential niche construction.
How the case is made
The case is made through conceptual distinctions, biological examples, and comparison with AI systems.
Where the idea has limits
Love treats biology-AI comparison as early-stage and warns against over-interpreting either organisms or AI agents from surface behavior alone.
A question to take away · from Digna Legi
When does goal-like behavior require agency, and when is it only our interpretation of an efficient process?
What the original adds
The source adds Love’s separate treatment of directionality and function, plus his claim that AI experimentation may reveal breaking points and possibility spaces hard to test in living systems.
About this brief
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Digna legi. Worth reading.