#The claim

A cognitive actor is any system, carbon or silicon, that processes signals and converts them into actions or plans in pursuit of an objective. The definition is deliberately functional: it says nothing about neurons, consciousness, or personhood. Whatever can perceive, decide, and act toward a goal qualifies — a person, a firm, a trained model, a person wielding a model. By this definition, the fundamental unit of agency in a knowledge system is not a person but a capability. And a capability is a physical thing: the cognitive actor is where knowledge, compute, and energy co-locate — a twenty-watt brain or a megawatt datacenter. Without the watts, the knowing cannot act.

Within each actor, the two interfaces already described meet. The beliefs and frameworks refined by the evolution interface are the linkage that couples it to the agency interface: evolution learns, agency acts, and the cognitive actor is where the two are joined in one system. And each actor is monad-like — a seeker carrying its own view of the world, reasoning within its own beliefs, frameworks, and plans. No two model reality identically.

#The mechanism

The convergence of the two substrates was anticipated before it was possible. Licklider sketched man–computer symbiosis as a partnership of complementary strengths,1Licklider (1960), “Man-Computer Symbiosis.” Complementary cognition: humans set goals, machines handle the computable middle. and Turing had already put the underlying question — can machines think? — on functional footing.2Turing (1950), “Computing Machinery and Intelligence.” The question of machine thought recast as a functional test. What has changed is that the line is no longer speculative. These systems are not mere tools anymore; they create new knowledge. Protein-structure prediction closed a fifty-year-old problem and generated predictions for over 200 million proteins.3Jumper et al. (2021), AlphaFold. The fifty-year protein-folding problem closed by a learned system. Automated research systems now run the full lifecycle from hypothesis to peer-reviewed manuscript.4Sakana AI (2024), “The AI Scientist.” Hypothesis, experiment, paper — the research loop, automated end to end. Foundation models discover artificial-life simulations no human conceived.5Kumar et al. (2024). Foundation models searching the space of artificial-life simulations humans never wrote down.

Cognitive technology widens the spectrum between the two poles. By computing directly over the symbolic layer — turning language into plans and actions — large language models lower the threshold for what can function as a cognitive actor, extending agency across tasks and substrates once reserved for humans.

#Why it matters

Cognitive actors are the locus where knowledge converts to decision — where information gains consequence, and where agency meets accountability. That last pairing is not incidental: if the unit of agency is a capability, then responsibility attaches to capabilities and to those who deploy them, a question the network’s scoring systems will have to price.

Knowledge translated into action in light of reality generates trajectories of experience, and their accumulation is history. The network does not distinguish between carbon and silicon. It recognizes only the capacity to act and to know — which is exactly why the next factors, about how actors transmit and coordinate, apply to both without amendment.