Library.Reference:9.3.10 - Sources, currentness and the strength of the claims

The general explanation joins conceptual distinctions, professional Methods and bounded empirical findings. These have different evidential roles.

Anatoly Levenchuk’s Person Engineering (R9) supplies a useful role and transformation account. The account here preserves developing person’s agency, the distinction between professional content and didactics, the contribution of teaching and provision, and concurrent development. It does not adopt a universal learning sequence, quantitative promises of accelerated learning or the claim that a motor-learning mechanism has already explained every kind of professional acquisition.

Dietz and Mulder’s Enterprise Ontology, second edition, 2024 supplies the coordination distinctions and their human social premises. It helps analyse what was requested, promised, declared, accepted or reopened. Wagner’s account of the opportunistic principal supplies a conceptual counterweight to treating only delegates as possible opportunists. Neither source makes every participant selfish or settles the governing obligations of a particular institution.

Team Topologies supplies professional distinctions among interactions in software delivery. The current ETAP account relates enabling work to platform improvement. The AI-era discussion motivates attention to continuing value and accountability. Its analogy between human cognitive load and AI context is not an established equivalence of mechanisms. The present account therefore keeps human and technical interventions distinct.

Microsoft’s AI centre-of-excellence operating models provide a contemporary professional comparison of centralized, hybrid and federated arrangements in that domain. They inform the organizational alternatives without proving a best arrangement for quality, project management or literacy.

Intelligent AI Delegation proposes a framework connecting capability, authority, monitoring, adaptation and accountability across delegation. It is a design proposal, not a validated universal protocol. Use its questions to expose a needed condition; do not infer that a signed event or a technical verification proves the full professional result.

The Cybernetic Teammate reports a bounded workplace innovation experiment. Its team-performance findings do not establish long-term culture or personal learning. Shen and Tamkin distinguish immediate task performance from acquisition in a short study of developers learning a new library. That result motivates separate examination of learning; it does not prove inevitable deskilling with AI.

SCARCE examines conventions and relationships among agents competing for a resource. Whether the emerging arrangements improve collective task performance remains a separate question. GlossoGen examines formation and transmission of communication conventions under particular agent and memory conditions. Jones and colleagues show that mixed human–AI pairs in a reference game can improve while still coordinating less well than homogeneous pairs. These results justify examining actual transmission and mixed interaction; they establish neither a single mechanism of all culture nor a ceiling on later models.

Keep model, configuration, task, permitted assistance and observation interval with any AI performance claim. A benchmark’s human-time horizon is not the agent’s elapsed run time or a universal limit on organizational work. METR’s account of its time-horizon measurements explains that boundary. Refresh the particular premise when the model, tool or receiving task changes; a later publication date alone does not qualify the new use.