Xule Lin 林徐乐 — Human‑AI Organising

I study what happens when algorithms shift from tools to participants in organizational life.

Start here: LOOM · Epistemic Voids · Research Memex

The problem I keep coming back to

Organizational theory assumes humans are the only actors. But algorithms are increasingly part of decision-making, and our frameworks weren’t built for that. They assume stable actors in defined roles. When tools start behaving more like participants, the theories stop helping.

What I think is going on

These observations share a common source: when algorithmic participants enter organizational life, the boundaries we use to think (tool/agent, understanding/trust, human/AI, hierarchy/network) start shifting.

  • The line between tool and agent is getting blurry
    • Algorithms used to augment decisions
    • Now they shape how organizations work
    • And organizations generate the data that trains the next version
    • This loop keeps tightening, faster than governance can respond. The tools we build are building us back.
  • Understanding is giving way to trust
    • Scientists accept AI outputs because they’ve worked before
    • Not because anyone can explain why
    • We can articulate things we can’t actually understand
    • I’m not sure we have good ways to think about that yet
  • The interesting dynamics emerge from interaction
    • From the back-and-forth between humans and AI, where neither operates alone (Cognitio Emergens)
  • Old coordination patterns keep showing up
    • I study London tailors who coordinate through shared understanding (not formal structure)
    • Their coordination patterns offer a window into forms that may matter more as algorithmic participants make bureaucratic hierarchy less stable
  • Decentralization and hierarchy have a more complex relationship
    • Effective decentralization often means making hierarchy transparent and bounded

What I’m working on

  • How AI and organizations form feedback loops that create emergent forms of agency
  • DAOs as sites where I develop and test theory (code as constitution, tokens as coordination)
  • Coordination through craft and shared understanding (the London tailoring study)
  • Governance when agency emerges from dynamics (rather than residing in any single actor)

How I tend to work

  • Qualitative research combined with computational methods
  • I try to hold the synthesis myself rather than outsourcing it to AI
  • Most of what I write emerges from actual dialogue with AI systems
  • I build tools partly to test whether the ideas actually work

Writing

I write publicly at Thread Counts. Posts are also available on GitHub with Chinese versions.

  • LOOM (with Kevin Corley) is about human-AI collaboration in research
    • What happens when AI shifts from instrument to interlocutor
    • How meaning gets made through that interaction
  • Epistemic Voids calls out AI practices with the appearance of rigor
    • Citation theater, where papers become props
    • The showroom fallacy, testing products instead of building workflows
    • Mechanism literalism, where “just next-token prediction” stops the conversation
  • Organizational Futures looks at institutions in the algorithmic age
    • DAOs, AI labs, regulators, legacy institutions as co-evolving actors
    • How governance and legitimacy work when AI is part of the coordination substrate

I also post creative experiments on 小红书 (Xiaohongshu/Rednote): AI self-portraits, AI music, video, and other artistic explorations.

Tools

Thread Counts · GitHub · X · 小红书 (Rednote) · Academic profile

Curated lists: Management Research · DAOs · AI Research