The Postdigital Is Messy — and That Is Fine

The first time I read Petar Jandrić and colleagues' definition of «postdigital», I stopped. Read it again. Then felt something close to recognition.

They write: «The postdigital is hard to define; messy; unpredictable; digital and analogue; technological and non-technological; biological and informational. The postdigital is both a rupture in our existing theories and their continuation» (Jandrić et al., 2018).

A definition that embraces its own indeterminacy. That treats disorder as a constitutive feature rather than a problem to be solved. That refuses clean dichotomies — digital/analogue, technological/human — to insist on their tangled, irreducible coexistence.

I have spent years configuring organizational workflows, mostly in large Italian public institutions and private groups. What I observe in every effective system is exactly this: automation mixed with informal conversation, algorithms sitting alongside situated judgment, standardized procedures punctuated by improvisation. The postdigital is not an academic category. It is a description of what working organizations actually look like when you stop forcing them into tidy diagrams.

Peters and Besley (2019) push this framework further in «Critical Philosophy of the Postdigital», connecting cybernetics, complexity theory, quantum computing, and deep learning into a single critical frame. Their core claim: complex systems are by definition not fully predictable. The attempt to eliminate uncertainty, variability, every human element, is structurally destined to fail. Imprevedibility is a systemic feature, not a defect.

This connects to a passage Peters lifts from Heidegger that stays with me: «The essence of technology is by no means anything technological» (Being and Time, SCM Press, 1962). The essay uses it to make a point about knowledge socialism — that its essence lies in cultural practices, social relations, communities of inquiry — but the observation lands equally well anywhere tools are involved. A Jira workflow is not software. It is an assemblage, to use Jacques Ellul's term (The Technological Society, Random House, 1964): hardware, scripts, writing conventions, peer review rituals, power relations, the physical screens it runs on, the network infrastructure that makes it reachable. The whole of that entanglement is the technology, not the application layer alone.

Designing with this in mind changes what you optimize for. Effective workflows function the way radio telescopes do: they capture weak signals in organizational background noise, amplify them, make them legible to a community. But they are postdigital radio telescopes — complex assemblages whose performance depends on the entire socio-technical ecology, not on the precision of any single instrument.

Peters introduces a further concept, «the biologization of digital reason» (Peters & Besley, 2019): the convergence of AI, deep learning, quantum computing, nanotechnology, and new genetic biology into what he calls «bio-informationalism». In this phase, the individual knowing subject is either entirely surpassed or radically decentered.

This is the part of the framework I hold with most friction. The claim is intellectually serious; the empirical question of when and how far individual subjectivity is actually displaced remains open. I take it as a horizon worth watching, not a fait accompli.

What is already observable, and what the postdigital framework articulates well, is this: the boundary between physical, digital, and biological has become a zone of continuous transit rather than a line. Designing systems that treat it as a fixed boundary produces fragility.

Three practical consequences I carry forward from this reading.

Effective systems are always messy. Mixing formal and informal, procedural and improvised, automated and craft-made is not a failure of design — it is a condition of resilience. Eliminating this complexity produces rigidity.

What gets designed is never a tool in isolation. It is a socio-technical assemblage that includes practices, language, power expectations, cultural habits. A design that addresses only the software layer will be undermined by every layer it ignores.

The aim of design is to create conditions for emergence, not to achieve total control. Complex systems produce unpredictable behaviors. The productive move is to open space for communities of inquiry — in Dewey and Peirce's sense — where knowledge cultures can form and sustain themselves.

The next stop in this itinerary is the one I approach with the most caution: cognitive capitalism and deep learning as «the final phase of automation». The radical possibilities of knowledge socialism must eventually face their own shadows.

References

Ellul, J. (1964). The Technological Society. New York: Random House.

Heidegger, M. (1962). Being and Time. London: SCM Press.

Jandrić, P., Knox, J., Besley, T., Ryberg, T., Suoranta, J., & Hayes, S. (2018). Postdigital Science and Education. Educational Philosophy and Theory, 50(10), 893–899. https://doi.org/10.1080/00131857.2018.1454000

Peters, M. A., & Besley, T. (2019). Critical Philosophy of the Postdigital. Postdigital Science and Education, 1(1), 29–42. https://doi.org/10.1007/s42438-018-0004-9

Peters, M. A., Besley, T., Jandrić, P., & Zhu, X. (Eds.). (2020). Knowledge Socialism: The Rise of Peer Production: Collegiality, Collaboration, and Collective Intelligence. Singapore: Springer. https://doi.org/10.1007/978-981-13-8126-3