Perspective Page

The humanities at the workbench

AI has made an old question newly practical: what can a person do with what they know?

The ability to write software, structure information and produce a working interface is becoming accessible to more people. A subject specialist can test a product idea. An operator can prototype an improvement. Someone with a formation in letters can participate much more directly in technical construction.

The opportunity interests me because it brings intellectual life closer to the means of making things.

Language is part of construction

Before a useful system can be built, someone must describe a need precisely.

What does the user mean? Which distinction changes the result? What is being assumed? Which exceptions belong within the process? These questions demand command of language and attention to context.

Close reading is practical work when a market standard becomes a system requirement or a regulatory obligation becomes a workflow. With AI, the quality of that interpretation influences what gets built.

Imagination gives direction to capability

A new capability does not arrive with a complete account of its worthwhile uses.

Imagination helps connect a technical possibility with a human situation: a colleague struggling to understand a process, a team navigating unfamiliar regulation, a patient trying to find the right information.

Literature, history and philosophy offer a vast field of motives, conflicts and possible arrangements to think with. Their value includes the capacity to enlarge what we can conceive, before we decide what to implement.

Institutions determine what survives

A prototype enters a world of existing responsibilities, systems, budgets and habits.

Organisational theory helps explain what happens next. Knowledge may reside in an experienced colleague rather than a manual. A workaround may be compensating for an unresolved design problem. A team may resist a change because it transfers risk to them without transferring authority.

Implementation requires discovering these conditions and working through them. That is part of building the product.

Acceleration requires a destination

Accelerationism interests me because it takes technological change seriously as a force capable of altering economic and political arrangements. Debates around the Dark Enlightenment press uncomfortable questions about competence, authority and institutional failure.

The questions deserve examination. So do the proposed answers.

Greater speed and concentrated power leave fundamental decisions unresolved: towards which ends, under whose judgment, with what means of correction? Any serious account of development must consider both the capacity to act and the ability to recognise and remedy failure.

My practical interest is in enlarging our capacity to learn, build and improve, while keeping decisions open to examination.

Standards make coordination possible

Financial infrastructure depends on shared definitions, communication standards and rules that allow institutions to work together. Their value becomes visible when a change must travel across systems, organisations and markets.

Distributed ledgers introduce different ways of maintaining shared records and coordinating participants. Their usefulness depends on how the technical architecture relates to governance, legal recognition, interoperability and the activity being supported.

This is why I am interested in blockchain as institutional design as well as technology. The larger question is how an infrastructure enables independent actors to cooperate reliably.

Judgment belongs inside the workflow

AI can assist with research, comparison and verification as well as generation. The challenge is to design the work so that relevant evidence, unresolved questions and consequential decisions remain visible.

Scholastic reasoning offers a useful habit here: give the objection enough force to challenge the answer. Organisational learning adds another: let experience alter the process.

I want to build with both. The ambition is to make more of our knowledge usable, more of our ideas testable and more of our work open to improvement.