What should technology help humans become?

The technologies change quickly. The questions do not.

Across more than two decades of work in cultural heritage, media, institutions, and emerging technology, I keep encountering the same critical questions under new technological conditions.

Five Critical Questions

Choose any critical question as a place to enter.

Blogs, podcasts, wikis, mobile devices, social platforms, digital collections, algorithms, open knowledge systems, and artificial intelligence have each altered the conditions under which these questions are asked. None has made them disappear.

These ideas do not form a straight line. They branch, recur, intersect, and return under new technological conditions.

Each critical question opens onto a different part of the archive. The same artifact may appear in more than one place. That is intentional. A project about open knowledge may also be about participation. A preservation experiment may become a lesson about infrastructure. A piece about technology may turn out, years later, to have been about attention.

The relationships matter as much as the chronology.

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Who gets to speak, and whose knowledge is heard?

New technologies have always promised broader participation. But access to a platform does not automatically create visibility, authority, or trust.

A lot of the early Voices work followed people who were already doing meaningful cultural work and asked what happened when they gained new ways to speak directly to wider publics. The deeper question was not simply whether they could publish. It was whether their knowledge could travel.

2014–2015 — GLAM-Wiki Program Report / Case Study

What changed here: the question expanded from individual voice to shared public knowledge infrastructure. Institutional expertise could enter systems larger than the institution itself.

Still matters: public knowledge grows stronger when participation is distributed rather than tightly contained.

Taken together, these artifacts show a recurring pattern: new media keep changing the mechanisms of participation, but the human problem remains remarkably stable. Who gets heard? Who is trusted? Whose knowledge travels? And what kinds of systems help useful expertise move beyond the people and institutions that already possess it?

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How do we make knowledge more accessible without losing its meaning?

Digital technology can make cultural knowledge easier to reach. That does not necessarily make it easier to understand.

A story can travel without its context. A tutorial can become useful because expertise is translated into practical guidance. A collection can become vastly more discoverable when it enters Wikipedia or another shared knowledge system. In each case, access changes the relationship between knowledge and the people encountering it.

The challenge is not simply to make more information available. It is to make knowledge more accessible without severing it from the evidence, place, expertise, and context that give it meaning.

2014–2015 — GLAM-Wiki Program Report / Case Study

What changed here: cultural institutions could move knowledge beyond their own websites and into shared public information systems where people were already searching, reading, and making connections.

Still matters: discoverability increases the public value of cultural knowledge only when provenance, context, and pathways back to deeper sources travel with it.

These artifacts move from visibility, to usefulness, to discoverability at scale. Each technological shift solves one access problem while creating another interpretive problem. Information can become easier to reach and easier to detach from the context that gives it meaning.

Technology repeatedly changes how knowledge travels. The harder work is making sure meaning travels with it.

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What happens when useful tools become systems we depend on?

Most technologies arrive as tools. The important shift happens when they stop being optional and start shaping how the work itself gets done.

At that point, the question is no longer whether a tool is useful. It is whether the people, workflows, knowledge, and institutional capacity around it are strong enough to make that usefulness sustainable.

Beyond Vizcaya — a stronger iteration

Beyond Vizcaya carried the HistChem idea into a different organization and became a more successful iteration of the same underlying practice: use media and technology to extend an institution beyond the physical room, test the concept in public, learn from audience response, and refine the model through use.

That matters because it turns HistChem from a one-off experiment into evidence of transferability. The practice could survive a change of institution, context, audience, and implementation—and improve.

Taken together, these artifacts show a movement from tool → behavior → strategy → experiment → iteration → system.

The deeper question is what happens after adoption. A useful technology begins attracting workflows, expectations, skills, dependencies, documentation, and institutional memory around itself. Eventually, the technology is no longer the system. The system is the relationship among people, process, knowledge, and tools.

Current edge — Workflow Studio

The present version of this question is less about selecting technology than creating a repeatable institutional practice where people can identify problems, test technology-enabled workflows, document what works, share what they learn, and build capability that remains useful beyond any single platform.

In that sense, Workflow Studio is not another tool experiment. It is an attempt to make experimentation, learning, and reuse part of the institution’s operating capacity.

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How do we preserve memory when the systems carrying it keep changing?

Digital technology made cultural knowledge easier to reproduce, distribute, search, and connect. It also made that knowledge dependent on systems with surprisingly short lives.

Websites disappear. Platforms decline. File formats age. APIs close. Links break. Collections can remain technically intact while becoming practically invisible.

Keeping something is not the same as keeping it usable, understandable, and connected to its context.

2014–2015 — GLAM-Wiki Program Report / Case Study

What changed here: the continuity question expanded beyond institutional repositories. Wikimedia showed how cultural knowledge could live inside shared public infrastructure with enormous reach, reuse, and distributed stewardship.

Still matters: some knowledge survives precisely because no single institution controls its only copy or pathway of access.

Current edge — recovering Voices itself

The archive behind this page has become its own experiment in continuity across changing systems. Recovering Voices has meant locating old media, reconciling migrated WordPress content, tracing broken or transformed platforms, recovering audience evidence, verifying links, and distinguishing what merely survived from what remains intelligible.

That experience makes the old problem more concrete. The challenge is not simply whether an artifact still exists. It is whether someone can find it, understand it, reconnect it to its context, and use it again.

These artifacts move from survival → context → reuse → continuity across systems.

The underlying problem is not technological obsolescence by itself. It is dependency. Every digital artifact depends on layers of software, metadata, documentation, links, institutions, people, and practices that make it usable. A surviving file that nobody can find, interpret, or reconnect to its context is only partially useful as memory.

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What should technology strengthen in us—and what should it never replace?

For much of my career, technological questions were framed around capability: what could become faster, easier, more connected, more automated, or more scalable.

Eventually that stopped being enough.

Every technology also creates habits. It changes what we notice, what we practice, what we delegate, what we expect from ourselves, and sometimes what we stop knowing how to do.

So the question has become less about what technology can do and more about what using it trains us to become.

2022 — E-ink Thinking

What changed here: I stopped treating maximum technological capability as an unquestioned good and began testing whether deliberately limited devices could improve attention, writing, memory, and agency.

Still matters: sometimes a constraint is not a technological deficiency. It is a design choice that gives a human capacity room to work.

2026 — In Training

What changed here: the experiment moved from devices into embodied practice. Attention, patience, boredom tolerance, endurance, physical presence, and agency became capacities I could deliberately train—and observe with technology without allowing the measurements to become the experience itself.

Still matters: a tool can help us see ourselves more clearly without becoming the authority on what the experience means.

Use the machine to observe the human, not replace the human experience.

Current edge — AI and human capability

Artificial intelligence makes this question much harder to avoid. We can increasingly delegate drafting, recall, synthesis, analysis, navigation, and parts of judgment itself.

The meaningful question is not whether those capabilities should be used. It is what we want them to strengthen in us—and which capacities become weaker if they are no longer exercised.

The goal is not human purity from technology. It is augmentation without abdication.

These artifacts move from mediation → intentional constraint → embodied practice → augmentation.

The question beneath all of them is not whether technology is good or bad. It is whether the systems we build and use leave us more capable of paying attention, exercising judgment, making meaning, acting with agency, connecting with other people, and inhabiting the world we are supposedly using technology to understand.

The measure of a technology is not only what it can do for us. It is also what practicing with it trains us to become.

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A Scholar-Practitioner Approach

I have come to understand this work less as a sequence of projects than as a recurring practice.

A shift appears in the field. I try something. The experiment produces evidence. Research and reflection help explain what happened. A principle begins to emerge. Then a new technology, institution, audience, or problem creates another opportunity to test it.

Observe → Experiment → Document → Reflect → Extract → Test Again

Practice generates questions. Research sharpens them. Reflection makes patterns visible. Then the ideas return to practice.

That is why the archive matters now. Not because every early experiment predicted what would come next. Not because every platform survived. And certainly not because everything fits neatly into a retrospective master plan. The value is in seeing which questions survived the technologies that first brought them into view.

New technologies keep giving us new ways to answer old human questions. The scholar-practitioner’s job is to test those answers in the world—and notice what they change.

Explore the Archive

There is no single correct way through this material.

Follow a critical question

Trace one recurring human question across different technologies, projects, and periods.

Follow a person

Move through practitioners, thinkers, storytellers, collaborators, and interview subjects.

Follow an idea through time

Start with one artifact and see where the question reappears years later under different technological conditions.

Browse by year

Use chronology without treating it as progress.

Explore everything

Enter the full Voices of the Past archive and make your own connections.

The same artifact may lead somewhere different depending on where you enter.

That is not a flaw in the structure. It is the structure.