Why Are You Making the Thing You’re Making?

When I first started mapping in OpenStreetMap, I walked every trail in my neighborhood. I’d walk trails that were already perfectly visible from satellite imagery. I didn’t need to do it, I could hand digitize if I wanted. But I was mapping those trails as a one person protest. You see the neighborhood next door had all the same resources but big “no trespassing” signs for non-residents. I coined the act “spite mapping” and the act of trespassing to collect the data was part of the point. Was it a little petty? Maybe.

Later in OpenStreetMap more options of machine learning became available. Facebook began adding data that had automatically been extracted. The debate became:

“Why should I map this when the robots can do it for me?”
“Why would you let the robots do it for you?”

This is a simplification of the debate, but it really got down to purpose.

I used to think and sometimes question out loud in these debates: “Are we knitting or are we industrial weaving?”

And that question once again is coming to mind as we think about our relationship with AI.

Handcraft vs. Industrial Production

Humans have been making things by hand for tens of thousands of years. Weaving is at least 27,000 years old. When weaving began to be industrialized punch cards first were invented for some looms. I think it is ironic as I think of punch cards as the oldest way of programming and they were first used for weaving in 1725. The first major way of programming was also used to automate hand crafting.

The tension between using AI and how physical objects are made has a lot in common:

Are we making something because we enjoy the craft?​
​Or because we want the output at scale?Is the item something so specialized that it should be made by a craftsperson?Are we making art and is that the purpose?Is there some other meaning in the act itself?

Knitting a sweater by hand is slow, intricate, and deeply human. Using a sewing machine is faster. Industrial textile production is thousands of times faster still. Is the goal to create 1,000 shirts, a few highly custom ones or a one of a kind?

None of these are “wrong.”
They’re just different answers to different purposes:

• Handcrafted → identity, mastery, intimacy, meaning, high level of expertise
• Assisted tool use → efficiency + creativity
• Industrialized production → scale, consistency, reach, less experts required

And we all float between these modes depending on what we’re making and why.

The same is true for writing, coding, mapping, research, teaching, policy drafting, poetry and design.

What Happens When AI Enters the Craft?

AI brings a question we all now have to answer:

Why are you making the thing you’re making?

If you’re writing a novel because you love the act of writing, then letting an agent generate chapters might feel hollow. If you write to think, or to uncover meaning, or to feel your own ideas forming then outsourcing that to a machine removes the part that matters. I frequently use an LLM as a partner including by feeding it my voice notes to synthesize them. I am not asking it to do my thinking for me.

When at in person events I often take notes by hand. I seldom ever look at those notes. The purpose is to increase my attention and help me remember. A perfect AI transcription would give me words I’d never absorb.

But if you are able to create a custom application built just for a few people or summarize 1,000s of pages of policy into something that is actionable or discover a way to use all the ingredients in your kitchen. Only then by using automated help might strengthen the purpose rather than take it away.

I’ve watched this play out across crowdsourced communities for years:

OpenStreetMap

In 2010 when humanitarian mapping got big in OpenStreetMap the tension between motivations of mapping as fast a possible to potentially save lives and mapping for the community/craft of it originally came into tension. Large corporations began to see the potential of OpenStreetMap and looked for ways to get data into it more quickly. I remember a particular argument in Thailand where Facebook was using machine learning to map roads more quickly, the community was upset about how the data was coming in without consultation on the process.

Multiple team members working with me in Indonesia were bewildered why people would want to do manual work when there were automated ways to help.

Mappers were all coming from very different motivations.

Wikipedia

A colleague at Wikimedia told me that some Wikipedia editors are not concerned at all about people reading their articles. The articles they contribute to serve as a monument to knowledge. This is a tension when people come to Wikipedia for a quick high level overview of a topic and instead find expert level information.

Zooniverse

Early in Zooniverse some volunteers became upset when they learned that their work was being used to train machine learning models. They did not want to be replaced by automation and wanted to make the discoveries themselves. Some felt betrayed because their purpose was different than the project’s purpose.

GIS Analysts and Crowdsourced Map Data

When I began working more formally with organizations to adopt OpenStreetMap, the people most resistant were often GIS professionals. There were many different reasons they might be concerned. The tension was their professional identity was being questioned.

The Expertise Still Matters, Deeply

It is astonishing to think about the layered knowledge humans have built across generations. Think about how we started weaving 27,000 years ago and then began using punch cards for weaving and then computers and now Frontier AI. Frontier AI exists because of that accumulation.

Knowledge for its own sake. I’m old enough to remember when you could have a bar debate around facts. I also remember the transition to when many people began to reference their phones to prove a point.

Does my friend’s son who is obsessed with dinosaurs need to stop learning all their names because an LLM could just tell him? No, why take the joy out of dinosaurs for him. Additionally by learning something fun and learning to learn will serve him in the future.

And expertise still matters, even when AI can create something impressive.

We lose more than skills when we automate everything.
We lose ways of thinking, ways of sensing, ways of understanding the world.

There Are Also Practical Reasons to Choose the Slower Path

Sometimes you choose a human-only process because:

• the data is sensitive
• the system isn’t trustworthy yet
• you’re attempting to avoid hidden bias
• you’re protecting privacy
• the climate or cost implications matter
• your governance structures require human oversight

Sometimes the handcraft way is simply the responsible way.

Just like sometimes using AI is the responsible way.

Even Art Isn’t Exempt

Years ago, I saw an exhibit where robots were installed behind drywall. They’d punch through it, making holes, learning how to break it more efficiently. It was eerie to hear creepy tapping and a hole to break in the dry wall and a bright robot light to shine back at you.

I mentally reference this exhibit when thinking about how there are artistic ways to use AI and robotics. The art works because a human gave it intention. The machine was a medium.

Same with AI writing poems.
Is a perfect haiku written by a language model actually poetry?
Or is poetry the human struggle to find the right word?

These questions matter more than they seem.

Responsibility Doesn’t Go Away

Whether you wrote every line of code by hand or collaborated with a powerful agent, the outcome is yours. If a robot-assisted builder constructs a house that collapses, we still blame the builder.

Tools don’t remove responsibility.
Tools change the shape of how you ensure quality.

So Ask Yourself: Why Are You Making the Thing?

Not everything needs to be handcrafted.
Not everything should be automated.

When building ask yourself:

• What you’re trying to achieve
• How you want to feel during the making
• How your community defines meaning
• What expertise you want to preserve
• What responsibilities you’re willing to hold
• And whether the process itself is part of the purpose

We have to get clear on the why before we decide the how.

Otherwise, we risk using AI in ways that flatten the very human richness we’ve spent thousands of years cultivating or rejecting AI in ways that keep us from building the tools that could free us for deeper craft.

Neither extreme honors what humans are capable of.

We’ve always moved between craft and scale, between knitting by hand and weaving with machines. AI is just a new kind of loom.

The sweet spot is intentionality.

What matters is choosing the method that matches the meaning.

I’m still figuring this out. Some days I want to automate everything; other days I want to walk every trail myself just to prove I can. If you’re wrestling with this too, I’d love to hear how you’re thinking about it.

Why are you making the thing?

-Kate