What Victorian Naturalists Can Teach Us About AI

In 1859, a naturalist named Alfred Russel Wallace sent Charles Darwin a letter from the Malay Archipelago. He'd been collecting beetles, observing birds, cataloguing species across thousands of islands. The work was meticulous, patient, obsessive. He wasn't discovering individual facts - he was mapping patterns across an entire ecosystem.

That letter prompted Darwin to finally publish On the Origin of Species. Wallace had independently arrived at the same conclusion through the same method: systematic observation, careful categorization, pattern recognition across massive variation.

The breakthrough wasn't from studying one species intensely. It was from cataloguing hundreds and seeing the patterns that emerged.

I think about this often while building Scotoma.

We're Doing Something Similar

Not with beetles or birds. With something more elusive: the invisible mental models people bring to AI.

When organizations adopt AI, they don't just implement technology. They unconsciously adopt a theory about what that technology is supposed to do. A manager says "AI will help the team" - but that word "help" contains an entire framework about control, agency, and the nature of expertise.

Another manager says "AI is becoming part of the environment." Same technology. Completely different theory. Those theories imply different outcomes, create different blind spots, require different management approaches.

For three years, I've been doing what Victorian naturalists did: systematic observation across massive variation. Not islands - academic patterns. Studying how professionals interact with AI, each one a specimen revealing how organizations think about this technology.

What emerged was a taxonomy: 6 distinct orientation patterns, each with recognizable signals, plausible risks, and specific blind spots.

Scotoma is that taxonomy, made usable.

Why Cataloguing Matters

Here's what naturalists understood that most people miss: you can't understand a single organism without understanding the ecosystem it inhabits.

A beetle isn't just a beetle - it's a pollinator in a specific habitat, prey for certain birds, dependent on particular plants, shaped by millennia of environmental pressures. To understand the beetle, you need to understand everything it relates to.

Same with AI mindsets.

You can't understand why Naturalist-level integration can hide dependency without understanding how it differs from Craftsperson-style control. You can't appreciate why Architect reflection can preserve expertise without seeing how unexamined acceleration can erode it. The meaning emerges from the relationships, the contrasts, the ecosystem of possibilities.

That's what the 6 mindsets provide: a map of the possibility space. When you encounter AI implementation challenges, you're not starting from scratch. You're navigating terrain that's been carefully mapped by examining hundreds of organizational experiences.

The Aesthetic of Careful Observation

There's something beautiful about Victorian natural history illustration. Not flashy or dramatic - beautiful in its precision, its patience, its respect for the subject.

Look at Ernst Haeckel's illustrations of radiolarians, or Maria Sibylla Merian's butterflies, or John James Audubon's birds. Every detail rendered with care. Colors chosen thoughtfully. The specimens shown in context - not isolated, but in relationship with their environment.

That's the aesthetic we're trying to channel with Scotoma.

We could have made it look like every other AI tool - sleek gradients, dark mode, aggressive angles. "Disruptive innovation." "Move fast and break things."

But that would betray what the project actually is. This isn't disruption - it's systematic observation. This isn't innovation theater - it's careful taxonomy. The work is patient, grounded, respectful of complexity.

So we chose earth tones. Natural colors. Gentle gradients that remind you of botanical illustrations. The visual language says: this is thoughtful work, not hype. This is about understanding, not selling.

What "Solarpunk" Actually Means Here

People hear "solarpunk" and think solar panels and vertical gardens. That's the aesthetic shorthand. But the deeper idea is more interesting: technology in service of human flourishing, not replacement. Knowledge systems that enhance rather than extract. Optimism grounded in systems thinking, not naive enthusiasm.

Victorian naturalists were proto-solarpunk.

They believed human knowledge could advance through systematic observation. They thought careful cataloguing could reveal patterns that improved understanding. They were optimistic about human capacity to comprehend complex systems - but that optimism was grounded in patient, meticulous work.

Not "technology will save us." More like: "Thoughtful humans using good tools and rigorous methods can develop genuine insight."

That's the spirit we're channeling. AI isn't magic. It's a tool. Used thoughtlessly, it erodes expertise. Used with intention and understanding of its actual nature, it can enhance human capability.

The work is figuring out which approach you're taking - and whether it's the one you think you're taking.

The Collector's Patience

Darwin spent eight years studying barnacles before publishing on evolution. Eight years. On barnacles. People thought he was wasting time.

But he wasn't studying barnacles - he was developing the observational capacity to see variation. To notice subtle differences. To understand how small changes compound into major divergences. The barnacles were training for the bigger insight.

Building Scotoma required similar patience. Three years reviewing papers. Hundreds of conversations with practitioners. Multiple false starts on the taxonomy. Throwing away frameworks that seemed elegant but didn't map to reality.

You can't rush taxonomy. If you force it, you get clean categories that don't actually capture the messy variation in the real world. Victorian naturalists knew this. You have to let the patterns emerge from patient observation, not impose them from theory.

The 6 mindsets aren't arbitrary. They emerged from systematic analysis of how organizations actually talk about, implement, and respond to AI. Some appear frequently, such as Craftsperson and Symbiont orientations. Some are rarer but important, such as Architect and Naturalist patterns. Some are concerning in particular contexts, but pretending they don't exist won't help anyone.

Why We Made It Free

Victorian naturalists didn't keep their catalogs secret. They published. They shared specimens. They corresponded across continents. Knowledge advanced through collective observation, not individual hoarding.

That's why the current individual assessment is open to complete. The basic reflection should be easy to access while team pilots and workshops fund the deeper work. Organizations are implementing AI right now, mostly blind to the invisible mental models shaping outcomes. Making the basic framework accessible isn't charity - it's how knowledge systems advance.

You take the assessment, see your orientation signals, and get the basics of what they may reveal and what blind spots to watch for. That's genuinely useful. For many people, it's enough.

If you want the deeper dive - team distribution analysis, facilitated discussion, and a recommendation memo - that is what a team diagnostic pilot provides. The individual reflection remains the starting point.

The goal isn't to extract maximum revenue from knowledge. It's to make invisible patterns visible to as many people as possible.

What Comes After Cataloguing

Here's what happened after Victorian naturalists completed their catalogues: the real work began.

Once species were categorized and relationships were mapped, scientists could ask deeper questions. Why do these patterns exist? How do ecosystems maintain stability? What happens when you remove a keystone species? What makes some systems resilient and others fragile?

Cataloguing wasn't the endpoint - it was the foundation for ecological thinking.

Same here. The 6 mindsets are a foundation, not a conclusion. They let us ask better questions:

• Why do some AI-use patterns narrow innovation while others support it?
• What organizational conditions push people from Craftsperson-style control toward naturalized dependency?
• How do you design AI systems that reinforce healthy orientation patterns rather than brittle ones?
• What happens when different parts of an organization hold incompatible orientation patterns?

You can't investigate these questions until you can namewhat you're investigating. The taxonomy makes the invisible visible. That's where understanding begins.

The Conversation We're Not Having

Most discourse about AI splits into two camps:

Camp 1: Uncritical enthusiasm. "AI will transform everything! Productivity will soar! This is the future!"

Camp 2: Reactionary fear. "AI will destroy jobs! Humans will become obsolete! Resist adoption!"

Both camps treat AI as a monolithic force with predetermined effects. One sees only benefits, the other only threats. Neither is asking the more interesting question:

That's not a technology question. It's a design question. A systems question. A question about relationships between human expertise and computational capability.

It's the question Victorian naturalists would ask:not "is this organism good or bad?" but "how does it function within its ecosystem, and what relationships sustain or threaten it?"

Scotoma is built for people who want to have that conversation. Not hype versus fear. Not disruption versus resistance. But thoughtful, grounded, systems-aware thinking about how to navigate this transition intelligently.

An Invitation to Observe

Victorian naturalists extended an invitation: look closely at the world around you. Notice variation. Track patterns. Question assumptions. Build knowledge through patient observation.

Scotoma extends the same invitation, just pointed at a different subject:look closely at how your organization thinks about AI.

Not what you say in strategy documents. What language actually appears in meetings. How people respond when AI fails. Whether verification is celebrated or seen as inefficiency. What gets measured and what gets ignored.

The mental models are there. You just have to learn to see them.

Take the assessment. It'll show you patterns you might not have noticed. That's where understanding begins - not with pronouncements about what AI will inevitably do, but with careful observation of what's actually happening in your context.

The naturalist's method, reimagined for organizational ecosystems navigating technological change.

Thorough. Patient. Grounded. Optimistic about human capacity to understand complex systems - but only through rigorous observation, not wishful thinking.

That's what we're building. That's why we built it this way.