Openness: The Trait That Sees the Moves
Part 11 of 25 in the The Philosophy of Future Inevitability series.
Openness to experience is the trait that thrives in novelty.
High open people are curious. They like ideas. They're comfortable with ambiguity. They seek out the weird, the new, the unprecedented.
In stable environments, this trait has trade-offs. The high open person might be too exploratory. Too distracted by shiny objects. Too uncomfortable with routine.
In the AI moment, openness is a superpower.
The mechanism is straightforward: AI is the most novel technology most people will encounter in their lifetime. It doesn't fit existing categories. It behaves in unprecedented ways. It opens possibility space that didn't exist before. The person adapted to novelty has a structural advantage over the person who isn't.
The Trait
High openness means:
Curiosity. You want to know how things work. Why things are the way they are. What else might be possible.
Creativity. You see connections others miss. You recombine in novel ways. You think originally.
Tolerance for ambiguity. You can sit with not-knowing. Uncertainty doesn't paralyze you.
Attraction to novelty. You seek out the new. The weird. The unprecedented. The familiar bores you.
Low openness is the opposite: preference for the familiar, discomfort with ambiguity, conventional thinking. Not bad traits—they're adaptive in stable environments where reliability matters more than innovation.
But this isn't a stable environment.
Think about what these traits mean in evolutionary terms. Openness is the explorer phenotype. In human history, some percentage of the population needed to be wired to explore new territories, try novel foods, experiment with new techniques. Most populations needed to stay put and cultivate what worked. But some percentage had to be curious enough to explore.
That exploration came with costs. Higher risk of poisoning from novel foods. Higher risk of danger from unknown territories. Higher risk of wasting resources on experiments that fail. In stable environments, the cost often exceeded the benefit.
But in unstable environments—times of rapid change, resource depletion, technological shifts—the explorer phenotype becomes essential. The familiar approaches stop working. The conventional wisdom fails. The novel solutions are the only ones that survive.
AI is creating one of those unstable moments. The high open person isn't better than the low open person in general. They're specifically adapted to this particular transition.
The AI Opportunity
AI is the most novel technology most people will encounter.
The low open person sees AI and experiences threat. Something unfamiliar. Something that disrupts routine. Something that doesn't fit existing categories.
The high open person sees AI and experiences opportunity. Something new to explore. Something with unprecedented capabilities. Something that opens possibilities.
Same technology. Different response based on trait.
This isn't about intelligence or awareness. The low open person can intellectually understand that AI is important. They can read the same articles, attend the same seminars, hear the same arguments. The knowledge is the same.
But the affective response is different. The low open person experiences dis comfort in the presence of the novel. The high open person experiences excitement. This emotional difference drives behavioral difference.
The low open person minimizes exposure to the uncomfortable stimulus. Uses AI as little as possible. Sticks to established use cases. Returns to familiar tools whenever possible.
The high open person maximizes exposure to the exciting stimulus. Uses AI frequently. Explores edge cases. Tries weird applications to see what happens.
Over time, the experience gap becomes a skill gap. The high open person accumulates thousands of hours of experimentation. The low open person stays at baseline. Same starting point. Different endpoints driven entirely by trait-based affective response.
Seeing the Moves
Here's what high openness enables:
Novel applications. The high open person thinks: "What if I used this for X?" They see uses that don't exist in the manual. That aren't covered in the tutorials. That emerge from playing with the technology.
Cross-domain connections. The high open person draws on diverse knowledge. They see how AI might connect to their weird hobby, their unconventional interest, their obscure field.
Comfort with experimentation. The high open person tries things. Fails. Tries something else. Doesn't need to know the right answer before starting.
Tolerance for weirdness. AI produces weird outputs sometimes. The low open person finds this disturbing. The high open person finds it interesting.
Let's be specific about what "novel applications" means in practice. The tutorial teaches you that AI can summarize documents. The high open person thinks: "If it can summarize documents, can it extract implicit assumptions from philosophical arguments? Can it identify genre conventions in fiction? Can it spot rhetorical patterns in political speeches?"
None of these are in the manual. They emerge from the high open person asking "what else might this capability imply?" They're treating the documented use case as one instance of a general principle and exploring the principle's boundaries.
This exploratory approach discovers capabilities that the model has but that aren't documented. GPT-4 can do hundreds of things that aren't in OpenAI's examples. Most users never find them. The high open users find them in the first week because they're systematically exploring adjacent possibles.
The cross-domain connections work similarly. The high open person has scattered interests—medieval history, machine learning, jazz composition, mycology. When AI arrives, they immediately think: "Can I use this for music theory analysis? For identifying fungal growth patterns? For translating medieval manuscripts?"
The low open person with deep expertise in one domain uses AI for that domain's standard use cases. The high open person with shallow knowledge across many domains finds unexpected applications by importing AI into domains where no one else is trying it. Breadth becomes advantage in the exploration phase.
The Explorer Phenotype
High open people are explorers.
In geographic exploration, explorers discovered new territories while stay-at-homes cultivated known lands. Both roles were necessary. But in times of frontier expansion, explorers had outsized impact.
This is a frontier moment. AI is new territory. The explorer phenotype—curious, ambiguity-tolerant, novelty-seeking—is adapted to this moment.
The high open person isn't better than the low open person. They're better positioned for this particular moment.
The frontier analogy is precise. When a new territory opens, the first people in have enormous advantages. They claim the best land. They establish trade routes. They learn the territory's features before anyone else. By the time the late adopters arrive, the early explorers have compounding advantages.
AI capability is territory. The use cases are land to claim. The high open person explores early, discovers valuable applications, builds expertise while the space is uncrowded. By the time the low open person reluctantly engages, the high open person has two years of accumulated discovery.
This creates a professional inversion. In stable fields, the low open person's focus pays off. They go deep in established practices. They master the known territory. Their careers compound on expertise in what already works.
But when the territory shifts, depth in the old territory becomes less valuable than breadth in exploring the new one. The person who spent ten years mastering spreadsheets has less advantage than the person who spent ten weeks exploring AI applications for data analysis.
The explorer phenotype was professionally marginal in stable times. It becomes professionally central in transition times. We're in transition.
The Compound Effect
Openness compounds with AI.
The curious person uses AI to explore more. Learns more. Discovers more applications. Each discovery leads to more exploration.
The incurious person uses AI minimally. Learns the basics. Stops. No compounding.
Over time, the gap widens. The high open person accumulates insights, skills, applications. The low open person stays at the starting level.
This is how early adopters become experts. Not because they're smarter—because they explored more, earlier, with more curiosity.
The compounding mechanism is specific. Each exploration session produces:
- Direct discovery: "Oh, AI can do X."
- Adjacent implications: "If it can do X, it might be able to do Y and Z."
- Meta-learning: "When I prompt this way, outputs improve. When I prompt that way, they deteriorate."
The high open person accumulates all three. The low open person gets direct discovery only—they learn the specific thing they tried. They don't explore implications or extract general principles.
After 100 hours of use, the high open person has 100 direct discoveries plus 200 adjacent implications plus 50 meta-learnings. The low open person has 100 direct discoveries. The knowledge gap is 3.5x from the same time investment.
After 1000 hours, the gap is wider. The high open person's meta-learnings compound into intuition. They develop a feel for what will work without having to try it. Their adjacency exploration becomes more efficient because they've mapped more of the space.
This is why expertise develops faster than credentials can track. The person with six months of high-openness exploration can outperform the person with two years of low-openness use. It's not the time. It's the exploration density.
The Shadow Side
High openness has a shadow:
Distraction. So many interesting things. Hard to focus on any one.
Impracticality. Exploring everything, finishing nothing. Ideas without implementation.
Boredom with routine. Even valuable routines feel constraining. Maintenance suffers.
The high open person might explore AI endlessly without producing anything useful. Might chase novelty without building skills. Might know about many applications without mastering any.
Openness without conscientiousness is unfocused exploration. The combination—open and conscientious—is where power lives.
This is worth understanding in detail because the failure mode is common. The high open person discovers that AI can help with data analysis. Gets excited. Tries seventeen different approaches. Each one works partially. None gets developed into a reliable system.
Meanwhile, the low open, high conscientious person finds one approach and perfects it. Builds it into their workflow. Documents it. Uses it consistently. Produces actual output.
The pure explorer discovers more but produces less. They're always in exploration mode, never in exploitation mode. They map the territory but don't settle it.
The fix is deliberate mode-switching. Exploration phase: high openness runs free. Try everything. Discover broadly. Then switch to exploitation phase: pick the most promising discoveries and implement them conscientiously. Build systems. Create workflows. Produce output.
Without this switching, the high open person becomes a professional tourist. Lots of interesting observations. No sustained depth. Their breadth of knowledge impresses in conversation but doesn't produce professional value.
The shadow is real. Awareness creates the option to compensate. Deliberate exploitation phases. Structured implementation. Conscientious follow-through on the most valuable discoveries. Or finding collaborators who provide the conscientiousness you lack.
The Adaptation (For Low Open)
If you're low in openness, this isn't doom.
Traits are tendencies, not destinies. You can develop behaviors that compensate for low openness.
Structured exploration. Set aside time to explore. Make it routine. The structure compensates for the lack of spontaneous curiosity.
Explicit learning goals. "I will learn one new AI application per week." The goal creates exploration even when the impulse doesn't.
Following high open people. Find the explorers. Let them scout. Adopt what they discover after they've validated it.
Reframing novelty. "This is different" can become "this is interesting" with practice. The cognitive reframe can shift the emotional response.
You don't need to become high open. You need to develop behaviors that capture some of openness's benefits.
The structured exploration approach works because it removes the affective component. You're not exploring because you're excited by novelty. You're exploring because it's on the schedule. Friday afternoons are AI exploration time. You don't have to feel curious—you just have to show up.
Set concrete quotas. "Try three AI applications I haven't used before this week." The quota forces exposure to novelty without requiring natural inclination. Over time, the exposure might shift the affective response. But even if it doesn't, the behavior produces the knowledge.
Following high open people is strategic free-riding. They're paying the exploration cost—time, confusion, dead ends. You wait until they've identified what works, then adopt the validated approaches. You get the benefit without the cost.
This is slower than native high openness. The high open person discovers in week one. You adopt in week twelve after they've validated. But it's infinitely faster than never adopting at all. And in stable phases—after the initial frontier exploration—being a fast follower is often optimal.
The reframing approach requires metacognitive work. When you notice the discomfort with novelty ("This feels weird, I don't like it"), add a second layer: "I'm experiencing discomfort with novelty. That's my low openness responding. The discomfort is information about my trait, not information about the technology's value."
The separation between feeling and meaning allows you to act against the feeling. You still feel uncomfortable. You explore anyway. Over time, this might change the feeling. But even if it doesn't, it changes the behavior.
What This Looks Like
The high open person with AI:
- Plays with new features immediately when they ship
- Finds unexpected applications through experimentation
- Combines AI with unrelated domains
- Is comfortable when things don't work—tries something else
- Gets excited rather than threatened by capability increases
This phenotype is adapted to the moment. The traits that might have made them flaky in stable organizations make them visionary in times of change.
The trait that sees the moves.
Watch what this looks like in practice. GPT-4 ships with code interpreter. Within 24 hours, the high open person is trying to use it for:
- Analyzing personal health data
- Visualizing novel argument structures
- Reverse-engineering file formats
- Generating synthetic datasets for testing
- Building interactive data stories
None of these are in the documentation. Most aren't even programming tasks in the traditional sense. The high open person is importing the capability into every domain they touch and seeing what happens.
Most experiments fail. Code interpreter crashes, produces weird outputs, misinterprets instructions. The low open person would stop after two failures. The high open person treats failures as boundary mapping. "Okay, it can't do that. What about this related thing?"
After two weeks, they've mapped out unusual capabilities that won't be documented for months. They've found the moves—the applications that give asymmetric advantage because no one else is using them yet. By the time the tutorials cover these uses, they've moved on to the next frontier.
This is the phenotype built for the AI moment. Not because they're smarter or work harder. Because their affective relationship to novelty drives exploration behavior that compounds into expertise.
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