Excellent piece David. Whilst the jury may be out as regards how big and pervasive the Zone of Existential Dread may become, I still back humankind to use AI to augment creative thinking. But what do I know?
I love this post, David. I totally relate to these zones.
One thing I find interesting is most of the market seems to be currently focused on the zone of relief - taking things we can do and doing them faster and cheaper. But I am really interested in the zone of excitement bc I am focused on how we can create and use these tools to do some of the sorts of complex, integrative thinking that people and teams struggle to do alone.
What I find is that many people exist in the zone of existential dread, but for some reason I am constantly in the zone of excitement. I think that this has a lot to do with personality profiles. In a conversation I recently with had with ChatGPT about why some knowledge workers lean into AI and other are resistant, I found these points useful:
Adoption splits less by age or role and more by psychology. Here’s a compact map of what’s going on and how it shows up at work.
The core psychological drivers
1. Curiosity & Openness
High: “Let me poke it and see what happens.” (Openness to Experience, Need for Cognition)
I think… I may try out boardy now
Excellent piece David. Whilst the jury may be out as regards how big and pervasive the Zone of Existential Dread may become, I still back humankind to use AI to augment creative thinking. But what do I know?
I love this post, David. I totally relate to these zones.
One thing I find interesting is most of the market seems to be currently focused on the zone of relief - taking things we can do and doing them faster and cheaper. But I am really interested in the zone of excitement bc I am focused on how we can create and use these tools to do some of the sorts of complex, integrative thinking that people and teams struggle to do alone.
What I find is that many people exist in the zone of existential dread, but for some reason I am constantly in the zone of excitement. I think that this has a lot to do with personality profiles. In a conversation I recently with had with ChatGPT about why some knowledge workers lean into AI and other are resistant, I found these points useful:
Adoption splits less by age or role and more by psychology. Here’s a compact map of what’s going on and how it shows up at work.
The core psychological drivers
1. Curiosity & Openness
High: “Let me poke it and see what happens.” (Openness to Experience, Need for Cognition)
Low: “New = distraction.” Prefers proven routines.
2. Self-efficacy & Locus of control
High: “I can learn this.” Experiments, iterates.
Low: “Tech beats me.” Avoids first steps; small misfires confirm “I’m bad at this.”
3. Risk orientation & Ambiguity tolerance
Promotion-focused: chases upside; treats errors as tuition.
Prevention-focused: guards against downside; hates opaque failure modes.
4. Identity & Craft attachment
Outcome identity (“I solve client problems”): AI feels like leverage.
Process identity (“I write perfect memos”): AI feels like a threat to craft and status.
5. Status/competence protection
Low evaluation anxiety: happy to learn in public.
High evaluation anxiety: fears “looking dumb with AI,” so silently opts out.
6. Perfectionism vs Iterative comfort
Iterators: fine with messy first drafts; edit aggressively.
Perfectionists: AI’s occasional errors feel intolerable.
7. Time scarcity mindset
Slack mindset: will invest now to save later.
Scarcity mindset: “No time to learn,” even when the tool could repay quickly.
8. Moral/ethical stance & algorithm aversion
Trust-with-verification: adopts with guardrails.
Principle-first skepticism: resists until assurance on privacy, fairness, IP, attribution.
9. Autonomy needs
Choice & co-design: adoption rises.
Mandate & monitoring: adoption drops (reactance).
10. Conscientiousness & habit strength
High conscientiousness can cut both ways: disciplined pilots vs rigid routines that repel change.
Four common archetypes (and what works for each)
1) Explorers (curious, high efficacy, promotion focus)
Motive: learning & edge.
Friction: boredom, lack of challenge.
What works: sandboxes, advanced prompts, stretch use-cases, recognition as coaches.
2) Optimizers (pragmatic, ROI-driven, moderate curiosity)
Motive: clear payoff.
Friction: vague benefits.
What works: before/after demos on their tasks, timered sprints, KPIs (e.g., cycle time ↓20%).
3) Worriers (low efficacy, prevention focus, high evaluation anxiety)
Motive: safety and competence.
Friction: fear of public errors.
What works: private practice spaces, checklists, templates, buddy systems, “first output is a draft” norms.
4) Guardians (strong craft identity, ethical salience, high standards)
Motive: integrity of work.
Friction: quality, IP, privacy concerns.
What works: explicit standards (citation, review, red-teaming), audit trails, “human-in-the-loop” roles that elevate judgment, not replace it.