AI: Supercharging Experience and Expertise Development
I’ve been thinking a lot about AI lately and exploring different ways to use it in various settings. One question that’s been on my mind is how AI might help with gaining experience, I was wondering if there’s a way to supercharge the learning process using these tools.
TL;DR:
- Experience builds through exposure, reflection, pattern recognition, mental models, and judgment though most people get stuck in early stages
- AI can help with cognitive work: simulating scenarios, spotting patterns, and guiding systematic reflection
- However, real-world action builds judgment under pressure, relationships, and emotional resilience
- The key is using AI to think more systematically around your actions, rather than to replace them
AI’s Role in Skill Development
The Core Idea: Most people think experience just accumulates with time, but research in expertise development shows it’s actually built through specific cognitive processes. I wanted to understand where AI fits into skill development based on how expertise actually develops.
What’s Involved in Building Experience
Experience development involves these interconnected processes:
Exposure — Encountering new situations and challenges (where many people stop, according to deliberate practice research)
Reflection — Systematically analyzing what worked and what failed (a key component of deliberate practice)
Pattern Recognition — Experts develop the ability to spot where a problem is similar to one previously encountered
Mental Models — Internal frameworks that help experts make faster decisions
Judgment — Making better decisions when stakes are high and time is short (requires real-world consequences to develop properly)
Relationships — Building trust through consistent action over time
Feedback Loops — Learning from actual consequences, not hypothetical ones (a core principle of deliberate practice)
Emotional Calibration — Developing resilience through actual setbacks and recoveries
Reputation — Earning credibility that opens doors
Important Note: These processes happen simultaneously and recursively, not in any particular order.
How AI Can Help
| Experience Component | How AI Can Help | What AI Cannot Do |
|---|---|---|
| Exposure | Simulate scenarios, role-play conversations, test approaches before high-stakes moments | Replicate the full emotional pressure and unpredictability of real decision environments |
| Reflection | Guide systematic post-mortems, help identify decision points, surface potential blind spots | Force genuine self-examination or push through “uncomfortable but necessary” realizations about performance |
| Pattern Recognition | Connect situations to patterns from other fields, highlight potential similarities across experiences | Develop intuitive pattern matching from thousands of real encounters |
| Mental Models | Introduce frameworks from expertise research, help adapt proven models to specific contexts | Help truly internalize models until they’ve been tested under pressure conditions |
| Judgment | Stress-test decisions, run scenarios, challenge assumptions | Replicate the cognitive load and emotional stakes of real decision-making |
| Relationships | Prepare for conversations, draft communications, rehearse discussions | Build trust through consistent action over time |
| Feedback Loops | Provide immediate critique on logic and approach | Replicate real-world consequences or stakeholder reactions |
| Emotional Calibration | Help process setbacks, reframe challenges, plan for responses | Build genuine resilience through experiencing actual failure and recovery |
| Reputation | Polish communication, test how words might land with different audiences | Earn credibility, which requires delivering results over time |
Caveat: AI capabilities are evolving rapidly. These limitations may not hold as systems develop.
What This Means
Expert performance traces to “active engagement in deliberate practice,” where training focuses on improving particular tasks and involves immediate feedback and time for problem-solving. Thinking alone isn’t enough, you need practice with real consequences.
AI works best as a “thinking partner” rather than a replacement for action. Experts perceive situations and pattern match against “a collection of prototypes” and you can only build those prototypes through actual experience.
The core insight: AI can help you prepare more systematically and reflect more deeply, but deliberate practice in real-world conditions remains essential for building genuine expertise.
Further Reading
Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. (1993). “The Role of Deliberate Practice in the Acquisition of Expert Performance.” Psychological Review, 100(3), 363-406.
Kahneman, D., & Klein, G. (2009). “Conditions for Intuitive Expertise: A Failure to Disagree.” American Psychologist, 64(6), 515-526.
Klein, G. (1998). Sources of Power: How People Make Decisions. MIT Press.
