Original Experience
Session 12.2 · ~5 min read
AI Generates Generality. You Generate Specificity.
AI can write a paragraph about running a business. It can produce competent prose about the challenges of scaling, the importance of cash flow, and the difficulty of hiring. Every sentence will be true in a generic way. None of it will be specific enough to be useful.
What AI cannot write: the specific Tuesday when your best-selling product started cannibalizing your second-best product and you had to make a call with incomplete data and a warehouse lease expiring in 30 days. That is original experience. It is specific. It is yours. And in a world saturated with AI-generated generality, specificity is the new premium.
Original Experience: Knowledge that comes from doing the work, not reading about the work. It is characterized by specificity (exact details only you know), surprise (things that turned out differently than expected), and consequence (decisions that had real stakes). AI cannot generate it because it was never published for the model to train on.
The Specificity Spectrum
Content ranges from completely generic to completely specific. The more specific your content, the harder it is for AI to replicate.
'Many businesses face challenges
with cash flow management'"] --> B["Somewhat Specific
'Service businesses often
struggle with 60-day
payment terms'"] B --> C["Specific
'Our agency lost $40K
in Q3 2024 because
three clients paid at 90 days
instead of 30'"] C --> D["Irreplaceably Specific
'That $40K gap meant
I personally covered payroll
from savings for two months
before changing our contract
terms to net-15 with a
2% early payment discount'"] style A fill:#c47a5a,color:#111 style B fill:#c8a882,color:#111 style C fill:#6b8f71,color:#111 style D fill:#6b8f71,color:#111
AI operates in the first two zones. It can generate generic and somewhat specific content because the patterns exist in its training data. The third and fourth zones require lived experience. That is where your content should live.
Three Types of Original Experience
| Type | Description | Example | Content Value |
|---|---|---|---|
| Surprising outcomes | Things that happened differently than expected | "We A/B tested the shorter landing page expecting lower conversion. It converted 3x better." | Very high. Contradicts conventional wisdom with evidence. |
| Decision under uncertainty | Choices made without complete information | "We chose to build in-house rather than use a SaaS tool. Nine months later, that decision saved us $200K/year." | High. Shows the reasoning process, not just the outcome. |
| Failure analysis | What went wrong and what you learned | "Our first product launch missed revenue targets by 60%. The pricing was right. The positioning was wrong." | Very high. Honest failure analysis is rare and valuable. |
The Experience Extraction Process
Most practitioners have more original experience than they realize. The problem is not that the experience does not exist. The problem is that it sits in your memory, unstructured and unarticulated. You need an extraction process.
For each project you have completed, each product you have shipped, each client you have served, ask these five questions:
- What did I expect to happen? Document the prediction.
- What actually happened? Document the reality.
- Where did the gap appear? The gap between expectation and reality is the interesting part.
- What decision did I make? Not what the textbook says to do. What you actually did.
- What would I do differently? Hindsight is not weakness. It is matured judgment.
Each set of answers is a content seed. The gap between expectation and reality is the hook. The decision is the substance. The hindsight is the takeaway. AI cannot generate any of this.
Making Experience Publishable
Raw experience is not content. It needs structure. The structure is simple:
- Set the scene. What were the conditions? What was at stake? (2-3 sentences)
- State the expectation. What was supposed to happen? (1 sentence)
- Describe what happened. Specific details. Numbers if available. Timeline. (3-5 sentences)
- Explain what you did. The decision and the reasoning behind it. (3-5 sentences)
- Deliver the takeaway. What someone else can learn from this. Not a platitude. A specific, actionable insight. (2-3 sentences)
This structure turns a memory into a publishable unit. Each unit can stand alone as a short post, or serve as a section in a longer article. Stack five of these units on the same theme and you have an essay that no AI can replicate.
The Mandatory Inclusion Rule
Every piece of content you publish should contain at least one moment of irreplaceable originality. One specific detail, one personal observation, one data point from your own work that AI could not generate. If a piece contains none of these, it fails the differentiation test from Session 12.1 and should not be published under your name.
Further Reading
- What's Your Edge? Rethinking Expertise in the Age of AI, MIT Sloan Management Review
- Artificial Knowledge Generation: The Role of Generative AI in Knowledge Management, ScienceDirect (2025)
- Instagram Chief Outlines the Challenges of AI Content, Social Media Today
- The Impact of Generative AI on Academic Reading and Writing: A Synthesis of Recent Evidence, Frontiers in Education (2025)
Assignment
Identify 5 original experiences relevant to your content area that no AI could generate. For each, apply the five-question extraction process and then structure the experience using the publishable format from this session. These 5 structured experiences are content assets. Save them. Use at least one in every piece you publish going forward.