March 5, 2025
Quick question: What’s the difference between an assumption, an opinion, and an experience?
Most people use these words interchangeably. I did too, until I started really thinking about how beliefs actually form.
But they’re not the same. And once you see the difference, you can’t unsee it.
The Three Types
Here’s how I think about it:
ASSUMPTION:
- Something you believe based on your life so far
- No external validation required
- Built from pattern recognition and common sense
- Example: “If I prepare thoroughly, people will see me as competent”
OPINION:
- An assumption influenced by outside information
- Could be from research, other people, news, whatever
- Still subjective, but has external inputs
- Example: “Based on what my mentor said, I think Client A prefers morning meetings”
EXPERIENCE:
- Immutable fact you personally witnessed
- Can’t be argued with (it happened to you)
- Strongest form of evidence
- Example: “Client A told me directly they hate being late to meetings”
Why the Hierarchy Matters
An assumption can become an opinion when you read something that supports it. An opinion can become an experience when you test it and see the result.
But here’s what’s wild: Experiences can also create new assumptions.
If you have three experiences where being late to meetings caused problems, your brain forms the assumption: “Being late causes problems.”
That assumption then influences how you behave in future meetings, even with different people.
The Learning Loop
This is how humans learn. We build beliefs from the bottom up:
- Experience something
- Form an assumption about the pattern
- Refine it with opinions from others
- Test it with more experiences
- Strengthen or weaken the belief
AI agents need this same loop.
Right now, most AI systems either:
- Have no beliefs (start from scratch every time)
- Have fixed beliefs (can’t update based on evidence)
Neither works for professional work.
What I’m Building
An AI that tracks the evidence behind every belief. It knows:
- “I believe X based on 3 experiences and 2 assumptions”
- “I believe Y based on 1 opinion from the user”
When new evidence comes in, the belief updates. Strong experiences override weak assumptions. Contradictory patterns weaken old beliefs.
It’s not magic. It’s just modeling how humans actually think.
If you’re building AI and you’re not tracking the difference between assumptions, opinions, and experiences, you’re missing the foundation of learning.
How does your AI handle conflicting information? Does it even know which evidence is stronger?