AIYING — The Discipline of Accomplishing with AI

AIYING is not about using AI tools faster.
It is a discipline for clarifying intent, designing outcomes, and using AI as a thinking partner to accomplish meaningful work.
It shifts the focus from asking for outputs to working towards outcomes — especially in high-judgment domains.

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AIYING in Practice — Equity Research (Assumption Stress Testing)

Intermediate · 20–30 minutes · For investors, analysts, serious learners

Purpose of This Practice

To strengthen an investment thesis by identifying, questioning, and stress-testing assumptions before markets do it for you.

This practice is about fragility detection, not prediction.

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Step 1 — The Hidden Risk in Most Research

Most equity research looks confident because assumptions stay invisible.

Typical statements:

• “Margins will normalise”

• “Demand will remain strong”

• “Execution risk is manageable”

These are not facts.
They are assumptions wearing confidence.

Markets punish unexamined ones.

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Step 2 — Apply the AIYING Outcome Clarifier

Before involving AI, write this down:

1️⃣ Outcome
What decision depends on this research?
☐ Buy ☐ Hold ☐ Avoid ☐ Position sizing ☐ Risk framing
✍️ Outcome: _______________________

2️⃣ Thesis Core
In one sentence, what must be true for this investment to work?
✍️ Core belief: _____________________

3️⃣ Assumption Density
Which part of the thesis feels most fragile?
☐ Revenue ☐ Margins ☐ Costs ☐ Timing ☐ Execution ☐ External factors
✍️ Fragile zone: ____________________

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Step 3 — Surface All Assumptions (Critical Step)

Ask yourself (or list manually):

• What am I assuming about customers?

• What am I assuming about management capability?

• What am I assuming about competitors’ behaviour?

• What am I assuming will not happen?

Write them all down.
No filtering yet.

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Step 4 — Reframe the AI Ask (Key Shift)

Instead of asking AI to validate your view, ask it to attack it:

“List the key assumptions underlying this investment thesis, explain why each could fail, and identify which assumptions are most sensitive to small changes.”

This is AIYING in Practice:
using AI to reduce self-deception.

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Step 5 — Stress Test, Don’t Defend

Now take the top 3 assumptions and ask AI:

• What early signals would indicate this assumption is weakening?

• What data would confirm it is breaking?

• What second-order effects follow if it fails?

✍️ Insight: _______________________

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Step 6 — Human Judgment Check (Non-Negotiable)

Step away from AI and answer:

1. Which assumption do I want to be true?

2. Which assumption would hurt the most if wrong?

3. Which assumption am I least prepared to monitor?

✍️ Reflection: _______________________

This step cannot be automated.

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Step 7 — Build the Assumption Watchlist

Turn stress-tested assumptions into a live monitoring list:

• Metric to watch

• Update frequency

• Threshold that changes your decision

This converts research into ongoing judgment, not static reports.

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Why This Is AIYING in Practice

AI cannot eliminate uncertainty.
But it can expose where your thinking is brittle.

In equity research:

• Returns come from insight

• Losses come from ignored assumptions

AIYING in Practice — Assumption Stress Testing
is how analysts stay ahead of reality instead of reacting to it.