The idea behind my AI investing process is simple:
AI solves a different problem at each stage of the investing process.
For New ideas: AI helps research more companies, faster, so I can see more opportunities.
Going deeper: AI helps understand the business better and surface things I may have missed manually.
Building the thesis: AI will challenge my assumptions and find what could prove me wrong.
After I buy: AI will track earnings, new risks, and what materially changed in the thesis.
Here is how I use AI at each step.
The 4-Step AI Investing Process:
1. Overview: Understand more businesses, faster
Let’s say someone sends me a stock I have never looked at before.
I do not start by reading the entire annual report.
First, I want a fast answer to a simpler question:
Is this company interesting enough to deserve more of my time?
I use AI to understand things like:
How does the company make money?
Who are the customers?
What drives revenue growth?
Where do margins come from?
How does the industry work?
Who are the main competitors?
Is the business cyclical?
Does it need a lot of capital to grow?
Here is a video of an example with Claude:
The goal is not to understand everything.
It is to understand enough, quickly, to decide whether the company deserves deeper work.
That lets me look at more businesses without spending hours on every idea.
2. Deep Dive: let AI do the heavy analytical work
If the company still looks interesting, I go deeper.
Now I am no longer trying to understand the broad story.
I want to know:
What does the evidence actually show?
This is where I spend time in:
annual reports
financial statements
transcripts
proxy statements
investor presentations
historical disclosures
And this is where AI becomes especially useful.
For example, in this video I ran my Business Evolution Map Skill on Microsoft.
Instead of reading years of annual reports one by one, Claude compared the filings and looked for things like:
KPIs or disclosures that appeared or disappeared
changes in how management describes the business
shifts in risks and segment reporting
accounting changes
changes in useful-life assumptions
AI helps me find what I might have missed manually.
3. Building the thesis: use AI to challenge yourself
This is where AI should push back my assumptions.
For example, I give Claude my thesis and run a Skill that asks it to test things like:
whether my growth assumptions are realistic
whether the margin expansion is supported by history
what has to be true for my expected return to work
which risks could permanently damage the thesis
what a smart short seller would attack first
The goal is simple:
Find the weak points before the market does.
4. After I buy: track what changed
Once I own the stock, I do not want to restart the research every quarter.
I already know the business.
I already have a thesis.
So after earnings, I want AI to answer a much narrower question:
What changed relative to what I already believed?
For example, in this Uber video, I run my Earnings Update Engine inside Claude.
It pulls the new earnings data, compares it with the previous quarters and my existing research, then checks:
what changed in the key KPIs
whether guidance improved or weakened
whether new risks appeared
whether management credibility changed
whether the thesis is stronger or weaker
How to build your own process
Start with one company you know well.
Use AI for one part of your research.
Keep what saves time or improves the analysis.
Turn it into a workflow you can reuse.
Repeat for the rest of your process.
Over time, AI stops being random prompts and becomes part of your investing system.
See you,
Mostapha
P.S. Want to build a process like this without starting from zero?
Join as a paid member and get my full AI Investing Skill & Prompt Library, including the workflows I use for business overviews, deep dives, thesis testing, and earnings updates.