To The Moon

Speaker: Ada Tan Jia Ying

Hey Tigers 🐯!

AI is quickly becoming part of the investing workflow.

But there is an important difference between using AI to process information and using AI to make decisions for you.

During the latest Tiger First Stripes: Getting Started with Tiger Trade App session, Ada Tan Jia Ying introduced Tiger AI and walked through how investors can use it for stock research, market interpretation and earnings analysis.

At the same time, she stressed one point that matters more than any individual feature:

AI can help investors work faster, but it should not replace independent judgment.

That is probably the most important takeaway from this part of the session.

🤖 What Tiger AI Can Help With

Tiger AI is designed to help users process investment information more efficiently.

According to the session, users can use it for tasks such as:

•         individual stock analysis

•         market trend interpretation

•         earnings report summaries

•         answering preset investment-related questions

For investors, the value is obvious.

Markets generate far more information than any one person can realistically read in full.

Earnings reports are long.

News moves quickly.

Sector narratives change.

And by the time an investor has processed everything manually, the market may already have moved.

This is where AI can be useful:

It helps compress information.

Instead of replacing research, it can reduce the time needed to get from:

raw information → useful questions

That is a very different role from simply telling users what to buy.

📊 From Information Overload to Faster Research

One of the biggest problems investors face today is not a lack of information.

It is the opposite.

There is too much information.

A single company can generate:

•         quarterly earnings

•         management commentary

•         analyst revisions

•         industry news

•         macro exposure

•         competitor developments

For a retail investor, keeping up with all of this can be difficult.

AI tools can help by summarizing the first layer.

For example, an earnings report can be reduced into:

•         revenue trends

•         profit changes

•         management guidance

•         major business drivers

•         key risks

That gives investors a faster starting point.

But Ada’s message is important here:

The better way to use AI is to let it tell you where to look next.

Not where to stop thinking.

🧠 The Best Use of AI Is Better Questions

The most useful shift for investors may be this:

Instead of asking AI:

“Should I buy this stock?”

Ask:

“What are the three biggest drivers of this company’s earnings?”

Or:

“What changed in management guidance this quarter?”

Or:

“What risks could challenge the current bullish thesis?”

Those are much stronger research questions.

Why?

Because they force the investor to think in terms of:

•         business drivers

•         evidence

•         risks

•         changing assumptions

AI becomes more useful when it helps structure the research process.

Good AI usage is less about getting an answer and more about asking a sharper question.

That is especially important in investing, where there is rarely one objectively “correct” answer.

⚠️ AI Can Sound Confident and Still Be Wrong

This was also where Ada added an important reminder.

AI-generated content should be treated as a reference, not as financial advice.

That matters because AI can sometimes present information in a very confident tone even when:

•         the data is incomplete

•         the context is missing

•         the information is outdated

•         the interpretation is uncertain

Confidence is not the same as accuracy.

And in financial markets, small factual errors can lead to very different conclusions.

So investors should still verify important information through:

•         company filings

•         official announcements

•         earnings releases

•         other reliable sources

The more important the decision, the more important the verification.

That is a good rule not only for AI — but for investment research in general.

🔍 AI Should Shorten Research, Not Shorten Thinking

This is probably the most important distinction from the session.

There are two very different ways to use AI.

The weak way:

AI gives an answer → user accepts it → trade is placed.

The stronger way:

AI summarizes information → user identifies key drivers → user verifies evidence → user forms a view.

The second process takes more effort.

But it is also much more useful.

Because investing is ultimately about judgment under uncertainty.

No tool can fully remove that uncertainty.

AI can accelerate the process. It cannot eliminate the responsibility.

👤 Why Human Support Still Matters

Interestingly, the session did not position AI as the only source of help.

Ada also introduced the role of the dedicated investment representative.

Users can contact representatives for questions related to:

•         account usage

•         platform functions

•         trading-related operations

That creates an important contrast.

AI is useful for speed and information processing.

Human support is useful when context, clarification or account-specific questions matter.

The two are not necessarily substitutes.

For investors, the better question is:

If the issue is summarizing an earnings report, AI may be useful.

If the issue is understanding an account setting or platform rule, human support may be more appropriate.

Knowing when to use each one is part of becoming a more capable user.

🔄 A Better AI Investing Workflow

If investors want to use AI effectively, a simple process can help:

1️⃣ Ask

Use AI to identify key information or summarize a topic.

2️⃣ Question

Look for assumptions, missing information and potential risks.

3️⃣ Verify

Check important claims against reliable sources.

4️⃣ Decide

Form your own view based on your goals and risk tolerance.

That creates a much healthier sequence:

AI → Research → Verification → Judgment

Instead of:

AI → Trade

The difference is small on paper.

But in practice, it can be huge.

🐯 Ada’s Key Takeaways

Tiger AI can help investors:

Analyze stocks
→ Organize company information more efficiently

Interpret market trends
→ Build a quicker first view of what may be driving the market

Summarize earnings
→ Extract key points from longer financial information

Generate research questions
→ Help users explore a company or thesis more systematically

But the bigger lesson from Ada’s session is:

The best investors will probably not be the ones who ignore AI.

And they will not be the ones who blindly follow it either.

They will be the ones who use AI to move faster without giving up judgment.

Let AI do more of the processing. Keep the decision-making human. 🧠🤖

🐯 How do you use AI in your investing process — stock research, earnings summaries, market analysis, or idea generation?

AI-generated information may contain errors or omissions and should be used for reference only. Investing involves risk. Investors should conduct independent research and make decisions based on their own circumstances.