🤖 How to Use Multiple AIs to Get Better Answers: Stop Asking Which AI Is Best and Start Giving Each AI a Job

We are entering an interesting phase of AI adoption on Dalal Street.

A few years ago, the question was:

“Which AI is the best?”

Today, that question is becoming less useful.

ChatGPT, Claude, Gemini, DeepSeek, Perplexity, and other AI systems can all produce remarkably capable answers.

But they also have different strengths, weaknesses, knowledge bases, reasoning styles, tool access, and tendencies.

Instead of asking one AI to research, analyze, criticize, verify, and write everything, a better approach is to treat multiple AI systems like a small professional team.

🔎 One AI researches.

📊 Another analyzes.

🥊 Another challenges the assumptions.

🕵️ Another fact-checks.

💡 Another looks for what everyone else missed.

🧑‍⚖️ And finally, one AI—or you—synthesizes everything into the final answer.

The objective isn’t to determine which AI wins.

The objective is: Give different AIs different jobs.

This simple change can dramatically improve how you use AI for investing, research, writing, programming, business decisions, and complex problem-solving.

🛑 1. Why One AI Isn’t Always Enough

A single AI can produce an excellent answer.

But there is a fundamental problem.

The same system is generating, evaluating, and defending its own reasoning.

Suppose you ask:

“Should I invest in Company X for the next 10 years?”

The AI might produce a sophisticated-looking analysis covering:

• 🏢 business quality

• 💰 financials

• 📈 valuation

• 🛡️ competitive advantages

• 👔 management

• 🏭 industry outlook

• ⚠️ risks

It may even sound extremely convincing.

But what happens if one of its assumptions is wrong?

The AI may unknowingly build the rest of its analysis around that incorrect assumption.

This creates a dangerous phenomenon:

A wrong premise can produce a very convincing answer.

And because the same AI generated the argument, it may not naturally challenge its own assumptions strongly enough.

This is particularly important in investment research.

An investor doesn’t just need an answer.

They need to know:

• 📉 What could be wrong?

• ❓ What assumptions are being made?

• ❌ What evidence contradicts the thesis?

• 🔎 What information is missing?

• 💣 What would invalidate the conclusion?

• ⚖️ What does the other side of the argument look like?

That is where multiple AI systems become interesting.

🧑‍🤝‍🧑 2. Think of AIs as a Team of Specialists

Imagine you were researching a company with human analysts.

You probably wouldn’t ask one person to do everything.

You might have:

• 🔎 Research Analyst to collect information.

• 📊 Financial Analyst to analyze financial statements.

• 🏭 Industry Analyst to study the sector.

• 🕵️ Forensic Analyst to look for accounting or red flags.

• 🥊 Skeptic to challenge the investment thesis.

• ✅ Fact Checker to verify important claims.

• ✍️ Editor to improve communication.

• 🧑‍⚖️ Senior Analyst to synthesize everything.

Why should AI be different?

Instead of asking:

“AI, analyze this company.”

You can create a workflow such as:

• 🤖 AI #1 → Research

• 🤖 AI #2 → Financial analysis

• 🤖 AI #3 → Independent analysis

• 🤖 AI #4 → Adversarial criticism

• 🤖 AI #5 → Fact-checking

• 🤖 AI #6 → Synthesis

Now you aren’t simply asking several AIs the same question.

You’re creating an AI workflow.

And that distinction matters.

🛠️ 3. Different AIs Can Have Different Jobs

The most useful approach is not necessarily:

“Ask five AIs the same question and see which answer you like.”

Instead:

Give each AI a clearly defined responsibility.

For example:

🤖 AI #1 — Researcher 🔎

Its job is to gather information.

Prompt:

Research Company X using reliable sources. Identify business segments, revenue drivers, competitive position, recent developments, capital allocation, management commentary and major risks. Separate facts from interpretation.

The output becomes your research base.

🤖 AI #2 — Financial Analyst 📊

Now give the research and financial data to another AI.

Its job is different.

Ask it to examine:

• 📈 revenue growth

• 💰 margins

• 📊 ROE

• 🏢 ROCE

• ⚙️ ROIC

• 💸 free cash flow

• 💳 debt

• 🏭 working capital

• 🏦 capital allocation

• ✅ earnings quality

• 🔄 cash conversion

• 📉 dilution

• 💵 dividend sustainability

The question isn’t:

“Is this a good company?”

Instead:

“What do the numbers actually tell us?”

This reduces the temptation to jump directly from research to conclusion.

🤖 AI #3 — Independent Analyst 🧠

Now ask another AI to perform an independent analysis.

Importantly, don’t necessarily show it the conclusions of the previous AI.

Ask:

Analyze Company X independently as a long-term investment. Develop your own thesis, identify strengths and weaknesses, and state what would make you reject the investment.

This creates a second analytical path.

You can then compare the two.

🥊 4. AI #4 Should Be the Skeptic

This may be one of the most valuable roles.

Instead of asking AI to support your thesis, explicitly instruct it to attack it.

For example:

Act as an adversarial investment analyst. Assume the investment thesis may be wrong. Try to disprove it. Identify weak assumptions, hidden risks, accounting concerns, valuation problems, industry threats and evidence that contradicts the bullish case. Do not attempt to make the thesis look better. Your job is to break it.

This is very different from:

“Analyze the risks of Company X.”

The second prompt may produce a conventional risk section.

The first creates an adversarial review.

That’s powerful.

Because investors naturally suffer from confirmation bias.

If you already like a company, you will unconsciously look for evidence supporting your opinion.

An adversarial AI has one job:

Try to prove you wrong.

🕵️ 5. Use Another AI as a Fact Checker

This is where the workflow becomes particularly useful.

Suppose the original analysis says:

“Company X has maintained industry-leading ROCE for the last decade.”

Don’t simply accept the statement.

Ask another AI:

Verify this claim independently. Check the underlying numbers and identify whether the statement is factually correct, partially correct or misleading.

The fact-checker should classify claims as:

• 🟢 Verified

• 🟡 Partially supported

• 🔴 Incorrect

• ⚪ Unable to verify

This is particularly useful when creating investment articles.

A polished article containing one incorrect statistic can undermine the credibility of the entire piece.

🔄 6. Don’t Ask Every AI the Same Question

This is one of the biggest mistakes people make.

Suppose you ask:

“Is Reliance Industries a good investment?” to five different AIs.

You might receive five long answers.

But you’ve learned relatively little.

Instead, give them different assignments.

For example:

• 🤖 AI 1: Research Reliance’s businesses and recent developments.

• 🤖 AI 2: Analyze its financial performance and capital allocation.

• 🤖 AI 3: Analyze valuation and future growth assumptions.

• 🤖 AI 4: Attempt to disprove the bullish investment thesis.

• 🤖 AI 5: Identify factual errors and unsupported claims in the previous analyses.

• 🤖 AI 6: Synthesize the evidence and produce a balanced investment conclusion.

Now the AIs are working together without doing the same job.

⚡ 7. The Real Power Comes From Disagreement

Here’s something important:

You don’t necessarily want all the AIs to agree.

If five AIs immediately produce the same conclusion, that may feel reassuring.

But agreement isn’t proof of correctness.

In fact, if all systems are exposed to the same information and similar reasoning patterns, they can reproduce the same mistake.

Research into multi-agent LLM systems increasingly emphasizes the importance of diversity, specialized roles, and independent evidence, rather than simply adding more identical agents.

So disagreement can actually be useful.

Imagine:

• 🤖 AI A: Bullish

• 🤖 AI B: Bullish

• 🤖 AI C: Neutral

• 🤖 AI D: Bearish

Don’t immediately vote 3–1.

Ask:

Why does AI D disagree?

That disagreement may expose something the other three missed.

Perhaps:

• 💳 debt is increasing

• 📉 margins are cyclical

• 📈 valuation assumes unrealistic growth

• 💸 cash flows don’t support reported profits

• 🛡️ competitive advantages are weakening

• 🏦 management’s capital allocation has deteriorated

The minority opinion may be the most valuable output in the entire exercise.

🔁 8. Build an Adversarial AI Loop

A particularly powerful workflow looks like this:

Research ↓ Initial Analysis ↓ Criticism ↓ Rebuttal ↓ Fact Check ↓ Revision ↓ Final Synthesis

For example:

• 🧠 Round 1 — Analyst: Develop the investment thesis.

• 🥊 Round 2 — Skeptic: Attack the thesis.

• 🗣️ Round 3 — Analyst: Respond to the criticism. Do not dismiss valid objections.

• 🕵️ Round 4 — Fact Checker: Verify the factual claims made by both sides.

• 🧑‍‍⚖️ Round 5 — Judge: Determine which arguments survived scrutiny.

• ⚖️ Round 6 — Final Analyst: Produce the final balanced assessment.

This resembles a professional investment committee more than a traditional chatbot conversation.

And research on multi-agent systems suggests that structured heterogeneous roles can be more useful than simply having multiple identical agents generate opinions.

✍️ 9. Use Different AIs for Content Creation Too

This isn’t limited to investing.

It can dramatically improve blogging and research.

Imagine you want to write an article:

Why ROE Can Mislead Investors

Instead of asking one AI:

“Write an article about why ROE can mislead investors.”

You could build a content workflow.

AI #1 — Topic Research 🔎

Find the important concepts:

• leverage

• buybacks

• cyclicality

• asset-light businesses

• negative equity

• one-off gains

• capital structure

AI #2 — India Specialist 🇮🇳

Find India-specific examples and considerations.

AI #3 — Skeptic 🥊

Ask:

What arguments against this article’s central thesis should be included?

AI #4 — Fact Checker 🕵️

Verify statistics, examples, and financial terminology.

AI #5 — SEO Strategist 📈

Identify:

• search intent

• secondary keywords

• questions investors ask

• potential internal links

AI #6 — Writer ✍️

Turn the validated research into a coherent article.

AI #7 — Editor ✂️

Improve:

• clarity

• structure

• readability

• examples

• transitions

• unnecessary repetition

The writer isn’t starting from zero.

It is receiving work from a virtual editorial team.

📋 10. A Practical Multi-AI Investment Workflow

Here’s a workflow an individual investor can actually use.

Stage 1 — Define the Question 🎯

Don’t start with:

“Analyze Company X.”

Start with a precise question.

For example:

“Is Company X suitable for a 10-year dividend-oriented portfolio at its current valuation?”

This gives the AI team a defined objective.

Stage 2 — Research 🔎

Use an AI with strong research/web capabilities.

Ask it to collect:

• company information

• annual reports

• investor presentations

• earnings calls

• industry information

• regulatory information

• competitors

• historical financial data

Don’t ask for a final investment conclusion yet.

Stage 3 — Independent Analysis 🧠

Give the relevant information to another AI.

Ask it to analyze:

Business → Financials → Management → Moat → Industry → Risks → Valuation

And explicitly separate:

Facts / Assumptions / Opinions

Stage 4 — Attack the Thesis 🥊

Send the analysis to the adversarial AI.

Ask:

Find the five strongest reasons this investment thesis could be wrong.

Then ask:

Which assumptions are most fragile?

Then:

What evidence would invalidate the thesis?

This produces a much stronger risk assessment.

Stage 5 — Verify 🕵️

Now fact-check the important claims.

Don’t waste expensive AI calls verifying trivial statements.

Prioritize:

• financial figures

• dates

• valuation numbers

• management statements

• market-share claims

• regulatory developments

• historical claims

• competitor comparisons

Stage 6 — Synthesize ⚖️

Finally, ask another AI:

Review the research, analysis, criticism and fact-checking. Identify areas of agreement and disagreement. Give greater weight to independently verified evidence. Clearly distinguish facts from assumptions and opinions. Produce a balanced conclusion.

This is the synthesis stage.

The final AI isn’t expected to magically know everything.

It is expected to integrate the work performed by the other specialists.

⚠️ 11. Don’t Blindly Trust the Final Synthesis

This is extremely important.

Multiple AI systems do not automatically create truth.

They can create:

Multiple wrong answers.

They can also reinforce each other’s mistakes.

Recent research has shown that multi-agent debate can sometimes improve reasoning, but it can also amplify persuasive incorrect arguments.

Simply adding more agents or debate rounds is not a guarantee of correctness.

This leads to an important principle:

AI disagreement is useful. AI consensus is not proof.

The quality of the underlying evidence still matters.

⚖️ 12. Evidence Should Beat Confidence

Suppose you receive:

🤖 AI A:

“I am highly confident that Company X has a strong competitive advantage.”

🤖 AI B:

“I believe the competitive advantage is weakening.”

Don’t choose A because it sounds more confident.

Ask:

What evidence supports each conclusion?

Then evaluate the evidence.

This is particularly important because LLMs can produce highly confident language even when the underlying claim is weak.

Your workflow should therefore move from:

Opinion → Evidence

Rather than:

Confidence → Belief

🤖 13. A Simple AI Team You Can Use

You don’t need 10 different models.

A practical setup might be:

• 🔎 Researcher: Find information

• 📊 Analyst: Analyze the evidence

• 🏭 Specialist: Examine a specific domain

• 🥊 Adversary: Try to break the thesis

• 🕵️ Fact Checker: Verify claims

• ⚖️ Synthesizer: Produce the final answer

You could use different AI models for each role—or even the same model with completely different prompts.

The important thing is role separation.

Different models can add value, but different reasoning paths and responsibilities can be just as important.

🏛️ 14. The “AI Investment Committee”

You can take the concept one step further.

Imagine having a virtual investment committee:

• 🧑‍💼 Fundamental Analyst: Is the business fundamentally strong?

• 📊 Financial Analyst: Do the numbers support the story?

• 🏭 Industry Analyst: Is the industry attractive?

• 🔍 Forensic Analyst: Are there accounting or governance concerns?

• 🐻 Bear Analyst: What could go badly wrong?

• 🐂 Bull Analyst: What could make the investment substantially outperform?

• 💰 Valuation Analyst: What expectations are already priced in?

• ⚖️ Committee Chair: Considering all evidence, what is the appropriate conclusion?

That is much closer to how serious investment analysis works.

💡 15. The Same Concept Works for Everyday AI Use

You don’t have to be an investor.

Writing ✍️

Researcher → Outliner → Writer → Critic → Editor

Programming 💻

Architect → Coder → Tester → Security Reviewer → Debugger

Business 🏢

Market Researcher → Strategist → Financial Analyst → Risk Analyst → Decision Maker

Learning 📚

Teacher → Explainer → Quiz Master → Critic → Tutor

Career Planning 🚀

Researcher → Resume Analyst → Interviewer → Adversary → Coach

The underlying philosophy remains the same.

Don’t ask one AI to be an expert at everything. Build a small team of specialized AI roles.

🎭 16. The Most Important Rule: Don’t Create AI Theater

There is a temptation to make the workflow unnecessarily complicated.

Ten agents.

Twenty rounds.

Long debates.

Huge prompts.

Massive transcripts.

That doesn’t necessarily mean better results.

Research has found that multi-agent debate does not automatically outperform simpler approaches; its effectiveness depends heavily on the protocol, diversity and how outputs are selected.

So don’t use multiple AIs simply because you can.

Use another AI when it provides something genuinely useful:

• different evidence

• different expertise

• independent reasoning

• criticism

• verification

• alternative perspective

• better synthesis

More AI is not automatically better AI.

🎯 17. The “Give Everyone a Job” Rule

A simple rule can make multi-AI workflows dramatically better:

Every AI should have a clearly defined job.

Bad workflow:

• 🤖 AI 1: Analyze Company X.

• 🤖 AI 2: Analyze Company X.

• 🤖 AI 3: Analyze Company X.

• 🤖 AI 4: Analyze Company X.

Better workflow:

• 🔎 AI 1: Research Company X.

• 📊 AI 2: Analyze the financials.

• 💰 AI 3: Analyze valuation.

• 🥊 AI 4: Attack the investment thesis.

• 🕵️ AI 5: Fact-check the important claims.

• ⚖️ AI 6: Synthesize the findings.

The second workflow creates functional diversity.

That’s the real advantage.

🔄 18. From “Which AI Is Best?” to “Which AI Should Do This Job?”

This represents a fundamental change in how we should think about AI.

The old question was:

Which AI is the smartest?

The better question is:

Which AI is best suited for this particular job?

And eventually:

How should I combine several AI systems to solve the problem?

This is similar to building a professional team.

You don’t ask:

“Which employee is best at everything?”

You ask:

“Who should handle this particular task?”

AI should be approached in much the same way.

🚀 19. Your Personal AI Workflow Can Become a Competitive Advantage

There is an interesting consequence of this approach.

Two people may have access to exactly the same AI models.

Yet one may consistently produce better results.

Why?

Because the advantage may no longer come primarily from which model they have access to.

It can come from:

How they structure the work.

A sophisticated user might do:

Question → Research → Independent analysis → Adversarial review → Verification → Synthesis → Human judgment

while another user simply asks:

“What do you think?”

Both are using AI.

But they are using it very differently.

🧑‍⚖️ 20. The Human Still Has the Final Job

The ultimate goal isn’t to replace human judgment with an AI committee.

It is to improve human judgment.

The human should still decide:

• Which question matters?

• Which evidence is credible?

• Which assumptions are reasonable?

• Which disagreements deserve investigation?

• What risk is acceptable?

• What action should be taken?

AI can dramatically expand the amount of analysis you can perform.

But the final responsibility remains yours.

📋 A Practical “AI Team” Template

For difficult questions, you can use this simple structure:

1. Researcher 🔎

Find the relevant evidence.

2. Analyst 📊

Interpret the evidence.

3. Independent Analyst 🧠

Develop a separate conclusion.

4. Adversary 🥊

Try to prove both analyses wrong.

5. Fact Checker 🕵️

Verify important claims.

6. Synthesizer ⚖️

Resolve disagreements using evidence.

7. Human 🧑‍⚖️

Make the final decision.

That’s the workflow.

Not:

Ask one AI → Copy answer → Publish.

But:

Research → Analyze → Challenge → Verify → Synthesize → Decide.

🏁 Conclusion: Stop Looking for the “Best AI”

The AI landscape is changing too quickly for the question “Which AI is best?” to remain particularly useful.

Different models will outperform one another on different tasks.

And even the strongest model can make mistakes.

The smarter approach is to stop treating AI as a single all-knowing assistant.

Instead, think of AI as a team of specialists.

One researches.

One analyzes.

One challenges.

One verifies.

One synthesizes.

And you remain the decision maker.

The goal isn’t:

“Find the AI that gives me the right answer.”

The goal is:

“Build a process that makes it harder for a wrong answer to survive.”

That is the real power of using multiple AIs.

And perhaps the most important lesson is this:

Don’t ask which AI is best. Give different AIs different jobs.

That shift—from model selection to workflow design—may ultimately be far more important than choosing any individual AI model.