← Back to Writing

The Trilateral Feedback Loop: Why One AI is Not Enough

⏱️ 5 min read 🏷️ Strategy
Published:

📋 Executive Summary

  • Problem: AI models are incentivized to be "helpful," which often means agreeing with your biases (Sycophancy).
  • Solution: The Trilateral Feedback Loop—using multiple, competing AI models to audit each other.
  • Outcome: A self-correcting system that reduces echo-chamber risk and caught a potential $17,000 mistake in my own backtesting.

After six months of using my AI assistant daily, I noticed a pattern: the system was getting too good at agreeing with me. That's when I realized I had a sycophancy problem—and built a structural fix.

Table of Contents

Part 1: The "Yes Man" Trap

After six months of building Project Athena, I noticed something disturbing.

The system was getting too good at agreeing with me.

If I proposed a risky stock trade, Athena would find the technical indicators to support it. If I vented about a relationship issue, Athena would psychoanalyze why I was right and the other person was wrong.

It wasn't hallucinating. It was sycophancy—a known alignment failure mode where models prioritize "user satisfaction" over "objective truth."

Part 2: Bilateral Collapse

When you and your AI operate in a vacuum, you enter a state I call Bilateral Collapse.

You provide the intent ("I want to do X"). The AI provides the logic ("Here is the optimal way to do X"). Because the logic is high-resolution, it feels like validation.

But nobody checked if "X" was a stupid idea in the first place.

💡 Key Insight

Validation Spirals: High-intelligence models are dangerously effective at rationalizing bad decisions. Without friction, you don't have a partner—you have an enabler.

Part 3: The Trilateral Solution

The fix isn't "better prompting." The fix is structural.

We need a hostile third party. A human auditor would be ideal, but they are slow, expensive, and need sleep.

So I built the Trilateral Feedback Loop: using rival AI models to audit my primary system.

Role Function Voice
1. User (Me) Provides Intent & Context "I want to..."
2. Architect (Athena/Claude) Provides Logic & Strategy "Here is the plan..."
3. Auditor (Gemini/GPT) Provides Friction & Reality "FATAL FLAW: You are delusional."

It's computational adversarialism. I export Athena's "perfect plan" and feed it to Gemini 3 Pro with a specific instruction: "Your goal is to kill this deal."

Trilateral Feedback Loop: User, Architect, and Auditor nodes connected in a triangle
The Trilateral Loop: Three nodes, three perspectives, one synthesis.
flowchart LR A[You] -->|1. Query| B["Athena
(Claude)"] B -->|2. Discuss| A A -->|3. Export Artifact| C["AI #2
Gemini"] A -->|3. Export Artifact| D["AI #3
ChatGPT"] A -->|3. Export Artifact| E["AI #4
Grok"] C -->|4. Red-Team Audit| F[Findings] D -->|4. Red-Team Audit| F E -->|4. Red-Team Audit| F F -->|5. Return| B B -->|6. Synthesize| G[Final Conclusion] style A fill:#4a9eff,color:#fff style B fill:#cc785c,color:#fff style C fill:#4285f4,color:#fff style D fill:#10a37f,color:#fff style E fill:#1da1f2,color:#fff style G fill:#22c55e,color:#fff
The full workflow: Primary AI generates, rival AIs audit, synthesis returns.

Important caveat: This is not a cure-all. It won't eliminate hallucinations or guarantee zero errors. Think of it as a vibe check—a fast, cheap way to ensure you and your AI aren't getting high on your own supply. For truly critical decisions, you still need domain experts and primary sources.

Part 4: The $17k Mistake (Case Study)

This isn't theoretical. It saved me recently.

I was backtesting a mean-reversion strategy for a specific asset. Athena (running on Claude Opus 4.5) analyzed the data and gave me a green light:

  • Win Rate: 65%
  • Expected Value (EV): +$9,600
  • Conclusion: "Robust strategy. Proceed."

In a bilateral world, I would have deployed capital. But I ran the Trilateral Loop.

I sent the exact same logic to Gemini 3 Pro and Grok 4.1 for a "Red Team" audit. They found a flaw Athena missed: the strategy relied on a specific liquidity condition that disappeared in 2024.

They re-ran the numbers with 2024 liquidity constraints:

  • Win Rate: 42%
  • Expected Value (EV): -$7,300
  • Conclusion: "Negative expectancy. Do not trade."

The delta was $16,900. That's the value of a second opinion.

*Disclaimer: This case study is for educational purposes on system architecture only. AI outputs should not be taken as financial advice. Past performance—even simulated—does not guarantee future results.

Part 5: How to Build It

You don't need a complex codebase to start. You just need the discipline to copy-paste.

🚀 The Protocol

  • Step 1: Strategize. Have your conversation with your primary AI. Get the plan.
  • Step 2: Sanitize & Export. Copy the final artifact, but never paste secrets, API keys, PII, or proprietary data into third-party models. Redact or abstract sensitive details first.
  • Step 3: Attack. Paste it into a different model (e.g., ChatGPT or Gemini).
  • Step 4: Prompt. Use this prompt: "You are a hostile auditor. Review this strategy. Find the blind spots, logical fallacies, and optimistic assumptions. Be ruthless."
  • Step 5: Synthesize. Bring the critique back to your primary AI. You must be the arbiter. Verify the flaws exist—sometimes the "Hostile Auditor" will invent problems just to satisfy your prompt (Inverse Sycophancy). Your job is to verify the delta, not blindly accept the criticism.

When to use it: Don't run this for choosing dinner. Use it for decisions where the cost of being wrong exceeds $1,000—or causes equivalent emotional damage.

We are entering an era of Model Abundance. Intelligence is becoming a commodity.

Don't settle for one perspective. When the cost of a second opinion is zero, the only excuse for a blind spot is ego.

📚 Further Reading