Man and his AI friends

Most people treat AI like a calculator for thoughts — plug in a question, get an answer, move on. But what if you could train AI to think with you, not just for you? What if AI could act as a recursive thought amplifier, refining and expanding your ideas rather than just regurgitating pre-processed knowledge?

That’s exactly how I use AI.

Instead of taking one model’s response as the final word, I triangulate across multiple AI systems, feeding responses back into other models like GPT, Claude, DeepSeek, Mistral, and Perplexity. I treat these models as a collaborative thought engine, refining concepts through iteration and synthesis.

My friends think this approach is novel — that I’m using AI in a way that most people don’t even consider. Maybe they’re right. Maybe more people should be thinking about AI not as a tool for answers, but as a process for thinking itself.

The Problem: How Most People Use AI (And Why It’s Limiting)

Most AI interactions fall into one of two categories:

  1. Quick Answers — A user asks a question, gets a response, and moves on.
  2. Long-Form Assistance — AI helps draft an article, summarize research, or refine writing.

Both are useful, but they miss the bigger opportunity: AI as a recursive thinking tool.

The problem with the one-and-done approach is that it assumes AI has the right answer the first time. But AI models aren’t infallible. Each has biases, gaps, and different strengths. Relying on a single model means limiting yourself to one perspective, one dataset, one way of thinking.

The reality? AI is most valuable when it’s interrogated, challenged, and refined.

The Process: AI as a Recursive Thought Partner

Here’s how I do it:

  1. Initial Prompt & Response — I start a conversation with GPT (or another model), exploring an idea or problem.
  2. Divergent Feedback — I take that response and feed it into other models like Claude, DeepSeek, Perplexity, and Mistral.
  3. Synthesis & Iteration — I compare responses, looking for new angles, contradictions, or deeper insights.
  4. Refinement Loop — I feed the best ideas back into GPT (or another model), iterating until I hit a point where insights start to converge.

This process isn’t just about getting better answers — it’s about sharpening my own thinking.

The Benefits: Why This Works

1. You Get Multiple AI Perspectives, Not Just One

Each AI model has its own training data, biases, and strengths. Claude leans more toward contextual understanding, DeepSeek is stronger in research synthesis, and Perplexity excels in surfacing fresh information.

By feeding responses across these models, I get a wider spectrum of insights — just like consulting multiple experts rather than relying on a single opinion.

Example:
I was recently working through the idea of how coding will evolve in an AI-driven world. I started with GPT, which argued that knowing coding fundamentals (rather than deep expertise) will be the most valuable skill in the future AI space.

I then fed that response into Claude and Perplexity, which challenged the premise:

  • Claude suggested that human coders will shift towards “meta-coding”, focusing on how AI models structure and optimize code.
  • Perplexity provided recent papers and expert discussions, highlighting how low-code/no-code adoption is already reshaping software development.

Bringing these insights back to GPT, I synthesized a more nuanced perspective:

✅ Knowing coding fundamentals is essential.
✅ AI won’t replace coders, but will change what coding looks like.
✅ The real value lies in understanding AI-augmented development workflows, not just syntax.

A single model wouldn’t have gotten me there. The recursive feedback loop did.

2. You Sharpen Your Own Critical Thinking

AI is a mirror — it reflects back the way you ask questions. By forcing models to challenge and refine ideas, you develop stronger arguments, identify weak points, and uncover overlooked insights.

Example:
I recently discussed my leadership philosophy here. I started by outlining my belief in:

  1. Dissent as a powerful tool — Encouraging pushback and critical thinking.
  2. Disagree and commit — Ensuring alignment once a decision is made.

After feeding these ideas into multiple models, I realized something:

  • While I embrace dissent, I sometimes frame friction as necessary rather than optional.
  • Mistral pushed me to consider: Can collaboration be just as effective without friction?

That led me to rethink how I approach certain leadership discussions — ensuring I’m not just fostering healthy debate, but also recognizing when consensus-driven progress is the better path.

This wasn’t AI giving me a single answer. It was AI helping me interrogate my own assumptions.

3. You Build a Dynamic, AI-Powered Think Tank

Most people use AI passively. I use it like a think tank — a recursive system that continuously evolves ideas.

By structuring AI feedback loops this way, you can:

✅ Generate and test new hypotheses.
✅ Surface hidden insights.
✅ Explore counterarguments.
✅ Push your thinking beyond first-order logic.

Challenges & Optimizations: Where This Approach Struggles

This method isn’t perfect. Some challenges I’ve run into:

  • Time Investment — It takes longer than a simple AI query. But the depth of insight is worth it.
  • Signal vs. Noise — Some models over-index on filler content. Filtering out weak responses is key.
  • Contradictions — Sometimes different models completely disagree. That’s not a bad thing — it forces you to think critically rather than just accept one answer. Don’t be afraid to call the model out!

Ways to optimize:

  • Use AI models strategically (e.g., Perplexity for research, Claude for reasoning, GPT for synthesis).
  • Focus on refining prompts to get better responses.
  • Recognize when to stop iterating (not every idea needs endless refinement).

The Bigger Implication: AI as a Thinking Partner, Not Just a Tool

Most AI discussions focus on productivity — how to get things done faster. But AI’s real power is in thought amplification — helping you think at a higher level than you could alone.

This approach isn’t just about AI. It’s about reframing how we interact with knowledge itself:

✅ AI isn’t an answer machine; it’s an idea generator. ✅ The best insights come from synthesis, not single outputs.
✅ The future belongs to those who can integrate multiple perspectives into a coherent strategy.

This recursive AI workflow has changed the way I think. Maybe it could do the same for you.

What’s Next?

If this resonates with you, try it yourself:

  1. Start with a question.
  2. Run it through multiple AI models.
  3. Compare, refine, and iterate.
  4. See where it leads you.

And if you’ve used AI in a similar way — or have other methods for AI-assisted critical thinking — let’s talk.

I’m always looking for better ways to push the boundaries of thought.