Build Your Ultimate AI Tool Stack

Specialists, Not Generalists

January 22, 2026

Last week I watched this talented creator do something I almost never see.
He didn’t argue about which AI is “best.”
He treated AI tools the way pros treat camera lenses: different jobs, different gear.
That hit me, because most of us are still forcing one chatbot to do everything.
And when you do that, you don’t feel “productive”… you feel weirdly stuck.
So here’s the mental switch that makes the whole thing click.

Most people treat AI chatbots like a Swiss Army knife, using a single tool for writing, coding, and research, but that is actually slowing you down.
The reality is that while tools like ChatGPT, Claude, and Gemini look similar on the surface, they have developed distinct personalities and strengths that make them non-interchangeable.

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I just watched a deep-dive analysis by a productivity expert who spent three years testing these tools daily to figure out exactly where each one wins. This creator doesn’t just list features. He gives you a simple way to think so you stop guessing, stop tool-hopping, and start picking the right “engine” for the right moment.

The “MO” Mental Model
The core concept this industry pro shares is that every AI model has a specific MO, or mode of operation, where it consistently beats the others. When you force a creative model to be rigidly obedient, or you demand that a reasoning model behave like a search engine, you get output that’s fine…but never great.

His fix is clean: stop looking for one tool to rule them all. Build a small toolkit where each AI handles a specific stage of your workflow. Once you understand a model’s “superpower,” you can chain them together. One model plans the structure, another processes messy inputs like media, and a third adds the final human polish.

The Battle for Everyday AI Dominance
The expert categorizes the big three—ChatGPT, Gemini, and Claude—as “Everyday AI,” but he stresses their roles are very different.

ChatGPT is the king of obedience. In his tests, he gave ChatGPT and Gemini the same complex rubric with lots of constraints. ChatGPT followed the rules cleanly. Gemini produced something that looked good, but quietly dropped requirements. The takeaway: if you have a strict checklist or lots of moving parts where accuracy matters, ChatGPT is your workhorse.

Claude is the master of the first draft. The creator argues Claude often produces the best “version one,” especially for writing and coding. In one test, it was the only model that generated a functional Go script on the first attempt. For writers, Claude’s edge is style matching. It captures tone and nuance better, which makes it perfect for the last mile—turning an outline into something that reads like a human wrote it.

Gemini is the multimodal powerhouse. Gemini wins when your input isn’t just text. With a huge context window and native handling of video and audio, you can upload a meeting recording, a slide deck, and a whiteboard photo in one shot and get a coherent synthesis. If you’re drowning in files, long PDFs, or mixed media, this is where Gemini shines.

The Specialist Scalpels: Precision Over Reasoning
The second category is Specialist AI: fine-tuned tools designed for specific kinds of accuracy.

Perplexity is for fetching, not thinking. The expert draws a line between reasoning (like planning a strategy) and fetching (like verifying a spec or checking hours). Treat Perplexity like a search scalpel: fast, direct, and citation-forward. Use it when you need the exact fact, right now, without the fluff.

NotebookLM is the hallucination killer. This is the “walled garden” tool. It only answers based on what you upload, which makes it perfect for compliance, fact-checking, and staying honest. The creator uses it to audit scripts: upload the draft and the sources, then ask it to flag anything that isn’t actually supported by the documents.

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Other awesome AI guides you may enjoy

Building the Integrated Workflow
The real magic is when you stop trying to crown a winner and start building a system.

Phase 1: Structure and logic
Start with ChatGPT to outline, create checklists, and enforce constraints. Let it do the logical heavy lifting without skipping steps.

Phase 2: Research and verification
Use Perplexity to fetch data points, recent info, and specific facts with sources. Don’t force a general chatbot to “be Google” when precision matters.

Phase 3: The creative finish
Move to Claude once you have structure + facts. Feed it the outline and research and ask for the final draft. This is how you get writing (or code) that feels clean on the first pass.

Phase 4: The audit
If accuracy matters, run the final output through NotebookLM using your source docs. It’s the fastest way to catch accidental drift before you publish or present.

If you want to see the specific prompts the expert uses to test these models or get the full list of tools he recommends, you should definitely check out the full breakdown.

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