A few months ago I was sitting with Evan — he runs a coworking space and had just come back from an AI workshop I hosted. He'd spent two days learning to build agents, and now had a list of 16 different automations he wanted to create.
His question was basically: which one do I build first? And also — when do I use ChatGPT versus Lindy?
I get that question a lot. And the honest answer isn't “it depends” — there's a clean split that most people miss, and once you see it, everything gets easier.
The Two Jobs AI Tools Do
Every AI tool in your stack is doing one of two things.
Thinking. You go here when the problem isn't fully formed yet. You need to brainstorm, explore options, work through trade-offs, or just clarify what you actually want. The AI pushes back, asks questions, helps you think out loud. Reasoning quality matters here. ChatGPT and Claude in chat mode are strong here.
Executing. You go here when you know what you want and you need it to happen. Send this email. Create this document. Pull the relevant items from this spreadsheet. Update the project tracker. The AI is wired into your real tools — email, calendar, Google Drive, Airtable — and it can chain multiple actions together in one pass. Lindy, n8n, and purpose-built agents live here.
The problem is that most people use one tool for both jobs, or they collect multiple AI tools with no clear logic for which to use when. Either way, they're not getting what they want from either.
What This Looks Like In Practice
I was planning content strategy for Q1 earlier this year. I had a bunch of loose ideas — directions I'd been noodling on, things clients kept asking me about, content I'd been meaning to write for months.
First I took everything into ChatGPT. Let it ask me questions. Explored what resonated. Argued a little. An hour later, I had a clearer picture of what I actually wanted to do — a structured quarter of content by theme.
Then I took that output to Claude Code and said: structure this into a content calendar by week, create individual docs for each piece, save them to my Drive folder, and update the project tracker.
Same problem. Two different tools. Two different phases.
Neither could have done the other's job as well. ChatGPT doesn't have access to my Drive or my tracker. And prompting a super agent through vague, half-formed ideas is a mess — you get confused outputs because the prompt was confused.
I've also seen this with a film producer I was coaching. He'd built a habit of running every new idea through three different models — ChatGPT first for initial ideation, then Claude for technical architecture, then Gemini for creative angles. Each one gave him something the others missed. He wasn't being indecisive. He was being strategic.
The Two-Step Workflow Pattern
Once you understand the thinking/execution split, a simple two-step workflow starts to emerge naturally:
- Work out the idea, the direction, and the output spec in chat (ChatGPT or Claude)
- Pass the final output to your agent/workflow tool and say: do this, create this, update this, send this
A common version of this: you have a meeting, you dump the transcript into ChatGPT, you get a structured summary and the decisions made. Then you drop that into your super agent and say: create a Google Doc from this, email it to the team, update the project memo, create Todoist tasks for the action items.
Twelve seconds instead of thirty minutes. And the quality is better because each tool did its own job.
What This Means for Your Tool Stack
At Asian Efficiency, we teach AI fluency as a progression — from using AI as an assistant, to building workflows, to deploying agents that take actions autonomously. Understanding the thinking/execution split is part of moving from the first level to the second.
The question to ask about every AI tool in your stack: is this where I think, or is this where I execute?
ChatGPT: thinking. Super agent: execution. Notebook LM: synthesis. Claude Code: building.
Each one has a job. The skill isn't picking the best AI. It's knowing which AI to pick for which phase of work.
That's actually what fluency looks like.
Want to build a workflow system that puts the right AI in the right place? The 25X Productivity System walks through how to design your AI stack so thinking, execution, and review each have their own tool and their own time.
