Models, Apps, and Harnesses: How Tech Writers Should Select AI Tools
A practical way to think about AI tools based on how your work actually gets done
Tech writers are living through a super strange moment. We’re supposed to calmly evaluate AI tools while the rest of the software world sprints from headline to headline shouting about “breakthroughs,” “agents,” and “the end of work as we know it.”
Meanwhile, we’re just trying to decide which tool helps us update documentation that aligns with our style guide.
So the reasonable question remains: Which AI should a technical writer use?
That question used to be simple. “Use ChatGPT” was a complete sentence. And one that most people understood. Today, that answer feels like saying “Use the internet.” It’s technically correct, and yet, wildly insufficient advice.
Ethan Mollick’s article, A Guide to Which AI to Use in the Agentic Era, provides one of the clearest explanations of why this question has become more complicated — and advice on how to think about it in a way that won’t fall apart the next time a model gets renamed. Mollick is a professor at the Wharton School of the University of Pennsylvania.
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What follows is a summary of his core ideas, hopefully useful to tech writers.
The Big Shift: From Answers to Actions
For a while, AI tools functioned mostly as conversational partners. You typed a prompt, the system generated text, you refined the prompt, and eventually you copied the output into your documentation tool. Then you quietly edited it because you are a professional and the AI is not.
That’s still happening. But it’s no longer the most important development.
