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How can we use AI to do better thinking, not skip thinking?

6 min read

Ethan Mollick recently wrote an article demonstrating what you can do with AI in under a minute. He shows that you can write a product launch, plan a course syllabus, design a kitchen and do market research all in under a minute. It’s a great demonstration of what we can generate with AI tools today.

I thought this was a great insight from Ethan’s article:

When a middle manager writes a weekly report on the status of a major initiative, the report may not be the point. Instead, it serves as a signal that the middle manager has done their job, speaking to the relevant employees, keeping an eye on the status of the project, and making corrections as needed. And it has always worked well enough - a senior manager could tell at a glance if the report was seemingly substantive (showing effort) and well-written (showing quality). But now every employee with Copilot can produce work that checks all the boxes of a formal report without necessarily representing underlying effort.

I think this is a big deal - while previously the ‘output’ (the report or summary) was proof of underlying thinking, now these outputs can be generated without much thinking at all. While these AI tools allow us to jump straight to the end result, they work better when we do more thinking and give them more context. In this blog post, I want to focus on how AI tools can help us do better thinking, rather than skip thinking.

Thinking and output

When thinking about work, it can be useful to break a task into two components:

There are certain tasks that don’t require much thinking. You might have a meeting agenda that you need to prepare. You know the topics that need to be covered, and you know the order that they need to be covered in. If you have a template for the meeting agenda, the work is mostly about filling in the blanks.

There are other tasks that require more thinking. You might have an idea for a new way of working that will save time. You need to think about the potential benefits and challenges of using this new approach, and write a pitch to present to your manager.

With the recent wave of AI tools such as ChatGPT, people can jump straight to the output without doing much thinking. While this means we can get things done a lot faster, we need to be careful to not skip the ‘thinking’ part.

AI can create better outputs if you do more thinking

For an AI to create a good output, it needs to be given contextual information in its prompt. Without the proper context, it will generate a generic output that might not be relevant to your specific situation. Here’s an example of a generic prompt:

Write a list of pros and cons of using Jira for task management.

If you take some time to think about the task, prepare some rough notes, and then use those notes in your prompt, the AI will be able to generate a much better output. Here’s an example of a more contextual prompt:

I’m considering using Jira for task management. I work at an agency where we make websites for clients. Some of our team members are not technical and some are. We are currently organising tasks using a combination of Trello and Google Sheets. We are having trouble keeping track of tasks and changing deadlines. We need software that can help us manage tasks, deadlines, and deal with external clients. Can you write a list of pros and cons of using Jira for task management in this context?

Tools like ChatGPT can really shine when you give them more context. I think this is what sets them apart from traditional search engines.

AI tools can also help with the thinking part

While AI tools are great for generating outputs, they can also help you with the thinking part.

If you’re trying to decide between two different approaches, you can ask ChatGPT to write a summary arguing for one approach, and another summary arguing for the other approach. You can then read through those outputs and think about them deeply. In this way, ChatGPT is not making the decision for you, but is helping you to think about the problem from different angles.

ChatGPT can also generate a list of ideas for you to consider. For example, if you want to create an app that helps people save money, you could ask ChatGPT to generate a list of 20 different app ideas that help people save money. In this way, ChatGPT can be more like a generative design tool that prepares many options or combinations of ideas.

I have been using the brainstorming app Brainstory to help me think deeper about my ideas. In the app, an AI coach asks me thought-provoking questions and I answer the questions using my voice. The app transcribes the conversation and generates a concise summary. I think this is a great example of how AI can help users think deeper, rather than skip thinking.

It’s worth noting that it is still early days for AI in the workplace. As time goes on, AI tools are going to do more and more of the thinking for us. Tasks that require ‘thinking’ today, such as finding relevant contextual information and making connections between different pieces of information, will be completed by AI in the future. So our concept of ‘thinking’ will keep changing over time.

Let me know what you think

I think it’s fascinating to think about how AI tools are changing the way we work.

If this is interesting to you, feel free to reach out on LinkedIn or Twitter. I’d love to hear what you think, and if you have any ideas for using AI tools to help you think better.

Originally published on by Larry Hudson