Part 1 of my motivation series.

We all struggle with motivation at times, including myself. As the founder of an AI company that develops applications to solve various problems, I decided to create an AI to assist me in my own battle to stay motivated. It generated 15 rules that have significantly helped me stay motivated. Here are the first five.

A Self-Starter’s Challenge

Since leaving my previous company four and a half months ago, I’ve written over 70 blog posts, served on several advisory boards, developed a dozen AI applications, and discussed potential projects with hundreds of new contacts. By any measure, I am a motivated self-starter.

However, I still struggle with self-motivation. I constantly feel like I could achieve more. While binge-watching 33 seasons of “Survivor” was entertaining, I realize I could have used some of that time more productively. I’m all for rest and relaxation, but I’m also all for hard work. Thus, I decided to test my AI products and create an AI to motivate myself. The ultimate goal is to develop a motivational tool to help others, but I started with myself.

Insights on Motivation

I recently listened to Influence by Robert Cialdini. Though primarily about persuasion, Cialdini mentions that psychology has extensively studied motivation but has not effectively shared these findings with the public. This seemed like an opportunity.

Given my work with Retrieval-Augmented Generation AI, which excels at applying knowledge to real-world scenarios, I thought it would be fun to see if I could leverage these studies and my skills to improve my own motivation.

Gathering the Data

Scholarly data, especially abstracts, is readily available. I searched Google Scholar for “motivation,” “intrinsic motivation theory,” and “how to motivate myself.” I selected the top ten relevant results from each search, filtering out non-pertinent ones, and fed them into my AI to “learn” about motivation.

Using an advanced technique called Graph, I extracted key concepts from these 30 papers, clustered them into similar themes, and had the AI clearly define these concepts. Here are the first five:

1. Action Precedes Motivation: The first step is the hardest. Taking action often generates momentum, boosting motivation to continue. Overcoming the initial hurdle of starting is the key step. For example, decide to write just one sentence of your essay now and tell yourself after one sentence you can take a 15-minute break. Often, once you start, you usually end up doing much more before taking a break. Getting past the starting line is the key.
2. Autonomy and Choice: Giving people control over what and how they learn enhances motivation. Allowing them to choose topics or methods that interest them increases their ownership and motivation. Students who can select their own research topics or choose how to approach assignments tend to be more enthusiastic and invested in their work.
3. Feedback Needs Vary: Beginners need encouragement to build confidence, while experts need constructive criticism to improve. For instance, someone giving their first speech deserves strong encouragement as any public speaking is difficult. In contrast, someone with more experience needs detailed feedback on improvement areas.
4. Optimal Challenge: Goals should be challenging enough to promote growth but not so difficult that they seem unattainable. For example, if you are learning a new language, setting a goal to hold a basic conversation within three months is challenging yet achievable. However, expecting to become fluent in the same timeframe might be too daunting and could lead to discouragement.
5. Extrinsic vs. Intrinsic Incentives: Both external rewards (like money or grades) and personal satisfaction are important. However, finding joy in the task itself is the key to long-term motivation. Helping people see how tasks contribute to their growth can be more motivating than external rewards.

Applying the Concepts

I stored these concepts in a database alongside my goals, values, and needs. I also track all questions I ask the AI and the feedback I provide. This information is analyzed by the AI each time I interact with it.

This approach has really increased my motivation, and I’m back up to working 10 solid hours or more daily while feeling quite fulfilled. When I update the AI on my achievements it brings me great satisfaction. When my motivation wanes, the AI encourages me based on my accomplishments, reminding me of all I’ve done and learned.

It prompts me to start with small tasks, knowing that once I begin, I usually continue. It also pushes me to tackle challenging, but not impossible, tasks. If it makes a suggestion it always is clear that the choice is mine.

Most importantly, it proves that my motivational AI truly works. There’s nothing more gratifying than seeing it in action. I hope these concepts help motivate you too.

References:

1. Latham, G. P., & Pinder, C. C. (2005). Work motivation theory and research at the dawn of the twenty-first century. Annual Review of Psychology, 56, 485-516.
2. Lai, E. R. (2011). Motivation: A literature review. Pearson Research Report.
3. Garcia, T. (1993). The role of motivational strategies in self-regulated learning. In P.R. Pintrich (Ed.), New Directions for College Teaching and Learning: Self-Regulated Learning in the College Classroom. Jossey-Bass.
4. Schroeder, J., & Fishbach, A. (2015). How to motivate yourself and others? Intended and unintended consequences. Research in Organizational Behavior, 35, 123-141.
5. Pinder, C. C. (1998). Work motivation in organizational behavior. Upper Saddle River, NJ: Prentice Hall.