I have been logging my weight since November 16, 2016. As I approach a full decade of tracking in 2025, I realized I was sitting on a mountain of data—hundreds of entries comprising dates, timestamps, numbers, and scribbled notes about how I felt or what I ate. For years, I looked at these numbers in isolation, happy when they went down and frustrated when they went up.

But when I fed this ten-year history into an AI, it didn’t just calculate averages; it held up a mirror to my habits. It found patterns I was too close to see. Here is how AI helped me understand the “why” behind the numbers that I wasn’t fully aware of.

1. The “Hardware Anxiety” Loop

I always knew I was skeptical of scales, but the AI highlighted just how much this distrust skewed my reality. The analysis showed I was constantly “shopping” for a number I liked by switching devices. I jumped from “Ashley’s scales” (which I noted were “unreliable” and “6 pounds off”) to “mom’s scales,” “Henry’s scales,” “store scales,” “package scales,” and eventually “new scales” .

The AI pointed out that my data wasn’t fluctuating because of my body; it was fluctuating because of my equipment. For example, in 2018, I noted that Ashley’s scales matched the gym scales, yet I still questioned them constantly . The AI showed me that consistency in the *device* is more important than the *accuracy* of the specific number.

2. The “Clothing Tax”

I didn’t realize how often I sabotaged my own data until the AI flagged the inconsistency in my weigh-in conditions. The analysis contrasted entries where I weighed “naked” or “without clothes” against entries where I wore “four layers of clothing and shoes” or “two hoodies on, jeans and shoes” .

Seeing the data laid out, it became obvious why I saw sudden spikes. In January 2025, I recorded a weight of 176 while wearing four layers and drinking Diet Pepsi, admitting I felt the number was wrong . The AI helped me learn that I wasn’t necessarily gaining weight—I was just weighing my wardrobe.

3. The Binge-Bloat Correlation

While I knew eating junk food wasn’t good for me, the AI drew a straight line between specific “trigger” foods and my physical sensation of bloating. The logs show a repeated cycle: consuming large quantities of specific items like an “entire gallon of ice cream,” an “entire big bag of nerds candy,” or “pizza before bed” .

Almost immediately following these entries, my notes would consistently report, “I feel very bloated” or “my stomach looks huge” . The AI helped me see that the immediate scale jump wasn’t always fat gain; it was often acute water retention and bloating caused by high sugar and sodium intake, which I frequently recorded alongside drinking sodas like “diet Pepsi” or “coke zero” .

4. The “Morning After” Mathematics

Finally, the AI highlighted how biological functions impact my data in real-time. I had multiple entries where I weighed myself, had a bowel movement, and immediately weighed again to find I had lost fractions of a pound, such as dropping from 171 to 169 or 164.2 to 164.0 in minutes .

Furthermore, the AI identified a timing error I was unaware of: I frequently weighed myself at inconsistent times, such as 2 PM or late at night after having “eaten and drunk quite a bit” . Comparing these to my morning fasted weigh-ins explained the erratic “gains” that were actually just food volume sitting in my system .

The Takeaway

For ten years, I thought I was tracking my weight. The AI showed me I was actually tracking my variables—my clothes, my water retention, and my choice of scale. Understanding this distinction has finally given me the clarity to move forward.

If you want to do something similar, you may be surprised at what you can uncover with today’s modern tools. Below is an AI discussion about the ten years of data I have collected.  Maybe you can discover a few things from the information and compare it to your own journey.