A CGM CONTINUOUS GLUCOSE MONITORING, ARTIFICIAL INTELLIGENE AND THE LIMITS OF CULTURAL UNDERSTANDING

 

---

A CGM, Artificial Intelligence, and the Limits of Cultural Understanding

For the past week, I have been wearing a continuous glucose monitor, a device largely designed and marketed for the United States. During this time, I have been in France.

Two observations have become strikingly clear.

First, the artificial intelligence embedded within these systems—useful as it is—does not yet understand culture. It issued repeated warnings, almost alarmist in tone, predicting adverse glucose responses based on the foods I was consuming. Yet it has no capacity to distinguish where and how that food is prepared: whether industrially processed or carefully crafted within a French home or kitchen.

Not once did these predictions materialize.

( the above recording is over 12 hours ...lunch and dinner and you can see how consistent it is )

After one evening meal—champagne, a glass of Douro wine, a kosher croque monsieur (without ham), vegetable soup, steamed vegetables, and a slice of apple pie—my glucose levels remained remarkably stable. There was no meaningful excursion.

This is not an isolated observation, but a pattern.

It reinforces a broader lesson: foods that appear similar across countries are not metabolically equivalent. An apple pie in the United States is not the same as one in France. Regulatory standards, ingredient quality, and culinary traditions differ profoundly. In France, there is closer scrutiny of additives and chemicals, and a relative absence of highly processed foods in everyday life.

There is also an economic paradox. A well-prepared meal, often including wine, can be obtained here for significantly less than what one might pay in Miami—where industrialized food dominates much of the landscape.

One could argue that my observations reflect some peculiarity of absorption. But that explanation is difficult to sustain when the same individual, consuming comparable foods in different environments, demonstrates entirely different glycemic responses.

Bread is a simple example. In the United States, I approach it with caution. Here, fresh baguettes—made with few ingredients and traditional methods—do not produce the glycemic rise that the device confidently predicts.

Perhaps, in time, artificial intelligence will evolve to incorporate such nuances—cultural, agricultural, and culinary. For now, it remains reductionist, interpreting food as a set of macronutrients, rather than as a lived, contextual experience.

I have been eating with freedom, even generosity—and my glucose has remained stable.

There is a lesson here, not only for technology, but for how we understand food itself.