Yes, the processed food industry has gotten pretty good at making food to please the masses. New Yorker staff writer Helen Rosner once argued that anyone who’s tasted chicken tenders loves them, even if they no longer choose to ingest them. New York Times reporter Michael Moss famously explained how snack food companies have learned to lure us by tweaking proportions of salt, sugar, and fat to a “bliss point” ratio most human beings find irresistible. But Cohen’s argument is that existing models of flavor design only work in crude broad strokes. And he thinks his artificial intelligence (AI) tool is the doorway into a new landscape where food and beverage companies know more about us than ever before, with product offerings that respond ever more individualized hungers.

In its quest to make food that knows more about you, Analytic Flavor Systems’s main data collection tool its smartphone app, Gastrograph. The app’s central feature is a wheel with twenty-four spokes, where each sliver represents a discrete category of sensory experience—such as “meaty,” “bitter,” or “mouthfeel.” Tasters map the contours of flavor perception by tracing the spokes corresponding to the qualities they detect, designating the intensity of each on a scale from one to five. A submenu allows for a more granular record of experience: specifying that “meaty” quality, for instance, as beefy, sausage-like, or more exotic options (moose, kangaroo). Tasters are then prompted to give the product a preference rating, on a scale from one to seven.

Analytic Flavor Systems

The Gastrograph app also gathers data about the person doing the tasting—demographic information, socioeconomic status, past experience with the product, smoking habits, and more— as well as information about the ambient environment, such as temperature, barometric pressure, and noise levels, all of which can shade our experience of how things taste. “We literally turn on every sensor that the device has,” Cohen explains, including the microphone, light meter, and GPS. “We even collect magnetic field data, which is right now not predictive of anything, but one day…” He shrugs. Who knows what the data might reveal about the influence of the magnetosphere on our ability to detect saltiness?

All this is an attempt to crack open most that most private realm: the intimate, ineffable world of flavor. The tastes that tantalize and repel us, after all, are highly individual, shaped by biology, culture, and personal history. But the power of the Gastrograph A.I. lies in its ability to model and predict the flavor preferences of increasingly narrow slices of the consuming public, giving food and beverage companies the information they need to develop products optimized for more and more specific sensibilities. Cohen dreams of a day when we’ll each have a Dorito of our own.

An algorithm has no tastebuds; a neural net never gets the munchies. So can a robot brain really tell us what we’ll want to eat? The question is whether A.I. systems will be able to excel in the sensual, creative work of tasting and developing new foods—and what we stand to gain or lose by inventing foods that really have our number.

???

How should food be made to taste? This question has vexed manufacturers since the earliest days of factory-made foods, when industrial processing created new challenges—and new possibilities—for flavor. The unprecedented ability to manipulate raw ingredients raised two connected conundrums, both still top-of-mind for the industry today. The first has to do with consistency. No grain of wheat, no cocoa bean, is identical—yet each Oreo that tumbles off the production line must be, as far as possible, indistinguishable from the next. How can nature’s variety be commoditized and rendered uniform, with sensory experience that’s guaranteed? Second, there is the problem of deliciousness. What makes one crème-filled cookie preferable to another crème-filled cookie? How can pleasure be measured?