When Will You Die? This AI-Powered ‘Death Clock’ Claims to Know The Answer

A new AI app, Death Clock, has caught attention by predicting users’ life expectancy based on lifestyle habits. Since its launch in July, the app has been downloaded over 125,000 times, appealing to health enthusiasts and financial planners alike. Developed by Brent Franson, Death Clock uses data on diet, exercise, stress, and sleep to calculate a likely date of death. With a data drawn from 1,200 life expectancy studies and 53 million participants, the app delivers personalized predictions for its users.

Though the concept may seem 'grim', it even displays a “fond farewell” death-day card with the Grim Reaper—it has gained popularity among those seeking healthier lives. Beyond personal health, its potential extends into financial and economic planning.

Franson, the app’s developer, mentions that the prediction of one’s death date is likely the most important and impactful date in a person’s life. While the Death Clock may seem morbid, its potential to influence health behaviors and financial planning is a sign of how AI can reshape our understanding of longevity - and its profound implications for economics.

Life expectancy predictions are crucial for governments, companies, and individuals. These predictions might influence retirement planning, life insurance policies, and pension fund calculations. For instance, the U.S. Social Security Administration estimates that an 85-year-old man has a 10% chance of dying within a year and an average of 5.6 years left to live. Franson argues that averages like these are often inaccurate, emphasizing that Death Clock’s algorithms provide more tailored and accurate results.

Recent research by the National Bureau of Economic Research highlights the importance of nuanced life expectancy data. A study titled On the Limits of Chronological Age found that aging affects physiological capacities differently and that chronological age is a poor predictor of economic behaviors like workforce readiness. Another study, The Value of Statistical Life for Seniors, explored how age and health influence the “value per statistical life” (VSL), a metric used in areas like pollution regulation and workplace safety.

Economic inequality also plays a significant role in life expectancy. Research from the American Medical Association found a 15-year longevity gap at age 40 between the wealthiest and poorest 1% of men and a 10-year gap for women. Similarly, Nobel laureate Angus Deaton’s work on “deaths of despair” underscores the disparity between rich and poor Americans.

Lifestyle factors further shape life expectancy. Loneliness has been shown to reduce lifespan, while gratitude may increase it. A Harvard study found that women who reported high gratitude had a 9% lower risk of dying within three years compared to their less grateful peers.

For its $40 annual subscription, Death Clock offers more than just predictions. It suggests lifestyle changes to delay mortality and even provides a second-by-second countdown of one’s estimated remaining time.