Scientists Uncover Oldest Signs of Life on Earth Using AI

A team of scientists has identified some of the oldest chemical traces of life on Earth, utilizing a groundbreaking analytical method that leverages artificial intelligence. This innovative approach can differentiate between organic molecules produced by living organisms and those formed through non-biological processes with over 90 percent accuracy. The findings, published in the Proceedings of the National Academy of Sciences, reveal vital insights into early life, significantly enhancing the understanding of Earth’s ancient biological history.

The research, led by Robert Hazen and Anirudh Prabhu from the Carnegie Institution for Science, focused on analyzing rocks from South Africa that date back approximately 3.3 billion years. These rocks contain molecular remnants that indicate the presence of early microbial life. Additionally, signs of primitive photosynthetic organisms have been detected in rock formations that are around 2.5 billion years old. These revelations align with previous isotopic evidence found in Greenland, suggesting microbial activity as early as 3.8 billion years ago.

Innovative Method Detects Ancient Life

The core of the study’s innovation lies in its ability to analyze highly degraded organic molecules preserved in ancient rocks. Over billions of years, original biomolecules such as sugars and lipids deteriorate, leaving behind fragments trapped within mineral structures. Traditional methods often struggle to interpret these remnants, but the new machine learning technique can analyze thousands of molecular peaks to identify unique patterns indicative of biological activity.

Hazen noted that whereas human researchers may encounter a confusing array of molecular signals, the machine learning model can discern subtle chemical fingerprints that suggest biological origins. By isolating carbon-rich materials and examining the distribution of molecular fragments, this method effectively separates signals from living organisms from those produced through non-living chemical reactions. This advancement marks a significant turning point in the analysis of early life, allowing scientists to interpret evidence previously deemed inaccessible.

New Insights into Early Photosynthesis

Among the most notable findings from the research is the evidence for the origin of oxygen-producing photosynthesis. The machine learning analysis detected molecular characteristics associated with organisms capable of splitting water and releasing oxygen, suggesting that oxygenic photosynthesis was occurring more than 2.5 billion years ago. This challenges previous assumptions that such activity began later, indicating that early oxygen-producing processes may have started at least 800 million years earlier than previously recorded.

These discoveries imply a more complex and gradual emergence of early photosynthetic organisms than previously understood. While the original biomolecules have long since vanished, the chemical fragments they left behind retain identifiable patterns that machine learning can still recognize.

The new analytical approach expands the time frame for detecting life in Earth’s geological record, nearly doubling the range from approximately 1.6 billion years to 3.3 billion years. This extension is particularly valuable given the limited and often ambiguous fossil record from this period. Hazen and Prabhu’s method also differentiates between various life forms, enhancing the understanding of early ecosystems and their metabolic diversity during a time when Earth’s environment was drastically different from today.

As the implications of this research extend beyond Earth, the technique offers promising opportunities for astrobiology. The ability to identify biological patterns in degraded organic material could revolutionize the analysis of samples collected from Mars by NASA’s rover missions. If ancient Martian rocks contain organic molecules, even in a degraded state, this method could help ascertain their biological origin.

The same analytical approach could be applied to the organic-rich plumes of Saturn’s moon Enceladus, the complex hydrocarbons on Titan, or the icy crust of Jupiter’s moon Europa. These celestial bodies exhibit signs of chemical activity related to organic molecules, and this new technique provides a means to determine whether any of these signatures bear biological imprints.

The combination of machine learning and geochemistry represents a monumental step toward reconstructing the earliest chapters of Earth’s biological history. The findings not only push the detectable record of life deeper into the past but also suggest that early microbial ecosystems may have been more diverse and widespread than previously recognized.

As supported by NASA, the researchers are refining the technique for future planetary missions. As scientific tools advance, the capacity to interpret chemical signals from ancient rocks will continue to evolve, bringing humanity closer to understanding the origins of life on Earth and potentially identifying life beyond our planet.