The model learns from vast datasets of genomic sequences, allowing it to identify biologically meaningful patterns that can lead to new insights in biology.

The model can interpret genetic mutations and provide insights into their functional consequences, which is state-of-the-art in computational biology.

Biology is fundamentally digital; every organism is made up of sequences of DNA, and understanding how to program that DNA allows us to design biology to perform specific tasks.

There's a significant opportunity in generating in vivo data, which is currently limited, to better understand drug interactions in real biological systems, and we're seeing breakthroughs in data-generating technology in biology and chemistry that will significantly impact how we model these fields.

The future may involve programming proteins as a new form of technology, bridging the gap between computers and human health, which is currently underexploited. With advancements in machine learning, like AlphaFold, we can now predict how DNA sequences fold into proteins in just minutes, potentially revolutionizing how we build machines that interact with the human body and our environment.

In the DNA sequencing space, the deflation rate is 40% for every cumulative doubling, and we're starting from a very low base, which will increase rapidly.

Our model, EVO, is designed to predict the next base in a DNA sequence, which can determine health outcomes, such as the presence of sickle cell anemia mutations.

Nanotechnology, particularly DNA manipulation, shows promise in curing diseases and enhancing health.

We are training models inspired by evolutionary forces to connect biological sequences directly to their functions, leveraging the effects of natural selection through DNA mutations.