The Information Age was a consequence of two developments: the ability to work with information programmatically and the democratization of that capability. We are now entering a similar period in biology. We can now program life itself with the ever cheaper tools of synthetic biology. We believe this era of programmable biology will spark a new wave of entrepreneurs who push the limits of what biology can do.

Before “computers” were machines, they were people who computed things by hand– a slow, expensive, and operationally complex process. They were eventually replaced by devices that could be programmed to perform calculations automatically. As computers became cheaper and easier to use, there were ever more people empowered to explore the software design space. Small teams could cheaply build software products, distribute them over the internet, and quickly iterate on them with user feedback. This decentralization of computer programming unleashed the software revolution that has dominated venture capital over the last few decades.

Biology is undergoing a similar transformation today. Just as human computers performed calculations by hand, scientists have had to carry out experiments manually, spending their precious time on physical tasks like pipetting. Furthermore, scientists have lacked general-purpose tools to manipulate biology. Instead of being able to program biological systems to do what they’d like, they’ve historically relied on working with whatever they happened to find in nature. GLP-1s, for example, come from studying the venom of Gila monsters, which were known to eat extremely infrequently.

Synthetic biology turned the old paradigm on its head. Scientists can now read the code of life with DNA sequencing and can rationally design biological systems by writing and editing genomes. Therapies like Casgevy don’t manage the symptoms of genetic disease: they treat them directly by editing errors in a patient’s genome. And Moderna didn’t develop their COVID vaccine by harvesting the virus and growing a weakened form; instead, they wrote mRNA code to instruct the body to produce the COVID spike protein itself.

The basic functions of synthetic biology have become dramatically cheaper, making them far more widely accessible. DNA sequencing in particular has come down in cost by a million-fold, going from a billion dollar government program to a consumer product. These advances coincided with recent progress in AI. We now have large genomic data sets and the AI infrastructure to train biological models on them. Meanwhile, tools like Opentrons are making labs themselves programmable, making it easier to run experiments without as much manual work. 

Put together: AI models make it easier to explore biological design space, synthetic biology makes it possible to physically implement novel hypotheses, and lab automation is tightening the loop from software to physical feedback. 

This pattern of radically cheaper design and tightening feedback loops in biology closely resembles the dynamics that launched the software revolution. Small teams outside of established labs can increasingly carry out cutting-edge research. They will push the frontiers of everything from diagnostics and therapeutics to food, agriculture, materials, and beyond.

Transitions like this one are rare. Having seen this story before with software, we can’t help but pay attention. We recently announced our first bio investment in Humane Genomics, and we are now actively investing in the space. Some areas we’re exploring are scaling DNA synthesis so we can actually make the millions of sequences AI models will propose, simulating the safety of new compounds in silico, and developing new ways to deliver genetic code. We’re especially interested in technologies that make it possible to collect new kinds of data to train a model that then gets better as people use it: data flywheels only unlocked by technical breakthroughs. 

We are excited to back the biotechnologists of the future. If that sounds like you, we would love to meet.