The Natural Future for AI in Biotech: The Next Generation of Machine Learning Demands Partnership with Biodiversity

Abstract

Progress in biotechnology is critically dependent on continued access to new biological "components" (genes, proteins, organisms) from nature. Over recent decades, the way that researchers access and use these components has changed dramatically in response to similarly dramatic developments in technology and regulation. The net effect of these changes has been to severely restrict the availability of high-quality genetic data from biodiversity. This bottleneck limits the potential of machine learning (AI) in biotechnology and is a threat to progress across the industry. We suggest that the inevitable demand for high-quality genetic data to train the next generation of biological AI models has the potential to align the economic and technical interests of the bioeconomy with those of biodiverse "provider" countries and communities. The impending era of big data in biotechnology will therefore require the industry to break its dependence on "digital biopiracy" and embrace sustainable partnership-based data supply chains.