AI at the Intersection: The A16z Investment Thesis on AI in Bio + Health

This transformation begins with less-complex, one-off models (typically referred to as machine learning) to do simple tasks that are forgiving to mistakes; for example, Netflix using AI to recommend shows.

In time, the ratio of human work lessens, eventually getting closer to full automation even in areas which require a human specialist—i.e., where small mistakes can have disastrous effects—but likely not without a human somewhere in the loop, especially in areas that are particularly unforgiving to mistakes, such as diagnoses, drug prescriptions, o... See more

First, the cost of healthcare. The exponential increase in cost stems partiallyfrom the need for highly trained staff (PhDs, MDs, nurses, etc)—especially as the cost of skilled labor is growing far faster than inflation. As AI becomes increasingly able to function as a technical expert, there are opportunities to extend the abilities of our existin... See more

Over time, this progression leads to the potential for AI-driven co-pilots for life sciences and healthcare that greatly scale skilled labor or that uplevel less-skilled labor. For example, AI could suggest answers or ideas, allowing trained humans to choose the best ones, curating results and skipping over any wrong answers. This approach integrat... See more