I am a founder of a startup (Octant - a16z backed) that has a small & growing software engineering and data science team (see the Nov who's hiring post). Some thoughts on some of the discussion here:
1. Compensation – In academia, you will likely take a big salary hit (much of this is discussed). There are a few exceptions like newer institutes like Chan Zuckerberg, Arc Institute, etc that are paying much more competitive salaries though. In well-backed startups and larger biotech/pharma, cash is likely equal (or often more) to software comps elsewhere – the bigger hit you take is usually in equity – no one has been able to match FAANG on total comp with RSUs in the mix. Startups can provide options, but it's not very fungible. For example, we benchmark salary on comparable A16Z pre-public non-bio companies use as well as stats from the broader SV SWE salary datasets. There are startups in bio that pay even higher to lure talent.
2. Research vs Product – Over the last decade, there are a bunch of highly profitable tech companies and large funded new startups (e.g., Calico, Altos, Deepmind, etc) trying to take on bio as the next frontier. These places (like those named in the blog post) pay very competitively. Thus far, these places often turn into a big mess because it becomes hard to deliver products (like drugs) in a mostly academic-y atmosphere. I don't think anyone has really cracked this nut yet (or if it's even possible).
2. Culture of SW importance – In a lot of startups these days, this has changed quite a bit over the last 5 years. Lots of software & data science first startups. I think in the larger pharma/biotech though, the centrality of drug discovery takes a lot more oxygen than software, which are often thought of as innovation bets and different places have different levels of long term commitment.
3. I think one important difference is the type of company. There are many software companies in healthcare/bio that are software products supporting R&D, healthcare, drug development etc. Many of them have done quite well (e.g., Benchling, Komodo Health etc in A16Z portfolio alone) and are basically just software companies that just happen to be in bio. There are many others like most drug discovery companies (like us) where software and data science is enabling, but the product is often ultimately drugs. For a lot of SWEs, this becomes problematic because people often want the satisfaction of having externally deployed software products to push into the world. The heroes and heroines of this world are often drug hunters over tool developers, and this has cultural consequences as well. Some people are really good with this (getting a lot of satisfaction out of enabling new drugs to treat serious disease), but a lot of folks aren't.
4. The current biotech crash has been bigger and more sustained than the tech crash thus far. High interest rates impact this industry much more than others, because revenue on new drugs, which drive a large part of the industry usually take a decade or more to develop before revenues are flowing. This is less of an issue in healthtech companies that can often deploy much more quickly (90% of healthcare costs are not drugs).
5. Finally, there are many happy SWEs and DS in bio at companies that value software and can build good careers in it building products that ultimately help human health in new ways. It's a pretty amazing time in biology, with a suite of new technologies to read, interpret, write, edit, deploy molecules/DNA/cells that are really unlocking many of the mysteries of human diseases. I feel lucky every day we get to continue building in this space.
1. Compensation – In academia, you will likely take a big salary hit (much of this is discussed). There are a few exceptions like newer institutes like Chan Zuckerberg, Arc Institute, etc that are paying much more competitive salaries though. In well-backed startups and larger biotech/pharma, cash is likely equal (or often more) to software comps elsewhere – the bigger hit you take is usually in equity – no one has been able to match FAANG on total comp with RSUs in the mix. Startups can provide options, but it's not very fungible. For example, we benchmark salary on comparable A16Z pre-public non-bio companies use as well as stats from the broader SV SWE salary datasets. There are startups in bio that pay even higher to lure talent.
2. Research vs Product – Over the last decade, there are a bunch of highly profitable tech companies and large funded new startups (e.g., Calico, Altos, Deepmind, etc) trying to take on bio as the next frontier. These places (like those named in the blog post) pay very competitively. Thus far, these places often turn into a big mess because it becomes hard to deliver products (like drugs) in a mostly academic-y atmosphere. I don't think anyone has really cracked this nut yet (or if it's even possible).
2. Culture of SW importance – In a lot of startups these days, this has changed quite a bit over the last 5 years. Lots of software & data science first startups. I think in the larger pharma/biotech though, the centrality of drug discovery takes a lot more oxygen than software, which are often thought of as innovation bets and different places have different levels of long term commitment.
3. I think one important difference is the type of company. There are many software companies in healthcare/bio that are software products supporting R&D, healthcare, drug development etc. Many of them have done quite well (e.g., Benchling, Komodo Health etc in A16Z portfolio alone) and are basically just software companies that just happen to be in bio. There are many others like most drug discovery companies (like us) where software and data science is enabling, but the product is often ultimately drugs. For a lot of SWEs, this becomes problematic because people often want the satisfaction of having externally deployed software products to push into the world. The heroes and heroines of this world are often drug hunters over tool developers, and this has cultural consequences as well. Some people are really good with this (getting a lot of satisfaction out of enabling new drugs to treat serious disease), but a lot of folks aren't.
4. The current biotech crash has been bigger and more sustained than the tech crash thus far. High interest rates impact this industry much more than others, because revenue on new drugs, which drive a large part of the industry usually take a decade or more to develop before revenues are flowing. This is less of an issue in healthtech companies that can often deploy much more quickly (90% of healthcare costs are not drugs).
5. Finally, there are many happy SWEs and DS in bio at companies that value software and can build good careers in it building products that ultimately help human health in new ways. It's a pretty amazing time in biology, with a suite of new technologies to read, interpret, write, edit, deploy molecules/DNA/cells that are really unlocking many of the mysteries of human diseases. I feel lucky every day we get to continue building in this space.