There are three groups of people who do research: academics, employees, and founders. There’s a lot of benefits and drawbacks to all three, but ultimately it comes down to the type of person you are. If you like risks and doing exciting things, building a startup is probably more for you. In my case, I think that this is going to be the trajectory that I will take in the future. There’s a lot of reasoning behind this, but I need to give context first.
Here’s a quick overview of what I’ve wanted to do in life. When I was little, I literally had no clue how the world worked, let alone myself. For this reason, everything that I wanted to do during this part of my time was either something that I thought was cool (like making pizza) or something I had been exposed to (like being an engineer). After I entered high school and really started to gain an understanding of how the world works, I moved on to wanting to be a doctor. I’ll admit that this was mostly because I was kind of interested in medicine and that’s basically the only field that existed in the world of biology.
In the past few months though, I’ve experienced a lot of growth due to the environment I’m in and the projects I’m building. As soon as I was opened to these possibilities for building the future, I decided that I wanted to be a Ph.D researcher in a lab. When I made this decision though, I was still very uncertain about which path I would take: a PhD position or being a doctor. The two main points of contention were money and stability. While doing research would be great, I was concerned about how underpaid PhDs and people in the research field in general are. Comparing this to the salary of a doctor, plus their stability, it was hard to give that up just to have a shot at making an impact. So why did I end up choosing neither of those things and end up going a completely different route?
If academia is played right, there are a lot of places where someone can go with their career. They can become a world-class researcher and win a Nobel Prize while securing great funding towards their lab. However, there are a lot of inherent flaws that are built into the system. This is because it was built in a world that was not rapidly accelerating every day. However, technology has brought the world to a level of acceleration that academia is just not keeping pace with. For an ambitious person, academia isn’t the right place to be for three reasons:
Publications: while publications used to be a good way to track how impactful research is, its design is now based on volume instead of quality and impact. For example, professors with a lot of publications will easily get positions at prestigious, well-funded universities while younger thinkers will have an extremely hard time breaking into the field. This exacerbates their risk aversion as these young scientists will be less willing to risk not getting publications, leaving the experienced professors to take more risk and eventually getting the results. The cycle then repeats until we have a very risk-avert research force that cannot take any risks in their work.
Innovation: Due to a less risk-taking research force, there will not be much innovation in younger people. In a system that is designed for this, it is hard for them to break from this as well inside academia. For a person who wants to create new things and actually discover impactful things, there’s going to be failure and risk involved. So if there’s no risk, there’s no reward.
Action: One thing I’ve noticed is that even if there are people who do innovate and find very impactful research, it often takes a long time for it to actually get implemented. For example, CRISPR was discovered a long time ago but it’s only until recently that the first therapy has come out. Obviously regulations play a role but in general, companies have always been much faster at implementing this technology than academia.
So for a young person who wants to have an impact on the world, discover new things, and not waste their time, it’s almost impossible as an academic. While companies are the ones who primarily implement these discoveries, your individual impact here is very little and you’re back to the idea of not having a big impact.
One of the major parts of building a startup is it’s inherent risk of failing. I think that I, and possibly most people in general, are risk-avert by default. There’s a lot of evidence for this in my past decisions. When I was exposed to all these new technologies, I was very convinced that the only path that came naturally to me was being an academic. Even though I knew about startups, I always thought that it was too much risk and I could be more successful without my own company. The fact is, I was just being risk avert and was just thinking of the ways that I could take the least risk possible.
I think that while it may be hard, building a startup satisfies all the needs that people are looking for in academia, with some extra things. Startups are special in that they can be very versatile. This means that you can have a big impact while also being able to discover new things about the world. Furthermore, you can start building right away, and the market can decide whether or not your startup deserves to survive. Finally, you can also get great financial return from these startups, making them seem like a no-brainer.
Since the first tech startups started popping up, there was a great need for innovative startups. And they came in large numbers. While many died, some are still thriving today and continue to be great examples of how startups can be a good choice for many people. However, this is overlooking a critical aspect of startups: their field. Internet and software companies are great because they don’t need to rely on a lot of hardware and physical materials to run. If you know some coding, you can build a great startup. However, this isn’t the same for biotech.
Biotech startups are notoriously difficult to start. First, you need to get a massive amount of funding to start building one of these. This poses a challenge as investors will probably invest in companies only if their research and ideas are proven (so the investors can make their money back). However, this would mean that the founder would need to have a lab before starting their company, leading us back to academia. Software companies are a lot easier to startup because they don’t require vast resources like biotech. Furthermore, the financial nature of these companies is very variable. Sometimes they can’t make any money, but they can make extremely large amounts if they either get acquired or start producing a product (which could take 10+ years). So what can founders do? How can we get the best results, have an impact, all in a short amount of time?
One of the limits of biotech startups is their inability to start from nothing. They need resources and this can be hard to get when the founders don’t have access to research facilities to start with. This is not just a problem within biology, but also other fields that require large resources. I think that the best solution to this is to start building companies inside of other entities. This will give that smaller entity the funding needed to do great work while also maintaining financial freedom. However, it’s important that this doesn’t work in the same way as a university, where these small inter-company startups are not able to do risky things. Otherwise, this will lead to the same situation we are facing right now.
I think a good example of this is with Google. They are able to fund both DeepMind and X’s Moonshot Factory, both very successful projects. This is able to work though because Google has good money and supports their teams taking risks. We also have proof that this works, as these two companies have done great things in their spaces. X has released the Waymo and DeepMind released AlphaFold, both breakthroughs in the problems they tackle. Having a similar model for biotech startups may lead to more breakthroughs, and at a faster rate. While this idea works really well, there are also very few companies that would be willing to throw money at these risky ideas like Google. DeepMind has actually been losing Google money but they are still funding them, a rare event for big companies.
Another model that may work is the development of better funding systems. An interesting situation we are approaching is the aging of the population. With so many people starting to think about living longer, many investors may be willing to invest in biotech ideas rather than only concrete testing. This could mean an era of more longevity startups. We can see this with new venture capital firms such as age1. They structure their firm to not always look for concrete tests, but also look at founders even before they have an idea. Another path may also come from self-funding. A situation like this could come up when a wealthy person starts a biotech venture backed by their own funds, removing the risk of upsetting investors, the company, or a university. However, this can pose a challenge for first-time founders who have no capital to break into the field with. It ends up more like a rich get richer scenario where new people cannot start something of their own.
To fix this, a superpower that these founders can use is computational biology. This part of biology does not require nearly as much resources but can still have a big impact. So for founders that are interested in starting companies in the biotech space, the computational space may be the way to go. An example of a company like this is Benchling. They provide software for research labs to organize themselves better. This is extremely useful and user-friendly, making it financially successful. Now that founders have this capital, they can choose to start a pure-biology company if they wish.
Now, this is assuming that you want to do this. Many people in biology don’t like computer science and that’s okay. I think that if you have the drive to do something like starting a pure-biology company, and the work doesn’t feel like work to you, it’s going to be much easier to succeed. Furthermore, a higher barrier to entry usually means a lower amount of competition, meaning that you may find it a little easier to expand once you have passed the giant walls. In general, good ideas that fit the market win so as long as your company fits a problem and users like your solution, the company will probably succeed.
In general though, computational biotech startups will see an impact right away but it may not be huge with the general population. We see this a lot with AI tools. Once a new paper comes out on a better way of using AI, it gets implemented in a very short amount of time. On the other hand, CRISPR was discovered many years ago and we only recently got the first approval for its use in sickle-cell disease. However, in terms of impact, wet-lab companies have a bigger potential for a direct impact on the world. While computational tools are useful, they are mostly used by researchers, not by the general public. However, these companies that distribute therapies are directly impacting the population with their discovery. So computational startups are quick, but lower impact, while wet-lab startups are slow, but high impact. The question is: how long are you willing to wait and what kind of impact do you want to have?
While all of this logic may make sense right now, it’s impossible to see how the future may play out. For example, if I was going to start a company, it would probably be around 7-8 years from now. That’s a pretty long time. Seven years ago we didn’t have ChatGPT, AI wasn’t that big, and people were still having a lot of meetings over the phone or in person. That’s the thing about the future, it often goes faster than we expect. So 7 years from now, the landscape of biotech could be completely different and this essay may not be applicable anymore. However, the core skills that come with starting a company will probably stay the same.
This is why I think that instead of building startups at a young age, it is more important to build these skills and learn from people that are in the space right now. This way, you can gain experience and your field and not build a startup with gaps in it. When people hear this, one argument they may have is that you will get a lot of experience just by building a startup. My counter to this is that it costs a lot to make a startup, mostly your time. If it’s built on gaps that you don’t know about, it will most likely be a pain and you will have lost time that could have been spent on learning more.
A concern here though is the thought of wasting time in a corporate job or in a similar institution. This should not be the case as everything you do in life should be done to boost you up towards your goals. To combat this, it may be worth more to work at a startup, especially when you are younger. This will still train you to have the skills necessary for your own work, but also allow you to see first-hand how a startup is built, potentially even some things to avoid.
I’m interested in entering research but am concerned about how little PhDs make for their expertise
Academia is a very slow-paced environment that focuses more on discovering than building
Academia is for those who only care about exploring things, while startups are meant more for those who want to have an impact
Computational biology can be really useful for fast-paced, ambitious people who want to build things
Before building a startup, it’s necessary to get the skills required to build something useful and impactful
Thank you for reading.