- The speaker introduces Elizabeth Irons, a cancer biologist and founder of Science Exchange.
- Science Exchange is an online marketplace for outsourcing science experiments.
- Irons started the company in 2011 and was part of the Y Combinator summer 2011 batch.
- Irons is also the chairman of Reformer Therapeutics and a part-time partner at Y Combinator.
- The speaker wants to discuss running a marketplace company and Irons' perspective on the biotech industry.
- Irons explains that Science Exchange was created to address the inefficiencies and fragmentation in scientific research collaborations.
- The platform helps researchers find collaborators, evaluate their abilities, and manage ownership of scientific results.
- Irons gives an example of how she struggled to find the necessary resources for her breast cancer research and experienced price differences due to the lack of information.
- Irons shares her journey from academia to starting a tech company and the challenges she faced with tech transfer.
- She advises academics to join Y Combinator after generating a significant amount of research and discusses the growing trend of academics starting their own companies.

- LabCentral and QB3 provide cost-effective lab space, lowering barriers to entry for scientists and researchers.
- The current model for scientists to become entrepreneurs is challenging, often requiring a well-established PI to co-found the company for legitimacy and funding.
- Science Exchange is a marketplace that connects scientists with lab services, focusing on B2B interactions and providing a curated marketplace with quality assurance.
- The demand side of Science Exchange was more difficult to establish than the supply side.
- The first customers of Science Exchange were friends of the founder, providing valuable user feedback.
- Product-market fit was achieved when a large pharmaceutical client, Amgen, started using Science Exchange for their discovery research.
- The main competitor for Science Exchange is the status quo of using inefficient processes like SharePoint to manage external vendors.
- Science Exchange has also launched the reproducibility initiative to validate and reproduce scientific experiments.

- The Reproducibility Initiative, a project by Science Exchange, aims to improve the quality and efficiency of scientific research.
- The initiative covers various aspects such as antibody validation, reanalysis of epidemiology results, and replication of published results.
- Replication studies are controversial in academia due to concerns about the value of replication and the potential negative impact on researchers' careers.
- The main reason for non-reproducibility is the lack of quality assay validation, which is more common in academia compared to the pharmaceutical industry.
- Science Exchange faces challenges in scaling and staying focused while pursuing multiple opportunities.
- The biotech industry is experiencing a surge in startups due to increased access to capital and advancements in therapeutic modalities.
- The convergence of experienced industry professionals entering the biotech sector is also contributing to the growth of startups.

- The speaker discusses the differences and similarities between biotech and software startups.
- In biotech, the outcome of the science cannot be changed, unlike software where pivoting is possible.
- Biotech companies focus on key milestones to demonstrate the effectiveness of their drugs.
- Most biotech companies do not have commercial revenue until they have an approved product, but some are starting to commercialize their own products.
- Access to capital and FDA regulations have made it easier for biotech companies to bring their products to market without getting acquired.
- Common mistakes made by biotech founders include not doing crucial experiments and not recognizing their own limitations.
- A business co-founder is not necessary for success in the biotech industry, common sense and learning from others can be sufficient.
- For programmers interested in biotech, going back to school or obtaining a PhD may be a potential path.

- Silicon Valley is showing a lot of interest in biotech, including personalized understanding of biology and self-hacking.
- The future of biotech may involve a strong component of user pays, focusing on indications that patients are willing to pay for.
- Migraine drugs have been successful because people are willing to pay for them due to the debilitation caused by migraines.
- Computer programmers can get involved in biotech through bioinformatics and other analysis tools without lab experience.
- The decision to do the reproducibility initiative was a game changer for Science Exchange, opening doors and leading to success.
- Science Exchange's purpose is to enable scientific breakthroughs through connections, and this will continue in the future.
- The industry should consider new approaches to look for causality instead of just correlations in research.
- To quickly go from an idea to a minimum viable product, it is important to dedicate time and resources to the project.
- Credentials are important in the biotech space, but having credentials will give a significant advantage.

- Having credentials in a field can provide a huge advantage, but it is not always necessary for success.
- Being knowledgeable and credible about the field can compensate for a lack of credentials.
- Personal motivation and purpose can serve as a surrogate to get meetings and start a company in the biotech industry.
- Building a scientific team is important for a biotech company, but a co-founder does not necessarily need a PhD.
- The speaker had the support of her boss at the University of Miami, who allowed her to take time off to start Science Exchange.
- Quality control is a core value proposition of Science Exchange, with suppliers being qualified and performance being continuously monitored.
- The platform clearly outlines expectations and deliverables, leading to better performance by suppliers.
- Science Exchange has a high net promoter score, indicating customer satisfaction.

- Elizabeth Irons is the founder of Science Exchange, an online marketplace for outsourcing science experiments.
- Science Exchange was created to address inefficiencies and fragmentation in scientific research collaborations.
- The platform helps researchers find collaborators, evaluate their abilities, and manage ownership of scientific results.
- Irons shares her journey from academia to starting a tech company and the challenges she faced with tech transfer.
- Science Exchange's main competitor is using inefficient processes like SharePoint to manage external vendors.
- The Reproducibility Initiative by Science Exchange aims to improve the quality and efficiency of scientific research.
- The biotech industry is experiencing a surge in startups due to increased access to capital and advancements in therapeutic modalities.
- Biotech companies focus on key milestones to demonstrate the effectiveness of their drugs.
- Access to capital and FDA regulations have made it easier for biotech companies to bring their products to market.
- Common mistakes made by biotech founders include not doing crucial experiments and not recognizing their own limitations.
- Silicon Valley is showing interest in biotech, including personalized understanding of biology and self-hacking.
- The future of biotech may involve a strong component of user pays, focusing on indications that patients are willing to pay for.
- Science Exchange's purpose is to enable scientific breakthroughs through connections.
- Building a scientific team is important for a biotech company, but a co-founder does not necessarily need a PhD.
- Quality control is a core value proposition of Science Exchange, with suppliers being qualified and performance being continuously monitored.

YC Partner Adora Cheung talks to Science Exchange Co-Founder and CEO Elizabeth Iorns about her experience as a biotech startup founder.

Learn more at https://www.startupschool.org/

Chapters (Powered by https://bit.ly/chapterme-yc) -
00:00:00 Introduction
00:01:50 Shift from academic to startup route. Where did the idea come from?
00:03:47 How Science Exchange helps organize the very fragmented market for research?
00:05:54 Was it difficult to make the switch and not work on the one thing that you focused on during research?
00:07:56 Advice for people who want start a startup from a research at University? Sometimes it’s a huge obstacle from universities. How can they navigate?
00:09:08 Do you think there is a overall trend in people moving from academics into startups?
00:11:46 How did you get your first users for Science Exchange marketplace?
00:14:08 What was more difficult – Supply or demand?
00:15:54 Do you remember on the demand side, the very first customer? What were they? What did they do?
00:17:04 So we talked a lot about at Startup School, product market fit. For you what  was that like? When did you know you had that? And how did you define  it? And what metrics were you looking at?
00:19:51 Reproducibility Initiative (reproduce to validate experiments) -  why has this never been done? Was it a money problem or can only Science Exchange solve?
00:23:09 What are the two top two complex factors?
00:25:10 What is the biggest challenge as a CEO of  a marketplace company that you have today? And how has that challenge morphed over time?
00:27:03 There has been this implosion of biotech startups recently, especially in Silicon Valley, what do you think that is? What are the causes for this?
00:30:04 Are there any similarities between biotech and software?
00:31:25 What does an MVP mean in biotech?
00:33:13 What has changed that has allowed a small companies product to go-to-market without getting acquired?
00:34:36 Science Exchange is helping speed up experiments, trolls, and getting through the FDA process.
00:35:48 Common mistakes you've seen biotech founders make? What do you just need and what do you avoid at all costs?
00:37:33 What do you think about the need for a business co-founder and should that be a goal?
00:39:30 Non-scientists who are interested in biotech, what should they do? Get a PhD or what's the potential path?
00:42:17 Decisions you’ve made that was a game changer (inflection point)?
00:43:29 100 years from now, what do you think Science Exchange will be?
00:45:16 Q1 - whether we should look at new approaches too, instead of just looking at correlations to look for causality in a space with unknowns?
00:46:40 Q2 - Idea to an MVP in a short space of time, and the steps that required to do that?
00:49:27 Q3 - Importance of credentials in the biotech space?
00:51:29 Q4 - when did you decide to leave academics?
00:53:18 Q5 - Quality control in a two sided marketplace?

So Elizabeth is a cancer biologist by training.

You got your PhD in cancer biology from University of London, and you did your post-doc and then became an assistant professor, which you're still at, at University of Miami School of Medicine.

So in 2011, she started Science Exchange, then called The Bench, which was an online marketplace for outsourcing science experiments and she was part of the Y Cominator summer 2011 batch.

And I think you might have been like the very first biotech-y type company in YC, right?

And she's also the chairman of Reformer Therapeutics, which just went through YC as well, last batch.

She's also a part-time partner at YC, which means in her free time, she helps all our biotech companies, which at this point is like hundreds.

I don't know what the exact number is, but hundreds of biotech companies.

So today I want to spend time talking about 2 things in particular.

1 is running a marketplace company, since that's what Science Exchange is.

And 2 is getting your perspective as someone who's now advised hundreds of biotech companies, what you think about what's going on in the space.

And I'm in particular because I think there's an increase in slope of scientists coming into the field.

So to get started, like I said, you're a cancer biologist by training.

You were, I believe, doing breast cancer research right after college, I guess.

Why, how did you even, I mean, you're kind of going down the academic route, like why, how did you even come upon startups?

Yeah, yeah, it was kind of random, especially to start a technology company like Science Exchange rather than a biotech.

So I was actually sort of, I guess, taking Y Combinator's advice without knowing about Y Combinator's advice to solve your own problem.

So I was looking at the way that scientific research was evolving and it was becoming much more specialized, much more multidisciplinary, had to form these collaborations with other labs in order to access, you know, all of the latest technologies.

And I experienced that myself and realized that it was incredibly inefficient.

And so that process of how you find somebody to work with, how you evaluate the quality of their ability to do that specialized work, and then even just the infrastructure to actually work with them.

So in biotech and in scientific research, the actual ownership of the scientific results is very important, And so just figuring out who is actually the owner of those results, who has the intellectual property, publication rights, all of those things is very difficult.

And so I was talking about this with my co-founder and we sort of looked at other examples of industries that had solved a similar problem.

So we're looking at ODesk and Elance at the time, which have now become Upwork.

But that was really an example of you could solve this with a marketplace of experts and that was the basis of Science Exchange and it's never really kind of evolved much more beyond that in terms of the fundamental goal of what the company wants to achieve.

So before, if I wanted to do some research and run an experiment, basically what it sounds like is this big disorganized process of I need 10 different things super fragmented where this stuff is.

Can you give it like, I guess, maybe give an example of, related to the research you were doing of how it was so disorganized and then how Science Exchange now collapses all that into 1 place.

Yeah, definitely super disorganized, lots of fragmentation, both in terms of who you work with, but also how you find the information.

And in some cases, the information is actually not really available.

So at the time when I was working as a breast cancer researcher, I was running multiple different types of experiments.

And 1 example was microarray analysis, which is definitely showing my time away from the lab because that's a very old school technique now.

But at the time, it was, you know, what we used to analyze gene expression profiling.

And we just didn't have the types of arrays that I wanted to use, and so I was trying to use Google and asking my friends and asking my old lab, can we use your infrastructure?

Also, actually, the inefficiency drives a lot of inefficiency in pricing in the market.

So that was something that was a big learning point for me at that time point was you could actually create a company that had margin in that space because the market was so inefficient due to the lack of information.

So for example, I was seeing literally 10 times price differences for an equivalent experiment because people really just didn't have access to what should it actually cost.

And we see that still on science exchange today is that when people go and look for multiple options and they actually don't have the barriers of being able to work with them, they see very frequently price differences that are quite extraordinary for the same results that could be generated.

Just to take 1 step back, when you were an academic, I don't know if that's a bad word, but when you're an academic.

Was that a tough choice to just make that switch?

Because I mean you spent so many years studying for this 1 thing and then you just are starting essentially what is a tech company, that is not solving that 1 thing that you've spent up until that point in your entire life going after.

Yeah, and actually at the time I didn't really have a lot of role models to sort of look to either.

So I was in Miami, which was not a very startup place.

And now it's actually, it's interesting to see the evolution not just in the biotech world, but just generally in the startup ecosystem and all of the infrastructure that's been created.

Like I think University of Miami even has now an incubator associated with the School of Meds and it helps people kind of create companies.

I remember at the time having this idea for a company and I actually asked the university because I was a little bit worried if I have this idea while I'm still working there, will they try to get some ownership stake up from the idea?

Because actually when you work in academia, the results you generate don't belong to you.

So I think that's kind of a common misperception that universities actually own your work.

So it's often a challenge to spin those results out of the university.

So I was kind of worried, well, if I have this idea, will they own it?

And so I went and talked to the tech transfer office and they were just like, whatever.

And it was kind of interesting, and they didn't really have any guidance on how to do that.

But coincidentally I read about Y Combinator in Wired Magazine.

So there was actually a Wired Magazine interview all those years ago about Y Combinator and that was where I first learned about it and I was like, wow, that sounds super cool.

We didn't know anybody that had gone through Y Combinator.

We just applied with this idea and we're fortunate enough to get in.

So you talked a little bit about tech transfer.

I mean I do think that a lot of people in academia who are thinking about starting a startup do think that is a huge issue.

And in some sense there are, from some universities we've had to work with with some of our startups, it is a huge issue.

What's your, do you have any advice for them on how to navigate that process?

Like should that be the barrier in your mind?

Particularly for scientific startups, because if you're trying to spin out actual research that was done already, and I think people often ask me, like, what's a good time to join Y Combinator if you're creating a science startup?

And actually I do think a good time to join is once you've already generated a lot of the R&D from grant-funded work because you don't want to spend years and all of this money trying to pursue a research hypothesis.

You really want to be in the development phase.

And so I think that gets very difficult with a lot of the universities.

I think some of them are trying to be more sort of founder friendly and create ways to more readily license the results that were generated.

Do you see that academics, is the trend they're moving more into startups or what do you see, or maybe we just get the bubble view here in San Francisco.

But I mean, that's certainly the thing I see anecdotally.

But do you think that's a growing trend overall?

Yeah, I think people are more willing to just create companies, I think, generally.

Like, I think that's sort of a macro trend way beyond science is, you know, really just entrepreneurship has become this viable career path and people are like, oh yeah, I could create a company and it's just become a lot easier.

Like the infrastructure that's been created through Y Combinator but also through things like Stripe and things like AWS has made it so much easier for people to create companies.

And I think in the science space, we're starting to see that.

So obviously, science has changed as 1 way to do that.

But there's some really interesting other evolutions like LabCentral and other lab space, QB3, that provide lab space by the bench that's very cost effective, and you can just rent 1 bench per month and be able to get started really cost effectively.

So those things are lowering the barriers to entry.

And so I think academics, what I would like to see more of is how can we allow, you know, the true, I would say the analogy to what we've seen in the engineering world, the software engineering world, is that the actual developers, the software developers become the entrepreneurs and they maintain that and really build this company around them.

I think that's still very difficult for scientists, particularly PhD and postdoc scientists.

So they actually make the discoveries in these labs, so they're the ones doing the work.

But a lot of the time for them to actually be the ones to be the founder of the company is pretty challenging.

And usually what you'll see is a well-established, famous PI, so principal investigator, kind of being a co-founder of that company, just to give it legitimacy.

And then that usually allows people to bring in funding.

Why do we have to have these sort of figureheads who own large stakes of these companies in order for them to get off the ground and get started?

Yeah, that's something we, as you know, talk a lot about to people who, when they apply, and then we see that kind of structure, and then we try to go help them fix it, essentially.

Don't wanna lose too much of your company too soon.

So, you already described what problem you're trying to solve.

And then you've decided at this point, in 2011 to start the startup.

And you have 2 sides of the marketplace, so it's doubly hard for you.

Yeah, I think marketplaces are really challenging because you don't control really the success of the business so you don't I mean you do but you don't have you're not just kind of building something and selling it you have to then make sure you have the things to sell on the other side.

I think when we first started, we were very MVP-like in our approach.

So I think the first version of Science Exchange, which as you mentioned was actually called The Bench, which was renamed during Y Combinator thanks to Paul Graham's very good advice about our poor naming choice.

So, yeah, we kind of threw up this website and it was literally like, put your, what you need on this website and then other people will see it and they will give you options which is, even at the time as a scientist I was like, I wouldn't use this.

It seemed like very quick to get something up and see who would test it out but it was obvious right from the start they would actually have to create, you know, a true marketplace and have supply that was visible and actually even more so with Science Exchange, what we had to do is create a curated marketplace that operates in the B2B sector.

So what we had to do was build QAs, so quality assurance, we had to actually qualify and contract every provider that's available through the marketplace and then surface all of the results so that when somebody comes to the marketplace they can literally find what they need and they can give them the information that's required for them to be able to quote accurately because they already know there's confidentiality agreements in place.

And then they actually also had to manage those projects because these aren't just put it in your cart and check out, you have to actually manage a project with somebody who works in another part of the world from you and so all this had to be built into the software so that was many iterations of learning about where really is the value that comes from what we are providing.

And I think marketplaces have evolved a lot from being predominantly consumer focused.

There's a lot of interesting B2B examples that are emerging, and Science Exchange obviously sits very firmly in that category.

And what was more difficult, supply or demand?

So supply for us is and was pretty straightforward in the sense that providers are looking for work and they, providers of these services, they, it's a very fast growing industry so also highly fragmented and they actually also suffer the same challenges that the demand side has.

So for them, if they want to sell 1 of their projects, they actually have to go on contract with each client they work with, which can take several months.

And they have to go through quality assurance and get set up.

And so for them, if they're on Science Exchange, now all of a sudden, every 1 of our clients can work with them instantly.

It took us a lot longer to convince the large players in the market, so the large CROs, that this would be something that they should be part of, but actually we have been able to do that as well.

So I think 1 of the important lessons that we learned quickly was for this to be a really effective solution, it had to be the platform that was used for everything at the companies that were using it.

So not just one-off, come and shop on the marketplace, but actually use it as a system for managing all of the projects that are going on with the external partners.

And in order to do that, you truly do have to have all of the major players that they wanna work with on the platform.

And I've seen other companies who have B2B marketplaces struggle a little bit where they haven't had the large established players on the supply side.

And without that, I think it's very difficult to have an enterprise partnership that uses the platform.

Do you remember the very first, on the demand side, the very first customer?

I remember, actually, so our first customers were totally my friends who were scientists, who I would tell, if you have experiments you're running, you have to try out Science Exchange.

And they, actually, it was great user feedback, because for them, it did turn out to be this really high-touch, very concierge service, so they found great options that were really cost effective.

So it wasn't like, you know, they just used it because we made them.

But I think they taught us, you know, an enormous amount.

But I also remember the first customer that ever was just, you know, not related to us, like just came onto the platform and used it.

We were actually in Italy at my brother's wedding.

And I was like, oh my God, somebody used our website, yay.

Do you know, what was the experiment they were trying to run, do you remember?

So, we talk a lot about, at start of school, product market fit.

When did you know you had that and how did you define it and what metrics were you looking at?

Yeah, for us I think we defined product market fit when we had our first large pharmaceutical client actually use the platform for whole sectors of their business.

So for us that was Amgen and they launched Science Exchange for all of their discovery research, which was a really big deal for us at the time.

And we sort of realized, oh, we actually have something that they do a lot of steps manually at the moment, and this product actually automates, you know, automates providing all the information about who they should work with, who is, you know, all of these new options for new types of technologies that they might be just exploring, and then even actually their business intelligence around looking at spend and supply performance.

So those things took us a while to kind of figure out where the core value props were.

You know you're on to something when a big company who spent many years and money trying to build that problem, solve that problem internally has all of a sudden just shifted to your product and it's starting to pay you for that.

Yeah, I think, yeah, I mean, much more so, I think, in software, there's, it's really interesting because people always ask me today, like, who are your main competitors and, you know, how do you kind of think about competitive differentiation and all that, and still, to me, like, our main competitor is status quo, so that people still set up these really crazy SharePoint processes to manage their external vendors.

And I always go in there, I'm like, this is horrible.

And big companies, they just love SharePoint.

They're like, oh yeah, my SharePoint site, I have created all of these like really Byzantine processes which only apply to like 5 vendors I work with and everyone else has to go through some other crazy process.

And I'm like, okay, but you can just transition all of that onto this platform.

And it's always this interesting kind of battle of who set up that process, because trying to convince them that the software automates all that hard work is often actually 1 of our biggest challenges.

So 1 of the things I really love that you're doing, and announced a long time ago, was the reproducibility initiative.

So the idea that, it seems obvious to do, which is, you know, if you run an experiment and you publish results, someone else should be able to reproduce it to validate that it's being done.

So can you talk a little bit about why you're doing that, what you're doing, and why has this never been done?

Is it just been a money problem or is it something that only science exchange actually can do because you have the process in place?

Yeah, the Reproducibility Initiative is, I still think it's a really cool project.

It's controversial because of a lot of reasons.

I think the main reason is that people are not sure of the value of it, which I think is legitimate.

I think there's still a question of how efficient is it to replicate experiments.

And that was actually, you know, when you say is it science exchange was the only way we could do it, like our hypothesis was that, yeah, having a network where people already had all of the, you know, right assays or the right infrastructure or the right animal model would be the only way that was actually practically possible to do it.

Previously obviously people didn't have that.

They had that same challenge of, oh I have to go find somebody who has that same animal model or has that same instrument and then I have to convince them and contract them and all that kind of stuff.

So yeah, the Reproducibility Initiative actually is kind of science exchanges part of our mission to improve the quality and efficiency of scientific research.

And that project is quite broad, so it covers both things like antibody validation, which we've done a lot of, so reagent validation, things like reanalysis of epidemiology results, So we worked with the Gates Foundation to do that.

And then the projects that we're most well known for, which is actually replicating published results.

And we do that both for the pharmaceutical industry, which is way easier and way less controversial because they just come and say, hey, I'm interested in this result, and then we quickly find, you know, a facility that can replicate the key results and provide this back to them.

However, the Cancer Biology Reproducibility Project and the Prostate Cancer Foundation Project are the 2 ones that people have followed the most, which is public replications, where we've actually published the replication studies.

And that's controversial because people don't generally do this, So I think it goes against the cultural norm of really not publishing replication studies.

I also think people are really afraid and they sort of mix up failed replication with things like fraud.

So people are really afraid that if their result is found to be non-reproducible, that that will have a negative impact on their career, which I think is probably true, but it shouldn't be.

So the reality is that the results that have been generated show that most published results are not reproducible.

So if that's true, then we should try to understand that and study it, which is my opinion as a scientist, like we should study the science behind why is this the case rather than try to go after individual people and say, like, oh, your science is horrible.

Clearly it's that most things are not reproducible when they're published because of a variety of complex factors of which we've started to unpack, but I think there's still a lot of work to do.

What are like the 1 or the top 2 complex factors?

So I think the main reason like that I've seen things are not reproducible is the quality of assay validation.

So it's really interesting when you go and talk to pharmaceutical companies because they also transfer experiments.

So they replicate experiments in the sense that they'll set something up at their facility and then they'll outsource to a CRO to run that assay for them over and over again.

And they call it tech transfer or assay transfer.

And that process is incredibly efficient.

And it's got a high probability of working.

And so when you look at that, I don't think it's much different than if you take a published result and you try and rerun that assay in another lab.

But the main difference is that when you work in a pharmaceutical company, you have SOPs, you have everything documented, you look at assay variability, you look at positive and negative controls, there's just a lot more actual validation of the type, the actual experimental assay that's used before it's transferred and that's very different than in academia.

So in academia you tend to take, oh yeah, I've got this animal model in my lab, or I've got this cell line in my lab, I'm just gonna run this experiment.

And hopefully you include positive and negative control, but maybe not.

And you also don't look at things like variability of the assay, reproducibility of the assay.

And I think a lot of what we see in published results is likely to be noise rather than true experimental effects.

And so until we kind of understand that more, we will have an issue with reproducing those results.

You're saving the world, so save us from this problem.

So going back to Science Exchange, what is the biggest challenge as a CEO of a marketplace company that you have today?

And how has that challenge morphed over time?

Like what was the biggest challenge when you just started to what is the biggest challenge today?

Oh yeah, I'm sure like so many challenges along the way.

When we first started Science Exchange, the biggest challenge was, can we get anybody to use it?

I think that was kind of the first challenge.

And then can we build something where the very conservative pharmaceutical industry will use it.

And that was important for us because when you look at spend breakdown, the market is predominantly for outsourced research focused in the pharmaceutical industry.

So that they control over $100 billion of spend per year and so if we couldn't get them to use the platform, we would have a lot of issue with really creating a truly large company.

Today I think our biggest issues are more around scaling.

So how do we, we're still only 85 people.

We've just signed a couple of extremely large partnerships and so we're really trying to execute against some pretty tight deadlines with staff that are focused on just 1 of these large integrations.

And so trying to kind of make sure that we don't take on too much while also realizing there's enormous opportunities so we don't want to sort of overlook that.

So I think just staying focused, executing on the right things, and being really disciplined about that, which is always really hard when you have multiple opportunities that you can go after.

I think that discipline is incredibly important.

Yeah, focus is really hard, especially as you get more employees and you get more funding and stuff like that.

So, I want to switch gears a little bit and talk about biotech and startups.

As we talked about earlier, there has been this implosion of biotech startups recently, especially in Silicon Valley.

Yeah, the biotech industry is so interesting right now and it's actually amazing.

It's not just here in Silicon Valley, it's everywhere.

So the UK, Cambridge, San Diego, you know, everywhere there's like lots and lots of biotech startups and I think it's a really cool time for that sector of the industry.

1, capital, so access to capital is unprecedented and the amount of capital that's going into biotech is definitely unprecedented.

There's also, you know, a sort of interesting evolution in terms of where biology is in terms of the actual therapeutic modalities that are available.

So I'm not sure that people who are outside the industry may not kind of recognize this as a real turning point for biotech.

So up until very recently, there was really only small molecule inhibitors and then after that, there was Genentech and Amgen, so there was actual biologics, so antibodies and proteins.

But just in the last year, we had gene therapies approved, we had cell-based therapies approved, we had RNAi approved as actual marketable products for the first time.

So all of a sudden, you have this huge window opened up for you and the different types of approaches you can take that are viable to create a commercial product.

So I think the science has caught up to a point where there's just some phenomenal opportunities to tackle diseases in a way that was not possible previously.

Yeah, it's a really, really exciting time.

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A Conversation with Elizabeth Iorns - Advice for Biotech Founders