00:00welcome to the a 16z podcast today we're
00:03having another of our holy style
00:04conversations based on videos that are
00:06also available on our YouTube channel at
00:08youtube.com slash c / a 16z videos
00:12how can bio and therapeutics founders
00:15learn to speak the languages of tech and
00:16bio in this hallway cell a conversation
00:19a 16 z bio team partners including
00:21general partners Jorge Conte and BJ
00:23Pandi with Geoff lo discussed the
00:25mindset shifts involved in building bio
00:27companies therapeutics companies in
00:29particular they cover paths to market
00:31timelines and more so welcome here today
00:35we have the a 16 Z bio team Vijay Pandey
00:38Jorge Conde and Jeffrey Lowe joining and
00:42what we want to talk about today is some
00:45of the challenges for entrepreneurs
00:48investing and building companies in the
00:50bio space broadly and especially in ways
00:53that that might deliver a differ a bit
00:55from the tech in the tech companies the
00:57traditional tech companies and certainly
00:59I want to make sure we focus on many of
01:01the common pitfalls that we see
01:03entrepreneurs have stumbled into in this
01:07Jeffrey you spent a lot of time thinking
01:10about that in your mind what do you
01:12think is the top issue that an
01:15entrepreneur in this space faces when
01:17they start to think about innovating and
01:20building a company in the bio space I
01:21think it's about speaking a new language
01:22so you have you know tech entrepreneurs
01:25and technical founders coming in and
01:27speaking a language that is very geared
01:29toward a tech audience having a new
01:31technology and I think what we're so
01:33interested in is bringing these new
01:34technologies to the bio space and how do
01:37you translate that to your partners how
01:39do you translate that to your potential
01:40funders how do you translate that to the
01:42media because there's something new here
01:44and and something new and different is
01:46something that they often have
01:48difficulty translating to people doing
01:50something used to doing something in the
01:52same way it's previously yeah or even I
01:54mean some of these founders while
01:56they're in computer science or
01:57engineering they've actually never
01:59founded a company before so they're not
02:01even familiar with how the tech side
02:02works either way there's tons of
02:04pitfalls going into the bio space and
02:06what do you think is different about
02:08these this class of companies that you
02:10know makes it interesting in the first
02:12I mean first off is that often these
02:14companies have to prove that their
02:16technology works and as useful and the
02:19elements necessary for proof are
02:21relatively high bar high barrier to pass
02:23and so you know you could get early
02:26proof-of-concept deals and that will
02:27look good you might have five deals and
02:29I'll be like a couple hundred K each but
02:31in reality those are so easy to get that
02:34that's maybe it sounds more impressive
02:35than it is getting really big up fronts
02:38and obviously big Baba collect deals
02:39that's a whole different game what do
02:41you think is different this time
02:42computational techniques and in drug
02:44discovery have been around for quite a
02:46long time you know what's exciting about
02:47what's going on now and what's different
02:50this time yeah you know you think about
02:51is that uh that people point to AI and
02:54machine learning but really that's I
02:56think just a surrogate for the fact that
02:58data is being used very efficiently and
03:00we have so much more data and we do have
03:02techniques that we didn't exist before
03:04but in many ways is it's the data that
03:06makes things different and that we see
03:09pharma companies and biotechs and
03:11startups seeking themselves not as
03:12biotechs but as eventually I think as
03:15data science companies and that become
03:17that's a very huge sort of mindset shift
03:19yeah and I think on that on that point
03:21it's a really interesting moment in time
03:23because and one of the big challenges
03:24that startups have had in this space
03:26traditionally is that if the time to get
03:30to a validation point or a value
03:33inflection point more specifically is
03:35very long and risky and time-consuming
03:37you do have this temptation to start
03:40with a pilot and I think the trying to
03:42get a bunch of pilot deals has multiple
03:45pitfalls associated with it first of all
03:46they tend to be relatively small upfront
03:48and so you have to generate a lot of
03:50them to actually generate enough
03:52additional cash runway to drive you
03:54forward but second of all most pilot Pro
03:57projects come with expectations
03:59associated with them and so you know I
04:01don't think it's unique to the bio space
04:03but this concept of death by a thousand
04:05pilots is a real one and so if you're
04:07trying to find a company in this space
04:10and trying to build a company this space
04:12I should say you have to be very careful
04:14about who you decide to work with on the
04:16pilot stamps on the pilot side because
04:19not only do you run the risk of working
04:21on a lot of small projects small
04:23projects with large companies tend to
04:26creep and timeline creep and so if you
04:29were you thought you would finish pilot
04:31you know pilot project and get paid x
04:33and y amount of time oftentimes you're
04:36getting paid X in something like 2 X 2
04:40times the Y amount of time and you're
04:42also working a lot harder if there's
04:43been scope creep so it gets really
04:44really challenging to deliver on
04:47multiple pilot projects in one given
04:51time so what's the solution to that
04:53pitfall I mean do you just not do the
04:54pilot projects and wait for the bigger
04:55deals you try to execute them
04:57differently well one thing I noticed is
04:59I think this is where a lot of these
05:00companies start is they start with
05:02pilots and they start as a service
05:04company they start by saying hey I'm
05:06gonna go to Big Pharma I have a new
05:08technological breakthrough I'm gonna
05:10sell that as a pilot as a service to big
05:13pharma companies and then they can't
05:15generate the economics that it would
05:17make for a sustainable business and then
05:18they're they're really in a sort of
05:20tough spot and what ultimately you know
05:22they go through this idea maze and you
05:24know that what they decide to do often
05:26is hey I need to develop assets in-house
05:28that is because in the bio space value
05:31is created in these huge technical
05:34milestones in the very beginning where a
05:36lot of these services are used these are
05:38preclinical assets in which there's
05:40really not too much value until you
05:42bring a drug candidate into the clinic
05:44so these companies go and say if I want
05:46to capture the value for my technology I
05:48need to advance this asset a little bit
05:50further down and then they get into the
05:52space which say well I now I'm
05:53developing drugs yeah and I think so I
05:58think it's exactly right to go back to
05:59to Vijay's comment or question rather
06:01and how do you how do you address this
06:03issue of the death by a thousand pilots
06:06the first one is you have to be very
06:08selective with who you work with in
06:12other words it has to be with and this
06:14could be a different company in any
06:15given in different contexts but it has
06:18to be a company that fundamentally
06:19believes in what you're doing and and
06:22that if you can get to a proof point for
06:25them you are immensely valuable for them
06:28the big risk is that everyone does the
06:31you know I'll just do a you know the
06:32free sampling yeah I'm alright I'll try
06:34a little bit of everything that's always
06:35fun and that's yeah it's fun if you're
06:37sampling or if you have to make all the
06:39um and so I think that's the the big
06:42challenge you have to be very careful
06:43with who you you know you align with on
06:45I think Geoffrey's point which I think
06:46is exactly right is you also have to be
06:49very thoughtful about where you are and
06:50the value chain and so you know there
06:52have been there were a lot of bio
06:53companies for example that came around
06:55around really really neat science but
06:57that the end for the science was to for
07:00example identify a novel target and in
07:05this case we're talking about a novel
07:06target we're talking about the piece of
07:08biology that you want a drug to to in or
07:11to intersect with to impact that the the
07:13progression of the disease and so the
07:15problem with that is pharma companies
07:17are swimming in targets right the
07:20problem they're trying to solve for is
07:21how do I fill in my pipeline yeah and
07:23often they think target should be free
07:24anyway exactly and then a lot of people
07:26competitive efforts to make targets free
07:27and basically win on on the third on the
07:29drug development piece and so if your
07:32platform is to identify novel targets
07:34it's gonna actually be very hard that's
07:35sort of the end of the line for you what
07:38your technology can do it's really hard
07:40to capture value there and certainly
07:43hard to get that from a collaboration
07:44standpoint if on the other hand you
07:46somehow had a magical tool that could
07:48predict which drug was likely to fail in
07:50a phase three trial you can imagine that
07:52that you'd be able to capture a lot of
07:54value there so it really is where you
07:55are in the value chain well another
07:57pitfall I see is that you and I see
07:59those many different types of
08:00technologies it could be you know
08:02machine learning or compute or it could
08:03be like a new type of structural biology
08:05technique whatever the technology is
08:06they got the cool technology they think
08:09this will change drug design and so then
08:10they go to farm they try sell it and
08:12farmers not convinced yet it's like my
08:15kids with new foods and I don't want to
08:17try it until I really know that they
08:18really like it and then they love it and
08:21goes but the beginning is really hard
08:22and so then you decide okay we can't try
08:24it we can't sell to Pharma for what we
08:26think it's worth so we're gonna design
08:28drugs but we don't know anything about
08:29designing drugs and and and then that
08:32can also be a very dangerous road so I
08:33don't know how you deal with that
08:35pitfall do you just sort of try to stay
08:37away from that from the beginning and
08:38stand Oh sir honestly never cross that
08:40line or do you plan to do that from the
08:42beginning yeah well I you know Jeffrey
08:45alluded to this a few minutes back I
08:47think if you look at the history of sort
08:49of the biotech startup ecosystem
08:53you know a relatively common journey is
08:55that is say I have this interesting
08:57technology or platform I'm gonna do a
09:00couple of early-stage business
09:01development deals and then my grand
09:03vision and hope is it eventually so I
09:05can develop drugs for them for my
09:06partners and capture some piece of the
09:09economics of a downstream value but my
09:11ultimate vision and goal for my company
09:13is that I'll eventually get into
09:14developing my own drugs and where I'll
09:16keep you know all of the economics for
09:18the majority of the economics and it's
09:20that's a really hard sort of transition
09:23to make and frankly not many good
09:25company and the teams aren't set up for
09:27and they aren't set up for it so I would
09:29say there's two things here one is going
09:31back to the pilot concept that who you
09:32partner with matters what they want how
09:35they value and how they see what value
09:37you bring to the table matters which I
09:39think leads to a second point which is
09:41business development in the bio space is
09:43a fundamentally strategic advantage to
09:48have if you have a team that is good at
09:51structuring business development deals
09:53in other words that has experience in
09:54the space that allows you to actually
09:56help bridge what is often a very fatal
09:59chasm for a lot of companies how to
10:01figure out how to set up early stage
10:02deals with pharma companies where you're
10:06getting value from the larger partner
10:08because the larger partner obviously can
10:10deliver a lot of value how you're
10:12figuring out ways that you can capture
10:14some of the downstream value if your
10:16approach works right so this you know
10:18downstream milestones and royalties and
10:19all of those things and importantly how
10:21to make sure that you structure these
10:22things in a way that you haven't fully
10:24encumbered your platform so that you can
10:26in fact do things on your own in the
10:28future if you choose to or you do thing
10:31with do things with other partners in
10:32the future if it makes sense right I
10:34mean well you know the pitfalls at the
10:36platform sometimes is the thing that's
10:38we find very exciting huh but then the
10:40company sort of has finally actually get
10:43through all the things we talked about
10:44and now they gotta deal with the fact
10:45that that they were successful and with
10:48this one asset and now maybe they're
10:49tempted to ditch to the platform and I
10:52could see the value and the surge is
10:53pushing ahead with what works but you
10:55know how we've sort of balanced that
10:56especially nowadays where the platform
10:58really could be really valuable if these
11:00technologies are are as powerful as we
11:02think they could be well I've certainly
11:04seen that in in you know in
11:06the first-hand experience where you have
11:09a platform that could be very valuable
11:10and precisely because it's a productive
11:13platform you find a an asset in this
11:15case a specific potential drug and very
11:18quickly all focus goes towards you know
11:21how we make sure you maximize the value
11:22minimize the risk and successfully
11:25commercializing said drug and it's a
11:27really interesting thing and it happens
11:29almost overnight then as soon as you
11:31have that all of the conversations start
11:33to focus on well we could always use
11:35more resources on making sure that the
11:38drug program succeeds and there's only a
11:40fixed pool of resources generally
11:42speaking and so what ends up happening
11:43is the platform gets start and I think
11:44that happens time and time again I think
11:47one way to address that is of course
11:49through business development
11:49partnerships and even then you don't
11:53entirely alleviate the pressure on
11:56funding the program ahead of the
11:58platform unless you've specifically
11:59structured your business development
12:01agreement to fully fund the program
12:02right I mean that's the that's and
12:04that's hard to do especially if you're
12:05not a stage company but a second way to
12:07do that is through innovative structures
12:09and so we've seen this in a couple of
12:11examples where companies develop an LLC
12:13structure and they basically are able to
12:16say for investors that are really the
12:18believe in the future of this platform
12:20and the productivity of the platform to
12:23not just develop an one drug asset but
12:26multiple drug assets over time that
12:28becomes sort of a parent company of the
12:30and then you have separate LLC's under
12:33that that you basically say for drug
12:35asset a I'll bring in new investors and
12:38new investors are essentially betting
12:39primarily on the success of that drug
12:41asset and so the the the parent company
12:44has taken a much smaller piece of the
12:46economics but they can now replicate it
12:48many many times because they have the
12:49resources to invest in the platform so
12:52ensure that future products let's be
12:53clear why we do this
12:55a unvalidated platform really doesn't
12:58have a lot of value and that's why
12:59there's on the first asset you know the
13:01whole company's value may be riding on
13:03this asset because not only is that
13:04asset itself valuable but it also
13:06validates the the efficacy and
13:08usefulness of the platform itself and
13:11then you're kind of in this in this
13:12state where you might have to say well I
13:15am now a single asset bet because of my
13:17first asset from my platform fails well
13:20throw out a good platform with a bad
13:22asset or maybe I just have a bad
13:24platform you know and a bad asset some
13:26of these other legal structures or kind
13:29of ways to get around that but you know
13:31doesn't get around this problem of hey
13:33the first asset validates the platform
13:35and its success or failure you know
13:38means a lot for this business going
13:39forward yeah and we and we've spoken a
13:42little bit about this before and I think
13:44it's an important point to bring up is
13:46that there are platforms that generate a
13:53and therefore you don't know their
13:55unvalidated by definition because you
13:57have one drug yes and so until that drug
13:58acid is approved you don't really know
14:00if the platform was valuable or not and
14:02so therefore all the value does accrue
14:03and all the risk does accrue to that
14:05that lead-acid but then there are
14:07platforms that could be so fundamental
14:09in understanding biology and so
14:11generalizable that you know that you
14:15actually want a structure a deal in such
14:17a way that someone that only believes in
14:19assets with fund that and you still
14:22leave room for folks that believe in the
14:24platform to support them now it's hard
14:26to know a priori which is which and I
14:28think that's one of the big challenges
14:29here our belief or at least I think I
14:32speak collectively for the team is that
14:34you know for platforms that have a
14:36engineering like been to them they're
14:39more likely to fall in the second camp
14:41than the first but that's obviously the
14:44the hope and the bet that we're making
14:46in the entrepreneurs and the companies
14:47that we're supporting but yeah I think
14:49that's it's gonna it remains to be seen
14:51how over time you make sure that you're
14:53very clear on what kind of platform
14:55you're dealing with when you're making
14:57it we've been talking to some
14:57generalities here you know Vijay you're
14:59involved pretty early on in Schroedinger
15:00which had a very successful
15:02collaboration with Nimbus you know maybe
15:04you could talk about that example is one
15:06where the the legal structure was you
15:08know really successful made helped make
15:10that company successful yeah I mean in
15:11this was I think when the early
15:13companies have thought about this LLC
15:14structure and it's kind of interesting
15:15that pitfalls can even be just and how
15:18do you structure the company you'd think
15:20a C core would be a pretty standard
15:21thing to do these days
15:23and so I mean I think it's so early and
15:25I think it's a new thing and I therefore
15:28the venture community and stand star
15:30Bunch members will need to get
15:31socialized to this and that will have
15:33in time you know I think we've been
15:34spent a lot of time talking about
15:35therapeutics I think you know there's
15:37also analogous pitfalls in other areas I
15:39can in diagnostics and especially the
15:41bottle fund we're interested in biology
15:43quite broadly and other sort of
15:45applications when I think about that I
15:47mean there's some applications that
15:48might look more like tech companies
15:50depending on how they're built but some
15:52things like Diagnostics will still have
15:54to get through regulatory agencies all
15:56right they're Clio or FDA and there's a
15:58whole bunch of pitfalls just there and I
16:00think a lot of times what we're seeing
16:01is that it's very important to address
16:03those pitfalls by understating the
16:04marillion and handling them so I mean we
16:06could talk a little bit about the
16:07pitfalls that we see in that diagnostic
16:08space so what why don't we start there
16:11what do you ask in in diagnostic
16:14specific yeah ironically I think you
16:15know most people think about the FDA or
16:18CLIA being your big concern
16:20I think reimbursement is part the first
16:22place to start because I wouldn't want
16:23to sort of be designing a test without
16:25having a confidence that would get
16:26reimbursed otherwise why bother doing
16:28the whole thing and so you know when
16:31I've been involved in drug design we
16:33usually start with thinking about how
16:34we're gonna run the clinical trial and
16:36then work backwards on the drug design
16:38side I want to do the same thing on the
16:40diagnostic but start to even further
16:41upstream and downstream talk about how
16:44we gonna get reimbursed what's the value
16:45that we're going to add and if that's
16:47there than the rest actually we can make
16:48our arguments for and and when there's
16:51time early on especially when tech is so
16:53powerful that you could go of lots of
16:54different avenues and early it's sort of
16:56directing where you want to take the
16:57ship thinking about that won't get you
16:59the right place long term rather than
17:01getting the wrong place and then having
17:02to figure out how to pivot from there
17:03maybe you did helpful if you backed up
17:05and said well how is uh what would a
17:08company that's a Diagnostics company
17:09that's tech driven our don't like a tech
17:12company look like and how would that be
17:13different from a biology driven
17:15diagnostics company yeah I mean as a
17:17great point I think one things that's we
17:18see that's really different is that when
17:20you can engineer biology you don't have
17:22this very bespoke process of sort of
17:24having scientific discovery but you have
17:26the ability to engineer a process and
17:27then repeat this in different
17:28indications so if you're having a cancer
17:30test it could be for whichever cancer
17:33you know indication you want based on
17:35the data that you have so why not pick
17:37an indication where you feel like the
17:38go-to-market is strongest and for that
17:41it's now talking thinking about the
17:42go-to-market at the very least earliest
17:44of stages so to just
17:46to take your words out of context a
17:49little bit earliest of stages and
17:51diagnostics and you're talking about go
17:52to market with a reimbursement one of
17:55the challenges and I think this is more
17:57a thought experiment than sort of how
17:59hard data but one of the challenges that
18:02at least I've heard about the early
18:04stage screening type diagnostics and
18:06reimbursement is that a lot of patients
18:08don't stay on a plan for a long period
18:10of time and so getting broad-based
18:12reimbursement for early screening may
18:14not make sense from an ROI perspective
18:15for their payer how do we get around
18:19that question yeah this is one the
18:22fundamental questions in healthcare
18:23because how do you pay for things where
18:25the ROI is is back-loaded like five or
18:28ten years and so if your insurance
18:29company maybe that doesn't make sense
18:31for your economics there's a couple
18:32different ways one is that I mean
18:34outside the u.s. obviously things are
18:35different so that's like that cheating
18:37answer that you don't get they only get
18:39partial credit for I think the maybe a
18:42somewhat deeper answer is to think about
18:45that self-insured payer sorry some
18:48employers might be a little more
18:50motivated because while people may
18:51change plans they've changed jobs
18:53slightly less frequently but I think you
18:55know I think we've talked about this
18:57we've seen other sort of more
18:58interesting financial mechanisms that
19:00are coming on board where insurance
19:02companies can find ways to make these
19:05incentives and I think we'll have to
19:06change how paying is done but at least
19:08the different proposals for that yeah
19:10and I think that's that that was one of
19:12the interesting ones that I've sort of
19:13heard thrown around is the idea you know
19:15we talk a lot about doing pilot projects
19:17with you know pharma companies if you
19:18try and develop a therapeutic there is
19:20this sort of tantalizing potential to do
19:24pilot projects with insurers or payers
19:26if you're trying to develop something
19:28that they need to prove the ROI ROI out
19:31for themselves and that gets really
19:33interesting because then you can start
19:34with the end in mind as you sort of laid
19:35out yeah great well you know so we
19:38covered therapeutics and diagnostics
19:40maybe one last thing we could talk about
19:41then we're maybe running close to time
19:43is sort of biology more broadly like so
19:45maybe you're not doing something that's
19:48going to sort of connect with human
19:50health so she'd have to get it done but
19:52like you're Alesi a company designing
19:53bacteria to do something new the
19:56bacteria is not be ingested by a human
19:57or anything like that
19:59now there's sort of different challenges
20:01I mean there's things that may be
20:02tempting to look like a temp company
20:04because you may have text behind it but
20:06I and the deals could be really large I
20:08think this maybe just comes us takes us
20:10back full circle to what we talked about
20:11on the therapeutic side that the POC
20:13still become a problem yeah and I think
20:16it's I think those those are big I mean
20:17that is a big challenge on the
20:19therapeutic side and it's a big
20:20challenge on sort of the broader
20:21platform you know biology side and to us
20:25or at least you know as we've talked
20:27about it as a group collectively I think
20:28what's really interesting is what do you
20:30do with that challenge right so one way
20:32to do it is to say it now to go all the
20:35way back to the beginning of this
20:35conversation is to say well maybe you do
20:38start off as a service model or you know
20:41in the case of you know I don't know
20:43what a box a magic box that can make
20:45engineer bacteria would look like but
20:46let's assume that this is something you
20:47could productize as an instrument kind
20:50of like what Illumina has done where you
20:51sort of now go into this box and and
20:54sort of consumables model and what you
20:57do there is you basically start and
20:58yourself to the high end of the market
20:59first right so we don't you know what we
21:02saw with happen with sequencing was that
21:04obviously in the very early days when
21:07you know the the throughput of the
21:09sequencer was low where the cost was
21:11very high the only natural buyer for
21:13that was you know large research
21:15institutions and those sort of became
21:17the initial sites and as the as the you
21:19know sort of performance got better the
21:22market opened up and opened up and
21:23opened up and of course you know today
21:25if you can get a benchtop sequencer for
21:27relatively low amount of money and
21:29basically any lab can afford that and so
21:31I think the same is true when you have
21:34these other technology platforms so the
21:35big question becomes how do you get to
21:38fundamental POC in the case of
21:40sequencing it was straightforward you
21:41could sequence known DNA and basically
21:44show that you can generate that same
21:45result in the case of sort of let's take
21:48your you know engineered bacteria is you
21:51would want to design a simple set of
21:53experiments and I'll go back to your
21:54Apollo mission sort of analogy right
21:56where you put a set of experiments that
21:58say I can design basic functionality
22:01it's going to have very high
22:01predictability and then over time the
22:04level of complexity that you can design
22:06into a system goes up or the throughput
22:08goes up or the quality goes up or the
22:10cost comes down and so they're sort
22:12these various variables that folks I
22:14think feel comfortable around you can
22:16start to sort of march towards Inc you
22:19know increased proof points and
22:22eventually get into what you know POC
22:24and the other thing I'd mention is POC
22:26is not the same for all parties that's
22:28right so you'll have early adopter POC
22:30is obviously a near-term target then
22:32your late adopter POC and so you should
22:34always design for the early adopter POC
22:35with a clear path to how you can
22:37actually eventually engineer in all the
22:39way to the end of the market now the one
22:40thing I would say I think this is true
22:42for all entrepreneurs in this space and
22:44this has been my humble experience is
22:45you know if you're developing technology
22:48in the biology space you should know
22:51what your near-term killer experiment is
22:54and the experiment that if it does not
22:56work it's it's you know kill mode for
22:59whatever you're developing and you
23:01should know what that is you should have
23:02in your mind what could I show in the
23:04next you know six to 12 months that
23:07would you know essentially cause me to
23:08kill this idea I don't know CA you
23:10should know what it is and B you should
23:13be doing that experiment the number of
23:15times I've talked to entrepreneur and
23:16that's scary because it's a very
23:17essential thing but that's exactly why
23:19you got to do it exactly the number of
23:21times that I've talked entrepreneurs
23:22that have said well yes that we think
23:24that's a big experiment we have all this
23:25other stuff going on so we're gonna get
23:28to it and that's I think you know for
23:30everyone's sake it's important to do the
23:32kill you know the kill test early yeah
23:34yeah so that's that's always been my
23:36view on there's POC and then well
23:38there's the equivalent of proof Tim you
23:40know it's proof of concept and then you
23:41know it proved to me that this hasn't
23:42failed yes you know in a fundamental
23:44mode and so you should know what that
23:46looks like and do it early so maybe one
23:47last pitfall we could talk about is just
23:49pitfall for getting yourself funded so
23:51if you think about like three polls we
23:53got consumer Enterprise and biotech and
23:55the space that we're talking about
23:57sometimes looks more or it's like some
23:59combination of those you might have a
24:02company that's using technology and
24:04healthcare but direct-to-consumer
24:05something that's more Enterprise focused
24:07something that is maybe more therapeutic
24:09or diagnostic focused and each one of
24:12those has fairly different ways of
24:13proving to investors that you're making
24:15progress you know consumer maybe it's
24:17about the graph you know da you versus
24:19Mau and so on for enterprise might be
24:22revenue for biotech monthly hitting
24:23milestones and so a lot of the
24:26year I think is uh you know trying to
24:27figure out where you know the pitfalls
24:29are figuring out work how can you prove
24:31that that's really working when is some
24:34complicated mix of all these things and
24:35then which is the best investor actually
24:37at each stage of the business so I mean
24:39there there could be periods of time
24:41where it's you know much more tech like
24:43building a platform and there are
24:44periods of time where there might be a
24:46lot of science risk and you know tech
24:48investors I mean ultimately they're just
24:49not set up to take on science risk in
24:52the way that biotech investors you know
24:53have arranged themselves so you know how
24:55do these companies go and pick which
24:57thing it is that they should be doing it
24:59and who they should get money from yeah
25:03I would say a couple of things one is
25:04you know in some cases it might make
25:07sense to actually have a hybrid right
25:09because these companies are hybrid I
25:10just think of what exactly spends their
25:14time thinking at they're they at both
25:15levels right from the bio side and from
25:17the sort of the the the the more tech
25:19side and it also makes sense to think
25:22about how you would build up your
25:23investors syndicate right and so I think
25:25one thing that's a truism for all
25:26entrepreneurs is when you're raising
25:28this round you should always be thinking
25:29about what the next round is going to
25:31look like and that's not just in terms
25:33of what you know valuation metrics are
25:35worth to hit what milestones you hope to
25:37clear to make sure you hit the
25:38inflection points to allow you to hit
25:39the valuation metrics but also who's
25:41involved today and who might be involved
25:43tomorrow and you know one thing that
25:45we've seen work before is you know you
25:47see a traditional bio investor connect
25:50with the traditional tech investor so
25:51they can each side can help the other
25:53know what they don't know and sort of
25:56get comfortable and then as the sort of
25:58company gets more mature that mix of the
26:00syndicate might change either becomes
26:01more traditional bio a more traditional
26:03tech and then eventually you get to a
26:06course late stage type investors so
26:07that's one way to approach it the other
26:09one is to go back to where you started
26:10Vijay which is to be very clear on what
26:13metrics do you think you can prove over
26:16the course of whatever the funding
26:17amount of the funding period is the
26:19runway and then base your investors on
26:22what you're going to prove because again
26:24different audiences care about different
26:27pocs whether it's the graph or the
26:29milestone or what have you and I think
26:31being very clear to yourself about that
26:33upfront should help you define how you
26:35seek an investor base and then the final
26:38point I would make is I think the
26:40comment that VG excuse me that Jeffrey
26:42made which is sometimes we speak to two
26:45different languages and so you know you
26:48need to be if you if you're not fluent
26:50in two languages you should definitely
26:52be fluent in one and you know functional
26:54and the other and I think it's important
26:56to understand what the two languages are
26:57so that you can ensure that if you
26:59intend to bring bio investors online in
27:02the future they do set up a company and
27:04set up a sort of a common set of
27:06languages and ideas that they can
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