SPEAKER_00: we created interval which is a gamified running app you run around the block and you claim territory on a live global map people are just so much more motivated to go out and do that activity when they get a notification that their territory has just been stolen it becomes quite personal

David Friedberg: you're taking the competitive spirit you're taking the slot machine nature of apps and smartphones

SPEAKER_05: and you're using it for good all right everybody welcome back to twist we're talking to the founder

SPEAKER_06: of interval it is a running app that's gamified jason so you don't just do your daily run you claim the territory around which you've run uh and so it turns it into sort of a social community uh you know sort of feature let's meet the founder louis phillips louis louis how are you louis very well

SPEAKER_02: thanks thanks so much for having me on really excited thank you so much for showing up great

SPEAKER_09: studio there louis is in australia jason so it is the middle of the night hold on let me hear the

SPEAKER_11: accent say the quick brown fox jumped over the lazy dog go ahead let me hear it the quick brown

SPEAKER_12: fox jumped over the lazy dog louis seems kind of tough yeah so i was gonna go with melbourne

SPEAKER_16: but he's not that tough he seems kind of nice more like a brisbane guy you're thinking now no it's the

Chamath Palihapitiya: sydney guys are a little softer on all right so i'm gonna go sydney where are you from i'm from melbourne

SPEAKER_23: i'm calling calling in from melbourne so you're you're performative right now you're being a little professional but you talk how you actually talk exactly no this is this is it this is how i actually

SPEAKER_26: talk um how about if i said hey mate can you get the out of the way and let me get to the bathroom

SPEAKER_00: what would you say yeah i feel like i'm home i feel like i'm i was actually born in western australia

SPEAKER_29: which is wow you notice the difference loud you hear him now yeah a little bit let it down yeah this is a melbourne guy trying to sound fancy like the sydney guys yeah you should just embrace your melbourne

SPEAKER_33: you ever see this uh mr nobody i haven't no are you talking about mr nobody or mr in between

SPEAKER_06: the guy who's like a hitman gangster wasn't that mr in between i think that that's the australian series yeah yeah the scott ryan i'm pretty sure that's what you're thinking of oh because you told me to watch it you you were like lon you guys yeah it's mr in between

SPEAKER_33: look at this guy this is the classic melbourne guy yeah nice i have i've seen shorts of him on on tick

SPEAKER_24: tock that's that's no but that's parts of melbourne for sure no this this is your classic melbourne

SPEAKER_29: ryan you shaved ryan is that guy's name and this guy stopped doing it he's got the greatest character of all time this character is literally the level of tony soprano or walter white wow

SPEAKER_33: high praise breaking bad i praise or the guy in the shield what's the guy from the shield um oh oh oh god vick something vick mackie vick mackie of course how did i forget if you want a canonical

SPEAKER_44: tough guy anti-hero this guy is so tough they need to make a crossover between him and walter white for a series where like one's trying to get walter white's dead the breaking bad spoilers folks

SPEAKER_56: maybe or you know maybe you could do an integration all right all right luis we've uh a little that's

SPEAKER_57: just a little australian shenanigans i've missed australia you know we used to have a great partnership with uh sydney yeah and we would do launch festival there and i'm uh considering bringing founder university back to australia or new zealand us yes i just love going there i've never been yeah

SPEAKER_33: i've never been uh hamilton island great barrier reef you're in some good spots there oh man how

SPEAKER_00: great is that man have you been up there cairns uh i've never been to cairns i've been to hamilton

SPEAKER_66: island though and that's essential tell them about hamilton just yeah hamilton island is a beautiful

SPEAKER_00: island off the kind of coast of queensland um and so it's in the pacific ocean and it is absolutely stunning it's just classic kind of australian tropical kind of beachy uh it's the ultimate relaxation spot um i think they just got acquired by they did yeah i don't know who bought it to go

SPEAKER_28: through yeah it was private equity uh i'm pretty sure so it was owned by a family uh some family

SPEAKER_57: owned this island and i went there on vacation one time when i was in sydney for launch festival and then we went there and i rented a little boat and we did a little scuba diving trip and we brought like

SPEAKER_25: eight of us on a like overnight thing but hamilton island sunday beaches black sundays if you pull

SPEAKER_23: up with sundays with sunday's beaches is the most beautiful beach on planet earth according to the people who go out and gallivant around the world incredible have you have you hit the wit sundays

SPEAKER_00: oh yeah well i mean that's it's kind of in the wit sundays but yeah i've i've been there the other one is you know i'm i was born there but western australia i'd say that is like peak australian kind of postcard if you if you ever get a chance i'd recommend heading across it's a long flight but

SPEAKER_80: uh what is it six hours seven hours to get from the east to the west it's it's like four and a half on

SPEAKER_00: the way there three and a half on the way back because you've got the wind okay so it's basically

SPEAKER_81: like going from california to new york something like that that's not exactly yeah yeah yeah all right

SPEAKER_85: thanks for tuning into this week in australia blackstone the private equity firm they bought

SPEAKER_06: hamilton island in december 2025 for 1.2 billion australian dollars it's about 804 million u.s

SPEAKER_89: i mean i kind of think bezo should have bought it if it's if that's the price i would have he could afford it why not that i mean it's unbelievable when you go there beautiful but i want to go to

SPEAKER_93: the west because that's like raw right the west coast is raw red dirt that's proper australia that's where you'll see you know the types that we spoke about before in that tv show that's that's

SPEAKER_57: proper proper in other words if you were part of the penal colony that's kind of where you stayed

SPEAKER_99: you didn't go to these fancy dancy cities to get your flat white exactly you're right kangaroo and your bowl i'm gonna flat white in a bowl bowl culture you know about bowl culture alan i don't

SPEAKER_57: i don't know what you're talking about so australians started like bowl culture you go for breakfast or lunch they have bowls the bowl's got a little quinoa it's got a little salmon it got a little this little of that everybody likes to eat a bowl okay you know we have sandwich culture and

SPEAKER_105: sammy culture here in the united states we also kind of have a bowl there's like a lot of cribbed it poke bowls and uh you know we cribbed it from australia i didn't realize i didn't realize

SPEAKER_108: where's your favorite flat white where's your favorite bowl yeah yeah well as acai balls is big here uh it's original kind of like that breakfast yeah um and then favorite spot i mean we just have

SPEAKER_28: the best coffee here in melbourne that's what we're known for um so any coffee shop you can't beat it

SPEAKER_110: we love a flat white yeah yeah flat white or off is uh basically they're british those guys are pretty

SPEAKER_113: pretty much if you want a cappuccino go to back to italy or new jersey all right yeah why don't you

SPEAKER_114: show us show us what you built sure sure sure absolutely i'll share my screen and i can kind of

SPEAKER_00: um walk us through it so we created interval which is a gamified running app um it's essentially a game where you run around the block and you claim territory on a live global map um for example we are here in austin for those that are watching and there's the lake all those different colors are different people's territories so we can see here if we click on this specific run michael has gone for a 67 kilometer run i think that's around like 40 miles yeah wow and he's captured a lot of austin so what happens is michael went out for his run in the morning and he aimed to do 40 miles he ran around a perimeter wherever he decided to run and then finished his run within 200 meters of where he started after he pressed stop he uh claimed that territory so everyone inside of his territory gets notified that their territory has just been stolen so a pretty simple concept a global game of turf wars as you can see we've also got kind of leaderboards where the goal is to climb the leaderboards michael is obviously the king of the area in austin uh with a fair few following as well um on top of that we have a complete like community feed where people kind of upload you know different posts and stuff there's some very funny ones you can chuck things that like comment on them uh and so on so a pretty simple concept that seems to work really well and the reason we brought it to market was we found that no one else has done this concept as well as what we could have done we found the types of people who tend to do it we're kind of like into medieval games and different kind of you know very computer game-esque where we wanted to take that strava level ui and ux and implement that into a cool game that people can

SPEAKER_118: use day to day uh which has led us to so do i win by making a longer run and encircling him at plus 10k

David Friedberg: kilometers or can i just do another circle within his and beat his speed maybe because there are multiple

SPEAKER_120: vectors for running one is distance one is speed absolutely so right now there is nothing for

SPEAKER_28: the speed inside interval which was intentional we love an exclusive here on the pod and i have an

SPEAKER_122: amazing deal to announce that's just for twist listeners go right now to agree.com sign up for their easy to use all-in-one contract to cash stack and if you tell them jason sent you you're going to get 50 off for life that's right 50 off for life it's really important to have a tight system to get paid agree is the number one fastest way to go from contract to cash that means gathering e-signatures invoicing billing payments and revenue recovery if it comes to that no more jumping between four or five different platforms just to write out a contract get it signed set up billing and start sending invoices check out these numbers the average time from contracts that to signature is under seven hours using agree.com and the median time from invoice sent to invoice paid is just 36 hours so if you want to stop chasing invoices go right now to agree.com if you tell them jason sent you

SPEAKER_02: you'll get 50 off for life so i've found you know i'm a runner myself not a very good runner but i i

SPEAKER_00: can run and i found on strava i just i cannot ever compete with people because it's essentially olympians at this point what i can do though is i can go out and i can run you know in a volume like i can do multiple runs a week i can run slowly i can run and kind of be more committed than people in general so interval is not based on speed you run around the block and you claim land but other people can steal small bits of your territory so for example if i did a run around my local block the grandma who lives next door to me can technically go and walk around the block in whatever pace she wants to capture

SPEAKER_28: that territory off me um and what it's done is it just brings in this element of anyone can compete

SPEAKER_125: against anyone uh and and it's a lot of fun so lon can go and beat this guy's ass just walking and lollygagging with his dog as he does want to do dripping sweat i i did have a question though uh i've

SPEAKER_06: actually two i have two questions jason if you'll if you'll allow me the first one uh it feels to me like if if it's just whoever ran the most recently like how how do you keep that sort of interesting in an ongoing gamified sort of way like if i run around my three blocks and then somebody takes a run an hour after me and they claim those three blocks like well now it's theirs not mine like i am i motivated to go back and reclaim that territory the next day it just it feels a little

SPEAKER_00: ephemeral in some ways yeah for sure so i mean initially it it is the the game is literally just you go out capture territory and then someone captures it back off you and then we have kind of leaderboards and different kind of local battles where you're competing against that specific individual right i get fun and exciting yeah we do have things for solving that so right now there is a the game is fun at a specific level of density and we've pretty much got that particularly in melbourne

SPEAKER_28: australia i'll i'll go across to melbourne you can see we're pretty popular here um particularly in

SPEAKER_00: melbourne like there is a lot of density so if you go for a run and then you come back your run some of it might already be captured what we want to do with that is creating like an onion skin around the globe where you can climb up the levels by capturing more and more territory yes yes on top of that we do have a solve for um the pace element so uh oh sorry we do have a solve for the pace element so what we're going to create and what is in in works at the moment is something called arenas where to capture a certain really you know uh active spot let's say it's central park in new york you need to be the fastest around that spot on that day and then you get the you know the yellow jersey or you get that territory for that day we'll have specific leaderboards for that that are based on time and then we'll also have a volume based leaderboard as well so if you want to capture territory by you know walking or or just going about your day then the rest of the territory map is for that whereas

SPEAKER_28: if you want to really lock in and run at a fast pace um this will be resetting every single day uh then go

SPEAKER_06: to one of the arenas yeah i like that too because i i think um one one thing this made me think of right away was four square you guys remember like where you become the mayor you would check in at your favorite coffee shop or arcade or whatever bar and you could become like the mayor of that place if you checked in the most and for like there was a summer or two there where i everybody i knew was like obsessed with becoming the mayor of their favorite sandwich shop or whatever like they wanted to be and then and then it sort of burned out so i think there's a huge opportunity here but you do have to be like you got to keep it fresh and new and exciting for people my my other thought was having just been on uh i went i went to uh europe with a friend and she's a big pokemon go fan and every time we went to a new place like a new landmark she'd have to pull up her phone and check what are the pokemon go things happening around here that's a little that's a bit annoying

SPEAKER_33: i think how how long did it take her to check in long and then what is it is this a special friend

SPEAKER_06: that i'm unaware of it's just a friend a companion of traveling buddy that i went to europe with but but uh but you know like i feel like there's an element there where you're visiting somewhere different if you want to like do a run in rome and claim rome as separate from your home like i think there's an interesting element there too like of getting encouraging travel and checking in

SPEAKER_00: wherever you go i think is a something sticky absolutely and an adventure is the whole point of interval we don't want people just doing their average out and back runs every single day the idea is that you go out and go and explore new areas a big thing for us as well as we found that people are just so much more motivated to go out and do that activity when they get a notification that their territory has just been stolen sure so you're like so much more likely if you get told oh you've just been sold you know and and then you have like an individual's name and face put to that territory it becomes quite personal right so um yeah louise you know what it is you're doing gamification for

David Friedberg: good you're taking the competitive spirit you're taking the slot machine nature of apps and smartphones and you're using it for good fantastic you know strava has a little bit of an issue with speed runs and people getting hurt um and they've had to be a little bit careful because people started bombing

SPEAKER_57: and running red lights and they crashed into people and tragically literally in san francisco

SPEAKER_74: somebody died uh a vice i i believe this is 20 years 15 years ago i think now yours is not encouraging

SPEAKER_57: people to like do a lap in an ungodly amount of time and run red lights in order to accomplish that so great and i'm not blaming the people like strava for what their users do it's just the nature of competition people who are competitive um and we there's just a great tv show on right now

SPEAKER_81: dark wizard about free climbing and free soloing and and just the competitive nature of that and people dying or you know risking their lives i think a really interesting way for you to expand this would be um to do uh say skiing or biking or you know other kayaking whatever it happens to be and let people claim the water the mountain etc and then you could also do it based on i like not doing speed because again speed equals death in a lot of these pursuits like skiing but you could do completeness and so you know when i you ski a certain mountain let's say there's 50 runs how many of the runs and this might include some element of speed but just how comprehensive are you how many times have you done

SPEAKER_148: the run you know not speed just percentage of the mountain you covered and okay so today i did

SPEAKER_81: 80 percent of the mountain lawn did 82 he wins today tomorrow i do 85 he does 75 boom wonderful um and these become viable these apps in the days of vibe coding this app would take a company of 12 people but five or ten years ago if you were going to seed invest in a company like this you'd say 12 people to build the app two platforms customer support marketing administrative everything yeah a minimum of 12 which means you got to raise about three to five million dollars to do this so louise give us an idea of in the age of ai what it costs to to stand up this app and get to revenue because uh you're charging for this i'm assuming you charge 50 or 100 bucks a year is my guess

SPEAKER_00: yeah yeah yeah so uh we've got a team of five of us total three developers my co-founder built this from the ground up pretty much to what you see the app is right now and what i just showed you in 30 days we're launching a complete ui ux overhaul and also launching bike mode which will be our biggest launch yet um with that the the team we've got on so two extra engineers uh has just meant our speed is

SPEAKER_28: is so much faster uh obviously but we've managed to keep it pretty lean like jordan building it from the ground up we got to profitability which was pretty cool and then i was doing the marketing side just through social media without any paid media um and we grew that to about a million downloads and about a hundred thousand followers on instagram wow um i heard that you're uh i heard

SPEAKER_57: your um your uh paid and social game is strong that's what my producers tell me maybe you could

SPEAKER_159: talk a little bit about tactically what's work in terms of acquiring producer jacob actually saw an

SPEAKER_06: instagram ad for this product and that's how louie got booked on our show today yeah so tell us a little

SPEAKER_57: bit about that because uh most people in the app business are like oh my god i can't make it work

SPEAKER_163: the it's too expensive to do a paid motion in the world any new company needs to focus on their

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SPEAKER_123: twist that's q uo.com twist it is yeah yeah well so for us i think the biggest thing with social media

SPEAKER_00: is you've got to prepare to suck and you've got to prepare to suck publicly and i think i've found a lot of founders particularly in australia are not willing to fail publicly and look like an idiot online whereas i i don't really like i obviously care about my image online but i've been doing social media for about four or five years now and i'm not too worried about looking like an idiot so getting on on camera getting in front of camera was huge for our our growth and you know if you can get prolific with social media you essentially get free marketing so for us the kind of content that worked was game explanations it's a little bit like complicated to understand if it's just a video without anyone talking so i would literally jump in this studio or back at my house and explain the game with some overlays above my head and that in itself got us to a hundred thousand followers pretty quickly so just that like talking head style uh of content really helped and for a little tactical

SPEAKER_145: practical tip for folks you know everybody tunes in here for tactical practical meta has an ad library and here's interval and here's their ads so anybody can do competitive intelligence on other people's ads and you can see here a range of ads lon what type of ads work for you in combination like there's the one with the meme see that one with the woman with the blonde hair the second one over like go ahead and play that one this ad seems to have worked or not i don't know i can't see the stats

SPEAKER_113: there but yeah there is that you or is that your partner that's that's my no that's that's max he's

SPEAKER_169: head of content and look he just did the austin route and that's probably what my guy saw and

SPEAKER_145: there it is and like this is a beautiful it's your same studio it matters so you do a podcast studio you show these 3d graphics really cool yeah so and it makes it look fun you're like oh okay i get it

SPEAKER_06: it's a game i run around i get to claim territory like it's very immediate do these ads work yet what is

SPEAKER_57: the cost of acquiring a free user a paid user what's the what's the economics here yeah absolutely

SPEAKER_00: so the the ads has been great because it take it adds a level of predictability into our uh into our business previously with uh organic content we're just solely reliant on hitting the algorithm and

SPEAKER_28: it meant that we had months which were astronomical and we couldn't believe we could you know get this many downloads and subsequently make money uh verse other months which were just absolute flops and it's kind of crickets you can't get anyone to download the app so ads really ironed that out for us um and the cost per trial start for us currently is about 12 um on meta uh and then yeah we're we're seeing you know average customer lifetime is about 17 months um the app changes a lot so it's it's hard to get really ironed out metrics on that but where the the ad side has just been yeah revolutionary for for us um and we've got a good ad that helps helps things out as well

SPEAKER_145: you're doing it all internal or using external consultants to help you with it or you believe inside your company you need to have this expertise what's your philosophy

SPEAKER_28: louise yeah so we're actually using a uh third party it's called scale um and they have just been incredible you essentially paid them a monthly fee uh and they handle a flat rate or on top of your

SPEAKER_177: spend like a percentage of spend or just uh it it scales with this with the spend yeah gone and they

SPEAKER_145: can't charge you more than your economics make work and so 12 to start a trial the product on average

SPEAKER_180: costs 50 bucks a year is that yeah about 60 60 us a year perfect yeah so i went through this with comms

SPEAKER_81: so that means if you get one in five people to convert uh you know five times 12 uh you hit that 60 and you said they last for 17 months which means on average they make you 90 or 85 so you can uh and then maybe they tell a friend about it if it has an internal feature launch so you can maybe add a factor of like one in five at a friend which you divide the 17 months by five you get another three

SPEAKER_145: months and each month costs five dollars you got an extra fifteen dollars in value so there's all kinds of return on ad spend roas uh and cost per install and it's a really interesting science and there are

SPEAKER_81: funds that can help you i went through all this with calm fit pod um we have a great company called tone base uh that does music musician that does music steezy that does dance and it just becomes really hard to get this right but if you do get it right you can have an incredible flywheel and build an incredible brand like calm and fitbot did uh and tone base steezy didn't work out exactly for that

SPEAKER_144: was a harder one to make work dance uh but yeah continued success louise and thank you so much for

SPEAKER_183: sharing uh all your secrets yeah yeah thanks louis continued success i'll see you when i'm down under

SPEAKER_184: sounds good i'm joined i'm joining for that one i'm coming along on the australia trip yes you are

David Friedberg: yeah that's definitely well you know what i'd like to do is there's four cities there perth sydney melbourne what's the other one that always competes for startups brisbane brisbane so there's like four centers of excellence um so what i want to try to do is get you know two or three or all four of them to join forces to bring my stack to australia yeah um so i want to fire up this week in startups

SPEAKER_57: australia again mark pesci used to do it uh for me we did like 12 seasons many years ago we started

SPEAKER_33: doing that yeah so it'd be great to get that fired up again to bring founder university there and to

SPEAKER_57: bring the launch accelerator there and then my vision for it would be to get those three cities to collaborate chop up the cost of doing this oh sure and then rotate it so founder university is in perth then it's in sydney then it's in australia then it's in brisbane and it just rotates so maybe canva also that's whoever wants to you know chip in yeah to get the flywheel going i just want to have an excuse to go there frankly what my family wants hey everybody welcome back to twist this is alex

SPEAKER_197: now ai is having a moment people are mad about data centers people are mad about anthropic people that like anthropic are mad at open ai space xai is suddenly a hyperscaler job loss is either here or never coming and ai regulation is becoming a battlefield are you tired of all the negativity well something that many ai believers love to trot out is that ai is going to cure cancer bro and the thing is maybe that's why i wanted to get alice zhang from verge labs on the show to tell us about the state of using ai to discover new drugs to tackle our most intractable species level diseases and maladies so please join me in welcoming the show it's alice hey how you doing good thank you for having me on the show alex i'm so glad you're here we're also talking to you uh mere days after the company were branded from verge genomics the name that i've always known it under to verge labs so one congratulations on the rebrand and two uh from a very high level why was this the right moment to uh kind of change the name

SPEAKER_200: of the company and redirect in a new direction so we started 10 years ago really with the the mission that drug discovery could really be turned from a guess and check problem to really a prediction problem

SPEAKER_203: and that the missing piece was really missing data so we over the last decade have built one of the field's largest brain data sets directly from patients over 12 000 human brains and 6 000 patients and we initially used that to develop our own drugs and we went through that experience which was really useful but that experience really taught us the importance of an even kind of more valuable problem which is once you've developed the drug how do you actually predict what patient will respond to that drug which is something we did not originally foresee so the first thing is that we learned this really hard lesson about this very valuable problem and we also had the data sets that were necessary to really solve that problem the second is that the architectures in ai have finally gotten to a point where they can actually solve one of the key challenges that actually prevented us from solving that problem in the first place which is the kind of incomplete and fragmented state of most patient data sets so it was really the kind of intersection of the fact that our data actually achieved the scale we needed and the multiple architectures were advancing to the point where we could actually make use of these data sets that let us see this much larger opportunity which is instead of just buying a lottery ticket and developing a drug ourselves can we actually make a better machine that sells those lottery tickets and that's really what drove the shift is this kind of

SPEAKER_207: culmination of all of the above i've never heard a startup founder come on the show and say you know

SPEAKER_209: what we're doing now is we're selling the lottery tickets instead of scratching them ourselves you can invest right here uh no i i really appreciate that summary i now want to go through that in a bit slower pace to let people know what's changed and how technology has kind of brought you to this point so i think it's a very important story so in the earlier days of verge genomics you guys were working on converge one which actually helped you select a candidate drug that you then took into testing if i'm right and i'm curious about the the process to getting converge one built and how surprised or not surprised were you when you took it to the real world with this drug you put

SPEAKER_213: together uh the results didn't quite match what you were hoping for most ai tools are adding friction

SPEAKER_122: not making your life simpler and it's another tab to switch to and maybe you forget to even do it it's arduous what you really want is one system that's going to make you more efficient and save your time every single time you do work that's why i love superhuman go from the amazing team behind grammarly which i have insisted all my team members use since day one now it's an ai chat that lives on the side of your browser it's always there maybe you're drafting an email mid-meeting it goes and helps you finish it without switching apps maybe you got a 40 email thread to get through before that call it's going to summarize it for you in seconds without losing your place no new tabs no starting from scratch no context switching superhuman go has the context of everything you're working on it works inside the tools and sites you already use your inbox your docs your browser maybe you're doing social media all day long like me it's part of my job you can try many of superhuman ghost features for free find out more superhuman.com that's superhuman.com yeah so what we built

SPEAKER_203: converge originally is what we call it's called a target discovery engine so it's how do you actually find the proteins to go after that cause disease and then design drugs around them so to do that we started accumulating this very large data set which is that instead of starting with a mouse or cell which is how most researchers start we asked why not actually go directly to the source which is the brain uh for neurological diseases because that's where it happens and so we started sequencing these brains we paired them with multimodal data like their clinical records how they progressed in

SPEAKER_218: the disease can i can i ask a question about that just because i'm really curious uh my brain's inside of my skull and hasn't ever to my knowledge left um so when you're talking about getting brain samples number of patients number of brains how much tissue are you getting are these from um politely living people are these the recently deceased i i don't know and i just thought i'd ask for everyone out there

SPEAKER_203: is curious so they're from a deceased patients this is why it's actually it's so hard is because you know in cancer the problem of how do we actually find the right patient and match them to the right drug has been partly solved because you can actually take a tumor right from a living person you can profile it and analyze it and then you can match it to the therapy that you want that patient to be on in the brain you know um you can't take a brain from a living person right in neurological disease and so you can only take it from autopsy patients and so that is what we have done is that we've partnered with more than 24 different tissue banks hospitals academic centers across the world that have thousands and thousands of patient brains from people that have passed away from disease and donated their bodies for research and then we've built an end-to-end infrastructure that can actually ingest these samples quality control them dissect them um for data consistency quality and traceability and then we essentially digitize them which means that we sequence them so we capture the behavior of all 30 000 genes in the genome at multiple levels from the dna to

SPEAKER_209: rna to protein okay that's super cool but i think when you guys were working on converge one the first iteration of this engine uh there was a mismatch between the samples of data that you could collect from the i guess tissue banks of the world and maybe the brain of someone who had a particular uh disease you were going after trying to fix and there was a bit of a uh a gap between the two

SPEAKER_203: yeah what what you can right now you can only get brains from deceased individuals and one of the challenges is that when you actually go into clinical trials right you're actually going into a living person so how do you actually measure what's happening in that person's brain which is the really only window into what is happening with disease and so what we've developed in the last year is a world model of disease that can ingest all this brain tissue that we've collected combine that with patient data from living patients and essentially create what we call a virtual biopsy of the brain so that's essentially a reconstructed picture of what's happening in your brain that can be built from just a single blood draw and so that allows us in a living patient to actually say hey what stage is your disease at and how might you actually respond to a given therapy so with the information you have from

SPEAKER_209: the deceased and these tissue banks and uh some information about living patients you can kind of bring the two halves together using ai which is what's changed since you started the company and therefore kind of close the gap using i guess the power of generative ai yeah and that's what the power

SPEAKER_203: of what these models have brought in the last few years is if you look at traditional deep learning or machine learning models they've really had required every patient to have every single measurement so you have to have the brain the blood the clinical treatment data all in one but that's not how it happens in the real world in the real world you might have a patient that goes into a clinical trial and you might have a different patient that gives their blood and then you might have yet a different patient that donates their brain tissue and the power of these transformer based architectures is that it allows you to actually piece together missing data and infer missing data from what you have so you can start creating a a unified representation of what a patient looks like

SPEAKER_209: and start filling in missing data modalities now you guys said that brain tissue is the lidar of neuroscience applying the kind of world models we've heard about from self-driving uh companies like wave and i think also lobby and so forth are working on that and you think that brain tissue is going to help your world model have high fidelity and high accuracy are people out there trying to build similar world models for similar tasks without using actual brain tissue as part of

SPEAKER_203: the data grounding for that work yeah so we are using world the very kind of same models that some of the self-driving cars are because it allows you to not just pattern match based on observational data like it doesn't just pattern match how their previous driving scenarios happen but it can predict a new person in the road and similarly that's what we're doing with our world models um there are world models in oncology because that's a much easier space to get data in fact that's kind of a pattern you see in the space that ai companies get just simply built because of where it's easiest to get data set but we've kind of taken the opposite approach is we've actually asked what's the biggest problem right now and then how do we actually do the hard work of collecting the right data so with neuroscience most of the data um it's not that there are people building world models with the proxy data it's just that that's where most of the data is today so it's probably tempting to go there first um but the issue with the proxy data when i say proxy data i mean things like blood you know brain imaging you know spinal fluid that can easily be collected from a living person is that they're all just downstream consequences of the disease they're like shadows of the disease right so in order to really understand what is happening in disease you need to go into the brain where it's happening and so the reason it's a bit like lidar is it's like thinking about self-driving if you were to build a model only on just camera data alone like kind of tesla has you have limited information but we saw that when waymo integrated cameras with lidar which was an actual direct reading of 3d depth that could vastly increase the speed at which they could get accuracy and self-driving and so in a very similar way that's why i say brain tissue is like the lidar of neuroscience and that it's just the molecular ground truth of disease and for the model to work you need that anchor to be able to anchor the

SPEAKER_231: relationships between blood between your brain images and to actually what's happening in the brain

SPEAKER_209: okay so some people are spinning up uh drug discovery companies using ai and they're going to where there's a lot of data because everyone knows if you can bring a lot of data in you can fine tune a model you can therefore do a lot of work with it but you know honestly alice if everyone's going to go just to where there's easy data it seems like they're all going to be competing kind of along the same vector whereas you guys having done uh years of data collection that's special and unique will have a different approach okay that makes good sense to me now when it comes to world models for this work i'm a little bit confused because when i think about a world model in the self-driving context i almost imagine like a video game if you will like a place where there's you know

SPEAKER_197: physics and people moving around and interactions and so forth when you're doing world models for brains what does that look like or does it actually look like anything or is it just code so it's what a world

SPEAKER_203: model looks like is that so in the self-driving world instead of pattern matching on a previous scenario it creates an internal representation of how the world works you know and so that it can anticipate new scenarios so similarly you know instead of a road our road is essentially the patient or the human exactly but what we do is that we take all of these inputs ranging from your genetics from your blood your brain images and your brain tissue and we fuse those into a single internal representation of each patient so actually each patient is represented essentially as a 512 uh dimensional vector oh okay

SPEAKER_237: so this boils down to a series of numbers in a list yeah exactly it's not okay a lot like a lot like the

SPEAKER_238: kind of current uh large language model architectures not to be a total brat but uh vectors are i think one

SPEAKER_218: dimensional tensors do you actually use vectors or do you use higher dimensional tensors so the actual

SPEAKER_203: model architecture is at the kind of core it's a transformer in the same vein as chat gpt clod and other lm so it leverages the flexibility of those transformers but it has several innovations that are unique in the bio the first is that each data actually gets its own encoder each data type so it's multimodal and that maps it to the shared kind of mathematical space so blood brain and genetics can all kind of live in the same kind of mathematical language and then we fuse all the data layers into a single unified vector that represents each patient and the way you think about it is essentially like a patient fingerprint okay right and then the last thing we do is we use what we call contrastive alignment so this is actually a new architecture we use a form of it that's a new architecture that's only been developed in the last 18 months um which is called contrastive multimodal learning so unlike your kind of classic contrastive alignment which you know image models often used and that only keeps two types of um the kind of data that two types of data agree on ours keeps three things which is you know what the blood knows on its own what the brain knows on its own and then what's the synergy between both that combines them and in biology the synergy is huge because it's where kind of real signal hides where no kind of single measurement can capture and so lastly once we have that fingerprint then we freeze it and we can build a bunch of task heads on top of it that answers specific biological questions like who is going to respond to this drug what does their brain look like and what is cool is that this form of training because we're using masking actually starts to learn tasks that it was never explicitly trained on can you explain masking

SPEAKER_245: for me in that context it's a bit similar to um kind of how ai does masking right which is that um so in large language models ais do masking by actually you know hiding a word and then predicting what that word is for us we have all types of data you know blood genetics brain and what we do is we can hide one type of data and the model trains by learning what data type

SPEAKER_203: is missing and how to fill that in so as a result it can start learning tasks that it wasn't taught so we have seen that our own model with high accuracy can actually accurately reconstruct brain activity

SPEAKER_246: from blood alone and that's actually not a task that it was asked to do it's just a simply emergent

SPEAKER_209: property of this training task i love ai it always finds some new way to delight me and make me excited about the world okay so you went from the first iteration of the company verge genomics we're going to identify candidate drugs and test them and bring them to market and now you realize that your technology is probably a better tool for other people to go out there and do the um very expensive guessing and uh trials work which makes a lot of sense to me uh who is the the target customer for

SPEAKER_203: this new iteration of verge so it's really anyone that's developing a drug right it's the whole pharmaceutical business um companies can work with us essentially three ways they can first come to us with a specific problem and we can run our targets against it we can also directly license insights or targets that we've already found or we can license the data and models directly so for example if you're a company with a phase two drug in schizophrenia and you're like holy cow there's my drug this drug is behaving differently in every patient you can come to us and we can help you pick

SPEAKER_238: out which patients to roll in your next trial that actually respond to your drug um and let you design

SPEAKER_227: a much smaller and cheaper clinical trial that's so many ways to make money and the companies that you're

SPEAKER_209: going to have as customers are uh famously large and frankly quite wealthy which is good for you guys um do you charge for this on like a per case basis it sounds a little bit custom on the pricing side

SPEAKER_203: if that makes sense um so we have you know we've done two major partnerships already actually with eli lily and um astrazeneca alexion um those are target discovery partnerships are more traditionally structured so it's um in those cases it was a 25 to 42 million up front with then milestones that total up to anywhere between 700 to 800 million dollars each as a very traditional therapeutic structure now we've opened up new platform models as well that allow you to engage with it more kind of how you might used to be engaging with kind of a direct model license right so um you know companies like you know in the space like chai and noatech have done kind of these multi-year licenses to pharma companies we also work with smaller biotechs as well in a kind of platform as a service format where they have actually a specific question they can come to us and we can kind of answer on a on a question

SPEAKER_196: by question basis the deals you're talking about uh back when you raised your series b in uh 2000 i

SPEAKER_209: think it was late 21 you said that the company had announced a 706 million dollar partnership with lily to quote develop new treatments for oh hell oh als there you go using this platform so how did that contract go did the milestones come in because one thing i noticed alice is that you guys haven't raised money in a while which is fine but also may imply that there was some revenue along the way

SPEAKER_234: we did um well we haven't uh announced publicly any additional funding um but we have done those

SPEAKER_203: actually a few major deals um and we've raised some unannounced funding in between um those partnerships are also did provide some milestones so lily actually in 2024 announced that they actually optioned two of those targets into their internal als pipeline so it's actually the first ai derived targets that were actually internalized into their als pipeline which we're well done we're quite proud of um and one thing that was actually really quite striking from that partnership was going into the partnership lily had said to us um you know even if 20 of these targets validate in the lab we would be very that would far surpass our expectations and we actually found at the end of that partnership that 83 of those targets actually validated in wet lab experiments so that kind of far surpassed even our own internal expectations and starts to create this kind of surplus bullpen

SPEAKER_209: of targets that we can continue licensing and by targets we're talking about uh ideas for drugs that

SPEAKER_203: might solve okay cool sorry it's actually like what are the proteins to go after with a drug that might

SPEAKER_209: cause disease the target proteins to go after to help either reduce or resolve als in this case yeah exactly yeah okay so you guys are focused on the brain which i think is fantastic because i'm a big fan of having my brain and working in all those good things and also i would like to live for a long time with my mental faculties um but i'm curious about the the idea of taking in people's information tissue samples and applying ai to them does that work in a similar way for example in my liver or is this more of a system that is set up because the brain works a certain way and it wouldn't be applicable

SPEAKER_203: to other organs in my body yeah absolutely and there are other companies that are doing something similar um in cancer um the reason it is such a big problem and so hard in the brain though is because the brain is the hardest organ to access so pretty much in any other disease in cancer in fact standard of care to get your tumor kind of taken out to get it analyzed in ibd you often do that that most tissues you can actually go and take a sample of that tissue and the patient can continue living with a brain you simply can't do that so being able to accurately reconstruct what's happening in the brain has been one of the field's longest standing challenges and it's why i think you know neuroscience is long behind cancer by 10 20 years and it's really honestly probably the biggest driver of mortalities in the next generation as we get all i'll get older it will really be alzheimer's disease and dementias

SPEAKER_209: yeah no i'm i mean i'm at the age now and my parents are in their mid-70s and you start to have thoughts and fears about how they're going to do and what we can do for them and how to care for them so this is this is very apropos to you know things that are near and dear to my heart one thing though that i've heard from basically every ai-ish ceo that i've spoken to and i include you in that bucket of course uh is that if they have more compute and they have more data they can do a much better job over time it's kind of a standard kind of like path that direction does that same relationship apply to the second version of converge and also like understanding which proteins in the

SPEAKER_207: brain we want to go after or is there a limit that is uh different from other applications of ai in

SPEAKER_234: that context so actually what we have found so far we actually have not deliberately chased

SPEAKER_203: parameter counts in count yet because the biggest gains we've seen have actually come from scaling data and modalities so it's not about making the ai bigger it's about feeding it the right pair of data but you kind of contract like something interesting between kind of text models and bio models are of course in text models scaling just works you have this kind of everyone believes that if you just make it bigger and it gets better but you know the reason why that is in text and i don't think most people realize why is because when you're actually training a text model you're predicting the next word in a sentence and that task inherently forces the model to learn everything about reasoning for example reasoning code tone everything but when you interact with the real world like biology self-driving robotics it's much much harder because first of all there's no single task we're predicting the next thing can teach you whether or not a drug works in which patients whether it be toxic most biological data are actually proxies so they're kind of shadows right of what's happening and most biological data is observational but you're actually wanting to ask counterfactual questions like what if i take this drug what will happen and kind of analogous is kind of self-driving again because you know waymo didn't solve self-driving by collecting just simply more and more camera footage they fused sensors cameras lidar radar maps etc and so biology is the same you really need to fuse modalities rather than just scaling one and but it's even harder because you don't have a perfect geometric representation of the world like lidar does biology doesn't have that kind of same sensor so the takeaway in biology is that scale really only matters when it's pointed in the right data in the right direction um so it's not to

SPEAKER_238: say that scaling doesn't matter but it's i think in the beginning the gains will come from kind of

SPEAKER_209: combining the right data sets and scaling the right data so then would a major unlock for the company then being able to access more brain tissue samples to expand your underlying data yeah and that's what

SPEAKER_225: we're doing not just more brain tissue but more modalities so on the roadmap for us next is

SPEAKER_203: bringing in imaging bringing in proteomics bringing in even longitudinal data so that we can not only predict a snapshot of the brain but we can actually create a virtual model of the patient in time where we can actually run forward each person see when they'll get the disease how the disease will ah wait no

SPEAKER_271: i don't like no wait a minute are you everything you've set up at this point has been fantastic but then you just told me you're gonna tell me what i'm gonna die and i i know i don't know alice if

SPEAKER_273: i'm on board for that more knowledge is power is it though i sometimes ignorance really is bliss uh okay

SPEAKER_209: but if if i'm being serious if you were to tell me you are at risk of getting alzheimer's or whatever dementia early then i presume that i could take at least some steps to limit that risk and manage it

SPEAKER_276: okay that that makes a lot of sense and in alzheimer's disease a lot of people think it's

SPEAKER_203: actually not even just finding the right drug it's actually being able to intervene early enough

SPEAKER_245: to change your trajectory so that becomes even more important but i want to get back to the the the data

SPEAKER_271: points so scaling parameters not that important having the right data very important is there uh when it

SPEAKER_209: comes to text you can scan books right it's a little bit easier to talk about than deceased people's brains but is there a a good pipeline of fresh deceased brains that you can if you wanted to access collect more and then expand your data sets over time as you learn more and tune your own models

SPEAKER_234: i mean that's really what we still spent the kind of last 10 years building is that end-to-end

SPEAKER_203: infrastructure and it really took us 10 years so people always ask you know why aren't just big pharma companies doing this themselves yeah i mean the real answer it's not impossible but it will just simply take a very long time and it's very hard and so it's really the kind of unsexy blood sweat and tears that we put in over the last 10 years that have created the moat for us yeah and it's how we'll continue scaling these data sets and what's exciting is that we are seeing scaling laws in our data right where we're then they're non-linear and increase as we add samples and

SPEAKER_238: we haven't even started working on scaling the the compute um and the parameters yet so there's

SPEAKER_209: still massive head room for growth okay so basically you've done all the hard work to have a pipeline of of of useful brain tissue samples other companies don't have that so not only are you ahead of the game in your particular niche but also you have a unique advantage of having more data okay i want to spin the clock and look ahead a bit like like not this year not next year but a couple years down down the road i think some people have been impatient incorrectly but impatient with the pace of medical progress in the ai era i think people have been seeing i've seen coding agents do so well and say hey why aren't we there with drug discovery and health yet so if you could take a like like a 50 confidence interval guess about where both verge is and other companies in the bio ai space where are we in five years what have we unlocked and are we going to feel that difference in our kind of lived

SPEAKER_203: medical reality yeah i mean i think even with some of the text models right that progress all happened very quickly uh and there was also ongoing you know work that was going on behind the scenes that enabled it i think with every technology it's always a process of iteration and learning and facing setbacks and then learning from that and then once things start clicking right progress gets made exponentially right now i think that what's really exciting is just some of these transformer-based models and these world models are just performing in ways that we didn't expect even with us we're starting to see you know performance on tasks like brain prediction directly from blood that it wasn't trained on that are far exceeding current clinical tools we're seeing you know prediction of responders and so what i see in five years is really i think ai will come into the pipeline at multiple points from multiple different models right i think you'll have models that are able to you know predict hey what patients will respond to what drugs and i think the future vision for that is you can have a continuous monitoring of your health state right you can figure out you know when you're going to get disease when you want to intervene and that really brings us to a world of true personalized medicine where we're no longer just thinking of alzheimer's disease as one disease but we're thinking about hundreds of diseases where you might just have one one form of a disease and you can really then seek a therapy that perfectly matches to the specific disease that you have as alex or that i have as as alice um and that's really the way to start extending you know health span and age span is really by

SPEAKER_209: being able to address these chronic diseases so when we sequence the human genome it costs like a bajillion dollars and took a while now we can do it for like four dollars or something crazy uh what you're describing to me sounds fantastic but i'm curious about the price curve and uh if you think it's going to become something that is accessible to people let's say on medicaid versus with all our

SPEAKER_221: friends and their concierge doctors will get it first but will it make it down to the people that

SPEAKER_225: are less resourced well so i in terms of pricing the thing i always think about is why are drugs so

SPEAKER_203: expensive now it's expensive because it costs five billion dollars on average all in to develop a single drug right and so that's reflected in the price why does it cost five billion dollars actually the vast majority of that five billion is getting spent on failures it's because nine out of the ten attempts fail at the last stage in the most expensive stage so if you can actually be able to reduce that even by a small amount that is huge implications for how much is saved and that ultimately is going to be the thing that drives down the cost of prices sustainably is actually being able to be much more efficient at how you develop drugs so that's really what i see is the long-term solution is if you can perfectly with accuracy predict kind of which drug will succeed it goes from five billion to really you know tens tens of millions to really get a drug all the way through and so you can see orders of magnitude reduction kind of then get pulled through to actual you know what the average consumer will see in and how much is it for drugs so as we have better

SPEAKER_227: uh selection of possible drugs we'll have a lower failure rate therefore we'll spend less money

SPEAKER_209: spinning our wheels spend less time wasting there we can therefore offer better more targeted drugs at a lower price point keeping this in everyone's uh medicine cabinet to use an analogy i suppose that's fantastically good news i'm pretty excited about all this is there anything that like you're worried about that might not work out because this all feels like you have the tools you have technology you have the data and off to work you go but are there any like science risks left i mean i think the

SPEAKER_203: biggest thing i always like to warn people about is that you know techno people always like to be very reductionist about how they view technologies yes you know they always like to say oh you know this drug has failed in clinical trials ai doesn't work at all right and rarely in the case of any transformational technology has the first attempt ever been the blockbuster success in fact actually transformational technologies get built because people continue to learn from setbacks they feed that back into their platforms and then they improve from those and so i think the biggest risk is more of um a sought like a human one which is that we kind of lose interest in ai or in the application of ai and healthcare just because we kind of face one step back and we generalize that about the promise of the

SPEAKER_289: whole technology um but i think that technology is built through iteration um and transformation and

SPEAKER_209: kind of continued persistence yeah i i hope that no one takes an early failure as indication that things don't work i mean if we believed that we would never be in rockets for example as a species because if you go back to the early days of rockets it wasn't exactly like they were coming out the assembly line and going straight up they were not yeah so it takes a lot of time and in pharma there's this

SPEAKER_203: tendency when you have a clinical trial failure to essentially just look away and just move on to the next thing and that's why when we had our clinical trial which didn't pan out instead of looking away we published the details and results in detail we took all that data and we fed it back in the platform and we said hey this taught us a really hard one lesson about what's important in this space it gave us all the data to be able to address that challenge now let's feed it in make the next version actually address what we missed and then actually build an even better kind of tool on top

SPEAKER_209: of that well you have me feeling both optimistic and excited uh because i'm starting to reach the age in which my body gets dings and scrapes and nicks and needs a little bit of help here and there

SPEAKER_197: so i'm really glad that you're working on this problem and other companies are working on cancers and so forth because who doesn't want to live forever alice you know uh all right for folks

SPEAKER_218: who want to know more it's no longer verge genomics it's verge labs what's the url and is there a job you

SPEAKER_245: want to shout out to the audience in case the right candidate is tuned in um verge labs.com v-e-r-g-e labs.com and we are always looking for great ai research talent so if you are interested in ai and

SPEAKER_297: biology give us a shot how how hard is it to hire right now in that particular space i know it's crazy no i i'm actually i'm actually curious because i'm not sure if the people that are going to work

SPEAKER_209: for anthropic are interested in the same problem space so i'm kind of curious if your your focus gives you access to talent that might otherwise be absorbed by the major labs yeah it's kind of in the

SPEAKER_225: space what is hard is finding the intersection of you kind of have to ask do you want ai do you want

SPEAKER_203: biology expertise because there's kind of folks from the frontier ai labs and then there are folks with kind of biology training that develop foundation models we kind of sit in between both so it's actually more about finding the unicorns that like are interested in both so it's either people that have had deep frontier ai experience that may have had a personal experience really with one of these diseases and so actually what we find that once we find those individuals it's actually quite easy to recruit them because there's such a strong mission alignment and it's so kind of what we're doing

SPEAKER_302: is so differentiated from a lot of the other companies out there but it's actually about finding those people that that have both if you're curious founders what people mean when they say

SPEAKER_209: mission that is mission not improving barbershrap cms phone call cold outreach response rates all right alice an absolute treat please come back on in six or eight months when you have more news i really want to keep track of what you're doing because i think it's fantastic thank you thank you so

SPEAKER_306: much alex thanks for watching this week in startups if you liked this episode check out more if you're a startup founder founder university cohort 13 kicks off this fall it's a 12-week program that provides guidance on building your product launching to real customers and pitching to investors top startups receive 25 000 or 125 000 in investment apply now at founder dot university slash twist already have traction the launch accelerator invests 125 000 and connects you with 500 plus investors to help you raise your next round apply at launch accelerator dot co if you're an accredited investor looking to gain access to quality deal flow apply for jason's angel syndicate at the syndicate dot com we find two to three deals a month and check out this week in ai jason's experts only roundtable with top ai founders and operators every week find it this week in ai dot ai check out the twist ticker our daily newsletter at this week in startups dot com slash ticker follow the show on instagram follow the show on x.com this week in startups publishes three days a week monday wednesday and friday at 5 pm central time you can submit an audio or video file question by emailing it to this week in dot com