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Desci Rising x Qure | ep 9
Recorded: June 5, 2025 Duration: 0:55:39Space Recording
Short Summary
Cure is making waves in the biotech sector with its innovative AI co-pilot for scientists, currently in fundraising mode and forming strategic partnerships to enhance research efficiency. The conversation highlights a significant trend towards decentralized science, reflecting a shift in how research is funded and conducted.
Full Transcription
Thank you. Thank you. Hello, hello.
Hello, hello.
Hey, guys.
Sorry for this late start.
There was a little bit of technical shenanigans.
Joshua, thank you for coming in and helping with that.
Char, hey, how you doing, buddy?
Hey, Jelani, I'm well. How are you?
I'm good, boss.
I've been really looking forward to this particular one.
You have been in the space for a very long time,
and I feel like you haven't gotten the shine that you deserve
and the work that you guys are doing.
So I'm glad to have you here, my friend.
And for context, yes, Char is a close friend of mine, I consider, and so so I'm glad to have you here my friend for context yes Char is a close friend of mine I consider and so I'm
super happy to have him here oh that's that's very kind Jelani happy to be here
thanks so much for having me on really really excited love love the work
Eastside world's been doing and yeah yeah happy to happy to get this going
all right cool let's kick it off like we usually do our normal intro so hi
everybody my name is Jelani if you don't know um welcome to season two episode nine of the
DSiRising series put on by DSiWorld where our goal here is always to provide an opportunity
for many of the wonderful projects on our growing DSiWorld dashboard to just
formally introduce themselves update their progress highlight their achievements and just
generally engage with both the DSiWorld and wider DSi community. The idea behind all this is always
that at DSiWorld, we very much believe that more than the tech, more than the funding, it is the
community leveraging these tools that will effectively usher in a lot of the changes that
we're all kind of hoping for DSi to bring to legacy STEM industries. And so as I mentioned today,
bring to legacy stem industries and so as i mentioned today special episode good friend of
mine shar koreshi founder of cure and if you guys are wondering why cure is spelt q-u-r-e well that's
derivative of his last name koreshi which i only just realized my friend for the longest time i've
been confused and then it clicked like the other day i was like oh shit it makes sense so that's
great branding um but cure is effectively
a real-time i'm gonna read it off of what you provided us right so a real-time scientist
training execution collaboration platform addressing slow costly and inefficient collaboration
in the life sciences and i really put an emphasis on those because that is what really defines the
legacy stem industry and what we're hoping to fix, right?
And you're doing this by mixing in, I guess, originally VR and now XR or mixed reality content creation tools.
So you guys on your website very poignantly put it as effectively an AI co-pilot for science and engineering workflows.
And I think that is a fantastic tagline.
a fantastic tagline. So, Shar, again, welcome. It's great to have you. Happy to learn more
So, Shar, again, welcome.
It's great to have you.
myself about Cure, but also let other people learn about the great work you guys are doing.
Yeah, thanks so much, Ilani. It's wonderful to be back. I think last time we were in season
one, so it's great to hear season two. That's awesome. Yeah, season one, we've come quite some way.
So I'll give a little background, some lore.
I started this in 2021 with the initial idea
to solve for the reproducibility crisis
and preclinical research.
I was a scientist for 10 years before that in the lab,
and I wrapped up my time at the lab as a field scientist for a biotechnology company,
an organ-on-a-chip company.
So it was quite a sophisticated platform.
But the thing that bothered me, and I had identified a lot of things in science that didn't sit right with me prior, but at Emulate as a field scientist,
I really got kind of a first row seat to the reproducibility crisis in action, and not just at one place or an isolated place but like you
know throughout and so you know these organ on a chip workflows they take a
long time so emulates system it's 40 days worth of touch points in the lab
right and the training that I conducted was five days long. And so the question is, and the question that became very apparent, was how do you do the same thing as someone else for 40 days straight in the lab and get the same results?
results. That's the reproducibility crisis right there. And the answer to my rhetorical
question is that you can't. It's actually quite hard to reproduce 40 days worth of nuanced
technical touch points. And so through that experience, I went into corporate and aerospace
and biotech and healthcare, but my passion for the lab really never left.
And so, you know, I decided to really do something about it.
And I felt that the reproducibility crisis, the replication crisis, whatever you want to call it,
was not only a detriment to science, but I saw it as actually a detriment to humanity in that, you know,
science was becoming more and more blatant and the incentives were not lined up, right? And with reproducibility, with irreproducibility, right? With this reproducibility
crisis, what that means effectively for all of us is that science is, you know, honesty and rigor
of scientists are actually assumed, right? There's not a way to, if it's not been replicated, then it's just your
word saying that, hey, A plus B equals C, right? But if it hasn't been reproduced by someone else,
then it's not. You know, looking back, you know, season one, I gave the king of Egypt example,
Season one I gave the the king of Egypt example right in season two I'll give you another example in like
in the Enlightenment there were royal societies and these royal societies still exist
Royal Society of chemistry
physics etc and
These royal societies their job they kind of function as
publishing houses of peer-reviewed journals.
However, the idea behind them was actually to less publish it.
But if someone came in with a claim, like Sir Isaac Newton came with a claim,
the Royal Society of Physics would reproduce his experiment
to see if his claims were reproducible.
That was their job. That was actually their function for society,
was to serve as this reproducibility checkpoint.
And now we've actually lost that.
Now we have a system of science that is irreproducible.
I think more than half of scientists in the US.S. agree that there's a reproducibility crisis,
that 50 to 70 percent of the literature, primarily in the biological science space,
is irreproducible. And we've got things like publish and perish culture really churning that that that crisis even further yielding things like a publication a publication
farming or citation mafias which are really just uh scientists who are gaining the system
to increase their h index or to to get more grants to get more clout etc And so I identified all of these kind of issues and I saw that
reproducibility was kind of at the core of it, right? By increasing our our rigor
and ensuring that science is reproduced, now we've got kind of a
quality metric. But the current metrics are, you know, how many publications you have, you know, what's the measure of a scientist?
The measure of a scientist is how many publications you have, what journals you publish in.
And so, you know, once a metric becomes a, once a measure becomes a metric, it no longer serves as a good metric or something like that.
A metric becomes a measure.
You know, once a metric becomes measured, it no longer serves as a metric,
as a good one. So how many publications you have, that's being gained. People are farming
publications, they're taking one study that could be published by itself and chopping it up to two,
three, four, five papers just to boost that metric. And then citation mafias, etc.
that metric. And then citation mafias, etc. These are all artificial, right? This further
deepens the reproducibility crisis. And it was from my perspective that the elimination
of a reproducibility checkpoint to consider science to be distributed to humanity was
actually at the core of this issue.
And by bringing in reproducibility back and bringing reproducible science back and making it more efficient, easier to reproduce science,
that we could really usher in a new golden age of science and innovation.
And that's my goal, ultimately, and that's why I founded Cure.
I mean, it's great.
I don't think anybody can touch upon it
as well as you just did from the realm of reproducibility and the only thing that I
would add there is effectively reproducibility is one metric of validation and quality assurance,
right? So can your science be done by independent bodies? That is a measure of how true how close to the actual tangent you know
objective truth is your your claim are your claims but woven into that and around that is well what
was your methodology of achieving that truth right because there are multiple ways to skin a cat as
well and to me like a lot of the in addition to the reproducibility crisis that you guys are kind of, you know, contributing as a solution for, to your point, there's a lot of things in science that are hard to capture and therefore hard to audit.
that you can now verifiably ascertain how something was done
or be able to capture what was previously intangible
by virtue of technological limitations.
Now by using XR, VR, virtual reality
and all these kinds of applications,
you can now start to bring this auditability,
this traceability, this verification
into the quote unquote meat space, right?
Because people at one point thought that IoT, so the, the meat, the quote unquote meat space, right? Because people at one point thought that IOT, so the internet of things, having
every, uh, machine plugged into some measurable capacity would allow us to do
that and it, and it does, and it can, but you cannot account for human error
because that exists offline.
But now with cure, you can start to think, take things that were offline online in a way like never before.
And I think that is a fantastic space that has, you know, only the advent of AI and some of these new technologies are allowing us to get closer to that source of truth.
So that's really what excites me with what you guys are doing.
In addition to all the other stuff, it definitely backs all that.
But you're effectively generating new
assets for verifiability that before just couldn't be done unless you were
sitting in the lab or had everybody who was gonna peer review you in lab with
you watching you do it exactly right and and you know there are you know when it
comes like things like physics you know maybe physics experience or slight well
not modern physics experiments right but when like during the enlightenment if
a physics experiment would be i think uh kind of pretty straightforward and and you'd be able to
write down what you did and have someone else you know execute it without running into too
many issues but now you know with technology progressing so much, equipment progressing
so much, you know, chemicals, assays, kits, biomarkers, you know, different ways to identify
things, there are a hundred ways to skin a cat, right? And, you know, and science, you know, wet
lab science, biological science is extremely nuanced. It's extremely technical and you
know just following a protocol as I've seen firsthand is not a great way
to reproduce an experiment, right? Especially when you're throwing in you
know like like you know the scanning electron microscopy right or yes an
endpoint right. How are you how are you to do, right? Or, yeah, as an endpoint, right?
How are you to do that, right?
You have to make sure all the settings are the same.
You have to make sure the software is the same.
It's the same instrument, right?
You're using all the same oil, et cetera.
There's so much variability that there needed to be some sort of structure.
And to your point, when I discovered DSi, I got my blockchain certificate from
MIT in like 2021. And I was thinking of, I was currently in aerospace at that time and
saw its applications in supply chain. And it has killer apps in supply chain. But I then started thinking about how it could be used
in kind of the scientific ledger
and really enable kind of more of a reproducible version
of science, really enable a more democratized version
of science.
And that's when I came across DSi.
And it was very small and 21
very nascent and that's when I actually discovered DSi world like 21, 22 and I was like, whoa,
you know, this is, you know, there's people here that they see the same thing that I see. So a big shout out to Josh there for having that vision and seeing that.
But I really then started thinking, okay, things really could change for the better with the system.
And at the same time, AI had just kind of hit the scene.
And the world has really fundamentally changed since 2020.
So, now fast forward to today.
Yeah, we really can't think the same way about things because now we're in an AI era.
And so being in an AI era, how do we organize ourselves?
How do we organize our data?
How do we do science in an AI era?
I think that's a very prudent question.
And we're working on that in a number of ways.
But there's really a lot of opportunity in that.
And just as an example, a DSi kind of principle, like incremental publishing.
Incremental publishing in an AI era makes a lot more sense than the traditional publishing pathways that we've been using.
And for those who know, incremental publishing is rather than publishing traditionally, which is,
you know, a scientist will do a set of experiments, five experiments, to answer a certain question,
a hypothesis, and then they'll publish a narrative of what they did, how they did, why
they did those experiments, what the results of all those experiments in a sequence or together
mean for answering that question, that hypothesis. You know, that's a very traditional way of doing
it. It's narrative. It's kind of narrative- based. But in an age of AI, incremental publishing now looks more like instead of putting those five
narratives, putting those five data sets together, the data from those five experiments together
and publishing it, you publish each of those data sets by itself with its own relevant metadata of why you did it, how you did it,
what equipment you used, what answer you're trying to solve, what cells you used.
You put all that data just like you would into a regular experiment,
into a regular publication, you now just publish that sole piece of data, that data set.
And now if everyone starts doing this into a more of a centralized
location, now investigators and scientists are able to come to that centralized data
storage of these incrementally published data sets and say, I'm looking for inflammation
markers in kidney from this certain type of drug.
And now all of the data sets that have been published about kidneys,
looking at inflammation markers from a certain subset of drugs,
now are in front of you and you're able to select them and now use those.
And those are now automatically referenced in the data set that you published.
Right? That, you know, okay, understanding what all of these data
together and the AI insights from all of this data together
lead me to now do this experiment with a new type of drug.
And I'm going to now publish that incrementally
into the same data set.
It's now linked to the sets that were used to execute that
and the reasoning behind why they executed that. So, you know, the insights, the traceability, and, you know, of course, this is also with blockchain, right?
I mean, if you have a kind of a blockchain framework, a DSi framework to this, now names are connected, institutions are connected,
keywords are connected, metadata is connected, and the AI is able to dance on top of this
this framework and really enable kind of new insights. And that's just one example, right, prudent way of innovating the current scientific system in an age of AI and design.
I agree with you. I think that really what we're talking about is lower latency science,
right? So whether it be incremental or whatever, the way that legacy science has operated has been in a very high latency
type ecosystem or type paradigm almost and and partially by design right by fear of being scooped
and all this kind of stuff you keep these things hoarded until you can publish a particular what
you consider to be a whole unit but effectively that that slows down by definition the movement
capacity of science so it creates that latency
and so i think now i mean ideally through dsai but also just the advent of this novel technology
we can start to get real-time streamlined science for the very first time in humanity and i i you
know it's just the friction point of transitioning into that new paradigm of real-time science.
And it's just that, right?
But once we can get there, it speaks for itself, right?
Lower latency science is there's no argument that you can really make to say that that will not help usher in at least the level of innovation that we're hoping the future to have.
at least the level of innovation that we're hoping the future to have.
And I like to highly consider that across this series and across the entire space,
we are dealing with peoples and persons who understand in maybe their own contextual,
in their own contextual piece, you know, this, this future of lower latency science.
And so to that end, and clearly you are somebody who has thought about this in a number of ways over the last few years, and this has led you to build Cure VR and then Cure XR and then whatever future iteration it'll be.
I'd really like to kind of hone into how you guys are contributing to that.
So talk to me about what Cure effectively is.
Yeah, effectively what Cure is, is a means of collaboration between scientists.
If I were just to not use any buzzwords, that is what it is.
And to use your word, I love low latency science.
It really enables low latency science.
What you mentioned prior, operations in science are very costly, slow and ineffective, inefficient.
And that adds to the high latency.
We are breaking down some walls within that, within operations, and I like to use operations because operations considers training, it considers execution, and it considers collaboration. And all of
these are extremely important in science and to scientists. And so, you know, just
just focusing on those kind of things, what CURE is really doing is we are
bringing in lower latency science, we are bringing down the bar or the cost for collaboration in science.
And I see reproducibility as a major part of collaboration in science. So effectively what we've built today and what we've gotten to the market is a content
creation tool for scientists and engineers using mixed reality.
And so this what effectively that does and I put our I linked our demo in there is that
a scientist is able now to in a hands manner, capture exactly how they do what they do in the lab.
Right? With all the nuances, with exactly the technologies, the materials, the equipment that they use,
and exactly how they do an experiment, how they do what they do,
and they're able to instantaneously, rapidly, distribute those methods, how they do what they do. And they're able to instantaneously, rapidly distribute those
methods, how they do what they do. So now, whether it's for an internal training and internal
standardization of, hey, this is how it needs to be done every time, or towards a external
collaboration, hey, this is how I did it. Now you can do it too, as if I'm standing right next to you,
but I don't need to get on a plane, I don't need to book a hotel,
I don't need to travel to Europe, you can just do it.
This is exactly how I do it, now you do it too.
That's effectively what we've made as far as our mixed reality content creation tool.
And from there, it's been very exciting the last few months.
You know, the product has been on the market for about eight months now.
And we've got our first three customers.
We just closed another one, another university yesterday.
So it's been very fast-paced, very exciting.
But also in March, we started our AI R&D for Cure AI.
And what Cure AI does is it effectively kind of 10xs, 100xs our value proposition
from lowering latency in science, from allowing, you know,
from allowing, you know, accelerating operations in science to just do that 10x, 100x faster.
And so by creating a simulation, and I just came from a lab just now before this,
we created a simulation with environmental health and safety, right? For them to standardize how safety procedures
are done at the lab at this university, Wright State University here in Dayton, Ohio.
You know, we're in their school of medicine, we're in their school of nursing, and this was their
environmental health and safety getting onboarded now too. The applications are very broad, right? It's
actually quite a potent tool. Not just scientists need lower latency science. Engineers need lower
latency engineering, right? Nurses need lower latency training as well, operations as well.
You know, sharing best standards and practices. And that's what CURE is really about is it's a means
to enable rapid sharing of best standards and practices. And that's what CURE is really about is it's a means to enable rapid sharing
of best standards and practices, rapid sharing of methods of the how and
why you do what you do so that it doesn't matter what institution you're at,
doesn't matter where you are, you are able to do science the way that it's
meant to be done.
And now our AI trains on those simulations, right? It trains on the content
that is created by these subject matter experts, right? We're standardizing these procedures.
And what that now enables is a real-time AI co-scientist, an AI co-pilot for scientists and engineers for their workflows.
You know, I try to relate it to The Matrix.
Actually, there's like two really good scenes from The Matrix that feels a lot like Cure is kind of the beginning
of what we're seeing in The Matrix in these two scenes.
One of them is, you know, the classic I know Kung Fu, right of them is you know the classic i know kung fu right neo's
in inside of the this the simulation he's inside of like a virtual dojo and he immediately gets
uploaded with kung fu and now he knows kung fu right uh that was most likely because it was trained
on a someone who who the ai got trained on how to do Kung Fu, right? Someone had to
train it on that. That is what CURE is enabling for really any type of
procedural workflow. Whether it's in the lab, in a manufacturing facility, in an
assembly line, whether it's a DOD, how do you load artillery, how do you, you know,
repair a boat, a plane, a truck, a tank?
Right?
All of that is able to be done through our pipeline
of content creation and Cure AI,
being able to train on these protocols,
on these simulations, and now yielding an assistant
that is able to guide you through,
regardless of your skill level,
whether you're a scientist or an engineer or not,
you are
now able you know kung fu right uh and the other one is uh you know they're they got into this
helicopter i think it was like neo and morpheus and he's like do you know how to fly a helicopter
and you know his like eyes go in the back of his head and he's like for a second he's like i do now
and then he takes off right that is effectively uh what we're building uh at cure
that is so cool man because i mean in fact you talk about it you know it's verification it's it's
it's acceleration of education i also want to throw in there things like data enrichment
right like you know you talk about most of the ways that we convey knowledge and convey intellect is very static, right?
It's a protocol.
It's in the case of a flight, it's a flight manual.
But, you know, to your point, imagine if, and we haven't quite gotten to the point where you just download the instructions to your brain, but I'm sure we're not too far from there.
But imagine if you grab the throttle, you grab the steering, and you have this overlay
of hand placement.
You have this overlay of how the top tier person who is the best in their field actually
operates the process.
That's an enrichment of how somebody can acquire, can convey and acquire that level
of information.
And I think this is something that's not trivial, right?
Like it may seem incremental to a certain extent, but if you think about it,
I like what comes to the back of my mind is like your grandmother's favorite recipe,
or the favorite food that your grandmother makes.
Like you can look at the cookbook and try to follow the same steps,
but unless you're in the kitchen with your grandmother,
you're never going to be able to make that actual that actual dish that tastes the exact same way because there's a flavor that comes
for that only she knows how to do it there's a little bit of extra that is not really written
down but is repeatedly done that gives it that extra feel and the same is true in i think any
any human endeavor we write down these very static, like, you know, quote unquote,
standardized processes,
but there's always a little bit of nuance in there.
And for all the scientists out there,
I'm sure you can relate to receiving a protocol
from the lab, from a postdoc,
or from somebody who's a little bit higher than you.
And they have the, yes, everything written down,
but then you have like notes jotted on the side,
like, okay, add a little bit more of this, add a little bit more of this add a little bit more of that and these are the things that are caught in
between that don't often get conveyed but now with cure you actually get to see that in real time
and so you not only are you like you're creating a next level of standardization by virtue and
that's where i'm assuming the the cure ai is going to come in because effectively you're going to distill out what that true, again, closest to true standardization form is.
But effectively you're contributing to a better way to capture or to devise best practices, best practice standards based off of everybody's or accumulate an aggregated amount of experience that people are conveying.
or accumulate an aggregated amount of experience that people are conveying.
And I think that's really, really cool.
That's a huge data enrichment piece there
for things that are not as tangibly captured
in this more static framework that we traditionally have been doing.
Precisely. Yeah, exactly.
It's the nuance, right?
Like your grandma's cooking is very nuanced.
She knows what she's doing.
She's probably not following a protocol. Exactly. She's done it. She is the expert. She knows what she's doing. She's probably not. She's not following a protocol.
Exactly. She's done it. She's she is the expert. She is the subject matter expert.
Right. So this is a way Cure is basically a pipeline to get technical complex siloed knowledge out of a subject matter expert's brain and be able to rapidly scale that across the world to enable others to do it, right?
That's fundamentally what we're building.
And so I love the cooking example.
But, you know, and it is.
It's a new type of data, right?
This is kind of like a new methods section, right?
We have, you know, our early adopting customers,
they are interested in their next publication.
They've already made the simulation for how they did the experiment.
For their next publication, they actually want to send in the simulation file, that data, with their publication for it to be in the official filing of that pub.
I love that. Whether that's just the videos, like, you know, that's, so, you know, this year where
they're sending that in, they're going to be sending in those videos of them using Cure to do it.
And, you know, it's not just the how, right?
Because also within, right, we allow, you know, it's content creation.
That's really what it is.
We've created a content creation tool
for scientists and engineers.
And so, right, we basically created like the Canva
or the PowerPoint for mixed reality
and for these technical complex workflows.
So what you're also able to do is you're able to upload
reference materials, right?
Which are like PowerPoints, PDFs, work instructions,
blueprint, schematics, PDFs, Word docs, you know, PPTX, whatever. And you're able to open
it in a spatial environment, right? So not only do the users see kind of your first person
perspective, hear your voice, but now they're also supplying you with these materials, right? Maybe it's a
PowerPoint slide, or maybe it's a PDF that they've written themselves, and they're able to now go
into the theory behind what they're doing, right? So not only is it real-time training,
you know, real-time operations support, but it's also real-time education. It's not only answering
how do you do what you do, but it's also answering why do you do what you do. And you know that added context
is very important for you know that it's a huge huge like for for our customers.
They love the the reference material aspect because it allows them to become
more creative and how they're able to convey certain ideas,
processes, and otherwise with more nuance,
more illustrative nuance.
But also this is great for our AI training as well.
And so the beauty of our AI platform
is that we're really creating our own data sets.
We're not going out.
And that really gives us a leg up as far as AI for scientific advancement goes
because we're relying on subject matter expert simulations and data.
And that goes into kind of methods.
We've been asked to open our own methods journal,
a journal of XR methods.
We've got something cooking along those lines.
We'd love to work with Deci Labs, who is basically
doing just that.
The current publication system is absolutely extortionary and needs to go away.
So I'm interested to see what they're doing.
You know, time for me, for us in the biomedical space, like, you know, one of
the major tenants that we talk about in this side, but also just across the
biomedical spaces is patient engagement, getting patients more involved and people
who were traditionally
left out as stakeholders, but, you know, as actual stakeholders in the ecosystem.
And so to your same point of being able to convey information in a more easily digestible
format for patients, for people who are undergoing surgeries and therapies and are contributing
biospecimens to research, to be able to get that reference material
around how it's being leveraged, how it's actually being processed from both the angle of understanding
what you're contributing to, but also in getting that education of like, okay, well, this is
actually how this operates. This is what I'm contributing to. All of that information is
extremely valuable in terms of pushing industries
forward and filling in a lot of the gaps that currently exist across these various industries.
And so, I mean, you guys are talking about a journal. That's fantastic. Methods journal,
it just seems perfect for that. But there are so many different applications I can envision
you guys being a part of. Sorry, I know you and I have spoken about things on the side. And
as things start to tangibilize more in different endeavors, definitely looking forward to wrapping you in.
But like, I feel like there's so much, there's so many different ways that you can apply what you're generating.
And to your last point, you're generating data sets.
And not only are you generating your own data sets, you're generating these novel forms.
These almost like unique asset data sets.
And so you guys are
sitting on something super powerful um and i mean you know me i'm super i've always been excited
about it but it's been great to see it kind of flourish over time yes i know it's an uphill
battle i'm sure things are slow to adopt but there is no doubt that you guys are building something
and those like you are building things that are really going to be what is considered normal science moving forward. I appreciate that, Jelani. Yeah,
it's definitely not easy. Startups are not easy. You know, changing a traditional system that's so
ingrained in society is not easy. It's got institutions. It's got a lot of stakeholders
involved. But, you. But I think the direction
that Desai is going is really, really wonderful. We have a vision here. Things are really moving
in a great direction in the US as far as stable coin bills being passed. Next up is kind of
definition, regulation and policy around crypto, NFTs, etc.
And I think that's really when institutions are, right, it's going to be like 2020, right?
Everybody, all the consultants were like, hey, what's your AI strategy?
Everybody needs to have an AI strategy. What's your AI strategy?
Now it's going to be, what's your digital asset strategy, right?
Okay, hey, there's different ways to do things now. what's your digital asset strategy? There's different ways to do things now.
What's your digital asset strategy?
And so I think institutions like academia, pharma, biotech, and analogous are going to have to come up with a way.
And that's why I think it's so wonderful for DSi because we're here.
We've been building.
We've thought this through.
There's multiple ways that people can harness value through DSci, through digital assets,
once that's, you know, not something so scary and lawyers are like, oh no, we can't do that.
So yeah, I mean, and we're going to be setting up, I'm joining as a ad in some DSI kind of mechanisms in there.
The timing is incredible, to be honest, right?
For all of this, I mean, it's not great that it's a great timing because it's quite consequential
for science right now. But with the NSF and NIH cuts, there's a vacuum.
A vacuum is formed.
Latency is no longer acceptable in this high-state, low-resource environment.
So it's put fire to ask at this point for this innovation to actually come in and talk and actually become
something that is adoptable and applicable across the scientific landscape. Now, to your point,
though, and this, you know, there's always this element of like Web3 blockchain when we talk
about DSA, and we haven't talked about that really with you guys. And I know, so can you talk about
that actually? Are there any, you know, quote unquote
web three leaning features involved in the cure ecosystem
either prospectively or currently implemented?
Currently implemented, no, but.
I love hearing that chart.
I love hearing that.
Okay.
Yeah.
I'm gonna put that out there for everyone else is listening.
I love hearing that because these things are secondary third order applications effectively, right?
And I think that, and I just want to, I just want to exclaim on that because the theme of season two has been a number of projects that have lasted somewhat the testament of time and are not leading with Web3, but are open to leveraging it where it makes sense.
Sorry, I digress. you're it's it's
totally on point right like that it's a web3 d side products are not there's no product market
fit for them yes right now okay the market's not ready for it you can make a product it's
the market's not ready to adopt it okay i mean we can just take yeah pick
your pick your example right uh so no right but we we are very strategically seeing that and and my
confidence in uh in dsai becoming a very mainstream theme has been growing over time right so this nsf
nih vacuum that's been caused, right, in funding.
This is great news for digital assets, for decentralized research funding, right?
And so, you know, shout out to Molecule and Bio for what they're trying to do with these DAOs.
I'm very interested, right?
I mean, I'd be very interested where there's a new center of excellence being opened up in the department that I'll be joining.
I'm very interested in opening something like a DAO there, decentralized research funding it there. we're what we're positioning Cure as a tool as is as a tool for decentralized science,
is a tool for DAOs, is a tool for CROs, CDMOs, academia, biotech, pharma for operations.
And that includes training, execution, collaboration, but also for, you know, a real dsci future looks like which is anyone can be a scientist
number one and number two is that anyone with access to a lab uh can basically serve as their
own kind of contractor their own contract research organization right uh that that's really what it
is because those those incentives aren't in science right now, right?
Your incentives in science come from a centralized source, probably grant funding,
a department, a biotech, a pharma. And if you want to start your own kind of innovation in science
or in biotech, you have to raise, get grants, go to VCs, and you're still held to those same constraints. Creativity is not allowed
to flow. But I really do like what some of the experiments in the current Deesha ecosystem have
been and what they're leading to and the lessons that we can learn from them. And I'm still waiting
to get some more transparent lessons that have been learned from the efforts that have been made.
So as far as kind of where we are going, the ecosystem is being built so beautifully,
regardless of how it's going. It's a long-term vision, it's a long-term game.
So from a decentralized research funding model,
that's great, right?
An incremental published model,
we're able to kind of come in there, right?
When you incrementally publish,
here's the simulation of exactly how you do it.
Now you can do it too.
From a funding model,
hey, we want,
you know, science is expensive, right?
Hey, I did this experiment, here's exactly how I did it.
We are going for, you know,
let's say we want to solve a problem.
Let's say we want to create a ovarian cancer vaccine.
Okay, how are we gonna do that, right? right okay you have one scientist in a lab how's
he going to do that does he know anything about raising money is he going to get a co-founder is
it going to work let's focus on the science he has a hypothesis he executes the hypothesis once
okay he gets some preliminary data he creates a simulation when he does it right with cure
some preliminary data, he creates a simulation
when he does it, right, with Cure.
He now puts that simulation on a smart contract
and he can say, hey, I want to decentralize,
you know, raise funding in a decentralized manner
because I want nine other scientists
to do exactly what I did.
I want them to reproduce it before I move forward with my hypothesis. Okay. This is
how I did it. I'm not going to tell you my results. Okay. But I want 10 other people to do it so that
we can aggregate those results and, and, and, um, and get it done. I've already put in, you know,
the, the money required to do it once. Okay. I want nine other people to do it so that we have an N of 10.
All right.
Cool.
Other people see it.
They can invest into it, right?
It's a smart contract.
Now they are maybe the earliest investors into whatever this is going to become, whether
it's a biotech company, a biodao, et cetera.
But they all put it in.
Now you've crowd-sourced research okay crowd-funded research
and and it's in this incremental fashion which is a more scientifically responsible way of doing it
right not only that bro first of all what you just said is amazing because not only that you're
bringing the peer review at the forefront and not as an after effect. And so you're validating from the ground up.
That is okay. Yeah. And so you, so now, okay. You know, each experiment is going to cost $1,000
and hey, you know, these guys aren't doing it for free. So we need another $1,000 of like labor,
right? For them to do it. So, okay we and we need nine people to do it so we
need we need eighteen thousand dollars okay raised okay uh you know d gens independent investors
they all throw in oh this is this could lead to an ovarian cancer vaccine a 500 billion
a billion dollar market sure okay let's do it um it. So they throw in whatever, it gets crowdfunded, they raise
the 18,000. And now scientists are able to bid on it, they have to meet some certain
requirements, same equipment, right, most likely same, same kind of research focus area,
right, oncology for this, for this example, oncology, you know, oncology researchers bid
on it, they get the contract contract they get the money to get
the exact same reagents materials to do the experiment they get paid for the labor after
they execute it they have to go through the simulation they have to use the simulation
so that we ensure reproducibility right of those nine scientists doing it they all execute and they
submit the raw data.
Then they get paid for their labor and we're done. They're done there. Now all of that data, those nine data sets now go to the original investigator. He compiles those data, he
analyzes the data. Is the hypothesis positive or negative? If it is positive, now he says,
okay, next we're going to do this. Okay. And because of the results of the last one, we're now going to do this experiment to dive deeper.
And we need $20,000 for the next set of experiments.
And we're crowdsourcing the science and the funding for it.
right so now more people are able to come in now let's say the hypothesis was
So now more people are able to come in.
Now let's say the hypothesis was false.
false now he's like okay that did that you know that obviously is not the
pathway the mechanism the target the receptor that we're going for we're
going to now do since that was negative we're now going to do this so now
effectively what I'm trying to convey is that now we were enabling what I see is
it as a future where scientists are able to build in public,
share their work in public, super low latency. I mean, it's all being done right in front of
everybody. Everybody sees it. It worked. It didn't work. What do you know? Does everybody else agree
with me? You know, you can throw in your two cents. You know, what should we do next? Okay,
I already know what to do next. Here's what we're going to do next i did the experiment here's the simulation i want nine other people to do it
this is the sequence and so now what we're doing in this in this context right this is this is kind
of what what i see as a as a real possibility and this is just one you know my my crazy idea of for
what can happen in dsci uh but what we're doing now effectively is number one, we are executing science in
very low latency, a very reproducible manner. We are decentralizing the research funding.
We are giving people ownership, like independent investors, consumer investors, the ability
to get into very early stage by bioscience innovation. We are incrementally publishing data, right, if they so choose,
and more, right? So we're hitting a lot of DDSI themes and points in this framework, right? Now,
who are the scientists doing the experiments, right? This is anybody, this could be anybody,
anyone with access to a lab. They're now getting a new revenue stream.
They have new incentives.
They can also propose peer-reviewed smart contracts, right?
Ask other scientists to do this work.
Now you've got an entirely new market economy.
You've got an entirely new market for contract research, contracted research, right?
Now these major CROs, they're gonna see that,
and we'll go, hey, Charles River,
what's your digital asset strategy?
Okay, these CROs are already having issues,
these CDMOs, these pharmaceutical manufacturers,
are already having issues.
I was just talking to Mike, our friend, Mike in Colorado,
I know we're running out of time,
but he said that they've wasted millions of dollars because
a CDMO, a pharmaceutical manufacturer, cannot scale the small batch manufacturing to a large
batch, right?
They've wasted millions of dollars trying to do so, and they've tried twice.
And they've already given them the protocol.
So he was like, hey, can I make a simulation of how I do it so that I can give it to them
and they do it exactly how I do it?
Yeah, of course. That is the point, right? It's a problem. It's persistent. I wish we had
more time, Zilani, but I'll stop there and then hand it to you. So we will make more time. Shara,
we'll have you back up because these are, and probably for our T-Sci event, because these are
like conceptually the conversations we need to have around DISA. Like how do we actually, actually build these novel economic flywheels
paradigms to help power? And we as builders are just like, we contribute the tools to allow for
this, but how do we weave these into tangible paradigms that can be leveraged in research to
help support research, especially in unsettling times like what we currently have.
But yes, to your point, we are reaching the top.
So I will say, Char, this is always a great conversation with you, men.
You have so much insight and it's always good to see people
who have lived the experience of traditional science
and are also looking to build in decentralized science
or whatever form of futuristic science you're appealing to I think that's important particularly for those
who are looking to participate in this ecosystem get in touch with different
groups and projects like have that on your your rubric of who and where you
decide to participate now with that said man I'll kind of leave you with the
closing remarks of course this is a space for you and I think you've done a fantastic job. Just let us know what the future, what you can share about
the future of Cure in like one or two lines in a minute or so, and then close it out with whatever
call to action you want, man. Thank you, Jelani. Thank you again for the opportunity to come up.
We're moving really fast at Cure. Our AI development is going really well. We'll have
an MVP later this month, full-time integration in Q3, and really 10X, 100X is our platform.
We're in manufacturing, we're in the labs, we're in defense right now.
And we still have our AngelRound round open, anyone knows, is interested
in being part of cure, you know, do do let me know, reach out through x or LinkedIn or
wherever. Big shout out to you, Jelani. Shout out to Amino chain, the work they're doing.
I mean, the they are the pioneers in B2B D-Sci right now
and really showing what real value looks like from a D-Sci perspective.
Shout out to Josh and all the D-Sci builders.
I think that, you know, what the future looks like is very good.
You know, check out the Maha report that just came out a few weeks ago,
the Make America Healthy Again report. The last page has 10 recommendations from the Maha
committee, and the very first recommendation is to solve the reproducibility crisis. They see it,
I see it. There's a vacuum in in science today especially in academic science and research
funding I think DCI is here to to solve that we need more real builders we need more scientists
who have who have lived the experience who have seen the problems and who are passionate on
solving it and yeah I'm really looking forward to next time. And let's cure together. Let's cure together.
Well, with that, everybody, we're going to close off the space.
Thank you again, guys.
This was a great space.
Char, thank you.
We're going to have you up again.
If you guys have any questions, any comments, any ideas, please do not hesitate to reach out to myself, the DeSci World official account, Josh, who is the mysterious name we keep dropping, our DSiJinger, DSiJesus, Char himself, reach out to him.
We'd love to participate and kind of work together to build this future that we all spoke about.
So with that, guys, have a great rest of your week.
Tune in to our next episode, which will probably be in two weeks.
And if you're a DSi project that is looking to get more visibility, please list on the Desai World dashboard.
Also reach out to us.
We'd love to have you up for your own dedicated episode of Desai Rising.
So with that, guys, have a great rest of your day, and we'll talk soon.
Take care.
Bye.
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DeSciWorld
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