Original author: elliot hershberg and jocelynn pearl
Compiler: llamac
(Portfolio: burning man 2016, about tomo: eth foundation illustrator)
"Recommended message: This article helps you understand why binance invests in bio protocol, and discusses the funding of scientific research changes, the structure of existing scientific research institutions, and web3 technology is currently desci How narrative is changing the way scientific research is funded and organized.
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Science is a fundamental tool for human progress.
It is a system we have built to derive detailed explanations of objective realities that are difficult to change. These types of explanations require coherent models that can explain empirical observations. The only way to arrive at these types of explanations is to perform challenging experimental and theoretical work to ensure that all details of the explanation are functional and tightly connected to objective reality. Explanations of this nature are central to our transition from myth to physics, from caves to skyscrapers. Physicist David Deutsch believes that this is the core idea of the scientific revolution. "Since then, our knowledge of the physical world and how to adapt it to our wishes has been continuously growing."
The guiding light of scientific discovery It is one of our most valuable resources and must be managed with care. In addition to developing new explanations, we have built a complex human system that transforms new knowledge into the inventions that drive the modern world. Studying and improving this system is critical—it requires interacting with countless complex human systems. As the visionary Vannevar Bush argued, "We need legislators, courts, and the public to have a better understanding of the entire complex matter. There will be no shortage of inventions; real inventors just can't help but invent. But we want more Many successful inventions, and achieving them requires a better understanding."
Guided by a mission to accelerate scientific progress, Vannevar Bush led efforts to expand the U.S. research funding system to the scale it is today. This powerful system is what scientist and former Office of Science and Technology Policy Director Eric Lander calls a "miracle machine."
Our systematic efforts to fund basic science ultimately led to such miracles as the Internet, artificial intelligence, cancer immunotherapy, and gene-editing technologies like CRISPR. While the results so far have been miraculous, this machine does not run on its own: maintaining this system is absolutely crucial.
However, over time we became complacent in maintaining this machine.
lander coined the term while passionately arguing for an increase in the federal research budget, which has actually been declining in recent years. Over time, "adjusted for inflation, the budget of the National Institutes of Health, the federal medical research agency, has declined by nearly 25 percent since 2003." The challenge of funding science goes beyond advocating for a bigger budget. Our actual funding mechanisms are becoming increasingly rigid, inefficient, and consensus-driven. One U.S. government study estimates that professors now "spend about 40 percent of their research time navigating the bureaucratic maze" necessary to fund their labs. In another sobering survey, 78% of researchers said they would change their research plans "significantly" if they received unrestricted funding. Young scientists also face severe bottlenecks in obtaining funding early in their careers, even though this may be the most productive and groundbreaking period of their lives.
Beyond laboratory funding, there are serious constraints on how we translate scientific discoveries into new drugs and products. Structural bottleneck. This is what former National Institutes of Health Director Elias Zerhouni calls the "Valley of Death." The pace of company creation in the biotech sector has slowed in recent years. Physician and scientist Eric Topol recently noted that while we have made profound advances in understanding the human genome, this knowledge has yet to be practically applied clinically.
Any optimist and advocate of human progress should regard the health and efficiency of our "miracle machines" as of central importance, and we are clearly far from operating to our maximum capacity.
So, what should we do?
challenges and inefficiencies represent new opportunities. Innovation in research funding mechanisms has exploded in recent years. Metascience—the study of science itself—has become an applied discipline. Will the wonder machines of the future be modernized versions of our current systems, or will they be something entirely new? Where and how will the next round of scientific progress occur? These are questions at the heart of nearly all types of innovation. To quote R. Buckminster Fuller: "You can never change things by fighting against existing reality. To change something is to build a new model that makes the existing model obsolete."
in Analysis With When dealing with complex human systems with multiple layers of incentives, following the flow of money is often surprisingly good advice.
all the president's men | follow the money scene | warner bros. entertainment
https://youtu.be/qodgxd19_as
The goal of our exploration here is to better understand how we currently fuel the miracle machine. How do we actually fund scientific innovation and commercialization? From there, we'll examine ideas, technologies, and projects aimed at changing this process.
takes a deep dive into some of the innovations in research funding in recent years, from private capital to cryptocurrencies to the creation of new dedicated research institutions targeting uncharted territories in our scientific understanding.
We'll explore:
- A macro view of current science funding
- Killer web3 use cases
- Faster gas, faster funding
- Fully bucky (built from the ground up)
A macro view of current science funding
Current miracle machines in action How is it built?
Almost all scientific disciplines are roughly divided into three types of organizations:
- Academic institutions (universities, non-profit research institutes, etc.)
- Startups
- Corporations (established businesses with R&D labs)
Let’s look at how biomedicine works to crystallize this. With an annual budget of approximately $45 billion, the National Institutes of Health (NIH) is a major source of funding for biomedical research. Other agencies such as the National Science Foundation, with an annual budget of about $8 billion, are also important funding agencies. These large government agencies allocate funds through a variety of different grant mechanisms to principal investigators (PIS) who apply for funding. A pis is usually a professor at a research university or medical school who manages a laboratory. The actual research work is carried out by graduate students, temporary postdoctoral fellows (postdocs) and some professional staff, while the PI takes on the role of manager.
This tiered funding and organizational structure is not the only way we do laboratory science. The eminent chemist and microbiologist Louis Pasteur (for whom the process of pasteurization is named) carefully conducted many of the experiments (as mentioned above) himself, with the help of experimental assistants. This was actually a key part of his research process: he trained himself to maintain a "prepared mind" to notice even subtle results in his experiments. Today, it has become a common joke to "be careful when principal investigators enter the laboratory" because their experimental skills are rusty.
emily noël broke the laboratory equipment due to excessive force during an operation in the laboratory.
https://x.com/noelresearchlab/status/1171376608437047296
It is difficult to pinpoint when the transition to modern laboratory systems occurred, but World War II was a key turning point. Given the importance of the Manhattan Project in the war effort, science funding underwent an important transformation: it no longer simply supported intellectual pursuits—scientific funding had a direct impact on national security and economic growth. These ideas were best expressed in Vannevar Bush's 1945 report, Science—The Endless Frontier.
In the years that followed, many of our current scientific and biomedical research institutions came into being.The number of medical schools in the United States has doubled since World War II. Between 1945 and 1965, faculty positions increased by 400 percent. Science is no longer a solitary intellectual profession but increasingly a team endeavor funded by government grants. This is often referred to as the increasing "bureaucratization" of science.
So the first major cog in the miracle machine was the government-funded research laboratory. The
laboratory is responsible for building fundamental explanations of the world and making its transformation possible. Commercialization of science is achieved through spin-out companies formed around specific intellectual property (IP) with translational potential. These spin-offs are funded by venture capital companies (VCs), which are primarily funded by limited partners (LPs). Limited partners are institutions such as university endowments, pension funds and family offices.
This is the second cog in the miracle machine: startups and university spin-outs backed by private capital.
Biotech startups are primarily focused on scaling and expanding the initial science they are built around and going through the arduous and lengthy process of getting new drugs approved. The process does not end with approval. Drugs must be produced, marketed and sold globally. This phase of the work is done by pharmaceutical companies, many of which are large multinational corporations that have been around for more than a century and in some cases even predate the creation of the Food and Drug Administration (FDA), which is responsible for drug approval. Rather than developing drugs themselves, pharmaceutical companies primarily purchase assets from biotech companies, which often involves acquiring entire companies.
Massive R&D enterprises like Big Pharma are the third major cog in our current miracle machine.
This machine really does wonders.
The Genentech story is just one example. Groundbreaking academic work at Stanford University is being spun off into a venture-backed company. The company has successfully used genetic engineering to turn bacterial cells into mini-insulin-producing factories - significantly alleviating shortages of a vital drug. In 2009, Genentech merged with Swiss pharmaceutical giant Roche in a $47 billion deal that promised global scale.
The story does not end here. Breakthrough technologies such as cell therapies and crispr gene editing are still making the transition from academic laboratories to clinical applications. Academic labs are still developing new theories and models, and companies are still being founded and funded based on the most promising advances. The pharmaceutical industry remains a major global buyer and distributor. The system achieves some kind of stable equilibrium among its various participants.
While miracle machines improved our world, systemic challenges emerged over time. We provide this bird's-eye view of the current system in order to make some of these problems easier to understand and to provide context for understanding new projects that seek to address these problems.
Large funding agencies like the National Institutes of Health (NIH) have become increasingly bureaucratic over time, with an inherent bias toward funding more conservative and progressive work. We're pretty sure no one really thinks scientists should spend up to 40% of their time slogging through tedious government paperwork. As the funding process becomes increasingly complex and committee-driven, it becomes increasingly difficult for new and promising research directions to gain support.
The National Institutes of Health (NIH) has also become interested in "big science" programs, which organize large research groups to fund projects that cannot be completed by individual laboratories. While this seems important in principle, such projects have had mixed results and consume resources that could otherwise be used to fund laboratories focused on basic discovery science. As Berkeley biologist Michael Eisen puts it, "Large-scale biology projects are not a boon to individual-driven discovery science. Ironically and tragically, they are becoming the greatest threat to the latter's continued existence."
Government Research Large-scale structural changes in funding have shaped and limited the types of scientific questions researchers can pursue. The relay between universities and startups has also become more complex.During the conversion phase, terms for university spin-offs proved to vary widely, in some cases putting the companies in trouble before they even got off the ground. Universities have strong incentives to closely protect their intellectual property, which can lead to worse terms for scientists, and may even lead to terms so unfavorable that investors lose interest in funding translational efforts.
Government agencies are not the only part of the system with financial blind spots. Venture capitalists also have inherent limitations on what they can invest in—a company must have the potential to be a massive $1 billion-plus exit for the math to make it worth investing in. Not all technologies or public goods can generate such returns, especially within investors' constrained time horizons. Only a small portion of society has the opportunity to earn real wealth as accredited investors by backing these private investments, further exacerbating inequality.
Pharmaceutical companies are also constrained by their financial structures and incentives. The clear incentive is to develop or acquire the drug with the largest market while minimizing R&D costs. This distorts the entire pipeline in a suboptimal manner, with real consequences: "Despite significant unmet need and disease burden, there are few products in the pipeline to address antimicrobial resistance, tuberculosis, and opioid dependence." . In contrast, many new products are new versions of existing products with only minor changes to existing drugs."
So where should we look for new ideas and approaches?
It is unlikely that we will get radical solutions from the leaders of our current institutions because they have incentives to perpetuate the system they are in. One interesting direction for finding new ideas is to explore side projects that innovative scientists are working on. As Paul Graham said about great startup ideas, "The best ideas almost have to start as side projects because they are always so different that your conscious mind rejects them as ideas for a company."
takes this approach approach, it’s hard to ignore the steady expansion of activity in the decentralized science community.
killer web3 application case
Personally, I was initially highly skeptical of web3. As a scientist and engineer, one of my core areas of focus is leveraging the power of web2 technology—efficient central databases, fast servers, powerful modern browsers—to build cutting-edge research tools for scientists. Measurements and technical assessments like moxie marlinspike's initial impressions of web3 have been fundamental to my thinking in this area.
But over time, I became a cautious optimist - ironically, this happened just as the cryptocurrency market was collapsing and doubts about web3 were growing. Why? As I talk to smart people like packy, jocelynn, and some of the leading founders in the space, I get excited about what this new set of protocols, tools, and ideas could potentially excel at. We are observing some important social experiments that seek to establish new models of collaboration and organization. From my direct experience in academic science, I know that our research institutions could benefit from changing the status quo.
Not boring readers may be familiar with the huge web3 use case debate our fearless leader packy has been embroiled in lately. A real advantage of web3 is that it provides a new set of tools for creating financial instruments. As Michael Nielsen points out, "New financial tools can in turn be used to create new markets and enable new forms of collective human behavior."
If one of the killer applications of this new tool stack is to radically improve the research funding process Woolen cloth?
As we have emphasized so far, research funding once fell broadly into two categories: public or private financing. Once cryptocurrency investors began generating significant wealth, a third category of funding sources emerged, and many of these new investors wanted to use their funds for good.
alone is worth briefly thinking about. The expansion of cryptocurrencies has created a new breed of billionaires, primarily those willing to be early adopters of a new financial system.As Tyler Cowen has argued, this could change philanthropy, as these new tech elites will have a greater interest in "quirky independent projects." We’re already seeing this dynamic happening, with both Vitalik Buterin and Brian Armstrong making massive investments in longevity science projects.
This difference is not limited to the birth of a group of younger and more technical investors and philanthropists. web3 technology is being used to enhance funding for new and exotic scientific projects. Today, new financing mechanisms including token sales and cryptocurrency-backed crowdfunding are introducing an entirely new way of financing projects.
Crowdfunding has traditionally been a challenge for scientific research, but cryptocurrency crowdfunding may be changing that. A range of new open protocols and tools have emerged aimed at scaling up the funding of public goods. One example is gitcoin, an organization dedicated to building and funding public goods. Every quarter, they run a crowdfunding campaign backed by big donors like Vitalik Buterin. The interesting innovation here is that the grants are matched quadratically - meaning the number of donors has a greater impact on the match than the amount donated. In the latest gr15 grant round, decentralized science (desci) was listed as one of four impact categories, once again highlighting the growing interest in scientific research in the web3 space.
gitcoingr15 grant round
https://x.com/umarkhaneth/status/1575147449752207360
desci round received donations from 2,309 unique contributors, supported 83 projects, and raised a total of $567,983. Matching donation funding was provided by an interesting group of large donors; including vitalik buterin (co-founder of ethereum), stefan george (co-founder and CTO of gnosis), protocol labs, and... Springer · Natural group.
The scientific community is borrowing from another blockchain technology innovation: decentralized autonomous organizations (DAOS).
As Packy described before, dao is an innovation in web3 governance. dao "run on a blockchain, giving decision-making power to stakeholders rather than executives or board members." They are "autonomous" because they rely on software protocols recorded on a publicly accessible blockchain , "If certain conditions are met, action is triggered without human intervention."
As is the case with gitcoin and quadratic funding, one of the most exciting early use cases for dao is accelerating the building and funding of scientific communities. Over the past year, science has experienced a kind of Cambrian explosion. Here is an overview of some of the DAOs and projects in the field:
If we think of traditional science as taking place within established and highly centralized university centers" "Top-down approach", then science shows the upward trend of "bottom-up" scientific development. Many of the communities showcased in this field are formed when a group of people adopt a common goal—advancing research in agriculture or hair loss, for example. These are not just reddit-like discussion forums; most DAOs contain specialized working groups, often mixing experts with amateur scientists, working on things like conducting new literature reviews for their areas of interest or evaluating projects for funding. Task. One of the original promises of
desci was the democratization of access to funding; essentially, research that would otherwise not be funded is now being funded. But is this true in a community-funded project like vitadao’s Transaction Process Group? Among the funded projects listed on their websites, several university researchers have received grants of approximately $200,000-300,000.
vitadao How do researchers funded by NIH differ from those receiving traditional NIH funding? For example, Dr. Evandro Fang, whose project investigating novel mitophagy activators recently received a $300,000 investment from Vitadao, has received multiple NIH and other government grants for his work, according to his resume.Another argument for the novelty of Vitadao's approach is that their community reviews and funds these projects more quickly than NIH's, even though there is a high degree of overlap among grantees.
So far, crowdfunding projects like gitcoin and organizations like vitadao in the desci community have set their sights on accelerating and simplifying the funding process for basic research. Other projects have begun to target the shortcomings of the biopharmaceutical industry that we highlighted, such as rare disease drug development. Another early selling point for the
desci space is its potential to advance treatments for underserved patient groups, such as those with ultra-rare diseases. Traditional biotech companies typically do not pursue drug development for smaller patient populations because they cannot generate enough profit from the final product to justify the high costs of clinical development. But dispersed global teams are advancing efforts to identify drugs for repurposing for patients with rare diseases. Examples include perlara and phage directory, neither of which rely on blockchain technology but do support the argument that knowledge from decentralized networks can advance the development of treatments.
In terms of organizing on the blockchain, vibe bio is a new company that is embracing web3 as a way to find "every cure for every community." Vibe founders Alok Tayi and Joshua Forman plan to build a Web3 protocol for setting up patient community DAOs that can jointly own and manage their drug development. This is an exciting innovation in a space where patient communities have been organizing themselves for decades, but often with companies owning the data and assets. This poses a risk to patient foundations, which often provide seed funding for science. The companies could choose to shelve these projects, as Taysha Gene Therapeutics recently did with its Leigh syndrome project.
vibe recently raised $12 million from traditional venture capital, including Not Boring Capital; a positive sign that connecting patient communities through DAOs could be a beneficial process for developing treatments for rare diseases. Founder alok tayi was inspired to create vibe after his daughter was born with an incurable disease. In an interview with the not boring podcast, when asked "Why web3?", tayi responded as follows:
Our goal is to create an infrastructure approach through which we can potentially solve all the overlooked and Neglected diseases. So the first things we need to look at are technology and governance solutions that allow us to achieve infinite scalability of engagement, but also a whole new source of capital that's interested in taking bold action and getting big things done. .
Constraints on biotech venture capital have pushed them to make slightly more conservative investments rather than broader disease coverage. Another aspect that I would also highlight here is that when you look at the approach that others might take, whether it's a charity, an academic institution, or even a C Corporation or LLC, ultimately there's a difference in the amount of funding, the type of expertise, and There are inherent limitations in terms of the number of owners and participants who can actually participate in the process. So our ambition at vibe, our mission is to find every cure for every community, not just those 250 accredited investors or qualified purchasers who are allowed to participate in these traditional types of mechanism.
Beyond cryptocurrency funding and DAOs, there are many novel ideas exploring how to apply token economics to science and ameliorate some of its shortcomings. Among these strategies is ip-nft; essentially intellectual property tied to non-fungible tokens. A company called molecule has implemented this proof-of-concept for the first time for a biopharmaceutical asset. They hope to create an "open market for drug development." The integration of
web3 with science is still in its very early stages; time will tell how these new experiments in scientific funding, ownership, and organization will develop.We are optimistic that even if blockchain is not the answer to the crisis in the scientific ecosystem, at least it has reignited the discussion about what needs to be fixed and begins to allocate this new form of liquidity to one of the best use cases.
Faster gas, rapid funding
Experiments in decentralized science show that the web3 community has great enthusiasm for funding scientific research and commercial transformation. This should not be taken lightly. Although the National Institutes of Health (NIH) has an annual budget of $50 billion, it continues to engage in deep political maneuvering to try to convince American taxpayers to increase the scale and scope of science spending. Given this vast disparity in enthusiasm, it’s entirely conceivable to imagine a world in which the cryptocurrency market spends $1 trillion more than the U.S. government spends on science funding.
Beyond cryptocurrencies, tech philanthropists are also targeting some of the major inefficiencies in our modern science funding system. A striking example is the way emergency funding has been deployed during the pandemic. Even in the face of a global emergency, NIH has shown its inability to deviate from its rigid funding structure:
The cumbersome process scientists need to follow to obtain emergency NIH funding during the pandemic
https://x.com/patrickc/status/ 1399795033084096512
In order to deploy funds faster, the fast grants project came into being. The project, launched by Emergent Ventures and backed by a range of high-profile tech leaders including Elon Musk, Paul Graham and the Collison brothers, aims to significantly shorten important COVID-19 related research projects Time required to start. Their argument is simple: "In normal times, the science funding mechanism is too slow, and it is likely to be even slower during the covid-19 pandemic. Fast Grants is an effort to correct this."
There is an important lesson here , requires us to review our mental model of how the National Institutes of Health (NIH) came to be in the first place. As we currently see, our current funding system was largely designed by the visionary Vannevar Bush, a key member of the National Defense Research Council (NDRC) that achieved rapid results during World War II. Part of the mission of the rapid funding program is to return to the kind of efficient system that Bush himself advocated. In his memoir, Bush recalled: "Within a week, the ndrc could review the project. The next day, the director could authorize it, the business office could issue a letter of intent, and actual work could begin."
The program was originally intended to Accelerating research and understanding of covid-19 during the global pandemic, but this model appears to have appeal beyond this use case as well. In an article for Future, tyler cowen, patrick hsu, and patrick collison reflect on some of the results of the project:
We originally expected to receive a few hundred applications at most. However, within a week we received 4,000 serious applications and almost no spam. Within days, we began distributing millions of dollars in grants, and during 2020, we raised over $50 million and awarded over 260 grants. All of this was accomplished with Mercatus overhead of less than 3%, in part thanks to the infrastructure put together for emergent ventures that was also designed to disburse (non-biomedical) grants quickly and efficiently.
Incredibly, approved grants received funding within 48 hours. A second round of funding will follow in two weeks. Grantees are required to publish results publicly and share a brief update each month.
Among some interesting findings, many applicants came from top universities, a group organizers had thought were already well supported by traditional NIH-style funding. And 64% of funders surveyed said the research would not have happened without rapid funding. To quote collison, cowen and hsu again:
Quickly fund the pursuit of low-hanging fruit, choosing the most obvious bets. What's unusual about it is not coming up with clever things to fund, but finding a mechanism to actually do it.To us, this suggests that there may be a lack of smart managers in mainstream institutions who can be trusted to handle flexible budgets and allocate funds quickly without triggering massive red tape or committee-driven consensus.
Rapid funding is an approach being adopted by several organizations. These include longevity research impetus grants founded and led by 22-year-old Thiel scholar Lada Nuzhna. The initial round funded 98 projects with the goals of accelerating aging biomarker research, understanding mechanisms of aging, and improving the translation of research to the clinic. While an explicit goal of the program is to fund research that may be overlooked by traditional sources, the list of grant recipients includes several well-known longevity researchers and its admissions rate is actually more rigorous than that of the National Institutes of Health (NIH). grants is 15%, while NIH is about 20%). It's important to note that an important aspect of this type of experiment is that it could push the NIH to adopt and scale up some of the most promising new strategies. The Rapid Accelerator for Diagnostics (RADX) was launched by NIH around the same time that FAST grants were launched.
Over the next few years, it will be interesting to compare how funding is rapidly changing the mix of people who can conduct research and the types of results these researchers produce. These various projects highlight two interesting trends.
First, beyond the crypto market, a new generation of tech philanthropists have shown a genuine interest in funding science in new ways.
Second, sometimes less is more.
As we explore new forms of funding, it is worth recognizing that writing grant applications should be secondary and actually conducting the science come first. Sometimes the best solution is to quickly evaluate and fund the most promising proposals and then not impede progress.
Fully Adopted Bucky (Built from the Ground Up)
So far we have sketched out a rough picture of how institutions currently operate, and seen how the crypto market, web3 technology, and tech philanthropists contribute to the research funding landscape. We now live in a world where vitalik buterin provides quadratic crowdfunding support for science projects and collison brothers support low-overhead grant mechanisms to alleviate government inefficiencies. These new ideas are being explored to accelerate and expand Miracle Machines in exciting and important ways.
With all these new efforts, an interesting question emerges: What if some problems in science funding cannot be solved simply by new funding sources or funding mechanisms?
Ultimately, our current scientific institutions represent only a small sample of the full space of possible organizational structures. The miracle machines we have are the byproduct of a very specific set of historical pressures and ideas. Some of the new funding ideas being explored today require the construction of an entirely new set of 21st century science institutions. In other words, they are practicing Buckminster Fuller's philosophy and exploring new ways of funding and organizing science from the ground up.
How can new real-life (irl) institutes be built to address missing links in science?
One approach is a focused research organization (fros), a new type of institution dedicated to solving a specific scientific challenge, such as blue-sky neurotechnology or longevity research. Other proposed focus areas for FROS include antibodies that recognize each protein, mathematics, artificial intelligence, and developing super-durable organ transplants. The core idea of the fro model is that these types of scientific projects fall into an institutional void. They are too capital-intensive and team-oriented for academia, yet outside the realm of startups or large corporations because they are more like public goods than products with clear commercial value. fros aims to fill this gap:
Convergence Research was co-founded by Adam Marblestone and Anastasia Gamick to incubate new fros. This spring, cr hosted a metascience symposium that brought together thought leaders such as institute directors, policymakers from Washington, D.C., and the United Kingdom, as well as authors and changemakers in the metascience field. The main goal of the workshop was to brainstorm ideas on how the new organization can contribute to the advancement of science.
A common theme among attendees’ presentations was that something is wrong with the scientific ecosystem.To summarize this working hypothesis: The dominant model of university-based research published in traditional scientific journals is creating a fragile ecosystem that needs to be disrupted.
In a talk given by ilan gur (then CEO of activate.org, now CEO of aria research) we were shown a pie chart showing the distribution of research funding over time.
This chart shows something very interesting. The massive reorganization of scientific research funding after World War II that we mentioned earlier coincided with a major shift in the composition of our scientific institutions. Basic research funding in the United States has shifted from primarily funding federal laboratories (1953, left pie chart) to primarily funding university research (2020, right pie chart). Is this shift toward a university-centric funding model to blame for some of the flaws in our current ecosystem?
In another presentation, we watched a video clip of scientists at the Santa Fe Institute talking about the magic of setting:
Sample footage of the Santa Fe Institute's upcoming documentary
https://www.youtube.com/watch ?v=xc6ihzosky8
"What we did at the Santa Fe Institute was to escape society; to build a community in the mountains, in the shadow of the atomic bomb." — David Krakauer, president of the Santa Fe Institute.
The intimacy and beauty of this environment are intoxicating. Santa Fe Institute represents a real departure from the institutional structure of a traditional research university—and as such it has its own unique culture. It provides a venue for renegade scientists to pursue their boldest and most unique ideas. While watching the video, we wondered: How can we build more places like this? What would it take to design a space that would nurture the world’s next Feynman or Einstein? How big is the team? How about leadership?
Many metascientific innovators or renegade scientists are following Buckminster Fuller principles to build new institutions in the real world.
Among leading research institutions, Arcadia Science, led by Seemay Chou and Prachee Avasti, stands out. arcadia is an experiment in applied metascience. The institute is structured as an R&D company but focuses primarily on basic science and technology development. One of its core ideas is that we fundamentally misunderstand the value of basic science, especially if institutional design helps scientists effectively translate their work into new products and technologies.
in the process arcadia is experimenting with every part of its research process. For example, they are disrupting the status quo in the scientific publishing ecosystem by prohibiting scientists from publishing in traditional journals; instead, they publish journal-like articles on their website, including links to project descriptions, data, reviews, and even tweets. While this may seem trivial, it is actually a conscious move away from the strange dynamics and exploitative nature of the existing academic publishing system. Self-published experiments may improve the way code, data, and results are shared with other scientists who wish to build on them.
Another interesting institution-building applied experiment is new science. The organization was largely the brainchild of writer and researcher Alexey Guzey, who spent a year writing a classic blog post, "How Life Sciences Really Works," which explored the current realities of biomedical institutions. One of the main observations that impressed Alexey was the lack of funding opportunities for young scientists:
Over time, an increasing proportion of research funding is used to support older (literally) scientists, which leaves younger scientists It’s harder to get initial funding for their labs. This chart doesn’t even reflect the full picture: it only reflects the difficulty young professors have in obtaining funding. Young scientists who are pursuing PhDs or doing postdoctoral research have less autonomy—they mostly work on projects for which professors can get funding. While technology has greatly expanded the agency of young people—providing them with ways to found, finance, and lead their own companies—young academics are often unable to actually develop or obtain funding for their own projects. One of the core goals of
new science is to fill this gap.They have launched a short-term fellowship program for young scientists to pursue their own ideas and projects. Over time, the plan is to create longer-term fellowship programs and eventually independent institutes to give young scientists back control of their work:
Much like arcadia, they will conduct various applied metasciences along the way experiment. For example, they let researchers share articles about their ideas and work on their substack – you should really consider subscribing. They also fund more research and writing about how our current life sciences institutions actually work, like their big report on nih , or elliot's article on life science software funding .
One criticism of these new scientific institutions so far is that they rely heavily on the support of large donors, such as Eric Schmidt. Nadia Asparukhova documents some of the ways that emerging tech elites have pursued philanthropy in the life sciences in recent years, a trend that shows no signs of slowing down. In addition to the Chan Zuckerberg BioCenter, we have also seen the establishment of the Arc Institute, another life sciences center supported by the tech community. Within the world of independent research institutions, there is some debate about the best type of financing - whether a single donor can give an institute ultimate freedom of thought, versus whether the wishes and biases of multiple donors can lead to research being Pulled in too many directions?
This question highlights a key philosophical difference between decentralized science and many emerging institutions. The decentralized science movement is trying to build new protocols and tools to empower decentralized networks of scientists and technologists to organize and act more effectively. Why create a fro if there is a significant funding gap? Why not just build a new dao and let the scientific community naturally figure out how to solve the problem once it has the resources?
Lauraminquini on We now see all of these experiments happening simultaneously. As we argue, science is one of the most valuable and productive endeavors we pursue as humans, and as such there should be ample room for new ideas and resources. Nonetheless, there may be some competition between the different approaches. As nadia points out, "I'm particularly interested in watching how the tension between tech-native and crypto-native approaches unfolds. While they are at different stages of maturity, at a macro level these are two major experiments going on simultaneously ."
Conclusion
Science is one of the most powerful tools we have for progress as a species. As Packy argues, this is an inherently optimistic process: "Conducting experiments to better understand the universe, assuming that we can discover more than we already know and use it to improve the world." We are lucky to live in a world where In an interpretable world, as our knowledge increases, the world can be changed in new ways.
Because of the central role of scientific research in World War II, American leaders like Vannevar Bush designed a vast government apparatus to expand funding for science at the national level. We now live in a world driven by the wonders produced by this machine. On top of our vast federal funding system, there are several layers necessary to ultimately produce the product. Technology needs to be separated from universities and receive additional private financing. These spin-off companies also need to interface with large R&D giant companies that control all aspects of sales and commercialization.
Although the Miracle Machine has earned its nickname many times, we have highlighted several reasons why it is now necessary to try out the new scientific system. It's almost a natural law that bureaucracy increases over time, and nih is no exception. Our brightest minds now spend up to half their time applying for complex government grants that can be rejected over minor issues like fonts.Over time, government funding has become anchored in conservative, consensus-driven projects led by senior researchers.
The desire for change is clearly part of the current zeitgeist. We are experiencing a Cambrian explosion of new funding and institutional models for science. The goal of this article is to provide you with a mental model of how current systems operate and to provide a field guide for further exploration of the many exciting applied experiments in metascience.
If you happen to think there are use cases for web3, and we've successfully convinced you that funding science is one of them, you should head over to the desci wiki and consider joining the project that excites you. If you are a scientist looking for a faster way to get funding for your project, we hope that the resources we have listed on Fast Grants will be useful to you. If helping to build a new kind of 21st century scientific research institution sounds like it might be your life's work, many of the projects we've mentioned are expanding rapidly and looking for contributors in both science and non-science. The overedge catalog curated by samuel arbesman provides a great starting point for a comprehensive understanding of new research institutes.
One topic we are thinking about right now is the tension between centralization and decentralization. As Packy recently wrote, "The battle between centralization and decentralization is reaching fever pitch in many areas, with a quasi-Cold War playing out on multiple fronts. Web2 versus Web3, Russia and China versus the West, OpenAI versus OpenAI." The story of science is no exception. It will be interesting to see how these different philosophies interact with each other over time. As balaji discusses in "Network Nations," perhaps communities can form in the digital world in a decentralized manner and then build new systems in the physical world, like new nations, or in the case of decentralized science , establish a new laboratory or research institute. In turn, centralized institutes can adopt web3 technologies and use their skills and expertise as part of wider scientific networks, adopting new protocols and ways of collaborating.
Whether it is experiments in new research facilities in physical laboratories or explorations in blockchain and new network laboratories, this is an exciting time to observe innovations in organization and financing unfolding. We are hopeful for the future and the progress these ideas will bring.
Original author: elliot hershberg and jocelynn pearl
Compiler: llamac
"Recommended message: This article helps you understand why binance invests in bio protocol, and discusses the funding of scientific research changes, the structure of existing scientific research institutions, and web3 technology is currently desci How narrative is changing the way scientific research is funded and organized.
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Science is a fundamental tool for human progress.
It is a system we have built to derive detailed explanations of objective realities that are difficult to change. These types of explanations require coherent models that can explain empirical observations. The only way to arrive at these types of explanations is to perform challenging experimental and theoretical work to ensure that all details of the explanation are functional and tightly connected to objective reality. Explanations of this nature are central to our transition from myth to physics, from caves to skyscrapers. Physicist David Deutsch believes that this is the core idea of the scientific revolution. "Since then, our knowledge of the physical world and how to adapt it to our wishes has been continuously growing."
The guiding light of scientific discovery It is one of our most valuable resources and must be managed with care. In addition to developing new explanations, we have built a complex human system that transforms new knowledge into the inventions that drive the modern world. Studying and improving this system is critical—it requires interacting with countless complex human systems. As the visionary Vannevar Bush argued, "We need legislators, courts, and the public to have a better understanding of the entire complex matter. There will be no shortage of inventions; real inventors just can't help but invent. But we want more Many successful inventions, and achieving them requires a better understanding."
Guided by a mission to accelerate scientific progress, Vannevar Bush led efforts to expand the U.S. research funding system to the scale it is today. This powerful system is what scientist and former Office of Science and Technology Policy Director Eric Lander calls a "miracle machine."
Our systematic efforts to fund basic science ultimately led to such miracles as the Internet, artificial intelligence, cancer immunotherapy, and gene-editing technologies like CRISPR. While the results so far have been miraculous, this machine does not run on its own: maintaining this system is absolutely crucial.
However, over time we became complacent in maintaining this machine.
lander coined the term while passionately arguing for an increase in the federal research budget, which has actually been declining in recent years. Over time, "adjusted for inflation, the budget of the National Institutes of Health, the federal medical research agency, has declined by nearly 25 percent since 2003." The challenge of funding science goes beyond advocating for a bigger budget. Our actual funding mechanisms are becoming increasingly rigid, inefficient, and consensus-driven. One U.S. government study estimates that professors now "spend about 40 percent of their research time navigating the bureaucratic maze" necessary to fund their labs. In another sobering survey, 78% of researchers said they would change their research plans "significantly" if they received unrestricted funding. Young scientists also face severe bottlenecks in obtaining funding early in their careers, even though this may be the most productive and groundbreaking period of their lives.
Beyond laboratory funding, there are serious constraints on how we translate scientific discoveries into new drugs and products. Structural bottleneck. This is what former National Institutes of Health Director Elias Zerhouni calls the "Valley of Death." The pace of company creation in the biotech sector has slowed in recent years. Physician and scientist Eric Topol recently noted that while we have made profound advances in understanding the human genome, this knowledge has yet to be practically applied clinically.
Any optimist and advocate of human progress should regard the health and efficiency of our "miracle machines" as of central importance, and we are clearly far from operating to our maximum capacity.
So, what should we do?
challenges and inefficiencies represent new opportunities. Innovation in research funding mechanisms has exploded in recent years. Metascience—the study of science itself—has become an applied discipline. Will the wonder machines of the future be modernized versions of our current systems, or will they be something entirely new? Where and how will the next round of scientific progress occur? These are questions at the heart of nearly all types of innovation. To quote R. Buckminster Fuller: "You can never change things by fighting against existing reality. To change something is to build a new model that makes the existing model obsolete."
in Analysis With When dealing with complex human systems with multiple layers of incentives, following the flow of money is often surprisingly good advice.
all the president's men | follow the money scene | warner bros. entertainment
https://youtu.be/qodgxd19_as
The goal of our exploration here is to better understand how we currently fuel the miracle machine. How do we actually fund scientific innovation and commercialization? From there, we'll examine ideas, technologies, and projects aimed at changing this process.
takes a deep dive into some of the innovations in research funding in recent years, from private capital to cryptocurrencies to the creation of new dedicated research institutions targeting uncharted territories in our scientific understanding.
We'll explore:
- A macro view of current science funding
- Killer web3 use cases
- Faster gas, faster funding
- Fully bucky (built from the ground up)
A macro view of current science funding
Current miracle machines in action How is it built?
Almost all scientific disciplines are roughly divided into three types of organizations:
- Academic institutions (universities, non-profit research institutes, etc.)
- Startups
- Corporations (established businesses with R&D labs)
Let’s look at how biomedicine works to crystallize this. With an annual budget of approximately $45 billion, the National Institutes of Health (NIH) is a major source of funding for biomedical research. Other agencies such as the National Science Foundation, with an annual budget of about $8 billion, are also important funding agencies. These large government agencies allocate funds through a variety of different grant mechanisms to principal investigators (PIS) who apply for funding. A pis is usually a professor at a research university or medical school who manages a laboratory. The actual research work is carried out by graduate students, temporary postdoctoral fellows (postdocs) and some professional staff, while the PI takes on the role of manager.
This tiered funding and organizational structure is not the only way we do laboratory science. The eminent chemist and microbiologist Louis Pasteur (for whom the process of pasteurization is named) carefully conducted many of the experiments (as mentioned above) himself, with the help of experimental assistants. This was actually a key part of his research process: he trained himself to maintain a "prepared mind" to notice even subtle results in his experiments. Today, it has become a common joke to "be careful when principal investigators enter the laboratory" because their experimental skills are rusty.
emily noël broke the laboratory equipment due to excessive force during an operation in the laboratory.
https://x.com/noelresearchlab/status/1171376608437047296
It is difficult to pinpoint when the transition to modern laboratory systems occurred, but World War II was a key turning point. Given the importance of the Manhattan Project in the war effort, science funding underwent an important transformation: it no longer simply supported intellectual pursuits—scientific funding had a direct impact on national security and economic growth. These ideas were best expressed in Vannevar Bush's 1945 report, Science—The Endless Frontier.
In the years that followed, many of our current scientific and biomedical research institutions came into being.The number of medical schools in the United States has doubled since World War II. Between 1945 and 1965, faculty positions increased by 400 percent. Science is no longer a solitary intellectual profession but increasingly a team endeavor funded by government grants. This is often referred to as the increasing "bureaucratization" of science.
So the first major cog in the miracle machine was the government-funded research laboratory. The
laboratory is responsible for building fundamental explanations of the world and making its transformation possible. Commercialization of science is achieved through spin-out companies formed around specific intellectual property (IP) with translational potential. These spin-offs are funded by venture capital companies (VCs), which are primarily funded by limited partners (LPs). Limited partners are institutions such as university endowments, pension funds and family offices.
This is the second cog in the miracle machine: startups and university spin-outs backed by private capital.
Biotech startups are primarily focused on scaling and expanding the initial science they are built around and going through the arduous and lengthy process of getting new drugs approved. The process does not end with approval. Drugs must be produced, marketed and sold globally. This phase of the work is done by pharmaceutical companies, many of which are large multinational corporations that have been around for more than a century and in some cases even predate the creation of the Food and Drug Administration (FDA), which is responsible for drug approval. Rather than developing drugs themselves, pharmaceutical companies primarily purchase assets from biotech companies, which often involves acquiring entire companies.
Massive R&D enterprises like Big Pharma are the third major cog in our current miracle machine.
This machine really does wonders.
The Genentech story is just one example. Groundbreaking academic work at Stanford University is being spun off into a venture-backed company. The company has successfully used genetic engineering to turn bacterial cells into mini-insulin-producing factories - significantly alleviating shortages of a vital drug. In 2009, Genentech merged with Swiss pharmaceutical giant Roche in a $47 billion deal that promised global scale.
The story does not end here. Breakthrough technologies such as cell therapies and crispr gene editing are still making the transition from academic laboratories to clinical applications. Academic labs are still developing new theories and models, and companies are still being founded and funded based on the most promising advances. The pharmaceutical industry remains a major global buyer and distributor. The system achieves some kind of stable equilibrium among its various participants.
While miracle machines improved our world, systemic challenges emerged over time. We provide this bird's-eye view of the current system in order to make some of these problems easier to understand and to provide context for understanding new projects that seek to address these problems.
Large funding agencies like the National Institutes of Health (NIH) have become increasingly bureaucratic over time, with an inherent bias toward funding more conservative and progressive work. We're pretty sure no one really thinks scientists should spend up to 40% of their time slogging through tedious government paperwork. As the funding process becomes increasingly complex and committee-driven, it becomes increasingly difficult for new and promising research directions to gain support.
The National Institutes of Health (NIH) has also become interested in "big science" programs, which organize large research groups to fund projects that cannot be completed by individual laboratories. While this seems important in principle, such projects have had mixed results and consume resources that could otherwise be used to fund laboratories focused on basic discovery science. As Berkeley biologist Michael Eisen puts it, "Large-scale biology projects are not a boon to individual-driven discovery science. Ironically and tragically, they are becoming the greatest threat to the latter's continued existence."
Government Research Large-scale structural changes in funding have shaped and limited the types of scientific questions researchers can pursue. The relay between universities and startups has also become more complex.During the conversion phase, terms for university spin-offs proved to vary widely, in some cases putting the companies in trouble before they even got off the ground. Universities have strong incentives to closely protect their intellectual property, which can lead to worse terms for scientists, and may even lead to terms so unfavorable that investors lose interest in funding translational efforts.
Government agencies are not the only part of the system with financial blind spots. Venture capitalists also have inherent limitations on what they can invest in—a company must have the potential to be a massive $1 billion-plus exit for the math to make it worth investing in. Not all technologies or public goods can generate such returns, especially within investors' constrained time horizons. Only a small portion of society has the opportunity to earn real wealth as accredited investors by backing these private investments, further exacerbating inequality.
Pharmaceutical companies are also constrained by their financial structures and incentives. The clear incentive is to develop or acquire the drug with the largest market while minimizing R&D costs. This distorts the entire pipeline in a suboptimal manner, with real consequences: "Despite significant unmet need and disease burden, there are few products in the pipeline to address antimicrobial resistance, tuberculosis, and opioid dependence." . In contrast, many new products are new versions of existing products with only minor changes to existing drugs."
So where should we look for new ideas and approaches?
It is unlikely that we will get radical solutions from the leaders of our current institutions because they have incentives to perpetuate the system they are in. One interesting direction for finding new ideas is to explore side projects that innovative scientists are working on. As Paul Graham said about great startup ideas, "The best ideas almost have to start as side projects because they are always so different that your conscious mind rejects them as ideas for a company."
takes this approach approach, it’s hard to ignore the steady expansion of activity in the decentralized science community.
killer web3 application case
Personally, I was initially highly skeptical of web3. As a scientist and engineer, one of my core areas of focus is leveraging the power of web2 technology—efficient central databases, fast servers, powerful modern browsers—to build cutting-edge research tools for scientists. Measurements and technical assessments like moxie marlinspike's initial impressions of web3 have been fundamental to my thinking in this area.
But over time, I became a cautious optimist - ironically, this happened just as the cryptocurrency market was collapsing and doubts about web3 were growing. Why? As I talk to smart people like packy, jocelynn, and some of the leading founders in the space, I get excited about what this new set of protocols, tools, and ideas could potentially excel at. We are observing some important social experiments that seek to establish new models of collaboration and organization. From my direct experience in academic science, I know that our research institutions could benefit from changing the status quo.
Not boring readers may be familiar with the huge web3 use case debate our fearless leader packy has been embroiled in lately. A real advantage of web3 is that it provides a new set of tools for creating financial instruments. As Michael Nielsen points out, "New financial tools can in turn be used to create new markets and enable new forms of collective human behavior."
If one of the killer applications of this new tool stack is to radically improve the research funding process Woolen cloth?
As we have emphasized so far, research funding once fell broadly into two categories: public or private financing. Once cryptocurrency investors began generating significant wealth, a third category of funding sources emerged, and many of these new investors wanted to use their funds for good.
alone is worth briefly thinking about. The expansion of cryptocurrencies has created a new breed of billionaires, primarily those willing to be early adopters of a new financial system.As Tyler Cowen has argued, this could change philanthropy, as these new tech elites will have a greater interest in "quirky independent projects." We’re already seeing this dynamic happening, with both Vitalik Buterin and Brian Armstrong making massive investments in longevity science projects.
This difference is not limited to the birth of a group of younger and more technical investors and philanthropists. web3 technology is being used to enhance funding for new and exotic scientific projects. Today, new financing mechanisms including token sales and cryptocurrency-backed crowdfunding are introducing an entirely new way of financing projects.
Crowdfunding has traditionally been a challenge for scientific research, but cryptocurrency crowdfunding may be changing that. A range of new open protocols and tools have emerged aimed at scaling up the funding of public goods. One example is gitcoin, an organization dedicated to building and funding public goods. Every quarter, they run a crowdfunding campaign backed by big donors like Vitalik Buterin. The interesting innovation here is that the grants are matched quadratically - meaning the number of donors has a greater impact on the match than the amount donated. In the latest gr15 grant round, decentralized science (desci) was listed as one of four impact categories, once again highlighting the growing interest in scientific research in the web3 space.
gitcoingr15 grant round
https://x.com/umarkhaneth/status/1575147449752207360
desci round received donations from 2,309 unique contributors, supported 83 projects, and raised a total of $567,983. Matching donation funding was provided by an interesting group of large donors; including vitalik buterin (co-founder of ethereum), stefan george (co-founder and CTO of gnosis), protocol labs, and... Springer · Natural group.
The scientific community is borrowing from another blockchain technology innovation: decentralized autonomous organizations (DAOS).
As Packy described before, dao is an innovation in web3 governance. dao "run on a blockchain, giving decision-making power to stakeholders rather than executives or board members." They are "autonomous" because they rely on software protocols recorded on a publicly accessible blockchain , "If certain conditions are met, action is triggered without human intervention."
As is the case with gitcoin and quadratic funding, one of the most exciting early use cases for dao is accelerating the building and funding of scientific communities. Over the past year, science has experienced a kind of Cambrian explosion. Here is an overview of some of the DAOs and projects in the field:
If we think of traditional science as taking place within established and highly centralized university centers" "Top-down approach", then science shows the upward trend of "bottom-up" scientific development. Many of the communities showcased in this field are formed when a group of people adopt a common goal—advancing research in agriculture or hair loss, for example. These are not just reddit-like discussion forums; most DAOs contain specialized working groups, often mixing experts with amateur scientists, working on things like conducting new literature reviews for their areas of interest or evaluating projects for funding. Task. One of the original promises of
desci was the democratization of access to funding; essentially, research that would otherwise not be funded is now being funded. But is this true in a community-funded project like vitadao’s Transaction Process Group? Among the funded projects listed on their websites, several university researchers have received grants of approximately $200,000-300,000.
vitadao How do researchers funded by NIH differ from those receiving traditional NIH funding? For example, Dr. Evandro Fang, whose project investigating novel mitophagy activators recently received a $300,000 investment from Vitadao, has received multiple NIH and other government grants for his work, according to his resume.Another argument for the novelty of Vitadao's approach is that their community reviews and funds these projects more quickly than NIH's, even though there is a high degree of overlap among grantees.
So far, crowdfunding projects like gitcoin and organizations like vitadao in the desci community have set their sights on accelerating and simplifying the funding process for basic research. Other projects have begun to target the shortcomings of the biopharmaceutical industry that we highlighted, such as rare disease drug development. Another early selling point for the
desci space is its potential to advance treatments for underserved patient groups, such as those with ultra-rare diseases. Traditional biotech companies typically do not pursue drug development for smaller patient populations because they cannot generate enough profit from the final product to justify the high costs of clinical development. But dispersed global teams are advancing efforts to identify drugs for repurposing for patients with rare diseases. Examples include perlara and phage directory, neither of which rely on blockchain technology but do support the argument that knowledge from decentralized networks can advance the development of treatments.
In terms of organizing on the blockchain, vibe bio is a new company that is embracing web3 as a way to find "every cure for every community." Vibe founders Alok Tayi and Joshua Forman plan to build a Web3 protocol for setting up patient community DAOs that can jointly own and manage their drug development. This is an exciting innovation in a space where patient communities have been organizing themselves for decades, but often with companies owning the data and assets. This poses a risk to patient foundations, which often provide seed funding for science. The companies could choose to shelve these projects, as Taysha Gene Therapeutics recently did with its Leigh syndrome project.
vibe recently raised $12 million from traditional venture capital, including Not Boring Capital; a positive sign that connecting patient communities through DAOs could be a beneficial process for developing treatments for rare diseases. Founder alok tayi was inspired to create vibe after his daughter was born with an incurable disease. In an interview with the not boring podcast, when asked "Why web3?", tayi responded as follows:
Our goal is to create an infrastructure approach through which we can potentially solve all the overlooked and Neglected diseases. So the first things we need to look at are technology and governance solutions that allow us to achieve infinite scalability of engagement, but also a whole new source of capital that's interested in taking bold action and getting big things done. .
Constraints on biotech venture capital have pushed them to make slightly more conservative investments rather than broader disease coverage. Another aspect that I would also highlight here is that when you look at the approach that others might take, whether it's a charity, an academic institution, or even a C Corporation or LLC, ultimately there's a difference in the amount of funding, the type of expertise, and There are inherent limitations in terms of the number of owners and participants who can actually participate in the process. So our ambition at vibe, our mission is to find every cure for every community, not just those 250 accredited investors or qualified purchasers who are allowed to participate in these traditional types of mechanism.
Beyond cryptocurrency funding and DAOs, there are many novel ideas exploring how to apply token economics to science and ameliorate some of its shortcomings. Among these strategies is ip-nft; essentially intellectual property tied to non-fungible tokens. A company called molecule has implemented this proof-of-concept for the first time for a biopharmaceutical asset. They hope to create an "open market for drug development." The integration of
web3 with science is still in its very early stages; time will tell how these new experiments in scientific funding, ownership, and organization will develop.We are optimistic that even if blockchain is not the answer to the crisis in the scientific ecosystem, at least it has reignited the discussion about what needs to be fixed and begins to allocate this new form of liquidity to one of the best use cases.
Faster gas, rapid funding
Experiments in decentralized science show that the web3 community has great enthusiasm for funding scientific research and commercial transformation. This should not be taken lightly. Although the National Institutes of Health (NIH) has an annual budget of $50 billion, it continues to engage in deep political maneuvering to try to convince American taxpayers to increase the scale and scope of science spending. Given this vast disparity in enthusiasm, it’s entirely conceivable to imagine a world in which the cryptocurrency market spends $1 trillion more than the U.S. government spends on science funding.
Beyond cryptocurrencies, tech philanthropists are also targeting some of the major inefficiencies in our modern science funding system. A striking example is the way emergency funding has been deployed during the pandemic. Even in the face of a global emergency, NIH has shown its inability to deviate from its rigid funding structure:
The cumbersome process scientists need to follow to obtain emergency NIH funding during the pandemic
https://x.com/patrickc/status/ 1399795033084096512
In order to deploy funds faster, the fast grants project came into being. The project, launched by Emergent Ventures and backed by a range of high-profile tech leaders including Elon Musk, Paul Graham and the Collison brothers, aims to significantly shorten important COVID-19 related research projects Time required to start. Their argument is simple: "In normal times, the science funding mechanism is too slow, and it is likely to be even slower during the covid-19 pandemic. Fast Grants is an effort to correct this."
There is an important lesson here , requires us to review our mental model of how the National Institutes of Health (NIH) came to be in the first place. As we currently see, our current funding system was largely designed by the visionary Vannevar Bush, a key member of the National Defense Research Council (NDRC) that achieved rapid results during World War II. Part of the mission of the rapid funding program is to return to the kind of efficient system that Bush himself advocated. In his memoir, Bush recalled: "Within a week, the ndrc could review the project. The next day, the director could authorize it, the business office could issue a letter of intent, and actual work could begin."
The program was originally intended to Accelerating research and understanding of covid-19 during the global pandemic, but this model appears to have appeal beyond this use case as well. In an article for Future, tyler cowen, patrick hsu, and patrick collison reflect on some of the results of the project:
We originally expected to receive a few hundred applications at most. However, within a week we received 4,000 serious applications and almost no spam. Within days, we began distributing millions of dollars in grants, and during 2020, we raised over $50 million and awarded over 260 grants. All of this was accomplished with Mercatus overhead of less than 3%, in part thanks to the infrastructure put together for emergent ventures that was also designed to disburse (non-biomedical) grants quickly and efficiently.
Incredibly, approved grants received funding within 48 hours. A second round of funding will follow in two weeks. Grantees are required to publish results publicly and share a brief update each month.
Among some interesting findings, many applicants came from top universities, a group organizers had thought were already well supported by traditional NIH-style funding. And 64% of funders surveyed said the research would not have happened without rapid funding. To quote collison, cowen and hsu again:
Quickly fund the pursuit of low-hanging fruit, choosing the most obvious bets. What's unusual about it is not coming up with clever things to fund, but finding a mechanism to actually do it.To us, this suggests that there may be a lack of smart managers in mainstream institutions who can be trusted to handle flexible budgets and allocate funds quickly without triggering massive red tape or committee-driven consensus.
Rapid funding is an approach being adopted by several organizations. These include longevity research impetus grants founded and led by 22-year-old Thiel scholar Lada Nuzhna. The initial round funded 98 projects with the goals of accelerating aging biomarker research, understanding mechanisms of aging, and improving the translation of research to the clinic. While an explicit goal of the program is to fund research that may be overlooked by traditional sources, the list of grant recipients includes several well-known longevity researchers and its admissions rate is actually more rigorous than that of the National Institutes of Health (NIH). grants is 15%, while NIH is about 20%). It's important to note that an important aspect of this type of experiment is that it could push the NIH to adopt and scale up some of the most promising new strategies. The Rapid Accelerator for Diagnostics (RADX) was launched by NIH around the same time that FAST grants were launched.
Over the next few years, it will be interesting to compare how funding is rapidly changing the mix of people who can conduct research and the types of results these researchers produce. These various projects highlight two interesting trends.
First, beyond the crypto market, a new generation of tech philanthropists have shown a genuine interest in funding science in new ways.
Second, sometimes less is more.
As we explore new forms of funding, it is worth recognizing that writing grant applications should be secondary and actually conducting the science come first. Sometimes the best solution is to quickly evaluate and fund the most promising proposals and then not impede progress.
Fully Adopted Bucky (Built from the Ground Up)
So far we have sketched out a rough picture of how institutions currently operate, and seen how the crypto market, web3 technology, and tech philanthropists contribute to the research funding landscape. We now live in a world where vitalik buterin provides quadratic crowdfunding support for science projects and collison brothers support low-overhead grant mechanisms to alleviate government inefficiencies. These new ideas are being explored to accelerate and expand Miracle Machines in exciting and important ways.
With all these new efforts, an interesting question emerges: What if some problems in science funding cannot be solved simply by new funding sources or funding mechanisms?
Ultimately, our current scientific institutions represent only a small sample of the full space of possible organizational structures. The miracle machines we have are the byproduct of a very specific set of historical pressures and ideas. Some of the new funding ideas being explored today require the construction of an entirely new set of 21st century science institutions. In other words, they are practicing Buckminster Fuller's philosophy and exploring new ways of funding and organizing science from the ground up.
How can new real-life (irl) institutes be built to address missing links in science?
One approach is a focused research organization (fros), a new type of institution dedicated to solving a specific scientific challenge, such as blue-sky neurotechnology or longevity research. Other proposed focus areas for FROS include antibodies that recognize each protein, mathematics, artificial intelligence, and developing super-durable organ transplants. The core idea of the fro model is that these types of scientific projects fall into an institutional void. They are too capital-intensive and team-oriented for academia, yet outside the realm of startups or large corporations because they are more like public goods than products with clear commercial value. fros aims to fill this gap:
Convergence Research was co-founded by Adam Marblestone and Anastasia Gamick to incubate new fros. This spring, cr hosted a metascience symposium that brought together thought leaders such as institute directors, policymakers from Washington, D.C., and the United Kingdom, as well as authors and changemakers in the metascience field. The main goal of the workshop was to brainstorm ideas on how the new organization can contribute to the advancement of science.
A common theme among attendees’ presentations was that something is wrong with the scientific ecosystem.To summarize this working hypothesis: The dominant model of university-based research published in traditional scientific journals is creating a fragile ecosystem that needs to be disrupted.
In a talk given by ilan gur (then CEO of activate.org, now CEO of aria research) we were shown a pie chart showing the distribution of research funding over time.
This chart shows something very interesting. The massive reorganization of scientific research funding after World War II that we mentioned earlier coincided with a major shift in the composition of our scientific institutions. Basic research funding in the United States has shifted from primarily funding federal laboratories (1953, left pie chart) to primarily funding university research (2020, right pie chart). Is this shift toward a university-centric funding model to blame for some of the flaws in our current ecosystem?
In another presentation, we watched a video clip of scientists at the Santa Fe Institute talking about the magic of setting:
Sample footage of the Santa Fe Institute's upcoming documentary
https://www.youtube.com/watch ?v=xc6ihzosky8
"What we did at the Santa Fe Institute was to escape society; to build a community in the mountains, in the shadow of the atomic bomb." — David Krakauer, president of the Santa Fe Institute.
The intimacy and beauty of this environment are intoxicating. Santa Fe Institute represents a real departure from the institutional structure of a traditional research university—and as such it has its own unique culture. It provides a venue for renegade scientists to pursue their boldest and most unique ideas. While watching the video, we wondered: How can we build more places like this? What would it take to design a space that would nurture the world’s next Feynman or Einstein? How big is the team? How about leadership?
Many metascientific innovators or renegade scientists are following Buckminster Fuller principles to build new institutions in the real world.
Among leading research institutions, Arcadia Science, led by Seemay Chou and Prachee Avasti, stands out. arcadia is an experiment in applied metascience. The institute is structured as an R&D company but focuses primarily on basic science and technology development. One of its core ideas is that we fundamentally misunderstand the value of basic science, especially if institutional design helps scientists effectively translate their work into new products and technologies.
in the process arcadia is experimenting with every part of its research process. For example, they are disrupting the status quo in the scientific publishing ecosystem by prohibiting scientists from publishing in traditional journals; instead, they publish journal-like articles on their website, including links to project descriptions, data, reviews, and even tweets. While this may seem trivial, it is actually a conscious move away from the strange dynamics and exploitative nature of the existing academic publishing system. Self-published experiments may improve the way code, data, and results are shared with other scientists who wish to build on them.
Another interesting institution-building applied experiment is new science. The organization was largely the brainchild of writer and researcher Alexey Guzey, who spent a year writing a classic blog post, "How Life Sciences Really Works," which explored the current realities of biomedical institutions. One of the main observations that impressed Alexey was the lack of funding opportunities for young scientists:
Over time, an increasing proportion of research funding is used to support older (literally) scientists, which leaves younger scientists It’s harder to get initial funding for their labs. This chart doesn’t even reflect the full picture: it only reflects the difficulty young professors have in obtaining funding. Young scientists who are pursuing PhDs or doing postdoctoral research have less autonomy—they mostly work on projects for which professors can get funding. While technology has greatly expanded the agency of young people—providing them with ways to found, finance, and lead their own companies—young academics are often unable to actually develop or obtain funding for their own projects. One of the core goals of
new science is to fill this gap.They have launched a short-term fellowship program for young scientists to pursue their own ideas and projects. Over time, the plan is to create longer-term fellowship programs and eventually independent institutes to give young scientists back control of their work:
Much like arcadia, they will conduct various applied metasciences along the way experiment. For example, they let researchers share articles about their ideas and work on their substack – you should really consider subscribing. They also fund more research and writing about how our current life sciences institutions actually work, like their big report on nih , or elliot's article on life science software funding .
One criticism of these new scientific institutions so far is that they rely heavily on the support of large donors, such as Eric Schmidt. Nadia Asparukhova documents some of the ways that emerging tech elites have pursued philanthropy in the life sciences in recent years, a trend that shows no signs of slowing down. In addition to the Chan Zuckerberg BioCenter, we have also seen the establishment of the Arc Institute, another life sciences center supported by the tech community. Within the world of independent research institutions, there is some debate about the best type of financing - whether a single donor can give an institute ultimate freedom of thought, versus whether the wishes and biases of multiple donors can lead to research being Pulled in too many directions?
This question highlights a key philosophical difference between decentralized science and many emerging institutions. The decentralized science movement is trying to build new protocols and tools to empower decentralized networks of scientists and technologists to organize and act more effectively. Why create a fro if there is a significant funding gap? Why not just build a new dao and let the scientific community naturally figure out how to solve the problem once it has the resources?
Lauraminquini on We now see all of these experiments happening simultaneously. As we argue, science is one of the most valuable and productive endeavors we pursue as humans, and as such there should be ample room for new ideas and resources. Nonetheless, there may be some competition between the different approaches. As nadia points out, "I'm particularly interested in watching how the tension between tech-native and crypto-native approaches unfolds. While they are at different stages of maturity, at a macro level these are two major experiments going on simultaneously ."
Conclusion
Science is one of the most powerful tools we have for progress as a species. As Packy argues, this is an inherently optimistic process: "Conducting experiments to better understand the universe, assuming that we can discover more than we already know and use it to improve the world." We are lucky to live in a world where In an interpretable world, as our knowledge increases, the world can be changed in new ways.
Because of the central role of scientific research in World War II, American leaders like Vannevar Bush designed a vast government apparatus to expand funding for science at the national level. We now live in a world driven by the wonders produced by this machine. On top of our vast federal funding system, there are several layers necessary to ultimately produce the product. Technology needs to be separated from universities and receive additional private financing. These spin-off companies also need to interface with large R&D giant companies that control all aspects of sales and commercialization.
Although the Miracle Machine has earned its nickname many times, we have highlighted several reasons why it is now necessary to try out the new scientific system. It's almost a natural law that bureaucracy increases over time, and nih is no exception. Our brightest minds now spend up to half their time applying for complex government grants that can be rejected over minor issues like fonts.Over time, government funding has become anchored in conservative, consensus-driven projects led by senior researchers.
The desire for change is clearly part of the current zeitgeist. We are experiencing a Cambrian explosion of new funding and institutional models for science. The goal of this article is to provide you with a mental model of how current systems operate and to provide a field guide for further exploration of the many exciting applied experiments in metascience.
If you happen to think there are use cases for web3, and we've successfully convinced you that funding science is one of them, you should head over to the desci wiki and consider joining the project that excites you. If you are a scientist looking for a faster way to get funding for your project, we hope that the resources we have listed on Fast Grants will be useful to you. If helping to build a new kind of 21st century scientific research institution sounds like it might be your life's work, many of the projects we've mentioned are expanding rapidly and looking for contributors in both science and non-science. The overedge catalog curated by samuel arbesman provides a great starting point for a comprehensive understanding of new research institutes.
One topic we are thinking about right now is the tension between centralization and decentralization. As Packy recently wrote, "The battle between centralization and decentralization is reaching fever pitch in many areas, with a quasi-Cold War playing out on multiple fronts. Web2 versus Web3, Russia and China versus the West, OpenAI versus OpenAI." The story of science is no exception. It will be interesting to see how these different philosophies interact with each other over time. As balaji discusses in "Network Nations," perhaps communities can form in the digital world in a decentralized manner and then build new systems in the physical world, like new nations, or in the case of decentralized science , establish a new laboratory or research institute. In turn, centralized institutes can adopt web3 technologies and use their skills and expertise as part of wider scientific networks, adopting new protocols and ways of collaborating.
Whether it is experiments in new research facilities in physical laboratories or explorations in blockchain and new network laboratories, this is an exciting time to observe innovations in organization and financing unfolding. We are hopeful for the future and the progress these ideas will bring.
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