Original author: Nadia Asparouhova
Compiled by: LlamaC
“Recommendation: This article explores the methods and attempts of the scientific and technological community in the field of biological sciences to innovate and reform scientific research funding and institutions from 2011 to 2021. In this article, you can get a separate perspective from cryptocurrency to take a comprehensive look at all the innovative solutions for scientific research funding in the world today, so as to derive the substantial advantages and disadvantages of Crypto in this paradigm.”
Main Text👇
For those who live and breathe science and technology, it’s hard not to notice the plethora of new initiatives that have emerged over the past two years that are designed to improve the life sciences sector in particular.
While I have no background in science and no personal connection to the field (other than knowing and liking many of the people involved), I became interested in understanding why the field was suddenly changing, especially from a philanthropic perspective. Figuring out what works in science could help us solve other similarly shaped problems in the world.
To understand what’s going on, I looked at examples of science-related efforts in the tech space over the past decade (roughly 2011-2021). I looked for patterns that helped me infer the norms and values of the time, as well as turning points that changed those attitudes. I also interviewed many people in the field to help me fill in the gaps and understand their values and what success looks like.
A word of warning: complex questions like "why did this culture change?" rarely, if ever, produce clear answers, so please consider this post a starting point for further research.
Problems in Science
When people say they want to "do science better", what problems are they trying to solve, and how?
There are several observations that seem to be generally recognized by people working in and around science. These topics have been discussed extensively and in more detail elsewhere, so I will only briefly mention them:
As a scientist, the process of getting funding is slow and bureaucratic
The popularity of Fast Grants, a rapid grantmaking program launched in response to the COVID-19 pandemic, illustrates the lack of options for scientists. Its founders noted in retrospect that they were surprised by the number of applicants from the top 20 research institutions: “We didn’t expect that people at top universities would struggle so much with funding during the pandemic.” Yet in a survey sent to grant recipients, 64% of respondents said that their work would simply not have been possible without Fast Grants.
Although the reward system in academia is sound, it does not select the best work
Scientists are expected to publish their work in journals, and their reputations can be measured by the number of citations they receive. But peer review tends to choose consensus over risk-taking, and scientists feel pressure to pursue quantity over quality, among many other problems.
Early career scientists at a disadvantage
Science is trending toward older, more experienced scientists. Most NIH grants go to older scientists, and the age of scientists making Nobel Prize-winning discoveries is also increasing.
Defining a Theory of Change
Why do these questions matter? If we had to ask a "so what" question for the above observations, we might say that scientific progress is not as robust as it could be due to these systemic challenges. Compared to other historical periods, such as the Victorian era or the Cold War, it seems difficult for promising, talented scientists to pursue their work today, especially when their ideas are experimental or unproven.
In a 2019 survey of the life sciences, New Science founder Alexey Guzey noted that scientists have learned to work around these issues by, for example, applying for grants for their "boring" ideas and then using some of that money to fund their "experimental" ideas. Regardless, it's reasonable to assume that more work might be accomplished if scientists didn't have to go through this round of detours. For example, 78% of respondents from the aforementioned Fast Grants survey said they would "substantially" change their research plans if they could get "unrestricted, permanent funding."
If we had to write a theory of change for science with a tech flavor, it might look something like this:
Ensure scientific progress can flourish by removing the financial and institutional barriers facing the world’s top scientists, allowing them to fully pursue their curiosity and produce research that can be applied to benefit humanity.
Within this statement, there is disagreement among practitioners on what they consider to be the most important activities:
Some people I spoke to believed that insufficient funding for research or a slow funding process was the biggest lever to pull: giving scientists money and letting them use their ideas freely.
Others see academic norms as a bigger obstacle: research should be run more like a startup culture.
Others see a divide between those who focus on basic research and those who want to apply the results of their research: the latter want to bring their research results to market more quickly so that humanity can benefit from the scientists' work.
I will describe some of these methods in more detail in the following chapters.
Science can also be viewed as a subset of a broader problem statement: “How do we support a culture of research in science and technology?” For example, artificial intelligence falls into this category, but has a different development trajectory and funding history. So does human-computer interaction (HCI) and “tools for thinking.” Even “science” itself is an extremely broad category, as we will see in the following sections (note that a particular focus on improving the scientific process is sometimes called “metascience”).
In this case study, I focus only on the overlap between scientific research and technology over the past decade. However, there are many cases where technology’s attitude toward research also influences our attitude toward science, and vice versa, which I will occasionally touch on here.
Now that I’ve made those caveats clear, let’s look at what practitioners today have in common. Recalling the theory of change above, what’s unusual or important about a tech-native approach to science?
One area that stood out to me was the focus on supporting and attracting top scientific talent. There’s an underlying assumption here that the quality of individual scientists matters, and that perhaps the biggest leaps forward in science are due to the contributions of a few talented people rather than the scientific community as a whole. (A meta-analysis by José Luis Ricón seems to support this assumption, although he notes that these conclusions may vary by field.)
This focus on "top talent" feels very tech to me, and akin to the way founders think about startups. While no meritocracy is perfect, tech culture thrives in part because companies tend to place less weight on markers like pedigree or years of experience and more on what a person has actually accomplished. Prioritizing high-quality talent also helps organizations avoid decline as they grow. So it's no surprise that the tech world has applied this mindset to science.
Second, there is always an emphasis on output, especially bringing research results to market. Third, this "results-oriented" approach feels very consistent with the characteristics of the technology industry to me: the belief that basic research should ultimately serve a long-term goal to benefit humanity - and we should shorten this timeline as much as possible.
Most people I talk to think that if you can commercialize your work, you should—assuming, of course, that not everything can be commercialized. Even nonprofit science projects tend to emphasize startup-inspired values like speed, proving power, and collaboration.
Finally, there is an implicit belief prevalent among practitioners today that change is exogenous: we must work outside the institution and influence from the outside to achieve these goals. While some organizations do collaborate with universities, they still operate outside of traditional academic career paths.
These values may seem obvious to people working in science and technology, but if we return to the high-level vision of “ensuring that scientific progress can flourish,” applying them would rule out some options that non-science and technology workers might pursue: for example, establishing postdoctoral programs, improving tooling in university research labs, increasing enrollment in STEM graduate programs, and so on.
With these values in mind, let’s look at how research funding in the tech sector has evolved over the past decade.
Promoting technological innovation through start-ups (2011-2014)
A common theme I heard from conversations was that the statement of the problem for science has not changed significantly over the past decade. There has long been a widespread awareness that science is not working as well as it should, and a desire to take action to change that. However, views on how to address this problem have changed.
Ten years ago, most people believed that startups were the best way to advance science: either start a company or fund one.
At the time, economist and author Tyler Cowen provided a philosophical basis for scientific progress in his 2011 book The Great Stagnation. Cowen made a broader argument about the stagnation of the U.S. economy, but he pointed to a lack of scientific breakthroughs and a general slowdown in the rate of technological progress as one of the causes.
Cowen dedicated the book to Peter Thiel, who has spoken publicly about the decline of technological innovation. In The Great Stagnation, Cowen quotes an interview with Thiel in which he said: "Pharmaceuticals, robotics, artificial intelligence, nanotechnology - progress in all of these areas has been much more limited than people thought. The question is why."
Around this time in 2011, Thiel also adopted the now-infamous tagline for Founders Fund, the venture capital firm he founded in 2005: “We were promised flying cars, we got 140 characters.” Thiel decided to turn this statement into an investing philosophy, revealing his theory of change: scientific progress would be solved through markets, not by funding basic research.
While it’s hard to pinpoint why startups became the preferred way to get into science at that time, the simplest explanation is that it has to do with the general popularity of startups in the 2010s. Y Combinator, the accelerator that played a big role in making entrepreneurship more attractive and easier to start, was founded in 2005 but reached its cultural peak in the 2010s. Many of its most successful alumni came from companies that founded or achieved breakout growth in the 2010s. Marc Andreessen’s 2011 opinion piece “Software is Eating the World” captured the mood of the time: software-driven startups could be applied to solve many different problems across industries.
With the exception of Breakout Labs (which, while a grant program, was structured as a revolving fund with income coming from grantee intellectual property and/or royalties), notable science projects at the time were often startups or venture capital funds. Examples include:
Outside of startups, there were two notable research sponsors in the tech space at the time who were closer to science but also tell us a lot about how people viewed research at the time:
Google X: Google X was quietly founded in 2010, and its existence was first revealed by The New York Times, which described it as a secretive lab inside Google focused on "ideas that aim for the stars." Google X popularized the term "moonshots" and now describes itself as a "moonshot factory."
MIT Media Lab: The MIT Media Lab now describes itself as an "interdisciplinary research laboratory." While not focused on science, it is often cited as a symbol of the intersection of technology and academic research culture. It flourished in the 2010s under the guidance of its charismatic leader, Joi Ito, until he abruptly resigned in 2019 due to controversial financial relationships.
Early Philanthropy Approach (2015-2017)
By the mid-2010s, tech exits had generated enough personal wealth to cause some investors to try traditional philanthropy.
In 2015, Y Combinator announced the creation of a nonprofit research organization, YC Research, initially funded by a $10 million personal donation from its president, Sam Altman. While not directly involved in science (their first research projects focused on universal basic income, cities, and human-computer interaction), YC Research can be understood as a bellwether of changing cultural attitudes. As Sam Altman explained in his announcement post, sometimes "startups are not well suited to certain types of innovation," which was a novel perspective at the time:
Our mission at YC is to foster innovation as much as possible. That primarily means funding startups. But startups aren't ideal for some kinds of innovation -- for example, work that requires very long cycles, seeks to answer very open-ended questions, or develops technology that shouldn't be owned by any one company.
However, he stressed that YC Research still aims to do things differently than a typical research organization (emphasis mine):
We think the research community can be better than it is today…Researcher compensation and power will not be driven by publishing lots of low-impact papers or speaking at lots of conferences – the whole system seems broken. Instead, we will focus on the quality of output.
That same year, Mark Zuckerberg and Priscilla Chan announced that they would donate 99% of their Facebook shares to philanthropic causes, which would be managed by the Chan Zuckerberg Initiative. Similar to Y Combinator, Chan and Zuckerberg chose to do things a slightly different way, structuring CZI as an LLC rather than a 501c3 nonprofit (like most charitable foundations), arguing that this would give them "the flexibility to more effectively execute their mission."
CZI's first investment is a $3 billion commitment to "cure, prevent, and manage all human disease in our lifetimes," to be distributed over a decade. $600 million of that is earmarked to create the Biohub, a research center based at the University of California, San Francisco (UCSF), in partnership with Stanford University and UC Berkeley.
In their joint statement, Zuckerberg explained that the slow progress in the life sciences is related to the current way science is funded and organized (emphasis mine):
Building tools requires new ways of funding and organizing science…Our current funding environment doesn’t really incentivize much tool development…Solving big problems requires bringing scientists and engineers together to work in new ways: sharing data, coordinating, and collaborating.
The following year, in 2016, Sean Parker founded the Parker Institute for Cancer Immunotherapy. Again, Parker’s statement echoed similar concerns about the way science is conducted (emphasis mine):
The cancer problem is not just a problem of resources, but a problem of how we allocate those resources… The system is broken to some extent… The agencies responsible for funding most scientific research generally don’t encourage scientists to pursue their boldest ideas, so we don’t get ambitious science.
Compared to the first half of the 2010s, this period saw a renewed interest in basic research funding and a tacit recognition that startups could not fully achieve their goals – even as donors stressed the importance of an innovative research culture itself, with a greater focus on science-oriented outputs, collaboration, and tool development.
Other projects that launched around the same time and reflect these trends include:
Open Philanthropy: A research and grantmaking organization focused more broadly on improving philanthropy, but its initial focus areas include funding biological research. Open Philanthropy became an independent organization in 2017, but it grew out of a collaboration between Good Ventures (Dustin Moskovitz and Kari Tuner) and Givewell in the previous few years.
OpenAI: A nonprofit organization originally described as a "nonprofit research company" launched in 2015 by Elon Musk, Sam Altman, and others with a $1 billion commitment. (OpenAI later transitioned to a for-profit structure.) While not focused on science, OpenAI became one of the largest research projects in tech in recent years. Their initial announcement emphasized the importance of open publishing, open patents, and collaboration.
During this period, despite a stated interest in improving collaboration between researchers, one thing seemed to be missing: coordination between donors. Instead, it felt like each effort was centered around the donor itself, rather than working together to solve a well-defined problem through multiple approaches.
This is not meant as a criticism, but rather to highlight the very difficult challenge that early major donors were still learning—compared to today’s cohort—how to strategically address scientific problems in non-entrepreneurial ways and how to define their philanthropic work outside of traditional expectations.
Field Building and New Institutions (2018-2021)
In recent years, there has been closer coordination between funders and founders, which has helped spawn a range of new scientific initiatives.
A 2017 NBER working paper, “Are Ideas Getting Harder to Find?”, renewed the discussion about scientific innovation by suggesting that “research effort is increasing dramatically, while research productivity is falling dramatically.” In 2018, Patrick Collison and Michael Nielsen published a review article in The Atlantic that included original research making a similar argument: Although “there are more scientists, more research funding, and more scientific papers published than ever before…is our scientific understanding growing commensurately?”
The following year, Patrick Collison and Tyler Cowen published a related article in The Atlantic, “We Need a New Science of Progress,” arguing that “the world would benefit from an organized effort to understand” how progress is achieved, including identifying talent, spurring innovation, and the benefits of collaboration.
While their review article focuses more broadly on progress, science stands out as an example. Collison and Cowen say that "while science generates much of our prosperity, scientists and researchers themselves have not paid enough attention to how science should be organized," and that "critical assessments of how science is practiced and funded are in short supply, perhaps for unsurprising reasons."
The Atlantic Monthly commentary (and a host of subsequent efforts) led to the formation and growth of the "progress studies" community, providing a much-needed intellectual home and community for those interested in issues such as scientific progress.
While today’s scientific practitioners are not formally affiliated with progressive studies (most would probably say they are not part of the field), and progressive studies focuses on issues far beyond science, my sense is that the formation of such a community would be helpful:
Act as a coordination point for like-minded people to attract more talents into the field, and
Legitimize the work of practitioners.
In 2021, a group of people came together for an in-person "Technology Bottleneck Workshop," based on the premise that bottlenecks "exist throughout science and technology, and solving them could lead to huge advances for the field as a whole." Attendees included founders and investors, many of whom are already working on science-related projects, including Fast Grants, Convergent Research, and Rejuvenome.
The workshop was well received by the participants. It helped people get to know each other better, strengthened common approaches and interests in the field, and even sparked new collaborations.
Below are some of the scientific initiatives launched in recent years. Particularly noteworthy is the diversity of experiments within a common problem space, and the increased coordination among funders and founders (note the degree of overlap between initiatives). These are signs of a healthy, thriving field, compared to the more monolithic, closed approaches of the 2010s.
Most of these initiatives are concentrated in the life sciences. I asked several people why they thought this was the case. Some thoughts included:
Personal connections and interests: Some funders and founders have pre-existing connections or backgrounds in the life sciences field.
Storytelling and public narratives: Life sciences means addressing problems such as curing disease, extending lifespan, fertility medicine, and genetics. The benefits of pursuing this type of work are easier for the public to understand than existential risk or space exploration, especially in the wake of a global pandemic.
As mentioned earlier, this group is characterized by a diversity of approaches: a mix of for-profit and nonprofit pursuits, and a combination of funding and operating organizations. We can also note a diversity of approaches in terms of the level of systemic change (organizational vs. individual), type of research (basic vs. applied), and project time span (short-term vs. long-term).
Why are there so many new initiatives today?
While there has long been a community of enthusiastic scientific practitioners, only a recent influx of funding has made it possible to put these long-standing ideas into practice. (For example, Adam Marblestone and Sam Rodriques had been thinking about focused research organizations for years before they successfully secured funding.)
Some funders tend to downplay their role as "providers of funds," but I think it's important to emphasize the importance of good funding practices. Specifically, I want to emphasize that rather than "throwing money at a problem," science funders in technology today are taking a strategic, yet classically philanthropic approach to building a new field. Two major efforts that have been particularly useful have laid the foundation for this field:
Better coordination: Greater coordination and co-funding among funders, which helps them learn from each other and make greater investments, while also giving practitioners peace of mind as they pursue long-term work;
Field building: showing that these are interesting and worthy problems to study, attracting others to the field and legitimizing the work of practitioners.
What has led to this renewed interest in funding science? There are likely several factors, some of which are external and others the result of conscious efforts:
Global COVID-19 Pandemic
By forcing people to grapple with large, immutable systems, the pandemic has helped us realize that the world is more malleable than it previously seemed. People have grown frustrated with the bureaucracy, unable to escape it, and realized that they can take action now — not in the distant future — to improve the status quo.
The success of the Rapid Grants program, which was launched in direct response to the COVID-19 pandemic, appears to have influenced the Arc Institute’s vision. The Longevity Dynamics Grants program was also inspired by the Rapid Grants model, but focuses on a different topic.
Arcadia Science's founder directly noted that the COVID-19 pandemic "has sparked a sense of urgency, collaboration, and passion for scientific advancement outside of our usual circles. The resulting vaccine development demonstrates how powerful science and collaboration between scientists can be."
One person I spoke to suggested that the geographical dispersal caused by the pandemic may also have had the effect of breaking up Silicon Valley groupthink, exposing people to new ways of thinking and making them more receptive to non-startup approaches.
Successful field building and better coordination between players
Publishing review articles, hosting workshops, and forming progressive research communities make it easier for like-minded people to find each other and coordinate. As Luke Muehlhauser noted in his report on Open Phil’s early field growth, while these approaches may seem “obvious,” they are also “often effective.”
Long-time practitioners in my conversations have commented that people have been interested in this problem area for decades, but only in recent years have they been surprised to find (quote) "there are more people like us than I thought."
Even among practitioners who have known and collaborated with each other for years, field building has the effect of making their work more status-quo than before—more like that of startup founders—which will continue to attract others into the field.
Several people commented on this effect during our conversation. One person said that this type of project (i.e. starting an ambitious non-startup project) was considered "unfundable" until recently because now a few people have "made it cool." Another felt that while the average person in the tech industry might not understand what they are doing yet, they feel like their work is no longer seen as "low status."
Cryptocurrency Wealth Boom
2017 and 2021 were two major turning points in cryptocurrency wealth creation. We are starting to see the downstream effects of the first boom and will likely see the effects of the second boom in the coming years.
Cryptocurrency has had both direct and indirect impacts on the science funding landscape. First, from a practical perspective, it has created a new set of potential funders. Cryptocurrency funders active in science today are primarily beneficiaries of the first cryptocurrency boom in 2017 — just as Mark Zuckerberg, Dustin Moskovitz, and Sean Parker were beneficiaries of Facebook’s 2012 IPO and became active philanthropic funders a few years later.
Second, crypto wealth becomes a driving force for "traditional tech" to take greater risks in terms of cultural construction. While it's hard to prove this is true, we can think of it as a shift in the Overton Window, where the emergence of a group that holds more extreme views than the median can make previously seemingly radical positions plausible. The fact that the cryptocurrency industry unironically wants to rebuild society from scratch in terms of tech makes, say, the creation of a new 501c3 research organization seem less weird.
There are several other macro conditions that may have contributed to the shift in tech’s interest in funding new science: a bull market that made capital cheap; a growing disillusionment among the general public with traditional institutions; a wave of liquidity events that generated new wealth in the late 2010s; and a fundamental shift in tech’s relationship to mainstream culture starting in the mid-2010s. These topics are beyond the scope of what I want to discuss here, but they are worth noting as other contributing factors.
Measuring Success
Finally, I wanted to understand how participants in today’s group think about measuring impact. In ten years, how will we know if these efforts were successful?
Nearly everyone I spoke to mentioned some version of the "$100 billion problem" (a term attributed to David Lang), referring to the relative smallness of private capital compared to federal R&D funding, which in the United States amounts to more than $100 billion per year. The latest wave of initiatives, as best we can guess, represents a few billion dollars in total. While significant, it's a tiny fraction of what the government can do.
Because of these relative financial constraints, participants I spoke with are instead thinking about how to inspire improvements in federal funding (particularly NIH funding in the life sciences) by demonstrating what is possible, rather than trying to compete one-on-one for funding. This approach is more consistent with the role of philanthropic capital in civil society, where the goal is not to compete with or replace government but to seed new ideas through private experimentation that does not affect public tax revenues. For example, America’s public libraries, public schools, and universities were all shaped by early philanthropic work.
Practitioners who choose to start companies rather than nonprofits are similarly driven by a desire to extend the life of capital. If a company succeeds, it can inspire the creation of other tech companies because there is plenty of startup capital available. In contrast, successful nonprofits tend not to inspire the creation of more nonprofits (even if they influence each other’s practices and interests) because philanthropic capital is limited, creating a more competitive zero-sum situation.
Here are some of the immediate and long-term goals I hear in conversations, along with suggestions for how to measure them.
Epilogue: DeSci and new cryptographic primitives
There is one more chapter to this story, which I have put in a separate "Epilogue" section because it is both new and significantly different from the above approach, but also serves as an important counterpoint to everything we have covered so far.
If we look at the big picture and consider how science is funded and supported, there are a number of approaches we can take. Public goods are not only funded by governments; they can also be influenced by markets (i.e. starting companies) and philanthropic capital. The examples we have seen so far, no matter how new or different they may seem, fall into one of these existing categories.
There is another, more radical approach, which I would (grudgingly) call the crypto-native approach. Proponents of this approach argue that the efforts above, while positive developments, ultimately replicate the same problems of our existing traditional systems. They would say that creating new institutions without rewriting their fundamental incentives solves nothing in the long run: it simply resets the timer on institutional decay.
Even within the “traditional tech” community, there is a wide range of answers to the question, “Are we trying to create new public institutions, or just make existing ones better?” Some initiatives are thinking long-term about how to avoid institutional decay, such as limiting funding or organizational size. Regardless, most people I spoke with seemed to agree on the “$100 billion problem” approach: deploy limited funds efficiently to make an impact at the larger federal level.
In contrast, in the crypto-native approach, proponents hope to create entirely new ways of funding public goods. While they share the long-term vision of improving scientific progress, attracting top talent, and bringing research results to market, their strategy is different. Their theory of change might look like this:
By inventing new ways to reward scientists, improve collaboration, and evaluate and enhance the quality of their work, we ensure that scientific progress can flourish, allowing them to fully pursue their curiosity and produce research results that can be applied to benefit humanity.
In my conversations, I heard those who support different approaches say almost verbatim: “The existing systems in academia, research, and government are designed to produce a certain set of outcomes. Unless we invent new rules of the game, nothing will change.” However, in traditional tech, it seems that the new rules of the game are creating new institutions (but the underlying organizing principles are considered static), while in crypto, it’s about designing new incentive systems entirely (where the organizing principles are considered malleable).
At "Funding the Commons," a 2021 virtual conference on funding public goods hosted by Protocol Labs, founder Juan Benet gave a talk on "Crossing the Innovation Chasm." He noted that over the past decade, the startup ecosystem has made significant progress in R&D innovation by productizing new technologies. From his perspective, Y Combinator has contributed far more to R&D innovation than Alphabet or Ethereum.
But while basic research efforts focus on solving problems in the “blue triangle” area above, they do not address the missing “black square”: translating research into real-world innovation. Just as the tech ecosystem has generated billions of dollars in venture capital funding for startups, the crypto ecosystem can do the same for funding public goods.
To me, this gets to the core difference between tech-native and crypto-native approaches to solving the public goods problem. In the best case, the tech approach is to generate wealth through startups, which then use their surplus wealth for philanthropic purposes (whether through for-profit or nonprofit initiatives). The crypto approach, on the other hand, is to create a native funding system for public goods, allowing participants to generate wealth through the development of the public goods themselves.
Vitalik Buterin echoed these sentiments in his speech at Funding the Commons. He explained that blockchain communities are built more on public goods than private goods, such as open source code, protocol research, documentation, and community building. Therefore, he emphasized that "public goods funding needs to be long-term and systematic," meaning that funding needs to come "not only from individuals, but also from applications and/or protocols." New cryptographic primitives can help address these needs, such as DAOs or token rewards.
Some differences between encryption and traditional technology native methods:
Belief in limited upside vs. uncapped upside. While those in traditional tech recognize the limitations of the $100 billion problem, crypto takes a more expansive view of the possibilities. One person I interviewed believes that cryptocurrency networks could rival federal funding levels in the next decade. A new set of crypto primitives would also make it possible to dramatically increase financial rewards for scientists. Whether or not this is achievable, I find this belief in uncapped upside encouraging.
Centralization vs. decentralization of talent. As mentioned earlier, traditional tech seems to focus its efforts on helping the best scientists who are being slowly destroyed by a decaying bureaucracy. Crypto, on the other hand, takes a more decentralized approach to talent, attracting and coordinating a larger network of contributors. (As one person told me: "Scientific progress is a coordination problem.") Crypto's approach aims to provide the world with tools that allow anyone to experiment (which will eventually filter out the best talent), rather than actively identifying and recruiting the best talent into the organization. We can think of this as the open source vs. Coase approach to talent, which is also the theme difference between crypto and traditional tech at a broader level.
While traditional tech and crypto offer two different approaches to solving scientific problems, there is still crossover activity among funders. Funders are not grouped based on where they work, but rather based on differences in theory of change. Some funders, like Vitalik, may support both traditional tech and crypto efforts, which could be called a "diversified portfolio" approach to improving science.
Focusing further on the cryptocurrency space, there is an emerging movement to apply new primitives to science, which in the Web3 space is sometimes referred to as DeSci, or decentralized science. While not everyone agrees with this term, I will use it as shorthand in this section to refer to improved scientific methods centered around crypto, because, well, it’s catchier.
Surprisingly, many DeSci practitioners have a scientific background. These aren’t just cryptocurrency evangelists who have decided to apply their skills to a new industry: there are also scientists who are leaving their positions in academia or industry to devote themselves to DeSci full-time.
Jessica Sacher, a microbiologist turned co-founder of Phage Directory, describes herself as living an intensely “analog life”:
I come from a bench in a molecular microbiology lab, where I wrote my methods and data in paper notebooks (on good days; the rest of the time I wrote on paper towels and rubber gloves). In the 7 years I worked at the bench, I rarely even used Excel.
Nonetheless, she was drawn to decentralized science (DeSci) because it offered a vision of optimism that she could not get in academia (emphasis mine):
[As] I spend more time talking to people in the tech/startup space, I realize more and more that the problems in science come from artificial incentive systems, not from fundamental truths about the universe… This may be obvious to people already [in tech], but it’s not obvious to me as a biologist.
Another DeSci supporter is Joseph Cook, an environmental scientist who specializes in computing at Aarhus University in Denmark. While he agrees with other scientists that “our current [scientific research] infrastructure is no longer adequate,” he believes that “decentralized models can be used to rewrite the rules of professional science.”
Interestingly, many DeSci participants also appear to have a life science background, or focus on life science initiatives, just like their traditional tech counterparts.
While the field of decentralized science is still evolving, here are a few examples of experiments that have been launched in the past year:
VitaDAO
VitaDAO is a DAO-managed community fund that "funds and advances longevity research in an open and democratic way." They have over 4,500 members on Discord and fund projects ranging in size from $25,000 to $500,000. As of January 2022, they have funded two projects with a total of $1.5 million in research funding.
VitaDAO’s revenue model is similar to Thiel’s Breakout Labs, but with a cryptocurrency twist: VitaDAO members own the intellectual property of the projects they fund (though they say this is negotiable), which theoretically increases the financial value of the $VITA token. VitaDAO has partnered with Molecule, which calls itself the “OpenSea of biotech IP,” to develop an IP-NFT framework to manage its IP. (Molecule is launching a similar project for psychiatric drug research, called PsyDAO.)
CryoDAO
CryoDAO is a community fund managed by a DAO dedicated to advancing cryopreservation research, such as developing new cryoprotectants to reduce toxicity, or developing different cryoprotection protocols based on ischemic conditions.
CryoDAO aims to support cryopreservation research projects that have high potential to advance the quality and capabilities of cryopreservation, a technology that has many current and potential applications in the preservation of viable organs and even humans.
OpScientia
OpScientia is a platform that is developing a new set of research workflows based on the principles of openness, accessibility, and decentralization. Some examples include: decentralized file storage for research data, verifiable reputation systems, and "game-theoretic peer review."
It’s again useful to compare OpScientia’s language to traditional tech theories of change in terms of talent; OpScientia describes itself as “a community of open science activists, researchers, organizers, and enthusiasts” that is “building a scientific ecosystem, unlocking data silos, orchestrating collaborations, and democratizing funding.”
LabDAO
LabDAO aims to create a community-run network of wet and dry lab services where members can conduct experiments, exchange reagents, and share data. Its founder, Niklas Rindtorff, is a physician scientist at the German Cancer Research Center in Heidelberg, Germany. LabDAO has not yet officially launched, but is under active development, and its Discord community has nearly 700 members.
Planck
Planck hopes to improve the way scientific knowledge is created and rewarded by putting digital manuscripts on the blockchain, which they call "alt-IP." Its founder, Matt Stephenson, is a behavioral economist who once sold an NFT containing independent data analysis for $24,000.
summary
There are more ways to improve scientific research methods now than in previous years, thanks to:
Changes in macro conditions, such as the coronavirus pandemic, a series of liquidity events in the tech sector, and the cryptocurrency boom have raised the bar for what’s possible;
Deliberate field-building efforts (writing, community building, and conferences) to legitimize scientific work and attract talent to the field;
Better coordination between funders (including co-funding opportunities) and practitioners
There are still new science startups being built today, like New Limit, Arcadia Science, and Altos Labs. But there are also examples of research institutions now, like the Arc Institute and New Science, and even emerging examples of crypto-native experiments, like VitaDAO and LabDAO. It’s not that one approach has replaced another, but there are more people trying different things now, which is a sign of a growing, thriving field.
The tech industry is still largely dominated by startups, and will likely continue to be so for a long time. But as tech matures as an industry, and as more extreme wealth outcomes emerge, there is now (as one would expect) growing interest in using philanthropic capital to solve ambitious problems.
Cryptocurrencies take this one step further by developing new primitives for public goods. Concerned that traditional philanthropic strategies will repeat the mistakes of traditional institutions, they seek to develop new ways to reward scientists and help them share in uncapped gains, which, if successful, could do for science (and other public goods) what startups did for venture capital.
There are fundamental differences in the theories of change native to crypto and tech. Tech focuses on recruiting top talent, but borrows similar reward structures from today’s science and startups. Crypto takes a more decentralized, networked approach to attracting talent and is more willing to reimagine fundamental structures like patents, intellectual property, and even research labs themselves. Both types of practitioners believe in improving traditional institutions through external work.
On the traditional technology side, it will be interesting to see whether the first wave of “anchor” funders can attract more funders into the space. If their efforts are successful, we should see:
Scientists publish high-quality work that is recognized by the wider scientific community;
The new initiative continues to attract top talent and is seen as an ideal place to build a scientific career;
Changes are coming to NIH and elsewhere in the federal sector thanks to new initiatives that show what’s possible
When it comes to cryptocurrencies, we should be looking at whether new initiatives:
Ability to generate and allocate funds for scientific work;
Produce research that is recognized by the wider scientific community;
Generate uncapped rewards (financial or otherwise) for participating scientists
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 grand experiments happening simultaneously.
This technology story fits in pretty well with philanthropic efforts over the past few decades, which means it has a higher probability of success: it’s a model that people understand more easily. The cryptocurrency story is very different, requiring us to reimagine what it means to fund and develop public goods, starting from a whole new set of assumptions. It’s more likely to fail, or to succeed only in limited circumstances. But if it does succeed, the potential gains are unimaginably large.
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