DeSci: Revolutionizing Science Funding

Author: Nadia Asparouhova Translator: LlamaC

(Portfolio: Burning Man 2016, About Tomo: eth Foundation Illustrator)

For those who are in the middle of science and technology, it is hard not to notice the large number of new initiatives that have emerged in the past two years, which are aimed at improving the field of life sciences in particular.

While I have no scientific background and no personal relationship with this field (other than knowing and liking many of the people involved), I became interested in understanding why this field is suddenly changing, especially from a philanthropic perspective. Figuring out what works in the scientific field can help us solve other similarly shaped problems in the world.

To understand what happened, I looked at examples of science-related efforts in the tech field 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 caveat: 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.

Questions 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 universally recognized by people working in and around science. These topics have been discussed extensively and in greater 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 grant 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 twenty research institutions: “We didn’t expect people from top universities to struggle so much with funding during the pandemic.” Yet in a survey sent to grant recipients, 64% of respondents said that without Fast Grants, their work simply would not have been possible.

Academia’s reward system, while robust, does not select for the best work

Scientists are expected to publish their research in journals, and their reputations can be measured by citation counts. 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 Are at a Disadvantage

Science is trending toward older and experienced scientists. The majority of NIH grants go to older scientists, and the age of scientists making Nobel Prize-winning discoveries is increasing.

Define a Theory of Change

Why do these issues matter? If we had to pose 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 today to pursue their work, especially when their ideas are experimental or unproven.

In a 2019 survey of the life sciences, Alexey Guzey, founder of New Science, noted that scientists have learned to work around these problems by, for example, applying for grants for their “boring” ideas and then using some of those grants 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 rounds. For example, from the aforementioned Fast Grants survey, 78% of respondents said they would “substantially” change their research plans if they had access to “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 that scientific progress can flourish by removing the financial and institutional barriers facing the world’s top scientists, allowing them to fully follow their curiosity and produce research that can be applied to benefit humanity.

In this statement, there is a split among practitioners about what they consider to be the most important activities:

• Some people I talked to thought that insufficient funding for research or slow funding processes were the levers with the greatest impact: giving scientists money to pursue their ideas.

• Others thought that academic norms were a bigger obstacle: research should be run more like a startup culture.

• Still others saw a split between those who focus on basic research and those who want to apply the results of their research: the latter want to get their research results to market more quickly so that humanity can benefit from the scientists' work.

I will describe some of these approaches in more detail in the following sections.

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, AI falls into this category, but has a different trajectory and funding history. So does human-computer interaction (HCI) and “tools for thinking.” Even “science” itself is an extremely broad category, as we’ll see in the following sections (note that a particular focus on improving the scientific process is sometimes referred to as “metascience”).

In this case study, I’ve focused only on the overlap between scientific research and technology over the past decade. However, in many cases, technology’s attitude toward research also influences how we think about science, and vice versa, and I’ll occasionally touch on this 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 stands out to me is 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 geniuses, rather than the scientific community as a whole. (A meta-analysis by José Luis Ricón seems to support this assumption, though he notes that these conclusions may vary by field.)

The focus on “top talent” feels very tech-y to me, and akin to the way founders think about startups. While there’s no perfect meritocracy, tech culture thrives in part because companies tend to place less emphasis 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 not surprising that the tech world applies this mindset to science.

Second, there’s always an emphasis on output, especially getting research results to market. Third, this “results-focused” approach feels very tech-y to me: the belief that basic research should ultimately serve a long-term goal to benefit humanity — and that we should try to shorten that timeline as much as possible.

Most people I talked to believed that if you can commercialize your work, you should do so—assuming, of course, that not everything can be commercialized. Even nonprofit science programs tend to emphasize some entrepreneurial-inspired values, such as speed, proving power, and collaboration.

Finally, there is an implicit belief among today’s practitioners that change is exogenous: we must work outside of institutions and influence from the outside to achieve these goals. While some organizations do work with universities, they still operate outside of traditional academic career paths.

These values ​​may seem obvious to someone working in tech, but if we go back to the high-level vision of “ensuring that scientific progress can flourish,” applying these values ​​would rule out some options that non-tech practitioners might pursue: for example, establishing postdoc programs, improving tools in university research labs, and increasing enrollment in STEM graduate programs.

With these values ​​in mind, let’s look at how science funding has evolved in tech over the past decade.

Driving Technological Innovation Through Startups (2011-2014)

A common theme I heard in my conversations was that the scientific problem statement had 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.

A decade 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’s 2011 book The Great Stagnation provided a philosophical basis for scientific progress. Cowen made a broader argument about the stagnation of the U.S. economy, but he pointed to the lack of scientific breakthroughs and a general slowdown in the rate of technological progress as one of the reasons.

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 fields has been much more limited than people thought. The question is why.”

Around this time in 2011, Thiel also adopted the now-infamous slogan 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 investment philosophy, revealing his theory of change: scientific progress would be solved through markets, not by funding basic research.

While it is difficult to pinpoint why startups became the preferred way to do science at the 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 major 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 grantees’ intellectual property and/or royalties), notable science projects at the time were typically startups or venture capital funds. Examples include:

Outside of startups, there were two notable research sponsors in tech at the time that 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, describing it as a secretive lab within 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 lab." While not focused on science, it is often cited as a symbol of tech and academic research culture. It flourished in the 2010s under the guidance of its charismatic leader, Joi Ito, until he abruptly resigned in 2019 amid controversial financial ties.

Early Philanthropic Approaches (2015-2017)

• By the mid-2010s, tech exits had generated enough personal wealth to cause some investors to begin experimenting with traditional philanthropic approaches.

• In 2015, Y Combinator announced the creation of a nonprofit research arm, 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 for changing cultural attitudes. As Sam Altman explained in his announcement post, sometimes "startups aren't ideal for certain kinds 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 for certain kinds of innovation, startups aren't ideal — 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 from a typical research organization (emphasis mine):

We think research organizations can be better than they are today…Researcher compensation and power won't be driven by publishing lots of low-impact papers or speaking at lots of conferences — that whole system seems broken. Instead, we'll focus on the quality of the output.

That same year, Mark Zuckerberg and Priscilla Chan announced that they would be donating 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 was 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 was earmarked for the creation of the Biohub, a research center based at the University of California, San Francisco (UCSF), in partnership with Stanford and UC Berkeley.

In their joint statement, Zuckerberg explained that the slow progress in the life sciences was related to the current way science is funded and organized (emphasis mine):

Building tools requires new ways to fund and organize 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. Parker’s statement again echoed similar concerns about the way science is done (emphasis mine):

The cancer problem isn’t just a problem of resources, it’s a problem of how we allocate those resources…The system is broken in ways that…The institutions responsible for funding most scientific research often 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 deliver – even as donors stressed the importance of an innovative research culture in itself, with a greater focus on science-oriented outputs, collaboration, and tool development.

Some other initiatives launched around the same time that reflect these trends include:

• Open Philanthropy: A research and grantmaking body focused more broadly on improving philanthropy, but whose 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 Cary Tuner) and Givewell in the previous few years.

• OpenAI: A nonprofit organization initially described as a “nonprofit research company” was 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 stressed the importance of open publishing, open patents, and collaboration.

During this period, despite professed interest in improving collaboration among researchers, one thing seemed to be missing — coordination among 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 of early major donors still learning how to strategically solve scientific problems in non-entrepreneurial ways, and how to define their philanthropic work outside of traditional expectations — compared to today’s cohort.

Field Building and New Institutions (2018-2021)

In recent years, coordination between funders and founders has become closer, which has helped to spawn a series of new scientific programs.

A 2017 NBER working paper, "Are Creativity Getting Harder to Find?", proposed that "research efforts are increasing dramatically, while research productivity is declining sharply", which triggered a renewed discussion on scientific innovation. In 2018, Patrick Collison and Michael Nielsen published an opinion piece 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. Although their opinion piece focused on progress more broadly, science stands out as an example. Collison and Cowen state that “while science generates much of our prosperity, scientists and researchers themselves have not paid sufficient 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 op-ed (and a host of subsequent efforts) led to the formation and growth of the “Progressive Studies” community, providing a much-needed intellectual home and community for those interested in issues such as scientific progress.

While today’s practitioners of science are not formally affiliated with Progressive Studies (most would probably say they are not), and Progressive Studies focuses on issues far beyond science, my sense is that the formation of such a community is helpful:

1. serving as a coordination point for like-minded people, attracting more talent into the field, and

2. legitimizing the work of practitioners.

   In 2021, a group of people came together for an in-person "Science and Technology Bottlenecks 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 were already working on science-related programs including Fast Grants, Convergent Research, and Rejuvenome.

   The workshop was well received by participants. It helped more people get to know each other, reinforced a shared approach and interest in the field, and even inspired new collaborations.

   Here are some of the science initiatives that have been launched in recent years. Particularly noteworthy is the diversity of experiments within a common problem space, and the increased coordination between funders and founders (note the degree of overlap between initiatives). These are signs of a healthy, thriving field, compared to the more singular, closed approach of the 2010s.

   

   Most of these initiatives are focused on the life sciences. I asked several people why they thought this was the case. Some thoughts include:

   • Personal connections and interests: Some funders and founders have pre-existing connections or backgrounds in the life sciences sector.

   • 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 diverse approach: a mix of for-profit and nonprofit pursuits, and a combination of funding and operating organizations. We can also note the diversity of approaches in terms of level of system 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 practitioners who are passionate about science, only the 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 obtained funding.)

   Some funders tend to downplay their role as “funders,” 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 problems,” science funders in tech today are taking a strategic, yet classically philanthropic approach to building a new field. Two major efforts that are particularly helpful have laid the foundation for this field:

   1. 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;

   2. Field building: Showing that these are interesting and worthwhile problems to study, attracts others to the field and legitimizes the work of practitioners.

   What has led to this renewed interest in funding science? There are likely several factors, some of which are external conditions and others the result of conscious efforts:

   The 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 bureaucracy, unable to escape it, and realized that they can take action now—not in the distant future—to improve the status quo.

   The Rapid Grants program was launched in direct response to the COVID-19 pandemic, and its success seems to have influenced the vision of the Arc Institute. The Longevity Dynamics grant program was also inspired by the Rapid Grants model, but focuses on a different theme.

   Arcadia Science’s founder noted directly that the pandemic “has sparked a sense of urgency, collaboration, and enthusiasm for scientific advancement outside of our usual circles. The resulting vaccine development has demonstrated how powerful science and collaboration between scientists can be.”

   One ​​person I spoke with suggested that the geographic dispersion of people due to 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 among participants

   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 Open Phil Early Field Growth Report, while these approaches may seem “obvious,” they are also “often effective.”

   In my conversations, long-time practitioners commented that people have been interested in this problem area for decades, but only in recent years have they been surprised to find (quote) “more people like us than I thought.”

   Even among practitioners who have known and collaborated with each other for years, field building has had the effect of making their work more status-based than before—more like startup founders—which will continue to attract others into the field.

   Several people commented on this effect in our conversations. One person said that this type of project (i.e., starting an ambitious non-startup project) would have been considered “unfundable” until recently, because now a few people have “made it cool.” Another person felt that while the average person in the tech industry might not yet understand what they are doing, they feel like their work is no longer considered “low status.”

   Crypto Wealth Boom

   2017 and 2021 were two major turning points in crypto 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.

   Crypto has had both direct and indirect effects on the science funding landscape. First, from a practical perspective, it has created a new set of potential funders. Crypto funders active in science today are primarily beneficiaries of the first crypto 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 has become an enabler for “traditional tech” to take greater risks in terms of culture building. 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. In the case of tech, the fact that the cryptocurrency industry unironically wants to rebuild society from the ground up makes, say, the creation of a new 501c3 research organization seem less odd.

   There are several other macro conditions that may have contributed to the shift in tech’s interest in funding new scientific projects: a bull market that made capital cheap; a growing disillusionment among the general public with traditional institutions; a wave of liquidity events in the late 2010s that generated new wealth; 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’re worth noting as other contributing factors.

   Measuring Success

   Finally, I wanted to understand how participants in today’s group think about measuring impact. In a decade, how will we know if these efforts have been successful?

   Almost 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 amounts to more than $100 billion per year in the United States. The latest wave of initiatives, as best we can surmise, represents a few billion dollars in total. While significant, it is a small fraction of what the government can do.

   Given these relative financial constraints, participants I spoke with instead thought 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. America’s public libraries, public schools, and universities, for example, were all shaped by earlier philanthropic work.

   Practitioners who choose to start companies rather than nonprofits are similarly driven by a desire to extend the life of capital. If one company succeeds, it can inspire the creation of other tech companies because there is plenty of startup money available. In contrast, successful nonprofits tend not to inspire the creation of more nonprofits (even if they can influence each other’s practices and interests) because philanthropic capital is limited, creating a more competitive, zero-sum situation.

   Here are some of the near- and long-term goals I heard 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 approaches, 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 variety of approaches we can take. Public goods are not exclusively funded by governments; they can also be influenced by markets (i.e., starting companies) and philanthropic capital. The examples we’ve seen so far, no matter how novel or different they may seem, fall into one of these existing categories.

   There is another, more radical approach, which I will (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’ll say that creating new institutions without rewriting their fundamental incentives doesn’t solve anything 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’ve talked to seem to agree on the “$100 billion problem” approach: deploying limited funds efficiently to have an impact at a larger federal level.

   In contrast, in the crypto-native approach, proponents want 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:

   Ensure that scientific progress can flourish by inventing new ways to reward scientists, improve collaboration, and evaluate and improve the quality of their work, so that they can 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 is 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 speech on "Crossing the Innovation Chasm." He pointed out that over the past decade, the entrepreneurial ecosystem has made remarkable achievements in R&D innovation by productizing new technologies. From his perspective, Y Combinator's contribution to R&D innovation far exceeds that of Alphabet or Ethereum.

   

   However, while basic research efforts focus on solving problems in the "blue triangle" area mentioned above, they do not solve the missing "black square": turning research into real-world innovation. Just as the technology ecosystem has created 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 and then use their surplus wealth for philanthropic purposes (whether through for-profit or non-profit initiatives). The crypto approach, on the other hand, is to create a native funding system for public goods that allows participants to generate wealth through the development of the public goods themselves.

   Vitalik Buterin's speech at Funding the Commons echoed these points. He explained that the blockchain community is 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 systemic," which means that funds need to "come not only from individuals, but also from applications and/or protocols." New crypto primitives can help address these needs, such as DAOs or token rewards.

   Some differences between crypto and traditional tech native approaches:

   • Belief in limited upside vs. uncapped upside. People in traditional tech recognize the limitations of the $100 billion problem, while 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 will also make it possible to dramatically increase financial rewards for scientists. Whether or not this is achievable, I find this belief in uncapped upside inspiring.

   • Centralization vs. decentralization of talent. As mentioned earlier, traditional tech seems to be focusing its efforts on helping talented 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 organizations. We can think of this as the open source vs. Coase approach to talent, which is also the thematic difference between crypto and traditional tech more broadly.

   While traditional tech and crypto offer two different approaches to solving scientific problems, there is still crossover activity among funders. Funders are not categorized based on where they work, but rather based on differences in theory of change. Some funders, like Vitalik, can support both traditional tech and crypto efforts, which can be called a “diversified portfolio” approach to improving science.

   Focusing further on the crypto space, there is an emerging movement to apply new primitives to science, which in the Web3 space is sometimes called DeSci, or decentralized science. While not everyone agrees with the term, I’ll use it as shorthand for crypto-centric improved scientific methods in this section because, well, it’s catchier.

   Surprisingly, many DeSci practitioners have scientific backgrounds. These aren’t just cryptocurrency evangelists who’ve decided to apply their skills to a new industry: There are also scientists who are leaving positions in academia or industry to pursue DeSci full-time.

   Jessica Sacher, a microbiologist-turned-co-founder of Phage Directory, describes her previous life as one that was intensely “analog”:

   I came from a bench in a molecular microbiology lab, where I wrote my methods and data in paper notebooks (on a good day; the rest of the time I wrote on tissues and rubber gloves). In my seven years at the bench, I barely even used Excel.

   Nevertheless, 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 spent more time talking to people in the tech/startup world, I increasingly realized that the problems with science come from artificial incentive systems, not from fundamental truths about the universe… This may be obvious to someone already [in tech], but it was not obvious to me as a biologist.

   Another DeSci supporter is Joseph Cook, an environmental scientist at Aarhus University in Denmark who focuses on computation. While he, like other scientists, believes 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 seem to have backgrounds in life sciences, or focus on life science initiatives, just like their traditional tech counterparts.

   While the field of decentralized science is still developing, here are a few examples of experiments that have 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 between $25,000 and $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 (although 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 psychotropic research, called PsyDAO.) CryoDAO is a DAO-managed community fund dedicated to advancing cryopreservation research, such as developing new cryoprotectants to reduce toxicity, or developing different cryopreservation protocols based on ischemia. CryoDAO’s goal is to support cryopreservation research projects that have high potential to improve the quality and capacity of cryopreservation, which has many current and potential applications in the field of viable organ and even human preservation.

   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."

   Again, it's useful to compare OpScientia's language to traditional theories of change in tech for talent; OpScientia describes itself as "a community of open science activists, researchers, organizers, and enthusiasts" that is "building a scientific ecosystem that unlocks data silos, coordinates collaboration, and democratizes funding."

   LabDAO

   LabDAO aims to create a community-run network of wet and dry lab services where members can run 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 it 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.

   Abstract

   There are more avenues to improve how science is done now than in previous years, thanks to:

   • Changes in macro conditions like the COVID-19 pandemic, a series of liquidity events in the tech space, 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

   new science startups are still being built today, like New Limit, Arcadia Science, and Altos Labs. But there are now examples of research institutes, 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 that there are now more people trying different things, which is a sign of a growing, thriving field.

   The tech industry is still largely dominated by startups, and it 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 a step further by developing new primitives for public goods. They worry that traditional philanthropic strategies will repeat the mistakes of traditional institutions, so 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 between crypto and tech-native. 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 basic structures such as patents, intellectual property, and even research labs themselves. Both types of practitioners believe in improving traditional institutions through external work.

   On the traditional tech side, it will be worth watching whether the first batch of "anchor" funders can attract more funders into the field. If their efforts are successful, we should see:

   • scientists publish high-quality work that is recognized by the broader scientific community

   • the new initiative continues to attract top talent and is seen as a great place to build a scientific career;

   • changes at the NIH and elsewhere in the federal sector as a result of the new initiative demonstrating what is possible

   On the cryptocurrency side, we should watch for new initiatives that:

   • are able to generate and distribute funding for scientific work;

   • produce research that is recognized by the broader scientific community;

3. 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’re at different stages of maturity, at a macro level these are two grand experiments going on simultaneously.

The tech story fits in pretty well with philanthropic efforts of the past few decades, which means it has a higher likelihood of success: it’s a model that people understand more easily. The cryptocurrency story is radically different, requiring us to reimagine what it means to fund and develop public goods, starting with an entirely 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.