This article explores the methods and attempts made by the tech community to innovate and reform scientific research funding and institutions in the field of biosciences from 2011 to 2021. In this article, you can gain a separate perspective beyond cryptocurrency to overview all innovative funding solutions in current scientific research worldwide, in order to draw conclusions about the substantive advantages and disadvantages of Crypto in this paradigm.

Written by: Nadia Asparouhova

Translated by: LlamaC

_ (Portfolio: Burning Man 2016, about Tomo: eth foundation illustrator)_

For those straddling the line between science and technology, it is hard not to notice the plethora of new initiatives that have emerged over the past two years, aimed specifically at improving the life sciences sector.

Although I do not have a scientific background and have no personal ties to the field (other than knowing and liking many of the people involved), I became interested in understanding why this field has suddenly changed, particularly from a philanthropic perspective. Figuring out what works in the scientific domain can help us address other similarly shaped problems in the world.

To understand what has happened, I looked at examples of science-related efforts in the tech field over the past decade (approximately 2011-2021). I sought patterns that could help me infer the norms and values of the time, as well as turning points that changed these attitudes. I also interviewed many people in the field to help fill in the gaps and understand their values and what success looks like.

A caveat: for complex questions like "why has this culture changed," it is rare, if not impossible, to produce clear answers, so please consider this article a starting point for further exploration.

Problems in Science

When people say they want to "do science better," what problems are they trying to solve, and how are they addressing them?

Those working in and around the scientific field seem to generally recognize several observations. These themes have been widely and more thoroughly discussed elsewhere, so I will only briefly mention them:

The process of obtaining funding as a scientist is slow and bureaucratic

The popularity of Fast Grants (a rapid funding initiative 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 did not expect people from top universities to struggle so much for funding during the pandemic." However, in a survey sent to grant recipients, 64% of respondents indicated that their work would not have been possible without Fast Grant.

The reward system in academia, while sound, does not select the best work

Scientists are expected to publish their research findings in journals, and their reputation can be measured by citation counts. However, peer review tends to favor consensus over risk-taking, and scientists feel pressured to pursue quantity over quality, along with many other issues.

Early-career scientists are at a disadvantage

Science is trending towards older and more experienced scientists. Most funding from the National Institutes of Health in the U.S. is awarded to older scientists, and the age at which scientists make Nobel Prize-winning discoveries is also increasing.

Defining the Theory of Change

Why are these issues important? If we had to pose a "so what" question for the observations above, we might say that due to these systemic challenges, scientific progress is not as robust as it could be. Compared to other historical periods, such as the Victorian era or the Cold War, promising and talented scientists today seem to struggle to pursue their work, especially when their ideas are experimental or unproven.

Alexey Guzey, founder of New Science, pointed out in a 2019 survey of life sciences that scientists have learned to address these issues through various means, such as applying for funding for their "boring" ideas and then using part of that to fund their "experimental" ideas. In any case, it is reasonable to assume that if scientists did not have to engage in such maneuvering, they might accomplish more work. For example, from the aforementioned Fast Grants survey, 78% of respondents indicated that if they could obtain "unrestricted, permanent funding," they would "significantly" change their research plans.

If we had to write a theory of change for science with a tech flavor, it might look like this:

By eliminating the financial and institutional barriers faced by the world's top scientists, we ensure that scientific progress can thrive, allowing them to fully pursue their curiosity and produce research outcomes that can be applied for the benefit of humanity.

In this statement, there is a divergence among practitioners regarding what they consider the most important activities:

• Some people I spoke with believe that insufficient research funding or slow funding processes are the most significant levers: give scientists money and let them freely explore their ideas.
• Others believe that academic norms are a greater barrier: research should operate more like a startup culture.
• Still, others believe there is a divide between those focused on basic research and those who wish to apply research outcomes: the latter want to bring research results to market more quickly so that humanity can benefit from scientists' work.

I will elaborate on some of these approaches in the following sections.

Science can also be viewed as a subset of a broader problem statement: "How do we support a research culture in the tech field?" For example, artificial intelligence falls into this category but has different development trajectories and funding histories. Human-computer interaction (HCI) and "thinking tools" are similar. Even "science" itself is an extremely broad category, as we will see in the following sections (note that particular attention to improving scientific processes is sometimes referred to as "meta-science").

In this case study, I will focus solely on the overlap between scientific research and technology over the past decade. However, in many cases, the tech community's attitude towards research also influences our views on science, and vice versa, which I will occasionally mention here.

Now that I have clarified those considerations, let’s look at what commonalities exist among today’s practitioners. Reflecting on the theory of change above, what is unusual or significant about tech-native approaches to science?

For me, a prominent aspect is the focus on supporting and attracting top scientific talent. There is a potential hypothesis here that the quality of individual scientists matters, and that significant leaps in science may be attributed to the contributions of a few geniuses rather than the entire scientific community. (A meta-analysis by José Luis Ricón seems to support this hypothesis, although he notes that these conclusions may vary by field.)

The focus on "top talent" feels very tech-like to me, akin to how founders treat startups. While there is no perfect elite system, part of the reason tech culture thrives is that companies tend to place less emphasis on markers like background 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. Therefore, it is not surprising that the tech community applies this mindset to the scientific field.

Secondly, there is a constant emphasis on output, particularly in bringing research results to market. Again, this "results-oriented" approach feels very much in line with the characteristics of the tech industry: the belief that basic research should ultimately serve a long-term goal of benefiting humanity—and that we should shorten this timeline as much as possible.

Most of the people I spoke with believe that if you can commercialize your work, you should do so—of course, provided that not everything can be commercialized. Even nonprofit scientific projects tend to emphasize some entrepreneurial-inspired values, such as speed, proof of capability, and collaboration.

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

These values may seem obvious to those working in the tech field, but if we return to the high-level vision of "ensuring that scientific progress can thrive," applying these values may exclude some options that non-tech practitioners might pursue: for example, establishing postdoctoral programs, improving tools in university research labs, increasing enrollment in STEM graduate programs, etc.

Considering these values, let’s look at how research funding in the tech field has evolved over the past decade.

Driving Tech Innovation through Startups (2011-2014)

A common theme I heard in conversations is that the problem statements in science have not significantly changed over the past decade. For a long time, there has been a general awareness that science is not operating as effectively as expected, and there has been a desire to take action to change this. However, views on how to address this issue have shifted.

Ten years ago, most people believed that startups were the best way to drive scientific progress: either by founding companies or funding them.

At that time, economist and writer Tyler Cowen's 2011 book "The Great Stagnation" provided a philosophical foundation for scientific progress. Cowen made a broader argument about the stagnation of the U.S. economy, but he pointed out that the lack of scientific breakthroughs and the general slowdown of technological progress were among the reasons.

Cowen dedicated this book to Peter Thiel, who had publicly discussed the decline of technological innovation. In "The Great Stagnation," Cowen quoted an interview with Thiel, who stated, "Progress in pharmaceuticals, robotics, artificial intelligence, nanotechnology—all of these fields have been much more limited than people think. The question is why."

Around 2011, Thiel also adopted the now-infamous slogan for his venture capital firm Founders Fund, which he founded in 2005: "We were promised flying cars, and instead we got 140 characters." Thiel decided to translate this saying into an investment philosophy, revealing his theory of change: scientific progress would be solved through the market rather than through funding basic research.

While it is difficult to pinpoint why startups became the preferred method in the scientific field at that time, the simplest explanation is that it was related to the general popularity of startups in the 2010s. The Y Combinator accelerator played a significant role in making entrepreneurship more attractive and easier to start; it was founded in 2005 but reached cultural peaks in the 2010s. Many of its most successful alumni came from companies founded or achieving breakthrough growth in the 2010s. Marc Andreessen's 2011 op-ed "Why Software Is Eating the World" captured the sentiment of the time: software-driven startups could be applied to solve many different problems across industries.

Aside from Breakout Labs (which, although a funding initiative, was structured as a revolving fund with income from the intellectual property and/or royalties of the funded), the notable scientific projects at that time were typically startups or venture capital funds. Examples include:

Outside of startups, there were two notable research sponsors in the tech field at the time, who were closer to science but also reflected how people viewed research back then:

Google X: Google X was quietly established in 2010, with The New York Times first revealing its existence, describing it as a secret lab within Google focused on "moonshot ideas." 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 solely on science, it is often cited as a symbol of the culture of technology and academic research. In the 2010s, it thrived under the charismatic leadership of Joi Ito until he abruptly resigned in 2019 due to controversial financial relationships.

Early Philanthropic Approaches (2015-2017)

• By the mid-2010s, the tech industry had generated enough personal wealth that some investors began to experiment with traditional philanthropic methods.
• In 2015, Y Combinator announced the establishment of a nonprofit research organization, YC Research, initially funded by its president Sam Altman with a personal donation of $10 million. Although not directly involved in science (their initial research projects focused on universal basic income, urban studies, 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 suited for certain types of innovation," which was a novel perspective at the time:

Our mission at YC is to promote innovation as much as possible. This primarily means funding startups. But for certain types of innovation, startups are not ideal—such as work that requires long time horizons, seeks to answer very open-ended questions, or develops technologies that should not be owned by any one company.

However, he emphasized that YC Research still aimed to operate differently from typical research institutions (emphasis mine):

We believe research institutions can do better than they do now… Researchers' compensation and power should not be driven by publishing a large number of low-impact papers or speaking at numerous conferences—the whole system seems to be broken. Instead, we will focus on the quality of output.

In the same year, Mark Zuckerberg and Priscilla Chan announced that they would donate 99% of their Facebook shares to charity, managed by the Chan-Zuckerberg Initiative. Similar to Y Combinator, Chan and Zuckerberg chose to operate in a slightly different manner, structuring CZI as a limited liability company rather than a 501c3 nonprofit organization (like most charitable foundations), believing this would give them "the flexibility to execute their mission more effectively."

CZI's first investment was a $3 billion commitment aimed at "curing, preventing, and managing all human diseases in our lifetime," planned to be distributed over ten years. Of this, $600 million was earmarked for the creation of Biohub, a research center located at the University of California, San Francisco (UCSF), established in collaboration with Stanford University and the University of California, Berkeley.

In their joint statement, Zuckerberg explained that the slow progress in life sciences was related to the current ways of funding and organizing science (emphasis mine):

Building tools requires new ways of funding and organizing science… Our current funding environment does not 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 echoed similar concerns about the way scientific research is conducted (emphasis mine):

The cancer problem is not just a matter of resources, but how we allocate those resources… The system is problematic to some extent… The institutions that fund most scientific research typically do not encourage scientists to pursue their boldest ideas, so we do not get ambitious science.

Compared to the first half of the 2010s, this period saw a burgeoning interest in funding basic research, and there was a growing recognition that startups could not fully achieve the goals—despite donors emphasizing the importance of an innovative research culture, with a greater focus on tech-oriented output, collaboration, and tool development.

Other projects launched around the same time that reflected these trends included:

• Open Philanthropy: A research and funding organization that broadly focuses on improving philanthropy, but its initial focus areas included funding biological research. Open Philanthropy became an independent organization in 2017, but it originated from the collaboration between Good Ventures (Dustin Moskovitz and Cari Tuna) and GiveWell in the preceding years.
• OpenAI: A nonprofit organization initially described as a "nonprofit research company," launched in 2015 with a $1 billion commitment from Elon Musk, Sam Altman, and others. (OpenAI later transitioned to a for-profit structure.) Although not focused on science, OpenAI became one of the largest research projects in the tech field in recent years. Their initial announcement emphasized the importance of open publishing, open patents, and collaboration.

During this period, despite claims of interest in improving collaboration among researchers, there seemed to be a missing element—coordination among donors. Instead, it felt as though each effort was donor-centric rather than collectively addressing a clearly defined problem through various methods.

This is not a criticism but rather highlights the very difficult challenge that early major donors faced in learning how to strategically address scientific problems through non-startup means and how to define their philanthropic work outside traditional expectations—compared to today's groups.

Field Building and New Institutions (2018-2021)

In recent years, coordination between funders and founders has become tighter, helping to spawn a series of new scientific initiatives.

A 2017 NBER working paper titled "Are Ideas Getting Harder to Find?" raised the alarm that "research efforts are increasing dramatically, while research productivity is plummeting," sparking renewed discussions about scientific innovation. In 2018, Patrick Collison and Michael Nielsen published a commentary in The Atlantic, which included original research making a similar argument: despite "the number of scientists, research funding, and published scientific papers being greater than ever… has our scientific understanding grown correspondingly?"

The following year, Patrick Collison and Tyler Cowen published a related article in The Atlantic titled "We Need a New Science of Progress," proposing that "the world would benefit from an organized effort to understand" how to achieve progress, including identifying talent, incentivizing innovation, and the benefits of collaboration.

Although their commentary broadly addressed progress, science was a prominent example. Collison and Cowen stated, "While science has produced most of our prosperity, scientists and researchers themselves have not adequately focused on how science should be organized," and "there is a lack of critical assessment of how science is practiced and funded, which may be for unsurprising reasons."

The commentary in The Atlantic (along with numerous subsequent efforts) contributed to the formation and development of the "Progress Studies" community, providing a much-needed intellectual home and community for those interested in issues like scientific progress.

While today's scientific practitioners are not formally affiliated with Progress Studies (most would likely say they do not belong to this field), and the issues Progress Studies focuses on extend far beyond science, I feel that the formation of such a community has been helpful:

1. As a coordination point for like-minded individuals, attracting more talent into the field, and
2. Legitimizing the work of practitioners.

In 2021, a group gathered for an in-person "Tech Bottleneck Workshop," premised on the idea that bottlenecks "exist throughout the science and technology landscape, and addressing these bottlenecks could yield tremendous advances for the entire field." Attendees included founders and investors, many of whom were already engaged in science-related projects, including Fast Grants, Convergent Research, and Rejuvenome.

The workshop received positive feedback from participants. It helped more people get to know each other and understand one another, strengthening a shared approach and interest in the field, and even sparking new collaborations.

Here are some scientific initiatives launched in recent years. Notably, the diversity of experiments within the shared problem space and the strengthened coordination between funders and founders (note the degree of overlap among the various initiatives). Compared to the more singular, closed approaches of the early 2010s, these are signs of a healthy, thriving field.

Most of these initiatives focus on the life sciences. I asked several people why this might be the case. Some thoughts include:

• Personal relationships and interests: Some funders and founders have pre-existing connections or backgrounds in the life sciences.
• Storytelling and public narrative: Life sciences involve addressing issues like curing diseases, extending life, reproductive medicine, and genetics. The benefits of pursuing such work are easier for the public to understand, especially in the wake of a global pandemic, compared to risks or space exploration.

As mentioned earlier, this group is characterized by a diverse approach: a mix of for-profit and nonprofit pursuits, as well as a combination of funding and operational organizations. We can also note the diversity of approaches at the systemic change level (organizational vs. individual), types of research (basic vs. applied), and project time spans (short-term vs. long-term).

Why Are There So Many New Initiatives Today?

While there has long been a group of passionate practitioners in science, only recent influxes of funding have 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 successfully securing funding.)

Some funders tend to downplay their role as "funders," but I believe it is important to emphasize the significance of good funding practices. Specifically, I want to highlight that today's scientific funders in the tech field are not "throwing money at problems," but rather taking a strategic yet classic philanthropic approach to build a new field. Two particularly useful primary efforts have laid the groundwork for this field:

1. Better Coordination: Strengthened coordination and co-funding among funders helps them learn from each other and make larger contributions, while also providing practitioners with reassurance as they pursue long-term work.
2. Field Building: Indicating that these are interesting and worthy research questions attracts others into the field and legitimizes the work of practitioners.

What Led to the Renewed Interest in Funding Science? There may be several factors, some of which are external conditions and others are the results of conscious efforts:

Global COVID-19 Pandemic

By forcing people to confront large, immutable systems, the pandemic helped us realize that the world is more malleable than it previously appeared. People became frustrated with bureaucracy, unable to escape it, and realized they could take immediate action—rather than waiting for a distant future—to improve the situation.

Fast funding initiatives were launched to directly address the COVID-19 pandemic, and their success seems to have influenced the vision of the Arc Institute. The Longevity Fund was also inspired by the fast funding model, but focused on different themes.

The founder of Arcadia Science directly pointed out that the pandemic "sparked a sense of urgency, collaboration, and enthusiasm for scientific progress beyond our usual circles. The resulting vaccine development demonstrated how powerful collaboration can be between science and scientists."

One person I spoke with suggested that the pandemic, which geographically dispersed people, may have also had the effect of breaking the Silicon Valley groupthink, exposing individuals to new ways of thinking and making them more receptive to non-startup approaches.

Successful Field Building and Better Coordination Among Participants

Commentary articles, workshops, and the formation of the Progress Studies community have made it easier for like-minded individuals to find and coordinate with each other. As Luke Muehlhauser noted in his early field growth report for Open Phil, while these methods may seem "obvious," they are also "often effective."

In my conversations, long-time practitioners commented that people have been interested in this problem space for decades, but only in recent years have they been surprised to discover (quote) "that there are more people like us than I imagined."

Even among practitioners who have known and collaborated with each other for years, field building has had the effect of elevating their work to a status more akin to that of startup founders—this will continue to attract others into the field.

In our discussions, several individuals commented on this effect. One person said that such projects (i.e., starting an ambitious non-startup project) were considered "unfundable" until recently, as now there are several people "making it cool." Another felt that while the average person in the tech industry might still not understand what they are doing, they sensed that their work is no longer viewed as "low status."

Cryptocurrency Wealth Boom

2017 and 2021 were two major turning points for wealth creation in cryptocurrency. We began to see the downstream effects of the first boom and may see the effects of a second boom in the coming years.

Cryptocurrency has had both direct and indirect impacts on the field of scientific funding. First, from a practical standpoint, it has created a new class of potential funders. Today's active cryptocurrency funders in the scientific field are primarily beneficiaries of the first cryptocurrency boom in 2017—similar to how Mark Zuckerberg, Dustin Moskovitz, and Sean Parker benefited from Facebook's IPO in 2012 and became active philanthropic funders a few years later.

Second, crypto wealth has become a driving force for "traditional tech" to take greater risks in cultural building. While it is difficult to prove this, we can view it as a shift in the Overton window, where the emergence of a group holding more extreme views than the median makes previously radical positions seem reasonable and feasible. In terms of technology, the fact that the cryptocurrency industry non-ironically wants to rebuild society from scratch makes, for example, the establishment of a new 501c3 research institution seem less strange.

Several macro conditions may also have prompted a shift in the tech community'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 that generated new wealth in the late 2010s; and a fundamental shift in the relationship between technology and mainstream culture starting in the mid-2010s. These topics are beyond the scope of what I want to discuss here, but it is worth noting that they are other contributing factors.

Measuring Success

Finally, I want to understand how participants in today's community view measuring impact. How will we know in ten years whether these efforts have been successful?

Almost everyone I spoke with mentioned some version of the "100 Billion Dollar Question" (a term attributed to David Lang), referring to the relatively small amount of private capital compared to federal R&D funding, which exceeds $100 billion annually in the U.S. Based on what we can infer, the latest wave of initiatives collectively represents billions of dollars in scale. While the amount is substantial, it is just a small fraction of what the government can do.

Due to these relative financial constraints, the participants I spoke with are instead thinking about how to stimulate improvements in federal funding (especially National Institutes of Health funding in the life sciences) by demonstrating possibilities, rather than trying to compete dollar-for-dollar. This approach aligns more closely with the role of philanthropic capital in civil society, which aims not to compete with or replace the government but to seed new ideas through private experiments that do not affect public tax revenue. For example, public libraries, public schools, and universities in the U.S. were all shaped by early philanthropic work.

Practitioners who choose to start companies rather than nonprofits are similarly driven by the desire to extend the lifespan of capital. If a company succeeds, it can inspire the founding of other tech companies, as there is ample startup funding available. In contrast, successful nonprofits often do not inspire the founding 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 recent and long-term goals I heard in conversations, along with suggestions on how to measure these goals.

Epilogue: DeSci and New Crypto Natives

This story has another chapter, which I have placed in a separate "Epilogue" section because it is both new and distinctly different from the approaches discussed above, but also serves as an important contrast to everything we have covered so far.

If we look at the big picture of how science is funded and supported, we can take multiple approaches. Public goods are not solely funded by the government; they can also be influenced by the market (i.e., startups) and philanthropic capital. So far, the examples we have seen, no matter how novel or different they may appear, fall into one of these existing categories.

There is another, more radical approach that I will (reluctantly) call the crypto-native approach. Proponents of this approach argue that while the above efforts are positive developments, they ultimately replicate the same problems of our existing traditional systems. They would argue that creating new institutions without rewriting their fundamental incentive mechanisms will not solve any problems in the long run: it merely resets the timer on institutional decay.

Even within the "traditional tech" community, there are widely varying answers to the question, "Are we trying to create new public institutions, or merely make existing ones better?" Some initiatives are thinking long-term about how to avoid institutional decay, such as limiting funding or organizational scale. Regardless, most of the people I spoke with seem to agree with the "100 Billion Dollar Question" approach: efficiently deploying limited funds to create impact at a larger federal level.

In contrast, in the crypto-native approach, supporters hope to create entirely new ways of funding public goods. While they share a long-term vision of improving scientific progress, attracting top talent, and bringing research results to market, their strategies differ. Their theory of change might look like this:

By inventing new ways to reward scientists, improve collaboration, assess, and enhance the quality of their work, we ensure that scientific progress can thrive, allowing them to fully pursue their curiosity and produce research outcomes that can be applied for the benefit of humanity.

In my conversations, I heard those supporting different approaches almost verbatim say: "The existing systems of academia, research, and government are designed to produce a certain set of outcomes. Nothing will change unless we invent new rules of the game." However, in the traditional tech space, it seems that new rules of the game are creating new institutions (but the basic organizational principles are considered static), while in the cryptocurrency space, there is a complete redesign of the incentive systems (where organizational principles are considered malleable).

At the "Funding the Commons" virtual conference hosted by Protocol Labs in 2021, founder Juan Benet gave a talk about "Crossing the Innovation Chasm." He pointed out that over the past decade, the startup ecosystem has achieved significant results in R&D innovation by productizing new technological advancements. From his perspective, Y Combinator's contributions to R&D innovation far exceed those of Alphabet or Ethereum.

However, while foundational research efforts focus on solving the aforementioned "blue triangle" issues, they do not address the missing "black box": translating research into real-world innovations. Just as the tech ecosystem has created billions of dollars in venture capital for startups, the crypto ecosystem can do the same for funding public goods.

For me, this touches on the core distinction between the tech-native and crypto-native approaches to addressing public goods issues. In the best-case scenario, the tech approach generates wealth through startups and then uses the remaining wealth for philanthropic purposes (whether through for-profit or nonprofit initiatives). On the other hand, the crypto approach creates a native funding system for public goods, allowing participants to generate wealth through the development of public goods themselves.

Vitalik Buterin's talk at Funding the Commons echoed these sentiments. He explained that the blockchain community is more built on public goods rather than private products, 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 just from individuals, but from applications and/or protocols." New crypto primitives can help address these needs, such as DAOs or token rewards.

Some distinctions between crypto and traditional tech-native approaches:

• The belief in limited upward space versus unlimited upward space. Traditional tech individuals recognize the limitations of the $100 billion question, while cryptocurrency holds a broader view of possibilities. One person I interviewed believes that cryptocurrency networks may rival federal funding levels in the next decade. A new set of crypto primitives will also make it possible to significantly increase financial rewards for scientists. Whether this is achievable or not, I find this belief in unlimited upward space encouraging.
• Centralization versus decentralization of talent. As mentioned earlier, traditional tech seems to focus on helping outstanding scientists who are slowly being destroyed by failing bureaucracies. In contrast, cryptocurrency takes a more decentralized approach to talent, attracting and coordinating a larger network of contributors. (As someone told me: "Scientific progress is a coordination problem.") The cryptocurrency approach aims to provide tools for the world that allow anyone to experiment (ultimately filtering out the best talent), rather than actively identifying and recruiting the best talent into organizations. We can view this as an open-source vs. Coasean approach to talent, which is also a thematic difference between cryptocurrency and traditional tech on a broader level.

While traditional tech and crypto provide two different approaches to solving scientific problems, there is still cross-activity among funders. Funders are not categorized by their workplace but by differences in their theories of change. Some funders, like Vitalik, can support both traditional tech and crypto efforts simultaneously, which can be referred to as a "diversified portfolio" approach to improving science.

Focusing further on the cryptocurrency space, there is an emerging movement applying new primitives to science, sometimes referred to in the Web3 space as DeSci, or decentralized science. While not everyone agrees with this term, I will use it as shorthand in this section to refer to crypto-centered approaches to improving science because, well, it’s more catchy.

Surprisingly, many DeSci practitioners have scientific backgrounds. These individuals are not just cryptocurrency evangelists deciding to apply their skills to a new industry: some scientists are leaving positions in academia or industry to fully immerse themselves in DeSci.

Jessica Sacher, a former microbiologist turned co-founder of Phage Directory, describes her previous life as intensely "analog":

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

Despite this, she is drawn to decentralized science (DeSci) because it offers an optimistic vision that she could not find in academia (emphasis on mine):

As I spent more time engaging with people in tech/startup spaces, I became increasingly aware that the problems in science stem from artificial incentive systems rather than from fundamental truths of the universe… For those already in tech, this may be obvious, but it wasn’t to me as a biologist.

Joseph Cook is another DeSci supporter, an environmental scientist at Aarhus University in Denmark, focusing on computational fields. While he, like other scientists, believes that "our current [scientific research] infrastructure is no longer fit for purpose," 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 programs, just like their traditional tech counterparts.

While the decentralized science field is still developing, here are several experimental examples launched in the past year:

VitaDAO

VitaDAO is a community fund managed by a DAO, "funding and advancing longevity research in an open and democratic way." They have over 4,500 members on Discord, funding projects ranging from $25,000 to $500,000. As of January 2022, they have funded two projects with a total research funding of $1.5 million.

VitaDAO's revenue model is similar to Thiel's Breakout Labs but features cryptocurrency: VitaDAO members own the intellectual property of the projects they fund (though they state this is negotiable), which theoretically increases the financial value of the $VITA token. VitaDAO collaborates with Molecule, which calls itself the "OpenSea of biotech intellectual property," to develop an IP-NFT framework to manage its intellectual property. (Molecule is launching a similar project for psychedelic 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 formulating different cryopreservation protocols based on ischemic conditions.

CryoDAO aims to support cryopreservation research projects that have high potential to enhance the quality and capability of cryopreservation, with many current and potential applications in the fields of usable organs and even human preservation.

OpScientia

OpScientia is developing a platform for a new research workflow based on principles of openness, accessibility, and decentralization. Some examples include: decentralized file storage for research data, verifiable reputation systems, and "game-theoretic peer review."

It is again useful to compare OpScientia's language with the theories of change in traditional tech regarding talent; OpScientia describes itself as "a community of open science activists, researchers, organizers, and enthusiasts" that is "building a scientific ecosystem to unlock data silos, coordinate collaboration, and democratize funding."

LabDAO

LabDAO aims to create a community-operated 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 officially launched yet but is actively in development, with nearly 700 members in its Discord community.

Planck

Planck aims to improve the creation and reward mechanisms of scientific knowledge by placing digital manuscripts on the blockchain, which they call "alt-IP." Its founder, Matt Stephenson, is a behavioral economist who sold an NFT containing independent data analysis for $24,000.

Summary

Compared to previous years, there are now more pathways to improve scientific research methods, thanks to:

• Changes in macro conditions, such as the COVID-19 pandemic, a series of liquidity events in the tech sector, and the cryptocurrency boom raising the bar for possibilities;
• Conscious field-building efforts (writing, community building, and conferences) to legitimize scientific work and attract talent into the field;
• Better coordination between funders (including co-funding opportunities) and practitioners.

New scientific startups continue to emerge, such as New Limit, Arcadia Science, and Altos Labs. But there are also examples of research institutions, like the Arc Institute and New Science, and even emerging examples of crypto-native experiments, such as VitaDAO and LabDAO. It is not that one approach replaces another; rather, more people are now trying different things, which is a sign of a growing and thriving field.

The tech industry remains primarily dominated by startups, and it is likely to continue this way for a long time. However, as the tech sector matures and more extreme wealth outcomes emerge, there is now (as expected) increasing interest in using philanthropic capital to tackle ambitious problems.

Cryptocurrency takes this a step further by developing new primitives for public goods. They are concerned 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 unlimited returns, which, if successful, could have an impact on science (and other public goods) similar to what startups have done for venture capital.

There are fundamental differences in the theories of change between crypto and tech natives. Tech focuses on recruiting top talent but borrows from similar reward structures in today’s science and startups. Crypto takes a more decentralized, networked approach to attracting talent and is more willing to reimagine fundamental structures such as patents, intellectual property, and even research laboratories themselves. Both types of practitioners believe in improving traditional institutions through external work.

In traditional tech, it is worth noting whether the first batch of "anchor" funders can attract more funders into the field. If their efforts succeed, we should see:

• Scientists publishing high-quality work that gains broader recognition in the scientific community;
• New initiatives continuously attracting top talent, being seen as ideal places to build scientific careers;
• Changes occurring in the National Institutes of Health and other federal departments due to new initiatives demonstrating possibilities.

In cryptocurrency, we should focus on whether new initiatives can:

• Generate and allocate funding for scientific work;
• Produce research recognized by the broader scientific community;
• Generate unlimited rewards for participating scientists (financially or otherwise).

I am particularly interested in observing how the tension between tech-native and crypto-native approaches unfolds. While they are at different stages of maturity, from a macro perspective, these are two significant experiments happening simultaneously.

This tech story aligns quite well with philanthropic efforts over the past few decades, suggesting a higher likelihood of success: it is a pattern that people find easier to understand. The cryptocurrency story, however, is entirely different, requiring us to start from a completely new set of assumptions and reimagine what it means to fund and develop public goods. It is more likely to fail or only succeed in limited circumstances. But if it does succeed, the potential rewards could be unimaginably large.

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