Decentralized AI + science

Decentralized Science and Biotech

Decentralized Science

Science is essential for human progress. Decentralized science is a way to make science more open and frictionless, from publishing to funding. This will enable more scientific progress by allowing for more people to participate in the scientific process.

What is decentralized science?

Decentralized science is a movement that promotes open-source research in order to create a more democratic and equitable scientific system. DeSci allows for a more decentralized and distributed scientific research model, making it more resistant to censorship and control by central authorities. DeSci hopes to create an environment where new and unconventional ideas can flourish by decentralizing access to funding, scientific tools, and communication channels.

The goal is to make science more open, collaborative and accountable.  

Science could be better

Science is failing to keep up with the demands of the modern world because the mechanisms that have governed scientific inquiry for centuries are no longer providing all the necessary advancements. Decentralized science can help address these issues by allowing for more diverse funding sources, easier access to data and methods, and by providing incentives for reproducibility.

An incomplete list of key problems in science and how decentralized science can help to address these issues

Decentralized scienceTraditional science
Distribution of funds is determined by the public using mechanisms such as quadratic donations or DAOs.Small, closed, centralized groups control the distribution of funds.
You collaborate with peers from all over the globe in dynamic teams.Funding organizations and home institutions limit your collaborations.
Funding decisions are made online and transparently. New funding mechanisms are explored.Funding decisions are made with a long turnaround time and limited transparency. Few funding mechanisms exist.
Sharing laboratory services is made easier and more transparent using Web3 primitives.Sharing laboratory resources is often slow and opaque.
New models for publishing can be developed that use Web3 primitives for trust, transparency and universal access.You publish through established pathways frequently acknowledged as inefficient, biased and exploitative.
You can earn tokens and reputation for peer-reviewing work.Your peer-review work is unpaid, benefiting for-profit publishers.
You own the intellectual property (IP) you generate and distribute it according to transparent terms.Your home institution owns the IP you generate. Access to the IP is not transparent.
Sharing all of the research, including the data from unsuccessful efforts, by having all steps on-chain.Publication bias means that researchers are more likely to share experiments that had successful results.

Truly global

Biotech is becoming increasingly decentralized, with startups launching outside of major hubs, sharing lab space, hiring across borders, and collaborating on research projects. Decentralized models offer several advantages, such as lowering the barrier to entry for smaller companies and harnessing the talents of a more diverse pool of scientists.

It enables more research to happen and succeed outside of the incumbent system, which is held back by bureaucracy. Decentralized science has the potential to bring funding to researchers more quickly and advance their research more seamlessly. To make it truly successful, we need coordination between desci organizations and demonstration of the advantages of decentralized research, clinical trials etc. Biotech is already moving away from heavy physical setups and footprints, toward more virtual ones. It is outsourcing research processes to CROs and making its information and resources available to more people.

Why Crypto?

The decentralized science movement is an effort to improve science using blockchain. Similar to how blockchain is disrupting other industries, where web2 models of centralized ownership are being challenged by web3 models of decentralized, shared ownership. DeSci efforts range from purely theoretical ideas and small-scale technological experiments to more established players funding university research and launching multiple DAOs of their own.

As academic institutions increasingly shift their learning online, DeSci ecosystems may form an attractive alternative to traditional scientific education.

A better way with decentralized science

In decentralized science, the public determines how funds are distributed, through mechanisms such as quadratic voting. This is in contrast to traditional science, where funding decisions are made by small, centralized groups, with little transparency. DeSci is open to anyone, and allows for the free trade of laboratory services. Work is published online, on the author’s own terms. Peer reviewers can earn tokens and reputation for their work. And finally, in decentralized science, authors own the IP they generate and can distribute it according to transparent terms.

Use cases


The current standard model for funding science is that individuals or groups of scientists make written applications to a funding agency. A small panel of trusted individuals score the applications and then interview candidates before awarding funds to a small portion of applicants. Aside from creating bottlenecks that lead to sometimes years of waiting time between applying for and receiving a grant, this model is known to be highly vulnerable to the biases, self-interests and politics of the review panel.

Studies have shown that grant review panels do a poor job of selecting high-quality proposals as the same proposals given to different panels have wildly different outcomes. As funding has become more scarce, it has concentrated into a smaller pool of more senior researchers with more intellectually conservative projects. The effect has created a hyper-competitive funding landscape, entrenching perverse incentives and stifling innovation.

Web3 has the potential to disrupt this broken funding model by experimenting with different incentive models developed by DAOs and Web3 broadly. Retroactive public goods funding, quadratic funding, DAO governance and tokenized incentive structures are some of the Web3 tools that could revolutionize science funding.


Science publishing is a controversial topic because it is usually managed by publishing houses that rely on free labor from scientists, reviewers, and editors to generate papers, but then charge high publishing fees. The public, who have often paid for the work and the publication costs through taxation, can often not access that same work without paying the publisher again. The total fees for publishing individual science papers can be five figures ($USD), which undermines the whole concept of scientific knowledge as a public good while generating enormous profits for a small group of publishers.

Free and open-access platforms, such as ArXiv, exist as an alternative to traditional publishing houses. However, these platforms lack quality control, anti-sybil mechanisms, and do not generally track article-level metrics, meaning they are usually only used to publicize work before submission to a traditional publisher. SciHub also makes published papers free to access, but not legally, and only after the publishers have already taken their payment and wrapped the work in strict copyright legislation. This leaves a critical gap for accessible science papers and data with an embedded legitimacy mechanism and incentive model. The tools for building such a system exist in Web3.

Reproducibility and replicability

Reproducibility and replicability are the foundations of quality scientific discovery.

New Web3-native tools can ensure that reproducibility and replicability are the basis of discovery. Quality science can be woven into the technological fabric of academia using these tools. Each analysis component- raw data, computational engine, and application result- can be attested to in a consensus system. This system would then be responsible for reproducing the calculation and validating each result.

IP ownership and development

The ownership of digital assets such as scientific data or articles is a big problem in traditional science. However, this is something that Web3 does exceptionally well using NFTs.

NFTs can help ensure that the original creators of digital assets receive a portion of the revenue from future transactions. In the same way, transparent value attribution chains can be used to reward researchers, governing bodies, or even the subjects whose data is collected. This would help create a more equitable distribution of rewards and value within the digital economy.

IP-NFTs can serve as a key to a decentralized data repository of research experiments, plugging into NFT and DeFi financialization. This also allows entities like VitaDAO to conduct research directly on-chain.


“Soulbound” tokens may also play an important role in DeSci by allowing individuals to prove their experience and credentials linked to their Ethereum address for example.

Data storage, access and architecture

Web3 patterns can make scientific data vastly more accessible, and distributed storage will enable research to survive cataclysmic events.

A system accessible by any decentralized identity holding the proper verifiable credentials is the starting point. This allows sensitive data to be securely replicated by trusted parties, enabling redundancy and censorship resistance, reproduction of results, and even the ability for multiple parties to collaborate and add new data to the dataset. Confidential computing methods like compute-to-data provide alternative access mechanisms to raw data replication, creating Trusted Research Environments for the most sensitive data.

Flexible Web3 data solutions support the scenarios above and provide the foundation for truly Open Science, where researchers can create public goods without access permissions or fees. Web3 public data solutions such as IPFS, Arweave and Filecoin are optimized for decentralization. dClimate, for example, provides universal access to climate and weather data, including from weather stations and predictive climate models.

Biotech DAOs

Biotech DAOs are a new way of organizing and incentivizing collaborative research in the biotech space. They have the potential to break Eroom’s law (drug discovery becoming slower and more expensive over time) by enabling new methods of optimizing collaboration, talent, and capital allocation.

VitaDAO is the first example of a biotech DAO and it is focused on funding early-stage preclinical drug development in the context of longevity.  





Insights from Michael Nielsens Book “Reinventing Discovery”

In his book “Reinventing Discovery: The New Era of Networked Science,” Michael Nielsen discusses the potential for decentralized, networked science to accelerate the pace of scientific discovery. Some key actionable insights from the book include:

Overall, the key actionable insights from “Reinventing Discovery” highlight the potential for decentralized networks to accelerate scientific discovery and improve the accessibility and inclusivity of scientific research


Quotes from “Understanding Science Funding “ by Nadia

By contrast, in a crypto-native approach, proponents hope to create entirely new ways of funding public goods. While they share the same long-term vision of improving scientific progress, as well as attracting top talent and bringing research to market, their strategies are different. Their theory of change might look something like:

Ensure that scientific progress can flourish by inventing new ways to reward scientists, improve collaboration, and assess and amplify the quality of their work, so that they can fully pursue their curiosity and produce research that finds its way into applications that benefit humanity.

In my conversations, I heard near-verbatim statements made by those espousing different approaches, to the effect of “The current systems in academia, research, and government are designed to produce a certain set of outcomes. Unless we invent new games, nothing is going to change.” Among trad tech, however, it seems that the new games are creating new institutions (but the underlying organization principles are assumed to be static), whereas among crypto, it’s designing new incentive systems entirely (where organizing principles are assumed to be malleable).

But while basic research efforts focus on fixing problems in the “blue triangle” area above, they don’t address the missing “black square”: translating research into real-world innovation. Just as the tech ecosystem has created billions of dollars in venture capital funding for startups, then, the crypto ecosystem can do the same for funding public goods.

This, to me, gets to the heart of the difference between tech-native and crypto-native approaches to solving public goods problems. In a best-case scenario, the tech approach is to generate wealth via startups, then use their surplus wealth for philanthropic means (whether through for-profit or nonprofit initiatives). The crypto approach, on the other hand, is to create a native funding system for public goods, so that participants can generate wealth through the development of public goods themselves. [6]

Vitalik Buterin also gave a talk at Funding the Commons that echoed these sentiments. He explains that blockchain communities are built more on public goods than private goods, such as open source code, protocol research, documentation, and community building. He therefore emphasizes 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 those needs, such as DAOs or token awards.

A few differences between crypto- and trad tech-native approaches:

Belief in limited vs. uncapped upside. Whereas those in trad tech recognized the limitations of the $100B problem, crypto takes a much wider view of what’s possible. One person I spoke to thought that crypto networks could rival federal funding levels in the next decade. A new set of crypto primitives would also make it possible to significantly increase financial rewards for scientists. Whether this is achievable or not, I find this belief in uncapped upside to be inspiring.

Centralization vs. decentralization of talent. As previously mentioned, trad tech seems to focus their efforts on helping the really good scientists who are slowly being destroyed by an ailing bureaucracy. Crypto, on the other hand, takes a more diffuse approach to talent, attracting and coordinating a larger network of contributors. (As one person told me: “Science progress is a coordination problem.”) With a crypto approach, the goal is to equip the world with tools that allow anyone to experiment (which eventually filters for the best talent), rather than proactively identifying and recruiting the best talent into an organization. We can think of this as an open source vs. Coasean approach to talent, which is a thematic difference between crypto and trad tech more broadly.


Specific use cases

Interesting Reads


Disclaimer: I’m directly involved with VitaDAO, PsyDAO, labDAO and Molecule and might indirectly with the others


Decentralized Science Landscape

Decentralized Science Landscape, curated by Jocelynn Pearl —  

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