This post is a summary of a presentation I gave at in September 2023. The (very high level slides) are linked at the bottom of this page.

The decentralised science movement is very close to our heart. We perceive scientific research as the bedrock of human progress, innovation and flourishing. This is why we have been part of the community since its inception when we backed in late 2019 and Labdao in 2022.


99 Problems

In the post WW2 era the scientific ecosystem evolved into a complex bureaucracy composed of various different stakeholder groups and relationships between them - a complex system. This system is currently facing many structural challenges such as (1) paid access to research which is financed by the public, (2) opaque and inefficient funding mechanisms, (3) a lack of data sharing initiatives across countries, institutions and disciplines preventing deeper and broader insights, (4) intransparency around contributions and many, many more.

Science is broken and everybody knows it.

Having broad baed consensus on the 99 problems and some of the smartest, purpose driven talents of a generation working on it - why is it so hard to make progress? The root cause is inertia: the more complex a system, the larger its resistance to change.

To share some examples:

(1) Institutional biases: the brand of an institution counts more than the actual scientific contribution to a field when it comes to publishing in top journals. Exclusivity instead of meritocracy and truth seeking became the norm.

(2) Social biases: in its current form the ‘scientific method’ is defined very narrowly and hampers us to to get insights from experiments which require hard-to-replicate human interventions like in the field of psychedelic therapy where the subjects are typically guided through the journey what makes it harder to assign the causality of the results to the compounds as opposed to the human therapist’s influence.

(3) Feedback Loops: Publish or perish is a terrible feedback loop which leads to more frequent publishing creating group think and incrementalism. Quality became a vanity metric for scientific researchers.

So how do we overcome such inertia in a complex system?

We need to tackle (1) the ‘right leverage points’ and (2) build up sufficient ‘activation energy’. But what does that mean?

Finding the right leverage points

Leverage points in our context are activities that are yielding maximal effects with minimal effort. Ideally, those are changes which unlock other changes. Changes that can create ripple or network effects of some sort. To generate maximal impact, the leverage points should be composable and integrate with one another.

Intuitively, the DeSci ecosystem picked a mix of technologies and behavioural patterns as leverage points to start with. I’m currently seeing 3 major categories that I’d consider as the ‘right’ leverage points and one that I perceive as ‘less relevant’.

(1) Data & Collaboration tools can be used to facilitate data sharing and to track contributions and research provenance - e.g. through NFT issuance and provable claims of contributions (not your keys, not your contribution!).

(2) On top of such tools a social-knowledge graph can be layered, e.g. to represent research trees which can be leveraged for

(3) novel incentive structures such as programmatic royalties for researchers, paid peer-reviews, retro-active funding or fractional, liquid IP markets

The leverage point I am much less excited about is ‘Public Policy’. Building momentum on the regulatory front requires insider access, knowledge and obscene budgets while driving marginal (if any) results. It might only become relevant at a much later point in time once we built up momentum aka activation energy.

I’d be very curious to learn more about alternative or additional leverage points in this context. What do you see in terms of novel products and approaches to address some of the 99 problems?

Activation Energy

Now, assuming that we figured out some of the right initial leverage points we need to think about activation energy - the energy that must be delivered to a system in order to initiate a reaction; to break bonds so that new ones can form.

Activation energy in socio-technological - let alone political - contexts is a very tricky thing. We can borrow some analogies from the Arab Spring and many other (failed) revolutions of the past. In most cases it only requires a small but very active minority of say 10% of a given population to rebel against the status quo. In Tunesia, Lybia, Egypt, Algeria, Jordan, Morocco and some other countries such highly active, minority groups were amplifying their goals through new means of technology - social media. The problem many of them were facing though, was a lack of activation energy. In order to execute a revolution successfully it doesn’t suffice to overthrow the powers that be and to create a power-vacuum. One needs to have a concrete plan about how to seize power, how to provide and maintain public infrastructure like health, education and basic supplies like electricity and water. One needs to have a plan for how to arrange new governance models and how one deals with opposition forces short-, mid- and longterm to avoid an immediate counter-revolution or seeing the military taking over. The latter is sort of a default for most revolutions.

For more context about those topics I highly recommend the book Revolt of the Public by Martin Gurri.

Applying some of those learnings to the DeSci movement - what would need to happen in order to change the state of a highly complex system with huge inertia? More than a few things:

  • Ideology
    • Talent
      • Capital
        • Technology
          • Adoption
            • Public Support
              • Governance

                Those things are partially path-dependent in the sense that we cannot have certain things without others (e.g. no capital, no technology or no public support, no governance). In order to navigate through what might probably be a multi-decade, maybe never-ending state transition we need to be aware of where we are in the cycle.

                And to be aware of where we are in the cycle we need data.

                Reality Check

                So, how far are we in the transition towards are more open, more collaborative, more accountable and efficient scientific ecosystem? Are we 5% in, 15%, 50%? In order to get any sense of orientation we could look at the inputs and outputs of the system from a 10,000 foot view. Some metrics coming to mind:

                • Input: 95 DeSci Projects (40% funding, 25% data, tools, 25% Publishing, 10% Other); $65M in funding raised, about 150 talents working FT in Desci,
                  • Output: about 2,400 users of DeSci Products, 9 IP-NFTs issued

                    Those numbers do barely yield any insights - they are too shallow, too fragmented and mostly incorrect or outdated. We used the Messari Report - The Decentralised Science EcosystemAwesome-Desci Github (for #Projects with URLs), Crunchbase (for funding data) and (for users + categories) to pull some of those numbers.

                    Going forward we’d like to collaborate with any of the above initiatives and the broader ecosystem to put up some (self reporting) dashboards where we can gather more insightful fundamental data. Please reach out in case you have ideas or would like to contribute!

                    Growing Pains; Builder’s 101

                    Assuming we’d be able to get more meaningful data - a next step would be to identify our community’s blank spots and improvement areas to accelerate progress. Below I am sharing some observations of the last few years in the space which I believe are critical for survival:

                    Measure Fundamentals, not vanity metrics

                    ❌ Discord Activity, X Followers, Token Holders, Token $Price, Trading Vol, Projects Sourced 

                    ✅ Utility, Growth, Conversions, Usage, Retention, Transaction Value, Revenue

                    Referring back to the ‘Activation Energy Section’ the phase of rising public awareness feeding into public policy and governance comes at a later stage. For the near future of a couple of years we should rather focusing on show casing to the world what is possible and how a better science system could look like. Flash-in-the-pan traffic, PR campaigns and social media noise won’t drive results, they rather distract from what is essential - namely fundamental utility. Do we solve a critical problem for someone? Even if that that someone represents a small user base it would be the right starting point. The utility of the product can be measured by retention rates and going forward by willingness to pay.

                    Build products, not decentralisation theaters

                    ❌ Complex Gov / Voting Schemes early on

                    ✅ Solving someone’s problem, Deep Customer Exploration, Simple

                    Again, referring back to the section ‘Activation Energy’ the ‘Governance’ phase comes years after the utility phase. Most ‘DAOs’ today suffer from immense over-head and noise in their communities. Very few people make regular, meaningful contributions - pareto is everywhere. Some of the decentralisation theatre is driven by regulatory uncertainties, some of it is driven by a real desire for more equitable governance. The reality is that the opportunity costs are organisational efficiency and building something useful - in the early years only the latter is existential while the former is nice to have.

                    Seek long term alignment, not short term speculation

                    ❌ Liquidity Carousels, pump & dumps

                    ✅ Long lock ups, Deep Customer Exploration

                    Attracting the right talent, finding the right leverage points and getting to sufficient activation energy to drive change are critical. Focusing on token prices, pre-mature asset issuance, market making and speculation will be counter-productive as it attracts (1) the wrong people, (2) the wrong money and (3) it confuses internal speculative demand with actual outside demand verification. Defi doesn’t have product-market fit as long as it serves a niche tech- and finance savvy community of speculators and doesn’t integrate with real-world assets and payments infra. Similarly, DeSci won’t find product market fit if we can’t channel Bio-pharma demand and resources into our products.

                    Let’s be the ‘American Revolution’, not the ‘French Revolution’ or the ‘Arab Spring’.