Yesterday, we explored how technology acts as a powerful amplifier, magnifying the impact of any system it's applied to. When the underlying system is sound, the results can be revolutionary.

But when it's riddled with flaws, the consequences can be disastrous as a heated paintball tournament at a nudist colony.

For example…

The rise of powerful AI language models like ChatGPT is exposing deep cracks in the scientific establishment -- from rampant fraud and sloppy methodology to perverse incentives that prioritize publishing over rigorous research.

Thankfully, the shedding of this long, dark veil will lead to a thirst for more transparency, more truth, and more (wait for it) science.

I suspect it will lead to more citizen science, which social media has already helped to facilitate.

iNaturalist, for example, is an app and social network that allows users to photograph and share plants, animals, and organisms in their areas, contributing to a global database that helps scientists track biodiversity.

Zooniverse is a citizen science network that leverages volunteers in tasks ranging from classifying galaxies to transcribing medieval texts.

After the Fukushima nuclear disaster, Safecast leveraged social media to gather and share radiation measurements from volunteers using Geiger counters across Japan. This created a crowd-sourced map of radiation levels, providing transparent data that was not otherwise available.

And then there’s DeSci -- the final piece of the puzzle -- leveraging the power of immutable ledgers and AI to realign incentives and accelerate discovery.

How does DeSci plan to do so, you ask? It’s simple.

Science As it Should Be

At its core, DeSci is about two things: permissionless provenance and participatory capital formation.

Provenance just means tracking the origin and history of something. Permissionless means anyone can use and access those records without needing approval from gatekeepers.

By timestamping lab data and publishing results to blockchains in real-time, researchers can prove conclusively that their work came first.

No more hiding findings in fear of getting scooped. No more disputes over authorship and attribution. With provenance assured, scientists are free to embrace the kind of open, collaborative approach that's key to scientific progress.

No more politics in research.

Furthermore, participatory capital formation means creating new ways for anyone to fund scientific research. Instead of relying solely on volunteers and grants from governments or big institutions, DeSci platforms allow researchers to crowdfund support from individuals worldwide who share their interests.

Imagine a biotech startup issuing "lab tokens" that give holders a stake in the IP they produce. Or a Zooniverse-like platform that pays individuals for completing microtasks in a global research database.

Or a DAO (decentralized autonomous organization) where members vote on which research projects to back with the group's pooled funds. By aligning incentives between researchers and supporters, DeSci encourages greater accountability and focus on delivering real-world impact.

A few projects are leading the charge:

VitaDAO and PsyDAO allow token holders to vote on grant allocations for research projects in longevity science and psychedelic medicine respectively.

Meanwhile, Molecule and LabDAO leverage IP-NFTs and quadratic funding to launch biotech ventures and crowdfund experiments.

While still early stage, these examples demonstrate how Web3 primitives like tokens, NFTs and DAOs can create new markets and funding mechanisms to support scientific innovation in more open, accessible, and rewarding ways. Especially when compared to the traditional grant-based or closed-door venture models.

Meanwhile, AI's potential impacts on the scientific process itself cannot be overstated, either.

Crypto x AI

Tools like DeepMind's AlphaFold are solving grand challenges like protein folding in a fraction of the time humans can.

Language models help researchers efficiently parse millions of studies, while other AI systems can propose more promising hypotheses and automatically design optimal solutions to engineering problems.

Before long, we’ll have fleets of AI-powered robotic labs sharing data in an open marketplace for other labs to build upon.

This is where crypto and AI find a natural fit:

Two major blockchain players are creating decentralized marketplaces for AI algorithms and datasets. (One of them is well-known to our Altucher’s Investment Network readers.)

Through these protocols, researchers can monetize their AI tools and data by offering them for use by others through these platforms. This incentivizes open sharing of valuable AI resources for scientific applications, while keeping proprietary data private.

Of course, major hurdles remain before this techno-optimist vision of the scientific future materializes.

But it could be closer than many think… and the potential upside is immense.

More tomorrow.