Science Problems
Why does the best method humanity has for discovering truth run on broken incentives?
The scientific method works. Hypothesis, experiment, conclude, iterate — the loop that turns questions into principles. The problem is upstream: who funds the questions, who owns the data, who decides what gets replicated.
The Failures
| Problem | Current State | Why It Persists |
|---|---|---|
| Funding centralised | Only 2% goes to researchers under 35 | Gatekeepers optimise for prestige, not discovery |
| Replication siloed | Hard to reproduce across institutions | No incentive to replicate — only novel results publish |
| Data paywalled | Lives behind institutional access | Publishers extract rent from publicly funded research |
| Incentives misaligned | Publish or perish, not discover or validate | Career survival beats truth-seeking |
Same pattern as every coordination failure — local optimisation that destroys global outcomes.
Decentralised Fix
DeSci applies the ABCD stack to science:
| Layer | What It Fixes | Projects |
|---|---|---|
| AI | Accelerates hypothesis generation and data analysis | Automated literature review, pattern detection |
| Blockchain | Makes results verifiable and immutable | On-chain proofs, open peer review |
| Crypto | Aligns incentives toward replication and validation | VitaDAO, Molecule, AthenaDAO |
| DePIN | Distributes lab infrastructure and compute | Decentralised compute, sensor networks |
Token-gated grants and IP NFTs let researchers fund work directly — no gatekeeper, no 18-month review cycle.
Context
- Science — The method itself
- Evolution — The method running across generations
- Standards — Where surviving hypotheses become replicable truths
- Financialization — Same extraction pattern, different domain
- The Meta Problem — Trust infrastructure for verification