One-Shot Grants Are a Bad Bet. Continuous Funding Is the Obvious Fix.

Traditional grants compress an entire research trajectory into a single decision point. A committee reads a proposal, predicts what will happen over three to five years, and commits a lump sum. If the prediction is wrong — and predictions about research are wrong most of the time — the money is already spent and the correction comes years later, if at all.

Software engineering solved this problem decades ago. Nobody ships a monolithic product after a single planning meeting. Teams iterate. They ship small increments, measure what works, adjust what does not, and allocate resources continuously based on demonstrated progress. The feedback loop is days or weeks, not years.

DeSci projects are starting to apply this same logic to research funding. Instead of front-loading all resources into a grant proposal, continuous funding models release support incrementally based on ongoing contribution. A researcher publishes a dataset — funding flows. They release a method others build on — funding increases. They hit a dead end and report it transparently — funding continues because honest null results have value.

This inverts the risk structure. Under one-shot grants, the funder bears all the prediction risk. Under continuous models, risk distributes across time. Bad bets get caught early. Good bets compound. Researchers who consistently deliver value receive increasing support without re-applying from scratch every cycle.

The technical infrastructure for this exists. Contribution tracking, transparent metrics, and programmable funding flows are all buildable today. What is missing is the willingness to abandon the comfort of single-decision funding.

The question: would you rather fund a promising proposal or fund demonstrated progress? Explore the DeSci DAO approach to continuous research support.