Trust at Scale: The Economic Limits of Cryptocurrencies and Blockchains
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Satoshi Nakamoto (2008) invented a new kind of economic system that does not need the support of government or rule of law. Trust and security instead arise from a combination of cryptography and economic incentives, all in a completely anonymous and decentralized system. This article shows that Nakamoto’s novel form of trust, while undeniably ingenious, is deeply economically limited. The core argument is three equations. A zero-profit condition on the quantity of honest blockchain “trust support” (work, stake, etc.) and an incentive-compatibility condition on the system’s security against majority attack (the Achilles heel of all forms of permissionless consensus) together imply an equilibrium constraint, which says that the “flow” cost of blockchain trust has to be large at all times relative to the benefits of attacking the system. This is extremely expensive relative to traditional forms of trust and scales linearly with the value of attack. In scenarios that represent Nakamoto trust becoming a more significant part of the global financial system, the cost of trust would exceed global GDP. Nakamoto trust would become more attractive if an attacker lost the stock value of their capital in addition to paying the flow cost of attack, but this requires either collapse of the system (hardly reassuring) or external support from rule of law. The key difference between Nakamoto trust and traditional trust grounded in rule of law and complementary sources, such as reputations, relationships, and collateral, is economies of scale: society or a firm pays a fixed cost to enjoy trust over a large quantity of economic activity at low or zero marginal cost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it