Zero-Knowledge Proofs and the Economics of Verification
Scientific and engineering fields usually move in long stretches of incremental work. Then, every so often, a new tool or idea changes the constraints and the whole field jumps. Most of the time progress comes from thousands of small improvements made by researchers, engineers, and companies. But occasionally something arrives that changes what is practical, not just what is theoretically possible.
The public usually notices breakthroughs in areas like aerospace, energy, or AI. Cryptography moves more quietly, even when the consequences are just as large. During the COVID years, the field made real progress, especially in zero-knowledge proofs and other primitives that make verification cheaper and privacy easier to preserve. The bet in this piece is that these advances will matter well beyond cryptography itself.
One of the most important changes has been practical speed. Zero-knowledge proofs have existed since the 1980s, but for a long time they lived mostly in papers because the proving costs were too high for ordinary systems. Over the last decade, and especially in the last few years, that changed. Engineers pushed them much closer to real-world use.
The financial system still depends on intermediaries: auditors, regulators, accountants, banks, and payment networks. That system works when the institutions inside it are trusted and the surrounding state has enough legitimacy to enforce the rules. Bitcoin introduced a different model: a permissionless monetary network where users can move value without asking an intermediary for access. In places where inflation, capital controls, or institutional weakness are part of daily life, that difference is not theoretical. It is immediate.
As trust becomes more uneven, systems that can be independently verified become more attractive. That does not mean social trust disappears. It means more parts of the stack will be built so they rely on less of it.
Bitcoin was designed primarily as a monetary asset and settlement network, so expressive computation on top of it has always been limited by design. Ethereum expanded that design space by allowing more complex programs, which is why lending, exchanges, and other financial applications grew there first. But blockchains still impose severe constraints. Computation is expensive, throughput is limited, and the cost of moving meaningful value is often too high.
This is where zero-knowledge proofs and related distributed-systems primitives become useful. A zero-knowledge proof lets one party show that a computation was done correctly without revealing the underlying data and without forcing everyone else to rerun the computation. The crucial asymmetry is that proving is expensive, but verification is much cheaper. That is what makes the technique economically meaningful rather than just mathematically elegant.
At the beginning it’s difficult to grasp, even for engineers, that this technology is possible. The mathematics behind it, until recently, seemed magical, and that’s why it was called moon math. Thanks to ZKPs, transferring money in systems similar to Bitcoin becomes cheaper and much faster because there is no need for every node to re-execute each transaction. In some architectures, one node can process all the transactions and prove them using ZKPs, while the rest simply verify them, saving valuable computing resources. Among other things, ZKPs enable creating a financial system that doesn’t depend on social trust like traditional finance and that doesn’t depend as much on re-executing algorithms as Bitcoin.
Zero Knowledge Proofs facilitate the development of an entirely new range of applications that are executed and proven on a single computer outside the blockchain, with verification happening within Ethereum. The cost of verification is much lower than the cost of proving or executing the computation. Ethereum will evolve from a slow yet secure distributed mainframe, where execution time is shared among all users to run small programs, into a distributed computer that stores and verifies proofs generated externally from the blockchain.
Blockchains are not the only systems that benefit from these primitives. As AI-generated content begins to overshadow human-generated content on the internet, ZKPs become useful for verifying that content came from a specific model, system, or approved pipeline. “Proof of humanity” systems are already using ZKPs to verify that a human is accessing specific resources.
More broadly, some industries will face growing pressure to prove they are operating correctly rather than asking users to trust them. Online gaming, ad networks, and other opaque intermediaries are obvious candidates. Fully Homomorphic Encryption, a related primitive that enables computation on encrypted data without exposing it, will play a similar role wherever privacy constraints are binding.
As these primitives mature, the gap between what is theoretically possible and what is practically deployable will continue to narrow. The systems that benefit most will be the ones where verification cost has been the binding constraint, and those exist well beyond finance.