For Crypto At Scale, Look To Blockchain … And Academia?

Academia blockchain cryptocurrency

For cryptos to be used in the real world to buy and sell things  — what a concept! — might academics succeed where entrepreneurs have not, at least so far?

News comes that a number of high-powered bastions of higher learning have banded together to bring cryptocurrencies to the mainstream, in a way that bitcoin seemingly cannot, eyeing scale and processing speed.

Simply put, by designing a system that can process thousands of transactions a second, rather than the dribs and drabs that have marked bitcoin and even Ethereum, cryptos may find use cases beyond the mere speculation and hype (and hope) that has marked the arena into the present day.

The colleges working on the project — with the backing of Pantera Capital, a hedge fund — are based in the United States, and include Stanford, Massachusetts Institute of Technology (MIT) and the University of California at Berkeley. The effort is being funded by Distributed Technologies Research (DTR), which is a nonprofit foundation based in Switzerland.

The researchers being funded by DTR have created a digital currency known as Unit-e, which uses decentralized ledger technology, and should be able to scale at rates leagues above what has been thus far.

The crypto will come to market as bitcoin has some widely-reported flaws, among them self-imposed limits that have been there from the beginning and seemingly always will. How hobbling are the output caps that limit blocks to 1 MB, and processing times that span minutes, so there can be only about seven transactions per second? Ethereum can be a bit quicker, where the processing comes in at around 30 transactions per second.

The consortium has said that the transaction speeds might be comparable to, or even overtake Visa, which of course processes several thousand transactions per second.

The crypto is slated to launch in the second half of this year, where time is of the essence for cryptos, as noted even by a principal backing the academic push into blockchain and Unit-e.

As interviewed by Fortune, Joey Krug, who serves as co-chief investment officer at Pantera Capital, said that with bitcoin and others “the mainstream public is aware that these networks don’t scale … We are on the cusp of something where if this doesn’t scale relatively soon, it may be relegated to ideas that were nice but didn’t work in practice: more like 3D printing than the internet.’’

The overarching aim of the academia consortium is to support a range of cryptocurrencies — and to boost transaction uptake by a process known as sharding, where computing and storage workloads are shared across nodes.

In an interview with PYMNTS conducted by written exchange, Giulia Fanti, a lead researcher for DTR and assistant professor of electrical and computer engineering at Carnegie Mellon University, acknowledged that the low scalability mentioned above and volatility have “led to relatively low adoption by point of sale merchants” and as a result, digital currencies have been seen more “as a store of value than a medium of exchange that yields measurable benefits to its users. In a self-fulfilling cycle, this has encouraged speculation, which created even more volatility,” adding that “a payment system that cannot scale has limited inherent value, so it is difficult to price accurately in the short term.”

Asked how DTR and its as-yet-to-debut crypto address the “trust issue,” Fanti stated that the goal is “not to try to suppress” initial volatility that may mark the digital currency “but to give merchants a way to manage it” until adoption leads to liquidity and stable pricing. DTR is targeting mechanisms that will let merchants convert Unit-e into fiat without friction. She said that such mechanisms may include partnerships with exchanges and POS systems.

As to whether some observers might look askance at academics and not-for-profits delving into the scalability problem that currently bedevils cryptos, Fanti stated that “historically, many groundbreaking technologies have been most successful when industry and academia work together; the two sides have complementary resources. For instance, many of the algorithms, architectures, and hardware that make our cell phones or computers work emerged from academic research labs. This may be partially because academic labs have the freedom to pursue projects on a longer timescale, without worrying about the next release cycle.”


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