Is Crypto Back, Boosted by Artificial Intelligence?

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Paris, FRANCE With more than double the attendance from last year’s event, over 250 side events, and just about every event and side event oversubscribed, the mood for last week’s EthCC was elated, leaving attendees at the Ethereum-focused conference wondering if the worst of the bear market is over.

Windy summer weather and blue skies in Paris didn’t hurt, nor did the jolt of positive sentiment from Ripples’ recent partial victory in its case against the US Securities and Exchange Commission (SEC), a catalyst for price increases not only in the related XRP token but other altcoins, including Ether, the native token of the Ethereum blockchain. The past few weeks have also seen application announcements for bitcoin (BTC) exchange-traded funds (ETFs) from some of the world’s largest asset managers, not really related to the Ethereum blockchain per se, but an important and encouraging development for the digital asset industry.

Another hot topic at the event was the rise of artificial intelligence or AI, with multiple conferences and side events being held on the subject.

Most investors and founders I spoke to were optimistic about the intersection of the two fields, although they disagreed on exactly how they should intersect.

One camp sees practical, more immediate applications of AI in crypto markets and communities as the best use case. For example, Ken Timsit of Cronos Labs, the Layer 1 blockchain exchange Crypto.com, sees AI as a tool to increase productivity in crypto financial markets, increasing the dollar value of funds managed by crypto firms.

It might sound a bit like launching chatbots on exchanges right now, but fundamentally bringing analytical capabilities to traders and crypto institutions could end up being a powerful force. High-frequency trading firms and market makers have been using AI for some time now, but their advantage often lies in the in-house technology.

Along those lines is Upshot, which uses AI to price non-fungible tokens (NFTs), and they’ve been working on it for three years. According to them, NFTs are the rails on which trillions of dollars of assets can be brought into Web 3, such as art, luxury goods, insurance and eventually real estate. But these non-fungible assets need a lot of human power to keep running. AI could be the answer for determining pricing mechanisms and how to balance markets.

One of the buzzwords in crypto development over the past year has been zero-knowledge (ZK) proofs, a type of cryptographic process that can verify the validity of a statement without revealing any other information about the statement itself.

ZK could be applied to machine learning to check which model was used to produce a given result in a mashup known as ZKML, or perhaps which datasets were used in training the data. Essentially, blockchain could create a decentralized trust system for AI.

According to Eli Ben-Sasson, co-founder and president of StarkWare, and a major figure in the development of ZKs, blockchains are particularly good for social coordination, and ZKs are a privacy technology, which is not necessarily what machine learning needs.

A venture capitalist said the best use case for blockchain is tracking data used in training AI systems. It is an incredibly demanding computational task. Keeping track of what algorithms are doing is another crucial task, perhaps even more important.

A key bottleneck is the computational power, performance, and usability of ZK proofs, which remain relatively slow. Companies like Manta Network and Fabric Cryptography are working on these issues.

Other projects use blockchains to gather and coordinate computing power that could then be used to train and run machine learning models.

Enough to fuel discussions in Paris around croissants and champagne on the rooftops with a view of the Eiffel Tower.

Exactly what use cases stem from all of this remains to be seen.

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