Brian Brooks: Token Securities Framework and Launching a Network

Brian Brooks: Token Securities Framework and Launching a Network

The final week of a16z’s Crypto Startup School kicks off with former Coinbase Chief Legal Officer Brian Brooks discussing “Token Securities Frameworks and Launching a Network.”

The final week of a16z’s Crypto Startup School kicks off with former Coinbase Chief Legal Officer Brian Brooks discussing “Token Securities Frameworks and Launching a Network.” Brooks starts off calling crypto the “most perfect intersection of tech and finance,” but he cautions that crypto builders must navigate traditional financial-services regulatory structures. This takes on special importance because tokens, the native assets of crypto networks, can be deemed securities by regulators, making them illegal to list on exchanges and subject to disclosures and other legal requirements.

Brooks explains the four-part Howey test, the Supreme Court ruling that has come to define when a given transaction is a securities transaction. Because crypto is still relatively new, however, the path to legality is still developing. In the meantime, the crypto industry has created the Crypto Rating Council, a new tool to objectively rate tokens and gauge their risk of being deemed securities. Broadly, the tokens that carry the most risk of being labeled securities are those issued before a crypto network is fully decentralized, and while the actions of the management team remain critical to a network’s success. (Bitcoin, for example, is not a security, because it is completely decentralized and there is no core management team.)

Brooks introduces some promising new regulatory paths for crypto including membership models — similar to cooperatives or mutuals — in which token holders agree to only sell the token to other members of the network, avoiding a secondary sales market and thus steering clear of securities issues. While this model hasn’t been tested with the SEC, it has a long track record in other industries and bears further study.

neural networks

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