Digital Currency Group’s Barry Silbert Bets Big on AI Blockchain Bittensor

As a seasoned analyst with over two decades of observing and participating in the evolution of technology, I find myself intrigued by Barry Silbert’s latest venture – Yuma, an incubator for the decentralized AI ecosystem, Bittensor. Having witnessed the transformative potential of Bitcoin and other digital assets, I can’t help but see parallels in Silbert’s assertion that Bittensor could be just as groundbreaking.

Digital Currency Group (DCG), a pioneer in the field of cryptocurrencies and the digital assets sector, is now expanding its focus to include Artificial Intelligence (AI). Specifically, they are looking into a decentralized form of AI that powers the Bittensor ecosystem.

Barry Silbert’s group has recently introduced a new venture called Yuma, which aims to nurture and develop businesses leveraging decentralized artificial intelligence for task completion and reward earning.

Fundamentally, Bittensor operates as a decentralized artificial intelligence (AI) network, encouraging individuals to share data and computational resources for a variety of tasks such as text translation, data preservation, and even forecasting the structures of intricate protein chains. This contribution results in incentives within the system.

As a seasoned cryptocurrency investor and advocate, I, Silbert, have observed that when posing the question, “What is Bittensor?”, you’ll likely receive five distinct responses. This reminds me of the early days of Bitcoin, where some individuals perceived it as a form of currency, others as digital gold. Similarly, there were those who saw it as a blockchain technology […]. To me, Bittensor represents the World Wide Web for Artificial Intelligence.

It’s clear that AI is a fundamental technology with numerous potential applications, yet it carries the risk of granting excessive power to companies like Microsoft, Facebook, and Google by amassing users’ data in their powerful computer systems. Decentralized AI could potentially mitigate this issue, not only for utilizing previously untapped computational resources but also for improving the transparency and reducing the unsettling perception often associated with AI technology.

Bittensor’s very own digital currency, TAO, serves as motivation for a vast network of decentralized laborers. These workers can be either miners who provide sophisticated computational resources for specific jobs, or evaluators who scrutinize the work done and distribute prizes based on quality.

This interest in AI didn’t happen overnight. DCG made its first investment into Bittensor in 2021. More recently, DCG’s asset management firm, Grayscale, added funds dedicated to AI including the $TAO token. Underscoring his belief in the project, Silbert will step into the CEO position at Yuma, which will have some 25 employees on day one.

The Yuma Incubation and Design Studio plays a role similar to Joe Lubin’s Consensys in relation to Ethereum, but with a key difference: unlike Yuma overseeing all the subnets it fosters, it operates more like a combination of a venture capital firm (such as Y Combinator) and an accelerator. As per Silbert’s explanation.

Silbert stated that there are two types of partnerships related to subnets. For those who are startups or businesses with an innovative idea and an interest in exploring the Bittensor world and launching a subnet, we offer assistance. Additionally, for those looking to collaborate, we have a subnet incubator program where we will work together to construct a brand-new subnet from the ground up.

As an analyst, I can share that so far, I’ve found five active subnets within our system. Four of these were part of our accelerator program, while the fifth one was nurtured through incubation. Interestingly, there are nine more in the pipeline, with one still under incubation and the rest moving forward through the accelerator. These upcoming additions promise a diverse set of use cases, as mentioned by Evan Malanga, our Chief Revenue Officer.

In an interview, Malanga explained, “We have developed systems capable of recognizing humans, similar to how we identify bots. We are also introducing prediction models for time series data. Additionally, we have some academic projects, including AI research that uses the network’s computational power for research tasks, various security projects, a role-play simulation, and sports forecasting. There are numerous sports predictions being developed.