Grayscale: How Crypto Can Accelerate the Arrival of the AI Era?

Author: Grayscale Research

Compilation: Felix, PANews

Grayscale announced yesterday the launch of a new fund, Grayscale Decentralized AI Fund LLC, which focuses on DecentralizationAI. The first batch of projects in the fund includes Bittensor (TAO), FIL (FIL), Livepeer (LPT), Near (NEAR), and Render (RNDR), with Near, FIL, and Render being the highest weighted assets in the fund. As a result of this news, the related Tokens experienced a significant pump. Subsequently, Grayscale published an article interpreting AI and DecentralizationAI, explaining the reasons for their importance. The following is the full text of the content.

Artificial Intelligence (AI) is one of the most promising emerging technologies of this century, with the potential to exponentially increase human productivity and drive medical breakthroughs. While AI may be important today, its influence will only continue to grow. According to PwC estimates, AI will become a $15 trillion industry by 2030.

However, this promising technology also faces challenges. As AI technology becomes more powerful, the power of the AI industry is concentrated in the hands of a few companies, which has potential harm to society. This has also raised serious concerns about the fraud, embedded bias, and data privacy risks in Depth. Fortunately, encryption technology provides potential solutions to some of these issues, with the characteristics of Decentralization and transparency.

This article will explore the problems brought about by centralization and how DecentralizationAI helps to solve some of these drawbacks. In addition, it will discuss the intersection of Crypto and AI, with a focus on encryption applications that have shown early adoption signs in this field.

The problem of centralized AI

The current development of AI is facing certain risks and challenges. The network effect and intensive capital requirements of AI are very significant, to the point that many AI developers outside of large technology companies, such as small companies or academic researchers, either struggle to obtain the resources required for AI development or are unable to monetize their work. This limits the overall competition and innovation of AI.

As a result, the influence of this key technology is mainly concentrated in the hands of a few companies such as OpenAI and Google, triggering serious questions about AI governance. For example, in February of this year, Google’s AI image generator Gemini was exposed for racial discrimination and historical errors, suspected of manipulating models. In addition, in November last year, a six-person board decided to dismiss OpenAI CEO Sam Altman, exposing the fact that a few people control the development of these models.

As the influence and importance of AI continue to grow, many people are concerned that a company may have the decision-making power over AI models that have a significant impact on society. It may even set up barriers and manipulate models for personal gain at the expense of others.

DecentralizationAIHow to Provide Help

DecentralizationAI refers to the AI services that use blockchain technology to distribute AI ownership and governance in a way that enhances transparency and accessibility. Grayscale Research believes that DecentralizationAI has the potential to release these important decisions from closed environments and make them publicly owned.

Blockchain technology can help developers increase access to AI, drop the threshold for independent developers to build and monetize their work. This will help enhance overall AI innovation and competition, and keep pace with models developed by tech giants.

In addition, Decentralization’s AI can help democratize AI investment. Currently, apart from investing in some tech stocks, there are few ways to gain profits related to AI development. Meanwhile, a large amount of private capital has been allocated to AI startups and private companies (USD 47 billion in 2022, USD 42 billion in 2023). As a result, only a small number of venture capitalists and accredited investors can access the profits of these companies. In contrast, Decentralization’s encrypted AI assets are open to everyone, allowing anyone to participate in the future of AI.

What is the current development of the convergence field?

The intersection of Crypto and AI is still in its early stages in terms of maturity, but the market’s response is exciting. As of May 2024, the AI field of encryption assets has a return rate of 20%, outperforming most encryption tracks. In addition, according to Kaito data, compared with other tracks such as Decentralized Finance, Layer2, MEME, and RWA, the AI track currently has the highest “narrative intelligence share” on social platforms (the highest market attention).

Recently, some well-known figures have begun to embrace this emerging field, dedicated to addressing the shortcomings of centralized AI. In March of this year, Emad Mostaque, founder of the AI company Stability AI, left the company to pursue DecentralizationAI, stating, “It’s time to ensure that AI remains open and decentralized.” In addition, Erik Vorhees, founder of ShapeShift, recently launched Venice.ai, a privacy-focused AI service with end-to-end encryption capabilities.

Figure 1: AI Universe has outperformed almost all encryption tracks so far this year.

The intersection of Crypto and AI can be divided into three main subclasses:

• Infrastructure layer: The network that provides a platform for AI development (e.g. NEAR, TAO, FET)
• Resources required for AI: provide assets (such as RNDR, AKT, LPT, FIL, AR, MASA) for the key resources (computing, storage, data) required for AI development
• Solve AI problems: Assets attempting to address AI-related issues, such as the rise of fake robots and Depth, as well as model validation (e.g., WLD, TRAC, NUM)

Picture 2*:** AI** and Crypto Market Map*

Data source: Grayscale Investments. The protocol included is for illustrative purposes

Network providing infrastructure for AI development

The first type is to provide a network with an open architecture that does not require permission, built specifically for the overall development of AI services. These assets do not focus on a specific AI product or service, but focus on creating underlying infrastructure and incentive mechanisms for various AI applications.

NEAR stands out in this category, with its founder being a co-founder of the “Transformer” architecture, which powers AI systems like ChatGPT. In May of this year, NEAR announced its focus on building a user-owned AI ecosystem, dedicated to optimizing user privacy and sovereignty. In late June, NEAR launched its AI incubator program for developing NEAR-native base models, data platforms for AI applications, AI agent frameworks, and computing markets.

Bittensor is a platform that economically incentivizes AI development using TAOToken. As the underlying platform for 38 subnets, each subnet has different use cases such as chatbots, image generation, financial forecasting, language translation, model training, storage, and computation. The Bittensor network rewards the best performing Miners and validators in each subnet with TAOToken, and provides developers with permissionless API to build specific AI applications by querying the Miner in Bittensor subnet.

This category also includes other protocols such as Fetch.ai and the Allora network. Fetch.ai is a platform for developers to create complex AI agents, recently merged with AGIX and OCEAN, with a total market capitalization of about $7.5 billion. Another one is the Allora network, which is a platform focused on applying AI to financial applications, including automatic trading strategies for DEX and prediction markets. Allora has not yet issued tokens and conducted a strategic financing round in June, with a total private sale financing of $35 million.

Resources Needed for AI Development

The second category includes assets that provide the necessary resources for AI development in the form of computation, storage, or data.

The rise of AI has generated a massive demand for GPU computing resources. Decentralized GPU markets like Render (RNDR), Akash (AKT), and Livepeer (LPT) provide idle GPU supplies for developers working on model training, model inference, or rendering 3D generative AI. Render is estimated to offer around 10,000 GPUs, targeting artists and generative AI. Akash offers 400 GPUs, focusing on AI developers and researchers. Meanwhile, Livepeer has recently announced a new AI subnet plan, aiming to execute AI inference tasks such as text-to-image, text-to-video, and image-to-video by August 2024.

In addition to requiring a large amount of computing resources, AI models also require a large amount of data. Therefore, the demand for data storage has increased significantly. FIL (FIL) and Arweave (AR) are data storage solutions that can serve as decentralized and secure alternatives to storing AI data on centralized AWS servers. These solutions not only provide cost-effective and scalable storage, but also enhance data security and integrity by eliminating single points of failure and dropping data leakage risks.

Finally, existing AI services such as OpenAI and Gemini can access real-time data through Bing and Google searches, respectively. This puts all other AI model developers at a disadvantage except for technology companies. However, data scraping services like Grass and Masa can help create a fair competitive environment by allowing individuals to profit from providing application data for AI model training while maintaining control and privacy over personal data.

Assets that attempt to solve AI-related issues

The third category includes assets that attempt to solve AI-related problems, including robots, Depth forgery, and the rise of content sources.

Another significant issue with AI is the proliferation of robots and misinformation. The Depth forgery generated by AI has already affected presidential elections in India and Europe, and experts are “very concerned” about the upcoming US presidential election, with a massive influx of “fake news” driven by Depth forgery. Assets aimed at helping to address the issue of Depth forgery by establishing verifiable sources of content include Origin Trail (TRAC), Numbers Protocol (NUM), and Story Protocol. In addition, Worldcoin (WLD) attempts to address the robot problem by using unique biometric identification for verification.

Another risk of AI is ensuring trust in the model itself. How can we trust that the received AI results have not been tampered with or manipulated? Currently, there are several protocols that help address this issue through cryptography, Zero-Knowledge Proof, and Fully Homomorphic Encryption (FHE), such as Modulus Labs and Zama.

Conclusion

Although these Decentralization AI assets have made initial progress, they are still in the early stages. Earlier this year, venture capitalist Fred Wilson said that AI and Crypto are “two sides of the same coin,” and “Web3 will help us trust AI.” As the AI industry continues to mature, Grayscale Research believes that these encryption use cases related to AI will become increasingly important, and these two rapidly developing technologies may complement each other.

Many signs indicate that the AI era is coming and will have profound, positive or negative effects. By leveraging the characteristics of blockchain technology, it is believed that Crypto will eventually help alleviate some of the dangers of AI.

Related reading: Why are venture capital firms heavily betting on Crypto x AI