Author: Will Ogden Moore, Translator: 0xjs@Golden Finance
AI is one of the most promising emerging technologies of this century, with the potential to exponentially increase human productivity and drive medical breakthroughs. As important as AI may be today, its impact is only going to grow, with PwC estimating that it will grow into a $15 trillion industry by 2030.
However, this promising technology also faces challenges. As AI technology becomes more powerful, the AI industry has become extremely centralized, with power concentrated in the hands of a few companies, which can harm society. This has also raised serious concerns about deep fakes, built-in bias, and data privacy risks. Fortunately, Crypto and its decentralized and transparent nature offer potential solutions to some of these issues.
In this article, we’ll explore the problems caused by centralized AI and how decentralized AI can help address some of its ills, and discuss the current intersection of Crypto and AI, highlighting crypto applications in the space that have shown early signs of adoption.
The Problem with Centralized AI
Today, the development of AI faces certain challenges and risks. The network effects and intensive capital requirements of AI are so significant that many AI developers outside of large tech companies, such as small companies or academic researchers, either struggle to obtain the resources needed for AI development or are unable to monetize their work . This limits overall competition and innovation in AI.
As a result, the influence on this critical technology is mainly concentrated in the hands of a few companies such as OpenAI and Google, which raises serious questions about AI governance. For example, in February this year, Google's AI image generator Gemini exposed racial bias and historical errors, illustrating how companies manipulate their models. In addition, last November, the six-member board of directors decided to fire OpenAI CEO Sam Altman, exposing the fact that a few people control the companies that develop these models.
As AI grows in influence and importance, many worry that a single company could gain decision-making power over an AI model that has a huge impact on society, and could put up guardrails, operate behind closed doors, or manipulate the model to its own advantage — at the expense of the rest of society.
How Decentralized AI Can Help
Decentralized AI refers to AI services that use blockchain technology to distribute AI ownership and governance in a way that is designed to increase transparency and accessibility. Grayscale Research believes that decentralized AI has the potential to free these important decisions from closed institutions and bring them into public ownership.
Blockchain technology can help developers gain more access to AI and lower the barrier for independent developers to develop and monetize their work. We believe this can help improve overall AI innovation and competition and maintain a balance with the models developed by tech giants.
Additionally, decentralized AI helps democratize AI investing. Currently, there are few ways to access the financial gains associated with AI developments other than through a handful of tech stocks. At the same time, a large amount of private capital is allocated to AI startups and private companies ($47 billion in 2022 and $42 billion in 2023). As a result, only a small group of venture capitalists and accredited investors can access the financial gains of these companies. In contrast, decentralized AI crypto assets are available to everyone, allowing everyone to own a part of the AI future.
Where is the intersection of Crypto and AI today?
Today, the intersection of cryptocurrencies and AI is still in its early stages in terms of maturity, but the market response has been encouraging. As of May 2024, the AI sector of crypto assets has returned 20%, outperforming every crypto track except the Currencies track (Figure 1). In addition, according to data provider Kaito, the AI theme currently accounts for the most "narrative mind share" on social platforms compared to other themes such as decentralized finance, Layer 2, meme coins, and real-world assets.
Recently, some well-known figures have begun to embrace this emerging cross-field and are committed to solving the shortcomings of centralized AI. In March of this year, Emad Mostaque, the founder of the well-known AI company Stability AI, left the company to pursue decentralized AI, saying that "now is the time to ensure that AI remains open and decentralized." In addition, cryptocurrency entrepreneur Erik Vorhees recently launched Venice.ai, a privacy-focused AI service with end-to-end encryption.
Figure 1: So far this year, the AI track has outperformed almost all crypto tracks
We can divide the intersection of Crypto and AI into three main subcategories:
1. Infrastructure layer: Networks that provide platforms for AI development (e.g. NEAR, TAO, FET)
2. Resources required for AI: Assets that provide key resources (computing, storage, data) required for AI development (e.g. RNDR, AKT, LPT, FIL, AR, MASA)
3. Solving AI problems: Assets that attempt to solve AI-related problems, such as the rise of bots and deepfakes, and model validation (e.g. WLD, TRAC, NUM)
Figure 2: AI and Crypto Market Map
Source: Grayscale Investments
A network that provides infrastructure for AI development
The first category is networks that provide permissionless, open architectures built for the general development of AI services. These assets are not focused on a single AI product or service, but rather on creating the underlying infrastructure and incentives for a variety of AI applications.
Near stands out in this category, with its founders being the co-founders of the “Transformer” architecture that powers AI systems like ChatGPT. However, the company recently leveraged its AI expertise to unveil efforts to develop “user-owned AI” through an R&D division led by a former OpenAI research engineer advisor. In late June 2024, Near launched its AI Incubator program to develop Near-native base models, an AI application data platform, an AI agent framework, and a compute marketplace.
Bittensor provides another potentially compelling example. Bittensor is a platform that uses TAO tokens to economically encourage AI development. Bittensor is the underlying platform for 38 subnetworks (subnets), each with 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 TAO tokens and provides a permissionless API for developers to build specific AI applications by querying miners in the Bittensor subnet.
This category also includes other protocols such as Fetch.ai and Allora Network. Fetch.ai is a platform for developers to create sophisticated AI assistants (or “AI agents”) and recently merged with AGIX and OCEAN, with a total value of approximately $7.5 billion. Another is the Allora Network, a platform focused on applying AI to financial applications, including decentralized exchanges and automated trading strategies for prediction markets. Allora has not yet launched a token and conducted a strategic round of financing in June, bringing its total financing to $35 million in private capital.
Resources needed for AI development
The second category includes assets that provide the resources needed for AI development in the form of compute, storage, or data.
The rise of AI has created an unprecedented demand for compute resources in the form of GPUs. Decentralized GPU marketplaces such as Render (RNDR), Akash (AKT), and Livepeer (LPT) provide a supply of idle GPUs to developers who need compute for model training, model inference, or rendering 3D generative AI. Today, Render is estimated to offer around 10,000 GPUs, with a focus on artists and generative AI, while Akash offers 400 GPUs, with a focus on AI developers and researchers. Meanwhile, Livepeer recently announced its new AI subnet initiative, which aims to complete tasks such as text-to-image, text-to-video, and image-to-video by August 2024.
In addition to requiring a lot of computation, AI models also require a lot of data. As a result, the demand for data storage has increased significantly. Data storage solutions such as Filecoin (FIL) and Arweave (AR) can serve as decentralized secure network 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 reducing the risk of data breaches.
Finally, existing AI services such as OpenAI and Gemini have continuous access to live data through Bing and Google Search, respectively. This puts all other AI model developers outside of these tech companies at a disadvantage. However, data scraping services such as Grass and Masa (MASA) can help level the playing field by allowing individuals to monetize their application data by using it for AI model training, while maintaining control and privacy over their personal data.
Assets that attempt to solve AI-related problems
The third category includes assets that attempt to address issues related to AI, including the rise of bots, deepfakes, and content provenance.
One of the major issues exacerbated by AI is the proliferation of bots and disinformation. AI-generated deepfakes have already impacted presidential elections in India and Europe, and experts are “very afraid” that the upcoming presidential race will involve a “tsunami of disinformation” heavily driven by deepfakes. Assets that hope to help address issues related to deepfakes by establishing verifiable provenance of content include Origin Trail (TRAC), Numbers Protocol (NUM), and Story Protocol. Additionally, Worldcoin (WLD) seeks to address the bot problem by proving a person’s humanity through unique biometric identifiers.
Another risk with AI is ensuring trust in the models themselves. How can we trust that the AI results we receive have not been tampered with or manipulated? Several protocols are working to help solve this problem through cryptography, zero-knowledge proofs, and fully homomorphic encryption (FHE), including Modulus Labs and Zama.
in conclusion
While these decentralized AI assets have made initial progress, we are still in the first innings of this intersection. Earlier this year, well-known venture capitalist Fred Wilson said that AI and cryptocurrency are "two sides of the same coin" and that "web3 will help us trust AI." As the AI industry continues to mature, Grayscale Research believes that these AI-related crypto use cases will become increasingly important, and these two rapidly developing technologies have the potential to support each other's growth.
There are many signs that AI is coming and will have profound impacts, both positive and negative. By leveraging the properties of blockchain technology, we believe encryption can ultimately help mitigate some of the dangers posed by AI.
