Artificial intelligence (“AI”) is one of the most promising emerging technologies of this century, with the potential to exponentially improve human productivity and power medical breakthroughs. While AI may be important today, its influence is only growing, as PwC estimates that it will grow to be a $15 trillion industry by 2030.[1]

However, this promising technology has its challenges. As AI technology has become increasingly powerful, the AI industry has become extremely centralized, concentrating power in the hands of a few companies to the potential detriment of society. It has also raised serious concerns regarding deepfakes, embedded biases, and data privacy risks. Fortunately, crypto — and its properties of decentralization and transparency — offers potential solutions to some of these problems.

Below, we explore the issues caused by centralization and how decentralized AI can help solve some of its ills, and we discuss where the intersection of crypto and AI stands today, highlighting the crypto applications in this space that have shown early signs of adoption.

The Problem with Centralized AI

Today, AI development presents certain challenges and risks. Network effects and intensive capital requirements in AI are so significant that many AI developers outside of large tech companies, such as small companies or academic researchers, either have difficulty gaining access to needed resources for AI development or are unable to monetize their work. This limits overall AI competition and innovation.

As a result, influence over this critical technology is largely concentrated in the hands of a few companies such as OpenAI and Google, leading to serious questions about AI governance. For example, this past February, Google’s AI image generator Gemini revealed racial biases and historical inaccuracies, illustrating how companies can manipulate their models.[2] In addition, a board of six individuals decided to fire OpenAI CEO Sam Altman last November, exposing the fact that a small handful of people wield control over the companies developing these models.[3]

As AI grows in influence and importance, many worry that one company could hold decision-making power over the AI models that have an outsize influence on society, potentially imposing guardrails, operating behind closed doors, or manipulating models to their benefit — but at the expense of the rest of society.

How Decentralized AI Can Help

Decentralized AI refers to AI services that leverage blockchain technology to distribute ownership and governance of AI in a manner that is designed to increase transparency and accessibility. Grayscale Research believes that decentralized AI holds the potential to bring these important decisions out from walled gardens and into public ownership.

Blockchain technology can help increase developer access to AI, lowering the barrier for independent developers to build and monetize their work. We believe this could help improve overall AI innovation and competition as well as provide balance with the models developed by tech giants.

In addition, decentralized AI can help democratize access to investing in AI. Currently, there are very few ways to gain access to the financial upside associated with AI development besides through a few tech stocks. Meanwhile, significant amounts of private capital have been allocated towards AI startups and private companies ($47 billion in 2022 and $42 billion in 2023).[4] As a result, the financial upside of these companies is only available to a small portion of venture capitalists and accredited investors. In contrast, decentralized AI crypto assets are available to everyone, allowing all to own a part of an AI future.

Where Does This Intersection Stand Today?

Today, the intersection of crypto and AI is still early in terms of maturity, yet the market has responded encouragingly. In 2024, through May, the AI universe of crypto assets[5] has returned 20%, outperforming each of the Crypto Sectors except for the Currencies Sector (Exhibit 1). In addition, according to data provider Kaito, the AI theme is currently taking up the most amount of “narrative mindshare” on social platforms — as opposed to other themes such as decentralized finance, Layer 2s, memecoins, and real-world assets.[6]

Recently, several prominent figures have embraced this nascent intersection, focusing on addressing the shortcomings of centralized AI. In March, Emad Mostaque, the founder of a prominent incumbent AI company called Stability AI, left the company to pursue decentralized AI, citing that “it is now time to ensure AI remains open and decentralized.”[7] In addition, crypto entrepreneur Erik Vorhees recently launched Venice.ai, a privacy-focused AI service with end-to-end encryption.[8]

Figure 1: AI Universe has outperformed almost all Crypto Sectors year to date

Today, we can break down the crypto and AI intersection into three primary subcategories:[9]

1. Infrastructure Layer: Networks that provide platforms for AI development (e.g., NEAR, TAO, FET)
2. Resources Needed for AI: Assets that provide critical resources (compute, storage, data) needed 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 verification (e.g., WLD, TRAC, NUM)

Figure 2: AI and Crypto Market Map

Source: Grayscale Investments. Protocols included are illustrative examples.

Networks That Provide the Infrastructure for AI Development

The first category involves networks that provide a permissionless, open architecture purposely built for the general development of AI services. Instead of focusing on one AI product or service, these assets concentrate on creating the underlying infrastructure and incentive mechanisms for a wide variety of AI applications.

Near stands out in this category, having been founded by the co-creator of the “Transformer” architecture that powers AI systems like ChatGPT. [10] However, it recently leaned into its AI expertise, unveiling efforts to develop “user-owned AI”[11] through its R&D arm, led by a former OpenAI research engineer consultant.[12] In late June 2024, Near launched its AI incubator program for the development of Near-native foundational models, data platforms for AI applications, AI agent frameworks, and compute marketplaces.[13]

Bittensor offers another potentially compelling example. Bittensor is a platform that uses the TAO token to economically encourage the development of AI. Bittensor serves as the underlying platform for 38 subnetworks (subnets),[14] each with different use cases such as chatbots, image generation, financial predictions, language translation, model training, storage, and compute. The Bittensor network rewards top-performing miners and validators in each subnet with TAO token rewards and provides a permissionless API for developers to build specific AI applications by querying miners from Bittensor subnets.

This category also includes other protocols such as Fetch.ai and Allora network. Fetch.ai, a platform for developers to create sophisticated AI assistants (i.e., “AI agents”) that recently merged with AGIX and OCEAN for a combined value of around $7.5 billion.[15] Another is Allora network, a platform focused on applying AI to financial applications including automated trading strategies for decentralized exchanges and prediction markets.[16] Allora has not launched a token yet and raised a strategic funding round in June, bringing its total amount of funding to $35mm in private capital.[17]

Resources Needed for AI Development

The second category includes assets that offer resources needed for AI development in the form of either compute, storage, or data.

The rise of AI has produced an unprecedented demand for computing resources in the form of GPUs.[18] Decentralized GPU marketplaces such as Render (RNDR), Akash (AKT), and Livepeer (LPT) offer access to idle GPU supply to developers in need of compute for model training, model inference, or rendering 3D generative AI. Today, it is estimated that Render offers around 10K GPUs with a focus on artists and generative AI, while Akash offers a capacity of 400 GPUs with a focus on AI developers and researchers[19]. Meanwhile, Livepeer recently announced its plans for a new AI subnet targeting August 2024 for tasks such as text-to-image, text-to-video, and image-to-video.[20]

In addition to requiring significant levels of compute, AI models also require massive amounts of data. As a result, there’s been a huge increase in demand for data storage.[21] Data storage solutions like Filecoin (FIL) and Arweave (AR) can serve as decentralized and 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, incumbent AI services like OpenAI and Gemini have continuous access to real-time data through Bing and Google Search, respectively. This puts all other AI model developers outside these tech companies at a disadvantage. However, data-scraping services like Grass and Masa (MASA) could help level the playing field as they allow individuals to monetize their application data by offering it for AI model training while maintaining control and privacy over personal data.

Assets Attempting to Solve AI-Related Problems

The third category includes assets that attempt to solve AI-related problems, including the rise of bots, deepfakes and content provenance.

A significant problem exacerbated by AI is the proliferation of bots and misinformation. AI-generated deepfakes have already been shown to impact presidential elections in India and Europe,[22] and experts are “completely terrified” that the upcoming presidential race will involve a “tsunami of misinformation” driven heavily by deepfakes.[23] Assets looking to help solve issues related to deepfakes through establishing verifiable content provenance include Origin Trail (TRAC), Numbers Protocol (NUM), and Story Protocol. In addition, Worldcoin (WLD) attempts to solve the issue of bots by proving a person’s humanity through unique biometric identifiers.

Another risk in AI is ensuring trust in the models themselves. How do we trust that the AI results that we receive are not doctored or manipulated? Currently, there are several protocols working to help solve this problem through cryptography, zero-knowledge proofs, and Fully Homomorphic Encryption (FHE), including Modulus Labs and Zama.[24]

Conclusion

While these decentralized AI assets have made initial progress, we are still in the first inning of this intersection. At the beginning of this year, prominent venture capitalist Fred Wilson stated that AI and crypto are “two sides of the same coin” and “web3 will help us trust AI.”[25] As the AI industry continues to mature, Grayscale Research believes that these AI-related crypto use cases will become increasingly important and that these two rapidly evolving technologies have the potential to mutually support each other’s growth.

By many indications, AI is on the horizon and is poised to have a profound impact, both positive and negative. By leveraging the attributes of blockchain technology, we believe crypto can ultimately help mitigate some of the dangers posed by AI.

Disclaimer：

1. This article is reprinted from[Grayscale]. All copyrights belong to the original author [Will Ogden Moore]. If there are objections to this reprint, please contact the Gate Learn team, and they will handle it promptly.
2. Liability Disclaimer: The views and opinions expressed in this article are solely those of the author and do not constitute any investment advice.
3. Translations of the article into other languages are done by the Gate Learn team. Unless mentioned, copying, distributing, or plagiarizing the translated articles is prohibited.