AI Is Coming — Crypto Can Help Make It Right
Will Ogden Moore
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 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.
Where Does This Intersection Stand Today?
Figure 1: AI Universe has outperformed almost all Crypto Sectors year to date
Infrastructure Layer: Networks that provide platforms for AI development (e.g., NEAR, TAO, FET)
Resources Needed for AI: Assets that provide critical resources (compute, storage, data) needed for AI development (e.g., RNDR, AKT, LPT, FIL, AR, MASA)
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.
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.
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.
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.
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.
[5] AI Universe as defined by the Grayscale Researchwith minimum assets at $500mm in market cap and with a quarterly rebalance at 4/1/2024. The assets in the Universe include NEAR, FET, RNDR, FIL, TAO, THETA, AKT, AGIX, WLD, AIOZ, TFUEL, GLM, PRIME, OCEAN, ARKM, and LTP.
[6] Kaito. Narrative mindshare measures the relative frequency of social media mentions for specific crypto market themes or narratives, which is helpful in evaluating crypto assets that are driven by the community of believers and supporters who often express their views on social media platforms.
[9] Assets are illustrative examples, listed largest to smallest by market cap
[14] A subnet is a smaller, segmented part of a larger network designed to improve efficiency and security by isolating sections of the network for specific purposes or groups of users. As of June 23, 2024
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