What are AI tokens? What is the future direction of combining AI with Web3?
With the rise of artificial intelligence in the past two years, AI has penetrated into various industries such as manufacturing, e-commerce, advertising, and medicine. The field of cryptocurrency is no exception. The integration of artificial intelligence and blockchain has allowed us to see a unique digital asset-AI crypto tokens.
Its popularity began at the end of 2022. With the popularity of OpenAI’s intelligent chatbot ChatGPT, many people realized that artificial intelligence is no longer just in movies. More applications have been illuminated in reality. AI has been applied to actual industries as an efficient productivity.
The AI craze has also affected institutional giants. For example, Google announced that it will start developing its own artificial intelligence chatbot Bard. In addition, it is worth noting that Microsoft acquired OpenAI for $10 billion and proposed to integrate it into its Bing search engine. This continued mainstream interest in AI technology has led to an explosive growth in the market value of various AI tokens, some of which have increased by as much as 1,600%!
So what are AI tokens? How to combine with Web3 and what is the future direction? Let’s discuss these issues below.
What are AI tokens?
AI tokens are encrypted assets that integrate AI principles into blockchain technology. The AI element of such tokens enables them to develop better automated strategies that can solve specific problems. They have an advantage over other crypto assets because their intelligence can better adapt to market conditions.
AI tokens are cryptocurrencies that support AI-based projects, applications, and services in the blockchain ecosystem. They play three key roles:
- Facilitate transactions. They are a medium of exchange within AI-driven platforms, where users can pay for services, access data, and participate in platform activities.
- They can be used as governance tokens, where these tokens give their holders governance rights and enable holders to participate in shaping the development of AI projects or platforms.
- They can also serve as rewards to motivate users to contribute to AI protocols or projects, generally by contributing data, providing computing resources, etc.
AI+web3 Infrastructure
The main projects in the infrastructure layer of the AI+Web3 industry are basically based on decentralized computing networks as the main narrative, low cost as the main advantage, token incentives as the main way to expand the network, and serving AI+Web3 customers as the main goal.
Infrastructure is the definite growth direction of AI development
Explosive growth in AI computing power demand
In recent years, computing power demand has grown rapidly, especially after the launch of the LLM large model, AI computing power demand has detonated the high-performance computing power market. OpenAI data shows that since 2012, the computing usage used to train the largest AI model has grown exponentially, doubling every 3–4 months on average, and its growth rate has greatly exceeded Moore’s Law.
At the same time, the need for massive data also puts forward requirements for storage and hardware memory, especially in the model training stage, which requires a large number of parameter inputs and a large amount of data storage. The storage chips used in AI servers mainly include: high bandwidth memory (HBM), DRAM and SSD, which need to provide larger capacity, higher performance, lower latency and higher response speed for the working scenarios of AI servers.
Imbalance between supply and demand drives high computing power costs
With the development of large models, computing complexity has also risen sharply, requiring more high-end hardware to meet model training needs. Taking GPT3 as an example, based on the situation of 13 million independent users accessing, the corresponding chip demand is more than 30,000 A100 GPUs. Then the initial investment cost will reach a staggering $800 million, and the daily model inference fee is estimated to cost $700,000. Therefore, the rising demand for high-end GPUs and the obstructed supply have driven the high prices of current hardware such as GPUs.
AI infrastructure occupies the core value growth of the industry chain
Grand View Research’s report shows that the size of the global cloud AI market is estimated to be $62.63 billion in 2023, and is expected to grow to $647.6 billion by 2030, with a compound annual growth rate of 39.6%. This data reflects the growth potential of cloud AI services and their important share in the entire AI industry chain.
Narrative logic of AI+Web3 infrastructure projects
Distributed AI infrastructure has strong demand and long-term growth potential, so it is an area that is easy to narrate and favored by capital. At present, the main projects of the infrastructure layer of the AI+Web3 industry are basically based on decentralized computing networks as the main narrative, low cost as the main advantage, token incentives as the main way to expand the network, and serving AI+Web3 customers as the main goal. It mainly includes two levels:
1. A relatively pure decentralized cloud computing resource sharing and leasing platform: there are many early AI projects, such as Render Network, Akash Network, etc.
- Computing power resources are the main competitive advantage: the core competitive advantage and resources are usually the ability to access a large number of computing power providers, quickly establish their basic network, and provide easy-to-use products for customers to use.
- Low product threshold and fast launch speed: For mature products such as Render Network and Akash Network, we can already see real growth data and have a certain leading advantage.
- Product homogeneity of new entrants: Due to the current hot spots in the track and the low threshold characteristics of such products, a large number of projects that share computing power, computing power leasing and other narratives have recently entered, but the products are relatively homogeneous, and more differentiated competitive advantages need to be seen.
- Customers who tend to serve simple computing needs: For example, Render Network mainly serves rendering needs, and Akash Nerwork provides more CPU in its resources.
2. Provide decentralized computing + ML workflow services: There are many emerging projects that have recently received high financing, such as Gensyn, io.net, Ritual, etc.
- Decentralized computing raises the valuation foundation. Since computing power is a deterministic narrative for the development of AI, projects with computing power foundation usually have more stable and high-potential business models, which makes them have higher valuations than pure middle-layer projects.
- Middle-layer services create differentiated advantages. Middle-layer services are the competitive parts of these computing power infrastructures, such as oracles and validators that serve the synchronization of on-chain and off-chain computing of AI, and deployment and management tools that serve the overall workflow of AI.
About Starcoin
Starcoin provides the utmost security from the origin via its enhanced PoW consensus and Secure smart contract, using the language of Move. Through layered and flexible interoperability, it optimizes the building of ecosystems such as DeFi, NFTs, Gaming, etc., with higher efficiency and convenience. This process redefines value by empowering every participant in the ecosystem to enjoy the multiplication of values.
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