In recent years, with the rise of artificial intelligence, AI has penetrated various industries such as manufacturing, e-commerce, advertising, and pharmaceuticals; the cryptocurrency field is no exception. The fusion of artificial intelligence and blockchain has led us to see a unique digital asset — AI crypto tokens.
Its popularity began at the end of 2022, with the explosion of OpenAI's intelligent chatbot ChatGPT, many people realized that artificial intelligence is no longer just in movies; more applications have entered reality, and AI has been applied as an efficient productivity tool in actual industries.
The AI frenzy has also impacted institutional giants. For example, Google announced it would develop its own AI chatbot, Bard. Additionally, noteworthy news is that Microsoft acquired OpenAI for $10 billion and proposed to integrate it into its Bing search engine. This sustained mainstream interest in AI technology has led to explosive growth in the market value of various AI tokens, with some increasing by as much as 1600%!
So what are AI tokens? How do they integrate with Web3, and what is the future direction? Let's discuss these issues below.
What are AI tokens?
AI tokens are crypto assets that integrate AI principles into blockchain technology. The AI elements of such tokens allow them to develop better automated strategies to solve specific problems. Since their intelligence can better adapt to market conditions, they have advantages over other crypto assets.
AI tokens are cryptocurrencies that support AI-based projects, applications, and services within blockchain ecosystems. They primarily play three key roles:
Facilitating transactions, they serve as exchange media within AI-driven platforms, allowing users to pay service fees, access data, and participate in platform activities.
They can also be used as governance tokens, which grant their holders governance rights, enabling holders to participate in shaping the development of AI projects or platforms.
They can also serve as rewards to incentivize users to contribute to AI protocols or projects, generally awarded for contributions such as providing data or computing resources.
AI + Web3 infrastructure
The main projects in the infrastructure layer of the AI + Web3 industry are primarily based on decentralized computing networks as the main narrative, with low cost as the main advantage, using token incentives as the primary method to expand the network, and serving AI + Web3 clients as the main goal.
Infrastructure is a certain growth direction for AI development
Explosive growth in AI computing demand
In recent years, the demand for computing power has rapidly increased, especially after the emergence of large language models (LLM), which has ignited the high-performance computing market. OpenAI data shows that since 2012, the computing usage for training the largest AI models has increased exponentially, doubling every 3-4 months, and its growth rate far exceeds Moore's Law.
At the same time, the massive data requirements also impose demands on storage and hardware memory, especially during the model training phase, which requires a large amount of parameter input and storage of vast amounts of data. The storage chips used in AI servers mainly include: High Bandwidth Memory (HBM), DRAM, and SSD, which need to provide greater capacity, higher performance, lower latency, and faster response speeds for AI server working scenarios.
Supply and demand imbalance drives high computing costs
With the development of large models, computational complexity has also skyrocketed, requiring more high-end hardware to meet model training needs. Taking GPT-3 as an example, with 13 million independent users accessing it, the corresponding chip demand is over 30,000 A100 GPUs. Thus, the initial investment cost will reach an astonishing $800 million, with estimated daily model inference costs of $700,000. Therefore, the rising demand for high-end GPUs and supply constraints have driven the current high prices of hardware like GPUs.
AI infrastructure occupies the core value growth of the industrial chain
A report by Grand View Research indicates that 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 significant share in the entire AI industry chain.
The narrative logic of AI + Web3 infrastructure projects
The demand for distributed AI infrastructure is strong and has long-term growth potential, making it a narrative-friendly area favored by capital. Currently, the main projects in the infrastructure layer of the AI + Web3 industry are primarily based on decentralized computing networks as the main narrative, with low cost as the main advantage, using token incentives as the primary method to expand the network, and serving AI + Web3 clients as the main goal. This mainly includes two aspects:
1. A comparison of pure decentralized cloud computing resource sharing and leasing platforms: There are many early AI projects, such as Render Network, Akash Network, etc.;
Computing power resources are the main competitive advantage: Core competitive advantages and resources usually involve accessing a large number of computing power providers to quickly establish their foundational network while providing user-friendly products for clients.
Low product barriers and fast launch speed: For mature products like Render Network and Akash Network, tangible growth data can already be seen, possessing a certain leading advantage.
Homogenization of new entrant products: Due to the current hot track and the low barriers of such products, many recently entered projects are doing narratives around shared computing power and computing resource leasing, but the products are relatively homogeneous and need to see more differentiated competitive advantages.
Tending to serve customers with simple computing needs: For example, Render Network primarily serves rendering needs, and Akash Network offers more CPU resources.
2. Providing decentralized computing + ML workflow services: There are many emerging projects that have recently received significant funding, such as Gensyn, io.net, Ritual, etc.;
Decentralized computing raises the valuation foundation. Since computing power is a deterministic narrative for AI development, projects with a computing power foundation typically have more stable and high-potential business models, leading to higher valuations compared to purely intermediary projects.
Intermediary services showcase differentiated advantages. The services of the intermediary layer are those that provide competitive advantages based on these computing power infrastructures, such as oracles and validators that synchronize on-chain and off-chain computation for AI, and deployment and management tools for the overall AI workflow.
