AI+Web3: Towers and Squares

TL;DR

1. Web3 projects with AI concepts are becoming attractive targets for capital in both primary and secondary markets.

2. The opportunities of Web3 in the AI industry are reflected in: using distributed incentives to coordinate potential supply in the long tail ------ across data, storage, and computation; at the same time, establishing an open-source model and a decentralized market for AI Agents.

3. AI's main application in the Web3 industry is on-chain finance (crypto payments, trading, data analysis) and assisting development.

4. The utility of AI+Web3 is reflected in the complementarity of the two: Web3 is expected to counteract AI centralization, while AI is expected to help Web3 break through its boundaries.

Introduction

In the past two years, the development of AI has been accelerated like pressing a fast-forward button. This butterfly effect triggered by ChatGPT has not only opened a new world of generative artificial intelligence but has also stirred up strong currents in Web3 on the other side.

With the support of AI concepts, the fundraising boost in the relatively slowing cryptocurrency market is significant. Media statistics indicate that in the first half of 2024 alone, a total of 64 Web3+AI projects completed fundraising, with the AI-based operating system Zyber365 achieving a maximum funding amount of 100 million dollars in its Series A round.

The secondary market is more prosperous, and data from cryptocurrency aggregator websites shows that in just over a year, the total market value of the AI sector has reached $48.5 billion, with a 24-hour trading volume close to $8.6 billion; the positive impact brought by the progress of mainstream AI technology is evident, as after the release of OpenAI's Sora text-to-video model, the average price of the AI sector increased by 151%; the AI effect has also radiated to one of the cryptocurrency fundraising sectors, Meme: the first AI Agent concept MemeCoin------GOAT has quickly become popular and achieved a valuation of $1.4 billion, successfully sparking the AI Meme craze.

The research and discussions around AI+Web3 are equally heated, from AI+Depin to AI Memecoin and currently AI Agent and AI DAO, the FOMO sentiment can no longer keep up with the speed of the new narrative rotation.

AI + Web3, this combination of terms filled with hot money, market trends, and future fantasies, is inevitably seen by some as a marriage arranged by capital. It seems difficult for us to discern whether beneath this splendid robe lies the playground of speculators or the eve of a dawn explosion?

To answer this question, a key consideration for both parties is whether it would be better with the other involved. Can one benefit from the other's model? In this article, we also attempt to examine this pattern by standing on the shoulders of our predecessors: how Web3 can play a role in various aspects of the AI technology stack, and what new vitality AI can bring to Web3?

Part.1 What opportunities does Web3 have under the AI stack?

Before diving into this topic, we need to understand the technology stack of AI large models:

To express the whole process in more common language: "The large model" is like the human brain. In the early stages, this brain belongs to a newborn baby that has just come into the world and needs to observe and absorb massive amounts of information from its surroundings to understand this world. This is the "collection" phase of data. Since computers do not possess multiple senses like human vision and hearing, before training, the large amounts of unlabelled information from the outside world need to be transformed into a format that computers can understand and use through "preprocessing."

After inputting data, the AI constructs a model with understanding and predictive capabilities through "training," which can be seen as a process similar to a baby gradually understanding and learning about the outside world. The model's parameters are akin to the language skills that a baby continuously adjusts during the learning process. When the learning content begins to be categorized, or feedback is received from interactions with others and corrections are made, it enters the "fine-tuning" phase of the large model.

When children gradually grow up and learn to speak, they can understand meanings and express their feelings and thoughts in new conversations. This stage is similar to the "reasoning" of AI large models, where the model can predict and analyze new language and text inputs. Infants express feelings, describe objects, and solve various problems through language abilities, which is also similar to how AI large models apply reasoning in various specific tasks after completing training and being put into use, such as image classification, speech recognition, etc.

The AI Agent is more akin to the next form of large models - capable of independently executing tasks and pursuing complex goals. It not only possesses thinking ability but also can remember, plan, and interact with the world using tools.

Currently, in response to the pain points of AI across various stacks, Web3 has preliminarily formed a multi-layered, interconnected ecosystem that covers all stages of the AI model process.

1. Basic Layer: Computing Power and Data's Airbnb

▎Hash Rate

Currently, one of the highest costs of AI is the computational power and energy required for training and inference models.

One example is that Meta's LLAMA3 requires 16,000 H100 GPUs produced by NVIDIA (which is a top graphics processing unit designed for artificial intelligence and high-performance computing workloads) and takes 30 days to complete training. The unit price of the latter's 80GB version ranges from $30,000 to $40,000, which necessitates an investment of $400 million to $700 million in computing hardware (GPU + network chips), while monthly training consumes 1.6 billion kilowatt-hours, leading to energy costs of nearly $20 million per month.

The relaxation of AI computing power is also one of the earliest areas where Web3 intersects with AI------DePin (Decentralized Physical Infrastructure Network). Currently, data websites have listed over 1,400 projects, among which representative projects for GPU computing power sharing include io.net, Aethir, Akash, Render Network, and so on.

The main logic lies in that the platform allows individuals or entities with idle GPU resources to contribute their computing power in a permissionless decentralized manner. By creating an online marketplace for buyers and sellers similar to Uber or Airbnb, it increases the utilization rate of underutilized GPU resources, allowing end users to access more cost-effective and efficient computing resources; at the same time, the staking mechanism also ensures that if there are violations of the quality control mechanisms or network interruptions, the resource providers will face corresponding penalties.

Its characteristics are:

• Aggregating Idle GPU Resources: The suppliers mainly include third-party independent small and medium-sized data centers, surplus computing power resources from operators such as cryptocurrency mining farms, and mining hardware with a consensus mechanism of PoS, such as FileCoin and ETH miners. Currently, there are also projects dedicated to launching devices with lower entry barriers, such as exolab, which utilizes local devices like MacBook, iPhone, and iPad to establish a computing power network for running large model inference.

• Facing the long-tail market of AI computing power:

a. "In terms of the technical aspect," the decentralized computing power market is more suitable for inference steps. Training relies more on the data processing capabilities brought by the scale of ultra-large GPU clusters, while inference has relatively lower requirements for GPU computing performance, such as Aethir focusing on low-latency rendering work and AI inference applications.

b. "From the demand side perspective," small to medium computing power demanders will not train their own large models individually, but will only choose to optimize and fine-tune around a few leading large models, and these scenarios are naturally suitable for distributed idle computing power resources.

• Decentralized ownership: The technological significance of blockchain lies in the fact that resource owners always retain control over their resources, flexibly adjust based on demand, and simultaneously gain profits.

▎Data

Data is the foundation of AI. Without data, computation is as useless as floating duckweed; the relationship between data and models is akin to the saying "Garbage in, Garbage out," where the quantity of data and the quality of input determine the final output quality of the model. For the training of current AI models, data determines the model's language ability, comprehension ability, even values, and human-like performance. Currently, the data demand dilemma of AI mainly focuses on the following four aspects:

• Data hunger: AI model training relies on a large amount of data input. Public information shows that OpenAI trained GPT-4 with a parameter count in the trillions.

• Data Quality: With the integration of AI and various industries, the timeliness of data, the diversity of data, the professionalism of vertical data, and the ingestion of emerging data sources such as social media sentiment have also raised new demands on its quality.

• Privacy and compliance issues: Currently, countries and companies are gradually recognizing the importance of high-quality datasets and are imposing restrictions on dataset scraping.

• High cost of data processing: large data volume and complex processing. Public information shows that more than 30% of AI companies' R&D costs are used for basic data collection and processing.

Currently, web3 solutions are reflected in the following four aspects:

1. Data Collection: The availability of freely accessible real-world data for scraping is rapidly diminishing, and AI companies' expenditures on data are increasing year by year. However, this expenditure is not being returned to the true contributors of the data; instead, platforms are reaping the rewards of the value creation brought by the data. For example, Reddit generated a total revenue of $203 million through data licensing agreements with AI companies.

The vision of Web3 is to allow users who genuinely contribute to also participate in the value creation brought by data, and to obtain more private and valuable data from users in a cost-effective manner through distributed networks and incentive mechanisms.

• Grass is a decentralized data layer and network where users can run Grass nodes to contribute idle bandwidth and relay traffic to capture real-time data from the entire internet, and earn token rewards;

• Vana introduces a unique Data Liquidity Pool (DLP) concept, allowing users to upload their personal data (such as shopping records, browsing habits, social media activities, etc.) to a specific DLP and flexibly choose whether to authorize specific third parties to use this data;

• On a certain AI platform, users can use #AI 或#Web3 as a category tag on social media and @ the platform to achieve data collection.

2. Data Preprocessing: In the data processing of AI, the collected data is often noisy and contains errors, so it must be cleaned and converted into a usable format before training the model, involving standardization, filtering, and handling missing values through repetitive tasks. This stage is one of the few manual processes in the AI industry, giving rise to the profession of data annotators. As the model's requirements for data quality increase, the threshold for data annotators also rises, and this task is inherently suitable for the decentralized incentive mechanism of Web3.

• Currently, Grass and OpenLayer are both considering incorporating data annotation as a key component.

• Synesis introduced the concept of "Train2earn," emphasizing data quality. Users can earn rewards by providing labeled data, annotations, or other forms of input.

• The data labeling project Sapien gamifies the labeling tasks and allows users to stake points to earn more points.

3. Data Privacy and Security: It is important to clarify that data privacy and security are two different concepts. Data privacy involves the handling of sensitive data, while data security protects data from unauthorized access, destruction, and theft. Thus, the advantages of Web3 privacy technologies and potential application scenarios are reflected in two aspects: (1) training of sensitive data; (2) data collaboration: multiple data owners can participate in AI training together without sharing their raw data.

The currently common privacy technologies in Web3 include:

• Trusted Execution Environment ( TEE ), such as Super Protocol;

• Fully Homomorphic Encryption (FHE), such as BasedAI, Fhenix.io, or Inco Network;

• Zero-knowledge technology (zk), such as the Reclaim Protocol which uses zkTLS technology, generates zero-knowledge proofs of HTTPS traffic, allowing users to securely import activity, reputation, and identity data from external websites without exposing sensitive information.

However, the field is still in its early stages, and most projects are still exploring. One current dilemma is that the computing costs are too high, some examples include:

• zkML Framework E