AI+Web3: Towers and Squares

TL;DR

1. Web3 projects with AI concepts have become targets for capital attraction in both primary and secondary markets.

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

3. The main application of AI 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 the centralization of AI, while AI is expected to help Web3 break out of its niche.

Introduction

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

With the support of AI concepts, the financing boost in the slowing cryptocurrency market is evident. Statistics show that in the first half of 2024 alone, 64 Web3+AI projects completed financing, with the AI-based operating system Zyber365 achieving a maximum financing amount of 100 million dollars in its Series A round.

The secondary market is more prosperous, and crypto aggregation data shows that in just over a year, the total market value of the AI sector has reached 48.5 billion USD, with a 24-hour trading volume approaching 8.6 billion USD; the positive impact brought by mainstream AI technology advancements is evident, with the average price of the AI sector rising by 151% after the release of OpenAI's Sora text-to-video model; the AI effect is also radiating to one of the cryptocurrency capital-raising sectors, Meme: the first AI Agent concept MemeCoin - GOAT has quickly gained popularity and achieved a valuation of 1.4 billion USD, successfully sparking an AI Meme craze.

The research and topics surrounding AI+Web3 are equally hot, from AI+Depin to AI Memecoin to the current AI Agent and AI DAO, the FOMO sentiment has already fallen behind the speed of the new narrative rotation.

AI + Web3, this combination of terms filled with hot money, opportunities, and future fantasies, is inevitably viewed as a marriage arranged by capital. It seems difficult for us to discern whether under this glamorous robe lies the arena of speculators or the eve of a dawn explosion.

To answer this question, a crucial consideration for both parties is whether the other will become better. Can one benefit from the other's model? In this article, we also attempt to examine this pattern from 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 delving into this topic, we need to understand the technology stack of AI large models:

In simpler terms, the entire process can be described as follows: a "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, needing to observe and absorb a vast amount of external information to understand this world. This is the "data collection" phase. Since computers do not possess human senses like vision and hearing, prior to training, the large-scale unlabelled information from the external world needs to be converted 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 the process of a baby gradually understanding and learning about the outside world. The parameters of the model are like the language abilities that the baby adjusts continuously during the learning process. When the content of learning starts to be specialized, or when feedback is received through communication with others and corrections are made, it enters the "fine-tuning" stage of the large model.

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

The AI Agent is closer to the next form of large models - capable of independently executing tasks and pursuing complex goals, not only possessing thinking abilities but also capable of memory, planning, and able to use tools to interact with the world.

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

1. Basic Layer: The Airbnb of Computing Power and Data

Hash Rate

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

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

The release of AI computing power is also the earliest intersection of Web3 and AI - DePin (Decentralized Physical Infrastructure Network). Currently, data statistics 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 is that the platform allows individuals or entities with idle GPU resources to contribute their computing power in a decentralized manner without permission, creating an online marketplace for buyers and sellers similar to Uber or Airbnb. This increases the utilization rate of underutilized GPU resources, allowing end users to obtain more cost-effective and efficient computing resources. Meanwhile, the staking mechanism also ensures that resource providers face corresponding penalties in the event of violations of the quality control mechanism or network interruptions.

Its characteristics are:

• Gather idle GPU resources: The suppliers mainly consist of third-party independent small and medium-sized data centers, surplus computing power resources of 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, iPad, etc., to establish a computing power network for running large model inference.

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

a. "From a technical perspective," decentralized computing power markets are more suitable for inference steps. Training relies more on the data processing capabilities brought by large-scale GPU clusters, while inference has relatively lower demands on GPU computing performance, such as Aethir focusing on low-latency rendering tasks and AI inference applications.

b. "From the demand side perspective," small and medium computing power demanders will not train their own large models separately, 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, allowing for flexible adjustments according to demand, while also generating profits.

Data

Data is the foundation of AI. Without data, computation is as useless as floating duckweed, and the relationship between data and models is like the saying "Garbage in, Garbage out"; 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, understanding ability, and even values and human-like performance. Currently, the challenges in AI's data demand mainly focus 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 reaching trillions.

• Data Quality: With the integration of AI and various industries, the timeliness of data, the diversity of data, the specialization of vertical data, and the incorporation of emerging data sources such as social media sentiment have raised new requirements for its quality.

• Privacy and Compliance Issues: Currently, countries and enterprises are gradually recognizing the importance of high-quality datasets and are imposing restrictions on dataset scraping.

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

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

1. Data Collection: The availability of free real-world data that can be scraped is rapidly diminishing, and the expenditures of AI companies on data have been increasing year by year. However, this spending has not benefited the actual contributors of the data, as platforms have entirely reaped the value created 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 low-cost manner through distributed networks and incentive mechanisms.

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

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

• In PublicAI, users can use #AI 或#Web3 as a classification label on X and @PublicAI 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 repetitive tasks such as normalization, filtering, and handling missing values. This stage is one of the few human elements 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 has also risen, and this task is inherently suitable for the decentralized incentive mechanisms of Web3.

• Currently, Grass and OpenLayer are both considering joining this key step of data annotation.

• Synesis has proposed the concept of "Train2earn", emphasizing data quality, where users can earn rewards by providing labeled data, annotations, or other forms of input.

• The data labeling project Sapien gamifies the tagging 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 information from unauthorized access, destruction, and theft. Thus, the advantages of Web3 privacy technology and its potential application scenarios are reflected in two aspects: (1) Training with sensitive data; (2) Data collaboration: Multiple data owners can participate in AI training together without sharing their original data.

The currently prevalent 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 using zkTLS technology, generates zero-knowledge proofs for 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, and some examples are:

• The zkML framework EZKL takes about 80 minutes.