Written by: Black Rice
I am honored to complete this research under the guidance of Meteorite Labs, based on experience exchanges with hundreds of Web2 AI applications.
PIN AI is a selected project of the a16z Crypto Startup Accelerator Fall Program, with a seed round of financing of $10 million. In addition to a16z Crypto, well-known VCs include Stanford Blockchain Accelerator, Hack VC, and Foresight Ventures. Angel investors include NEAR Protocol founder Illia Polosukhin, Gitcoin co-founder Scott Moore, Solana Foundation Chairman Lily Liu, SUI/Mysten Labs CEO Evan Cheng, Worldcoin research engineer DCBuilder, etc.
I just finished reading the article co-written by the three co-founders of PIN AI. I found that it is the most attractive Web3 AI project besides Sahara recently, and the application scenarios are very interesting. (Article link: https://www.pinai.io/post/pin-ai-the-open-platform-for-personal-ai)
PIN AI is an open AI network where developers can build useful AI applications. "Useful AI Applications" is the focus of its products. It is a bit like AI Agents such as MultiOn and Jace.ai of Web2, and is committed to providing users with applications that are useful in daily life and realizing the intentions proposed by users, such as online purchases, travel planning, and investment planning.
Let me briefly introduce Jace.ai, an AI agent that can complete browser tasks autonomously. It is based on LLM and its proprietary model AWA-1 (Autonomous Web Agent-1), which supports AI to perform operations on web pages.
Jace's greatest ability is that it can plan tasks autonomously and perform operations in the browser on behalf of the user.
Let's take an example to understand the application scenario. Tell Jace, "I plan to travel to Beijing for a week on September 20th, with a budget of 5,000 yuan. Please help me plan it." Jace will automatically plan a travel plan, including the attractions to visit, the hotels to stay in, and the food to eat. If I agree to this travel plan, it will help me book all the scenic spots, find the most cost-effective hotel on Meituan, and place an order. I only need to enter my personal information and pay with one click.
In fact, what PIN AI does is very similar. The biggest difference from generative AI is that this type of AI project focuses on users' daily lives rather than work.
1. Deconstructing the PIN AI design concept
Simply put, PIN AI = AI + DePIN
The PIN AI network consists of two types of AI:
Personal AI: A personalized AI agent that adapts to user preferences in real time. It is the connection point between the user and the agent service, a bit like a coordinator. Users can download it to their mobile phone or computer device for use.
Agentic Services: AI Agents built on-chain for Web2 platforms that can perform tasks on some of the top Web2 platforms, with the execution process and completion status recorded on the blockchain
The official also mentioned external AI, which may support interaction with other LLM or Web2 AI Agents in the future.
PIN AI architecture core:
PIN Protocol, a DePIN distributed data storage network, allows anyone to connect their devices to the network and share data. It integrates a BERT-based model to anonymize data at all stages of processing user data, ensuring privacy and compliance with data protection regulations.
Personal AI is built into it. On one hand, it provides personalized data for personal AI, and on the other hand, it provides the most relevant data for agent services.
The PIN Protocol is built from three components:
1. Private storage and computing layer: Distributed storage data, securely storing device data shared by users (including photos, videos, etc.), and making the most relevant data readily available for personal AI and proxy services. Users can connect their devices to the network, provide device data, and receive native token $PIN rewards.
2. Data connector: Use zk technology to track and verify user data connected to the network. I think it is equivalent to the node of the PIN network. The node operator needs to stake $PIN tokens to do verification, and some token holders can stake tokens to the node, and both can get token staking rewards.
3. Agent Linking: Aims to match personal AI with agent services. It consists of an agent registry and a transaction mechanism, the former is used to track performance indicators, while the latter "thinks" about how to match personal AI with agent services (based on the cost, performance, and completion quality of each agent service)
User usage pattern / business logic:
When a user makes a specific request, PIN AI will follow the steps below:
Step 1: Personal AI - Collecting user requirements
The user makes a request to the personal AI, and the personal AI forwards the request to the PIN Protocol
Step 2: PIN Protocol - Preparation before task execution
Break down user intent into specific steps, find the most suitable and cost-effective proxy service, retrieve the most relevant data, and provide it to the proxy service. (If multiple Web2 platforms are involved, the intent needs to be split into different proxy services)
Step 3: Proxy service - Execute specific steps
Step 4: PIN Protocol - Feedback to the User
After all, most of the daily life needs require money transactions. In PIN AI, the flow of funds should be:
The user pays the Gas fee to the PIN Protocol (I guess it is to activate this intent transaction). Since the PIN Protocol first disassembles the user's intent, and then indexes and sends the most relevant data to the proxy service, after the proxy service completes the task, it will give part of the service fee back to the PIN Protocol as a tip.
Therefore, both PIN Protocol and proxy services can earn commissions from the service fees given by users.
For example:
Users can download personal AI to their computers or mobile phones, make requests to personal AI, such as "buy the cheapest GTX 3080 graphics card on Amazon", and pay fees (purchase fee + service fee + PIN Protocol Gas fee).
The personal AI communicates this requirement to the PIN Protocol.
After understanding and deconstructing the user's intent, PIN Protocol will break down the user's intent into specific task steps and send them to the proxy service along with the most relevant data. There may be dozens of proxy services dedicated to Amazon shopping at the same time, so PIN Protocol needs to comprehensively consider their costs, performance, and past completion records and select the most suitable one.
The proxy service finds the most cost-effective GTX 3080 graphics card on Amazon and places an order. After completion, the intention disassembly fee and data call fee will be paid to PIN Protocol. PIN Protocol and personal AI feedback the results to the user and send the user PIN tokens as a reward.
Network Participants
Personal AI users: Install personal AI on your computer or mobile phone, connect your personal data to the PIN Protocol, and receive PIN token rewards.
Value transfer users: Like the above usage model, users who conduct valuable transactions will also receive PIN token rewards.
PIN Protocol Node: Tracks and verifies user data connected to the network. Operators need to stake, and token holders can stake tokens to nodes, and both can receive staking rewards.
Agency services: Developers can earn service fees.
2. Core Development Team
Davide Crapis - Co-founder
Background in blockchain protocol design, with some AI background
He worked as a senior data scientist at Lyft, where he designed and implemented an incentive allocation algorithm that issued $xx in growth incentives to passengers and drivers each year. After resigning, he worked as an independent researcher for a while, studying incentive schemes and token allocation. Before founding PIN AI, he worked as a research scientist in the Ethereum Foundation’s “robust incentives” direction.
He has developed a machine learning model for "consumer sensitivity to investment/credit product interest rates" and worked as a researcher and mentor in machine learning at Columbia Business School for four years. He has joined the Web2 developer community "South Park" to explore the intersection of large language models and blockchain.
Ben Wu - Co-founder
Operational background, may provide strategic direction and AI product ideas
Graduated from MIT and a Y Combinator alumnus. Before founding PIN AI, he worked as the Director of Database and Operations in Yahoo's Strategic Data Solutions Department, responsible for the operation and management of large-scale data projects.
Bill Sun - Co-founder, Chief Scientist
Quantitative Trading and AI Background
A PhD in Mathematics from Stanford University, he worked on AI research at Google DeepMind. He worked as an AI/quantitative trading stock investment manager at a Wall Street asset management company. He founded the AI research organization AI+Club and the AI technology community AGI House. He is an angel investor of the a16z scout fund. He is also the founder of Generative Alpha, which provides enterprise-level AI solutions.
3. Thoughts and Conclusions
In the first industrial revolution, machinery freed our hands;
The second industrial revolution, electricity broke the boundary between day and night;
In the third industrial revolution, the Internet merges the boundaries between the virtual and the real.
The emergence of AI is generally considered to be a sign of the Fourth Industrial Revolution, and AI Agent is the ticket for this journey of exploration. Each of us can board this ship heading for the future of "human-computer interaction".
Over the past few decades, a large amount of activities have taken place on the Internet every day and massive amounts of data have been generated. However, users do not have ownership of this data.
iPhone16 has just been released, bringing Apple Intelligence, but PIN AI has the opportunity to build an AI Agent ecosystem that is more open than Apple Intelligence.
Among them, developers can get rewards by developing innovative Web2 platform agent services, which will give birth to AI agents with higher and higher performance and quality, triggering a wave of innovation.
Billions of mobile phone users can not only use personalized personal AI, but also share device data to earn rewards.
User data supports the entire PIN AI ecosystem. This is the power of users, and it is also the starting point of Web3 - decentralization and ownership.
I hope to see the implementation of the PIN AI network as soon as possible and whether the incentive mechanism it brings can play an effective role, so that a large number of open source contributors can flock to it and then create a larger wave of innovation. The test network may be launched in October, and the main network and TGE will be launched in January next year, which is worth looking forward to.
