Author: Paul Veradittakit, Partner at Pantera Capital
Compiled by: Xiaozou, Golden Finance
Sahara AI's mission is to create a more open, fair, and collaborative artificial intelligence economy, making it as easy as possible for people to participate. By leveraging blockchain, Sahara ensures that all contributors (data contributors, labelers, model developers, etc.) receive fair compensation, data and models maintain sovereignty, AI assets are secure, and permissions can be created, shared, and traded.
1. Current State of AI Stack
The current AI stack can be divided into the following layers:
Data Collection and Labeling
Data is collected from various sources (e.g., web scraping, public datasets, user-generated data) and must comply with licensing requirements to avoid legal issues. Data is labeled according to the task at hand (e.g., classification, object recognition).
Model Training and Services
Data is input into the model, which adjusts its internal parameters (weights) to minimize errors. This requires quite expensive and time-consuming computation.
Creation and Deployment of AI Agents
The user experience of creating AI agents often involves using tools like TensorFlow, requiring technical expertise.
Computational Resources
Model training requires expensive processing.
Each layer is highly competitive and diverse, and largely, one execution method has proven to be the most effective. For example, data collection is best done using large public datasets (like books), with specialized data (research papers) used for fine-tuning. Model training is best done on specialized hardware, and AI agents should be easy to use with plug-and-play resources to build a developer community, while computational resources should be distributed to accurately reward resource providers. These combinations will lead to better AI models and a more robust community.
Web2 companies are working towards this direction, but they face serious limitations due to their centralized designers. From both enterprise and technical perspectives, these companies aim to restrict access and isolate the different parts of the stack, leading to different security standards, database designs, backend integrations, and monetization strategies. In reality, such designs are poor and cannot cope with the shift in the AI economic model.
For example, OpenAI has built a very powerful foundational model and started attracting community builders through its no-license GPT wrapper marketplace, but only allows superficial prompt customization and does not support the reconstruction of the underlying model. All of the company's computational resources are purchased with investor money, and it is expected to lose $5 billion by the end of this year.
2. AI Collaborative Economy
The Sahara platform provides a one-stop service for all AI development needs throughout the entire artificial intelligence lifecycle: from data collection and labeling, to model training and services, creation and deployment of AI agents, multi-agent communication, trading of AI assets, and crowdsourcing of AI resources. By democratizing the AI development process and lowering the entry barriers of existing systems, Sahara AI provides equal access for individuals, businesses, and communities to collaboratively build the future of artificial intelligence.
The above diagram summarizes the user journey, depicting how AI assets in the Sahara AI ecosystem progress from creation to use to achieving user stickiness. Notably, all transactions within the platform are immutable and traceable, ownership is protected, and the source of assets is recorded. This supports a transparent and fair revenue-sharing model, ensuring that developers and data providers receive appropriate compensation for generating revenue.
Sahara's goal is to make it easier for people to participate in the AI economy. Developers and users can use Sahara in this way:
Experienced AI Developers:
Developers can use the Sahara SDK and API to interact with any layer of the Sahara blockchain and its AI stack, such as personalized computing power, data storage, and incentive structures to form their own Sahara AI agents, which can be authorized and monetized for others to use.
AI Development Novices:
Through no-code/low-code environments, developers can create and deploy AI assets using an intuitive interface and pre-built templates.
AI Training:
To participate in artificial intelligence model training, users only need to access a website where they can complete AI training tasks and then receive tradeable token compensation. The task range varies from solving basic math problems to short video descriptions.
AI Users:
Users can easily use AI agents through an intuitive UI. Users can flexibly purchase licensing rights for access and further development, and even trade AI asset shares.
Users will be able to create their own personalized data 'knowledge base' and use their own data to create specialized artificial intelligence. Like other AIs, this will allow others to access it while the training data remains completely private and secure.
Companies:
Companies can also create AI agents (or 'business agents') and train them on their proprietary data. Since the system runs on the Sahara blockchain, it benefits from decentralized AI agent generation and services, leading to significantly lower costs.
Businesses can also pay to generate Sahara data, which integrates AI auto-labeling and manual labeling, effectively creating high-quality, privacy-protected multi-model datasets.
In addition to enterprise products already being used by some well-known clients, all other features have not yet been released, but there are plans for release.
3. Technical Overview
The Sahara team has designed the system to be as simple and user-friendly as possible, abstracting the complexities needed to ensure the compatibility, profitability, and security of various parts of the AI stack. Behind the scenes, the Sahara team has developed countless innovations to achieve this goal. Here are a few examples:
Sahara blockchain minimizes gas fees, is fully compatible with EVM, and the Sahara Cross-Chain Communication (SCC) protocol enables secure, permissionless data transfer across blockchains, facilitating trustless interoperability.
Sahara AI-Native Precompiles (SAPs) are pre-compiled smart contracts designed to optimize the performance of AI tasks, reducing computational overhead, including training execution SAPs and inference execution SAPs.
The Sahara Blockchain Protocol (SBPs) manages artificial intelligence assets to ensure accountability, such as AI Attribution tracking contributions and distributing rewards, AI Asset Registry for managing AI assets, AI licenses, and registrations and sources of AI ownership.
Data management occurs both on-chain and off-chain, with AI asset metadata, commitments, and proofs on-chain, while important datasets, AI models, and supplementary information occur off-chain to optimize data retrieval, security, and data availability.
Collaborative Execution Protocols support joint AI model development and deployment across AI training, aggregation, and services. Other models like PEFT allow for technical fine-tuning, Privacy Preserving Compute supports differential privacy, homomorphic encryption, and secret sharing, and the functionalities of Fraud Proofs are as their name suggests.
4. Fully Integrated AI Stack
The team is led by Sean Ren, a tenured professor at the University of Southern California, and Tyler Z, an alumnus of the University of California, Berkeley. The former was named one of MIT Technology Review's 35 Innovators Under 35 and was awarded Samsung Researcher of the Year in 2023, while the latter previously served as the investment director for Binance Labs. Other team members have backgrounds or experiences from Stanford University, University of California, Berkeley, AI2, Toloka, Stability AI, Microsoft, Binance, Google, Chainlink, LinkedIn, Avalanche, and more, contributing valuable expertise.
Sahara also has top AI-native researchers and enterprise clients providing advice:
Laksh Vaaman Sehgal (Vice Chairman of Motherson Group)
Rohan Taori (Human Research Scientist)
Teknium (Co-founder of Nous Research)
Vipul Prakash (CEO of Together AI)
Elvis Zhang (Founding Member of Midjourney)
Sahara AI is currently used by over 35 leading technology innovation projects and research institutions, including Microsoft, Amazon, MIT, Motherson Group, and Snap, for various artificial intelligence services such as Shara Data for data collection/labeling and Sahara Agents for personalized domain intelligence.
Generative AI is still in its infancy in terms of technology and market scale; due to the difficulty of integrating the entire AI stack into one product, the coverage of today's centralized chat and video tools is limited. Sahara AI is the only company addressing this bottleneck through modular design, using blockchain as the pillar for permissionless access, token distribution, and security. For everyone to participate, the future of AI must be accessible and fair, and Sahara AI is the only company moving towards this vision.
