Pantera Partner A Comprehensive Analysis of Sahara AI

Coinworld.com Report:
Author: Paul Veradittakit, Partner at Pantera Capital; Translation by: xiaozou
The mission of Sahara AI is to create a more open, fair, and collaborative artificial intelligence economy, making it as easy as possible for people to participate. Using blockchain, Sahara ensures that all contributors (data contributors, labelers, model developers, etc.) are fairly compensated, data and models maintain sovereignty, and artificial intelligence assets have security, permissions to create, share, and trade.
1. AI Stack Status
The current AI stack can be divided into the following layers:
Data Collection and Annotation
Data is collected from various sources (such as web scraping, public datasets, user-generated data) and must comply with licensing requirements to avoid legal issues. Data is annotated based on the task at hand (such as classification, object recognition).
Model Training and Services
Data is input into models, and the models adjust their internal parameters (weights) to minimize errors. This requires expensive and time-consuming computation.
Creation and Deployment of AI Agents
The user experience for creating AI agents typically involves using tools like TensorFlow and requires technical expertise.
Computational Resources
Model training requires expensive processing power.
Each layer is highly competitive and diverse, and to a large extent, there is a proven most effective way of execution. For example, data collection is best done using large public datasets (such as books) and fine-tuning with specialized data (research papers). Model training is best done on dedicated hardware, and AI agents should be easy to use with plug-and-play resources to build a developer community. Computational resources should be distributed to accurately reward resource providers. This combination will result in better AI models and a stronger community.
Web2 companies are striving in this direction, but due to their centralized design, they face serious limitations. From a business and technical perspective, these companies aim to restrict access and isolate different parts of the stack, leading to different security standards, database designs, backend integrations, and monetization strategies. In fact, such designs are poorly equipped to handle the transformation of the AI economy model.
For example, OpenAI has built a very powerful base model and is starting to attract community builders through its permissionless GPT wrapper marketplace, but it only allows surface-level prompt customization and does not support the reconstruction of the underlying model. All of the company’s computational resources are purchased with investors’ 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 AI lifecycle: from data collection and annotation, to model training and services, creation and deployment of AI agents, multi-agent communication, AI asset trading, and crowdsourcing of AI resources. By democratizing the AI development process and lowering the entry barriers of existing systems, Sahara AI provides equal access to individuals, companies, and communities to collectively build the future of artificial intelligence.
The above figure summarizes the user journey, depicting how AI assets are created, used, and achieve user stickiness in the Sahara AI ecosystem. It is worth noting that all transactions within the platform are immutable and traceable, ownership is protected, and asset sources are 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 the following ways:
Experienced AI Developers:
Developers can interact with any layer of the Sahara blockchain and its AI stack using Sahara SDK and APIs, such as personalized computing power, data storage, and incentive structures, to form their own Sahara AI agents that can be authorized and monetized for others to use.
Novice AI Developers:
Using a no-code/low-code environment, developers can create and deploy AI assets through intuitive interfaces and pre-built templates.
AI Training:
To participate in AI model training, users simply need to access a website where they can complete AI training tasks and receive compensation in the form of tradable tokens, ranging from solving basic math problems to describing short videos.
AI Users:
Users can easily use AI agents through an intuitive UI. Users can flexibly purchase access and further development licenses, 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 artificial intelligence, this will allow others to access it while keeping the training data completely private and secure.
Companies:
Companies can also create AI agents (or “business proxies”) and train their proprietary data. As the system runs on the Sahara blockchain, it benefits from decentralized AI agent generation and services, resulting in much lower costs.
Companies can also pay to generate Sahara data, which combines AI auto-labeling and manual labeling to effectively create high-quality, privacy-protected multi-model datasets.
Except for the products targeting enterprises that have already been used by some well-known clients, all other features have not been released yet but have release plans.
3. Technical Overview
The Sahara team has designed the system to be as simple and user-friendly as possible, abstracting the complexity required to ensure compatibility, profitability, and security for each part of the AI stack. Behind the scenes, the Sahara team has developed numerous innovations to achieve this goal. Here are a few examples:
The Sahara blockchain minimizes gas fees and is fully compatible with EVM. The Sahara Cross-Chain Communication (SCC) protocol enables secure and permissionless data transfer across blockchains, facilitating trustless interoperability.
Sahara AI-Native Precompiles (SAPs) are pre-compiled smart contracts used to optimize the performance of AI tasks, reducing computational overhead, including training execution SAPs and inference execution SAPs.
Sahara Blockchain Protocols (SBPs) manage AI assets to ensure accounting accountability, such as AI Attribution for tracking contributions and allocating rewards, and AI Asset Registry for managing AI assets, AI licenses, and AI ownership registration and sources.
Data management is done on-chain and off-chain, with AI asset metadata, commitments, and proofs on-chain, while important datasets, AI models, and supplemental information are stored off-chain to optimize data retrieval, security, and availability.
Collaborative Execution Protocols support collaborative 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 Fraud Proofs provide fraud-proofing capabilities as the name suggests.
4. Fully Integrated AI Stack
The team is led by Professor Sean Ren, a lifetime professor at the University of Southern California, and Tyler Z, an alumnus of the University of California, Berkeley, who was named one of MIT Technology Review’s Innovators Under 35 and awarded the 2023 Samsung Fellow. Other team members have backgrounds or experience from companies such as Stanford University, University of California, Berkeley, AI2, Toloka, Stability AI, Microsoft, Binance, Google, Chainlink, LinkedIn, Avalanche, etc., contributing valuable expertise.
Sahara also has top AI native researchers and corporate 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 being used by over 35 leading technology innovation projects and research institutions, including Microsoft, Amazon, MIT, Motherson Group, and Snap, for various AI services such as Shara Data for data collection/annotation and Sahara Agents for personalized domain agents.
Generative AI is still in its early stages in terms of technology and market size. Due to the difficulty of integrating the entire AI stack into one product, today’s centralized chat and video tools have limited coverage. Sahara AI is the only company that addresses this bottleneck through modular design, using blockchain as the pillar for permissionless access, token distribution, and security. To make the future of artificial intelligence accessible and fair for everyone, Sahara AI is the only company moving towards this vision.