Sahara AI, a decentralized AI data network, is a 10,000-word research report: The data gold mine gatekeeper in the Web3 era? A panoramic analysis of its development background, technical principles, ecological status, pros and cons, risks, and future prospects

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When Silicon Valley's generative AI giants swear by the Scaling Law and frantically burn computing power budgets, people quickly realize that the only strategic asset in this game has formed: high-quality training data has become the "digital oil" that every model manufacturer craves. The supplier of this "new oil", Chinese-American genius Alexandr Wang and the data annotation company he single-handedly created, Scale AI, have also been in the limelight. He not only became the designated data service provider for companies like Microsoft, Meta, and OpenAI that are heavily investing in model training, but also became a guest of the US Department of Defense. The market's expectation of a shortage of high-quality data has made companies like Scale AI, which make money by processing data, a recognized good company. After completing a $1 billion Series F financing in 2024, the company's valuation has doubled to $13.8 billion compared to the previous round. This young CEO, born in 1997, has leveraged the wave of machine learning and generative AI to lead Scale AI to break through the $1 billion annual recurring revenue threshold in 2024, which is 4 times more than before 2023. Not only Scale AI, but companies like Encord, Label Box, and Snorkel AI have also quickly grown into important players in this emerging market with their unique technologies.

However, this feast will not last forever. According to researchers' judgments, publicly available corpus data on the internet will soon be exhausted, and AI giants urgently need professionals from various fields to contribute their annotated data and vertical domain knowledge to build better AI. The data stored by these experts in private domains cannot be easily grabbed by giants from the internet, which means they have the opportunity to gain greater benefits by controlling these data, rather than letting them flow away for nothing. This may be a good opportunity for Web3 to intervene.

Author: Hendrix, Web3Caff Research Researcher

Cover: Logo and background photo by Sahara, Typography by Web3Caff Research

Word Count: Total of 10,400+ words

Table of Contents

  • Sahara AI: Web3 Data Factory
  • Data & AI 101: Web2 & Web3 Perspective
  • Step One: Sahara Data Service Platform Reshaping Production Relations
    • Platform Function Introduction
    • Sahara Legend
    • Annotation Case: Myshell
    • Comparison with Similar Data Annotation Platforms
  • Step Two: AI Studio Completing Data Commodification
    • Platform Function Interpretation
    • Comparison with Competitors
  • Step Three: Asset Chaining + Ecosystem Operation
    • Multi-layer Architecture
    • Ecosystem Supplement
  • Sahara's Opportunities and Challenges
    • Opportunities: Niche Market Bringing Better Financial Performance
    • Challenges: AI Challenges Data Annotation
  • Summary
  • Key Structure Diagram

Source
Disclaimer: The content above is only the author's opinion which does not represent any position of Followin, and is not intended as, and shall not be understood or construed as, investment advice from Followin.
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