Sahara is going live today, quickly connecting to all major exchanges. The current on-chain value is $0.1448, which means the market's valuation before its TGE is $1.45 billion. The initial circulation is about 20%, which is around a market cap of $290 million. Compared to recent projects with TGE, Sahara's valuation and market cap are among the highest—only $NXPC can compare with it.
This also reflects the capabilities of the Sahara team ⬇️
1. Funding and Resources: "Quickly connecting to all major exchanges" proves that this team is strong in terms of both funding and resources. Additionally, they have taken retail investors into consideration by implementing IDO in the TGE issuance rules, allowing retail investors to benefit.
2. Narrative Control: Sahara's IDO this time adopted USD1 fundraising, which effectively rides on the narrative of Trump WLFI stablecoin, further enhancing the market's valuation due to the previous retail investors' preference for USD1 narrative (the subsequent quick connections to major exchanges further increased the market's valuation).
However, since we are discussing Sahara, it's necessary to talk about what this project is actually doing.
Overall, Sahara is a comprehensive decentralized AI platform that supports AI developers in deploying and training AI models. It has a decentralized AI marketplace that supports trading of abstract assets such as computing power, models, and datasets. They have also introduced a Knowledge Agent, aimed at consumers (which allows users to ask the Knowledge Agent questions; it summarizes answers for you after reviewing various materials, understanding and remembering knowledge, and using it to perform certain operations).
However, in my opinion, these are not the main points. The core product of Sahara is actually in data labeling. This is also a sector I have frequently mentioned in previous tweets. The @TensorplexLabs invested by Yzi Labs is also a product in the same space.
Simply put, the concept of data labeling is: AI training requires data, but the data quality varies; there is both high-quality and low-quality data. Data labeling involves filtering the data and classifying and summarizing it based on different types.
The key to this type of work is incentives—if you don’t offer rewards, users have no motivation to do this. Tensorplex's method is to provide incentives to users through registering for the BIttensor subnet. Sahara is more direct this time, with airdrop expectations before the token launch and using $SAHARA for incentives after the token launch.
We can directly envision the future development path of the Sahara ecosystem: Sahara incentivizes users to label data through tokens —> dataset uploads —> B-end clients purchase data through the AI marketplace and use it for AI training. If the project isn’t just a one-off (though I think a $1.5 billion valuation is a bit high), Sahara will also need to attract more people to participate in data labeling by increasing token prices, providing B-end clients with richer dataset options, and earning profits from it (essentially operating as a cash flow income platform). This is basically the play.
This approach is likely the final path for all Crypto AI infrastructure projects—providing services to Web2 AI giants at a lower price through token incentives (Web3 crowdsourcing). Another major category of Crypto AI projects, AI Agents, represents a different Ponzi scheme.
Of course, Sahara is a large platform, and in the future, besides data labeling, there are many product lines that can be developed. This will depend on the team's ideas and execution.
