Finding the Next Dark Horse in the AI赛道: Investment Logic and Token Recommendations
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Finding the Next Dark Horse in the AI赛道: Investment Logic and Token Recommendations
Artificial intelligence is a technology that will disrupt almost every industry, including Web3.
Navigating Web3 tides with focused insightsAuthor: hitesh.eth
Translation: TechFlow
Introduction
AI coins will perform strongly in this bull market. Over the coming months, the entire AI sector will be flooded with over 100 AI tokens.
90% of these AI coins won't last long. Therefore, you need to identify the other 10% that could potentially deliver 100x returns on your investment.
We need a logical framework for identifying the next high-return AI coin—explained in detail below.
Main Content
Artificial intelligence is a technology poised to disrupt nearly every industry, including Web3.
One aspect of AI use cases involves building tools and applications that can help crypto users earn more money.
This area remains largely unexplored—an opportunity worth closely monitoring.
You should look for projects that enable retail users to easily complete complex trading and investment tasks.
DeFi farming is something everyone wants to do, but due to its complexity, not everyone understands various DeFi strategies.
I believe AI-trained models will see extensive adoption in this field in the near future.
Mozaic is a prime example applying AI to DeFi farming; we’ll certainly see even better projects emerge beyond them.
Twitter: @Mozaic_Fi
Website: https://mozaic.finance/
There are also projects like PAAL AI, which helps you build chatbots, and AEGIS AI, which assists in screening cryptocurrency security.
Pay attention to such infrastructure enabling developers to build different types of AI applications (primarily bots).
Now let’s discuss another aspect: how blockchains enhance the efficiency, cost-effectiveness, and privacy of artificial intelligence / machine learning.
We can call this the decentralized infrastructure sub-sector, which includes promising use cases such as decentralized AI/ML, computing, and ZKML.
Decentralized AI/ML platforms like Bittensor are leading examples, allowing AI researchers to build diverse applications based on their LLM models.
Developers can monetize through their apps, and the entire economy is powered by the $TAO token.
A few months ago, TAO had a market cap of $200 million; it now exceeds $4 billion.
It primarily rose during the bear market, so most people completely missed this move—and are now eager for the next TAO.
If you're tracking the next major decentralized AI infrastructure project (like TAO), here's what to look for: performance of the LLM model.
Last year, Bittensor partnered with Cerebras to build a state-of-the-art lightweight LLM model, making them a hot topic within the AI community.
This was a breakthrough success, validating their technology.
They built a strong community focused on sharing and explaining all the complex fundamentals behind the project.
Therefore, the next $TAO must receive similar validation from the AI community and have a passionate following.
Strong backing from top-tier investment firms could accelerate partnership development and network effects.
So also watch VC-backed AI projects such as Ritual and gensyn.
Ritual
Twitter: @ritualnet
Website: ritual.net
Gensyn
Twitter: @gensynai
Website: gensyn.ai
We've covered AI/ML infrastructure—now let’s turn to computing. Here, Akash Network and Render Network are industry leaders.
Both operate decentralized GPU markets where you can rent GPUs to earn token rewards.
GPU configuration and the cost of using GPU infrastructure will be key factors in this sub-sector.
New entrants like io.net are emerging.
They’re collaborating with Render, Solana, and Filecoin to aggregate GPU resources and dominate the market—so keep an eye on them too.
Additionally, I believe another sub-sector set to become the next big thing is ZkML. Click here to read my deeper analysis on ZkML.
I think this covers my observations regarding all these existing and upcoming AI projects. Thanks for reading.
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