Grass is moving towards a transformative AI development platform.

Author: Aylo

Compiled by: Deep Tide TechFlow

The real gold rush lies in AI data, and there is a project building foundational tools for this "gold rush."

Imagine if you could invest in a quietly rising crypto project that is expected to become the "Google" of the AI field…

In yesterday's Discord live stream, @0xdrej shared the latest developments of @getgrass_io. The biggest highlight is that Grass is moving towards a transformative AI development platform that could unleash tremendous potential value.

Here are the key points from the seminar:

• Massive Data Advantage: Grass has indexed over 3 billion video data, far exceeding the 200 million video data used by companies like NVIDIA to train top video models. This gives Grass an currently unmatched multimodal dataset.

• Technical Upgrades: With the Scion upgrade, Grass's data retrieval efficiency and scale have significantly improved. After the upgrade went live, the amount of data captured has noticeably increased. The second phase of the upgrade will further expand its capabilities.

• Semantic Multimodal Search: Grass is about to launch a semantic multimodal search feature across its vast data index. This means users can efficiently search and extract video, audio, and image clips that are highly relevant to their needs. Currently, no other platform can achieve such functionality at the scale of Grass.

• Future-Oriented Demand: As AI gradually shifts towards multimodal models in fields like robotics, the demand for specialized datasets will continue to grow. Grass's unique advantages in this area make it an ideal platform to meet this demand.

• Vertical Integration: Grass plans to evolve from a data retrieval network into an end-to-end AI development platform for training models. Additionally, they may open-source some of their powerful models to provide more support for developers.

• Hardware Innovation: Grass is exploring new node distribution methods, including dedicated hardware technology. This innovation could bring disruptive changes, allowing Grass to implement what only a few companies can do today—loading web scraping scripts directly onto hardware, significantly increasing efficiency. If successful, this will further solidify Grass's competitive advantage in cost and scale.

• Platform Expansion: The seminar also revealed an upcoming new product that will introduce "horizontal scaling" and "change the dimensions in which you view Grass." While specific details have not been disclosed, this indicates that Grass is developing an important expansion feature that could open up new application scenarios and markets.

Overall, Grass is building critical infrastructure for the next generation of AI—including large-scale multimodal datasets and efficient scaling solutions. As the demand for specialization grows for models like NVIDIA's, Grass is poised to become the preferred platform for developers to easily search and fine-tune custom datasets.

If Grass can realize its vision, the potential value creation will be immense. It will provide the "foundational tools" for the wave of multimodal AI, becoming a key player in this technological revolution.

Whether in web development or robotics, nearly every field will attempt to leverage this technology. Grass is expected to occupy a significant position in this multi-trillion-dollar market.

Imagine you are designing a robot that needs to understand the surrounding world. To achieve this, the robot requires not only training on text data but also support from image, video, and audio data. This is what is known as "multimodal" data, and Grass is providing core support for this demand.

Current large AI models can handle multimodal data, but their functionalities are often too generic. If you want your robot to excel at specific tasks, such as recognizing various fruits or navigating a warehouse, you need to fine-tune the model with specialized datasets for those tasks.

This is where Grass excels. They have indexed a massive amount of multimodal data from the internet, including billions of images, videos, and audio files. But what is truly disruptive is their upcoming "semantic multimodal search" feature.

The so-called "semantic multimodal search" allows you to search for data not only by keywords but also based on the actual meaning of the content. For example, if you need videos of a robot picking apples, you can precisely find those videos, rather than just those that mention "robot" and "apple" in their descriptions.

The significance of this feature lies in the fact that no other platform can provide such precise and content-aware search at the scale of Grass. As more companies and developers seek to apply AI in fields like robotics, they will need these specialized datasets to make AI more efficient. Grass is born to meet this demand.

Of course, Grass also faces execution risks, and the competition in this field is fierce. However, Grass's data scale, decentralized efficiency, and semantic search capabilities give it a unique advantage in the competition. The seminar also revealed that they are developing a brand new horizontal scaling product, which will further expand Grass's application scenarios and market space.

Overall, Grass is still in the early stages of development. This reminds me of Chainlink in 2018/2019, when it gradually showcased immense potential as a key infrastructure unlocking global DeFi. Grass's vision and positioning are similar to Chainlink; it aims to unlock multimodal data for AI developers and enterprises, which could create significant value for the world.

Currently, Grass stores 0.5 PB (petabytes) of data daily, and this number may soon grow significantly. It is easy to imagine that storage costs are very high, so the protocol must generate enough revenue to cover these costs (a large amount of data scraping is done based on customer demand).

In the AI and crypto space, I have yet to see another protocol that, like Grass, has a product that is genuinely paid for by Web2 AI customers while continuously innovating and delivering technology to unlock new value for AI. While there are some interesting AI agent platforms on the market (I also hold some), they currently remain in a more speculative phase (more like "casino logic"). Grass's LCR product will also serve AI agents.

If Grass can succeed, its future potential is limitless. It is expected to become a landmark technology platform in the AI field. The next 6 to 12 months will be a critical period, as Grass needs to seize its current lead. If it can achieve this, the current market value will seem trivial in a few years.

Disclaimer: The above content does not constitute investment advice. I am a holder of Grass. While I am optimistic about its prospects, it is also important to recognize the existence of execution risks and potential challenges.

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