At the juncture of the old and new year, reflection and anticipation remain timeless themes.
If asked which tracks leave a lasting impression and still hold development potential next year, DePIN certainly claims a spot.
Top-tier crypto VCs repeatedly mention DePIN in their annual outlooks, and with the year-end momentum of Solana’s ecosystem recovery, tokens of various DePIN concept projects like Helium Mobile, DIMO, and HONEY have seen a frenzy of surges. Recently, Grass also announced a $3.5 million financing, quickly attracting interest in the gaming community for ‘0-lift mining.’
Amidst the collective climax of these tracks, it might be worthwhile to calmly consider with an investment mindset:
What is the intrinsic logic behind the growth of the DePIN track? Is diving into the trend now still a good choice?
As the Solana ecosystem buzzes with activity, the rush of players entering to grab profits is gradually declining. Should one be proactive and observe other undervalued assets?
If you are seeking new opportunities in the DePIN track in the new year, you need to penetrate the narrative logic of DePIN, look beyond the market focus, and find projects that can support this logic but have not yet gained widespread recognition.

Transitioning from DePin to AI, gathering public data to raise the ceiling of the track.

Regarding DePin, different perspectives on its narrative logic may lead to varying investment strategies and outcomes.

• Surface-level perspective: DePin equals device mining

From the end-user’s standpoint, this perspective has its merits. The lower the threshold for device requirements, the earlier you connect to the project network, and the more project tokens you can earn. The more you mine, withdraw, and sell, the greater your short-term gains.
However, this is a strategy geared towards short-term profits.

• Deep-level perspective: DePin equals a decentralized collection of public resources (data)

Have you ever considered what exactly you are mining?
In DePin, individual mining rewards are essentially incentives for contributing resources (driving data, IP, geographic location, bandwidth, etc.).
Under the incentive system of Web3, a large amount of public data can be rapidly collected in a decentralized manner. The more dispersed and diverse the data, the greater the value various industries can derive from it.
However, in traditional models, no centralized organization or institution can efficiently acquire such massive, scattered, and diverse data.
Therefore, the overt narrative of DePin is device mining, but the core logic behind it is the ‘efficient collection of public data.’
Let’s simplify this logic further:
You have the right to produce data — — You use devices to generate data — — You transfer ownership of the data — — You contribute to other data needs — — Ultimately, you receive rewards.
So, which industry requires the use of extensively collected public data? Undoubtedly, the most apparent need is for AI model training.
Take the well-known GPT as an example; its outstanding performance is the result of OpenAI collecting a vast amount of publicly available text data from the entire human internet for training.
On the other side, who is likely to contribute a large amount of public data? Naturally, it’s DePIN projects with low entry barriers, low device costs, and minimal technical constraints.
However, it’s important to note that the abundance of internet IP addresses directly affects the efficiency of crawling public data for AI training. Therefore, theoretically, the richer the IP resources of a DePIN project, the more direct its contribution to AI’s access to public data.
Therefore, as a hardware layer collecting data for AI or other industries, with the support of a dual narrative, DePIN projects can push their limits higher.
This approach has been validated by the recent success of Grass.
Chris Nguyen, CTO of Grass’s parent company Wynd Network, publicly stated:
We aim to make public web data more accessible to open-source AI projects, and decentralization is the only way to achieve this goal in a manner that balances ethics and efficiency.’
Clearly, Grass understands its role not only at the hardware level of DePIN but also as a booster for assisting AI in accessing data for training.
Top research firm Messari has also categorized various Web3 projects in a study on AI. Interestingly, in the category of ‘AI Data Collection,’ Grass seems to be the only project recognized for its value.”