Over the past few months, due to the vigorous development of the entire AI industry, a large number of crypto industry talents have shifted to AI. Researchers involved in both fields are also exploring a proposition that no one has yet successfully realized:
Can blockchain become a part of AI infrastructure?
In the past two years, the market has seen many versions of the integration of AI and Crypto: AI Agents, on-chain inference, data markets, compute power leasing. The hype is high, but there aren't many projects that have truly formed a closed business loop. The reason is simple: most projects remain at the "AI application layer." But Gensyn is targeting the most core and expensive layer of the AI industry:
"Model Training"
How to achieve this? By organizing globally distributed GPU resources into an open AI training network. Developers can submit training tasks, nodes provide computing power, the network is responsible for verifying the training results and completing incentive distribution. What is truly worth paying attention to behind this is not "decentralization" itself, but a problem that is becoming increasingly impossible to ignore in the AI industry:
Computing resources have rapidly concentrated in the hands of a few giants. Large companies are already competing for chips several years in advance. Over the past year, a clear trend has gradually formed in the AI industry: whoever controls GPUs controls the speed of AI development, especially in the era of large models, where training resources have become a core barrier to entry.
H100 supply is tight, cloud service prices continue to rise. The first step for major domestic companies to develop AI is not to expand teams, but to lock in computing resources. This is also why behind OpenAI, Anthropic, and xAI, there are large cloud vendors. Because behind model competition, it has essentially become infrastructure competition. And the significance of Gensyn lies in:
Providing a new way to organize resources for AI training.
1. It Targets the Most Core Infrastructure Layer of the AI Industry
Many AI+Crypto projects lean more towards application-layer narratives. To put it bluntly, everyone is just building apps. But Gensyn directly enters the training phase. This is the part of the entire AI value chain with the highest technical barriers and the greatest resource consumption, and it is currently the layer most prone to forming platform barriers. Because once the training network reaches scale, it is not just a compute marketplace; it may become an important entry point for future AI development. This is also why the market continues to pay attention to Gensyn, and why A16Z has made two major investments leading the rounds.
2. It Provides a More Open Model of Compute Collaboration
Traditional AI training heavily relies on centralized cloud platforms. The advantage is stability, but costs are also continuously rising. Especially for small and medium-sized AI teams, training resources have gradually become a limiting factor for innovation. The idea Gensyn provides is: bring more idle GPUs into the network, allowing training resources to be dynamically scheduled, thereby improving overall compute utilization. Behind this logic is somewhat similar to when cloud computing first emerged: not reinventing computing, but reorganizing computing resources. If this model can be consistently proven, it will bring not only cost optimization but also potentially improve the resource efficiency of the entire AI industry.
3. Technical Barriers Are Its Important Moat
The truly difficult part of a training network is never "connecting GPUs," but rather: how to verify training results, how to ensure nodes honestly execute tasks, how to maintain training reliability in a distributed environment. What Gensyn has been working on is precisely this part, including mechanisms like probabilistic verification, task distribution models, node coordination systems, etc. These things might not be as "eye-catching" as Agent narratives, but they determine whether the network is truly usable. To some extent, Gensyn is more like a deep-tech infrastructure company. This is also its biggest difference from many projects in the same track.
4. It Has Formed a Closed Business Loop
One of the biggest controversies in the Crypto industry in the past has been: many projects have narratives but lack real demand. However, AI training is different. This is a proven, fast-growing real market. Global AI training demand continues to expand, the GPU resource gap persists in the long term, and Gensyn is targeting an industrial chain segment where clear demand already exists. In other words, it's not "on-chain for the sake of being on-chain," but because the AI industry itself needs a more flexible, more open resource scheduling system. This is also why more and more capital is starting to focus on the AI Infra direction. Because compared to short-cycle applications, infrastructure, once it forms network effects, often has a longer lifecycle.
Finally, a very interesting change is taking place. In the past, people always thought: Crypto is a financial system, AI is a technical system.
But now, the boundary between the two is becoming increasingly blurred. AI needs resource coordination, incentive mechanisms, and global collaboration. And these are precisely the areas where Crypto excels the most. It's about making training capability no longer belong only to a few giants, but becoming a more open, more collaborative system. At least from what we can see now, this is no longer just a conceptual story but is evolving towards a real AI infrastructure. And the most valuable companies of the AI era often also emerge from the infrastructure layer.
