GensynAI: Don't let AI repeat the mistakes of the internet

Original Title: GensynAI: Don’t Let AI Repeat the Mistakes of the Internet

Original Author: Rhythm BlockBeats

Original Source:

Reprinted from: Mars Finance

In the past few months, due to the booming development of the entire AI industry, a large number of crypto industry talents have shifted towards AI. Researchers involved in both fields are also exploring a question that no one has successfully answered:

Can blockchain become part of AI infrastructure?

Over the past two years, the combination of AI and crypto has seen many versions in the market—AI agents, on-chain reasoning, data marketplaces, compute rental. The hype is high, but there are actually not many projects that form a complete commercial cycle. The reason is simple: most projects remain at the “AI application layer.” However, Gensyn is targeting the most core and also the most expensive layer of the AI industry:

“Model training”

How to achieve this? Organize the globally dispersed GPU resources into an open AI training network, where developers can submit training tasks and nodes provide computing power. The network verifies training results and distributes incentives. What’s truly worth paying attention to behind this is not just “decentralization,” but an increasingly unavoidable issue in the AI industry:

Compute resources are rapidly consolidating into the hands of oligarchs. Major companies have been competing for GPUs for years. Over the past year, a clear trend has emerged in the AI industry: whoever controls the GPU, controls the pace of AI development. Especially in the era of large models, training resources have become a core barrier.

H100 supply is tight, cloud service prices are rising continuously. The first step for domestic giants to develop AI is not expanding their teams but securing compute resources. This is also why behind OpenAI, Anthropic, and xAI, there are large cloud providers bound to them. Because behind model competition, the essence has shifted into infrastructure competition. The significance of Gensyn lies in:

Providing a new way to organize AI training resources

1. It targets the most core infrastructure layer of the AI industry

Many AI + crypto projects tend to focus on application-level narratives—simply put, they are just building apps. But Gensyn directly enters the training stage, which is the most technically challenging and resource-intensive part of the entire AI value chain. It’s also the layer most likely to form platform barriers. Once a training network scales, it’s not just a compute market but could become an important gateway for future AI development. That’s why the market continues to pay attention to Gensyn and why A16Z has invested heavily twice.

2. It offers a more open compute collaboration model

Traditional AI training heavily relies on centralized cloud platforms—stable but increasingly costly. For small and medium-sized AI teams, training resources are becoming a significant barrier to innovation. Gensyn’s approach is to bring more idle GPUs into the network, enabling dynamic scheduling of training resources. This is somewhat similar to the logic of early cloud computing—reorganizing computing resources rather than reinventing computation itself. If this model can be sustained, it could lead to cost optimization and improve overall resource efficiency in the AI industry.

3. Technical barriers are actually its key moat

The real difficulty in training networks has never been “connecting GPUs,” but rather: how to verify training results, ensure nodes honestly perform tasks, and maintain training reliability in a distributed environment. Gensyn has been solving exactly this—through probabilistic verification mechanisms, task distribution models, and node collaboration systems. These elements may not be as “flashy” as agent narratives, but they determine whether the network is truly usable. To some extent, Gensyn is more like a deep-tech infrastructure company, which is its biggest difference from many other projects in the same track.

4. It has already formed a commercial closed loop

One of the biggest controversies in the crypto industry has been: many projects have narratives but lack real demand. But AI training is different—it’s a verified, rapidly growing real market. Global demand for AI training continues to expand, and the GPU resource gap has persisted long-term. Gensyn is targeting a segment of the industry chain with clear existing demand. In other words, it’s not just “on-chain for the sake of on-chain,” but because the AI industry itself needs a more flexible and open resource scheduling system. This is also why more capital is paying attention to AI infrastructure—once a network effect is formed, the lifecycle tends to be longer than short-cycle applications.

Finally, an interesting change is happening. In the past, people thought: crypto is the financial system, AI is the technological system.

But now, the boundaries between the two are becoming increasingly blurred. AI needs resource coordination, incentive mechanisms, and global collaboration. And these are exactly the areas where crypto excels—making training capabilities no longer exclusive to a few giants but accessible to a more open and collaborative system. At least for now, this is no longer just a conceptual story but an evolution toward genuine AI infrastructure. The most valuable companies in the AI era are often born from the infrastructure layer.