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GensynAI: Don’t Let AI Repeat the Internet’s Mistakes

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In the past, people always thought: Crypto is the financial system, AI is the tech system. But now, the boundaries between the two are becoming increasingly blurred.

Over the past few months, due to the vigorous development of the AI industry as a whole, a large number of talents from the crypto industry have turned to AI. Researchers who have experience in both fields are also discussing a proposition that has never been fully proven:

Blockchain, Can It Become Part of AI Infrastructure

Over the past two years, the combination of AI and crypto has seen many versions in the market, such as AI Agents, on-chain reasoning, data markets, and computing power leasing. While the enthusiasm is high, there are actually not many projects that have truly formed a commercial ecosystem, for a simple reason: most projects remain at the "AI application layer." However, what Gensyn is delving into is the most core and expensive layer of the AI industry:

"Model Training"

How is this achieved? By organizing globally distributed GPU resources into an open AI training network, where developers can submit training tasks to nodes providing computing power. The network is responsible for validating the training results and completing incentive distribution. What is truly worth paying attention to behind this is not the "decentralization" itself, but rather a problem that is becoming increasingly hard to ignore in the AI industry:

Computing power resources have rapidly concentrated in the hands of a few major players. The big tech companies have been hoarding GPUs for years. Over the past year, the AI industry has gradually formed a clear trend: whoever controls the GPUs controls the pace of AI development, especially in the era of large models, where training resources have become a core barrier.

With an H100 supply shortage and continuous price increases in cloud services, the first step for domestic tech giants to advance in AI is not to expand their teams, but to lock in computing resources. This is why behind OpenAI, Anthropic, and xAI, there are partnerships with large cloud providers because behind the model competition lies fundamentally an infrastructure competition. The significance of Gensyn lies in:

Providing a new way of organizing resources for AI training

1. It Targets the Most Core Infrastructure Layer of the AI Industry

Many AI+Crypto projects tend to focus more on the application layer narrative; in other words, everyone is just building apps. However, Gensyn directly enters the training process, which is the most technically challenging and resource-intensive part of the entire AI value chain. It is also the layer that is currently most likely to establish a platform barrier. Once the training network reaches scale, it will not only be a computing power market but could also become a crucial entry point for future AI development. This is why the market continues to keep an eye on Gensyn, and this is why A16Z has made significant investments twice.

2. It Provides a More Open Computing Power Collaboration Model

Traditional AI training relies heavily on centralized cloud platforms. While stable, the cost is continuously increasing. This is especially challenging for small to medium-sized AI teams, where training resources have become a limiting factor for innovation. The approach offered by Gensyn is to bring more idle GPUs into the network, allowing training resources to be dynamically allocated to improve overall computing power utilization. This concept is somewhat similar to the early days of cloud computing logic—not reinventing computation but reorganizing computational resources. If this model can be consistently implemented, it will not only optimize costs but also potentially enhance the overall resource efficiency of the AI industry.

3. Technical Barrier Has Become Its Key Moat

The real challenge in training networks is never "connecting GPUs" but rather: how to validate training results, how to ensure nodes honestly perform tasks, how to maintain training reliability in a distributed environment. Gensyn has been addressing this aspect, including probabilistic verification mechanisms, task distribution models, node coordination systems, and more. These may not be as "visible" 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 also the biggest difference between it and many projects in the same field.

4. It Has Formed a Commercial Closed Loop

One of the biggest controversies in the crypto industry in the past was: many projects had narratives but lacked real demand. However, AI training is different. It is a verified and rapidly growing real market. Global AI training demand is continuously expanding, with a long-standing GPU resource gap. Gensyn is entering a sector where there is already a clear demand in the industry chain. In other words, it is not just for the sake of being "on-chain" but because the AI industry itself needs a more flexible and open resource scheduling system. This is why more and more capital is beginning to focus on the AI Infra direction because, compared to short-term applications, once an infrastructure forms a network effect, its lifecycle is often longer.

Finally, a very interesting change is taking place. In the past, people always thought: Crypto is a financial system, and AI is a technical system.

But now, the boundaries between the two are becoming increasingly blurred. AI needs resource coordination, incentive mechanisms, and global collaboration. And these are precisely where Crypto excels, allowing training capabilities not only to belong to a few giants but to become a more open and collaborative system. At least for now, this is no longer just a conceptual story but an evolution towards true AI infrastructure. The most valuable companies in the AI era often emerge at the infrastructure layer.