Guest: Harry Grieve, Co-founder of Gensyn
Editor: momo, ChainCatcher

As the thirst for computing power from AI models meets the natural bottleneck of centralized supply, a computing revolution is quietly taking place. The two co-founders of Gensyn, Harry Grieve and Ben Fielding, have realized that the key to breaking the deadlock lies in activating the dormant computing potential of billions of edge devices worldwide, and the path to this is decentralization.

Gensyn is dedicated to building a distributed machine learning network that connects idle computing devices globally through blockchain protocols, ensuring the credibility of training results with its innovative verifiable computing technology. Its testnet has attracted 150,000 users and is running stably. With the successful completion of the testnet phase, Gensyn's mainnet will also be launched soon.

Gensyn has raised $43 million in Series A funding led by a16z, with total funding exceeding $50 million. In this exclusive interview, Harry Grieve systematically explains how Gensyn is building the technical blueprint and business thinking for the next generation of AI infrastructure, starting from the core proposition of "breaking the scale barrier."

The Purpose of Decentralization is to Break the Scale Limit of Computing Power

1. ChainCatcher: Please introduce yourself. What were the three most critical experiences before founding Gensyn? How did they shape your entry into the "decentralized AI computing" space?

Harry Grieve: I am part of the early generation that encountered the internet. Back then, the web was more open and decentralized, filled with file-sharing networks and various information repositories. This shaped my understanding of information and networks and led me to lean towards open-source and decentralized ideas early on.

During my time at university and afterward, I was exposed to classical liberal thought, which made me more focused on individual rights and freedoms, and I began to question centralization and censorship. This directly relates to today's AI models—when models make decisions for us, who decides their "rights" and how they behave? This sparked my thoughts on the relationship between AI, sovereignty, and ethics.

After graduation, I worked at a machine learning company in London, where I personally experienced the immense difficulty of acquiring large-scale computing resources and high-quality data. I realized that to continuously develop more powerful models, the issues of access to and scaling of underlying resources (computing and data) must be addressed, which is why I later firmly entered the decentralized AI computing space and co-founded Gensyn.

2. ChainCatcher: What was the impetus for the birth of Gensyn? How did you and Ben Fielding decide to go "All-in" on this direction in just 8 weeks at Entrepreneur First?

Harry Grieve: We met at a social event before the UK accelerator program Entrepreneur First began. Our rapid decision to go "All-in" was based on two key consensus points:

First, we firmly believe that machine learning is the future. In 2020 (before the emergence of ChatGPT), we were both highly confident that machine learning would be the next technological wave. Although this was not a consensus at the time, we saw technological breakthroughs in areas like image generation and interaction, and we believed in its potential.

Second, we both opposed "centralization." I was constrained by the bottlenecks of centralized computing and data sources, while Ben focused on personal privacy and data security in his doctoral research and entrepreneurship. We both held a critical view of centralization. Initially, we focused on technologies like "federated learning," but later realized that to solve the trust issues involved, a decentralized state recording and accountability mechanism was needed, which ultimately led us to blockchain. We transitioned from "AI-native" founders to "AI + crypto" explorers.

3. ChainCatcher: At that time, AI training seemed to rely mainly on cloud giants (like AWS). Why did you firmly believe that "decentralized computing power" would have opportunities? What did you think were the main driving forces behind choosing decentralized computing power?

Harry Grieve: The driving factors are multifaceted, but the core answer is scale.

Currently, the vast majority of available internet data has been used to train models. Future performance improvements depend on accessing data that is located at the "edge" and currently inaccessible. To utilize this data, you must move towards the edge, which inherently requires decentralization.

Although centralized computing investments are substantial, the demand for computing power in AI is "endless." This thirst will drive the demand for computing power to all underutilized devices. To connect and scale these dispersed resources without centralizing them all, decentralization is the only way.

So, scale is the only answer. Decentralization is to unlock an unprecedented scale of computing and data resources.

What is Gensyn's Core Differentiation?

4. ChainCatcher: If you had to explain in one sentence to a non-technical audience, what system is Gensyn actually "building"?

Harry Grieve: Gensyn is a system that allows you to access all the core resources (such as computing power and data) needed to build machine learning systems at an unprecedented scale.

5. ChainCatcher: There are players like Akash, Render, and io.net in the decentralized computing power space. What is Gensyn's focus or core differentiation?

Harry Grieve: We have great respect for early players like Akash. Our core differentiation lies in our perspective on resources: other projects mainly offer single, containerized GPU computing power leasing. Gensyn has a broader perspective; we consider various machine learning resources (computing power, data, models), and these resources are interwoven and reusable.

For example, the output generated by a node performing model inference is data that can be used to train other models. In our network, the boundaries between inference, training, computing, and data become blurred. The system we are building is designed to adapt to this dynamic, chaotic new paradigm of machine learning.

6. ChainCatcher: Can you systematically explain the current product layout of Gensyn? How are you systematically achieving decentralized computing power?

Harry Grieve: This is a technical description: it is a decentralized cryptographic network where users can access various resources through our native token—whether it is verifiable computing resources for training or inference, or mechanisms to incentivize different model training by setting objective standards. This system consists of three core components that together form a powerful closed loop:

Verification System: This is our core technology. We have developed a proprietary compiler and verification framework that can achieve bit-level precise verification across different hardware and software. This means we can prove that the training results of a model on one device are identical to those verified on another completely different device. This is the cornerstone of building trust in the network and preventing fraud.

Scaling Technology (Swarm): This is a peer-to-peer training framework (such as for human feedback reinforcement learning). It allows you to connect countless devices globally for horizontal scaling, utilizing computing and data on edge devices for training, thereby creating more powerful models.

Assist Technology (Assist Agent): We have autonomous AI assistants that can be integrated into applications. They can learn without guidance and assist users in completing tasks. When these assistants are trained, they can utilize our scaling technology to train across devices, thus self-evolving and becoming stronger.

In summary, when users integrate intelligent assistants into applications, they continuously generate interaction data during task execution; subsequently, this data is input into our scaling technology framework, continuously optimizing the model through distributed training across devices; during this process, the core verification technology ensures the accuracy and credibility of the training process, ultimately producing a new generation of models with significantly enhanced performance. This process forms a nonlinear, continuously reinforcing machine learning ecosystem, allowing the system to maintain reliability and evolutionary capability while scaling up.

7. ChainCatcher: From the Series A funding in 2023 to the public testing of the testnet in 2025, what has been the biggest technical milestone achieved by Gensyn? Was there a moment that made the team "scream together"? What do you think is Gensyn's most underrated technological innovation?

Harry Grieve: Frankly, we may have screamed more out of "fear" than "excitement"—entrepreneurship is tough.

I believe the most underrated technological innovation is actually our verification system. The construction of this technology is extremely complex, requiring comprehensive solutions to all possible factors that could lead to non-determinism (even including GPU bit flips caused by cosmic rays), from the compiler and machine learning framework to the hardware layer. There is a huge gap between its value and external recognition. It is this technology that ensures the security and scalability of our network, allowing us to confidently permit any device to join the network and be verified without worrying about security dilution.

Over 150,000 Testnet Users, Mainnet Launch Approaching

8. ChainCatcher: Compared to centralized cloud computing giants or other decentralized computing networks, do you currently have any advantages in performance efficiency and cost?

Harry Grieve: In terms of absolute cluster scale, we cannot yet compare with giants like AWS, but this is mainly an issue of network adoption rather than technical limitations. Our advantage lies in unlocking new resource scales (especially computing and data at the edge) and becoming the infrastructure for future machine intelligence civilization. We believe that truly autonomous AI, capable of self-evolution and existing within a cryptoeconomic system, will require a decentralized, permissionless network as its "habitat," which is precisely what we are committed to building.

9. ChainCatcher: How active is your network now? Are there any noteworthy data to share?

Harry Grieve: During the testnet phase, we have made very positive progress: we have over 150,000 users, most of whom have grown organically through product appeal; about 40,000 nodes are running on the network; and the system has trained over 800,000 models.

10. ChainCatcher: What is the "last mile" obstacle to launching the mainnet? What timeline have you set for the team regarding the mainnet? Is there a clear timeline for TGE?

Harry Grieve: The launch of the mainnet is currently a top priority, and TGE will follow. We are about 3-4 weeks away from the mainnet launch, after which we will begin the mainnet audit.

Before that, the main focus is to ensure that all mechanisms are in place, functioning correctly, and fully operational, and most importantly, to ensure that the economic activities of the network are secure.

11. ChainCatcher: Compared to when you first started, what changes in market demand is Gensyn facing? What impact do you think the arrival of the machine intelligence era will have on you?

Harry Grieve: Compared to the early days of founding Gensyn, the market environment we face has undergone a fundamental transformation. Looking back to 2020 when we just started, we had to repeatedly explain the importance of machine learning to investors. However, with the emergence of ChatGPT, AI has become a consensus across society. This shift in perception has also led to a more intense competitive market environment, with various AI and computing power startups springing up like mushrooms after rain. At the same time, the focus of industry discussions has changed significantly—topics such as the ethical boundaries of open-source models and the regulatory framework for AI governance, which were rarely discussed a few years ago, have now become hot topics in policy-making across countries.

It is against this backdrop that the accelerated arrival of the machine intelligence era precisely confirms the value of Gensyn's existence. The decentralized computing network we are building is essentially designed to provide the underlying support for the upcoming self-evolving machine intelligence. When AI systems need to break through existing computing power bottlenecks to achieve true autonomous learning and rapid iteration, the infrastructure we construct will become a key cornerstone of this new era.

12. ChainCatcher: You mentioned in public speeches the "economic, ethical, and regulatory challenges of AI." What regulatory risks are you most concerned about? How does Gensyn's protocol design achieve a balance between "compliance-friendly" and "anti-censorship"?

Harry Grieve: When discussing AI regulation, my biggest concern is that regulatory policies may mistakenly target the infrastructure layer. Imagine if future policies were to limit the number of GPUs, the scale of datasets, or even impose limits on the proportion of electricity used for AI training; such a crude regulatory approach would severely hinder progress in the entire technology field. From our perspective, AI models should essentially be open-source and shared like mathematical formulas, without excessive restrictions.

At the protocol design level, we are exploring ways to find a balance. Currently, the model weights and data transmission in the network are primarily in plaintext, which provides the necessary transparency for compliance regulation. At the same time, since we are built on underlying public chains like Ethereum, we naturally inherit their decentralized characteristics and verification mechanisms. This architecture maintains the necessary regulatory visibility while ensuring the system's anti-censorship capability.

As AI capabilities continue to break through, finding a balance between openness and control will become an important issue that we and the entire industry need to continuously face in the coming years.

13. ChainCatcher: Looking back from 2030, what are the key indicators of Gensyn's success?

Harry Grieve: The key indicators of success are not simply financial data or user numbers. I hope Gensyn's greatest contribution is to become the economic foundation of a parallel machine civilization.

By 2030, I hope to see a completely parallel society, civilization, and economy operating on the blockchain, without any humans. This civilization can produce economic output comparable to or even greater than that of humans, possess true creativity, and significantly advance scientific development and address major issues facing humanity (such as extending lifespan and reducing inequality). If Gensyn is the cornerstone that makes all of this possible, that will be the ultimate mark of our success.

免责声明：本文章仅代表作者个人观点，不代表本平台的立场和观点。本文章仅供信息分享，不构成对任何人的任何投资建议。用户与作者之间的任何争议，与本平台无关。如网页中刊载的文章或图片涉及侵权，请提供相关的权利证明和身份证明发送邮件到support@aicoin.com，本平台相关工作人员将会进行核查。