{"id":"ben-fielding","title":"Ben Fielding","content":"**Ben Fielding** is a researcher, technologist, and entrepreneur specializing in artificial intelligence (AI), machine learning (ML), and computer vision. He is the co-founder and chief executive officer (CEO) of Gensyn, a company developing decentralized infrastructure for training machine learning models. His work is primarily motivated by the computational resource constraints he encountered in academia. [\\[1\\]](#cite-id-AqRCrUaFH13xXQcA)​\n\n## Education\n\nFielding attended Northumbria University, where he earned a Bachelor of Science (BSc) in Computer Science in 2015. He continued his studies at the same institution, completing a PhD in Computer Science in 2019. His doctoral research focused on artificial intelligence, machine learning, and computer vision, with a particular emphasis on the application of evolutionary and swarm-based methods for neural network design and training. During his doctoral studies, he also served as a demonstrator from 2016 to 2018, teaching laboratory and classroom sessions to undergraduate computer science students. [\\[2\\]](#cite-id-K2lMo2OOBlENnZir)​\n\n## Career\n\nFielding began his career in 2013 as a junior database administrator at CDL (Cheshire Datasystems Ltd), where he worked on Oracle database administration, Linux server maintenance, environment provisioning, ETL processes, and database performance optimization. In 2015, he worked briefly as a senior research associate on an industrial collaboration project focused on health and well-being applications using machine learning and computer vision. From 2015 to 2019, Fielding was a PhD researcher at Northumbria University, conducting research in artificial intelligence, machine learning, and computer vision, with a focus on evolutionary and swarm-based methods for neural network design and training. During this period, he also served as a demonstrator from 2016 to 2018, teaching undergraduate computer science laboratory and classroom sessions. \n\nBetween 2017 and early 2020, he worked as a director at Research Analytics, providing machine learning and computer vision consultancy services. From late 2018 to early 2020, Fielding co-founded and served as CEO of Fair Custodian, a company focused on personal data management, while also working as a machine learning consultant at Ricochet AI from late 2019 to early 2020. In 2020, he participated in the Entrepreneur First LD14 cohort in the United Kingdom. Beginning in 2020, Fielding co-founded Gensyn and served as its CEO, working on the development of distributed infrastructure for machine intelligence. From 2022 onward, he also acted as an angel investor, supporting early-stage technology companies. [\\[3\\]](#cite-id-uzeM4EYCgKTLdZ4F) \n\n## Interviews\n\n### Decentralized AI Infrastructure\n\nIn an interview on the DWebDecoded Podcast in May 2025, Fielding discussed Gensyn, a decentralized AI company founded several years earlier that initially focused on privacy-preserving machine learning for financial institutions. He described the company’s work on low-level machine learning infrastructure, including execution, communication, verification, and coordination frameworks designed to support decentralized computation. Fielding reflected on how consumer-facing AI tools had shifted public perceptions of artificial intelligence and explained his move from academia into industry as a response to scalability and resource constraints in machine learning. He noted that decentralized AI continued to face technical and adoption challenges but was gradually addressing practical use cases, and he discussed approaches to talent recruitment, the role of AI tools in supporting human work, and the early-stage nature of decentralized AI relative to centralized systems. [\\[6\\]](#cite-id-Bid4YacltPIi63J7) \n\n[YOUTUBE@VID](https://youtube.com/watch?v=nQggsoNGWMc)\n\n### Gensyn & AI Internet\n\nIn an interview on The Delphi Podcast in March 2025, Fielding discussed his work on Gensyn Network and its focus on decentralized compute infrastructure for artificial intelligence. He described the rapid volatility of the AI landscape and outlined an approach centered on building low-level, model-agnostic infrastructure that remains relevant despite shifting techniques and architectures. Fielding addressed limitations of centralized data centers as AI workloads scaled, and explained how decentralized, peer-to-peer communication frameworks could support distributed machine learning without reliance on a central authority. He also discussed the concept of reinforcement learning swarms, in which models iteratively evaluate and learn from one another, as well as longer-term views on open-source versus closed models, regulatory pressures, and the development of AI systems that could evolve continuously. [\\[8\\]](#cite-id-R54GdSHmMIUxl7OZ) \n\n[YOUTUBE@VID](https://youtube.com/watch?v=euaM1tK2e7I)\n\n### Building Gensyn\n\nIn an interview with [a16z Crypto](https://iq.wiki/wiki/a16z-crypto) in September 2024, Fielding and Harry Grieve discussed their work as co-founders of Gensyn and the development of a decentralized machine learning compute protocol. They reflected on their backgrounds in econometrics, deep learning, and data privacy. They described meeting through the Entrepreneur First accelerator in 2020, while each had independently encountered compute constraints in machine learning. Fielding and Grieve explained that Gensyn was formed to address limitations imposed by centralized cloud providers by creating an open market for machine learning compute, with an emphasis on decentralized training rather than inference. They also discussed the role of automation in future machine learning systems, the importance of censorship resistance and individual agency amid increasing regulation, their focus on early-stage machine learning teams as primary users, and their decision to operate with a small, non-hierarchical team structure. [\\[4\\]](#cite-id-Wbq3O0eCsA18dIy4) \n\n[YOUTUBE@VID](https://youtube.com/watch?v=a2GE-7YbW8s)\n\n## Presentations\n\n### Gensyn’s Machine Intelligence\n\nIn a talk at DePIN Founders Day in October 2024, Fielding reflected on his background in machine learning research, including deep learning and neural architecture search, and described the limited access to computational resources he experienced during his PhD. He explained that Gensyn was developed as a machine learning compute protocol intended to connect machine learning–capable devices globally and broaden access beyond centralized cloud providers such as AWS and Google. Fielding outlined the design of a trustless, [peer-to-peer](https://iq.wiki/wiki/peer-to-peer-trading-p2p) network for renting compute resources, supported by a [proof-of-stake](https://iq.wiki/wiki/proof-of-stake) consensus model and an off-chain, cryptographically secure verification system to validate contributed work. He also discussed ongoing research into improving efficiency across distributed resources and presented a longer-term view in which machine learning functioned as a continuous mechanism for compressing real-world data, shifting how knowledge was curated. The talk concluded with a discussion of machine agency, the need for protocols governing machine access to resources, and a vision of Gensyn supporting decentralized infrastructure for machine intelligence and large-scale digital representations of the world. [\\[7\\]](#cite-id-O2k3mvF8OuoImfTC) \n\n[YOUTUBE@VID](https://youtube.com/watch?v=YkWv9wsU_HU)\n\n### AI and Crypto\n\nIn a presentation at [ETHDenver](https://iq.wiki/wiki/ethdenver) in February 2024, Fielding introduced himself as a co-founder of Gensyn and framed the session as an interactive discussion on why artificial intelligence benefited from cryptographic and decentralized systems. He reflected on his academic background in machine learning and described a compute shortage he encountered during his PhD, driven by limited access to capital and computational resources. Fielding explained that Gensyn was developed as a decentralized machine learning compute protocol intended to connect engineers with available hardware, reduce costs, and lessen reliance on dominant cloud providers such as AWS and Google. He argued that decentralization addressed rent-seeking behavior in centralized markets and enabled future scenarios in which machine learning models autonomously interacted with compute resources. The talk also examined limitations of existing centralized platforms, scalability challenges, and the role of decentralized trust and verification, alongside discussions of data privacy techniques, fairness in access to compute hardware, and the need for protocol designs that allowed researchers to focus on model development rather than infrastructure constraints. [\\[5\\]](#cite-id-ejMlVyzTWyOkY9p5) \n\n[YOUTUBE@VID](https://youtube.com/watch?v=UDISNqga8w0)","summary":"Ben Fielding is a researcher, technologist, and angel investor. He is the co-founder and CEO of Gensyn, a decentralized machine learning compute protocol. 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