The ever-growing appetite for artificial intelligence (AI) and machine learning (ML) is creating a double whammy: a shortage of powerful hardware and skyrocketing cloud service costs. Enter the intriguing possibility of decentralized protocols – could they revolutionize the infrastructure powering machine learning?

Harry Grieve, co-founder of Gensyn, a machine learning compute network in development, believes so. In a recent interview with Cointelegraph, Grieve envisioned a future where peer-to-peer computing networks challenge the dominance of centralized players like Amazon Web Services (AWS).

Gensyn's decentralized network, currently under construction, aims to connect various devices across the internet, enabling them to collaborate on training machine learning models. This project, backed by prominent Web3 venture capital firms and boasting a $50 million investment from Andreessen Horowitz in 2023, holds immense potential. Grieve sees it as a catalyst for "self-sovereignty and computational liberty online," aligning with the evolving, dynamic nature of the internet.

Founded in 2020, Gensyn's journey began with Grieve and co-founder Ben Fielding's exploration of machine learning computing for training and decentralized verifiable systems. Their mission? To tackle a three-pronged challenge with blockchain technology:

1. Untrusted Devices: How can devices collaborate on training a machine learning model without complete trust?

2. Model Fragmentation: How can models exceeding the capacity of a single device be trained collaboratively?

3. Cost-Effectiveness: Can a decentralized system achieve performance and economic efficiency comparable to AWS?

Gensyn's solution? A "layer-1 trustless protocol for deep learning computation," as described in their lite paper. This network directly rewards participants who contribute computing resources and complete ML tasks. However, Grieve acknowledges the key challenge – verifying the completion of complex ML work. This hurdle lies at the intersection of intricate fields like complexity theory, game theory, cryptography, and optimization. As Grieve puts it, "We realized that if you want to build this in a decentralized way, you need a way to reach a decentralized consensus about who did what. That's basically all a blockchain is."

Gensyn draws inspiration from the original Bitcoin protocol. Grieve, a self-proclaimed believer in the early days of Bitcoin laptop mining, sees parallels: "Satoshi gave people the right to their money again; they could generate it from a laptop. They could convert electricity into money. They could convert fiat into something harder."

While the long-term goal is to create a platform accessible to a wide range of users and hardware for ML training, the initial rollout will target users with powerful graphics processing units (GPUs) for faster feedback loops. Grieve anticipates a vibrant ecosystem built upon Gensyn, with user-friendly applications emerging for both contributing and accessing computing resources. He envisions a future where "an individual with a laptop will be able to download our client and run it in a way that connects you to the network."

The rise of Apple Silicon chips adds another exciting dimension. Research suggests that M2 and M3 chips possess processing power comparable to mid-tier Nvidia RTX GPUs. This translates to two potential benefits for protocols like Gensyn: a broader pool of devices to contribute to its global supercomputer and a more democratized approach to resource contribution. Grieve elaborates, "I think people are more likely to have something that looks like a MacBook than a standalone GPU, so it's a much more like democratizing force if you can contribute that."

The versatility of Apple Silicon chips, functioning as "system on a chip" designs, presents an additional advantage. This design has the potential to be emulated by other chip manufacturers, paving the way for a future populated by edge devices significantly more powerful than today's smartphones. For Grieve, the path forward lies in decentralization across a wide range of devices and fostering verification methods that are device-agnostic. As the boundaries between AI and everyday life continue to blur, decentralized protocols like Gensyn offer a glimpse into a future where collaborative computing power fuels the next generation of machine learning.