As Artificial Intelligence (AI) continues to make significant strides worldwide, a growing number of enthusiasts are pivoting their focus from cryptocurrency to this emergent tech trend. Yet, it’s important to note that these two dynamic concepts are not necessarily rivals. Rather, they can be harnessed to complement and enhance each other. And Phoenix is such a platform that is working on just that.
Phoenix is a robust blockchain infrastructure platform for decentralized AI, computation scaling, and data-driven Web3. The platform provides decentralized access to GPU computation and a low-cost infrastructure for this decentralized AI computation. Furthermore, it is a platform for scaling AI and analytics-intensive Web 3 applications.
With names like Binance, ByteDance, Tencent Cloud, Alibaba Cloud, Alchemy Pay, and Anker working with Phoenix as investors and partners, the platform aims to penetrate and establish a strong presence in several industries.
A year ago, the company launched its Layer 1 mainnet, and a few months later, the testnet for AI Computation Layer was released, which is the infrastructure for on-chain data-related applications and AI. Instead of developing a DeFi ecosystem on L1, the firm chose to focus on data-driven applications as its differentiator.
Phoenix’s ecosystem has a variety of Web 3 and AI-driven applications, including two native dApps; AlphaNet, an AI platform for the crypto trading market, and NYBL, an AI-driven metaverse platform that will use AI Node Network to scale its AI and GPU-based video and image processing.
The governance and growth of Phoenix are driven by primarily three organizations that have strong AI development capabilities: APEX Technologies, a leading China-based enterprise AI company; Federated Learning Consortium (FLC), an HK-based AI research organization, and Tensor Investment Corporation, an AI-driven trading firm.
The Bridge Between Blockchain and AI Models
According to Phoenix, data computation scaling and AI support are crucial for the future development of Web3.
This is why Phoenix has built an extension of its L1, which is also the first infrastructure platform integrated with a public blockchain that serves as an efficient computation layer for privacy-enabled data analytics and AI.
The Computation Layer is the protocol-level bridge linking a blockchain with mainstream AI frameworks. Functioning as an L2, this layer provides a robust Web3-based infrastructure platform specifically designed for AI and computation-related tasks.
Such tasks encompass AI model computing service, multi-party computation (MPC)—allowing for the analysis, computation, and sharing of value without infringing data privacy—and various decentralized AI processes, including federated learning and edge computing.
Phoenix’s AI Computation Layer has been growing rapidly, with hundreds of users leveraging the platform. Since March this year, there has also been nearly a 4x increase in AI & Privacy Computation Jobs.
Late last month, the Phoenix team made a significant upgrade called SkyNet to its Computation Layer, which further increases its utility, scalability, and ecosystem growth factor.
This update included complete decentralization of compute resources that enable ecosystem participants such as miners, enterprises, gamers, and community members to provide CPU/GPU resources in return for tokenized rewards for an infinitely scalable AI computing platform.
Moreover, the upgrade enables more effective aggregation, and smart allocation of GPU compute resources. This, according to the Phoenix team, will be key to cost-effectively scaling deep learning and high-workload AI models. The SkyNet upgrade is expected to help save as much as 80% from traditional GPU-enabled cloud computing resources.
Besides making AI and GPU computation scalable and cost-effective, this fresh update also aims to optimize accessibility, ease of use, and time-to-value. This can already be seen in features such as rapid AI model deployment within the Computation Layer Control Panel. The Phoenix team is planning to implement additional technical features that’ll make it easier for developers to deploy AI models with less code and DevOps.
Moreover, the upgrade optimized token economics to enable maximum ecosystem and community participation. This means using Computation Credits (CCD) to incentivize both users and resource providers as well as synergize with the PHB token economy.
The native token PHB has a max supply of 64 million and the current total supply of around 44 million, out of which over 14,000,000 PHB are staked. Meanwhile, the token CCD (available in ERC-20, BSC, PHB Mainnet), which is the unit of value exchange for various computational resource tasks on the Computation Layer, is obtained via a one-way swap from PHB.
More importantly, the SkyNet upgrade will involve two major milestones that one should keep an eye on. The first one is ‘Local Distributed Compute,’ where each AI job or process can scale within one data center (or local network), and different jobs will be routed independently to local networks determined by SkyNet’s routing system. Slated to launch this quarter, the project will be inviting enterprise partners and mining firms to test the product.
The next would be ‘Global Distributed Compute,’ where each AI job can scale across multiple local networks and geographic locations and will process asynchronously. In this case, SkyNet’s routing system will allocate resources based on resource cost & efficiency, as determined by the core MAPPO model. This is expected to launch in Q4 of 2023.