Both Web3 and AI have taken the world by storm in recent years, disrupting traditional models and processes across industries. They bring immense potential for change, especially from the point of view of building community-oriented, value-driven systems. I recently spoke with Jet Liu, Product Director at Phoenix and VP of Product at Apex Technologies, who is working at the intersection of AI and Web3. With Jet, who has more than ten years of experience building enterprise-grade AI applications and data infrastructures, we discussed how a combination of AI, Web3, and innovations in computation technology can help shape the future. Hi Jet. It’s great to speak with you. Please tell us about your journey and how you got interested in AI. Hey, thanks for having me. Having worked in the marketing technology (Martech) space for many years, I eventually arrived at APEX Technologies, Shanghai, where I'm currently the VP of Product. I've helped them build an end-to-end product suite for enterprises needing AI-powered consumer data solutions. Over 400 large enterprises and multinationals have used the company's technology, including the likes of Starbucks, Maserati, PingAn Group, Walmart, McLaren, Michael Kors, Smart Automotive, and many more. While I've been interested in exploring AI deeply for several years, especially alongside other emerging technologies like blockchain and cryptography, the real chance and scope to do so arrived when I joined APEX. I haven't looked back ever since—I didn't need to. Deep down, I've always wanted to do this, and I am also a part of this exciting project, Phoenix. So it's all going great. What problems are you looking to solve with this latest project you mentioned? When it's about emerging tech, you have two options. One, you can sulk and whine about past glories, missing out on the future, clinging on to nostalgia—think of the 'AI will destroy humans' camp. Two, you can get your hands dirty building the future. That's what we're doing with Phoenix. It's an answer to the currently inaccessible AI computation landscape characterized by restrictively high costs and resource-intensive frameworks that most people (or even enterprises) can't use. AI's development has been highly centralized so far, dominated by capital-rich giants like OpenAI, Microsoft, and so on. High entry barriers are a key enabling factor here—something we aim to fix. So, overall, Phoenix is all about democratizing AI computation and making it widely accessible for innovation-driven enterprises. We're leveraging blockchain technology and cryptography to this end, fostering decentralization for a better future. Phoenix has an interesting organizational structure. It's not a typical DAO. Tell us more about this and why it matters. Sure. The Phoenix DAO is unique because it combines three industry leaders in AI data research and development: APEX Technologies, FLC, and Tensor Investment. Backed by NewMargin Ventures, Tencent, and Anker Investment, APEX Technologies is mainly responsible for building Phoenix's enterprise-grade Layer 1 blockchain. FLC is a decentralized AI research organization based in Hong Kong. Their specialization includes federated learning, multi-party computation, and decentralized edge computing. Both APEX and FLC are working on the Phoenix Computation Layer, which enables multi-party computation and AI on-demand decentralized compute. Besides this, FLC is also helping with Phoenix's blockchain integration and the upcoming AIGC NFT Portal dApp. Tensor, an AI-driven proprietary trading firm specializing in emerging asset classes, is the third pillar of Phoenix DAO. They're focusing on the development, modeling, and partnerships for AlphaNet. Plus, they're supporting the ecosystem with Deep Learning and Deep Reinforcement Learning tech and GPU computation resources. It should be clear why Phoenix DAO's structure matters. Something as powerful and broadly impactful as decentralized AI computation demands collaboration among the best of the best, and that's what we're trying to achieve. And we're widening the scope by involving a globally-distributed, engaged, and driven Web3 community. What are the main products offered by Phoenix, and how do they contribute to supporting Web3 communities and enterprises? Currently, we're building three main products for the Phoenix ecosystem. The first and most important one is the decentralized computation layer, of course. That's like the backbone for everything to come. It's a novel infrastructure combining decentralized AI and privacy-oriented multi-party computation (MPC). This'll help intelligent Web3-native applications scale easily and increase the speed/efficiency of deploying AI models. The two other products include AlphaNet and NYBL. AlphaNet is a robust AI-powered platform for high-frequency crypto traders and an integrated scaling infrastructure. NYBL is a metaverse by AIGC that'll utilize our computation layer's AI Node Network for scaling and GPU-based video/image processing. Besides these, Phoenix also has an L1 enterprise-grade blockchain and Oracle platform for max efficiency and scope. Altogether, we can enable decentralized deep learning, GPU resource scaling, codeless AI deployment, and multi-party computation across industries like health, retail, consumer IoT, financial services, supply chain, and Web3. Zooming out a little, what's the biggest advantage/strength of integrating AI with Web3? Thanks for this question. Naysayers spin up AI vs. Web3 debates, but I feel that's pretty naive. These two are the most complimentary technologies we've ever got to date. We're in the age of data, where everything from consumer behaviors to impressions on social media is quantified/quantifiable. So much so that it's now justified to say—data is power. But this also implies a greater need to break up silos and promote decentralization. While AI is the tool for humanity to leverage massive unstructured data sets, monetizing them in an equitable manner, Web3 unlocks infrastructure for decentralization. Web3 also introduces novel organizational structures—like DAOs—besides new asset classes and revenue streams. By efficiently adopting the AI-Web3 combinations, we can finally build the community-oriented and user-centric world that has been on the cards since the Internet's advent. That'll be a truly global world without arbitrary barriers, hierarchical domination, and resource misutilization. What are the challenges of adopting Web3 and AI? And where do you see them a few years down the line? The biggest challenge is misinformation and FUD. The noise-signal ratio is disturbingly high because Web3 and AI are still in the nascent stages of market development and technical design. People are ready to believe anything the self-proclaimed gurus say on Twitter or other social media platforms. There are technical limitations for us to overcome. No doubt. The scalability of L1 blockchains is one example. Then there's the question of AI's accuracy and overcoming bias. But none of these make the concerned technologies inherently bad or harmful. And surely AI isn't here to destroy us—let's get real. It's a powerful tool instead and can significantly boost our progress. Anyway, all this FUD will evaporate once we start seeing more groundbreaking innovations. It's happening already and will only take so much time. The Internet had its critics alright, and we know how things went for them—they became Internet users themselves because what else would they do? It'll be the same with Web3 and AI. Soon these emerging technologies will disrupt almost every aspect of our lives. And once we realize the benefits on a practical, day-to-day level, there'll be no turning back. It's not a question of if but when. The future is inevitable—how we embrace it is what matters.

Between Two Computer Monitors: This story includes an interview between the writer and guest/interviewee. 

Navigating the Landscape of Innovative Brand Content: A Conversation With Thomas Ma

Shaping the Future of Blockchain and Crypto Education: A Conversation With Cryptonauts' Nathan Leung

Unraveling Decentralized AI Computation & Web3 with Jet Liu, Product Director at Phoenix

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

AI Needs Better Data, Not Just Bigger Models

The Noonification: Use This 7-Step McKinsey Framework to Solve Any Problem (1/10/2023)

The Noonification: A Taxonomy of Inclusiveness (1/11/2024)

The Noonification: What is the InfiniteNature-Zero AI Model? (11/19/2022)

10 Ways AI Has Changed Our Lives

AI Needs Better Data, Not Just Bigger Models

The Noonification: Use This 7-Step McKinsey Framework to Solve Any Problem (1/10/2023)

The Noonification: A Taxonomy of Inclusiveness (1/11/2024)

The Noonification: What is the InfiniteNature-Zero AI Model? (11/19/2022)

10 Ways AI Has Changed Our Lives

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps