MagpieNews Interviews MAAS CTO: Why Did MAAS Attract $50 Million in AI Financing?

What helped MAAS attract $50 million in financing, and how does the company plan to turn its AI technology into practical enterprise solutions?

In this interview with Gina of Magpie News, Dr. Zhifeng Li, Chief Technology Officer of Maase Inc. (NASDAQ: MAAS), discusses the company’s technology, business model, and plans for growth. The conversation covers distributed computing infrastructure, the Lingyanmiaoyu ("Lingyan") large language model, and the challenges businesses face when deploying AI—from cost and energy efficiency to data protection and control over AI agents.

Dr. Li also explains MAAS's approach to combining computing infrastructure, software, and industry-specific models, before outlining the planned use of financing proceeds and the company's priorities for the next few years.

00:00 — Introduction
00:24 — Gina: What helped MAAS attract this $50 million PIPE financing, and what technical advantages distinguish the company?
Dr. Li cites proprietary technology, deployment experience, and an integrated offering spanning computing infrastructure, software, and industry-specific models.
02:38 — Gina: Many AI platforms claim to be standardized but end up requiring custom development for every customer, increasing costs. How does MAAS avoid this problem and build a repeatable business model?
Dr. Li describes a standardized technical foundation with adaptable API modules, enabling scalable deployment and customization.
03:24 — Gina: Is that standardized foundation the combination of distributed computing and large models you mentioned? How does distributed computing help customers lower costs?
Dr. Li highlights deployment within 90 days, PUE below 1.15 through liquid cooling and hardware optimization, and responsive local service.
04:40 — Gina: Businesses are increasingly concerned about AI-related data leakage, and some hesitate to adopt AI because of security risks. How do you see this trend, and how do MAAS's products protect users?
Dr. Li outlines Lingyan’s efficient architecture and security controls covering data access, agent permissions, tool calls, human approval, and auditing. He also mentions post-quantum cryptography research.
07:07 — Gina: Is this approach also why MAAS has recently secured several major orders, including the three consecutive orders mentioned in its announcements?
Dr. Li links customer interest to local computing, industry-specific models, manageable costs, and control over sensitive data.
08:17 — Gina: AI computing centers in the United States face power constraints. Does China face similar challenges, has MAAS encountered them, and how does the company address the issue?
Dr. Li contrasts large data centers’ power requirements with distributed infrastructure’s potential cost advantages for smaller enterprises, supported by integrated models and deployment services.
09:37 — Gina: How does MAAS plan to use the $50 million?
Dr. Li identifies two priorities: advancing Lingyan’s capabilities and safety, and expanding the Star distributed intelligent computing network to improve efficiency, reliability, and affordability.
10:36 — Gina: Looking ahead three to five years, what direction will MAAS take?
Dr. Li outlines continued investment in models, distributed computing hardware, and industry expansion, alongside work on next-generation computing architecture and edge computing.