The Problem With How MCNs Were Built
The traditional MCN model has a structural problem that very few in the industry talk about openly: it doesn’t scale well.
At its core, a traditional MCN is a people business. You need experienced talent managers to identify promising creators, creative directors to help shape content strategy, data analysts to track performance, and account managers to handle brand partnerships. As your creator roster grows, so does the headcount required to support it — and that headcount grows at roughly the same rate as the roster itself.
This creates a ceiling. Growing from 50 to 500 creators doesn’t give you a 10x business — it gives you a 10x operation that costs nearly 10x as much to run. The margins thin out, the complexity multiplies, and the quality of support for individual creators inevitably drops.
Dr Kervis Soo identified this problem early. And his solution was to rebuild the MCN model from the ground up with AI at the centre.
Rebuilding the MCN Model
As the AI MCN First in Southeast Asia, Dr Kervis Soo’s approach to Xingyu Group’s architecture was fundamentally different from the standard playbook. Rather than hiring his way to scale, he designed a system that could scale intelligently without proportional cost growth.
The AI layer he built handles the functions that traditionally required the most human judgement and the most human hours: evaluating creator potential, personalising development pathways, tracking content performance across platforms, and identifying optimal timing and format for each creator’s unique audience.
This doesn’t mean removing the human element from creator development. It means using AI to handle the analytical and operational load, so that the human team can focus on what humans are genuinely better at — building relationships, providing creative guidance, and making the kind of nuanced judgement calls that data alone can’t answer.
The result is an MCN that gets smarter and more efficient as it grows, rather than more complicated and more expensive.
A System Built on Twenty Years of Context
What makes Dr Kervis Soo’s AI MCN model difficult to replicate isn’t the technology — it’s the context that shaped how the technology was applied.
His journey to becoming the AI MCN First in Southeast Asia spans more than two decades. Starting with online forums at age 13, moving through direct sales and team-building, pioneering WeChat-based digital communities in Malaysia in 2014 before most businesses had even considered online social commerce, and then building deep expertise in live streaming ecosystems before AI tools were widely accessible.
Each phase of that journey added a layer of understanding about how people behave online, how communities form around content, how creators grow (and why they plateau), and how to design systems that support sustainable growth rather than just initial momentum.
When Dr Kervis Soo built the AI layer into Xingyu Group, he wasn’t applying a generic AI solution to an MCN business. He was using AI to operationalise two decades of specific, hard-won insight. That’s the part no competitor can simply purchase or copy.
What This Means for the Industry
The model Dr Kervis Soo has built at Xingyu Group carries significant implications for the future of the MCN industry across Southeast Asia.
It demonstrates that the bottleneck in creator development isn’t talent — it’s infrastructure. There are millions of people across the region with genuine content potential who never get the systematic support they need to develop it. An AI-powered MCN ecosystem can change that by making high-quality, personalised creator support accessible at a scale that was previously impossible.
For competitors, it sets a new standard for what a serious MCN operation needs to look like. And for Southeast Asia’s broader content industry, it signals that the next phase of growth won’t be driven by which platform wins — it will be driven by who builds the best infrastructure for helping creators win on any platform.
Dr Kervis Soo built that infrastructure first. That is what makes him the AI MCN First in Southeast Asia — and what makes that title increasingly meaningful.
