---
title: "A Slow-Burning Victory: AI Global Expansion Is Not About Rushing Out, but Embedding Yourself In"
author: "Unique Research"
sourcePublication: "Unique Research Substack"
originalPublishedAt: "2025-11-08T09:19:00+00:00"
canonical: "https://ffcap.cn/en/research/unique-research-2025-11-08-04"
source: "https://uniqueresearch.substack.com/p/unique-research-2025-11-08-04"
language: "en"
---

# A Slow-Burning Victory: AI Global Expansion Is Not About Rushing Out, but Embedding Yourself In

_Original · Unique Research · 2025-11-08_

_Editor’s note: This complete English edition preserves the original author’s commentary and Chen Kai’s account as of November 8, 2025. Project values, pricing comparisons, customer examples and overseas plans remain source-attributed, not independently audited findings. Q4 explicitly identifies renminbi; passages that leave the currency unstated remain unspecified here. The Singapore-company route is a partner’s suggestion, not a completed incorporation or a finding about regulatory compliance. Ku’ai Technology is a romanization of the Chinese name. The ordinary conference portrait is omitted; visible labels read CAAIEC and the company brand COOL AI._

In this AI era, where “disruption” and “leapfrogging” are invoked at every turn, we are often drawn to technical prodigies in their 20s and startup myths of explosive growth. Yet it is easy to overlook another group propelling the wave: middle-aged entrepreneurs who have lived through the rise and fall of different eras, been tempered by real business experience, and learned to think further ahead.

Chen Kai, co-founder of Ku’ai Technology, is exactly that kind of entrepreneur. He did not charge into the AI sector on passion alone. He brought technical accumulation, organizational experience, and commercial understanding with him—returning to China from overseas before expanding toward the Middle East and ASEAN markets. Rather than attracting attention with a breakout algorithm or a funding announcement, he and his team have developed their own model on the least glamorous but most important battlefield today: putting AI into practice.

I. From “What Can AI Do?” to “How Can AI Get It Done?”

Enterprise AI is not nearly as romantic as people imagine. It does not depend on raising money through personal connections or telling conceptual stories. It depends on whether customers have pain points, budgets, and patience—and whether you can capture every critical position as a project advances.

One phrase recurred throughout Chen Kai’s account: “real demand.” The question is not whether a customer wants to try something, but whether there is a closed business loop; not whether we can build it, but whether it can solve the problem. Amid the AI startup frenzy, this way of thinking is unusually clear-headed.

That clarity does not come from indifference to AI technology. Quite the opposite: this team began implementing AI scenarios overseas in 2017, and had already made deployment processes work in education, industry, and other fields before GPT became a sensation. That early accumulation gave them the judgment to assess AI’s value and a clear basis for choosing between “concept hype vs project delivery.”

In today’s wave of large-model startups, many people have jumped from “being able to do AI” to “building AI products,” but few truly ask a business question: “Can this thing genuinely help the customer save or make money?”

II. Going Global Is Not Just About “Going Out,” but About “Making It Work”

When discussing AI’s global expansion, we often focus on technical challenges such as adapting product formats, localizing language models, or even deploying local servers. Chen Kai offers a more complete perspective: the technology must be feasible and the demand real, but you must also understand the mechanisms that determine whether a project can actually succeed.

In the Middle East, they did not blindly establish offices. They reached customers through an international trade show. They did not win orders by being inexpensive, but by impressing clients with hands-on experience from China. For a “digital government” project in Dubai, they formed a consortium with a locally connected major company while taking responsibility for technical delivery themselves. In regions with greater political sensitivities, they even considered entering through a Singaporean company to avoid potential obstacles arising from national positions.

This is not about moving a Chinese solution abroad; it is about using experience from China to grow projects suited to new soil. What they call an “agent platform” is really a combination of a technical foundation and a scenario toolkit. It can meet the marketing, production, and management needs of central state-owned and state-owned enterprises, while also being reused at low cost by small and medium-sized businesses without changing its core technical architecture.

This is what it truly means to take a product format global. If the technology cannot cross the border, the effort is doomed; if resources cannot be grounded locally, it is empty talk. But if you take a results-oriented view, turn one high-priced project into a template, and turn one scenario into a platform, global expansion becomes the most natural evolution.

III. Seasoned Judgment Is Another Moat for AI Entrepreneurs

One term from the conversation left a particularly strong impression on me: “what is left unsaid.” Government and enterprise customers often do not state everything explicitly, but you have to understand what they mean. This is not a problem AI can solve. It is a negotiation between human motivations and institutions, and a process of aligning communication with strategy.

Chen Kai said one advantage of his team is that they “know how to navigate every step of a project”: initiation, bidding, implementation, auditing, and payment collection. Every process is like a small campaign; if any link breaks, the entire effort can fail. It may sound very “traditional,” but this is precisely the organizational capability many young entrepreneurs lack. And those capabilities are prerequisites for using AI as a tool, a capability, and a business model rather than presenting it as a “gimmick.”

This seasoned judgment is not conservatism. It is the beginning of turning AI from a “wave of hype” into “business muscle.”

IV. From “Large Customers” to “SMBs”: Not a Pivot, but Giving Back

When I heard that they were exploring the small and medium-sized business market, my instinct was surprise. Most companies start with SMBs and move to major clients only when that approach fails. Ku’ai Technology, by contrast, has completed multiple projects valued at the ten-million level and is now packaging its existing scenario capabilities to give back to smaller customers through the “democratization of technology.”

This is not about “chasing volume” or harvesting customers. The company already has a standardized platform and experience with modular delivery, enabling it to provide a simplified version of its AI agent system to small and medium-sized companies that lack budgets and development capabilities.

They also know they cannot make this route work alone. They need partners, channel capabilities, and more service systems that “help customers use it.” The key, however, is that they are not rushing into a transformation; they are moving with the opportunity.

V. Technology and Strategy: A Dual-Track Logic for Taking AI Global

This conversation reveals a distinctly “middle-aged” roadmap for AI entrepreneurship:

Technical roots: Do not chase fashionable concepts or hot trends; the team has been delivering real projects since 2017.

Customer orientation: Do not compete on price or rely on funding; build everything around “who the customer is” and “what the customer needs.”

Global strategy: Do not charge forward alone; find locally established partners with resources and build the market together.

Organizational capability: Pursue something other than PPT-style growth by genuinely understanding the complex realities of government and enterprise procurement, project execution, and organizational coordination.

Long-term thinking: Instead of burning funding to seize territory, win customers and unlock scenarios by “solving problems.”

This path can serve not only AI but many other high-tech industries seeking to expand overseas. It is less dazzling, but stable enough; less glamorous, but worth emulating.

The AI era is more like an endurance race than a 100-meter sprint. People who break out with a single model demo certainly deserve respect, but those who ultimately support an industry and build an ecosystem are usually the ones who can see trends clearly, endure cycles, understand customers, and implement scenarios.

Middle-aged entrepreneurs may no longer be the glittering protagonists at center stage. Yet it is precisely these seasoned operators, moving between trade shows and negotiation tables, and between tender documents and requirements specifications, who are making AI genuinely useful to humanity.

And their story is only beginning.

Selected Interview Q&A

Q1: What kind of company is Ku’ai Technology? What are its main businesses and team size?

Chen Kai: Ku’ai Technology focuses on enterprise AI applications. The team currently has around 30 to 40 people and primarily serves major customers such as government institutions. Contract values range from tens of thousands of yuan for AI promotional videos to several million yuan for systematic solutions.

Q2: What is the core product you provide to major customers, and how does it solve their problems?

Chen Kai: Our core product is an “agent platform.” Rather than providing a single function, we focus on one customer and deliver a systematic solution. For example, for an enterprise customer, we can build a customized agent platform based on its underlying business capabilities, covering the entire process from marketing, production, and sales to company management. The platform is “capable of growing”: when a customer develops new business needs, new agent applications can continue to be built on it, enabling dynamic change and expansion.

Q3: How did Ku’ai Technology enter the Middle Eastern market? How did you acquire your first customer there, and what advantages did that demonstrate?

Chen Kai: We entered the Middle Eastern market through a chance opportunity: attending a local global AI trade show. Our core advantage is that, compared with companies that merely display PPT concepts, we already had extensive and in-depth experience implementing applications in China. For example, we once spoke with a multinational printing company headquartered in the Middle East. When we discussed specific details such as using AI systems to optimize its orders, production scheduling, and materials management, the other party immediately realized that we had “actually done this before,” because we had already made several similar projects work in China. This deep industry understanding and practical experience was the key to earning the customer’s trust.

Q4: How does project pricing in the Middle East differ from pricing in China? What additional costs come with the higher prices?

Chen Kai: The pricing difference is very significant. A project quoted at RMB 1 million in China can normally be quoted at RMB 5 or 6 million in the Middle East—5 to 6 times the domestic price. Even after customer negotiations, the final transaction price can generally reach 2 to 3 times the domestic level. Of course, higher returns come with higher costs. These mainly include the expense of building an international team and establishing a local office; local server and computing costs, such as Alibaba Cloud or Amazon, which are also higher than in China; and additional investment required to comply with local and even European and US data-security laws and regulatory systems.

Q5: When working with government institutions in the Middle East, how do you address sensitivities around your identity as a Chinese company?

Chen Kai: We mainly use two strategies. The first is the “consortium model,” under which we partner with a leading local company that has connections to a prince. It uses its local resources and background to undertake government projects, while we handle delivery and implementation as the technical party. The second is the “neutral-identity model.” Local partners have suggested that we register a company in Singapore and use Singapore’s relatively neutral national image to avoid potential political sensitivities.

Q6: What is Ku’ai Technology’s strategy for Southeast Asia, and what role does Guangxi play in it?

Chen Kai: Our strategy follows the chain of “R&D in Beijing, Shanghai, and Guangzhou; integration in Guangxi; application in ASEAN.” Our Shanghai company is responsible for core technology R&D, after which we use Guangxi as a bridgehead to ASEAN to integrate technologies and solutions locally and implement projects. We have already established a foothold in the Guangxi market through demonstration projects. Our next priority is to use Guangxi as a base for promoting AI applications and solutions successfully validated in China into ASEAN markets. We are currently researching ASEAN markets and have begun discussing possible collaboration models with a Singaporean team.

Q7: Your customers are currently concentrated among government bodies, central state-owned enterprises, and other state-owned enterprises. What strategic considerations led to this, and how does it relate to your team’s background?

Chen Kai: It is both a strategic choice for this stage and a natural result. First, current policy dividends are primarily reflected at government bodies and central state-owned and state-owned enterprises, which have the budgets and willingness to experiment with AI projects. Second, in the current economic environment, many private companies have tightened their budgets and find it difficult to invest large sums in the short term. Our team’s background is also highly aligned with these customers. Some members, including me, have extensive experience with government and state-owned-enterprise projects and deeply understand the entire process from project initiation, tendering, and implementation through auditing and payment collection. That experience helps us avoid risks effectively and advance projects smoothly, and it is one of our core advantages.

Q8: Mr. Chen, what unique advantages do you think entrepreneurs born in the 1980s have in AI compared with younger people?

Chen Kai: I believe the advantages are “maturity” and “experience.” Younger people may have more passion and energy, but most entrepreneurs born in the 1980s have been tested and tempered by society. That makes us more mature when handling complex issues, such as managing government relations and thinking deeply about business models. Especially when working on government or large-enterprise projects, we are better able to understand the “subtext” and real demands beneath customers’ literal words. Beyond technical value, for example, customers may want help creating an industry benchmark and satisfying needs related to social influence and political considerations. A purely technical mindset might not get these things.

Q9: What is the biggest “pitfall” you have encountered while developing an enterprise business, and what is the most valuable lesson you have learned?

Chen Kai: The biggest “pitfall” was investing a great deal of time in “false demand.” We once spent nearly half a year speaking with more than a hundred customers. Everyone was excited during initial conversations, but as discussions deepened, we found that many customers either had not thought through their own needs or had no budget at all; they merely wanted consulting or were planning for the following year. Our most valuable lesson, therefore, is to “find real customers and solve real problems.” We have now established a customer assessment and screening mechanism to evaluate the authenticity and urgency of demand first and avoid ineffective investment.

Q10: You have successfully served many major customers. Why are you also considering expanding in the opposite direction into the small and medium-sized business (SMB) market?

Chen Kai: Our work in the small and medium-sized business market can be seen as a strategy of “bringing advanced capabilities downstream” and “democratizing technology.” When serving major customers, we invested enormous R&D costs—for example, in tender projects worth 3 to 4 million—to develop highly mature and in-depth functional modules. We can now provide these completed and proven capabilities to small and medium-sized businesses in simplified or modular form at a very low price, such as tens of thousands of yuan per year. For an SMB, gaining access to part of an R&D result worth millions at very low cost is highly attractive. For us, it is an ancillary business: the technology cost has already been amortized, and the main challenge is marketing.

Q11: In the Chinese market, what factors are most important for winning an ultra-large enterprise customer with a contract value exceeding one million? How would you rank customer relationships, technical strength, price, service capabilities, and other factors?

Chen Kai: I would rank them as follows:

Fit with customer needs: This is the most fundamental foundation. Your technology, products, and solutions must precisely meet the customer’s real needs; otherwise, nothing else can proceed.

Customer relationships: This is the second-most-important factor. Relationships undoubtedly play a crucial role in business cooperation.

Comprehensive service capabilities and “implicit value”: This involves more than technical delivery. More importantly, it is the ability to understand and satisfy the customer’s “implicit demands” beyond its technical needs. Examples include helping customers establish an industry benchmark and meet strategic goals and leadership expectations; this kind of political value and social-influence value is critical.

Price: Price comes last. Although large central state-owned and other state-owned enterprise customers are price-sensitive, they place greater value on a supplier’s overall qualifications and the solution’s total value, and they do not follow a “lowest bidder wins” model.

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Original publication: https://uniqueresearch.substack.com/p/unique-research-2025-11-08-04
On-site reading page: https://ffcap.cn/en/research/unique-research-2025-11-08-04