AI Hay, a Vietnam-based AI-powered knowledge discovery platform, has raised US$10 million in a series A round led by Argor Capital.

Existing investors Square Peg, Northstar Ventures, AppWorks, and Phoenix Holdings also joined, bringing total funding to over US$18 million.

Launched in May 2024, AI Hay combines generative AI and social networking to provide context-rich answers.

It has reached over 15 million downloads and facilitates more than 100 million questions monthly.

The funding will support its web-based expansion and innovation in education and entertainment, including developing AI companions.

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🔗 Source: AI Hay

🧠 Food for thought

1️⃣ Vietnam’s AI landscape represents one of Southeast Asia’s most aggressive adoption stories

Vietnam is leading the region’s AI revolution, with 44% of SMEs prioritizing AI investments in 2024, double the 22% rate from just a year earlier1.

This rapid adoption is producing tangible results: 88% of Vietnamese businesses report improved profitability from tech investments, the highest rate in the Asia-Pacific region1.

AI Hay’s success is occurring within this broader context, where 77% of Vietnamese online sellers are already familiar with AI technology, significantly above the regional average2.

The company’s rapid growth to 15 million downloads aligns with the country’s exceptional AI readiness, where over 47% of SMEs now utilize AI tools for core business functions1.

Vietnam’s strategic advantages include a young workforce (with 60% of Southeast Asia’s population under 35) and strong government support through initiatives like the “AI for All” program, which promotes AI accessibility1.

2️⃣ Vietnam faces critical AI talent challenges despite investment momentum

Despite surging capital into Vietnam’s AI sector, the country faces a significant challenge in building a talent pool of world-class AI experts to compete globally3.

The education system requires a fundamental shift toward quality training in AI, with industry experts stressing that theoretical knowledge alone is insufficient for practical application in real-world business contexts3.

This talent gap explains why AI Hay’s founding team—veterans from Vietnam’s leading consumer platforms with a dedicated Vietnamese engineering team—represents such a valuable asset in the country’s tech ecosystem.

The talent shortage has prompted government initiatives to recruit scientists and AI experts from abroad and provide scholarships for students to study at leading AI institutions, creating a more competitive environment for skilled developers3.

For AI Hay and similar ventures, this context highlights why their success depends not just on technology but on their ability to attract and retain specialized AI talent in a highly competitive market.

3️⃣ Local AI applications show higher adoption when culturally optimized

AI Hay’s focus on “hyperlocal, intelligent, and accessible AI tools” reflects a critical success factor in Vietnam’s emerging AI ecosystem. Tools designed specifically for local context consistently outperform generic solutions.

This pattern is evident in the economic data: while 61% of Vietnamese businesses question the overall usefulness of generic AI tools, those with localized applications report significantly higher ROI, such as Cỏ Mềm HomeLab achieving a 1.4x return on ad spend through culturally-adapted AI analytics14.

The combination of generative AI with social networking in AI Hay’s platform addresses a specific cultural expectation in Vietnam, where 73% of consumers are comfortable interacting with businesses via AI chatbots, particularly when these interactions feel locally relevant4.

Meta’s Project ViGen similarly demonstrates this principle, creating local datasets specifically for training AI models that reflect Vietnamese culture and language nuances, recognizing that generic global AI models often underperform in specific cultural contexts4.

The social community aspect of AI Hay’s platform, allowing users to connect with peers and experts, aligns with Vietnam’s collaborative business culture, where technology adoption decisions are heavily influenced by community recommendations and shared experiences.

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