In this ION Influencers Fireside Chat, host Giovanni Amodeo sat down with Johnson Cheng, Partner at Voyager Capital, to discuss the rapidly evolving AI landscape, the differences between enterprise vs. consumer AI, and the venture capital trends shaping the future of innovation.

Key Topics Discussed

1. Johnson Cheng’s Background and Voyager Capital

• Began career at Deloitte Consulting in New York, working on strategy and tech projects for Fortune 500 companies.

• Later took over his family’s e-commerce business in China, scaling it to 180M RMB in GMV per year.

• Founded Voyager Capital in Hong Kong, the family’s venture arm, with investments in AI and enterprise tech companies such as AutoX, PhysicalNotes, Sonatus, and Food Truck Alliance.

• Today, Voyager not only invests but also helps enterprises implement AI agents and solutions.

2. Lessons from Enterprise Implementation

Johnson shared three critical lessons learned from implementing enterprise AI solutions:

1. It’s not just about the tech → success requires customization, service, and cultural adoption.

2. Vendors vs. in-house → external vendors bring cross-industry knowledge, but in-house requires rare, specialized talent.

3. Speed matters → family businesses and enterprises must move quickly to capture the advantages of AI before competitors do.

3. Moving Too Fast or Too Slow in AI Adoption

• Depends on industry: marketing firms must adopt rapidly; nuclear energy companies may move cautiously.

• Pilot projects are key: start with non-critical functions, build organizational confidence, then scale.

• Ultimately, AI is as much about cultural transformation as technology adoption.

4. Consumer AI vs. Enterprise AI

• Consumer AI:

  • Fast-moving, capital-intensive, weekly iteration cycles.

  • Low switching costs and little user loyalty.

  • Success requires being on top of trends, fads, and user marketing.

• Enterprise AI:

  • Focused on solving pain points through customization and service.

  • Slower but more sustainable growth.

  • Example: Sonatus, a Voyager-backed company, enables OEMs to manage vehicle data in the software-defined vehicle revolution, growing 15x since Voyager’s investment in 2022.

5. Price Sensitivity in Enterprise AI

• Large enterprises weigh service quality vs. cost.

• SMEs can be easier to work with: owners quickly evaluate ROI.

• Example: Voyager’s AI agents reduced processing time by 90%, making adoption a no-brainer even at higher prices.

6. Assessing Market Size & Pain Points

• AI’s total addressable market is vastly underestimated—we’re only at the dawn of an industrial revolution.

• Market sizing must start with shared pain points: how many companies have the same challenge, and how much will they pay to solve it?

• Scaling strategy: start with one niche pain point, then expand into adjacent problems.

7. What Makes a Strong Founder (and Red Flags)

• Resilience and adaptability matter more than the original idea. Example: an MIT founder pivoted from AI-driven e-commerce to social commerce and scaled successfully.

• Red flags:

  1. Founders overly fluent in financial engineering language rather than product focus.

  2. Too many simultaneous ideas, spreading attention thin.

8. Key Metrics and Investment Approach

• Voyager focuses on Series A companies with product-market fit.

• Benchmarks: at least $1M ARR/revenue, signaling that enterprises are paying to solve a real problem.

• Investment evaluation blends quantitative metrics (CAC, LTV, growth rates) with qualitative founder assessments.

9. U.S. vs. Asia VC Ecosystems

• U.S. VC ecosystem: robust, risk-taking, invests early in breakthrough tech (AI, quantum computing).

• Asia VC ecosystem: more cautious, revenue- and traction-focused, though interest remains strong, particularly from family offices.

• Current sentiment: some struggle in fundraising, but U.S. firms expanding into Asia are finding success.

10. Exits: IPOs vs. M&A

• Hong Kong IPO market is rebounding, but:

  • Small IPOs risk low liquidity, no analyst coverage, and high compliance costs.

  • M&A may often be the more practical exit path for startups.

Key timestamps:

00:06 Introduction to the Fireside Chat
01:38 Lessons from Family Business and Consulting
03:50 Understanding Market Timing in AI Adoption
05:28 Consumer vs. Enterprise AI Dynamics
08:32 Unlocking Value in Enterprise AI
10:36 Assessing Total Addressable Market
12:46 Identifying Customer Pain Points
13:59 Red Flags in Founders
16:50 Metrics for Investment Justification
19:01 Predicting Winners and Losers in AI Startups
20:43 Shifting Focus to the VC Landscape
22:22 Challenges Facing Asian VC Firms
23:06 The Evolving Model of Diversification in VC
24:17 The Current State of the IPO Market in Asia
25:48 Conclusion and Final Thoughts