VC says healthcare AI, robotics, space ripe for investments

Key Concepts

• Fusion Fund: A Silicon Valley venture capital firm focusing on early-stage investments in Enterprise AI, Healthcare AI, and Industry Automation.
• Enterprise AI: AI applications geared towards business operations and integration within large organizations, contrasting with consumer-facing AI.
• AI Phases: The evolution of AI, currently transitioning from Large Language Models (LLM) to incorporating multi-dimensional, real-world data (“world models”).
• Industry Readiness: The crucial factor determining successful AI deployment, encompassing data availability, infrastructure, and cost-effectiveness.
• Space Economy: The growing economic activity surrounding space infrastructure, including lunar development and satellite technology.
• AI on the Edge: Deploying AI processing directly on devices (like microphones) rather than relying solely on cloud computing.

Investment Focus of Fusion Fund

Since its founding in 2015, Fusion Fund has concentrated its investments on three key verticals: Enterprise AI, Healthcare AI, and Industry Automation. Lu Jong emphasizes a focus on early-stage companies, specifically those transitioning “from zero to one” – moving from initial technology and market validation to generating substantial revenue (hundreds of millions, approaching billion-dollar IPO stage). The firm’s team comprises former entrepreneurs and technologists, reflecting a hands-on approach to supporting founders. A deliberate choice is made to invest only in Enterprise AI, excluding consumer AI due to the greater sustainability and higher integration costs associated with enterprise solutions.

The AI “Bubble” and Sustainable Growth

Lu Jong addresses concerns about an AI bubble, differentiating between capital market fluctuations and the underlying technological advancements. She acknowledges a “bubble period” in both private and public markets, characterized by inflated valuations and unsustainable metrics. However, she argues that the true value lies in the deployment of Enterprise AI, where integration with mature, sophisticated enterprise customers drives rapid and sustainable business automation and optimization. She notes the high migration costs for enterprises, ensuring a more considered and long-term investment in AI solutions.

She highlights the accelerating pace of AI innovation, moving from quarterly to weekly advancements, and describes the current evolution from language-based models (LLMs) to “world models” incorporating three-dimensional and real-world data. She uses the analogy of “small earthquakes” to describe necessary market corrections, preferable to a catastrophic “big earthquake” crash.

Challenges and Concerns in the AI Landscape

Despite her optimism, Lu Jong acknowledges significant concerns. She admits to limited sleep in 2025 due to the rapid pace of change and anticipates even faster evolution in 2026. Her primary worry centers on the disparity between the pace of technology and industry readiness. She stresses the need to make AI not only better and faster but also cheaper.

A critical point raised is the potential for energy infrastructure to become a limiting factor: “before we run out of GPU, we’re going to run out of energy first.” She emphasizes the practical considerations of cost and integration cycles alongside technological advancements.

Evaluating Startups: Beyond Technology

Lu Jong dispels the misconception that early-stage investment is simple. She describes it as “dancing with the risk,” requiring careful evaluation of various risk aspects and potential mitigation strategies. Market size and timing are identified as the most crucial factors, regardless of technology or founder quality. She explains that the current industry readiness, coupled with the vast amounts of data collected during the pandemic, has created a favorable environment for AI deployment.

When evaluating technology, she emphasizes a “better, faster, cheaper” framework, highlighting that cost-effectiveness is as important as performance improvements.

Future Tech Sectors: Healthcare AI and the Space Economy

Looking ahead, Lu Jong expresses strong bullishness about Healthcare AI, particularly its potential for personalized diagnostics and therapeutics in areas like cancer, heart disease, mental health, and neurodegenerative diseases. She believes AI can deliver low-cost, personalized solutions in these critical areas. She anticipates increased capital flow into this sector, citing upcoming conferences like CES and JP Morgan Healthcare as key indicators.

She also highlights the burgeoning Space Economy, extending beyond launch services (like SpaceX) to encompass the development of infrastructure on the moon and in space. Fusion Fund has been investing in this sector since 2017 and sees significant momentum.

Finally, she points to the need for new AI interfaces, specifically “AI on the edge” – enabling AI processing on small devices like microphones. This requires innovation across the entire technology stack, from chip architecture to infrastructure and model layers.

Notable Quotes

• “We want to have some small release of pressure like a small earthquake happen here out there instead of everything is disable until we have a big earthquake that would be the crash.” – Lu Jong, on the need for market corrections.
• “Before we run out of GPU, we’re going to run out of energy first.” – Lu Jong, highlighting the potential energy constraints of AI development.
• “Market size and market timing is the number one thing because regardless of the technology and founder you cannot really shift the market timing and market size.” – Lu Jong, on the importance of market factors in investment decisions.

Synthesis/Conclusion

Lu Jong and Fusion Fund are strategically positioned at the forefront of the AI revolution, focusing on sustainable, enterprise-level applications. Her perspective emphasizes the importance of balancing technological innovation with practical considerations like cost, energy infrastructure, and industry readiness. Beyond AI, she identifies significant opportunities in Healthcare AI and the Space Economy, advocating for increased investment in these transformative sectors. Her insights underscore the dynamic and rapidly evolving nature of venture capital, requiring constant adaptation and a forward-looking vision. The key takeaway is that successful AI deployment isn’t solely about developing advanced algorithms; it’s about integrating those algorithms into real-world systems in a cost-effective and sustainable manner.