Return of the rack: Are hardware and deep tech reclaiming the VC stack?

Beyond the Cloud: Why Hardware and Deep Tech are Reclaiming the VC Spotlight

The venture capital landscape is shifting, with hardware and deep tech reclaiming focus from a decade dominated by cloud and SaaS. Mr. Alex Niehenke, Partner at Scale Venture Partners, highlighted this “physical world striking back,” noting massive hyperscaler buildouts and specialized deep tech in robotics, space, biotech, and autonomous cars. Ms. Lu Zhang, Founder of Fusion Fund, emphasized her firm’s thesis of marrying deep tech with strong data moats, crucial for practical, large-scale AI deployment across non-tech industries.

Effective AI requires deep control over the hardware layer for data input. Ms. Zhang cited SenseNet, a Vancouver-based company using unique gas sensors for wildfire detection, as an example of hardware-software integration creating proprietary data moats. This approach unlocks vast market opportunities in non-tech sectors. Mr. Niehenke explained that while initial investment in robotics like Dusty Robotics or Motive assesses hardware capability, long-term value is driven by the sophisticated software and AI built on top, as seen with Motive’s real-time AI models.

Founders in this space need a “system mindset,” offering comprehensive solutions and strategically leveraging existing hardware components. AI is accelerating R&D, with companies like Medra operating autonomous robotics AI labs and space factories exploring new material discoveries. Human intuition remains crucial for setting research direction. The current robotics cycle is distinct due to AI’s data-driven learning, enabling adaptation beyond rigid rule-based systems, though acquiring high-quality, varied 3D data is a primary bottleneck.

Mr. Niehenke expressed skepticism about humanoid robots. He highlighted the surprisingly low global robot deployment (around 3 million), creating a “chicken and egg” problem for foundational robotics models. Geopolitical and supply chain risks significantly impact hardware investments. Ms. Zhang recommended early strategic partnerships with key suppliers like TSMC or Nvidia to secure access to critical components and infrastructure.

Mr. Niehenke acknowledged these macro risks, citing unexpected tariff chargebacks as an example of how global complexities can severely affect business forecasts. Key investment opportunities include robotics for high-skilled labor in constrained markets. Ms. Zhang also pointed to hardware innovations addressing AI’s energy consumption, GPU usage, and edge deployment challenges, such as analog computing chips (reducing AI energy by 100x), and micro-LEDs for data centers, all vital for future AI infrastructure.