Research by:

Eric Markowitz, Managing Partner | Director of Research

Daniel Crowley, CFA, Managing Partner | Portfolio Manager

There were dozens of important trends at CES 2025, from robotic dogs to massive Chinese drones. But perhaps the most interesting was the rise of edge AI inference—i.e., the ability for devices to handle AI tasks locally, without relying on the cloud.

We believe this is an important shift. It’s being driven by a combination of factors: physics, economics, and the practicality of modern computing. From TSMC’s advancements in chip manufacturing that enable powerful AI processing at the edge—to Qualcomm’s vision of “AI is the new UI”—edge AI could very well reshape how we use technology.

What’s more, this shift could alter where technology companies fall in the stack. In the future, the key differentiator may not be the sheer compute power available but where that compute happens—on centralized cloud servers or localized edge devices. This has the potential to redefine the roles of hardware, software, and platform companies in the tech ecosystem.

Here’s a few ideas based on panels and conversations we experienced at CES.

1. The Case for Edge AI: Why It Makes Sense

The argument for running AI at the edge is rooted in practicality.

  • Latency and Immediacy: Processing AI tasks locally eliminates the delays of sending data to the cloud and back, resulting in real-time responsiveness. At a panel discussion, Qualcomm’s SVP and GM Durga Malladi emphasized that immediacy is central to creating better user experiences. Whether it’s an AI assistant responding to a voice command—or an autonomous vehicle making split-second decisions—the edge enables faster, more reliable outcomes.
  • Cost Efficiency: By reducing the need to pipe vast amounts of data back and forth to the cloud, edge AI cuts down on bandwidth costs and energy consumption—a win for both businesses and consumers.
  • Privacy and Context: On-device AI safeguards sensitive data by keeping it local. For example, your AI assistant can access your medical appointments without that information leaving your device. This contextual and private approach enhances trust and usability, especially for enterprise applications involving proprietary data.

2. Hardware Innovation: The Return of Silicon

For years, hardware was seen as a commodity, overshadowed by the rise of cloud computing. In my opinion, hardware matters again, especially in the context of edge AI.

  • Qualcomm’s Vision: Qualcomm’s Snapdragon Summit introduced the idea of “AI as the new UI”, where on-device AI agents replace traditional app-centric interfaces. These agents adapt to the user over time, building personal knowledge graphs that deliver hyper-customized experiences.
  • Smaller Innovators: Startups like Innatera Nanosystems are pushing the envelope with intelligent microprocessors for edge devices. Their Spiking Neural Processor T1 combines ultra-low latency with submilliwatt power consumption, paving the way for smarter sensors in everyday devices like smoke detectors.

3. Edge AI’s Transformative Impact on Daily Life

The integration of AI into physical spaces is creating “intelligent” environments that feel increasingly natural.

  • Consumer Applications: LG showcased AI-driven appliances that manage schedules, optimize power usage, and even order coffee on the go. These are just small examples of how edge AI is transforming home and automotive experiences.
  • AI as the New Interface: Qualcomm’s Malladi described a future where edge AI agents interact with apps and services on your behalf. Instead of navigating menus, you simply make a request—like asking your AI assistant to coordinate your schedule—and the system connects the dots across apps seamlessly. This shift represents a move away from traditional, tactile interfaces toward something more human-centered.
  • Enterprise Use Cases: In the business world, edge AI allows companies to fine-tune models with proprietary data without risking exposure. This approach enhances AI outcomes while maintaining strict data privacy.

4. The Physics and Economics of Edge AI

The edge isn’t just about smartphones or PCs. It includes any device or system close to the user. This is important because:

  • Physics: The further data has to travel, the more latency it introduces. Running AI at the edge reduces this delay, ensuring a smoother, faster user experience.
  • Economics: The cost of transmitting data to the cloud is nontrivial. Edge AI alleviates this burden by keeping processing local, making it a more sustainable and scalable approach.

A Few Key Takeaways

  • Edge AI is essential for scaling AI globally, enabling real-time responsiveness, cost savings, and improved data privacy.
  • Hardware innovation is back in the spotlight, with advances in silicon driving the edge-to-cloud continuum.
  • AI is becoming the new interface, creating more personalized and intuitive user experiences that transcend traditional app-based paradigms.

Disclosures

The opinions expressed herein are those of Nightview Capital and are subject to change without notice. The opinions referenced are as of the date of publication, may be modified due to changes in the market or economic conditions, and may not necessarily come to pass. Forward-looking statements cannot be guaranteed. This is not a recommendation to buy, sell, or hold any particular security. There is no assurance that any securities discussed herein will remain in an account’s portfolio at the time you receive this report or that securities sold have not been repurchased. It should not be assumed that any of the securities transactions, holdings or sectors discussed were or will be profitable, or that the investment recommendations or decisions Nightview Capital makes in the future will be profitable or equal the performance of the securities discussed herein. Nightview Capital reserves the right to modify its current investment strategies and techniques based on changing market dynamics or client needs. Recommendations made in the last 12 months are available upon request.

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