Recent signals from across the industry are pointing in the same direction. Forbes coverage featuring Won-Joon Choi from Samsung Electronics highlighted a hardware-centric AI vision. At the same time, Mark Gurman’s reporting on Apple indicates how Apple is doubling down on integrating AI into its device ecosystem. Discussions following our recent commentary on Meta’s push into prescription smart glasses, in partnership with optical vendors, further underscore a broader industry shift.
Taken together, these signals point to a clear emerging theme: AI is becoming deeply embedded in hardware, and the next phase of competition will be defined by how it is delivered to consumers and gain consumers trust.
From Forced Surveillance to Voluntary Participation
When George Orwell wrote Nineteen Eighty-Four, he imagined a world where surveillance was imposed by authority. Today, the dynamic has fundamentally changed. Consumers are voluntarily adopting devices that can see, hear, and understand their daily lives, because AI makes these devices more useful, more personalized, and more integrated into everyday routines.
However, this shift also introduces new trade-offs. As devices become more intelligent and more aware of their surroundings, the boundary between convenience and surveillance becomes increasingly blurred. This tension sits at the core of the next phase of AI adoption.
The Cost of AI: Privacy, Regulation, and Economics
As AI becomes more pervasive, consumer concerns around privacy are rising, and regulatory frameworks are beginning to reflect this shift. The European Union, for example, has taken a cautious stance toward always-on AI devices such as Meta’s display smart glasses, driven in part by compliance with the General Data Protection Regulation (GDPR). GDPR imposes strict requirements on how personal data is collected, processed, and stored, raising the bar for any device that continuously captures real-world data.
At the same time, recent legal setbacks for Meta highlight a broader challenge facing AI companies. Court rulings scrutinizing research practices and user safety implications signal increasing headwinds around public trust. As AI becomes more embedded in daily life, trust is no longer a secondary consideration. It is becoming a fundamental prerequisite for adoption.
Cost presents another challenge. AI is not purely a scale-driven business. Unlike traditional software, where marginal costs tend toward zero, large AI models require significant and ongoing compute resources. As usage increases, so do operational costs. While monetization models are starting to take shape in areas such as programming and enterprise applications, the path for mass consumer adoption remains uncertain. The industry is still in the early stages of defining how AI should be priced and monetized sustainably. Even developments such as Sora being scaled back by OpenAI underscore the difficulty of balancing demand, cost, and long-term viability.
Why Hardware-Led AI Matters
SAG believe hardware-led AI is emerging as a more compelling path for consumer adoption.
Processing more AI on-device, or tightly integrating it with hardware, directly addresses several of the biggest challenges mentioned above facing AI today. It helps reduce perceived privacy risks by keeping more data local, lowers latency to enable real-time interactions, and mitigates the growing issue of usage-based costs that are inherent in cloud-based AI models. As AI usage scales, these factors become increasingly important for both users and providers.
More importantly, hardware provides a stable and scalable distribution layer. Smartphones, PCs, and other connected devices are already deeply embedded in consumers’ daily lives. Over the next five years, these devices are not expected to disappear. Instead, they will evolve into the connection layer and control layer for AI adoption.
This is where users will access AI services, manage multiple AI agents, and generate and control their data. It is also where monetization will naturally occur. At the same time, user interfaces will gradually shift from app-based interactions to more agent-driven experiences, where AI operates across applications and contexts in a seamless and proactive way.
The Rise of the Multi-Agent Ecosystem
The industry is now entering a multi-agent era, where multiple AI agents coexist and collaborate across devices, platforms, and applications. This represents a shift from single-assistant models toward more distributed and specialized intelligence.
Early signals of this transition are already visible. The Samsung Galaxy S26 introduces initial forms of multi-agent structure, while Apple is moving in a similar direction through deeper system-level AI integration. In this emerging model, devices act as the structure layer, AI agents become the service layer, and ecosystems form the foundation of competitive advantage.
Trust as the New Competitive Advantage
The recent challenges faced by Meta indicates a critical shift in the AI landscape. The competition is no longer defined solely by model performance. It is increasingly shaped by public and consumers’ trust.
Companies that face trust deficits are likely to encounter regulatory friction, slower adoption, and greater resistance from users. In contrast, established hardware players such as Apple and Samsung enter this phase with clear advantages. They benefit from large hardware installed bas and user bases, established distribution channels, and long-standing consumer relationships built around hardware reliability and ecosystem integration.
These factors position them well to distribute AI services at scale, integrate multiple AI agents into a unified experience, and monetize AI through a combination of devices, services, and ecosystem lock-in.
SAG Takeaway
AI models will continue to improve rapidly, but the next phase of competition will be defined more by who controls the interface, the distribution layer, and the trust relationship with users.
In this context, hardware-led ecosystems have a strong opportunity to lead the consumer AI market, not only in driving adoption and usage, but also in capturing long-term monetization.
The AI race is becoming an ecosystem and trust race.