Meta aims to be the device-based AI pioneer. With over a billion consumers using its apps, the company is in an exceptional position. Proponents argue that Meta can be big enough as a platform to compete against Apple and Google. However, structural, technical, and strategic obstacles cast many doubts on whether this ambition can work in the U.S. market.
Industry analysts have noted that Meta’s strategy resembles a climb with little chance of reaching the peak. Apple and Google already control the most critical layers of the digital ecosystem. Their dominance comes from the platforms users rely on daily. Meta, meanwhile, remains a tenant inside operating systems it does not own.
The Operating System Reality Check
The first roadblock for Meta is the lack of a proprietary operating system. Analysts stressed that AI devices depend on deep access to system-level data. Ian Fogg from CCS Insight explained that operating systems enable personal intelligence by tailoring AI with user-specific information. Without that layer, Meta cannot compete with the device-native models built by Apple or Google.
Apple and Google benefit from owning the core platforms where AI lives. They decide which features are prioritized and how user data flows into applications. Ben Barringer from Quilter Cheviot noted that Meta is merely a collection of apps hosted on others’ systems. This dependency ensures that Apple, Google, and Microsoft can deliver AI directly to users, while Meta must wait for access.
The Talent War Meta Is Losing
Meta has also found it extremely difficult to attract the best AI talent. It was reported that the company provided compensation of hundreds of millions of dollars to attract professionals. In one instance, Ruoming Pang at Apple was thrown a switch of 200 million dollars to go elsewhere. Still, even with such propositions accepted by a great number of candidates, they were still fearful to proceed because of internal instability and confusion within the Meta AI department.
Industry insiders noted that Meta’s reputation has worsened after controversies over performance claims. In April, critics accused the company of gaming benchmarks to exaggerate its model quality. The subsequent delay of a flagship model reinforced doubts about its progress. For researchers in a tight-knit community, such missteps signal uncertainty that no paycheck can fully erase.
The Technical Reality Behind the Marketing
Underlying these staffing issues is the fact that Meta simply does a poor job of AI implementation. The limitation to Meta AI is that it has been reported to lack memory, forgetting context in a discussion. The system may lose its way in terms of budget or location preferences in the middle of planning tasks, and may require users to repeat instructions. These lapses point to the fundamental weaknesses in providing high-quality device AI.
Image generation presents another problem. Users observed that Meta’s models fail to replicate diverse art styles, often producing generic outputs. Integration across Facebook, Instagram, and WhatsApp provides reach, but it comes with limitations. Analysts stressed that device AI must feel universal and contextual across every aspect of digital life. Meta’s social-first strategy instead funnels users back into its own platforms.
The Distribution Mirage
Supporters argue that Meta’s 3.48 billion monthly users give it unmatched distribution power. Clare Pleydell-Bouverie from Liontrust Asset Management called this reach an extraordinary advantage. Yet analysts countered that those users still rely on Apple and Google devices. This dependency means Meta’s access can be restricted at any moment by its rivals.
Apple already demonstrated its leverage with App Tracking Transparency, cutting into Meta’s advertising model. Google could follow with similar policies. Reports also showed that Apple rejected partnership opportunities with Meta earlier this year. Without system-level cooperation, Meta’s distribution strength is not an advantage but a fragile dependency subject to competitors’ rules.
The Ecosystem Reality
Apple’s strategy depicts the importance of platform control decisiveness. The company has over two billion active iOS devices, which gives it a fully integrated ecosystem. Apple devices allow AI to control and access calendars, messaging, maps, and more smoothly. As analysts pointed out, the high level of integration that Apple introduces into its products would automatically make its AI initiatives more personal and applicable in practice than those of Meta.
Meta has powers through sharing social data on Facebook, Instagram, and WhatsApp. Though useful, it does not deliver contextual breadth when compared to usage at the device level. Apple has displayed its patience in its long-term strategy regarding AI, including a so-called gap year in 2025. Experts noted that increasing Siri and other services in 2026 may end up providing an AI ecosystem that is even more comprehensive than the way Meta is doing it.
The Hardware Reality Check
Meta has invested heavily in hardware through its Ray-Ban smart glasses and VR headsets. Since 2021, it has sold over two million units of its glasses. However, analysts noted that these figures pale compared to Apple’s iPhone shipments, which exceed 200 million annually. Meta’s hardware remains a niche product rather than a mass-market anchor for device AI.
There is also an increasing competition in the smart glasses sphere. Other firms such as Halliday and Xiaomi are creating high-end models that have better displays and increased integration into their ecosystems. In the meantime, Apple plans to introduce its pair of glasses by 2027 having been preparing years and enjoying the benefits of its supply chain prowess. Meta has made an early foray into the world of creating apps and may serve a guide, but a lesson will probably be the power of integration that Apple has on its side.
The Strategic Delusion
Meta’s strategy reflects what some analysts call misplaced ambition. Ben Thompson of Stratechery stated that Meta might be wasting resources on AI in areas where it lacks advantages. The company has no hyperscaler business like Microsoft or Google, yet spends billions chasing device AI dominance. This pursuit highlights a mismatch between resources and structural realities.
Open-source projects such as Llama models have enhanced the reputation of Meta towards the masses. The company has also collaborated with chip makers like Qualcomm and MediaTek in order to increase access. Nevertheless, such attempts are not able to compensate for platform control. The hardware partners work with more than one company, including the competitors. Meta’s investments are exposed to external decisions, even though the investments do not own the ecosystem.
