Gemini 3.0: From Super Tool to AI OS Prototype, Google's Opportunities and Concerns

1. In What Areas Does Gemini 3.0 Exceed Expectations?

Gemini 3 adopts a Transformer-based Mixture of Experts (MoE) architecture and natively supports multi-modal inputs including text, vision, and audio, building the strongest full-modal reasoning capability. Specifically, Gemini 3's advancements are manifested in:

a) Significant Advances in Multi-Step Reasoning and Reliability: Previous generation models were prone to losing coherence after 5-6 steps of complex logical deduction, whereas Gemini 3.0 can reliably complete 10-15 steps of consecutive reasoning. Its score in Humanity's Last Exam surged from 21.6% (Gemini 2.5 Pro) to 37.5% (without tools), and in ARC-AGI-2—known as the "Turing Test for AI"—it achieved 31.1%, nearly doubling GPT-5.1's 17.6% and demonstrating abstract reasoning capabilities close to humans.

b) Breakthrough In Proactive Intelligent Capabilities: Gemini 3.0 transitions from passive intelligence (responding to user queries/instructions) to proactive intelligence. For example, Gemini 3.0 can automatically scan users’ email content, classify it by importance, mark items requiring responses, draft suggested replies, categorize similar emails, and learn users' daily habits to predict subsequent actions. This capability is not found in other mobile large language models, and allows it to learn user habits and provide optimized recommendations.

c) Distribution Efficiency Through Ecosystem Integration: Gemini 3.0 was integrated into all of Google's ecosystems (including Search, Cloud Services, Gmail, YouTube, Maps, etc.) upon release, allowing users to access it without downloading new apps or registering new accounts. Leveraging Google's robust ecosystem (billions of daily searches, hundreds of millions of daily Gmail emails, billions of daily views on YouTube) , it achieves rapid adoption.

2. Reinforcement of Google's Existing Advantages

Software Enhancement: Gemini 25Q1 DAU 35 million --> 25Q2 MAU 450 million --> 25Q3 MAU 650 million.  The launch of Gemini 3 and the subsequent integration of NanoBanana2 (offering enhanced semantic understanding, simpler natural language interaction, and improved Chinese language capabilities) are expected to further boost user numbers and stickiness for the Gemini app and Google AI series.

Reinforced Developer Ecosystem Stickiness: Google Antigravity, a new agent development platform centered around Gemini 3.0, supports natural language conversion to fully functional code, thus lowering the barrier to entry for developers. The Android XR system leverages Gemini as its underlying AI core, providing developers with a low-power, always-on application development environment and expanding ecological application scenarios.

Enterprise Ecosystem: Gemini Enterprise is rapidly penetrating the market, with over 2 million enterprise users covering 700+ enterprises; its API throughput is remarkable (processing 7 billion tokens per minute), supporting large-scale to-B tasks.

Self-developed TPUs Reduce Costs and Accelerate Commercialization: Gemini 3 was trained using Google's proprietary TPUs, which features high-bandwidth memory and scalable TPU Pods, significantly improving large language model training speed and supporting larger model scales. The model is also built on JAX and ML Pathways architectures for training workflows. Pathways excels in multi-task scheduling and cross-device parallelism, further enhancing computing power utilization.

3. AI OS As the Ultimate Industry Paradigm; Lack of Hardware Entry Points Becomes Google's Weakness

The industry consensus that “large models are the next-generation operating system (LLM: The Next OS)” has solidified, with its core being LLM+ Tool Use ​​. Furthermore, the evolution of Gemini 3.0's capabilities and Google's ecosystem strategy strongly suggest that: in the future, large language models will no longer be standalone applications but will evolve into an AI OS form that runs through hardware, systems, and services, becoming the underlying hub for all digital interactions.

Google has taken a crucial step via the Android XR system by deeply integrating Gemini into the system's underlying layer. This enables low-power, always-on operation and integrated perception of real and virtual scenarios, a core characteristic of AI OS. However, if the "entry-distribution-execution" processes of AI OS occur primarily at the edge hardware/system layer, then whoever controls the entry, defines the user experience and commercial distribution. In North America, the dominance of hardware in AI entry distribution is even more pronounced. Integrated hardware-software collaboration and system-level distribution on phones and PCs will strengthen manufacturers' influence in the AI OS era. Consequently, Google's shortcomings are prominent: its hardware layout relies on a collaborative model. After abandoning self-developed AI glasses, it shifted to jointly launching hardware products with manufacturers such as Samsung and XREAL, lacking its own core hardware platform.

Although the current Android ecosystem still holds a market advantage, risks are emerging. If Android-based mobile phone manufacturers like Huawei and Xiaomi deeply integrate their self-developed large language models into their proprietary OS to achieve vertical integration of "hardware - system - large language model," it will directly divert core traffic from Google's ecosystem. Google's hardware model, which relies on partners, makes it difficult to achieve optimal adaptation between AI OS and hardware, nor can it control the key user access points. Ultimately, it may face an awkward situation of "strong core capabilities but weak traffic acquisition".

Gemini 3.0 provides Google with a first-mover advantage in the AI OS arena, but the absence of a hardware entry point represents the critical variable in its long-term development.

The key to future industry competition will be the battle for an ecological closed loop of "AI OS + core hardware." If Google fails to make up for its hardware shortcomings, even with its leading large language model technology, it may face severe challenges of ecosystem traffic diversion amid the wave of vertical integration by mobile phone manufacturers.

Chart: Comparative Analysis of Gemini 3.0 Core Capabilities and Industry Competitors