Foreword: Recently, after lying low for eight months, Google launched its new-generation AI model, Gemini 3.0, in a near-surprise move, instantly rewriting the global competitive landscape for large models. OpenAI CEO Altman rarely posted to congratulate, stating, '[This looks like an amazing model],' while Tesla CEO Musk quickly followed with a like. Berkshire Hathaway, under Buffett, even heavily bought Google stocks in the third quarter.

Author | Fang Wensan Image Source | Network

Google's Counterattack After Eight Months of Silence

Since the release of Gemini 2.5 Pro in March 2025, Google has been mired in an eight-month '[silence period]' in the foundational model domain, while competitors have continuously iterated their offerings.

OpenAI has upgraded from GPT-5 to GPT-5.1, and Anthropic has dense (this Chinese word means 'intensively' and may not fit well here; consider revising to 'released') launched multiple versions, from Claude 3.7 Sonnet to Haiku 4.5. Google's presence in the developer community has been continuously diluted.

In July-August 2025, Microsoft launched a fierce talent offensive, poaching over 20 core experts from DeepMind, including key figures like Amar Subramanya, Vice President of Gemini Engineering, directly impacting the R&D process.

At the same time, Google has invested significant effort in tackling AI image generation technologies. The Nano Banana team believes that only by solving three major challenges—role consistency, contextual editing, and text rendering—can foundational models truly achieve commercial viability.

This '[sharpening the axe will not interfere with cutting the firewood]' strategy, while delaying version updates, has laid a solid foundation for Gemini 3.0's multimodal capabilities.

Starting in October, '[accidental leaks]' of internal calendar screenshots, API document identifiers, and test data followed one after another. Each exposure precisely showcased Gemini 3.0's core advantages, pushing industry expectations to their peak.

When officially released on November 19, Google responded to all doubts with a resolute '[release and go live]' stance.

The new model was integrated into products like Search AI Mode, Gmail, and Android on the same day, directly reaching 2 billion monthly active search users, 650 million Gemini App users, and 13 million developers, setting a new record for AI model distribution efficiency.

Three Core Advantages Redefine AI's Capability Boundaries

The disruptiveness of Gemini 3.0 is concentrated in three dimensions: task execution, distribution efficiency, and an ecological moat.

① Task Execution: From Q&A to Full Process Agency

The most core breakthrough of Gemini 3.0 is the qualitative change in multi-step reasoning capabilities. DeepMind CEO Demis Hassabis emphasized that the new model can engage in complex thinking like humans, without interruptions or loss of coherence midway.

Users only need to specify their goals, and Gemini 3.0 can autonomously decompose tasks, allocate resources, and advance implementation.

Google's evaluation metrics for AI have also shifted accordingly, from the number of questions answered and the quality of content generated to the number of tasks completed to help users.

This shift reflects a re-evaluation of AI's capability boundaries in the industry. Truly powerful AI is not '[smarter]' but '[more useful].'

In the Humanity's Last Exam, a test of top human-level challenges, Gemini 3.0 scored 37.5% without tools, nearly doubling the previous generation's 21.6%.

In the AIME 2025 mathematics test, with code execution, it achieved 100% accuracy, proving its core advantage of '[getting things done]' with hard skills.

② Distribution Efficiency: From Active Seeking to Seamless Integration

Another record created by Gemini 3.0 is the miracle of reaching billions of users on the day of release, which stems from Google's distinct path choice compared to other AI companies.

Most AI products adopt a standalone application model, requiring users to actively download, register, and learn to use them. In contrast, Gemini 3.0 is directly embedded into tools that users are already accustomed to, enabling zero-learning-cost usage.

In search scenarios, when a user queries '[how does RNA polymerase work],' AI Mode generates an immersive layout with interactive visualizations, using code to create operable scientific animations rather than simply listing links.

In the Android system, it replaces Google Assistant, supporting cross-application voice tasks, such as '[find the meeting minutes from last week with CEO Zhang and send them to Manager Li],' completing the process automatically.

In Docs, it can summarize documents in real-time, supplement materials, generate charts, and seamlessly integrate with the editing process.

Josh Woodward, Vice President of Google Labs, pointed out the key, '[AI integration should be seamless, allowing users to receive help without changing any habits.]'

This embedded distribution model enables Gemini 3.0 to activate the potential value of existing products without spending time acquiring new users.

③ Ecological Moat: Full-Stack Layout Constructs an Irreplicable Barrier

As CEO Sundar Pichai stated, Google's differentiated advantage lies in '[full-link control from chips to data centers, from models to products, and from users to developers.]'

The first layer is computational autonomy. While OpenAI spent over $8.6 billion on computational power in the first nine months of 2025, and Anthropic purchased $30 billion worth of computational power from Azure, Google achieved autonomous control over computational costs and performance with its self-developed TPU chips.

The second layer is the data closed loop. Google's search handles billions of queries daily, Gmail processes hundreds of billions of emails, and YouTube records billions of views. This data serves as high-quality material for model training and a continuous source of optimization feedback.

The third layer is the product matrix. Gemini 3.0 can test its understanding capabilities in Search, verify its generation capabilities in Gmail, and refine its agent capabilities in Android. Each product serves as a real-world validation ground for its abilities.

This positive cycle of '[model-product-data]' allows Google to quickly identify issues, optimize models, and form a continuously evolving ecological closed loop.

Four Technological Breakthroughs Unlock AI's Future Form

If the three core advantages are the '[skeleton]' of Gemini 3.0, then the four technological breakthroughs are the '[muscles]' supporting its capabilities.

These breakthroughs not only enable the model to achieve overwhelming performance but also unlock entirely new application scenarios for AI, driving the industry from '[chatbots]' to '[digital colleagues.]'

① Native Multimodality: Covering Full-Dimensional Information Processing

Gemini 3.0 elevates multimodal capabilities to a new height, supporting not only the collaborative processing of text, images, videos, music, and code but also achieving native support for special modalities like 3D and geospatial data, becoming the only model currently capable of covering full-dimensional information.

In the multimodal reasoning test MMMU-Pro, it set a new record with a score of 81%; in the Video-MMMU test, it reached a high score of 87.6%, demonstrating powerful cross-modal understanding capabilities.

② Deep Think+ Multi-Agent: Endowing AI with '[Slow Thinking]' and '[Collaboration]',

The Deep Think reasoning architecture introduced by Gemini 3.0 enables AI to '[slow down and think.]'

Unlike previous models' '[intuitive rapid answers],' it engages in multi-step analysis, hypothesis verification, and logical reasoning when faced with complex problems before providing the optimal answer.

In the MathArena Apex mathematics competition, after enabling Deep Think mode, the model's score surged from around 1% with ordinary models to 23.4%, showcasing reasoning capabilities close to those of human experts.

At the same time, the model supports multi-agent collaboration, enabling multiple agents to execute complex tasks simultaneously and cooperate with each other.

In the tool usage capability test 12-bench, Gemini 3.0 Pro scored 85.4%, a significant improvement from the previous generation's 54.9%, proving the practical effectiveness of its multi-agent collaboration.

③ Long Context and Long-Term Memory: Making AI '[Understand You Better]'

Gemini 3.0 adopts a new context and retrieval mechanism, supporting an ultra-large context window of 1 million tokens, capable of processing extremely long documents or video content.

In the MRCR v2 benchmark, its average score for 28k context was 77.0%, far exceeding competitors; its point-by-point score for 1M context was 26.3%, indicating that it does not simply '[stuff in]' more information but truly understands and utilizes key content in long documents.

More importantly, its long-term memory capabilities have improved, allowing the model to remember users' style preferences, tone habits, and even past task details, enabling each interaction to build upon historical foundations rather than starting anew as '[a complete stranger]' every time.

④ Antigravity Platform: Reconstructing the AI Programming Experience

Google simultaneously launched the Agent-First programming tool, Google Antigravity, which has completely transformed the software development model.

This is not a simple code editor but an agent-first development environment that integrates the reasoning capabilities of Gemini 3.0, the browser control capabilities of Gemini 2.5, and the image generation capabilities of Nano Banana, forming a complete development closed loop.

In Antigravity, developers no longer need to write code line by line. They only need to issue target instructions, and the agent can autonomously decompose tasks, write code, run tests, consult API documentation, and fix errors.

When a user requests '[make an adjustable-speed electric fan],' the system can generate complete code including rotation animations, speed control sliders, and on/off buttons within 30 seconds.

When developing a shopping cart feature for an e-commerce app, the model automatically integrates the Stripe payment module and generates task lists, implementation plans, and operational screen recordings for user review.

Developers can collaborate with multiple intelligent agents, with different agents responsible for backend, testing, UI verification, and other aspects, advancing tasks in parallel.

This model enables developers to upgrade from '[code monkeys]' to '[architects],' focusing on core goals rather than specific implementations, greatly enhancing development efficiency.

In the Design Arena programming competition, Gemini 3.0 Pro ranked first in four categories: website development, game development, 3D design, and UI components, proving its industry-leading programming capabilities.

An Industry Watershed Defined by '[Models]'

For traditional software vendors like Yonyou, Kingdee, Seeyon, and Fanwei, which have long been deep cultivation (this Chinese word means 'deeply cultivated' and may not fit well here; consider revising to 'focused') on enterprise services, Gemini 3.0 brings not a shock but a once-in-a-lifetime opportunity for dimension upgrading.

For a long time, the core value of enterprise applications like ERP, CRM, and OA has lied in solidifying processes and recording data. However, pain points have become increasingly prominent, with complex system operations and data precipitate (this Chinese word means 'precipitation' and may not fit well here; consider revising to 'accumulation') into '[dumb assets.]'

The slow thinking reasoning capabilities and native multimodal interactions of Gemini 3.0 will completely transform this status quo.

① Interaction Interface Demystification: Natural language interaction (LUI) will gradually replace complex graphical interfaces (GUI). Future enterprise software may only have a simple dialog box.

Business personnel no longer need to learn complex menus. A simple '[analyze abnormal payment collection (this Chinese term means 'payment collections') from last month]' will prompt the system to automatically retrieve ERP data and generate an analysis report.

② Monetization of Data Asset Value: The core advantage of traditional vendors lies in their understanding of business and possession of data. Relying on the long context and reasoning capabilities of large models, originally dormant business data will be activated, transforming from '[recording history]' to '[predicting the future.]'

For AI entrepreneurs, Gemini 3.0 sends a clear signal that the window of opportunity for general intermediate layers is closing, and the survival space for shell vendors will be significantly squeezed.

Early on, many AI applications served as patches for large models, supplementing functionalities like memory, internet connectivity, and tool invocation. However, as Gemini 3.0 internalizes these capabilities as native features, thin-shell applications relying solely on Prompt engineering or simple orchestration will struggle to survive.

The restructuring of industry division is now clear. MaaS-layer giants are responsible for pushing the '[IQ]' of models to the extreme, providing universal AI '[utilities.]'

SaaS and ISV-layer application vendors are responsible for solving the last-mile business challenges, translating model capabilities into productivity that enterprises can understand and utilize.

Ending:

The release of Gemini 3.0 has also ignited a new war in the AI industry. This competition is no longer a simple performance showdown of single models but a comprehensive game of ecology, entry points, and scenarios. Giants like Google, OpenAI, Apple, Alibaba, and Tencent have all entered the fray, reshaping the industry landscape.

Partial reference materials: Zhongzhi Observation: '[Gemini 3.0 Released, A Watershed for the Software Industry],' Direct AI: '[Altman Likes It Too, What Makes Google's Gemini 3 Pro So Strong?],' Tencent Technology: '[One Article to Understand Google's Most Powerful Model Gemini 3: The Biggest Surprise of the Second Half of the Year, the Return of the Google Dynasty],' China Entrepreneur Magazine: '[Google Has Awakened, the Battle for New Entry Points Begins.']