Google AI Mode in Action: How a Real Land Dispute Revealed the True Capabilities and Limits of AI Tools

Snippet: Google AI Mode for Search delivered stunning accuracy in local legal policy research for a land dispute, using verifiable footnotes to identify land use classifications and transfer regulations, helping recover a 30,000 yuan deposit. Its synergy with Gemini Deep Think creates a “research + reasoning” powerhouse that mitigates AI hallucinations, yet it refuses complex case judgments—demonstrating remarkably clear product positioning and well-defined capability boundaries.

How a Land Dispute Became the Ultimate AI Tool Stress Test

If you’re anything like me—drowning daily in “AI is changing everything” marketing hype—you’ve probably developed a natural immunity to such claims. But when I found myself facing a thorny real-world problem—helping a friend resolve a land sale dispute involving a 30,000 yuan deposit—I discovered that the actual capability gaps between different AI tools are far more real and stark than any marketing brochure would admit.

It started on September 22. My friend had paid a deposit to purchase a plot of land locally. Without any written agreement, the seller unilaterally demanded the full balance by December 31, threatening to forfeit the deposit otherwise. The complications multiplied: the land involved a privately-developed plot, my friend was a non-employee, and the underlying land was state-owned—all factors governed by hyper-local policies with layers of regional specificity. When my friend approached me, armed only with a receipt and a bank transfer record (made to the seller’s wife), we also suspected the seller had double-sold the plot to another buyer. Whether this constituted fraud required professional judgment.

My initial approach seemed straightforward: let the most powerful AI tools handle it. After all, I’d spent months deeply testing at least eight AI products—Gemini Deep Think, Gemini 3, NotebookLM, ChatGPT, Perplexity, Claude, Zhihu Direct, Doubao, and Yuanbao. Each had clear positioning: Gemini Deep Think excels at parallel thinking and complex reasoning; NotebookLM dominates academic material analysis. But when I applied these “ace” tools to this real legal scenario, the cracks exploded wide open.

Why Traditional AI Tools Fail at Localized Legal Research

My first instinct was Gemini Deep Think. Its deep reasoning capabilities are genuinely impressive for tackling serious problems requiring multi-dimensional analysis. But when I input the core questions about local land classification, the legality of private development, and the specific legal foundations, its response cited the Civil Code and other generic statutes. Yet on the crucial region-specific policies? It became vague and started hallucinating, misapplying land policies from other provinces to our locality.

That’s when I pinpointed its fatal flaw: Deep Think’s IQ is entirely hostage to the quality of user-provided information. It tests the user’s ability to search and filter information manually—essentially forcing you to perform RAG (Retrieval-Augmented Generation) by hand. Bottom line: you must first find the right legal statutes, then feed them in, before it can reason effectively. Feed it regionally biased foundational info, and its “deep thinking” only amplifies errors.

Next, I tried NotebookLM. This “learning powerhouse” is genuinely formidable when parsing uploaded documents. But its data sources are locked to Google Scholar and foreign academic paper repositories, offering zero coverage of domestic local regulations and policy documents. Zhihu Direct? Over 70% of its sources come from Zhihu answer posts and CNKI/VIP academic databases—hardly credible for serious legal disputes requiring official government notices.

The real kicker came with Gemini 3 and Gemini Deep Research’s web-enabled modes. Their returned information was 90% sourced from foreign websites. Worse? The traceability function was completely missing—I couldn’t click to verify which webpage a specific claim came from, nor view the original text. Legal arguments built on such shaky ground wouldn’t stand up anywhere.

Google AI Mode’s First Real-World Test: Precision That Shocked Me

Just as I was ready to abandon AI and manually scour local government portals, the “AI Mode” button next to the Google search box caught my eye. Honestly, I hadn’t given it a second glance before, assuming it was just another chatbot interface. But in a “what do I have to lose?” moment, I entered my core question:

“What is the nature of privately-developed land in this locality? If someone is a non-employee who privately developed commercial-use land, can this land be privately sold? What is the legal basis?”

The search results completely upended my expectations.

Instead of dumping a pile of blue links like traditional Google, or spewing an unverifiable summary like other AI tools, it generated a research report with footnotes—each key assertion tagged with [1][2][3]. Clicking instantly jumped to the relevant government portal or policy document, even highlighting the quoted passage.

What truly mattered was the accuracy. Having spent months battling local authorities in court, I knew our area’s land law framework well enough to judge the output’s quality instantly:

First, it precisely distinguished between “identity land” and “commercial land”—nuanced categories that exist only in our local policy documents. This granular understanding proved it had genuinely digested local regulations, not just slapped on national-level generic laws.

Second, it proactively corrected my conceptual framing—this isn’t “selling,” it’s legally termed “transfer of rights.” That single-word difference determined the contract’s nature and applicable legal clauses, becoming the case’s critical breakthrough.

Third, it cited specific policy titles, explicitly referencing the source stating “private sales are strictly prohibited.” Clicking the footnote opened the official notice page for the “Interim Measures for the Transfer of Identity Land Management Rights (Trial)” with the relevant paragraph highlighted yellow. Verification took under 15 seconds.

This WYSIWYG fact-checking experience made one thing crystal-clear: Google AI Mode’s product logic isn’t competing with other AIs on “who chats better”—it’s filling the biggest gap in generative AI: verifiable information accuracy.

The Core Difference: Fact-Checker vs. Content Creator

To clarify Google AI Mode’s positioning, I compiled its fundamental differences from Gemini’s web-search mode based on test data:

This table reveals the key conclusion: While both may use Gemini model tech, their product positioning is diametrically opposite. Google AI Mode’s mission is helping you find objective facts online and providing links for personal verification. Gemini Deep Research focuses on creating new content or deep reasoning based on existing information.

Like using a microscope to hammer nails—wrong tool for the job. Choosing requires understanding its design intent. Google AI Mode is essentially a super-efficient “information reader + citation organizer,” not a legal consultant or case reasoning expert.

Boundary Testing: When It Refuses Service, That’s Actually a Feature

Emboldened, I got carried away. If Google AI Mode was so brilliant at fact-finding, could I just dump the entire case’s complexity on it and demand a litigation strategy?

So I ran a stress test: I input the full backstory—600+ words covering the September 22 receipt details, bank transfer to the seller’s wife, suspected double-selling timeline, and legal questions about fraud—and explicitly demanded: “Act as a lawyer and help me strategize, even determine if this constitutes fraud.”

The system responded with a single red line: “An error has occurred. Please try again later.”

I repeated the attempt three times. Each triggered the same error after inputting the full case details. This failure perfectly validated its product positioning: When a question cannot be answered through web search and fact-summarization alone, it actively refuses service rather than risk misleading users with fabricated analysis.

This design is brilliantly responsible. Legal case judgment requires evaluating evidence chain completeness, party intent, and local judicial interpretations—information impossible to retrieve from public web pages. Google AI Mode, recognizing its boundaries, chooses to say “no.” This prevents the most severe AI abuse risk: users believing they’ve received professional legal advice when it’s actually hallucinations from incomplete information.

The Golden Workflow: Google Search + Deep Think = Complete AI System

Since no single tool is perfect, how do you combine them for maximum effect? From this battle-test, I distilled a two-step golden workflow:

Step 1: Use Google AI Mode as “Eyes,” Outputting Verified Fact Sheets

Concrete process:

• ✦ Break complex problems into verifiable factual sub-questions (e.g., land classification, transfer procedures, deposit forfeiture rules)
• ✦ Query each in Google AI Mode; capture at least 3-5 footnoted policy sources per question
• ✦ Click every footnote to verify the original text contains the cited content, filtering out AI misreads
• ✦ Export all verified info into a PDF or structured notes, annotating source URLs and publication dates

In this case, four rounds of queries yielded over 12 precise clauses from local policy documents covering land classification, transfer rules, and deposit legal effects—all verifiable source material.

Step 2: Use Gemini Deep Think as “Brain” for Complex Reasoning

With 100% accurate “fuel” in hand, Deep Think’s IQ truly activates. I uploaded the Google AI Mode-cleansed policy PDF with explicit instructions:

“These are verified local land policies and legal foundations. Based on this concrete material, analyze my friend’s case—contract validity, litigation strategy, and fraud assessment.”

The results were instant and grounded. This time, Deep Think didn’t invent; it reasoned rigorously from accurate policy documents, precisely deriving that:

• ✦ Private sales of identity land management rights violate Article X of the Interim Measures, rendering the contract void
• ✦ Deposit forfeiture requires a valid contract; void contracts mandate full refund
• ✦ Double-selling intent requires evaluation under local judicial practice; civil litigation is recommended

The logic chain was airtight, each step citing specific policy sources—a stark contrast to its earlier vague Civil Code references. With this combo, we built an evidence chain that helped my friend recover the 30,000 yuan deposit three days later. Confronted with irrefutable policy clauses, the seller agreed to settle.

Real-World Reboot: Which Tool When?

This stress-test gave me clear mental models for each AI tool’s boundaries and best-use scenarios:

When to Use Google AI Mode for Search:

• ✦ Querying localized, time-sensitive policies/regulations (e.g., land management, tax incentives, subsidies)
• ✦ Research requiring precise source traceability and verification
• ✦ Quickly building factual frameworks on topics (e.g., industry definitions, historical evolution)
• ✦ Constructing “evidence libraries” for legal documents or research reports

When to Use Gemini Deep Think:

• ✦ Complex logical reasoning from verified materials
• ✦ Strategic decision analysis requiring multi-dimensional parallel thinking
• ✦ Creative work like writing frameworks or brainstorming
• ✦ Interpreting causality behind data

When to Use NotebookLM:

• ✦ Parsing academic papers or technical documentation
• ✦ Generating podcasts/summaries from uploaded materials
• ✦ Cross-document information integration and comparison

When to Use Zhihu Direct:

• ✦ Gathering industry practitioner experiences
• ✦ Understanding general public sentiment on topics
• ✦ Locating Chinese academic paper abstracts

Absolute Don’ts:

• ✦ Never use Google AI Mode for subjective case consultations
• ✦ Never use Deep Think for localized policy questions requiring fresh info
• ✦ Never input full personal privacy or sensitive case details into any tool

How to Start Using Google AI Mode Today

Google AI Mode isn’t fully rolled out yet, but some users see an “AI Mode” button next to the search bar. If you have access, test these question types immediately to experience its unique value:

Test Type 1: Localized Policy Queries

• ✦ “What are the specific R&D expense ratio requirements for Beijing’s 2024 high-tech enterprise recognition? Provide policy source links.”
• ✦ “What’s the latest work permit application process for foreign talent in Shanghai’s Free Trade Zone? What documents are needed?”

Test Type 2: Time-Sensitive Fact-Checking

• ✦ “What were the main research contributions of the 2024 Nobel Prize in Physics winners? Cite sources.”
• ✦ “What specific revenue figures did Company X mention in its Q3 2024 earnings report? Provide the official announcement link.”

Test Type 3: Precise Concept Definitions

• ✦ “What does the ‘three-rights separation’ in data factor market allocation specifically refer to? Which policy document first introduced this?”
• ✦ “How does the generative AI filing system define ‘large models’? Cite the official Cyberspace Administration definition.”

For every query, cultivate the habit of clicking footnotes to verify originals. This isn’t just about confirming accuracy—it’s the core of AI literacy: always remember AI provides clues, not conclusions.

The Real Measure of an AI Tool: From “Best Talker” to “Can Stand Accountability”

This experience reframed my AI evaluation criteria. Most reviews focus on fluency, creativity, or human-likeness—metrics that may completely fail in serious scenarios. What truly determines an AI tool’s value is whether it can provide information that is verifiable, traceable, and accountable when it matters most.

Google AI Mode didn’t excel at localized legal questions because it’s “smarter”—it excelled because it prioritizes accuracy above all, even refusing service to avoid misleading users. This design philosophy aligns perfectly with knowledge graph and authoritative source principles: trustworthiness trumps operability.

Contrast this with AIs that fabricate policy clauses to deliver “satisfactory answers.” They seem to solve user problems while planting catastrophic risk seeds. If someone acts on hallucinated legal advice, the consequences could be disastrous.

So when choosing AI tools, ask three questions:

1. Can I find the original source for its claims within 10 seconds?
2. When I feed it uncertain info, does it warn me or simply agree?
3. Does it clearly state its capability boundaries?

If all three answers are yes, you’ve likely found a trustworthy fact-checking tool. If not, it’s better suited as a creative assistant, not a decision-making foundation.

Real Results and Next Steps: When AI Tools Return to Real-World Value

That land dispute from the opening? Using the “Google AI Mode + Deep Think” workflow, we achieved breakthrough resolution by day three. Armed with 12 policy documents and AI-generated logical analysis, I negotiated face-to-face with the seller. Confronted with irrefutable clauses, they agreed to a full refund. The entire rights-protection cycle compressed from typical months to 72 hours.

This validated the core thesis: AI tools’ value isn’t replacing human experts, but boosting information acquisition and logical reasoning efficiency by an order of magnitude.

I’ll publish a full case复盘 (post-mortem) on my WeChat public account 【稀有学生】(Rare Student), detailing the evidence chain construction and negotiation scripts. Unlike those 400,000+ view marketing hits that are 97% AI-generated fluff, every detail here is verified through real judicial practice.

If you’re tired of AI circle hype and want to see genuine real-world problem-solving, this experience-driven, results-verified approach is what matters. Because when real money and legal rights are on the line, we don’t need AIs that chat well. We need tools bold enough to have their information verified, brave enough to refuse service, and wise enough to stay silent beyond their boundaries.

Frequently Asked Questions (FAQ)

Q1: What’s the fundamental difference between Google AI Mode and Gemini Deep Think?
A: Their core objectives are polar opposites. Google AI Mode is a “fact-checker,” focused on searching the web, summarizing facts, and providing verifiable footnote links. Its primary mission is accuracy. Gemini Deep Think is a “reasoner,” excelling at complex logical analysis, parallel thinking, and creative problem-solving from existing materials. Google helps you “find the right raw materials,” while Gemini helps you “make the best decisions from those materials.”

Q2: Why does Google AI Mode error out when I ask complex legal case questions?
A: This is a deliberate self-protection mechanism. When the system judges that a question cannot be reliably answered through public web searches (e.g., requiring evaluation of evidence chain completeness, party intent, etc.), it refuses service rather than risk generating misleading hallucinations. This “clear boundary” design is safer than fumbling out wrong analysis.

Q3: How can I tell if an AI tool’s information is trustworthy?
A: Follow the “Triple-Verifiable” principle: ① Source-Verifiable—Can you click through to the original webpage within 10 seconds? ② Content-Verifiable—Does the source actually contain the quoted sentence? ③ Timeliness-Verifiable—Does the policy document show publication date and validity? Google AI Mode shines on all three; pure generative AIs often fail.

Q4: What query types does Google AI Mode support best?
A: Based on testing, it dominates three scenarios: ① Localized policy/regulation queries (e.g., land management, local subsidies). ② Time-sensitive fact-checking (e.g., latest financial data, policy changes). ③ Precise concept definitions (e.g., specific terms in official documents). In these cases, its footnoted answers are ideal for building evidence chains.

Q5: What should I watch out for when using Google AI Mode?
A: The golden rule: Always click footnotes to verify the original. AI can misinterpret web content or mis-summarize. Only by verifying can you unlock its true value. Also, never input full personal privacy or sensitive case details. It’s for publicly verifiable factual questions, not confidential case consultations.

Q6: Can I fully rely on Google AI Mode for legal decisions?
A: Absolutely not. It only serves as an “information gathering assistant” to help you locate relevant policies rapidly. Legal decisions require integrating evidence completeness, local judicial interpretations, and case specifics—all beyond its scope. The correct workflow: use it to compile factual materials, then consult a qualified lawyer for final decisions.

Q7: Why can’t Gemini Deep Think find local policies in web mode?
A: Its data sources and algorithmic weighting skew toward international knowledge bases, academic papers, and English content. It crawls domestic municipal/county government portals and local policy documents at low frequency and priority, causing coverage and timeliness gaps. This isn’t a model capability issue—it’s a product positioning and data strategy limitation.

Q8: How do I build my own “AI tool combination workflow”?
A: Replicate this case’s two-step method: Step 1—Use Google AI Mode to gather verified factual materials and export to PDF. Step 2—Feed that to a reasoning AI like Gemini Deep Think for analysis. The key is letting specialized tools do specialized tasks: search tools for accuracy, reasoning tools for creativity. They complement, not replace, each other.

Q9: Who currently has access to Google AI Mode?
A: The feature isn’t fully launched. Some users see an “AI Mode” button next to Google’s search bar. If you have access, immediately test it on localized policy queries to experience its unique value. If not, Perplexity can serve as a temporary alternative with source tracing, though its localization capability remains inferior.

Q10: Can this 30,000 yuan deposit case methodology be replicated?
A: The core methodology is replicable: ① For localized policy problems, prioritize Google AI Mode for fact-finding. ② Feed verified materials to a reasoning AI. ③ Use the analysis for stakeholder negotiations. However, specific legal clauses vary by region and require fresh research. Success hinges on information accuracy and logical rigor, not templated scripts.