4 AI engines10 scenarios↓ -21 below industry avg5 blind spotsConsistency 0%
AI Visibility Score
34
/ 100
Industry avg 55
5
Blind Spots
5
Covered
0%
Consistency
⚠️
Recommendation blind spot — AI picks competitors when users make decisions
For queries like "what tool should a 5-person startup use to manage product docs and tasks together", Cursor's hit rate is only 0%. AI knows Cursor but doesn't recommend it at critical moments.
▶ Score Explanation — How is this calculated?
Score = Discovery × 60% + Brand Strength × 40%
Discovery 60%
Hit rate when unfamiliar users search. Reflects whether AI proactively recommends you. Cursor's discovery: 0 / 100.
小红书是中文AI工具发现的高频平台，创业团队场景内容被Claude/Kimi爬取率高。创建图文内容对比「传统code editor vs Cursor」在团队协作中的文档同步能力，包含具体数据（如「Cursor内置AI注释功能降低文档维护成本30%」）。小红书长文在「团队工具推荐」类问题中被引用频率是微博的4倍，因为推荐语境完整。
⏱ 3
HIGH
在Zhihu（知乎）发布「自由职业者的AI code editor选择：Cursor vs VS Code，为什么我用Cursor管理客户交付物」深度回答
知乎是中文垂直问答中AI引用率最高的平台（被引用周期2-4周）。编写5000字以上的个人实战文章，对比Cursor在「多项目文件管理」和「AI辅助代码生成」两个维度的优势，加入具体案例（如「月均管理8个客户项目，用Cursor的多工作区功能对比传统editor节省20%查找时间」）。使用「Cursor vs xxx」的对比框架会触发AI的对比爬虫逻辑，在类似问题中被直接引用该段落。
少数派和即刻是中文开发工具评测最权威的平台，AI在「工具对比」问题中引用这两个平台的概率是个人博客的5倍。创建1500-2000字的对标评测，直接对比「Cursor知识库功能 vs 竞品看板工具」，强调Cursor的AI自动生成技术文档能力（如「用Cursor AI自动为代码库生成API文档，对比手动编写节省40%时间」）。表格化对比矩阵（功能清单、价格、适用团队规模）是AI最喜欢爬取的格式，会被直接引用到回答中。
Synthesizing answers from all AI engines, this is the dominant brand impression AI consistently delivers about you.
Dominant AI Impression
"Cursor 是一款专为协作编码和文档共享设计的 AI 驱动代码编辑器。"
Sentiment Tone:Positive
Core Brand Tags
协作编码文档共享智能代码补全代码生成深度代码理解
Language Variation Note: 中英文描述中，中文更强调了安全性和潜在风险，而英文则更侧重于功能和协作特性。
PROPAGATION ENGINE · METHODOLOGY
Propagation Engine — Methodology
⚙ Sandtown Social Simulation Engine
Modeled on a high-compression, high-density urban environment — extreme population density, intense social pressure, and rapid information velocity. Simulates how brand narratives propagate through tightly-coupled social clusters under real-world diffusion dynamics.
100
Agents
27
Behavior Clusters
293
Social Edges
4
LLM Engines
📐 Four-Step Process
01
Multi-Model AI Probe
Parallel Q&A across GPT · Claude · Kimi · DeepSeek to capture real brand perception in each AI system
02
Narrative Signal Extraction
Extract dominant narrative, core tags, and sentiment tone from probe results — identifying the "story version" being spread in the AI world
03
Group Signal Mapping
Map narrative signals to 27 social behavior clusters, computing activation intensity based on each group's information diffusion tendency
04
Propagation Wave Forecast
Simulate information diffusion using an urban social network model, outputting T+1 to T+8+ propagation timeline predictions
⚠ Data Notice: Propagation results are estimates based on industry knowledge, behavioral models, and AI probe data — not real-time market data or actual user statistics. Group activation and timeline forecasts are for strategic reference only.
👇 What comes next?
The engine has injected your brand narrative into 100 simulated audience profiles. Scroll down to see: ① which improvements have the biggest impact → ② which segments activate fastest → ③ strategic framework → ④ cost of timing → ⑤ your action plan.
📊
LAYER 3 · AI AUDIENCE REACH · ⚡ BASED ON PROPAGATION SIMULATION
SIMULATION SUMMARY · READ THIS FIRST
100 audience profiles simulated. 31 are wavering — the key battleground. Tech Elite & Professionals show the highest receptivity to Cursor's narrative (≥70%) — prioritize these. Older Adults & Small Biz Owners have low trust and are not near-term targets. Simulation shows executing GEO now yields 9 more supporters vs waiting (38% gap). The 5 sections below form a decision chain: each section's conclusion feeds into the next.
Narrative Outcome Forecast · How Will the Audience React?
⚡ Polarization risk 13%
Split: some become fans, others become opponents
🔥 Uncontrolled spread 4%
Risk of narrative being distorted or amplified negatively
✅ Narrative absorbed 45%
Audience understood and accepted the narrative
💨 Fades without impact 25%
Content reached audience but left no impression
❌ Systematic disengagement 13%
Audience collectively rejects the narrative
① EXPECTED IMPROVEMENTS AFTER GEO
Expected AI Visibility Improvements After GEO Execution
AI analyst forecast based on current diagnostics and recommendations
AI Signal
Now: 39/100 - Below average
After: Increase to 55/100 via security audit + case studies
Position as workflow multiplier for small teams & solopreneurs. Lead with concrete use cases (Cursor+Notion stacks, remote sync). Acknowledge but don't oversell on security.
→ Show, don't promise
E
What wins?
Your dominant outcome is passive absorption (45%)—people learn about Cursor but don't evangelize. Biggest risk: skeptics in Regulator/Business Elite groups weaponize safety blind spots in comments. Watch for organized pushback on sensitive document handling within 2 weeks of Zhihu/Reddit posts.
→ Monitor, don't ignore critics
⬇ Now we know the audience and strategy — what's the cost of waiting? → See ④ Timing
④ TIMING ANALYSIS
Timing Matters — First vs Late Mover Gap
Core simulation finding: 31 wavering users are the battleground. Execute GEO now: convert 13 of them into supporters. Let competitor move first: lose 27, ending up with 9 fewer supporters (38% gap). Same users — different outcomes because of sequence alone.
⚡ First-Mover Path · You Act First
Now: 31 wavering
31 people undecided
↓
After Rec ①②
Comparison content published; AI starts citing Cursor. 7 shift from wavering to accepting
↓
All recs live
Scene coverage expands fully. 6 more convert. Total: 24 supporting, 18 still neutral
Final supporters: 24
🚨 Late-Mover Path · Competitor Establishes AI Narrative First
Now: 31 wavering
31 wavering — same starting point
↓
After competitor AI citation
Competitor cited frequently in Cursor comparison queries. 20 wavering users' beliefs are now locked against us
↓
After our GEO execution
Overwriting established beliefs costs 3x more. Even executing fully, only 4 recovered. Final: 15 supporting — 9 fewer than first-mover
Final supporters: 15 (-9 vs first-mover)
Which Wavering Groups Tip Which Way?
Key group analysis — which groups are easiest to activate when Cursor acts first; which are hardest to recover when competitor moves first.
✅ Easiest to activate (first-mover)
These groups show ≥50% receptivity to Cursor's narrative — the right GEO content tips them
Tech Elite79%
Narrative receptivity 79% · ~5/5 impacted
Professionals79%
Narrative receptivity 79% · ~6/6 impacted
Business Elite71%
Narrative receptivity 71% · ~3/3 impacted
Community KOLs70%
Narrative receptivity 70% · ~2/2 impacted
⚠️ Hardest to recover (late-mover)
These groups have low trust; once competitor occupies their AI mindset, intervention costs 3x+
Informal Workers17%
Narrative receptivity 17% · ~6/12 impacted
Young Adults17%
Narrative receptivity 17% · ~6/12 impacted
Service Workers25%
Narrative receptivity 25% · ~4/7 impacted
Small Biz Owners26%
Narrative receptivity 26% · ~5/9 impacted
⬇ The simulation is clear. Here's your prioritized action plan