| name | startup-trend-prediction |
| description | Analyze 2-3 year historical trends in technology, market, and business models to predict 1-2 years ahead. Uses pattern recognition, adoption curves, and cycle analysis to identify timing windows and emerging opportunities. History is cyclical - products and markets follow predictable patterns.
|
| globs | ["**/*.md","**/research/**","**/trends/**","**/analysis/**"] |
Startup Trend Prediction
Systematic framework for analyzing historical trends to predict future opportunities. Look back 2-3 years to predict 1-2 years ahead.
When to Use This Skill
| Trigger | Action |
|---|
| "When should I enter this market?" | Run timing analysis |
| "What's trending in [technology/market]?" | Run trend identification |
| "Is this trend rising or peaking?" | Run adoption curve analysis |
| "What comes after [current trend]?" | Run cycle prediction |
| "Historical patterns for [topic]" | Run pattern recognition |
| "2-3 year trends" or "predict 1-2 years" | Full trend prediction workflow |
Quick Reference: Trend Categories
Technology Trends
| Trend Area | 2022 State | 2023 State | 2024 State | 2025-26 Prediction |
|---|
| AI/ML | GPT-3, ChatGPT launch | GPT-4, AI hype peak | Agents, RAG, fine-tuning | Agentic AI mainstream, multi-modal default |
| Infrastructure | Cloud-native default | Serverless growth | Edge computing rise | Edge AI, hybrid deployments |
| Developer Tools | GitHub Copilot launch | AI assistants proliferate | AI-native IDEs | Autonomous coding, AI PR reviews |
| Data | Lakehouse emergence | Real-time analytics | Streaming-first | Embedded analytics, AI-native data |
Market Trends
| Trend Area | 2022 State | 2023 State | 2024 State | 2025-26 Prediction |
|---|
| GTM Motion | PLG dominant | PLG + Sales hybrid | AI-assisted everything | Agent-to-agent sales |
| Pricing | Subscription default | Usage-based rise | Hybrid models | Outcome-based pricing |
| Consolidation | Point solutions | Platform plays begin | Vertical platforms | Industry-specific AI |
| Buyer Behavior | Self-serve preference | Research-heavy buying | AI-assisted procurement | Autonomous buying |
Business Model Trends
| Trend Area | 2022 State | 2023 State | 2024 State | 2025-26 Prediction |
|---|
| Revenue | SaaS dominant | Usage-based growth | Hybrid SaaS + usage | Outcome/success fees |
| Distribution | Marketplace growth | Embedded solutions | API-first | Agent marketplaces |
| Moats | Data moats | Network effects | Workflow lock-in | Agent ecosystems |
| Funding | Peak valuations | Down rounds, efficiency | Recovery, AI focus | AI-native premium |
Adoption Curve Framework
Rogers Diffusion Model
ADOPTION CURVE
│
│ ╭────────╮
│ ╭───╯Late │
│ ╭───╯Majority │
│ ╭───╯Early │
│ ╭───╯Majority │
│ ╭───╯Early │
│ ╭───╯Adopters │
│──╯Innovators ╰──────
│ │ │ │ │ │
│ 2.5% 13.5% 34% 34% 16%
└─────────────────────────────────────────▶
TIME
Position Identification
| Position | Market Penetration | Characteristics | Strategy |
|---|
| Innovators | <2.5% | Tech enthusiasts, high risk tolerance | Enter now, shape market |
| Early Adopters | 2.5-16% | Visionaries, want competitive edge | Enter now, premium pricing |
| Early Majority | 16-50% | Pragmatists, need proof | Enter with differentiation |
| Late Majority | 50-84% | Conservatives, follow herd | Compete on price/features |
| Laggards | 84-100% | Skeptics, forced adoption | Avoid or disrupt |
Gartner Hype Cycle Mapping
HYPE CYCLE
│
│ Peak of
│ Inflated ╭─────────────
│ Expectations ╭───╯ Plateau of
│ ╭────╯ Productivity
│ ╭────╯
│ ╭────╯ Slope of
│──╯ Enlightenment
│ Technology ╲_____╱
│ Trigger Trough of
│ Disillusionment
└─────────────────────────────────────▶
TIME
| Phase | Duration | Action |
|---|
| Technology Trigger | 0-2 years | Monitor, experiment |
| Peak of Inflated Expectations | 1-3 years | Caution, don't overbuild |
| Trough of Disillusionment | 1-3 years | Build foundations |
| Slope of Enlightenment | 2-4 years | Scale solutions |
| Plateau of Productivity | 5+ years | Optimize, commoditize |
Cycle Pattern Library
Technology Cycles (7-10 years)
| Cycle | Previous Instance | Current Instance | Pattern |
|---|
| Client → Cloud → Edge | Desktop → Web → Mobile | Cloud → Edge → Device AI | Compute moves to data |
| Monolith → Services → Agents | SOA → Microservices | Microservices → AI Agents | Decomposition continues |
| Batch → Stream → Real-time | ETL → Streaming | Streaming → Real-time AI | Latency shrinks |
| Manual → Assisted → Autonomous | IDE → Copilot | Copilot → Autonomous | Automation increases |
Market Cycles (5-7 years)
| Cycle | Previous Instance | Current Instance | Pattern |
|---|
| Fragmentation → Consolidation | 2015-2020 point solutions | 2020-2025 platforms | Bundling/unbundling |
| Horizontal → Vertical | Horizontal SaaS | Vertical AI platforms | Specialization wins |
| Self-serve → High-touch → Hybrid | PLG pure | PLG + Sales | Motion evolves |
Business Model Cycles (3-5 years)
| Cycle | Previous Instance | Current Instance | Pattern |
|---|
| Perpetual → Subscription → Usage | License → SaaS | SaaS → Usage-based | Payment follows value |
| Direct → Marketplace → Embedded | Direct sales | Marketplace → Embedded | Distribution evolves |
Signal vs Noise Framework
Strong Signals (High Confidence)
| Signal Type | Detection Method | Weight |
|---|
| VC funding patterns | Track quarterly investment | High |
| Big tech acquisitions | Monitor M&A announcements | High |
| Job posting trends | Analyze LinkedIn/Indeed data | High |
| GitHub activity | Stars, forks, contributors | High |
| Enterprise adoption | Gartner/Forrester reports | Very High |
Moderate Signals (Validate)
| Signal Type | Detection Method | Weight |
|---|
| Conference talk themes | Track KubeCon, AWS re:Invent | Medium |
| Hacker News sentiment | Algolia search trends | Medium |
| Reddit discussions | Subreddit growth, sentiment | Medium |
| Influencer adoption | Key voices tweeting about | Medium |
Weak Signals (Monitor)
| Signal Type | Detection Method | Weight |
|---|
| ProductHunt launches | Daily tracking | Low |
| Blog post frequency | Content analysis | Low |
| Podcast mentions | Episode scanning | Low |
| Media hype | TechCrunch, Wired articles | Low (often lagging) |
Noise Filters
Exclude from prediction:
- Single viral tweet without follow-up
- PR-driven announcements without product
- Predictions from parties with financial interest
- Old data recycled as "new trend"
Prediction Methodology
Step 1: Define Scope
Domain: [Technology / Market / Business Model]
Lookback Period: [2-3 years]
Prediction Horizon: [1-2 years]
Geography: [Global / Region-specific]
Industry: [Horizontal / Specific vertical]
Step 2: Gather Historical Data
| Year | State | Key Events | Metrics |
|---|
| {{YEAR-3}} | | | |
| {{YEAR-2}} | | | |
| {{YEAR-1}} | | | |
| {{NOW}} | | | |
Step 3: Identify Patterns
Step 4: Generate Prediction
## Prediction: [TOPIC]
**Thesis**: [1-2 sentence prediction]
**Confidence**: High / Medium / Low
**Timing**: [When this will happen]
**Evidence**: [3-5 supporting data points]
**Counter-evidence**: [What could invalidate]
Step 5: Identify Opportunities
| Opportunity | Timing Window | Competition | Action |
|---|
| {{OPP_1}} | {{WINDOW}} | Low/Med/High | Build/Watch/Avoid |
| {{OPP_2}} | {{WINDOW}} | | |
Navigation
Resources (Deep Dives)
Templates (Outputs)
Data
| File | Contents |
|---|
| sources.json | Trend data sources (Gartner, CB Insights, State of AI, etc.) |
Key Principles
History Rhymes
Past patterns repeat with new technology:
- Client-server → Web apps → Mobile → Edge AI
- Mainframe → PC → Cloud → Distributed
- Manual → Automated → AI-assisted → Autonomous
Timing Beats Being Right
Being right about a trend but wrong about timing = failure:
- Too early: Market not ready, burn runway
- Too late: Established players, commoditized
- Just right: Ride the wave
Multiple Signals Required
Never bet on single signal:
- Funding + Hiring + GitHub activity = Strong signal
- Just media coverage = Hype, validate further
- Just VC interest = May be speculative
Update Predictions
Predictions are living documents:
- Revisit quarterly
- Track accuracy over time
- Adjust for new data
- Document what changed and why
Integration Points
Feeds Into
Receives From
