---

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

• Linear growth/decline
• Exponential growth/decline
• Cyclical pattern
• S-curve adoption
• Plateau reached
• Disruption event

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)

Resource	Purpose
technology-cycle-patterns.md	Technology adoption curves and cycles
market-cycle-patterns.md	Market evolution and consolidation patterns
business-model-evolution.md	Revenue model cycles and transitions
signal-vs-noise-filtering.md	Separating hype from substance
prediction-accuracy-tracking.md	Validating predictions over time

Templates (Outputs)

Template	Use For
trend-analysis-report.md	Full trend prediction report
technology-adoption-curve.md	Adoption stage mapping
market-timing-assessment.md	When to enter decision
cyclical-pattern-map.md	Historical pattern matching
prediction-hypothesis.md	Prediction with evidence
trend-opportunity-matrix.md	Trends → Opportunities

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

• startup-idea-validation - Market timing score
• startup-mega-router - Trend context for analysis
• product-management - Roadmap prioritization

Receives From

• startup-review-mining - Pain point trends over time
• startup-competitive-analysis - Competitor movement patterns