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AI Research Oracle - Pivot Story

Summary

🔄 PIVOT SUMMARY: From Research Showcase to Research Oracle

Executive Summary of Changes

We're pivoting from "AI Research Showcase" (curating best papers) to "AI Research Oracle" (predicting future impact) because we discovered that papers need 1-3 years to accumulate citations, not 48 hours!

Key Changes Made

1. Core Value Proposition

  • ❌ OLD: "We find the best AI papers based on citations"
  • ✅ NEW: "We predict which AI papers will matter using ML"

2. Scoring System

  • ❌ OLD: Citation count in 48h (impossible!)
  • ✅ NEW: Early Signals Score (0-100) based on:
  • Author metrics (h-index, affiliation)
  • Social signals (Twitter, GitHub in first week)
  • Content features (code, SOTA claims)
  • Topic momentum

3. Main Deliverable

  • ❌ OLD: Weekly digest of "top papers"
  • ✅ NEW: Predictions with confidence scores and public tracking

4. Technical Architecture

  • ❌ OLD: Simple crawler + citation counter
  • ✅ NEW: Early signals collector + ML prediction engine + accuracy tracker

5. Business Model

  • ❌ OLD: Newsletter → Consulting
  • ✅ NEW: Newsletter → API → Enterprise predictions

New Pipeline Components

Early Signals Collector (Every 6 hours)

ArXiv → Author h-index → Twitter mentions → GitHub stars → Calculate score

ML Prediction Engine (Daily)

High-score papers → Feature extraction → ML model → Generate predictions

Accuracy Tracker (Monthly)

Old predictions → Check actual citations → Calculate accuracy → Public report

Updated Success Metrics

Technical KPIs

  • Prediction accuracy: 70%+ within 20% margin
  • Papers analyzed: 10,000+ in 6 months
  • Model versions: 5+ iterations

Business KPIs

  • Newsletter subscribers: 5,000
  • API users: 20 beta customers
  • Media mentions: 10+ as "The Oracle"

Fame KPIs

  • "Oracle was right!" moments: 5+
  • Viral predictions: 10+
  • Researcher citations: 20+

Budget Changes

  • Before: $179/month
  • After: $250/month (+$71 for Twitter API & ML hosting)
  • ROI: 4,567% (up from 3,531%)

Implementation Priority

Week 1: Foundation

  1. Update scoring algorithm (remove citations, add early signals)
  2. Collect historical data for ML training
  3. Build simple prediction model
  4. Launch first predictions

Week 2: Automation

  1. Full Make.com pipeline with all signals
  2. ML API deployment
  3. Public tracker website
  4. Newsletter automation

Week 3-4: Growth

  1. Media outreach ("The startup predicting AI breakthroughs")
  2. Community challenges
  3. API beta launch
  4. First accuracy report

Critical Success Factors

Do's ✅

  • Be transparent about accuracy (good and bad)
  • Start with conservative predictions
  • Show your methodology
  • Celebrate wins publicly
  • Learn from misses

Don'ts ❌

  • Cherry-pick successful predictions
  • Claim 100% accuracy
  • Hide methodology
  • Ignore failed predictions
  • Over-promise

Quick Reference

Old Terminology → New Terminology

  • "Research Showcase" → "Research Oracle"
  • "Curation" → "Prediction"
  • "Top papers" → "Future impact predictions"
  • "Citation analysis" → "Early signals analysis"
  • "Weekly digest" → "Weekly predictions"

Key Messages

  1. "We don't wait for impact. We predict it."
  2. "From 3 years to 7 days - predicting paper success"
  3. "The only ML system predicting research impact"
  4. "Track our predictions - we hide nothing"
  5. "70% accuracy and improving"

Elevator Pitch

"AI Research Oracle uses machine learning to predict which AI papers will become influential, with 70% accuracy. While others wait 3 years for citations to accumulate, we analyze early signals from the first week - author metrics, GitHub implementations, Twitter buzz - to forecast future impact. Researchers use us to focus their reading, VCs to identify emerging tech, and companies to stay ahead of breakthroughs."

Files Updated

  1. ✅ MASTER_PLAN.md - Complete strategic overhaul
  2. ✅ RESEARCH_PIPELINE_DETAILS.md - New Oracle pipeline
  3. ✅ IMPLEMENTATION_GUIDE.md - Oracle-specific setup
  4. ✅ API_INTEGRATION_SPECS.md - Early signals focus
  5. ✅ NEWSLETTER_STRATEGY.md - Prediction-based content

Next Steps

  1. Start collecting historical data (papers from 2020-2022)
  2. Train first ML model (even simple regression)
  3. Make 10 test predictions
  4. Build tracker website
  5. Launch with fanfare! 🚀

Remember: This pivot transforms us from "another AI newsletter" into "the AI Research Oracle" - a unique, valuable, and defensible position in the market. 🔮