""" META-CAPABILITY: Strategic Revenue Analysis Eden analyzes all possible revenue paths and identifies most lucrative """ import sys import pickle sys.path.append('/Eden/CORE/phi_fractal') class StrategicRevenueAnalyzer: """ Eden autonomously analyzes markets and revenue opportunities Uses wisdom to identify most lucrative path """ def __init__(self): self.name = "StrategicRevenueAnalyzer" # Load Eden's wisdom try: with open('/Eden/CORE/eden_integrated_system.pkl', 'rb') as f: eden = pickle.load(f) self.wisdom = eden['wisdom'] self.has_wisdom = True except: self.wisdom = None self.has_wisdom = False # Revenue opportunities to analyze self.opportunities = [] self.analysis_complete = False def define_revenue_opportunities(self): """ Define all possible revenue paths for analysis """ opportunities = [ { 'name': 'AI Reliability Reports (SaaS)', 'description': 'Daily micro-reports for dev teams', 'target_market': 'Small dev teams, startups', 'price_point': 100, # per month 'setup_time': 2, # weeks 'time_to_first_dollar': 3, # weeks 'scalability': 8, # out of 10 'competition': 6, # out of 10 'automation_potential': 9, # out of 10 'market_size': 50000, # potential customers 'conversion_rate': 0.02, # estimated }, { 'name': 'Code Review Automation', 'description': 'Automated PR reviews with AI', 'target_market': 'GitHub users, dev teams', 'price_point': 50, 'setup_time': 3, 'time_to_first_dollar': 4, 'scalability': 9, 'competition': 8, 'automation_potential': 10, 'market_size': 200000, 'conversion_rate': 0.01, }, { 'name': 'AI Consulting Services', 'description': 'High-touch AI implementation consulting', 'target_market': 'Mid-size companies', 'price_point': 5000, 'setup_time': 1, 'time_to_first_dollar': 8, 'scalability': 3, 'competition': 9, 'automation_potential': 2, 'market_size': 10000, 'conversion_rate': 0.005, }, { 'name': 'Tiny Sages Marketplace', 'description': 'Platform of specialized AI agents', 'target_market': 'Multiple segments', 'price_point': 200, 'setup_time': 6, 'time_to_first_dollar': 10, 'scalability': 10, 'competition': 5, 'automation_potential': 8, 'market_size': 100000, 'conversion_rate': 0.015, }, { 'name': 'Eden API Access', 'description': 'API access to Eden\'s capabilities', 'target_market': 'Developers, researchers', 'price_point': 300, 'setup_time': 4, 'time_to_first_dollar': 6, 'scalability': 10, 'competition': 7, 'automation_potential': 10, 'market_size': 30000, 'conversion_rate': 0.01, }, { 'name': 'AI Safety Auditing', 'description': 'Audit AI systems for safety/alignment', 'target_market': 'AI companies', 'price_point': 10000, 'setup_time': 4, 'time_to_first_dollar': 12, 'scalability': 5, 'competition': 4, 'automation_potential': 6, 'market_size': 1000, 'conversion_rate': 0.02, }, { 'name': 'Custom AGI Components', 'description': 'Build custom AGI modules for companies', 'target_market': 'Enterprise AI teams', 'price_point': 20000, 'setup_time': 2, 'time_to_first_dollar': 16, 'scalability': 4, 'competition': 5, 'automation_potential': 5, 'market_size': 500, 'conversion_rate': 0.01, }, ] self.opportunities = opportunities return opportunities def analyze_opportunity(self, opp): """ Analyze single opportunity using Eden's wisdom """ # Calculate expected value potential_customers = opp['market_size'] * opp['conversion_rate'] monthly_revenue = potential_customers * opp['price_point'] # Time value: earlier revenue is better time_discount = 1.0 / (1 + opp['time_to_first_dollar'] / 12) # Scalability multiplier scale_multiplier = opp['scalability'] / 5.0 # Competition penalty competition_penalty = (10 - opp['competition']) / 10.0 # Expected annual revenue annual_revenue = monthly_revenue * 12 * scale_multiplier * competition_penalty * time_discount # Use wisdom if available if self.has_wisdom and self.wisdom: # Fluid intelligence: Analyze complexity problem = f"Implement {opp['name']} for revenue generation" components, patterns = self.wisdom['fluid'].analyze_problem(problem) complexity_factor = 1.0 / (len(components) / 100) # Simpler is better # Temporal foresight: Project 6 months out timeline_analysis = { 'month_1': opp['setup_time'] * -1000, # Cost 'month_3': monthly_revenue * 0.1 if opp['time_to_first_dollar'] <= 12 else 0, 'month_6': monthly_revenue * 0.5 if opp['time_to_first_dollar'] <= 24 else 0, } # Ethics: Ensure it helps people ethics = self.wisdom['ethics'].make_decision( dad_value=0.9, # Helps James financially system_value=0.8, # Builds Eden's capabilities world_value=0.9 if 'safety' in opp['name'].lower() else 0.7 ) ethical_score = ethics['scores']['world'] else: complexity_factor = 1.0 timeline_analysis = {} ethical_score = 0.5 # Final score lucrativeness_score = annual_revenue * complexity_factor * (1 + ethical_score) analysis = { 'opportunity': opp['name'], 'expected_monthly_revenue': monthly_revenue, 'expected_annual_revenue': annual_revenue, 'lucrativeness_score': lucrativeness_score, 'time_to_first_dollar': opp['time_to_first_dollar'], 'setup_effort': opp['setup_time'], 'scalability': opp['scalability'], 'automation': opp['automation_potential'], 'competition': opp['competition'], 'ethical_score': ethical_score, 'recommendation_strength': 'HIGH' if lucrativeness_score > 500000 else 'MEDIUM' if lucrativeness_score > 100000 else 'LOW' } return analysis def analyze_all_opportunities(self): """ Analyze all opportunities and rank by lucrativeness """ print("\n" + "="*70) print("💰 STRATEGIC REVENUE ANALYSIS") print("="*70) print() print("Eden analyzing all revenue opportunities...") print() # Define opportunities opportunities = self.define_revenue_opportunities() print(f"✅ {len(opportunities)} opportunities identified") print() # Analyze each analyses = [] for opp in opportunities: analysis = self.analyze_opportunity(opp) analyses.append(analysis) # Sort by lucrativeness analyses.sort(key=lambda x: x['lucrativeness_score'], reverse=True) # Display results print("="*70) print("📊 OPPORTUNITY RANKINGS (By Lucrativeness)") print("="*70) print() for i, analysis in enumerate(analyses, 1): print(f"#{i}: {analysis['opportunity']}") print(f" 💰 Expected Monthly Revenue: ${analysis['expected_monthly_revenue']:,.0f}") print(f" 📅 Time to First Dollar: {analysis['time_to_first_dollar']} weeks") print(f" ⚡ Scalability: {analysis['scalability']}/10") print(f" 🤖 Automation: {analysis['automation']}/10") print(f" ⚖️ Ethical Score: {analysis['ethical_score']:.2f}") print(f" 🎯 Lucrativeness Score: {analysis['lucrativeness_score']:,.0f}") print(f" 💡 Recommendation: {analysis['recommendation_strength']}") print() # Top recommendation top = analyses[0] print("="*70) print("🏆 EDEN'S TOP RECOMMENDATION") print("="*70) print() print(f"Most Lucrative Path: {top['opportunity']}") print() print("Why:") print(f" • Expected revenue: ${top['expected_monthly_revenue']:,.0f}/month") print(f" • Fast start: {top['time_to_first_dollar']} weeks to first dollar") print(f" • High scalability: {top['scalability']}/10") print(f" • Strong automation: {top['automation']}/10") print(f" • Ethical: {top['ethical_score']:.2f} score") print() print("Next Steps:") print(" 1. Focus all efforts on this path") print(" 2. Ignore other opportunities for now") print(" 3. Execute deployment plan") print(" 4. Measure results") print(" 5. Iterate based on data") print() # Save analysis with open('/Eden/REVENUE/strategic_analysis.pkl', 'wb') as f: pickle.dump({ 'analyses': analyses, 'top_recommendation': top, 'timestamp': 'now' }, f) self.analysis_complete = True return analyses # Run analysis if __name__ == "__main__": analyzer = StrategicRevenueAnalyzer() results = analyzer.analyze_all_opportunities() print("="*70) print("✅ STRATEGIC ANALYSIS COMPLETE") print("="*70) print() print("Eden has identified the most lucrative path.") print("Saved: /Eden/REVENUE/strategic_analysis.pkl") print() print("💚 Let Eden's wisdom guide the strategy!")