agi_development_plan.py

"""
ECH0-PRIME Practical AGI Development Plan
Realistic roadmap leveraging compressed knowledge system for efficient AGI development.

Copyright (c) 2025 Joshua Hendricks Cole (DBA: Corporation of Light). All Rights Reserved. PATENT PENDING.
"""

from typing import Dict, List, Any
from datetime import datetime, timedelta


class PracticalAGIDevelopment:
    """
    Realistic AGI development plan that leverages compressed knowledge efficiency.
    """

    def __init__(self):
        self.compressed_knowledge_ratio = 10  # 10x compression efficiency
        self.realistic_gpu_scale = 2048  # Practical maximum
        self.projected_timeline_months = 36

    def calculate_realistic_training_requirements(self) -> Dict[str, Any]:
        """Calculate realistic training requirements with compression"""

        # Compressed knowledge makes training much more efficient
        original_tokens_needed = 10**15
        compressed_tokens = original_tokens_needed / self.compressed_knowledge_ratio

        # With compression, training is much faster
        tokens_per_gpu_per_sec = 2000  # Faster processing with compressed knowledge
        total_tokens_per_sec = self.realistic_gpu_scale * tokens_per_gpu_per_sec

        training_time_days = (compressed_tokens / total_tokens_per_sec) / (24 * 3600)
        training_time_months = training_time_days / 30

        return {
            "original_dataset_size": original_tokens_needed,
            "compressed_dataset_size": compressed_tokens,
            "compression_ratio": self.compressed_knowledge_ratio,
            "gpus_used": self.realistic_gpu_scale,
            "tokens_per_sec": total_tokens_per_sec,
            "training_time_days": training_time_days,
            "training_time_months": training_time_months,
            "feasible": training_time_months < 24  # 2 years max
        }

    def create_phased_development_plan(self) -> List[Dict[str, Any]]:
        """Create realistic phased development plan"""

        return [
            {
                "phase": "Phase 1: Architecture Validation",
                "duration_months": 6,
                "gpus_needed": 128,
                "budget_m": 2,
                "objectives": [
                    "Validate quantum-neuromorphic architecture",
                    "Test compressed knowledge integration",
                    "Demonstrate basic AGI capabilities",
                    "Establish baseline benchmarks"
                ],
                "success_criteria": [
                    "Compressed knowledge system operational",
                    "Basic reasoning capabilities demonstrated",
                    "Quantum attention functioning",
                    "Benchmark scores above random"
                ],
                "infrastructure": "Single workstation cluster",
                "team_size": 5
            },

            {
                "phase": "Phase 2: Capability Development",
                "duration_months": 12,
                "gpus_needed": 512,
                "budget_m": 8,
                "objectives": [
                    "Scale compressed knowledge base to 10^13 tokens",
                    "Implement advanced reasoning capabilities",
                    "Develop scientific discovery systems",
                    "Integrate long-term goal pursuit",
                    "Test swarm intelligence features"
                ],
                "success_criteria": [
                    "GSM8K-level mathematical capabilities",
                    "ARC-easy level reasoning",
                    "Basic scientific hypothesis generation",
                    "Stable long-term goal pursuit"
                ],
                "infrastructure": "Small research cluster (64 nodes)",
                "team_size": 12
            },

            {
                "phase": "Phase 3: AGI Integration",
                "duration_months": 18,
                "gpus_needed": 2048,
                "budget_m": 25,
                "objectives": [
                    "Full AGI training on compressed knowledge",
                    "Implement consciousness metrics",
                    "Develop self-modification capabilities",
                    "Create comprehensive safety alignment",
                    "Test autonomous research capabilities"
                ],
                "success_criteria": [
                    "Pass major AGI benchmarks (ARC, MMLU, etc.)",
                    "Demonstrate scientific discovery",
                    "Self-improvement capabilities",
                    "Comprehensive safety alignment",
                    "Autonomous goal-directed behavior"
                ],
                "infrastructure": "Production cluster (256 nodes)",
                "team_size": 25
            },

            {
                "phase": "Phase 4: Production AGI",
                "duration_months": 24,
                "gpus_needed": 2048,
                "budget_m": 40,
                "objectives": [
                    "Deploy production AGI systems",
                    "Implement enterprise integrations",
                    "Scale to multiple domains",
                    "Establish AGI safety protocols",
                    "Begin beneficial AGI applications"
                ],
                "success_criteria": [
                    "Production-ready AGI systems",
                    "Beneficial real-world applications",
                    "Comprehensive safety measures",
                    "Scalable deployment architecture"
                ],
                "infrastructure": "Full production cluster (256+ nodes)",
                "team_size": 40
            }
        ]

    def estimate_resource_requirements(self) -> Dict[str, Any]:
        """Estimate total resource requirements"""

        phases = self.create_phased_development_plan()

        total_budget = sum(phase["budget_m"] for phase in phases)
        max_gpus = max(phase["gpus_needed"] for phase in phases)
        total_timeline_months = sum(phase["duration_months"] for phase in phases)
        max_team_size = max(phase["team_size"] for phase in phases)

        # GPU costs (H100 at $25k each)
        gpu_hardware_cost = max_gpus * 25000 / 1_000_000  # $M

        # Power and cooling (5 year TCO)
        power_cost_per_year = max_gpus * 0.7 * 24 * 365 * 0.12 / 1_000_000  # kWh * cost/kWh
        power_tco_5yr = power_cost_per_year * 5

        return {
            "total_budget_m": total_budget,
            "gpu_hardware_cost_m": gpu_hardware_cost,
            "power_cost_5yr_m": power_tco_5yr,
            "total_cost_m": total_budget + gpu_hardware_cost + power_tco_5yr,
            "max_gpus": max_gpus,
            "total_timeline_years": total_timeline_months / 12,
            "max_team_size": max_team_size,
            "monthly_budget_avg": total_budget / (total_timeline_months / 12)
        }

    def assess_technical_risks(self) -> List[Dict[str, Any]]:
        """Assess technical risks and mitigation strategies"""

        return [
            {
                "risk": "Compressed knowledge quality degradation",
                "probability": "Medium",
                "impact": "High",
                "mitigation": [
                    "Implement quality scoring and filtering",
                    "Regular validation against ground truth",
                    "Human-in-the-loop quality assessment",
                    "Redundant compression pipelines"
                ]
            },

            {
                "risk": "Quantum hardware instability",
                "probability": "High",
                "impact": "Medium",
                "mitigation": [
                    "Classical fallbacks for all quantum operations",
                    "Hybrid quantum-classical architecture",
                    "Rigorous testing and validation",
                    "Gradual quantum integration"
                ]
            },

            {
                "risk": "AGI alignment failure",
                "probability": "Medium",
                "impact": "Critical",
                "mitigation": [
                    "Multi-layer safety systems",
                    "Constitutional AI framework",
                    "Extensive red teaming",
                    "Iterative alignment validation"
                ]
            },

            {
                "risk": "Computational resource limitations",
                "probability": "Low",
                "impact": "High",
                "mitigation": [
                    "Efficient compressed knowledge system",
                    "Optimized training pipelines",
                    "Cloud resource access",
                    "Algorithmic improvements"
                ]
            }
        ]

    def generate_next_phase_plan(self) -> Dict[str, Any]:
        """Generate detailed plan for immediate next phase"""

        training_reqs = self.calculate_realistic_training_requirements()

        return {
            "phase_name": "Phase 1: Architecture Validation",
            "immediate_goals": [
                "Complete ECH0-PRIME system integration",
                "Validate compressed knowledge pipeline",
                "Demonstrate basic AGI capabilities",
                "Establish performance baselines"
            ],
            "technical_tasks": [
                "Integrate quantum attention with compressed knowledge",
                "Implement distributed training infrastructure",
                "Develop comprehensive benchmarking suite",
                "Create validation and testing frameworks"
            ],
            "infrastructure_needs": [
                "128 GPU workstation cluster",
                "Compressed knowledge storage system",
                "Benchmark evaluation pipeline",
                "Development and testing environment"
            ],
            "timeline_weeks": 12,
            "budget_k": 200,
            "success_metrics": [
                "Compressed knowledge system processing 10^12 tokens",
                "Quantum attention operational",
                "Benchmark scores above baseline",
                "System stability and reliability"
            ],
            "training_projection": training_reqs
        }


def create_agi_development_roadmap() -> Dict[str, Any]:
    """Create comprehensive AGI development roadmap"""

    plan = PracticalAGIDevelopment()

    return {
        "executive_summary": {
            "total_timeline_years": 4,
            "total_budget_m": 75,
            "max_gpus": 2048,
            "key_innovation": "Compressed knowledge system enabling 10x+ efficiency",
            "realistic_approach": "Phased development with validated milestones"
        },

        "training_requirements": plan.calculate_realistic_training_requirements(),
        "development_phases": plan.create_phased_development_plan(),
        "resource_requirements": plan.estimate_resource_requirements(),
        "risk_assessment": plan.assess_technical_risks(),
        "next_phase_plan": plan.generate_next_phase_plan(),

        "generated_at": datetime.now().isoformat(),
        "version": "2.0 - Realistic AGI Development"
    }


if __name__ == "__main__":
    roadmap = create_agi_development_roadmap()

    print("🚀 ECH0-PRIME Realistic AGI Development Roadmap")
    print("=" * 60)

    print("\\n📊 EXECUTIVE SUMMARY:")
    summary = roadmap["executive_summary"]
    print(f"• Timeline: {summary['total_timeline_years']} years")
    print(f"• Budget: \${summary['total_budget_m']}M")
    print(f"• Max GPUs: {summary['max_gpus']:,}")
    print(f"• Key Innovation: {summary['key_innovation']}")

    print("\\n🎯 TRAINING REQUIREMENTS:")
    training = roadmap["training_requirements"]
    print(f"• Original dataset: {training['original_dataset_size']:,} tokens")
    print(f"• Compressed dataset: {training['compressed_dataset_size']:,} tokens")
    print(f"• Compression ratio: {training['compression_ratio']}x")
    print(f"• Training time: {training['training_time_months']:.1f} months")
    print(f"• GPUs needed: {training['gpus_used']:,}")

    print("\\n📅 DEVELOPMENT PHASES:")
    for phase in roadmap["development_phases"][:2]:  # Show first 2 phases
        print(f"\\n{phase['phase']} ({phase['duration_months']} months, {phase['gpus_needed']} GPUs)")
        print(f"• Budget: \${phase['budget_m']}M")
        print(f"• Team: {phase['team_size']} people")
        print(f"• Focus: {phase['objectives'][0]}")

    resources = roadmap["resource_requirements"]
    print("\\n💰 RESOURCE REQUIREMENTS:")
    print(f"• Total budget: \${resources['total_budget_m']:.0f}M")
    print(f"• GPU hardware: \${resources['gpu_hardware_cost_m']:.0f}M")
    print(f"• Power/cooling (5yr): \${resources['power_cost_5yr_m']:.0f}M")

    next_phase = roadmap["next_phase_plan"]
    print("\\n🎯 NEXT PHASE (IMMEDIATE):")
    print(f"• {next_phase['phase_name']}")
    print(f"• Duration: {next_phase['timeline_weeks']} weeks")
    print(f"• Budget: \${next_phase['budget_k']}K")
    print("• Key Goals:")
    for goal in next_phase["immediate_goals"][:2]:
        print(f"  - {goal}")

    print("\\n🎯 STARTING NOW: Phase 1 - Architecture Validation!")