Deep Dive into Microsoft Frontier Company: How a $2.5B FDE Deployment is Reshaping AI Commercialization

package main

import (
    "context"
    "encoding/json"
    "fmt"
    "log"
    "math/rand"
    "sort"
    "strings"
    "sync"
    "time"
)

// ── Data Types ──

type ModelProvider string
const (
    ProviderOpenAI     ModelProvider = "openai"
    ProviderAnthropic  ModelProvider = "anthropic"
    ProviderAzure      ModelProvider = "azure"
    ProviderOpenSource ModelProvider = "opensource"
)

type ModelCapability string
const (
    CapCoding      ModelCapability = "coding"
    CapReasoning   ModelCapability = "reasoning"
    CapToolUse     ModelCapability = "tool_use"
    CapLongContext ModelCapability = "long_context"
    CapLowCost     ModelCapability = "low_cost"
)

type ModelSpec struct {
    Name         string            `json:"name"`
    Provider     ModelProvider     `json:"provider"`
    Capabilities []ModelCapability  `json:"capabilities"`
    InputPrice   float64           `json:"input_price_per_mtok"`
    OutputPrice  float64           `json:"output_price_per_mtok"`
    Weight       float64           `json:"weight"`
    Healthy      bool              `json:"healthy"`
}

type RoutingRequest struct {
    Prompt       string            `json:"prompt"`
    RequiredCaps []ModelCapability `json:"required_capabilities"`
    Priority     string            `json:"priority"` // "cost", "quality", "latency"
}

type RoutingDecision struct {
    SelectedModel  string   `json:"selected_model"`
    Provider       string   `json:"provider"`
    EstimatedCost  float64  `json:"estimated_cost"`
    Confidence     float64  `json:"confidence"`
    FallbackModels []string `json:"fallback_models"`
}

type MultiModelRouter struct {
    mu              sync.RWMutex
    models          map[string]*ModelSpec
    latencyHistory  map[string][]time.Duration
}

func NewMultiModelRouter() *MultiModelRouter {
    r := &MultiModelRouter{
        models:         make(map[string]*ModelSpec),
        latencyHistory: make(map[string][]time.Duration),
    }
    go r.healthCheckLoop()
    return r
}

func (r *MultiModelRouter) RegisterModel(spec *ModelSpec) {
    r.mu.Lock()
    defer r.mu.Unlock()
    spec.Healthy = true
    r.models[spec.Name] = spec
    log.Printf("[Router] Registered: %s ($%.2f/$%.2f per Mtok)",
        spec.Name, spec.InputPrice, spec.OutputPrice)
}

func (r *MultiModelRouter) Route(ctx context.Context, req *RoutingRequest) (*RoutingDecision, error) {
    r.mu.RLock()
    defer r.mu.RUnlock()

    var candidates []*ModelSpec
    for _, model := range r.models {
        if !model.Healthy { continue }
        if !r.hasAllCaps(model, req.RequiredCaps) { continue }
        candidates = append(candidates, model)
    }

    if len(candidates) == 0 {
        return nil, fmt.Errorf("no healthy model with required capabilities")
    }

    switch req.Priority {
    case "cost":
        sort.Slice(candidates, func(i, j int) bool {
            return candidates[i].InputPrice+candidates[i].OutputPrice <
                candidates[j].InputPrice+candidates[j].OutputPrice
        })
    case "quality":
        sort.Slice(candidates, func(i, j int) bool {
            return len(candidates[i].Capabilities) > len(candidates[j].Capabilities)
        })
    default:
        return r.weightedSelect(candidates), nil
    }

    selected := candidates[0]
    var fallbacks []string
    for i := 1; i < len(candidates) && i < 3; i++ {
        fallbacks = append(fallbacks, candidates[i].Name)
    }

    return &RoutingDecision{
        SelectedModel:  selected.Name,
        Provider:       string(selected.Provider),
        EstimatedCost:  r.estimateCost(selected, req.Prompt),
        Confidence:     0.9,
        FallbackModels: fallbacks,
    }, nil
}

func (r *MultiModelRouter) weightedSelect(candidates []*ModelSpec) *RoutingDecision {
    totalWeight := 0.0
    for _, c := range candidates { totalWeight += c.Weight }
    roll := rand.Float64() * totalWeight
    cumulative := 0.0
    for _, c := range candidates {
        cumulative += c.Weight
        if roll <= cumulative {
            return &RoutingDecision{SelectedModel: c.Name, Provider: string(c.Provider), Confidence: 0.85}
        }
    }
    last := candidates[len(candidates)-1]
    return &RoutingDecision{SelectedModel: last.Name, Provider: string(last.Provider), Confidence: 0.75}
}

func (r *MultiModelRouter) hasAllCaps(model *ModelSpec, required []ModelCapability) bool {
    capSet := make(map[ModelCapability]bool)
    for _, c := range model.Capabilities { capSet[c] = true }
    for _, req := range required {
        if !capSet[req] { return false }
    }
    return true
}

func (r *MultiModelRouter) estimateCost(model *ModelSpec, prompt string) float64 {
    inputTokens := len(strings.Fields(prompt)) * 2
    outputTokens := inputTokens / 3
    inputCost := (float64(inputTokens) / 1_000_000.0) * model.InputPrice
    outputCost := (float64(outputTokens) / 1_000_000.0) * model.OutputPrice
    return inputCost + outputCost
}

func (r *MultiModelRouter) healthCheckLoop() {
    ticker := time.NewTicker(30 * time.Second)
    for range ticker.C {
        r.mu.Lock()
        for name, model := range r.models {
            model.Healthy = true // In production: actual health check
        }
        r.mu.Unlock()
    }
}

func main() {
    router := NewMultiModelRouter()
    
    router.RegisterModel(&ModelSpec{
        Name: "gpt-5.6-sol", Provider: ProviderOpenAI,
        Capabilities: []ModelCapability{CapCoding, CapReasoning, CapToolUse},
        InputPrice: 15.0, OutputPrice: 75.0, Weight: 1.0,
    })
    router.RegisterModel(&ModelSpec{
        Name: "claude-fable-5", Provider: ProviderAnthropic,
        Capabilities: []ModelCapability{CapReasoning, CapCoding, CapLongContext},
        InputPrice: 5.0, OutputPrice: 25.0, Weight: 1.5,
    })
    router.RegisterModel(&ModelSpec{
        Name: "deepseek-v4", Provider: ProviderOpenSource,
        Capabilities: []ModelCapability{CapCoding, CapLowCost, CapReasoning},
        InputPrice: 0.5, OutputPrice: 2.0, Weight: 2.5,
    })

    req := &RoutingRequest{
        Prompt: "Implement a production-grade Kubernetes operator for model deployment",
        RequiredCaps: []ModelCapability{CapCoding, CapToolUse},
        Priority: "cost",
    }
    
    decision, _ := router.Route(context.Background(), req)
    result, _ := json.MarshalIndent(decision, "", "  ")
    fmt.Printf("Routing Decision:\n%s\n", string(result))
    
    fmt.Println("\n=== Monthly Cost Comparison (1M requests @ 2K tokens) ===")
    for _, name := range []string{"gpt-5.6-sol", "claude-fable-5", "deepseek-v4"} {
        if m, ok := router.models[name]; ok {
            cost := (6_000_000.0 / 1_000_000.0) * (m.InputPrice + m.OutputPrice) * 30
            fmt.Printf("  %-20s: $%.2f/month\n", name, cost)
        }
    }
}

type DataSovereigntyGateway struct {
    customerID    string
    auditLog      []AuditEntry
}

type AuditEntry struct {
    Timestamp time.Time `json:"timestamp"`
    Operation string    `json:"operation"`
    ModelName string    `json:"model_name"`
    DataHash  string    `json:"data_hash"`
    DataSize  int       `json:"data_size_bytes"`
}

func (g *DataSovereigntyGateway) LogAudit(operation, modelName string, data []byte) {
    g.auditLog = append(g.auditLog, AuditEntry{
        Timestamp: time.Now(),
        Operation: operation,
        ModelName: modelName,
        DataHash:  fmt.Sprintf("%x", sha256.Sum256(data)),
        DataSize:  len(data),
    })
}

from enum import Enum
from dataclasses import dataclass
from typing import List
import datetime


class ProjectStage(Enum):
    DISCOVERY = "discovery"
    FEASIBILITY = "feasibility"
    PROTOTYPE = "prototype"
    DEPLOYMENT = "deployment"
    OPTIMIZATION = "optimization"


class RiskLevel(Enum):
    LOW = "low"
    MEDIUM = "medium"
    HIGH = "high"


@dataclass
class BusinessProcess:
    name: str
    data_readiness: float
    pain_points: List[str]
    integration_complexity: RiskLevel


@dataclass
class FDEMetrics:
    stage: ProjectStage
    time_elapsed_days: int
    model_calls: int
    cost_saved: float
    business_impact: float


class FDEProjectPipeline:
    def __init__(self, project_name: str, customer: str, team_size: int):
        self.project_name = project_name
        self.customer = customer
        self.team_size = team_size
        self.stage = ProjectStage.DISCOVERY
        self.start_date = datetime.date.today()
    
    def discovery_phase(self, processes: List[BusinessProcess]) -> dict:
        """Phase 1: Identify high-value AI deployment opportunities."""
        self.stage = ProjectStage.DISCOVERY
        
        readiness_scores = []
        for p in processes:
            score = self._calc_ai_readiness(p)
            readiness_scores.append((p.name, score))
        
        readiness_scores.sort(key=lambda x: x[1], reverse=True)
        return {
            "total_processes": len(processes),
            "ai_ready": sum(1 for _, s in readiness_scores if s > 0.7),
            "top_priority": readiness_scores[:3],
        }
    
    def _calc_ai_readiness(self, process: BusinessProcess) -> float:
        score = process.data_readiness * 0.4
        pain_score = min(len(process.pain_points) / 10.0, 1.0)
        score += pain_score * 0.3
        complexity_penalty = {RiskLevel.LOW: 0.2, RiskLevel.MEDIUM: 0.1, RiskLevel.HIGH: -0.1}
        score += complexity_penalty[process.integration_complexity]
        return max(0.0, min(score, 1.0))
    
    def prototype_phase(self, days: int = 14) -> str:
        """Phase 3: Deliver a demonstrable prototype in 2 weeks."""
        self.stage = ProjectStage.PROTOTYPE
        deliverables = [
            "Multi-model router config",
            "Data pipeline (ETL + vectorization)",
            "RAG engine prototype",
            "Security gateway (RBAC + audit)",
            "Monitoring dashboard",
            "A/B testing framework",
        ]
        for d in deliverables:
            print(f"  ✓ {d}")
        return "prototype_ready"
    
    def deployment_phase(self) -> FDEMetrics:
        """Phase 4: Production deployment."""
        self.stage = ProjectStage.DEPLOYMENT
        return FDEMetrics(
            stage=ProjectStage.DEPLOYMENT,
            time_elapsed_days=(datetime.date.today() - self.start_date).days,
            model_calls=100000,
            cost_saved=50000.0,
            business_impact=0.75,
        )


def simulate_fde_pipeline():
    pipeline = FDEProjectPipeline(
        project_name="AI Customer Service Transformation",
        customer="Global Retail Co.",
        team_size=12,
    )
    
    processes = [
        BusinessProcess("ticket_routing", 0.85, ["High latency", "30% misrouting"], RiskLevel.MEDIUM),
        BusinessProcess("recommendation", 0.70, ["Low conversion 2.1%"], RiskLevel.LOW),
        BusinessProcess("supplier_comms", 0.30, ["Unstructured", "Multi-language"], RiskLevel.HIGH),
    ]
    
    result = pipeline.discovery_phase(processes)
    print(f"Discovery: {result['ai_ready']}/{result['total_processes']} AI-ready processes")
    pipeline.prototype_phase()
    metrics = pipeline.deployment_phase()
    print(f"Deployed: {metrics.model_calls:,} calls, ${metrics.cost_saved:,.0f} saved")
    
    return pipeline


if __name__ == "__main__":
    simulate_fde_pipeline()

def analyze_capex_risk():
    companies = {
        "Microsoft": {"fcf_b": 15.0, "ai_capex_b": 95.6, "ai_revenue_b": 28.0},
        "Amazon":    {"fcf_b": 3.2,  "ai_capex_b": 114.8, "ai_revenue_b": 22.0},
        "Google":    {"fcf_b": 28.0, "ai_capex_b": 75.0,  "ai_revenue_b": 18.0},
    }
    
    print("=== AI Capex Risk Analysis ===")
    for company, data in companies.items():
        revenue_ratio = (data["ai_revenue_b"] / data["ai_capex_b"]) * 100
        risk = "CRITICAL" if revenue_ratio < 20 else "HIGH" if revenue_ratio < 30 else "MODERATE"
        print(f"{company:12s} | Revenue/Capex: {revenue_ratio:5.1f}% | Risk: {risk}")

analyze_capex_risk()