IBM 25% Crash Deep Dive: How AI CapEx Is Devouring Traditional Software, and the $5.8 Trillion Infrastructure Gold Rush

"""
IBM Revenue Analysis and AI Budget Crowding Effect
"""

import numpy as np
import pandas as pd

class IBMBudgetAnalysis:
    def __init__(self):
        self.revenue_data = {
            'Software': {
                '2025Q2': 7.1, '2025Q3': 7.0, '2025Q4': 7.5, '2026Q1': 7.2,
                '2026Q2_actual': 7.0, '2026Q2_expected': 7.6
            },
            'Consulting': {
                '2025Q2': 5.2, '2025Q3': 5.1, '2025Q4': 5.3, '2026Q1': 5.0,
                '2026Q2_actual': 4.9, '2026Q2_expected': 5.1
            },
            'Infrastructure': {
                '2025Q2': 3.8, '2025Q3': 3.7, '2025Q4': 4.0, '2026Q1': 3.6,
                '2026Q2_actual': 3.5, '2026Q2_expected': 3.7
            }
        }
        
        self.it_budget_allocation = {
            '2022': {'traditional_software': 0.45, 'cloud': 0.30, 'ai': 0.10, 'infra': 0.15},
            '2023': {'traditional_software': 0.40, 'cloud': 0.32, 'ai': 0.15, 'infra': 0.13},
            '2024': {'traditional_software': 0.35, 'cloud': 0.33, 'ai': 0.20, 'infra': 0.12},
            '2025': {'traditional_software': 0.28, 'cloud': 0.32, 'ai': 0.30, 'infra': 0.10},
            '2026E': {'traditional_software': 0.22, 'cloud': 0.30, 'ai': 0.40, 'infra': 0.08},
        }
    
    def simulate_budget_crowding_out(self):
        print("=== Enterprise IT Budget Allocation Evolution ===")
        for year, alloc in self.it_budget_allocation.items():
            print(f"{year}: Traditional SW={alloc['traditional_software']:.0%}, "
                  f"Cloud={alloc['cloud']:.0%}, AI={alloc['ai']:.0%}, "
                  f"Infra={alloc['infra']:.0%}")
        
        base = self.it_budget_allocation['2022']['traditional_software']
        current = self.it_budget_allocation['2026E']['traditional_software']
        print(f"\nTraditional software: {base:.0%} (2022) → {current:.0%} (2026E)")
        print(f"Cumulative crowding out: {(base-current)/base*100:.1f}%")

analyzer = IBMBudgetAnalysis()
analyzer.simulate_budget_crowding_out()

// AI CapEx Scale Analysis
package main

import "fmt"

type CloudCapEx struct {
    Company       string
    FiveYearCapEx float64
    AnnualCapEx   float64
    MainFocus     string
    DataCenterMW  int
}

func analyzeCapExScale() {
    companies := []CloudCapEx{
        {"Microsoft", 1.8, 0.36, "Copilot+Azure AI+Infra", 15000},
        {"Amazon", 1.5, 0.30, "AWS AI+Trainium+Bedrock", 12000},
        {"Meta", 1.0, 0.20, "Llama+AI Research+Infra", 8000},
        {"Google", 0.9, 0.18, "Gemini+TPU+DeepMind", 10000},
        {"Oracle", 0.6, 0.12, "OCI+GPU Cloud", 5000},
    }

    total := 0.0
    fmt.Println("=== Top 5 Cloud AI CapEx (2026-2030) ===")
    for _, c := range companies {
        fmt.Printf("%-15s $%.1fT ($%.2fT/yr) | %dMW DC\n",
            c.Company, c.FiveYearCapEx, c.AnnualCapEx, c.DataCenterMW)
        total += c.FiveYearCapEx
    }
    
    fmt.Printf("\nTotal: $%.1fT\n", total)
    fmt.Printf("Annual: $%.2fT/yr\n", total/5.0)
    
    // Scale comparisons
    annualAI := total / 5.0
    globalSoftwareMarket := 0.75
    fmt.Printf("\n=== Scale Comparison ===")
    fmt.Printf("\nAnnual AI CapEx vs Global Software Market: %.0f%%",
        annualAI/globalSoftwareMarket*100)
    fmt.Printf("\n5yr AI CapEx could buy %.1f global software markets",
        total/globalSoftwareMarket)
    fmt.Printf("\n\nThis is why IBM crashed: CIOs are spending on AI servers, not software licenses")
}

func main() {
    analyzeCapExScale()
}

"""
Software Valuation Restructuring Model
"""

class SoftwareValuationModel:
    def __init__(self):
        self.valuation_metrics = {
            '2019': {'EV/Revenue': 8.5, 'EV/EBITDA': 25.0, 'P/E': 35.0},
            '2020': {'EV/Revenue': 12.0, 'EV/EBITDA': 30.0, 'P/E': 40.0},
            '2021': {'EV/Revenue': 15.0, 'EV/EBITDA': 35.0, 'P/E': 50.0},
            '2022': {'EV/Revenue': 6.0, 'EV/EBITDA': 18.0, 'P/E': 25.0},
            '2023': {'EV/Revenue': 7.0, 'EV/EBITDA': 20.0, 'P/E': 28.0},
            '2024': {'EV/Revenue': 8.0, 'EV/EBITDA': 22.0, 'P/E': 30.0},
            '2025': {'EV/Revenue': 6.5, 'EV/EBITDA': 18.0, 'P/E': 25.0},
            '2026E': {'EV/Revenue': 5.0, 'EV/EBITDA': 14.0, 'P/E': 20.0},
        }
    
    def analyze_valuation_trend(self):
        print("=== Software Valuation Multiples (2019-2026E) ===")
        for year, metrics in self.valuation_metrics.items():
            print(f"{year}: EV/Rev={metrics['EV/Revenue']:.1f}x, "
                  f"EV/EBITDA={metrics['EV/EBITDA']:.1f}x, "
                  f"P/E={metrics['P/E']:.1f}x")
        
        peak = self.valuation_metrics['2021']['EV/Revenue']
        current = self.valuation_metrics['2026E']['EV/Revenue']
        print(f"\nEV/Revenue compression: {peak:.1f}x → {current:.1f}x")
        print(f"Compression: {(peak-current)/peak*100:.1f}%")

model = SoftwareValuationModel()
model.analyze_valuation_trend()