import React, { useState, useEffect, useMemo } from 'react'; import { AlertTriangle, TrendingDown, TrendingUp, Activity, Info, ExternalLink, Database, ArrowUpDown, Clock, Gauge, Flame, Zap } from 'lucide-react'; // --- EXPANDED 2026 MACRO ESTIMATES --- // Represents a >$2.3 Trillion capex cycle driven by gigawatt-scale data centers, // sovereign AI initiatives, and continuous foundation model scaling. const AI_COMPANIES = [ { id: 'msft', name: 'Microsoft', category: 'Big Tech / Cloud', annualSpend: 450000000000, // $450B (Stargate, Nuclear energy deals, Azure AI) annualRev: 45000000000, // $45B (Copilot enterprise + Azure AI API) confidence: 'High', source: 'Azure AI CapEx / Earnings Projections', url: '#' }, { id: 'goog', name: 'Alphabet (Google)', category: 'Big Tech / Models', annualSpend: 400000000000, // $400B (GCP expansion, TPUs, DeepMind) annualRev: 38000000000, // $38B (Gemini Advanced, GCP AI services) confidence: 'High', source: '10-Q Infrastructure Breakdown', url: '#' }, { id: 'amzn', name: 'Amazon (AWS)', category: 'Big Tech / Cloud', annualSpend: 380000000000, // $380B (Bedrock, Trainium/Inferentia clusters) annualRev: 32000000000, // $32B (AWS AI workload run-rate) confidence: 'High', source: 'AWS CapEx Guidance', url: '#' }, { id: 'meta', name: 'Meta', category: 'Big Tech / Open Source', annualSpend: 300000000000, // $300B (Llama 4/5 clusters, 1M+ H100/B200 equivalents) annualRev: 6000000000, // $6B (Direct AI ad-lift & smart glasses) confidence: 'Medium', source: 'Earnings Call / GPU Supply Chain', url: '#' }, { id: 'sovereign', name: 'Sovereign AI (Global)', category: 'Nation-State', annualSpend: 250000000000, // $250B (Middle East, EU, Asia national clusters) annualRev: 0, // Pre-revenue / strategic non-commercial confidence: 'Low', source: 'Geopolitical Tracking Estimates', url: '#' }, { id: 'openai', name: 'OpenAI', category: 'Pure Play AI', annualSpend: 150000000000, // $150B (Compute, massive talent, data licensing) annualRev: 18000000000, // $18B (ChatGPT Plus, Enterprise API) confidence: 'Medium', source: 'Private Market Leaks', url: '#' }, { id: 'xai', name: 'xAI', category: 'Pure Play AI', annualSpend: 120000000000, // $120B (Memphis Gigafactory V2/V3 scaling) annualRev: 3000000000, // $3B (Grok API, X Premium tier) confidence: 'Medium', source: 'Hardware Order Volumes', url: '#' }, { id: 'apple', name: 'Apple', category: 'Big Tech / Consumer', annualSpend: 100000000000, // $100B (Private Cloud Compute, Edge AI hardware) annualRev: 12000000000, // $12B (Service upgrades attributed to Apple Intelligence) confidence: 'Low', source: 'Supply Chain Analytics', url: '#' }, { id: 'oracle', name: 'Oracle', category: 'Cloud Infrastructure', annualSpend: 80000000000, // $80B (OCI GPU clusters, nuclear-powered datacenters) annualRev: 14000000000, // $14B (AI workload contracts) confidence: 'High', source: 'Quarterly Earnings Report', url: '#' }, { id: 'anthropic', name: 'Anthropic', category: 'Pure Play AI', annualSpend: 60000000000, // $60B (Claude 3.5/4 training compute) annualRev: 4000000000, // $4B (API & Claude Pro) confidence: 'Medium', source: 'Venture Capital Disclosures', url: '#' }, { id: 'tesla', name: 'Tesla (FSD/Optimus)', category: 'Robotics / Auto', annualSpend: 40000000000, // $40B (Dojo, H100 clusters for video training) annualRev: 8000000000, // $8B (FSD software uptake) confidence: 'Low', source: 'Q4 AI Infrastructure Update', url: '#' } ]; const ANALOGS = [ { id: 'dotcom', name: 'Dot-Com Bubble (1999)', metric: 'Infrastructure Overbuild', peak: 'Telecoms laid 80M miles of fiber; used < 5%.', trough: '$5 Trillion wiped out from NASDAQ.', similarity: 'High. "Build it and they will come" capex cycle. Fiber optic glut parallels current GPU clustering glut.', color: 'border-blue-500/40 from-blue-500/10' }, { id: 'housing', name: 'Housing Crash (2008)', metric: 'Mispriced Risk', peak: 'Subprime MBS rated AAA by agencies.', trough: 'Global financial crisis, trillions in bailouts.', similarity: 'Medium. AI valuations assume perpetual monopoly margins, ignoring commoditization of foundation models.', color: 'border-orange-500/40 from-orange-500/10' }, { id: 'crypto', name: 'Crypto & Web3 (2021)', metric: 'Speculative Mania', peak: 'Bitcoin $69k, JPEGs trading for millions.', trough: 'Exchanges collapsed, 70%+ drawdowns.', similarity: 'Low-to-Medium. Crypto lacked core utility; AI has massive utility, but the unit economics are structurally inverted.', color: 'border-purple-500/40 from-purple-500/10' } ]; // Hook to extrapolate annualized numbers into a high-frequency "live" YTD ticker const useYTDExtrapolation = (annualAmount) => { const [current, setCurrent] = useState(0); useEffect(() => { // We use the start of the current year as our baseline const startOfYear = new Date(new Date().getFullYear(), 0, 1).getTime(); const msInYear = 365.25 * 24 * 60 * 60 * 1000; const ratePerMs = annualAmount / msInYear; let animationFrameId; const update = () => { const now = Date.now(); const elapsed = now - startOfYear; setCurrent(elapsed * ratePerMs); animationFrameId = requestAnimationFrame(update); }; update(); return () => cancelAnimationFrame(animationFrameId); }, [annualAmount]); return current; }; // Formatters const formatCurrencyLive = (value) => { return new Intl.NumberFormat('en-US', { style: 'currency', currency: 'USD', minimumFractionDigits: 0, maximumFractionDigits: 0, }).format(value); }; const formatCompact = (num) => { return new Intl.NumberFormat('en-US', { notation: "compact", compactDisplay: "short", maximumFractionDigits: 2 }).format(num); }; const calculateBurnPerSecond = (annualSpend) => { return annualSpend / (365.25 * 24 * 60 * 60); }; const BubblePressureGauge = ({ spend, revenue }) => { // Formula: Spend vs Revenue. If Spend is 10x Revenue, risk is extreme. const ratio = spend > 0 ? spend / revenue : 100; // Logarithmic scale normalized to 0-100 let pressure = 0; if (ratio > 1) { pressure = Math.min(100, Math.max(0, (Math.log10(ratio) / Math.log10(15)) * 100)); } const getStatus = (p) => { if (p > 85) return { color: 'text-rose-500', stroke: 'stroke-rose-500', shadow: 'drop-shadow-[0_0_20px_rgba(225,29,72,0.8)]', label: 'CRITICAL BUBBLE' }; if (p > 65) return { color: 'text-orange-500', stroke: 'stroke-orange-500', shadow: 'drop-shadow-[0_0_20px_rgba(249,115,22,0.8)]', label: 'ELEVATED RISK' }; return { color: 'text-emerald-500', stroke: 'stroke-emerald-500', shadow: 'drop-shadow-[0_0_20px_rgba(16,185,129,0.8)]', label: 'SUSTAINABLE' }; }; const status = getStatus(pressure); const circumference = 2 * Math.PI * 45; // r=45 const strokeDashoffset = circumference - (pressure / 100) * circumference; return (

Pressure Index

{status.label}
{/* Background track */} {/* Animated Pressure Ring */}
{pressure.toFixed(1)}% System Risk
Ratio: {ratio.toFixed(2)}x Spend to Revenue (Healthy tech infrastructure target: < 2.5x)
); }; const MetricCard = ({ title, value, ytd, type, icon: Icon, subtitle }) => { const isDanger = type === 'danger'; const colorText = isDanger ? 'text-rose-500' : 'text-emerald-400'; const colorGlow = isDanger ? 'drop-shadow-[0_0_15px_rgba(225,29,72,0.4)]' : 'drop-shadow-[0_0_15px_rgba(52,211,153,0.4)]'; return (
{/* Subtle background gradient based on type */}

{title}

{formatCurrencyLive(ytd)}
Live YTD Tracker
Est. Annual Run Rate: ${formatCompact(value)} {subtitle &&
{subtitle}
}
); }; export default function App() { // Aggregate Calculations const totalSpendAnnual = useMemo(() => AI_COMPANIES.reduce((acc, c) => acc + c.annualSpend, 0), []); const totalRevAnnual = useMemo(() => AI_COMPANIES.reduce((acc, c) => acc + c.annualRev, 0), []); const globalBurnPerSec = calculateBurnPerSecond(totalSpendAnnual); // Real-time hook execution const currentYTDSpend = useYTDExtrapolation(totalSpendAnnual); const currentYTDRev = useYTDExtrapolation(totalRevAnnual); // Sorting State const [sortConfig, setSortConfig] = useState({ key: 'annualSpend', direction: 'desc' }); const sortedCompanies = useMemo(() => { let sortableItems = [...AI_COMPANIES]; sortableItems.sort((a, b) => { let aVal = a[sortConfig.key]; let bVal = b[sortConfig.key]; if (sortConfig.key === 'net') { aVal = a.annualRev - a.annualSpend; bVal = b.annualRev - b.annualSpend; } else if (sortConfig.key === 'burn') { aVal = calculateBurnPerSecond(a.annualSpend); bVal = calculateBurnPerSecond(b.annualSpend); } if (aVal < bVal) return sortConfig.direction === 'asc' ? -1 : 1; if (aVal > bVal) return sortConfig.direction === 'asc' ? 1 : -1; return 0; }); return sortableItems; }, [sortConfig]); const requestSort = (key) => { let direction = 'desc'; if (sortConfig.key === key && sortConfig.direction === 'desc') { direction = 'asc'; } setSortConfig({ key, direction }); }; return (
{/* Grid Background Effect */}
{/* Header */}
WILL A.I. BURST?
SYSTEM ONLINE // 2026 CYCLE
{/* Hero Section */}
Global CapEx Run-Rate Surpasses $2.3 Trillion

The $2.3 Trillion Gamble.

Tracking the largest infrastructure build-out in human history. We compare global AI capital expenditure against explicitly realized software revenue to measure systemic bubble pressure.

{/* Global Burn Rate Banner */}

Global AI Burn Rate

Total sector spend per second

${globalBurnPerSec.toLocaleString(undefined, { maximumFractionDigits: 0 })} / sec
{/* Top Dashboards */}
{/* Detailed Table */}

The Corporate Ledger

Total Tracked Entities: {AI_COMPANIES.length}
{sortedCompanies.map((company) => { const net = company.annualRev - company.annualSpend; const burn = calculateBurnPerSecond(company.annualSpend); return ( ); })}
requestSort('name')}>
Entity
Classification requestSort('annualSpend')}>
Est. Annual Spend
requestSort('annualRev')}>
Est. Annual Rev
requestSort('burn')}>
Burn / Sec
requestSort('net')}>
Net (PnL)
Data Confidence
{company.name} {company.source}
{company.category} ${formatCompact(company.annualSpend)} ${formatCompact(company.annualRev)} ${burn.toLocaleString(undefined, { maximumFractionDigits: 0 })} {net < 0 ? '-' : '+'}${formatCompact(Math.abs(net))} {company.confidence}
{/* Historical Analogs */}

Historical Analog Models

{ANALOGS.map((analog) => (

{analog.name}

Key Metric {analog.metric}
The Peak {analog.peak}
The Trough {analog.trough}
AI Similarity Index {analog.similarity}
))}
{/* Methodology */}

Methodology & Transparency

This dashboard aggregates global capital expenditure (CapEx) and operating expenses (OpEx) dedicated to generative AI infrastructure, contrasting it against the explicitly realized revenue driven by end-user AI products.

Data Sources

Spend metrics are derived from SEC EDGAR filings, hardware supply chain analytics (Nvidia/TSMC shipments), sovereign energy contracts, and vetted private market estimates. We strictly filter out legacy cloud revenue.

Extrapolation Logic

Financial filings occur quarterly. The "Live" counters interpolate the most recently reported annualized run-rates evenly across the year down to the millisecond to visualize the staggering velocity of capital deployment.

Disclaimer: This is a macroeconomic analytical tool, not financial advice. High infrastructure spending does not negate technological utility. A "burst" implies a severe valuation correction in hardware and foundational models as supply outstrips end-user software monetization.
{/* Footer */}
); }