Why Getting Cheaper is Getting More Expensive
How the pursuit of affordable AI is creating the most capital-intensive technology race in history
We're living through one of the most counterintuitive economic phenomena in tech history. As artificial intelligence becomes cheaper per unit, total AI spending is exploding. It's a paradox that's reshaping entire industries and creating what might be the most capital-intensive arms race ever witnessed.
The numbers tell a remarkable story. Training a single AI model costs around $100 million today. But as Dario Amodei, CEO of Anthropic, recently revealed, "there are models in training today that are more like a billion" dollars, with $10 billion models expected to start training sometime in 2025.
Yet here's the twist: these astronomical training costs aren't even the main story anymore.
The Great Inversion
The real revolution is happening in inference—the cost of actually running AI models to answer queries, generate content, and make decisions. While training happens episodically (you build a model once), inference happens constantly—billions of times per day across millions of applications.
THE AI COST SHIFT
==================
BEFORE (Training-Heavy) AFTER (Inference-Heavy)
┌─────────────────────┐ ┌─────────────────────┐
│ 🏭 TRAINING │ --> │ ⚡ INFERENCE │
│ $100M+ once │ │ $$ constantly │
│ ================ │ │ ~~~~~~~~~~~~~~~ │
│ Build the factory │ │ Run the factory │
│ (Episodic cost) │ │ (Operational cost)│
└─────────────────────┘ └─────────────────────┘
ONE-TIME CONTINUOUS
Big upfront Growing with usage
Amazon CEO Andy Jassy captured this shift perfectly in his 2024 shareholder letter: "While model training still accounts for a large amount of the total AI spend, inference will represent the overwhelming majority of future AI cost because customers train their models periodically but produce inferences constantly."
This represents a fundamental inversion of AI economics. We're moving from a world where the biggest costs were one-time training expenses to one where operational inference costs dominate. Think of it as the difference between building an expensive factory once versus paying for electricity to run it forever.
The Flywheel That Won't Stop
But here's where the paradox gets really interesting. As inference becomes cheaper per unit, something unexpected happens: total usage explodes. More usage drives higher infrastructure demands. Higher infrastructure demands push total costs back up, despite unit economics improving.
THE AI ECONOMICS FLYWHEEL
==========================
💰 Cheaper
Per-Unit Costs
|
↓
🔧 Need to ←─────────→ 📈 More AI
Optimize Usage Explodes
| |
↓ ↓
🏗️ Higher ⚡ Higher Infra
Total Costs ←───────── Demand Grows
💸 THE PARADOX 💸
"Getting cheaper gets expensive!"
NVIDIA's Jensen Huang recently highlighted just how dramatic this effect has become. "Inference is exploding," , explaining that "reasoning AI agents require orders of magnitude more compute" than traditional models. These new reasoning models can require 100 times more computational power per task than standard AI inference.
The result is a self-reinforcing flywheel:
- Cheaper per-unit inference leads to
- Massive increases in AI usage which drives
- Exponential infrastructure demand resulting in
- Higher total costs that pressure providers to
- Optimize and scale further completing the cycle
The Infrastructure Reality Check
This flywheel effect is creating unprecedented pressure across the entire technology ecosystem:
THE PRESSURE POINTS
===================
☁️ CLOUD PROVIDERS 🔧 CHIPMAKERS 🏢 ENTERPRISES
┌─────────────────────┐ ┌─────────────────────┐ ┌─────────────────────┐
│ Amazon, Microsoft, │ │ NVIDIA struggling │ │ Stretched budgets │
│ Google deploying │ │ with demand for │ │ Can't be left │
│ massive capital │ │ expensive AI chips │ │ behind, can't │
│ │ │ │ │ afford to compete │
│ 💸💸💸💸💸💸💸💸💸 │ │ 🚀📈💰⚡🔥 │ │ 😰💸📊⚖️💼 │
└─────────────────────┘ └─────────────────────┘ └─────────────────────┘
↓ ↓ ↓
"Unusually high "Single chip "Can't afford the
demand periods" provider pricing infrastructure
- Andy Jassy power" bottlenecks requirements"
Cloud Providers like Amazon, Microsoft, and Google are deploying capital at rates that would have seemed impossible just a few years ago. Amazon's Jassy described the current moment as "periods of unusually high demand" where "you're deploying a lot of capital."
Chipmakers like NVIDIA are struggling to keep up with demand for increasingly expensive AI chips. Most AI development has been built on a single chip provider's technology, creating both bottlenecks and massive pricing power.
Enterprise Budgets are being stretched as companies realize they can't afford to be left behind in the AI race, yet can't afford the infrastructure requirements either.
The Startup Extinction Event
Perhaps most dramatically, these economics are creating what Anthropic's Amodei calls a barrier that many companies simply can't cross. "Most startups won't be able to afford to sign up for the AI race," he acknowledged.
THE FUNDING GAP REALITY
=======================
💰 TYPICAL STARTUP 🚀 AI FRONTIER COMPANY
┌─────────────────────┐ ┌─────────────────────┐
│ Series C Funding │ │ Anthropic Example │
│ │ │ │
│ $59M 💵 │ VS. │ $8B+ 💰💰💰 │
│ │ │ │
│ ████ │ │ ████████████████ │
│ 20% of what's │ │ 100% - What's │
│ needed for AI │ │ actually needed │
│ frontier research │ │ to compete │
└─────────────────────┘ └─────────────────────┘
😰 "Left out" 🎯 "Can compete"
Result: Market concentration in hands of ultra-funded players
The numbers back this up starkly. The average U.S. startup raises about $59 million in Series C funding. Anthropic raised $450 million in their Series C and has raised over $8 billion total. The scale difference isn't just significant—it's existential.
This is creating a bifurcated market where only the most well-funded companies can compete at the frontier, while everyone else relies on their APIs and services—a dynamic that concentrates power in ways we've never seen before.
The Three Phases of AI Economics
Understanding where we're headed requires recognizing the three distinct phases of AI economic evolution:
THE AI ECONOMICS TIMELINE
=========================
PHASE 1: 2024 PHASE 2: 2025-2026 PHASE 3: 2026+
Training-Heavy Era Transition Period Inference-Dominated
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ 🏭 $100M+ │ │ ⚖️ $1B+ │ │ ⚡ $10B+ │
│ │ │ │ │ │
│ Training: ████ │ │ Training: ████ │ │ Training: ████ │
│ Inference: ██ │ │ Inference: ████ │ │ Inference: ████ │
│ │ │ │ │ ████ │
│ Episodic costs │ -> │ Both significant│ -> │ Operational │
│ dominate │ │ costs │ │ costs dwarf │
│ │ │ │ │ everything │
└─────────────────┘ └─────────────────┘ └─────────────────┘
Current Transitioning Future State
Reality Now! Inference Rules
Phase 1: Training-Heavy Era (2024)
- Dominated by episodic but massive capital hits
- $100M+ models setting the bar
- Infrastructure built primarily for training workloads
Phase 2: Transition Period (2025-2026)
- Both training and inference costs significant
- $1B+ training costs becoming normal
- Infrastructure scaling for both workloads
Phase 3: Inference-Dominated Future (2026+)
- Constant operational costs dwarf periodic training expenses
- $10B+ training costs for cutting-edge models
- Infrastructure optimized primarily for inference at scale
The Reasoning Revolution
What's driving this acceleration isn't just more AI usage—it's fundamentally different AI that requires vastly more computational power. The emergence of reasoning models like OpenAI's o3, DeepSeek R1, and others represents a qualitative shift in what AI can do and what it costs to run.
OLD AI vs. REASONING AI
=======================
🤖 TRADITIONAL AI 🧠 REASONING AI
┌─────────────────────────┐ ┌─────────────────────────┐
│ "What's 2+2?" │ │ "Solve this complex │
│ │ │ physics problem..." │
│ Input → Output │ │ │
│ ⚡ (1 unit compute) │ │ Input → 🤔💭🧮🔍📊 │
│ │ │ Think → Reason → Check │
│ ████ │ │ ⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡ │
│ Fast, simple │ │ (100x more compute) │
│ │ │ │
│ "4" ✓ │ │ "Here's my step-by-step │
│ │ │ solution..." ✓ │
└─────────────────────────┘ └─────────────────────────┘
One-shot response Multi-step reasoning
Cheap to run Expensive but powerful
These models don't just generate responses; they "think" through problems step by step, applying logical reasoning and strategic decision-making. But this thinking comes at a steep computational cost. As NVIDIA's Huang explained, "The more the model thinks, the smarter the answer"—but also the more expensive it becomes.
What This Means for Everyone
For Businesses: The window for AI adoption isn't just about competitive advantage anymore—it's about survival. But the infrastructure requirements mean most companies will become dependent on a small number of AI providers rather than building in-house capabilities.
For Investors: Traditional software metrics don't apply. Success in AI requires massive upfront capital with long payback periods, more similar to infrastructure or energy investments than typical tech plays.
For Society: We're witnessing the creation of the most capital-intensive industry in human history, with power concentrated among a few players who can afford to play at scale.
The Ultimate Paradox
The most striking aspect of the AI economics paradox is how it inverts our intuitions about technology. Usually, technological progress makes things cheaper and more accessible over time. With AI, technological progress is making the underlying infrastructure more expensive and less accessible, even as the end-user experience becomes cheaper and more ubiquitous.
THE GREAT TECHNOLOGY INVERSION
===============================
🎭 THE PARADOX AT WORK 🎭
FOR USERS: FOR BUILDERS:
┌─────────────────────────┐ ┌─────────────────────────┐
│ 📱 AI gets cheaper │ │ 🏗️ Infrastructure gets │
│ 🚀 AI gets faster │ │ exponentially │
│ ✨ AI gets smarter │ │ more expensive │
│ 🌐 AI gets everywhere │ │ │
│ │ │ 💸💸💸💸💸💸💸💸💸 │
│ 😊 Better experience │ │ 😰 Higher barriers │
│ Lower barriers │ │ Fewer competitors │
└─────────────────────────┘ └─────────────────────────┘
↑ ↑
DEMOCRATIZED CONCENTRATED
More accessible Less accessible
🌍 "AI for everyone" 🏛️ "AI by the few"
We're getting the consumer benefits of democratized AI while simultaneously creating the most exclusive and capital-intensive development environment in tech history. It's as if the internet became cheaper to use while becoming vastly more expensive to actually build and operate.
Looking Ahead
The trajectory seems clear: AI will continue getting cheaper per query while getting exponentially more expensive to operate at scale. The companies that can navigate this paradox—balancing massive capital requirements with sustainable unit economics—will likely define the next era of technology.
As we stand at this inflection point, one thing is certain: the old rules of technology economics don't apply. We're writing new ones in real-time, and the stakes couldn't be higher.
The AI revolution isn't just changing what computers can do—it's fundamentally reshaping how technology companies operate, compete, and survive. In this new world, getting cheaper really is getting more expensive, and that paradox is just getting started.
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