- Google’s $85 billion 2025 CapEx signals the culmination of a 10-year vertical integration play centered on custom AI silicon (TPUs).
- What began as an internal experiment in 2014 is now a strategic differentiator, enabling superior price-performance, tighter integration, and unrivaled inference scale.
- TPU v7 “Ironwood” delivers 42.5 exaflops per pod—enough capacity to train frontier models while undercutting GPU-based clouds on cost.
- The result: TPUs are no longer a science project—they’re Google’s industrial weapon in the AI infrastructure war.
1. Context: The Hidden Decade
When Google unveiled the first TPU in 2015, it was viewed as an eccentric side project—a niche accelerator optimized for TensorFlow inference.
But beneath the surface, it marked a fundamental strategic shift:
owning the hardware substrate of intelligence rather than renting general-purpose compute from NVIDIA or others.
From TPU v1 (internal use) to TPU v7 (multi-tenant commercial scale), Google’s approach was methodical, compounding, and largely invisible.
While competitors optimized for GPUs, Google invested in systemic control—from silicon to software orchestration.
By 2025, that patience has paid off. TPU v7 “Ironwood” has become the backbone of Gemini, powering billions of daily queries and securing multi-billion-dollar deals such as Anthropic’s one-million-TPU commitment.
2. The Timeline: From Risky Bet to Competitive Weapon
2014–2015: TPU v1 — The Risky Bet
- Designed purely for inference, used internally for Search and Gmail.
- Goal: Reduce latency and energy per query.
- Outcome: 10× efficiency gain vs CPUs, proving the concept viable.
2017: TPU v2 — Opening Up
- Added training capability, launched on Google Cloud.
- Marked Google’s first move toward external commercialization.
2018–2020: TPU v3 & v4 — Maturing Fast
- Introduced liquid cooling, massive performance jump.
- Integrated into YouTube and Ads recommendation systems.
- Trained BERT, Meena, and early multimodal models.
2023–2024: TPU v5 & v6 — The Gemini Era
- Fully dedicated to large-scale model training.
- 10+ exaflops per pod, cloud enterprise adoption.
- Gemini 1.0–2.0 training ran entirely on TPU clusters.
2025: TPU v7 “Ironwood” — The Payoff
- 42.5 exaflops per pod, $106 billion cloud backlog.
- Integrated across 42 cloud regions.
- Adopted by Anthropic, DeepMind, and external enterprises.
Ten years of compounding engineering discipline have turned Google’s silicon into the AI industry’s most reliable backbone.
3. Why TPUs Are Google’s Secret Weapon
a. Price-Performance
TPUs deliver 2–3× better price-performance than equivalent GPU clusters.
At hyperscale, these unit-economics matter more than raw speed—they determine who can profitably serve intelligence.
Economic levers:
- No external vendor margin (vs. NVIDIA’s 70% gross).
- Lower total cost of ownership through vertical power delivery.
- Efficient batch inference for Search, Ads, and Gemini.
- Proven ability to attract cost-sensitive enterprise AI workloads.
Anthropic’s CTO summed it up: “We chose TPUs for price-performance and efficiency.”
b. Deep Integration
Google designed TPUs not as standalone chips, but as a native layer in its software stack.
- TensorFlow and JAX optimized at compiler level.
- Tight coupling with Kubernetes for workload scheduling.
- Seamless integration into Vertex AI and Google Cloud.
- Native compatibility with Gemini and DeepMind’s pipelines.
This co-optimization—hardware for software, software for hardware—creates an efficiency loop no third-party vendor can replicate.
Owning both layers converts integration cost into structural advantage.
c. Battle-Tested
TPUs are the only AI chips hardened by planetary-scale production.
- Power Search inference (billions of queries daily).
- Run Gmail’s classification and anti-spam models.
- Serve YouTube recommendations and ad matching.
- Trained every Gemini model from 1.0 to 2.0.
With more than a decade of operational telemetry, Google has debugged its silicon through billions of daily transactions.
This isn’t an experiment—it’s infrastructure at civilization scale.
d. Inference King
In AI economics, training is only 10% of cost; inference is 90%.
TPUs were architected for inference first—low latency, low power, high throughput.
That focus now compounds:
- Ultra-low-latency serving (<10 ms).
- 10× power efficiency vs GPU clusters.
- Optimized routing through Google’s edge networks.
Every Google product—from Search to Bard to Ads—benefits from these compounding cost efficiencies.
4. The Economic Flywheel
TPU integration has triggered a multi-layer compounding loop:
- Lower cost per inference → cheaper AI operations across Search and Ads.
- Higher margins → greater reinvestment capacity in next-gen chips.
- Better performance → more enterprise demand for TPU-based Cloud.
- Increased utilization → higher ROI on CapEx.
Each iteration tightens the feedback loop between Google Cloud and Gemini.
As utilization rises, the TPU business transitions from cost center to profit engine.
By Q3 2025, TPU utilization reached record highs, driving $15.2 billion in Google Cloud revenue (+26% YoY) with a 23.7% operating margin—a 650-bps improvement directly tied to TPU economics.
5. Strategic Positioning: What Google Built That Others Can’t
| Player | Silicon Strategy | Integration Depth | Strategic Risk | Moat Type |
|---|---|---|---|---|
| Fully custom (TPU v7) | End-to-end | High CapEx | Cost + Integration | |
| OpenAI | Partner-led (Azure GPU) | Medium | Vendor dependence | Model + API |
| Anthropic | Multi-cloud TPU + Trainium | Medium | Coordination | Flexibility |
| Meta | NVIDIA GPU reliance | Low | Vendor lock-in | Ecosystem |
| Amazon | Trainium/Inferentia mix | Moderate | Late to performance parity | Hybrid margin |
| NVIDIA | Merchant supplier | High margins | Customer dependency | Chip monopoly |
Unlike its peers, Google operates across the entire stack—from silicon to software to distribution.
That full control translates into durable economics and ecosystem gravity.
While others rent compute, Google has turned compute into a defensible product category.
6. The Long-Term Payoff: Decade of Patience, Decade of Dominance
By 2025, the TPU strategy delivers on every axis:
- Technical: Consistent 2× improvement per generation.
- Economic: Structural cost advantage at global inference scale.
- Strategic: Retention of AI independence against NVIDIA, AWS, and OpenAI.
The result is an asset class few companies can emulate:
- 42 cloud regions running on proprietary chips.
- $106 billion cloud backlog.
- One million TPUs contracted by Anthropic.
What started as a risky internal experiment now underwrites Google’s most profitable and defensible growth vector.
7. The Strategic Lesson: Integration Outlasts Acceleration
The AI race is crowded with firms chasing speed.
Google’s bet was on patience: investing in compounding integration rather than transient performance spikes.
That patience yielded structural resilience.
As chip scarcity, export controls, and supply shocks hit competitors, Google’s in-house silicon provides insulation and optionality.
In a capital-intensive cycle, control beats speed—and integration compounds faster than hype.
8. Implications: From Product to Platform
The TPU network has evolved from internal compute fabric to multi-tenant AI platform, anchoring Google’s transformation into an infrastructure-first company.
- TPU APIs available to developers through Gemini API.
- Partnerships with Anthropic, Samsung, and NVIDIA expand reach.
- TPU efficiency directly monetized through Ads and Cloud divisions.
TPUs now sit at the intersection of Google’s three revenue engines: Search, Ads, and Cloud—creating synergy across the company’s core and emerging businesses.
Conclusion: The Decade That Built Google’s Next Moat
The TPU program demonstrates how strategic compounding beats tactical iteration.
Ten years ago, Google bet that custom silicon would become the ultimate competitive moat in AI.
In 2025, that bet has matured into a self-reinforcing flywheel of scale, cost, and integration.
The lesson:
The future of AI will not belong to whoever trains the biggest model first, but to whoever controls the economics of intelligence at scale.
Google does.
