Google’s Ironwood TPU v7 cements a decade-long lead in custom AI silicon, underpinning a $15.2B Cloud quarter with 23.7% operating margins—up 650 bps YoY.
Anthropic’s 1M TPU commitment and 9 of 10 top AI labs running on Google Cloud validate TPU as the preferred enterprise AI substrate.
Vertical integration—across hardware, software, and supply chain—translates to compounding cost, performance, and margin advantages.
For years, Google’s strategic edge lay in data and algorithms. By 2025, the center of gravity shifted: the new competitive frontier in AI is infrastructure. Compute capacity—its cost, control, and scalability—now dictates the pace of innovation and profitability.
While NVIDIA dominates merchant silicon, Alphabet’s long bet on Tensor Processing Units (TPUs) has matured into a full-stack differentiator. The company’s ability to self-design, optimize, and deploy its own chips allows it to control cost curves, accelerate iteration, and maintain independence from global GPU scarcity.
This vertical integration now functions as both a defensive moat and profit amplifier. The financial outcome is visible: Google Cloud’s Q3 2025 operating margin expanded to 23.7%, with revenue hitting $15.2B, up 26% YoY—its most profitable quarter to date.
1. TPU Ironwood (v7): From Experiment to Ecosystem
Google introduced its first TPU in 2016; by 2025, it’s on its seventh generation—TPU v7 Ironwood. Unlike earlier iterations built primarily for internal workloads, Ironwood is designed for commercial scalability, powering both Google Cloud customers and Alphabet’s own AI stack (Gemini, Search, Ads).
Key milestone: Anthropic’s commitment for 1 million TPUs, the largest publicly disclosed AI infrastructure deal in the industry. This single agreement validated TPU as enterprise-grade silicon, not a proprietary niche. It also signals Google’s deepening role as the infrastructure provider of record for the AI ecosystem.
The Ironwood generation delivers massive throughput gains per watt and per dollar. It’s not just faster—it’s more capital-efficient. That’s crucial in a world where AI compute demand outpaces supply.
Customer validation:
9 of the top 10 AI labs now use Google Cloud, citing performance stability and scale economics.
Ironwood’s integration with TensorFlow and JAX simplifies model deployment, enabling labs to train frontier models with lower latency and higher reliability than on mixed GPU stacks.
The takeaway: Alphabet’s hardware edge is no longer technical—it’s economic.
2. Google Cloud: Profitable Scale through Vertical Integration
Q3 2025 marks a strategic milestone: Google Cloud reached $15.2B in quarterly revenue and sustained profitability. The key driver is vertical integration, which compounds advantages across four vectors:
Lower COGS: Self-designed chips eliminate merchant markups, translating directly into lower cost of goods sold. Each TPU generation reduces silicon dependency costs by 10–15%.
Faster Iteration: Unlike competitors bound to NVIDIA’s product cadence, Google controls its own roadmap. TPU development cycles align with Gemini’s release cadence, allowing faster AI improvements and deployment synergies.
Software–Hardware Co-Optimization: Google’s full-stack design (TPU + TensorFlow + JAX) extracts maximum performance per watt. AI workloads tuned to TPU architecture achieve better cost-performance ratios than general-purpose GPUs.
Supply Chain Control: Owning chip design mitigates the risk of allocation bottlenecks during global shortages. Google secures fabrication capacity years ahead, shielding its AI roadmap from market volatility.
Outcome: a 650-basis-point operating margin expansion in one year. The underlying mechanism: each TPU cycle compounds efficiency—lower compute cost, faster inference, higher throughput—feeding directly into both gross margin and market share.
3. Gemini API: The Developer Flywheel
On the demand side, Google’s AI infrastructure scales through the Gemini API, now handling 7 billion tokens per minute across 13 million developers. This developer layer converts TPU investment into recurring usage, creating a flywheel between compute supply and model consumption.
The loop works as follows:
Developers build on Gemini → generating demand for inference.
Inference volume → monetized via TPU-powered API usage.
TPU performance improvements → lower per-token cost → more adoption.
This closed-loop system turns infrastructure scale into ecosystem growth. Google’s advantage isn’t just having better chips—it’s that every chip powers both first-party AI products (Search, Ads, Workspace) and third-party developer activity. Each reinforces the other.
The Gemini developer ecosystem becomes the distribution engine for TPU economics. Every model call deepens Google’s dataset, reinforces its inference load, and strengthens pricing power across the AI stack.
4. CapEx and Supply Economics: Scaling the Advantage
Alphabet expects $91–93B in 2025 CapEx, with a “significant increase in 2026.” Roughly 60% will fund servers and chips (TPU + GPU + CPU) and 40% will go into data centers.
This scale of investment underscores Google’s intent: to outspend rivals not for vanity metrics, but to own the cost curve. In a market where AI compute has become the scarce resource, whoever controls cost structure controls margin destiny.
Critical constraint: global AI demand-supply imbalance. By manufacturing TPUs for internal and Anthropic workloads, Google insulates itself from this constraint. Each incremental dollar of CapEx earns an outsize return because it secures capacity unavailable to competitors dependent on NVIDIA’s allocation system.
This is Alphabet’s equivalent of AWS in 2010: early vertical integration creating long-term cost asymmetry.
5. Strategic Mechanisms: TPU as Leverage Multiplier
The strategic potency of TPUs lies in compounding integration across the stack. Each layer amplifies the next:
Layer
Function
Strategic Effect
Hardware (TPU)
Compute efficiency
Lowers cost per training/inference cycle
Software (TensorFlow/JAX)
Optimization
Maximizes performance and developer lock-in
Platform (Gemini API)
Distribution
Expands demand surface for TPU workloads
Cloud Infrastructure
Monetization
Converts utilization into recurring profit
Together, they create a vertically reinforced loop: more TPU usage → more data → better models → more demand for TPUs.
This loop gives Alphabet a self-funding AI infrastructure model—each dollar of TPU investment generates both compute efficiency and API revenue.
6. Competitive Implications: Beyond the NVIDIA Dependency
Where AWS and Microsoft Azure rely heavily on NVIDIA for AI scalability, Alphabet’s self-sufficiency grants strategic freedom. TPU allows:
Pricing autonomy: independent of GPU market inflation.
Performance edge: software-hardware synergy delivers superior inference-per-dollar.
Capital leverage: every CapEx cycle compounds efficiency rather than depreciation.
Anthropic’s 1M TPU deal signals an inflection point: hyperscalers are no longer buying compute—they’re aligning with ecosystems. By controlling its own silicon, Google isn’t just scaling; it’s shaping the economics of AI infrastructure itself.
Conclusion: The Hardware Flywheel Behind Alphabet’s AI Empire
Alphabet’s decade-long TPU bet is maturing into a system-level advantage few can replicate. Ironwood (v7) unifies the hardware, software, and economic layers of Google’s AI stack—transforming silicon design into strategic leverage.
Q3 2025’s record cloud margins and Anthropic’s infrastructure commitment prove that TPUs are not a side project—they are Alphabet’s new economic engine. Where others rent compute, Google manufactures margin.
If AI is the new electricity, Alphabet just built its own grid.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
