Google Will Clearly Win the AI Race
Tue Nov 25 2025
There’s no denying it: Google is on an AI winning streak. The new Gemini 3 model is making headlines, viral demos and benchmark-records. But the real story isn’t simply “Gemini 3 is better”—it’s that Google now owns the entire vertical stack of AI. In this article I walk you through why Google’s dominance isn’t just about a model—but about hardware, cloud, operating systems, apps, data and so much more.
The Full Stack Advantage
Here’s the full stack Google is operating today:
- Hardware & Silicon (TPUs)
Google designs and deploys its own Tensor Processing Units (TPUs), custom built for its AI workloads. - Data Centres & Infrastructure
Google’s global data-centre fleet, connected to its cloud and internal services, gives it unparalleled scale. - Cloud Architecture (GCP/Vertex AI)
With Google Cloud and Vertex AI, the company offers end-to-end AI training, serving and orchestration. - The Model (Gemini family)
Gemini 3 is the visible tip of the iceberg—but the model is deeply integrated with Google’s stack. - Application Layer (Workspace, Antigravity, Search mode etc.)
Google isn’t just delivering models—it's embedding them into widely used apps and tools. - Operating Systems & Platforms (Android, Chrome, Edge devices)
Google’s control of OS + device layer gives it reach into billions of endpoints.
No other major AI company currently matches that full-stack breadth. And that’s why the conversation should be about stack power, not just “who built the model”.
Why the Stack Really Matters
Hardware + Model = Cost & Performance Advantage
By owning silicon (TPUs), Google controls major cost levers and performance optimizations. That means faster iteration, lower unit cost and major scale advantage.
Data & Cloud Network Effects
Google’s vast user base (Search, Maps, YouTube) generates signals and data that feed into training and improvement. Its cloud network means models can train and serve at unprecedented scale.
Integration Into Everyday Apps
Even if Gemini 3 wasn’t best-in-class, Google’s ability to push AI into productivity tools, mobile devices and enterprise workflows means scale and entrenchment.
Platform & Ecosystem Lock-in
Control of OS + cloud + devices means Google can retain users and usage in a way others can’t easily replicate. The result: deep ecosystem lock-in and high switching cost.
What Does “Not Because of Gemini 3” Actually Mean?
It doesn’t mean Gemini 3 isn’t great—just that it’s necessary but not sufficient for Google’s dominance. The broader infrastructure and ecosystem around Gemini matter more. Some important points:
- The model may get all the publicity, but the hardware, data infrastructure, ecosystem and applications are what turn model quality into strategic power.
- Competitors may release strong models—but without owning the stack, they’ll rent or rely on third-party hardware, third-party apps or third-party platforms, limiting margin, scale and integrate-ability.
- Google’s advantage is structural: you can improve your model, but you cannot easily build the global data-cloud-device-silicon stack overnight.
Implications for the AI Race
For Developers & Start-ups
If you’re building AI products, this stack dynamic matters. You may still use models from others (OpenAI, Anthropic, Meta), but the platform effect of Google might tilt enterprise deals, integration partnerships or cloud cost structures in favour of Google.
For Enterprises
When selecting AI infrastructure, you’ll likely consider not just accuracy metrics, but which ecosystem can deploy, manage, scale and embed the AI. Google’s vertical stack offers one such path.
For Competition
Other players must either build out similar stacks (hardware + cloud + apps) or specialise into niches. Vertical integration becomes a strategic axis, not just “better model”.
For Investors
Model-only bets are riskier. Investing in companies aligned with the stack—devices, cloud, hardware, data pipelines—may yield more durable advantage.
Are There Risks to Google’s Approach?
Yes—no dominance is guaranteed. Potential headwinds:
- Regulation & antitrust scrutiny may pressure how freely Google can leverage its stack for competitive advantage.
- Open models and open hardware could somewhat mitigate Google’s moat if other ecosystems standardize.
- Model breakthroughs from competitors or academia might disrupt performance leadership.
- Ecosystem complexity: owning everything is powerful, but also requires integration, cost control and cultural alignment.
Nevertheless, the structural advantage remains compelling.
My Take: Long, Not Short
If I had to pick a side today: I lean long on Google—not only because of Gemini 3, but because of the stack that powers it and the ecosystem around it. The model is the front-end; the real club is the infrastructure, data, devices and platform integration behind it.
If you believe the AI race isn’t just about “who has the smartest model” but “who can embed smart models into real-world use at scale and cost”, then Google is uniquely positioned.
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Tue Nov 25 2025
