Why Google’s Vertical Integration Creates a Formidable Moat in the AI Race
The company that owns search, chips, models, and cloud infrastructure just posted 35% growth in enterprise AI. Every other player is missing at least two of those pieces.
Google Just Proved Why Vertical Integration Beats Best-of-Breed in AI Infrastructure
Five years ago, Google was doing $50 billion quarters. Today? They just posted $102 billion in revenue for Q3 2025. Their first $100 billion quarter ever. And here’s what matters: every single part of the business is executing at the same time.
Search revenue up 15%. Cloud up 35%. YouTube growing double digits. Net income jumped from $26 billion to $35 billion year over year. The stock popped 5% after hours. Wall Street got what it wanted, but I don’t think they fully grasp what just happened. Google didn’t just beat earnings. They proved something bigger: they’re the only company in the world that owns every single piece of the AI puzzle, and that flywheel is starting to spin faster.
Let me show you why this matters more than you think.
The Full Stack Advantage Nobody Else Has
Here’s the thing about Google that everyone’s finally starting to understand. They’re not just good at one thing. They’re the only hyperscaler that controls the entire vertical, from the silicon in the chips to the models processing your queries to the cloud infrastructure running it all.
Start with infrastructure. Google spent $85 billion on capital expenditures this year. They just raised that guidance to $91-93 billion. That’s not a typo. Nearly $100 billion going into data centers, network infrastructure, and compute capacity. Why? Because demand for their cloud services is so strong they can’t build fast enough. They’ve got a $155 billion backlog of future cloud revenue sitting in contracts. That’s not hope. That’s signed deals from enterprises who’ve already committed.
And here’s where it gets interesting. Google doesn’t just rent space in data centers. They build their own custom chips. The Ironwood TPU, their seventh-generation tensor processing unit, delivers 10x more compute power than the previous generation and runs 50% more efficiently. Think about that for a second. While everyone else is scrambling to get access to Nvidia GPUs (which are expensive and often in short supply), Google is manufacturing their own specialized AI accelerators that cost less per operation and run cooler. Even Anthropic, the company behind Claude, just signed a deal for up to 1 million TPUs worth tens of billions of dollars. When your competitors are buying your infrastructure, you’ve built something defensible.
But the hardware is just the beginning. Google’s been doing AI research longer than almost anyone. They acquired DeepMind back in 2014. Their models, the Gemini family, are now processing 7 billion tokens per minute through direct API calls. This is the equivalent of processing the entire 25-million-book text collection of the Library of Congress roughly every 6 hours. The Gemini app has 650 million monthly active users, up from 450 million just last quarter. That’s 44% growth in 90 days. And nine out of ten AI labs use Google Cloud as their platform. This isn’t a side project. This is the center of gravity for the entire AI industry.
What makes this powerful is how it all connects. Better models need more compute. Google’s TPUs deliver that compute cheaper and faster than alternatives. More compute means better model performance. Better models attract more enterprise customers. More customers generate more data. More data makes the models smarter. The cycle reinforces itself. That’s the flywheel.
The Numbers Tell the Real Story
Google Cloud revenue hit $15.16 billion this quarter, growing 35% year over year. That growth is accelerating, not slowing down. Microsoft’s Azure grew 40%, sure, but Google’s starting from a smaller base and closing the gap fast. And profitability? Google Cloud’s operating margin crossed 20% earlier this year. Two years ago, this business was losing money. Now it’s a cash machine that’s funding the next wave of AI infrastructure.
Search revenue, which some analysts worried might get disrupted by AI chatbots, grew 15% to $56.6 billion. AI isn’t cannibalizing search. It’s making search better. Google’s AI Overviews feature now reaches over 1.5 billion monthly users. When people use AI Mode, they come back to Google more, not less. The fear that ChatGPT would replace search hasn’t played out. Instead, Google integrated AI directly into the search box and gave two billion people access to it without asking them to download a new app.
YouTube ads brought in $10.26 billion, up 15% year over year. The company now has over 300 million paid subscriptions across its services. This isn’t a one-trick pony. This is a machine with multiple revenue engines all firing at once.
Why Everyone Else Is Playing Catch-Up
Think about what it takes to compete at Google’s level. You need world-class AI researchers. Google’s got them. You need billions of dollars to build data centers. Google’s spending $93 billion this year. You need custom silicon to drive down costs. Google designs and manufactures their own TPUs. You need distribution at scale. Google owns Search, Android, YouTube, Chrome, and Gmail. That’s billions of users across the planet who interact with Google products every single day.
But the real advantage isn’t any one of these things. It’s that Google is the only company that has all of them at the same time. Microsoft has cloud infrastructure and is partnered with OpenAI, but they don’t make their own chips and they don’t have Google’s consumer distribution. Amazon has AWS and good margins, but their AI models lag behind. Meta is strong in AI research but doesn’t have enterprise cloud sales motion. Anthropic, OpenAI, and other AI labs have great models but no infrastructure of their own. They’re renting from the big three.
Google is the only player who can take an AI breakthrough from research, optimize it on custom hardware, deploy it at global scale through their cloud, and distribute it to billions of people through products they already use daily. That vertical integration is nearly impossible to replicate. And as they scale, the cost advantages compound. The more TPUs they manufacture, the cheaper each one gets. The more enterprises adopt Google Cloud, the stickier the platform becomes. The more data flows through their models, the smarter those models get.
This is the definition of a moat that gets wider over time.
What Could Go Wrong?
I’d be lying if I said there were no risks. Regulators in the US and Europe are watching Google closely. There’s an ongoing antitrust case focused on search, and while the recent ruling was less punitive than feared, Google will have to share some search data with competitors. That could help rivals close the gap, at least at the margins.
OpenAI isn’t sitting still. ChatGPT has more weekly users than Gemini. They just launched a web browser that competes directly with Chrome and Search. If they can convert that user base into a sustainable business and maintain their lead in model quality, they could chip away at Google’s dominance in specific use cases.
And there’s execution risk. Google is spending nearly $100 billion on infrastructure. If demand slows or if they overbuilt, those billions turn into stranded assets. Free cash flow dropped 61% in Q2 because of the spending surge. That’s a bet that AI demand continues to grow exponentially. If that bet is wrong, the market will punish them.
But here’s what I think: the risk of under-investing is higher than the risk of over-investing right now. The companies that build the rails for AI are going to capture outsized value. Google is laying track faster than anyone else.
What This Means for You on Monday Morning
If you’re a business leader trying to figure out your AI strategy, pay attention to where the momentum is building. Google Cloud is signing billion-dollar deals faster than ever. Over 70% of their cloud customers are already using AI products. That’s not a trial. That’s production workloads at scale. The enterprises making big bets are voting with their budgets, and they’re choosing Google’s stack.
If you’re evaluating cloud providers, look past the surface-level feature comparisons. The real differentiator is cost and performance at scale. Google’s TPUs give them a structural cost advantage that shows up in your bill over time. And with a $155 billion backlog, you’re not the only one who sees it.
If you’re an investor, the thesis is simple: Google is the only company that can win in every layer of the AI value chain simultaneously. They’re not dependent on a single product or partnership. They’re building a self-reinforcing system where each part makes the other parts stronger.
Watch the cloud growth rate. Watch the TPU adoption numbers from other AI labs. Watch the CapEx guidance. Those are the signals that tell you if the flywheel is accelerating or slowing down.
Right now? It’s accelerating. And I don’t see anyone positioned to slow it down.
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Contact: steve@intelligencebyintent.com