Google Unloads its Warchest

Alphabet just put up the kind of quarter that usually quiets every debate. Fourth quarter revenue rose 18% year over year to $113.828 billion and earnings per share climbed 31% to $2.82, while Google Cloud grew 48% to $17.7 billion.

The reason the stock wobbled anyway was not the quarter. It was the message about spending. Management said 2026 capital expenditures are expected to land between $175 billion and $185 billion, a massive step up from 2025, and investors immediately worried about cash flow and margins.

It is easy to hear “higher CapEx” and translate it into “higher risk.” In the abstract, that is fair. A data center is expensive, and depreciation does not care about your narrative. Alphabet’s own CFO emphasized that higher infrastructure investment will pressure the income statement through depreciation and data center operating costs like energy, and pointed out that depreciation jumped sharply in 2025 and is expected to accelerate in 2026.

But here is the bullish interpretation: companies do not voluntarily choose this kind of spending ramp unless demand is already pulling them forward. Alphabet said it is operating in a tight supply environment in Cloud and is investing to meet customer demand and capitalize on opportunities ahead. That is not a defensive “keep up” posture. That is an offensive “we can sell what we build” posture.

Alphabet is unloading its warchest at a precise moment when the AI race is becoming brutally capital intensive for everyone. Alphabet, Microsoft, Amazon, and Meta are all signaling historically large AI infrastructure budgets, pushing combined spending into territory the market has never had to seriously price before.

That matters because the stock market is no longer giving “AI investment” a blank check. Investors are increasingly skeptical of large capital programs that do not come with a clear story around return on invested capital. In that environment, matching Alphabet’s escalation can become a trap for rivals. Spend too little and you fall behind in capability and customer momentum. Spend too much without credible ROI and you invite margin compression, cash flow concerns, and investor pushback. This is how competitors get squeezed.

Most people lump all hyperscaler spending into one bucket: more data centers. That misses a critical quality difference. Alphabet is not simply building generic capacity and hoping someone rents it. Management tied the CapEx plan directly to multiple monetization engines: frontier model development at Google DeepMind, improved user experience and higher advertiser ROI in Google Services, meeting Cloud customer demand, and strategic investments in Other Bets.

Even the plumbing tells you this is not a one lane bet. Alphabet has been clear that the vast majority of its capital spending goes into technical infrastructure, with a meaningful portion dedicated to machine learning compute that directly supports the Cloud business. This is not speculative buildout. It is capacity being added where revenue is already visible.

Alphabet’s most underappreciated advantage is that it is not buying all its AI horsepower from the same place, at the same price, with the same constraints as everyone else. It operates its own custom silicon stack and has spent over a decade building accelerators across chips, systems, networking, and software. That level of vertical integration changes the economics of large scale AI spending.

More importantly, those TPUs are not sitting idle or serving second tier workloads. They are increasingly being used to operate what many now consider the strongest frontier models in production, while also serving fast growing external demand. Anthropic’s expanding reliance on Google infrastructure is a clear example. As Anthropic pulls ahead in enterprise adoption, its dependence on Google’s TPU based infrastructure deepens, reinforcing Google’s position as the underlying utility layer. At this point, Anthropic increasingly resembles a high powered enterprise AI sidekick inside Google’s broader ecosystem, and the close alignment between Demis Hassabis and Dario Amodei is a visible signal of how intertwined the frontier has become.

What makes this dynamic especially powerful is the kind of customers being pulled onto Google’s infrastructure. Enterprise AI workloads are sticky, usage driven, and economically meaningful. As Anthropic’s enterprise footprint expands, Google’s infrastructure investments gain leverage without Google having to carry all of the go to market burden itself.

This is a crucial distinction versus generic cloud buildouts. When external frontier model developers choose your stack because it is faster, cheaper, or better suited to their models, your capital spending starts to behave like platform investment rather than rental property. It raises switching costs, deepens ecosystem lock in, and improves the long term return profile of every additional dollar invested.

Alphabet’s biggest advantage over peers is that improvements in AI capability pay off in more than one place at once. Cloud growth is not isolated from the rest of the business. Better models improve enterprise demand, consumer products, subscriptions, and core advertising performance simultaneously.

That flywheel is already visible. Cloud demand has been strong enough to drive rapid backlog growth, while Cloud margins have continued to improve even as the business scales. At the same time, Gemini has been pushed into mass market distribution through Search, consumer apps, and enterprise offerings, turning model improvements directly into usage and revenue rather than leaving them trapped in demos.

Alphabet is not trying to earn a return from AI compute in only one way. YouTube alone is already a massive business, generating tens of billions of dollars in annual revenue and maintaining dominant share of global streaming attention. That matters because it gives Alphabet the financial flexibility to reinvest aggressively while still owning one of the most valuable media platforms in the world.

Waymo adds a different kind of optionality. While still loss making, it has crossed key operational thresholds in autonomous driving that few competitors have matched. If autonomous transport scales, the payoff is not incremental. It is structural, and it sits inside an ecosystem that can promote, distribute, and monetize it across Search, Maps, Android, and advertising.

One of the simplest tells in AI is who consistently shows up together discussing the hardest problems. The visible alignment between the leaders of Google DeepMind and Anthropic is not about personalities. It is about where frontier development and real world deployment are converging.

When a leading enterprise focused model provider is scaling on Google infrastructure, and Google is simultaneously investing to expand compute capacity, improve its own models, and sell AI infrastructure to the broader market, capital spending stops looking like a cost and starts looking like a wedge. It hardens the ecosystem and makes each successive investment more productive than the last.

Alphabet’s bull case is not that rivals are incompetent. It is that the math of this moment is unforgiving. If Alphabet can translate spending into better models, stronger Cloud momentum, and improved advertising performance across an enormous installed base, competitors are forced to respond with similar spending just to avoid losing strategic ground.

And if they do, the market will demand proof that the returns justify the expense. Alphabet is betting that its unique combination of frontier models, custom silicon, distribution, and monetization engines gives it a far higher ROI ceiling than peers chasing narrower outcomes. In that light, the CapEx surge is not a warning sign. It is a signal that Alphabet believes it is about to widen the gap, and that it can afford to make everyone else try to keep up.