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Sundar Pichai confirmed that Google Cloud’s revenue is not meeting its potential as capacity limitations prevent the company from serving all its waiting customers.
Google Cloud’s $462 billion in debt, which nearly doubled in one quarter, is more than 10 times annual revenue, with most of the conversions occurring within 24 months.
Google’s mandate requiring engineers to use AI to generate code creates internal competition for GPUs that further strains the same infrastructure it already sells to enterprise customers.
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Artificial intelligence has evolved from a software race to an infrastructure arms race. The companies building the biggest artificial intelligence models are finding that the limiting factors are chips, data centers and electricity, not customer interests.
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A company spending billions due to low demand is a warning sign. It’s a different story if the company does it because it can’t ramp up capacity fast enough to satisfy customers. This seems to be the problem being faced Alphabet(NASDAQ:GOOG) Google. The company is not trying to find buyers for artificial intelligence services; it simply cannot produce enough computing power to service them.
Google’s AI Spending Is Growing Faster Than Expected
Google has its foot on the AI spending pedal. In the fourth quarter of 2025, capital expenditures will reach $175-185 billion this year. The market has questioned whether the company is spending too aggressively as it nearly doubled Google’s spending in 2025. Investors feared that big tech companies’ investments in artificial intelligence could become a costly spending spree.
A quarter later, however, Google reported capital expenditures of $35.7 billion in the first quarter alone. Instead of slowing down, company management raised its annual capital investment forecast to $180–190 billion. The reason was simple: demand exceeded supply. During the earnings call, CEO Sundar Pichai said revenue from Google Cloud would have been higher if the company had enough capacity to meet customer demand.
This means that Google is not building infrastructure and hoping that customers will appear. They are already here – and they are waiting.
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$462 billion backlog shows demand for AI is real
The clearest evidence is Google Cloud’s $462 billion backlog, which nearly doubled in one quarter. Management expects more than 50% of this backlog to be converted into revenue within 24 months. By comparison, Google Cloud generated $43.2 billion in revenue in 2025. The backlog is more than 10 times the annual revenue base.
The story continues
The size of enterprise obligations is also expanding. Google said the number of billion-dollar-plus cloud deals signed in 2025 exceeded the total of the previous three years. Google’s problem isn’t finding AI clients. It keeps pace with them.
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Google is caught in a race to the $462 billion gap, where chips and power are the main limiting factors, resulting in a massive $190 billion infrastructure growth. © 24/7 Wall Street.
Domestic demand for AI adds pressure
Bloomberg reported that Google delayed the launch of Gemini 3.5 Pro as engineers struggled to meet internal performance targets. It also highlighted an unusual problem: Google employees are becoming major consumers of artificial intelligence computing.
The company required engineers to use artificial intelligence tools to generate code. The initiative is intended to improve productivity, but it also increases demand for the same computing resources that Google sells to third-party customers. In other words, Google is competing with itself for GPUs.
This creates a rare situation. A company that lacks artificial intelligence capabilities for its own engineers but has a $462 billion cloud technology reserve has no reason to cut costs. In fact, you may have to spend even more.
Agree, there are risks. Spending on AI infrastructure requires a huge upfront investment, and returns will depend on whether enterprise demand remains strong enough to justify the cost. The industry has not yet reached the point where every dollar spent on AI guarantees a dollar earned.
However, Google’s current constraint is the type of constraint investors typically want to see: too much demand, not too little.
Key Takeaway
In short, Google’s rising capex isn’t just a spending story. This is a story of potential. The company has enterprise clients waiting on a $462 billion backlog, but its own engineers are consuming more AI resources. Management is increasing costs as existing infrastructure cannot keep up.
The question for investors isn’t whether Google can find demand for AI. It may need to spend even more money on infrastructure while accelerating the expansion of capacity it has already contracted for. This is not necessarily a bad problem.
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