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Google profits QUADRUPLE to $112billion after investments in AI

Google profits QUADRUPLE to $112billion after investments in AI
Google profits QUADRUPLE to $112billion after investments in AI

Google's profits have quadrupled to $112billion (£84billion), with the search engine monolith citing its investments in AI as the reason for the windfall.

Google's profits have quadrupled to $112billion (£84billion), with the search engine monolith citing its investments in AI as the reason for the massive windfall. 

In the firm's latest quarterly update, CEO Sundar Pichai said that the successful quarter was due in part to growth of its AI model Gemini and AI search queries, as well as YouTube where more than 1.7 billion unique viewers tuned in to videos related to the 2026 FIFA World Cup.

'Our AI investments are redefining what's possible across every part of our business,' Pichai said.

The company recorded more than $119billion in total revenue between April and June, up 24 percent compared to the same period last year.

Its cloud business recorded more than $24billion in the quarter, and its net profit quadrupled to over $112billion - boosted massively by its AI-related investments in SpaceX and Anthropic. 

Google said gains tied to those investments were valued at about $99billion, while contributing to $77billion in overall profit. 

The boost was also helped by SpaceX going public earlier this month, though the Elon Musk-run company has dropped well below its IPO price of $135 per share, trading this morning at just over $115 per share. 

Google's Gemini app has grown to 950 million monthly active users.

CEO Sundar Pichai (pictured) said that the successful quarter was due in part to growth of its AI model Gemini and AI search queries
CEO Sundar Pichai (pictured) said that the successful quarter was due in part to growth of its AI model Gemini and AI search queries
Google's Gemini app has grown to 950 million monthly active users, the firm said (File image)
Google's Gemini app has grown to 950 million monthly active users, the firm said (File image) 

That puts its AI chat app within striking distance of OpenAI's ChatGPT which hit 1 billion monthly active users in June, according to reports.

Alphabet spent nearly $45billion in the quarter, 'the vast majority' of which went towards AI investments, CFO Anat Ashkenazi said during a call with analysts.

The company also increased its capital expenditure (capex) estimate for the full year to as much as $205billion, a jump from $190billion last quarter.

The increase is 'primarily due to an acceleration in the delivery of capacity to meet growing demand,' Ashkenazi continued. 'We continue to expect our capex to increase significantly in 2027, and will provide more details at a later date.'

The company's stock fluctuated after hours and dropped by more than 4 percent.  

Major tech companies in the US have been spending billions to build out AI infrastructure and develop increasingly more advanced AI models.

In June, Google parent company Alphabet announced it would raise up to $80billion in stock to fund its AI efforts, with Warren Buffett's Berkshire Hathaway committing $10billion.

The spending is part of a larger race across the tech sector to stay competitive amid soaring demand for computing power.

Amazon, Microsoft, Alphabet and Meta are collectively on track to pour roughly $700billion into AI data centers, chips and computing infrastructure this year.

Amidst competition for customers, AI developers are also designing lower-cost versions of their own models.

On Tuesday, Google launched three new models under the name 'Flash' which the company described as cheaper, faster and more reliable.

Cheaper and faster models coming from developers in China - particularly from DeepSeek and Moonshot - have ruffled feathers, and raised concerns about intellectual property theft, in the US.

This week, White House technology official Michael Kratsios accused Moonshot of creating its new Kimi K3 model off of Anthropic's powerful new model known as Fable.

Distillation is a technique in which a smaller AI model is trained to mimic the outputs of a larger, more capable one.

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