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Google Q3 financial report: Cloud business grew by 103%, net profit decreased by 25% year-on-year

Alphabet, Google's parent company, announced its third-quarter financial report for 2026 in the early hours of October 29, Beijing time. The quarterly revenue was $127.6 billion, a year-on-year increase of 25%, achieving double-digit growth for the 13th consecutive quarter, with an annualized revenue run rate of $500 billion. Revenue slightly missed Wall Street expectations, and net profit declined year-on-year, overall falling short of market expectations.Google Cloud revenue grew 103% year-on-year to $30.7 billion, a record increase, mainly driven by demand for enterprise AI infrastructure, AI solutions, and TPU-related needs. Google Services revenue increased by 11% to $96.3 billion, with search and other advertising revenue growing by 12% year-on-year, and YouTube advertising revenue increasing by about 9% to $11.2 billion. The company stated that the year-on-year growth rate of search queries was the highest in nearly five years, with AI-related features contributing to increased usage.Net profit attributable to common shareholders was $26.2 billion, a year-on-year decrease of about 25%, with earnings per share of $2.12. The profit decline was mainly affected by losses from equity securities investments, with related losses for the quarter amounting to about $8.7 billion, whereas this item had contributed significantly to profits in the same period last year. Operating profit increased by 30% year-on-year to $40.6 billion, with an operating margin rising to 31.8%. The free cash flow for the quarter was negative, and the full-year capital expenditure guidance remained at $195 billion to $205 billion. After the financial report was released, Alphabet's stock price fell by 9.84% in after-hours trading.

first_img Google DeepMind released XYEval, assessing whether agents comply with misleading suggestions

Google DeepMind researchers Zhengxuan Wu, Yuxuan Li, Oyvind Tafjord, and Been Kim released a preprint paper titled "XYEval: Agents say yes to bad advice," proposing a meta-evaluation framework XYEval to assess the performance of agents when faced with misleading suggestions from users. The paper extends the "XY problem" to the agent scenario, where the user inquires about their attempted solution X rather than the actual problem Y.XYEval retains the original task environment and verifiable answers, only adding seemingly reasonable but misleading user suggestions in the instructions. The misleading suggestions are generated by a generator after seeing the complete task and are appended in the form of confident suggestions rather than hard requirements, allowing the original task to remain solvable. The evaluation covers six benchmarks: τ2-bench, SWE-bench Verified, SWE-bench Pro, Terminal-Bench, Humanity's Last Exam, and MCP-Atlas, along with five models, where the misleading suggestions for τ2-bench are generated based on rules set by human experts.The paper states that the performance significantly declines after the XY mutation, with the relative drop reaching up to 46.7%. On τ2-bench, when faced with users who require a detailed explanation before accepting a better solution, the performance drops even more. Encouraging models to recognize the XY problem through simple system instructions only partially alleviates the issue. The related code is published in the GitHub repository google-deepmind/xyeval.
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