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Market map S&P 500, sized by market cap, shaded by year-to-date change

Alphabet (GOOGL) stock plunged after a third-quarter miss: revenue of $127.57B vs $128.2B expected, and Google Search revenue of $63.4B below $64.9B forecast. Negative free cash flow of $9.1B vs an estimated $3.5B shortfall heightened concerns about Big Tech profitability as AI-era advertising shifts emerge. Analysts point to AI-driven changes hurting search and potential YouTube monetization issues. The stock fell about 10% in pre-market, underscoring worries about AI's impact on core businesses and the pace of monetization despite Google Cloud growth. Viewed as a rare double miss with earnings per share of $2.12 versus $3.02: signaling broader profit and cash-flow pressure for Alphabet.

Alphabet reported 3Q revenue of $127.6B, up 25% y/y, with Google Search & other ad revenue growing 12% to $63.3B, slower than Q2’s 17% due to FX headwinds and Asia-Pacific ad-spend dips tied to EU rules. Q2 benefited from World Cup advertising, a factor that normalized in Q3. While Google Cloud grew a record 103% to $30.7B, overall ad sales rose 10.6% to $82.01B. AI-driven search (AI Mode) showed rising usage but fewer baked-in ads, pressuring monetization. Alphabet’s shares fell ~10% after results, highlighting ongoing concerns about core advertising momentum in the AI era despite cloud gains. Retail sentiment for GOOGL dipped from extremely bullish to bullish. Executives signaled macro pressures and currency effects as drivers of the slowdown.

Nvidia’s GTC Berlin highlighted a shift from AI training to large-scale inference in “AI factories,” powered by a new comprehensive server rack combining 72 Vera Rubin GPUs with 256 new custom LPU chips and Nvidia’s CPUs. Management cites a 1-gigawatt AI factory costing about $50 billion and generating roughly $40 billion in annual revenue, with a three-month delay costing $10 billion in potential sales. The move expands monetization per gigawatt of compute (from $18B to $25B to $40B) as AI inference becomes more central. This broadens Nvidia’s exposure beyond GPUs to integrated accelerators and CPUs, aiming to capture substantial data-center and AI-infrastructure demand while markets weigh valuation concerns. Critics note mixed sentiment, but the blueprint underscores substantial potential for AI compute ecosystems.

28 October