Key Takeaways

• Strong AI Adoption & Monetization: AI Overviews reached 1 billion+ monthly users with ads monetizing at similar rates to traditional search, addressing key investor concerns about the AI transition. Gemini API calls grew 14x in 6 months, while machine costs per query dropped 90% over 18 months despite doubling model size.
• Infrastructure Strategy Delivering Results: Google’s dual-track approach using both NVIDIA GPUs and custom TPUs (now in the 6th generation) is showing efficiency gains. ~60% of technical infrastructure spending went to servers (TPUs/GPUs) and 40% to data centers/networking. Management expressed strong confidence in their TPU roadmap.
• Google Cloud Momentum: Cloud revenue grew 35% YoY to $11.4B with margins expanding to 17%, driven by AI infrastructure and solutions adoption. Customer examples showed significant cost savings (e.g., LG AI Research achieved 50% faster inference and 72% lower costs using TPUs/GPUs).
• Capital Investment Clarity: CapEx was $13B in Q3, with similar levels expected in Q4. The new CFO provided improved transparency on 2025 infrastructure investments, indicating an increase but not at the same percentage step-up as 2023-2024, helping investors better understand the investment trajectory.
• Strong Overall Financial Performance: Revenue grew 15% YoY to $88.3B with operating margins expanding to 32% despite a $600M real estate charge, demonstrating a successful balance of AI investments and cost discipline.

Alphabet delivered a strong Q3 2024 with revenue growing 15% year-over-year to $88.3 billion, demonstrating robust momentum across core businesses. Operating income increased significantly by 34% to $28.5 billion, with operating margins expanding to 32%.

The company’s AI infrastructure strategy and execution was a highlight of the quarter. Management emphasized Google’s differentiated “full stack” approach to AI, combining robust infrastructure, research capabilities, and product deployment. Pichai highlighted the company’s comprehensive AI accelerator strategy, leveraging both NVIDIA GPUs and their custom-built TPUs, now in their sixth generation (Trillium). The dual-track approach appears to be delivering results – Pichai pointed to external Gemini model pricing as evidence of their infrastructure efficiency, noting that costs are notably lower compared to similar capability models from competitors. This was further evidenced by a 90% reduction in machine costs per query for AI Overviews over 18 months, while simultaneously doubling the size of their Gemini model.

Google Cloud’s performance reflected this infrastructure advantage, growing 35% year-over-year to $11.4 billion with operating margins expanding to 17%. The company shared that Gemini API calls have grown 14x over six months, suggesting strong customer adoption of their AI offerings. While management didn’t break out specific training versus inference workload metrics, they did highlight customer examples like LG AI Research, which achieved 50% faster inference processing time and 72% lower operating costs using a combination of TPUs and GPUs.

Capital expenditure strategy and AI infrastructure investment was a major focus for investors. Q3 CapEx was $13 billion, primarily directed toward technical infrastructure, with approximately 60% allocated to servers (including both TPUs and GPUs) and 40% to data centers and networking equipment. New CFO Anat Ashkenazi provided increased transparency around infrastructure spending, noting Q4 CapEx would remain at similar levels with an increase expected in 2025, though likely not at the same percentage step-up seen between 2023 and 2024. She emphasized the company’s flexibility to use both GPUs and TPUs based on specific customer and internal needs.

Core products showed strong AI-driven momentum. AI Overviews is now reaching over 1 billion monthly users with strong engagement trends. Importantly for investors, ads within AI Overviews are monetizing at similar rates to traditional search, helping allay concerns about potential revenue headwinds from the AI transition. The company also highlighted that 25% of all new code at Google is now generated by AI, demonstrating internal productivity gains from their AI investments.

The Q&A session revealed a strong investor focus on AI monetization and infrastructure efficiency. Management appeared confident in addressing concerns about AI product adoption and scaling. When questioned about Cloud margins, Ashkenazi provided a balanced view, acknowledging the opportunity for further expansion while noting the necessary ongoing investments in infrastructure. Pichai emphasized their forward-looking TPU roadmap, expressing strong confidence in future generations of their custom AI accelerators.

The overall tone of the call suggested management is successfully balancing aggressive AI investments with cost discipline while maintaining their competitive position in AI infrastructure. Numerous analysts raised their 2025/2026 revenue estimates by 1%/1.5% and free cash flow estimates by approximately 7%, reflecting confidence in both near-term execution and long-term strategic positioning in AI. While questions remain about the pace and scale of return on AI investments, analysts appeared satisfied with management’s clear articulation of strategy and improved disclosure around key metrics, particularly regarding their AI infrastructure approach and efficiency gains.

Join the newsletter and stay up to date