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Monolithic Power Systems and Analog Devices linked to AI data center power demand

Markets are being pulled by elections in Brazil and Serbia, oil worries in the Middle East, and a busy earnings week for Alphabet, Microsoft and Amazon. The article uses this backdrop to highlight three AI‑focused stocks, focusing on two: Monolithic Power Systems and Analog Devices.

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Key points

  • Monolithic Power Systems reports US$3.3 billion revenue and US$56.0 billion market cap.
  • Enterprise data growth at Monolithic floors at 130 % YoY, with ~50 % revenue from data centers.
  • Analog Devices generates about US$13.9 billion from chips used in AI and cloud hardware.

Monolithic Power Systems makes power‑management semiconductors used in AI data centers, cloud servers, autos and industrial gear. It generates about US$3.3 billion in revenue, with a market cap of US$56.0 billion, and roughly half of its sales now come from data‑center exposure. Management says enterprise data growth is now flooring at 130 % year‑over‑year. The company’s outlook hinges on whether AI‑driven workloads continue to need higher power density, a factor that could lift revenue and earnings if the trend holds.

Analog Devices designs high‑performance chips that turn analog signals into digital data for AI, cloud, automotive and industrial hardware. It reports about US$13.9 billion in revenue. Both firms sit behind the heavy computing loads that power AI models, making their performance a bellwether for the broader AI infrastructure ecosystem.

Full story from finance.yahoo.com · by Sasha Jovanovic · via Search: MicrosoftOpen source ↗

3 AI Infrastructure Stocks Linked To Data Center Power Demand

finance.yahoo.com · 26 October 2026

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This text was published by finance.yahoo.com and written by Sasha Jovanovic. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗

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Monolithic Power SystemsAnalog DevicesAlphabetMicrosoftAmazon

The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.

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