Caterpillar Climbs on AI Data Center Expansion and Strong Backlog

Yahoo Finance ·

U.S. industrial and manufacturing equities are riding a massive wave of growth driven by the artificial intelligence data center boom, with heavy machinery giant Caterpillar Inc. CAT emerging as a prime beneficiary. Caterpillar reported a record order backlog of $72 billion at the end of the second quarter of 2026, marking a 92% surge year over year, with 59% slated for delivery within the next 12 months. Driven by escalating power requirements for generative AI and cloud computing, the company has seen soaring demand for reciprocating engines. CAT shares have jumped 36% year to date. With major hyperscalers lifting their 2026 AI capital expenditures to $750 billion, management anticipates robust sales and revenue gains for the third quarter of 2026, alongside mid-to-high teens revenue growth for the full year 2026.

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US heavy equipment company Caterpillar (CAT) recorded an order backlog of $72.0 billion at the end of the second quarter, surging 92% year-over-year, driven by the AI data center boom. As the 2026 AI capital expenditure budget of the four major hyperscalers was revised upward to $750 billion, demand for power and generation equipment is expanding significantly. Investors should pay attention to solid earnings growth and the expansion of the AI ecosystem through collaboration with NVDA.

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Caterpillar's annual revenue growth rate for 2026 is expected to reach the mid-to-high teens due to a surge in demand for reciprocating engines and power generation equipment essential for building AI data centers. Notably, 59% of the order backlog is scheduled for delivery within the next 12 months, and the stock price has jumped 36% since the beginning of the year.

In future scenarios, further stock price gains are possible if AI data center investments exceed $1 trillion as planned, but margins could be eroded if supply chain constraints or tariff cost pressures arise. Therefore, quarterly margin trends and power equipment shipment volumes should be used as key monitoring indicators.

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