•Data centre revenue rose around 90% year-on-year, driven by AI infrastructure build-out.

•Industrial and automotive demand also recovered, as inventories normalised after post-pandemic correction.

What happened

Texas Instruments reported stronger-than-expected first-quarter results and raised its second-quarter outlook, signalling improving demand across both cyclical and structural segments of the semiconductor market.

The company forecast Q2 revenue of $5.0bn to $5.4bn, above Wall Street expectations of $4.86bn, according to LSEG data. It also guided earnings per share of $1.77 to $2.05, compared with forecasts of $1.57.

First-quarter revenue came in at $4.83bn, also ahead of estimates.

The most significant growth came from its data centre-related business, which rose around 90% year-on-year, driven by rapid expansion of AI infrastructure.

TI CEO Haviv Ilan said demand strength was broadening beyond early AI adopters, reflecting sustained investment in large-scale computing systems.

At the same time, industrial and automotive segments showed recovery. Industrial demand improved as inventories normalised, while automotive revenue rose in the mid-single-digit range.

Why it's important

The key signal in TI's results is not earnings strength alone, but the widening reach of AI demand across the semiconductor stack.

The around 90% surge in data centre activity suggests AI investment is extending beyond high-end processors into supporting components such as analog chips, which manage power, signal conversion and system stability in large-scale computing infrastructure.

At the same time, recovery in industrial and automotive markets points to the end of the post-pandemic inventory correction. This creates a dual cycle effect, where cyclical recovery and structural AI demand reinforce each other.

For Texas Instruments, this matters because it operates at the intersection of industrial electronics and AI infrastructure demand. The company is therefore increasingly exposed not just to cyclical swings, but to the deeper build-out of AI-driven systems.


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