Power efficiency and uptime are becoming the limiting factors in scaling AI infrastructure. The upside case depends on keeping premium sockets while layering stickier telemetry/security value.

As AI infrastructure becomes power- and reliability-limited, MPS can keep taking premium “power bottleneck” sockets (controllers + integrated modules) and add sticky software/security layers (telemetry, provenance) that defend pricing and raise dollar-content per rack and per vehicle through 2030.

Compute scaling shifts the bottleneck to power efficiency, thermals, and uptime; MPS sells the shovels and can extend into verification/telemetry where trust and reliability become scarce assets.

MPS is positioned in the part of semiconductors where AI-driven system constraints show up first: converting watts into reliable compute. Its advantage is not just silicon performance but also system-level power architecture plus “design-win gravity” from deep apps engineering. If it keeps expanding content per platform (more integrated modules, more rails, more monitoring) while extending into lightweight software/security features, it can sustain above-market growth while remaining relatively capital-light versus integrated analog peers.

The company-risk is not “can they build power ICs,” but whether premium sockets stay premium. The main failure mode is customer-driven commoditization (multi-sourcing, in-house designs, standard rack architectures) that compresses both growth and valuation even if MPS executes well operationally.

Higher near-term revenue momentum plus a lower starting valuation versus the prior run increased modeled upside.

$1180.93