Semiconductor IP Market to hit $18.64B by 2032, fueled by AI

The global semiconductor IP market, estimated at USD 9.30 billion in 2025, will expand to USD 18.64 billion by 2032, according to a new report. This growth, registering a 10.2% compound annual growth rate from 2026 to 2032, is fueled by demand for artificial intelligence, custom silicon, and the increasingly adopted RISC-V architecture. Rising semiconductor development costs are driving chipmakers toward pre-validated IP solutions that accelerate innovation and reduce engineering complexity across industries like automotive and data centers.

AI, Custom Silicon, and RISC-V Drive Semiconductor IP to $18.64B by 2032

The semiconductor intellectual property (IP) market is forecast to more than double in size, reaching USD 18.64 billion by 2032, a substantial increase from the USD 9.30 billion estimated valuation in 2025. This expansion, registering a 10.2% compound annual growth rate, is driven by companies increasingly turning to pre-validated IP solutions to address the rising costs and complexities of modern semiconductor development, accelerating innovation and improving productivity. Investments in custom silicon, particularly AI-enabled processors and chiplet-based designs, are significantly bolstering demand for semiconductor IP across diverse sectors including consumer electronics, automotive, telecommunications, industrial automation, and data centers.

The shift towards heterogeneous computing, advanced packaging techniques, and the open-source RISC-V instruction set architecture are creating new monetization opportunities for IP providers and fundamentally reshaping the competitive dynamics within the semiconductor industry. A report released by Research and Markets, titled “Semiconductor IP Market by Design IP, IP Core, IP Source, IP Consumer, Architecture, Vertical, and Region – Global Forecast to 2032,” details these trends.

Security IP is projected to be the fastest-growing segment within design IP through 2032, reflecting a critical need for robust hardware-level protection. Demand is surging for technologies like root-of-trust solutions, cryptographic engines, secure boot implementations, and increasingly, post-quantum cryptography blocks, driven by the escalating frequency of cyberattacks, the proliferation of internet-connected devices, the rise of software-defined vehicles, and the expansion of cloud-based AI infrastructure.

As security transitions from a feature to a core requirement in chip design, IP providers are positioned to capitalize on opportunities spanning automotive, industrial, consumer electronics, and data center applications. Hard IP cores are also expected to outpace soft IP in growth during the forecast period. Solutions like SerDes PHYs, PCIe PHYs, DDR PHYs, HBM PHYs, RF blocks, and analog circuits are in high demand as companies adopt advanced process nodes, high-bandwidth memory architectures, and AI accelerators.

The focus on power efficiency, performance optimization, and faster time-to-market is making hard IP increasingly vital for next-generation chip development. The RISC-V architecture is anticipated to experience the fastest growth of any architecture segment through 2032, offering customized processor development without the constraints of traditional licensing. Asia Pacific is forecast to remain the fastest-growing regional market, supported by investments in semiconductor self-sufficiency and expanding domestic chip design ecosystems.

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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