Saturday, June 28, 2025 5:11 PM EDT

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Introduction

The AI hardware-related (semiconductor/computer chip/wafer) segment consists of hundreds of companies, big and small, in 7 distinct sub-segments, each with its own unique dynamics and growth trajectories. On average, the 7 model MunAiMarkets portfolios of 31 constituents, in total, were UP 13.1% in June, as follows:

 

The MunAiMarkets Model AI Hardware Stock Portfolio

As mentioned in the Introduction, the segments are tracked below as to their performances in June (month-ending June 27th) and YTD in 7 similarly focused model portfolios along with their constituents.

The first 4 stages (involved in the exclusive production and distribution of semiconductor devices) are referred to as the semiconductor value chain.

1. The Pure-Play EDA Software Chip Design Model Portfolio: UP 7.6% in June; UP 2.8% YTD

• Focus: design of complex semiconductor chips using sophisticated electronic design automation (EDA) software.
• Constituents: Cadence Design Systems (CDNS); and Synopsys (SNPS).

2. The Pure-Play Semiconductor Supplier Model Portfolio: UP 15.8% in June; UP 23.6% YTD

• Focus: provide essential equipment for manufacturing chips such as lithography machines, etching, test and packaging equipment, automation and inspection systems and production materials such as chemicals, gases, wafers and packaging.
• Constituents: Applied Materials (AMAT); ASML Holding (ASML); Entegris (ENTG);  KLAC Corp. (KLAC); and Lam Research (LRCX).

3. The Pure-Play Semiconductor Foundries Model Portfolio: UP 15.2% in June; UP 6.6% YTD

• Focus: factories (called foundries in semiconductor jargon) concentrate all their efforts in the manufacture of chips based on the designs provided by other semiconductor companies.
• Constituents: GlobalFoundries (GFS); Taiwan Semiconductor (TSM); Tower Semiconductor (TSEM); and United Microelectronics (UMC).

4. The Pure-Play Outsourced Semiconductor Assembly and Test Model Portfolio: UP 15.8% in June; DOWN 11.2% YTD

• Focus: assemble chips into finished semiconductor components, tests for defects and do the very specialized packaging of the chips for shipping.
• Constituents: Amkor Technology (AMKR) and ASE Technology (ASX). 

In addition to the value chain there are 3 other segments and they, too, are highlighted below:

5. The MunAiMarkets Pure-Play Fabless Semiconductor Companies Model Portfolio: UP 13.8% in June; UP 14.9% YTD

• Focus: manufacturers design and sell hardware devices and semiconductor chips themselves while outsourcing their fabrication (i.e. are fabless) to a specialized manufacturer.
• Constituents: Advanced Micro Devices (AMD);  Broadcom (AVGO); Marvell (MRVL); Monolithic Power (MPWR);   Nvidia (NVDA); and Qualcomm (QCOM).

6. The MunAiMarkets Integrated Device Manufacturer (IDM) Model Portfolio: UP 15.4% in June; UP 15.0% YTD 

• Focus: companies do everything themselves in house to control the entire production process.
• Constituents: Analog Devices (ADI); Infineon Technologies (IFNNY); Intel Corp. (INTC). Microchip Technology (MCHP); Micron Technology (MU); NXP Semiconductors (NXPI); STMicroelectronics (STM); and Texas Instruments (TXN). 

7. The MunAiMarkets Pure-Play Quantum Computing Model Portfolio: UP 7.3% in June; UP 0.8% YTD 

• Focus: involved exclusively in the research and development of quantum computers.
• Constituents: D-Wave Computing (QBTS); IonQ (IONQ); Quantum Computing Inc. (QUBT); and Rigetti Computing (RGTI).

Summary

On average, the 31 constituents in the above 7 Model AI Hardware Stock Portfolios were UP 13.1% in June and are also UP 13.1%, on average, YTD. In comparison, the tradable iShares Semiconductor ETF (SOXX), with 35 constituents, was UP 16.6% in June and is now UP 10.8% YTD.

Please note that hyperscaler companies such as Google, Amazon, Meta, and Microsoft, are developing and deploying their own proprietary AI chips to optimize costs, enhance performance, and reduce reliance on third-party suppliers. These hyperscalers are not presented here in a portfolio of their own as the companies are not exclusively AI hardware operations, but their major contributions to the research, development and deployment of AI chips are hereby acknowledged.

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