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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, consisting of 31 constituents were up 3.0%, on average, week-ending July 4th as follows:

The MunAiMarkets Model AI Hardware Stocks Portfolio

The segments are tracked below as to their performances week-ending July 4th 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 8.4% w/e July 4th

• 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 2.3% w/e July 4th

• 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 3.4% w/e July 4th

• 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 2.7% w/e July 4th

• 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 segments there are 3 other segments:

5. The Pure-Play Fabless Semiconductor Companies Model Portfolio: UP 1.7% w/e July 4th

• 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 Integrated Device Manufacturer (IDM) Model Portfolio: UP 3.8% w/e July 4th

• 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 Pure-Play Quantum Computing Model Portfolio: UP 15.9% w/e July 4th

• 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 3.0%, on average, w/e July 4th. In comparison, the tradable iShares Semiconductor ETF (SOXX), with 35 constituents, was UP 1.9% while the S&P 500 ETF Trust was UP 1.7% and the Invesco QQQ Trust ETF was UP 1.5% .

Please note that hyperscalers such as Google‘s TPU, Amazon’s Inferentia and Trainium, Meta’s MTIA, and Microsoft‘s Maia accelerators, are developing and deploying their own proprietary AI chips to optimize costs, enhance performance, and reduce reliance on third-party suppliers. This signals a potential fragmentation of the market and a shift towards more integrated, ecosystem-specific solutions. These hyperscalers are not presented 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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