At the end of the interest rate hiking cycle, the defensive value of dividend pharmaceutical stocks becomes prominent, while the proliferation of AI computing chips is driving pharmaceutical companies to accelerate tech transformation. By leveraging AI for new drug R&D, they enhance growth potential, balancing dividend stability and growth, creating new investment opportunities for investors.
1. Defensive Value of Dividend Pharmaceutical Stocks: A Safe Haven at the End of the Interest Rate Hiking Cycle
In August 2026, the Federal Reserve's interest rate hiking cycle is nearing its end, with market expectations of interest rate cuts strengthening. At this time, the defensive value of dividend stocks regains attention. As a traditional defensive sector, U.S. dividend pharmaceutical stocks, with their stable cash flows and continuously growing dividends, become the preferred choice for investors to hedge against market volatility. For example, major pharmaceutical companies like Merck, Eli Lilly, and Pfizer have all maintained consecutive dividend growth in recent years. Merck has raised its dividend for 10 consecutive years, Eli Lilly for 8 consecutive years, and Pfizer for 7 consecutive years. The dividend yields of these companies generally exceed 3%, far higher than the market average.
However, with the rise of AI technology, dividend pharmaceutical stocks are facing new transformation opportunities. The advancement of AI computing chips enables pharmaceutical companies to use machine learning to accelerate drug R&D processes, from target discovery to clinical trials, significantly shortening R&D cycles and reducing costs. This tech empowerment not only enhances pharmaceutical companies' innovation capabilities but also injects growth momentum into dividend stocks, transforming them from "defensive" to "defensive + growth-oriented".
2. AI Computing Chips: The "Accelerator" for Pharmaceutical R&D
1. Development Status of AI Computing Chips
AI computing chips are the core hardware of AI technology. In recent years, chip giants like NVIDIA and AMD have continuously launched high-performance products to meet the needs of AI training and inference. For example, NVIDIA's H100 GPU and the B200 chip based on the Blackwell architecture, as well as AMD's MI400X GPU, all have powerful parallel computing capabilities to process large-scale drug molecular data. The popularization of these chips allows pharmaceutical companies to use AI models to analyze massive biological data, accelerating the discovery of drug targets and compound screening.
According to IDC data, the global AI server market size is expected to reach $50 billion in 2026, with the demand from the pharmaceutical R&D field accounting for about 15%, becoming one of the important application scenarios for AI computing power. As the performance of AI computing chips improves and costs decrease, more and more pharmaceutical companies are starting to deploy AI new drug R&D, using it as a key means to enhance competitiveness.
2. Applications of AI Computing Chips in Pharmaceutical R&D
The applications of AI computing chips in pharmaceutical R&D are mainly reflected in the following aspects:
- Target Discovery: By using AI models to analyze genomic, proteomic, and other data, quickly identify disease-related targets, which is several times faster than traditional methods. For example, Merck used AI computing chips to analyze over 1 billion molecular structures, discovering new tumor targets and laying the foundation for subsequent drug R&D.
- Compound Screening: Use AI models to predict the activity and toxicity of compounds, significantly reducing the number of experiments. For example, Eli Lilly used NVIDIA's H100 chips to shorten compound screening time from several months to a few weeks, improving R&D efficiency.
- Clinical Trial Optimization: By using AI models to analyze clinical trial data, optimize trial designs, and improve success rates. For example, Pfizer used AI computing chips to analyze clinical trial data from the past 10 years, adjusted the trial protocol, and increased the success rate of a new drug's clinical trial by 20%.
3. AI Transformation Cases of Dividend Pharmaceutical Stocks
1. Merck: Dividend Raised by 10%, Bolsters AI New Drug R&D
Merck is one of the leaders in U.S. dividend pharmaceutical stocks, with its dividend raised for 10 consecutive years, reaching a 3.5% dividend yield in 2026. In recent years, Merck has increased its investment in AI new drug R&D, collaborating with NVIDIA to use Blackwell chips to accelerate the development of oncology drugs. Merck's Q3 2026 financial report shows that 3 drugs in its AI new drug pipeline have entered Phase II clinical trials, expected to be launched in 2027, bringing new growth points to the company.
Merck's AI layout not only improves R&D efficiency but also enhances the sustainability of its dividends. The company stated that the success of AI new drugs will bring higher revenue, thereby supporting the continuous growth of dividends.
2. Eli Lilly: Weight Loss Drug Revenue Surges 60%, AI Boosts Pipeline Expansion
Eli Lilly is another high-dividend pharmaceutical company, with its dividend raised for 8 consecutive years, reaching a 3.2% dividend yield in 2026. Eli Lilly's weight loss drug Zepbound (tirzepatide) achieved $15 billion in revenue in 2026, a 60% year-on-year increase, becoming the company's core product. In addition, Eli Lilly used AI computing chips to accelerate the pipeline R&D of diabetes and oncology drugs, with 5 new drugs entering Phase I clinical trials in 2026, expected to be launched in 2028.
Eli Lilly's AI layout has enabled it to maintain a leading position in the weight loss drug market while expanding into other disease areas, providing momentum for future growth. The company stated that the application of AI technology will reduce its R&D costs by 30% and speed up the launch of new drugs.
3. Pfizer: Dividend Raised by 8%, Breakthrough in AI Drug Discovery Platform
Pfizer is one of the largest pharmaceutical companies in the U.S., with its dividend raised for 7 consecutive years, reaching a 3.0% dividend yield in 2026. In recent years, Pfizer has launched an AI drug discovery platform, using NVIDIA's MI400X chips to accelerate the development of antiviral drugs. In 2026, Pfizer's AI platform successfully discovered two new antiviral drugs, entering the preclinical research stage, expected to enter clinical trials in 2027.
Pfizer's AI layout not only improves R&D capabilities but also enhances the stability of its dividends. The company stated that the success of AI new drugs will bring higher profits, thereby supporting the continuous growth of dividends.
4. Investment Logic: Balancing Defense and Growth
For investors, the AI transformation of dividend pharmaceutical stocks brings dual advantages: dividend stability and growth potential. At the end of the interest rate hiking cycle, market risk appetite increases, and the defensive nature of dividend stocks makes them a safe-haven asset, while AI technology empowerment gives them growth potential, attracting more capital inflows.
In terms of valuation, the P/E ratios of dividend pharmaceutical stocks generally range between 15-20 times, lower than that of tech stocks but higher than traditional consumer stocks. As AI new drug R&D progresses, pharmaceutical companies' revenue and profits will gradually increase, and valuations are expected to be reshaped. For example, Merck's P/E ratio increased from 18 times in 2025 to 22 times in 2026, Eli Lilly's from 20 times to 25 times, and Pfizer's from 17 times to 21 times.
In addition, dividend pharmaceutical stocks have relatively high dividend yields, averaging 3.2% in 2026, higher than the market average of 2.5%, providing investors with stable cash flows. The success of AI new drugs will further enhance the sustainability of dividends, forming a positive cycle of "dividends + growth".
5. Risk Warnings
Although the AI transformation of dividend pharmaceutical stocks brings opportunities, there are also certain risks. First, AI new drug R&D has high uncertainty and may face the risk of clinical trial failures. For example, one of Merck's AI new drugs failed in Phase II clinical trials, leading to increased R&D costs. Second, the application of AI technology requires large capital investments, which may affect pharmaceutical companies' cash flows. For example, Eli Lilly's investment in AI R&D reached $1 billion, accounting for 20% of its R&D budget. If AI new drugs fail, it may affect dividend payments.
In addition, market competition is also a major risk. As more and more pharmaceutical companies deploy AI new drug R&D, intensified competition may lead to rising R&D costs and declining profit margins. For example, Pfizer's AI drug discovery platform faces competition from Merck and Eli Lilly, requiring continuous investment to maintain its lead.
6. Conclusion
At the end of the interest rate hiking cycle, the defensive value of dividend pharmaceutical stocks becomes prominent, while the empowerment of AI computing chips gives them growth potential. Pharmaceutical companies like Merck, Eli Lilly, and Pfizer have accelerated new drug R&D through AI technology, improving R&D efficiency and success rates, laying the foundation for future growth. For investors, the "defensive + growth" characteristics of dividend pharmaceutical stocks make them an ideal investment target, but attention should be paid to the risks of AI R&D and the impact of market competition.
