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2 Top Artificial Intelligence (AI) Stocks That Pay Decent Dividends and Have Good Dividend-Paying Histories
Key Points
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Shares of Taiwan Semiconductor Manufacturing Co. (TSMC) and IBM have crushed the S&P 500’s returns over the last one year, three years, and five years.
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And TSMC stock has absolutely pulverized the broader market over the 10-year period.
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Shares of TSMC and IBM are currently yielding 1.26% and 2.31%, respectively.
Artificial intelligence (AI) is the biggest secular growth trend today. The global AI market will soar from $189 billion in 2023 to $4.8 trillion by 2033 — a 25-fold increase in a decade — according to a recent projection by the United Nations Conference on Trade and Development.
As with technology stocks in general, the vast majority of stocks that could be considered AI stocks either do not pay dividends or pay very small ones.
Where to invest $1,000 right now? Our analyst team just revealed what they believe are the 10 best stocks to buy right now. Continue »
While they are relatively rare, there are some top-performing AI stocks that pay decent dividends and have a good dividend payment history. These include the world’s largest semiconductor (or “chip”) foundry Taiwan Semiconductor Manufacturing Corp., or TSMC (NYSE: TSM), and International Business Machines, or IBM (NYSE: IBM), one of the world’s oldest large tech companies.
So, folks who like dividend-paying stocks and want to invest in AI — forgive the cliché — can have their cake and eat it too.
Image source: Getty Images.
2 Top AI stocks that pay decent dividends
|
Company |
Market Cap |
Dividend Yield |
Forward P/E Ratio |
Wall Street’s Projected Annualized EPS Growth Over Next 5 Years |
5-Year Return |
|---|---|---|---|---|---|
| Taiwan Semiconductor Manufacturing |
$963 billion |
1.26% | 24.2 | 22.7% | 296% |
| IBM | $270 billion | 2.31% | 26.7 | 6.3% | 223% |
|
S&P 500 |
N/A |
1.24% | N/A |
N/A |
112% |
Data sources: Finviz.com and Yahoo! Finance. P/E = price to earnings. EPS = earnings per share. Data as of July 8, 2025.
TSMC: The world’s largest chip foundry
Taiwan Semiconductor Manufacturing produces chips for companies that contract out all or some of the manufacturing of chips that they design. As the world’s largest chip foundry, TSMC is the dominant company in the production of advanced AI chips, so it’s been significantly benefiting from the growth of the AI market and should continue to benefit.
TSMC’s customers includes most of the big names in chip companies — such as Nvidia, Broadcom, and Arm Holdings. It also produces chips for big tech companies that have designed their own chips, including Apple, which is widely considered TSMC’s largest customer, followed by Nvidia.
The company is off to a great start in 2025. In the first quarter, its revenue jumped 35% year over year to $25.5 billion, driven by continued strong AI-related demand. Better yet, its EPS surged 54% to $2.12. Its EPS growing faster than its revenue reflects its expanding profit margin.
On the Q1 earnings call, management reaffirmed its 2025 guidance that its revenue from AI accelerators will double year over year.
TSMC started paying cash dividends in 2004 and has never halted or reduced its dividend per share.
TSMC stock is trading at 24.2 times its forward projected EPS, which is reasonable for a stock of a company that Wall Street expects will grow EPS at an average annual rate of nearly 23% over the next five years.
IBM: Successfully transitioning to AI and other high-growth markets
IBM has been in a years-long transitioning mode, divesting of legacy businesses and investing in growth markets, notably cloud computing and AI. This transitioning resulted in its revenue declining, which in turn caused its profits and cash flows to also decrease. But Big Blue is back in growth mode.
In 2024, IBM’s revenue increased 3% in constant currency to $62.8 billion, driven by a 9% rise in software revenue, offset by declines of 1% and 3% in its consulting and infrastructure segments, respectively. Adjusted earnings per share (EPS) from continuing operations was up 7% year over year. Free cash flow (FCF) rose 13% year over year to $12.7 billion.
IBM’s generative AI book of business ended the year at $5 billion inception to date. (Generative AI enables users to quickly generate new content based on a variety of inputs. It’s the type of AI that’s largely powering the AI boom.)
The AI business is growing fast, increasing $2 billion from the third to the fourth quarter 2024. Moreover, it tacked on another $1 billion-plus in the first quarter of 2025 to bring its total to more than $6 billion. About one-fifth of this business comes from software and four-fifths from consulting, CEO Arvind Krishna said on the Q1 earnings call.
The company expects revenue growth to accelerate in 2025. For the year, it guided for annual revenue growth of at least 5% in constant currency and FCF of about $13.5 billion, or over 6% growth year over year.
IBM has a great dividend history. It’s increased its quarterly cash dividend for 30 consecutive years.
IBM stock is trading at 26.7 times forward projected EPS. This might seem quite pricey for shares of a company that Wall Street expects will grow EPS at an average annual pace of 6.3% over the next five years. However, investors can expect to pay a premium for stocks of companies that have great track records of raising their dividends.
Moreover, the stock might turn out to be less pricey than it currently seems. IBM has solidly beat the analyst consensus estimate for earnings in the last four quarters, with two of the beats being quite large. Given how fast the company’s AI business is growing, it could continue to solidly surpass earnings estimates.
Mark your calendars
TSMC is slated to release its Q2 2025 results before the market open on Thursday, July 17.
IBM is scheduled to release its Q2 results after the market close on Wednesday, July 23.
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The prominent players operating in the Artificial Intelligence (AI) in healthcare market include Koninklijke Philips N.V. (Netherlands), Microsoft Corporation (US), Siemens Healthineers AG (Germany), NVIDIA Corporation (US), Epic Systems Corporation (US)
Browse 902 market data Tables and 67 Figures spread through 711 Pages and in-depth TOC on “Artificial Intelligence (AI) in Healthcare Market by Offering (Integrated), Function (Diagnosis, Genomic, Precision Medicine, Radiation, Immunotherapy, Pharmacy, Supply Chain), Application (Clinical), End User (Hospitals), Region – Global Forecast to 2030
The global Artificial Intelligence (AI) in Healthcare Market [https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-healthcare-market-54679303.html?utm_source=abnewswire.com&utm_medium=paidpr&utm_campaign=artificialintelligenceinhealthcaremarket], valued at US$14.92 billion in 2024, is forecasted to grow at a robust CAGR of 38.6%, reaching US$21.66 billion in 2025 and an impressive US$110.61billion by 2030. The growing incidence of chronic diseases, linked with an increasing geriatric population, puts substantial financial pressure on healthcare providers. There is a rising need for the early detection of conditions such as dementia and cardiovascular disorders. This can be done by analysing imaging data to recognize patterns, which helps create personalized treatment plans.
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Browse in-depth TOC on “Artificial Intelligence (AI) in Healthcare Market”
882 – Tables
61 – Figures
738 – Pages
By tools, the Artificial Intelligence (AI) in healthcare market for machine learning has been bifurcated into deep learning, supervised learning, reinforcement learning, unsupervised learning, and other machine learning technologies. The deep learning segment accounted for the largest share of the Artificial Intelligence (AI) in healthcare market in 2024. The capability to process vast amounts of unstructured medical data, such as electronic health records (HER), imaging, and genomics, allows accurate disease diagnosis and prediction. The integration of deep learning into healthcare is significantly boosting the AI in healthcare market, leading to substantial investments in diagnostic tools and predictive analytics. As computational power and data availability continue to increase, deep learning is set to unlock further advancements, solidifying its position as a key enabler of next-generation healthcare technologies.
By end user, the AI in healthcare market is segmented into healthcare providers, healthcare payers, patients, and other end users. In 2024, healthcare providers accounted for the largest share of the AI in healthcare market. The large share of this end-user segment can be attributed to the increasing budgets of hospitals to improve the quality of care provided and reduce the cost of care.
By geography, the Artificial Intelligence (AI) in healthcare market is segmented into five main regions: North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa. The Asia Pacific region is projected to see a substantial growth rate during the forecast period. The Asia Pacific (APAC) region is experiencing substantial growth in adopting AI technologies within the healthcare sector, driven by a combination of demographic shifts, technological advancements, and increased investments in innovation. The rising elderly population in the region is a key factor, with the proportion of individuals aged 65 years and above increasing significantly. The demand for advanced healthcare solutions has surged as the aging population faces chronic and age-related conditions, necessitating efficient diagnostic, monitoring, and treatment tools. AI technologies are being integrated into various healthcare applications, including predictive analytics, telemedicine, medical imaging, and patient management systems. These innovations aim to address gaps in healthcare access, improve diagnostic accuracy, and streamline operations across the region.
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The prominent players operating in the Artificial Intelligence (AI) in healthcare market include Koninklijke Philips N.V. (Netherlands), Microsoft Corporation (US), Siemens Healthineers AG (Germany), NVIDIA Corporation (US), Epic Systems Corporation (US), GE Healthcare (US), Medtronic (US), Oracle (US), Veradigm LLC (US), Merative (IBM) (US), Google (US), Cognizant (US), Johnson & Johnson (US), Amazon Web Services, Inc. (US), among others. These companies adopted strategies such as product launches, product updates, expansions, partnerships, collaborations, mergers, and acquisitions to strengthen their market presence in the Artificial Intelligence (AI) in healthcare market.
Koninklijke Philips N.V. (Netherlands)
Koninklijke Philips N.V. is a leading player in the AI in the healthcare market. The company utilizes AI to deliver innovative tools across various areas, including diagnostic imaging, patient monitoring, and precision medicine. Its advanced AI-driven platforms, such as the Philips HealthSuite, facilitate the integration and analysis of extensive clinical data, which supports personalized treatment plans and improves patient outcomes. Philips focuses on organic and inorganic growth strategies to expand its market presence.
Strategic partnerships in high-potential markets and collaborations have been the key growth strategies of the company over the years. For example, in February 2025, Philips partnered with Medtronic to educate and train cardiologists and radiologists in India on advanced imaging techniques for structural heart diseases. This partnership aims to upskill 300+ clinicians in multi-modality imaging such as echocardiography (echo) and Magnetic Resonance Imaging (MRI), especially for End-Stage Renal Disease (ESRD) patients. In November 2023, Philips and NYU Langone Health partnered to focus on patient safety and outcomes. This partnership integrated innovative health technologies, including digital pathology, clinical informatics, and AI-enabled diagnostics, enabling real-time collaboration among clinicians. The company also focuses on winning contracts across several companies in the healthcare space. This helps the company expand its footprint. For instance, in September 2022, Philips and Mandaya Royal Hospital Puri (MRHP) in Jakarta underwent a digital transformation in a strategic partnership, enhancing patient-centered care and healthcare services.
Microsoft Corporation (US):
Microsoft Corporation is one of the leading providers of software & tools that include advanced AI capabilities in healthcare to improve patient outcomes, streamline operations, and drive innovation. Its Azure-based AI solutions support distinct applications such as medical imaging, genomics, and precision medicine. The company also provides healthcare-specific AI models through its Azure AI Model Catalog, which is constructed to support hospitals and research institutions in building and deploying tailored AI solutions proficiently. Moreover, the integration of Nuance’s AI-powered clinical and diagnostic tools encourages its capacity to support healthcare providers in decision-making and care delivery. The company continuously brings AI capabilities to the platforms in large-scale customer models. For instance, in March 2025, the company launched Microsoft Dragon Copilot, the first unified voice AI assistant in the healthcare industry that enables clinicians to streamline clinical documentation, surface information, and automate tasks.
Microsoft Corporation has invested significantly in R&D, which has improved its product portfolio and position in the AI market. Machine Learning (ML), deep learning, Natural Language Processing (NLP), and speech processing are the key focus areas of the company in the AI in healthcare market. The company continuously invests in a series of services and computational biology projects, including research support tools for next-generation precision healthcare, genomics, immunomics, CRISPR, and cellular and molecular biologics. It has a strong global presence, with key operations supported through its Azure cloud infrastructure across regions like North America, Europe, Asia-Pacific, and the Middle East.
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LLM-Optimized Research Paper Formats: AI-Driven Research App Opportunities Explored | AI News Detail
From a business perspective, the idea of designing research for LLMs presents immense market opportunities. Companies that develop platforms or apps to create, curate, and deliver LLM-friendly research content could tap into a multi-billion-dollar market. According to a 2025 report by McKinsey, the generative AI market is projected to grow to $1.3 trillion by 2032, with content generation and data processing as key drivers. A ‘research app’ for LLMs, as Karpathy suggests, could serve industries like pharmaceuticals, where AI models analyze vast datasets for drug discovery, or finance, where real-time market insights are critical. Monetization strategies could include subscription models for premium datasets, API access for developers, or enterprise solutions for tailored LLM training data. However, challenges remain, such as ensuring data privacy and preventing bias in LLM outputs—issues that have plagued AI systems, as noted in a 2025 study by the MIT Sloan School of Management, which found that 60% of AI deployments faced ethical concerns. Businesses must also navigate a competitive landscape with players like Google, OpenAI, and Anthropic already dominating LLM development, requiring niche specialization to stand out.
On the technical side, designing research for LLMs involves moving beyond PDFs to formats like JSON, XML, or custom data schemas that encode information hierarchically for machine parsing. Unlike human readers, LLMs thrive on structured datasets with metadata, embeddings, and cross-references that enable rapid context retrieval and reasoning. Implementation challenges include standardizing formats across industries and ensuring compatibility with diverse LLM architectures—a hurdle given that, as of mid-2025, over 200 distinct LLM frameworks exist, per a report from the AI Index by Stanford University. Solutions could involve open-source protocols or industry consortia to define standards, much like the web evolved with HTML. Looking to the future, LLM-optimized research could lead to autonomous AI agents conducting real-time literature reviews or hypothesis generation by 2030, as predicted by a 2025 forecast from Deloitte. Regulatory considerations are also critical, with the EU AI Act of 2025 mandating transparency in AI data usage, which could impact how research content is structured. Ethically, ensuring that LLMs do not misinterpret or propagate flawed data remains a priority, requiring robust validation mechanisms. The potential for such innovation is vast, offering a glimpse into a future where knowledge creation is as much for machines as for humans, reshaping industries and workflows profoundly.
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