Top AI stocks and ETFs by market capitalization

What are AI stocks?

AI stocks are shares of companies that generate meaningful revenue from artificial intelligence technologies. Their exposure can be direct – like chipmakers and server suppliers – or indirect, through businesses embedding AI features into software and services.

A few years ago, ‘AI stocks’ referred mainly to semiconductor makers, but the category now includes software platforms, cloud providers and even consumer apps adding generative features. That expansion reflects how AI has moved from research labs into mainstream products.

Another feature of this theme is how investors track performance. Earnings reports often highlight metrics such as AI-related revenue growth, capital spending on data centers or commentary on GPU availability. These disclosures help frame how much of a company’s trajectory is tied to AI – even if it is not their only line of business.

Find out how to choose the best stockbroker

The AI ecosystem can be grouped into three broad categories:

• Infrastructure – chipmakers, server manufacturers and networking companies building the physical layer.
• Platforms – hyperscale cloud and model providers enabling large-scale access to AI.
• Applications – firms applying AI in security, analytics, automation, consumer apps and robotics.

This segmentation helps frame how different parts of the value chain capture AI-driven demand. It also underlines that the space is dynamic. Names listed here are illustrative, not exhaustive.

Why AI stocks are in focus in 2025

Artificial intelligence has been a recurring theme in markets for several years, but 2025 brings a new set of catalysts that make it especially prominent.

1. Enterprise adoption is accelerating

Companies are no longer experimenting with small pilots – many are now deploying AI at scale. Productivity software, coding assistants and customer service automation are being rolled out across entire organizations. This transition means AI is starting to influence not just technology budgets, but also broader measures like employee efficiency and revenue growth.

2. Capital expenditure cycles remain robust 

Hyperscale cloud providers and large corporations continue to expand their data centers, driving demand for servers, networking equipment and cooling solutions. Power availability and grid constraints are now part of the conversation, highlighting that AI is not just a software story but also a physical infrastructure challenge. These investments tend to be long term, which is why capex signals are closely monitored in quarterly reports.

3. Model scaling and inference costs are reshaping economics 

Training large-scale models requires more powerful hardware, while inference – the process of running models in production – is increasingly sensitive to cost. Companies are looking for efficiency gains, whether through new chip architectures, better networking, or optimized data center layouts. This balance between capability and cost influences where demand flows across the AI value chain.

4. Earnings commentary highlights near-term catalysts

In quarterly calls, executives frequently mention GPU availability, uptake of cloud-based AI services and the share of revenue tied to AI. Investors and analysts parse this language carefully, since it offers a direct view into how AI demand is trending. Mentions of order backlogs, customer demand for AI features, or partnerships in AI infrastructure can move share prices in the short term.

5. Broader sector implications are emerging

AI demand now affects industries beyond technology. Utilities are discussing electricity load from new data centers, real estate firms are considering locations for server capacity, and telecom providers are building higher-bandwidth networks. This ecosystem effect shows why AI is seen as a multi-sector catalyst, not just a technology trend.

Taken together, these factors explain why AI stocks are central to many market discussions in 2025. The theme is no longer about early adoption – it’s about scaling, costs and the broader economic ripple effects.

Learn how to invest in stocks in the US

Categories of AI stocks to know

AI exposure takes different forms across the value chain. Breaking the theme into categories helps explain how demand flows through the ecosystem and why each segment carries distinct opportunities and risks.

1. Infrastructure 

This layer includes GPUs, accelerators, AI-optimized servers, networking hardware and memory components. These companies generate revenue directly from AI model training and inference. Demand here is highly cyclical: when model sizes increase or new applications emerge, orders for GPUs and servers spike. 

At the same time, supply bottlenecks can create backlogs, making quarterly shipment updates particularly important. Infrastructure providers are also sensitive to capex cycles from cloud giants, since a small number of customers account for a large share of sales.

2. Platforms 

Sitting above the hardware, platforms offer model hosting, APIs, copilots and marketplaces for deploying AI at scale. Cloud providers dominate this space, integrating AI into productivity software, developer tools and enterprise workflows. KPIs often include cloud segment growth, customer adoption of new AI features and usage intensity across regions. 

Platform players are differentiated not just by scale, but by ecosystem – the breadth of tools, developer communities and partnerships that encourage adoption. Because platforms distribute models, they can act as gatekeepers, shaping which applications reach users fastest.

3. Applications 

The most visible category involves companies embedding AI into end-user products. Examples include cybersecurity firms using AI for threat detection, analytics companies enhancing data pipelines, automation tools streamlining workflows, and consumer apps adding generative features. Here, success is often measured by user adoption, churn rates and expansion of feature usage rather than raw infrastructure spend. 

Applications also face competitive risk: features that seem differentiated today can become commoditized quickly if rivals roll them out across large user bases.

4. Why segmentation matters 

Each layer responds to different signals. Infrastructure companies track chip supply and data center build-outs. Platforms focus on cloud usage growth and the adoption of AI APIs. Applications depend on end-user engagement and competitive positioning. Understanding where a company sits on this spectrum helps explain both its growth drivers and its risk profile.

This framework also shows how interconnected the layers are. A shortage of GPUs affects cloud service capacity, which in turn limits the rollout of AI features in applications. Conversely, rising demand for consumer AI tools can filter back into greater infrastructure orders. The value chain moves in both directions and companies are often exposed to more than one layer.

Top AI stocks by market capitalization in 2025

1. NVIDIA (NVDA)
2. Microsoft (MSFT)
3. Alphabet (GOOGL)
4. Amazon (AMZN)
5. Meta (META)
6. Broadcom (AVGO)
7. Palantir (PLTR)
8. Advanced Micro Devices (AMD)
9. IBM (IBM)
10. Adobe (ADBE)

Note: These companies aren’t necessarily the best AI stocks, but they’re among the most widely followed and/or based on market capitalization.¹ The examples below are for informational purposes only – not recommendations.

Learn how to buy stocks

1. NVIDIA (NVDA)

NVIDIA remains the backbone of the AI infrastructure story. Its GPUs dominate training and inference workloads, with data center revenue now surpassing its gaming business by a wide margin. 

Currently, attention is on the rollout of its Blackwell architecture, designed for greater efficiency in large-scale training. Partnerships with hyperscalers, along with updates on GPU supply constraints, are key earnings call highlights. NVIDIA is also expanding into networking and software ecosystems to reinforce its role across the AI stack.

2. Microsoft (MSFT)

Microsoft has positioned itself as a leader in AI adoption across enterprise software. Its Azure cloud platform powers large-scale model training and deployment, while Copilot integrations in Office and GitHub represent new monetization routes. Analysts often look for signals about GPU capacity and the pace of Copilot adoption. 

Beyond infrastructure, Microsoft’s investment in OpenAI continues to give it early access to model capabilities that can be embedded across its ecosystem.

3. Alphabet (GOOGL)

Alphabet plays a dual role as both a platform and an application leader. Google Cloud has become a core part of its AI strategy, offering customers custom TPUs alongside mainstream GPUs. 

Search, YouTube and Workspace all integrate generative AI, providing consumer-facing use cases that drive engagement. Investors closely track commentary on how AI is shaping advertising efficiency and whether generative features can add revenue without eroding margins. Alphabet’s open models and research efforts also position it as an important voice in AI development.

4. Amazon (AMZN)

Amazon Web Services (AWS) is central to the company’s AI ambitions. AWS Bedrock provides access to multiple foundation models through an API, while Trainium and Inferentia chips give customers in-house options for training and inference. 

Enterprise adoption is a recurring theme in Amazon’s earnings, with executives highlighting how clients are embedding AI into industry workflows. Beyond cloud, Amazon also integrates AI in logistics, retail and media, showing how the theme extends across its business model.

5. Meta (META)

Meta’s AI focus is primarily infrastructure-heavy, reflecting its scale in recommendations, ranking systems and advertising delivery. Its Llama open-source model family has made it a significant player in model development, while major investment in training clusters and networking has reshaped its capex profile. 

For investors, the key questions are how efficiently Meta can deploy these resources and how AI is contributing to user engagement across platforms. The company continues to emphasize that AI infrastructure spend is foundational to its future strategy.

6. Broadcom (AVGO)

Broadcom has carved out a niche in AI networking and custom silicon. Its products help move data within and between servers, a critical bottleneck in large-scale training. AI now accounts for a growing percentage of its semiconductor revenue, making earnings commentary on customer adoption a focal point. 

Broadcom’s strength lies in its ability to design tailored solutions for hyperscalers, often securing long-term contracts that lock in visibility. This positions it as an essential, if less visible, player in AI infrastructure.

7. Palantir (PLTR)

Palantir’s AI strategy revolves around enterprise platforms that help organizations integrate AI into decision-making. Its Foundry and Gotham products are widely used in government, while its Artificial Intelligence Platform (AIP) is gaining commercial traction. 

A key metric is how effectively Palantir converts pilots into production contracts, demonstrating customer confidence in long-term use. The company’s pitch often emphasizes safety, governance and practical deployment – areas that resonate as AI adoption scales.

8. Advanced Micro Devices (AMD)

AMD has emerged as a credible competitor to NVIDIA in AI accelerators. Its Instinct MI series of GPUs are increasingly adopted by cloud providers, offering an alternative supply line at a time of high demand. 

Analysts often watch for design wins, performance benchmarks and roadmap updates to assess AMD’s share in the fast-growing AI compute market. While smaller than NVIDIA, AMD benefits from a diversified product base in CPUs and gaming, providing stability as it builds its AI footprint.

9. IBM (IBM)

IBM has positioned itself as an enterprise AI partner focused on governance, trust and integration within business workflows. Its watsonx platform unifies data, AI, and automation tools, allowing organizations to train, tune and deploy models securely across hybrid environments.

The company’s AI focus leans heavily toward enterprise productivity and compliance rather than consumer-facing applications. Investors monitor updates on how watsonx adoption supports IBM’s shift toward higher-margin software and consulting revenue streams, especially as traditional infrastructure segments mature.

10. Adobe (ADBE)

Adobe has integrated generative AI into its core creative and marketing suite through the Firefly model family and Sensei framework. These tools power AI-driven features in Photoshop, Illustrator and Experience Cloud, enhancing productivity while maintaining control over intellectual property.

The key theme for Adobe is balancing innovation with responsible AI use – particularly around content authenticity and licensing. Analysts watch adoption rates of Firefly-powered tools and the impact of AI subscriptions on recurring revenue growth.

ETF routes for AI exposure

For those tracking broader AI themes, exchange-traded funds (ETFs) provide a way to gain exposure to multiple companies through a single vehicle. Each fund follows its own methodology, meaning holdings and sector weightings can differ significantly. 

Note: The ETFs mentioned below are not recommendations – they represent ten of the largest and most widely followed funds² in the robotics and AI sector by market capitalization.

1. XAIX: The Xtrackers Artificial Intelligence & Big Data UCITS ETF (XAIX) focuses on companies leading advancements in AI, machine learning and large-scale data analytics. Its portfolio spans global tech innovators and specialized AI developers, offering broad exposure to data-driven technologies shaping multiple industries. Because it targets both software and infrastructure leaders, XAIX often reflects the overall momentum of the AI and big-data ecosystem.

2. AIQ: The Global X Artificial Intelligence & Technology ETF (AIQ) focuses on companies driving innovation in AI hardware, software and related technologies. Its holdings include large-cap names such as Microsoft, NVIDIA and Alphabet, offering diversified exposure across the broader AI ecosystem. Because it tilts toward tech giants, its performance tends to track the overall momentum of the AI sector rather than niche themes.

3. BOTZ: The Global X Robotics & Artificial Intelligence ETF (BOTZ) concentrates on companies in robotics, automation and semiconductor hardware. Its holdings range from industrial robotics manufacturers to leading chip designers. With its strong hardware tilt, BOTZ can be particularly sensitive to semiconductor cycles and capital-intensive automation trends.

4. ARTY: The iShares Future Artificial Intelligence & Technology ETF (ARTY) invests in companies positioned to benefit from long-term AI adoption, spanning AI software, automation tools and high-performance computing. ARTY’s diversified global portfolio captures both foundational AI technologies and commercial applications, making it a broad thematic play across the AI value chain.

5. XMLD/AIAI: The L&G Artificial Intelligence UCITS ETF targets companies using AI to transform sectors such as healthcare, finance and manufacturing. By focusing on firms with measurable AI exposure, the fund offers more selective access than broad tech ETFs. Its methodology emphasizes companies where AI adoption plays a meaningful role in revenue growth.

6. GOAI: The Amundi MSCI Robotics & AI UCITS ETF (GOAI) tracks global companies developing robotics, automation systems and AI-enhanced technology. Its holdings include industrial robotics leaders and semiconductor firms powering automation. GOAI provides geographically diversified access to AI and robotics trends, including significant non-US exposure.

7. CHAT: The Roundhill Generative AI & Technology ETF (CHAT) focuses on companies driving progress in generative AI, including model developers, cloud infrastructure providers and firms commercializing large-scale AI systems. With its concentrated exposure to one of AI’s fastest-growing segments, CHAT offers high-growth potential but can also experience elevated volatility.

8. WTI2/WTAI: The WisdomTree Artificial Intelligence UCITS ETF invests in global companies expected to benefit from AI innovation, including semiconductor manufacturers, enterprise software firms and automation specialists. Its rules-based index methodology emphasizes companies with high AI innovation scores, creating diversified exposure across both hardware and software.

9. ROBT: The First Trust Nasdaq Artificial Intelligence & Robotics ETF (ROBT) uses a tiered weighting approach to categorize companies as AI creators, enablers or adopters. Its holdings span automation software, machine-learning infrastructure and applied robotics. This structure provides balanced exposure across the AI supply chain, from foundational technologies to end-market applications.

10. WTAI: The WisdomTree Artificial Intelligence and Innovation Fund (WTAI) targets firms developing or applying AI in transformative ways. Holdings include cloud-computing leaders, data-analytics firms and robotics manufacturers. Its diversified approach captures both emerging innovators and established corporations advancing AI-driven technologies.

How ETFs differ from single name stocks 

ETFs can help smooth company-specific risks, such as earnings misses, competitive threats or supply constraints. They also rebalance periodically, ensuring exposure evolves as the sector changes. However, investors tracking ETFs often note trade-offs: performance may lag the biggest winners in the space, and management fees (while generally low) reduce returns slightly compared to owning individual stocks.

Some investors use ETFs to gain exposure to international names that may not trade on their domestic exchange. Others prefer ETFs as a thematic ‘basket’, avoiding the need to research and monitor each stock individually. Still, single-name exposure remains attractive for those seeking higher conviction in specific leaders like NVIDIA or Microsoft.

How to invest in ETFs with 0% commission

Risks of AI stocks and what to consider

AI investing comes with risks that make the theme volatile and sometimes unpredictable.

Valuation – Many AI-related companies trade at premium multiples compared to their historical averages. Elevated pricing reflects high growth expectations, which can amplify volatility if results disappoint. Even small changes in earnings forecasts or demand commentary can trigger outsized moves in share prices.

Supply constraints – GPU availability remains tight, and demand for advanced chips often exceeds supply. Power and data center capacity are also becoming bottlenecks, especially as larger models require significant energy and cooling. These physical limits can slow adoption, even if customer demand is strong.

Platform reliance – Application-layer companies often depend heavily on cloud platforms for model access and distribution. This creates concentration risk: changes in pricing, partnerships or platform policies can directly affect smaller firms that build on top of these ecosystems.

Fast cycles – The pace of model development is rapid, and competitive advantages can erode quickly. Features that feel differentiated today may be commoditized tomorrow if rivals roll them out at scale. This speed creates uncertainty in forecasting which applications will sustain long-term traction.

Regulatory scrutiny – Policymakers are stepping up oversight of AI, with attention on privacy, safety, intellectual property and workforce implications. Proposed regulations could reshape how companies develop and deploy AI products, adding compliance costs or limiting certain business models.

Interconnected risks – Many of these factors overlap. For example, high valuations make companies more vulnerable to earnings downgrades tied to supply constraints. Similarly, reliance on platforms can compound regulatory risk if new rules affect access to cloud infrastructure.

Ready to get started?

1. Sign up to Crypto.com and create a Crypto.com Stocks account.
2. Explore 10,000+ US stocks and ETFs in the Crypto.com App.
3. Invest in stocks or buy fractional shares.
4. Follow market updates to stay on top of AI news and catalysts.

FAQs about AI stocks

Are AI stocks overvalued?
Valuations are often stretched, though growth expectations underpin current pricing. Regular earnings reports provide context.

What moves AI stocks day to day?
Key drivers include earnings, AI revenue commentary, GPU supply updates and broader market conditions.

What’s the difference between infrastructure and application exposure?
Infrastructure firms sell the hardware and platforms powering AI, while application firms embed AI in products.

Can I invest directly in OpenAI?
Not at present – OpenAI remains privately held. Exposure comes indirectly through partnerships with Microsoft and others.

Are AI ETFs better than picking stocks?
ETFs offer diversification and simpler access. Single stocks can provide higher potential upside but carry more concentrated risk.

How often should this list be updated?
AI is a rapidly evolving sector – updates are best made quarterly, in line with earnings and product cycles.

¹ Companies Market Cap, November 2025

² Companies Market Cap, November 2025

Foris Capital US LLC (FCUL) is a broker-dealer registered with the U.S. Securities and Exchange Commission (SEC) and a Member of the Financial Industry Regulatory Authority (FINRA) and the Securities Investor Protection Corporation (SIPC). For further information about FCUL, please visit FINRA BrokerCheck.  

FCUL is a subsidiary of Crypto.com. FCUL is a separate entity from Crypto.com, Foris DAX, Inc., and other affiliated Foris companies. FCUL does not engage in the sale, transfer or custody of crypto currencies or digital assets. Crypto.com is a separate entity from FCUL and does not engage in the securities business. Customer balances and crypto holdings held and transacted at Crypto.com and other entities outside of FCUL are not covered by SIPC insurance and are separate from securities transactions and holdings at FCUL.

All investments involve risk, and not all risks are suitable for every investor. The value of securities may fluctuate and as a result, clients may lose more than their original investment. The past performance of a security, or financial product does not guarantee future results or returns. Keep in mind that while diversification may help spread risk, it does not assure a profit or protect against loss in a down market. There is always the potential of losing money when you invest in securities or other financial products. Investors should consider their investment objectives and risks carefully before investing.

​​This is informational content sponsored by Crypto.com and should not be considered as investment advice.