Best AI Stocks 2026 to Watch

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Best AI Stocks 2026 to Watch, artificial intelligence has revolutionized the way we live, work, and invest. The tech has transformed industries, improved lives, and created new opportunities for growth. As we move into 2026, investors are keen to know which AI stocks will be worth keeping an eye on. In this comprehensive guide, we’ll explore the emerging trends in AI, top-performing stocks, and ETFs that will help you make informed investment decisions.

The AI landscape is rapidly evolving, with new innovations and breakthroughs happening every day. From computer vision to natural language processing, AI is being applied in various fields, making it an exciting time for investors. But which stocks are poised to capitalize on this growth? Let’s dive in and find out.

Emerging Trends in Artificial Intelligence that can Impact Stocks in 2026

As we approach 2026, the field of Artificial Intelligence (AI) is expected to continue its rapid growth and transformation, driven by advancements in technologies like machine learning, natural language processing, and computer vision. With its increasing applications in various industries, AI is poised to have a significant impact on the stock market in the coming years.

The current state of AI research is focused on developing more sophisticated and efficient algorithms, as well as exploring new areas of application such as explainable AI, transfer learning, and multimodal processing. One of the key challenges in AI research today is the need to develop more robust and reliable systems that can adapt to complex and dynamic environments. This requires the development of new architectures and learning methods that can handle uncertainty and ambiguity.

One of the key areas of application for AI in the next decade is in the field of healthcare, where AI is being used to develop personalized medicine, diagnose diseases, and improve patient outcomes. For example, Google’s DeepMind Health is using AI to analyze medical images and develop new treatments for diseases such as cancer and Alzheimer’s. Similarly, IBM is using AI to develop personalized medicine approaches that take into account a patient’s genetic profile and medical history.

Emergence of Edge AI

One of the key trends in AI research today is the emergence of edge AI, which involves the development of AI systems that can run on edge devices such as smartphones, smart home devices, and autonomous vehicles. Edge AI is important because it enables AI systems to operate in real-time, without the need for centralized processing and data transmission, which can be slow and energy-intensive.

Edge AI is particularly important in areas such as computer vision, which involves the processing of visual data from cameras, depth sensors, and other devices. With edge AI, computer vision systems can be deployed in a wide range of applications, including surveillance, robotics, and autonomous vehicles.

Rise of Transfer Learning

Another key trend in AI research today is the rise of transfer learning, which involves the transfer of knowledge and skills learned in one domain to a different domain. Transfer learning is important because it enables AI systems to learn from small datasets and adapt to new situations, without the need for extensive retraining.

Transfer learning is particularly important in areas such as natural language processing (NLP), where AI systems need to learn to understand and generate human language. With transfer learning, NLP systems can be fine-tuned for specific applications, such as language translation, sentiment analysis, and text summarization.

Increased Adoption of Explainable AI

Explainable AI (XAI) is an important trend in AI research today, as companies increasingly seek to develop AI systems that can provide insights and explanations for their decisions. XAI is particularly important in areas such as finance, healthcare, and law, where AI systems need to be transparent and accountable for their actions.

XAI involves the development of techniques such as sensitivity analysis, feature importance, and model interpretability, which enable AI systems to provide insights into their decision-making processes. With XAI, AI systems can be more trusted and reliable, and can provide business leaders with the insights they need to make informed decisions.

Impact on Stocks in 2026

The trends discussed above are expected to have a significant impact on the stock market in 2026. Companies that are investing heavily in AI research and development, such as Google, Amazon, and Microsoft, are likely to see significant growth and returns in the coming years. Similarly, companies that are developing innovative AI-based products and services, such as autonomous vehicles and smart home devices, are likely to see significant demand and market growth.

The increasing adoption of edge AI, transfer learning, XAI, and other AI-related technologies is expected to drive growth in industries such as healthcare, finance, and transportation. As a result, companies that are investing in these areas are likely to see significant returns and growth in the coming years.

  • Google
  • Amazon
  • Microsoft

AI-Driven Companies that are Revolutionizing Traditional Industries

These companies are transforming the global economy by introducing innovative AI-powered solutions, disrupting traditional industries, and creating new business models. From healthcare to finance, transportation to energy, AI-powered startups are revolutionizing the way we live and work.

  1. Healthcare:
    • AI-powered diagnosis and treatment platforms are improving patient outcomes and reducing healthcare costs.
    • Personalized medicine and targeted therapies are being developed using AI-driven data analysis and machine learning algorithms.
  2. Finance:
    • AI-powered trading platforms are enabling real-time portfolio management and risk assessment.
    • Robo-advisors are offering low-cost investment advice and portfolio optimization.
  3. Transportation:
    • Autonomous vehicles are being developed using AI-powered sensor data and machine learning algorithms.
    • Smart traffic management systems are optimizing traffic flow and reducing congestion.
  4. Energy:
    • AI-powered energy management systems are optimizing energy consumption and reducing waste.
    • Renewable energy sources are being integrated into the grid using AI-driven prediction and optimization algorithms.

Business Models of Successful AI-Driven Companies

The business models of successful AI-driven companies are often fundamentally different from traditional ones. These companies are capitalizing on the opportunities presented by AI to create new revenue streams, reduce costs, and improve customer experience.

  1. Subscription-based models:
    • Companies like Netflix and Spotify are offering subscription-based services that provide access to a vast library of content.
    • AI-powered recommendation engines are improving the user experience and increasing engagement.
  2. Data-driven business models:
    • Companies like Google and Facebook are monetizing user data to offer targeted advertising.
    • AI-powered data analysis is enabling real-time decision-making and optimization.
  3. Service-based models:
    • Companies like Amazon and Airbnb are offering on-demand services that use AI-powered logistics and scheduling.
    • Customer experience is being optimized using AI-driven sentiment analysis and chatbots.

Comparison of AI-Driven Business Models with Traditional Ones

The AI-driven business models are often more agile and adaptable than traditional ones. AI-powered startups are able to quickly pivot and adjust to changing market conditions, while traditional companies may struggle to keep up.

AI-powered companies are not just replacing traditional industries, but creating new ones.

AI-driven business models are often more customer-centric and data-driven, allowing companies to create personalized experiences and optimize customer engagement. Traditional companies, on the other hand, may rely on outdated business models that do not adapt to changing customer needs.

AI-powered companies are not just disrupting traditional industries, but also creating new opportunities for innovation and growth. As the AI revolution continues to unfold, we can expect to see even more exciting developments in the years to come.

Investing in AI-focused Exchange-Traded Funds (ETFs) offers a unique opportunity to diversify an investment portfolio and capitalize on the rapid growth of the artificial intelligence market. By pooling investments in a fund focused on AI-related stocks, ETFs provide accessible exposure to this high-growth sector.

AI-focused ETFs invest in a basket of stocks that are significantly involved in the development, deployment, or application of artificial intelligence technologies. These funds can include stocks from a wide range of industries, such as cloud computing, software, semiconductors, and cybersecurity.

AI ETFs can be a valuable addition to a long-term investment plan, providing exposure to a growing industry that is transforming the way businesses operate. Some key strategies for incorporating AI ETFs into a long-term investment plan include:

  • A dividend investing strategy, where investors seek to generate income while the underlying technology continues to evolve. This strategy is particularly useful for investors seeking a stable income stream and willing to hold investments for the long term.
  • A growth investing strategy, where investors focus on companies with high growth potential, often in the areas of machine learning, natural language processing, and computer vision. This strategy requires a higher tolerance for volatility and the ability to ride the waves of market fluctuations.
  • A core position strategy, where AI ETFs serve as a core holding in a diversified investment portfolio. This approach can help reduce overall portfolio risk while providing exposure to the AI sector.

Incorporating AI ETFs into a long-term investment plan can be a valuable way to tap into the growing demand for artificial intelligence technologies. By using a dividend, growth, or core position strategy, investors can find a fitting approach to suit their investment goals and risk tolerance.

  • To minimize risk, investors can consider diversifying their AI ETF holdings among different sectors, such as cloud computing, software, semiconductors, and cybersecurity.
  • Investors should also keep an eye on the overall market trend, sector rotation, and potential regulatory changes that may impact the AI ecosystem.
  • Regular portfolio rebalancing and monitoring can help maintain an optimal asset allocation and minimize any significant losses.

While AI ETFs can be a valuable addition to a long-term investment plan, it’s essential to conduct thorough research, understand the underlying holdings, and consider your overall investment goals before investing.

Here are some key FAQs regarding AI ETFs:

  • What is the average expense ratio for AI ETFs?
    The average expense ratio for AI ETFs ranges from 0.25% to 1.00% annually, with the majority falling between 0.25% to 0.75%.
  • Can AI ETFs be used as a core holding in a diversified investment portfolio?
    Yes, AI ETFs can serve as a core holding in a diversified investment portfolio, providing exposure to the AI sector while reducing risk.
  • Which sectors are represented by AI ETFs?
    AI ETFs represent a range of sectors, including cloud computing, software, semiconductors, and cybersecurity.

The Impact of AI on Human Resources and Talent Acquisition: Best Ai Stocks 2026

The rise of Artificial Intelligence (AI) has significantly impacted various industries, including Human Resources (HR). AI technologies such as Machine Learning (ML) and Natural Language Processing (NLP) are transforming the HR landscape in several ways. This transformation has both positive and negative effects on the way HR professionals operate and on the overall business.

HR Automation and Process Optimization

With AI, HR tasks such as recruitment, talent management, and employee engagement can be automated, freeing up HR professionals to focus on strategic and high-value tasks. For instance, AI-powered chatbots can assist with candidate screening, reducing the workload of HR teams. Additionally, AI-driven HR software can analyze vast amounts of data to identify trends, predict employee turnover, and provide insights on employee engagement.

  • Candidate Sourcing: AI can help identify top talent by analyzing resumes, cover letters, and social media profiles, making it easier for HR teams to find the best candidates.
  • Talent Acquisition: AI-driven recruitment platforms can automate tasks such as scheduling interviews, sending notifications, and even conducting initial interviews.
  • Employee Onboarding: AI can help personalize the onboarding experience by analyzing employee data and providing tailored recommendations for training and development.

Risks and Challenges in AI-driven HR

While AI can bring numerous benefits to HR, it also poses several risks and challenges. For instance, AI-driven recruitment tools can perpetuate bias if they are not designed and trained to account for various biases. Additionally, the increasing use of AI in HR may lead to job displacement, as some tasks are automated, and HR professionals may need to adapt to new roles.

Recent studies have shown that up to 60% of HR tasks can be automated using AI, freeing up HR professionals to focus on strategic tasks.

  • Risk of Bias: AI-driven tools can perpetuate existing biases and stereotypes, leading to unfair treatment of certain groups.
  • Job Displacement: The increasing use of AI in HR may lead to job displacement, as some tasks are automated.
  • Data Security: AI-powered HR tools often require access to sensitive personal data, making data security a significant concern.

The Future of HR and AI, Best ai stocks 2026

As AI continues to evolve and improve, it is likely to play an increasingly important role in shaping the HR landscape. By leveraging AI technologies, HR professionals can focus on high-value tasks, make data-driven decisions, and improve the overall employee experience.

The future of HR will be shaped by the strategic use of AI technologies to drive business outcomes, improve employee engagement, and enhance the overall work experience.

AI and Cybersecurity

As AI becomes increasingly ingrained in our daily lives, the need for robust cybersecurity measures has grown exponentially. Traditional security systems are struggling to cope with the evolving nature of cybersecurity threats, and this is where AI comes into play. By leveraging AI’s capabilities, we can create more effective security systems that can adapt to the ever-changing threat landscape.

AI-driven security systems use machine learning algorithms to analyze and identify patterns within large datasets, allowing them to detect and respond to threats in real-time. This not only improves the speed and accuracy of threat detection but also reduces the reliance on human input, making the process more efficient.

Evolving Nature of Cybersecurity Threats

The nature of cybersecurity threats is constantly evolving, with hackers becoming more sophisticated and brazen in their attacks. Advanced Persistent Threats (APTs), ransomware, and phishing attacks have become increasingly common, targeting individuals and organizations alike. These threats are not just limited to individual computers but can also impact entire networks and systems.

  1. Advanced Persistent Threats (APTs)
  2. Ransomware attacks
  3. Phishing attacks
  4. Data breaches

These threats are not only causing financial losses but also compromising sensitive information, including personal data and intellectual property. To combat these threats, AI-driven security systems are being deployed to detect and respond to threats in real-time.

Effectiveness of AI-Driven Security Systems

AI-driven security systems are proving to be more effective than traditional security measures in detecting and responding to threats. By analyzing vast amounts of data, AI systems can identify patterns and anomalies that may indicate a security breach. This not only improves the speed and accuracy of threat detection but also reduces the reliance on human input, making the process more efficient.

Advantages of AI-Driven Security Systems Traditional Security Measures
Improved speed and accuracy of threat detection Limited by human input and potential for human error
Real-time monitoring and response Reacting to threats after they have occurred
Reduced reliance on human input Highly dependent on human expertise and input

By leveraging AI’s capabilities, we can create more effective security systems that can adapt to the ever-changing threat landscape. This not only improves the overall security posture of an organization but also reduces the likelihood of a security breach occurring in the first place.

“AI can help us stay one step ahead of the bad guys by analyzing vast amounts of data and identifying patterns that may indicate a security breach.” – Cybersecurity Expert

AI-driven security systems are not just a tool for detecting threats but also for predicting and preventing them. By analyzing historical data and identifying patterns, AI systems can predict potential security risks and take proactive measures to prevent them.

Limitations of AI-Driven Security Systems

While AI-driven security systems are proving to be effective in detecting and responding to threats, they are not without limitations. One of the major limitations is the reliance on high-quality data to train AI algorithms. If the data is poor or biased, the AI system’s accuracy and effectiveness will also suffer.

  1. Relevance and accuracy of data used to train AI algorithms
  2. Limited ability to detect zero-day attacks
  3. Reliance on continuous updates and maintenance

Despite these limitations, AI-driven security systems are proving to be a valuable tool in the fight against cybersecurity threats. By leveraging AI’s capabilities, we can create more effective security systems that can adapt to the ever-changing threat landscape.

AI and Data Monetization Strategies for Businesses

In today’s digital age, data has become the lifeblood of any business. With the help of AI, companies can unlock new revenue streams and transform their data into a valuable asset. Data monetization is the process of generating revenue from the data that a business collects, processes, and analyzes. This can be done through various means such as data sharing, data analytics, and data-based services.

For instance, companies like Uber and Lyft collect vast amounts of data on their users, including their travel patterns and preferences. By leveraging AI, these companies can gain valuable insights into their users’ behavior, which can be used to offer targeted advertising, personalized services, and optimized routes. This data can also be shared with third-party companies, which can use it to develop their own products and services.

Data Monetization Strategies

Companies can leverage AI to unlock new revenue streams through various data monetization strategies.

When it comes to AI and data monetization, there are several strategies that businesses can adopt to maximize their revenue potential. One such strategy is to focus on the quality and accuracy of the data being collected. This can be achieved through the use of AI-powered data validation and cleaning tools, which can identify and eliminate errors and inconsistencies in the data.

Another strategy is to use AI to develop data-driven products and services that can be sold to customers. For example, companies like Salesforce and SAP offer data analytics and AI-powered services that can help businesses make better decisions and improve their operations. These services can be sold to customers at a premium price, generating significant revenue for the company.

AI-Driven Data Monetization Models

There are various AI-driven data monetization models that businesses can adopt to generate revenue from their data.

Some of the key AI-driven data monetization models include:

  1. Subscription-based model: This model involves offering access to data and AI-powered services to customers on a subscription basis. For example, companies like AWS and Azure offer cloud-based data storage and AI-powered services to their customers on a subscription basis.
  2. Data licensing model: This model involves selling data to third-party companies that can use it to develop their own products and services. For example, companies like Google and Facebook license their data to third-party companies that can use it to develop their own advertising products.
  3. Data brokerage model: This model involves selling data to multiple buyers at the same time. For example, companies like Acxiom and Experian sell data to multiple buyers at the same time, providing them with a revenue stream from each sale.

In conclusion, AI and data monetization go hand in hand. By leveraging AI to unlock new revenue streams and transform their data into a valuable asset, businesses can create new opportunities for growth and profitability. With the right data monetization strategy in place, companies can generate significant revenue from their data and stay ahead of the competition.

Data is the new oil, and AI is the drill that unlocks its value.

As Artificial Intelligence (AI) continues to advance and transform industries worldwide, governments and regulatory bodies are working to establish guidelines and laws to ensure the safe and responsible development and deployment of AI technologies. The complexity of these regulations and laws varies from country to country, but their importance in shaping the future of AI is undeniable. In this section, we’ll delve into the current regulatory landscape, the benefits and drawbacks of global regulatory frameworks, and the impact they have on AI development and adoption.

The Current Regulatory Landscape

The regulatory landscape for AI is constantly evolving, with different countries and regions adopting their own approaches to govern the development and deployment of AI technologies. Some of the key regulatory frameworks include:

  • The General Data Protection Regulation (GDPR) in the European Union, which provides robust data protection laws and guidelines for the use of AI in data-driven applications.
  • The California Consumer Privacy Act (CCPA), which sets strict standards for consumer data protection and requires companies to provide transparency and control over personal data.
  • The US Federal Trade Commission’s (FTC) guidelines on AI, which focus on the commission’s role in promoting competition and protecting consumers in the digital economy.
  • The Chinese government’s AI development plans, which emphasize the importance of AI in driving national development and growth.

These regulatory frameworks have both direct and indirect impacts on AI companies, affecting their business models, innovation, and growth prospects. Companies must navigate these regulations to ensure compliance and remain competitive in the market.

The Benefits of Global Regulatory Frameworks

Global regulatory frameworks have several benefits, including:

  • Consistency and predictability: A unified set of regulations can provide clarity for companies and investors, reducing uncertainty and encouraging investment in AI technologies.
  • Enhanced consumer protection: Stricter regulations can help protect consumers’ personal data, safety, and rights, leading to increased trust in AI systems and services.
  • Innovation and standardization: A globally accepted set of standards can facilitate innovation, collaboration, and knowledge-sharing among companies, governments, and research institutions.

Global regulatory frameworks also facilitate international trade, investment, and cooperation, promoting a more level playing field for companies operating in the global AI market.

The Drawbacks of Global Regulatory Frameworks

Global regulatory frameworks also have some drawbacks, including:

  • Complexity and fragmentation: Multiple regulations from different countries can create a complex and fragmented regulatory environment, making it difficult for companies to comply.
  • Competition and innovation stifling: Overly restrictive regulations can stifle competition, inhibit innovation, and lead to decreased investment in AI technologies.
  • Implementation and enforcement challenges: Enforcement mechanisms and resources can be inadequate, making it difficult for regulatory bodies to ensure compliance.

Furthermore, global regulatory frameworks can face challenges related to data protection, intellectual property, and the need for international cooperation and harmonization.

AI-driven Disruption in Supply Chain Management

With the increasing complexity of global supply chains, artificial intelligence (AI) is transforming the way companies manage their logistics, procurement, and inventory management. By leveraging AI, businesses can gain real-time visibility into their supply chains, predict demand and supply fluctuations, and streamline their operations to improve efficiency and reduce costs.

AI-Driven Predictive Analytics in Supply Chain Management

Predictive analytics is one of the key areas where AI is making a significant impact in supply chain management. By analyzing historical data, AI algorithms can identify patterns and predict future trends, allowing companies to make informed decisions about inventory management, demand forecasting, and supply chain optimization. This enables businesses to respond quickly to changes in demand, prevent stockouts and overstocking, and reduce inventory costs.

AI-powered predictive analytics can be applied in various areas of supply chain management, including:

  • Demand Forecasting: AI algorithms can analyze historical data, seasonal trends, and external factors to predict future demand, enabling companies to adjust their production and inventory levels accordingly.
  • Inventory Optimization: By analyzing inventory levels, lead times, and demand patterns, AI can help companies optimize their inventory levels, reduce stockouts and overstocking, and minimize inventory costs.
  • Supply Chain Risk Management: AI-powered predictive analytics can identify potential supply chain disruptions, such as natural disasters, supplier insolvency, or transportation delays, enabling companies to take proactive measures to mitigate these risks.

AI-Driven Automation in Supply Chain Management

Another area where AI is making a significant impact in supply chain management is automation. By automating repetitive tasks, such as data processing, reporting, and decision-making, businesses can free up resources to focus on higher-value activities, such as strategy and innovation.

AI-powered automation can be applied in various areas of supply chain management, including:

  • Data Processing and Reporting: AI algorithms can automate data processing, reporting, and analytics, enabling companies to make data-driven decisions quickly and efficiently.
  • Order Management: AI-powered automation can automate order processing, fulfillment, and tracking, reducing errors and improving customer satisfaction.
  • Supply Chain Visibility: AI-powered automation can provide real-time visibility into supply chain operations, enabling companies to track shipments, manage inventory, and respond quickly to changes in demand.

AI-Driven Blockchain in Supply Chain Management

Blockchain technology, combined with AI, is another area where significant innovation is happening in supply chain management. By providing a transparent, secure, and decentralized ledger, blockchain can help companies track goods throughout the supply chain, ensuring authenticity and provenance.

AI-powered blockchain can be applied in various areas of supply chain management, including:

  • Supply Chain Tracking: AI-powered blockchain can track goods throughout the supply chain, enabling companies to verify authenticity, prove ownership, and ensure compliance with regulations.
  • Inventory Management: AI-powered blockchain can help companies manage inventory levels, track inventory movements, and optimize inventory levels to reduce costs and improve efficiency.
  • Supply Chain Financing: AI-powered blockchain can provide a secure and transparent platform for supply chain financing, enabling companies to access funding and reduce the costs associated with traditional financing methods.

AI and Environmental Sustainability

AI and environmental sustainability have a complex relationship. On one hand, AI has the potential to mitigate the environmental impacts of business operations by increasing efficiency and reducing waste. On the other hand, the production and operation of AI systems themselves can have environmental consequences. As AI continues to grow in importance, it is essential to understand its potential to address environmental sustainability challenges.

The Environmental Impacts of AI

The development and operation of AI systems require significant computational resources, which are typically provided by data centers and cloud computing services. These operations consume a substantial amount of electricity and generate greenhouse gas emissions. For example, a study by the Natural Resources Defense Council found that the carbon footprint of data centers is comparable to that of a small country. Furthermore, the production of AI-powered devices, such as smartphones and laptops, also contributes to environmental pollution and resource depletion.

AI Applications for Environmental Sustainability

Despite the environmental impacts of AI, it also offers significant opportunities for environmental sustainability. For instance, AI can be used to monitor and predict environmental phenomena, such as weather patterns and water usage. This information can help organizations optimize their operations and reduce their environmental footprint. AI can also be used to develop more efficient and sustainable technologies, such as renewable energy systems and electric vehicles.

Examples of AI for Environmental Sustainability

  • Google’s AI-powered weather forecasting system, which provides more accurate and detailed weather forecasts, reducing the need for energy-intensive weather monitoring systems.
  • IBM’s AI-powered energy management system, which optimizes energy consumption and reduces waste in commercial buildings and data centers.
  • Microsoft’s AI-powered water management system, which uses satellite imagery and machine learning to monitor and predict water usage, helping to reduce water waste and improve conservation efforts.

Future Directions for AI and Environmental Sustainability

As AI continues to evolve, it is likely to play an increasingly important role in addressing environmental sustainability challenges. For example, AI-powered sensors and drones can be used to monitor and track environmental pollutants, while AI-driven predictive analytics can help organizations anticipate and respond to environmental events. Additionally, AI can be used to develop more sustainable infrastructure, such as smart grids and green buildings.

Conclusion

In conclusion, AI has both positive and negative impacts on environmental sustainability. While the production and operation of AI systems contribute to environmental pollution and resource depletion, AI also offers significant opportunities for environmental sustainability. As AI continues to grow in importance, it is essential to address its environmental consequences while leveraging its potential to mitigate environmental impacts.

Ending Remarks

In conclusion, the best AI stocks 2026 will likely come from companies that are driving innovation and delivering results. By staying informed about emerging trends, understanding the risks and opportunities, and making data-driven decisions, investors can capitalize on the AI revolution. Whether you’re an experienced investor or just starting out, this guide has provided valuable insights to help you make informed investment decisions.

Quick FAQs

What’s the best way to invest in AI stocks?

There are several ways to invest in AI stocks, including through ETFs, mutual funds, or individual stocks. Research and due diligence are essential before investing in any stock or fund.

How do I stay up-to-date with AI trends?

Attend industry conferences, follow reputable sources, and stay informed about breakthroughs and innovations in AI. This will help you stay ahead of the curve and make informed investment decisions.

What are some popular AI ETFs?

Some popular AI ETFs include the ROBO Global Robotics and Automation Index ETF (ROBO) and the First Trust Indxx Innovative Transaction & Process ETF (LEGR).

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