The Essential Guide to AI Stocks

November 18, 2024

How to Use This Guide

This guide is a comprehensive resource for understanding and investing in AI stocks. It covers AI’s history, applications, technologies, and limitations to build a strong industry foundation. Each section delves into the AI stack, exploring hardware, data, development, applications, and security, demonstrating how companies contribute within each category.

Whether you’re a beginner or experienced investor, this guide will help you identify top AI stocks, key trends, and growth opportunities, equipping you to make informed investment decisions in this evolving sector.

A Brief History of AI

AI’s origins date back to the 1950s, when pioneers like Alan Turing and John McCarthy laid the groundwork. Early AI efforts were constrained by limited computing power and high costs. In the 21st century, breakthroughs in machine learning, combined with vast data from computers, mobile devices, and sensors, propelled AI from theoretical concepts to practical applications.

Today, AI is embedded in daily life, often in ways people don’t immediately recognise. Examples include Netflix’s recommendation engine, targeted advertising, and fraud detection in banking. These applications showcase how AI has become integral to modern life, enhancing personalisation, efficiency, and security in ways that were once unimaginable.

With this understanding of AI’s evolution, let’s explore what AI achieves today and its future ambitions.

What Does AI Do, and What Is It Aiming to Achieve?

Today, AI powers applications ranging from recommendation engines and chatbots to complex data analysis in healthcare and finance. At its core, AI automates tasks, processes large datasets, recognises patterns, and makes accurate predictions. Future AI aims to achieve higher autonomy, adaptability, and human-like decision-making. This trajectory could eventually lead to Strong AI and even Super AI, holding transformative potential across industries.

Understanding the Types of AI

  • Weak AI (Narrow AI): Today’s most common AI form, designed for specific tasks like voice recognition or recommendation engines. It’s powerful but lacks the general intelligence seen in humans.

  • Strong AI (General AI): Currently theoretical, this would have human-like intelligence adaptable across tasks. Research is moving steadily in this direction.

  • Super AI: Hypothetical AI that surpasses human intelligence in all areas, a concept that remains speculative.

Most AI investments today focus on advancing Weak AI, with an eye toward potential future developments in Strong AI and, eventually, Super AI. Understanding these distinctions frames the future directions and potential of AI.

Limitations of AI: Recognising the Gaps

AI’s effectiveness depends on the quality of data it relies on. If data is incomplete, biased, or unstructured, AI models may produce inaccurate or misleading results, sometimes referred to as “hallucinations.” Additionally, biases in training data can impact AI decision-making. Recognising these limitations underscores the importance of quality data and responsible development as AI progresses.

Foundational AI Technologies

AI’s growth stems from three core capabilities that serve as enablers for various applications across industries:

  1. Generative AI
    Generative AI powers text, image, and code creation, with versatile applications that enhance productivity and creativity.

    • Text Generation: Language models like ChatGPT are transforming customer support, content creation, and real-time interactions.

    • Image and Code Generation: Tools like Midjourney and AI code generators streamline workflows in design and software development.

  2. Automation and Robotics
    Automation and robotics drive operational efficiency, especially in manufacturing, logistics, and retail.

    • Industrial Automation: Robots enhance productivity and cost-efficiency in manufacturing processes.

    • Service and Delivery Robotics: Autonomous systems in warehouses and last-mile delivery improve logistics and fulfilment speed.

  3. Predictive and Analytical AI
    Predictive and analytical AI processes and analyses large datasets, delivering insights essential for sectors like healthcare, finance, and climate science.

    • Predictive Analytics: AI forecasts trends in financial markets and consumer behaviours.

    • Medical and Climate Modelling: AI supports climate prediction and medical diagnostics, improving planning and patient care.

These foundational technologies drive innovation across sectors, enabling targeted applications and advancements.

With these core technologies as enablers, AI is reshaping industries through targeted applications and innovations. Here’s how these trends are impacting specific sectors, making them attractive to investors:

1. AI in Healthcare

  • Drug Discovery: AI accelerates drug development by analysing biological data, offering significant potential for healthcare innovation.

  • Medical Imaging: AI-powered tools improve diagnostic accuracy, enhancing patient outcomes and reducing human error.

  • Personalised Medicine: AI enables customised treatment plans based on patient data, increasing treatment effectiveness.

2. AI in Finance

  • Algorithmic Trading: AI algorithms enable real-time, data-driven trading decisions, creating opportunities for rapid, responsive investments.

  • Fraud Detection: AI systems detect and prevent fraudulent activities, adding critical security in financial transactions.

  • Risk Assessment: AI improves credit scoring and portfolio management, supporting more accurate and data-driven decision-making.

3. AI in Climate Change

  • Climate Modelling: AI enhances climate predictions, supporting sustainable environmental planning.

  • Sustainable Energy: AI optimises energy consumption and renewable energy generation, contributing to lower carbon footprints and efficient resource use.

4. AI Ethics and Regulation

  • Bias and Fairness: Efforts are underway to ensure fairness in AI models, preventing biases from affecting decisions.

  • Transparency and Explainability: AI models are becoming more interpretable, which builds trust and facilitates regulatory compliance.

  • Ethical Guidelines: Emerging standards and regulations aim to support responsible AI development, particularly around data privacy and ethical practices.

AI’s Energy Demands: Powering AI’s Growth

AI’s exponential growth comes with substantial energy requirements. Advanced model training and deployment, particularly for large language models, consume significant electricity. By 2030, AI-related data centres could account for 10-15% of global electricity consumption. For instance, training a single large AI model can require thousands of megawatt-hours—comparable to the annual electricity usage of hundreds of homes.

Meeting these demands involves advanced cooling technologies, energy management, and increased renewable energy integration. This highlights the importance of sustainable energy sources and energy-efficient infrastructure to support AI’s future growth.

What Are AI Stocks?

AI stocks are companies that either develop AI technology or leverage AI to drive business. These include AI software developers, hardware providers, and firms integrating AI across sectors from finance to healthcare. The AI sector’s influence reaches beyond traditional tech, impacting numerous industries as companies seek AI-driven competitive advantages.

Why Invest in AI Stocks?

  • Growth Potential: AI is transforming healthcare, finance, tech, and manufacturing. Companies leading this revolution are experiencing rapid growth. Many AI stocks and ETFs saw considerable gains in 2024, though past performance isn’t a guarantee of future results.

  • AI in Everyday Life: From healthcare innovations to autonomous driving, AI is becoming a core part of daily life. Investing in AI stocks allows participation in the next wave of technological progress, from personalised healthcare to automated logistics.

Our Curated List of AI Stocks

AI’s impact is broad, making it challenging to capture the entire AI stock universe. Our curated list of 153 stocks focuses on ETFs (Exchange Traded Funds) and Index list with a strong and pure focus on AI, concentrating on companies directly involved in AI infrastructure, innovation, and applications. Unlike broader tech-focused ETFs, like those that may include robotics or automation, our picks prioritise exposure to companies at the core of AI development and implementation.

While this list reflects our top choices, other AI-related ETFs exist, which may appeal to investors seeking broader exposure beyond AI-specific sectors.

Simplifying the AI Stack

Think of the AI stack as a “car” with essential parts working together for optimal performance. Each layer in the stack plays a specific role:

  1. Hardware, Infrastructure & Power (Engine & Chassis)
    This layer powers AI systems, like a car’s engine. Companies such as Nvidia and Intel provide GPUs—the “engines” behind AI. Cloud providers like AWS and Google Cloud offer the infrastructure to keep AI running efficiently.

    Example of some stocks in this layer: Amazon, Dell, Google, Nvidia, Advanced Micro Devices, Astera Labs, Micron Technology.

  2. Data & Connectivity (Fuel & Roads)
    Data is AI’s fuel, and connectivity ensures smooth operation. Companies like Snowflake and Cloudflare specialise in data integration and connectivity, ensuring clean, prepared data flows seamlessly for real-time processing.

    Example of some stocks in this layer: Snowflake, MongoDB, Arista Networks, Cisco, Cloudflare, Marvell Technology.

  3. AI Development & Applications (Driver & Usage)
    This layer covers AI model creation and deployment. Companies like C3.ai focus on building AI models, while firms like Adobe and Tesla use AI to drive creativity, enable autonomous driving, and optimise operations.

    Example of some stocks in this layer: C3.ai, Adobe, Block, Datadog, Tesla, Docusign, Hubspot, Salesforce, Spotify, Palantir.

  4. Security, Compliance & Sustainability (Seatbelt & Safety)
    Just as cars need safety features, AI systems require security and sustainability practices. Companies like Palo Alto Networks provide cybersecurity, while IBM and others focus on energy efficiency to reduce AI’s environmental impact.

    Example of some stocks in this layer: Crowdstrike, Okta, Sentinel One, Palo Alto Networks, Zscaler, IBM.

Ethical Considerations in AI

As AI technology advances, so does the need for ethical oversight in data privacy, transparency, and environmental impact. Minimising biases, ensuring transparent decision-making, and developing energy-efficient solutions are essential for responsible AI. Robust data governance protects user privacy and ensures compliance with regulatory standards, fostering trust in AI technology.

Understanding the Risks of Investing in AI Stocks

AI stocks present both opportunities and risks. Key considerations include:

  • Market Volatility: AI stocks can be highly volatile due to tech trends and investor sentiment.

  • Regulatory Uncertainty: Data privacy and ethical use regulations may impact operations and profitability.

  • Technological Competition: AI is highly competitive, with rapid innovations that can shift market positions.

  • Resource Demands: Developing AI requires substantial capital and energy, affecting growth strategies.

  • Long-Term Uncertainty: Concepts like Strong AI and Super AI are still theoretical, with high R&D costs and uncertain returns.

Future Outlook for AI Investments

The AI industry is set for substantial growth, with innovations in generative AI, autonomous systems, and robotics expanding applications. Quantum computing could revolutionise AI by enabling it to solve complex problems faster than ever. As research moves toward Strong AI, companies may shift from narrow applications to adaptable solutions. However, regulatory, ethical, and environmental factors will continue shaping AI’s future, impacting its investment landscape.

Glossary of Key AI Terms

This glossary provides clear definitions of AI terminology to help navigate the concepts and technologies discussed.

  • AI (Artificial Intelligence): Systems designed to mimic human intelligence by performing tasks like learning, problem-solving, and recognising patterns.

  • Algorithm: A step-by-step procedure or formula for solving a problem or completing a task.

  • Big Data: Extremely large data sets used by AI systems to identify patterns and trends, especially when traditional data processing is insufficient.

  • Cloud Computing: A model for delivering computing resources (storage, processing) over the internet, essential for scaling AI applications.

  • Computer Vision: An AI field focused on enabling machines to interpret and process visual information like images or videos.

  • Data Mining: The process of extracting useful information from large datasets to find patterns and relationships.

  • Deep Learning: A type of ML using neural networks with multiple layers to model complex patterns in data.

  • Generative AI: AI models that create content, such as text, images, or music, based on input data.

  • General AI (Strong AI): Theoretical AI with human-like intelligence, capable of performing various tasks and adapting to new situations.

  • Hallucination: An AI term referring to instances where a model generates plausible-sounding but factually incorrect or entirely fabricated responses.

  • Machine Learning (ML): A subset of AI where algorithms learn from data to make predictions or decisions without explicit programming.

  • Narrow AI (Weak AI): AI designed for specific tasks (e.g., image recognition) without general intelligence.

  • Neural Network: A computational model inspired by the human brain, used in deep learning to identify patterns in data.

  • NLP (Natural Language Processing): A branch of AI focused on understanding and generating human language.

  • Overfitting: A modelling error in which an AI model learns noise or irrelevant details in training data, impacting its accuracy with new data.

  • Quantum Computing: A revolutionary computing method that could exponentially increase computational power, benefiting AI research.

  • Recommendation Engine: AI technology that analyses user behaviour to suggest products, content, or information.

  • Super AI: Hypothetical AI surpassing human intelligence in all areas, remaining speculative.

  • Training Data: The data used to teach machine learning models, helping them recognise patterns or make predictions.

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Disclaimer - The information contained on this site does not constitute investment advice or a personal recommendation, nor is it an invitation or inducement to engage in investment activity. You should seek independent financial and, if appropriate, legal advice as to the suitability of any investment decision. Past performance is not a guide to future performance. The value of investments, and the income from them, can fall as well as rise. You may not get back the full amount invested and, in some cases, nothing at all. The information presented is based on publicly available data and sources believed to be reliable; however, its accuracy or completeness cannot be guaranteed. Market conditions can change rapidly, and the information provided may no longer be up to date. This content is for informational purposes only and should not be construed as financial, legal, or tax advice.