How To Invest in Artificial Intelligence Infrastructure

The AI infrastructure landscape presents a multifaceted investment opportunity, driven by the rapid advancement and adoption of artificial intelligence technologies, making it a crucial aspect of AI investing.

While the puzzle of AI infrastructure can be confusing and is constantly evolving, we’ll break it down for you into three primary layers: hardware, data infrastructure, and applications.  Think of these layers as the buildout of a “modern city”:

• Hardware: Consider this the city’s underlying infrastructure and utilities, such as the power lines, water pipes and roads. This translates to powerful processors like CPUs and GPUs, along with high-speed memory, storage solutions, and custom AI chips. 

• Data Infrastructure: This includes the vast area of city planning and architectural design, data administrative centers that store the city’s plans, and maintenance crews who continuously work to ensure the city remains functional. In AI terms, this equates cloud platforms, foundational models, and specialized databases that manage and process the large datasets needed to train and deploy AI models. 

• Applications: The most visible part of a city includes buildings tailored for specific purposes such as hospitals, schools and businesses – where services meet user needs. Translate this to business and consumer apps, including those specific to various industries which is potentially the largest segment but also the most competitive.

  Investors who grasp the early development stage of the AI infrastructure market and recognize its growth potential will be well-positioned to benefit. Imagine investing in a burgeoning city and capturing value as its economy grows, as we break down how to invest in artificial intelligence infrastructure throughout this article.

1. Hardware Layer: Power Lines, Water Pipes, and Roads, aka “the Infrastructure”

The hardware layer of AI infrastructure forms the physical foundation that enables artificial intelligence systems to operate. At its core, it consists of powerful processors like CPUs (central processing units) and GPUs (graphics processing units), which act as the “infrastructure” for computations. The potential implications of advanced AI chips, especially in the context of certain regions becoming more protective of supply, are significant as they highlight the strategic positioning of governments to harness the potential of AI and how the competition in this area is likely to intensify.

Training & Inference

In addition to the processing units, AI hardware infrastructure includes memory and storage solutions that can handle the vast amounts of data required for training AI models. High-speed RAM (random access memory) and fast storage options like SSDs (solid-state drives) are crucial for ensuring that data can be accessed and processed quickly. Distributed storage systems are often used in large-scale AI deployments to manage data across multiple servers, providing the necessary scalability and redundancy.

High-speed networking hardware facilitates rapid data movement between components to accelerate AI workloads, particularly for training and inference tasks. Training hardware is essential for developing AI models, while inference chips are optimized for efficient execution of pre-trained models in real-time applications. For training, which requires massive computational power, hardware often includes high-end GPUs or specialized training chips arranged in clusters to handle large datasets and complex models. Inference tends to use more compact and energy-efficient hardware, including ASICs (application-specific integrated circuits) or edge devices, optimized for real-time processing and lower power consumption.

Custom Solutions

Custom-designed AI chips and accelerators, such as photonic chips, have also been developed to further enhance performance for specific AI tasks. This layer is crucial because AI requires immense computational power and the ability to process vast amounts of data quickly. The exponential growth in compute requirements for AI models has led to a surge in demand for specialized hardware, creating significant investment opportunities.

2. Data Layer: City Planning, Administrative Centers, and Maintenance Crews

The data infrastructure layer encompasses cloud platforms, foundational models, data management systems, specialized databases, and model development and optimization. AI companies play a pivotal role in developing and providing data infrastructure solutions that support the AI ecosystem. Cloud providers deliver comprehensive AI platforms that enable users to develop, train, and deploy models using scalable cloud resources.

Foundational Models

Foundational model companies develop versatile, large-scale AI models that power LLMs (large language models) and generative AI across various applications. These pre-trained models, built on extensive datasets and requiring substantial computational resources, form the backbone of numerous AI applications. By lowering the barrier to entry for AI deployment, foundational models allow more organizations to efficiently build AI-driven solutions.

Closed vs Open Source

A key consideration in the AI landscape is the distinction between closed-source and open-source foundational models. Closed-source models are managed by private organizations and are proprietary. They offer high performance, ease of integration, and robust support, making them suitable for businesses seeking reliable, ready-made solutions. They also come with higher costs, potential vendor lock-in, and lack transparency regarding the underlying code and data. On the other hand, open-source models provide greater flexibility, transparency, and community-driven innovation, allowing users to customize and control the models extensively. They are often more cost-effective but require significant expertise and resources to implement and maintain.

Model Hubs

Serving as another alternative, model hubs have emerged as centralized repositories for accessing and deploying various pre-trained models. These platforms allow users to access, download, and deploy different language models and typically offer tools and interfaces to easily integrate these models into applications, providing a range of options for developers and researchers looking to leverage AI for natural language processing tasks.

Data Storage, Collection & Management

Data Management

Database companies play a crucial role in the AI ecosystem by providing tools to store, manage, and process vast amounts of high-quality data, which is the lifeblood of AI. Data management and labeling are vital for ensuring high-quality inputs for training accurate AI models. Data management companies manage and optimize effective use of data by providing tools and platforms for efficient storage, management, and processing of large datasets. Ensuring data quality, consistency, and accessibility is crucial for training accurate and reliable AI models.

Data Labeling

Data labeling companies specialize in annotating and categorizing data, ensuring its accuracy and quality, which directly impact AI effectiveness. In generative AI, their role is even more critical, as they provide well-structured, accurately labeled data for more reliable models. Generative AI and LLMs handle vast, often unstructured data like text, images, and audio.

Vector Databases

Vector databases have emerged as key technology for efficiently storing and manipulating the high-dimensional data points used in AI applications. Vector databases efficiently manage, index, and retrieve these large datasets by converting them into vector representations. This enhances advanced search capabilities, enabling nuanced, context-aware searches essential for applications like personalized content recommendations, image recognition, and natural language processing.

Model Development & Optimization

AI & Machine Learning Operations

AI/ML companies offer a wide range of platforms, tools, and interfaces that simplify the use, management, and optimization of Large Language Models. These tools enable both specialists and non-experts to interact with, fine-tune, and evaluate LLMs, democratizing access to advanced language AI capabilities.

Orchestration

Agent orchestration represents a novel and advancing frontier in AI applications. AI agent orchestration involves coordinating multiple AI agents to function together seamlessly within a single application or process. This concept allows for the building of more complex, intelligent systems that can handle various tasks through the use of large language models.

AI Testing & Optimization

AI testing, optimization, and model evaluation are crucial stages in the development of artificial intelligence systems. These processes ensure that AI models not only function as intended across various scenarios but also maintain efficiency, accuracy, and fairness. Testing ensures the AI behaves as expected in varied scenarios and under stress, while optimization improves its efficiency, accuracy, and decision-making capabilities, making it robust for deployment in real-world applications. Evaluation assesses how well the AI model predicts outcomes based on new data which is essential for confirming the model’s reliability before deployment in real-world applications.

3. Application Layer: the Buildings

The application layer, while potentially the largest market, is also the most competitive. It includes both startups and incumbent players developing AI-powered software and services. AI startups in this sector attract venture capital investors and can yield significant rewards if successful. The application layer of AI consists of both enterprise and consumer apps, encompassing horizontal and vertical applications. Overall, within AI applications, the application layer is the segment where AI’s capabilities become practical and accessible for users. This layer integrates sophisticated AI technologies into user-friendly interfaces, making advanced AI tools available to the general public. It represents the crucial final step in the AI infrastructure, transforming complex algorithms into everyday applications that empower end-users. This layer also encompasses end-to-end AI applications that integrate AI in every stage of the process within a unified system.

Horizontal Applications

Horizontal applications in generative AI and LLMs are versatile solutions addressing common needs across multiple sectors. These apps enhance workflow efficiency by automating routine tasks and handling work traditionally done by humans, using advanced approaches like agents and multi-step reasoning. They benefit a broad range of industries by improving communication, collaboration, learning, and prediction capabilities and include some of the names we all associate with AI Chatbots such as ChatGPT, Claude, and xAI, as well as publicly traded companies such as Meta’s Llama and Amazon’s Siri, utilizing their vast datasets and learning models to assist with various tasks and organization.

Vertical Applications

Vertical applications, in contrast, are specialized solutions tailored to the unique requirements of specific industries, such as Viz.ai, known for pioneering the use of AI algorithms and machine learning to increase the speed of diagnosis and care. These applications integrate deeply into industry-specific workflows, processes, and regulatory frameworks, offering customized solutions that address the distinct challenges of each sector. By focusing on the particular needs of their target industries, vertical applications deliver more effective and relevant AI-driven enhancements.

Diversifying across AI Infrastructure

For investors, it’s essential to recognize that the AI infrastructure market is still in its early stages, making it a prime area for investing opportunities although the complexities and challenges of the AI market require careful consideration and strategic planning.

As a reminder, the hardware layer is expected to be broader and more durable than many anticipate, presenting both opportunities and risks associated with investing in AI stocks. The data infrastructure layer, essential for AI and powered by data, is rapidly evolving and presents significant growth opportunities. In the application layer, comparing the valuation and growth expectations of tech stocks in the context of AI, companies with access to unique data sets, strong distribution channels, and large capital expenditure spend, are well-positioned to capture value.

When considering investing in publicly traded AI companies, evaluating stock prices through metrics like earnings-per-share and forward price-to-earnings ratios is important for making informed investment decisions. When evaluating privately held AI investments you may need to dig a little deeper to evaluate and conduct due diligence, although getting in at this early, private stage, may present more growth opportunities. Interested in getting an early investment opportunity? Linqto offers some of the most sought-after AI infrastructure companies across each layer, available on our platform here. When in doubt, it is best practice to diversify across artificial intelligence infrastructure to spread risk and capture value across the stack.