AI and ML: Current Trends and Investment Insights

The artificial intelligence (AI) and machine learning (ML) sector continues to grow at an unprecedented pace, driven by advancements in technology and increasing adoption across various industries. Let's analyze the current state of AI and ML, focusing on key investment opportunities, market trends, and the distinction between vertical and horizontal platforms.

Key Investment Areas

AI data centers are among the leading investment opportunities for AI and ML applications. These centers are split into advanced hardware, intelligent software, and conventional software. AI's significant impact on software applications is evident as it transforms both front-end and back-end operations, streamlining processes and enhancing user interactions. For example, AI-powered conversational interfaces allow users to interact with software through natural language, eliminating the need for traditional click-and-type methods.

Supplementing Human Tasks

AI is not replacing humans but supplementing various tasks, including basic communication, repetitive office tasks, and strategic thinking. This integration is particularly evident in sectors that involve complex workflows, voice communication, and strategic decision-making processes. AI's ability to handle repetitive and mundane tasks allows human workers to focus on more strategic and creative aspects of their jobs.

Generative AI vs. Top-Down AI

There is a clear distinction between generative AI (GenAI) and top-down AI approaches. GenAI typically involves a bottom-up framework (similarly to the foundational model use cases) where applications are built leveraging LLMs. On the other hand, top-down AI consists of integrating AI into existing infrastructure and industrial applications.

The AI and ML market, which reached $262.5 billion in 2023, is distinguished by generative AI (GenAI) and top-down approaches. GenAI typically involves creating AI applications from scratch, while top-down approaches integrate AI into existing infrastructure and industrial applications. Foundational models (GenAI), which enables the development of new horizontal applications, is poised to be the fastest-growing category, with a projected growth rate of nearly 120% year over year until 2026. Foundational models are the driver behind the very strong growth of horizontal platforms, making it the fastest-growing segment in the market.

Market Overview

The AI and ML market reached $262.5 billion in 2023. We can separate AI applications into vertical and horizontal applications. Horizontal applications are defined as AI solutions that can be applied across many different verticals (generalized LLMs), analytics solutions, etc.

Vertical applications, on the other side, are AI technology that is implemented (often) in existing software in one specific vertical (e.g., healthcare). Vertical applications are a significant part of this market, but horizontal platforms are the fastest-growing segment, with an annual growth rate of nearly 120%. Unlike vertical platforms, which focus on one specific sector, horizontal platforms can be used in many different industries. In 2023, horizontal computer vision and natural language software technologies were valued at $14.8 billion and $23.6 billion, respectively.

Foundational model applications, like the ones created by companies such as OpenAI and Anthropic, are quickly becoming more common. Foundational model application spending saw a market value of $1.5 billion last year and is expected to grow significantly in the coming years. Another significant category, business intelligence, was valued at $16.1 billion. AI lifecycle platforms, such as those offered by AWS, contributed $12.9 billion in 2023, with a projected annual growth rate of 50% through 2026.

Vertical vs. Horizontal Platforms

Vertical Platforms

Vertical platforms focus on specific industries, such as healthcare, finance, or manufacturing. They are tailored to meet the unique needs and challenges of a particular sector, making them highly specialized but limited in their scope of application.

Horizontal Platforms

Horizontal platforms, on the other hand, offer broad applicability across multiple industries. They provide foundational models and tools for various contexts, from healthcare to financial services. Horizontal platforms are the fastest-growing segment in the AI market, with a rate of 119.7% growth and a record $333 billion raised in 2023.

Key Horizontal Platform Categories

AI Core Technology

AI core technologies include tools and services that facilitate AI and ML deployments. These encompass AI as a service, AI/ML developer tools, AI platforms as a service, and automated AI solutions. Developer tools like Flanks enable faster coding and deployment, while AI platforms provide comprehensive environments for building and deploying AI applications.

Computer Vision

Computer vision technology analyzes visual data to make meaningful predictions. It has applications in healthcare, geospatial analysis, augmented reality, and facial recognition. This technology can process both physical and digital imagery, providing insights and predictions based on visual data.

Foundational Models

Foundational models, such as those developed by OpenAI and Anthropic, are designed to achieve artificial general intelligence (AGI). These models focus on natural language processing, conversational AI, and neural language generation. Training foundational models is expensive, but recent advancements have reduced these costs significantly.

AI Automation Platforms

AI automation platforms leverage AI to automate critical business processes, such as predictive analytics and business intelligence. These platforms enable enterprises to make data-driven decisions and improve operational efficiency.

Industry Drivers and Challenges

Cost Reduction in Model Training

The cost of training foundational models has decreased significantly, from an estimated $100 million to train GPT-4 to $10 million for Databricks' state-of-the-art open-source model. This reduction makes AI more accessible and affordable for broader applications.

Increased Developer Adoption

The adoption of AI among developers has surged. In 2023, 65% of developers used AI for software development, up from 26% five years ago. Tools like GitHub Copilot are instrumental in this shift, enabling faster and more efficient development processes.

Business Models and Cost Drivers

The primary cost drivers for horizontal platforms include storage, network traffic, and computational resources. Additionally, compliance with data privacy and security regulations adds to the operational costs. Despite these expenses, the benefits of AI, such as enhanced efficiency and decision-making capabilities, make it a worthwhile investment for many enterprises.

Conclusion

The AI and machine learning (ML) market is expected to keep growing, with horizontal platforms leading the way. The CAGR (Compounded Annual Growth Rate) for various AI and ML applications illustrates these technologies’ rapid advancement and investment. Foundational Models exhibit an impressive growth rate of nearly 120% year over year, underlining the momentum horizontal platforms have, which led the segment to set a VC record in 2023 with $33B raised.

AI lifecycle platforms, such as AWS and other API-based foundational models, are projected to grow at a 50% rate from 2023 to 2026. The overall AI and ML market reached a substantial $262.5 billion in 2023. A new market category for foundational models, which hit $1.5 billion last year, is anticipated to expand rapidly. These figures underscore the significant momentum in AI and ML, particularly in foundational models and horizontal platforms. Investors are focusing on foundational model applications and broad-use technologies that can be applied across multiple industries. As AI evolves, it will increasingly supplement human tasks, driving productivity and efficiency across various sectors.

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