Aside from all else, artificial reality Iis an investment theme. A study by Morgan Stanley, for example, argues that AI's materiality to investment theses has increased significantly, affecting at least 446 stocks, worth $15 trillion, in 2024.
That is logical enough, given the importance of AI enabling technology including semiconductors, servers, cloud computing, data centers, energy sources for data centers; as well as AI implications for the functionality of enterprise and consumer software.
In 2023 and 2024, much of that financial impact has centered on generative AI, eclipsing machine learning and natural language processing, for example, which already are used by many business and consumer applications.
Facial recognition, for example, uses ML algorithms to unlock user smartphones, while digital voice assistants such as Siri and Alexa use AI, NLP and ML to understand commands and carry out a range of tasks.
AI algorithms are used in e-commerce to make personalized shopping recommendations; in clinical trials to improve drug discovery and efficiency and elsewhere across an array of industries to automate a host of back-office tasks.
As often is the case, suppliers of “picks and shovels” were among early winners. Though we might quibble about what firms are in that category, many would say suppliers of graphics processor units, acceleration chips, memory, cloud computing suppliers and even electrical power companies are among firms and industries supplying “picks and shovels.”
There arguably is greater disagreement about whether large language models are enablers--and therefore in the “picks and shovels” category--or in the many other categories of beneficiaries of AI.
Large language models are where large amounts of disagreement could occur. Large language models can be viewed as both beneficiaries of AI and, to a lesser extent, enablers (picks and shovels). GPUs, memory, power, data centers and cloud computing enable AI to run.
In that sense, AI platforms and apps are beneficiaries, not picks and shovels. LLMs are a product of advanced AI techniques such as deep learning and natural language processing, but are applications in their own right. And AI applications are not generally considered to be picks and shovels.
Still, many could argue that LLMs are enablers to the extent they support creation of software features and applications. LLMs will power search, social media, many forms of app personalization, smartphone image processing, speech to text functions, text summarization, image processing, notetaking and so forth.
So LLMs are where the distinction between AI “picks and shovels” enablers and and AI beneficiaries is mixed.
Further muddling exists because some beneficiaries of AI also are developers of LLMs, and are revenue generators. Think of Microsoft’s Copilot, Google’s Gemini or other GenAI apps sold as subscriptions. GenAI is available both as a feature of Microsoft and Alphabet (Google) products as well as a subscription-based application.
In some instances the LLM is an enabler or feature; in other cases an app. For some firms, GenAI and LLMs are both enablers (picks and shovels) and beneficiaries of capabilities and features offered by most products and processes.
That matters for equity investors as well as all sorts of firms, industries and products.
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