Artificial Intelligence: An Accelerating RevolutionArtificialIntelligence:AnAcceleratingRevolution

A rising tide of demand 

One of the earliest impacts of AI has been surging demand for the semiconductors and infrastructure needed to support AI model training and running scaled AI-enabled applications. A source of this demand is large hyperscale cloud computing companies, which continue to be the major buyers of AI hardware (e.g., GPUs). Capital expenditures and spending forecasts for many company platforms have risen significantly.

These companies plan to rent this hardware to customers of all sizes, including software firms, to train models and run production AI applications. As demand for these services has grown, the companies have continued to build out the scale required to bring products to market. In turn, we believe this is a critical part of the “AI wave” that is preceding the creation and adoption of the products by end users.

While AI has already had a measurable impact on technology hardware, its influence on the rest of technology—and the economy—is unfolding in real time. AI-related development has been a priority at many companies for several years. Software applications that use generative AI have been introduced, and firms are employing these new tools in their workflows. As a result, software developers are gathering data and user feedback, which is being used to make the software offerings more effective. As these new generations of AI-powered products come online, we are looking for evidence of improved processes and efficiencies for their customers. Over time, we believe this should result in higher productivity, lower costs, and stronger economic growth. 

Some of the earliest examples of AI-driven improvements have been developed by software firms dedicated to customer service, marketing, and sales. Several firms reported that a sizable portion of their enterprise customers are already using AI features, and they expect their AI capabilities will improve significantly within a year. These tools are initially targeting routine tasks that can be automated, accelerated, or enhanced. For example, AI-related software can draft emails (ranging in style from formal to casual) and combine it with AI-generated images for use in a marketing campaign. The campaign may also include meeting follow ups and automated scheduling. Internally, the software tracks and analyzes data, offering a transparent view of a company’s sales pipeline and the effectiveness of its sales efforts.

We are already seeing the impact of generative AI on specific industries such as advertising. Today, it can be expensive to create a video advertisement for a medium-sized business. In our view, AI tools will cut production costs dramatically over time, which will lower the barrier to entry for small- and medium-sized businesses to adopt video advertising. Social media platforms and the largest video hosting services should be the primary beneficiaries of this development.

Another area for wide-spread AI use is cybersecurity, where firms are developing AI tools to help companies keep their networks and data secure. Much of this is designed to help a firm’s tech security identify a specific threat, uncover where it could exploit the firm’s network, and implement remediation. These tools are being marketed as stand-alone products that can free up security and data teams to do more important tasks—a critical need due to a massive shortage of cybersecurity professionals and growing threats from cybercriminals. 

AI agents are a new development framework that may be the next frontier in the use of large language models (LLMs). An AI agent is software designed to perform complex tasks autonomously or semi-autonomously. AI agents can create action plans, reason, determine the need for external actions or tools, gather additional information from the user in a conversational format. They can also interact with other AI agents, use tools to interact with external systems (websites, software APIs), revise its plan based on its research, and proactively start a series of actions. 

An AI agent can broaden the range of use cases for LLMs, allowing them to take on more complex multi-step tasks and take actions in external systems to achieve specific goals. Agents should help reduce hallucinations and allow the systems to take actions, thus expanding the range of tasks that an AI system can execute. 

 

Challenges and opportunities

Dramatic change, however, rarely occurs without some challenges. Most companies outside of technology understand the importance of AI, but they have little experience or expertise in the infrastructure needed to build out their AI capabilities. Making it more difficult, the collection of software programs supporting the AI features is new and always improving. Another major challenge is data purity. As companies look to build internal AI applications, their proprietary data may be scattered, stored inefficiently, or inaccessible. As a result, we think most companies will likely consume their first generative AI software products through new and existing Service as a Software (SaaS) vendors. 

Over time, we see more non-technology companies using their proprietary data to build their own internal applications. These tools could drive meaningful competitive and business achievements, but they will likely require more time to build and put into production. We also note that competition for talented workers is fierce, especially from giant, well-resourced tech companies.

From our perspective, the falling cost to run AI applications will help to catalyze the move from R&D into production. Pricing for LLMs and multimodal models fell dramatically in 2023 because of improvements in model efficiency and the scaling up of GPU infrastructure from cloud providers. Use cases that were previously very expensive to run at production scale are now much more affordable.

Overall, the appearance of AI-driven tools has been uneven. Definitive, widely accepted AI applications within the enterprise have not appeared, but promising uses of AI are already evident among early adopters. It remains a process of trial and error, and we expect many failures. For investors, we believe extra vigilance is warranted. If individual companies stumble—e.g., through poor execution, management uncertainty, or bad publicity—the decline could be swift and result in significant losses. However, we are convinced that “AI haves and have nots” will appear, with those companies that have successfully embraced AI in stronger competitive positions versus their slower, or less skilled, peers. Ultimately, we are looking for the companies that get it right—that develop “killer use cases” and build effective AI infrastructure and apps that lead to monetization.

In our view, these represent attractive long-term opportunities, and we believe that investors with resources, experience, expertise, and a disciplined approach are best positioned to exploit them.