Generative AI: Reshaping investment landscapes 

The new arms race in the technology sector 

Time will tell, but November 30, 2022, could potentially mark a moment as significant in tech history as Steve Jobs’ unveiling of the iPhone in 2007. This pivotal day witnessed the launch of ChatGPT by OpenAI, a San Francisco-based company. ChatGPT quickly gained global attention, thanks to its utilization of natural language processing and generative algorithms. These AI components enabled ChatGPT to gather information from the web, contextualize it within ongoing conversations, and reorganize it to provide responses—a leap beyond previous AI capabilities focused on aiding human comprehension. 

ChatGPT’s rapid adoption was unprecedented; within a week of its November 30, 2022 launch, it amassed over 1 million users, and within two months, it reportedly reached 100 million users, outpacing the growth rates of other popular applications like Instagram. 

Dom Rizzo, portfolio manager, observed that major tech companies were caught off guard by ChatGPT’s overwhelming consumer response. This triggered what he referred to as an “arms race” among these companies to acquire and refine AI capabilities. 

The core innovations driving generative AI 

Our managers have been intensely curious about the potential impacts of AI. And they have closely monitored and invested in several critical innovations crucial for building ChatGPT and other foundational models. These include cloud computing and the accumulation of sheer computing power enabled by ever‑faster chips and processors. Importantly, it also reflects the development of the industry’s “platform” structure, in which companies allow outside developers to build new applications on top of their intellectual property. 

Moreover, it’s believed that an array of companies, irrespective of size, stand to benefit from AI application development. AI’s future landscape might comprise a small set of massive foundation models sitting between many more specialized AI models and user interfaces. 

Generative AI’s evolution signifies a shift from mere perception and understanding of data towards the creation of entirely new outputs. This achievement involves analyzing user prompts, retrieving relevant information from its training data, discerning appropriateness, and generating responses—a major leap in machine learning capabilities. 

Exploring problem-solving potential 

AI’s journey toward generative capabilities finds roots in its deep learning through neural networks, exemplified by DeepMind’s AlphaZero mastering the complex game of “Go” in 2018 by playing millions of games against itself. This marked a significant milestone in machine learning’s advancement. 

Machine learning dates back to the ‘50s, while Deep Blue’s victory against chess grandmaster Garry Kasparov in 1997 showcased early machine learning progress. However, these systems relied on substantial human guidance and were designed for specific purposes.

Giants leading the charge in development 

Tech giants have made substantial strides in advancing AI’s foundational models. However, the massive costs associated with developing and operating AI applications have soared due to their growing complexity. For instance, training these models incurs significant expenses, with one estimation suggesting OpenAI spends a penny per 30-word answer generated by ChatGPT—a seemingly small sum that adds up quickly when the system has high volumes of users.  

Additionally, the escalating expenses of inferencing, or running the model to process user queries, are anticipated to outweigh model training costs in the future, setting AI apart from traditional software models. While this rapid scaling benefits larger corporations, it poses significant financial burdens. Companies like Alphabet, Microsoft, Meta and Amazon may need substantial investments to sustain their AI competitiveness. 

Alphabet’s supremacy and potential challenges 

Alphabet, despite its innovative AI efforts, faced market scrutiny post-ChatGPT’s launch, partly due to concerns about potential competition for Google’s search dominance.

However, there are signs that Google is adapting by incorporating advanced AI capabilities into its search engine and other products, while leveraging its extensive user base and distribution advantage.

Nevertheless, questions persist regarding the monetization and cost implications of implementing compute-intensive large language models for AI-generated responses. Adapting advertising models while balancing increased cost burdens remains a challenge for Google. 

Microsoft’s AI integration and role in AI advancement

Microsoft’s partnership with OpenAI has propelled its strides in AI, integrating chatbots into its Bing search engine and Office suite. CEO Satya Nadella plans to further integrate OpenAI into Microsoft’s software ecosystem. These moves, coupled with Microsoft’s AI history, position it as a formidable player in the AI race.  

Investing in the linchpins  

As the AI landscape evolves, Rizzo focuses on companies pivotal to AI development, particularly those providing indispensable tools. He compares them to suppliers during the California gold rush, poised to benefit from the AI spending surge.  

He highlights semiconductor companies like NVIDIA and AMD as linchpins due to their crucial role in developing AI-powered systems. NVIDIA, renowned for its GPUs initially designed for gaming, has become a leader in AI development. Its parallel processing capability significantly contributes to building complex neural networks essential for rapid data querying.

Furthermore, Advanced Micro Devices (AMD) stands out for its innovative MI300 chip, combining the strengths of both CPUs and GPUs. This breakthrough holds immense promise for advancing AI capabilities.  

The expanding role of AI in chip technology

AI’s growing demands in computing resources have significantly impacted the chip industry, with AI servers consuming more DRAM and storage than traditional servers. Companies like Taiwan Semiconductor Manufacturing Corporation (TSMC) play a pivotal role in creating smaller process nodes crucial for fitting more transistors on chips. ASML Holding’s extreme ultraviolet (EUV) lithography machines have been instrumental in TSMC’s advancements.   

Synopsys, a leader in chip design technology, harnesses AI to design faster and more energy-efficient chips, adding another layer to the complex AI-chip relationship. 

Disruption spreads beyond tech

AI’s impact extends far beyond the tech industry, disrupting conventional paradigms. Tools like ChatGPT expedite code writing, potentially creating a new breed of highly productive software developers. This might reduce barriers to software development and alter innovation rates in the industry.  

Healthcare has witnessed transformative AI advancements, notably Google’s DeepMind predicting the three-dimensional structure of almost all human proteins. This breakthrough has immense potential in drug development and genomic research, offering insights into disease mechanisms and potential treatments.  

Our ongoing analysis  

T. Rowe Price’s analysts and managers are actively exploring AI’s implications across various sectors, asking smart questions to get the right answers about this emerging space. The unprecedented pace of AI’s evolution presents uncertainties but also immense opportunities.   

The unfolding revolution in AI holds tremendous promise while demanding astute observation, curiosity, and adaptability from investors and industries alike. 

The specific securities identified and described are for informational purposes only and do not represent recommendations. 

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