AI’s Economic Ripple Effect: How Four Phases of Adoption May Shape Industries and Markets

By Gene Balas, CFA®
Investment Strategist

We can think of the adoption of artificial intelligence (AI) into four phases, and these four phases weave together a variety of industries, transforming the fabric of the global economy. Each phase of AI adoption not only marks a step forward in technological integration but also highlights the sectors primed to benefit from these advancements. The journey of AI adoption across industries, as delineated in the “Next Phases of the AI Trade,” by Goldman Sachs (published April 16^th, 2024), illustrates a transformative path from foundational technologies to broad economic impacts.

Is this a repeat of the Dot Com Bubble?

To start, some pundits have said that the AI boom is a repeat of the early days of the internet bubble and, as a result, that the market as a whole is overvalued. We note that the market is expensive by some measures, but it pales in comparison to the overvalued markets of the late 1990s or during the Covid mania, replete with meme stocks. Consider the nearby graph which illustrates the price to earnings (P/E) ratio of the Russell 3000 index, which is a very broad measure of about 3,000 stocks. The P/E ratio of the Russell 3000, while above that of recent years, is still well below that of the Dot Com Bubble or Covid Mania peaks.

The Four Phases of AI Adoption

Phase 1: Nvidia and the Emergence of AI Technologies

In this foundational phase – and the phase we are currently in – the semiconductor industry, represented by companies like Nvidia (NVDA), stands at the forefront. These companies may specialize in the production of graphic processing units (GPUs), which is a specialized processor originally designed to accelerate graphics rendering. GPUs and other critical components lay the groundwork for AI’s computational needs. Beyond semiconductors, this phase also emphasizes the importance of hardware manufacturers who create the servers and storage solutions essential for AI’s data-intensive operations.

Phase 2: Infrastructure Expansion

As AI’s potential expands, so does the need for a robust infrastructure to support its growth. This phase benefits a wider array of sectors:

• Semiconductors and Electronics: Beyond GPUs, there is a rising demand for specialized chips and electronic components tailored for AI applications.
• Cloud Computing: Companies like Amazon (AWS), Microsoft (Azure), and Google (Cloud) become pivotal as cloud services provide the scalable computing resources AI systems require.
• Utilities: The significant energy demands of data centers and AI computations highlight the role of utilities in providing reliable power sources.

• Data Centers and Real Estate: With AI’s dependency on big data, companies specializing in data storage and management solutions become indispensable. This includes traditional storage solutions and more innovative, AI-driven data management platforms.
• Telecommunications: The rollout of 5G technology is a boon for AI, significantly increasing the speed and reliability of data transmission, thereby enabling real-time AI applications, as well as between the devices themselves, autonomous vehicles, and more.
• Renewable Energy Providers: Given the environmental concerns surrounding the energy consumption of data centers, renewable energy providers play a critical role in powering AI’s infrastructure sustainably.
• Security: Companies will need to rely on software companies for endpoint security.
• Servers and Networking: To build and operate data centers and fit companies to use AI, various hardware and equipment will be required.

Phase 3: Revenue Enhancement through AI Integration

This phase sees the integration of AI into products and services, generating new revenue opportunities across diverse sectors:

• Software and IT Services: Companies in this sector leverage AI to develop new applications, enhance existing products, and offer innovative services that improve efficiency and customer experiences.
• Healthcare: AI applications in diagnostics, personalized medicine, and patient management systems begin to have a profound impact.
• Financial Services: From algorithmic trading to fraud detection and personalized banking services, AI enables new capabilities and efficiencies.
• Retail and E-commerce: AI-driven analytics, personalized shopping experiences, and inventory management transform the retail sector.

• E-commerce and Customer Service: AI-driven chatbots and recommendation engines personalize the shopping experience, boosting sales and customer satisfaction. Companies like Amazon and Alibaba are at the forefront, utilizing AI to refine customer interactions and backend logistics.
• Media and Entertainment: Streaming services like Netflix and Spotify use AI for content recommendation algorithms, enhancing user engagement and retention by tailoring offerings to individual tastes.
• Automotive Industry: The integration of AI in vehicles, from advanced driver-assistance systems (ADAS) to full autonomous driving capabilities, opens new revenue avenues for automakers and tech companies alike.

Phase 4: Productivity and Efficiency Gains

The final phase of AI adoption focuses on leveraging technology for operational efficiency and productivity improvements, impacting a broad range of industries:

• Manufacturing: AI and robotics revolutionize production lines, from automation to predictive maintenance, significantly enhancing efficiency.
• Professional Services: AI tools automate routine tasks, allowing professionals in fields such as law, finance, and consulting to focus on higher-value activities.
• Transportation and Logistics: AI optimizes routing, improves supply chain management, and paves the way for autonomous vehicle technologies.
• Agriculture: Precision farming techniques, powered by AI, enable better crop management and yield predictions, reducing waste and improving efficiency.
• Energy Sector: AI optimizes energy production and distribution, from predictive maintenance in oil and gas exploration to grid management in electricity networks, highlighting AI’s role in enhancing operational efficiencies.
• Education and Training: AI personalizes learning experiences, adapting content to suit individual learning styles and pacing, thereby improving educational outcomes and efficiency.
• Healthcare Delivery: Beyond diagnostics and patient management, AI streamlines hospital operations, from scheduling to inventory management, demonstrating the widespread applicability of AI-driven efficiency gains.

As AI progresses through these phases, the interconnectedness of industries becomes apparent. The advancements in foundational technologies during the initial phases enable transformative applications in subsequent stages, leading to widespread economic impacts. The sectors initially driving AI’s development, such as semiconductors and cloud computing, lay the foundation for its application across a myriad of industries, ultimately leading to enhanced productivity and efficiency on a global scale.

Understanding the specific industries within each phase not only provides insights into AI’s current impact but also forecasts future trends. As investors navigate this landscape, recognizing the phase each industry is in can guide investment and portfolio construction decisions, positioning investors to capitalize on AI’s transformative potential.

As we move forward into greater adoption of AI-related technologies, it’s likely that your portfolio already includes investments that include an emphasis on these opportunities. We understand how AI can influence the performance of both individual stocks as well as economic ramifications and their resultant influence on the broader market, beyond just AI stocks.

These are just a handful of examples of how AI can influence not just those stocks that are the immediate beneficiaries of AI, but also other stocks and asset classes. The companies include not just those that make software and equipment that are directly involved in developing and facilitating the adoption of AI technologies, but also companies that are deploying AI into their business models.

Consult with your SEIA advisor to help navigate this evolving landscape in the context of your investment portfolio and financial plan.

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The information contained herein is for informational purposes only and should not be considered investment advice or a recommendation to buy, hold, or sell any types of securities. Investing in securities carries a risk of loss, including the potential loss of principal. Different types of investments involve varying degrees of risk, and there can be no assurance that any specific investment will be profitable or suitable for a particular investor’s financial situation or risk tolerance. The information contained herein was carefully compiled from sources SEIA believes to be reliable, but we cannot guarantee the accuracy or completeness of the information provided. The Russell 3000 is a market capitalization-weighted index designed to measure the performance of the 3,000 largest companies in the U.S. equity market. Past performance does not guarantee future results. For details on the professional designations displayed herein, including descriptions, minimum requirements, and ongoing education requirements, please visit seia.com/disclosures. Signature Estate & Investment Advisors, LLC (SEIA) is an SEC-registered investment adviser; however, such registration does not imply a certain level of skill or training and no inference to the contrary should be made. Securities offered through Signature Estate Securities, LLC member FINRA/SIPC. Investment advisory services offered through SEIA, 2121 Avenue of the Stars, Suite 1600, Los Angeles, CA 90067, (310) 712-2323.

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