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What Is AI and Why Has Interest Taken Off Now?

Jonathan McMullan, Global Sector Specialist, Public Equities:
Artificial intelligence (AI) essentially means any technique that enables computers to complete tasks that typically require human intelligence. The concept of AI has been around for a long time, dating back to as early as 1950 when Alan Turing introduced the Turing Test for machine intelligence. Like many conceptual technologies, its progress has ebbed and flowed over the years, and its journey has been marked by alternating periods of heightened excitement and subsequent disillusionment.

We already encounter AI a lot in our daily lives: facial or voice recognition on your smartphone, TV show recommendations on streaming platforms, and smart replies in emails and text messages. But generative AI, and particularly the unveiling of ChatGPT in 2022, has sparked peoples’ imaginations.

What really distinguishes generative AI from other hyped technologies of recent years, such as cryptocurrency or the metaverse, is its tangible, practical nature; it’s not just an abstract idea. Generative AI is already finding its way into everyday workflows, and we don’t need to stretch our imaginations too far to see its transformative potential.



What Is Artificial Intelligence?

Source: Schroders

The swift adoption of ChatGPT suggests that societal habits are forming around the use of text-based generative AI—and these tools are here to stay.


Michael White, Global Sector Specialist, Public Equities:
ChatGPT’s initial success has been astonishing. At its release, it took only two months to reach the 100 million user milestone—far faster than its competitors at the time (FIGURE 2). The swift adoption of the chatbot suggests that societal habits are forming around the use of text-based generative AI—and these tools are here to stay.



Months to Reach 100 Million Global Monthly Active Users

Source: UBS, 2023.


What’s Behind the New Wave of Generative AI Apps?

Paddy Flood, Global Sector Specialist, Public Equities:
There are multiple factors contributing to the emergence of generative AI, including:

1. Improved architecture: There are many different architectural approaches to AI, but in 2017 Google introduced a new architecture based on transformers. This architecture is an essential building block for the large language models (LLMs) we see today as it, among other things, means that models can contextualize whole questions and conversations (as opposed to words or phrases in isolation) and be trained faster.

2. Enhanced computing power: Semiconductors have become smaller and more powerful, allowing tasks to be completed faster and more efficiently. In addition, cloud computing has taken off and has enabled companies to outsource their IT infrastructure to third parties. Without this, companies would have had to invest in expensive AI-related infrastructure, potentially slowing generative AI’s adoption.

3. Data: The increased availability and usability of data, a key part of LLMs, is another reason. The world continues to generate this data, but as cloud computing advances, it becomes easier to access and store it.

4. AI at the edge: Finally, we now have techniques for deploying AI at the edge. This means the AI computations are done on the device where the data is created, rather than on a distant data center. This is crucial for applications such as autonomous driving where data instructions must be acted on immediately with no latency or delay.


What Kinds of Companies Operate in the Generative AI Segment?

Ankur Dubey, Investment Director, Private Equity:
To understand what kinds of companies are at work in the AI universe, we need to understand the technologies needed to build a generative AI application—the technology stack (FIGURE 3). There are four layers:

1. Compute Layer: Generative AI systems require large amounts of computing power and storage capacity to train and run the models. Hardware (semiconductor chips) provides the computing power and cloud platforms like Amazon Web Services, Microsoft Azure, or Google Cloud Platform provide services such as virtual machines and storage.

2. Foundational Model Layer: Foundation models are systems with broad capabilities that can be adapted to a range of different, more specific purposes. This is arguably the most important layer of the generative AI stack. These foundation models are large statistical models built using sophisticated machine learning algorithms that generate human-like responses derived from large volumes of data on which they're trained. Foundation models are split into closed- and open-source models. Closed-source software is proprietary—only the company that owns it can modify it. Alternatively, open source means the source code is publicly available and programmers can change it.

3. Infrastructure Layer: These are the tooling or infrastructure companies for apps that don’t use proprietary foundational models. Such apps need the infrastructure companies to help them fully utilize the technology available at the foundational level. Apps with proprietary models (e.g., ChatGPT) don’t need to rely on third parties in the infrastructure or the layers in foundational models.

4. Application Layer: This is the software that allows users to interact with the underlying AI technology. This can include OpenAI’s ChatGPT product or an internally built solution such as Schroders’ in-house AI product, named Genie.



The Generative AI Technology Stack

Source: Schroders Capital, 2023.


What Kinds of Companies Could Benefit the Most from Generative AI?

Ankur Dubey:
The technology is quite young, so the jury is still out on which of these layers will accrue the most value. However, we can agree that, so far, the compute layer has emerged as a winner, and the market agrees: NVIDIA’s share price is up around 190% year-to-date.1 That said, questions remain regarding whether NVIDIA’s cutting-edge technology could be commoditized over time.


Michael White:
For now, the picks and shovels companies2 in the compute layer look like winners thanks to their existing dominant positions. As generative AI use cases grow, the demand for chips will grow, too, and NVIDIA is an expert with a dominant market share in the graphic-processing units that are essential for AI processing.

For now, the cloud computing market remains an oligopoly, as a small number of players reign supreme. Companies such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform are likely to retain their advantage because they invested significantly in infrastructure and established customer relationships in recent years.

We must remember, though, that new tech enables new ways of doing things, creating entirely new businesses in the process. Netflix, for example, was enabled by the internet allowing them to offer a superior product to traditional TV and flourish in a way that threatened existing media companies. Similarly, Uber’s business model can only exist because of smartphones and the mobile internet. AI technology will also provide a new way of doing things and likely carve out the space for new and innovative businesses. It may be too early yet for these businesses to have emerged, but this is what we’re looking for.


AI is an increasingly important element of the types of companies being created in the market today.


Mike McLean, Senior Investment Director, Private Equity:
Tech industry aside, data-rich companies (those that own a large amount of proprietary user-generated content) could become valuable simply because of the value derived from data in training AI models. Flows from AI companies have surged in recent years (FIGURE 4), growing much faster than the venture-capital market overall. Last year saw a drop off in investments but reflects a decline in the venture market more generally. Bottom line: AI is an increasingly important element of the types of companies being created in the market today.



Total Invested in Venture Capital Backed AI Companies Globally (USD)

As of 5/26/23. Source: Pitchbook, Schroders Capital.

To learn more about opportunities in AI, talk to your financial professional. 


1 As of 6/30/23. Source: FactSet.

2 A pick-and-shovel play is an investment strategy consisting of buying shares of companies in the tools or services an industry uses to produce a product.

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The views expressed herein are those of Schroders Investment Management (Schroders), are for informational purposes only, and are subject to change based on prevailing market, economic, and other conditions. The views expressed may not reflect the opinions of Hartford Funds or any other sub-adviser to our funds. The opinions stated in this document include some forecasted views. Schroders believes that they are basing their expectations and beliefs on reasonable assumptions within the bounds of what they currently know. The views and information discussed should not be construed as research, a recommendation, or investment advice, nor should they be considered an offer or solicitation to buy or sell any security. This information is current at the time of writing and may not be reproduced or distributed in whole or in part, for any purpose, without the express written consent of Schroders or Hartford Funds.

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