On May 15th, the Global X Future Analytics Tech ETF (AIQ) began trading on Nasdaq. The Global X Future Analytics Tech ETF (AIQ) seeks to invest in companies that potentially stand to benefit from the further development and utilization of artificial intelligence (AI) technology in their products and services, as well as in companies that provide hardware facilitating the use of AI for the analysis of big data. AIQ is the 7th ETF in Global X’s Thematic Tech suite of ETFs.

AI & Big Data is emerging as one of the most potentially disruptive themes in the digital world. As the world’s data grows exponentially, AI capabilities are tracking close behind, the far-reaching implications of which are becoming clearer every day. In this piece, we explore the AI and Big Data theme by discussing:

• What is AI?
• Why is Big Data key to AI’s success?
• What is the potential impact of this theme?
• What areas of the economy are expected to be most affected?
• Which types of companies potentially stand to benefit from the emergence of AI & Big Data?

What is AI?

Artificial intelligence is the concept of machines performing tasks that once required human intelligence to complete. Many use the terms AI, machine learning (ML), and deep learning (DL) interchangeably, but there are key differences between them. AI broadly encompasses the entire field of study, of which ML and DL are sub-segments.

— Artificial intelligence can be divided into two distinct fields. Applied AI refers to an application optimized to perform one specific task, like suggesting a movie or optimizing a driving route. General AI involves broader applications of AI, like a computer learning a variety of tasks and the ability to problem solve, much like a human.

— Machine learning is the process of building machines or programs that can access data, apply algorithms to the data, derive valuable insights, and then apply what they learned to other scenarios or new data sets.

— Deep learning takes AI to the next level. Inspired by the human brain, it utilizes artificial neural networks to make sense of unstructured data like images and sounds. To do so, neurons in the network analyze attributes like the shape, color, and size of an object within an image to determine whether or not the object is, in fact, a cat.

Why is Big Data key to AI’s Success?

Big Data is AI’s fuel. It is both what trains AI to become increasingly powerful and what AI systems are ultimately applied to in order to generate real-world insights. The more data AI systems can tap, the greater their intelligence and disruptive potential.

While AI as a concept has been around for more than 50 years, a shortage of structured data for much of that span and computational limits stunted AI’s growth. For example, good speech-recognition technology requires about 150,000 hours (i.e., 10 years) of audio data. Facial recognition applications require roughly 15 million images.¹

Only until recently was that much image and audio data readily available. In fact, 90% of the world’s data has been generated since 2015.¹ That year, the digital universe, i.e., the reservoir of data created and copied, totaled less than 10 zettabytes—that would be 10, followed by 21 zeros. By 2020, it is expected to grow more than four times to 44 zettabytes. Just five years after that, it could reach 180 zettabytes.²

Much of this growth can be attributed to the increased adoption of the Internet of Things and advancements in deep learning. With more connected devices recording videos, measuring heart rates, or tracking deliveries, the world’s information is becoming increasingly digitized. Combining this data creation with advancements in deep learning for image and speech recognition, more and more information is not just saved and stored now, it is structured and analyzed by AI systems.

What is the potential economic impact of this theme?

Nourished by an ever-growing reservoir of data, the stage has been set for AI to become a disruptive force across the global economy. According to one report, AI could contribute up to $15.7 trillion to global GDP in 2030, with $9.1 trillion coming from consumption-side effects and $6.6 trillion coming from increased productivity. For context, that would add about 14% to global GDP, or more than China and India’s combined output.³

However, these gains do not come without costs, particularly in labor productivity, which is the measure of output per worker. On one hand, workers will be freed from unstimulating, repetitive tasks. This will leave them to focus on higher value-add work that requires creativity and problem solving, resulting in higher potential output.

On the other hand, jobs that are largely based on repeatable or low value-add tasks could soon be replaced by AI systems, potentially resulting in a smaller workforce until people can be retrained.

What areas of the economy are expected to be most affected?

As mentioned, the accelerated adoption of the Internet of Things is digitizing information across the economy that can be now processed or analyzed by AI systems. Therefore, AI’s reach continues to expand across a variety of sectors and businesses. Some areas that are primed for AI disruption include:

— Industrial automation: Spurred by surging global investment in robotics that could surpass $180 billion by 2020, industrial automation is at the forefront of AI’s implementation in the physical world.⁴ With robotics representing a machine’s body and AI a machine’s mind, advancements in both fields are combining to create smarter, more capable machines than ever. This means that robots are no longer confined to simple, repeated tasks, but now operate more freely in unstructured environments like warehouses or factories and can collaborate more closely with humans on assembly lines.

Increasingly, AI programs are also used in the manufacturing process for design simulation and testing, predictive maintenance, supply chain optimization, and customized production.

— Autonomous vehicles: AI is at the helm of autonomous vehicles (AVs), which appears set to cause massive disruption within the transportation industry. By taking inputs from sophisticated sensors, GPS, cameras, and radar systems, AI software embedded in an AV computes billions of data points every second to effectively see the road and navigate the vehicle.

Obstacles remain until full automation, but high-end cars are already capable of performing basic driving functions with limited human interaction. And testing has commenced with AVs that control all driving aspects without human assistance under specific conditions.

— Health care: Applying AI to the US health care system could result in $150 billion in annual savings by 2026 and improvements in patient outcomes.⁵ From robot-conducted surgery, aided by integrating diagnostic imaging and pre-op medical data, to virtual nursing assistants that help with initial diagnoses and patient logistics, AI is expected to revolutionize a variety of aspects of health care.

— Consumer retail and E-commerce: AI is already used to generate personalized recommendations to help stores interact better with their customers and grow revenue. However, AI can also be used on the operations side to reduce costs, such as predicting customer orders, which may reduce shipping, inventory, and supply chain costs.

— Smart Assistants: Breakthroughs in voice recognition, predictive analytics, and natural language processing are making digital assistants increasingly dynamic and useful. An analysis by comScore expects that 50% of all Internet searches to be voice searches by 2020 as users increasingly shift away from the keyboard.⁶

Which types of companies stand to benefit from the emergence of AI & Big Data?

A variety of companies involved in AI & Big Data are well-positioned to potentially benefit as this theme emerges. Briefly, we believe it includes companies that own large proprietary data sets, are developing cutting-edge AI programs, or building the computer hardware that can perform these complex computations.

More specifically, these companies can be defined as the following four groups:

AI Developers: Companies that have developed internal AI capabilities (organically or through acquisition) and are applying artificial intelligence technology to enhance their products and services.

AI-as-a-Service (AIaaS): Companies that provide artificial intelligence capabilities to their customers as a service. The firms in this segment typically offer cloud-based platforms that allow their customers to apply artificial intelligence techniques without needing to make a direct investment in AI-related infrastructure.

AI Hardware: Companies that produce semiconductors, memory storage, and other hardware utilized for artificial intelligence applications.

Quantum Computing: Companies at the forefront of developing quantum computing technology, which is in the process of being commercialized, are expected to be significant players in AI & Big Data in the future.

Not surprisingly, tech giants often score well across these four categories.  Many own vast pools of proprietary data that create deep economic moats to help protect against new entrants, when used effectively to improve and monetize their offerings through AI. As these companies refine their products, they collect even more data and the AI becomes increasingly powerful.

But the Tech giants are not the only players in AI & Big Data. More specialized companies developing cutting-edge processors and pioneering quantum computing are essential to meeting the computing demands of AI. In addition, firms offering AI-as-a-Service to firms that bring their own data (BYOD) are expanding AI’s reach to the business world beyond just the tech giants.

Related ETFs

AIQ: The Global X Future Analytics Tech ETF seeks to invest in companies that may benefit from the further development and utilization of artificial intelligence (AI) technology in their products and services, as well as in companies that provide hardware facilitating the use of AI for the analysis of big data.

BOTZ: The Global X Robotics & Artificial Intelligence ETF seeks to invest in companies that may benefit from increased adoption and utilization of robotics and artificial intelligence (AI), including those involved with industrial robotics and automation, non-industrial robots, and autonomous vehicles.

SNSR: The Global X Internet of Things ETF seeks to invest in companies that may benefit from the broader adoption of the Internet of Things (IoT). This includes the development and manufacturing of semiconductors and sensors, integrated products and solutions, and applications serving smart grids, smart homes, connected cars, and the industrial internet.

DRIV: The Global X Autonomous & Electric Vehicles ETF seeks to invest in companies involved in the development of autonomous vehicle technology, electric vehicles (EVs), and EV components and materials. This includes companies involved in the development of autonomous vehicle software and hardware, as well as companies that produce EVs, EV components such as lithium batteries, and critical EV materials such as lithium and cobalt.