Artificial intelligence (AI) is increasingly pervasive in our daily lives, and even if you’re unaware of the ways in which it is, you can rest assured it is no longer strictly the stuff of science fiction. When your bank contacts you about potential fraudulent use of your credit card, it’s AI that noticed it. When Netflix recommends a show to you, or Amazon recommends a product, that’s AI at work. The increasing pervasiveness of AI means that market revenues are set to grow rapidly, creating what could be a very significant investment opportunity indeed. Adding the right Robotics & AI exposure could help keep your portfolio on the right side of this change.
Capture the theme with Lyxor’s new Robotics and AI ETF
Embracing the AI and robotics revolution could help future-proof your portfolio. Our new ETF gives you exposure to 150 companies – at least 50% more than other like for like products2 – harnessing the power of AI, whether for themselves or their customers. It is unique in three ways:
- Better balanced à Our ETF is more heavily weighted to AI leaders than competing European products, but it doesn’t ignore the benefits robotics can bring.2
- More capable of capturing change à We look beyond industrials and technology to AI innovators in areas such as retail, health care, energy and finance to better capture the theme.
- More likely to evolve à We’ve introduced a new way of classifying what constitutes a robotics or AI company – a combination of big data analytics and human expertise – ensuring our ETF won’t get left behind as the industry evolves.
5 Surprising Companies in Our Underlying Index
While some of the stocks we track in our ETF do overlap with other like-for-like products in Europe, 64 (or around 40%) of them are unique to us. In our view, this helps us capture the trend more broadly and accurately than other strategies. The companies below are in our underlying index and are, at least in terms of blended robotics and AI portfolios, entirely unique to us. Three of the five can’t even be found in a new pure play AI ETF.2
Alibaba: The Chinese government is committed to becoming the largest AI player in the world, to the benefit of companies like Alibaba. In late 2017, the e-commerce giant announced its plans to invest $15 billion over three years in a global research and development programme, with projects focusing on data intelligence, machine learning, the Internet of Things, and natural language processing.
Alibaba has transformed around a million physical ‘mom-and-pop’ shops and 100 superstores into “smart stores” by decking them out with AI-backed apps and heat sensors tracking foot traffic to better manage inventory and boost sales. Another example of Alibaba’s command of AI is its “City Brain” traffic project in Hangzhou. Using a cloud-based system, the technology stores and processes data about the movements of everyone in the city. AI algorithms can then help reduce traffic jams, and better respond to accidents and crimes.
Netflix: The Netflix name may have become synonymous with a quiet night in but the importance of AI to its business model is far less familiar. Netflix uses machine learning algorithms to make personalised viewing recommendations. It compares the collective feedback of a group of subscribers who have watched a film or show with your past viewing habits. If your taste matches that of the group, the film or show they watched will pop up as a suggestion for you. Netflix claims over 80% of TV shows people watch are a direct result of a recommendation they make.
Another underrated way in which Netflix leverages AI lies in its actual streaming technology. The company’s main goal is to deliver as much content as possible to its customers, within the limits imposed by internet providers (e.g. bandwidth speed, data caps…etc). To achieve this, Netflix recently re-encoded its entire library of content with an AI-powered video optimiser that aims to maximise image quality and minimise bandwidth consumption. The result? A growing, happy subscriber base capable of enjoying content without wiping out their data plans.
TripAdvisor: According to a comScore report commissioned by travel website TripAdvisor, they were the most visited pre-transaction travel site in the world over the second and third quarters of 2017. One reason for TripAdvisor’s success lies in its ability to deliver the right reviews to the right customers at the right time, depending on where they are in their holiday planning. To do this, their recommendation technology employs a machine learning technique called ‘collaborative filtering.’ The idea is to match users with specific interests to reviews with relevant content. In its view, “not every millennial wants to live like a local; not every family wants to live like a tourist.” By tapping into the website’s massive data sets and giving users a personalised experience, AI can really make a difference.
Salesforce.com: US software behemoth Salesforce.com is known for its cloud-based client relationship management product. And one of its self-proclaimed missions is to democratise AI, which it kick-started in 2016 with its announcement of Einstein, a programme that goes beyond simple filters and studies historical customer data such as e-mail threads and behavioural patterns to keep getting smarter over time. The result is a powerful and predictive customer experience designed to maximise sales figures by helping salespeople prioritise leads based on their likelihood to close, for example, effectively automating time-consuming tasks so the seller can focus on their hotter prospects. Marketers can also benefit by sorting recipients in large mailing lists by their likelihood to open an e-mail. In its latest upgrade, Einstein has also become a chatbot, minimising the need for costly customer service centres.
Tesla: Despite the eccentric antics of Elon Musk in recent months, the Tesla story remains intact and is founded on big data and AI. The company is desperate to win the race to build and market fully automated, driverless cars and doing so requires data – the thousands of Tesla-built cars on the road already mean they have a huge head start.
Data is extracted from every Tesla vehicle (as well as its driver) and used to generate detailed maps showing everything from the average increase in traffic speed over a stretch of road to the location of hazards which cause drivers to take action. Machine-learning in the cloud then educates the entire fleet, while edge computing decides what action any given individual car needs to take immediately. Cars can form networks with other Tesla vehicles nearby to share local information and insights.
If and when autonomous cars become as widespread as Musk maintains, these networks will be able to interface with cars from other manufacturers and other systems, such as traffic cameras, road-based sensors, or mobile phones.
This article is for informative purposes only, and should not be taken as investment advice. Lyxor ETF does not in any way endorse or promote the companies mentioned in this article. Capital at risk. Please read our Risk Warning below.
1Source: McKinsey Global Institute, June 2017.
2Source: Lyxor International Asset Management, as of 14 September 2018. Peer comparison made with European listed ETFs focused on Robotics and Automation, as well as a new ETF focused purely on AI.
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