What are the main opportunities of AI, how can investors gain exposure to them, and what are the risks to watch out for?
What is AI?
Artificial Intelligence (AI), in its simplest form, is about automating tasks and processes, but extends to solving more complex problems through machine learning and deep learning technologies. The global AI market is on track to reach over $554 billion in revenue by 2024 as it impacts a range of industries. PWC estimates that by 2030, AI will have added up to $15.7 trillion to the global economy.
AI is implicated in a whole host of new technologies, from virtual assistants, smart home appliances, futuristic shopping experiences, to powerful software like autonomous driving.
Benefits of AI
Sundar Pichai, CEO of Alphabet, believes that AI is the most significant discovery since fire, electricity or the internet.
The sectors likely to be most affected are travel and leisure, finance, technology, education, healthcare and automotive.
AI applications provide companies with the opportunity to adapt their business models and, in some cases, this technology is giving rise to disruptive companies. The sectors likely to be most affected are travel and leisure, finance, technology, education, healthcare and automotive. AI solutions are often designed for cloud-based systems and made to be easily adaptable.
AI generates a variety of business efficiencies and incremental improvements. For example, automated chat boxes save companies time by scheduling customer interaction and providing real-time support.
- Saves business resources that can be used for higher-level tasks.
- Reduces the risk of error, speeds up the execution of repetitive tasks and reduces costs.
- Collecting more data enables better analysis for smart decision making.
ii. High level customization
The large amount of data collected allows companies to create software that offers a high level of customization to end users. For example, YouTube and Netflix change their algorithm to personalize what end users see in their stream.
- Offers more value and helps keep users engaged
- Improve profitability with personalized ads and online shopping feeds
- Scalability: Lower costs allow companies to offer free services with paid features.
iii. State-of-the-art technology
We are still discovering all the features and applications of AI. At the more complex end of AI, machine learning can develop sets of algorithms designed for nonlinear reasoning.
Deep learning AI is slowly making its way into everyday life.
New AI technologies are being implemented in a wide range of applications, often combined with other technologies such as the cloud for real-time data, blockchain technologies for encryption, and the latest smart chips.
While many new technologies are likely still under the radar and likely won’t be commercialized for years, we’re also seeing deep learning AI slowly making its way into everyday life. Here are some examples:
- Automotive: Autonomous driving technologies, such as Tesla’s FSD.
- Social Media: Detecting Hate Speech and Sensitive Content
- Advertising: Machine Learning Trains Algorithms to Personalize Ads
- Finance: AI models to approve loans (examples: Upstart, LendingClub)
- Healthcare: Detect diseases more accurately and earlier
- Others: Language processing, speech and image recognition
Invest in AI
Big tech companies such as Microsoft, Alphabet, Meta, IBM, Amazon, and Netflix, which have well-established business cases, are expected to be among the biggest beneficiaries of AI, as they can leverage their vast amounts of data using machine learning to create smart AI software for productivity to generate more revenue and lower costs. Likewise, software companies such as Salesforce, Palantir, and UiPath are at the forefront of AI.
The big names in electronic chips, such as Nvidia and AMD, are developing components with powerful AI functions. For example, Nvidia is the leader in graphics cards (GPUs), which help power some of the world’s fastest computers and provide the hardware needed to support AI technologies, whether intelligent machines, voice recognition, autonomous driving or medical breakthroughs. After huge growth, Nvidia expects GPU performance to only grow by 10% per year.
Manufacturing robots are also benefiting from the rise of AI. Rockwell Automation, the world’s largest company dedicated to industrial automation and information, is using AI modules to make predictive analytics more accessible to help more workers make better production decisions. Its “FactoryTalk Analytics LogixAI” solution uses machine learning to create predictions.
Investors can gain exposure to AI through a basket of stocks or benchmarks that track the stocks of tech giants.
In healthcare, Intuitive Surgical, which sets the standard for robotic surgery, has developed a product called “Ion” that uses AI and computer vision to screen patients for lung disease.
iii. AI-focused funds
There are a series of solutions available in the market. The L&G Artificial Intelligence UCITS ETF tracks the ROBO Global Artificial Intelligence Index, which is a basket of companies from around the world with a distinct portion of their business and revenue derived from the field of artificial intelligence. There are similar thematic AI strategies offered by popular ETF managers such as iShares and Xtrackers. Investors should consider that AI ETFs can perform based on factors other than AI, as AI is for the most part a tool to bolster existing successful businesses.
Software ETFs, such as the iShares Expanded Tech-Software Sector (IGV) ETF, also offer significant exposure to AI as they are populated by companies that specialize in providing B2B software for implementing IT solutions. and automation.
An alternative for investors wanting “pure play” exposure to AI is to select a basket of companies with high AI exposure. A more basic approach is to follow the Nasdaq 100 or an index that tracks global technology leaders. Below we take a look at the major constituents of the Nasdaq 100, which are somewhat representative of the rise of AI.
Risks and limits
Human creativity and vision will be hard to replace, and so exceptional management teams will most likely be a key attribute of companies. Tech can also face challenges from time to time and require human talent. For example, in 2017, Facebook said it would limit the rollout of its Messenger chatbot platform after finding that many bots were unable to answer 70% of user queries. Likewise, its AI for analyzing sensitive content also requires human intervention for the final judgment on what is appropriate for the platform.
Finally, the regulation of big tech companies is most likely a significant threat, affecting tech giants in the US, Europe and China. Regulatory efforts are currently geared towards data collection, with an emphasis on privacy issues. In this sense, the EU has recently drafted rules for regulating AI, which concern the practical applications of AI and the prospects for data privacy. Regulation could also gradually move towards specific AI technologies, such as facial recognition. Facebook is currently being sued by Texas for allegedly collecting biometric data from millions of Texans, without their consent. The company had already reached an agreement with Illinois over a similar lawsuit. In other words, until the regulations are clearly defined, companies using consumer data can be challenged legally.
AI offers major opportunities for large, established companies to further automate, provide personalization and, in some cases, deploy advanced technologies. Investors can gain exposure to AI through a basket of stocks or benchmarks that track the stocks of tech giants. It cannot be ruled out that in the medium term, waves of regulation could slow down the progress of AI and its commercialization. Companies that process massive amounts of consumer data naturally put themselves in the crosshairs of regulators.
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