A recent query from a member of a Facebook community incited an interesting debate: how does one invest in Artificial Intelligence (AI)? With the surge in popularity of AI models such as OpenAI’s ChatGPT and the release of Google’s Bard, are we approaching the brink of an AI revolution that tech enthusiasts have speculated about for so long?
It seems evident that tech behemoths who have invested significantly in AI are poised to gain considerable benefits. Take, for instance, Microsoft’s investment in the development of OpenAI, which led to the creation of the increasingly embraced ChatGPT. Similarly, Google saw its market cap surge by over 5% mere days after their annual demonstration conference, Google I/O.
These high stakes point towards a possible repeat of the cutthroat competition witnessed during the smartphone era, where large corporations vied for market supremacy. Given the velocity of this technological transition, it’s essential to stay informed and identify potential investment opportunities in AI.
Three core technologies constitute the AI investment landscape: microchips, software applications, and cloud infrastructure, including databases and computational services.
When examining AI companies, it’s crucial to consider the areas they target, such as chips, cloud computing, or front-end technologies. Many corporations, including the likes of Apple, Tesla, and Google, have invested in several of these segments concurrently.
The chips and cloud services are the underpinning foundation of the AI revolution. The processors and cloud platforms driving these services are experiencing rapid advancement. Market leaders in this space include Nvidia, Intel, ARM Holdings, Google Cloud, Amazon Web Services (AWS), and Microsoft Azure.
Microchips are the beating heart of the computational magic. As the hardware-level processing needs of AI models are sophisticated, the chips have seen dramatic enhancements. Nvidia has been a significant beneficiary of this, thanks to its Graphics Processing Unit (GPU) technology, which is exceptionally suited for AI computation. Other prominent players in the chip manufacturing industry include ASML, TSMC, AMD, and Intel.
The next stage is software applications, where the breakthroughs in innovation are genuinely visible. The core of AI, machine learning algorithms, has seen substantial advancements, enabling machines to learn from data and make accurate decisions or predictions.
These sophisticated algorithms power a plethora of applications, ranging from recommendation engines to self-driving cars. Consequently, the advent of AI has led to a boom in the value of data and algorithms, two indispensable assets in today’s digital economy.
Further, the emergence of cloud computing has given AI the infrastructure it needs to operate on a grand scale. The endless processing power and storage capabilities of the cloud have enabled AI to analyze vast data sets and complex models, something that was prohibitively expensive until a few years ago.
Moreover, the rise of AI has led to an explosion in the value of data and algorithms, two critical assets in today’s digital economy. Furthermore, the advent of cloud computing has provided AI with the infrastructure it needs to function on a large scale. Entities leading the charge in cloud-based data are likely to have a predictable advantage in this AI-dominated landscape.
In terms of cloud services, databases, and cloud computation, supporting the vast computational needs of the cloud necessitates significant servers operating at what is termed ‘hyper-scale.’ These entities power the internet and mobile applications that have become integral to our lives. The demand for cloud computing is escalating so quickly that Microsoft’s Azure and Google may need to collaborate to cater to OpenAI’s cloud requirements.
When considering investments, have you thought about AI Exchange Traded Funds (ETFs)? It’s another avenue to explore. AI ETFs, such as the ROBO Global Robotics and Automation Index ETF (ROBO), Global X Robotics and Artificial Intelligence ETF (AIQ), iShares Robotics and Artificial Intelligence ETF (IRBO), First Trust Nasdaq AI and Robotics ETF (NOAR), iShares Exponential Technologies ETF (IBUY), offer potential benefits for investors.
Most of these so-called AI ETFs heavily emphasize robotics, an intriguing but distinct field. Robotics technologies tend to be costlier to build, have a different margin profile than sectors like software and chip-making, and can be capital-intensive, potentially diluting investors’ interests.
There are also ways to invest in private AI companies, although this is typically out of reach for most investors unless they are accredited and have connections to invest. These investments are high-risk and challenging to evaluate as many private startups claim to utilize AI in their operations.
The conclusion of this exploration into the AI investment landscape suggests that AI will permeate many businesses, so trying to secure a unique alpha for returns might be challenging unless you narrow your focus. It’s also crucial to be cautious of another potential hype cycle.
The most favorable outcomes will likely emerge in businesses with a service component that AI can readily improve the margin profile of, for example, law firms and accounting firms. Investing in individual companies is complex and might not yield the best results, but if you’re committed to selecting a company, consider tracking ones that are discussing AI in their earnings releases. Monitor their progress in terms of business efficiency, revenue per employee, and improved margins.
The real potential of AI lies in its deflationary nature and ability to enhance efficiency so that fewer people can produce more. It’s perhaps best to invest in AI by employing it in our ventures and businesses.
