Artificial intelligence (AI) has been a hot topic of discussion for many years now, and for good reason. AI has the potential to revolutionize many industries and aspects of our lives. Recently, the pace of AI innovation has accelerated sharply and easy to use AI tools have started to become readily available to the broader population. Recent breakthroughs have the potential to impact workflows across industries and may have significant impacts on investment portfolios.
AI – What Is It:
There are a lot of different kinds of AI tools being developed as our skill at building new machine learning models increases. In particular, deep learning and large language models have made rapid advancements. Deep learning is a type of machine learning that uses artificial neural networks to learn from data. Neural networks are inspired by the human brain, and they are able to learn complex patterns from data that would be difficult or impossible for traditional machine learning algorithms to learn.
Deep learning has been used to achieve state-of-the-art results in a variety of tasks, including image recognition, speech recognition, and natural language processing.
A similar process is used in the development of large language models (LLMs). These models are able to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. These models have recently burst onto the scene as they are easy to work with and highly interactive. They have many uses for day to day productivity, and show promise in assisting with a broad variety of content generation ranging from helping write code to generating articles or other creative content.
AI Constraints:
There are two primary inputs that act as limitations on the development of new AI models; data and processing power. Both resources are crucial. Data without processing power won’t yield a very accurate model, and processing power with low quality data will yield an AI tool full of biases or inaccuracies.
Processing power is the more straightforward of the two constraints. Bigger data sets or more complex AI set ups require more processing power, which means more chips and more electricity. Not all chips are made equal though – AI computing is substantially more efficient when done on graphics cards like those made by Nvidia, rather than on standard computational processors like those that currently dominate most servers across the world. Nvidia’s recent sharp stock price rise is almost entirely due to their raising guidance around expected graphics card revenues for use in AI computing. While Nvidia is currently the top player in the space, Microsoft
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has committed billions of dollars to help AMD design better chips for AI, and several other companies like Apple
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, Meta, and Alphabet all have internal chip design programs that will likely focus on AI chips as demand in that sector grows.
Data is the trickier constraint that AI models face. While processing power is primarily a question of cost versus quality, data set availability is more nuanced. The internet as a whole is a great database, but it’s very generalized and the quality is often questionable. More specialized data sets may be harder to access or may face privacy concerns. Furthermore, there are broad concerns around how data is presented when training an AI tool – will the data or the training parameters end up with the AI having a bias or tilt or blind spot. For instance, think about the difference in political perspective if the AI was sampling news from Fox News verses MSNBC. Similarly, what if it’s an automated driving AI and it’s trained without sufficient data for severe weather – the output will only be as good as the data input.
Fields AI May Disrupt:
As AI continues to develop, it is likely to disrupt almost every industry. However, some areas we’d like to highlight as being on the front end of the AI curve are:
Healthcare: Deep machine learning is being used to help diagnose cancers and diseases that can be hard to detect. It can ingest more data than an individual can handle and augments the doctor’s knowledge. It is also being used to help researchers find new proteins and compounds that can treat new diseases.
Finance: Automation has been occurring in the financial space for decades, and AI will simply accelerate the process. From proposing trades to building pitch decks to helping monitor risks across portfolios, AI is everywhere, though privacy is a concern here.
Transportation: AI is already being used in transportation to develop self-driving cars and trucks. It is also being used to help supply chains be managed more efficiently, both through improvements in ordering and shipping. It may also start to replace drivers as technology advances.
Education: AI is already being used in education to provide personalized learning experiences, grade papers, and even teach classes. While there are concerns about student use of AI, the potential upside of AI tools to help teachers balance their workloads and accelerate student engagement is huge.
Media and entertainment: AI is already being used in media and entertainment to create everything from news articles to personalized playlists. This space is likely to be one of the most noticeably disrupted by AI in the near future.
How Do You Invest In AI:
There are a lot of angles through which investors can try to ride the AI wave. We see three primary ways AI will impact the investing landscape.
First, there’s direct AI investing. You can buy the shares of companies who are designing and distributing LLMs and other productivity enhancing AI tools. Companies like Microsoft, Alphabet, and Adobe
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have been investing heavily in the space and are rushing to deploy AI into many of their products. Microsoft for example is working on integrating AI into their office suite, while Adobe is working to integrate AI into image editing and image generation. Alphabet and Microsoft are both working to integrate AI into their search engines. The tricky part of investing in AI this way though is that while AI is exciting, it’s also just a small part of what some of these mega software companies do. Investing in these companies isn’t investing in a pure AI play. On the other hand, there are a few companies who are exclusively focused on AI, but most of them don’t have real earnings yet and their share prices have been exceptionally volatile.
Second, you can participate in the shift to AI by investing in the semiconductors that power the processing. Right now it looks like Nvidia is the clear leader in graphics computing and AI computing, and their stock price has reflected that recently, but they will eventually have competition. AMD has experience in the graphics space and is keenly aware of the shift in data center demand towards more AI heavy computing. Intel
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will also be a player, though they appear to be less nimble than Nvidia or AMD. Furthermore, aside from direct AI computing, datacenters will need to spend a lot on connectivity products like those provided by Broadcom and Marvel.
Third, you can look for companies where AI will make substantial improvements to their business model. While AI will likely bring productivity improvements at a lot of levels, the big gains for AI come not just from productivity growth, but from process automation or deep data analysis that improves margins. Framing that another way, the big gains for AI come not just from having employees work more, but from getting better results from your proprietary databases. For example, using AI to make faster and better loan decisions could lead to a bank improving loss ratios or have better capital efficiency. Using it to speed up and improve the underwriting and issuance of insurance contracts could be incredibly lucrative if done properly. We could go on and on with different efficiency scenarios, but the central takeaway is that large high quality data sets and repetitive processes provide opportunities for more AI integration.
Finally, this is just the beginning of the AI wave. We’re going to see a lot of opportunities start coming down the pipeline as AI designers get better at building AI models and as companies get more comfortable with AI capabilities.
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