Artificial intelligence is on track to be a truly revolutionary technology. Here’s what investors need to know.

The rise of artificial intelligence (AI) has the potential to be one of the most significant developments in human history. Today’s machines are already improving in interpreting data, recognizing patterns and finding more efficient ways to perform tasks. And major tech companies are working hard on breakthroughs that could detract from what has been achieved so far. 

There are huge incentives to achieve and maintain a leadership position in the industry, and investors who back its top players will see huge rewards in the long run. Before we get into individual stocks that have the potential to be the winners among investments in the field of artificial intelligence, it is worth establishing some concepts and trends to better understand why AI has such great potential. 

This will allow you to identify and understand investment opportunities in space.


Artificial intelligence involves the use of algorithms that guide the behavior of the machine to solve problems intelligently or humanly. Within the broader category of AI, there are also several specialized sub-categories.

  • Machine learning

Machine learning is a branch of AI focused on the idea that computer algorithms can recognize patterns and “learn” while continuing to improve the amount of data that is fed. While the broader term “artificial intelligence” refers to systems that can produce intelligent results in a variety of situations, “machine learning” refers to systems that can infer from experience and adapt.

  • Neural networks

Neural networks are algorithms of many densely interconnected processing nodes that have been inspired by the human brain. They are initially defined without specific operating rules, instead, they infer rules and connections through pattern recognition.

  • Deep learning

Deep learning is a sub-category of machine learning that uses neural networks to analyze a set of data across a wide variety of different dimensions, identifying patterns and stacking these patterns on top of each other to create categories that can be used for classification. For example, a deep learning or deep learning artificial intelligence may be able to look at a collection of photographs without receiving further guiding data and identify people as a distinct category from their environment or faces as a distinct part of a person.


While there are no universally standardized means of measuring the value of the AI ​​market or its potential, there are numerous estimates that suggest that enormous value will be created directly and indirectly unlocked with technology. A report by PricewaterhouseCoopers (PwC) estimates that artificial intelligence technology will lead to improvements in labor productivity, product value and consumption that will lead to $ 15.7 trillion in the global annual gross domestic product (GDP) by 2030.

For comparison, the United States is the largest economy in the world and had an annual GDP of about $ 20.5 trillion in 2018, according to the International Monetary Fund. Meanwhile, China had a GDP of around $ 13.4 trillion for the year.

Of course, the PwC forecast takes into account some of the broader impacts of AI, so it’s also advisable to look at narrower projections. A report by MarketsandMarkets estimates that sales generated by hardware, software and services directly related to AI categories including machine learning, natural language processing, context-sensitive computing and computer vision will increase from $ 21.46 billion in 2018 to $ 190.61 billion in 2025 – which represents a compound annual growth rate of 36.6%.

The estimates can be used to get a rough idea of ​​what the future of the broader AI market or its subsections might look like, but the individual projections and figures are likely less important than the big picture painted by the range of estimates suggests. huge growth potential in space. The AI ​​revolution is already underway. Its progression will likely continue to accelerate and have a growing impact. Leading companies in space will see huge rewards if they establish and maintain success in the field.


The functionality of computers has historically relied on predefined instructions given to them. Machines depend on instructions or algorithms to function. The lines of code are assigned to a device or application and, in turn, the machine performs the functions strictly defined by that code. This still describes most of the computer systems that are in use today, but there are examples of machine learning technology already in use.

Here’s a quote from machine learning researcher Pedro Domingos’s acclaimed artificial intelligence book “The Master Algorithm” :

Computers aren’t supposed to be creative; they should do what you tell them. If what you tell them to do is creative, you get machine learning. A learning algorithm is like a master craftsman: each of its productions is different and exquisitely adapted to the customer’s needs. But instead of turning stone into masonry or gold into jewelry, students turn data into algorithms. And the more data they have, the more intricate the algorithms can be.

Machine learning revolves around the accumulation of data that can be inserted into an algorithm to detect patterns and make inferences that will model the evolution of a new algorithm. A machine or algorithm that can improve itself when presented with new data has the potential to continue to evolve and become more and more efficient at a rapid pace. Therefore, companies that establish early leadership in artificial intelligence have the potential to quickly establish advantages that will be difficult for competitors to outrun.


From news displayed in social media feeds to pages returned by a search engine query, the results are typically customized based on previous queries and network activity. AI algorithms are already shaping consumer-level experiences. When you interact with these systems, you are teaching the machines things about you – who you like to talk to, what you want to see and other dimensions. The quality of your user experience largely depends on how a given algorithm has identified relevant data about you and adapted its responses.

  • Main AI trends to keep an eye on

The uses of AI go far beyond search engines and social media platforms. Advances in AI-based technology could have huge implications for the healthcare industry, with the potential for algorithms to dramatically speed up patient history analysis and, in some cases, offer predictive diagnoses.

AI can dramatically improve the quality of human life by recognizing the patterns in the data that are most valuable, and companies at the top of the AI ​​technology hierarchy can deliver outstanding stock market performance if they can transform that opportunity into reality. Paying attention to big trends in the AI ​​space can help investors estimate where the market might end and be in a better position to select the winners in the space. Investors should keep an eye on the technology trends described below and understand which companies are driving them.

  • Governments that collaborate with companies to ensure leadership in AI

The incredible potential of artificial intelligence means that world governments have crucial interests in securing an advantage over geopolitical rivals in space, and advanced artificial intelligence will be very important for cybersecurity at both corporate and government levels. This dynamic is fostering greater collaboration between technology leaders and government agencies and is also causing countries to be more protective of their technology assets and data.

The tensions created by the new technological arms race are evident in the trade war between the United States and China, with the desire to ensure leadership in crucial sectors such as artificial intelligence and the 5G Internet that represent a fundamental background for the disputes between the two countries. . The significance of the global competition for supremacy in artificial intelligence is likely to foster close relationships between governments and leading companies in space. Collaborations between private companies and state entities will have a great impact on the progression of AI-dependent fields such as the Internet of Things and IT security.

  • The boom in the role of data in enhancing the development of artificial intelligence

Valuable data is the key to building and improving artificial intelligence systems. Data can be thought of as the collection of materials for building artificial intelligence systems, while AI is the specific machine that you are trying to build. Not all pieces of material will be useful and huge portions may not be, but having more high-quality material at your disposal will greatly increase the quality of your machine.

Research firm IDC estimates that the total size of all digital data in the world will have seen a tenfold increase from 2017 to 2025. As mobile computing technology becomes more widespread around the world, more and more video ad high definition is uploaded to the network and the prevalence of machine-to-machine communication increases, there will be an explosion of new data that can be fed into artificial intelligence systems to improve algorithms.

  • Advances in computer vision that make great technological leaps

Artificial intelligence-based machine vision technology is at the heart of a wide range of major technology projects, including facial recognition, autonomous vehicles and robotics-driven manufacturing. The vast majority of the new data created over the next decade will come from video and images, which bodes well for progress in these fields.

High-quality cameras and other forms of visual sensor technology are becoming increasingly available thanks to technological improvements and falling costs. Improved hardware for collecting and processing visual data will pave the way for more powerful algorithms that can make better sense of the physical world and generate insights that inform more advanced, machine-driven reactions.


Having a qualitative understanding of artificial intelligence and the potential improvements and market opportunities it could create will get you well on your way to finding quality investments in space, but it is also important to have some quantitative tools at your disposal. Even large companies can be bad investments if you buy their stocks at too high a price, and having an understanding of the following performance and valuation metrics will put you in a better position to select AI stocks with strong return potential. 

  • Earnings growth

When you buy stock in a company, you are buying a share of that company’s assets and assets. This means that the size of the profit generated by a company will tend to be one of the most important factors in determining the performance of your investment. Earnings growth is a comparison of the number of earnings generated over two reporting periods and is calculated by taking the amount that earnings have increased from one period to the next and then dividing that value by earnings from the starting point of the comparison.

Sometimes investing in large projects like AI will mean that a company’s earnings growth slows down or even slips down, but generally, you will want to see that a stock’s earnings are on an upward trajectory. The faster a company increases its earnings, the faster it will generate enough profit to match and exceed the price you bought the stock at, and successful AI projects should eventually turn into a substantial earnings growth catalyst.

  • Sales growth

Sales growth is calculated by selecting two equivalent periods, determining how many sales have increased between periods and then dividing the amount of the increase by the sales value at the starting point of the comparison. For example, if a company had $ 1 billion in revenue in 2018 and $ 1.5 billion in 2019, you would subtract $ 1 billion from $ 1.5 billion to get an increase of $ 500 million. You would then divide the figure of $ 500 million by $ 1 billion to get 0.5 – or a growth rate of 50%.

Companies won’t always disclose specific sales figures for their AI projects. In many cases, the fruits of AI initiatives will be incorporated into multiple business segments of the company. However, it’s a good idea to keep track of sales momentum when investing in the AI ​​space.

If a company presents specific figures for a business segment focused on artificial intelligence, it is something worth paying attention to as it also evaluates the company’s overall sales trends. If the specific sales data of the AI ​​are not disclosed, the evaluation of the general dynamics of revenues should still provide a valid indicator of the demand for the company’s products.


The P / E ratio is one of the most useful and widely used tools for analyzing stock valuations. It is calculated by taking a company’s stock price and dividing it by its earnings over twelve months. The P / E value can be considered as a representation of how many years of profits at that level would be required to generate profits that were added to the share price at the time of purchasing the stock.

Investors can compare the P / E ratio of stock to other companies in the same sector, the average P / E value of that sector or the broader market. A lower P / E value may indicate that a stock is undervalued, but you should also evaluate how quickly a stock’s earnings are increasing because this also determines how quickly the stock will match and exceed the price at which you bought the stock.


Companies that are growing faster will tend to have higher P / E ratios because investors are willing to pay more for a stock if they expect that the profits it is generating will continue to increase at a rapid pace. The PEG ratio measures the relationship between a company’s price-to-earnings multiple and its earnings growth rate and is calculated by dividing the P / E ratio by the percentage rate at which earnings have increased (or is expected that increase) over twelve months.

A PEG ratio of less than one can indicate that a stock is undervalued by the market because it suggests that the rate of earnings growth is not fully reflected in the company’s share price. However, investors also need to keep in mind that earnings can be erratic and that investments in initiatives such as AI can mean that companies see a reduction in profits in the short term to create the potential for greater long-term profits.


Some AI stocks will not be consistently profitable or may have such small profits that the P / E ratio is relatively unreliable as a valuation tool. In these cases, it may be useful to measure the valuation of a company with the amount of revenue it is generating.

The P / S ratio is calculated by dividing a company’s stock price by its sales per share over an annual period. The P / S ratio can also be calculated by dividing the company’s market capitalization by its total sales over an annual period.

The companies that lead the charge to AI

As PricewaterhouseCoopers suggests for $ 15.7 trillion in global economic value generated by the effects of artificial intelligence in 2030, no shortage of companies will benefit from advances in AI technology. As with the Internet, the far-reaching effects of artificial intelligence mean that nearly every industry under the sun will be affected in some way. However, the big winners could be a fairly concentrated group.

The development of AI technology requires capital and resources, and the evolution of the advanced deep learning system depends on access to huge amounts of new data. As a result, the companies at the forefront of this revolutionary technology push tend to be tech giants with large capital and pre-existing business positions that have helped launch their AI initiatives.