Artificial intelligence (AI) is a broad concept which has been around since the 1950s. Some, like Ray Kurzweil, foresee a limitless positive future. Others, like Stephen Hawking, predict general AI to be humanity’s last invention. As investors, we see opportunities in the ‘narrow’ form of AI.
The attention for artificial intelligence has gone through periods of boom, bust, boom, bust, and now boom again. In order to avoid the next bust, it is crucially important to manage expectations. We believe that ‘narrow AI’ is here to stay, and that all other forms are probably still very far away.
There are many forms of AI. Often, a separation is made between weak AI and strong AI, where strong refers to everything a human can do and weak refers to specific tasks. Another, in our view better suited, specification of AI is the following:
Narrow intelligence is used to optimize one certain task or specializes in one specific area. An example is playing chess, or arranging timelines on social media platforms according to your interests. General intelligence would match everything a human can do and super intelligence exceeds general intelligence in that it is superior to the most intelligent benchmark. General intelligence is often seen as the holy grail, super intelligence as the feared and unknown future.
We see the best investment opportunities in artificial narrow intelligence. This is already being used today and advances in data and computing power in combination with a stronger focus on specific applications are important improvements in comparison with earlier AI boom periods.
We focus mainly on machine learning. The potential for this area to grow is very large, given the most recent advances in algorithms and data. Market size ranges from USD mid-teen-billions to USD one hundred and fifty billion by 2025. The vague definition of artificial intelligence leaves room to inflate estimates by means of including robotics or even parts of the car industry. We do not consider the exact size of the potential market to be of interest, we rather focus on how expectations are developing. Judging from that perspective it is clear that AI is hot again.
It has proven very hard to predict progress in AI. Current predictions are probably wrong again. Based on the required computing power and current data limits, it will become very hard to live up to the market’s current high expectations. This doesn’t mean we are downplaying the trend though. Narrow artificial intelligence is used today already and it is impacting the way we diagnose, interact and optimize. We believe in incremental progress from this base onwards. We do not believe in theoretical extremes, however, because the technical requirements for such scenarios are simply non-existent currently nor likely in the coming decade.
We build on previous publications by Steef Bergakker that discuss the impact of business models on integration of new technology. Companies can be categorized as either value chains, value shops or value networks. We argue value chains will likely integrate artificial intelligence into their current processes in order to make these more efficient. Ocado, the online retailer in the UK, is a good example of this, as the company uses AI to optimize all of its logistics.
Value shops on the other hand are the battlefield for AI. Value shops represent specialist goods and services. Often, AI solutions also provide these specialized products, such as translation services. Progress in natural language processing, for example, is a direct threat to today’s translation services. Many more examples of specialized services and goods can be thought of to be replaced by AI in the future.
Whereas we argue AI to be sustaining innovation for value chains, we see it as potentially disruptive for value shops. The final business model, value networks, is the holy grail in terms of disruption potential. Current networking companies, like Google, Facebook and Amazon, can integrate artificial intelligence into their current offering, but we think AI has potential to assist in the creation of new value networks, potentially replacing the current ones.
Winners are networking companies and value shops that focus on artificial intelligence services. Although most companies currently working on artificial intelligence are startups (and university spin-offs), the large technology companies are best positioned. Google, Facebook, IBM, Apple, Yahoo, Microsoft, Amazon, Baidu and Alibaba have large portfolios of artificial intelligence startups. These networks buy up companies that best fit into their current product and services offerings. The one who owns the data and provides ease-of-use is likely to be a long-term winner.
For a public equity investor, it is not possible to invest in unlisted startups and an investment in technology giants will only provide little exposure to the artificial intelligence theme. It is therefore best to consider using a basket approach until true winners stand out. Instead of looking at the companies that eventually provide artificial intelligence services, it is perhaps better at this point in time to look at companies that provide resources used in that process, the so-called ‘shovel suppliers during the gold rush’. Shovels in this case are Graphics Processing Units, processors, chips, sensors, voice recognition, and so on.
Challenged companies are mostly found in the value shop segment, as services offered, such as translation, are being replaced by artificial intelligence solutions. Within value chains we believe companies that do not invest in artificial intelligence are more likely to lose out in the long run.
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