Will the artificial intelligence bubble burst?

Hamidreza Mazandarani, a network and artificial intelligence researcher, discusses the artificial intelligence bubble in a special note he wrote for Digiato. He tries to answer the question whether the investments that are currently made in the artificial intelligence industry are beyond its intrinsic value.

The history of technology has experienced an unfortunate pattern many times: the emergence of a new technology or approach creates a bubble of excitement and investment; A bubble that will burst sooner or later. Its famous example is the dot-com bubble in the early 2000s, which caused heavy losses to many companies and people who hoped for the development of the Internet. But will this story be repeated for artificial intelligence?

Before addressing this issue, we must point out that “bubble” is a specialized concept in economics and as a result, we need a quantitative economic analysis (such as this article) to answer the above question accurately. But in this note, we will consider its intuitive meaning by focusing on the technical issues: are the investments currently being made in the AI ​​industry beyond its intrinsic value? Here, investment, beyond the financial aspect, can also include the contribution of artificial intelligence to the public’s attention and take on a social color. Accordingly, if the AI ​​bubble bursts, not only will a lot of capital be wasted, but there will be a global disillusionment that may slow the momentum of this promising technology, albeit for a limited time.

Another point is that artificial intelligence includes a wide range of technologies, each of which has its own potential. In the meantime, basic models have taken the largest share of financial and social investment. Foundational models are those models that are trained by large companies on a huge amount of data to generate text, images, and even videos. In this note, AI refers to the services provided by such models, assuming that classical AI has passed its heyday.

Considering the diversity of capital flows in the economy of artificial intelligence, Andrew Ng, a famous scientist and investor in this field, has proposed three separate axes for analyzing the future of artificial intelligence, which include “application”, “infrastructure for inference” and “infrastructure for training learning models”, which we will review below.

Application: competition in the shadow of elders

At the top tier are application-related investments, which include chatbots, content creation tools, recommender systems, and coding assistants. In this area, Andrew Ng believes that not only is there no bubble, but more investment is needed. Nevertheless, the presented applications should bring added value beyond the basic model. In other words, what scares investors is the provision of the same application by infrastructure companies. A clear example of this is the provision of automatic recording and summarizing of meetings, which caused existential threats to startups in this field.

Also, in non-English-speaking countries such as our own country, investment in models focused on the native language has expanded, which, considering the transferability of knowledge between different languages ​​in large models, this approach has probably met with relative failure. With this account, the flow of investment in this area should be directed in a direction that goes beyond “a thin user interface on a general base model”; For example, entering a space for which there is dedicated data, or creating a different user experience, or integrating with other organizational processes.

Infrastructure for inference: The growing need for intelligent agents

In discussing the infrastructure for inference, i.e. delivering the trained model in the form of a cloud service or on-premise, Andrew Ng states that we are currently more limited by supply than by demand. In the meantime, the most important factor that has created the growth of demand is intelligent agency, in which different tools, like human users, call the models. Among other things, we can mention coding environments called “webcoding”.

With this account, there is still plenty of capacity for investment; But due to the decrease in price per token, there is also a possibility of capital loss. In the meantime, a strong point for countries with energy resources is cost reduction on the supply side, which will be realized with the strategy of smart investment on cheap energy. The prerequisite for such a strategy will be economic stability and correct regulation.

Infrastructure for Education: Technology’s Achilles Heel

Finally, the third area is the development of infrastructure for training models, which has attracted large and concentrated funds. However, in the wake of the boom in open source models, Andrew Ng has a more cautious view of the future of the field. In other words, if a bubble is going to burst, this area will be the most likely place for it to happen. He also raises the concern that if such an event occurs, its negative effects may spread to the previous two areas in a domino way.

Nevertheless, it should be noted that Andrew Ng is considered one of the optimistic figures in this field and it is better to examine different points of view. In fact, many prominent figures, such as Yan Lecan, chief AI officer at Meta, do not have an optimistic view of the current trend, and this could be considered a “self-fulfilling” prediction; This means that the influence of these people may change investment flows. But beyond these, other issues have the potential to influence the future of artificial intelligence.

Hopes and worries

Fortunately, the infrastructure and applications of basic models are not limited to common data such as text, image and video. An interesting example is the analysis of tables and databases with basic models, which Farrokh Shahabi, an Iranian entrepreneur, has recently pointed out as promising. Improvements have also been made in this area, among which Kumo can be mentioned, which provides the possibility of accurate analysis of the mass of organizational information, which will facilitate the entry of new capital.

Various industries also play their part in boosting the artificial intelligence market and creating positive synergies: the network industry for the automatic management of communication networks, the automotive industry for the development of self-driving cars and advanced driver assistance systems, and the pharmaceutical industry for accelerating the process of discovering and designing new drugs. In addition, fields such as finance, energy, industrial production, and agriculture are also gradually taking advantage of the capabilities of artificial intelligence and help to expand the scope of the impact of this technology in various economic ecosystems.

On the other hand, “trust” is the cornerstone of the development of artificial intelligence in the mentioned applications. At the same time, the Edelman Institute’s polling report shows that nearly half of the people in the United States, England, and Germany do not have a positive view of the expansion of the use of artificial intelligence.

This is not good news for investors, and if this lack of trust is not compensated by the right policy at the global level, especially in the field of privacy and property rights, technical progress will not go anywhere. Additionally, concerns about the social and ethical implications of AI, such as automated hiring decisions, or the dissemination of misleading information, could be another obstacle to widespread adoption and development of this technology, at least in the short and medium term.