AI is a bubble (Source: here)
In November 2022, OpenAI launched ChatGPT, the world’s first free-to-use, publicly available Artificial Intelligence (AI) chatbot. Since then, a number of similar AI chatbots have been launched, including Google’s Gemini, Microsoft’s Bing, You.Com, etc. In addition, AI agents - artificial intelligence systems that can act autonomously to achieve specific goals - have also adopted in many other areas of business, including self-driving cars, service evaluation, gaming, and industrial robots.
Anyone who has used an AI agent, such as a chatbot, will agree that AI technology is impressive, perhaps even revolutionary. Still, the same can be said for most new technologies. The key question from an investment perspective is therefore not whether the technology can change they way we do things, but whether it lives up to the hype. Specifically, does the future growth potential from AI-related investments justify the current elevated equity valuations of AI companies?
To answer this question, let us start by examining valuations for AI companies, which are pretty eye-watering. Alphabet, Amazon, Apple, Meta Platforms, Microsoft, Nvidia, and Tesla, AI companies collectively known as the ‘Magnificent Seven’, saw their average share price rise by a whopping 65% in 2024 (see table below).
Company name | Symbol | YTD return (2024) |
Alphabet | GOOGL | 37.4% |
Amazon | AMZN | 50.7% |
Apple | AAPL | 28.8% |
Meta Platforms | META | 78.2% |
Microsoft | MSFT | 19.6% |
Nvidia | NVDA | 177.3% |
Tesla | TSLA | 68.3% |
Year-to-date 2024 returns for AI companies (Source: here)
Take NVIDIA, the chipmaker and advanced AI software producer. NVIDIA currently trades at price, which is 53 times higher than its actual earnings. This compares to a medium price to earnings ratio of 17 for S&P500 companies and a typical range of 20-30. The dramatic rise in their valuations means that ‘Magnificent Seven’ stocks now account for 34.6% of the total value of the S&P500 index, which comprises America’s 500 largest and most important listed companies.
It is not a sign of financial health when a mere seven companies account for more than one third of the total valuation of an index that comprises hundreds of mostly well-run American companies.
Another factor that points to a bubble is that the surge in AI company share prices has been accompanied by extreme cheer-leading by many of the AI companies themselves. The cheerleading appears to have been highly successful, contributing the kind of investor herd-dynamics one usually sees ahead of bursting bubbles.
The industry proponents of AI have been falling over themselves trying to convince investors of AI's massive future potential, even as the big companies still struggle to break even on their existing AI investments. Undeterred, the AI proponents claim that AI will soon perform the same intellectual tasks as any human, including learning, reasoning, adapting, and making collaborative decisions. But wait! Just over the horizon lies the holy grail, when 'Artificial Superintelligence' will improve machine intelligence exponentially. At this stage, it is alleged, machine brains will surpass human intelligence in all domains, operating beyond the limits of all human comprehension. This milestone, many say, will be reached within a decade (Source: here)
There are good reasons to discount the hype of AI insiders, because they have powerful incentives to exaggerate the potential of AI, especially at this very early stage of development of the technology. No one can yet tell what the future for AI holds, but the big AI players all fear they could miss out on some future game-changing moment that sorts the winners from the losers, exactly as happened in the Smartphone revolution, where Apple and Samsung emerged winners, while Nokia and Microsoft lost out. In order to have a shot at the hoped-for yet so far elusive future payday, today’s big AI players are ploughing enormous amounts of money into AI. They are aware that much of this investment is highly speculative and may only pay off in the far distant future, if at all. But they won't tell you.
Now, if only one company invested recklessly and lost then it would not be a major concern, but if an entire industry engages in over-investment then we are almost certainly in a classic bubble. After all, this is exactly what happened in the DotCom and Sub-prime bubbles earlier this century and indeed in every other bubble in the past.
Already now, the extraordinary claims of the AI proponents are being challenged in some quarters. Some label them as misleading, exaggerated, or even outright lies (Source: here). More importantly, the claims made on behalf of AI appear to run counter to well-established long-term empirical evidence showing how research ideas are becoming scarcer and less disruptive (See here). The charts below illustrate these points.
Research productivity is declining (Source: here)
Research findings are becoming less disruptive (Source: here)
So, even as investment in AI piles up and the stock prices of AI companies soar, there are indications that AI fundamentals are running into trouble. One area of particular concern is data, which just happens to be critical to AI growth. AI engines acquire their intelligence by analysing data. Lots of data. All else even, the greater the amount of data, the higher its quality, and the greater its diversity the greater the learning potential of AI engines. However, in order to sustain the momentum in AI performance it is required that AI has access to exponentially more data. And there is now evidence that not just access to but also the quality of data is beginning to constrain AI performance.
First, there is not enough diversity in the available data. The most important source of data for AI engines is human activity on the internet, such as what we take pictures of, what we chat about, what we watch, what we buy, what we upload. Unfortunately, while abundant this type of data only covers a very narrow section of what makes the world go around. For one, it only relates to our own species and it is mostly confined to retail commerce and entertainment of various kinds. This type of data is very promising if you are aiming to use AI for marketing purposes, but it will not get you very far if you are, say, trying to find a cure for cancer.
Second, the available data contains all kinds of biases, with certain types of information over-represented and others under-represented. Take, for example, what people look at on the YouTube. Most people prefer to look at cute kittens over rat pups. Cute animal stuff is therefore heavily over-represented in the data, while disgusting animal stuff, which may be equally if not more important in shaping a correct and balanced understanding of the world can barely be found.
Third, there is a strong skew in the data towards stuff that we can readily understand, hear, touch, smell, taste, and see, in other words, stuff we can relate to. However, the vast majority of the things that drive the world – from micro-organisms and chemical processes to the forces that move the planets in our solar system – occur on a scale that makes it very hard for us to generate data, since they are out of our immediate cognitive range.
As if the data limitations are not a serious enough concern, there are also major but poorly recognised issues with data quality, because so much online data is now distorted by monopolistic business practices. Specifically, what we search for, watch, buy, and interact with on the internet no longer reflects our unconstrained free will. Instead, our consumption and social choices take place on narrowly-defined and deliberately manipulated platforms, where they our choices are anything but free. Thus, if AI engines tap into behavioural data, which has been obtained on manipulated commercial e-platforms what do these data actually reveal? Do they really tell us anything about ourselves, reality, truth, and nature itself? Probably not. Garbage in, garbage out.
AI enthusiasts are quick to point out we may be able to generate the data we need, but this often fails to take economy into account. It costs money to generate data and each successive venture into progressively less data-rich areas requires progressively greater outlays, rapidly undermining the AI investment case. Data limitations can be offset to some extent by the development of better chips as well as enhancements in transformer architecture (neural networks that convert input sequences into an output sequences). Yet, in my opinion, these technological enhancements only get you so far. If the data – the raw material of AI – is in short supply, biased, bad, or even missing entirely then AI will perform accordingly.
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AI is likely to have a bright future in the context of operations closely associated with human activity about which we have abundant and cheap data. When it comes to fundamental breakthroughs across the knowledge frontiers in the realms of science, however, the outlook is far more uncertain. Investors are likely to have to scale back expectations about what AI can deliver, particularly with respect to some of the more outlandish predictions for AI.
Due to the importance of access to good, unbiased data on human activity to feed into AI engines, it is essential that human economic activity can unfold in an inclusive and competitive environment with the widest possible participation. This means policy should aim to counter the current trend towards ever-greater monopoly power in the online economy. One way to do this is to promote Open Transaction Networks and other technologies that introduce competition by design (see here).
Government funding of fundamental research has declined as a share of total research funding (Source: here)
Finally, it is critical to reverse the decline in the government's share of research & development (R&D) spending, which has declining since the 1960s (see chart above). Privately funded R&D has delivered great advances in many areas and must continue, but the focus of private R&D is mainly to develop commercially viable products rather than to gain fundamental insights. If AI is to find application in areas other than just satisfying humanity’s immediate consumption needs then data needs to be generated in areas where it are not commercially viable. And that requires government leadership.
The End
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