Is the AI Bubble Set to Burst the Way Big Data Did?

Enormous expectations and investment in AI technology continue, but a warning has emerged that if this AI bubble bursts, it could cause serious shocks across the economy. Studian stressed that while the ripple effects on the stock and job markets are hard to predict, we need to prepare for the future by analyzing past technology bubble collapses.

The real economic impact of AI development remains uncertain, and the possibility that it follows the path of rapid growth and collapse seen in the dot-com bubble cannot be ruled out, which calls for caution.

The book presents practical guidance Korean companies and individuals need to prepare for the AI age.

Studian called the book a 'Korean version of Dual Brain' and explained that it clearly points out how we should think and act in a work environment being transformed by AI.

There was a 'data omnipotence' faith that big data would provide the answer to every problem, but in reality it failed to deliver the solutions expected and led many companies into strategic failures. The limits of data-driven decision-making were clearly revealed by Nike's failed Direct-to-Consumer (D2C) strategy.

As the insights big data offered failed to translate into real business results, the myth that data would solve everything gradually began to collapse.

But this strategy failed to produce real results and instead dealt the company a heavy blow.

Relying solely on data, Nike neglected its veteran frontline staff, who then left the company. As a result, Nike failed to read the changing fashion trends properly, which became a major cause of the D2C strategy's failure.

This is because massive AI investment is likely to lead to weak actual results.

If companies devalue the expertise of veteran staff on the ground and focus solely on technology investment, they risk a structure where returns are negative relative to technology spending. This suggests that the AI era could repeat the same kind of strategic errors seen in the big data era.

Building data centers and investing in AI-related technology entails enormous costs, which are acting as a key factor worsening the financial structures of Big Tech companies. Critics point out that while it looks dazzling on the surface, the real change AI has brought to our lives so far remains minimal.

Google's case shows that massive AI investment is weighing heavily on corporate cash flow. Even one to two years after the arrival of the AI era, real innovation felt relative to investment is still lacking.

John Donahoe, the CEO who led Nike's data-driven D2C strategy, stepped down in 2024, and his successor Elliott Hill, who returned, is going back to basics, focusing on 'athletes' to restore the company's identity. This is interpreted as an effort to acknowledge the limits of a data-centric strategy and recover the brand's original value.

Stock prices cannot be sustained by expectations alone; they must be backed by earnings. Yet many AI-related companies' stock prices are rising excessively on expectations without earnings, and warnings are emerging that, as with Nike's stock collapse, a sharp decline could follow if the AI bubble bursts.

Investment in AI technology that fails to generate real profits can ultimately lead to a stock price collapse, which offers an important insight for the AI era.

In the AI era, as in the big data era, a gap is forming between excessive expectations for technology and reality.

Many believe AI is omnipotent, but in reality it is a tool for solving specific problems and cannot provide perfect solutions in every domain, and we need to recognize this.

This means AI is processing far more data than humans.

While the volume of quantitative work has exploded, analysts argue that the real innovation people actually feel in their lives from this AI activity remains below 10%. Although AI processes large volumes of work efficiently, its impact on our daily lives and business remains limited.

This phenomenon raises concerns that although AI is advancing while consuming enormous resources, its results may fall short of expectations.

Data centers, the core infrastructure of AI technology, are called 'money-guzzlers' that consume enormous amounts of electricity, water, and capital. This resource consumption is said to cause environmental and regional damage, and they are criticized as a 'black hole' in social and environmental terms.

Because building infrastructure for AI development could threaten sustainable development, a fundamental question arises about the efficiency of AI technology, which delivers low results relative to its huge costs.

That the ultimate output of AI development amounts to turning K-pop into trot is pointed out as a disappointing result given the massive infrastructure investment and expectations. In contrast to how the smartphone revolution fundamentally changed our lives, the analysis holds that AI has yet to bring change of that scale that people can feel.

Just as past IT bubbles such as ubiquitous computing and IoT followed a similar path, AI may also be a bubble inflated by expectations alone; if it fails to improve people's quality of life in practice, the bubble will inevitably deflate, a warning goes.

The metaverse became a representative case of a technology bubble, experiencing a stock price surge and crash. Despite enormous investment and promotion, it was rarely used in actual work, and among adult users it quietly faded away. This shows the typical pattern of bubble collapse that occurs when excessive expectations for a specific technology fail to translate into practical usability and results.

In the AI era, literacy is emerging as a strategic tool more important than ever. Users with low literacy cannot properly judge whether AI-generated output is valuable information or mere 'garbage.'

AI will churn out endless output on human request, but people lack the pace to actually review all of it in the real world and assess its value. Therefore, even in the AI era, human critical thinking and literacy will ultimately be the core competencies for finding meaningful value amid the flood of information AI produces.