The 600 Billion Dollar Question

As of 2024, the AI industry is shaping up to be one of the largest bubbles in history. Since the debut of ChatGPT in 2022, investment enthusiasm for AI has soared past even the levels witnessed during the dot-com bubble of the 1990s. The market is filled with claims of “this time is different,” yet this is the very refrain that has always greeted the start of every bubble.

Overinvestment in AI Infrastructure

One of the most alarming trends today is the massive overinvestment in AI infrastructure. Nvidia has risen to become the world’s most valuable company by market capitalization, and tech giants like Microsoft and Google have poured hundreds of billions of dollars into ventures like Anthropic. As of June 2024, the gap between the AI industry’s revenue and the investments flowing into it has ballooned to approximately $600 billion (about 780 trillion won)—a threefold increase from the $200 billion gap recorded in September 2023.

The calculation is straightforward:

  • Multiply Nvidia’s projected annual revenue by 2 to estimate the total cost of running AI data centers (GPUs account for only half of the total ownership cost, with the remainder coming from power, facilities, backup generators, etc.).
  • Multiply that result by 2 once more to determine the revenue that end users must generate (since companies using cloud services like AWS or Azure also need to turn a profit).

Key Challenges Facing AI Companies

Several pressing challenges confront AI companies today:

  1. Commoditization of GPU Computing:
    Unlike physical infrastructure, GPU computing is increasingly billed on an hourly basis. In contrast to CPU-based cloud services, where monopolies or oligopolies might form, new AI cloud providers continue to enter the market, a trend that will likely drive prices down over time.

  2. Rapid Depreciation of Hardware:
    Nvidia’s next-generation chip, the B100, is expected to deliver 2.5 times the performance of its predecessor, the H100, at a 25% higher price. This development implies that the value of the currently purchased H100 units may plummet significantly within the next three to four years.

  3. Skepticism Over Consumer Value:
    While OpenAI’s annual revenue has increased to $3.4 billion, this figure remains modest compared to the colossal investments made in the AI sector. When you compare the cost of AI services with Netflix’s monthly fee of $15.49 or Spotify’s $11.99, serious questions arise about the actual consumer value that these services can generate.

  4. Shifts in GPU Supply Dynamics:
    The GPU shortage that marked the end of 2023 appears to have been resolved—though this remains a contentious issue—with large cloud providers now amassing substantial GPU inventories. For instance, Microsoft’s large-scale purchases once accounted for 22% of Nvidia’s fourth-quarter revenue.

Revolutionary Potential, But at What Cost?

There is no doubt that AI is a revolutionary technology with enormous potential benefits—from boosting productivity and driving medical innovation to accelerating scientific research. However, these benefits will likely unfold gradually over the long term. The current hype around imminent artificial general intelligence (AGI) and the frenzy to hoard GPUs are signs of irrational exuberance that must be approached with caution.

Lessons from History

History teaches us that every bubble eventually meets a harsh reality. Whether it was the tulip mania of the 1630s in the Netherlands, the South Sea Bubble of 1720, the dot-com boom of the 1990s, or the cryptocurrency frenzy of the 2010s, no speculative bubble has lasted forever. AI is unlikely to be an exception. While companies that truly create lasting value—much like Amazon—might endure the burst, many investors could suffer significant losses in the process.

The Road Ahead

The journey toward an AI-powered future is poised to be long and arduous, filled with both ascents and inevitable declines. The key will be maintaining clear-headed judgment throughout this process. Now, more than ever, it is essential to recall the lessons of past bubbles and avoid the pitfalls of unbridled speculation.

In the end, while the path to a robust AI ecosystem may be challenging, it can also be profoundly rewarding—provided that we learn from history and proceed with rational, measured steps.

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About the Author: Drytree

About the Author: Drytree (Jaehoon Lee) I am a senior researcher specializing in deep tech startups, technology commercialization, and R&BD strategy at a national research institute in South Korea. With a deep curiosity about the rapid evolution of technology and its impact on society, I have been writing under the pen name Drytree since 2021, aiming to make complex technological trends more accessible and foster informed discussions on innovation and policy. My academic background is diverse, spanning Political Science (B.A.), International Politics (M.A.), and currently, a Ph.D. in Technology Management & Entrepreneurship . I have published 13 academic papers in domestic and international journals, covering topics such as patent analysis, technology security, and industrial policy . Books & Publications I have authored several books exploring technology, startups, and geopolitical implications of innovation , including: Elon Musk’s DOGE: The Dawn of a New Economic Exper...
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