Will AI become a €1 trillion bubble?

As tech giants pour billions into AI development, investors are questioning whether this massive bet will pay off.

By the CogX R&I team

As tech giants pour billions into AI development, investors are questioning whether this massive bet will pay off.

In tech, there's a long history of boom and bust. From national railways to telecom networks, the story often unfolds in a similar fashion: massive capital investments, promises of transformative change, and the potential for both immense rewards and significant risks for investors. The current AI frenzy is no exception.

While several technologies claim to be 'world-changing', there's no denying that AI technology seems to be in a league of its own— at least according to most tech CEOs and evangelists, who likely view the development of this technology every bit revolutionary as the invention of fire or the steam engine. Regardless of the hype surrounding this technology, there's no escaping the fact that investor FOMO is at an all-time high. And big money is certainly flowing into the sector.

OpenAI, the poster child of this world, is now valued at $80 billion, triple its worth from a year ago. Rivals like Anthropic and Mistral are seeing similar growth. Even Elon Musk's AI venture, xAI, has secured $6 billion to create advanced chatbots.

But as investments soar, so do the costs. Bigger AI models are more expensive to create and run, eating up vast amounts of computing power and energy.

So, how much money will the tech sector need to keep up with the hype? Recent industry estimates suggest a staggering $1 trillion investment in the coming years.

This eye-popping sum is what big tech firms, corporations, and even utility companies plan to spend to support AI. Already, tech behemoths the likes of Microsoft and Google have invested hundreds of billions into building this AI infrastructure. But while all this spending has certainly driven much of the S&P 500’s gains in recent months, all this money has to eventually be recuperated. Failure to do so could lead to a significant market correction or even a broader economic downturn.

The latest earnings season has injected a dose of reality into the market's AI euphoria. 

The market's reaction has been telling. Amazon's stock tumbled nearly 9% following earnings that revealed heavy AI spending with little to show for it. Intel faced an even harsher judgement, with its stock plunging 25% after announcing $10 billion in cost cuts and massive layoffs – a direct result of its AI-related spending spree. Google's parent Alphabet faced similar scepticism a week earlier.

Even the darlings of the AI boom aren't immune to market jitters. Nvidia, the chipmaker at the centre of the AI revolution, saw its $3 trillion market valuation wobble on reports of potential delays in the release of its next AI chip, Blackwell.

The repercussions of this AI frenzy extend far beyond Silicon Valley. In Asia, crucial players in the AI supply chain are feeling the heat. Taiwan Semiconductor Manufacturing Co. (TSMC) recently experienced its worst stock drop in decades, shedding $67 billion in market value in a single day.

Add to this rising geopolitical tensions – particularly between the U.S. and China – and you've got a recipe for some seriously fragile global AI supply chains.

The cost of innovation

According to Adam Sarhan, founder and CEO of 50 Park Investments, "Investors are entering a 'show me' phase, demanding concrete evidence of AI's impact on revenue and productivity." This shift in sentiment indicates that the market is no longer satisfied with promises of future AI dominance — it wants results now.

Analysts at Sequoia Capital estimate that tech companies will need to generate $600 billion in AI-related revenue to justify their current spending levels. This figure underscores the immense pressure on companies to monetize their AI capabilities quickly.

Many AI startups are now facing a classic Silicon Valley challenge: finding compelling applications for their shiny new AI products. Most chatbots, while impressive, haven't yet found mass market appeal, especially in the enterprise AI space.

This disconnect has prompted prominent voices in both finance and tech to raise concerns about the potential for AI hype to outpace reality.

However, it would be premature to dismiss AI entirely. The dot-com crash of the early 2000s, fueled by similar overinvestment and overvaluation, ultimately paved the way for today's tech giants like Google and Meta. AI could follow a similar trajectory, but there's no guarantee that today's AI frontrunners will be tomorrow's success stories.

Nonetheless, many analysts remain cautiously optimistic about AI's long-term potential. The technology's ability to enhance productivity, drive innovation, and solve complex problems is undeniable. The challenge lies in bridging the gap between AI's theoretical potential and its practical, revenue-generating applications.