Artificial Intelligence, Record-Breaking Investments, Billion-Dollar Delusions, and the Risk of a Tech Bubble

Amid record investments and AI fever, Big Tech is competition for control of global innovation. But behind the euphoria lies the growing risk that concentration will stifle creativity and slow true progress.

Recent weeks have seen a flurry of investments and strategic agreements among major AI players. In early September, OpenAI signed a deal with Oracle estimated at approximately $300 billion for the purchase of computing capacity over the next five years, sending the company's stock skyrocketing. A few days later, Nvidia announced an investment of up to $100 billion in OpenAI to build 10 gigawatt AI data centers, followed by the expansion of the OpenAI-CoreWeave partnership by another $6.5 billion. Meanwhile, Microsoft has signed $33 billion in deals with Nebius and CoreWeave, while Meta has announced a multi-year, $14 billion agreement with the latter. Further underscoring the investment momentum, OpenAI has also signed a deal with chipmaker AMD worth tens of billions of dollars, which will allow it to reduce its current reliance on Nvidia. Finally, Google has partnered with Oracle to bring Gemini AI to the group's cloud platform and will simultaneously invest £5 billion in the UK for research and data centers.

Overall, the investment pipeline exceeds $500 billion, a sign of Big Tech's massive effort to control AI infrastructure and secure a long-term competitive advantage.

But as industry euphoria grew, an analysis published by The Information dampened enthusiasm. According to the report, the profitability margins of the mega-deal between Oracle and OpenAI would actually be very low, as much of the expected revenue would be absorbed by energy, hardware, and maintenance costs. The news fueled skepticism among analysts who see expectations prevailing at the expense of real profits in the current acceleration.

So far, The Information observed, AI has proven profitable primarily for those providing the physical infrastructure: chip and server manufacturers like NVIDIA, AMD, and Dell. "For many others, it's a bottomless pit. We already knew this was true for OpenAI, but a new example is Oracle, a software company whose rapidly growing cloud business is threatening the company's traditionally high profit margins."

According to the publication's respected deputy managing editor, there are still few signs that AI is a profitable business for those who use servers to sell apps or develop AI models. Programming assistants, for example, have proven not to have high margins, and neither do other AI apps currently in circulation or still in development. Peers' comment is clear: "In years to come, we may look back on this period in the tech sector as one in which the entire industry was gripped by a collective delusion."

There is also a growing sense that the entire sector is pushing its economic sustainability to the limit. He warns that the need for capital to build AI infrastructure is already putting pressure on traditional funding sources, reviving tech bubble fears. 

"If a year from now we find ourselves at the point where an AI bubble has burst," in concludes, "this deal may have been one of the first signs." The fear isn't so much of an isolated speculative bubble, but of systemic risk. If AI as a whole were to enter a crisis and sink into one of those periods of stagnation that cyclically afflict it, the entire ecosystem could be dragged down, with significant macroeconomic consequences.

While excitement, financial risk, and investment pressures stream past analysts' monitors, the effects of the AI rush have already begun to ripple downstream. According to Nobel Prize-winning economist, the more money flows into AI, the more a growing number of companies believe they must follow suit.

The stakes are therefore structural, leading to the question of the development model taking shape behind the AI race. And yet, whether an ecosystem dominated by a few global players can foster the development of AI and progress in general or, conversely, paradoxically risks becoming its greatest constraint. Observing the history of technological cycles, a crucial factor in progress emerges: the alternation of phases of openness and stimulation of innovation with others of rationalization and consolidation.

Periods of great technological creativity arise not from the concentration of resources, but from the proliferation of parallel experiments, from the opportunity for many to try different paths, from the calculated risk of failure. Without this adaptive capacity—on the part of businesses, industrial sectors, and institutions—innovation tends to stall. Carl Benedikt Frey, an Oxford economist who studies digital transition and economic development, in his recently published work, "How Progress Ends," foresees the risk that this could happen right now in the United States. While the first discoveries—from transformers to generative AI itself—arose from open experimentation in universities and small laboratories, today everything is in the hands of Big Tech.

Startups are acquired for the talent they employ, not the projects they develop. Few venture to experiment in areas dominated by the big players. Those who control the platforms largely decide where innovation is directed. The institutional context, which at the beginning of the digital revolution favored the diversity of research by funding it through public agencies and protecting it with antitrust policies, has now radically changed. Funding for basic research has shrunk, while acquisitions that would once have been blocked now proceed unhindered.

Here lies the great risk for AI and for innovation in general: when market structures impede the transition from cycles of consolidation to new waves of decentralized discovery, the economy loses its ability to generate the factors that should boost its growth. We find ourselves stuck in an "exploitation" phase—optimization of the known—without the possibility of returning to the "exploration" phase, from which true innovations arise.