The AI-Bubble
“Artificial Intelligence (AI) is likely to be either the best or the worst thing to happen to humanity,” says entrepreneur and trillionaire Elon Musk. Musk, the CEO of SpaceX and Tesla, is known for his bold and often controversial statements on the future of technology. He has been a vocal advocate for developing AI, but has also warned about the potential dangers it could pose to humanity if not properly managed. While most discussions focus on AI’s direct social impacts, like automation and job insecurity, the bigger risks lie in its economic and institutional effects on governments and markets.
Recently, massive investments in AI have fueled what many call an “AI bubble,” driven by projected future returns, inflated stock prices, and a gap between the hype and the actual, sustainable profitability of AI technologies and business models. Experts are divided. Some see a bubble forming, while others argue that AI’s tangible utility and the financial strength of tech giants make the boom sustainable. Recent market movements underscore this tension. Investor Michael Burry, famous for predicting the 2008 financial crisis, has taken high-profile short positions against AI giants like Nvidia and Palantir, even as these companies continue to post record-breaking revenues and attract billion-dollar investments from Microsoft, Google, Meta, OpenAI, and major venture capital firms.
Between these companies, there is a growing competition for human capital. Major corporations have begun aggressively poaching AI researchers and engineers from one another, highlighting both the scarcity and the value of expertise. For example, Apple has recruited numerous AI specialists from Google, establishing a new research hub in Zurich, while Meta’s “superintelligence” division has drawn top talent from DeepMind and Scale AI. These moves are backed by staggering compensation packages. Meta’s top AI engineers earn base salaries up to $440,000, while senior researchers at OpenAI can make between $800,000 and $1.3 million annually when including equity and bonuses. Apple’s AI engineers, too, can reach base salaries above $300,000, reflecting a market where talent itself has become an asset class. This scramble for expertise not only concentrates innovation within a handful of dominant firms but also raises barriers for smaller competitors and public institutions.
These patterns mirror the 1990s Dot-com bubble, where companies with no profits received enormous investments and evaluations surrounding the internet’s potential, which resulted in a market collapse in 2000. While that crash resulted in significant financial losses, job cuts, and the failure of many dot-com companies, it nevertheless led to a more cautious approach in the tech industry, with a greater emphasis on profitability and stricter regulations on financial reporting and disclosure to protect investors. Critics of the AI bubble turn to these provisions to defend the investment in the AI market. They argue that AI is already delivering real value across various industries, where investment is coming from large, profitable tech companies with strong balance sheets. The AI industry’s growth is projected to continue for years, suggesting that the boom may not be a bubble but rather a genuine technological revolution. Yet, even if the boom isn’t a bubble, AI-driven investment is reshaping global markets and widening the world’s militant and economic divide.
AI’s Effect on Global Markets
AI-driven investment is heavily concentrated in a few countries, creating profound effects on international markets and economic power structures. In 2025, the United States leads global AI spending with approximately $470 billion, followed by China at $119 billion, the United Kingdom at $28 billion, and countries like Canada, Israel, Germany, and India trailing behind. This concentration gives these countries outsized influence over global trade in AI-related goods and services, including cloud computing, semiconductors, AI software, and high-performance computing resources. Companies and governments in these leading nations are able to set standards, control supply chains, and dictate pricing, while countries with limited investment struggle to compete in these emerging markets.
The imbalance also affects international capital flows. Wealthier nations attract the bulk of AI venture capital and access to AI infrastructure, where expertise remains concentrated in a few economies. Currently, just “100 firms, mainly in the US and China, account for 40% of global corporate research and development (R&D) spending,” accumulating a “market value of around $3 trillion, rivalling the gross domestic product of the whole African continent.” The U.S.’s partnership with Nvidia, which controls 85-90% of the global market for AI chips, gives the U.S. singular leverage over compute access. Similarly, China’s “Digital Silk Road” exports AI technology to more than 80 countries across Africa, Latin America, and Eastern Europe. India and African economies rely on imported computing capacity, where the majority of the networks for AI infrastructure come from companies headquartered in the U.S. or China.
This creates a feedback loop: countries that already dominate global markets receive more investment, strengthen their technological infrastructure, and expand influence, while smaller economies remain technologically dependent and economically marginalized. The result is a reinforced global hierarchy, where AI investment is not only a domestic economic strategy but also a tool for asserting international market dominance and shaping the rules of trade, intellectual property, and technology access.
AI’s Effects on Military Development
Beyond investment, AI’s rapid development has also transformed the global security landscape, intensifying competition between major powers and redefining the nature of warfare. Wealthier nations are investing heavily in AI-enabled defense systems, including autonomous drones, predictive intelligence platforms, cyberwarfare tools, and advanced command-and-control networks. The U.S., through initiatives like the Joint Artificial Intelligence Center (JAIC) and partnerships with OpenAI, integrates AI into both battlefield and intelligence operations, while China’s People’s Liberation Army (PLA) pursues “intelligentized warfare” to fuse AI with military command systems. For example, the U.S. uses programs like Project Maven for real-time target identification; DARPA tests autonomous drone swarms; and the Pentagon increasingly relies on AI-enabled command-and-control systems. Other nations, including Russia, Israel, and the U.K., are also developing AI-driven defense capabilities, but their investments are far smaller, leaving them dependent on alliances or technology imports. Israel’s IDF employs AI systems such as “Gospel” and “Fire Factory” for automated target selection and operational planning and exports AI-enabled drones to India, Azerbaijan, and the UAE, extending its influence. Meanwhile, Russia is experimenting with autonomous ground vehicles (Uran-9) in Syria and AI-assisted cyberwarfare tools used in operations against Ukraine.
This concentration of AI military capacity has significant diplomatic implications. Countries controlling advanced AI capabilities can leverage them to exert influence in international negotiations, peacekeeping, and strategic alliances. For example, a nation with superior autonomous surveillance and cyber capabilities may have greater leverage in trade agreements or conflict mediation. Smaller states or developing countries, especially in Africa, the Middle East, and Southeast Asia, are lacking domestic AI resources, are often forced into strategic dependence, relying on AI technologies from wealthier powers and limiting their ability to act independently in international affairs. Analysts warn that this uneven distribution risks creating a new global hierarchy of influence, where technological superiority translates directly into diplomatic and geopolitical advantage, reinforcing existing inequalities between the Global North and South.
Mitigating Global AI Inequality
These economic and military transformations ultimately converge into a broader global inequality problem where the development of AI is concentrated in a small group of wealthy states. The United States and China dominate AI infrastructure, research, and talent, giving them disproportionate control over markets, trade flows, and international standards. This accelerates their economic growth while limiting opportunities for developing countries, creating a technological and economic divide, and fostering dependency on imported AI systems. The same inequality extends to military and diplomatic power; nations with advanced AI defense systems, including autonomous weapons, cyberwarfare tools, and predictive intelligence platforms, can project influence globally, shaping negotiations, alliances, and conflict outcomes. Smaller or developing countries face strategic dependence, reducing their autonomy in international affairs and reinforcing global hierarchies (Europarl, 2024; Al Jazeera, 2025).
Yet, this trajectory is not inevitable. The AI bubble highlights both the risks of overconcentration and the potential for disproportionate influence by a few nations and firms. Global coordination, equitable investment in infrastructure, open-access AI initiatives, and international standards for ethical deployment can help mitigate disparities. Programs such as the EU Digital Europe Initiative, Horizon Europe, and UN-supported collaborative research provide models for sharing expertise, funding, and technology. Lessons from previous technological revolutions, like the internet and mobile communications, suggest that deliberate policy and international cooperation can prevent concentrated power from solidifying into long-term inequality. If nations act strategically, it is possible to balance innovation with equity, ensuring that AI delivers broad economic, social, and security benefits rather than reinforcing existing hierarchies.
Image source: public domain pictures