A trillion-dollar bet on superintelligence: What Altman, Musk and Co. are hiding from us

Warning about the AI ​​bubble: Why you shouldn't blindly trust the promises of tech bosses

Will money soon be irrelevant? Before the big crash? The true agenda behind Elon Musk's AI utopia

Whether it's Sam Altman, Elon Musk, or Dario Amodei – the most powerful minds in the tech industry are currently outdoing each other with almost utopian predictions about artificial intelligence. From an imminent superintelligence that will eclipse human capabilities on a massive scale, to a world where traditional work and money no longer matter: the visions of these AI prophets increasingly resemble science fiction. But behind the dramatic rhetoric lies a hard-nosed economic calculation. In a market environment that devours astronomical sums of investment and is characterized by extraordinary competition, bold promises are often a strategic necessity to secure investor funding and regulatory approval. While financial markets, researchers, and institutions are already warning of massive overreaction, a crucial question arises: How much of this hype corresponds to groundbreaking reality – and how much is simply clever marketing? The following analysis exposes the economic motives behind the superlatives and shows why a healthy dose of skepticism is essential in today's AI debate.

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Within just a few weeks in the summer of 2026, the most influential figures in the artificial intelligence industry engaged in a remarkable race of superlatives. Sam Altman, head of OpenAI, declared in an interview with Handelsblatt that AI incidents would occur in the future, without specifying how serious they might be. Elon Musk, founder and CEO of xAI and Tesla, went significantly further in an interview with the British business magazine The Economist, claiming that artificial intelligence would surpass the intelligence of all humankind within about five years. Dario Amodei, founder and CEO of Anthropic, seconded this, predicting that by 2027 at the latest, a so-called "land of geniuses" would exist in a data center, equipped with the intellectual capacity of fifty million Nobel laureates. These three statements, however different they may be in detail, follow a common rhetorical pattern that warrants closer economic analysis.

A systematic jumble of terms

Anyone following the debate surrounding artificial intelligence inevitably stumbles upon a Babel of terminology. Artificial General Intelligence, or AGI for short, is generally understood as a system capable of handling intellectual tasks at the level of an average adult human. A superintelligence, on the other hand, would be collectively superior to all of humanity—a qualitatively different state. Amodei himself deliberately avoids the term AGI because he finds it too vague and burdened with science fiction associations, preferring instead to speak of powerful AI. AI researcher Rolf Pfister from Lab42 in Davos succinctly captures the problem when he states that there are several hundred definitions of AGI and that the term is essentially a marketing buzzword introduced to distinguish itself from traditional AI research. This definitional ambiguity is not merely an academic fringe issue but has tangible economic consequences, as it allows every speaker to define the term in a way that best suits their own narrative.

The economic core of optimism

Sam Altman's announcement of future incidents may at first glance appear to be a serious warning, but it also serves a strategic function. Admitting that mistakes will happen allows companies to immunize themselves against future criticism and simultaneously position themselves as responsible actors who take the risks of their technology seriously. Elon Musk's vision of a world of abundance, in which money will no longer matter from 2036 onward, only reveals its full significance in the context of his professional position. As the world's richest person, who also sells humanoid robots through his company Tesla, Musk has a direct commercial interest in ensuring that investors, governments, and the general public believe in the imminent availability of such technologies. Amodei's rhetoric about a land of geniuses, in turn, serves, not least, to position Anthropic as the company best equipped to manage this power responsibly, which can translate directly into capital inflows and regulatory favor.

Why incentives distort the validity of the statement

From an economic perspective, public forecasts from business leaders are never neutral weather predictions, but rather strategic communication in an environment of asymmetric information. While the heads of OpenAI, xAI, and Anthropic have deeper insight into the technological advancements of their own models than external observers, they also have the strongest financial incentives to present these advancements optimistically. Investors, seeking to invest in an environment of enormous valuations and capital demands, are more likely to reward bold visions than cautious assessments. This pattern is familiar from other technology cycles, such as the dot-com era, when CEOs made similarly far-reaching promises about the internet, many of which were fulfilled decades later, or not at all. The crucial difference is that today's investment sums have reached historically unprecedented levels, thus significantly increasing the cost of miscalculations.

The dizzying sums behind the words

The five largest American hyperscalers—Microsoft, Amazon, Alphabet, Meta, and Oracle—will collectively invest more than one trillion US dollars in data centers, chips, and power supplies between 2025 and 2026, according to the Bank for International Settlements. While the expansion of AI infrastructure consumed roughly fourteen percent of these companies' revenue in 2023, this figure is expected to reach nearly fifty percent by 2026, a pace significantly outpacing underlying revenue growth. Morgan Stanley estimates that Amazon, Microsoft, Alphabet, and Meta alone will spend around 630 billion dollars on data centers and chips in 2026, while analysts at Panmure Liberum have calculated that even under the optimistic assumption of virtually negligible operating costs, the return on investment for many hyperscalers could remain negative until 2030. In its 2026 annual report, the Bank for International Settlements explicitly draws parallels to the dot-com bubble and the railway craze of the nineteenth century, warning that such episodes have historically always ended with a sudden reversal of investment and macroeconomic recessions.

Between arms race and overcapacity

A key economic problem of the current AI boom lies in the logic of the arms race between a few large providers. The Bank for International Settlements uses contrast-theoretic models to describe how intense competition tempts companies to invest ever more capital in projects with still uncertain returns, causing the sector's overall net economic surplus to shrink and, in unfavorable scenarios, even become negative. Paul Kedrosky, an investor and research fellow at the Massachusetts Institute of Technology, warns of a massive overproduction of data centers in the United States, largely financed with borrowed money, and states that there is no way to bring this plane back down to earth. At the same time, recent data from Data Center Watch shows that since mid-2024, projects worth over $64 billion have been halted or delayed by local opposition, with $18 billion being completely canceled and $46 billion postponed, mostly due to a lack of transformers, switchgear, and batteries in the supply chains. These physical limitations stand in peculiar contrast to the visions of an era of superintelligence dawning within a few years.

The academic counter-position to the rhetoric of genius

While tech executives speak of imminent breakthroughs, a significant portion of the scientific community remains considerably more cautious. A survey of leading researchers conducted in the spring of 2025 revealed that 76 percent of respondents considered it unlikely or highly unlikely that simply scaling up current AI approaches would lead to true general intelligence. Yann LeCun, one of the most influential AI researchers of our time, has repeatedly disputed that large language models are even the right path to general intelligence. On forecasting platforms where thousands of participants share their predictions, the median expected timeframe for weak general AI is February 2028, and for full-fledged general AI not until April 2033, while former OpenAI co-founder Andrej Karpathy anticipates more like a decade. Even Microsoft CEO Satya Nadella has stated that the term AGI, as defined in contracts, will probably never be achieved in the foreseeable future. This range of opinions within the scientific community itself significantly puts the claims of individual corporate leaders into perspective.

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Amodei's Land of Geniuses: A Fact Check

Dario Amodei's oft-quoted phrase about a nation of geniuses in a data center originated in his October 2024 essay, "Machines of Loving Grace," and has since been further elaborated upon. In a conversation with podcaster Dwarkesh Patel in February 2026, Amodei offered a personal assessment with only a 50 percent probability that this scenario could occur as early as 2026 or 2027, while emphasizing that this assessment could be entirely wrong. It is noteworthy that in his essay "The Adolescence of Technology," Amodei identifies powerful AI as one of the greatest national security risks in a century, without specifying the particular historical event he is referring to. This combination of dramatic warning and simultaneous entrepreneurial leadership in developing the very technology being warned against raises the fundamental question of whether security concerns and business interests can truly be clearly distinguished.

Musk's abundance promise under scrutiny

Elon Musk's statement that money will no longer matter in 2036 deserves particular economic attention because it comes from someone whose personal wealth and entrepreneurial success are inextricably linked to the continuation of the existing economic system. Musk bases this prediction on the expectation that humanoid robots, combined with superior artificial intelligence, will produce a virtually unlimited abundance of goods and services, thereby rendering traditional scarcity economically irrelevant. However, he himself concedes that this forecast could be thwarted by events such as a global thermonuclear war, demonstrating how presuppositional and ultimately speculative the entire construct is. When asked the obvious journalistic question of how his own companies would even generate profits in such a moneyless world, Musk failed to provide a truly convincing answer, instead resorting to the analogy of growing tomatoes as a hobby in a world of plenty.

The silent language of the capital markets

A revealing counter-indicator to the optimistic public pronouncements can be found in the behavior of the capital markets themselves. Investors like the hedge fund Elliott Management have already spoken of a veritable bubble land, while at the same time, a growing number of institutional investors are quietly beginning to prepare for a slowdown in the nearly one trillion dollar spending boom. The Swiss bank UBS estimates that hyperscalers' investment spending will still increase by 76 percent this year, but is likely to grow by only 25 percent next year and by a mere 6 percent in 2028 – a clear sign of waning euphoria. Deutsche Bank is already describing 2026 as the most difficult year yet for the industry, characterized by a triple test of disillusionment, misallocation, and dwindling confidence. These sober financial figures stand in striking contrast to the public pronouncements of company executives, suggesting that internal expectations are considerably more cautious than external communication.

The psychological dimension of technological forecasts

Beyond the purely economic incentive structure, it is also worthwhile to examine the psychological mechanisms that favor such predictions. People at the helm of disruptive technology companies tend to overestimate their own abilities and those of their creations for several reasons. Firstly, daily interaction with rapid technological advances within their own company creates an insider perspective that makes linear extrapolations appear exponential, even though many technological development paths actually follow an S-curve with diminishing marginal returns. Secondly, the media's attention economy rewards precisely the boldest, most sensational statements with the most headlines, creating an incentive for exaggerated predictions that operates independently of actual technical probability. In behavioral economics, this bias can be understood as a form of the overconfidence effect, which is particularly pronounced among founders and CEOs because their careers were only made possible by above-average optimism in earlier development phases.

Real progress despite exaggerated rhetoric

However, it would be wrong to conclude from justified skepticism towards individual forecasts that the underlying technology is meaningless or that its progress is merely marketing. Amodei's assessment that artificial intelligence could automate a significant portion of end-to-end software development within one to two years is based on measurable performance improvements in programming benchmarks such as SWE-Bench Pro, where current models already achieve scores of around 80 percent. The real-world investments in data centers, chips, and energy infrastructure, which could consume more than 500 terawatt-hours of electricity worldwide in 2026—equivalent to about two percent of global electricity consumption—also demonstrate that this is by no means a purely virtual narrative, but rather one of the greatest industrial transformations of our time. The economically appropriate way to deal with this ambivalence is to acknowledge the real technological dynamics without allowing those who directly profit financially from the respective narrative to dictate the time horizon or the extent of the change.

The role of regulation as a reality corrector

Another aspect of the debate that has been underestimated so far concerns the relationship between exponential technological progress and the significantly slower pace of adaptation in regulation, legislation, and procurement processes. Observers point out that models might be available even before the regulatory and societal frameworks that enable their widespread economic application are in place. This so-called diffusion gap between technical feasibility and actual societal penetration acts as a natural brake on overly optimistic time horizons, regardless of how powerful the underlying models actually become. At the same time, the growing local resistance to the construction of new data centers in the United States demonstrates that societal acceptance is not a given but must be actively cultivated, adding an additional dimension of uncertainty to purely technological forecasts.

What follows from this complex situation?

The appropriate response to the predictions of Sam Altman, Elon Musk, and Dario Amodei lies neither in blind faith nor in reflexive rejection, but in a structured, interest-driven evaluation of their statements. Anyone who speaks publicly about their own technology while simultaneously requiring billions in capital inflows for precisely that technology is caught in a structural conflict of interest, which should inherently apply a degree of skepticism to every statement. At the same time, the enormous sums of real-world investment, the progress made in technical benchmarks, and the serious warnings from international institutions such as the Bank for International Settlements demonstrate that the underlying developments should neither be ignored nor downplayed. For companies, investors, and policymakers, this necessitates developing their own scenarios and resilience tests, instead of relying on the self-referential time horizons of those whose business model directly depends on the credibility of those time horizons.

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