The OpenAI shock: What the postponed IPO means for your money

Artificial Intelligence: A Trillion-Dollar Trap? The Uncomfortable Secret of AI Stocks

Despite the mega-hype: Why investors could end up being the losers in the AI ​​boom

The surprising withdrawal of OpenAI CEO Sam Altman from an imminent IPO is more than just a Silicon Valley footnote—it marks a potential turning point in the biggest infrastructure cycle of the digital age. While tech giants pump hundreds of billions into data centers, chips, and global energy expansion, a dangerous gap is widening in the financial markets between astronomical valuations and demonstrably real returns. Are we on the cusp of an unprecedented productivity leap, or will we see the fatal pattern of the dot-com bubble repeat itself, where a revolutionary technology changed the world but ultimately bankrupted countless investors? This in-depth analysis sheds light on unresolved security risks, the threat of massive overcapacity, and the harsh economic truth about why AI as a technology can triumph without necessarily benefiting your stock portfolio.

AI boom: between productivity leap and capital destruction

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The withdrawal of OpenAI CEO Sam Altman from a potential 2026 IPO is more than just a personnel decision from Silicon Valley. It impacts a capital market that no longer views artificial intelligence as an isolated technology, but rather as a new economic order. Semiconductor manufacturers, cloud companies, data center operators, energy providers, and software vendors are investing sums that make even previous technology cycles seem small. At the same time, the gap is widening between the expectations of the financial markets and the returns seen so far from many AI applications. This is precisely where the real risk lies: not in the fact that artificial intelligence is meaningless, but in the fact that a genuine technological revolution is being capitalized on too early, too expensively, and in a one-sided manner.

The crucial question, therefore, is not whether AI is here to stay. There is reasonably little doubt about that. Rather, the decisive factor is which companies will generate a sustainable and reasonable return on their invested capital, which will only benefit temporarily from infrastructure development, and how much future growth is already priced into current valuations. A technology can profoundly transform the economy and yet deliver disappointing returns for its investors. Railroads, electricity, telecommunications, and the internet have demonstrated precisely this pattern: the societal benefits were enormous, while numerous investors lost money along the way.

When the clock suddenly brakes

Altman's rejection of an IPO in 2026 resonates so strongly because OpenAI, like few other companies, embodies the current AI cycle. ChatGPT made generative AI accessible to the mass market within a matter of months, triggering a global investment race. When the CEO of this company now cites unresolved security and regulatory issues as the reason for his reluctance, the debate takes on a new dimension. It no longer originates solely from regulators, academics, or skeptical investors, but from the very heart of the development of the most powerful models.

Economically, however, the decision should not be interpreted solely as a security measure. An IPO obligates a company to regular reporting, greater transparency, reliable forecasts, and continuous market evaluation. A private company can more easily negotiate enormous investments, substantial losses, and long-term projects with a small circle of strategic investors. On the stock exchange, by contrast, revenue growth, margins, infrastructure commitments, dependencies on partners, and the actual monetization of user numbers would be subject to much stricter scrutiny. The postponement therefore protects not only against societal pressure but also against a point at which investors would compare the exceptionally high private valuation with publicly verifiable cash flows.

This doesn't mean that the security justification is necessarily a pretext. Both can apply simultaneously. The technology may create real control risks, while the company's structure isn't yet ready for an IPO. This connection is particularly important for investors: security issues are not an ethical question separate from economics. They can slow down development cycles, increase liability risks, delay product launches, trigger regulatory requirements, and thus directly impact revenue, costs, and company value.

The IPO as a stress test

OpenAI was valued at $852 billion following its funding round in March 2026. According to the company, it raised $122 billion in committed capital. Such a valuation is not a neutrally determined market price, but rather the result of a private transaction between a few parties. It reflects the value that investors with substantial capital place on the company under specific contractual terms. However, it offers only limited insight into the price a broad public market would accept, given daily trading, greater transparency, and fluctuating risk appetite.

Private financing rounds often include special rights, liquidation preferences, or strategic motives that complicate a simple transfer to a stock market valuation. A cloud provider, chip manufacturer, or infrastructure partner might invest in an AI company because the investment simultaneously generates demand for its own services or secures access to a strategically important platform. For a typical shareholder, however, the primary factor is the long-term free cash flow attributable to their stake. This shifts the valuation metric from strategic option value to demonstrable economic returns.

With a market capitalization of $852 billion, OpenAI not only needs to grow rapidly, but also generate exceptionally high revenues over many years, control its high computing costs, and develop sustained pricing power. Furthermore, it would have to compete against financially powerful platform companies that don't necessarily treat AI as a standalone profit center. Microsoft, Alphabet, Amazon, and Meta can leverage AI capabilities to boost cloud revenue, advertising, productivity, customer retention, or existing software subscriptions. A pure AI provider, on the other hand, must earn a larger portion of its costs directly through model access, subscriptions, enterprise solutions, or new services.

Security becomes a balance sheet item

Warnings from leading AI developers increasingly concern systems that can independently plan, use tools, execute program code, and act across extended task chains. The more autonomous such agents become, the greater their economic benefits. At the same time, the costs of any errors rise. A text-based model that provides an inaccurate answer usually causes limited damage. In contrast, an agent with access to internal systems, payment processes, development environments, or the open internet can trigger operational, legal, and financial consequences.

This creates a new cost center for companies. They need auditing procedures, access controls, logging, red teaming, independent assessments, human approvals, and robust emergency processes. These measures initially increase costs and can slow down time to market. In the long run, however, they are a prerequisite for scaling. An AI that only functions reliably under idealized laboratory conditions has less economic value than a slightly less powerful system that can be securely integrated into real-world processes.

Security and profitability are therefore not necessarily mutually exclusive. A sound security architecture can build trust, improve insurability, and enable deployment in regulated industries. Banks, insurers, industrial companies, healthcare providers, and public administrations will only widely adopt autonomous systems if responsibilities and control mechanisms are clearly defined. For investors, this entails a significant shift: The winner is no longer solely determined by the best model performance, but rather by the ability to translate performance into controllable and economically viable products.

A coordinated slowdown in model development could improve the security situation, but would be economically difficult to implement. Each individual provider fears losing market share, skilled personnel, and technological leadership if competitors continue to accelerate. This is a classic coordination problem. Voluntary rules only work if they are verifiable and supported by the key players. National regulations, on the other hand, can force companies into different legal systems. International agreements would be more effective, but they encounter geopolitical rivalry and differing security interests.

$852 billion for a bet on the future

OpenAI's valuation encapsulates the logic of the entire AI market. Investors aren't paying for the current state, but for a potential future market structure. In this scenario, a few leading models will become a kind of operating system for knowledge work, software development, research, administration, and digital services. Whoever controls such a platform could capture a significant portion of global value creation. Under this assumption, even a valuation in the high hundreds of billions doesn't necessarily seem irrational.

The counterargument is that models could become interchangeable more quickly than expected. Performance differences between providers fluctuate, open models improve, and enterprise customers can build applications to use multiple models in parallel. Decreasing switching costs limit pricing power. At the same time, technological advances drive down the cost per computing unit, making AI cheaper and more widespread, but not automatically generating higher margins for the model provider. What is positive for the economy as a whole can be economically inconvenient for the producer.

Furthermore, there is a paradoxical relationship between scarcity and commodification. Today, modern chips, data centers, power connections, and top researchers are in short supply. This scarcity supports prices and valuations. However, the massive capacity expansion is intended to eliminate this scarcity. If this succeeds, usage costs will fall and AI will spread more rapidly. At the same time, overcapacity, price competition, and lower returns on infrastructure can emerge. The investment boom thus contains the seeds of its own margin erosion.

A reliable assessment would therefore need to consider several scenarios. In the optimistic scenario, dominant platforms emerge with high recurring revenues. In the moderate scenario, the market grows strongly, but competition and falling prices distribute the benefits primarily to customers. In the pessimistic scenario, security issues, regulation, energy shortages, or disappointing applications slow growth, while long-term infrastructure contracts remain in place. The higher the initial valuation, the less room there is for the moderate or negative scenarios.

The biggest infrastructure cycle of the digital age

The scale of the current expansion is historic. Alphabet, Amazon, Microsoft, and Meta planned to invest a combined total of approximately $725 billion in 2026, according to their updated figures. The corresponding figure for 2025 was around $410 billion. Not every dollar of this is earmarked solely for AI, but data centers, chips, network technology, and energy supply represent by far the most significant drivers. Within a single year, the capital intensity of a sector long considered particularly easy and scalable is thus rising to a level more reminiscent of energy, telecommunications, or heavy industry.

These investments initially generate real growth. Construction companies, semiconductor manufacturers, power producers, grid operators, cooling system providers, fiber optic producers, and specialized financiers all benefit. Regions with available land and power connections attract billion-dollar projects. The boom thus strengthens not only software companies but also a broad physical supply chain. It also explains why the AI ​​boom can temporarily mask overall economic weakness.

The key uncertainty lies in the timing. Infrastructure is being built today, while a large portion of the anticipated revenue is not expected for several years. Data centers require lengthy planning and approval processes. Power grids, power plants, and transformers can be expanded even more slowly. Companies must therefore order capacity before the demand is confirmed. If they wait for clear evidence, they risk falling behind the competition. If they invest too early, they risk unused assets and low returns on investment.

For large corporations, this gamble is more manageable than for highly indebted specialist providers. Hyperscalers have profitable core businesses, large customer bases, and easy access to capital. Nevertheless, their financial strength is not unlimited. Rising depreciation, higher electricity costs, and declining free cash flow can weigh on their valuations, even if revenue and operating profits continue to grow. For suppliers, the risk is different: They benefit greatly from expansion but are particularly vulnerable if orders suddenly decline after a period of overinvestment.

Where growth tips into overcapacity

An investment boom doesn't only become dangerous when the underlying technology fails. It's enough that the created capacity grows faster than the demand for investment. Then, utilization and prices fall, while depreciation, interest, and maintenance costs continue to accrue. This pattern has been observed with fiber optic networks after the dot-com era, with solar panels, in shipping, and in various commodity cycles. The infrastructure remained socially beneficial, but some owners lost money.

With AI, an additional factor comes into play: the uncertain lifespan of the technology. Buildings and power connections can be used for decades, but modern accelerator chips rapidly lose relative performance. If new generations become significantly more efficient, older hardware can become economically obsolete faster than the accounting depreciation schedules anticipate. This increases the risk that reported profits may temporarily underestimate the actual replacement investments required.

On the other hand, rapid efficiency gains can trigger new demand. If AI queries become cheaper, applications emerge that wouldn't be practical at high costs. This so-called rebound effect is well-known from energy and technology markets: Lower unit costs don't necessarily reduce overall consumption, but can multiply usage. Therefore, a simple calculation that more efficient chips automatically require fewer data centers is insufficient.

The critical metric is ultimately the level of paid usage. Billions of requests only have high economic value if customers pay for them directly or if they measurably increase other revenues and reduce costs. Free usage, discounted trial offers, and AI integrated into existing products can significantly increase activity without generating proportional cash flow. Investors should therefore focus less on user numbers and computing power and more on revenue per unit of use, gross margin, customer retention, and return on investment.

The cash flow puts an end to all euphoria

The economic test begins where investments transition into depreciation and ongoing costs. A data center already impacts cash flow during construction. The expense then appears in the profit and loss statement spread over several years. This allows a company to initially report solid operating profits, even though free cash flow declines significantly. With sharply increasing investments, the gap between accounting results and actual available cash grows.

This difference is particularly important in the AI ​​cycle. Large technology companies primarily finance expansion from existing businesses, but some market participants are increasingly relying on bonds, project financing, or private loans. As long as demand, valuations, and willingness to borrow rise, this system appears stable. However, if delays or lower capacity utilization occur, interest payments and repayments continue. The pressure then shifts from the stock market to the credit market.

The private lending sector deserves particular attention. Data centers, energy projects, and specialized operators are sometimes financed there outside of traditional bank balance sheets. This diversifies risks, but doesn't make them invisible. In a downturn, illiquid loans are difficult to value and hard to sell. If collateral loses value and several projects need to be refinanced simultaneously, a sector-specific problem can become a broader burden for funds, insurers, and institutional investors.

A company can be technologically successful and still be a poor investment if the price was too high or the capital requirements were underestimated. Conversely, a moderately growing company can be attractive if it reliably generates free cash flow and is favorably valued. This basic insight is often lost during boom times because investors confuse revenue growth with value creation. However, lasting corporate value only arises when returns, after all necessary investments, exceed the cost of capital.

Why the dot-com comparison is both apt and misleading

The comparison to the dot-com bubble is useful as long as it isn't used mechanically. Back then, the basic premise was correct: the internet transformed commerce, media, communication, and business processes. What was often wrong were the prices, business models, and expectations regarding speed. Many companies disappeared, while the infrastructure and the few eventual winners created enormous value. This is precisely why the statement that AI isn't a bubble because the technology is real is logically inadequate.

Today, however, leading companies differ significantly from many dot-com firms. Microsoft, Alphabet, Amazon, and Meta boast high revenues, profitable core businesses, global platforms, and substantial cash reserves. Their investments are not financed solely through speculative debt. This reduces the immediate risk of a widespread collapse. However, it doesn't prevent share price declines or misallocations. Even an outstanding company can be overpriced, and even a financially strong corporation can invest billions in projects with weak returns.

The greater similarity lies less in the balance sheets of market leaders than in the narrative of inevitable growth. In both phases, a plausible technological development is transformed into a seemingly secure financial forecast. However, several transitions lie between usage, revenue, profit, and shareholder return. Each of these can be weakened by competition, regulation, price pressure, or rising costs.

Furthermore, today's stock market is more concentrated. Large technology companies have significant weightings in broad US indices and, consequently, in many ETFs, retirement savings products, and international portfolios. A decline in just a few companies can therefore significantly impact broad indices, even if the rest of the economy is less affected. This can mislead a broadly diversified investor's risk perception: A fund with hundreds of holdings is not automatically economically balanced if a large portion of its performance depends on the same few AI and platform companies.

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The circular system behind the billions

Another warning sign is the increasing interconnectedness within the AI ​​ecosystem. Chip providers invest in model companies, cloud companies provide capital and computing power, model providers commit to long-term infrastructure purchases, and data center operators finance new facilities based on anticipated large orders. Each individual transaction may be strategically sound. However, collectively, they can make demand and financial strength appear greater than they actually are outside the system.

Such circular relationships are not inherently problematic. Strategic investments have a long tradition in emerging industries. They secure supply chains, share development risks, and accelerate standards. The danger arises when revenues are indirectly financed by capital previously provided by suppliers or partners, or when multiple company valuations are based on the same anticipated end-customer revenues. In such cases, a single euro of future demand counts multiple times as a growth argument in different balance sheets.

For a sound analysis, a distinction must therefore be made between internal ecosystem demand and independent end demand. The crucial factor is how much money companies and consumers will permanently pay from their own budgets for AI services. Computing power subsidized by investor capital can promote technological development, but it is not yet proof of a self-sustaining business model.

In a downturn, such interconnections amplify the correlation. If a major model provider scales back its expansion plans, not only does its own value decline, but cloud partners, chip orders, data center projects, power purchase agreements, and lenders could also be affected. This explains why international financial institutions warn of a synchronized decline in investment. The system is not necessarily unstable, but it is more interdependent than individual company analyses would suggest.

Electricity, networks, and chips set the limits

Artificial intelligence is not a purely virtual industry. It consumes space, semiconductors, copper, water, cooling technology, and above all, electricity. According to the International Energy Agency's central projection, the global electricity consumption of data centers could rise from approximately 485 terawatt-hours in 2025 to around 950 terawatt-hours in 2030. This would correspond to roughly three percent of global electricity demand. Globally, this share is manageable, but locally, the increase could place a heavy burden on grids, permits, and generation capacity.

The bottleneck often lies not in the total amount of energy available, but in its location and timing. A large data center requires a high-performance grid connection, reliable supply, and often predictable long-term pricing. New power lines and substations take years to build. This gives regions with fast permitting, stable grids, and affordable energy a competitive advantage. For operators, access to the power grid can become more valuable than the building itself.

Rising energy costs are changing the competitive landscape. Large providers can secure long-term supply contracts, promote their own generation, and distribute locations internationally. Smaller companies purchase computing power at market prices and thus bear a larger share of the fluctuations. At the same time, political pressure is increasing as data centers compete with households and industry for grid capacity. New levies, connection requirements, or demands for flexible loads could impact profitability.

For investors, this expansion opens up opportunities beyond obvious AI stocks. Network technology, power generation, cooling, transformers, and industrial components can all benefit from the increased demand. However, even here, structural need doesn't guarantee a good return. If too many projects price in the same growth assumption, valuations and capacity will rise faster than the market. The smarter question, therefore, isn't which sector will profit from the AI ​​boom, but rather which company possesses scarce capabilities, pricing power, and a reasonable valuation.

Productivity requires more than one model

The strongest justification for these high investments is the potential for productivity growth. AI can accelerate research, programming, customer service, design, documentation, and administrative processes. It can make knowledge more readily accessible, automatically check variants, and relieve skilled workers of routine tasks. If these effects are widely adopted by companies, they could boost growth, wages, and profits for years to come.

However, operational reality is lagging behind technological demonstrations. The Stanford AI Index 2026 reports that 88 percent of surveyed organizations are using some form of AI. Around 70 percent are using generative AI in at least one business function. This high adoption rate shows that it is no longer a niche topic. However, it does not yet prove a corresponding contribution to profitability.

Company surveys paint a mixed picture. Many users report cost reductions or revenue improvements in specific areas. At the same time, a large majority do not yet see a clear effect on the overall company result. A frequently cited study by the MIT project NANDA even concluded that around 95 percent of the generative AI pilot projects examined showed no measurable impact on profit or loss. This figure should not be misinterpreted as a universal failure rate for all AI projects. It stems from a limited research design and measures visible financial impact, not technical functionality. Nevertheless, it describes a real problem: There is a significant organizational gap between pilot projects and scaled value creation.

The bottleneck is often not the model, but the process. Data is incomplete, responsibilities are unclear, interfaces are missing, employees are not involved, and successes are not measured with reliable key performance indicators. A chatbot alongside an unchanged workflow rarely saves significant money in the long run. Greater effects occur when companies redesign processes, adjust approvals, integrate systems, and continuously monitor results. This takes time and makes the productivity gains less spectacular, but more economically credible.

The real battle takes place in the execution

For the next phase of the AI ​​market, competition is shifting from pure model performance to implementation. Basic models remain important, but their economic value increasingly stems from industry knowledge, proprietary data, sales, integration, and trust. A technically less powerful model can be more successful if it is reliably integrated into an enterprise resource planning (ERP) system, production planning, or a regulated approval process.

This creates opportunities for specialized providers. Industry, logistics, medicine, law, and finance all require solutions that take into account professional regulations, liability, and existing systems. However, specialized providers do not automatically possess lasting protection. Large platforms can take over functions, customers can build their own developments on standard models, and consulting firms can offer similar solutions. A sustainable competitive advantage only emerges when data, process integration, and user experience are difficult to copy.

For established companies, AI can primarily be a tool for defending existing margins. An insurer doesn't need a global AI model to accelerate claims processing. An industrial company can better analyze maintenance data without becoming a software company itself. A logistics provider can optimize routes, capacities, and documents. A significant portion of the overall economic benefits could therefore accrue to users, not the most prominent AI producers.

This opportunity is often underestimated on the stock market. Capital tends to flow preferentially to visible providers of chips, models, and cloud infrastructure. However, when AI becomes a mainstream tool, the benefits will be more widely distributed. Then, the margins of less conspicuous companies may increase, while the technology providers suffer from price competition. Therefore, the biggest technological winners are not necessarily the ones with the highest stock returns.

Not every AI star remains a winner

Nvidia exemplifies the first phase of the boom. The company benefited from a rare combination of powerful hardware, an established software ecosystem, and enormous demand. Such competitive advantages are real. Nevertheless, the future return of the stock depends on how long exceptional growth rates and margins persist and how much of this is already priced into the stock. Competition from cloud companies' own chips, more efficient models, or a pause in investment could slow growth without diminishing Nvidia's technological importance.

The situation is different for software and data analytics providers like Palantir. Here, investors are primarily paying for strong growth, high margins, and the expectation that AI will permanently accelerate demand for data-driven platforms. The risk lies less in physical overcapacity than in valuation. Even minor disappointments in growth or customer development can trigger large share price reactions if expectations are exceptionally high.

Cloud companies have broader business models but bear the brunt of infrastructure costs. They can gain market share while simultaneously diluting their return on equity. Data center operators and utilities benefit from long-term contracts but are dependent on financing costs, construction costs, and regulatory decisions. Small AI companies, on the other hand, can grow rapidly but face pressure from powerful platforms and high computing costs.

A blanket judgment about AI stocks is therefore not very useful. The market encompasses very different business models, balance sheets, and risk profiles. The shared narrative of the AI ​​boom obscures these differences. As the cycle progresses, the divergence between companies is likely to widen. In early phases, many stocks rise together. Later, the quality of cash flow, balance sheet, and competitive advantage will be the deciding factors.

The underestimated concentration in the portfolio

Many investors believe they are sufficiently diversified with a broadly diversified US stock or global ETF. While they may formally own shares in hundreds or thousands of companies, the portfolio can still be heavily dependent on a few technology companies because indices are weighted by market capitalization. If the largest companies perform particularly well, their weighting automatically increases. Past success thus also increases future concentration.

Additional tech, Nasdaq, or AI ETFs amplify this effect. They often contain the same large holdings, just in different weightings. An investor can therefore own several funds and still be heavily invested in Nvidia, Microsoft, Amazon, Alphabet, or Meta. Diversification is not measured by the number of securities, but by the independence of their economic drivers.

This doesn't mean that investors should categorically avoid successful technology companies. A complete exit would be just as one-sided a bet as extreme overweighting. Those who recognize the long-term productivity impact of AI can remain invested in leading companies while simultaneously limiting valuation and concentration risks. The key factors are the investment horizon, risk tolerance, and how heavily a portfolio depends on a single narrative.

Credit-financed speculation is particularly problematic. High volatility is normal for growth stocks. Investors who use borrowed capital or are dependent on short-term liquidity can be forced to sell at the wrong time. For long-term investors, however, not every price decline is a permanent loss. It becomes permanently dangerous primarily when the initial valuation was based on returns that never materialize.

Boring as a rational counter-position

The shift towards supposedly boring companies in the food industry, insurance, or healthcare is no proof that these stocks are automatically safe or cheap. Even defensive companies struggle with cost inflation, price pressure, weak growth, natural disasters, or regulatory burdens. Their advantage lies more in the fact that their returns depend on economic factors other than the AI ​​investment cycle.

Food is still purchased during a recession, but brand-name manufacturers lose market share when consumers switch to cheaper products. Reinsurers can profit from higher prices but bear high risks from natural disasters and major losses. Healthcare companies have structural demand but depend on research success, patents, and government regulation. Defensive industries, therefore, do not replace business analysis.

Such stocks can still be useful as portfolio components because they reduce dependence on technology spending. If AI stocks correct, food manufacturers or insurers don't necessarily have to fall to the same extent. This lower correlation is the real benefit. However, the opposing position shouldn't be chosen out of nostalgia or fear, but according to the same criteria as any other investment: valuation, balance sheet quality, pricing power, return on equity, and sustainable cash flow.

The term "boring" can also be misleading. Insurers, industrial companies, and utilities, in particular, are already using AI. They are therefore potential users of the productivity gains without having to bear the extreme valuations of pure AI providers. A balanced strategy can thus encompass both infrastructure and platform winners as well as profitable users. It doesn't rely on an either-or approach, but rather on different ways of extracting economic value from the technology.

Regulation changes the list of winners

Government regulations will not simply slow down or boost the AI ​​market, but will fundamentally alter its structure. Stricter requirements for testing, documentation, and liability increase fixed costs. Large providers can more easily absorb these costs than smaller competitors. Regulation can therefore improve safety while simultaneously increasing market concentration. For investors, this presents a mixed picture: market leaders are protected, while innovation and price competition may weaken.

Credible oversight could accelerate acceptance in sensitive industries. When companies know which standards apply and how liability is distributed, they are more likely to invest in productive applications. Uncertainty is often more costly than a strict but clear rule. However, conflicting national regulations are problematic, as they fragment global products and multiply compliance costs.

Geopolitics is exacerbating the conflict. High-performance chips, manufacturing facilities, cloud capacity, and power infrastructure are increasingly seen as strategic resources. Export controls and subsidies are impacting supply chains and location decisions. Companies must balance efficiency with security of supply. Additional inventory, regional data centers, and redundant supply chains increase resilience but reduce return on investment.

The politically desired technological sovereignty can create new markets, but it does not automatically lead to globally competitive companies. Subsidized capacity remains economically viable only if it is fully utilized in the long term. Europe has an opportunity to position itself in industrial AI, energy efficiency, data protection, and trustworthy applications. Simply copying American platform strategies would be less convincing given smaller capital markets and other strengths.

What a bursting of the bubble would mean

A potential bursting of the AI ​​bubble would likely not be a single moment in which the technology disappears. A more plausible scenario would be a chain reaction of disappointing returns, reduced investment plans, falling valuations, and tightened financing conditions. Initially, highly valued or unprofitable companies would be hit hardest. Then, orders for chip, construction, and infrastructure suppliers could decline. Delayed data center projects would strain regional investments, energy contracts, and loan portfolios.

The overall economic impact would depend heavily on financing. Because the largest technology companies have profitable core businesses, a collapse like that seen in highly leveraged real estate cycles is not inevitable. Nevertheless, a significant decline in the value of major stocks could weaken consumption through wealth effects, particularly in the US. International indices and funds would then reflect the correction globally.

A downturn would also have productive aspects. Cheaper computing power could enable new applications. Skilled workers and infrastructure would become available to companies that previously couldn't compete. After the dot-com crisis, many providers disappeared, but the networks built beforehand formed a foundation for the next wave of digitalization. A shakeout therefore not only destroys value but also corrects prices and shifts resources to more viable business models.

For the real economy, it is crucial whether companies abandon AI projects due to a lack of funding or continue to leverage already demonstrable productivity gains. The more AI is integrated into profitable processes, the more robust demand remains. The more it relies on subsidized experiments, the sharper the cuts will be. The transition from demonstrations to reliable use is therefore central not only for individual companies but also for the stability of the entire investment cycle.

Between warning signal and crash prophecy

The current situation warrants a clear warning signal, but not a certain crash prediction. Valuations are sometimes challenging, investments are historically high, and interconnections have increased. At the same time, usage is growing rapidly, leading corporations are profitable, and the technology is already generating measurable benefits. Both sides of the debate therefore have valid arguments.

The bullish view assumes that AI, much like the internet in the mid-1990s, is still in its infancy. According to this view, today's investments will appear small in retrospect because virtually every software, machine, and service will eventually incorporate AI capabilities. The skeptical view, on the other hand, emphasizes that even a huge end market doesn't justify arbitrary prices. If capital flows too quickly into the same bottlenecks, future returns will decline.

The most compelling perspective combines both statements. Artificial intelligence is likely to become one of the most important foundational technologies of the coming decades. At the same time, parts of the capital market are experiencing a period of inflated expectations and exceptional concentration. It is therefore possible to be very optimistic about the technology while remaining extremely cautious regarding individual valuations. This apparent contradiction is, in reality, the core of a dispassionate analysis.

Altman's warning signal should therefore be dismissed neither as proof of imminent collapse nor as mere publicity stunt. It shows that technological capability, social control, and economic maturity are not progressing at the same pace. This very asynchronicity is the central risk of the boom. The capital market is already valuing a large part of the potential future, while companies, governments, and infrastructures are still struggling to organize it safely and profitably.

A clear outlook for investors and the economy

The central danger is not that AI doesn't create value. It lies in the fact that the investment calculation only works with very high future revenues, and that many companies are simultaneously anticipating the same future. The more capital flows into chips, data centers, and investments, the higher the necessary cash flow becomes to justify these investments. If this cash flow fails to materialize, the correction could be severe, even though the technology continues to grow.

For companies, this translates into a disciplined priority: AI projects should be selected not based on public attention, but on their process impact. A clearly defined use case with measurable cost, quality, or revenue impact is more valuable than a broad strategy without operational accountability. Data access, integration, governance, and training are not secondary considerations, but rather the actual investment behind the model.

For investors, the rational answer is neither panic selling nor blind euphoria. Crucial factors are price, the quality of the business model, financing, and the overall portfolio concentration. A robust portfolio can participate in AI development without being entirely dependent on it. It can include both producers and users of the technology and consider sectors whose returns follow different cycles.

The AI ​​boom is thus both real and dangerous. The technological progress, rapid dissemination, and the development of a new infrastructure are real. The enormous upfront investments, high valuations, and the assumption that today's market leaders will automatically reap future economic benefits are dangerous. AI will likely transform the economy more profoundly than many skeptics believe. It is equally likely that a significant portion of the capital invested today will not generate the expected returns. The technology could win while numerous investors lose. This very possibility should be the focus of any serious economic assessment.

Xpert.Digital is a data-driven B2B industry hub led by Konrad Wolfenstein . The company acts as an external, quasi-in-house solution for industrial partners, closing operational gaps in marketing, content, and sales – without requiring additional resources on the client side.

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