Is the AI ​​boom heading for a correction? What Anthropic and Microsoft really show – billions for AI, fewer jobs with AI

Anthropic is worth almost a trillion dollars – but there's a catch to the calculation

AI stocks on a record course: Why even real growth can be overvalued

The AI ​​boom currently paints a picture that seems hardly compatible: Anthropic is growing rapidly and is valued at sums that are exceptional even in Silicon Valley. Microsoft, on the other hand, is investing enormous amounts in data centers, chips, and AI systems – while simultaneously offering voluntary severance packages to around 60,000 employees. This juxtaposition raises a pressing question: Is a new technological revolution underway, or are hopes for future growth already being traded at such high prices that a correction is almost inevitable?

A closer look reveals that the answer isn't simply "bubble" or "no problem." Anthropic is generating real revenue and winning enterprise customers; Microsoft is increasingly integrating AI into its own workflows. At the same time, valuations and long-term investment plans demand exceptionally high growth and productivity gains. The crucial question, therefore, is whether the technology will sustainably create more economic value than its development, computing power, and infrastructure costs. The employment implications are also becoming more concrete: AI doesn't necessarily have to immediately replace entire jobs, thus hindering new hires, redistributing tasks, and downsizing teams. Anthropic and Microsoft thus illustrate both sides of the AI ​​boom—its enormous potential and the risks that arise when expectations far outpace reality.

Anthropic, Microsoft and the fear of the AI ​​bubble: These three risks could slow down the boom

When trillion-dollar valuations meet job cuts, progress becomes a matter of faith

The debate about a potential artificial intelligence bubble has reached a new turning point. On the one hand, Anthropic presents growth rates, revenue forecasts, and valuations that are exceptional even for Silicon Valley, accustomed to large numbers. On the other hand, Microsoft is offering voluntary severance packages to around 60,000 employees while simultaneously investing enormous sums in data centers, chips, cloud capacity, and AI systems. Together, these two factors paint a disturbing picture: capital is being shifted on an unprecedented scale into machines, models, and infrastructure, while human labor increasingly appears as a cost center that can be reduced using the same technology.

The obvious conclusion is that AI euphoria has gotten out of control. But it's not that simple. Anthropic has a real, rapidly growing business, Microsoft is achieving real productivity gains, and the demand for computing power is by no means fabricated. At the same time, valuations, investment plans, and long-term purchase commitments are based on expectations that are only sustainable if AI generates exceptionally high growth and productivity rates for years to come. The crucial question, therefore, is not whether artificial intelligence is economically relevant. It is whether today's price for its anticipated future is still in reasonable proportion to the foreseeable returns.

Anthropic will become a stress test for the entire AI market

Anthropic is far more than just the manufacturer of a successful language model. The company has become a litmus test for how far investors are willing to anticipate future market leadership. In the first half of 2026, Anthropic generated revenue of approximately $16.3 billion. In the same period of the previous year, revenue was only about $1.2 billion. Growth of more than 1,300 percent is not only unusual, but virtually unprecedented for a company already worth billions. Total revenue for 2025 was around $4.6 billion.

These figures initially contradict the notion that it's just a pipe dream. Anthropic doesn't sell a mere vision, but rather models, programming interfaces, enterprise accounts, and increasingly automated development tools. More than 11,900 customers recently spent at least $100,000 each within a twelve-month period. Around 1,900 customers exceeded one million dollars, and approximately 250 even reached ten million dollars. This demonstrates that Claude and the services built upon it are no longer just an experiment in many companies. They are being integrated into software development, knowledge work, customer service, research, data analysis, and process automation.

At the same time, the speed of growth presents investors with a methodological problem. The steeper a curve rises, the more the chosen starting point influences the result. A projected sales rate does not automatically reflect the actual annual revenue. It assumes that the most recently achieved level will be maintained over twelve months. In a young, consumption-driven industry, such a projection can meaningfully depict the dynamics. However, it can also treat an exceptionally strong quarter as if it were already a permanent foundation.

The assessment assumes an almost flawless future

Anthropic was valued at approximately $965 billion in a funding round in May 2026. In the context of its planned IPO, valuations between $1.5 and $2 trillion are circulating. This would propel a company only a few years old directly into the ranks of the world's most valuable corporations. Such a valuation cannot be explained by current earnings, as a stable annual profit has not yet been achieved. It can only be justified if Anthropic quickly becomes a dominant infrastructure and application platform for the global economy.

Based on the projected revenue of the first half of the year, a valuation of $1.5 trillion would correspond to approximately 46 times that revenue. At $2 trillion, it would be more than 60 times the annual revenue. Using a higher current revenue rate as a basis, the ratio becomes more favorable, but remains very ambitious. Furthermore, revenue is not the same as readily available cash. Cloud fees, computing power, research, sales, personnel, security measures, and the expansion of capacity must all be paid from revenue.

Revenue forecasts for 2028 are around $190 to $200 billion. To reach this level, Anthropic would have to more than quadruple its already very high revenue growth rate from spring 2026. This is conceivable if AI agents become a universal layer for digital work and Anthropic maintains a large market share over the long term. However, it is by no means guaranteed. Competition, falling model prices, more efficient open-source systems, customer-specific models, and slowing growth could significantly alter the calculations.

A valuation of this magnitude therefore leaves little room for ordinary business errors. Delays in new models, a price war, security issues, regulatory interventions, or the loss of individual major clients would not only negatively impact a quarter's results. They could call into question the entire valuation logic. This is precisely the typical characteristic of speculative overvaluation: the problem is not the quality of the company, but the perfection already factored into the price.

The record loss looks worse than the business is actually doing

The net loss of approximately $80.7 billion in the first half of 2026 is particularly noteworthy. Viewed in isolation, this figure appears catastrophic. It far exceeds revenue and seems to contradict the notion of an economically viable business. However, a large portion of this loss did not arise from ongoing operations. Approximately $72.4 billion was attributable to valuation changes in convertible financial instruments and related liabilities.

The mechanism is paradoxical: If the assumed company valuation increases, the value of certain liabilities, which can later be converted into shares, also increases on the balance sheet. This revaluation appears as an expense and increases the net loss, even though no corresponding amount of money has flowed out of the company. In this way, a rising company valuation can generate an enormous accounting loss before the IPO. The net loss is therefore real in the accounting sense, but only of limited use in assessing operational performance.

More telling is the operating loss of approximately $693 million in the first half of the year. While this isn't a profit either, it appears significantly less dramatic in relation to $16.3 billion in revenue. In the same period last year, the operating loss was over $2 billion. For the second quarter, the company even reported an operating profit. This demonstrates that Anthropic's core business is fundamentally capable of becoming profitable.

However, correcting this initial impression must not lead to the opposite exaggeration. One-off accounting effects explain the majority of the net loss, but they do not eliminate the underlying economic risks. Anthropic must achieve consistently high gross margins while simultaneously defending its research leadership and meeting enormous infrastructure commitments. Under these conditions, a quarterly profit is not yet proof of a sustainably self-sustaining business model.

High gross margins mask the hunger for computing power

In the first half of 2026, revenues of approximately $16.3 billion were offset by costs of roughly $2.4 billion. This results in a calculated gross margin of about 85 percent. For a software company, this is attractive. It indicates that, after directly attributable operating expenses, a large portion remains to finance research, administration, sales, and profit.

However, for AI companies, the distinction is more complex than with traditional software. A portion of computing costs is directly attributable to customer usage, while another portion covers training, research, capacity reservation, and strategic infrastructure. Anthropic spent approximately $8.9 billion on computing power and infrastructure in the first half of the year, significantly exceeding the previous year's figure. Therefore, the reported gross margin only tells part of the story. The company can simultaneously have a high gross margin and enormous capital and infrastructure requirements.

The central risk lies in the long-term commitments. Depending on the definition, fixed and broader commitments for computing power add up to several hundred billion dollars. Clearly quantified minimum commitments alone amount to more than 400 billion dollars; including additional agreements, the sum rises to well over 500 billion dollars. Such contracts secure scarce capacity and can give Anthropic a strategic advantage. However, they also act as a massive lever for fixed costs.

As long as demand grows faster than obligations, this leverage improves market position. If growth is weaker, the same contracts become a burden. Furthermore, computing capacity is not a timeless asset. Chips become technologically obsolete quickly, new model architectures can change the required computing power, and more efficient systems can drive down prices. What is considered a scarce strategic resource today may be an expensive overcapacity tomorrow.

The growth is real, but its quality remains difficult to measure

Around 80 percent of Anthropic's revenue comes from usage-based models. Customers therefore pay largely based on actual usage. This is generally a positive sign because it indicates real activity rather than just long-term licenses. At the same time, it makes revenue more susceptible to optimization. Companies can reduce consumption through more efficient models, improved input, caching, smaller, specialized models, or their own infrastructure.

International expansion is progressing rapidly. In the first half of the year, approximately $4.5 billion, or nearly 28 percent of revenue, came from abroad. This broadens the demand base and reduces dependence on the US market. Nevertheless, economic concentration remains high. A significant portion of revenue is generated through the marketplaces of Amazon, Google, and Microsoft. These five largest customers account for a substantial share of the revenue. At the same time, some of these platforms are among the most important investors, infrastructure partners, and sales channels.

A particularly striking feature is the extremely high net revenue retention. This indicates that existing customers have multiplied their spending within a short period. In an early growth phase, this can occur due to the transition from a pilot project to widespread implementation. However, this same level of spending cannot be maintained indefinitely. If existing customers were to increase their spending several times over each year, their budgets would quickly become more than economically feasible. Therefore, this metric primarily describes the exceptional expansion phase and not necessarily a stable long-term situation.

The crucial question of quality is how much independent economic benefit is generated for the end customer. If a company purchases AI services for ten million dollars but saves twenty million in costs or generates thirty million in additional revenue, further growth is plausible. If usage consists primarily of experimentation, strategic conformity, or fear of technological backwardness, some demand will normalize. The market has a very precise understanding of the expenditures, but a much less accurate picture of the actual productivity gains achieved.

Microsoft is not cutting 60,000 jobs at once

The second figure fueling suspicions of a bubble also needs to be put into perspective. Microsoft has not decided to lay off 60,000 people immediately. The company has offered approximately 60,000 employees in the US, Canada, the UK, and India the option of voluntary severance. This represents roughly 27 percent of its global workforce of around 223,000. Employees in selected areas, up to and including the vice-presidential level, are eligible to participate.

The difference is economically significant. The figure of 60,000 describes the number of eligible employees, not the expected number of departures. Experience with comparable programs suggests an acceptance rate in the low single digits. Even if only three to five percent of eligible employees accept the offer, roughly 1,800 to 3,000 employees could leave the company. That would be significantly fewer than 60,000, but still a substantial reduction in staff.

In addition, there are already completed reductions. In the summer of 2026, Microsoft eliminated approximately 4,800 positions. The number of employees decreased compared to the previous year. The voluntary nature of the new program therefore does not diminish its strategic importance. Microsoft is creating a tool to selectively reduce its workforce, thin out hierarchical levels, and redirect resources to high-growth areas without immediately triggering a widespread wave of layoffs.

It's noteworthy who is partially exempt. Employees in data centers, security departments, selected sales functions, and other strategically important units are either not included in the program or only partially so, depending on the organization. Conversely, parts of Windows, Xbox, finance, human resources, and AI and Copilot-related units are fully included. This contradicts the simplistic notion that Microsoft is merely replacing routine, non-technical staff with AI. Rather, the company is reviewing its entire organizational structure, including those areas that develop or market AI.

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The alleged super software is a bundle of agents and process redesign

The idea that Microsoft has a single super-software that makes tens of thousands of employees redundant is misleading. In fact, the company combines Microsoft 365 Copilot, internal development tools, Work IQ, Agent 365, Copilot Studio, Dynamics 365, and numerous specialized agents. These systems write text and code, search company knowledge, analyze data, prepare meetings, process operations, and execute actions in business systems within defined permissions.

The greatest productivity gains don't come from a particularly clever chatbot, but from the complete reorganization of workflows. Microsoft, for example, reports having more than 100 specialized agents in selected supply chain processes. These agents investigate changes in demand, compare transportation options, analyze capacities, and can update or cancel orders within defined limits. In some processes, lead times have decreased by up to 75 percent. In one sales division, closing rates increased by 20 percent, while revenue per account manager rose by 9.4 percent.

Such figures are relevant but must be interpreted with caution. They are company-specific data from selected use cases. Successful pilot projects are not automatically representative of the entire corporation. Productivity can also be measured as faster processing, higher quality, greater quantity, or reduced staffing. Depending on the metric used, a different picture emerges.

Nevertheless, Microsoft's approach demonstrates where this development is headed. AI is not only being sold as a product to customers, but also deployed as an internal operating system for knowledge work. When tasks in sales, software development, human resources, finance, and supply chains are completed more quickly, a growing company needs fewer additional employees. Even if AI doesn't initially replace any single position entirely, it can prevent new hires, reduce team sizes, and leave vacant positions unfilled. The resulting employment effect is gradual and statistically more difficult to detect than a direct layoff.

Staff reductions finance machines, but not in a simple exchange ratio

Microsoft plans to invest approximately $175 billion in AI and infrastructure by 2026. Even significant staff reductions can only finance a small portion of this. While leaving the company would result in substantial and lasting savings in salaries, benefits, stock options, office space, and administration, this amount remains limited compared to the capital required for data centers, power connections, network components, and accelerator chips.

The connection is therefore more strategic than purely accounting. Microsoft isn't simply replacing a dollar of wages with a dollar of computing power. The company is changing the capital structure of its business. Variable and organizationally complex personnel costs are being replaced, in part, by substantial fixed infrastructure costs. This infrastructure can be sold to millions of customers via Azure and Copilot, but it's only profitable if utilization and pricing are right.

This represents a shift in entrepreneurial risk. Employees can be hired, transferred, or reduced through natural attrition. Data centers, electricity contracts, and long-term chip orders are significantly less flexible. Companies are thus exchanging some of their personnel risk for capital, technology, and capacity utilization risks. In a boom phase, this exchange improves scalability. In a downturn, it can exacerbate profitability.

For employees, this doesn't mean that human labor will disappear. Rather, activities that define goals, assess exceptions, assume responsibility, cultivate customer relationships, and monitor automated processes will become more valuable. Roles whose value lies primarily in standardized information processing, coordination, or documentation will come under pressure. The transformation will therefore resemble a permanent shift in job profiles rather than a one-off wave of automation.

The real bubble indicator is the capital intensity of the entire system

The four major hyperscalers Amazon, Alphabet, Microsoft, and Meta plan to invest a combined total of approximately $720 to $745 billion by 2026. While the figures are not entirely comparable depending on the accounting method used, the scale is clear. Within a short period, an investment wave has emerged that extends far beyond individual AI labs. It encompasses semiconductors, data centers, power generation, networks, cooling, fiber optics, land, and financing.

Such a wave can be an expression of a historic infrastructure transformation. Railways, electricity, telecommunications, and the internet also required enormous upfront investments before their full economic benefits became apparent. However, it can also lead to overcapacity. Technological revolutions and speculative bubbles are not mutually exclusive. On the contrary, technologies with genuine long-term potential often attract too much capital too early.

Investors face a timing problem. Expenditures are incurred today, while productivity gains are spread over many years. Stock market valuations, however, react immediately to expectations. If profits rise more slowly than hoped, a stock can fall sharply, even if the technology ultimately prevails. A correction would then not be evidence against AI, but rather against the price paid for the future.

The International Monetary Fund now considers the risks to be macroeconomically relevant. According to a stress scenario, an abrupt decline in AI investments due to disappointing returns could significantly weaken global economic output by 2028, hitting the US particularly hard. Conversely, successful implementation could boost global growth for years. This range illustrates the extent to which the global economy is becoming dependent on a technology whose long-term returns are not yet reliably measurable.

Circular money flows make it difficult to see genuine demand

Another reason for skepticism is the industry's close financial ties. Cloud providers invest in AI labs. These labs use a large portion of their capital to purchase computing power from the same cloud providers. Chip companies finance customers or infrastructure projects, who in turn purchase their chips. At the same time, several stakeholders report increasing revenues, order backlogs, or company valuations.

This structure is not automatically fraudulent or economically unsound. Vertical partnerships can share risks, accelerate the development of new markets, and finance scarce capacity. The problem arises when investors treat the resulting revenues as if they stem entirely from independent end-customer demand. Money circulating within a tightly integrated ecosystem does not prove that sufficient willingness to pay exists outside that system.

Anthropic exemplifies this ambivalence. Amazon, Google, and Microsoft are simultaneously investors, infrastructure partners, sales channels, and, in some cases, significant sources of revenue. At times, almost half of Anthropic's revenue was generated through the marketplaces of these major platforms. This gives Anthropic access to enterprise customers and global infrastructure. Simultaneously, it creates dependencies regarding pricing, distribution, financing, and technical capacity.

Therefore, assessing a bubble depends not only on revenue growth but also on the source of the funding. Revenue from independent customers who demonstrably reduce their own costs or generate new income is particularly reliable. Revenue primarily financed through venture capital, minimum purchase agreements, loans, or strategic cross-subsidization is less reliable. The AI ​​market contains both, and this very mix makes a clear diagnosis so difficult.

The comparison with the dot-com bubble is too simplistic, yet it still hits a nerve

The parallels to the year 2000 are obvious. Back then, companies were valued based on reach, growth, and future market dominance, even though stable profits were lacking. Today, revenue rates, model performance, computing power, and expected platform effects dominate. In both cases, a fundamental technology justifies very high investments, while competition forces companies to make ever greater upfront investments.

However, there are important differences. The leading investors in today's boom are predominantly highly profitable corporations with strong balance sheets, established customer relationships, and global infrastructure. Microsoft, Amazon, Alphabet, and Meta can absorb losses or lower returns for significantly longer than many internet companies of the late 1990s. Furthermore, generative AI is already generating substantial revenue and is being used productively in businesses.

This doesn't protect the market from misallocations, however. Even solvent companies can overinvest. Financial strength actually prolongs competition because multiple providers can simultaneously build up enormous capacities without having to give up early. This increases the risk of a temporary imbalance between supply and demand. Falling prices would be good for customers, but could put pressure on the returns of infrastructure operators and model providers.

The most likely parallel, therefore, is not a complete technological collapse. More probable is a phase in which valuations fall, providers disappear or are acquired, and infrastructure continues to be used at lower costs. After the dot-com bubble burst, the internet didn't disappear. Similarly, an AI correction would probably not mean the end of artificial intelligence, but rather the end of the assumption that virtually any leading company can consistently generate exceptional returns.

Three forces determine boom or bust

The first crucial factor is measurable customer benefit. AI must consistently generate more economic value than the costs of models, data, integration, control, and computing power. Isolated success stories are not enough. Broad productivity gains are needed in ordinary companies, including those outside the technology sector. Only when medium-sized businesses, industrial companies, logistics providers, banks, insurers, and public administrations achieve stable returns will the investment wave become a sustainable macroeconomic transformation.

The second driving force is price development. Models are becoming more powerful, while at the same time the cost per computing unit is decreasing. This can cause demand to explode. However, it can also lead to revenues not growing as fast as usage. For Anthropic and other providers, the crucial question is whether efficiency gains remain as higher margins or are passed on to customers in the face of competition. A company can be technologically successful and still achieve lower profits than its valuation suggests.

The third force is financing. As long as stock markets, bond investors, banks, and private lenders are willing to provide capital, companies can secure long-term capacity. Rising interest rates, disappointing results, or individual payment defaults could reverse this cycle. Then not only highly valued laboratories would suffer, but also data center developers, energy projects, chip suppliers, and lenders.

These three forces interact. Falling prices increase adoption but make it more difficult to refinance large investments. Productivity gains boost demand but can simultaneously put pressure on employment and wage growth. Extensive financing accelerates innovation but increases vulnerability to shifts in sentiment. The AI ​​economy is therefore not a typical software cycle, but a complex interplay of technology, capital, and the labor market.

For Europe, the greater risk lies in being on the sidelines

From a European perspective, the American investment wave appears both fascinating and threatening. Europe boasts strong industrial companies, a skilled workforce, and vast amounts of valuable process data. However, it faces a significant dependence on foreign providers for basic models, cloud platforms, and accelerator chips. If Anthropic, Microsoft, and others firmly integrate their platforms into European value chains, a growing share of digital value creation will flow to the USA.

A potential bubble is therefore not a convincing argument for postponing investments. Even if valuations in the US are inflated, the technology remains strategically relevant. However, German companies should not try to replicate the American capital outlay. A selective strategy is more sensible: clearly defined processes, reliable data, measurable profitability, multiple model providers, and contractual control over costs and data access.

The potential is particularly great in industry and logistics. Production planning, maintenance, quality management, purchasing, technical documentation, inventory optimization, and transport control involve many information-intensive work processes. AI can generate significant efficiency gains in these areas without requiring a company to train its own basic model. The prerequisites are clean data, integrated systems, and an organization that restructures decisions and responsibilities.

The labor market needs a realistic adaptation strategy in parallel. It's not enough to offer employees general AI training. Companies must define which tasks will be automated, which will be expanded, and which will be newly created. Further training should focus on specific processes. Those who can professionally monitor an AI agent, assess data quality, and make decisions in exceptional cases gain in value. Those who merely forward standardized information will come under increasing pressure.

A correction would reshuffle the winners and losers

A mild scenario would involve a gradual normalization. Revenue growth for AI providers slows but remains high. Prices fall while usage increases. Hyperscalers extend their investment plans instead of abruptly halting projects. Valuations recover without a drying up of funding. In this case, Anthropic could become a very large and profitable company, even if it doesn't initially maintain a $2 trillion valuation.

In a more severe shakeout scenario, companies would align their AI budgets more closely with demonstrable results. Less successful projects would be terminated, smaller providers would disappear, and the demand for additional computing capacity would grow more slowly. Cloud prices would come under pressure, while long-term offtake agreements would strain the margins of model providers. Large platforms could gain market share because they can absorb losses for longer and acquire weaker competitors.

A crisis scenario would arise if several factors tipped around the edge simultaneously: disappointing productivity, falling prices, rising financing costs, security incidents, and doubts about the solvency of major customers. Investment projects could then be canceled, assets written down, and loans revalued. The consequences would extend beyond the technology sector because energy suppliers, construction companies, semiconductor manufacturers, and financial investors are all involved in its expansion.

The most likely outcome is not one of the pure variants, but rather a mix. Certain applications will deliver high returns, while others will remain expensive experiments. Some data centers will be scarce and valuable, while others will be built in the wrong locations or with outdated technology. Leading models will continue to grow as their valuations fluctuate. The market will not rise or fall uniformly, but will replace its current reckless enthusiasm with a more nuanced distinction between genuine value creation and funded hope.

Anthropic and Microsoft are sending contradictory but honest signals

Anthropic demonstrates that AI can create a multi-billion dollar business in a very short time. The company also proves that enterprise customers are willing to spend significant sums on models and agent tools. At the same time, it reveals the business's extreme dependence on capital, computing power, partners, and future growth. The figures refute both the claim that it's all just hype and the assertion that economic success is already guaranteed.

Microsoft illustrates the other side of the same trend. The company boasts profits, customers, infrastructure, and one of the strongest balance sheets in the world. Yet, it is attempting to streamline its workforce while simultaneously shifting unprecedented capital into AI infrastructure. This isn't a sign of immediate financial distress. It's a sign of how aggressively even a highly profitable company is aligning its organization with the anticipated AI economy.

Voluntary severance packages are therefore neither mere personnel routine nor proof of a fully automated future. They are an early indicator. Companies are beginning to translate productivity promises into staffing plans. This transition will be socially contentious because the benefits may initially accrue to investors, platforms, and highly skilled employees, while other workers bear the costs of adjustment.

It is precisely this combination of rising valuations and declining personnel requirements that makes the debate politically explosive. A technology can increase gross domestic product and yet exacerbate distributional conflicts. It can make companies more productive and simultaneously create insecurity in the labor market. Economic progress does not automatically guarantee social acceptance.

The AI ​​bubble exists primarily in expectations

The objective perspective is this: The AI ​​market is not a pure bubble, but it clearly exhibits bubble-like characteristics in key areas. The technology works, revenues are real, and productivity opportunities are substantial. What seems excessive, however, are the speed at which long-term successes are factored into current valuations, the scale of infrastructure commitments, and the assumption that virtually unlimited demand will lead to permanently high margins.

Anthropic may be an outstanding company and yet, at a valuation of $1.5 to $2 trillion, still be overpriced. Microsoft may indeed become more productive through AI, while simultaneously overly optimistic in its public presentation of the benefits of its systems. Hyperscalers may invest strategically and still collectively create excess capacity. These statements are not contradictory. They illustrate the difference between technological potential and financial return.

The crucial question is whether today's influx of capital translates into a broad boost in productivity. If, in the coming years, AI not only generates text and code but also makes real business processes more reliable, faster, and cheaper, a large portion of the investment can be economically justified. If the savings achieved fall short of the costs for models, integration, control, and infrastructure, a reassessment will follow.

The greatest danger is not that artificial intelligence will suddenly become useless. It lies in a useful technology being burdened with unrealistic expectations of return on investment. Then it won't be AI that bursts, but rather the notion that growth can be permanently exponential, that capital is unlimited, and that every efficiency gain automatically translates into profit. Anthropic and Microsoft fuel concerns about a bubble because they highlight both sides of this contradiction: breathtaking economic opportunities and a price that already demands near-perfect success.

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