More than 60 percent revenue? Purchased demand? How Nvidia fuels its own growth with controversial investments.
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Published on: November 20, 2025 / Updated on: November 20, 2025 – Author: Konrad Wolfenstein
More than 60 percent revenue? Purchased demand? How Nvidia fuels its own growth with controversial investments – Image: Xpert.Digital
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California-based chipmaker Nvidia once again shattered Wall Street expectations in the third quarter of 2025, posting $57 billion in revenue and a year-over-year growth rate of 62 percent. But behind these impressive figures lies a fundamental debate that extends far beyond the usual quarterly analyses. The company's performance, which at the end of October 2025 became the first in history to break the five-trillion-dollar mark, raises critical questions. Is this healthy growth based on real demand, driven by the unstoppable artificial intelligence revolution? Or are we witnessing speculative overheating reminiscent of the excesses of past tech bubbles, artificially fueled by risky, circular financing models?
This extraordinary expansion is inextricably linked to the rise of generative AI since the introduction of ChatGPT. Nvidia GPUs have become indispensable infrastructure for training and running large language models, triggering exponential demand. At the heart of this development are the “hyperscalers”—technology giants like Microsoft, Amazon, Google, and Meta—who, with planned investments exceeding $300 billion by 2025, are the primary drivers of this boom. However, this massive reliance on just four major customers, who now account for 61 percent of revenue, carries significant concentration risks. At the same time, strategic investments in startups that are among Nvidia’s largest customers raise questions about the true nature of this demand. While proponents point to unprecedented profitability, the technological edge provided by architectures like Blackwell, and the firmly established CUDA software ecosystem, prominent critics and fund managers warn of a dangerous bubble that could be worse than the dot-com bubble. The following analysis delves deep into the anatomy of Nvidia's growth, highlighting the key drivers and growing risks, and poses the crucial question: Is digital capitalism built on a solid silicon foundation or on the speculative sands of an impending correction?
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Nvidia's exponential growth in the age of artificial intelligence
When a chip company becomes critical infrastructure of digital capitalism
California-based chipmaker Nvidia achieved $57 billion in revenue in the third quarter of 2025, exceeding Wall Street expectations. The 62 percent year-over-year growth rate is remarkable, but it raises fundamental questions that extend far beyond typical quarterly results. It's not just about how a single company achieved such performance, but also about whether this expansion rests on a solid economic foundation or shows signs of speculative overheating reminiscent of past tech bubbles.
The full extent of this development only becomes apparent when one considers the timeframe. Three years ago, Nvidia's market capitalization was around $400 billion. By the end of October 2025, the company had become the first in history to reach a market value of $5 trillion. This more than twelvefold increase in value within just three years is unprecedented in history. Even the most spectacular rising stars of the dot-com era did not achieve such absolute valuation increases.
This extraordinary development is closely linked to the rapid spread of generative artificial intelligence. Since the launch of ChatGPT in November 2022, the demand for high-performance graphics processors for training and running large language models has grown exponentially. Nvidia is at the heart of this transformation, as the company's GPUs are considered essential for building complex data centers. The key question now is whether this boom is based on fundamental economic mechanisms or whether it masks speculative dynamics reminiscent of past market excesses.
The Anatomy of Growth: Five Key Drivers of the Sales Explosion
Hyperscalers as the primary growth engine
The analysis of revenue drivers reveals a highly concentrated business model. The data center segment generated $51.2 billion in the third quarter, accounting for approximately 90 percent of total revenue. Within this segment, roughly 50 percent is attributable to so-called hyperscalers, i.e., the major cloud providers Microsoft, Amazon, Google, and Meta. These four companies are investing heavily in building infrastructure for artificial intelligence.
The investment appetite of hyperscalers has reached a remarkable level. These companies are planning capital expenditures exceeding $300 billion by 2025, with Amazon leading the way at over $100 billion, followed by Microsoft at $85-93 billion and Google at approximately $75 billion. These sums significantly surpass capital investments of recent years and represent a fundamental infrastructure build-up of unprecedented historical proportions.
However, the focus on these few major customers carries structural risks. Quarterly reports reveal that Nvidia's four largest customers now account for 61 percent of revenue, an increase of 56 percent in the previous quarter. Two unnamed customers alone are responsible for 39 percent of revenue. This extreme dependence on a small number of customers represents a concentration risk, which is highlighted as a critical point in the financial analysis.
Blackwell architecture as a technological catalyst
The second key growth driver is the launch of the new Blackwell chip generation. CEO Jensen Huang explained during the earnings presentation that sales figures for Blackwell chips are exceptionally high and that the processors for data centers are completely sold out. Demand significantly exceeds production capacity, so Nvidia has asked its manufacturing partner TSMC to increase production of three-nanometer wafers by 50 percent.
These supply bottlenecks are a double-edged sword. On the one hand, they demonstrate the structural demand for the most advanced chips. On the other hand, they show that Nvidia, despite its dominant market position, is reaching its capacity limits. TSMC plans to increase its monthly output from the current 100,000-110,000 wafers to 160,000 wafers, with 35,000 wafers per month reserved specifically for Nvidia. However, this production expansion is a process that will take months and will not have its full effect until 2026.
The Blackwell platform is not just a single chip, but a complete ecosystem of processors, network components, and circuitry. This integration gives Nvidia a competitive advantage over rivals who can only offer individual components. The complete system solution enables hyperscalers to operate their data centers more efficiently and maximize performance per dollar invested. At the same time, this strategy tightly binds customers to the Nvidia platform, resulting in a natural lock-in effect.
Product cycle shortening and permanent upgrade cycles
In 2024, Nvidia underwent a fundamental strategic shift, shortening its product cycle from 18 to 24 months to an annual rhythm. This acceleration is remarkable and fundamentally different from previous semiconductor cycles. Following Blackwell, the VeraRubin platform will be launched in the second half of 2026, followed by Rubin Ultra in 2027, and subsequent generations at annual intervals.
This strategy of continuous innovation creates a constant pull on demand. Hyperscalers and enterprise customers face the challenge of regularly modernizing their infrastructure to remain competitive. The technological leaps between generations are substantial. Compared to the previous Hopper generation, Blackwell offers significant improvements in computing power, energy efficiency, and storage connectivity. From the customer's perspective, these advancements justify the high investment costs, as they directly reduce operating costs per compute operation.
However, this strategy also carries risks. Shortening product lifecycles means that hardware loses its relative value more quickly. An H100 chip purchased today will be technologically obsolete in two years, even if it remains functionally usable. This planned obsolescence leads to higher capital turnover rates for customers and increases their dependence on continuous reinvestment. The question of whether these cycles are sustainable in the long term or whether they will lead to investment fatigue remains open.
Strategic partnerships and ecosystem building
The fourth growth driver consists of the extensive strategic partnerships that Nvidia has forged in recent months. In September 2025, the company announced an investment of up to $100 billion in OpenAI to fund the construction of data centers with a total capacity of ten gigawatts. In return, OpenAI commits to using millions of Nvidia GPUs. A similar agreement with Anthropic followed in November, in which Nvidia invests up to $10 billion, while Microsoft contributes an additional $5 billion. Anthropic, in turn, purchases $30 billion worth of computing capacity from Microsoft Azure, powered by Nvidia hardware.
These transactions follow a circular pattern that is increasingly being questioned. Nvidia invests in startups, which in turn buy Nvidia hardware. At the same time, a portion of the investments flows back to Nvidia via cloud providers in the form of chip purchases. This system creates a closed value loop in which capital circulates within a tightly integrated ecosystem. Critics call this artificial demand stimulation, while proponents argue that it is strategic vertical integration that spreads risk among the partners.
Brian Mulberry of Zacks Investment Management succinctly summarized the problem when he explained in an interview with TheStreet that it is becoming increasingly difficult to trace the financial entanglements. The question of who can ultimately claim what share of a dollar of revenue is becoming ever more complex. This lack of transparency makes rational market valuation difficult and opens the door to speculative excesses.
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Software ecosystem and CUDA moat
The fifth, and potentially most sustainable, growth factor is the software ecosystem that Nvidia has built over two decades. Its proprietary programming platform, CUDA, has become the de facto standard for developing AI applications. Over four million developers worldwide use CUDA, and virtually all major AI models have been trained on Nvidia hardware using CUDA software.
This network effect creates significant switching costs. Even if a competitor like AMD or Intel offers technologically equivalent hardware, developers would have to rewrite their entire software stack to use it. Compatibility with existing frameworks like PyTorch and TensorFlow is closely tied to CUDA. Google, with its Tensor Processing Units, and Amazon, with its Trainium chips, are pursuing alternative approaches, but remain confined to their own cloud ecosystems and fail to achieve the cross-platform adoption of CUDA.
This dominance, however, is not entirely secure. Pressure to cut costs is driving hyperscalers to develop their own chips. Google has now launched its fifth generation of TPUs, and the latest Ironwood TPU offers double the energy efficiency and six times the memory capacity compared to its predecessor. These custom solutions are optimized for specific workloads and can be more cost-effective in these scenarios than Nvidia's general-purpose solutions.
The fundamental question: Solid business or speculative bubble?
Arguments for sustainable fundamental strength
Proponents of the sustainability of Nvidia's growth point to several structural factors. The most important is the actual transformation of the economy through artificial intelligence. Unlike previous technology hype such as the metaverse or blockchain, AI is already demonstrating measurable productivity gains in companies. Studies show that organizations using generative AI achieve average returns of $3.70 per dollar invested, with leading implementations reaching returns of as high as $10.30.
The adoption rate in companies underscores this fundamental demand. In 2024, 78 percent of all organizations used AI in at least one business function, an increase of 55 percent from the previous year. Generative AI is used regularly by 71 percent of companies, a significant jump from 65 percent at the beginning of the year. These figures show that AI has moved beyond the experimental phase and is being integrated into the core operational processes of companies.
The financial results support this argument. Nvidia's gross margin is over 73 percent, and net income reached $31.9 billion in the third quarter, representing a net margin of 56 percent. This profitability is exceptional and demonstrates that Nvidia not only generates revenue but also efficiently converts it into profit. Free cash flow exceeds $25 billion per quarter, providing the company with significant financial flexibility for investments, share buybacks, and strategic acquisitions.
Demand visibility extends far into the future. Chief Financial Officer Colette Kress explained during the earnings presentation that Nvidia has visibility into revenues exceeding $500 billion through 2025 and 2026, based on existing contracts and orders for Blackwell and Rubin systems. This long-term planning certainty distinguishes the current situation from previous periods of speculation, in which valuations were based on vague promises of the future.
Analysts consider the company's valuation metrics to be reasonable. With a price-to-earnings ratio of approximately 52 to 53, Nvidia is significantly above the market average of 40, but below the technology sector average of 105. Based on the expected earnings growth rate of over 40 percent, this results in a PEG ratio of 1.34, which suggests a fair valuation.
Warning signs of speculative overheating
Despite these fundamental strengths, warning signs pointing to speculative elements are increasing. The investment bank Goldman Sachs has indicated in several analyses that market valuations in the AI sector may already have priced in all the economic gains of the next few decades. The cumulative increase in value of AI-related companies is reaching $19 trillion, which corresponds to the upper limit of the projected macroeconomic benefits. The increase in value in the semiconductor sector and among private AI model providers alone already exceeds the baseline scenario of $8 trillion.
Goldman Sachs analysts identify two key risks. The first is the aggregation fallacy, where investors extrapolate the impressive growth rates of individual companies across all potential winners. The combined market capitalization of chip designers, model developers, and hyperscalers could exceed the overall market they ultimately have to share. The second risk is that markets tend to overpay for future gains, even when the underlying innovations are real. The parallels to the innovation booms of the 1920s and 1990s are striking, although Goldman Sachs does not explicitly refer to the subsequent crashes of 1929 and 2000.
Hedge funds Elliott Management and Michael Burry's Scion Asset Management have issued more pointed warnings. Elliott, in a letter to investors, described Nvidia as being in bubble territory and expressed skepticism that hyperscalers would continue to buy chips in such large quantities. They argued that AI is overvalued and that many proposed use cases would never be cost-effective, would consume too much energy, or would prove unreliable. Michael Burry, known for his successful bet against the housing market before the 2008 financial crisis, has acquired put options on Nvidia shares, indicating an expectation of falling prices.
The extreme concentration of demand exacerbates these concerns. The fact that 61 percent of revenue comes from just four customers means that a change in investment strategy at a single hyperscaler would have a significant impact on Nvidia's business. Should Microsoft, Amazon, or Google decide to reduce their capital expenditures or focus more on their own chips, this would fundamentally alter the growth dynamics.
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The phenomenon of circular financing
The increasing circularity of financial flows in the AI ecosystem is viewed with particular concern. Nvidia invests in startups like OpenAI, CoreWeave, and Anthropic, which are among Nvidia's largest customers. These startups use the capital to rent computing capacity from cloud providers who also use Nvidia hardware. A portion of the invested capital thus flows back to Nvidia in the form of chip purchases. This closed loop raises the question of whether genuine external demand exists or whether Nvidia is partially financing its own demand.
The example of CoreWeave illustrates this dynamic particularly clearly. Nvidia holds a stake of over five percent in the cloud startup and committed in September 2025 to purchase $6.3 billion worth of cloud services from CoreWeave. This serves as a safeguard to ensure that CoreWeave can utilize its capacity and, in turn, allows the startup to purchase even more Nvidia chips. OpenAI, for its part, has signed contracts with CoreWeave totaling $22.4 billion. In this structure, Nvidia acts simultaneously as a supplier, investor, and customer, which significantly reduces transparency.
Critics argue that this structure resembles a house of cards, where the loss of a single link could jeopardize the entire chain. For example, if OpenAI fails to generate sufficient revenue to meet its obligations to CoreWeave, and CoreWeave, in turn, struggles to service its debts, this could backfire on Nvidia. The fact that these interconnections are not publicly transparent makes a sound risk assessment difficult.
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Return to investment by hyperscalers
A key question for the sustainability of AI investments is whether hyperscalers can actually achieve an adequate return on their massive capital expenditures. The combined spending of the four largest hyperscalers—Amazon, Microsoft, Google, and Meta—is projected to reach $315 billion in 2025, more than thirteen times the 2015 level.
The results so far are mixed. Google's latest quarterly results show that AI capabilities in search and Google Cloud are already generating revenue. Microsoft is also seeing success with its Copilot products in Office 365 and Azure. Meta, on the other hand, is investing heavily in AI without yet establishing clear revenue streams outside of its traditional advertising business. Analysts at Bernstein warned that Meta's grace period for showing investors something on the non-core AI front is almost over.
Amazon CEO Andy Jassy argued that companies' overall spending is increasing even as unit costs decrease because AI is opening up new possibilities that were previously unattainable. This creates an upward momentum in overall spending as companies rush to develop new applications. This explanation is plausible, but it raises the question of when these applications will actually generate profits.
The McKinsey study from 2025 raises further doubts. It documents a fundamental discrepancy between infrastructure investments and actual market volume. In 2024, the industry invested $57 billion in cloud infrastructure to support Large Language Model API services, while the actual market for these services was only $5.6 billion. This ten-to-one ratio is interpreted as an indication of a strategic miscalculation.
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Between technological leadership and speculative overheating – Nvidia caught between AI boom and valuation risk
Structural risks and challenges
Margin pressure due to production complexity
Despite impressive profitability, the first signs of margin pressure are emerging. The gross margin fell to 73.4 percent in the third quarter, below analysts' expectations of 73.7 percent and significantly lower than the 75.7 percent in the previous quarter. This is due to the higher production costs of the more complex Blackwell chips. Manufacturing on TSMC's most advanced three-nanometer process is considerably more expensive than previous generations, and the yield in the initial phase is lower.
Nvidia forecasts an improvement in margins to 75 percent for the fourth quarter, but this depends on the Blackwell platform reaching production maturity and the realization of economies of scale. Should yield fall short of expectations or should further technical challenges arise, this could negatively impact profitability. For investors accustomed to Nvidia's exceptional margins, any sustained decline would be a negative signal.
The annual shortening of product cycles further exacerbates this problem. Each new generation requires extensive research and development, which translates into increased operating expenses. In the third quarter, operating expenses rose by 36 percent year-over-year, raising concerns about the sustainability of profit margins. Nvidia must continuously invest in innovation to maintain its technological edge, which structurally means higher costs.
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Competitive pressure from customer-specific chips
The development of custom chips by hyperscalers poses a long-term threat to Nvidia's dominance. Google has already demonstrated with its TPUs that alternative architectures can be competitive for specific workloads. The latest Ironwood TPU offers twice the energy efficiency and six times the memory capacity compared to the previous generation. Amazon with Trainium and Microsoft with Maia are pursuing similar strategies.
These customized solutions have the advantage of being precisely tailored to the needs of the respective company and can offer long-term cost benefits. Analysts at Kearney predict that silicon solutions developed by hyperscalers could achieve a market share of up to 15 to 20 percent. While Nvidia is likely to remain dominant in the computationally intensive training of large models, the less demanding inference market could increasingly be served by more affordable alternatives.
Meta already uses AMD chips for certain inference tasks, and this diversification is likely to continue. For Nvidia, this means that the company must not only maintain its technological leadership but also adjust its pricing and cost structures to remain competitive. The question is whether it can do this without jeopardizing the exceptional margins it currently enjoys.
Efficiency gains as a dampener on demand
Another challenge, paradoxically, stems from the advancement of AI models themselves. In January 2025, the Chinese company DeepSeek unveiled a language model trained with significantly less computing power than comparable Western models. If DeepSeek's claims are true, this would mean that future AI developments would no longer require the same massive GPU clusters currently considered necessary.
These efficiency gains could dampen demand for high-end GPUs. If models with less hardware can achieve similar performance, the incentive to constantly upgrade to the latest generation decreases. Nvidia argues that efficiency improvements have historically always led to higher overall demand, according to the Jevons Paradox, which states that lower costs per unit lead to higher overall utilization. This argument is plausible, but it assumes there are unlimited new use cases that can absorb the freed-up capacity.
The reality is likely more complex. While efficiency gains at the individual chip level can indeed lead to increased demand, saturation could occur at the level of entire data centers once basic infrastructure needs are met. The question of whether we are in a phase of exponential growth or a temporary build-up phase is crucial for the long-term evaluation of Nvidia's business model.
Geopolitical risks and China exclusion
Nvidia's growth is occurring without contributions from the Chinese market, which was once a major revenue driver. US export restrictions prevent the sale of advanced chips to China, and countermeasures by the Chinese government have virtually halted the business. Chief Financial Officer Colette Kress stated that Nvidia expects zero revenue from its Chinese data center business for the fourth quarter.
This situation presents both an opportunity and a risk for Nvidia. On the one hand, it demonstrates that the company can achieve exceptional growth even without China, underscoring the strength of demand in Western markets. On the other hand, China remains the world's second-largest technology market, and its long-term exclusion represents lost revenue potential. Should geopolitical tensions ease, China could once again emerge as a growth market. Conversely, further escalation could also affect other markets.
Chinese competitors are developing their own AI chips to become independent of Western suppliers. Huawei is working on its own solutions, and the aforementioned DeepSeek trains its models on Huawei hardware. Should China catch up technologically, this could not only permanently close off the Chinese market but also create global competitive pressure if Chinese chips enter the world market.
Accounting valuation issues and depreciation practices
A more subtle, but potentially significant discussion concerns hyperscalers' accounting practices for depreciating their GPU investments. Michael Burry has publicly warned that hyperscalers could be artificially inflating their results by extending the depreciation periods of their server and network assets. Meta, for example, increased the useful life from five to five and a half years, which in the first nine months of 2025 alone reduced depreciation costs by $2.29 billion and increased profits by $1.96 billion.
The justification for longer depreciation periods lies in the actual usability of GPUs across multiple generations. While new Blackwell chips are optimal for training the most powerful models, older H100 or A100 chips can still be used effectively for less demanding inference tasks. This cascading of hardware through different usage levels can indeed extend its economic lifespan.
Critics argue, however, that a two-year depreciation period would be more realistic given Nvidia's annual product cycle. With a new generation launching every year, older chips lose relative value more rapidly. The discrepancy between technological obsolescence and accounting depreciation could lead to an overvaluation of assets on hyperscalers' balance sheets, which would ultimately reflect badly on Nvidia if these customers were forced to reduce their investments.
Nvidia between AI boom and valuation risk – future or overheating? The truth behind Nvidia's record valuation
The tightrope walk between reality and speculation
The analysis shows that Nvidia's growth is based on a combination of fundamental drivers and speculative elements. The fundamental factors are impressive. The actual transformation of the economy through artificial intelligence, the measurable productivity gains in companies, Nvidia's own high profitability, and the long-term demand visibility all point to a solid business model. Its dominance in the software ecosystem through CUDA and its technological leadership in the most advanced chips create high barriers to entry for competitors.
At the same time, the speculative warning signs are unmistakable. The extreme increase in valuations within three years, the concentration on a few large customers, the circular financing structures, the discrepancy between infrastructure investments and actual revenues in the AI services market, and the warnings from established investors like Goldman Sachs and Elliott Management all deserve serious attention. The parallels to previous tech bubbles, where fundamental innovations were real but valuations nevertheless collapsed, should not be ignored.
The key question is whether the hyperscalers can translate their massive investments into revenue and profits within a reasonable timeframe. If AI does indeed fundamentally boost the productivity of the global economy over the next decade, then today's investments are justified. However, if practical applications prove more limited than anticipated, or if efficiency gains materialize faster than new demand emerges, then a correction could be inevitable.
Scenarios for the coming years
The optimistic scenario envisions a continuous diffusion of AI into virtually all sectors of the economy. Autonomous systems, personalized medicine, scientific research, robotic manufacturing, and countless other applications create a sustained high demand for computing power. In this scenario, today's investments are fully justified, and Nvidia remains the central infrastructure company of the AI era. The five trillion dollar valuation proves, in retrospect, to be appropriate or even conservative.
The moderate scenario assumes a normalization of growth. The phase of explosive infrastructure development will end within the next two to three years, once the basic capacity is in place. Afterward, growth will slow to a still robust, but no longer exceptional, level. Competitors will gain market share in certain segments, and Nvidia's margins will normalize. The valuation will adjust to more realistic growth expectations, leading to sideways movement or a moderate correction.
The pessimistic scenario involves a significant disappointment of expectations. Practical applications of AI fall short of promises, or efficiency gains reduce hardware requirements faster than new use cases emerge. Hyperscalers curtail their investments to demonstrate profitability, and circular financing structures collapse. In this scenario, Nvidia's share price could experience a correction similar to that of other technology stocks in previous bubble phases, potentially falling 50 to 70 percent from its highs.
The truth likely lies somewhere between these extremes. The AI revolution is real and will fundamentally change the economy. At the same time, current valuations are ambitious and leave little room for disappointment. Investors should be aware that with Nvidia, they are not just investing in a technology company, but in a bet on the speed and scope of the AI transformation of the global economy.
Impact on the broader economy
Regardless of the specific outcome for Nvidia, current developments have important implications for the broader economy. Massive capital investments in AI infrastructure are fundamentally changing resource allocation. Capital is flowing into data centers, semiconductor manufacturing, and energy infrastructure, potentially disadvantaging other sectors of the economy. If these investments pay off, a new productivity cycle will emerge. If not, significant resources will have been tied up in infrastructure that is not being fully utilized.
The concentration of value creation in the hands of a few companies also raises socio-political questions. Nvidia, along with the hyperscalers, controls the critical infrastructure for the development of artificial intelligence. This concentration of power could become problematic in the long term, especially if AI is indeed as transformative as expected. Questions of regulation, competition, and democratic control over this infrastructure will gain importance in the coming years.
This development poses a strategic challenge for the German and European economies. The leading AI companies are predominantly American, with China as the second major player. Europe risks falling behind in this critical technology, which could result in long-term competitive disadvantages. Dependence on American hardware and software for AI applications is a structural risk that requires political action.
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AI market drives Nvidia to record valuation – will the trend continue?
Nvidia's 62 percent revenue increase in the third quarter of 2025 is the result of an exceptional combination of technological innovation, structural shifts in demand, and astute strategic positioning. The company has successfully established itself as an indispensable infrastructure provider for the AI era. The combination of hardware dominance, a robust software ecosystem, and strategic partnerships creates high barriers to entry and justifies a premium valuation.
At the same time, the speculative elements are undeniable. The extreme increase in valuations, the circular financing structures, the customer concentration, and the warnings from established market participants necessitate a nuanced analysis. The question is not whether artificial intelligence will transform the economy, but whether today's valuations already anticipate all future profits.
Nvidia's business is fundamentally sound financially, but its valuation leaves little room for disappointment. Investors should be aware of the risks and understand that they are not just investing in a single company, but in a broader thesis about the future of the digital economy. The coming years will show whether this thesis holds true to the extent implied by current valuations, or whether a correction is inevitable.
For a balanced assessment, characterizing Nvidia as a hybrid is the most accurate description. Nvidia's business is based on real, fundamental drivers, but is overlaid with speculative elements that significantly increase risk. It is neither a pure bubble without any substance nor a completely risk-free, fundamentally justified investment. The truth lies somewhere in between, and this ambivalence should be considered in every investment decision.
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