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AI chip hype meets reality: The future of data centers – in-house development versus market saturation

AI chip hype meets reality: The future of data centers – in-house development versus market saturation

AI chip hype meets reality: The future of data centers – in-house development versus market saturation – Image: Xpert.Digital

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The big showdown in the data center: In-house development meets impending market saturation

The world of artificial intelligence is experiencing an unprecedented boom, driven by a seemingly insatiable demand for computing power. At the center of this hype are AI chips, especially the GPUs from market leader Nvidia, which have become the gold of the digital era. But behind the scenes, a strategic shift is taking place that could reshape the power structure of the entire tech industry. The largest buyers of these chips—hyperscalers like Microsoft, Google, and Amazon—no longer want to be mere customers. With billions of dollars in investment, they are developing their own customized semiconductors like Microsoft's Maia, Google's TPUs, and Amazon's Trainium.

The motivation is clear: to cut costs, reduce dependence on individual vendors, and perfectly align the entire infrastructure, from chips to cooling, with the company's own AI models. What begins as a pragmatic business decision to optimize performance is sparking fundamental competition and seriously challenging Nvidia's dominance for the first time. But as an arms race for the most powerful AI infrastructure rages and hundreds of billions of dollars are invested, warnings of overheating are growing louder. Experts are drawing comparisons to previous speculative bubbles and warning of impending market saturation and overcapacity in the coming years.

This article delves deep into the AI ​​chip hype and sheds light on the reality behind it: Why are tech giants focusing on in-house development? How far along are they really with this? And what happens when exponential demand suddenly collapses and the dream of infinite AI growth collides with the harsh reality of an economic correction?

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What drives hyperscalers to develop their own chips?

The major cloud providers, also known as hyperscalers, are facing a fundamental strategic decision: Should they continue to rely on chips from established manufacturers like Nvidia and AMD or increasingly switch to their own semiconductor developments? Microsoft CTO Kevin Scott recently brought this issue into focus when he explained that Microsoft intends to rely primarily on its own Maia chips in the long term. This strategy is not new – both Google with its TPUs and Amazon with its Trainium chips are already pursuing similar approaches.

The main reason for this development is cost optimization. For hyperscalers, the price-performance ratio is the deciding factor, as Scott emphasizes: "We are not dogmatic about the chips we use. This means that Nvidia has been the best price-performance solution for many years. We are open to all options that ensure we have sufficient capacity to meet demand." This statement makes it clear that this is not a fundamental rejection of the incumbent vendors, but a pragmatic business decision.

Developing their own chips also allows hyperscalers to optimize their entire system architecture. Microsoft, for example, can use its Maia chips to not only customize the computing power but also tailor the cooling, networking, and other infrastructure elements to its specific requirements. Scott explains: "It's about the entire system design. It's the networking and cooling, and you want the freedom to make the decisions you need to make to truly optimize the computing for the workload."

How far are the various hyperscalers with their own developments?

The three major cloud providers are at different stages of developing their custom silicon strategies. Amazon Web Services is the pioneer in this area, having already laid the foundation with the first Graviton chip in 2018. AWS is now in the fourth generation of Graviton processors, designed for general-purpose computing workloads. In parallel, Amazon has developed specialized AI chips: Trainium for training and Inferentia for inferring machine learning models.

The numbers speak for the success of this strategy: In the last two years, Graviton processors accounted for over 50 percent of all CPU capacity installed in AWS data centers. AWS also reports that more than 50,000 customers are using Graviton-based services. The practical application is particularly impressive: During Prime Day 2024, Amazon deployed a quarter of a million Graviton chips and 80,000 of its custom AI chips.

Google has taken a different path with its Tensor Processing Units, focusing early on AI-specific hardware. The TPUs are already in their seventh generation and are offered exclusively through Google Cloud. Google also recently introduced its first Arm-based general-purpose processor, Axion, which, according to the company, is said to offer up to 30 percent better performance than comparable Arm-based instances from other cloud providers.

Microsoft is the latecomer in this race. The company only unveiled its first in-house developed chips at the end of 2023: the Azure Maia AI Accelerator and the Azure Cobalt CPU. The Cobalt CPU has been generally available since October 2024 and is based on a 64-bit architecture with 128 cores, manufactured on a 5-nanometer process from TSMC. Microsoft claims that Cobalt delivers up to 40 percent better performance than previous Arm-based offerings in Azure.

Why can't our own chips cover the entire demand?

Despite advances in in-house development, all hyperscalers are still far from meeting their entire needs with homegrown chips. The main reason is the sheer size of the market and the rapid increase in demand. Microsoft's Kevin Scott sums it up: "To say there is a massive shortage of compute capacity is probably an understatement. Since the launch of ChatGPT, it has been nearly impossible to scale capacity fast enough."

The figures illustrate the scale of the challenge: Global data center capacity is expected to increase by 50 percent by 2027, driven by AI demand. Major tech companies alone plan to invest over $300 billion in AI infrastructure by 2025. At such a pace of growth, it is physically impossible to meet all demand through internal chip development.

In addition, there are technical limitations in manufacturing. The most advanced chips are manufactured by only a few foundries like TSMC, and capacities are limited. Microsoft, Google, and Amazon have to share this production capacity with other customers, which limits the available quantities for their own chips. Another factor is development time: While demand is exploding, developing a new chip takes several years.

The hyperscalers are therefore pursuing a mixed strategy. They are developing their own chips for specific workloads where they see the greatest benefit, and complementing these with chips from Nvidia, AMD, and Intel for other use cases. Scott explains: "We're not dogmatic about the names on the chips. It's all about the best price-performance ratio."

What economic advantages do custom silicon solutions offer?

The economic incentives for developing your own chips are significant. Studies show that AWS Trainium and Google TPU v5e are 50 to 70 percent cheaper in terms of cost per token for large language models than high-end NVIDIA H100 clusters. In some analyses, TPU implementations have proven to be four to ten times more cost-effective than GPU solutions for training large language models.

These cost savings arise from several factors. First, the chips can be tailored precisely to the specific requirements of the workloads, enabling efficiency gains. Second, the chip manufacturer's margin is eliminated, leading to significant savings given the enormous volumes of hyperscalers. Third, vertical integration enables better control over the entire supply chain.

Amazon, for example, reports that SAP achieved a 35 percent performance increase in analytical workloads with Graviton-based EC2 instances. Google claims that its TPU v5e delivers three times more inference throughput per dollar than the previous TPU generation through continuous batching. Microsoft claims its Cobalt CPUs offer up to 1.5 times better performance in Java workloads and twice the performance in web servers.

The long-term financial impact is significant. With investments in the hundreds of billions of dollars, even small efficiency gains can lead to enormous cost savings. Experts estimate that the market for custom silicon in cloud environments could reach a volume of $60 billion by 2035.

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How is the competitive situation in the chip market developing?

The increasing in-house development of hyperscalers is fundamentally changing the traditional chip industry. Nvidia, long the undisputed market leader in AI accelerators, is facing serious competition for the first time. Analysts at Kearney predict that hyperscaler-developed silicon solutions such as Google's TPU, AWS Trainium, and Microsoft's Maia could achieve up to 15 to 20 percent market share as internal implementations.

This development is forcing traditional chip manufacturers to reposition themselves. AMD, for example, is attempting to directly challenge Nvidia with its MI300 series while simultaneously offering increased partnerships with cloud providers. Intel, although less strongly positioned in AI chips, continues to benefit from custom Xeon processors for hyperscalers, as demonstrated by the R8i instances recently announced by AWS.

The competitive dynamics are further intensified by the different strategies of the hyperscalers. While Google uses its TPUs exclusively internally and offers them via Google Cloud, other vendors may also market their chips externally in the future. This diversification of vendors leads to healthier competition and can accelerate innovation cycles.

The geopolitical dimension is also an important aspect. In light of the tensions between the US and China, American hyperscalers are increasingly investing in their own chip capabilities to become less dependent on Asian suppliers. At the same time, domestic champions are emerging in China, such as Baidu with its Kunlun chips.

 

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AI boom vs. chip shortage: When is the data center bubble imminent?

What does the current demand trend mean for the market?

Demand for computing capacity, especially for AI applications, is currently experiencing exponential growth. Nvidia estimates that reasoning model responses require more than 100 times more computing resources than previous generations. This development is leading to a structural shortage of advanced chips and data center capacity.

The McKinsey analysis shows that global demand for data center capacity could triple by 2030, with an annual growth rate of approximately 22 percent. In the US, demand could even grow by 20 to 25 percent annually. About 70 percent of this projected demand for 2030 will come from hyperscalers.

This increase in demand is leading to a paradigm shift in the industry. Synergy Research Group predicts that hyperscalers will control 61 percent of global data center capacity by 2030, up from 44 percent today. At the same time, the share of on-premises data centers will decline from 34 percent today to an expected 22 percent by 2030.

The high demand is also leading to bottlenecks throughout the supply chain. High-bandwidth memory, advanced packaging technologies such as CoWoS, and specialized substrates are already fully booked for months. Nvidia, for example, reports that the next generation of Blackwell GPUs is already sold out for a year or more.

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When might overcapacity occur?

The question of potential overcapacity in data centers is highly controversial. Various experts are already warning of an AI bubble that could be larger than the dot-com bubble of the 1990s. The MacroStrategy Partnership, an independent research firm, claims that the current AI bubble is 17 times larger than the dot-com bubble and four times larger than the 2008 real estate bubble.

Goldman Sachs CEO David Solomon warns of a stock market drawdown in the coming years due to the enormous sums flowing into AI projects. He explains: "I think a lot of capital is being deployed that will prove unprofitable, and when that happens, people are not going to feel good." Amazon CEO Jeff Bezos confirmed at the same conference that there is a bubble in the AI ​​industry.

The warning signs are mounting: Julien Garran of the MacroStrategy Partnership points out that enterprise adoption of large language models has already begun to decline. He also argues that ChatGPT may have "hit a brick wall," as the latest version costs ten times more but doesn't perform noticeably better than previous versions.

On the other hand, current market data shows that demand continues to exceed supply. CBRE reports that vacancy rates in primary data center markets in North America fell to a record low of 2.8 percent at the beginning of 2024. This occurred despite the largest annual increase in data center supply, suggesting that fundamentals remain strong.

What timeframes are realistic for a possible market consolidation?

Precisely predicting the timing of a potential market consolidation is extremely difficult, as it depends on many unknown factors. However, analysts identify several key periods during which market dynamics could change.

The first critical period is between 2026 and 2027. Several factors indicate that growth rates could slow during this period. Hyperscalers are already planning a 20 to 30 percent slowdown in their investments for 2026, indicating some saturation or re-evaluation of investments.

The semiconductor industry expects that demand for AI chips could reach a first plateau between 2026 and 2027. The annual growth rate for wafers could normalize from the current 14 to 17 percent to approximately 4 percent. This would represent a significant turning point in capacity planning.

A second critical period is around 2028 to 2030. By this time, the first generation of large-scale AI infrastructure investments may have to reach their payback point. If enough profitable use cases have not developed by then, a correction could set in. McKinsey predicts that demand for data center capacity will triple by 2030, but these forecasts are based on assumptions about AI adoption that may prove to be overly optimistic.

The decisive factor will be whether AI applications prove to be permanently profitable. Dario Perkins of TS Lombard warns that technology companies are taking on massive debt to build AI data centers without considering returns because they are competing for capital. This situation is reminiscent of previous bubbles and could lead to a correction if returns don't meet expectations.

What would be the impact of overcapacity?

Overcapacity in data centers would have far-reaching consequences for the entire technology industry. First, it would lead to a drastic drop in the price of cloud services. While this would be beneficial for customers in the short term, it could significantly impact the profitability of hyperscalers and lead to market consolidation.

The impact on employment would be significant. More than 250,000 workers in the technology industry were already affected by layoffs by 2025, and a market correction would exacerbate these trends. Data center operations, chip development, and related sectors would be particularly affected.

Overcapacity would be particularly painful for the semiconductor industry. The enormous investments in manufacturing capacity for advanced chips could prove excessive. Samsung already reported a 39 percent decline in profits in the second quarter of 2025 due to weaker AI chip demand, which could be a harbinger of things to come.

Market consolidation would likely lead to a concentration on the strongest providers. Smaller cloud providers and data center operators could be acquired by larger companies or forced out of the market. This could lead to less competition and higher prices in the long run.

On the other hand, a correction could also have positive effects. It would eliminate inefficient capacities and redirect resources to more productive uses. The surviving companies would likely be stronger and more sustainable. Furthermore, consolidation could promote the development of standards and interoperability.

How are companies preparing for different scenarios?

Given the uncertainty surrounding future market developments, hyperscalers and other companies are pursuing various strategies to mitigate risk. The most important is diversifying their chip strategies. As Microsoft CTO Kevin Scott emphasizes, they remain "open to all options" to ensure sufficient capacity is available.

Microsoft not only develops its own chips but also continues to invest in partnerships with Nvidia, AMD, and other vendors. This multi-vendor strategy reduces the risk of dependence on a single supplier and enables it to respond quickly to market changes. Amazon and Google pursue similar approaches, although they each have different focuses.

Another important aspect is geographical diversification. Given the NIMBY problems in established markets like Northern Virginia, hyperscalers are increasingly shifting their investments to secondary markets and overseas. This not only reduces costs but also regulatory risks.

Hyperscalers are also increasingly investing in energy efficiency and sustainable technologies. With data center energy consumption set to double by 2028, this is both an economic and regulatory imperative. Liquid cooling, more efficient chips, and renewable energy are becoming standard features.

Finally, many companies are developing more flexible business models. Instead of relying exclusively on self-ownership, they are increasingly using hybrid models with colocation providers and other partners. This allows them to scale or reduce capacity more quickly, depending on market conditions.

What role do regulatory factors play?

Regulatory developments could play a decisive role in the future development of the data center market. In the US, calls for stricter regulation of data center energy consumption are growing. Some states are already considering moratoriums on new large-scale consumers or stricter testing procedures.

Environmental impacts are increasingly in focus. Data centers could be responsible for 20 percent of global energy consumption by 2028, which could lead to stricter environmental regulations. The European Union has already introduced the Climate Neutral Data Center Pact, which over 40 data center operators have joined.

Geopolitical tensions are also impacting the industry. Potential tariffs on semiconductors could increase chip costs and disrupt supply chains. This could force hyperscalers to rethink their procurement strategies and rely more on regional suppliers.

Data protection and data sovereignty are also becoming important factors. Various countries require certain data to be processed locally, limiting the global scaling of data centers. This could lead to market fragmentation and reduce efficiency gains from economies of scale.

Regulation could also provide positive impetus. Investments in sustainable technologies and renewable energies are often supported by governments. Furthermore, regulatory requirements could promote standards that increase the efficiency of the entire industry in the long term.

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Navigating between growth and risk

The data center industry is at a critical inflection point. The development of proprietary chips by hyperscalers like Microsoft, Google, and Amazon is a logical response to the exploding costs and limited availability of standard solutions. This strategy offers significant economic advantages and enables greater control over the entire infrastructure.

At the same time, the risks of overcapacity are real and could lead to a significant market correction between 2026 and 2030. Warning signs are mounting, from the slowing adoption of AI technologies to prominent industry figures warning of a bubble. A potential consolidation would bring both opportunities and challenges.

The decisive factor for the future of the industry will be whether the enormous investments in AI infrastructure prove to be sustainably profitable. Hyperscalers are preparing for various scenarios through diversification, geographical spread, and flexible business models. Regulatory developments, particularly in the areas of environmental and energy, will add further complexity.

For companies and investors, this means they must keep an eye on both the enormous growth opportunities and the considerable risks. The winners will be those who can respond flexibly to market changes while continuously improving the efficiency of their operations. The coming years will show whether the current expansion is based on solid foundations or whether the warnings of a bubble prove true.

 

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