Last Friday marked a monumental day for Broadcom, a leading player in the semiconductor industryThe company witnessed a staggering 24% increase in its stock price, pushing it to an all-time high and elevating its market capitalization to an impressive $1.05 trillionThis translates into a remarkable single-day market gain of approximately $206 billion, an amount that rivals the GDP of many nations, with a rough equivalence of around 1.5 trillion yuan in ChinaBroadcom has now become the third semiconductor company globally, following Nvidia and TSMC, to surpass the trillion-dollar market value milestone, a feat that underscores its influential position in the industry.

This remarkable performance in the capital markets can be attributed to Broadcom's strategic acquisition of VMware for $69 billion

As the demand for high-performance AI chips grows exponentially amid the generative AI revolution, Broadcom has successfully tapped into this lucrative marketIn its fiscal year 2024's fourth quarter, the company reported revenues of $14.054 billion, representing a phenomenal growth of 51% year-over-year, with net income reaching $4.324 billionAdjusted EBITDA showcased an even more impressive figure at $9.089 billion, marking a 65% increase compared to the previous yearFor the entire fiscal year, Broadcom’s revenue touched $51.6 billion, a 44% annual growth.

At the core of Broadcom's impressive performance is its AI business, which acts as the key driver of growthIn fiscal year 2024, revenue from AI-related endeavors soared to $12.2 billion, witnessing a staggering rise of 220% compared to the previous period

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The engine powering this growth is none other than AI-specific XPU technology, which has propelled semiconductor revenues to a record high of $30.1 billion, marking a notable year-on-year growth of 58%. This robust performance not only reflects the increasing demand for AI capabilities but also positioned Broadcom as the ninth company globally to achieve a market capitalization exceeding one trillion dollars.

The burgeoning landscape of artificial intelligence has been monumental since the debut of ChatGPTThis event heralded the entrance into a new era of generative AI, spurring an unparalleled wave of innovation in the fieldToday, the market is flooded with various large models ranging from natural language processing to complex image generation, showcasing the breadth and depth of what AI can achieve.

However, training these large AI models necessitates constructing robust AI infrastructure

Both Nvidia and Broadcom have emerged as significant beneficiaries in this wave of artificial intelligence advancementsNotably, the development of large AI models has heavily relied on Nvidia's high-performance chipsIt is not an exaggeration to state that the strength of an AI model is often dictated by the number of Nvidia cards backing it.

Consequently, there has been an insatiable rush among tech giants to secure these scarce Nvidia chips to enhance their AI capabilitiesOver the past couple of years, the leading technology behemoths—Meta, Google, Microsoft, and Amazon—have collectively invested a staggering $200 billion into AI development, with some purchasing Nvidia chips worth tens of billions of dollarsThe result has been astronomical, with Nvidia's revenue soaring to unprecedented heights, ultimately leading to a market valuation of $3.29 trillion

This meteoric rise reflects the optimistic outlook of investors regarding the commercial potential of AI technologies.

It is essential to note that the current monopoly of Nvidia in the AI chip market has spurred tech giants to diversify their chip supplies, moving away from an over-reliance on one sourceThis diversification is not only strategic but pragmatic, allowing them to cater to diverse application needs in various operational scenariosIn response, many of these firms are turning their attention to customizable chips, aiming to meet specific requirements without being at the mercy of a single supplier.

In light of these shifts, Broadcom has reported that it is collaborating with three major cloud clients to develop custom AI chips, with plans for each client to deploy one million AI chips by 2027. During a recent earnings call, CEO Hock Tan noted that this custom AI chip business could potentially generate between $60 billion and $90 billion in revenue by 2027, with this scale representing four times their current revenue.

This indication of demand for custom AI chips reveals the burgeoning appetite among tech giants, looking not only to satisfy their internal needs better but also to lessen their dependence on Nvidia

Such trends suggest that Nvidia's era of dominance may soon face competition from Broadcom and others responding to market needsWall Street, for its part, is keenly observing the significant demand for ASICs from big cloud companies like Google.

ASICs, or application-specific integrated circuits, are specialized chips designed for specific tasks, including AI computations, thus making them core components of the custom AI chip category.

Broadcom is not the only company benefitting from the burgeoning demand for AI; Marvell is also seeing strong performance driven by AI needs, particularly in providing new types of custom AI chips for Amazon and other large data centersThe surge in AI-related business has been sufficient to offset declines in other sectors such as telecommunications and automotive industries.

By offering customized ASIC solutions, both Broadcom and Marvell have secured a competitive advantage in the market and delivered impressive financial results, illustrating the strength of ASIC demand.

Within the context of the AI large model competition and the demand for custom chips, Broadcom stands as a significant semiconductor manufacturer, with clients that include tech giants like Google, Apple, and Meta

For instance, Google’s TPUs (Tensor Processing Units) are custom-made by Broadcom, tailored specifically to Google's requirementsThere are also reports that Broadcom is collaborating with Apple to develop AI servers, further solidifying their position in the market.

In conclusion, despite the potential of large model companies, many have found it challenging to become profitableEven OpenAI, despite its prominence, is grappling with substantial losses, primarily due to the escalating costs associated with training AI models and maintaining servers.

The competitive edge of AI large models lies in their powerful computational infrastructureHowever, the high operational expenditures make it difficult for large model companies to convert these efforts into profitability