In recent weeks, the discussion surrounding artificial intelligence (AI) and its advancements has gained considerable momentum, particularly with the emergence of domestic models such as DeepSeek-R1 during the Spring Festival in ChinaThis model has demonstrated that achieving world-class performance does not require exorbitant costs or extensive computational powerThe launch of DeepSeek-R1 seems to signal a shift in the paradigm of large model development, challenging the era in which companies predominantly viewed enhanced chip capabilities as the key to superior AI performanceAs a testament to this shift, Nvidia’s stock price fell by nearly 17% on January 27, reflecting a potential reevaluation of investments in computational power by tech giants.

However, this raises a pivotal question: Is computational power becoming less critically important on the path toward artificial general intelligence (AGI)? For Silicon Valley's leading companies—Google, Microsoft, Meta, and Amazon—the answer appears to be a resounding "no." As these major players have been releasing their financial reports recently, a common theme has emerged: they plan to significantly increase their investments in computational power by 2025.

Alphabet, Google’s parent company, has set a capital expenditure target of $75 billion for 2025, representing a 42% increase from 2024. The motivation behind this bold move stems from their observation that demand for AI products was exceptionally high in the fourth quarter of 2024, exceeding their available capacityTo address this gap, Google is focused on expanding its capacity, which translates into investing more in computational resources.

In a similar vein, Microsoft has announced an investment of $80 billion in AI data centers for the fiscal year 2025, concluding in JuneThe rationale remains consistent with Google’s: sustained market demand is surpassing their current capacity, necessitating expansion

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Microsoft’s leadership has acknowledged that their planned investments are conservative, given the nature of technology advancements driven by Moore's LawThis law states that the number of transistors on a microchip doubles approximately every two years, thus enhancing hardware performanceMicrosoft executives caution against making excessively large purchases, as this could result in losses due to rapid performance improvements in subsequent hardware developments.

Meta, the company formerly known as Facebook, has earmarked $65 billion for capital expenditure in 2025, reflecting a substantial 66% increase over the previous yearCEO Mark Zuckerberg maintains that strong investments in capital expenditures and infrastructure will yield long-term strategic advantagesWhile he remains open to reevaluating this strategy in the future, he expresses a conviction that building robust infrastructure is crucial for competitive success.

Amazon also stands firm in its approach, with projected capital expenditures between $100 billion and $105 billion for 2025, a 24% increase compared to the previous year’s $83 billionChief Financial Officer Brian Olsavsky reaffirmed that the bulk of this expenditure is aimed at expanding its AI and cloud service capabilities through AWS.

The collective commitment from these tech giants to increase their investments in computational power is not indicative of a diminishing regard for DeepSeek; rather, it demonstrates their recognition of the model’s capabilitiesAmazon’s CEO, Andy Jassy, points out that lower inference costs do not equate to reduced overall expenditures, citing similar trends experienced in the cloud computing sector.

Furthermore, Microsoft’s CEO recently conveyed his stance on social media by highlighting the "Jevons Paradox," which posits that improvements in resource efficiency can paradoxically lead to increased overall consumption due to behavioral changes or market responses

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This concept is vital in understanding the complex relationship between technology and consumption.

A practical illustration of the Jevons Paradox can be seen in the automotive industryAs automotive technology improves, engines become more fuel-efficient, ostensibly reducing gasoline consumptionHowever, as costs decrease, consumers may opt to buy more vehicles or drive longer distances, ultimately leading to an increase in overall petroleum consumption.

In the context of AI, DeepSeek-R1 can be likened to an efficient engine whilst computational resources serve as the fuelDespite the individual vehicle's more efficient fuel consumption, the demand for fuel across the market may, in fact, increaseIt’s noteworthy that although DeepSeek-R1 is celebrated for its lower training costs, it still adheres to the principles of Scaling Law, suggesting that model performance and computational demand are closely linked.

Under the Scaling Law, one can conceptualize the relationship between model performance and computational needs as a proportional functionEarlier models had a relatively small slope, indicating lower efficiency where performance improved with increasing computational power, albeit slowlyIn contrast, DeepSeek exhibits a steeper slope, suggesting a significantly quicker enhancement of performance with rising computational resources.

This leads to a fundamental quandary: would increased efficiency encourage reduced investment, or might it provoke greater expenditure? Generally, one would be inclined to invest more due to heightened efficiencyThus, rather than fretting about an overabundance of computational power, the focus may need to shift towards the realization that while we might possess both efficiency and power, the quality data needed for training has become scarce.

Returning to the automotive analogy, when both fuel efficiency and gasoline resources are plentiful, the market could saturate with vehiclesHowever, should an unforeseen bottleneck manifest—the lack of quality roadways, or top-tier training data—progress may stagnate, akin to traffic congestion.

Looking to the future, it is plausible that fewer data annotators will remain available, giving way to a burgeoning number of "data producers." These individuals will occupy cubicles, harnessing their creativity to generate high-quality data capable of enhancing model performance

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