Author: 137Labs

Introduction: A Quarterly Earnings Season That Changed AI Investment Logic

After the market closed on April 29, 2026, Microsoft, Alphabet, Amazon, and Meta, four tech giants, simultaneously announced their quarterly earnings. This moment is seen by the market as a "midterm examination of the AI era," its importance stemming not only from the size of the companies themselves but also from their joint role as core suppliers of global artificial intelligence infrastructure.

From the perspective of the capital markets, these four companies not only hold a significant market cap weight in the S&P 500 index, but they are also direct beneficiaries and major drivers of the AI investment wave over the past three years. The competition surrounding large models, cloud computing, computing infrastructure, and data centers has, in some sense, made them synonymous with the "AI economy." Therefore, this round of earnings reports is not merely a performance disclosure but more like a concentrated answer to a core question: Has artificial intelligence entered a profit phase, or is it still in a capital-driven investment cycle?

Based on reports from multiple media outlets and data disclosed by the companies, it is evident that the answer to this question is not straightforward. The four companies have generally delivered strong results in revenue growth, profit performance, and business expansion, yet the capital market's responses have shown notable divergence. This very divergence reveals that the logic of AI investment is undergoing structural changes.

Overall Performance: Certainty of Growth and Capital Pressure Coexist

From an overall perspective, the most significant feature of this earnings season is the "strong fundamentals but a shift in valuation logic." Nearly all companies achieved year-on-year growth in revenue and profits, with most indicators surpassing market expectations. Especially driven by cloud computing and AI-related businesses, the quality of growth has visibly improved compared to the past few quarters.

From the company's official disclosures:

·Microsoft quarterly revenue approximately 61.8 billion dollars, net profit approximately 21.9 billion dollars

·Alphabet quarterly revenue approximately 109.9 billion dollars, net profit approximately 30.8 billion dollars

·Amazon quarterly revenue approximately 181.5 billion dollars, net profit approximately 10.4 billion dollars

·Meta quarterly revenue approximately 56.3 billion dollars, net profit approximately 15.6 billion dollars

However, alongside the growth, there has been a significant expansion in capital expenditures. According to company disclosures and guidance:

·Microsoft's full-year capital expenditure guidance is close to 190 billion dollars

·Amazon's capital expenditure grew over 70% year-on-year

·Meta's capital expenditure has been adjusted to a range of 125 to 145 billion dollars

·Alphabet's capital expenditure significantly increased year-on-year, but lower than prior market expectations

According to various statistics, the total AI-related investment scale of the four companies in 2026 has reached a range of 600 to 650 billion dollars. This figure not only sets a historical high but also signifies that artificial intelligence has fully upgraded from a "technological race" to a "capital-intensive industry competition."

It is against this backdrop that the market's focus has shifted significantly. Investors are no longer satisfied with companies showcasing AI capabilities or technological leadership but are beginning to assess the following dimensions more rigorously: first, whether AI can be translated into sustainable revenue; second, the relationship between capital expenditures and cash flow; third, whether long-term investment returns are clear and visible.

Thus, the core contradiction of this earnings season does not lie in "whether growth exists" but in "whether the growth is worth the current investment cost."

Individual Company Analysis: AI Commercialization Process on Different Paths

(1) Alphabet: The AI Winner with the Clearest Commercialization Path

Among the four companies, Alphabet's performance is the most certain and closest to the market's ideal "AI commercial closed loop." Its revenue reached approximately 109.9 billion dollars, with a year-on-year growth of more than 20%, and net profit achieved approximately 80% substantial growth. More importantly, its cloud business grew at a rate exceeding 60%, becoming the core engine driving overall performance.

From the official disclosures:

·Google Cloud quarterly revenue approximately 12.8 billion dollars

·Operating profit significantly increased year-on-year, with continually improving margins

·Cloud business backlog exceeds 460 billion dollars

Alphabet's advantage lies in its successful transformation from technical capability to commercial products. Whether it is generative AI tools, enterprise cloud services, or the external output of self-developed TPU chips, all indicate that its AI system not only serves internal efficiency improvements but also becomes products and services that can be sold directly. This complete chain of "from infrastructure to application layer" places it ahead of other competitors in the AI commercialization path.

Moreover, compared to its peers, Alphabet demonstrates stronger constraints regarding capital expenditures. The company disclosed that while its capital expenditure increased year-on-year, it fell below the previously expected market range, thus alleviating investor concerns about future cash flow. Consequently, its stock price received positive feedback following the earnings report.

From a market perspective, Alphabet's success is not just about leading in performance; more importantly, it proves one thing: AI can form scaled revenue in a short period, not merely a long-term vision.

(2) Microsoft: Misalignment Between Technological Leadership and Monetization Pace

Microsoft remains one of the most important players in the AI field, with its Azure cloud business maintaining a growth rate of approximately 40%, and enterprise AI products (such as Copilot) continuously expanding their user base. From the perspective of technological capability and ecosystem integration, Microsoft is still at the forefront of the industry.

According to the company's disclosures:

·Azure and related cloud business grew approximately 39% to 40%

·Intelligent cloud business revenue approximately 26.7 billion dollars

·AI-related annualized revenue scale approximately 37 billion dollars

·Copilot enterprise user penetration rate remains at a relatively low level

However, this earnings report exposes a key issue, namely that there is a certain degree of mismatch between the pace of AI commercialization and capital investment. Despite AI-related revenue having reached the hundreds of billions level, the actual adoption speed by enterprise customers is still below the market's previous high expectations. In other words, while the technological capability is in place, demand release is still in the gradual ramp-up stage.

At the same time, Microsoft continues to expand its investments in data centers, GPU procurement, and collaboration with OpenAI, with the company disclosing that capital expenditures remain at historically high levels. This model of "heavy upfront investment, slow monetization later" places certain short-term pressure on valuations.

As a result, the market's attitude towards Microsoft shows a "recognition but reservation" state. Investors do not question its long-term competitiveness, but they begin to assess its profitability timeline more cautiously.

(3) Amazon: The Long-Termism Logic of an Infrastructure Provider

Amazon's earnings report showed relatively steady performance, with its AWS cloud business's growth rate rebounding to a range of 25% to 28%, indicating that AI demand is driving cloud computing back into a growth cycle. Simultaneously, the company disclosed that its AI-related revenue has reached hundreds of billions, signifying substantial progress in commercialization in this area.

Official data further show:

·AWS quarterly revenue approximately 26.2 billion dollars

·AWS continues to contribute most of the company's operating profits

·AI-related business revenue around 15 billion dollars

·Self-developed AI chips (Trainium) start large-scale deployment

Unlike Alphabet, Amazon's AI strategy leans more towards the infrastructure level. By providing computing power, model hosting, and development platforms, it plays the role of a "platform provider" in the entire AI ecosystem. This model is akin to "tool suppliers in a gold rush," where its revenue does not depend on the success of any specific application but on the overall expansion of industry demand.

In addition, Amazon's investments in self-developed chips also reflect its attempt to establish a long-term competitive advantage in terms of computing costs. This strategy increases capital expenditures in the short term, but in the long run, it helps enhance profit margins and ecosystem stickiness.

Therefore, Amazon's core characteristic lies in "certain growth + delayed return." The market's response to it is relatively neutral, recognizing its strategic direction while remaining cautious about its short-term profitability.

(4) Meta: A Contradiction of High Growth and High Investment

Meta displayed the most evident "divergence between fundamentals and market performance" in this earnings report. The company achieved over 30% growth in revenue, with its advertising business performing robustly under AI recommendation algorithm optimization. However, its capital expenditure expectations were significantly raised to a range of 125 to 145 billion dollars, becoming the focus of market attention.

From the data disclosed by the company:

·Daily active users (DAU) exceeded 3.2 billion

·Advertising display efficiency significantly improved due to AI optimization

·Operating profit margins remain at a high level

Meta's AI strategy differs significantly from the other three companies. It primarily uses AI to enhance advertising efficiency and user experience rather than directly selling AI products or cloud services. This means that the return path of its AI investments is relatively indirect and difficult to quickly manifest as new revenue like cloud businesses.

At the same time, Meta is massively building its own computing infrastructure, attempting to grasp more underlying capabilities in the AI era. This "asset-heavy" path, while beneficial for long-term competitiveness, significantly compresses cash flow space in the short term.

Thus, the market's negative reaction to Meta arises not from its performance but from concerns over the sustainability of its capital expenditures. Investors are more concerned about whether such a scale of investment can be translated into measurable returns within a reasonable time frame.

Horizontal Comparison: AI Competition Enters a Stage of Structural Divergence

By comparing the four companies, it can be observed that AI competition has evolved from a single-dimensional technological contest into a multidimensional comprehensive competition. Alphabet leads in commercialization capabilities, Microsoft and Amazon possess advantages in infrastructure and enterprise services, while Meta holds a unique position in user data and application scenarios.

From a financial data perspective:

·Alphabet has the highest profit growth rate (approximately 80%)

·Microsoft has one of the largest cloud business scales

·Amazon has the largest revenue size

·Meta has obvious advantages in profit margins and user scale

However, the key variable that truly determines market evaluation is gradually shifting from "whose technology is more advanced" to "whose capital efficiency is higher." Under this standard, the advantages and disadvantages of different companies are further magnified, resulting in greater market divergence.

Core Trend: AI Enters the "Capital Efficiency-Driven" Second Stage

If we divide the development of AI over the past three years into phases, a clear turning point can be seen to have emerged.

In the first stage, the market primarily focused on technological breakthroughs and application potential, with valuation logic mainly driven by expectations; however, entering 2026, AI has begun its second stage, characterized by a significant increase in the importance of financial metrics and capital returns.

In this stage, companies need to answer not "can AI be made" but "how to make money with AI, and how much cost is required." Capital expenditures, cash flow, profit margins, and other traditional financial metrics have once again become the core of valuation, while AI becomes the key variable affecting these metrics.

Conclusion

Based on the earnings reports, a clear conclusion can be drawn: the artificial intelligence industry has completed its transition from technology-driven to capital-driven. Growth still exists, but the cost of growth is rising; opportunities remain vast, but market demands for efficiency are stricter.

In the near future, the capital market will favor companies that can maintain profitability while expanding AI investments and remain cautious towards those with excessive investments and uncertain returns.

Therefore, the true significance of this earnings report lies not in the short-term stock price volatility, but in its indication of a turning point of an era:

The logic of competition in AI has shifted from "who owns the technology" to "who can achieve scaled profitability at the lowest cost."

免责声明：本文章仅代表作者个人观点，不代表本平台的立场和观点。本文章仅供信息分享，不构成对任何人的任何投资建议。用户与作者之间的任何争议，与本平台无关。如网页中刊载的文章或图片涉及侵权，请提供相关的权利证明和身份证明发送邮件到support@aicoin.com，本平台相关工作人员将会进行核查。