Author: 137Labs
Introduction: A Earnings Season That Changed the Logic of AI Investment
On April 29, 2026, after the market closed, four tech giants—Microsoft, Alphabet, Amazon, and Meta—simultaneously released their quarterly earnings reports. This timing is seen by the market as a "mid-term test of the AI era," its importance stemming not only from the size of the companies themselves, but also from the fact that they collectively constitute the core suppliers of global artificial intelligence infrastructure.
From the perspective of capital market structure, these four companies not only account for a very high weighting in the S&P 500 index in terms of combined market capitalization, but they are also direct beneficiaries and major drivers of the AI investment boom over the past three years. Competition surrounding large-scale models, cloud computing, computing infrastructure, and data centers has, in a sense, made them synonymous with the "AI economy." Therefore, this round of financial reports is not simply a performance disclosure, but rather a concentrated answer to a core question: Has artificial intelligence entered the profit-making stage, or is it still in a capital-driven investment cycle?
A review of multiple media reports and company disclosures reveals that the answer to this question is not singular. While all four companies delivered strong results in revenue growth, profit performance, and business expansion, the capital market's response has been significantly divergent. This divergence itself reveals a structural shift in the logic of AI investment.
Overall performance: Growth certainty coexists with capital pressure.
From an overall perspective, the most significant characteristic of this round of earnings reports is "strong fundamentals but a shift in valuation logic." Almost all companies achieved year-on-year revenue and profit growth, with most indicators exceeding market expectations. In particular, driven by cloud computing and AI-related businesses, the quality of growth has improved significantly compared to the past few quarters.
According to the company's official disclosures:
Microsoft's quarterly revenue was approximately $61.8 billion , and its net profit was approximately $21.9 billion.
Alphabet's quarterly revenue was approximately $109.9 billion , and its net profit was approximately $30.8 billion.
Amazon's quarterly revenue was approximately $181.5 billion , and its net profit was approximately $10.4 billion.
Meta's quarterly revenue was approximately $56.3 billion , and its net profit was approximately $15.6 billion.
However, alongside the growth was a significant expansion in capital expenditures. According to company disclosures and guidance:
Microsoft's full-year capital expenditure guidance is close to $190 billion.
Amazon's capital expenditures increased by more than 70% year-over-year.
Meta's capital expenditure has been revised upward to the range of $125 billion to $145 billion .
Alphabet's capital expenditures increased significantly year-on-year, but fell short of market expectations.
According to multiple sources, the combined AI-related investments of the four companies in 2026 reached between $600 billion and $650 billion. This figure not only sets a new historical record but also signifies that artificial intelligence has evolved from a "technology 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 demonstrating AI capabilities or technological leadership, but have begun to more rigorously evaluate the following dimensions: first, whether AI can be converted into sustainable revenue; second, the matching relationship between capital expenditures and cash flow; and third, whether the long-term investment return cycle is clearly visible.
Therefore, the core issue in this round of financial reports is not whether growth exists, but whether the current investment costs are worthwhile.
Company-Specific Analysis: AI Commercialization Processes Along Different Paths
(I) Alphabet: The AI Winner with the Clearest Commercialization Path
Of the four companies, Alphabet's performance is the most certain and closest to the market's ideal "AI business loop." Its revenue reached approximately $109.9 billion, a year-on-year increase of over 20%, and its net profit saw a significant increase of approximately 80%. More importantly, its cloud business grew at a rate exceeding 60%, becoming the core engine driving its overall performance.
According to official disclosures:
Google Cloud's quarterly revenue was approximately $12.8 billion .
Operating profit increased significantly year-on-year, and profit margin continued to improve.
• Cloud business backlog exceeds $460 billion
Alphabet's advantage lies in its successful transformation from technological capabilities to commercial products. Whether it's generative AI tools, enterprise cloud services, or the external distribution of its self-developed TPU chips, all demonstrate that its AI system not only serves internal efficiency improvements but has also become products and services that can be directly sold. This complete chain "from infrastructure to application layer" puts it ahead of its competitors in the path to AI commercialization.
Furthermore, compared to its peers, Alphabet demonstrated stronger restraint in capital expenditures. The company disclosed that while its capital expenditures increased year-over-year, they were below market expectations, thus alleviating investor concerns about future cash flow. As a result, its stock price reacted positively after the earnings release.
From a market perspective, Alphabet's success is not just about leading in terms of performance; more importantly, it proves that AI can generate scalable revenue in the short term, rather than just being a long-term vision.
(II) Microsoft: The 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 its enterprise AI products (such as Copilot) continuing to expand their user base. From the perspective of technological capabilities and ecosystem integration, Microsoft remains at the forefront of the industry.
According to the company's disclosure:
• Azure and related cloud business grew by approximately 39%–40%.
• Revenue from intelligent cloud services was approximately US$26.7 billion.
AI-related annualized revenue is approximately $37 billion.
Copilot's enterprise user penetration rate remains at a low level.
However, this financial report revealed a key issue: a mismatch between the pace of AI commercialization and capital investment. Although AI-related revenue has reached tens of billions of dollars, the actual adoption rate by enterprise customers is still lower than the market's previous high expectations. In other words, the technological capabilities are in place, but demand is still in a gradual ramp-up phase.
Meanwhile, Microsoft continues to expand its investments in data centers, GPU procurement, and its collaboration with OpenAI, with the company disclosing that capital expenditures remain at historically high levels. This "heavy investment upfront, slow monetization later" model has put some downward pressure on valuations in the short term.
The market's attitude towards Microsoft has thus become one of "approval but reservation." Investors do not question its long-term competitiveness, but have begun to more cautiously assess its earnings realization timeline.
(III) Amazon: The Long-Term Logic of an Infrastructure Provider
Amazon's financial report showed relatively solid performance, with its AWS cloud business growth rebounding to the 25% to 28% range, indicating that AI demand is driving cloud computing back into a growth cycle. At the same time, the company disclosed that its AI-related revenue has reached tens of billions of dollars, demonstrating substantial progress in its commercialization in this area.
Official data further shows:
AWS quarterly revenue was approximately $26.2 billion.
AWS still contributes the majority of the company's operating profit.
AI-related revenue is approximately $15 billion.
• Self-developed AI chip (Trainium) begins 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 similar to that of a "tool provider during a gold rush," whose revenue does not depend on the success of a specific application, but rather on the expansion of overall industry demand.
Furthermore, Amazon's investment in developing its own chips reflects its attempt to establish a long-term competitive advantage in computing power costs. This strategy increases capital expenditure in the short term, but in the long run, it helps improve profit margins and enhance ecosystem stickiness.
Therefore, Amazon's core characteristics lie in "certain growth + delayed returns." The market's reaction to it is relatively neutral, acknowledging its strategic direction while remaining cautious about its short-term profitability.
(iv) Meta: The contradiction between high growth and high investment
Meta's latest earnings report reveals the most striking discrepancy between its fundamentals and market performance. While revenue grew by over 30%, with its advertising business performing strongly thanks to AI-powered recommendation algorithms, its capital expenditure forecast was significantly revised upwards to the $125 billion to $145 billion range, becoming a focal point of market attention.
Based on the data disclosed by the company:
Daily active users (DAP) exceed 3.2 billion
• Ad display efficiency has been 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 improve advertising efficiency and user experience, rather than directly selling AI products or cloud services. This means that the return on its AI investments is relatively indirect and unlikely to translate into new revenue as quickly as its cloud business.
Meanwhile, Meta is building its own computing infrastructure on a large scale, attempting to gain more control over underlying capabilities in the AI era. While this "asset-heavy" approach may contribute to long-term competitiveness, it significantly compresses cash flow in the short term.
Therefore, the market's negative reaction to Meta stemmed not from its performance, but from concerns about the sustainability of its capital expenditures. Investors were more concerned with whether such a large investment could translate into measurable returns within a reasonable timeframe.
Horizontal comparison: AI competition enters a stage of structural differentiation
A comparison of the four companies reveals that the AI competition has evolved from a single-dimensional technological contest to a multi-dimensional, comprehensive battle. Alphabet leads in commercialization capabilities, Microsoft and Amazon have advantages in infrastructure and enterprise services, while Meta holds a unique position in user data and application scenarios.
From a financial data perspective:
Alphabet had the highest profit growth rate (approximately 80%).
One of the largest cloud businesses in Microsoft's history
Amazon has the largest revenue.
Meta has significant advantages in profit margin and user scale.
However, the key variable that truly determines market evaluation has gradually shifted from "whose technology is more advanced" to "whose capital efficiency is higher." Under this standard, the strengths and weaknesses of different companies are further amplified, thus exacerbating market differentiation.
Key Trend: AI Enters the Second Phase of "Capital Efficiency Driven"
If we divide the past three years of AI development into stages, we can clearly see the emergence of a turning point.
In the first stage, the market mainly focused on technological breakthroughs and application potential, and valuation logic was mainly driven by expectations; after entering 2026, AI began to enter the second stage, the core feature of which is that the importance of financial indicators and capital returns has increased significantly.
At this stage, companies no longer need to answer the question "Can we create AI?", but rather "How can we make money with AI, and what costs are involved?". Traditional financial indicators such as capital expenditure, cash flow, and profit margin have once again become the core of valuation, while AI has become a key variable affecting these indicators.
in conclusion
Based on this round of financial reports, a clear conclusion can be drawn: the artificial intelligence industry has completed its transition from technology-driven to capital-driven growth. Growth still exists, but the cost of growth is rising; opportunities remain vast, but the market's demands for efficiency are also more stringent.
In the near future, the capital market will favor companies that can maintain profitability while expanding their AI investment, while remaining cautious about companies that invest too much and have uncertain returns.
Therefore, the true significance of this round of financial reports lies not in short-term stock price fluctuations, but in the fact that it marks a turning point in an era:
The competitive logic in AI has shifted from "who owns the technology" to "who can achieve large-scale profitability at the lowest cost".
