‘Biggest earnings day ever:’ US$12 trillion of combined market cap reported in tech

Key Concepts

Hyperscalers: Large-scale cloud computing providers (Microsoft, Amazon, Google) that provide massive infrastructure for AI and enterprise services.
Capex (Capital Expenditure): Funds used by companies to acquire or upgrade physical assets like data centers, memory, and CPUs.
Opex (Operating Expenditure): Ongoing costs for running a business.
Agentic Solutions: AI systems capable of performing tasks autonomously to achieve specific goals.
TPUs (Tensor Processing Units): Google’s proprietary AI accelerator chips, now being commercialized as a competitor to Nvidia’s GPUs.
Gemini: Google’s flagship AI model suite.

Analysis of Big Tech Earnings

1. Microsoft (Azure & Infrastructure)

Performance: Azure grew by 40% with a revenue run rate exceeding $100 billion. Overall revenue and earnings grew in the mid-to-high teens.
Capex Concerns: Investors are wary of rising capital expenditures. Gil Luria notes that this spending is driven by the rising costs of memory and CPUs rather than just physical data center construction.
Strategic Outlook: Luria views the spending as "very manageable." Microsoft is balancing internal builds with offloading capacity to "Neo clouds."
AI ROI: Microsoft reported over $35 billion in direct AI-related revenue, proving that early-stage investments are yielding tangible returns.

2. Amazon (Cloud & Retail)

Performance: Amazon’s cloud business (AWS) grew by 27% on a $140 billion base. Retail performance is at its strongest level since the COVID-19 pandemic.
Market Sentiment: Despite strong growth, investors are focused on the necessity for continued high investment, leading to a cautious reaction compared to Google’s results.

3. Meta (Social Media & AI Investment)

Performance: Revenue growth accelerated to 29%, the fastest in the group.
Investor Friction: Meta faces criticism for rising Capex and Opex, with guidance suggesting declining margins for the upcoming year.
Strategic Perspective: Mark Zuckerberg is "going all in" on compute infrastructure and AI models. Luria notes that while investors prefer fiscal discipline, this is a year of aggressive expansion to ensure Meta remains a leader in the AI race.

4. Alphabet (Google)

Performance: The standout performer. Cloud growth accelerated significantly from 48% to 63%.
Key Drivers:
- Gemini: Gaining traction with consumers and successfully entering the enterprise market.
- TPU Commercialization: Google has shifted from using TPUs solely for internal purposes to renting capacity and selling the chips, creating a direct competitor to Nvidia’s market dominance.

5. Apple (Upcoming Outlook)

Expectations: Anticipation of a "victory lap" for CEO Tim Cook.
Growth Drivers: A projected 15–20% growth in iPhone sales, fueled by a major upgrade cycle that drives demand for services and accessories.

Industry Trends and Future Outlook (12–18 Months)

Explosive AI Growth: The combined run-rate revenue for OpenAI and Anthropic has reached over $50 billion, up from zero two years ago. Luria predicts these firms will become the second and third-largest software companies within two years.
Economic Returns: As companies move toward "Agentic solutions," the utility of AI will increase, justifying the continued build-out of data centers.
Beneficiaries: The primary winners will be the hyperscalers (Google, Amazon, Microsoft) and the semiconductor supply chain, specifically Nvidia, AMD, Broadcom, and Micron.

Synthesis

The current tech landscape is defined by a massive, capital-intensive build-out of AI infrastructure. While investors are sensitive to the rising Capex required to sustain this growth, the underlying revenue acceleration—particularly in cloud services and AI-specific products—suggests that these investments are beginning to generate significant economic value. Google currently leads in momentum due to its cloud acceleration and TPU commercialization, while Meta remains the most aggressive in its long-term AI infrastructure spending. The sector is expected to maintain this growth trajectory as AI utility shifts from experimental to essential enterprise deployment.