Core Summary
Microsoft and Meta, which released their financial reports on the same day, both invested heavily in AI. However, the market reactions were vastly different: Microsoft's stock price rose 8% after the close due to its AI business generating clear revenue (such as from Azure cloud services and Copilot subscriptions), while Meta's stock price fell by more than 7% despite a healthy advertising business. This shift reflects a change in the capital market's attitude towards AI—from an enthusiasm for investing in computing power to a demand for tangible returns. Investors are no longer willing to support stories that promise future profits; they want to see how investments can be converted into actual profits, cash flows, and verifiable commercial revenue.
Detailed Analysis
1. Microsoft's Stock Rose, Meta's Fell: Why Such Different Market Reactions?
- Microsoft's Investment is Paying Off:
- The investment has led to tangible revenue growth: Azure cloud services saw a 43% increase (with AI services being a key driver of this growth), generating annual revenue exceeding $100 billion. There are over 30 million paid users for Microsoft 365 Copilot, indicating that corporate customers are willing to pay for AI solutions.
- A positive cycle has been established: The company has accumulated $678 billion in contract orders (up $51 billion from the previous quarter), ensuring future revenue. Even with high capital expenditure ($41 billion, a 70% increase year-over-year), the market is confident about its prospects.
- Meta's Investment Has Not Yet Yielded Returns:
- AI is still primarily used to enhance existing businesses: While Meta's AI systems have improved advertising efficiency, they have not generated new revenue sources (such as subscription-based services like Microsoft's Copilot).
- Increased capital expenditure ($130-145 billion for the year, all allocated to AI infrastructure) has led to a 91% decline in free cash flow. The conservative revenue forecast for the third quarter has raised concerns about when these investments will pay off.
2. The AI Arms Race Continues, but the Logic of Spending Has Changed
In the past two years, tech companies have been competing to acquire more GPUs and build data centers to maximize their computing power. However, the focus has shifted:
- Microsoft: While still investing heavily, it is focusing on converting computing power into revenue. For example, Azure sells AI capabilities to businesses, and Copilot provides tools for users, ensuring that every investment generates returns.
- Meta: It is still in the stage of accumulating computing power. Zuckerberg plans to build more data centers and purchase GPUs, even refusing to sell them at high prices, hoping to generate long-term profits through intelligent products (such as personal AI assistants). However, there is no clear short-term strategy for this approach.
- Other Giants: Google has increased its annual capital expenditure to $205 billion, resulting in negative free cash flow for the first time, which weakened its stock price after the close. Amazon's AWS is also investing heavily, but the market is waiting to see if it can replicate Microsoft's success in converting AI investments into growth.
3. The Capital Market Has Woken Up: From Computing Power to Returns
Investors no longer simply compare who has the most GPUs; they are concerned about how long it will take for these investments to generate profits:
- Morgan Stanley predicts that the five major cloud providers will spend over $800 billion on AI this year and $1.1 trillion next year, but the market is less enthusiastic because such spending may merely be a waste of money if it does not lead to revenue.
- Key indicators have changed: The focus is now on how often AI is used, how much money companies spend on related services (e.g., tokens), and the number of paid subscriptions—these directly affect profits.
- Apple is a typical example. Despite not investing heavily in AI infrastructure, the market focuses on whether its AI features (such as Siri) can drive user turnover and generate revenue.
4. AI Enters the "Inference Era": The Competition Is About Generating Revenue
The AI industry has moved from training large models (which requires significant capital investment) to applying these models to solve practical problems and generate revenue:
- Training Era: The focus was on having the largest and most powerful models (e.g., Meta's open-source models).
- Inference Era: The competition is about selling AI solutions to more customers. Microsoft's Copilot is a successful example, as businesses pay for subscription services to use AI for tasks like document writing and coding.
- Meta's challenge: Its AI is still primarily used to support its advertising business, and it has not found a model for selling AI capabilities externally, which is why the market is less impressed.
5. Looking Ahead at Apple and Amazon: New Directions for AI Commercialization
Apple and Amazon are about to release their financial reports, and the market is watching these two companies closely:
- Apple: Can it use its AI features (e.g., in the iPhone and Siri) to drive user turnover? This approach differs from cloud giants like Microsoft and Google, as it does not rely on large data centers but on hardware and an ecosystem.
- Amazon: Can AWS's AI business grow rapidly like Azure's? If AWS' AI revenue can cover its capital expenditure, the market will be positive; otherwise, it may face skepticism.
The performance of these companies will further shape the market's view of the value of AI investments. The question remains: Is the "cloud provider model" more stable, or is the "terminal AI ecosystem" more promising?
Conclusion
AI investment continues, but the era of reckless spending has passed. The key now is whether AI investments can be quickly converted into verifiable revenue and profits. Microsoft has already demonstrated a viable path, while Meta is still in the process of figuring it out. The financial reports from Apple and Amazon will provide more insights into the commercialization of AI. For investors, it is important to consider not only how much money companies are spending but also how much revenue they are generating.