#OpenAI第三季度营收升至100亿美元 #WCTCS9

In my view, artificial intelligence has officially moved past its experimental phase and entered a period of serious commercialization; however, profitability remains the greatest challenge ahead.

While OpenAI’s latest figures are impressive, they also highlight why investors should be cautious about conflating revenue growth with a sustainable business model.

1. Revenue growth is real, but profitability is nowhere to be seen

According to reports published on November 10, 2026:

* Quarterly revenue reached approximately $10 billion—an increase of about 50% compared to the previous quarter.
* Enterprise revenue doubled quarter-over-quarter.
* Operating losses rose from $10.1 billion to $10.9 billion.
* The adjusted operating margin improved from -150% to -109%.

Although this improvement in margins is encouraging, the growing operating loss serves as a serious warning.

The critical question is whether AI companies can generate significantly higher long-term revenue without simultaneously driving up computing and infrastructure costs at the same pace.

2. Market share or profitability?

I believe that market share should be a priority, but it should not be pursued at any cost.

Much like cloud computing in its early days, artificial intelligence is becoming a foundational infrastructure. Companies that succeed in building robust ecosystems, strong customer relationships, and effective distribution channels stand to gain tremendous long-term advantages. However, there is a distinction between investing for future growth and continuously spending money without improving unit economics.

Three indicators, in particular, warrant close attention:

* Customer retention: Are businesses renewing their AI contracts and expanding their scope?
* Unit economics: Is each new AI customer contributing to greater profitability for the company?
* Infrastructure efficiency: Are declining inference costs improving profit margins?

If these indicators improve simultaneously, current losses can be viewed as strategic investments rather than signs of an unsustainable business model.

3. Where would I look for investment opportunities?

I would categorize investment opportunities in the AI ​​space into three areas:

1. Infrastructure – NVIDIA

My preferred areas for investment within AI infrastructure include:

* GPUs and accelerated computing.
* Data center expansions.
* Demand driven by a large number of AI developers.

The advantage here is participating in the broader AI ecosystem, rather than placing a bet on a single model developer.

2. Distribution and Enterprise – Microsoft

* Azure cloud infrastructure.

* Enterprise software integration.

* Monetizing AI through existing customer relationships.

I particularly favor their diversified business model; however, valuation and capital expenditures remain significant risks.

3. AI Model Developers – OpenAI

* Subscription revenue.

* Enterprise AI services.

* APIs, agents, and AI applications. There is potentially enormous upside, but it comes with significant infrastructure costs and competitive pressure.

4. My Long-Term Perspective

I am optimistic about artificial intelligence over the next 5–10 years; however, I would like to distinguish between being optimistic about AI technology and being optimistic about every AI-related stock.

Some companies will become highly profitable. Others may struggle, as the costs of computing remain high while competition drives down the pricing of AI models.

Personally, I prefer a diversified approach—one that includes exposure to infrastructure and established technology platforms—while viewing individual AI developers as higher-risk growth investments.

A notable comparison: Anthropic reportedly generated $17 billion in revenue in the third quarter, yet recorded a much smaller operating loss of $567 million. This illustrates that different AI business models can have vastly different economic characteristics.

My firmest belief is that the true winners will not necessarily be the companies spending the most on AI, but rather those capable of translating AI adoption into recurring revenue, strong profit margins, and sustainable cash flow.