“Investors have been fretting over how Big Tech is going to make a return on all its AI spending.
Cloud computing increasingly looks like the answer.
Amazon.com, Microsoft and Alphabet's Google all have cloud businesses that have grown rapidly during the AI boom. Customers including AI labs and large corporations have clamored to rent access to chips and computing equipment from them -- to such an extent that their ability to supply it is more of a limitation than demand.
And unlike a lot of the other avenues companies have proposed to reap returns from AI investments, from chatbot ad sales to subscriptions for access to AI models, cloud computing's financial model is relatively well-understood.
The tech giants buy or lease buildings and computing gear, then rent it out via their cloud operations to recoup what they spent over a period of a few years. It is capital intensive but can be highly profitable: Amazon's cloud unit, Amazon Web Services, on Thursday reported a 39% operating profit margin in the second quarter.
Amazon Chief Executive Andy Jassy tried to paint a picture of AWS as both predictable and extremely fast-growing. It takes less than three years on average to break even on purchases of computing equipment, he said, while most of its contracts with AI-computing customers are at least five years long. That leaves a lot of juice for Amazon to squeeze.
"The resulting revenue, free cash flow and return on invested capital is very compelling," he said.
In the high-spending phase Amazon is going through now, it also means the company is free-cash-flow negative. Markets have been allergic to cash outflows in recent months amid concern about returns on hypsercalers' big investments.
But investors more recently seem to be coming around to the notion that Big Tech spending on AI can make sense -- as long as there is a cloud-computing business involved that can reap the benefits.
Amazon's stock surged on Friday after the company wowed Wall Street with 37% quarterly revenue growth for AWS, well above the roughly 31% analysts were projecting. Microsoft also got a massive stock-price boost after its results on Wednesday included a better-than-expected 43% revenue rise for its Azure cloud-computing unit. The two companies combined have added around $950 billion to their market values since reporting.
Google parent Alphabet's stock fell last week after it boosted capital spending for the year, but its cloud unit's 82% revenue growth has since tempered those concerns. The stock is up about 10% in the past week, more than recovering from its earlier losses.
AI is perhaps the most important driver of cloud-computing growth; companies and AI developers want more computing power, and it is easiest and often fastest to rent it in the cloud. Chunky deals with big customers are also helping inflate revenue and backlogs, like the more than $100 billion 10-year contract that AWS signed with Anthropic in April.
But there is also a shift predating the AI boom that has quietly persisted. Companies are moving more of their computing work to the cloud and using less of their own equipment. A Piper Sandler survey of IT leaders in June found they expected to boost spending by about 5% this year. Most planned to increase outlays on cloud computing, while relatively few plan to spend more on their own computing equipment.
Given those dual trends, it is a little easier to understand why Jassy suggested on Thursday that AWS could eventually grow into a business with $1 trillion in annual revenue, as outlandish as that figure may currently seem. Analysts expect AWS to grow to about $170 billion of revenue this year, according to FactSet.
Others also are looking to get a big piece of that expanding pie. Microsoft's and Google's cloud businesses have grown faster than Amazon's for some time, to the point that they pose a legitimate threat to AWS's dominance of an industry it virtually invented two decades ago. If their growth stays above AWS's, it isn't hard to foresee them surpassing their larger competitor by the end of the decade.
Of course, none of those businesses would fare well if the AI bubble bursts. Should Anthropic, OpenAI and other big buyers of AI computing power falter, massive contracts would likely be reworked and backlogs would shrink. For cloud-computing giants, though, there is something of a reprieve: At least they could fall back on the tried-and-true business model that sustained them before AI.
That is more than can be said for other AI players without cloud businesses, perhaps most prominently Meta Platforms. The social-media giant's stock is down about 5% since its earnings on Wednesday included a slight increase in the midpoint of its 2026 capital-spending range.
CEO Mark Zuckerberg said Wednesday that Meta is looking at starting a cloud-computing business of its own, but it would start far behind the three giants of the industry.
In sorting AI winners from losers, Wall Street has settled on a key differentiator.” [1]
Does running AI work on somebody's cloud and models increase chances of trade secrets' loss?
Yes, running AI work on a third-party cloud and external models increases the risk of losing trade secrets. This happens due to data ingestion in prompts, vendor training policies, and weak legal control over stored data.
Key Risk Factors
• Data Absorption: Inputs, code, or strategic text sent via prompts can be saved by external providers.
• Model Training: Standard or consumer-grade cloud tools may use user inputs to train future public versions of their models.
• Legal Waiver: Under trade secret laws, voluntarily sharing confidential information with an unmanaged third-party platform can destroy the required legal status of "secrecy".
Ways to Reduce Risk
• Enterprise Agreements: Use paid enterprise tiers that explicitly prohibit vendors from using customer data for training.
• Zero-Retention Policies: Select cloud options with strict data-deletion and zero-retention guarantees.
• Self-Hosting: Run open-weight models on local or dedicated private infrastructure for maximum security.
This why China goes to open weights containing AI. It could be used on our own servers, improved using our own trade secrets and applied for real work in real companies, not only for chats about love. There is no way around open weights.
Open-weights AI models offer key benefits including local control, data privacy, and customization.
Benefits of Open-Weights AI
• Local Hosting: Run models on private servers without sending data to outside companies.
• Custom Training: Improve the model using your own private business data and trade secrets.
• Real Work: Build specialized tools for real jobs instead of just general chat.
1. Wall Street Thinks It Knows How AI Will Pay Off --- Growth in cloud computing is driving share-price rallies and allaying fears of overinvestment. Fitch, Asa. Wall Street Journal, Eastern edition; New York, N.Y.. 03 Aug 2026: B9.
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