AI Green Shoots from Microsoft, Amazon and Google

Microsoft and Amazon quarterly earnings will not put all concerns about artificial intelligence capital investment to permanent rest. 

But the news is encouraging. Microsoft’s commercial remaining performance obligation (RPO) hit $678 billion in the second quarter, up 84 percent year over year. 

That’s more than double the company’s entire annual revenue of $332 billion. 

Microsoft CFO Amy Hood confirmed that all sequential RPO growth came from customers outside the major AI model makers. 

In other words, regular enterprises are locking in multi-year Azure commitments. 

Google capex continues to worry investors, as do Oracle debt burdens and Meta cash flow. But revenue performance of the sort Microsoft and Amazon are showing, plus growth at Google, suggest high-performance-computing services are generating direct revenue growth. 

Company	Cloud business	Q2 2026 evidence	Why it indicates AI demand
Amazon	AWS	AWS revenue grew 37% YoY to $42.2B, the fastest growth in more than four years. Contract backlog reached $496B. AWS AI and custom-chip businesses each exceeded a $25B annual run rate. (reuters.com)	Management attributed much of the acceleration to generative AI training and inference workloads. Capacity remains constrained despite massive investment.
Microsoft	Azure	Azure revenue increased 43%. Microsoft Cloud reached roughly $59.3B quarterly revenue. Microsoft 365 Copilot surpassed 30 million paid seats. (AP News)	AI workloads are driving Azure consumption while Copilot directly monetizes generative AI software.
Alphabet	Google Cloud	Google Cloud continued exceptionally rapid growth (roughly 60%+), while management repeatedly highlighted AI infrastructure demand and Gemini adoption. (Futurum)	Google now sells both AI infrastructure and AI models, creating two complementary revenue streams.

Also, management teams increasingly argued that AI is now contributing to revenue growth not only in cloud computing but across multiple business lines.

Company	Business segment	Evidence AI contributes
Amazon	Advertising	Advertising revenue increased about 26%. Amazon attributes improvements partly to AI-powered advertising optimization and conversational shopping experiences that improve conversion. (Amazon News)
Amazon	E-commerce	AI-powered product discovery, recommendation systems, inventory forecasting, robotics and delivery optimization contribute to higher retail productivity and better customer experience. Management highlighted record Prime delivery speeds. (AP News)
Amazon	Semiconductor business	Trainium and Graviton chips are now substantial businesses supporting both AWS customers and Amazon's own infrastructure. (MarketWatch)
Microsoft	Microsoft 365	Copilot subscriptions generate entirely new recurring revenue while encouraging premium licensing upgrades. (AP News)
Microsoft	GitHub	GitHub Copilot has become one of Microsoft's fastest-growing developer products, increasing Azure consumption while adding subscription revenue. (The Times of India)
Microsoft	Dynamics & Business Apps	AI assistants increase customer willingness to purchase higher-value enterprise software bundles, although this remains a smaller contributor than Azure. (AP News)
Alphabet	Search	AI Overviews and Gemini improve search engagement while preserving advertising volume. Google continues to report healthy Search revenue despite AI-generated answers. (blog.google)
Alphabet	Advertising	AI improves targeting, campaign optimization and automated creative generation for advertisers. This raises advertising effectiveness rather than replacing advertising. (blog.google)
Alphabet	Workspace	Gemini subscriptions create a growing AI software revenue stream alongside traditional productivity software. (blog.google)

The strongest evidence of AI-driven revenue remains in the cloud businesses, where AWS, Azure, and Google Cloud all reported exceptionally strong growth tied directly to AI workloads. However, the second quarter 2026 results also suggest AI is beginning to enhance the economics of legacy businesses rather than simply creating new standalone AI products.

For Amazon, AI appears to be improving retail operations, logistics, advertising, and semiconductor sales. 

For Microsoft, AI is increasing the value of Microsoft 365, GitHub, and business applications in addition to Azure. 

For Alphabet, AI is strengthening Google Cloud while also supporting Search, advertising, and Workspace.

Though all concerns have not been vanquished, the revenue evidence investors wanted is starting to show up in the financial results, at least from some of the leading hyperscalers making huge investments.

Like Texas, With AI "Everything is Bigger"

In many ways, vendor financing of artificial intelligence infrastructure is a bit like Texas: “everything’s bigger.”

Nobody knows yet whether “circular financing” is going to be a major problem in the artificial intelligence business, but it’s reaching new levels. 

Nvidia, for example, is pondering commitments to OpenAI of about $600 billion, including:

• An OpenAI Ohio data center lease financial guarantee of $250 billion 

• Separately, financing another $350 billion of GPU purchases for OpenAI. 

If completed, that would represent one of the largest examples of vendor-supported infrastructure finance in technology history.

Vendor financing has been provided by companies such as Cisco, Lucent, IBM, and GE Capital in the past, but not at such scale.

But Nvidia has increasingly used several mechanisms to support customers beyond simply shipping chips.

Customer	Approximate size	Nvidia role	Similarity to Ohio deal
OpenAI (Ohio campus)	Project >$500B; reported $250B guarantee plus possible $350B GPU financing	Credit guarantee, GPU financing, hardware supplier	Most extensive
OpenAI (2025 infrastructure agreement)	Up to $100B investment commitment	Infrastructure investment tied to deployment of Nvidia systems	High (Fierce Network)
CoreWeave	Multi-billion-dollar	Equity investor; guaranteed purchases of unused cloud capacity	High (Reuters)
CoreWeave	Multiple equity rounds	Early strategic investor before IPO	Medium (Reuters)
xAI	Tens of billions in GPU systems	Large hardware supplier; strategic ecosystem partner	Moderate (Reuters)
Oracle / Stargate	Hundreds of billions of AI infrastructure	Hardware supplier and infrastructure partner	Moderate (SSRN)
Numerous AI startups	Hundreds of millions to billions	Venture investments through NVentures plus preferred GPU access	Lower, but follows same ecosystem strategy (NVIDIA)

To some extent, Nvidia’s moves are an example of how various contestants in the AI value chain are staking claims in broader roles within the value chain. High-performance computing services suppliers such as Amazon and Google create their own chips and sponsor or create their own language models.

So it might not be surprising to see Nvidia taking on new roles as well. 

Function	Nvidia role
GPU supplier	Sell chips
Systems supplier	Sell complete AI clusters
Platform company	CUDA, networking, software, AI factories
Capital provider	Equity investments, financing, guarantees, demand commitments

The reported Ohio arrangement is not simply a very large chip sale. 

It would make Nvidia part supplier, part infrastructure financier, and part credit guarantor.Nvidia has previously invested in customers such as CoreWeave and OpenAI,  and has used demand guarantees and equity investments to accelerate AI infrastructure.

But such financing has been a staple of the computing industry since the time of mainframes. 

Vendor financing has been a recurring feature of the computing industry for more than 60 years. It tends to emerge during periods when a new generation of computing requires exceptionally large up-front investment. 

The mechanism changes over time, from leases to loans to equity investments to purchase guarantees.

But the economic logic remains consistent: If customers cannot afford the infrastructure needed to create the next wave of demand, suppliers help finance that infrastructure.

The reported Nvidia/OpenAI proposal is best understood as the latest version of this long-running pattern.

Era	Dominant technology	Financing mechanism	Strategic purpose
1960s–1970s	Mainframes	Leasing	Reduce customer capital burden
1980s	Minicomputers	Vendor credit	Expand installed base
1990s	Enterprise networking	Vendor financing	Accelerate Internet buildout
2000s	Telecom & hosting	Vendor loans, export finance	Support infrastructure expansion
2010s	Cloud computing	Long-term purchase commitments	Enable hyperscale investment
2020s	AI infrastructure	Equity, guarantees, GPU financing	Accelerate AI ecosystem growth

AI infrastructure is so capital-intensive that financing has returned to center stage.

Supplier	Customer	Financing approach	Circular element
Nvidia	CoreWeave	Equity investment plus demand guarantees	Nvidia helps create GPU demand
Nvidia	OpenAI	Reported credit guarantees and GPU financing	Financing supports purchases of Nvidia GPUs
AMD	Various AI cloud providers	Strategic investments and joint development (smaller scale)	Encourages accelerator adoption
Microsoft	OpenAI	Multi-billion-dollar investments tied to Azure usage	Investment generates Azure revenue
Amazon	Anthropic	Multi-billion-dollar investment tied to AWS usage	Investment drives AWS consumption
Google	Anthropic	Large investment tied to Google Cloud	Investment increases cloud demand

History suggests such financing can work. But history also suggests it can fail. We still do not know what the AI outcome will be. 

Condition	IBM	Cisco	Nvidia
Technology creates lasting productivity gains	✔	✔	Likely
Customers eventually generate sustainable cash flow	✔	Mixed	Unknown
Vendor does not assume excessive credit risk	✔	No	Still uncertain

Across six decades, the industry has repeatedly followed the same sequence:

• A breakthrough technology emerges (mainframes, PCs, the Internet, cloud, AI)

• Infrastructure costs initially exceed customers' ability or willingness to pay

• Suppliers devise financing mechanisms to accelerate adoption

• If demand proves durable, the financing is remembered as visionary

• If demand disappoints, the same financing is criticized as excessive risk-taking.

The reported Nvidia–OpenAI arrangement is unprecedented in scale, but not in principle. The novelty lies less in the existence of vendor financing than in its magnitude: guarantees and financing measured in the hundreds of billions of dollars rather than millions or even billions.