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Quick View: Mag 7 earnings results – No AI hyperscaler hyperscare here

Portfolio Manager Alison Porter shares the main highlights from Amazon, Alphabet, Microsoft, and Meta’s latest quarterly earnings results and what they reveal about the next phase of AI development and its implications for investors.

1 May 2026
7 minute read

Key takeaways:

  • On AI monetisation, accelerating revenue growth particularly from cloud and record backlogs across the hyperscalers demonstrated robust AI demand, with revenue growth limited by capacity rather than customer uptake. Cost per token and efficiency of capital expenditure is improving with Amazon and Alphabet’s own silicon.
  • The scale of planned AI capex signals the companies’ conviction that AI spending will translate into durable monetisation, potentially extending the capex cycle.
  • We believe the build-out of infrastructure and applications for agentic AI is a multi-year process, with significant investment required to realise its full potential.

A quartet of Magnificent 7 (Mag 7) companies reported on their quarterly earnings results recently (29 April). Microsoft, Amazon and Alphabet (Google Cloud) are the three largest hyperscalers, representing broad demand for AI use cases, while Meta is a leading use case of AI monetisation in its own business. Hyperscaler revenue is likely to be the most important lead indicator of AI returns and monetisation this year – recent earnings results demonstrated accelerating growth, strong backlogs and product expansions.

Varied reactions to the earnings reflect our long-held belief that the Mag 7 are not a monolith (vastly different businesses, margins, valuations and growth rates). But we also note that in the results from these four companies, there were more commonalities than differences:

  1. Revenue is accelerating across all the companies with capacity rising to meet surging demand, as is backlog (the booking figure reported on cloud divisions showing future sales). The backlog of Amazon, Alphabet and Microsoft announced exceeded US$1.4 trillion.
  2. Agentic AI is inflecting as the shift from training workloads to inferencing workloads drives an inflection/ exponential step up in demand for compute power, storage and networking.
  3. AI hyperscalers continue to be supply constrained in fulfilling demand and are responding to their backlog with higher capital expenditure (capex).
  4. Capex is shifting higher still – the four companies have committed cumulatively to more than US$700 billion in capex in 2026.1 A combination of rising costs (eg. memory) and the strength of demand have led management teams to indicate ( to various degrees) that this capex cycle will be stronger for longer beyond 2027). This is key for the AI infrastructure (e.g. semiconductor supply chain etc.), which looks to have already been partially priced into valuations. We expect 2027 capex to grow further.
What recent earnings reveal about the AI build out

The latest earnings results were positive for the build out of the full AI technology stack and ecosystem. Below we highlight some of the nuances and key points from each of the companies:

Amazon

We’ve never seen a technology grow as rapidly as AI, Amazon is already a leader, and companies continue to choose AWS for AI.

 

Amazon CEO Andy Jassy

Amazon delivered impressive results. Given Amazon Web Services (AWS) is the largest cloud provider, investors had expected strong growth from the division – there was no disappointment with AWS revenue growth accelerating to 28% on a US$150 billion revenue run rate.

The CEO articulated strong visibility on attractive return on invested capital (ROIC) from rising capex. While Amazon did not provide explicit 2027 capex guidance, demand strength was evident in its US$364 billion backlog (of contracted future revenue), which excludes the recently announced north of US$100 billion Anthropic commitment towards AWS technologies over the next ten years.2 Amazon’s differentiated approach to customer semiconductor chips has continued to gain traction, with its Graviton CPU now being sold to third parties, including Meta, and its Trainium AI chip now at a US$20 billion run rate. The retail groceries division demonstrated improving margins and strength with 15% year-over-year unit growth –outpacing fulfilment‑network cost growth. Broad‑based momentum across the US, Europe, and Brazil may help reassure investors concerned about macroeconomic pressures.

Alphabet (Google)

Our enterprise AI solutions have become our primary growth driver for cloud for the first time in Q1.

 

Alphabet CEO Sundar Pichai

Alphabet has the least mature, fastest-growing cloud business, so investors have looked for evidence of its scale potential. The company announced very strong growth in Google Cloud revenues of 63%, with associated operating margins having almost doubled. There was also disclosure of an exceptional US$462 billion backlog (up 90% year‑on‑year), with Google expecting to work through 50% of that over the next two years. This is one of the largest backlogs ever reported, reflecting both a doubling of new customer acquisitions and deepening relationships with existing customers.

Alphabet is unique in its fully integrated, full‑stack AI approach — spanning frontier models, Google Cloud applications, cloud infrastructure, and its own tensor processing unit (TPU) silicon. Unusually, management commented on 2027 capex, signaling a significant increase in spending and confidence in continued AI monetisation. AI also benefited the core Google Services business, which grew 16% year-on-year in its first quarter. Meanwhile, Waymo, its autonomous driving division, is now serving 500,000 rides per week, illustrating another future growth vector in physical AI.

Microsoft

Weekly engagement is now at the same level as Outlook, as more and more users make Copilot a habit.

 

Microsoft CEO Satya Nadella

Investors were seeking a clear narrative on how the company’s capex would be balanced between internal operations and its third-party cloud/AI business.

Microsoft again reported strong results, with the Azure cloud business growing 40% in the quarter from a year ago, although management reiterated that growth continues to be constrained by compute availability. The AI business surpassed US$37 billion in annual recurring revenue (ARR), up 123% year-on-year, attributed to customers building and running AI solutions on the Azure platform. However, growth of the AI business was lower versus Alphabet and Amazon, while concerns about high exposure to Open AI and associated huge capex and monetisation remain.

Meta

People will be more important in the future, not less.

 

Meta CEO Mark Zuckerberg

Meta reported strong advertising growth, with revenues for the quarter up 31% year‑on‑year. The weaker market response to Meta’s earnings despite its 30% revenue growth (the strongest of all the four) reflects the fact that Meta has no backlog to report and hence there is less visibility on the future returns of its capex. A small decline in Family of Apps (Facebook, Instagram, WhatsApp, Messenger, Threads) daily active users was attributed to internet shutdowns in Iran and WhatsApp being blocked in Russia. Engagement trends remained strong, with improved monetisation for advertisers. Video engagement reached all‑time highs across Facebook and Instagram, driven by ranking improvements. Meta’s innovation in advertising saw higher adoption of tools such as the Value Optimization Suite and Partnership Ads. During the quarter, Meta launched Muse – its newest family of large language models LLMs, which the CEO described as being integrated into personal AI tools via Meta AI, as well as applications in shopping and social content. However, these initiatives drove a capex increase of US$10 billion. Q2 guidance was modestly raised, while full‑year operating expense guidance was maintained. There was no guidance on margins improvement from the recently announced 10% workforce cut.

Conclusion

Overall, the latest earnings results from the four of the Mag 7 showed accelerating momentum in AI demand and increasing monetisation potential from the large cloud platforms. However, this continues to be constrained by capacity as evidenced by their backlog numbers. This is clearly not a zero‑sum game: all major hyperscalers are seeing strengthening demand as agentic AI adoption creates another inflection point for compute and storage requirements.

We believe the build out of infrastructure and applications for AI is a multi-year process, as we saw during the internet and mobile compute tech waves where significant investment is required to realise its full potential.

Important Information

References made to individual securities do not constitute a recommendation to buy, sell or hold any security, investment strategy or market sector, and should not be assumed to be profitable. Janus Henderson Investors, its affiliated advisor, or its employees, may have a position in the securities mentioned.

All company information sourced from earnings results releases and call transcripts, unless otherwise specified.

Amazon Investor Relations; Q12026 earnings results; 29 April 2026.

Amazon News; “Amazon and Anthropic expand strategic collaboration”; 29 April 2026.

Google Blog; Q1 2026 earnings call: Remarks from our CEO; 29 April 2026.

Microsoft Investor Relations; Earnings Release FY26 Q3; 29 April 2026.

Motley Fool.com; Meta Q1 2026 earnings call transcript; 29 April 2026.

1 Reuters; “Google Cloud pulls ahead as big tech’s AI bet swells to $700 billion”; 30 April 2026.

2 Amazon News; “Amazon and Anthropic expand strategic collaboration”; 29 April 2026.

Agentic AI: An AI system that uses sophisticated reasoning and iterative planning to autonomously solve complex, multi-step problems. Vast amounts of data from multiple data sources and third-party applications are used to independently analyse challenges, develop strategies and execute tasks.

AI hyperscaler: Technology providers that provide IT architectures that scale dynamically to handle exponential increases in workload and data. Apart from capacity, they offer enterprise-grade cloud services, flexible hardware resources, and robust software environments that support a broad range of AI applications.

Annual Recurring Revenue (ARR): The total predictable subscription-based revenue a company expects to earn each calendar year.

Capex: Money a business spends on major, long-term assets such as property and equipment (tangible assets) or technology, software, trademarks, patents etc (intangible assets) to facilitate new projects or investments that support business growth and expansion.

Full-stack approach: Refers to a comprehensive approach to software development that covers all layers of an application or project. This includes both the front-end and back-end components, as well as any other layers necessary for the application to function fully.

Hyperscaler AI capacity: Relates to hyperscaler’s inability to meet massive AI demand mainly because of compute power constraints, power supply, grid interconnection delays, specialised hardware supply issues.

LLM (large language model): A specialised type of artificial intelligence that has been trained on vast amounts of text to understand existing content and generate original content.

Physical AI: The integration of sophisticated AI algorithms into tangible, interactive systems, enabling autonomous machines with cognitive reasoning and spatial knowledge to learn from their interactions and respond in real time. Examples include autonomous vehicles, surgical and humanoid robots.

Return on invested capital (ROIC): A measure of how efficiently a company uses its capital to generate profits. It is calculated by dividing net operating profit after tax by invested capital.

Revenue run rate: Often used by high growth companies, it is a financial performance metric that takes a company’s current revenue in a certain period (a week, month, quarter, etc.) and converts it to an annual figure to arrive at the full-year equivalent.

These are the views of the author at the time of publication and may differ from the views of other individuals/teams at Janus Henderson Investors. References made to individual securities do not constitute a recommendation to buy, sell or hold any security, investment strategy or market sector, and should not be assumed to be profitable. Janus Henderson Investors, its affiliated advisor, or its employees, may have a position in the securities mentioned.

 

Past performance does not predict future returns. The value of an investment and the income from it can fall as well as rise and you may not get back the amount originally invested.

 

The information in this article does not qualify as an investment recommendation.

 

There is no guarantee that past trends will continue, or forecasts will be realised.

 

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