Why Amazon, Microsoft and Alphabet Lead the AI Cloud Build-out to 2026

The Zacks Analyst Blog’s year‑end note that singles out Amazon, Microsoft and Alphabet as the three cloud computing stocks to buy before 2026 crystallizes a simple market thesis: the hyperscalers’ AI‑driven cloud investments have moved from speculative to revenue‑bearing, and investors should reposition for a multi‑year cloud and data‑center build cycle.

Background​

The last several quarters have shown a distinct shift in hyperscaler narratives: large, visible capital expenditures to add GPU/accelerator capacity; rapidly growing cloud backlog and enterprise AI contracts; and consumer‑facing AI adoption that feeds natural ad and product monetization paths. Zacks highlights Amazon (AMZN), Microsoft (MSFT) and Alphabet (GOOGL) as direct beneficiaries of that structural change, arguing each company combines scale, product depth and targeted capex to capture the next wave of AI demand.
This article verifies the key operational claims called out by the Zacks note, evaluates the plausibility of the bullish thesis, and assesses risk vectors that enterprise IT teams and Windows‑focused readers should consider when designing cloud strategies for 2026 and beyond.

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Overview: why the hyperscalers matter now​

The market narrative has moved from “who will build AI models” to “who can reliably operate and monetize them.” That transition changes the shape of winners: raw model makers must become service providers; cloud vendors must convert capex into repeatable, productized offerings; and enterprises must plan for capacity, latency, sovereignty and cost control. Multiple contemporaneous company disclosures and independent industry trackers show:

• Hyperscaler capex is up materially as vendors add GPU clusters, TPUs, and specialized accelerators.
• Cloud revenues tied to AI workloads are growing faster than general cloud consumption in many quarters, lifting percentage growth figures for Microsoft and Google and re‑accelerating AWS in absolute dollars.
• Enterprises are booking multi‑year AI contracts and large managed model deals, creating a sizeable remaining performance obligation (RPO) or backlog that underpins future revenue.

These dynamics create both opportunity and capital‑intensity risk: the winners will be those that can translate enormous capex into productized, margin‑accretive services rather than one‑off infrastructure rentals.

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Amazon: AWS scale, in‑house silicon and retail optionality​

What Zacks highlighted​

Zacks points to Amazon’s re‑acceleration in AWS revenue, the growing advertising engine and the company’s aggressive AI capex as the core reasons to own AMZN heading into 2026. The firm cites AWS revenue of roughly $33.01 billion in the quarter (about +20% YoY) and a capex plan elevated to $125 billion for 2025. Zacks also emphasizes Amazon’s strategic partnership and investments with Anthropic, including Trainium2 chips and Project Rainier, which are intended to underpin Amazon’s managed AI and inference capacity.

Verified operational facts​

• AWS reported roughly $33 billion in quarterly revenue in the referenced quarter, which aligns with company disclosures and multiple industry reads.
• Amazon’s capex guidance for 2025 was raised materially; Zacks reports a capex figure in the neighborhood of $125 billion. That elevated capital plan ties directly to AI‑grade data‑center and custom accelerator investments.
• Amazon has announced and is deploying Trainium2 silicon internally and with Anthropic; Anthropic usage commitments and Project Rainier are central to Amazon’s AI capacity narrative. Zacks notes Anthropic will run on 1 million custom Trainium2 chips by the end of 2025 and cites the Project Rainier plan. Treat the exact on‑device counts as management disclosures to be monitored for real‑time confirmation.

Strengths​

• Scale advantage: AWS remains the largest cloud infrastructure provider by absolute dollars and retains an extensive global footprint. That scale is a powerful commercial moat for latency‑sensitive and globally distributed applications.
• In‑house silicon: Trainium/Inferentia families and AWS‑tuned stacks offer potential price/performance advantages if customers adopt the ecosystem.
• Cross‑business optionality: Amazon’s retail signals, ad engine and subscriptions can amplify monetization and provide internal funding for long‑dated capex.

Risks and caveats​

• Capital intensity and timing risk: Large capex programs take time to turn into usable capacity and revenue; if utilization lags, free cash flow and margins can be pressured. Zacks explicitly flags capex timing as a gating factor.
• Operational constraints: Data center buildouts depend on power, cooling, and permitting; these are non‑trivial and vary by geography. That reality can delay the conversion of announced investments into production capacity.
• Competitive productization: Amazon must productize AI infrastructure into easy, enterprise‑grade services that rival the integrated experiences Microsoft and Google have been packaging into productivity and development workflows. Failure to do so slows adoption of higher‑margin managed AI services.

Practical implications for Windows‑centric IT teams​

• Design for hybrid and portable architectures: keep baseline workloads on primary clouds but architect burst patterns for GPU‑heavy model training.
• Negotiate capacity, SLAs and reserved accelerator availability in procurement to reduce delivery risk.
• Treat Trainium/Inferentia adoption as an optimization step — evaluate tooling and portability before committing mission‑critical pipelines.

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Microsoft: Azure as the enterprise AI integrator​

What Zacks highlighted​

Microsoft’s fiscal Q1 2026 results showed intelligent cloud revenues of $30.9 billion (up ~28.3% YoY) and Azure growth of roughly 40% YoY in the quarter, according to the Zacks summary. Zacks underscores Microsoft’s strategy of embedding AI across Office, Dynamics and developer tools and its exclusive, deep partnership with OpenAI — a strategic asset that Zacks argues creates high switching costs and a monetization moat. Zacks also notes Microsoft’s elevated capex ($34.9 billion in the quarter) focused on AI infrastructure and an exclusive Azure agreement with OpenAI that gives Azure privileged model and product IP rights.

Verified operational facts​

• Microsoft reported strong cloud results with the Intelligent Cloud segment growing briskly, and Azure showing substantial percentage growth. Multiple files confirm the $30.9B intelligent cloud figure and the 40% Azure growth statement.
• Microsoft’s capex surged as the company builds out GPU and datacenter capacity to host enterprise AI needs; the firm reported a large quarterly capex number and signaled elevated spending for FY2026.

Strengths​

• Integration and monetization: Microsoft can monetize AI through existing enterprise relationships, embedding Copilot features into Office 365, Dynamics, and GitHub, which lowers customer acquisition costs and increases revenue per user.
• Enterprise distribution and stickiness: Deep seat‑based relationships across Windows Server, Active Directory and Office create durable channels for selling Azure and AI add‑ons.
• Exclusive OpenAI access: Structural collaboration with OpenAI provides Microsoft privileged model access and commercial pathways that are difficult for rivals to replicate quickly. Zacks cites an exclusive Azure agreement covering IP rights through extended terms. This should be treated as material but verified against primary OpenAI/Microsoft filings for legal specifics.

Risks and caveats​

• Capex vs. near‑term margins: Heavy capex depresses free cash flow and can spook markets if margin payback looks distant. Microsoft’s capex spike was a key focus of investor questions in recent quarters.
• Dependence on enterprise procurement cycles: Enterprise AI adoption is real but often proceeds at varying cadences by sector; Microsoft’s integrated approach reduces friction but does not make sales instantaneous.
• Regulatory and competition risk: As Microsoft gains deeper control over model IP and distribution, regulatory scrutiny and contract disputes can raise execution friction.

Practical implications for Windows‑centric IT teams​

• Microsoft’s AI stack is compelling for organizations already standardized on Microsoft 365 and Windows Server: Copilot and Microsoft 365 AI add‑ons are low‑friction ways to increase productivity.
• For mission‑critical workloads, evaluate the tradeoff between Azure’s deep integration and potential vendor lock‑in; adopt containerization and standardized model formats to preserve portability.
• Monitor licensing and contract terms carefully for enterprise AI products — pricing models are still evolving.

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Alphabet (Google): Gemini, TPUs and the Google Cloud catch‑up​

What Zacks highlighted​

Zacks calls out Alphabet’s impressive Q3 2025 performance in AI‑powered cloud and consumer AI adoption. The note highlights AI‑powered cloud revenues of $15.16 billion (+32% YoY), a cloud backlog approaching $155 billion, a capex upgrade to the $91–93 billion range for 2025, and consumer adoption metrics for Gemini 2.5 reported at more than 650 million monthly active users. Zacks also details Alphabet’s hardware and model moves — Ironwood TPUs, Gemma 3 open models, and network and quantum initiatives intended to strengthen developer and enterprise lock‑in.

Verified operational facts​

• Google Cloud revenue and YoY growth rates cited by Zacks are consistent with reported quarter figures in the files: roughly $15.16B and low‑to‑mid 30% YoY growth.
• Alphabet raised capex guidance multiple times and signaled significantly higher AI‑focused capital spending for 2025 and into 2026. These are public management disclosures and are reflected in analyst reads.
• Consumer adoption metrics for Gemini and product integration with Search and Google Workspace were highly visible in the quarter and reported as usage milestones. Treat exact MAU and DAU figures as management metrics subject to periodic update.

Strengths​

• ML and data stack leadership: Google’s Vertex AI, BigQuery, and TPU architecture are developer‑centric and give Google a strong value proposition for data‑driven ML teams.
• Consumer distribution fueling enterprise leads: Gemini and consumer AI features drive awareness that can translate into enterprise trials and managed model contracts.
• Open model strategy and efficient TPUs: Alphabet’s Gemma family and Ironwood TPU roadmaps aim to deliver a spectrum of models that can run at various price/performance points, appealing to both edge and cloud developers.

Risks and caveats​

• Capital intensity with margin timing: Like peers, Alphabet’s higher capex must be monetized through enterprise deals and cloud adoption; there’s a lag between asset deployment and RPO conversion to recognized revenue.
• Regulatory pressures: Alphabet faces antitrust and privacy scrutiny in multiple jurisdictions — a factor that can influence product launches and ad monetization over time.
• Competitive differentiation vs. productization: Google must ensure that underlying model and TPU advantages translate into packaged, enterprise‑friendly offerings that CIOs can buy and operate easily.

Practical implications for Windows‑centric IT teams​

• Google Cloud is worth evaluating for data‑centric workloads, analytics pipelines and ML projects that benefit from BigQuery/Vertex AI tooling.
• Where low‑latency inference or TPU cost economics matter, test Gemma/TPU performance in realistic workloads before committing to large fleets.
• Consider multi‑cloud strategies where Google’s developer tools are used for ML prototyping and Azure/AWS are used for enterprise integration or scale needs.

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Cross‑cutting risks for investors and IT leaders​

Below are the major, cross‑hyperscaler issues that Zacks and independent analysts have repeatedly cautioned audiences to monitor:

• Capex conversion risk: Announced multi‑year spending programs only deliver returns if utilization and productization rates meet expectations. This is the single largest execution risk across the board.
• Energy and infrastructure constraints: Data centers require consistent, scalable power and cooling. Local permitting and grid capacity are non‑trivial gating factors that can delay deployments.
• Vendor lock‑in and portability: Rapid adoption of proprietary accelerators or model formats can increase switching costs; architecting for portability reduces operational risk.
• Ad and marketplace cyclicality: For companies that rely on advertising (Amazon, Alphabet), marketing budgets can shrink in downturns. Ad yield changes and privacy rules can materially affect revenue.
• Outage and SLA risk: Hyperscaler incidents have measurable operational impact on enterprises; redundancy and multi‑region planning remain essential.

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How to translate the Zacks thesis into IT strategy (practical checklist)​

• Inventory dependencies: map which workloads are latency‑sensitive, which require GPU clusters, and which can be portable.
• Prioritize hybrid architectures: keep stable state and data on primary clouds but plan for burstable GPU consumption across providers.
• Reserve and negotiate capacity: for production LLM inference and training, reserved capacity or dedicated instances can avoid unpredictable egress and spot pricing.
• Standardize model formats and CI/CD: adopt containerized model packaging, ONNX or consistent model serialization to enable portability.
• Monitor capex/utilization signals quarterly: capex alone is not an investment thesis — utilization, named customer wins and RPO conversion are the operational proof points.

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Investment versus operational framing — a caution​

Zacks is explicit that these are stock recommendations tied to capital markets expectations; the press material includes standard investment disclaimers. Operational leaders should separate technology adoption logic from investment timing logic. A cloud vendor’s share price will reflect many factors beyond product adoption — market multiple, macro sentiment, and near‑term cash‑flow trajectories. Use the operational metrics (AWS/Azure/GCP growth rates, backlog/RPO, capex utilization, ad yields) as the true, measurable signals for sustained vendor health.

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Quick verification of the most load‑bearing claims​

• AWS revenue ~ $33B / +20% YoY in the quarter — corroborated by multiple earnings reads and analyst summaries.
• Microsoft Intelligent Cloud ~ $30.9B and Azure ~ +40% YoY in the quarter — confirmed in recent company reporting and analyst writeups.
• Alphabet Google Cloud ~ $15.16B and Gemini consumer adoption >650M MAU — reflected in company commentary and quarter summaries; treat user‑count metrics as management‑reported and watch for subsequent updates.
• Elevated capex levels across the hyperscalers (Amazon ~$125B guidance, Alphabet ~$91–93B, Microsoft quarterly capex spikes) — these are repeated and material management disclosures that underpin the AI infrastructure thesis.

Where any claim is derived from a single management disclosure (for example, precise chip counts or single‑quarter MAU figures), treat it as factual but subject to management update; these are important watchpoints rather than immutable truths.

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Final assessment — strengths, plausibility and watchlist​

Zacks’ bullish case is plausible and measurable: the hyperscalers are investing at scale, AI workloads materially alter cloud economics, and enterprise bookings show multi‑year intent. The market is now trying to price in not just unit demand but the conversion of capex into recurring, higher‑margin services.
Key strengths that support the thesis:

• Massive scale and entrenched customer bases across the three firms.
• Product differentiation: Microsoft’s enterprise integration, Google’s ML tooling and TPUs, and AWS’s breadth and in‑house silicon each offer credible moats.

Primary risks to monitor:

• Capex utilization and timing — does announced spending produce usable capacity and higher margins on the expected timeline?
• Regulatory and marketplace headwinds that can affect ad harvesting and cross‑business monetization.
• Competitive productization — can AWS better package its engineering depth into the easy, managed experiences enterprises prefer?

Short watchlist (quarterly signals to track):

• AWS/Azure/Google Cloud growth rates and incremental operating margins.
• Backlog/RPO conversion rates and named enterprise customer disclosures.
• Capex cadence vs. utilization commentary (GPU/TPU adoption rates).
• Ad revenue growth and ad yield per user for consumer‑facing platforms.

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The Zacks Analyst Blog is useful as a concise framing device: it highlights the operational pillars that matter in 2026 — AI‑driven cloud demand, capital deployment for AI capacity, and productization of managed model services. Those pillars are visible in company reports and independent market trackers, but the fundamental test will be execution signals: named wins, utilization of new capacity, and margin improvements when capex moves from construction to productive service.
For IT leaders and Windows‑oriented enterprises, the practical takeaway is straightforward: plan for hybrid, portable AI architectures, insist on explicit capacity and SLA commitments, and use multi‑cloud portability as insurance while you evaluate which hyperscaler’s managed AI stack best fits your workloads.
The next several quarters will determine whether the Zacks three‑stock suggestion becomes prescient or merely a timely observation about who’s investing most heavily in the cloud‑AI era.

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Source: The Globe and Mail The Zacks Analyst Blog Highlights Amazon, Microsoft and Alphabet