Amazon: AI Powerhouse Driving Margin Transformation, Retail in Tariff Crosshairs

Amazon is building an AI powerhouse in AWS, with the segment becoming an increasingly large driver of Amazon’s earnings power despite contributing just one-sixth of Amazon’s revenue. Management remains committed to continuously expanding AI offerings and expanding GPU capacity to meet demand, seeing long-term tailwinds to its AI growth story.

E-commerce remains Amazon’s bread-and-butter, but it has now found itself caught in the crossfire of fluctuating tariff policy, being quite heavily exposed to China. Amazon could feel impacts to both revenue and margins as a result, given that peer Walmart pulled its operating income growth forecast of 0.5-2.0% and simply said it has “widened” on tariff uncertainties. For Amazon, retail margins have also improved over the past few quarters to help (minimally) offset the effects.

AWS has helped drive strong expansion in Amazon’s margins with operating profit up 61% YoY to a record $21.2 billion. With that said, this previously bulletproof segment is also exposed to the trickle-down effects of tariffs as cloud customers exposed to China will likely cut their budgets in response. Mizuho believes this could be up to 50% of cloud customers.

How much of this is priced in? That’s a question the I/O Fund is working hard to answer as the risks in the market are immense yet shares of Amazon are near historically low levels.

Building An AI Behemoth in AWS

Amazon’s approach in AI mirrors a one-stop-shop, where it is providing access to a wide range of the industry’s leading models alongside its internal models, access to the newest generations of GPUs alongside extensive custom silicon deployment, and any service that customers could need in-between. Amazon stated in Q3 2024 that it had launched nearly 2x as many genAI and ML features as the other CSPs combined over the prior 18 months.

This is what Amazon sees as its differentiator versus Azure or GCP – its ability to offer more AI/ML features and services, a wide range of powerful custom silicon and GPU instances, and end-to-end platforms for developers to build on.

Amazon’s strategy is centered around offering efficient access to AI on a price-performance basis at any size or scale, either using its custom AI accelerators for training (Trainium) or inference (Inferentia), a wide range of Nvidia GPUs, or custom CPU instances (discussed below).

Customers can test, build, and customize genAI apps on the latest LLMs from a wide range of leading providers on Amazon’s serverless platform Bedrock, while SageMaker AI is AWS’ fully managed AI/ML platform spanning the training, fine-tuning and deployment lifecycle. Customers also have access to Amazon’s genAI assistant Amazon Q, and a wide range of storage, analytics and security tools.

AWS has developed several networking innovations to improve performance and scalability for AI workloads, such as Elastic Fabric Adapter (EFA) and Nitro. EFA can communicate directly with networking hardware using a technology called operating-system bypass (OS-bypass), reducing latency and allowing AI and HPC applications to easily scale to thousands of GPU or CPU cores. Nitro offloads typical virtualization functions such as networking, storage, and management tasks to specific Nitro cards, freeing up server resources and reducing costs.

Breakdown of Amazon’s Custom Chips, Instances

AI chips are a performance game, with Nvidia quickly upping the ante with its GB200 NVL72 systems, which offer up to 30x faster inference performance on trillion-parameter LLMs. Google and Amazon are responding quickly with their custom accelerators, with Google recently unveiling its 7th-gen TPU, offering up to 10x faster AI processing than v5p. Amazon says that its next-gen Trainium3, due later this year, is 2x as fast and 40% more energy efficient than Trainium2.

Amazon boasts a wide range of custom accelerator and CPU instance offerings alongside leading Nvidia GPUs:

Trainium2 AI Accelerator Offers up to 40% More Price Performance

Amazon launched its Trainium2 chips and instances in November 2023, built primarily for AI training tasks, offering up to 4x the performance of its first generation Trainium chip. Trn2 instances combine 16 Trainium2 chips to offer 20.8 petaflops of dense FP8 compute. Compared to its next-most powerful EC2 instances, Trn2 offers “30% more compute and 25% more high bandwidth memory.”

Amazon says that the Trn2 are “typically 30% to 40% more price performant than other current GPU-powered instances available.” For example, Databricks uses Trn2 instances to help “lower TCO by up to 30% for its customers,” while DataDog says the instances help users “cut AI infrastructure costs by up to 50% and boost model training and deployment performance.”

Next-Gen Trainium3 Set to Launch Later in 2025

Amazon’s next-generation Trainium3 chip is set to debut later in 2025, and are expected to offer up to 4x the performance of Trn2 UltraServers. Amazon has been sparse on details for the new chip, aside from the performance boost, speed upgrade and energy efficiency improvements. AWS CEO Matt Garman said the new clusters will let customers “iterate even faster when building models and deliver superior real-time performance when deploying them.”

Inferentia Tailored for Fast, Low Cost Inference

Amazon’s Inferentia chips, first launched in 2019, were tailored specifically for AI inference applications and aimed to tackle the barrier of high inference costs, which at the time could account for up to 90% of the infrastructure cost for building and deploying an AI/ML app.

The first-generation chip offered up to 2.3x higher throughput and 70% lower cost than comparable EC2 instances on inference tasks. Each chip featured 4 first-generation NeuronCores with 128 TOPS performance for lower precision FP16, BF16, and INT8 calculations, scaling up to 16-chip instances offering 192 GB memory for heavy inference tasks. Inferentia helped Amazon deliver high-throughput, low cost AI inference due to the chip’s inference-optimized design and low instance cost.

Inferentia2 Provides 10x Lower Latency

Inferentia2 was built for large-scale AI inference workloads, with Amazon saying customers could effectively deploy 175B parameter models for inference workloads on a single Inf2 instance, which features 16 Inferentia2 chips.

Inferentia2 can deliver up to 4x higher throughput and up to 10x lower latency compared to Inferentia, as it has 4x more memory capacity and 16.4x higher memory bandwidth than Inferentia. In addition, Inf2 are the first inference-optimized instances to support scale-out distributed inference for large-scale LLMs with high-speed connectivity between chips.

Inf2 is a core part of Amazon’s strategy to lead in AI inference as inference costs come down, offering low-latency, low-cost inference-optimized performance. Inf2 is said to cost ~$0.40 per 1 million tokens for a 70B model, versus ~$1.00 per 1 million for an H100 GPU ran on Azure. AWS customers can also build across Amazon’s full stack, Trn2 and Inf2, for quick, efficient and cheap AI training and inference tasks.

Graviton4 CPU Offers 40% Better Price Performance

AWS’ Graviton CPUs are its in-house Arm-based custom data center chips, with the most recent Graviton4 launched alongside Trainium2 in 2023. Amazon says the next-gen Graviton4 chips can offer up to “nearly 40% better price performance versus other leading x86 processors,” with 30% better performance, 50% more cores and 75% more memory bandwidth than the Graviton3.

Amazon has built more than 2 million Graviton chips to date since the first generation launched in 2018, with a wide range of instances powered by the chips. AWS offers EC2 instances for general purpose compute, as well as compute optimized, memory optimized, or storage optimized instances.

Unique ‘Burstable’ T3 Instances

Amazon’s EC2 T3 instances are a low-cost, burstable instance that offers balanced compute, memory, and network resources primarily for general purpose workloads. What’s unique is that T3 instances provide a baseline level of performance from the CPU and an ability to ‘burst’ CPU usage to access more compute and reach full-core performance.

The unique ‘burst’ factor of T3 instances is built upon credits, which are earned when running below baseline or when idle. As needed, at any time, credits can be used to throttle to maximum performance until credits run out, or for as long as required with customers responsible for the additional cost.

New UltraServers for Scale-out Compute

UltraServers are a new offering for Amazon, where it is linking four Trn2 instances to form a 64-chip cluster (4 Trn2 instances) for 83.2 petaflops of dense FP8 compute, utilizing its proprietary chip-to-chip interconnect NeuronLink. UltraServers aim to accelerate the training process further, letting customers deploy larger models faster – Amazon says customers could train 300B parameter models in just weeks as opposed to months.

UltraServers are then linked together to form UltraClusters, letting customers scale-out to tens of thousands up to 100,000 Trainium2 chips. This is the backbone of Amazon’s Project Rainier supercluster project for key partner Anthropic.

Amazon Spending Same Amount as AWS’ Run Rate on Capex

Amazon has the benefit of becoming an AI behemoth due to AWS’ massive scale – the unit recently crossed a $115 billion annualized revenue run rate, more than doubling in the past four years, with revenue up 19% YoY to $28.8 billion in Q4. Unlike Microsoft, Amazon has not provided exact AI revenue figures for AWS, stating that it was at a “multi-billion dollar annualized revenue run rate” and growing triple digits YoY in Q4.

AWS has grown, and continues to grow, significantly quicker than Amazon as a whole. Ten years ago, AWS generated just $4.6 billion in revenue versus $89 billion for Amazon. Since then, AWS revenue has grown nearly 24x while Amazon has not even grown 7.5x.

AWS is also estimated to have nearly 4.2 million business customers, an enormous base to fuel continued growth in cloud services and AI. However, HG Insights estimated that 92% of these customers spend less than $1,000/month on AWS services, which might be an issue down the line considering that AI’s earliest adopters and largest spenders are likely to be in the Fortune 500.

Q4’s call also featured an important quote from CEO Andy Jassy about AI’s future at Amazon:

“I spent a fair bit of time thinking several years out. And while it may be hard for some to fathom a world where virtually every app has generative AI infusing it with inference being a core building block just like compute, storage and database, and most companies having their own agents that accomplish various tasks and interact with one another. This is the world we're thinking about all the time and we continue to believe that this world will mostly be built on top of the cloud with the largest portion of it on AWS.”

Amazon is buying fully into this vision, indicating that it is spending nearly AWS’ revenue amount in capex in 2025 to capture growing AI demand. Jassy explained that annualizing Q4 2024’s $26.3 billion capex would be a reasonably good way to view 2025’s capex. This implied Amazon is eyeing capex of ~$105 billion, up ~27% YoY after rising ~57% YoY in 2024. This would also be practically double Amazon’s $52.8 billion in capex from 2023, and comes after Amazon announced in early 2023 that it would be putting $150 billion towards new data centers over the next 15 years.

Amazon Quickly Outpacing Hyperscaler Peers When it Comes to Capex

We’ve closely tracked Big Tech’s capex for many quarters as a growth indicator for Nvidia and AI hardware suppliers. Amazon has been the largest spender of the hyperscalers (Amazon, Microsoft, Meta, Alphabet) over the last few years, accounting for nearly one-third of total capex in 2023 and 2024 and is set to be the top spender again in 2025.

On a quarterly basis, Amazon’s capex accelerated significantly beginning in Q2 2024, rising above $15 billion for the first time before ending the year $10 billion higher. Amazon far outspent Google and Meta, though Microsoft was a heavy spender as well in the second half of 2024.

Amazon is accelerating its capex as it continues to witness elevated AI demand that outstrips its available capacity. Jassy said both in Q3 2024, and in Amazon’s shareholder letter published in April 2025, that there was more demand AWS could fulfill even if they had more capacity, and that the “faster demand grows, the more datacenters, chips, and hardware we need to procure.” This suggests that AWS is continuing to see high levels of demand to give it the confidence to commit to $105B+ in capex for 2025.

AWS is monetizing this capex “many months” after it is spent and “over many years,” suggesting that 2024’s acceleration to 19% growth is likely from monetizing investments from 2023 with Nvidia’s Hopper GPU generation. This also suggests that the surge in capex in 2H 2024 has not been monetized yet and AWS will recognize the growth benefits through 2025, especially as component constraints ease in the second half of the year.

Data from Omdia shows Amazon was a significant deployer of both Nvidia’s GPUs and custom silicon in 2024, purchasing ~196K Hopper GPUs and deploying a combined 1.3 million Trainium and Inferentia chips (comparable to ~430K Hoppers). For 2025, Nvidia showed that the top CSPs (Amazon, Microsoft, Alphabet, Oracle) have already ordered 3.6 million Blackwell GPUs, versus 1.3 million at Hopper’s peak, with Amazon likely a large purchaser of the new generation.

Based on management’s comments about the monetization timeline, it’s possible that Amazon is only just beginning to recognize the growth tailwinds from these large investments and GPU deployments. This also sets the stage for consistent, strong growth through 2026 as Blackwell comes online along with Trainium3.

Despite this, there are still risks on the horizon –the recent tariff-fueled market turbulence has led to some heightened fears about the state of cloud spending, given that some customers may undergo budget optimization efforts in an effort to absorb higher costs in the coming months. For example, Mizuho’s James Lee predicts that up to 50% of cloud customers may eye budget reductions this year and be more “hesitant while assessing the economic impact before redeploying their capital.”

A Note on Amazon’s AI Pricing, 3P Model Support versus Azure, GCP

AWS is working to differentiate itself from Azure and Google Cloud (GCP) in two ways – offering a wide range of AI model support and on price-performance.

AWS offers access to more than 100 leading foundation models, from DeepSeek, Mistral, Meta, and others, as well as access to Anthropic’s models, Amazon’s internal models, and bring-your-own model support in Bedrock and SageMaker. Azure is tightly coupled with OpenAI due to Microsoft’s partnership, though it does also offer access to leading open-source models such as those from DeepSeek and Meta; however, not all of OpenAI’s leading models are accessible in every region. Google offers access to its full suite of models with broad open-source support, though usage of Google models is locked to GCP.

Amazon offers this range of model support to its entire custom silicon family of chips and Nvidia GPUs, while Azure lets customers build primarily on Nvidia’s ecosystem as its custom silicon effort is not nearly as extensive as AWS’. GCP mirrors Amazon with flexibility between TPUs and Nvidia GPUs.

For chip pricing, AWS and GCP can both offer custom silicon instances for fraction of the cost of Nvidia GPU instances, while also offering cheaper prices for inference workloads. A report from CloudExpat estimated that Amazon’s Trn1 instances cost ~$1.34 per hour versus $1.20/hour for GCP’s TPU v5e and $12.84/hr for an H100 on Azure; for inference, an Inferentia instance was estimated at $0.40 per 1M tokens, versus $0.30 on GCP and $1.00 for the H100 on Azure.

When it comes to running hosted inference on a model, such as for Meta’s Llama 405B, where costs range from as low as $6 per 1M tokens on Fireworks.ai to ~$21.33 on both Azure and AWS, all the way to $30 on Snowflake, Amazon’s performance sets it apart. While smaller platforms like Fireworks may hit users with ‘spike arrests’ where usage is throttled, Amazon can offer compellingly fast performance and token generation due to its breadth of instances. So for a model like Llama 3.1 405B, AWS says that independent inference performance tests “showed that Amazon Bedrock, running on Trn2 instances, delivers more than 3x higher token-generation throughput compared to other available offerings by major cloud providers.”

Amazon’s $8B Anthropic Partnership, Project Rainier

Amazon is extending its competition with Microsoft to the AI startup sphere, backing OpenAI competitor Anthropic with a total investment so far of $8 billion in the Claude developer.

Anthropic is quickly adding new models with improved capabilities and new features as it targets both consumer and enterprise AI demand as competition heats up. The startup unveiled an AI Research feature, which conducts multi-step autonomous research, with in-line citations and an ability to “synthesize findings and deliver holistic, source-backed summaries.” It also launched deeper integrations with Google Workspace, where Claude can now connect directly to Gmail, Google Calendar, and Google Docs, allowing it to scan emails, summarize docs, identify meetings, and surface files requested.

Anthropic also recently introduced new subscription plans for Claude, building on top of its $20/month Claude Pro plan and supplementing its free tier. The new Max plan for $100/month offers 5x the usage as Pro, while the $200/month option offers up to 20x the usage. Additionally, Anthropic is said to be preparing to roll out a new “voice mode” AI assistant that users can speak to, as early as this month, following ChatGPT’s footsteps. The company is also working on a new major ‘hybrid’ model that can switch between deep reasoning and quick responses, offering developers a sliding scale customization tool to shift speeds and help reduce costs.

Amazon is working to support Anthropic’s future growth with Project Rainier, a massive supercomputer being developed primarily to serve Anthropic’s growing compute needs. AWS is also optimizing its platform to offer faster access to Claude, with Bedrock offering a new "latency-optimized mode" for Claude 3.5 Haiku which runs 60% faster on Trainium2 instances.

Project Rainier, in a way, is Amazon’s internal competitor to OpenAI and Oracle’s Project Stargate, as the supercomputer cluster, uniquely housed in multiple facilities in different locations linked by Amazon’s Elastic Fabric Adapter, will feature hundreds of thousands of its Trainium2 chips. Rainier is also unique in that it will be built entirely with Amazon’s custom chip stack, while heavily leveraging AWS’ Nitro system and its Neuron SDK for maximum performance.

Rainier will be composed thousands of UltraServers linked together to form an UltraCluster accessing those chips. UltraClusters will help bring training times from weeks down to just a few days, according to AWS, as customers can access thousands of chips in tandem for large-scale training tasks.

When completed, it will be one of the largest clusters ever built globally, though specifics about the exact amount of chips, cost and scale are still secret. Amazon is no stranger to large-scale cluster development though, having collaborated with Nvidia on Project Ceiba, a 20,736-Blackwell GPU cluster offering 414 exaflops performance.

Investing in Anthropic can be seen as a net positive for Amazon despite the large capital outlays in its upfront investment and in Rainier – not only does Amazon have a guaranteed, leading AI customer to utilize the massive compute cluster it’s building, but it’s also receiving “intense feedback” from the startup on its custom accelerators and how to better optimize each new generation for performance or cost.

AWS Margins Very Strong, but Risks Ahead

AWS has historically enjoyed a strong operating margin profile that has strengthened considerably since early 2023, nearing the 40% range.

In Q4, AWS reported an operating margin of 36.9%, down from its peak of 38.1% in Q3. This was a substantial improvement from a 29.6% margin in Q4 2023. Management noted that the strength of margins was due to strong growth and cost control – operating income for AWS has risen >30% YoY in each of the last six quarters, and 48% YoY in Q4. However, quarterly fluctuation is expected depending on the level of investment Amazon is making in the segment.

On a TTM basis, AWS had only briefly surpassed 30% in the beginning of 2022 prior to a macro-induced growth slowdown that dented margins. Since then, AWS has managed to substantially improve its profitability, with TTM operating margin at 37% in Q4, up more than 12 points from 24.7% in Q2 2023.

Should AWS continue to see operating income grow in the double-digits YoY, it could possibly reach the 40% threshold by the end of the year, providing a strong tailwind to EPS growth as it contributed half of Amazon’s total operating income in Q4.

AI, Useful Life Classifications May Present Margin Headwind

Management has talked about the AI versus non-AI margins, with AI margins being significantly lower due to the massive investments Amazon is undertaking at the moment. CFO Brian Olsavsky explained that AI “does come originally with lower margins and a heavy investment load,” and in the short-term that will be a headwind to margins, but over the long-term, he expects “margins will be comparable in non-AI business as well.”

This is likely due to a much lower revenue return per dollar of spending presently, given that AI is only at a multi-billion dollar run rate. TD Cowen analysts put this in perspective, estimating that AWS historically has generated $4 in incremental revenue for every $1 of capital spending, but with surging AI investments, the ratio is now likely ~$0.20 cents for every $1. TD Cowen expects the incremental revenue to reapproach its usual $4 in the next several years.

Another risk to operating margins that lies ahead is useful life calculations for servers, primarily that useful lives may continue to decrease as the pace of GPU upgrades accelerates, as the performance gaps between each generation widens.

Amazon noted that AWS’ operating margin in Q4 benefited from a ~200 bp YoY positive impact from increasing the estimated useful life of servers in 2024. Stripping out this impact, operating margin would’ve been 34.9%.

However, Amazon said that in Q4, they “completed a useful life study for our servers and networking equipment and observed an increased pace of technology development,” and as a result decreased the useful life for a “subset” of servers and networking from 6 years to 5 years. Management estimated that this change would decrease FY25 operating income by ~$700 million.

Amazon also retired a subset of servers and networking equipment early, recording a $920 million accelerated depreciation expense, which they estimate will also negatively impact FY25 operating income by ~$600 million. Both of the impacts are expected to primarily be felt in AWS. This combines for a $1.3 billion negative headwind to operating income, which would be about a (2.7%) impact assuming 20% YoY growth in operating income.

However, Nvidia is pushing ahead with a break-neck product upgrade cycle for its GPUs, maintaining an annual cadence that rival AMD is working to match. Each generation upgrade, such as from its Hopper generation to its Blackwell generation, promises significant leaps in compute and thus performance.

These rapid performance upgrades could mean that current depreciation schedules still do not account for how quickly older generations of GPUs become obsolete simply from an inability to remain competitive performance-wise, whether that be in sheer compute or performance-per-watt. Should depreciation schedules more accurately be in the 3 to 4 year range, recognized depreciation expenses would be much larger, providing another headwind to operating margins.

E-Commerce in Tariff Territory

On the e-commerce side, Amazon is facing risks from increased tariff uncertainty, considering its rather high exposure to China. Online stores revenue growth has leveled off in the high-single digits, while third-party (3P) seller services revenue has decelerated rather sharply over the last few quarters. In light of the rising China risk, Amazon has also pushed forward with a large US warehouse expansion plan.

According to Bloomberg, Amazon canceled several inventory orders from China after tariffs on the country were raised to 104%. The orders were said to contain numerous consumer-oriented products, such as air conditioners and scooters. Additionally, Amazon has high China exposure from first-party vendors, which account for ~40% of its items; Morgan Stanley estimates that 25% of the cost of goods sold from 1P vendors comes from China. There are also reports that Chinese vendors are considering hiking prices or leaving the marketplace due to the high tariffs.

Tariffs also could weigh on 3P vendors, as higher import prices could squeeze margins and again lead to price hikes to offset higher costs. Amazon is said to have offered price concessions to some vendors with high-demand products in order to mitigate potential impacts.

In light of the heightened tariff risk, Amazon is doubling down on its domestic US business, as it is reportedly looking for capital partners for a $15 billion project to expand its warehouse footprint. The plan would call for construction of 80 new logistics facilities, with the majority being delivery hubs and a few being multi-story, larger-scale fulfilment centers.

North America revenue did reaccelerate to 10% YoY in Q4 to $115.6 billion, though growth has slowed over the past few quarters – Q2 and Q3 both saw the segment grow just 9% YoY, versus 11% YoY in the same period in 2023.
International revenue decelerated rather sharply in Q4, with growth of 9% YoY to $43.4 billion, down from 12% growth in Q3. This would mark the segment’s slowest growth since the start of 2023.

Segment Breakdown:

Decelerations in 3P seller services (which includes commissions, fulfillment and shipping fees) and advertising occurring simultaneously raise red flags for Amazon heading into Q1, as it suggests that its seller flywheel may be losing some momentum as tariff risks escalate. The softness of 3P seller services noted below hints that marketplace volume may be slowing, while the slowdown in advertising growth (in the holiday quarter) could mean sellers are spending less on advertising due to weaker sales performance, tighter margins or lower product demand.

Amazon’s online store revenue was $75.6 billion in Q4, up 8% YoY, maintaining the same growth pace from Q3 yet accelerating from the 6% growth seen in Q1.
On the other hand, 3P Seller Services growth has decelerated dramatically, from 19% in Q4 2023 to just 9% in Q4 2024.
Advertising growth has mirrored that deceleration, from 26% in Q4 2023 to just 18% in Q4 2024.

Recent Strengthening in E-commerce Margins Provides Some Cushion for Tariffs

Tariff risk looks to be amplifying some underlying weaknesses in 3P seller services and advertising; two signals of marketplace volume and demand. However, Amazon does have levers it can pull to mitigate these effects, as it is recognizing cost savings in inventory management and has a much stronger operating margin profile now that can absorb some costs.

Amazon’s management explained in Q4 that some genAI applications they built for inventory management have led to “10% better forecasting on our part and 20% better regional prediction,” and improvements in robotic fulfilment efficiency have combined for “significant productivity and cost savings.” Management sees more opportunities to further reduce costs via more refined inventory placement, expansion of same-day delivery networks, and accelerated robotics and automation throughout the fulfillment process.

On the margin side, Amazon has seen solid improvement in both North America and International that should offer it some leeway when it comes to absorbing tariff impacts:

North America operating margin reached 8.0% in Q4, up nearly 2 points YoY, while TTM operating margin reached 6.4%, up 2.2 points YoY.
In dollar terms, North America’s TTM operating income rose 68% to $25.0 billion, or a $10.1 billion YoY gain.

International operating margin was 3.0% in Q4, a 4 point YoY improvement from (1.0%), while TTM operating margin was 2.7%, a notable improvement from (2.7%) last year.

Revenue Guide Misses Estimates

Amazon’s soft Q1 guidance pointed to growth in the high-single digit range, a sharp sequential deceleration and what would be the first single-digit print in the last eight quarters.

For Q1, Amazon projected revenue between $151 billion and $155.5 billion, for 5% to 9% YoY growth; which was well below the consensus estimate for $158 billion.

Amazon said the softness was partially due to an “unusually large, unfavorable impact of approximately $2.1 billion” from foreign exchange rates (150 bp YoY growth), as well as the lack of leap-year impact, which added $1.5 billion of revenue (or 120 bp YoY growth impact). Even backing out both headwinds, growth would still be projected in the single-digits using the midpoint of the range. It also likely does not account for increased macro turbulence on the retail side as the full breadth and severity of tariffs on key trading partners was not known when guidance was provided.

At the midpoint of 7% YoY growth, this would be Amazon’s slowest quarterly growth rate since Q3 2001. This also does not account for some of the escalated tariff risks and seller impacts that have arisen throughout April. Analysts expect Amazon to fare much better, with the current consensus estimate calling for 8.2% growth to $155 billion in revenue, at the upper end of the guided range. Analyst estimates range from $153.2 billion to $157.2 billion, with none of the 45 analysts expecting Amazon to report below 7% growth this quarter.

EPS Growth Outpacing Revenue on Margin Strengths

Despite the weak revenue guide and sequential deceleration, Amazon’s margin strengths, primarily from AWS but also from improvements on the e-commerce side, are aiding robust earnings growth.

Amazon is currently estimated to generate $1.38 in EPS in Q1, up nearly 39% YoY, or more than 5x the rate of revenue growth at midpoint. EPS has rebounded from 2023’s lows, rising 216% in Q1 2024 and >50% YoY in each quarter of 2024.

Q1’s EPS estimate does face some risks from tariffs impacting margins or weaker revenue growth, while broader macro risks in consumer spending are becoming more prevalent. Consumer spending fell by the most in four years in January, and while it rebounded in February, much of the rebound was driven by price increases as inflation-adjusted spend barely rose. Consumer spending is being closely watched as consumers remain a bit more cautious, with Richmond Fed President Richard Barkin saying that while spending is not yet showing “troubling signs of a decline,” it is the metric he is most closely watching as the “trigger” on the economy.

EPS growth is forecast to slow rather dramatically as Amazon continues to face tough comps and slower growth – each quarter in 2025 is expected to see sub-10% revenue growth. Q2 and Q3 are expected to see EPS growth of just 12% to 13%, while Q4 runs the risk of falling flat, with estimates pointing to just 2% YoY growth.

For 2025, EPS growth is projected to outpace revenue growth by 5 points at just over 14% YoY, as Amazon benefits from the improved margin base it built throughout 2024, which drove EPS growth of 91% YoY last year.

Gross margin is on the verge of breaking 20% as high-margin AWS and advertising increase their revenue share, while operating margin sharply expanded to the double-digits for the first time, up from the low single digits at the start of 2023. Should Amazon be able to drive and maintain an operating margin >40% for AWS, it is well on the path to see mid-20% gross margins and mid-teens operating margins over the next few years.

While Amazon is lapping strong growth this year, EPS growth is expected to accelerate through FY27 on the back of this margin strengthening. FY26 EPS growth is currently projected at 19% YoY before accelerating to nearly 25% YoY in FY27, representing a 10 point acceleration in two years.

From FY25 to FY27, EPS growth is currently around a 19% CAGR. With the way that AWS is quickly reshaping Amazon’s margin profile and putting it on a trajectory for mid-teens operating margins, Amazon could likely see EPS growth accelerate to low to mid-20% CAGR simply from the growing margin and profit tailwinds from AWS.

Operating Cash Flows Remain Robust, FCF Impacted by Capex

Amazon has driven significant growth in operating cash flow, though FCF growth has been minimal due to accelerating capex.

Amazon generated $115.9 billion in operating cash flow in 2024, up 36% YoY. OCF margin expanded 3.4 points to 18.2%. This has been a remarkable turnaround from 2022, where TTM OCF dropped to just $35.6 billion.

However, FCF growth has stalled, with Amazon reporting minimal FCF generation in the first three quarters of 2024 due to sharply increased capex. For 2024, FCF rose just 4% YoY to $38.2 billion, but on a TTM basis, FCF has declined sharply from its peak at $53 billion in Q2. This was because Amazon reported just $4.7 billion in OCF in Q3 and $5.1 billion in Q1, or margins of just 3% and 3.5% respectively.

While FCF’s growth trajectory had tracked OCF rather closely through 2021 all the way to 2024, FCF has detached and began declining due to capex acceleration. With management signaling ~$26 billion in capex each quarter in 2025, it’s likely that FCF generation and growth will lag OCF through next year.

Valuation at Historic Lows

Amazon is trading at historically low valuations on an earnings and cash flow basis, while it continues to benefit from improving operating leverage as AWS and ads help drive margins and earnings growth higher.

Amazon is trading at a <29x forward earnings, at 2024 and below 2022 lows, which would be the lowest forward P/E ratio the company has traded at in more than a decade. It’s also well below the 36-38x multiple it commanded through much of 2024, and a ~30% discount to its 5-year average forward P/E of 39.4x.

Given the strong growth in OCF, it is no surprise that Amazon is trading at its lowest P/OCF multiple at just 17x. This is also a 30% discount to its 5-year average P/OCF multiple of 25.7x, and a 20% discount to retail peer Walmart, which is trading at 20.6x OCF for just 2% YoY growth compared to Amazon’s 36%.

While Amazon is trading at historic lows for forward P/E and P/OCF, its top-line multiples have been consistently expanding as high-growth, high-margin AWS and advertising continued to grow their share of revenue and boost Amazon’s earnings power. Amazon is currently trading at 2.8x forward P/S, nearly double its multiple from early 2023 but in line with its 5-year average at 2.7x.

Conclusion

E-commerce made Amazon famous, yet its AWS segment is quietly and quickly becoming a driver of Amazon’s growth story.

Amazon is positioning AWS to become an AI behemoth with a focus on offering everything customers will need to fully harness AI efficiently and cheaply. AWS continues to launch new Trainium and Inferentia instances to offer a broader range of powerful, affordable training and inference-optimized compute resources to its customers. It also provides access to 100+ leading models and bring-your-own model support, while launching 2x more AI features than other CSPs combined from early 2023 through Q3 2024. Project Rainier is showcasing AWS’ powerful in-house chips and networking, aiming to scale up to hundreds of thousands of chips housed in different facilities as one of the largest GPU clusters built to date.

Capex spending hints that growth for AWS could remain strong through 2025 and into 2026 as Amazon begins to monetize 2024’s accelerating investments. While the e-commerce side bears the brunt of potential tariff impacts, Amazon’s valuation has gotten attractive recently as P/E and P/OCF multiples have fallen toward historic lows.

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Please note: The I/O Fund conducts research and draws conclusions for the Fund’s positions. We then share that information with our readers. This is not a guarantee of a stock’s performance. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis.

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